Additional matrix functionality for R including: (1) wrappers
  for the base matrix function that allows matrices to be created from character 
  strings and lists (the former is especially useful for creating block 
  matrices), (ii) better printing of large matrices via a new generic function
  for "pretty" printing, and (iii) a number of convenience functions for users 
  more familiar with other scientific languages like 'Julia', 'Matlab'/'Octave', 
  or 'Python'+'NumPy'.
Provides an efficient procedure for fitting the entire solution path for high-dimensional regularized quadratic generalized linear models with interactions effects under the strong or weak heredity constraint.
RAMpath is a rewrite of RAMpath software developed by John McArdle and Steven Boker. In addition to performing regular SEM analysis through the R package lavaan, RAMpath has unique features.  First, it can generate path diagrams according to a given model. Second, it can display path tracing rules through path diagrams and decompose total effects into their respective direct and indirect effects as well as decompose variances and covariances into individual bridges. Furthermore, RAMpath can fit dynamic system models automatically based on latent change scores and generate vector field plots based upon results obtained from a bivariate dynamic system.
Bayesian geostatistical modeling of Gaussian processes using a reparameterized and marginalized posterior sampling (RAMPS) algorithm designed to lower autocorrelation in MCMC samples.  Package performance is tuned for large spatial datasets.
Provides a solution path for Reinforced Angle-based Multicategory Support Vector Machines, with linear learning, polynomial learning, and Gaussian kernel learning.
The deterministic part of the Fortuna cryptographic
        pseudorandom number generator, described by Schneier & Ferguson
        "Practical Cryptography"
Generates random names with additional information including fake
    SSNs, gender, location, zip, age, address, and nationality.
The true random number service provided by the RANDOM.ORG
 website created by Mads Haahr samples atmospheric noise via radio tuned to
 an unused broadcasting frequency together with a skew correction algorithm
 due to John von Neumann.  More background is available in the included
 vignette based on an essay by Mads Haahr.  In its current form, the package
 offers functions to retrieve random integers, randomized sequences and
 random strings.
Simple methods to generate attractive random colors. The random
  colors are from a wrapper of 'randomColor.js'
  <https://github.com/davidmerfield/randomColor>. In addition, it also generates
  optimally distinct colors based on k-means (inspired by 'IWantHue'
  <https://github.com/medialab/iwanthue>).
Methods for the inference on and the simulation of Gaussian fields are provided, as well as methods for the simulation of extreme value random fields.
Various utilities are provided that might be used in spatial statistics and elsewhere. It delivers a method for solving linear equations that checks the sparsity of the matrix before any algorithm is used. Furthermore, it includes the Struve functions.
Classification and regression based on a forest of trees
        using random inputs.
Distributed training and prediction of random forest models based upon 'randomForest' package using 'ddR' (Distributed Data Structures) API in the 'ddR' package.
A unified treatment of Breiman's random forests for survival, regression and classification problems based on Ishwaran and Kogalur's random survival forests (RSF) package.  The package runs in both serial and parallel (OpenMP) modes. Now extended to include multivariate and unsupervised forests.
The package implements a bagging predictor based on
        general linear models
Allows the user to conduct randomization-based inference for a wide variety of experimental scenarios. The package leverages a potential outcomes framework to output randomization-based p-values and null intervals for test statistics geared toward any estimands of interest, according to the specified null and alternative hypotheses. Users can define custom randomization schemes so that the randomization distributions are accurate for their experimental settings. The package also creates visualizations of randomization distributions and can test multiple test statistics simultaneously.
Contains a function to randomize subjects, patients in groups of 
   sequences (treatment sequences).
   If a blocksize is given, the randomization will be done within blocks.
   The randomization may be controlled by a Wald-Wolfowitz runs test.
   Functions to obtain the p-value of that test are included.
   The package is mainly intended for randomization of bioequivalence studies
   but may be used also for other clinical crossover studies.
   Contains two helper functions sequences() and williams() to get the sequences 
   of commonly used designs in BE studies.
This tool enables the user to choose a randomization procedure
    based on sound scientific criteria. It comprises the generation of
    randomization sequences as well the assessment of randomization procedures
    based on carefully selected criteria. Furthermore, randomizeR provides a
    function for the comparison of randomization procedures.
Generates random assignments for common experimental
    designs: simple, complete, blocked, and clustered.
Fits standard and random effects latent class models. The single level random effects model is described in Qu et al <doi:10.2307/2533043> and the two level random effects model in Beath and Heller <doi:10.1177/1471082X0800900302>. Examples are given for their use in diagnostic testing.
Function for generating random gender and ethnicity correct first and/or last names. Names are chosen proportionally based upon their probability of appearing in a large scale data base of real names.
The Function performs a parallel analysis using simulated polychoric correlation matrices. The nth-percentile of the eigenvalues distribution obtained from both the randomly generated and the real data polychoric correlation matrices is returned. A plot comparing the two types of eigenvalues (real and simulated) will help determine the number of real eigenvalues that outperform random data. The function is based on the idea that if real data are non-normal and the polychoric correlation matrix is needed to perform a Factor Analysis, then the Parallel Analysis method used to choose a non-random number of factors should also be based on randomly generated polychoric correlation matrices and not on Pearson correlation matrices. Random data sets are simulated assuming or a uniform or a multinomial distribution or via the bootstrap method of resampling (i.e., random permutations of cases). Also Multigroup Parallel analysis is made available for random (uniform and multinomial distribution and with or without difficulty factor) and bootstrap methods. An option to choose between default or full output is also available as well as a parameter to print Fit Statistics (Chi-squared, TLI, RMSEA, RMR and BIC) for the factor solutions indicated by the Parallel Analysis.
Ensemble model, for classification, regression
	and unsupervised learning, based on a forest of unpruned 
	and randomized binary decision trees. Each tree is grown 
	by sampling, with replacement, a set of variables at each node. 
	Each cut-point is generated randomly, according to the continuous 
	Uniform distribution. For each tree, data are either bootstrapped 
	or subsampled. The unsupervised mode introduces clustering, dimension reduction
	and variable importance, using a three-layer engine. Random Uniform Forests are mainly 
	aimed to lower correlation between trees (or trees residuals), to provide a deep analysis 
	of variable importance and to allow native distributed and incremental learning.
Generate random strings of a dictated size of symbol set and
    distribution of the lengths of strings.
Several non parametric randomness tests for numeric sequences
Provides (1) pseudo random generators - general linear congruential generators, multiple recursive generators and generalized feedback shift register (SF-Mersenne Twister algorithm and WELL generators); (2) quasi random generators - the Torus algorithm, the Sobol sequence, the Halton sequence (including the Van der Corput sequence) and (3) some RNG tests - the gap test, the serial test, the poker test. The package depends on rngWELL package but it can be provided without this dependency on demand to the maintainer. For true random number generation, use the 'random' package, for Latin Hypercube Sampling (a hybrid QMC method), use the 'lhs' package. A number of RNGs and tests for RNGs are also provided by 'RDieHarder', all available on CRAN. There is also a small stand-alone package 'rngwell19937' for the WELL19937a RNG.
Implements random variables by means of S4 classes and methods.
Provides tools for filtering occurrence records, generating alpha-hull-derived range polygons and mapping species richness.
Tools for easy generation of (life-history) traits maps based on
    species range (extent-of-occurrence) maps.
Generates expected values of species richness, with continuous or
              scattered ranges, for data across one or two dimensions.
A fast implementation of Random Forests, particularly suited for high dimensional data. Ensembles of
            classification, regression, survival and probability prediction trees are supported. Data from
            genome-wide association studies can be analyzed efficiently. In addition to data frames, datasets of
            class 'gwaa.data' (R package 'GenABEL') can be directly analyzed.
This package performs aggregation of ordered lists based
        on the ranks using several different algorithms: Borda count,
        Cross-Entropy Monte Carlo algorithm, Genetic algorithm, and a
        brute force algorithm (for small problems)
Implementation of a model-based clustering algorithm for
    ranking data. Multivariate rankings as well as partial rankings are taken
    into account. This algorithm is based on an extension of the Insertion
    Sorting Rank (ISR) model for ranking data, which is a meaningful and
    effective model parametrized by a position parameter (the modal ranking,
    quoted by mu) and a dispersion parameter (quoted by pi). The heterogeneity
    of the rank population is modelled by a mixture of ISR, whereas conditional
    independence assumption is considered for multivariate rankings.
Implements distance based probability models for ranking data. 
    The supported distance metrics include Kendall distance, Spearman distance, Footrule distance, Hamming distance,
    , Weighted-tau distance and Weighted Kendall distance.
    Phi-component model and mixture models are also supported.
The rankFD() function calculates the Wald-type statistic (WTS) and the ANOVA-type
	     statistic (ATS) for nonparametric factorial designs, e.g., for count, ordinal or score data
	     in a crossed design with an arbitrary number of factors.
Rank-hazard plots Karvanen and Harrell (2009) <DOI:10.1002/sim.3591> visualize the relative importance of covariates in a proportional hazards model. The key idea is to rank the covariate values and plot the relative hazard as a function of ranks scaled to interval [0,1]. The relative hazard is plotted in respect to the reference hazard, which can bee.g. the hazard related to the median of the covariate.
Methods for ranking responses of a single response question or a multiple response question
Implementation of Kernelized score functions and other semi-supervised learning algorithms for node label ranking in biomolecular networks. RANKS can be easily applied to a large set of different relevant problems in computational biology, ranging from automatic protein function prediction, to gene disease prioritization and drug repositioning, and more in general to any bioinformatics problem that can be formalized as a node label ranking problem in a graph. The modular nature of the implementation allows to experiment with different score functions and kernels and to easily compare the results with baseline network-based methods such as label propagation and random walk algorithms, as well as to enlarge the algorithmic scheme by adding novel user-defined score functions and kernels.
Finds the k nearest neighbours for every point in a given dataset
    in O(N log N) time using Arya and Mount's ANN library (v1.1.3). There is
    support for approximate as well as exact searches, fixed radius searches
    and bd as well as kd trees.
Finds the k nearest neighbours for every point in a given dataset
    in O(N log N) time using Arya and Mount's ANN library (v1.1.3). There is
    support for approximate as well as exact searches, fixed radius searches
    and 'bd' as well as 'kd' trees. The distance is computed using the L1
    (Manhattan, taxicab) metric. Please see package 'RANN' for the same
    functionality using the L2 (Euclidean) metric.
To find the reversal association between variables.
Facilitates efficient polygon search using kd trees.
    Coordinate level spatial data can be aggregated to higher geographical
    identities like census blocks, ZIP codes or police district boundaries.
    This process requires mapping each point in the given data set to a
    particular identity of the desired geographical hierarchy. Unless efficient
    data structures are used, this can be a daunting task. The operation
    point.in.polygon() from the package sp is computationally expensive.
    Here, we exploit kd-trees as efficient nearest neighbor search algorithm
    to dramatically reduce the effective number of polygons being searched.
Package to perform non-invasive fetal testing for aneuploidies
    using sequencing count data from cell-free DNA
This package provides other packages with access to the internal 
 R serialization code. Access to this code is provided at the C function
 level by using the registration of native function mechanism. Client
 packages simply include a single header file RApiSerializeAPI.h provided by
 this package.

    This packages builds on the Rhpc package by Junji Nakano and Ei-ji Nakama
 which also includes a (partial) copy of the file src/main/serialize.c from R
 itself. 

    The R Core group is the original author of the serialization code made
 available by this package.
Bindings to various methods in the kernel for enforcing security
    restrictions. AppArmor can apply mandatory access control (MAC) policies on
    a given task (process) via security profiles with detailed ACL definitions. 
    In addition the package has kernel bindings for setting the process hardware
    resource limits (rlimit), uid, gid, affinity and priority. The high level R
    function 'eval.secure' builds on these methods to do dynamic sandboxing:
    it evaluates a single R expression within a temporary fork which acts as a 
    sandbox by enforcing fine grained restrictions without affecting the main R 
    process. Recent versions on this package can also be installed on systems
    without libapparmor, in which case some features are automatically disabled.
An easy way to determine which directories on the users computer
    you should use to save data, caches and logs. A port of Python's 'Appdirs'
    (\url{https://github.com/ActiveState/appdirs}) to R.
Facilitating the creation of reproducible statistical
    report templates. Once created, rapport templates can be exported to
    various external formats (HTML, LaTeX, PDF, ODT etc.) with pandoc as the
    converter backend.
Helper functions that act as wrappers to more advanced statistical
    methods with the advantage of having sane defaults for quick reporting.
This package uses the functions written by Daniel 
 Morissette <danmo@videotron.ca> to read geographical information in Arc/Info 
 V 7.x format and E00 files to import the coverages into R variables.
Tests gene-environment interaction for rare genetic variants using Sequence Kernel Association Test (SKAT) type gene-based tests. Includes two tests for the interaction term only, and one joint test for genetic main effects and gene-environment interaction.
Randomization tests for the statistical comparison of \emph{i} = two or more individual-based, sample-based or coverage-based rarefaction curves. The ecological null hypothesis is that the \emph{i} samples were all drawn randomly from a single assemblage, with (necessarily) a single underlying species abundance distribution. The biogeographic null hypothesis is that the \emph{i} samples were all drawn from different assemblages that, nonetheless, share similar species richness and species abundance distributions
Allows calculation of rarity weights for species and indices of rarity for assemblages of species according to different methods (Leroy et al. 2012, 2013).
Previously an R wrapper of the 'ARPACK' library
    <http://www.caam.rice.edu/software/ARPACK/>, and now a shell of the
    R package 'RSpectra', an R interface to the 'Spectra' library
    <http://yixuan.cos.name/spectra/> for solving large scale
    eigenvalue/vector problems. The current version of 'rARPACK'
    simply imports and exports the functions provided by 'RSpectra'.
    New users of 'rARPACK' are advised to switch to the 'RSpectra' package.
MCMC based sampling of binary matrices with fixed margins as used in exact Rasch model tests.
Software to perform supervised and pixel based raster image classification. It has been designed to facilitate land-cover analysis. Five classification algorithms can be used: Maximum Likelihood Classification, Multinomial Logistic Regression, Neural Networks, Random Forests and Support Vector Machines. The output includes the classified raster and standard classification accuracy assessment such as the accuracy matrix, the overall accuracy and the kappa coefficient. An option for in-sample verification is available.
The rase package primarily implements the Range Ancestral State Estimation for phylogeography described in Quintero, I., Keil, P., Jetz, W., & Crawford, F. W. (2015). Historical Biogeography Using Species Geographical Ranges. Systematic biology, 64(6), 1059-1073. It also includes Bayesian inference of ancestral states under a Brownian Motion model of character evolution and Maximum Likelihood estimation of rase for n-dimensional data. Visualizing functions in 3D are implemented using the rgl package.
Reading, writing, manipulating, analyzing and modeling of gridded spatial data. The package implements basic and high-level functions. Processing of very large files is supported.
This is a wrapper function for image(), which makes reasonable
    raster plots with nice axis and other useful features.
Performs kernel based estimates on in-memory raster images 
  from the raster package.  These kernel estimates include local means
  variances, modes, and quantiles.  All results are in the form of 
  raster images, preserving original resolution and projection attributes.
Methods for enhanced visualization and interaction with raster data. It implements visualization methods for quantitative data and categorical data, both for univariate and multivariate rasters. It also provides methods to display spatiotemporal rasters, and vector fields. See the website for examples.
An implementation of routines for solving rate-distortion problems.
  Rate-distortion theory is a field within information theory that
  examines optimal lossy compression. That is, given that some
  information must be lost, how can a communication channel be designed
  that minimizes the cost of communication error? Rate-distortion
  theory is concerned with the optimal  (minimal cost) solution to such
  tradeoffs. An important tool for solving rate-distortion problems is
  the Blahut algorithm, developed by Richard Blahut and described in:

    Blahut, R. E. (1972). Computation of channel capacity and
  rate-distortion functions. IEEE Transactions on Information Theory,
  IT-18(4), 460-473.

    This package implements the basic Blahut algorithm, and additionally contains a number of `helper' functions, including a routine for searching for an information channel that minimizes cost subject to a constraint on information rate.
A function which performs exact rate ratio tests and returns an object of class htest.
The kappa statistic implemented by Fleiss is a very popular index for assessing the reliability of agreement among multiple observers. It is used both in the psychological and in the psychiatric field. Other fields of application are typically medicine, biology and engineering. Unfortunately,the kappa statistic may behave inconsistently in case of strong agreement between raters, since this index assumes lower values than it would have been expected. We propose a modification kappa implemented by Fleiss in case of nominal and ordinal variables. Monte Carlo simulations are used both to testing statistical hypotheses and to calculating percentile bootstrap confidence intervals based on proposed statistic in case of nominal and ordinal data.
This package provides several functions to
    manipulate rational functions, including basic
    arithmetic operators, derivatives and integrals with
    EXPLICIT forms.
This package provide an easy way to produce atmospheric profiles of Pressure,
 Temperature and Density according to the standard atmosphere 1976. 
 It provides also profiles of molecular volume backscatter coefficients for standard atmosphere,
 and approximated estimations of sunset, sunrise and solar zenith angle.
The R Analytic Tool To Learn Easily (Rattle) provides a 
  Gnome (RGtk2) based interface to R functionality for data mining. 
  The aim is to provide a simple and intuitive interface 
  that allows a user to quickly load data from a CSV file 
  (or via ODBC), transform and explore the data, 
  build and evaluate models, and export models as PMML (predictive
  modelling markup language) or as scores. All of this with knowing little 
  about R. All R commands are logged and commented through the log tab. Thus they
  are available to the user as a script file or as an aide for the user to 
  learn R or to copy-and-paste directly into R itself. 
  Rattle also exports a number of utility 
  functions and the graphical user interface, invoked as rattle(),  does 
  not need to be run to deploy these.
Parameter estimation for averaging models of Anderson's Information Integration Theory
Interface to <http://proyectoavis.com> database. 
    It provides means to download data filtered by species, order,
    family, and several other criteria. Provides also basic functionality to
    plot exploratory maps of the datasets.
In order to facilitate R instruction for actuaries, we have organized several 
  sets of publicly available data of interest to non-life actuaries. In addition, we suggest 
  a set of packages, which most practicing actuaries will use routinely. Finally, there is 
  an R markdown skeleton for basic reserve analysis.
Provides an interface to functions of the 'SAMtools' C-Library by Heng Li.
A Pure R implementation of Bayesian Global Optimization with Gaussian Processes.
Basic R package to access data structures offered by any
    BEFdata portal instance.
rbenchmark is inspired by the Perl module Benchmark, and
        is intended to facilitate benchmarking of arbitrary R code. The
        library consists of just one function, benchmark, which is a
        simple wrapper around system.time.  Given a specification of
        the benchmarking process (counts of replications, evaluation
        environment) and an arbitrary number of expressions, benchmark
        evaluates each of the expressions in the specified environment,
        replicating the evaluation as many times as specified, and
        returning the results conveniently wrapped into a data frame.
An implementation of robust bent line regression. It can fit the bent line regression and test the existence of change point,
    for the paper, "Feipeng Zhang and Qunhua Li (2016). Robust bent line regression, submitted."
Oracle Berkeley DB -
    <http://www.oracle.com/technetwork/database/database-technologies/berkeleydb/overview/index.html> -
    is a family of embedded key-value database
    libraries providing scalable high-performance data management services
    to applications. The Berkeley DB products use simple function-call APIs
    for data access and management. This package provides an R interface to
    the Berkeley DB API.
The package contains functions to generate random numbers
        from the beta distribution and random vectors from the
        Dirichlet distribution.
Facilities for working with Atlantis box-geometry model (BGM) 
 files. Atlantis is a deterministic, biogeochemical, whole-of-ecosystem model. 
 Functions are provided to read from BGM files directly, preserving their 
 internal topology, as well as helper functions to generate 'Spatial' objects.
 This functionality aims to simplify the creation and modification of box 
 and geometry as well as the ability to integrate with other data sources.
Interface to 'Biodiversity' 'Heritage' Library ('BHL') 'API'
    methods. 'BHL' is a repository of 'digitized' literature on 'biodiversity'
    studies, including 'floras', research papers, and more.
To facilitate retrieving and processing biochemical reaction data such as Rhea, MetaCyc, KEGG and Unipathway, the package provides the functions to download and parse data, instantiate generic reaction and check mass-balance. The package aims to construct an integrated metabolic network and genome-scale metabolic model.
Functions for connecting to BioUML server, querying BioUML repository and launching BioUML analyses.
Interface to the 'USGS' 'BISON' 'API', a 'database' for
    species occurrence data on the web. Data comes from species in the
    United States from participating data providers. You can get
    data via 'taxonomic' and location based queries. A simple function
    is provided to help visualize data.
Utilities related to Bitcoin. Unified markets API interface
    (bitstamp, kraken, btce, bitmarket). Both public and private API calls.
    Integration of data structures for all markets. Support SSL. Read Rbitcoin
    documentation (command: ?btc) for more information.
An R package for the BitCoinCharts.com API.
An R Interface to Bloomberg is provided via the Blp API.
Creation, manipulation, simulation of linear Gaussian Bayesian
             networks from text files and more...
R interface for creating plots in Bokeh. Bokeh by Continuum
    Analytics, \url{http://bokeh.pydata.org/en/latest/}.
Scalable decision tree training and prediction.
Takes matched and unmatched data and calculates Rosenbaum bounds for the treatment effect.  Calculates bounds for binary outcome data, Hodges-Lehmann point estimates, Wilcoxon signed-rank test for matched data and matched IV estimators, Wilcoxon sum rank test, and for data with multiple matched controls. Package is also designed to work with the Matching package and operate on Match() objects.
The RBP curve is a visual tool to assess the
    performance of prediction models.
Functions to prepare files needed for running BUGS in
        batch-mode, and running BUGS from R. Support for Linux and
        Windows systems with OpenBugs is emphasized.
Rbundler manages a project-specific library for dependency
    package installation. By specifying dependencies in a DESCRIPTION file
    in a project's root directory, one may install and use dependencies
    in a repeatable fashion without requiring manual maintenance.
    rbundler creates a project-specific R library in
    `PROJECT_ROOT/.Rbundle` (by default) and a project-specific
    `R_LIBS_USER` value, set in `PROJECT_ROOT/.Renviron`. It supports
    dependency management for R standard "Depends", "Imports",
    "Suggests", and "LinkingTo" package dependencies. rbundler also
    attempts to validate and install versioned dependencies, such
    as ">=", "==", "<=". Note that, due to the way R manages package
    installation, differing nested versioned dependencies are not
    allowed. For example, if your project depends on packages A (== 1),
    and B (== 2), but package A depends on B (== 1), then a nested
    dependency violation will cause rbundler to error out.
Implements the Ranking-Based Variable Selection
    algorithm for variable selection in high-dimensional data.
Relational Class Analysis (RCA) is a method for detecting
        heterogeneity in attitudinal data (as described in Goldberg
        A., 2011, Am. J. Soc, 116(5)).
Memoization can be used to speed up repetitive and computational expensive function calls.  The first time a function that implements memoization is called the results are stored in a cache memory.  The next time the function is called with the same set of parameters, the results are momentarily retrieved from the cache avoiding repeating the calculations.  With this package, any R object can be cached in a key-value storage where the key can be an arbitrary set of R objects.  The cache memory is persistent (on the file system).
Calculate the flow of particles between polygons by two integration methods: integration by a cubature method and integration on a grid of points.
Provides an interface to the National Topographic System (NTS),
    which is the way in which a number of freely available Canadian datasets are
    organized. CanVec and CanVec+ datasets, which include all data used to create
    Canadian topographic maps, are two such datasets that are useful in creating
    vector-based maps for locations across Canada. This packages searches CanVec
    data by location, plots it using pretty defaults, and exports it to human-
    readable shapefiles for use in another GIS.
Estimation of abundance and other of demographic parameters for closed 
             populations, open populations and the robust design in capture-recapture experiments 
             using loglinear models.
This package makes some maps using shapefiles and
        dataframes. Five kinds of maps are available : proportionnal
        circles, proportionnal circles colored by a discretized
        quantitative variable, proportionnal circles colored by the
        modalities of a qualitative variable, choropleth and typology.
This packages provides a direct interface (without the
	     use of Java) to the most basic functionality of Apache
	     Cassanda such as login, updates and queries.
Provides implementations of rule pruning algorithms based on the "Classification Based on Associations" (CBA). It can be used for building classification models from association rules. Rules are pruned in the order of precedence given by the sort criteria and a default rule is added. CBA was originally proposed by Liu, B. Hsu, W. and Ma, Y (1998). Integrating Classification and Association Rule Mining. Proceedings KDD-98, New York, 27-31 August. AAAI. pp80-86.
Tools for large, sparse optimal matching of treated units
	and control units in observational studies.  Provisions are
	made for refined covariate balance constraints, which include
	fine and near-fine balance as special cases.  Matches are 
	optimal in the sense that they are computed as solutions to
	network optimization problems rather than greedy algorithms.
Tools for optimal subset matching of treated units
	and control units in observational studies, with support
	for refined covariate balance constraints, (including
	fine and near-fine balance as special cases). A close 
	relative is the 'rcbalance' package.
Identify, convert and handle dates as used within the Swedish cancer register and associated cancer quality registers in Sweden. Especially the cancer register sometimes use nonstandard date variables where day and/or month can be "00" or were the date format is a mixture of"%Y-%m-%d", "%Y%m%d" and "%y%V" (two digit year and week number according to ISO 8601,which is not completely supported by R).These dates must be approximated to valid dates before being used in for example survival analysis. The package also includes some convenient functions for calculating "lead times" (relying on 'difftime') and introduce a "year" class with relevant S3-methods to handle yearly cohort.
Functions either required by other Swedish Regional Cancer Center packages or standalone functions outside the scope of other packages.
R interface to (some of) cddlib
    (http://www.ifor.math.ethz.ch/~fukuda/cdd_home/cdd.html).
    Converts back and forth between two representations of a convex polytope:
    as solution of a set of linear equalities and inequalities and as
    convex hull of set of points and rays.
    Also does linear programming and redundant generator elimination
    (for example, convex hull in n dimensions).  All functions can use exact
    infinite-precision rational arithmetic.
Allows the user to access functionality in the
    CDK, a Java framework for cheminformatics. This allows the user to load
    molecules, evaluate fingerprints, calculate molecular descriptors and so on.
    In addition the CDK API allows the user to view structures in 2D.
An R interface to the Chemistry Development Kit, a Java library
    for chemoinformatics. Given the size of the library itself, this package is
    not expected to change very frequently. To make use of the CDK within R, it is
    suggested that you use the rcdk package. Note that it is possible to directly
    interact with the CDK using rJava. However rcdk exposes functionality in a more
    idiomatic way. The CDK library itself is released as LGPL and the sources can be
    obtained from https://github.com/cdk/cdk.
RCEIM is a package implementing a stochastic heuristic method for performing multidimensional function optimization. The method is inspired in the Cross-Entropy Method. It does not relies on derivatives, neither imposes particularly strong requirements into the function to be optimized. Additionally, it takes profit from multicore processing to enable optimization of time-consuming functions.
Example dataset for 'Rcell' package. Contains images and cell data object.
To facilitate using 'cereal' with Rcpp.
  'cereal' is a header-only C++11 serialization library.
  'cereal' takes arbitrary data types and reversibly turns them into
  different representations, such as compact binary encodings, XML,
  or JSON. 'cereal' was designed to be fast, light-weight, and easy
  to extend - it has no external dependencies and can be easily
  bundled with other code or used standalone.
Conjugate gradient minimization of nonlinear functions
        with box constraints incorporating the Dai/Yuan update. This
        implementation should be used in place of the "CG" algorithm
        of the optim() function.
A simple data science challenge system using R Markdown and Dropbox <https://www.dropbox.com/>.
    It requires no network configuration, does not depend on external platforms
    like e.g. Kaggle <https://www.kaggle.com/> and can be easily installed on a personal computer.
R package for chess validations, pieces movements and check
    detection. Also integrates functions to plot chess boards given a
    Forsyth Edwards and Portable Game notations.
An implementation of simulated maximum likelihood method for the estimation of Binary (Probit and Logit), Ordered (Probit and Logit) and Poisson models with random parameters for cross-sectional and longitudinal data.
Collection of portable choice dialog widgets
Provides a list of functions for the statistical analysis and the post-processing of the Markov Chains simulated by ChronoModel (see <http://www.chronomodel.fr> for more information).  ChronoModel is a friendly software to construct a chronological model in a Bayesian framework.  Its output is  a sampled Markov chain from the posterior distribution of dates component  the chronology. The functions can also be applied  to the  analyse of mcmc output generated by Oxcal software.
Functions to generate stimuli and analyze data of reverse correlation image classification experiments (psychophysical tasks aimed at visualizing cognitive mental representations of faces).
A simple and flexible way to generate Circos 2D track plot images for genomic data visualization is implemented in this package. The types of plots include: heatmap, histogram, lines, scatterplot, tiles and plot items for further decorations include connector, link (lines and ribbons), and text (gene) label. All functions require only R graphics package that comes with R base installation.
This package contains wrapper functions and methods which allow to
    use "climdex.pcic" and "RMAWGEN" packages.  With this simple approach it is
    possible to calculate climate change indices, suggested by the WMO-CCL,
    CLIVAR, ETCCDMI(http://www.climdex.org),on stochastic generations of
    temperature and precipitation time series, obtained by the application of
    RMAWGEN.  Each index can be applied to both observed data and to synthetic
    time series produced by the Weather Generator, over a reference period
    (e.g. 1981-2010, as in the example). It contains also functions and methods
    to evaluate the generated time series of climate change indices consistency
    by statistical tests.Bugs/comments/questions/collaboration of any kind are
    warmly welcomed.
R tools for integrating with the www.clinicalcodes.org web
    repository
ClinicalTrials.gov is a registry and results database of publicly
    and privately supported clinical studies of human participants conducted
    around the world. Users can search for information about and results from
    those trials. This provides a set of functions to interact with the search
    and download features. Results are downloaded to temporary directories and
    returned as R objects.
R version of 'GenClone' (a computer program to analyse genotypic data, test for clonality and describe spatial clonal organization, Arnaud-Haond & Belkhir 2007, <http://wwz.ifremer.fr/clonix/content/download/68205/903914/file/GenClone2.0.setup.zip>), this package allows clone handling as 'GenClone' does, plus the possibility to work with several populations, MultiLocus Lineages (MLL) custom definition and use, and p-value calculation for psex statistic (probability of originating from distinct sexual events) and psex_Fis statistic (taking account of Hardy-Weinberg equilibrium departure) as 'MLGsim'/'MLGsim2' (a program for detecting clones using a simulation approach, Stenberg et al. 2003).
Provide flexible native clustering routines that can be
        linked against in downstream packages.
Tool for providing access to the Java version 'CMAEvolutionStrategy' of
    Nikolaus Hansen. 'CMA-ES' is the Covariance Matrix Adaptation Evolution Strategy,
    see https://www.lri.fr/~hansen/cmaes_inmatlab.html#java.
Run 'R CMD check' from 'R' programmatically, and capture the
    results of the individual checks.
A platform-independent basic-statistics GUI (graphical user interface) for R, based on the tcltk package.
Various statistical, graphics, and data-management functions used by the Rcmdr package in the R Commander GUI for R.
An Rcmdr "plug-in" to accompany the book Customer and 
		Business Analytics: Applied Data Mining for Business Decision
		Making Using R by Daniel S. Putler and Robert E. Krider.
A plug-in for R Commander ('Rcmdr'). The package is a Graphical User Interface (GUI) in which several biclustering methods can be executed, followed by diagnostics and plots of the results. Further, the GUI also has the possibility to connect the methods to more general diagnostic packages for biclustering. Biclustering methods from 'biclust', 'fabia', 's4vd', 'iBBiG', 'isa2', 'rqubic' and 'BicARE' are implemented. Additionally, 'superbiclust' and 'BcDiag' are also implemented to be able to further investigate results. The GUI also provides a couple of extra utilities to export, save, search through and plot the results. 'RcmdrPlugin.BiclustGUI' also provides a very specific framework for biclustering in which new methods, diagnostics and plots can be added. Scripts were prepared so that R-package developers can freely design their own dialogs in the GUI which can then be added by the maintainer of 'RcmdrPlugin.BiclustGUI'. These scripts do not required any knowledge of 'tcltk' and 'Rcmdr' and are easy to fill in.
This package provides a Rcmdr "plug-in" based on coin (Conditional Inference Procedures in a Permutation Test Framework).
This package provides an Rcmdr plug-in based on the
        depthTools package, which implements different robust
        statistical tools for the description and analysis of gene
        expression data based on the Modified Band Depth, namely, the
        scale curves for visualizing the dispersion of one or various
        groups of samples (e.g. types of tumors), a rank test to decide
        whether two groups of samples come from a single distribution
        and two methods of supervised classification techniques, the DS
        and TAD methods.
The package provides a platform-independent GUI for design of experiments.
 It is implemented as a plugin to the R-Commander, which is a more general 
 graphical user interface for statistics in R based on tcl/tk. 
 DoE functionality can be accessed through the menu Design that is added to the 
 R-Commander menus.
This package provides an Rcmdr "plug-in" based on the
        Design of experiments class Stat 4309
Este paquete proporciona una interfaz grafica de usuario (GUI) para algunos de los procedimientos estadisticos detallados en un curso de 'Estadistica aplicada a las Ciencias Sociales mediante el programa informatico R' (EACSPIR). LA GUI se ha desarrollado como un Plugin del programa R-Commander.
Rcmdr plug-in GUI extension for Evidence Based Medicine medical indicators calculations (Sensitivity, specificity, absolute risk reduction, relative risk, ...).
A Rcmdr "plug-in" for the EcoVirtual package, designed primarily for teaching ecological models using simulations.
This package provides an Rcmdr "plug-in" based on the time
        series functions.  Contributors: G. Jay Kerns, John Fox, and
        Richard Heiberger.
Export Rcmdr output to LaTeX or HTML code. The
        plug-in was originally intended to facilitate exporting Rcmdr
        output to formats other than ASCII text and to provide R
        novices with an easy-to-use, easy-to-access reference on
        exporting R objects to formats suited for printed output. The
        package documentation contains several pointers on creating
        reports, either by using conventional word processors or
        LaTeX/LyX.
EZR (Easy R) adds a variety of statistical functions, including survival analyses, ROC analyses, metaanalyses, sample size calculation, and so on, to the R commander. EZR enables point-and-click easy access to statistical functions, especially for medical statistics. EZR is platform-independent and runs on Windows, Mac OS X, and UNIX. Its complete manual is available only in Japanese (Chugai Igakusha, ISBN: 978-4-498-10901-8 or Nankodo, ISBN: 978-4-524-26158-1), but an report that introduced the investigation of EZR was published in Bone Marrow Transplantation (Nature Publishing Group) as an Open article. This report can be used as a simple manual. It can be freely downloaded from the journal website as shown below.
Rcmdr Plugin for the 'FactoMineR' package.
The R Commander Plug-in for Fuzzy Clustering Methods. This Plug-
    in provide Graphical User Interface of 2 methods of Fuzzy Clustering (Fuzzy C-
    Means /FCM and Gustafson Kessel-Babuska). For validation of clustering, this plug-
    in use Xie Beni Index, MPC index, and CE index. For statistical test (test
    of significant differences of grouping/clustering), this plug-in use MANOVA
    analysis with Pillai trace statistics. For stabilize the result, this package provide
    soft voting cluster ensemble function. Visualization of result are provided via plugin that must be load in Rcmdr file.
Provides an Rcmdr plug-in based on the 'GWRM' package.
Rcmdr menu support for many of the functions in the HH package.
        The focus is on menu items for functions we use in our introductory
        courses.
This package is an R Commander plugin that accompanies IPSUR, an Introduction to Probability and Statistics Using R.
A GUI front-end for ggplot2 allows Kaplan-Meier plot, histogram,
    Q-Q plot, box plot, errorbar plot, scatter plot, line chart, pie chart,
    bar chart, contour plot, and distribution plot.
Provides an Rcmdr "plug-in" based on the lfstat package for low flow analysis.
Easy to use interface for conducting meta-analysis in R. This
    package is an Rcmdr-plugin, which allows the user to conduct analyses in a
    menu-driven, graphical user interface environment (e.g., CMA, SPSS). It
    uses recommended procedures as described in The Handbook of Research
    Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009).
Rcmdr menu items to display mosaic and assoc plots Allows
        to visually restructure the underlying structables Developed
        after extended discussions with Rich Heiberger
Extends R Commander with a unified menu of new and pre-existing 
        statistical functions related to public management and policy analysis 
        statistics. Functions and menus have been renamed according to the 
        usage in PMGT 630 in the Master of Public Administration program at
        Brigham Young University.
An R Commander "plug-in" extending functionality of linear models and providing an interface to Partial Least Squares Regression and Linear and Quadratic Discriminant analysis. Several statistical summaries are extended, predictions are offered for additional types of analyses, and extra plots, tests and mixed models are available.
This package provides a GUI for the orloca package, it is
        developed as an Rcmdr plug-in.
Rcmdr menu support for some of the graphics by group in
        the lattice package
This package provides a Rcmdr "plug-in" to analyze questionnaire data.
This package provides an Rcmdr "plug-in" based on the
        Quality control class Stat 4300
Mixed Treatment Comparison is a methodology to compare directly and/or indirectly health strategies (drugs, treatments, devices). This package provides an 'Rcmdr' plugin to perform Mixed Treatment Comparison for binary outcome using BUGS code from Bristol University (Lu and Ades).
Rcmdr GUI extension plug-in for Receiver Operator Characteristic tools from pROC and ROCR packages. Also it ads a Rcmdr GUI extension for Hosmer and Lemeshow GOF test from the package ResourceSelection.
This package includes tools for calculating sample sizes and 
 selecting samples using various sampling designs. This package is an extension
 of RcmdrPlugin.EHESsampling which was developed as part of the EHES pilot project.
 The EHES Pilot project has received funding from the European Commission and 
 DG Sanco. The views expressed here are those of the authors and they do not represent 
 Commission's official position.
Provides a GUI for the SCVA, SCRT and SCMA packages. The package is written as an Rcmdr plugin.
Supports the text book Acevedo, M.F (2013) "Data Analysis
        and Statistics for Geography, Environmental Science, and
        Engineering" CRC Press
This package provides a GUI for the SLC package, it is
        written as an Rcmdr plug-in.
This package provides an Rcmdr "plug-in" for studying
        sport management data.
Rcmdr interface to the 'sos' package. The plug-in renders
  the 'sos' searching functionality easily accessible via the Rcmdr
  menus. It also simplifies the task of performing multiple searches and 
  subsequently obtaining the union or the intersection of the results.
This package provides a GUI for the steepness package, it
        is written as an Rcmdr plug-in.
An R Commander plug-in for the survival
  package, with dialogs for Cox models, parametric survival regression models,
  estimation of survival curves, and testing for differences in survival
  curves, along with data-management facilities and a variety of tests, 
  diagnostics and graphs.
This package provides an Rcmdr "plug-in" based on the
        TeachingDemos package, and is primarily for illustrative
        purposes.
An 'R Commander' plug-in providing an integrated solution to perform
    a series of text mining tasks such as importing and cleaning a corpus, and
    analyses like terms and documents counts, vocabulary tables, terms
    co-occurrences and documents similarity measures, time series analysis,
    correspondence analysis and hierarchical clustering. Corpora can be imported
    from spreadsheet-like files, directories of raw text files, 'Twitter' queries,
    as well as from 'Dow Jones Factiva', 'LexisNexis', 'Europresse' and 'Alceste' files.
Some extension to Rcmdr (R Commander) made by R-UCA project and used in teaching statistics at University of Cadiz (UCA).
Working with CMIP5 data can be tricky, forcing scientists to write
    custom scripts and programs. The `RCMIP5` package aims to ease this
    process, providing a standard, robust, and high-performance set of scripts
    to (i) explore what data have been downloaded, (ii) identify missing data,
    (iii) average (or apply other mathematical operations) across experimental
    ensembles, (iv) produce both temporal and spatial statistical summaries,
    and (v) produce easy-to-work-with graphical and data summaries.
Provides programmatic access to Colombos, a web based
    interface for exploring and analyzing comprehensive organism-specific
    cross-platform expression compendia of bacterial organisms.
Provides color schemes for maps (and other graphics)
        designed by Cynthia Brewer as described at http://colorbrewer2.org
Functions and datasets to support "Summary and Analysis of
             Extension Education Program Evaluation in R" and "An R
             Companion for the Handbook of Biological Statistics". 
             Vignettes are available at <http://rcompanion.org>.
Solve some conic related problems (intersection of conics with lines and conics, arc length of an ellipse, polar lines, etc.).
A collection of small text corpora of interesting data.
    It contains all data sets from https://github.com/dariusk/corpora.
    Some examples:
    names of animals: birds, dinosaurs, dogs; foods: beer categories,
    pizza toppings; geography: English towns, rivers, oceans;
    humans: authors, US presidents, occupations; science: elements,
    planets; words: adjectives, verbs, proverbs, US president quotes.
R interface to CPLEX solvers for linear, quadratic, and (linear and quadratic) mixed integer programs. Support for quadratically constrained programming is available. See the file "INSTALL" for details on how to install the Rcplex package in Linux/Unix-like and Windows systems. Support for sparse matrices is provided by an S3-style class "simple_triplet_matrix" from package slam and by objects from the Matrix package class hierarchy.
Identifies regions of common (species) profiles (RCPs), possibly when sampling artefacts are present.  Within a region the probability of sampling all species remains approximately constant.  This is performed using mixtures-of-experts models.  The package also contains associated methods, such as diagnostics.
The 'Rcpp' package provides R functions as well as C++ classes which
 offer a seamless integration of R and C++. Many R data types and objects can be
 mapped back and forth to C++ equivalents which facilitates both writing of new
 code as well as easier integration of third-party libraries. Documentation 
 about 'Rcpp' is provided by several vignettes included in this package, via the 
 'Rcpp Gallery' site at <http://gallery.rcpp.org>, the paper by Eddelbuettel and 
 Francois (2011, JSS), and the book by Eddelbuettel (2013, Springer); see 
 'citation("Rcpp")' for details on these last two.
Rcpp11 includes a header only C++11 library that facilitates 
  integration between R and modern C++.
'Annoy' is a small C++ library for Approximate Nearest Neighbors 
 written for efficient memory usage as well an ability to load from / save to
 disk. This package provides an R interface by relying on the 'Rcpp' package,
 exposing the same interface as the original Python wrapper to 'Annoy'. See
 <https://github.com/spotify/annoy> for more on 'Annoy'. 'Annoy' is released
 under Version 2.0 of the Apache License. Also included is a small Windows
 port of 'mmap' which is released under the MIT license.
The 'APT Package Management System' provides Debian and
 Debian-derived Linux systems with a powerful system to resolve package
 dependencies. This package offers access directly from R.
'Armadillo' is a templated C++ linear algebra library (by Conrad
 Sanderson) that aims towards a good balance between speed and ease of use. Integer,
 floating point and complex numbers are supported, as well as a subset of
 trigonometric and statistics functions. Various matrix decompositions are
 provided through optional integration with LAPACK and ATLAS libraries.
 The 'RcppArmadillo' package includes the header files from the templated
 'Armadillo' library. Thus users do not need to install 'Armadillo' itself in
 order to use 'RcppArmadillo'. 'Armadillo' is licensed under the MPL 2.0, while
 'RcppArmadillo' (the 'Rcpp' bindings/bridge to Armadillo) is licensed under the
 GNU GPL version 2 or later, as is the rest of 'Rcpp'.
This package provides R with access to Boost Date_Time
 functionality by using Rcpp modules. 

    Functionality from Boost Date_Time for dates, durations (both for days 
 and datetimes), timezones, and posix time ("ptime") is provided. The posix
 time implementation can support high-resolution of up to nano-second
 precision by using 96 bits (instead of R's 64) to present a ptime object.
'Rcpp' Access to the 'CCTZ' timezone library is provided. 'CCTZ' is
 a C++ library for translating between absolute and civil times using the rules
 of a time zone. The 'CCTZ' source code, released under the Apache 2.0 License,
 is included in this package. See <https://github.com/google/cctz> for more
 details.
The RcppClassic package provides a deprecated C++ library which
 facilitates the integration of R and C++. 

    New projects should use the new Rcpp API in the Rcpp package.
The Rcpp package contains a C++ library that facilitates
        the integration of R and C++ in various ways via a rich API.
        This API was preceded by an earlier version which has been
        deprecated since 2010 (but is still supported to provide
        backwards compatability in the package RcppClassic).  This
        package RcppClassicExamples provides usage examples for the
        older, deprecated API. There is also a corresponding package
        RcppExamples package with examples for the newer, current API
        which we strongly recommend as the basis for all new
        development.
The 'cnpy' library written by Carl Rogers provides read and write
 facilities for files created with (or for) the 'NumPy' extension for 'Python'.
 Vectors and matrices of numeric types can be read or written to and from
 files as well as compressed files. Support for integer files is available if
 the package has been built with -std=c++11 which is the default starting
 with release 0.2.3 following the release of R 3.1.0, and available on all
 platforms following the release of R 3.3.0 with the updated 'Rtools'.
An efficient C++ based implementation of the 'DEoptim'
 function which performs global optimization by differential evolution.  
 Its creation was motivated by trying to see if the old approximation "easier,
 shorter, faster: pick any two" could in fact be extended to achieving all
 three goals while moving the code from plain old C to modern C++.  The
 initial version did in fact do so, but a good part of the gain was due to 
 an implicit code review which eliminated a few inefficiencies which have
 since been eliminated in 'DEoptim'.
This package is based on the C++ code from Yusuke Sugomori,
             which implements basic machine learning methods with 
             many layers (deep learning), including dA (Denoising Autoencoder), 
             SdA (Stacked Denoising Autoencoder), RBM (Restricted Boltzmann machine) and 
             DBN (Deep Belief Nets).
R and 'Eigen' integration using 'Rcpp'.
 'Eigen' is a C++ template library for linear algebra: matrices,
 vectors, numerical solvers and related algorithms.  It supports dense
 and sparse matrices on integer, floating point and complex numbers,
 decompositions of such matrices, and solutions of linear systems. Its
 performance on many algorithms is comparable with some of the best
 implementations based on 'Lapack' and level-3 'BLAS'.

    The 'RcppEigen' package includes the header files from the 'Eigen' C++
 template library (currently version 3.2.9). Thus users do not need to
 install 'Eigen' itself in order to use 'RcppEigen'.

    Since version 3.1.1, 'Eigen' is licensed under the Mozilla Public License
 (version 2); earlier version were licensed under the GNU LGPL version 3 or
 later. 'RcppEigen' (the 'Rcpp' bindings/bridge to 'Eigen') is licensed under
 the GNU GPL version 2 or later, as is the rest of 'Rcpp'.
Examples for Seamless R and C++ integration
 The 'Rcpp' package contains a C++ library that facilitates the integration of
 R and C++ in various ways. This package provides some usage examples.

    Note that the documentation in this package currently does not cover all the
 features in the package. It is not even close. On the other hand, the site
 <http://gallery.rcpp.org> is regrouping a large number of examples for 'Rcpp'.
Access to a family of Gauss error functions for arbitrary complex arguments is provided via the 'Faddeeva' package by Steven G. Johnson (see <http://ab-initio.mit.edu/wiki/index.php/Faddeeva_Package> for more information).
The 'getconf' command-line tool provided by 'libc' allows
 querying of a large number of system variables. This package provides
 similar functionality.
'Rcpp' integration for 'GNU GSL' vectors and matrices
 The 'GNU Scientific Library' (or 'GSL') is a collection of numerical routines for
 scientific computing. It is particularly useful for C and C++ programs as it
 provides a standard C interface to a wide range of mathematical routines
 such as special functions, permutations, combinations, fast fourier
 transforms, eigensystems, random numbers, quadrature, random distributions,
 quasi-random sequences, Monte Carlo integration, N-tuples, differential
 equations, simulated annealing, numerical differentiation, interpolation,
 series acceleration, Chebyshev approximations, root-finding, discrete
 Hankel transforms physical constants, basis splines and wavelets.  There
 are over 1000 functions in total with an extensive test suite.

    The 'RcppGSL' package provides an easy-to-use interface between 'GSL' data
 structures and R using concepts from 'Rcpp' which is itself a package that
 eases the interfaces between R and C++.

    This package also serves as a prime example of how to build a package
 that uses 'Rcpp' to connect to another third-party library. The 'autoconf'
 script, 'inline' plugin and example package can all be used as a stanza to
 write a similar package against another library.
Creates an easy way to use expression templates with R
             semantics on any iterator based structure.
MLPACK is an intuitive, fast, scalable C++ machine learning
   library, meant to be a machine learning analog to LAPACK. It
   aims to implement a wide array of machine learning methods
   and function as a Swiss army knife for machine learning
   researchers: MLPACK is from http://www.mlpack.org/: sources are included in the package.
A collection of open source libraries for numerical computing
    (numerical integration, optimization, etc.) and their integration with
    'Rcpp'.
Direct interface to Octave. The primary goal is to facilitate the
    port of Matlab/Octave scripts to R. The package enables to call any Octave
    functions from R and as well as browsing their documentation, passing
    variables between R and Octave, using R core RNGs in Octave, which ensures
    that stochastic computations are also reproducible.
High level functions for parallel programming with 'Rcpp'.
    For example, the 'parallelFor()' function can be used to convert the work of
    a standard serial "for" loop into a parallel one and the 'parallelReduce()'
    function can be used for accumulating aggregate or other values.
This package allows to display a progress bar in the R
    console for long running computations taking place in c++ code,
    and support for interrupting those computations even in multithreaded
    code, typically using OpenMP.
Connection to the 'Redis' key/value store using the
  C-language client library 'hiredis'. 'MsgPack' encoding is optional
  if the 'RcppMsgPack' package is detected. You can install via from
  the 'ghrr' drat repository listed below.
Provides fast and efficient routines for
    common rolling / windowed operations. Routines for the
    efficient computation of windowed mean, median,
    sum, product, minimum, maximum, standard deviation
    and variance are provided.
An R interface to the C++/Boost Shark machine learning library.
This package provides R with access to the Sequential 
 Monte Carlo Template Classes by Johansen (Journal of Statistical 
 Software, 2009, v30, i6).

    At present, two additional examples have been added, and the first 
 example from the JSS paper has been extended. Further integration 
 and extensions are planned.
The 'Streamulus' (template, header-only) library by
 Irit Katriel (at <https://github.com/iritkatriel/streamulus>)
 provides a very powerful yet convenient framework for stream
 processing. This package connects 'Streamulus' to R by providing 
 both the header files and all examples.
The configuration format defined by 'TOML' (which expands to
 "Tom's Obvious Markup Language") specifies an excellent format (described at
 <https://github.com/toml-lang/toml>) suitable for both human editing as well
 as the common uses of a machine-readable format. This package uses 'Rcpp' to
 connect the 'cpptoml' parser written by Chase Geigle (in modern C++11) to R.
This package provides access to some of the C level
        functions of the xts package.

  In its current state, the package is mostly a proof-of-concept to
        support adding useful functions, and does not yet add any of
        its own.
The Ziggurat generator for normally distributed random numbers,
 originally proposed by Marsaglia and Tsang (JSS, 2000), has been improved 
 upon a few times starting with Leong et al (JSS, 2005). This package provides
 an aggregation in order to compare different implementations. The goal is to
 provide an 'faster but good enough' alternative for use with R and C++ code. 

    The package is still in an early state. Unless you know what you are doing,
 sticking with the generators provided by R may be a good idea as these have
 been extremely diligently tested.
Implements Corpus Query Protocol functions based on the
        CWB software. Rely on CWB (GPL v2), PCRE (BSD licence), glib2
        (LGPL).
Aims at loading Criteo online advertising campaign data into R.
    Criteo <http://www.criteo.com/> is an online advertising service that enables
    advertisers to display commercial ads to web users. The package provides
    an authentication process for R with the Criteo API <http://kb.criteo.com/
    advertising/content/5/27/en/api.html>. Moreover, the package features an
    interface to query campaign data from the Criteo API. The data can be downloaded
    and will be transformed into a R data frame.
Client for various 'CrossRef' APIs, including 'metadata' search
    with their old and newer search APIs, get 'citations' in various formats
    (including 'bibtex', 'citeproc-json', 'rdf-xml', etc.), convert 'DOIs'
    to 'PMIDs', and 'vice versa', get citations for 'DOIs', and get links to
    full text of articles when available.
Provides easy symmetric file encryption using GPG with
    cryptographically strong defaults. Only symmetric encryption is 
    supported. GPG is pre-installed with most Linux distributions. 
    Windows users will need to install 'Gpg4win' (http://www.gpg4win.org/). 
    OS X users will need to install 'GPGTools' (https://gpgtools.org/).
Basic access to market infomation from the public API for the Crypto-Currency exchange Cryptsy.
R interface to the CSDP semidefinite programming library. Installs version 6.1.1 of CSDP from the COIN-OR website if required. An existing installation of CSDP may be used by passing the proper configure arguments to the installation command. See the INSTALL file for further details.
The numerical treatment of optimal switching problems in a finite time setting when the state evolves as a controlled Markov chain consisting of a uncontrolled continuous component following linear dynamics and a controlled Markov chain taking values in a finite set. The reward functions are assumed to be convex and Lipschitz continuous in the continuous state. The action set is finite.
Functions and objects for CUR matrix decomposition.
A wrapper for 'libcurl' <http://curl.haxx.se/libcurl/>
	Provides functions to allow one to compose general HTTP requests
        and provides convenient functions to fetch URIs, get & post
        forms, etc. and process the results returned by the Web server.
        This provides a great deal of control over the HTTP/FTP/...
        connection and the form of the request while providing a
        higher-level interface than is available just using R socket
        connections.  Additionally, the underlying implementation is
        robust and extensive, supporting FTP/FTPS/TFTP (uploads and
        downloads), SSL/HTTPS, telnet, dict, ldap, and also supports
        cookies, redirects, authentication, etc.
Functions to convert Rd to roxygen documentation. It can parse an
    Rd file to a list, create the roxygen documentation and update the original
    R script (e.g. the one containing the definition of the function)
    accordingly. This package also provides utilities which can help developers
    build packages using roxygen more easily. The formatR package can be used
    to reformat the R code in the examples sections so that the code will be
    more readable.
Shrunken Centroids Regularized Discriminant Analysis for
        the classification purpose in high dimensional data.
Provides basic functionalities for writing a module
    for http://datacanvas.io. The http://datacanvas.io is a big data
    analytics platform that helps data scientists to build, manage
    and share data pipelines.
Client for the web service methods provided
    by 'DataCite' (<https://www.datacite.org/>), including functions to interface with
    their 'OAI-PMH' 'metadata' service, and a 'RESTful' search 'API'. The 'API'
    is backed by 'SOLR', allowing expressive queries, including faceting,
    statistics on variables, and 'more-like-this' queries.
Fetches data from DataMarket.com, either as
    timeseries in zoo form (dmseries) or as long-form data
    frames (dmlist). Metadata including dimension structure
    is fetched with dminfo, or just the dimensions with
    dmdims.
Provides the tools to undertake estimation in
    Regression Discontinuity Designs. Both sharp and fuzzy designs are
    supported. Estimation is accomplished using local linear regression.
    A provided function will utilize Imbens-Kalyanaraman optimal
    bandwidth calculation. A function is also included to test the
    assumption of no-sorting effects.
Set of functions for Regression Discontinuity Design ('RDD'), for
    data visualisation, estimation and testing.
Data Envelopment Analysis for R, estimating robust DEA scores without and with environmental variables and doing returns-to-scale tests.
Get data from DEFRA's UK-AIR website. It basically scraps the HTML content.
The package provides functions for estimating the relevant
        dimension of a data set in feature spaces, applications to
        model selection, graphical illustrations and prediction.
Functions for creating plots and image files in a unified way regardless of output format (EPS, PDF, PNG, SVG, TIFF, WMF, etc.).  Default device options as well as scales and aspect ratios are controlled in a uniform way across all device types.  Switching output format requires minimal changes in code.  This package is ideal for large-scale batch processing, because it will never leave open graphics devices or incomplete image files behind, even on errors or user interrupts.
A client library for 'The Guardian' (https://www.guardian.com/)
    and their API, this package allows users to search for Guardian articles and
    retrieve both the content and metadata.
The package has many function that helps to perform
        various quality check on the data.It basically provides many
        function that helps in performing Extrapolative data analysis.
The RDieHarder packages provides an R interface to 
  the dieharder suite of random number generators and tests that 
  was developed by Robert G. Brown and David Bauer, extending 
  earlier work by George Marsaglia and others.
Analysis of distance sampling data collected on line transect surveys.  Estimates distance-based detection functions and abundances.
Imports real-time thermo cycler (qPCR) data from Real-time PCR
    Data Markup Language (RDML) format files and transforms to the appropriate
    formats of the 'qpcR' and 'chipPCR' packages. Contains a dendrogram
    visualization for the structure of RDML object.
Control the Java software Discourse Network Analyzer (DNA) from 
  within R. Network matrices, statement frequency time series and attributes 
  of actors can be transferred directly into R.
Comprehensive toolkit for generating various numerical                                 representation schemes of DNA sequence. The descriptors and similarity
    scores included are extensively used in bioinformatics and chemogenomics.
A wrapper for the Deutsche Nationalbibliothek (German National Library) API, available at <http://www.dnb.de>. The German National Library is the German central archival library, collecting, archiving, bibliographically classifying all German and German-language publications from 1913, foreign publications about Germany, translations of German works, and the works of German-speaking emigrants published abroad between 1933 and 1945. A personal access token is required for usage.
Get the category of content hosted by a domain. Use Shallalist <http://shalla.de/>,  
    Virustotal (which provides access to lots of services) <https://www.virustotal.com/>, 
    McAfee <https://www.trustedsource.org/>, Alexa <https://aws.amazon.com/awis/>, 
    DMOZ <http://www.dmoz.org/>, or validated machine learning classifiers based on 
    Shallalist data to learn about the kind of content hosted by a domain.
Functions for manipulation of Rd objects, including
             function reprompt() for updating existing Rd
             documentation for functions, methods and classes and
             function rebib() for import of references from 'bibtex'
             files. There is also a macro for importing 'bibtex'
             references which an be used in Rd files and 'roxygen'
             comments without importing this package.
Regression-discontinuity (RD) designs are quasi-experimental research designs popular in social, behavioral and natural sciences. The RD design is usually employed to study the (local) causal effect of a treatment, intervention or policy. This package provides tools for data-driven graphical and analytical statistical inference in RD	designs: rdrobust() to construct local-polynomial point estimators and robust confidence intervals for average treatment effects at the 	cutoff in Sharp, Fuzzy and Kink RD settings, rdbwselect() to perform bandwidth selection for the different procedures implemented, and rdplot() to conduct exploratory data analysis (RD plots).
Provides full programmatic access to the Dropbox file hosting platform (dropbox.com), including support for all standard file operations.
Interface to the Dryad Solr API, their OAI-PMH service, and
    fetch datasets.
Provides functionality for carrying out estimation
    with data collected using Respondent-Driven Sampling. This includes
    Heckathorn's RDS-I and RDS-II estimators as well as Gile's Sequential
    Sampling estimator. The package is part of the "RDS Analyst" suite of
    packages for the analysis of respondent-driven sampling data.
R interface to DSDP semidefinite programming library. The DSDP software is a free open source implementation of an interior-point method for semidefinite programming. It provides primal and dual solutions, exploits low-rank structure and sparsity in the data, and has relatively low memory requirements for an interior-point method.
Provides a threads-type programming environment for R.
   The package gives the R programmer the clearer, more concise
   shared memory world view, and in some cases gives superior
   performance as well.  In addition, it enables parallel processing on
   very large, out-of-core matrices.
This package provides an R interface to Pete Warden's Data
        Science Toolkit.  See www.datasciencetoolkit.org for more
        information.  The source code for this package can be found at
        github.com/rtelmore/RDSTK Happy hacking!
The rDVR package allows you to start stop and save a video server
    from within R. It does this by way of a REST interface to a JAVA service.
    The jar binary relies on the screen recorder included in the Monte Media
    Library (CC BY 3.0 licence) developed by Werner Randelshofer
    (http://http://www.randelshofer.ch/monte/). The REST interface was modified
    from https://github.com/tuenti/VideoRecorderService which has an Apache
    licence.
Routines for developing models that describe reaction and advective-diffusive transport in one, two or three dimensions.
  Includes transport routines in porous media, in estuaries, and in bodies with variable shape.
Identifies and reads Windows BMP, JPEG and PNG format bitmap
    images. Identification defaults to the use of the magic number embedded in
    the file rather than the file extension. Reading of JPEG and PNG image
    depends on libjpg and libpng libraries. See file INSTALL for details if
    necessary.
Reads data files acquired by MALDI-TOF MS on Bruker Daltonics
    machines of the *flex series.
Combine multiple data files from a common directory.
    The data files will be read into R and bound together, creating a
    single large data.frame. A general function is provided along with
    a specific function for data that was collected using the open-source
    experiment builder 'OpenSesame' <http://osdoc.cogsci.nl/>.
Functions for reading and decompressing the DBC (compressed DBF) files. Please note that this is the file format used by the Brazilian Ministry of Health (DATASUS) to publish healthcare datasets. It is not related to the FoxPro or CANdb DBC file formats.
A set of functions to simplify reading data from files. The main function, reader(), should read most common R datafile types without needing any parameters except the filename. Other functions provide simple ways of handling file paths and extensions, and automatically detecting file format and structure.
Functions for reading data sets in different formats
  for testing machine learning tools are provided. This allows to run
  a loop over several data sets in their original form, for example
  if they are downloaded from UCI Machine Learning Repository.
  The data are not part of the package and have to be downloaded
  separately.
Functions for reading mass spectrometry data in mzXML format.
Read ODS (OpenDocument Spreadsheet) and puts them into data frames.
Read flat/tabular text files from disk (or a connection).
Function to read and write the 'Stata' file format.
Import excel files into R. Supports '.xls' via the embedded
    'libxls' C library (http://sourceforge.net/projects/libxls/) and '.xlsx' via
    the embedded 'RapidXML' C++ library (http://rapidxml.sourceforge.net). Works on
    Windows, Mac and Linux without external dependencies.
The RealVAMs package fits a multivariate value-added model (VAM) (see Broatch and Lohr 2012) with normally distributed test scores and a binary outcome indicator. This material is based upon work supported by the National Science Foundation under grants DRL-1336027 and DRL-1336265.
Resampling methods for adaptive linear model selection.
        These can be thought of as extensions of the Akaike information
        criterion that account for searching among candidate models.
The rearrangement operator (Hardy,
        Littlewood, and Polya 1952) for univariate, bivariate, and
        trivariate point estimates of monotonic functions. The package
        additionally provides a function that creates simultaneous
        confidence intervals for univariate functions and applies the
        rearrangement operator to these confidence intervals.
Collection of analysis methods used in regional and urban economics and (quantitative) economic geography, e.g. measures of inequality, regional disparities, regional specialization and spatial interaction models.
Kiefer-Wolfowitz maximum likelihood estimation for mixture models
    and some other density estimation and regression methods based on convex
    optimization.
A programmatic client for the eBird database, including functions
    for searching for bird observations by geographic location (latitude,
    longitude), eBird hotspots, location identifiers, by notable sightings, by
    region, and by taxonomic name.
R functions for random univariate and multivariate finite mixture model generation, estimation, clustering and classification. Variables can be continuous, discrete, independent or dependent and may follow normal, lognormal, Weibull, gamma, binomial, Poisson or Dirac parametric families.
Build regular expressions piece by piece using human readable code.
    This package is designed for interactive use.  For package development, use
    the rebus.* dependencies.
Build regular expressions piece by piece using human readable code.
    This package contains core functionality, and is primarily intended to be
    used by package developers.
Build regular expressions piece by piece using human readable code.
    This package contains date and time functionality, and is primarily intended
    to be used by package developers.
Build regular expressions piece by piece using human readable code.
    This package contains number-related functionality, and is primarily intended
    to be used by package developers.
Build regular expressions piece by piece using human readable code.
    This package contains Unicode functionality, and is primarily intended to be
    used by package developers.
Relevant Component Analysis (RCA) tries to find a linear
    transformation of the feature space such that the effect of irrelevant
    variability is reduced in the transformed space.
The 'Echo nest' <http://the.echonest.com> is the industry's leading
    music intelligence company, providing developer with deepest understanding of
    music content and music fans. This package can be used to access artist's data
    including songs, blogs, news, reviews etc. Song's data including audio summary,
    style, danceability, tempo etc can also be accessed.
Pathways in a database could have many redundancies among
        them. This package allows the user to set a maximum value for
        the proportion of these redundancies.
Beta-diversity indices provide dissimilarity matrices with particular distribution of data requiring specific treatment. For example the high frequency of ties and zero values in turnover indices produces hierarchical cluster dendrograms whose topology and bootstrap supports are affected by the order of rows in the original matrix. Moreover, biogeographical regionalization can be facilitated by a combination of hierarchical clustering and multi-dimensional scaling. The recluster package provides robust techniques to analyze pattern of similarity in species composition.
Provides an interface and a C++ implementation of the RecMap MP2
  construction heuristic (see 'citation("recmap")' for details). This algorithm
  draws maps according to a given statistical value (e.g. election results,
  population or epidemiological data). The basic idea of the RecMap algorithm is
  that each map region (e.g. different countries) is represented by a
  rectangle. The area of each rectangle represents the statistical value given
  as input (maintain zero cartographic error). Documentation about 'RecMap' is
  provided by a vignette included in this package and a 'RecMap gallery' site at
  <http://cartodraw.science/recmap/gallery>.
Simple, easy to use, and flexible functionality for recoding variables. It allows for simple piecewise definition of transformations.
Provides a research infrastructure to test and develop
    recommender algorithms including UBCF, IBCF, FunkSVD and association
    rule-based algorithms.
Provides the Book-Crossing Dataset for the package recommenderlab.
Provides the Jester Dataset for package recommenderlab.
Functions to reconstruct sessions from web log or other user trace data
             and calculate various metrics around them, producing tabular,
             output that is compatible with 'dplyr' or 'data.table' centered processes.
Provides functions for linking and de-duplicating data sets.
  Methods based on a stochastic approach are implemented as well as 
  classification algorithms from the machine learning domain.
Provide methods to record data provenance about R script
    executions. Provenance data includes files that were read and written by
    the script, along with information about the execution, such as start time
    end time, the R modules loaded during the execution, and other information
    describing the execution environment.
Functions for generating k-record values and k-record
        times
R wrapper of the 'libmf' library
    (http://www.csie.ntu.edu.tw/~cjlin/libmf/) for recommender
    system using matrix factorization. It is typically used to
    approximate an incomplete matrix using the product of two
    matrices in a latent space. Other common names for this task
    include "collaborative filtering", "matrix completion",
    "matrix recovery", etc. High performance multi-core parallel
    computing is supported in this package.
Includes algorithms to facilitate the assessment of extinction risk of species according to the IUCN (International Union for Conservation of Nature, see <http://www.iucn.org> for more information) red list criteria.
Functions that fit gamma frailty model with spline or piecewise
    constant baseline rate function for recurrent event data, compute and
    plot parametric mean cumulative function (MCF) from a fitted model
    as well as nonparametric sample MCF (Nelson-Aalen estimator) are provided.
    Most functions are S4 methods that produce S4 class objects.
Provides functions used in the 'R: Einführung durch angewandte Statistik' (second edition).
Access data stored in REDCap databases using the Application
    Programming Interface (API).  REDCap (Research Electronic Data CAPture) is
    a web application for building and managing online surveys and databases
    developed at Vanderbilt University.  The API allows users to access data
    and project meta data (such as the data dictionary) from the web
    programmatically.  The redcapAPI package facilitates the process of
    accessing data with options to prepare an analysis-ready data set
    consistent with the definitions in a database's data dictionary.
Encapsulates functions to streamline calls from R to the REDCap
    API.  REDCap (Research Electronic Data CAPture) is a web application for
    building and managing online surveys and databases developed at Vanderbilt
    University.  The Application Programming Interface (API) offers an avenue
    to access and modify data programmatically, improving the capacity for
    literate and reproducible programming.
Reddit is an online bulletin board and a social networking website
    where registered users can submit and discuss content. This package uses
    Reddit API to extract Reddit data using Reddit API. Note that due to the API
    limitations, the number of comments that can extracted is limited to 500 per
    thread. The package consists of 4 functions, one for extracting relevant URLs,
    one for extracting features out of given URLs, one that does both together and
    one that constructs graphs based on the structure of a thread.
Computes quality control to daily precipitation datasets, reconstructs the original series by estimating precipitation in missing values, creates new series in a specified pair of coordinates and creates grids.
Enables researchers to sample redistricting plans from a pre-specified target distribution using a Markov Chain Monte Carlo algorithm. The package allows for the implementation of various constraints in the redistricting process such as geographic compactness and population parity requirements.  The algorithm also can be used in combination with efficient simulation methods such as simulated and parallel tempering algorithms. Tools for analysis such as inverse probability reweighting and plotting functionality are included. The package implements methods described in Fifield, Higgins, Imai and Tarr (2015) ``A New Automated Redistricting Simulator Using Markov Chain Monte Carlo,'' working paper available at <http://http://imai.princeton.edu/research/files/redist.pdf>.
Provides methods to parse, query and serialize information
    stored in the Resource Description Framework (RDF). RDF is described at <http://www.w3.org/TR/rdf-primer>.
    This package supports RDF by implementing an R interface to the Redland RDF C library,
    described at <http://librdf.org/docs/api/index.html>. In brief, RDF provides a structured graph
    consisting of Statements composed of Subject, Predicate, and Object Nodes.
Contains C++ compiled objects that use time delay embedding to perform state-space reconstruction and nonlinear forecasting and an R interface to those objects using 'Rcpp'. It supports both the simplex projection method from Sugihara & May (1990) <DOI:10.1038/344734a0> and the S-map algorithm in Sugihara (1994) <DOI:10.1098/rsta.1994.0106>. In addition, this package implements convergent cross mapping as described by Sugihara et al. (2012) <DOI:10.1126/science.1227079>.
This package estimates regression trees with random
        effects as a way to use data mining techniques to describe
        longitudinal or panel data.
small package with functions for creating references,
        reading from and writing to references and a memory efficient
        refdata type that transparently encapsulates matrices and
        data.frames
This is a collection of tools to allow the medical professional to
 calculate appropriate reference ranges (intervals) with confidence intervals around
 the limits for diagnostic purposes.
Reference-free method for conducting EWAS while deconvoluting DNA methylation arising as mixtures of cell types.  
   The older method (Houseman et al., 2014) is similar to surrogate variable analysis (SVA and ISVA), except that it makes additional use of a biological mixture assumption.
   The newer method (Houseman et al., 2016) is similar to non-negative matrix factorization, with additional constraints and additional utilities.
Contains functionality for import and managing of downloaded genome annotation Data from Ensembl genome browser (European Bioinformatics Institute) and from UCSC genome browser (University of California, Santa Cruz) and annotation routines for genomic positions and splice site positions.
Provides tools for importing and working with bibliographic
    references. It greatly enhances the 'bibentry' class by providing a class
    'BibEntry' which stores 'BibTeX' and 'BibLaTeX' references, supports 'UTF-8'
    encoding, and can be easily searched by any field, by date ranges, and by
    various formats for name lists (author by last names, translator by full names,
    etc.). Entries can be updated, combined, sorted, printed in a number of styles,
    and exported. 'BibTeX' and 'BibLaTeX' '.bib' files can be read into 'R' and
    converted to 'BibEntry' objects. Interfaces to 'NCBI Entrez', 'CrossRef', and
    'Zotero' are provided for importing references and references can be created
    from locally stored 'PDF' files using 'Poppler'. Includes functions for citing
    and generating a bibliography with hyperlinks for documents prepared with
    'RMarkdown' or 'RHTML'.
A tool for refining data frame with formulas.
Provides subsets with reference semantics, i.e. subsets
    which automatically reflect changes in the original object, and which
    optionally update the original object when they are changed.
Methods for regression for functional
    data, including function-on-scalar, scalar-on-function, and
    function-on-function regression. Some of the functions are applicable to
    image data.
Interactive plotting for functional data analyses.
Methods for regressing scalar responses on functional or image predictors, via transformation to the wavelet domain and back.
Contains basic tools for visualizing, interpreting, and building regression models.  It has been designed for use with the book Introduction to Regression and Modeling with R by Adam Petrie, Cognella Publishers.
This package clusters regression coefficients using the methods of clustering through linear regression models (CLM) (Qin and Self 2006). Maximum likelihood approach is used to infer the parameters for each cluster. Bayesian information criterion (BIC) combined with Bootstrapped maximum volume (BMV) criterion are used to determine the number of clusters.
Detecting mislabeled samples in genetic data.
Produces population distribution of disease risk and statistical risk categories, and predicts risks for individuals with genotype information.
Provides wrappers around base::grep() where the first argument
    is standardized to take the data object. This makes it less of a pain to use
    regular expressions with 'magrittr' or other pipe operators.
An R framework for constructing and managing human
        readable regular expressions.  It aims to provide tools that
        enable the user to write regular expressions in a way that is
        similar to the ways R code is written. The tools allow the user
        to (1) write in smaller, modular, named, sub-expressions, (2)
        write top to bottom, rather than a single string (3) comment
        individual chunks, (4) indent expressions to clearly present
        regular expression groups, (5) add vertical line spaces and R
        comments (i.e., #), and (6) test the validity of the
        concatenated expression and the modular sub-expressions.
Provides a generic infrastructure for creating and using registries.
Regularized (polychotomous) logistic regression 
  by Gibbs sampling. The package implements subtly different 
  MCMC schemes with varying efficiency depending on the data type 
  (binary v. binomial, say) and the desired estimator (regularized maximum
  likelihood, or Bayesian maximum a posteriori/posterior mean, etc.) through a 
  unified interface.
Tools are provided for
  (1) nonparametric regression (kernel, local linear),
  (2) semiparametric regression (single index, additive models), and
  (3) quantile regression (linear, kernel).
Functions to fit Gaussian linear model by maximising the
        residual log likelihood where the covariance structure can be
        written as a linear combination of known matrices.  Can be used
        for multivariate models and random effects models.  Easy
        straight forward manner to specify random effects models,
        including random interactions. Code now optimised to use
        Sherman Morrison Woodbury identities for matrix inversion in
        random effects models. We've added the ability to fit models
        using any kernel as well as a function to return the mean and
        covariance of random effects conditional on the data (BLUPs).
The expander functions rely on the mathematics developed for the Hessian-definiteness invariance theorem for linear projection transformations of variables, described in authors' paper, to generate the full, high-dimensional gradient and Hessian from the lower-dimensional derivative objects. This greatly relieves the computational burden of generating the regression-function derivatives, which in turn can be fed into any optimization routine that utilizes such derivatives. The theorem guarantees that Hessian definiteness is preserved, meaning that reasoning about this property can be performed in the low-dimensional space of the base distribution. This is often a much easier task than its equivalent in the full, high-dimensional space. Definiteness of Hessian can be useful in selecting optimization/sampling algorithms such as Newton-Raphson optimization or its sampling equivalent, the Stochastic Newton Sampler. Finally, in addition to being a computational tool, the regression expansion framework is of conceptual value by offering new opportunities to generate novel regression problems.
Performs Random Subspace Method (RSM) for high-dimensional linear regression to obtain variable importance measures. The final model is chosen based on validation set or Generalized Information Criterion.
Functions for fitting linear and generalized linear models with variable selection. The functions can automatically do Stepwise Regression, Lasso or Elastic Net as variable selection methods. Lasso and Elastic net are improved and handle factors better (they can either include or exclude all factor levels).
Uses both ridge and lasso penalties (and extensions) to penalize
    specific parameters in structural equation models. The package offers additional
    cost functions, cross validation, and other extensions beyond traditional SEM.
Computes linear Bayesian spectral estimates from multirate
	data for second-order stationary time series. Provides credible
	intervals and methods for plotting various spectral estimates.
For a sequence of event occurence times, we are interested in
  finding subsequences in it that are too "regular". We define regular as being
  significantly different from a homogeneous Poisson process. The departure from
  the Poisson process is measured using a L1 distance. See Di and Perlman 2007
  for more details.
Functions for unary and binary regression tests
Functions for the detection of footprints of selection on
        dense SNP data using Extended Homozygosity Haplotype (EHH)
        based tests. The package includes computation of EHH, iHS
        (within population) and Rsb or XP-EHH (across pairs of populations)
        statistics. Various plotting functions are also included to
        facilitate visualization and interpretation of the results.
Search R files for not installed packages and run install.packages.
Derives point estimates with confidence intervals for Bennett et als S, Cohen's kappa and weighted kappa, Gwet's AC1 and AC2, Krippendorff's alpha, and Scott's pi.
Item analysis with alpha standard error and principal axis
        factoring for continuous variable scales (with plots).
relaimpo provides several metrics for assessing relative importance in linear models. These can be printed, plotted and bootstrapped. The recommended metric is lmg, which provides a decomposition of the model explained variance into non-negative contributions. There is a version of this package available that additionally provides a new and also recommended metric called pmvd. If you are a non-US user, you can download this extended version from Ulrike Groempings web site.
Inference of relatedness coefficients from a bi-allelic genotype matrix using a Maximum Likelihood estimation.
Data structures and algorithms for k-ary relations with
  arbitrary domains, featuring relational algebra, predicate functions,
  and fitters for consensus relations.
package relax contains some functions for report
    writing, presentation, and programming: relax(), tangleR(),
    weaveR(), (g)slider(). "relax" is written in R and Tcl/Tk.
    relax creates a new window (top level Tcl/Tk widget) that consists
    of two text fields and some buttons and menus.
    Text (chunks) and code (chunks) are inserted in the upper text field (report field).
    Code chunks are evaluated by clicking on EvalRCode.
    Results are shown in the lower text field (output field) and
    will be transferred to the report field by pressing on Insert.
    In this way you get correct reports. These reports can be
    loaded again in cause of presentation, modification and result checking.
    tangleR() and weaveR() implement a plain kind of tangling
    and weaving. gslider() and slider() are designed to define sliders for interactive
    experiments in a simple way.
    The syntax rules of code chunks and text chunks are defined by 
    the noweb system proposed by Norman Ramsey
    (http://www.eecs.harvard.edu/~nr/noweb/intro.html).
Extends the glmnet package with "relaxation", done by running glmnet once on the entire predictor matrix, then again on each different subset of variables from along the regularization path. Relaxation may lead to improved prediction accuracy for truly sparse data generating models, as well as fewer false positives (i.e. fewer noncontributing predictors in the final model). Penalty may be lasso (alpha = 1) or elastic net (0 < alpha < 1). For this version, family may be "gaussian" or "binomial" only. Takes advantage of fast FORTRAN code from the glmnet package.
Relaxed Lasso is a generalisation of the Lasso shrinkage
        technique for linear regression. Both variable selection and
        parameter estimation is achieved by regular Lasso, yet both
        steps do not necessarily use the same penalty parameter. The
        results include all standard Lasso solutions but allow often
        for sparser models while having similar or even slightly better
        predictive performance if many predictor variables are present.
        The package depends on the LARS package.
R functions for the comparison of distributions.
 This includes nonparametric estimation of the relative distribution
 PDF and CDF and numerical summaries as described in 
 "Relative Distribution Methods in the Social Sciences" 
 by Mark S. Handcock and Martina Morris, 
 Springer-Verlag, 1999, Springer-Verlag, ISBN 0387987789
This function computes the relative entropy (H) as an index for qualitative variation of a factor.
Functions for estimating parameters in software reliability models.
             Only infinite failure models are implemented so far.
A variety of tools useful for performing structural
        reliability analysis, such as with structure function and
        system signatures.  Plans to expand more widely.
A collection of utilities for some reliability
        models/probability distributions.
Functions to facilitate inference on the relative importance of predictors in a linear or generalized linear model, and a couple of useful Tcl/Tk widgets.
Makes relationship inference involving DNA mixtures with unknown profiles.
A set of tools to explore the behaviour statistics used for forensic DNA interpretation when close relatives are involved. The package also offers some useful tools for exploring other forensic DNA situations.
Various functions for relative survival analysis.
Classes and functions for analyzing the performance of portfolios relative to a benchmark.
Calculate endogenous network effects in event sequences and fit relational event models (REM): Using network event sequences (where each tie between a sender and a target in a network is time-stamped), REMs can measure how networks form and evolve over time. Endogenous patterns such as popularity effects, inertia, similarities, cycles or triads can be calculated and analyzed over time.
A small wrapper on 'regexpr' to extract the matches and
    captured groups from the match of a regular expression to a character
    vector.
remix provides remix, a quick and easy function for
        describing datasets. It can be view as a mix of cast (in
        package reshape) and summary.formula (in package Hmisc).
Implements TRACDS (Temporal Relationships 
    between Clusters for Data Streams), a generalization of 
    Extensible Markov Model (EMM). TRACDS adds a temporal or order model
    to data stream clustering by superimposing a dynamically adapting
    Markov Chain. Also provides an implementation of EMM (TRACDS on top of tNN 
    data stream clustering). Development of this 
    package was supported in part by NSF IIS-0948893 and R21HG005912 from 
    the National Human Genome Research Institute.
remMap is developed for fitting multivariate response regression models under the high-dimension-low-sample-size setting
Empirical orthogonal teleconnections in R.
    'remote' is short for 'R(-based) EMpirical Orthogonal TEleconnections'.
    It implements a collection of functions to facilitate empirical
    orthogonal teleconnection analysis. Empirical Orthogonal Teleconnections
    (EOTs) denote a regression based approach to decompose spatio-temporal
    fields into a set of independent orthogonal patterns. They are quite
    similar to Empirical Orthogonal Functions (EOFs) with EOTs producing
    less abstract results. In contrast to EOFs, which are orthogonal in both
    space and time, EOT analysis produces patterns that are orthogonal in
    either space or time.
A set of utilities for controlling a remote R session
    from a local one.  Simply set up a server (see package vignette
    for more details) and connect to it from your local R session,
    including 'RStudio'.  Network communication is handled
    by the 'ZeroMQ' library by way of the 'pbdZMQ' package.  The 
    client/server framework is a custom 'REPL'.
Download and install R packages stored in 'GitHub',
    'BitBucket', or plain 'subversion' or 'git' repositories. This package
    is a lightweight replacement of the 'install_*' functions in 'devtools'.
    Indeed most of the code was copied over from 'devtools'.
Fits linear models with endogenous regressors using Internal
    Instrumental Variables (IIV) methods. These are statistical techniques to
    correct for endogeneity when no strong, valid external instrumental variables
    are available. The first version of the package offers two methods, the Latent
    Instrumental Variables (Ebbes et al., 2005) and Lewbel's higher moments approach
    (Lewbel, 1997). In a second version of the package, two other methods will be
    added, joint estimation using copulas (Park and Gupta, 2012) and multilevel GMM
    (Kim and Frees, 2007).
Peaks Over Threshold (POT) or 'methode du renouvellement'. The distribution for the exceedances can be chosen, and heterogeneous data (including historical data or block data) can be used in a Maximum-Likelihood framework.
Provides an R interface to the NCBI's EUtils API
    allowing users to search databases like GenBank and PubMed, process the
    results of those searches and pull data into their R sessions.
An R interface to the Encyclopedia of Life API. Includes functions for downloading and extracting information off the EOL pages.
Tools for performing the leaf reordering for the dendrogram that preserves the hierarchical clustering result and at the same time tries to group instances from the same class together.
Performs genome-wide association studies on individuals that are both related and have repeated measurements.
Global tests for expression data of high-dimensional sets of
    molecular features.
Estimation of Bayes and local Bayes false discovery rates for
    replicability analysis.
Supplies classes and routines to convert data to and from EpiJSON files. This package provides conversion functions for data.frame, sp and obkData.
Test for effects of  both individual factors and their interaction on  replicated spatial patterns in a two factorial design.
A common problem faced by journal reviewers and authors is the question of
     whether the results of a replication study are consistent with the original
     published study. One solution to this problem is to examine the effect size
     from the original study and generate the range of effect sizes that could
     reasonably be obtained (due to random sampling) in a replication attempt
     (i.e., calculate a replication interval). If a replication effect size falls
     outside the replication interval, then that effect likely did not occur
     due to the effects of sampling error alone. Alternatively, if a replication
     effect size falls within the replication interval, then the replication
     effect could have reasonably occurred due to the effects of sampling error
     alone. This package has functions that calculate the replication interval for 
     the correlation (i.e., r), standardized mean difference (i.e., d-value), and mean. 
     The calculations used in version 2.0.0 and onward differ from past calculations 
     due to feedback during the journal review process. The new calculations allow 
     for a more precise interpretation of the replication interval.
Tools to load 'R' packages
    and automatically generate BibTeX files citing them as well as load and
    cache plain-text and 'Excel' formatted data stored on 'GitHub', and
    from other sources.
A data manager meant to avoid manual storage/retrieval of
    data to/from the file system. It builds one (or more) centralized
    repository where R objects are stored with annotations, tags,
    dependency notes, provenance traces. It also provides navigation
    tools to easily locate, load and edit previously stored resources.
Fits linear models to repeated ordinal scores using GEE methodology.
Create 'Microsoft Word' document (>=2007),
    'Microsoft PowerPoint' document (>=2007) and 'HTML' documents from R. There are
    several features to let you format and present R outputs ; e.g. Editable
    Vector Graphics, functions for complex tables reporting, reuse of corporate
    template document. You can use the package as a tool for fast reporting
    and as a tool for reporting automation. The package does not require
    any installation of Microsoft product to be able to write Microsoft files.
External jars required for package ReporteRs. ReporteRs is an 
	R package for creating Microsoft Word document (>=2007), Microsoft 
	Powerpoint document (>=2007) and HTML documents from R.
Provides a system for reporting messages, which provides certain useful features over the standard R system, such as the incorporation of output consolidation, message filtering, expression substitution, automatic generation of stack traces for debugging, and conditional reporting based on the current "output level".
reportRx is a set of tools that integrates with LaTeX and knitr to
    automatically generate generate reproducible clinical reports. Fuctions to
    automatically produce demoraphic tables, outcome summaries, univariate and
    multivariate analysis results and more are included.
Assists in writing reports and
        presentations by providing a frame work that brings together
        existing R, LaTeX/.docx and Pandoc tools. The package is
        designed to be used with RStudio, MiKTex/Tex Live/LibreOffice,
        knitr, knitcitations, Pandoc and pander. The user will want to
        download these free programs/packages to maximize the
        effectiveness of the reports package.  Functions with two
        letter names are general text formatting functions for copying
        text from articles for inclusion as a citation.
These functions are especially helpful when writing reports of data analysis using Sweave.
An R Interface to 'EPP-lab' v1.0. 'EPP-lab' is a Java program for
    projection pursuit using genetic algorithms written by Alain Berro and S. Larabi
    Marie-Sainte and is included in the package. The 'EPP-lab' sources are available
    under https://github.com/fischuu/EPP-lab.git.
String and binary representations of objects for several formats /
    mime types.
Contains workhorse function jrparams(), as well as two
        helper functions Mboxtest() and JRsMahaldist(), and four
        example data sets.
Reproducible research tools automates the creation of an analysis directory structure and work flow. There are R markdown
  skeletons which encapsulate typical analytic work flow steps. Functions will create appropriate modules which may
  pass data from one step to another.
The reproducer R package includes data analysis functions and data
    sets (e.g., related to software defect prediction) to streamline reproducible
    research in software engineering.
Predicting regulatory DNA elements based on epigenomic signatures. This package is more of a set of building blocks than a direct solution. REPTILE regulatory prediction pipeline is built on this R package. See <https://github.com/yupenghe/REPTILE> for more information.
This package contains the function run.eqs() which calls
        an EQS script file, executes the EQS estimation, and, finally,
        imports the results as R objects. These two steps can be
        performed separately: call.eqs() calls and executes EQS,
        whereas read.eqs() imports existing EQS outputs as objects into
        R. It requires EQS 6.2 (build 98 or higher).
High level and easy 'HTTP' client for 'R'. Provides functions for
    building 'HTTP' queries, including query parameters, body requests, headers,
    authentication, and more.
General purpose R client for 'ERDDAP' servers. Includes
    functions to search for 'datasets', get summary information on
    'datasets', and fetch 'datasets', in either 'csv' or 'netCDF' format.
    'ERDDAP' information: http://upwell.pfeg.noaa.gov/erddap/information.html.
Executes a post-rotation algorithm that REorders and/or REflects FACTors (REREFACT) for each replication of a simulation study with exploratory factor analysis.
A collection of regression models for recurrent event process and failure time.
Bootstrap, permutation tests, and other resampling functions,
	featuring easy-to-use syntax.
Package of data sets from "Mathematical Statistics
    with Resampling in R" (2011) by Laura Chihara and Tim Hesterberg.
Implementation of functions for spectral similarity/dissimilarity
    analysis and memory-based learning (MBL) for non-linear modeling
    in complex spectral datasets. In chemometrics MBL is also known as local modeling.
Measure single-storage water supply system performance using resilience,
    reliability, and vulnerability metrics; assess storage-yield-reliability
    relationships; determine no-fail storage with sequent peak analysis; optimize
    release decisions for water supply, hydropower, and multi-objective reservoirs
    using deterministic and stochastic dynamic programming; generate inflow
    replicates using parametric and non-parametric models; evaluate inflow
    persistence using the Hurst coefficient.
Reshape lets you flexibly restructure and aggregate data using just two functions: melt and cast.
Flexibly restructure and aggregate data using just two
    functions: melt and dcast (or acast).
A tool for learning how to use the functions, melt,
        acast/dcast, and ddply.
electrical properties of resistor networks.
Resource Selection (Probability) Functions for
  use-availability wildlife data as described in
  Lele and Keim (2006, Ecology 87, 3021--3028),
  and Lele (2009, J. Wildlife Management 73, 122--127).
Provides tools to enable the researcher to more precisely conduct
    respirometry experiments. Strong emphasis is on aquatic respirometry. Tools
    focus on helping the researcher setup and conduct experiments. Analysis of the
    resulting data is not a focus since analyses are often specific to a particular
    setup, and thus are better created by the researcher individually. This
    package provides tools for intermittent, flow-through, and closed respirometry
    techniques.
Contains three functions that query AuriQ Systems' Essentia Database and return the results in R. 'essQuery' takes a single Essentia command and captures the output in R, where you can save the output to a dataframe or stream it directly into additional analysis. 'read.essentia' takes an Essentia script and captures the output csv data into R, where you can save the output to a dataframe or stream it directly into additional analysis. 'capture.essentia' takes a file containing any number of Essentia commands and captures the output of the specified statements into R dataframes. Essentia can be downloaded for free at http://www.auriq.com/documentation/source/install/index.html.
Contains easy scripts which can be used to quickly create GUI windows for 'Rcmdr' Plugins. No knowledge about Tcl/Tk is required to make use of these scripts (These scripts are a generalisation of the template scripts in the 'RcmdrPlugin.BiclustGUI' package).
Provides the user with functions to develop their trading strategy,
    uncover actionable trading ideas, and monitor consensus shifts with
    crowdsourced earnings and economic estimate data directly from
    <www.estimize.com>. Further information regarding the web services this
    package invokes can be found at <www.estimize.com/api>.
The restlos package provides algorithms for robust estimation of location (mean and mode) and scatter based on minimum spanning trees (pMST), self-organizing maps (Flood Algorithm), Delaunay triangulations (RDELA), and nested minimum volume convex sets (MVCH). The functions are also suitable for outlier detection.
Debugging with restore points instead of break points. A restore
    point stores all local variables when called inside a function. The stored
    values can later be retrieved and evaluated in a modified R console that
    replicates the function's environment. To debug step by step, one can
    simply copy & paste the function body from the R script. Particularly
    convenient in combination with RStudio. See the github page for a tutorial.
Using a database, LaTeX and R easily build attractive resumes.
Reaction time analysis by maximum likelihood
Reconstructs retinae by morphing a flat surface with
    cuts (a dissected flat-mount retina) onto a curvilinear surface (the a
    standard retinal shape). It can estimate the position of a point on the
    intact adult retina to within 8 degrees of arc (3.6% of nasotemporal axis).
    The coordinates in reconstructed retinae can be transformed to visuotopic
    coordinates.
A collection of tools to import and structure the (currently) single-season
    event, game-log, roster, and schedule data available from http://www.retrosheet.org.
    In particular, the event (a.k.a. play-by-play) files can be especially difficult to parse.
    This package does the parsing on those files, returning the requested data in the most
    practical R structure to use for sabermetric or other analyses.
An interface to NCBI databases such as PubMed, GenBank, or GEO
    powered by the Entrez Programming Utilities (EUtils). The nine EUtils
    provide programmatic access to the NCBI Entrez query and database
    system for searching and retrieving biological data.
Simplified scenario testing and sensitivity analysis with R via a
    generalized function for one-factor-at-a-time (OFAT) sensitivity analysis,
    evaluation of parameter sets and (sampled) parameter permutations. Options
    for formatting output and parallel processing are also provided.
Computation of (direct and indirect) revealed preferences, fast non-parametric tests of rationality axioms (WARP, SARP, GARP), simulation of axiom-consistent data, and detection of axiom-consistent subpopulations.
R Markdown format for 'reveal.js' presentations, a framework 
  for easily creating beautiful presentations using HTML.
An implementation of the reverse ecology framework. Reverse ecology
    refers to the use of genomics to study ecology with no a priori assumptions
    about the organism(s) under consideration, linking organisms to their
    environment. It allows researchers to reconstruct the metabolic networks and
    study the ecology of poorly characterized microbial species from their
    genomic information, and has substantial potentials for microbial community
    ecological analysis.
Adjusts the weights of survey respondents so that the
        marginal distributions of certain variables fit more closely to
        those from a more precise source (e.g. Census Bureau's data).
A friendly interface for the construction of regular expressions.
Provides convenience functions to communicate with
    an Experigen server: Experigen (<http://github.com/aquincum/experigen>)
    is an online framework for creating  linguistic experiments,
    and it stores the results on a dedicated server. This package can be
    used to retrieve the results from the server, and it is especially
    helpful with registered experiments, as authentication with the server
    has to happen.
This package wraps some of the matrix exponentiation utilities from EXPOKIT (http://www.maths.uq.edu.au/expokit/), a FORTRAN library that is widely recommended for matrix exponentiation (Sidje RB, 1998. "Expokit: A Software Package for Computing Matrix Exponentials." ACM Trans. Math. Softw. 24(1): 130-156).  EXPOKIT includes functions for exponentiating both small, dense matrices, and large, sparse matrices (in sparse matrices, most of the cells have value 0).  Rapid matrix exponentiation is useful in phylogenetics when we have a large number of states (as we do when we are inferring the history of transitions between the possible geographic ranges of a species), but is probably useful in other ways as well.
Provides an interface to the Facebook API.
A collection of fast (utility) functions for data analysis. Fast covariance matrix calculation, Mahalanobis distance and column-wise variances are some of the functions.
Returns environmental data such as air temperature,
        precipitation rate and wind speed from the FetchClimate Web service (<http://fetchclimate.org/>)
        based on user specified arguments such as geographical regions or coordinates and time bounds.
This package facilitates searching for and downloading seismic time series in miniSEED format (a minimalist version of the Standard for the Exchange of Earthquake Data) from International Federation of Digital Seismograph Networks repositories. This package can also be used to gather information about seismic networks (stations, channels, locations, etc) and find historical earthquake data (origins, magnitudes, etc).
An R implementation of the random ferns classifier by Ozuysal et
    al., modified for generic and multi-label classification and featuring OOB error
    approximation and importance measure.
RFGLS  uses a generalized least-squares method to perform single-marker association analysis, 
  in datasets of nuclear families containing parents, twins, and/or adoptees
Variable selection tools for groups of variables and functional data based on a new grouped variable importance with random forests.
An interface to 'figshare' (http://figshare.com), a scientific repository to archive and assign 'DOIs' to data, software, figures, and more.
A file set refers to a set of files located in one or more directories on the file system.  This package provides classes and methods to locate, setup, subset, navigate and iterate such sets.  The API is designed such that these classes can be extended via inheritance to provide a richer API for special file formats.  Moreover, a specific name format is defined such that filenames and directories can be considered to have full names which consists of a name followed by comma-separated tags.  This adds additional flexibility to identify file sets and individual files.  NOTE: This package's API should be considered to be in an beta stage.  Its main purpose is currently to support the aroma.* packages, where it is one of the main core components; if you decide to build on top of this package, please contact the author first.
Japanese stock market from Yahoo!-finance-Japan
A set of add on tools for the 'randomForest' package.
A programmatic interface to http://www.fishbase.org, re-written
    based on an accompanying 'RESTful' API. Access tables describing over 30,000
    species of fish, their biology, ecology, morphology, and more. This package also
    supports experimental access to http://www.sealifebase.org data, which contains
    nearly 200,000 species records for all types of aquatic life not covered by
    'FishBase.'
A programmatic interface to 'openfisheries.org'. This package is
    part of the 'rOpenSci' suite (http://ropensci.org).
R estimation and inference for linear models.  Estimation
        is for general scores and a library of commonly used score
        functions is included.
Basic R interface for the FLANN C++ library written by Marius Muja and David Lowe.
             This package was written primarily for another package, 'rcss'. This packages utilises
             a few features from the FLANN C++ library. When I have time (and if there is sufficient
             demand), I will add more functions.
RFLPtools provides functions to analyse DNA fragment samples 
  (i.e. derived from RFLP-analysis) and standalone BLAST report files 
  (i.e. DNA sequence analysis).
A collection of tools for multivariate analysis of metabolomics
    data, which includes several preprocessing methods (normalization, scaling)
    and various exploration and data visualization techniques (Principal
    Components Analysis and Multi Dimensional Scaling). The core of the package
    is the Random Forest algorithm used for the construction, optimization and
    validation of classification models with the aim of identifying potentially
    relevant biomarkers.
Functionality required to efficiently use R with MarkLogic NoSQL Database Server, <http://www.marklogic.com/what-is-marklogic/>. Many basic and complex R operations are pushed down into the database, which removes the main memory boundary of R and allows to make full use of MarkLogic server. In order to use the package you need a MarkLogic Server version 8 or higher.
Various tools for handling fuzzy measures, calculating Shapley value and Interaction index,  Choquet and Sugeno integrals, as well as fitting fuzzy measures to empirical data are provided. Construction of fuzzy measures from empirical data is done by solving a linear programming problem by using 'lpsolve' package, whose source in C adapted to the R environment 
  is included. The description of the basic theory of fuzzy measures is in the manual in the Doc folder in this package.
R access to the 'FOAAS' (F... Off As A Service) web service is provided.
Graphics for statistics on a sphere, as applied to geological fault data, crystallography, earthquake focal mechanisms, radiation patterns, ternary plots and geographical/geological maps.  Non-double couple plotting of focal spheres and source type maps are included for statistical analysis of moment tensors.
Insert, update,
    retrieve, delete and bulk operate datasets with a SaaS based CRM
    Salesforce.com and a PaaS based application platform Force.com from R.
Contains all the code examples in the book "R for Dummies" (1st
    edition). You can view the table of contents as well as the sample code for each
    chapter.
A modified version (with great help from Ian J. Wilson) of Ian J. Wilson's program BATWING for calculating forensic trace-suspect match probabilities.
The R Formatter formats R source code. It is very much based on
    formatR, but tries to improve it by heuristics. For example, spaces can be
    forced around the division operator "/".
Estimate significance of importance metrics
    for a Random Forest model by permuting the response
    variable. Produces null distribution of importance
    metrics for each predictor variable and p-value of
    observed. Provides summary and visualization functions for 'randomForest' 
    results.
An R interface to a modified version of the Free Evolutionary Algorithm Kit FrEAK. FrEAK is a toolkit written in Java to design and analyze evolutionary algorithms. Both the R interface and an extended version of FrEAK are contained in the RFreak package. For more information on FrEAK see http://sourceforge.net/projects/freak427/.
An efficient C++ based implementation of "Follow The (Proximally) Regularized Leader" online learning algorithm.
  This algorithm was proposed in McMahan et al. (2013) <DOI:10.1145/2487575.2488200>.
Utilities for Random Forest model selection, class balance
    correction, significance test, cross validation and partial dependency
    plots.
Provides functions for accessing and retrieving data from the
    Google Analytics APIs (https://developers.google.com/analytics/). Supports
    OAuth 2.0 authorization. Package provides access to the Management, Core
    Reporting, Multi-Channel Funnels Reporting, Real Time Reporting and
    Metadata APIs. Access to all the Google Analytics accounts which the user
    has access to. Auto-pagination to return more than 10,000 rows of the
    results by combining multiple data requests. Also package provides
    shiny app to explore the core reporting API dimensions and metrics.
This package was created to analyze multi-level one-way
        experimental designs. It is designed to handle vectorized
        observation and factor data where there are unequal sample
        sizes and population variance homogeneity can not be assumed.
        To conduct the Gabriel test, create two vectors: one for your 
        observations and one for the factor level of each observation. 
        The function, rgabriel, conduct the test and save the output as
        a vector to input into the gabriel.plot function, which produces 
        a confidence interval plot for Multiple Comparison.
This package implements a Gamma convolution model for
        background correction.
A programmatic interface to the Web Service methods
    provided by the Global Biodiversity Information Facility ('GBIF').
    'GBIF' is a database of species occurrence records from sources all
    over the globe. 'rgbif' includes functions for searching for
    taxonomic names, retrieving information on data providers,
    getting species occurrence records, and getting counts of
    occurrence records.
We utilize  approximate Bayesian machinery to fit two-level conjugate hierarchical models on overdispersed Gaussian, Poisson, and Binomial data and evaluates whether the resulting approximate Bayesian interval estimates for random effects meet the nominal confidence levels via frequency coverage evaluation. The data that Rgbp assumes comprise observed sufficient statistic for each random effect, such as an average or a proportion of each group, without population-level data.  The approximate Bayesian tool equipped with the adjustment for density maximization produces approximate point and interval estimates for model parameters including second-level variance component, regression coefficients, and random effect. For the Binomial data, the package provides an option to produce  posterior samples of all the model parameters via the acceptance-rejection method. The package provides a quick way to evaluate coverage rates of the resultant Bayesian interval estimates for random effects via a parametric bootstrapping, which we call frequency method checking.
Multi-block data analysis concerns the analysis of several sets of variables (blocks) observed on the same group of individuals. The main aims of the RGCCA package are: (i) to study the relationships between blocks and (ii) to identify subsets of variables of each block which are active in their relationships with the other blocks.
Thin plate spline fitting and prediction
Provides bindings to Frank Warmerdam's Geospatial Data Abstraction Library (GDAL) (>= 1.6.3) and access to projection/transformation operations from the PROJ.4 library. The GDAL and PROJ.4 libraries are external to the package, and, when installing the package from source, must be correctly installed first. Both GDAL raster and OGR vector map data can be imported into R, and GDAL raster data and OGR vector data exported. Use is made of classes defined in the sp package. Windows and Mac Intel OS X binaries (including GDAL, PROJ.4 and Expat) are provided on CRAN.
A method 'generate' is implemented in this package for the random
    generation of vector time series according to models obtained by 'RMAWGEN',
    'vars' or other packages.  This package was created to generalize the
    algorithms of the RMAWGEN package for the analysis and generation of any
    environmental vector time series.
The method 'generate' is extended for spatial multi-site
    stochastic generation of daily precipitation. It generates precipitation
    occurrence in several sites using logit regression (Generalized Linear
    Models) and D.S. Wilks' approach (Journal of Hydrology, 1998).
A genetic algorithm plus derivative optimizer.
Connectors to online and offline sources for taking IP addresses
    and geolocating them to country, city, timezone and other geographic ranges. For
    individual connectors, see the package index.
Interface to Geometry Engine - Open Source (GEOS) using the C API for topology operations on geometries. The GEOS library is external to the package, and, when installing the package from source, must be correctly installed first. Windows and Mac Intel OS X binaries are provided on CRAN.
Provides native wrappers for the functions available
    via the spatial quality REST API. See <http://bit.ly/bioinformatics_btw057>
    for more information on the API.
Create, read and write GEXF (Graph Exchange XML Format) graph files (used in Gephi and others). Using the XML package, it allows the user to easily build/read graph files including attributes, GEXF viz attributes (such as color, size, and position), network dynamics (for both edges and nodes) and edge weighting. Users can build/handle graphs element-by-element or massively through data-frames, visualize the graph on a web browser through "sigmajs" (a javascript library) and interact with the igraph package.
A command-line interface to 'GGobi', an interactive and dynamic
    graphics package. 'Rggobi' complements the graphical user interface of
    'GGobi' providing a way to fluidly transition between analysis and
    exploration, as well as automating common tasks.
Access WHO Global Health Observatory
    data from R via the Athena web service,
    an application program interface providing
    a simple query interface to the World
    Health Organization's data and statistics content.
This package provides some functions to create quizzes
  in the GIFT format. This format is used by several Virtual Learning
  Environments such as Moodle.
Provides medium to high level functions for 3D interactive graphics, including
    functions modelled on base graphics (plot3d(), etc.) as well as functions for
    constructing representations of geometric objects (cube3d(), etc.).  Output
    may be on screen using OpenGL, or to various standard 3D file formats including
    WebGL, PLY, OBJ, STL as well as 2D image formats, including PNG, Postscript, SVG, PGF.
A programmatic interface to the web service methods
    provided by Global Biotic Interactions (GloBI). GloBI provides 
    access to spatial-temporal species interaction records from 
    sources all over the world. rglobi provides methods to search 
    species interactions by location, interaction type, and 
    taxonomic name. In addition, it supports Cypher, a graph query
    language, to allow for executing custom queries on the GloBI 
    aggregate species interaction data set.
R interface to the GNU Linear Programming Kit.
  GLPK is open source software for solving large-scale linear programming (LP),
  mixed integer linear programming (MILP) and other related problems.
The contents of this package have
  been merged into rgl, so it is no longer needed.
Interface for gnuplot
        Based on gnuplot_i version 1.11, the GPL code from Nicolas Devillard.
Provides functions for accessing and retrieving data from the
    Google Analytics API
It fires a query to the API to get the unsampled data in R for Google Analytics Premium Accounts. It retrieves data from the Google drive document and stores it into the local drive. The path to the excel file is returned by this package. The user can read data from the excel file into R using read.csv() function.
Provides interface to Google Fit REST API v1 (see <https://developers.google.com/fit/rest/v1/reference/>).
This package serves two purposes: (i) Provide a
        comfortable R interface to query the Google server for static
        maps, and (ii) Use the map as a background image to overlay
        plots within R. This requires proper coordinate scaling.
RGP is a simple modular Genetic Programming (GP) system build in
    pure R. In addition to general GP tasks, the system supports Symbolic
    Regression by GP through the familiar R model formula interface. GP
    individuals are represented as R expressions, an (optional) type system
    enables domain-specific function sets containing functions of diverse
    domain- and range types. A basic set of genetic operators for variation
    (mutation and crossover) and selection is provided.
RGP UI provides as modern web-based user interface to the modular
    Genetic Programming (GP) system RGP.
Geological Survey of Canada (GSC) functions for exploratory data analysis with applied geochemical data, with special application to the estimation of background ranges and identification of outliers, 'anomalies', to support mineral exploration and environmental studies.  Additional functions are provided to support analytical data QA/QC, ANOVA for investigations of field sampling and analytical variability, and utility tasks.  NOTE: function caplot() for concentration-area plots employs package 'akima', however, 'akima' is only licensed for not-for-profit use.  Therefore, not-for-profit users of 'rgr' will have to independently make package 'akima' available through library(....); and use of function caplot() by for-profit users will fail.
Data and Functions from the book R Graphics, Second Edition. There is a function to produce each figure in the book, plus several functions, classes, and methods defined in Chapter 8.
Interpreted interface between GRASS 7 geographical 
    information system and R, based on starting R from within the GRASS GIS
    environment, or running free-standing R in a temporary GRASS location;
    the package provides facilities for using all GRASS commands from the 
    R command line. This package may not be used for GRASS 6, for which
    spgrass6 should be used.
Integrates the Groovy scripting language with the R Project for Statistical Computing.
Facilities in the R language for programming
  graphical interfaces using Gtk, the Gimp Tool Kit.
DBI/RJDBC interface to h2 database. h2 version 1.3.175 is included.
An R interface to the 'Handsontable' JavaScript library, which is a
    minimalist Excel-like data grid editor.
An R interface for the HealthData.gov data API.  For each data resource, you can filter results (server-side) to select subsets of data.
Evaluating risk (that a patient arises a side effect) during hospitalization is the main purpose of this package. Several methods (Parametric, non parametric and De Vielder estimation) to estimate the risk constant (R) are implemented in this package. There are also functions to simulate the different models of this issue in order to quantify the previous estimators. It is necessary to read at least the first six pages of the report to understand the topic.
Function of apply style using 'MPI' provides better 'HPC' environment on R.
 and this package supports long vector, can deal with slightly big data.
Control the number of threads on 'BLAS' (Aka 'GotoBLAS', 'ACML' and 'MKL').
  and possible to control the number of threads in 'OpenMP'.
  get a number of logical cores and physical cores if feasible.
rHpcc is an R package providing an Interface between R and
        HPCC.Familiarity with ECL (Enterprise Control Language) is a
        must to use this package.HPCC is a massive parallel-processing
        computing platform that solves Big Data problems.ECL is the
        Enterprise Control Language designed specifically for huge data
        projects using the HPCC platform.Its extreme scalability comes
        from a design that allows you to leverage every query you
        create for re-use in subsequent queries as needed. To do this,
        ECL takes a dictionary approach to building queries wherein
        each ECL definition defines an Attribute. Each previously
        defined Attribute can then be used in succeeding ECL Attribute
        definitions as the language extends itself as you use it.
Methods and tools for performing Heart Rate Variability analysis 
 of ECG records. RHRV allows to import data files containing heartbeat positions
 in the most broadly used formats; eliminating outliers or spurious points 
 present in the time series with unacceptable physiological values; plotting HRV 
 data and performing time domain, frequency domain and nonlinear HRV analysis.
This package offers functions to perform regularized
        Hotelling's T-square test for pathway or gene set analysis. The
        package is tailored for but not limited to proteomics data, in
        which sample sizes are often small, a large proportion of the
        data are missing and/or correlations may be present.
DEPRECATED. Do not start building new projects based on this package. Cross-platform alternatives are the following packages: bigmemory (CRAN), ff (CRAN), BufferedMatrix (Bioconductor).  The main usage of it was inside the aroma.affymetrix package. (The package currently provides a class representing a matrix where the actual data is stored in a binary format on the local file system.  This way the size limit of the data is set by the file system and not the memory.)
This package provides a set of tools for conducting exact
        or approximate randomization-based inference for experiments of
        arbitrary design. The primary functionality of the package is
        in the generation, manipulation and use of permutation matrices
        implied by given experimental designs. Among other features,
        the package facilitates estimation of average treatment
        effects, constant effects variance estimation, randomization
        inference for significance testing against sharp null
        hypotheses and visualization of data and results.
Computes attributable effects based confidence set, permutation test confidence set, and asymptotic confidence interval for the average treatment effect on a binary outcome.
The Diceware method can be used to generate strong passphrases.
  In short, you roll a 6-faced dice 5 times in a row, the number obtained is
  matched against a dictionary of easily remembered words. By combining together 7
  words thus generated, you obtain a password that is relatively easy to remember,
  but would take several millions years (on average) for a powerful computer to guess.
Computes rarefaction curves, cumulated and mean species
        richness. Compares these estimates by means of randomization
        tests.
This package implements ridge fusion methodology for inverse covariance matrix estimation for use in quadratic discriminant analysis.  The package also contains function for model based clustering using ridge fusion for inverse matrix estimation, as well as tuning parameter selection functions.  We have also implemented QDA using joint inverse covariance estimation.
An interface to iDigBio's search API that allows downloading
    specimen records. Searches are returned as a data.frame. Other functions
    such as the metadata end points return lists of information. iDigBio is a US
    project focused on digitizing and serving museum specimen collections on the
    web. See <https://www.idigbio.org> for information on iDigBio.
An extension of the Kruskal-Wallis Test that allow
        selection of arbitrary reference group. Also provide Mean Ridit
        for each group. Mean Ridit of a group is an estimate of
        probability a random observation from that group will be
        greater than or equal to a random observation from reference
        group.
Allows to get weather data from Automated Surface Observing System (ASOS) stations (airports) in the
    whole world thanks to the Iowa Environment Mesonet website.
RIFS package provides functionality for generating &
        plotting prefractals in R^n with various protofractal sets and
        partition coefficient for iterative segments
Provides functions to read 'ImageJ' (http://imagej.nih.gov/ij/)
    Region of Interest (ROI) files, to plot the ROIs and to convert them to
    'spatstat' (http://spatstat.org/) spatial patterns.
A pure R implementation of the median cut algorithm.
    Extracts the dominant colors from an image, and turns them into
    a scale for use in plots or for fun!
Cloudera 'Impala', which is a massively parallel processing (MPP) SQL query engine runs natively in Apache Hadoop. 'RImpala' facilitates the connection and execution of distributed queries through 'R'. 'Impala' supports JDBC integration which 'RImpala' utilizes to establish the connection between 'R' and 'Impala'. Thanks to Mu Sigma for their continued support throughout the development of the package.
A programmatic interface to the
    API provided by the iNaturalist website.
Index structures allow quickly accessing elements from
        large collections. While btree are optimized for disk databases
        and ttree for ram databases we use hybrid static indexing which
        is quite optimal for R.
C++ classes to embed R in C++ applications
 The 'RInside' packages makes it easier to have "R inside" your C++ application
 by providing a C++ wrapper class providing the R interpreter.

    As R itself is embedded into your application, a shared library build of R
 is required. This works on Linux, OS X and even on Windows provided you use
 the same tools used to build R itself.  

    Numerous examples are provided in the eight subdirectories of the examples/
 directory of the installed package: standard, mpi (for parallel computing)
 qt (showing how to embed 'RInside' inside a Qt GUI application), wt (showing
 how to build a "web-application" using the Wt toolkit), armadillo (for
 'RInside' use with 'RcppArmadillo') and eigen (for 'RInside' use with 'RcppEigen').
 The example use GNUmakefile(s) with GNU extensions, so a GNU make is required
 (and will use the GNUmakefile automatically).

    Doxygen-generated documentation of the C++ classes is available at the
 'RInside' website as well.
Functions to calculate several ecological indices of individual and population niche width
   (Araujo's E, clustering and pairwise similarity among individuals, IS, Petraitis' W, and Roughgarden's
   WIC/TNW) to assess individual specialization based on data of resource use. Resource use can be
   quantified by counts of categories, measures of mass/lenght or proportions. Monte Carlo resampling
   procedures are available for hypothesis testing against multinomial null models.
A wrapper for the 'intro.js' library (For more info: <http://www.introjs.com>). 
  This package makes it easy to include step-by-step introductions, and clickable hints in a 'Shiny' 
  application. It supports both static introductions in the UI, and programmatic introductions from 
  the server-side.
Streamlined data import and export by making assumptions that the user is probably willing to make: 'import()' and 'export()' determine the data structure from the file extension, reasonable defaults are used for data import and export (e.g., 'stringsAsFactors=FALSE'), web-based import is natively supported (including from SSL/HTTPS), compressed files can be read directly without explicit decompression, and fast import packages are used where appropriate.
Functions for the analysis of Quaternary science data, including
         constrained clustering, WA, WAPLS, IKFA, MLRC and MAT transfer 
         functions, and stratigraphic diagrams.
Utility functions and S3 classes for IPv4 and IPv6 addresses, including 
             conversion to and from binary representation.
A package including various functions for image processing and analysis. With this package is possible to process and analyse RGB, LAN (multispectral) and AVIRIS (hyperspectral) images. This packages also provides functions for reading JPEG files, extracted from the archived 'rimage' package.
Computes risk measures from data, as well as performs risk management procedures.
Risk regression models for survival analysis with and without
    competing risks.
Compute time-dependent Incident/dynamic accuracy measures
        (ROC curve, AUC, integrated AUC )from censored survival data
        under proportional or non-proportional hazard assumption of
        Heagerty & Zheng (Biometrics, Vol 61 No 1, 2005, PP 92-105).
Implements efficient simulation procedures to estimate tail loss probabilities and conditional excess for a stock portfolio. The log-returns are assumed to follow a t-copula model with generalized hyperbolic or t marginals.
A set of tools to extract bibliographic content from the National Center for Biotechnology Information (NCBI) databases, including PubMed. The name RISmed is a portmanteau of RIS (for Research Information Systems, a common tag format for bibliographic data) and PubMed.
Implements the simulation and regression of
        integrated Isothermal Titration Calorimetry (ITC) data using
        the most commonly used one-to-one binding reaction model.
A simple yet powerful script editor built natively in R with tcltk.
An interface to the Integrated Taxonomic Information System ('ITIS')
    (<http://www.itis.gov>). Includes functions to work with the 'ITIS' REST
    'API' methods (<http://www.itis.gov/ws_description.html>), as well as the
    'Solr' web service (<http://www.itis.gov/solr_documentation.html>).
Finds a robust instrumental variables estimator using a
             high breakdown point S-estimator of multivariate location
             and scatter matrix.
Reads river network shape files and computes network distances.
    Also included are a variety of computation and graphical tools designed 
    for fisheries telemetry research, such as minimum home range, kernel density 
    estimation, and clustering analysis using empirical k-functions with 
    a bootstrap envelope.  Tools are also provided for editing the river 
    networks, meaning there is no reliance on external software.
Functions for reading, analysing and plotting river networks.
  For this package, river networks consist of sections and nodes with associated attributes, 
  e.g. to characterise their morphological, chemical and biological state.
  The package provides functions to read this data from text files, to analyse the network
  structure and network paths and regions consisting of sections and nodes that fulfill
  prescribed criteria, and to plot the river network and associated properties.
Sankey plots are a type of diagram that is convenient to
    illustrate how flow of information, resources etc. separates and joins,
    much like observing how rivers split and merge. For example, they can be
    used to compare different clusterings.
This R package is a flexible and efficient tool to 
    visualise both quantitative and qualitative data from river surveys. 
    It can be used to produce diagrams with the topological structure of 
    the river network.
This package is devoted to the IVIVC linear level A with
        numerical deconvolution method. The latter is working for
        inequal and incompatible timepoints between impulse and
        response curves. A numerical convolution method is also
        available. Application domains include pharamaceutical industry
        QA/QC and R&D together with academic research.
A tool for undergraduate and graduate courses in open-channel
    hydraulics. Provides functions for computing normal and critical depths,
    steady-state water surface profiles (e.g. backwater curves) and unsteady flow
    computations (e.g. flood wave routing).
Bayesian analysis of CGH microarrays fitting Hidden Markov
        Chain models. The selection of the number of states is made via
        their posterior probability computed by Reversible Jump Markov
        Chain Monte Carlo Methods. Also returns probabilistic common
        regions for gains/losses.
Jade is a high performance template engine heavily influenced by
    Haml and implemented with JavaScript for node and browsers.
Low-level interface to Java VM very much like .C/.Call and friends. Allows creation of objects, calling methods and accessing fields.
RJDBC is an implementation of R's DBI interface using JDBC as a back-end. This allows R to connect to any DBMS that has a JDBC driver.
A series of useful functions, some available in different
  forms in other packages, but which have been extended, sped up, or
  otherwise modified in some way considered useful to the author.
Performs the JIVE decompositions on a list of data sets when the data share a dimension, returning low-rank matrices that capture the joint and individual structure of the data. It provides two methods of rank selection when the rank is unknown, a permutation test and a BIC selection algorithm. Also included in the package are three plotting functions for visualizing the variance attributed to each data source: a bar plot that shows the percentages of the variability attributable to joint and individual structure, a heatmap that shows the structure of the variability, and principal component plots.
R-interface to gene drop programs from Alun Thomas' Java Programs for Statistical Genetics and Computational Statistics (JPSGCS)
R interface to statistical database organized by Japanese government (http://statdb.nstac.go.jp/)
Provides functions to retrieve data and metadata from providers 
			 that disseminate data by means of SDMX web services. 
			 SDMX (Statistical Data and Metadata eXchange) is a standard that 
			 has been developed with the aim of simplifying the exchange of 
			 statistical information. 
			 More about the SDMX standard and the SDMX Web Services 
			 can be found at: http://sdmx.org .
Converts R object into JSON objects and vice-versa
This is a package that allows conversion to and from 
  data in Javascript object notation (JSON) format.
  This allows R objects to be inserted into Javascript/ECMAScript/ActionScript code
  and allows R programmers to read and convert JSON content to R objects.
  This is an alternative to rjson package. Originally, that was too slow for converting large R objects to JSON
  and was not extensible.  rjson's performance is now similar to this package, and perhaps slightly faster in some cases.
  This package uses methods and is readily extensible by defining methods for different classes, 
  vectorized operations, and C code and callbacks to R functions for deserializing JSON objects to R. 
  The two packages intentionally share the same basic interface. This package (RJSONIO) has many additional
  options to allow customizing the generation and processing of JSON content.
  This package uses libjson rather than implementing yet another JSON parser. The aim is to support
  other general projects by building on their work, providing feedback and benefit from their ongoing development.
Read and write the 'JSON-stat' format (http://json-stat.org) to and
    from (lists of) R data frames. Not all features are supported, especially
    the extensive metadata features of 'JSON-stat'.
R interface to Python via Jython allowing R to call python
        code.
Apache 'Kafka' is an open-source message broker project developed by the Apache Software Foundation which can be thought of as a distributed, partitioned, replicated commit log service.At a high level, producers send messages over the network to the 'Kafka' cluster which in turn serves them up to consumers.See <http://kafka.apache.org/> for more information.Functions included in this package enable:1.Creating 'Kafka' producer 2.Writing messages to a topic 3.Closing 'Kafka' producer 4.Creating 'Kafka' consumer 5.Reading messages from a topic 6.Closing 'Kafka' consumer. The jars required for this package are included in a separate package 'rkafkajars'.Thanks to Mu Sigma for their continued support throughout the development of the package.
The 'rkafkajars' package collects all the external jars required for the 'rkafka' package.
An R interface to KEA (Version 5.0).
  KEA (for Keyphrase Extraction Algorithm) allows for extracting
  keyphrases from text documents. It can be either used for free
  indexing or for indexing with a controlled vocabulary. For more
  information see <http://www.nzdl.org/Kea/>.
External jars required for package RKEA.
KEEL is a popular Java software for a large number of different knowledge data discovery tasks.
    This package takes the advantages of KEEL and R, allowing to use KEEL algorithms in simple R code.
    The implemented R code layer between R and KEEL makes easy both using KEEL algorithms in R as implementing new algorithms for 'RKEEL' in a very simple way.
    It includes more than 100 algorithms for classification, regression and preprocess, which allows a more complete experimentation process.
    For more information about KEEL, see <http://www.keel.es/>.
KEEL is a popular Java software for a large number of different knowledge data discovery tasks. Furthermore, RKEEL is a package with a R code layer between R and KEEL, for using KEEL in R code. This package includes the datasets from KEEL in .dat format for its use in RKEEL package. For more information about KEEL, see <http://www.keel.es/>.
KEEL is a popular Java software for a large number of different knowledge data discovery tasks. Furthermore, 'RKEEL' is a package with a R code layer between R and KEEL, for using KEEL in R code. This package downloads and install the .jar files necessary for 'RKEEL' algorithms execution. For more information about KEEL, see <http://www.keel.es/>.
An interface of R to Klout API v2. It fetches Klout Score for a Twitter Username/handle in real time. Klout is a website and mobile app that uses social media analytics to rank its users according to online social influence via the "Klout Score", which is a numerical value between 1 and 100.  In determining the user score, Klout measures the size of a user's social media network and correlates the content created to measure how other users interact with that content.
Random knn classification and regression are implemented. Random knn based feature selection methods are also included. The approaches are mainly developed for high-dimensional data with small sample size.
Contains functions for the topological analysis of polymers, with a focus on protein structures.
Contains function rkt which computes the Mann-Kendall test (MK) and the Seasonal and the Regional Kendall Tests for trend (SKT and RKT) and  Theil-Sen's slope estimator.
This package provides functionality to read files
    containing observations which consist of arbitrary key/value
    pairs.
Functions and Datasets Required for ST370 class
Allow a user to discover, query and modify data from a LabKey Server.
Standardized methods for calculating common important derived
    physical features of lakes including water density based based on
    temperature, thermal layers, thermocline depth, lake number, Wedderburn
    number, Schmidt stability and others.
Display spatial data with interactive maps powered by the 
  open-source JavaScript library 'Leaflet' (see <http://leafletjs.com/>). Maps can be rendered in a web browser or
  displayed in the HTML viewer pane of 'RStudio'. This package is designed to be easy to use and
  can create complex maps with vector and raster data, web served map tiles and interface elements.
Provides an interface to the C implementation of the
        random number generator with multiple independent streams
        developed by L'Ecuyer et al (2002). The main purpose of this
        package is to enable the use of this random number generator in
        parallel R applications.
An R interface for C library libeemd for performing the ensemble
    empirical mode decomposition (EEMD), its complete variant (CEEMDAN) or the
    regular empirical mode decomposition (EMD).
Set of tools for reading, processing and visualizing small set 
	of LiDAR (Light Detection and Ranging) data for forest inventory applications.
An interface to LINDO API. Supports Linear, Integer, Quadratic, Conic, General Nonlinear, Global, and Stochastic Programming models. To download the trial version LINDO API, please visit www.lindo.com/rlindo.
A series of functions that allow users
  to access the LinkedIn API to get information about connections,
  search for people and jobs, share updates with their network,
  and create group discussions.  For more information about using
  the API please visit https://developer.linkedin.com/.
Provides a set of functions for data manipulation with
    list objects, including mapping, filtering, grouping, sorting,
    updating, searching, and other useful functions. Most functions
    are designed to be pipeline friendly so that data processing with
    lists can be chained.
Robust fitting of linear model which can take response in matrix form.
R parallel implementation of Local Outlier Factor(LOF) which uses multiple CPUs to significantly speed up the LOF computation for large datasets. (Note: The overall performance depends on the computers especially the number of the cores).It also supports multiple k values to be calculated in parallel, as well as various distance measures in addition to the default Euclidean distance.
Processing logical operations such as AND/OR/NOT operations
    dynamically. It also handles nesting in the operations.
Rapid, simulation-based exact (restricted) likelihood ratio tests
    for testing the presence of variance components/nonparametric terms for
    models fit with nlme::lme(),lme4::lmer(), gamm4::gamm4(), mgcv::gamm() and
    SemiPar::spm().
R interface to LTP-Cloud service for Natural Language Processing
    in Chinese. For more details please visit http://www.ltp-cloud.com/. Visit 
    https://github.com/hetong007/rLTP for up-to-date version.
A collection of functions to simulate luminescence signals in the
    mineral quartz based on published models.
A collection of 'shiny' applications for the R package
    'Luminescence'. These mainly, but not exclusively, include applications for
    plotting chronometric data from e.g. luminescence or radiocarbon dating. It
    further provides access to bootstraps tooltip and popover functionality and
    contains the 'jscolor.js' library with a custom 'shiny' output binding.
RM2 is a simple package that implements functions used in
        revenue management and pricing environments.
Uses refined moving average filter based on the optimal and data-driven moving average lag q or smoothing spline to estimate trend and seasonal components, as well as irregularity (residuals) for univariate time series or data.
An EM algorithm to fit Mallows' Models to full or partial
        rankings, with or without ties.
This package implements an algorithm family for continuous
    optimization called memetic algorithms with local search chains
    (MA-LS-Chains). Memetic algorithms are hybridizations of genetic
    algorithms with local search methods. They are especially suited
    for continuous optimization.
Edit and simplify 'geojson' and 'Spatial' objects.
    This is wrapper around the 'mapshaper' 'javascript' library
    <https://github.com/mbloch/mapshaper/> to perform topologically-aware
    polygon simplification, as well as other operations such as clipping,
    erasing, dissolving, and converting 'multi-part' to 'single-part' geometries.
    It relies on the 'geojsonio' package for working with 'geojson' objects,
    and the 'sp' and 'rgdal' packages for working with 'Spatial' objects.
Applies the Hobday et al. (2016) <DOI:10.1016/j.pocean.2015.12.014>
    marine heat wave definition to an input time series of daily temperature.
    It outputs properties of all detected marine heat waves (and marine cold
    spells). The package provides data of observed SST records for three
    historical MHWs: the 2011 Western Australia event, the 2012 Northwest
    Atlantic event and the 2003 Mediterranean event.
An interface to the software package MARK that constructs input
    files for MARK and extracts the output. MARK was developed by Gary White
    and is freely available at (http://www.phidot.org/software/mark/downloads/)
    but is not open source.
Convert R Markdown documents into a variety of formats.
rmatio is a package for reading and writing Matlab MAT
    files from R. The rmatio package supports reading MAT version 4,
    MAT version 5 and MAT compressed version 5. The rmatio package can
    write version 5 MAT files and version 5 files with variable
    compression.
Methods readMat() and writeMat() for reading and writing MAT files.  For user with MATLAB v6 or newer installed (either locally or on a remote host), the package also provides methods for controlling MATLAB (trademark) via R and sending and retrieving data between R and MATLAB.
S3 and S4 functions are implemented for spatial multi-site
    stochastic generation of daily time series of temperature and
    precipitation. These tools make use of Vector AutoRegressive models (VARs).
    The weather generator model is then saved as an object and is calibrated by
    daily instrumental "Gaussianized" time series through the 'vars' package
    tools. Once obtained this model, it can it can be used for weather
    generations and be adapted to work with several climatic monthly time
    series.
Functions for fitting, diagnosing and predicting from a
        class of Markov models.
MCFS-ID (Monte Carlo Feature Selection and Interdependency Discovery) is a 
  Monte Carlo method-based tool for feature selection. It also allows for the discovery of interdependencies between the relevant features. MCFS-ID is particularly suitable for the analysis of high-dimensional, 'small n large p' transactional and biological data.
Compute the repeated measures correlation, a statistical technique
    for determining the overall within-individual relationship among paired measures
    assessed on two or more occasions, first introduced by Bland and Altman (1995).
    Includes functions for diagnostics, p-value, effect size with confidence
    interval including optional bootstrapping, as well as graphing. Also includes
    several example datasets.
HTML formats and templates for 'rmarkdown' documents, with some extra
    features such as automatic table of contents, lightboxed figures, dynamic
    crosstab helper.
A series of functions to aid in repeated tasks for Rmd documents. All details are to my personal preference, though I am happy to add flexibility if there are use cases I am missing. I will continue updating with new functions as I add utility functions for myself.
'R' 'Markdown' format for 'shower' presentations, see
   <https://github.com/shower/shower>.
We provide functions to compute confidence
    intervals (CIs) for a well-defined nonlinear function of the model
    parameters (e.g., product of k coefficients) in single--level and
    multilevel structural equation models.
Functions for simple fixed and random effects
        meta-analysis for two-sample comparisons and cumulative
        meta-analyses. Draws standard summary plots, funnel plots, and
        computes summaries and tests for association and heterogeneity
An interface between R and the metasim simulation engine.
    The simulation environment is documented in: "Strand, A.(2002) <DOI:10.1046/j.1471-8286.2002.00208.x> Metasim 1.0: an individual-based environment for simulating population genetics of 
    complex population dynamics. Mol. Ecol. Notes. 
    Please see the vignettes CreatingLandscapes and Simulating to get some ideas on how to use the packages.  
    See the rmetasim vignette to get an overview and to see important changes to the 
    code in the most recent version.
Methods that simplify the setup of S3 generic functions and S3 methods.  Major effort has been made in making definition of methods as simple as possible with a minimum of maintenance for package developers.  For example, generic functions are created automatically, if missing, and naming conflict are automatically solved, if possible.  The method setMethodS3() is a good start for those who in the future may want to migrate to S4.  This is a cross-platform package implemented in pure R that generates standard S3 methods.
Feasible multivariate GARCH models including DCC, GO-GARCH and Copula-GARCH.
Facilitates the use of data mining algorithms in classification and regression (including time series forecasting) tasks by presenting a short and coherent set of functions. Versions: 1.4.2 new NMAE metric, "xgboost" and "cv.glmnet" models (16 classification and 18 regression models); 1.4.1 new tutorial and more robust version; 1.4 - new classification and regression models/algorithms, with a total of 14 classification and 15 regression methods, including: Decision Trees, Neural Networks, Support Vector Machines, Random Forests, Bagging and Boosting; 1.3 and 1.3.1 - new classification and regression metrics (improved mmetric function); 1.2 - new input importance methods (improved Importance function); 1.0 - first version.
The Rmisc library contains many functions useful for data analysis
    and utility operations.
Interface of MIXMOD software for supervised, unsupervised and semi-Supervised classification with MIXture MODelling.
The Rmixmod package provides model-based clustering by fitting a mixture model (e.g. Gaussian components for quantitative continuous data) to the data and identifying each cluster with one of its components. The number of components can be determined from the data, typically using the BIC criterion. In practice, however, individual clusters can be poorly fitted by Gaussian distributions, and in that case model-based clustering tends to represent one non-Gaussian cluster by a mixture of two or more Gaussian components. If the number of mixture components is interpreted as the number of clusters, this can lead to overestimation of the number of clusters. This is because BIC selects the number of mixture components needed to provide a good approximation to the density. This package, RmixmodCombi, according to \emph{Combining Mixture Components for Clustering} by J.P. Baudry, A.E. Raftery, G. Celeux, K. Lo, R. Gottardo, combines the components of the EM/BIC solution (provided by Rmixmod) hierarchically according to an entropy criterion. This yields a clustering for each number of clusters less than or equal to K. These clusterings can be compared on substantive grounds, and we also provide a way of selecting the number of clusters via a piecewise linear regression fit to the (possibly rescaled) entropy plot.
Provides an interface to many endpoints of Mixpanel's Data Export, Engage and JQL API. The R functions allow for event and profile data export as well as for segmentation, retention and addiction analysis. Results are always parsed into convenient R objects. Furthermore it is possible to load and update profiles.
Sundry discrete probability distributions and helper functions.
A collection of miscellaneous functions for medical data analysis. Visualization
 of multidimensional data, diagnostic test calibration, pairwise tests for qualitative variables and outcome simulation.
Connect R with MOA (Massive Online Analysis -
    http://moa.cms.waikato.ac.nz) to build classification models and
    regression models on streaming data or out-of-RAM data
External jars required for package RMOA. RMOA is a framework to
    build data stream models on top of MOA (Massive Online Analysis -
    http://moa.cms.waikato.ac.nz)
MongoDB Database interface for R. The interface is provided via Java calls to the mongo-java-driver.
An interface to the MOSEK optimization library designed to
        solve large-scale mathematical optimization problems. Supports
        linear, quadratic and second order cone optimization
        with/without integer variables, in addition to the more general
        separable convex problems.  Trial and free academic licenses
        available at http://www.mosek.com.
Performs probability mass function estimation with nonparametric mixtures of rounded kernels.
Arithmetic (via S4 classes and methods) for
  arbitrary precision floating point numbers, including transcendental
  ("special") functions.  To this end, Rmpfr interfaces to
  the LGPL'ed MPFR (Multiple Precision Floating-Point Reliable) Library
  which itself is based on the GMP (GNU Multiple Precision) Library.
An interface (wrapper) to MPI APIs. It also 
	     provides interactive R manager and worker environment.
This package contains functions and S3 methods for spatial
    multi-site stochastic generation of daily precipitation. It generates
    precipitation occurrence in several sites using Wilks' Approach (1998).
    Bugs/comments/questions/collaboration of any kind are warmly welcomed.
Regression modeling, testing, estimation, validation,
	graphics, prediction, and typesetting by storing enhanced model design
	attributes in the fit.  'rms' is a collection of functions that
	assist with and streamline modeling.  It also contains functions for
	binary and ordinal logistic regression models, ordinal models for
  continuous Y with a variety of distribution families, and the Buckley-James
	multiple regression model for right-censored responses, and implements
	penalized maximum likelihood estimation for logistic and ordinary
	linear models.  'rms' works with almost any regression model, but it
	was especially written to work with binary or ordinal regression
	models, Cox regression, accelerated failure time models,
	ordinary linear models,	the Buckley-James model, generalized least
	squares for serially or spatially correlated observations, generalized
	linear models, and quantile regression.
Display a randomly selected quote about Richard M. Stallman
 based on the collection in the 'GNU Octave' function 'fact()' which was
 aggregated by Jordi Gutiérrez Hermoso based on the (now defunct) site
 stallmanfacts.com (which is accessible only via <http://archive.org>).
This package can be used to test any simple null
        hypothesis using the root-mean-square goodness of fit test.
        Monte Carlo estimation is used to calculate the associated
        P-value.
An algorithm which can be used to determine an objective threshold for signal-noise separation in large random matrices (correlation matrices, mutual information matrices, network adjacency matrices) is provided. The package makes use of the results of Random Matrix Theory (RMT). The algorithm increments a suppositional threshold monotonically, thereby recording the eigenvalue spacing distribution of the matrix. According to RMT, that distribution undergoes a characteristic change when the threshold properly separates signal from noise. By using the algorithm, the modular structure of a matrix - or of the corresponding network - can be unraveled.
Functions for working with the Tracy-Widom laws and other distributions 
    related to the eigenvalues of large Wishart matrices.
    The tables for computing the Tracy-Widom densities and distribution
    functions were computed by Momar Dieng's MATLAB package "RMLab"
    (formerly available on his homepage at 
    http://math.arizona.edu/~momar/research.htm ).
    This package is part of a collaboration between Iain Johnstone, 
    Zongming Ma, Patrick Perry, and Morteza Shahram.  It will soon be
    replaced by a package with more accuracy and built-in support for
    relevant statistical tests.
Exported class can be used for storing sparse matrix
	and solving corresponding linear system with one or many right
	hand sides. There is a possibility to do separately symbolic analysis,
	LU factorization and system solving.
Rasch model and extensions for survey data, using Conditional Maximum likelihood (CML).
Implements 'DBI' Interface to 'MySQL' and 'MariaDB' Databases.
This package is designed to streamline several of the common steps for RNA-seq
              analysis, including differential expression and variant discovery.
              For the development build, or to contribute changes to this package,
              please see our repository at https://bitbucket.org/petersmp/rnaseqwrapper/
GUI to explore high dimensional data (including image data) using graphs as navigational infrastructure.
Image data used as examples in the RnavGraph R package.
        See the demos in the RnavGraph package.
Provides a set of functions to access a data visualization web service. For more information and a tutorial on how to use it, see https://navicell.curie.fr/pages/nav_web_service.html and https://github.com/sysbio-curie/RNaviCell.
Access to data held by the National Biodiversity Network (NBN, www.nbn.org.uk). The NBN acts as a data warehouse for biological records data in the UK and is the UK node of GBIF (Global Biodiversity Information Facility). In this package NBN data is accessed via its web-services.
Provides the libraries of the EUtils operations for the
        RNCBI package.
This package contains functions to retrieve, organize, and visualize weather data from the NCEP/NCAR Reanalysis (http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html) and NCEP/DOE Reanalysis II (http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis2.html) datasets.  Data are queried via the Internet and may be obtained for a specified spatial and temporal extent or interpolated to a point in space and time.  We also provide functions to visualize these weather data on a map.  There are also functions to simulate flight trajectories according to specified behavior using either NCEP wind data or data specified by the user.
An interface to the Nexus Class Library which allows parsing
    of NEXUS, Newick and other phylogenetic tree file formats. It provides
    elements of the file that can be used to build phylogenetic objects
    such as ape's 'phylo' or phylobase's 'phylo4(d)'.
This package is a collection of various functions to extract the implied risk neutral density from option.
Using as input a 'LaTeX' file with a multiple choice exam, this package will produce several versions with randomized contents of the same exam. Functions for grading and testing for cheating are also available.
Neo4j, a graph database, allows users to store their data as a property graph. A graph consists of nodes that are connected by relationships; both nodes and relationships can have properties, or key-value pairs. RNeo4j is Neo4j's R driver. It allows users to read and write data from and to Neo4j directly from their R environment by exposing an interface for interacting with nodes, relationships, paths, and more. Most notably, it allows users to retrieve Cypher query results as R data frames, where Cypher is Neo4j's graph query language. Visit <http://www.neo4j.com> to learn more about Neo4j.
Within this package the XML-RPC API to NEOS is implemented. This enables the user to pass optimization problems to NEOS and retrieve results within R.
It provides functions to compute the modularity and modularity-related roles in networks. It is a wrapper around the rgraph library (Guimera & Amaral, 2005, doi:10.1038/nature03288).
An interface to the NetCDF file format designed by Unidata
  for efficient storage of array-oriented scientific data and descriptions.
  The R interface is closely based on the C API of the NetCDF library,
  and it includes calendar conversions from the Unidata UDUNITS library.
  The current implementation supports all operations on NetCDF datasets
  in classic and 64-bit offset file formats, and NetCDF4-classic format
  is supported for reading and modification of existing files.
Interface to use and access Wilensky's NetLogo (Wilensky 1999) from R using either headless (no GUI) or interactive GUI mode. Provides functions to load models, execute commands, and get values from reporters. Mostly analogous to the NetLogo Mathematica Link https://github.com/NetLogo/Mathematica-Link.
A collection of tools for measuring the similarity of news content and tracing the flow of (news) messages over
    time and across media.
Provides access to phyloinformatic data in 'NeXML' format.  The
    package should add new functionality to R such as the possibility to
    manipulate 'NeXML' objects in more various and refined way and compatibility
    with 'ape' objects.
A function setVectorSeed() is provided. Its argument
        is a numeric vector of an arbitrary nonzero length, whose
        components have integer values from [0, 2^32-1]. The input
        vector is transformed using AES (Advanced Encryption Standard)
        algorithm into an initial state of Mersenne-Twister random
        number generator. The function provides a better alternative
        to the R base function set.seed(), if the input vector is
        a single integer. Initializing a stream of random numbers
        with a vector is a convenient way to obtain several streams,
        each of which is identified by several integer indices.
This package contains a set of functions for working with
    Random Number Generators (RNGs). In particular, it defines a generic
    S4 framework for getting/setting the current RNG, or RNG data
    that are embedded into objects for reproducibility.
    Notably, convenient default methods greatly facilitate the way current
    RNG settings can be changed.
It is a dedicated package to WELL pseudo random generators, which were introduced in Panneton et al. (2006), ``Improved Long-Period Generators Based on Linear Recurrences Modulo 2'', ACM Transactions on Mathematical Software. But this package is not intended to be used directly, you are strongly __encouraged__ to use the 'randtoolbox' package, which depends on this package.
Long period linear random number generator WELL19937a by
        F. Panneton, P. L'Ecuyer and M. Matsumoto. The initialization
        algorithm allows to seed the generator with a
        numeric vector of an arbitrary length and uses MRG32k5a by
        P. L'Ecuyer to achieve good quality of the initialization. The
        output function may be set to provide numbers from the interval
        (0,1) with 53 (the default) or 32 random bits. WELL19937a is of
        similar type as Mersenne Twister and has the same period.
        WELL19937a is slightly slower than Mersenne Twister, but has
        better equidistribution and "bit-mixing" properties and faster
        recovery from states with prevailing zeros than Mersenne
        Twister. All WELL generators with orders 512, 1024, 19937 and
        44497 can be found in randtoolbox package.
Provides very fast access to images stored in the NIfTI-1 file
    format <http://nifti.nimh.nih.gov/nifti-1>, with seamless synchronisation
    between compiled C and interpreted R code. Not to be confused with RNiftyReg,
    which provides tools for image registration.
Provides an R interface to the NiftyReg image registration tools
    <http://sourceforge.net/projects/niftyreg/>. Linear and nonlinear registration
    are supported, in two and three dimensions.
An implementation of robust nonnegative matrix factorization (rNMF). The rNMF algorithm decomposes a nonnegative high dimension data matrix into the product of two low rank nonnegative matrices, while detecting and trimming outliers. The main function is rnmf(). The package also includes a visualization tool, see(), that arranges and prints vectorized images.
Implementation of a Recurrent Neural Network in R.
Client for many 'NOAA' data sources including the 'NCDC' climate
    'API' at http://www.ncdc.noaa.gov/cdo-web/webservices/v2, with functions for
    each of the 'API' 'endpoints': data, data categories, data sets, data types,
    locations, location categories, and stations. In addition, we have an interface
    for 'NOAA' sea ice data, the 'NOAA' severe weather inventory, 'NOAA' Historical
    Observing 'Metadata' Repository ('HOMR') data, 'NOAA' storm data via 'IBTrACS',
    tornado data via the 'NOAA' storm prediction center, and more.
An interface to the National Oceanic and Atmospheric Administration's Operational Model Archive and Distribution System (NOMADS) that allows R users to quickly and efficiently download global and regional weather model data for processing.  rNOMADS currently supports a variety of models ranging from global weather data to an altitude of 40 km, to high resolution regional weather models, to wave and sea ice models. It can also retrieve archived NOMADS models.  rNOMADS can retrieve binary data in grib format as well as import ascii data directly into R by interfacing with the GrADS-DODS system.
Programmatic interface to the
    Web Service methods provided by the National 'Phenology' Network
    (<https://usanpn.org/>), which includes data on various life history
    events that occur at specific times.
Utility functions to retrieve data from the UK National River Flow
    Archive. The package contains R wrappers to the UK NRFA data temporary-API.
    There are functions to retrieve stations falling in a bounding box,
    to generate a map and extracting time series and general information.
A collection of methods for the robust analysis of univariate and
    multivariate functional data, possibly in high-dimensional cases, and hence
    with attention to computational efficiency and simplicity of use.
Provides an interface to the OAuth 1.0 specification
        allowing users to authenticate via OAuth to the
        server of their choice.
Base S4-classes and functions for robust asymptotic statistics.
Includes 'sysdata.rda' file for packages of the 'RobASt' - family of packages; is
        currently used by package 'RobExtremes' only.
Methods for analysis of compositional data including robust
    methods, imputation, methods to replace rounded zeros, (robust) outlier
    detection for compositional data, (robust) principal component analysis for
    compositional data, (robust) factor analysis for compositional data, (robust)
    discriminant analysis for compositional data (Fisher rule), robust regression
    with compositional predictors and (robust) Anderson-Darling normality tests for
    compositional data as well as popular log-ratio transformations (addLR, cenLR,
    isomLR, and their inverse transformations). In addition, visualisation and
    diagnostic tools are implemented as well as high and low-level plot functions
    for the ternary diagram.
Robust pairwise correlations based on estimates of scale,
  particularly on "FastQn" one-step M-estimate.
Locations problems, M-estimates of coefficients and scale
        in linear regression, Weights for bounded influence regression,
        Covariance matrix of the coefficient estimates, Asymptotic
        relative efficiency of regression M-estimates, Robust testing
        in linear models, High breakdown point regression, M-estimates
        of covariance matrices, M-estimates for discrete generalized
        linear models.
A set of functions to filter time series based on concepts
        from robust statistics.
Functions for the determination of optimally robust influence curves and
        estimators in case of normal location and/or scale.
Functions for the determination of optimally robust influence curves and
        estimators for preprocessing omics data, in particular gene expression data.
Perform mediation analysis via a bootstrap test.
Provides functions to download and parse 'robots.txt' files.
        Ultimately the package makes it easy to check if bots
        (spiders, scrapers, ...) are allowed to access specific
        resources on a domain.
Calculates periodograms based on (robustly) fitting periodic functions to light curves (irregularly observed time series, possibly with measurement accuracies, occurring in astroparticle physics). Three main functions are included: RobPer() calculates the periodogram. Outlying periodogram bars (indicating a period) can be detected with betaCvMfit(). Artificial light curves can be generated using the function tsgen(). For more details see the corresponding article: Thieler, Fried and Rathjens (2016), Journal of Statistical Software 69(9), 1-36, <doi:10.18637/jss.v069.i09>.
Three-step regression and inference for cellwise and casewise contamination.
Functions for the determination of optimally robust influence curves in case of
        linear regression with unknown scale and standard normal distributed errors where the
        regressor is random.
This package provides the function to calculate SVD, regularized SVD, robust SVD and robust regularized SVD method. The robust SVD methods use alternating iteratively reweighted least squares methods. The regularized SVD uses generalized cross validation to choose the optimal smoothing parameters.
Data management in hydrology and other fields is facilitated with functions to enter and modify data in a database according to the Observations Data Model (ODM) standard by CUASHI (Consortium of Universities for the Advancement of Hydrologic Science). While this data model has been developed in hydrology, it is also useful for other fields. RObsDat helps in the setup of the database within one of the free database systems MariaDB, PostgreSQL or SQLite. It imports the controlled water vocabulary from the CUASHI web service and provides a smart interface between the analyst and the database: Already existing data entries are detected and duplicates avoided. The data import function converts different data table designs to make import simple. Cleaning and modifications of data are handled with a simple version control system. Variable and location names are treated in a user friendly way, accepting and processing multiple versions. When querying data from the database, it is stored in a spacetime objects within R for subsequent processing.
Functions for conducting robust variance estimation (RVE) meta-regression using both large and small sample RVE estimators under various weighting schemes. These methods are distribution free and provide valid point estimates, standard errors and hypothesis tests even when the degree and structure of dependence between effect sizes is unknown. Also included are functions for conducting sensitivity analyses under correlated effects weighting and producing RVE-based forest plots.
R functions for the computation of the truncated maximum
	     likelihood and the robust accelerated failure time regression 
	     for gaussian and log-Weibull case.
"Essential" Robust Statistics.
 Tools allowing to analyze data with robust methods.  This includes
 regression methodology including model selections and multivariate
 statistics where we strive to cover the book "Robust Statistics,
 Theory and Methods" by 'Maronna, Martin and Yohai'; Wiley 2006.
Robust mixture discriminant analysis (RMDA, Bouveyron & Girard, 2009) allows to build a robust supervised classifier from learning data with label noise. The idea of the proposed method is to confront an unsupervised modeling of the data with the supervised information carried by the labels of the learning data in order to detect inconsistencies. The method is able afterward to build a robust classifier taking into account the detected inconsistencies into the labels.
The Spatial-EM is a new robust EM algorithm for the finite  
    mixture learning procedures. The algorithm utilizes median- 
    based location and rank-based scatter estimators to replace sample
    mean and sample covariance matrix in each M step, hence enhancing 
    stability and robustness of the algorithm. To understand more about 
    this algorithm, read the article
    ''Yu, K., Dang, X., Bart Jr, H. and Chen, Y. (2015). Robust Model-
    based Learning via Spatial-EM Algorithm. IEEE Transactions on 
    Knowledge and Data Engineering, 27(6), 1670-1682.   
    doi:10.1109/TKDE.2014.2373355''.
Testing homogeneity for generalized exponential tilt model. This package includes a collection of functions for (1) implementing methods for testing homogeneity for generalized exponential tilt model; and (2) implementing existing methods under comparison.
An object oriented solution for robust factor analysis. In the solution, new S4 classes "Fa", "FaClassic", "FaRobust", "FaCov", "SummaryFa" are created.
This package provides robust estimation for generalized
        additive models. It implements a fast and stable algorithm in
        Wong, Yao and Lee (2013). The implementation also contains
        three automatic selection methods for smoothing parameter. They
        are designed to be robust to outliers. For more details, see
        Wong, Yao and Lee (2013).
Robust parameter estimation and prediction of Gaussian stochastic process emulators.
             Important functions : rgasp(), predict.rgasp().
Robust methods for high-dimensional data, in particular linear
    model selection techniques based on least angle regression and sparse
    regression.
A method to fit linear mixed effects models robustly.
    Robustness is achieved by modification of the scoring equations
    combined with the Design Adaptive Scale approach.
Robust estimation of the generalized log gamma model is provided using Quantile Tau estimator, Weighted Likelihood estimator and Truncated Maximum Likelihood estimator. Functions for regression and censored data are also available.
Implements several rank-based tests, including the modified Wilcoxon-Mann-Whitney two sample location test, also known as the Fligner-Policello test.
Methods for aggregating ranked lists, especially lists of
        genes. It implements the Robust Rank Aggregation (Kolde et. al
        in preparation) and some other simple algorithms for the task.
        RRA method uses a probabilistic model for aggregation that is
        robust to noise and also facilitates the calculation of
        significance probabilities for all the elements in the final
        ranking.
A collection of functions to compute the Rao-Stirling diversity index
	(Porter and Rafols, 2009) <DOI:10.1007/s11192-008-2197-2> and its extension to
	acknowledge missing data (i.e.,	uncategorized references) by calculating its
	interval of uncertainty using	mathematical optimization as proposed in Calatrava
	et al. (2016) <DOI:10.1007/s11192-016-1842-4>.
	The Rao-Stirling diversity index is a well-established bibliometric indicator
	to measure the interdisciplinarity of scientific publications. Apart from the
	obligatory dataset of publications with their respective references and	a
	taxonomy of disciplines that categorizes references as well as a measure of
	similarity between the disciplines, the Rao-Stirling diversity index requires
	a complete categorization of all references of a publication into disciplines.
	Thus, it fails for a incomplete categorization; in this case, the robust
	extension has to be used, which encodes the uncertainty caused by missing
	bibliographic data as an uncertainty interval.
	Classification / ACM - 2012: Information systems ~ Similarity measures,
	Theory of computation ~ Quadratic	programming, Applied computing ~ Digital
	libraries and archives.
Linear regression functions using Huber and bisquare psi functions. Optimal weights are calculated using IRLS algorithm.
Robust Estimation of Variance Component Models by classic and composite robust procedures. The composite procedures are robust against outliers generated by the Independent Contamination Model.
eXperimental, eXtraneous, or eXtraordinary Functionality
        for Robust Statistics.  In other words, methods which are not
        yet well established, often related to methods in package
        robustbase.
This package computes traditional ROC curves and time-dependent ROC curves using the cross-validation, the 0.632 and the 0.632+ estimators. The 0.632+ estimator of time-dependent ROC curve is useful to estimate the predictive accuracy of prognostic signature based on high-dimensional data. For instance, it outperforms the other approaches, especially the cross-validation solution which is often used. The 0.632+ estimators correct the area under the curve in order to adequately estimate the prognostic capacities regardless of the overfitting level. This package also allows for the construction of diagnostic or prognostic scoring systems (penalized regressions). The methodology is adapted to complete data (penalized logistic regression associated with ROC curve) or incomplete time-to-event data (penalized Cox model associated with time-dependent ROC curve).
Functions for a classification method based on receiver
        operating characteristics (ROC). Briefly, features are selected
        according to their ranked AUC value in the training set. The
        selected features are merged by the mean value to form a
        metagene. The samples are ranked by their metagene value and
        the metagene threshold that has the highest accuracy in
        splitting the training samples is determined. A new sample is
        classified by its metagene value relative to the threshold. In
        the first place, the package is aimed at two class problems in
        gene expression data, but might also apply to other problems.
Cross validate large genetic data while specifying clinical variables that should always be in the model using the function cv(). An ROC plot from the cross validation data with AUC can be obtained using rocplot(), which also can be used to compare different models.
A collection of functions for interpretation and presentation
    of regression analysis.  These functions are used
    to produce the statistics lectures in
    http://pj.freefaculty.org/guides. Includes regression
    diagnostics, regression tables, and plots of interactions and
    "moderator" variables. The emphasis is on "mean-centered" and
    "residual-centered" predictors. The vignette 'rockchalk' offers a
    fairly comprehensive overview.  The vignette 'Rstyle' has advice
    about coding in R.  The package title 'rockchalk' refers to our
    school motto, 'Rock Chalk Jayhawk, Go K.U.'.
Provides functions to complete three-dimensional rock fabric and strain analyses following the Rf Phi, Fry, and normalized Fry methods. Also allows for plotting of results and interactive 3D visualization functionality.
The 'rococo' package provides a robust gamma rank correlation
	     coefficient along with a permutation-based rank correlation test.
	     The rank correlation coefficient and the test are explicitly
	     designed for dealing with noisy numerical data.
ROC graphs, sensitivity/specificity curves, lift charts,
  and precision/recall plots are popular examples of trade-off
  visualizations for specific pairs of performance measures. ROCR is a
  flexible tool for creating cutoff-parameterized 2D performance curves
  by freely combining two from over 25 performance measures (new
  performance measures can be added using a standard interface).
  Curves from different cross-validation or bootstrapping runs can be
  averaged by different methods, and standard deviations, standard
  errors or box plots can be used to visualize the variability across
  the runs. The parameterization can be visualized by printing cutoff
  values at the corresponding curve positions, or by coloring the
  curve according to cutoff. All components of a performance plot can
  be quickly adjusted using a flexible parameter dispatching
  mechanism. Despite its flexibility, ROCR is easy to use, with only
  three commands and reasonable default values for all optional
  parameters.
Plots the Receiver Operating Characteristics Surface for high-throughput class-skewed data, calculates the Volume under the Surface (VUS) and the FDR-Controlled Area Under the Curve (FCAUC), and conducts tests to compare two ROC surfaces. Computes eROC curve and the corresponding AUC for imperfect reference standard.
Contains functions in order to estimate diagnostic and prognostic capacities of continuous markers. More precisely, one function concerns the estimation of time-dependent ROC (ROCt) curve, as proposed by Heagerty et al. (2000) <DOI: 10.1111/j.0006-341X.2000.00337.x>. One function concerns the adaptation of the ROCt theory for studying the capacity of a marker to predict the excess of mortality of a specific population compared to the general population. This last part is based on additive relative survival models and the work of Pohar-Perme et al. (2012) <DOI: 10.1111/j.1541-0420.2011.01640.x>. We also propose two functions for cut-off estimation in medical decision making by maximizing time-dependent expected utility function. Finally, we propose confounder-adjusted estimators of ROC and ROCt curves by using the Inverse Probability Weighting (IPW) approach.  For the confounder-adjusted ROC curve (without censoring), we also proposed the implementation of the estimator based on placement values proposed by Pepe and Cai (2004) <DOI: 10.1111/j.0006-341X.2004.00200.x>.
Function to estimate the ROC Curve of a continuous-scaled
        diagnostic test with the help of a second imperfect diagnostic
        test with binary responses.
An implementation of R's DBI interface using ODBC package as a
    back-end. This allows R to connect to any DBMS that has a ODBC driver.
An extension for RODBC package adding support for parameterized
    queries.
A collection of functions for numerical construction of optimal discriminating designs. At the current moment T-optimal designs (which maximize the lower bound for the power of F-test for regression model discrimination), KL-optimal designs (for lognormal errors) and their robust analogues can be calculated with the package.
Provides a reference class and several utility methods to
    facilitate the implementation of models based on ordinary
    differential equations. The heart of the package is a code generator
    that creates compiled 'Fortran' (or 'R') code which can be passed to
    a numerical solver. There is direct support for solvers contained
    in packages 'deSolve' and 'rootSolve'.
Fetch and process Hillary Rodham Clinton's "personal" emails.
This package implements an interface to Oracle Data Mining
        (ODM). It provides an ideal environment for rapid development
        of demos and proof of concept data mining studies. It
        facilitates the prototyping of vertical applications and makes
        ODM and the RDBMS environment easily accessible to
        statisticians and data analysts familiar with R but not fluent
        in SQL or familiar with the database environment. It also
        facilitates the benchmarking and testing of ODM functionality
        including the production of summary statistics, performance
        metrics and graphics. It enables the scripting and control of
        production data mining methodologies from a high-level
        environment. Oracle Data Mining (ODM) is an option of Oracle
        Relational Database Management System (RDBMS) Enterprise
        Edition (EE). It contains several data mining and data analysis
        algorithms for classification, prediction, regression,
        clustering, associations, feature selection, anomaly detection,
        feature extraction, and specialized analytics. It provides
        means for the creation, management and operational deployment
        of data mining models inside the database environment. For more
        information consult the entry for "Oracle Data Mining" in
        Wikipedia (en.wikipedia.org).
The R Optimization Infrastructure (ROI) is a
    sophisticated framework for handling optimization problems in R.
Enhances the R Optimization Infrastructure (ROI) package by registering
	     the CPLEX commercial solver. It allows for solving mixed integer quadratically
	     constrained programming (MIQPQC) problems as well as all
	     variants/combinations of LP, QP, QCP, IP.
Enhances the R Optimization Infrastructure (ROI) package
	     with the Embedded Conic Solver (ECOS) for solving conic optimization problems.
Enhances the R Optimization Infrastructure (ROI) package by registering
	     the free GLPK solver. It allows for solving mixed integer linear programming (MILP)
	     problems as well as all variants/combinations of LP, IP.
Enhances the R Optimization Infrastructure ('ROI') package 
	     by registering the ipop solver from package 'kernlab'.
Enhances the R Optimization Infrastructure ('ROI') package
        with the 'NLopt' solver for solving nonlinear optimization problems.
Enhances the R Optimization Infrastructure (ROI) package
	     by registering the quadprog solver. It allows for solving quadratic programming (QP)
	     problems.
Enhances the R Optimization Infrastructure (ROI) package
         with the SCS solver for solving convex cone problems.
Enhances the R Optimization Infrastructure (ROI) package by registering
	     the SYMPHONY open-source solver from the COIN-OR suite. It allows for
	     solving mixed integer linear programming (MILP) problems as well as all
	     variants/combinations of LP, IP.
Parallel functions for computing rolling statistics of time-series
    data.
Reports errors and messages to Rollbar, the error tracking platform <https://rollbar.com>.
Apply a function in a moving window, then
    combine the results in a data frame.
Provides the possibility to plot Lexis surface maps (heat maps) of rates of mortality improvement. Raw data to be plotted can be read from the Human Mortality Database using code originally written by Tim Riffe. The European Research Council has provided financial support under the European Community's Seventh Framework Programme (FP7/2007-2013) / ERC grant agreement no. 263744.
Methods and classes for object-oriented programming in R with or without references.  Large effort has been made on making definition of methods as simple as possible with a minimum of maintenance for package developers.  The package has been developed since 2001 and is now considered very stable.  This is a cross-platform package implemented in pure R that defines standard S3 classes without any tricks.
This package contains the Rook specification and
 convenience software for building and running Rook applications. To
 get started, be sure and read the 'Rook' help file first.
Implementation of the Taylor Regression Estimator method described in Christopoulos (2014,<https://www.researchgate.net/publication/261562841>)
 for finding the root, extreme or inflection point of a curve, when we only have a set of probably noisy xy points for it. The method uses a suitable
 polynomial regression in order to find the coefficients of the relevant Taylor polynomial for the function that has generated our data.
Routines to find the root of nonlinear functions, and to perform steady-state and equilibrium analysis of ordinary differential equations (ODE). 
  Includes routines that: (1) generate gradient and Jacobian matrices (full and banded),
  (2) find roots of non-linear equations by the Newton-Raphson method, 
  (3) estimate steady-state conditions of a system of (differential) equations in full, banded or sparse form, using the Newton-Raphson method, or by dynamically running,
  (4) solve the steady-state conditions for uni-and multicomponent 1-D, 2-D, and 3-D partial differential equations, that have been converted to ODEs
    by numerical differencing (using the method-of-lines approach).
  Includes fortran code.
Functions for hypothesis testing in single and double Wishart
    settings, based on Roy's largest root. This test statistic is especially
    useful in multivariate analysis. The computations are based on results by
    Chiani (2014) <DOI:10.1016/j.jmva.2014.04.002> and Chiani (2016)
    <DOI:10.1016/j.jmva.2015.10.007>. They use the fact that the CDF is related
    to the Pfaffian of a matrix that can be computed in a finite number of
    iterations. This package takes advantage of the Boost and Eigen C++ libraries
    to perform multi-precision linear algebra.
Allows access to air quality data from the API of the OpenAQ platform <https://docs.openaq.org/>, with the different services the API offers (getting measurements for a given query, getting latest measurements, getting lists of available countries/cities/locations).
Provide a simple interface to Bloomberg's OpenFIGI API. Please
    see <https://openfigi.com> for API details and registration. You may be
    eligible to have an API key to accelerate your loading process.
An R package for the OpenSecrets.org web services API.
OpenWeatherMap (OWM) <http://openweathermap.org/api> is a service providing weather related data.
           This package can be used to access current weather data for one location or several locations.
           It can also be used to forecast weather for 5 days with data for every 3 hours.
Optimally robust estimation in general smoothly parameterized models using S4 
        classes and methods.
Optimally robust estimation using S4 classes and methods. Old version still needed
        for current versions of ROptRegTS and RobRex.
R package extracts optimizely test results and test information using Optimizely REST API. Only read functionality is supported for analysis and reporting.
Optimally robust estimation for regression-type models using S4 classes and
        methods
Oracle Database interface (DBI) driver for R.
   This is a DBI-compliant Oracle driver based on the OCI.
Client for the 'Orcid.org' 'API' (http://orcid.org/).
    Functions included for searching for people, searching by 'DOI',
    and searching by 'Orcid' 'ID'.
Implementation of Robust Ordinal Regression for value-based sorting with some extensions and additional tools. It is a novel Multiple-Criteria Decision Aiding (MCDA) framework.
The package provides functions to deal with binary classification
  problems in the presence of imbalanced classes. Synthetic balanced samples are  
  generated according to ROSE (Menardi and Torelli, 2013).  
  Functions that implement more traditional remedies to the class imbalance
  are also provided, as well as different metrics to evaluate a learner accuracy.
  These are estimated by holdout, bootstrap or cross-validation methods.
Rosette is an API for multilingual text analysis and information extraction. More information can be found at <https://developer.rosette.com>.
Download and plot Open Street Map <http://www.openstreetmap.org/>,
    Mapquest <http://www.mapquest.com/>, Bing Maps <http://www.bing.com/maps> and
    other tiled map sources in a way that works seamlessly with plotting from the
    'sp' package. Use to create high-resolution basemaps and add hillshade to vector
    based maps.
Fit and deploy rotation forest models ("Rodriguez, J.J., Kuncheva, L.I., 2006. Rotation forest: A new classifier ensemble method. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1619-1630") for binary classification. Rotation forest is an ensemble method where each base classifier (tree) is fit on the principal components of the variables of random partitions of the feature set.
Tools for working with rotational data, including
    simulation from the most commonly used distributions on SO(3),
    methods for different Bayes, mean and median type estimators for
    the central orientation of a sample, confidence/credible
    regions for the central orientation based on those estimators and
    a novel visualization technique for rotation data.  Most recently,
    functions to identify potentially discordant (outlying) values
    have been added.
R build of Rothermel's (1972) model for surface fire rate of spread with some additional utilities (uncertainty propagation, standard fuel model selection, fuel model optimization by genetic algorithm) and sample datasets.
An interface to the 'Open Tree of Life' API to retrieve
    phylogenetic trees, information about studies used to assemble the synthetic
    tree, and utilities to match taxonomic names to 'Open Tree identifiers'. The
    'Open Tree of Life' aims at assembling a comprehensive phylogenetic tree for all
    named species.
A set of functions to support Xiong K, 'Roughened Random Forests for Binary Classification' (2014). The functions include RRFA, RRFB, RRFC1-RRFC7, RRFD and RRFE. RRFB and RRFC6 are usually recommended. RRFB is much faster than RRFC6.
Rough Sets were introduced by Zdzislaw Pawlak on his book "Rough Sets: Theoretical Aspects of Reasoning About Data". Rough Sets provide a formal method to approximate crisp sets when the set-element belonging relationship is either known or undetermined. This enables the use of Rough Sets for reasoning about incomplete or contradictory knowledge. A decision table is a prescription of the decisions to make given some conditions. Such decision tables can be reduced without losing prescription ability. This package provides the classes and methods for knowledge reduction from decision tables as presented in the chapter 7 of the aforementioned book. This package provides functions for calculating the both the discernibility matrix and the essential parts of decision tables.
Implementations of algorithms for data analysis
    based on the rough set theory (RST) and the fuzzy rough set theory (FRST). We
    not only provide implementations for the basic concepts of RST and FRST but also
    popular algorithms that derive from those theories. The methods included in the
    package can be divided into several categories based on their functionality:
    discretization, feature selection, instance selection, rule induction and classification
    based on nearest neighbors. RST was introduced by Zdzisław Pawlak in 1982
    as a sophisticated mathematical tool to
    model and process imprecise or incomplete information. By using
    the indiscernibility relation for objects/instances, RST does not require
    additional parameters to analyze the data. FRST is an extension of RST. The
    FRST combines concepts of vagueness and indiscernibility that are expressed
    with fuzzy sets (as proposed by Zadeh, in 1965) and RST.
Allows the manipulation of R objects as if they were
    organized rows in a way that is familiar to people used to working with
    databases.  It allows more consistent and predictable output to common
    functions, and generalizes a number of utility functions to to be failsafe
    with any number and type of input objects.
A 'Doxygen'-like in-source documentation system
    for Rd, collation, and 'NAMESPACE' files.
Performs a multivariate normality test based on Royston's H test
Implements macroevolutionary analyses on phylogenetic trees. See Morlon et al. (2010) <DOI:10.1371/journal.pbio.1000493>, Morlon et al. (2011) <DOI:10.1073/pnas.1102543108>, Condamine et al. (2013) <DOI:10.1111/ele.12062>, Morlon et al. (2014) <DOI:10.1111/ele.12251>, Manceau et al. (2015) <DOI:10.1111/ele.12415>, Lewitus & Morlon (2016) <DOI:10.1093/sysbio/syv116>, and Drury et al. (2016) <DOI:10.1093/sysbio/syw020>.
rpanel provides a set of functions to build simple 
        GUI controls for R functions.  These are built on the tcltk package. 
        Uses could include changing a parameter on a graph by animating it 
        with a slider or a "doublebutton", up to more sophisticated control 
        panels.
        Some functions for specific graphical tasks, referred to as 'cartoons',
        are provided.
Recursive partitioning for classification, 
  regression and survival trees.  An implementation of most of the 
  functionality of the 1984 book by Breiman, Friedman, Olshen and Stone.
Provides algorithims for randomly sampling a feasible set defined
    by a given total and number of elements using integer partitioning.
Plot 'rpart' models. Extends plot.rpart() and text.rpart()
             in the 'rpart' package.
This package contains functions that allow to build
        classification trees for ordinal responses within the CART
        framework. The trees are grown using the Generalized Gini
        impurity function, where the misclassification costs are given
        by the absolute or squared differences in scores assigned to
        the categories of the response. Pruning is based on the total
        misclassification rate or on the total misclassification cost.
This package contains additional tools for working with rpart
    objects.  Most importantly, it includes methods for converting rpart rules
    into a series of structured tables sufficient for executing the decision
    tree completely in SQL.
Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Candes, E. J., Li, X., Ma, Y., & Wright, J. (2011). Robust principal component analysis?. Journal of the ACM (JACM), 58(3), 11. prove that we can recover each component individually under some suitable assumptions. It is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the L1 norm. This package implements this decomposition algorithm resulting with Robust PCA approach.
Allows users to identify relevant clinical codes and
    automate the construction of clinical code lists for primary care database
    studies. This package is analogous to the Stata command pcdsearch.
This package implements the R-PCLR (Random-Penalized
        Conditional Logistic Regression) algorithm for obtaining
        variable importance. The algorithm is applicable for the
        analysis of high dimensional data from matched case-control
        studies.
Provides tools to read, write, visualize PDB files and perform some structural manipulations.
Provides monthly Pacific Decadal Oscillation (PDO) index
    values from January 1900 to March 2016.
Implements the methodology of "Cannings, T. I. and Samworth, R. J. (2015) Random projection ensemble classification. http://arxiv.org/abs/1504.04595". The random projection ensemble classifier is a general method for classification of high-dimensional data, based on careful combination of the results of applying an arbitrary base classifier to random projections of the feature vectors into a lower-dimensional space. The random projections are divided into non-overlapping blocks, and within each block the projection yielding the smallest estimate of the test error is selected. The random projection ensemble classifier then aggregates the results of applying the base classifier on the selected projections, with a data-driven voting threshold to determine the final assignment.
The purpose of this package is to factor out logic and math common
    to Item Factor Analysis fitting, diagnostics, and analysis. It is
    envisioned as core support code suitable for more specialized IRT packages
    to build upon. Complete access to optimized C functions are made available
    with R_RegisterCCallable().
Allows ad hoc queries and reading and
    writing data frames to and from a database.
Provides an R interface to the PHAST software
    (Phylogenetic Analysis with Space/Time Models).  It can be used for
    many types of analysis in comparative and evolutionary genomics,
    such as estimating models of evolution from sequence data, scoring
    alignments for conservation or acceleration, and predicting
    elements based on conservation or custom phylogenetic hidden Markov
    models.  It can also perform many basic operations on multiple
    sequence alignments and phylogenetic trees.
Rphylip provides an R interface for the PHYLIP package. All users
    of Rphylip will thus first have to install the PHYLIP phylogeny methods
    program package (Felsenstein 2013). See http://www.phylip.com for more 
	information about installing PHYLIP.
Tools for performing phylogenetic comparative methods for datasets with with multiple observations per species (intraspecific variation or measurement error) and/or missing data. Performs ancestral state reconstruction and missing data imputation on the estimated evolutionary model, which can be specified as Brownian Motion, Ornstein-Uhlenbeck, Early-Burst, Pagel's lambda, kappa, or delta, or a star phylogeny.
Get information (boards, pins and
    users) from the Pinterest <http://www.pinterest.com>
    API.
Build powerful pivot tables (aka Pivot Grid, Pivot Chart, Cross-
    Tab) and dynamically slice & dice / drag 'n' drop your data. 'rpivotTable' is a
    wrapper of 'pivottable', a powerful open-source Pivot Table library implemented
    in 'JavaScript' by Nicolas Kruchten. Aligned to 'pivottable' v1.6.3.
Provides an interface to the the many computational resources iPlant offers through their RESTful application programming interface, see <http://www.iplantcollaborative.org/> for more information about iPlant, and <http://www.iplantcollaborative.org/ci/iplant-science-apis> for its APIs.  Currently, 'rPlant' functions interact with the iPlant Agave API, the Taxonomic Name Resolution Service API, and the Phylotastic Taxosaurus API. Before using 'rPlant', users will have to register with the iPlant Collaborative.  See <http://agaveapi.co/> for more information on the Agave API and <http://user.iplantcollaborative.org/> to register with iPlant.
Efficiently read and analyze 'PLEXOS' solutions by converting
    them into 'SQLite' databases that can be easily queried. It supports collation
    of solutions that may have been divided into different time partitions, as
    well as the comparison across different scenarios. 'PLEXOS' is a power systems
    production cost model, created and distributed by Energy Exemplar (see <http://energyexemplar.com/software/plexos-desktop-edition> 
    for more information).
A programmatic interface to the 'SOLR' based
    search 'API' (http://api.plos.org/) provided by the Public
    Library of Science journals to search their articles.
    Functions are included for searching for articles, retrieving
    articles, making plots, doing 'faceted' searches,
    'highlight' searches, and viewing results of 'highlighted'
    searches in a browser.
This package is intended for using R as a plotting engine either by custom
    applications, or from a small script launched from the system console, or even within
    the R console. Two ASCII text files are necessary:
  (1) The graph parameters file, which name is passed to the function 'rplotengine()'.
    The user can specify the titles, choose the type of the graph, graph output formats
    (e.g. png, eps), proportion of the X-axis and Y-axis, position of the legend,
    whether to show or not a grid at the background, etc.
  (2) The data to be plotted, which name is specified as a parameter ('data_filename')
    in the previous file. This data file has a tabulated format, with a single character
    (e.g. tab) between each column, and a headers line located in the first row.
    Optionally, the file could include data columns for showing confidence intervals.
Really Poor Man's Graphical User Interface, used to create interactive R analysis sessions with simple R commands.
Recursively Partitioned Mixture Model for Beta and Gaussian Mixtures.  
    This is a model-based clustering algorithm that returns a hierarchy
    of classes, similar to hierarchical clustering, but also similar to
    finite mixture models.
Pure R implementation of a simple (Reverse) Polish Notation (RPN) interpreter
  and converter.
A set of functions to analyze and print the development of a
    commodity using the Point and Figure (P&F) approach. A P&F processor can be used
    to calculate daily statistics for the time series. These statistics can be used
    for deeper investigations as well as to create plots. Plots can be generated as
    well known X/O Plots in plain text format, and additionally in a more graphical
    format.
PDF tools based on the Poppler PDF rendering library.
  See <http://poppler.freedesktop.org/> for more information on Poppler.
A collection of tools used to generate
        various types of random portfolios.  The weights of these
        portfolios are random variables derived from truncated
        continuous random variables.
Provides additional functions to the
    'RPostgreSQL' package to interface R with a 'PostGIS'-enabled
    database, as well as convenient wrappers to common 'PostgreSQL'
    queries.
Database interface and PostgreSQL driver for R This
        package provides a Database Interface (DBI) compliant driver
        for R to access PostgreSQL database systems.

   In order to build and install this package from source, PostgreSQL
        itself must be present your system to provide PostgreSQL
        functionality via its libraries and header files. These files
        are provided as postgresql-devel package under some Linux
        distributions.

   On Microsoft Windows system the attached libpq library source will be
        used.

   A wiki and issue tracking system for the package are available at
        Google Code at https://code.google.com/p/rpostgresql/ .
The significance of mean difference tests in clinical trials is established if at least r null hypotheses are rejected among m that are simultaneously tested. This package enables one to compute necessary sample sizes for single-step (Bonferroni) and step-wise procedures (Holm and Hochberg). These three procedures control the q-generalized family-wise error rate (probability of making at least q false rejections). Sample size is computed (for these single-step and step-wise procedures) in a such a way that the r-power (probability of rejecting at least r false null hypotheses, i.e. at least r significant endpoints among m) is above some given threshold, in the context of tests of difference of means for two groups of continuous endpoints (variables). Various types of structure of correlation are considered. It is also possible to analyse data (i.e., actually test difference in means) when these are available. The case r equals 1 is treated in separate functions that were used in Lafaye de Micheaux et al. (2014) <doi:10.1080/10543406.2013.860156>.
Using some association schemes to obtain a new series of resolvable partially pairwise balanced designs (RPPBD) and space-filling designs.
Reads in sample description and slide description files and
        annotates the expression values taken from GenePix results files
	(text file format used by many microarray scanner and software providers). 
	After normalization data can be visualized as boxplot, heatmap or dotplot.
Routines to select and visualize the maxima for a given strict
    partial order. This especially includes the computation of the Pareto
    frontier, also known as (Top-k) Skyline operator, and some
    generalizations (database preferences).
Implements a 'DBI' compliant interface to Presto. Presto is
    an open source distributed SQL query engine for running interactive
    analytic queries against data sources of all sizes ranging from
    gigabytes to petabytes: <https://prestodb.io/>.
Estimates disease prevalence for a given index date, using existing
    registry data extended with Monte Carlo simulations.
'Eprime' is a set of programs for administering psychological
    experiments by computer. This package provides functions for loading,
    parsing, filtering and exporting data in the text files produced by
    'Eprime' experiments.
Provides a set of functions to facilitate building formatted strings
    under various replacement rules: C-style formatting, variable-based formatting,
    and number-based formatting. C-style formatting is basically identical to built-in
    function 'sprintf'. Variable-based formatting allows users to put variable names
    in a formatted string which will be replaced by variable values. Number-based
    formatting allows users to use index numbers to represent the corresponding
    argument value to appear in the string.
Robust, reliable and flexible paths to files below a
    project root. The 'root' of a project is defined as a directory
    that matches a certain criterion, e.g., it contains a certain
    regular file.
Protocol Buffers are a way of encoding structured data in an
 efficient yet extensible format. Google uses Protocol Buffers for almost all
 of its internal 'RPC' protocols and file formats.  Additional documentation
 is available in two included vignettes one of which corresponds to our paper
 in the Journal of Statistical Software (2016, v71i02). Either version 2 or 3
 of the Protocol Buffers API is supported.
Implements methods described by the paper Robins and Tsiatis (1991) <DOI:10.1080/03610929108830654>. These use g-estimation to estimate the causal effect of a treatment in a two-armed randomised control trial where non-compliance exists and is measured, under an assumption of an accelerated failure time model and no unmeasured confounders.
The rpsychi offers a number of functions for psychiatry, psychiatric nursing, clinical psychology. Functions are primarily for statistical significance testing using published work. For example, you can conduct a factorial analysis of variance (ANOVA), which requires only the mean, standard deviation, and sample size for each cell, rather than the individual data. This package covers fundamental statistical tests such as t-test, chi-square test, analysis of variance, and multiple regression analysis. With some exceptions, you can obtain effect size and its confidence interval. These functions help you to obtain effect size from published work, and then to conduct a priori power analysis or meta-analysis, even if a researcher do not report effect size in a published work.
Performs goodness of fits tests for both high and low-dimensional linear models.
    It can test for a variety of model misspecifications including nonlinearity and heteroscedasticity.
    In addition one can test the significance of potentially large groups of variables, and also
    produce p-values for the significance of individual variables in high-dimensional linear
    regression.
Access PubChem data (compounds, substance, assays).
        Structural information is provided in the form of SMILES
        strings. This package only provides access to a subset of the
        precalculated data stored by PubChem. Bio-assay data can be
        accessed to obtain descriptions as well as the actual data. It
        is also possible to search for assay ID's by keyword. Currently
        the main limitation is that only 1000 molecules can be
        downloaded at a time from the PubChem servers
Client for accessing data journalism APIs from ProPublica <http://www.propublica.org/>.
An R interface to the Pushbullet messaging service which
 provides fast and efficient notifications (and file transfer) between
 computers, phones and tablets.  An account has to be registered at the site
 http://www.pushbullet.com site to obtain a (free) API key.
Provide access to (virtually any) ArcGIS Geoprocessing
        tool from within R by running Python geoprocessing scripts
        without writing Python code or touching ArcGIS. Requires ArcGIS
        >=9.2, a suitable version of Python (for ArcGIS 9.2: Python
        2.4; for ArcGIS 10.0: 2.6), and Windows.
Run Python code, make function calls,  assign and retrieve variables, etc. from R.
R package for Qualitative Data Analysis. Current version only supports plain text, but it can import PDF highlights if the Enhance package of rjpod, which is available on R-Forge, is installed.
Package RQGIS establishes an interface between R and QGIS, i.e. it allows
    the user to access QGIS functionalities from the R console. It achieves this
    by using the QGIS Python API via the command line. Hence, RQGIS extends R's 
    statistical power by the incredible vast geo-functionality of QGIS (including 
    also GDAL, SAGA- and GRASS-GIS among other third-party providers). This in turn 
    creates a powerful environment for advanced and innovative (geo-)statistical
    geocomputing.
Performs penalized quantile regression for LASSO, SCAD and MCP functions including group penalties. Provides a function that automatically generates lambdas and evaluates different models with cross validation or BIC, including a large p version of BIC.
Hypocenter estimation and analysis of seismic data collected continuously, or in trigger mode. The functions organize other functions from RSEIS and GEOmap to help researchers pick, locate, and store hypocenters for detailed seismic investigation.
The 'RQuantLib' package makes parts of 'QuantLib' accessible from R
 The 'QuantLib' project aims to provide a comprehensive software framework
 for quantitative finance. The goal is to provide a standard open source library
 for quantitative analysis, modeling, trading, and risk management of financial
 assets.
Enables researchers to conduct multivariate statistical analyses
    of survey data with randomized response technique items from several designs,
    including mirrored question, forced question, and unrelated question. This
    includes regression with the randomized response as the outcome and logistic
    regression with the randomized response item as a predictor. In addition,
    tools for conducting power analysis for designing randomized response items
    are included. The package implements methods described in Blair, Imai, and Zhou
    (2015) ''Design and Analysis of the Randomized Response Technique,'' Journal
    of the American Statistical Association 
    <http://graemeblair.com/papers/randresp.pdf>.
Analyzes and predicts from matrix population models in the way of the Ramas (c) software
Replication Rate (RR) is the probability of replicating a statistically significant association in genome-wide association studies. This R-package provide the estimation method for replication rate which makes use of the summary statistics from the primary study. We can use the estimated RR to determine the sample size of the replication study, and to check the consistency between the results of the primary study and those of the replication study.
Software for genomic prediction with the RR-BLUP mixed model. One application is to estimate marker effects by ridge regression; alternatively, BLUPs can be calculated based on an additive relationship matrix or a Gaussian kernel.
rrBlupMethod6 -- Re-parametrization of mixed model
        formulation to allow for a fixed residual variance when using
        RR-BLUP for genomwide estimation of marker effects and linear
        transformation of the adjusted means proposed by Piepho et
        al.(2011)
Robust Location and Scatter Estimation and Robust
        Multivariate Analysis with High Breakdown Point.
Robust multivariate methods for high dimensional data including
        outlier detection, PCA, PLS and classification.
Robust Location and Scatter Estimation and Robust
        Multivariate Analysis with High Breakdown Point for Incomplete
        Data.
Make use of the hardware random number accessed by the 'RdRand'
 instruction in recent Intel CPUs (Ivy Bridge and later).
 'DRNG' is "Digital Random Number Generator".
Provides implementations of several popular recommendation systems. They can process standard recommendation datasets (user/item matrix) as input and generate rating predictions and recommendation lists.  Standard algorithm implementations included in this package are: Global/Item/User-Average baselines, Item-Based KNN, FunkSVD, BPR and weighted ALS. They can be assessed according to the standard offline evaluation methodology for recommender systems using measures such as MAE, RMSE, Precision, Recall, AUC, NDCG, RankScore and coverage measures. The package is intended for rapid prototyping of recommendation algorithms and education purposes.
R client interface to the "Redis" key-value database.
'IUCN' Red List (<http://apiv3.iucnredlist.org/api/v3/docs>) client.
    The 'IUCN' Red List is a global list of threatened and endangered species.
    Functions cover all of the Red List 'API' routes. An 'API' key is required.
Superclasses 'PostgreSQL' connection to help enable full 'dplyr' functionality on 'Redshift'.
'OpenRefine' (formerly 'Google Refine') is a popular, open source data cleaning software. This package enables users to programmatically trigger data transfer between R and 'OpenRefine'. Available functionality includes project import, export and deletion.
An R and Repast integration tool for running individual-based
    (IbM) simulation models developed using 'Repast Simphony' Agent-Based framework
    directly from R code. This package integrates 'Repast Simphony' models within
    R environment, making easier the tasks of running and analyzing model output
    data for automated parameter calibration and for carrying out uncertainty and
    sensitivity analysis using the power of R environment.
Feature Selection with Regularized Random Forest. This
        package is based on the 'randomForest' package by Andy Liaw.
        The key difference is the RRF function that builds a
        regularized random forest.
Collection of functions for fitting distributions to given data or
    by known quantiles. Two main functions fit.perc() and fit.cont() provide
    users a GUI that allows to choose a most appropriate distribution without
    any knowledge of the R syntax. Note, this package is a part of the 'rrisk'
    project.
This package offers methods to perform robust regularized
        linear discriminant analysis.
Functions for creating and manipulating RNA secondary structure plots.
Univariate and multivariate methods to analyze
    randomized response (RR) survey designs (e.g., Warner, S. L. (1965).
    Randomized response: A survey technique for eliminating evasive answer
    bias. Journal of the American Statistical Association, 60, 63–69).
    Besides univariate estimates of true proportions, RR variables can be used
    for correlations, as dependent variable in a logistic regression (with or
    without random effects), as predictors in a linear regression, or as
    dependent variable in a beta-binomial ANOVA. For simulation and bootstrap
    purposes, RR data can be generated according to several models.
The RSP markup language makes any text-based document come alive.  RSP provides a powerful markup for controlling the content and output of LaTeX, HTML, Markdown, AsciiDoc, Sweave and knitr documents (and more), e.g. 'Today's date is <%=Sys.Date()%>'.  Contrary to many other literate programming languages, with RSP it is straightforward to loop over mixtures of code and text sections, e.g. in month-by-month summaries.  RSP has also several preprocessing directives for incorporating static and dynamic contents of external files (local or online) among other things.  Functions rstring() and rcat() make it easy to process RSP strings, rsource() sources an RSP file as it was an R script, while rfile() compiles it (even online) into its final output format, e.g. rfile('report.tex.rsp') generates 'report.pdf' and rfile('report.md.rsp') generates 'report.html'.  RSP is ideal for self-contained scientific reports and R package vignettes.  It's easy to use - if you know how to write an R script, you'll be up and running within minutes.
Point and interval estimation of linear parameters with data
    obtained from complex surveys (including stratified and clustered samples)
    when randomization techniques are used. The randomized response technique
    was developed to obtain estimates that are more valid when studying
    sensitive topics. Estimators and variances for 14 randomized response
    methods for qualitative variables and 7 randomized response methods for
    quantitative variables are also implemented. In addition, some data sets
    from surveys with these randomization methods are included in the package.
Advanced response surface analysis. The main function RSA computes
    and compares several nested polynomial regression models (full polynomial,
    shifted and rotated squared differences, rising ridge surfaces, basic
    squared differences). The package provides plotting functions for 3d
    wireframe surfaces, interactive 3d plots, and contour plots. Calculates
    many surface parameters (a1 to a4, principal axes, stationary point,
    eigenvalues) and provides standard, robust, or bootstrapped standard errors
    and confidence intervals for them.
The package contains all the data sets related to the book
        written by the maintainer of the package.
Robust Small Area Estimation. Robust Basic Unit- and Area-Level Models
Provides access to geocomputing and terrain analysis
    functions of the geographical information system (GIS) 'SAGA' (System for
    Automated Geoscientific Analyses) from within R by running the command 
    line version of SAGA. This package furthermore provides several R functions
    for handling ASCII grids, including a flexible framework for applying local
    functions (including predict methods of fitted models) and focal functions to
    multiple grids. SAGA GIS is available under GPLv2 / LGPLv2 licence from
    http://sourceforge.net/projects/saga-gis/.
Performs parameter estimation for geostatistical data using a resampling-based stochastic approximation (RSA) method.
Contains functions that are useful for pre- and post-processing the tree output from the MCMC-based C++ program sampletrees. The program sampletrees is used to sample gene genealogies conditional on either phased or unphased SNP genotype data. More information about sampletrees can be found at http://stat.sfu.ca/statgen/research/sampletrees.html . Pre-processing tasks involve creating the settings file, the haplotype estimation files (if input data is genotype), and error checking the settings. Post-processing involves reading in the MCMC output from a run of sampletrees and computing summary statistics.
Resampling Stats (http://www.resample.com) is an add-in for
    running randomization tests in Excel worksheets. The workflow is (1) to define
    a statistic of interest that can be calculated from a data table, (2) to
    randomize rows ad/or columns of a data table to simulate a null hypothesis
    and (3) and to score the value of the statistic from many randomizations. The
    relative frequency distribution of the statistic in the simulations is then
    used to infer the probability of the observed value be generated by the null
    process (probability of Type I error). This package intends to translate this
    logic for R for teaching purposes. Keeping the original workflow is favored over
    performance.
Tools to create, evaluate, critique,
    revise, track progress, and communicate a detailed wilderness or urban
    search plan to management. This package uses and creates csv files in the R
    working directory to document inputs and results. It also creates a series
    of PDF and PNG files to accomplish communication of the plan. The program
    creates and revises search plans using Bayesian models. The package
    includes functions bestsearch(), searchstatus() and searchme() to model the
    number of searchers and hours required to search an area, calculate the
    probability of detection, probability of success, and project the best plan
    given limited resources.
SaTScan(TM) (http://www.satscan.org) is software for finding regions in 
    Time, Space, or Time-Space that have excess risk, based on scan statistics, and 
	uses Monte Carlo hypothesis testing to generate P-values for these regions.  The 
	rsatscan package provides functions for writing R data frames in 
	SaTScan-readable formats, for setting SaTScan parameters, for running SaTScan in 
	the OS, and for reading the files that SaTScan creates.
This package implements a statistical tool for modeling multivariate relationships using a stepwise cluster analysis (SCA) method.
Performs the Rao-Scott Cochran-Armitage by Slices trend test (RSCABS) used 
	in analysis of histopathological endpoints. It has functions for both command line 
	operations along with a built in GUI.
The Scala interpreter is embedded in R and callbacks to R from the embedded interpreter are supported.  Conversely, the R interpreter is embedded in Scala.  Scala versions in the 2.11.x series are supported.
The SciMark 2.0 benchmark was originally developed in Java as a benchmark for numerical and scientific computational performance. It measures the performance of several computational kernels which are frequently occurring in scientific applications. This package is a simple wrapper around the ANSI C implementation of the benchmark.
Client for Rserve, allowing to connect to Rserve instances and issue commands.
Programmatic deployment interface for 'RPubs', 'shinyapps.io', and
    'RStudio Connect'. Supported content types include R Markdown documents,
    Shiny applications, plots, and static web content.
Uses Elsevier Scopus API to download information about authors and their citations.
A method to download Department of Education College Scorecard data
    using the public API. It is based on the dplyr model of
    piped commands to select and filter data in a single chained function call.
    An API key from the U.S. Department of Education is required.
rscproxy library provides an interface to R used by third
        party applications, most notable, but not limited to,
        statconnDCOM, ROOo and other systems.
Symbolic Data Analysis (SDA) was proposed by professor Edwin Diday in 1987, the main purpose of SDA is to substitute the set of rows (cases) in the data table for  a concept (second order statistical unit). This package implements, to the symbolic case, certain techniques of automatic classification, as well as some linear models.
Ray Shooting Depth functions are provided for bivariate analysis.
Set of classes and methods to read data and metadata documents
  exchanged through the Statistical Data and Metadata Exchange (SDMX) framework,
  currently focusing on the SDMX XML standard format (SDMX-ML).
an implementation of the analysis about seed components
        from borenstein 2008
Estimate the percentage of seeds in a seedlot that contain stacks
    of genetically modified traits.  Estimates are calculated using a
    multinomial group testing model with maximum likelihood estimation of the
    parameters.
Multiple interactive codes to view and analyze seismic data, via spectrum analysis, wavelet transforms, particle motion, hodograms.  Includes general time-series tools, plotting, filtering, interactive display.
The RSelenium package provides a set of R bindings for the
    Selenium 2.0 WebDriver using the JsonWireProtocol. Selenium automates web
    browsers (commonly referred to as browsers). Using RSelenium you can
    automate browsers locally or remotely.
A robust procedure is implemented to estimate means and covariance matrix of multiple variables with missing data using Huber weight and then to estimate a structural equation model.
Analyses sentiment of a sentence in English and assigns score to it. It can classify sentences to the following categories of sentiments:- Positive, Negative, very Positive, very negative, 
              Neutral or Sarcasm. For a vector of sentences, it counts the number of sentences in each
              category of sentiment.In calculating the score, negation and various degrees
              of adjectives are taken into consideration. It deals only with English sentences.
Rserve acts as a socket server (TCP/IP or local sockets) 
	     which allows binary requests to be sent to R. Every
	     connection has a separate workspace and working
	     directory. Client-side implementations are available
	     for popular languages such as C/C++ and Java, allowing
	     any application to use facilities of R without the need of
	     linking to R code. Rserve supports remote connection,
	     user authentication and file transfer. A simple R client
	     is included in this package as well.
Slow Feature Analysis in R, ported to R based on the
    matlab versions SFA toolkit 1.0 by Pietro Berkes and SFA toolkit
    2.8 by Wolfgang Konen for matlab.
This package provides functions for calculating
        associations between two genes with five correlation
        methods(e.g., the Gini correlation coefficient [GCC], the
        Pearson's product moment correlation coefficient [PCC], the
        Kendall tau rank correlation coefficient [KCC], the Spearman's
        rank correlation coefficient [SCC] and the Tukey's biweight
        correlation coefficient [BiWt], and three non-correlation
        methods (e.g., mutual information [MI] and the maximal
        information-based nonparametric exploration [MINE], and the
        euclidean distance [ED]). It can also been implemented to
        perform the correlation and clustering analysis of
        transcriptomic data profiled by microarray and RNA-Seq
        technologies. Additionally, this package can be further applied
        to construct gene co-expression networks (GCNs).
Robust estimation of sparse inverse covariance matrix via the gamma-divergence.
Functions for estimating models using a Hierarchical Bayesian (HB) framework. The flexibility comes in allowing the user to specify the likelihood function directly instead of assuming predetermined model structures. Types of models that can be estimated with this code include the family of discrete choice models (Multinomial Logit, Mixed Logit, Nested Logit, Error Components Logit and Latent Class) as well ordered response models like ordered probit and ordered logit. In addition, the package allows for flexibility in specifying parameters as either fixed (non-varying across individuals) or random with continuous distributions. Parameter distributions supported include normal, positive/negative log-normal, positive/negative censored normal, and the Johnson SB distribution. Kenneth Train's Matlab and Gauss code for doing Hierarchical Bayesian estimation has served as the basis for a few of the functions included in this package. These Matlab/Gauss functions have been rewritten to be optimized within R. Considerable code has been added to increase the flexibility and usability of the code base. Train's original Gauss and Matlab code can be found here: http://elsa.berkeley.edu/Software/abstracts/train1006mxlhb.html See Train's chapter on HB in Discrete Choice with Simulation here: http://elsa.berkeley.edu/books/choice2.html; and his paper on using HB with non-normal distributions here: http://eml.berkeley.edu//~train/trainsonnier.pdf.
Fits models to longitudinal network data
Robust and efficient feature selection algorithm to
    identify important features for predicting survival risk.
    The method is based on subsampling and averaging linear models
    obtained from the (preconditioned) Lasso algorithm, with an extra 
    shrinking procedure to reduce the size of signatures. An 
    evaluation procedure using subsampling is also provided.
This package provides a way to transform an image into a mosaic composed from a set of smaller images (tiles). It also contains a simple function for creating the tiles from a folder of images directly through R, without the need of any external code. At this moment only the JPEG format is supported, either as input (image and tiles) or output (mosaic transformed image).
Functions for interacting with the Adobe Analytics API V1.4
    (https://api.omniture.com/admin/1.4/rest/).
This RSKC package contains a function RSKC which runs the robust sparse K-means clustering algorithm.
Implements the "Stemming Algorithm for the Portuguese Language" <DOI:10.1109/SPIRE.2001.10024>.
Functions that simplify the R interface the 'SLURM' cluster workload 
    manager, and automate the process of dividing a parallel calculation across 
    cluster nodes.
Provides functions to generate response-surface designs, fit first- and second-order response-surface models, make surface plots, obtain the path of steepest ascent, and do canonical analysis.
Aims at loading Facebook and Instagram advertising data from
    Smartly.io into R. Smartly.io is an online advertising service that enables
    advertisers to display commercial ads on social media networks. The package
    offers an interface to query the Smartly.io API and loads data directly into R
    for further data processing and data analysis.
It manages snow and weather local time series as provided by MeteoIO (<http://models.slf.ch/docserver/meteoio/html/index.html>, <http://www.slf.ch/ueber/organisation/schnee_permafrost/projekte/MeteoIO/index_EN>, <https://www.openhub.net/p/MeteoIO>). MeteoIO is a C/C++ Open Source library which "has been designed to accomodate both the needs of carefully crafted simulations for a specific purpose/study and for the needs of operational simulations that run automatically and unattended". It is integrated in physical spatially-distributed models and tackles several issues with weather input/output data. Here a SMET S4 class object is defined and can be imported from/ exported to a SMET ini files of MeteoIO , allowing interoperability from R to MeteoIO and other SMET-compliant software.
Read and analyse Root System Markup Language (RSML) files, used to
    store plant root system architecture data. More information can be found
    at the address <http://rootsystemml.github.io/>.
The Stuttgart Neural Network Simulator (SNNS) is a library
    containing many standard implementations of neural networks. This
    package wraps the SNNS functionality to make it available from
    within R. Using the RSNNS low-level interface, all of the
    algorithmic functionality and flexibility of SNNS can be accessed.
    Furthermore, the package contains a convenient high-level
    interface, so that the most common neural network topologies and
    learning algorithms integrate seamlessly into R.
A programmatic interface to various SNP datasets
    on the web: openSNP, NBCI's dbSNP database, and Broad Institute SNP
    Annotation and Proxy Search. Functions are included for searching for
    SNPs for the Broad Institute and NCBI. For OpenSNP, functions are
    included for getting SNPs, and data for genotypes, phenotypes,
    annotations, and bulk downloads of data by user.
An implementation of the use of efficient score statistics
  in genome-wide SNP set analysis with complex traits. Three standard score statistics
  (Cox, binomial, and Gaussian) are provided, but the package is easily extensible to
  include others. Code implementing the inferential procedure is primarily written in C++ and
  utilizes parallelization of the analysis to reduce runtime. A supporting function offers
  simple computation of observed, permutation, and FWER and FDR adjusted p-values.
Provides easier interaction with
    Socrata open data portals http://dev.socrata.com.
    Users can provide a 'Socrata' data set resource URL,
    or a 'Socrata' Open Data API (SoDA) web query,
    or a 'Socrata' "human-friendly" URL,
    returns an R data frame. Converts dates to 'POSIX'
    format and manages throttling by 'Socrata'.
    Users can upload data to Socrata portals directly
    from R.
General Non-linear Optimization Using Augmented Lagrange Multiplier Method.
Somoclu is a massively parallel implementation of self-organizing maps.  It exploits multicore CPUs and it can be accelerated by CUDA. The topology of the map can be planar or toroid and the grid of neurons can be rectangular or hexagonal .
Based on (optionally sparse) quadratic optimization with the main algorithms implemented in C. Includes features for easy processing of many (smaller) records. The algorithm has been tested on fairly large optimization problems with up to a few million variables and several hundred thousand restrictions.
Conducts a spatially-explicit, simulation-based power analysis for detecting trends in population abundance through occupancy-based modeling. Applicable for evaluating monitoring designs in conservation and ecological settings.
R interface to the 'Spectra' library
    <http://yixuan.cos.name/spectra/> for large scale eigenvalue and SVD
    problems. It is typically used to compute a few
    eigenvalues/vectors of an n by n matrix, e.g., the k largest eigenvalues,
    which is usually more efficient than eigen() if k << n. This package
    provides the 'eigs()' function which does the similar job as in 'Matlab',
    'Octave', 'Python SciPy' and 'Julia'. It also provides the 'svds()' function
    to calculate the largest k singular values and corresponding
    singular vectors of a real matrix. Matrices can be given in either dense
    or sparse form.
Conversion between attitude representations: DCM, Euler angles, Quaternions, and Euler vectors.
    Plus conversion between 2 Euler angle set types (xyx, yzy, zxz, xzx, yxy, zyz, xyz, yzx, zxy, xzy, yxz, zyx).
    Fully vectorized code, with warnings/errors for Euler angles (singularity, out of range, invalid angle order), 
    DCM (orthogonality, not proper, exceeded tolerance to unity determinant) and Euler vectors(not unity).
    Also quaternion and other useful functions.
    Based on SpinCalc by John Fuller and SpinConv by Paolo de Leva.
Provides functions for estimating power or sample size for RNA-Seq studies. Empirical approach is used and the data is assumed to be count in nature. The underlying distribution of data is assumed to be Poisson or negative binomial. The package contains 6 function; 4 functions provide estimates of sample size or power for Poisson and Negative Binomial distribution; 2 functions provide plots of power for given sample size or sample size for given power.
Calculates a newly defined coefficient of determination, aka R^2, and coefficient of partial determination, aka partial R^2 by Zhang (2016) for generalized linear models (including quasi models with well defined variance functions). It also avoids overstatement of variation proportion explained by the model or a group of covariates.
This package embeds the SQLite database engine in R and
    provides an interface compliant with the DBI package.
    The source for the SQLite engine (version 3.8.6) is included.
Utilises The jTDS Project's JDBC 3.0 SQL Server
    driver to extend the RJDBC classes and DBI methods. It defines a
    SQLServerDriver, SQLServerConnection & SQLServerRsult S4 classes as
    extensions of the RJDBC equivalent classes with most DBI
    methods being thin extensions of the methods defined by RJDBC classes.
    However, the dbConnect interface is more convenient, and the data reading
    and writing capabilities found in RJDBC's fetch and dbWriteTable methods
    have been refined. The package also implements a SQL backend to the dplyr
    package.
Methods and tools for Singular Spectrum Analysis including decomposition, forecasting and gap-filling for univariate and multivariate time series.
A collection of implementations of semi-supervised classifiers and methods to evaluate their performance. The package includes implementations of, among others, Implicitly Constrained Learning, Moment Constrained Learning, the Transductive SVM, Manifold regularization, Maximum Contrastive Pessimistic Likelihood estimation, S4VM and WellSVM.
Reservoir Systems Standard Operation Policy. A system for simulation of supply reservoirs. It proposes functionalities for plotting and evaluation of supply reservoirs systems.
An extremely simple stack data type, implemented with 'R6'
    classes. The size of the stack increases as needed, and the amortized
    time complexity is O(1). The stack may contain arbitrary objects.
Provides fast, persistent (side-effect-free) stack, queue and
    deque (double-ended-queue) data structures. While deques include a superset
    of functionality provided by queues, in these implementations queues are
    more efficient in some specialized situations. See the documentation for
    rstack, rdeque, and rpqueue for details.
User-facing R functions are provided to parse, compile, test, 
    estimate, and analyze Stan models by accessing the header-only Stan library 
    provided by the 'StanHeaders' package. The Stan project develops a
    probabilistic programming language that implements full Bayesian statistical 
    inference via Markov Chain Monte Carlo, rough Bayesian inference via variational 
    approximation, and (optionally penalized) maximum likelihood estimation via 
    optimization. In all three cases, automatic differentiation is used to quickly 
    and accurately evaluate gradients without burdening the user with the need 
    to derive the partial derivatives.
Estimates pre-compiled regression models using the 'rstan' package, which provides
    the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the
    customary R syntax with a formula and data.frame plus some additional arguments for priors.
Access to the Digital Universe Data set API pulls data on
    exoplants, stars, and galaxies.
A simple R -> Stata interface allowing the user to
    execute Stata commands (both inline and from a .do file)
    from R.
R interface for china national data http://data.stats.gov.cn/, 
    some convenient functions for accessing the national data are provided.
This package simulates random orthonormal matrices from linear and quadratic exponential family distributions on the Stiefel manifold. The most general type of distribution covered is the matrix-variate  Bingham-von Mises-Fisher distribution. Most of the simulation methods are presented in Hoff(2009) "Simulation of the Matrix Bingham-von Mises-Fisher Distribution, With Applications to Multivariate and Relational Data."
Toolbox for remote sensing image processing and analysis such as
    calculating spectral indices, principal component transformation, unsupervised
    and supervised classification or fractional cover analyses.
While streaming processing provides opportunities to deal with extremely large and ever growing data sets in (near) real time, the development of streaming algorithms for complex models is often cumbersome: the software packages that facilitate streaming processing in production environments do not provide statisticians with the simulation, estimation, and plotting tools they are used to. Developers of streaming algorithms would thus benefit from the flexibility of [R] to create, plot and compute data while developing streaming algorithms. Package RStorm implements a streaming architecture modeled on Storm for easy development and testing of streaming algorithms in [R]. RStorm is not intended as a production package, but rather a development tool for streaming algorithms.
R implementation of generalized survival models, where g(S(t|x))=eta(t,x) for a link function g, survival S at time t with covariates x and a linear predictor eta(t,x). The main assumption is that the time effect(s) are smooth. For fully parametric models, this re-implements Stata's 'stpm2' function, which are flexible parametric survival models developed by Royston and colleagues. We have extended the parametric models to include any smooth parametric smoothers for time. We have also extended the model to include any smooth penalized smoothers from the 'mgcv' package, using penalized likelihood. These models include left truncation, right censoring, interval censoring, gamma frailties and normal random effects.
Unified object oriented interface for multiple independent streams of random numbers from different sources.
A convenience interface for communicating with the Stripe payment processor to accept payments online. See <https://stripe.com> for more information.
Access the RStudio API (if available) and provide informative error
    messages when it's not.
A collection of efficient and effective tools and
	algorithms for subgroup discovery and analytics. The package
	integrates an R interface to the org.vikamine.kernel library
	of the VIKAMINE system (http://www.vikamine.org) implementing
	subgroup discovery, pattern mining and analytics in Java.
This package provides an interface for the package of
        nonlinear differential algebraic equation solvers that comprise
        SUNDIALS. ODEs are expressed as R functions or as compiled
        code.
Interface to three 'Sunlight' Foundation 'APIs' (http://
    sunlightfoundation.com/api/) for government data, including the
    Congress 'API' 'v3', the Capitol Words 'API', and the Open States
    'API'. 'Sunlight' Foundation is a 'nonpartison' 'nonprofit' that collects and
    provides data on government activities, and those that influence government.
    Functions are provided to interact with each of the three 'APIs'.
Provides functions to estimate the proportion of treatment effect on the primary outcome that is explained by the treatment effect on the surrogate marker.
This package provides a range of functions for the design and
    analysis of disease surveillance activities. These functions were
    originally developed for animal health surveillance activities but can be
    equally applied to aquatic animal, wildlife, plant and human health
    surveillance activities. Utilities are included for sample size calculation
    and analysis of representative surveys for disease freedom, risk-based
    studies for disease freedom and for prevalence estimation.
A processing program for spatially distributed data.
  It features graphing tools, query building, and polygon clipping.
  A graphical user interface is provided.
Randomized singular value decomposition (rsvd) is a very fast probabilistic algorithm that can be used to compute the near optimal low-rank singular value decomposition of massive data sets with high accuracy. SVD plays a central role in data analysis and scientific computing. SVD is also widely used for computing (randomized) principal component analysis (PCA), a linear dimensionality reduction technique. Randomized PCA (rpca) uses the approximated singular value decomposition to compute the most significant principal components. This package also includes a function to compute (randomized) robust principal component analysis (RPCA). In addition several plot functions are provided.
Renders vector-based svg images into high-quality custom-size bitmap
    arrays using 'librsvg2'. The resulting bitmap can be written to e.g. png, jpeg
    or webp format. In addition, the package can convert images directly to various
    formats such as pdf or postscript.
A graphics device for R that uses the w3.org xml standard
        for Scalable Vector Graphics.
A graphics device for R that uses the w3.org xml standard
             for Scalable Vector Graphics.  This version supports
             tooltips with 1 to 3 lines, hyperlinks, and line styles.
An R interface to the SYMPHONY solver for mixed-integer linear programs.
Access SymPy computer algebra system from R via Jython
Provides tabular reporting functionalities to work with 'ReporteRs'
    package: 'as.FlexTable' methods are available for 'ftable' and 'xtable' objects,
    function 'FlexPivot' is producing a pivot table and 'freqtable' a percentage table,
    a 'knitr' print method and a 'shiny' render function are provided for 'FlexTable' objects.
Contains functions for random generation of R x C and 2 x 2 x K contingency tables. In addition to the generation of contingency tables over predetermined intraclass-correlated clusters, it is possible to generate contingency tables without intraclass correlations under product multinomial, multinomial, and Poisson sampling plans. It also consists of a function for generation of random data from a given discrete probability distribution function.
Storing huge data in RData format causes problems because
        of the necessity to load the whole file to the memory in order
        to access and manipulate objects inside such file; rtape is a
        simple solution to this problem. The package contains several
        wrappers of R built-in serialize/unserialize mechanism allowing
        user to quickly append objects to a tape-like file and later
        iterate over them requiring only one copy of each stored object
        to reside in memory a time.
Discretize AR(1) process following Tauchen (1986) <http://www.sciencedirect.com/science/article/pii/0165176586901680>. A discrete Markov chain that approximates in the sense of weak convergence a continuous-valued univariate Autoregressive process of first order is generated. It is a popular method used in economics and in finance.
Tools for analyzing sales report files of iTunes Connect.
Robust tail dependence estimation for bivariate models. This package is based on two papers by the authors:'Robust and bias-corrected estimation of the coefficient of tail dependence' and 'Robust and bias-corrected estimation of probabilities of extreme failure sets'. This work was supported by a research grant (VKR023480) from VILLUM FONDEN and an international project for scientific cooperation (PICS-6416).
Provides response time distributions (density/PDF, distribution
    function/CDF, quantile function, and random generation): (a) Ratcliff diffusion
    model based on C code by Andreas and Jochen Voss and (b) linear ballistic
    accumulator (LBA) with different distributions underlying the drift rate.
Exploit controlled vocabularies organized on tematres servers.
A set of tools for creation, manipulation, and modeling
    of tensors with arbitrary number of modes. A tensor in the context of data
    analysis is a multidimensional array. rTensor does this by providing a S4
    class 'Tensor' that wraps around the base 'array' class. rTensor
    provides common tensor operations as methods, including matrix unfolding,
    summing/averaging across modes, calculating the Frobenius norm, and taking
    the inner product between two tensors. Familiar array operations are
    overloaded, such as index subsetting via '[' and element-wise operations.
    rTensor also implements various tensor decomposition, including CP, GLRAM,
    MPCA, PVD, and Tucker. For tensors with 3 modes, rTensor also implements
    transpose, t-product, and t-SVD, as defined in Kilmer et al. (2013). Some
    auxiliary functions include the Khatri-Rao product, Kronecker product, and
    the Hamadard product for a list of matrices.
Reorder sentences for Korean text using TextRank algorithm.
RTextTools is a machine learning package for automatic
        text classification that makes it simple for novice users to
        get started with machine learning, while allowing experienced
        users to easily experiment with different settings and
        algorithm combinations. The package includes nine algorithms
        for ensemble classification (svm, slda, boosting, bagging,
        random forests, glmnet, decision trees, neural networks,
        maximum entropy), comprehensive analytics, and thorough
        documentation.
This package contains several functions for calculation of texture metrics for Grey Level Co-occurrence matrices
A set of R functions to output Rich Text Format (RTF) files with high resolution tables and graphics that may be edited with a standard word processor such as Microsoft Word.
Identifies and scores possible Transcription Factor
    Binding Sites and allows for FDR analysis and pruning.  It supports
    splitting of sequences based on size or a specified GFF, grouping
    by G+C content, and specification of Markov model order.  The heavy
    lifting is done in C while all results are made available via R.
A suite of custom R Markdown formats and templates for
  authoring journal articles and conference submissions.
Calculates tide heights based on tide station harmonics.
    It includes the harmonics data for 637 US stations.
    The harmonics data was converted from harmonics-dwf-20151227-free,
    NOAA web site data processed by David Flater for XTide.
    The code to calculate tide heights from the harmonics is based on XTide.
Reads and writes TIFF format images and
        returns them as a pixmap object. Because the resulting object
        can be very large for even modestly sized TIFF images, images
        can be reduced as they are read for improved performance.  This
        package is a wrapper around libtiff (www.libtiff.org), on which
        it depends (i.e. the libtiff shared library must be on your
        PATH for the binary to work, and tiffio.h must be on your
        system to build the package from source). By using libtiff's
        highlevel TIFFReadRGBAImage function, this package inherently
        supports a wide range of image formats and compression schemes.
        This package also provides an implementation of the Ridler
        Autothresholding algorithm for easy generation of binary masks.
Interface to Congress, Campaign Finance, Article Search, and
    Geographic 'APIs' from the New York Times. Documentation for New York 
    Times 'APIs' (http://developer.nytimes.com/docs/). This client covers 
    a subset of the New York Times 'APIs'.
R tools to measure and compare inequality, welfare and poverty using the EU statistics on income and living conditions surveys.
Allows normalization of very large abundance tables, from metagenomics applications or other sources, to an equal depth. Additionally diversity measures, such as richness, Shannon diversity, chao1, chao2 and others are calculated on the fly. The results may be plotted using the provided plotting functions.
STK++ (http://www.stkpp.org) is a collection of
    C++ classes for statistics, clustering, linear algebra, arrays (with an
    Eigen-like API), regression, dimension reduction, etc. The integration of
    the library to R is using Rcpp.

       The rtkore package includes the header files from the STK++ core library.
    All files contain only templated classes and/or inlined functions.

       STK++ is licensed under the GNU LGPL version 2 or later. rtkore
    (the stkpp integration into R) is licensed under the
    GNU GPL version 2 or later. See file LICENSE.note for details.
STK++ (http://www.stkpp.org) is a collection of
    C++ classes for statistics, clustering, linear algebra, arrays (with an
    Eigen-like API), regression, dimension reduction, etc. The integration of
    the library to R is using Rcpp.

       The rtkpp package includes the header files from the STK++
    library (currently version 0.8.99). Thus users do not need to
    install STK++ itself in order to use it.

       STK++ is licensed under the GNU LGPL version 2 or later. rtkpp
    (the stkpp integration into R) is licensed under the
    GNU GPL version 2 or later.
Truncated Newton function minimization with bounds constraints
	based on the 'Matlab'/'Octave' codes of Stephen Nash.
Aimed at seismic tomography, the package
  plots tomographic images, and allows one to interact and query
  three-dimensional tomographic models.
  Vertical cross-sectional cuts can be extracted by mouse click.
  Geographic information can be added easily.
Geostatistical interpolation of data with irregular spatial support such as runoff related data or data from administrative units.
This is a port of Jonathan Shewchuk's Triangle library to
    R. From his description: "Triangle generates exact Delaunay
    triangulations, constrained Delaunay triangulations, conforming
    Delaunay triangulations, Voronoi diagrams, and high-quality
    triangular meshes. The latter can be generated with no small or
    large angles, and are thus suitable for finite element analysis."
This framework aims to provide classes and methods for manipulating and processing of raster time series data (e.g. a time series of satellite images).
An R wrapper around the fast T-distributed Stochastic
    Neighbor Embedding implementation by Van der Maaten.
TSON, short for Typed JSON, is a binary-encoded serialization of
    JSON like document that support JavaScript typed data (https://github.com/tercen/TSON).
Contains the program 'ttf2pt1', for use with the
    'extrafont' package. This product includes software developed by the 'TTF2PT1'
    Project and its contributors.
Convert text into speech (voice file in 'wav' format) with API offered by ITRI TTS (Text-To-Speech service, Industrial Technology Research Institute, Taiwan. http://tts.itri.org.tw/). One main function is given, tts_ITRI(). English and Chinese (both traditional and simplified) are supported, and user can specify the speaker accent, speed and volume. Using this package requires internet connection.
Contains functions for launching R Tutorial & Plotting Apps. The R Tutorial app
    provides a set of most commonly performed data manipulation tasks in R. The
    app structures the contents into different topics and provides an interactive & dynamic interface
    for navigation.
    The plotting app provides an automated interface for generating plots using the 'ggplot2' package.
    Current version of this app supports 10 different plot types along with options to manipulate specific
    aesthetics and controls related to each plot type.
The 't-walk' is a general-purpose MCMC sampler for
      arbitrary continuous distributions that requires no tuning.
An implementation of calls designed to extract
    and organize Twitter data via Twitter's REST and stream
    APIs. Functions formulate GET and POST requests and
    convert response objects to more user friendly structures,
    e.g., data frames or lists. Specific consideration is
    given to functions designed to return tweets, friends,
    and followers.
A strong type system for R which supports
    symbol declaration and assignment with type checking
    and condition checking.
An R interface to the 'typeform' (https://typeform.com) application program interface.
  Also provides functions for downloading your results.
R port of 'universalchardet', that is the encoding detector
    library of Mozilla.
Unobserved Components Models (introduced in Harvey, A. (1989),
    Forecasting, structural time series models and the Kalman filter, Cambridge
    New York: Cambridge University Press) decomposes a time series into
    components such as trend, seasonal, cycle, and the regression effects due
    to predictor series which captures the salient features of the series to
    predict its behavior.
ARFIMA, in-mean, external regressors and various GARCH flavors, with methods for fit, forecast, simulation, inference and plotting.
Contains data and visualization functions for USA unemployment
    data. Data comes from the US Bureau of Labor Statistics (BLS). State data
    is in ?df_state_unemployment and covers 2000-2013. County data is in
    ?df_county_unemployment and covers 1990-2013. Choropleth maps of the data
    can be generated with ?state_unemployment_choropleth and
    ?county_unemployment_choropleth respectively. See the vignettes for
    additional information.
R functions implementing a standard Unit Testing
        framework, with additional code inspection and report
        generation tools.
Automatically convert a file or package worth of 'RUnit' test
    functions into 'testthat' tests.
This package contains some functions for converting R
        objects to Java style variables and XML. Generated Java code is
        interpretable by dynamic Java libraries such as Beanshell.
        Calling R externally and handling the Java or XML output is an
        other way to call R from other languages without native
        interfaces. For a Java implementation of this approach visit
        http://www.mhsatman.com/rcaller.php and
        http://stdioe.blogspot.com/search/label/rcaller
User-friendly interface utilities for MCMC models via
    Just Another Gibbs Sampler (JAGS), facilitating the use of parallel
    (or distributed) processors for multiple chains, automated control
    of convergence and sample length diagnostics, and evaluation of the
    performance of a model using drop-k validation or against simulated
    data. Template model specifications can be generated using a standard
    lme4-style formula interface to assist users less familiar with the
    BUGS syntax.  A JAGS extension module provides additional distributions
    including the Pareto family of distributions, the DuMouchel prior and
    the half-Cauchy prior.
Interface to the UNU.RAN library for Universal Non-Uniform RANdom variate generators. 
	     Thus it allows to build non-uniform random number generators from quite arbitrary
	     distributions. In particular, it provides an algorithm for fast numerical inversion
	     for distribution with given density function.
	     In addition, the package contains densities, distribution functions and quantiles
	     from a couple of distributions.
This package provides a GUI (Graphical User Interface) for
        the UNU.RAN random variate generators. Thus it allows to build
        non-uniform random number generators interactively for quite
        arbitrary distributions. In addition, R code for the required
        calls from package Runuran can be displayed and stored for
        later use. Some basic analysis like goodness-of-fit tests can
        be performed.
An interface to the web service methods provided by the United States Department of Agriculture (USDA). The Agricultural Research Service (ARS) provides a large set of databases. The current version of the package holds interfaces to the Systematic Mycology and Microbiology Laboratory (SMML), which consists of four databases: Fungus-Host Distributions, Specimens, Literature and the Nomenclature database. It provides functions for querying these databases. The main function is \code{associations}, which allows searching for fungus-host combinations.
Utility functions useful when programming and developing R packages.
Implements the 'RUV' (Remove Unwanted Variation) algorithms.  These algorithms attempt to adjust for systematic errors of unknown origin in high-dimensional data.  The algorithms were originally developed for use with genomic data, especially microarray data, but may be useful with other types of high-dimensional data as well.  These algorithms were proposed by Gagnon-Bartsch and Speed (2012), and by Gagnon-Bartsch, Jacob and Speed (2013).  The algorithms require the user to specify a set of negative control variables, as described in the references.  The algorithms included in this package are 'RUV-2', 'RUV-4', 'RUV-inv', and 'RUV-rinv', along with various supporting algorithms.
Contains diverse more or less complicated functions, written to simplify user's life: simplifications of existing functions, basic but not implemented tests, easy-to-use tools, bridges between functions of different packages... All functions are presented in the French book 'Aide-memoire de statistique appliquee a la biologie', written by the same author and available on CRAN.
A collection of functions for computing "r-values" from various
    kinds of user input such as MCMC output or a list of effect size estimates
    and associated standard errors. Given a large collection of measurement units,
    the r-value, r, of a particular unit is a reported percentile that may be
    interpreted as the smallest percentile at which the unit should be placed in the
    top r-fraction of units.
Operations on triangular meshes based on 'VCGLIB'. This package
    integrates nicely with the R-package 'rgl' to render the meshes processed by
    'Rvcg'. The Visualization and Computer Graphics Library (VCG for short) is
    an open source portable C++ templated library for manipulation, processing
    and displaying with OpenGL of triangle and tetrahedral meshes. The library,
    composed by more than 100k lines of code, is released under the GPL license,
    and it is the base of most of the software tools of the Visual Computing Lab of
    the Italian National Research Council Institute ISTI (http://vcg.isti.cnr.it),
    like 'metro' and 'MeshLab'. The 'VCGLIB' source is pulled from trunk (svn://
    svn.code.sf.net/p/vcg/code/trunk/vcglib) and patched to work with options
    determined by the configure script as well as to work with the header files
    included by 'RcppEigen'.
Check latest release version of R and R package (both in 'CRAN' or 'Bioconductor').
Query the main 'R' 'SVN' repository to find the
    versions 'r-release' and 'r-oldrel' refer to, and also all
    previous 'R' versions and their release dates.
Retrieve, map and summarize data from the 'VertNet.org' archives.
    Functions allow searching by many parameters, including 'taxonomic' names,
    places, and dates. In addition, there is an interface for conducting spatially
    delimited searches, and another for requesting large 'datasets' via email.
Wrappers around the 'xml2' and 'httr' packages to make it easy to
    download, then manipulate, HTML and XML.
The RVFam package provides functions to perform single SNP association analyses and gene-based tests for continuous, binary and survival traits against sequencing data (e.g. exome chip) using family data.
Vector Graphics devices for 'SVG', 'DrawingML' for
  Microsoft Word, PowerPoint and Excel.
Test suite for non-uniform pseudo-random number generators.
To detecting rare variants for binary traits using general pedigrees, the pedigree disequilibrium tests are proposed by collapsing rare haplotypes/variants with/without weights. To run the test, MERLIN is needed in Linux for haplotyping.
Play Video Poker with R, complete with a graphical user
        interface. So far, only "Jacks or Better" is implemented.
ViennaCL is a free open-source linear algebra library 
  for computations on many-core architectures (GPUs, MIC) and 
  multi-core CPUs. The library is written in C++ and supports CUDA, 
  OpenCL, and OpenMP (including switches at runtime). 
  I have placed these libraries in this package as a more efficient 
  distribution system for CRAN. The idea is that you can write a package 
  that depends on the ViennaCL library and yet you do not need to 
  distribute a copy of this code with your package.
Variable metric nonlinear function minimization with bounds constraints.
The 'Vowpal Wabbit' project is a fast out-of-core learning
 system sponsored by Microsoft Research (having started at Yahoo! Research)
 and written by John Langford along with a number of contributors. This R
 package does not include the distributed computing implementation of the
 cluster/ directory of the upstream sources. Use of the software as a network
 service is also not directly supported as the aim is a simpler direct call
 from R for validation and comparison.
This is a collection of the five region-based
    rare-variant genetic association tests. The following tests are
    currently implemented: ASKAT, ASKAT-Normalized, VC-C1, VC-C2 and
    VC-C3.
When a gene or a genetic region is significantly associated with a disease or a trait, the rare variant selection procedure is able to distinguish causal (risk or protective) rare variants from noncausal rare variants located within the same gene or the same genetic region.
Computes estimates of the probability of related individuals sharing a rare variant.
Used to compute population controls weighted rare variants transmission distortion test
Use multiple regression methods to test rare variants
        association with disease traits.
A set of R functions which provide an
        environment for the Time-Frequency analysis of 1-D signals (and
        especially for the wavelet and Gabor transforms of noisy
        signals). It was originally written for Splus by Rene Carmona,
        Bruno Torresani, and Wen L. Hwang, first at the University of
        California at Irvine and then at Princeton University.  Credit
        should also be given to Andrea Wang whose functions on the
        dyadic wavelet transform are included. Rwave is based on the
        book: "Practical Time-Frequency Analysis: Gabor and Wavelet
        Transforms with an Implementation in S", by Rene Carmona, Wen
        L. Hwang and Bruno Torresani, Academic Press, 1998.
This package will download model predictions from 15 different global circulation models in 20 year intervals from the world bank.  Users can also access historical data, and create maps at 2 different spatial scales.
a Bipartite graph and is constructed based on the spatial and/or non-spatial attributes of the spatial objects in the dataset. Secondly, RW techniques are utilized on the graphs to compute the outlierness for each point (the differences between spatial objects and their spatial neighbours). The top k objects with higher outlierness are recognized as outliers.
RWebLogo is a wrapper for the WebLogo python package
    that allows generating of customised sequence logos. Sequence logos are
    graphical representations of the sequence conservation of nucleotides (in a
    strand of DNA/RNA) or amino acids (in protein sequences). Each logo
    consists of stacks of symbols, one stack for each position in the sequence.
    The overall height of the stack indicates the sequence conservation at that
    position, while the height of symbols within the stack indicates the
    relative frequency of each amino or nucleic acid at that position. In
    general, a sequence logo provides a richer and more precise description of,
    for example, a binding site, than would a consensus sequence.
An R interface to Weka (Version 3.9.0).
   Weka is a collection of machine learning algorithms for data mining
   tasks written in Java, containing tools for data pre-processing,
   classification, regression, clustering, association rules, and
   visualization.  Package 'RWeka' contains the interface code, the
   Weka jar is in a separate package 'RWekajars'.  For more information
   on Weka see <http://www.cs.waikato.ac.nz/ml/weka/>.
External jars required for package 'RWeka'.
Communications simulation package supporting forward error correction.
This package provides Wiener process distribution functions,
  namely the Wiener first passage time density, CDF, quantile and random
  functions.
A plug in for using WinEdt as an editor for R.
The Rwinsteps package facilitates communication between R
        and the Rasch modeling software Winsteps. The package currently
        includes functions for reading and writing command files,
        sending them to Winsteps, reading and writing data according to
        command file specifications, reading output into R, and
        plotting various results.
A communications simulation package supporting common wireless modulation
    formats including BPSK, QPSK, 8-PSK, 16-PSK, 16-QAM and 64-QAM. The package includes AWGN
    noise generation, and raised cosine and square root raised cosine pulse shaping. It also
    includes convenience functions for plotting constellation diagrams, density plots, stem
    plots and eye diagrams. Together these functions enable the evaluation of bit error and
    symbol error rates, evaluation of pulse shaping and inter-symbol interference and support
    visualization of the respective signals and noise in a variety of formats.
Enables mapping of country level and gridded user datasets.
High resolution vector country boundaries derived from
        Natural Earth data, can be plotted in rworldmap.
Provides a set of functions for performing digital signal
    processing.
Implements various tests, visualizations, and metrics
    for diagnosing convergence of MCMC chains in phylogenetics.  It implements
    and automates many of the functions of the AWTY package in the R
    environment.
Tools for getting historical weather information and forecasts from wunderground.com.  Historical weather and forecast data includes, but is not limited to, temperature, humidity, windchill, wind speed, dew point, heat index.  Additionally, the weather underground weather API also includes information on sunrise/sunset, tidal conditions, satellite/webcam imagery, weather alerts, hurricane alerts and historical record high/low temperatures.
Fits the R x C inference model described in Greiner and
        Quinn (2009).  Allows incorporation of survey results.
Visualize your favorite XKCD comic strip directly from
        R. XKCD web comic content is provided under the Creative
        Commons Attribution-NonCommercial 2.5 License.
Functions which allow to read many XMCDA tags and transform them into R variables which are then usable in MCDA algorithms written in R. It also allows to write certain R variables into XML files respecting the XMCDA standard.
Methods to compute chemical similarity between two or more reactions and molecules. Allows masking of chemical substructures for weighted similarity computations. Uses packages 'rCDK' and 'fingerprint' for cheminformatics functionality.
Facilities for running simulations from ordinary 
   differential equation (ODE) models, such as pharmacometrics and other 
   compartmental models.  A compilation manager translates the ODE model 
   into C, compiles it, and dynamically loads the object code into R for 
   improved computational efficiency.  An event table object facilitates 
   the specification of complex dosing regimens (optional) and sampling 
   schedules.  NB: The use of this package requires both C and 
   Fortran compilers, for details on their use with R please see 
   Section 6.3, Appendix A, and Appendix D in the "R Administration and 
   Installation" manual.
Identify and display TRACEs for a specified shrinkage path and determine
  the extent of shrinkage most likely, under normal distribution theory, to produce an
  optimal reduction in MSE Risk in estimates of regression (beta) coefficients.
An interface to the yacas computer algebra system.
'Yandex Translate' (https://translate.yandex.com/) is a statistical machine translation system.
	The system translates separate words, complete texts, and webpages.
	This package can be used to detect language from text and to translate it to supported target language.
	For more info: https://tech.yandex.com/translate/doc/dg/concepts/About-docpage/ .
You can use this package to translate thousands of words. The Youdao translation open API is applied in this package. But, it just translates less than 1000 English words into Chinese.
Package contains some data and functions that 
    are used in my forthcoming "R you ready?" book.
A programmatic interface to web-services of YouTheria. YouTheria is
    an online database of mammalian trait data <http://www.utheria.org/>.
This package is a port to R of the SysGran program, written in Delphi by Camargo (2006). It contains functions for the analysis of grain size samples (in logarithmic (phi) and geometric (micrometers) scale) based on various methods, like Folk & Ward (1957) and Methods of Moments (Tanner, 1995), among others; textural classifications and distribution of unconsolidated sediments are shown in histograms, bivariated plots and ternary diagrams of Shepard (1954) and Pejrup (1988). English and Portuguese languages are supported in outputs
R is very powerful but it lacks some of the functionalities found in
  Stata or SPSS to manage survey data. The 'memisc' package provides these
  (variable labels, value labels, definable missing values and so on), but to
  efficiently work these functions need a graphical interface to allow the user
  to get an overview of the data. This package provides such a graphical interface,
  similar in fashion to SPSS's Variable View and data managing system. It uses the
  'memisc' package as its backend. Additionally, 'Rz' has a powerful plot assistant
  interface based on 'ggplot2'.
R interface to the 'Zabbix API' data <https://www.zabbix.com/documentation/3.0/manual/api/reference>. Enables easy and direct communication with 'Zabbix API' from 'R'.
Interface to the ZeroMQ lightweight messaging kernel (see <http://www.zeromq.org/> for more information).
A set of functions used in teaching STATS 201/208 Data Analysis at the University of Auckland. The functions are designed to make parts of R more accessible to a large undergraduate population who are mostly not statistics majors.
Set of tools to verify forecasts through the computation of typical prediction scores against one or more observational datasets or reanalyses (a reanalysis being a physical extrapolation of observations that relies on the equations from a model, not a pure observational dataset). Intended for seasonal to decadal climate forecasts although can be useful to verify other kinds of forecasts. The package can be helpful in climate sciences for other purposes than forecasting.
Fit a spatial instrumental-variable regression by two-stage least squares.
The main function s4vd() performs a biclustering via sparse
        singular value decomposition with a nested stability selection.
        The results is an biclust object and thus all methods of the
        biclust package can be applied.
Perform joint segmentation on two signal dimensions derived from 
    total read depth (intensity) and allele specific read depth (intensity) for 
    whole genome sequencing (WGS), whole exome sequencing (WES) and SNP array data.
A collection of baseball analytics functions for sabermetrics
    purposes. Among these functions include popular metrics such as FIP, wOBA, and
    runs created and other advanced metrics.
Semiparametric empirical likelihood ratio
    based test of changepoint with one-change or epidemic alternatives
    with data-based model diagnostic
Functions for detecting eye fixations in raw eye-tracking
    data.  The detection is done using a velocity-based algorithm for
    saccade detection proposed by Ralf Engbert and Reinhold Kliegl in
    2003.  The algorithm labels segments as saccades when the velocity of
    the eye movement exceeds a certain threshold.  Anything between two
    saccades is considered a fixation.  Thus the algorithm is not
    appropriate for data containing episodes of smooth pursuit eye
    movements.
Computes the Exposure-At-Default based on standardized approach
    of the Basel III Regulatory framework (SA-CCR). Currently, trade types of all
    the five major asset classes have been created and, given the inheritance-
    based structure of the application, the addition of further trade types is
    straightforward. The application automatically separates the trades on the
    corresponding hedging and netting sets including the basis and volatility
    transactions. All the examples appearing on the regulatory paper (including the
    margined and the un-margined workflow) have been implemented.
Performs constrained optimization for expensive black-box problems.
Provides the density, distribution, quantile and generation
    functions of some obscure probability distributions, including the doubly non-
    central t, F, Beta, and Eta distributions; Lecoutre's lambda-prime and K-prime;
    the upsilon distribution; the (weighted) sum of non-central chi-squares to a
    power; the (weighted) sum of log non-central chi-squares; the product of non-
    central chi-squares to powers; the product of doubly non-central F variates.
Maximum likelihood tools to fit and compare models of species
    abundance distributions and of species rank-abundance distributions.
Time series models for small area estimation based on area-level models.
The SAEMIX package implements the Stochastic Approximation EM algorithm for parameter estimation in (non)linear mixed effects models. The SAEM algorithm: - computes the maximum likelihood estimator of the population parameters, without any approximation of the model (linearisation, quadrature approximation,...), using the Stochastic Approximation Expectation Maximization (SAEM) algorithm, - provides standard errors for the maximum likelihood estimator - estimates the conditional modes, the conditional means and the conditional standard deviations of the individual parameters, using the Hastings-Metropolis algorithm. Several applications of SAEM in agronomy, animal breeding and PKPD analysis have been published by members of the Monolix group (http://group.monolix.org/).
An implementation of a stacked sparse autoencoder for dimension reduction of features and pre-training of feed-forward neural networks
		with the 'neuralnet' package is contained within this package. The package also includes a predict function for the stacked autoencoder object to generate the compressed
		representation of new data if required. For the purposes of this package, 'stacked' is defined in line with http://ufldl.stanford.edu/wiki/index.php/Stacked_Autoencoders .
		The underlying sparse autoencoder is defined in the documentation of 'autoencoder'.
Methods to fit robust alternatives to commonly used models used in
    Small Area Estimation. The methods here used are based on best linear
    unbiased predictions and linear mixed models. At this time available models
    include area level models incorporating spatial and temporal correlation in
    the random effects.
A complete set of functions to calculate several EBLUP (Empirical Best Linear Unbiased Predictor) estimators and their mean squared errors. All estimators are based on an area-level linear mixed model introduced by Rao and Yu in 1994 (see documentation). The REML method is used for fitting this model.
Tools for the simulation of data in the context of small area
    estimation. Combine all steps of your simulation - from data generation
    over drawing samples to model fitting - in one object. This enables easy
    modification and combination of different scenarios. You can store your
    results in a folder or start the simulation in parallel.
The aim of the package is to provide some basic functions
        for doing statistics with one dimensional Fuzzy Data (in the
        form of polygonal fuzzy numbers). In particular, the package
        contains functions for the basic operations on the class of
        fuzzy numbers (sum, scalar product, mean, median, Hukuhara difference) 
        as well as for calculating (Bertoluzza) distance,
        sample variance, sample covariance, and the
        Dempster-Shafer (levelwise) histogram. Moreover a function to
        simulate fuzzy random variables, bootstrap tests for the
        equality of means, and a function to do linear regression given
        trapezoidal fuzzy data is included. Version 1.0 fixes some bugs
        of previous versions.
Functions for Generalized and Safe-Bayesian Ridge and Lasso Regression models with both fixed and varying variance.
Overloads the glm function in the stats package so that
  a test for the existence of the maximum likelihood estimate is included
  in the fitting procedure for binary regression models.
Tools for the statistical analysis and visualization of (relative and absolute) quantitative (LFQ,TMT,HRM) Proteomics data.
Design and sensitivity analysis for computer experiments with scalar-valued output and functional input, e.g. over time or space. The aim is to explore the behavior of the sensitivity over the functional domain.
Implements an information theory approach to the analysis of line
    cross data providing model averaged results of parameter estimates and
    unconditional standard errors.  Also includes functions to provide a
    visualization of models space, custom plots of multi-model inference results, and traditional
    line cross analysis plots.
A coordinate descent algorithm for computing the solution path of the sparse and coupled sparse asymmetric least squares, including the elastic net and (adaptive) Lasso penalized SALES and COSALES regressions.
The SALTSampler package facilitates Monte Carlo Markov Chain (MCMC)
    sampling of random variables on a simplex. A Self-Adjusting Logit Transform
    (SALT) proposal is used so that sampling is still efficient even in difficult
    cases, such as those in high dimensions or with parameters that differ by orders
    of magnitude. Special care is also taken to maintain accuracy even when some
    coordinates approach 0 or 1 numerically. Diagnostic and graphic functions are
    included in the package, enabling easy assessment of the convergence and mixing
    of the chain within the constrained space.
The package SAM targets at high dimensional predictive
        modeling (regression and classification) for complex data
        analysis. SAM is short for sparse additive modeling, and adopts
        the computationally efficient basis spline technique. We solve
        the optimization problems by various computational algorithms
        including the block coordinate descent algorithm, fast
        iterative soft-thresholding algorithm, and newton method. The
        computation is further accelerated by warm-start and active-set
        tricks.
Programs for fitting Gaussian linear mixed models (LMM).
A framework for running sets of MCMC samplers on sets of
  distributions with a variety of tuning parameters, along with plotting
  functions to visualize the results of those simulations.  See sc-intro.pdf
  for an introduction.
Two-step estimation
   and maximum likelihood estimation
   of Heckman-type sample selection models:
   standard sample selection models (Tobit-2)
   and endogenous switching regression models (Tobit-5).
Computes sample size for Student's t-test and for the Wilcoxon-Mann-Whitney test for categorical data. The t-test function allows paired and unpaired (balanced / unbalanced) designs as well as homogeneous and heterogeneous variances. The Wilcoxon function allows for ties.
Computes the required sample size using the optimal designs with multiple constraints proposed in Mayo et al.(2010). This optimal method is designed for two-arm, randomized phase II clinical trials, and the required sample size can be optimized either using fixed or flexible randomization allocation ratios.
Computes the required sample size for estimation of totals, means
    and proportions under complex sampling designs.
A set of R functions for calculating sample size
        requirements using three different Bayesian criteria in the
        context of designing an experiment to estimate a normal mean or
        the difference between two normal means.  Functions for
        calculation of required sample sizes for the Average Length
        Criterion, the Average Coverage Criterion and the Worst Outcome
        Criterion in the context of normal means are provided.
        Functions for both the fully Bayesian and the mixed
        Bayesian/likelihood approaches are provided.
A set of R functions for calculating sample size
        requirements using three different Bayesian criteria in the
        context of designing an experiment to estimate the difference
        between two binomial proportions. Functions for calculation of
        required sample sizes for the Average Length Criterion, the
        Average Coverage Criterion and the Worst Outcome Criterion in
        the context of binomial observations are provided. In all
        cases, estimation of the difference between two binomial
        proportions is considered. Functions for both the fully
        Bayesian and the mixed Bayesian/likelihood approaches are
        provided.
Functions for drawing and calibrating samples.
Sampling procedures from the book 'Stichproben - Methoden
        und praktische Umsetzung mit R' by Goeran Kauermann and Helmut
        Kuechenhoff (2010).
Functions to estimate from survey data. This package is a user-friendly wrapper of the samplingVarEst package. It considers that the user is more familiar with practical survey data rather than with research on survey sampling (variance estimation). More functionalities are on the way.
In the field of stratified sampling design, this package
        offers an approach for the determination of the best
        stratification of a sampling frame, the one that ensures the
        minimum sample cost under the condition to satisfy precision
        constraints in a multivariate and multidomain case. This
        approach is based on the use of the genetic algorithm: each
        solution (i.e. a particular partition in strata of the sampling
        frame) is considered as an individual in a population; the
        fitness of all individuals is evaluated applying the
        Bethel-Chromy algorithm to calculate the sampling size
        satisfying precision constraints on the target estimates.
        Functions in the package allows to: (a) analyse the obtained
        results of the optimisation step; (b) assign the new strata
        labels to the sampling frame; (c) select a sample from the new
        frame accordingly to the best allocation. 
        Functions for the execution of the genetic algorithm are a modified 
        version of the functions in the 'genalg' package.
Functions to calculate some point estimators and estimating their variance under unequal probability sampling without replacement. Single and two stage sampling designs are considered. Some approximations for the second order inclusion probabilities are also available (sample and population based). A variety of Jackknife variance estimators are implemented.
Sampling surface simulation is useful in the comparison of different areal sampling methods
             in forestry, ecology and natural resources. The sampSurf package allows the simulation 
			 of numerous sampling methods for standing trees and downed woody debris in a spatial context.
			 It also provides an S4 class and method structure that facilitates the addition of new sampling
			 methods.
Augmenting a matched data set by generating multiple stochastic, matched samples from the data using a
  multi-dimensional histogram constructed from dropping the input matched data into a multi-dimensional grid built on
  the full data set. The resulting stochastic, matched sets will likely provide a collectively higher coverage of the full
  data set compared to the single matched set. Each stochastic match is without duplication, thus allowing downstream
  validation techniques such as cross-validation to be applied to each set without concern for overfitting.
This package contains R functions to gauge the impact of unpublished studies upon the meta-analytic summary effect of a set of published studies. (Credits: The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 282574.)
Data sets for the book 'Statistical Analysis of 
  Network Data with R'.
Model-robust standard error estimators for cross-sectional, time series, and longitudinal data.
Recent gcc and clang compiler versions provide functionality to
 memory violations and other undefined behaviour; this is often referred to as
 "Address Sanitizer" (or SAN) and "Undefined Behaviour Sanitizer" (UBSAN).
 The Writing R Extension manual describes this in some detail in Section 4.9.

    This feature has to be enabled in the corresponding binary, eg in R, which
 is somewhat involved as it also required a current compiler toolchain which 
 is not yet widely available, or in the case of Windows, not available at all
 (via the common Rtools mechanism).

    As an alternative, the pre-built Docker containers available via the Docker Hub
 at https://registry.hub.docker.com/u/eddelbuettel/docker-debian-r/ can be used
 on Linux, and via boot2docker on Windows and OS X.

    This package then provides a means of testing the compiler setup as the
 known code failures provides in the sample code here should be detected
 correctly, whereas a default build of R will let the package pass.

    The code samples are based on the examples from the Address Sanitizer
 Wiki at https://code.google.com/p/address-sanitizer/wiki/AddressSanitizer.
Sankey plots illustrate the flow of information or material.
There are several functions to implement the method for analysis in a randomized clinical trial with strata with following key features. A stratified Mann-Whitney estimator addresses the comparison between two randomized groups for a strictly ordinal response variable. The multivariate vector of such stratified Mann-Whitney estimators for multivariate response variables can be considered for one or more response variables such as in repeated measurements and these can have missing completely at random (MCAR) data. Non-parametric covariance adjustment is also considered with the minimal assumption of randomization. The p-value for hypothesis test and confidence interval are provided.
Software for the book Spectral Analysis for Physical
        Applications, Donald B. Percival and Andrew T. Walden,
        Cambridge University Press, 1993.
Functions for statistical analysis of point processes.
Read SAS files in the sas7bdat data format.
Using any importation code designed for SAS users to read
        ASCII files into sas7bdat files, the SAScii package parses
        through the INPUT block of a (.sas) syntax file to design the
        parameters needed for a read.fwf function call.  This allows
        the user to specify the location of the ASCII (often a .dat)
        file and the location of the .sas syntax file, and then load
        the data frame directly into R in just one step.
Data sets and sample lmer analyses corresponding
  to the examples in Littell, Milliken, Stroup and Wolfinger
  (1996), "SAS System for Mixed Models", SAS Institute.
A statistical method for significant analysis of
        comparative proteomics based on LC-MS/MS Experiments
Functions for reading, listing
    the contents of, and writing 'SAS' 'xport' format files.
    The functions support reading and writing of either
    individual data frames or sets of data frames.  Further,
    a mechanism has been provided for customizing how
    variables of different data types are stored.
This smorgasbord provides a variety of functions which are useful 
    for handling, manipulating and visualizing remote sensing data.
Bayesian inference of graphical model structures using spanning trees.
Implements Bayesian statistical methodology for the 
		analysis of complex computer models.
		It allows for the emulation, calibration, and validation of computer models, 
		following methodology described in Bayarri et al 2007, Technometrics.
The purpose of this package is to be able to save and load only
    the needed variables/columns of a dataframe in special binary files (tar
    archives) - which seems to be a lot faster method than loading the whole
    binary object (RData files) via load() function, or than loading columns
    from SQLite/MySQL databases via SQL commands (see vignettes). Performance
    gain on SSD drives is a lot more sensible compared to basic load()
    function. The performance improvement gained by loading only the chosen
    variables in binary format can be useful in some special cases (e.g. where
    merging data tables is not an option and very different datasets are needed
    for reporting), but be sure if using this package that you really need
    this, as non-standard file formats are used!
Tests coefficients with sandwich estimator of variance and with small samples. Regression types supported are gee, linear regression, and conditional logistic regression.
Smooth Backfitting for additive models using
        Nadaraya-Watson estimator
An MCMC algorithm for simultaneous feature selection and classification, 
    and visualization of the selected features and feature interactions. 
    An implementation of SBFC by Krakovna, Du and Liu (2015), <http://arxiv.org/abs/1506.02371>.
This package estimates parameters of a Gaussian copula,
        treating the univariate marginal distributions as nuisance
        parameters as described in Hoff(2007). It also provides a
        semiparametric imputation procedure for missing multivariate
        data.
sbioPN is a package suited to perform simulation of deterministic and stochastic systems of biochemical reaction
  networks with spatial effects.
  Models are defined using a subset of Petri Nets, in a way that is close at how chemical reactions are defined.
  For deterministic solutions, sbioPN creates the associated system of differential equations "on the fly", and
  solves it with a Runge Kutta Dormand Prince 45 explicit algorithm.
  For stochastic solutions, sbioPN offers two variants of Gillespie algorithm, or SSA.
  For hybrid deterministic/stochastic,
  it employs the Haseltine and Rawlings algorithm, that partitions the system in fast and slow reactions.
  sbioPN algorithms are developed in C to achieve adequate performance.
An ADMM implementation of SDP-1, a semidefinite programming relaxation of the maximum likelihood estimator for fitting a block model. SDP-1 has a tendency to produce equal-sized blocks and is ideal for producing a form of network histogram approximating a nonparametric graphon model. Alternatively, it can be used for community detection. (This is experimental code, proceed with caution.)
The package uses fitting axes-aligned rectangles to a time series in order to find structural breaks. The algorithm enclose the time series in a number of axes-aligned rectangles and tries to minimize their area and number. As these are conflicting aims, the user has to specify a parameter alpha in [0.0,1.0]. Values close to 0 result in more breakpoints, values close to 1 in fewer. The left edges of the rectangles are the breakpoints. The package supplies two  methods, computeBreakPoints(series,alpha) which returns the indices of the break points and computeRectangles(series,alpha) which returns the rectangles. The algorithm is randomised; it uses a genetic algorithm. Therefore, the break point sequence found can be different in different executions of the method on the same data, especially when used on  longer series of some thousand observations. The algorithm uses a range-tree as background data structure which makes i very fast and suited to analyse series with millions of observations. A detailed description can be found in Paul Fischer, Astrid Hilbert, Fast detection  of structural breaks, Proceedings of Compstat 2014.
An implementation of Scalable Bayesian Rule Lists Algorithm.
Tools for interacting with U.S. Geological Survey ScienceBase 
    <https://www.sciencebase.gov> interfaces. ScienceBase is a data cataloging and
    collaborative data management platform. Functions included for querying
    ScienceBase, and creating and fetching datasets.
Simple Component Analysis (SCA) often provides much more
   interpretable components than Principal Components (PCA) while still
   representing much of the variability in the data.
Calculates graph theoretic scagnostics. Scagnostics
        describe various measures of interest for pairs of variables,
        based on their appearance on a scatterplot.  They are useful
        tool for discovering interesting or unusual scatterplots from a
        scatterplot matrix, without having to look at every individual
        plot.
Provides the Scale class and corresponding functions, in order to facilitate data input for scale construction. Reverse items and alternative orders of administration are dealt with by the program. Computes reliability statistics, confirmatory, single factor loadings. It suggests item deletions and produces basic text output in English, for incorporation in reports. Returns list objects of all relevant functions from other packages (see Depends).
Graphical scales map data to aesthetics, and provide
    methods for automatically determining breaks and labels
    for axes and legends.
Algorithms for fitting scaled sparse linear regression and estimating precision matrices
Routines for generalized additive modelling under shape
        constraints on the component functions of the linear predictor.
        Models can contain multiple shape constrained (univariate
        and/or bivariate) and unconstrained terms. The routines of gam() 
        in package 'mgcv' are used for setting up the model matrix,  
        printing and plotting the results.  Penalized likelihood
        maximization based on Newton-Raphson method is used to fit a
        model with multiple smoothing parameter selection by GCV or
        UBRE/AIC.
Import, plot, and diagnose results from statistical
  catch-at-age models, used in fisheries stock assessment.
This package computes the maximum likelihood estimator of the generalised additive and index regression with shape constraints. Each additive component function is assumed to obey one of the nine possible shape restrictions: linear, increasing, decreasing, convex, convex increasing, convex decreasing, concave, concave increasing, or concave decreasing.
scaRabee is a port of the Scarabee toolkit originally
        written as a Matlab-based application. It provides a framework
        for simulation and optimization of pharmacokinetic-pharmacodynamic 
        models at the individual and population level. It is built on top of the
        neldermead package, which provides the direct search algorithm proposed 
        by Nelder and Mead for model optimization.
Creates 'D3' 'JavaScript' scatterplots from 'R' with interactive
    features : panning, zooming, tooltips, etc.
Plots a three dimensional (3D) point cloud.
This package implements statistical methods for estimating and inferring the mean of functional data. The methods are based on simultaneous confidence bands and local polynomial fitting. They feature: bandwidth selection by plug-in and cross-validation, goodness-of-fit tests for parametric models, equality tests for two-sample problems, and plotting functions.
Uses the neural gas algorithm to construct
    a scenario tree for use in multi-stage stochastic programming.
    The primary input is a set of initial scenarios or realizations
    of a disturbance. The scenario tree nodal structure must be
    predefined using a scenario tree nodal partition matrix.
SCEPtER pipeline for estimating the stellar age, mass, and radius
        given observational 
        effective temperature, [Fe/H], and astroseismic
	parameters. The results are obtained adopting a maximum likelihood
	technique over a grid of pre-computed stellar models.
SCEPtER pipeline for estimating the stellar age for double-lined detached binary systems. The observational constraints adopted in the recovery are the effective temperature, the metallicity [Fe/H], the mass, and the radius of the two stars. The results are obtained adopting a maximum likelihood technique over a grid of pre-computed stellar models.
The Fisher Scoring Algorithm is extended so as to combine Partial
    Least Squares regression with Generalized Linear Model estimation in the
    multivariate context.
Provides schema-on-read tools including a single function call (e.g., schemaOnRead('filename')) that reads text ('TXT'), comma separated value ('CSV'), raster image ('BMP', 'PNG', 'GIF', 'TIFF', and 'JPG'), R data ('RDS'), HDF5 ('H5'), NetCDF ('CS'), spreadsheet ('XLS', 'XLSX', 'ODS', and 'DIF'), Weka Attribute-Relation File Format ('ARFF'), Epi Info ('REC'), SPSS ('SAV'), Systat ('SYS'), and Stata ('DTA') files. It also recursively reads folders (e.g., schemaOnRead('folder')), returning a nested list of the contained elements.
Provides functions to extract citation data from Google
    Scholar.  Convenience functions are also provided for comparing
    multiple scholars and predicting future h-index values.
This package contains functions and datasets for math
        taught in school. A main focus is set to prime-calculation
Offers a helping hand to psychologists and other behavioral scientists who routinely deal with experimental data from factorial experiments. It includes several functions to format output from other R functions according to the style guidelines of the APA (American Psychological Association). This formatted output can be copied directly into manuscripts to facilitate data reporting. These features are backed up by a toolkit of several small helper functions, e.g., offering out-of-the-box outlier removal. The package lends its name to Georg "Schorsch" Schuessler, ingenious technician at the Department of Psychology III, University of Wuerzburg.
This is a shape preserving spline which is guaranteed
    to be monotonic and concave or convex if the data is monotonic
    and concave or convex. It does not use any optimisation and is
    therefore quick and smoothly converges to a fixed point in
    economic dynamics problems including value function iteration.
    It also automatically gives the first two derivatives of the
    spline and options for determining behaviour when evaluated
    outside the interpolation domain.
This package provides detailed functionality for working with the Schwartz 1997 two-factor commodity model. Essentially, it contains pricing formulas for futures and European options and the standard d/p/q/r functions for the distribution of the state variables and futures prices. In addition, a parameter estimation procedure is contained together with many utilities as filtering and plotting functionality. This package is accompanied by futures data of ten commodities.
Functions for generating Standardized Climate Indices (SCI). 
		SCI is a transformation of (smoothed) climate (or environmental)
		time series that  removes seasonality and forces the data to
		take values of the standard normal distribution. SCI was 
		originally developed for precipitation. In this case it is 
		known as the Standardized Precipitation Index (SPI).
An R interface to the SciDB array database (http://scidb.org).
Provide functions for analyzing elections and political behavior
    data, including measures of political fragmentation, seat apportionment, and
    small data visualization graphs.
It involves bibliometric indicators calculation from bibliometric data.It also deals pattern analysis using the text part of bibliometric data.The bibliometric data are obtained from mainly Web of Science and Scopus.
Sparse Column-wise Inverse Operator for estimating the inverse covariance matrix.  Note that this is a preliminary version accompanying the arXiv paper (arXiv:1203.3896) in 2012.   This version contains only the minimal set of functions for estimation and cross validation.
A collection of functions that creates graphs with error
        bars for data collected from one-way or higher factorial
        designs.
Functions to install SciViews additions to R, and more (various) tools
Provides functions to measure growth patterns and align
    sampling spots in chronologically deposited materials. The package is
    intended for the fields of sclerochronology, dendrochronology and geology.
An R package for single-case meta-analysis.
Given a matrix with results of different algorithms for different
    problems, the package uses statistical tests and corrections to assess the
    differences between algorithms.
Provides routines for scoring behavioral questionnaires. Includes scoring procedures for the 'International Physical Activity Questionnaire (IPAQ)' <http://www.ipaq.ki.se>. Compares physical functional performance to the age- and gender-specific normal ranges.
Estimate GGUM Person Parameters Using Pre-Calibrated Item Parameters and Binary or Graded Disagree-Agree Responses
A set of tools for quickly scoring models in data science and
    machine learning. This toolset is written in C++ for blazing fast performance.
This package contains the functions necessary to run the SCORER 2.0 algorithm. SCORER 2.0 can be used to differentiate between parallel dimeric and trimeric coiled-coil sequence, which are the two most more frequent coiled-coil structures observed naturally. As such, SCORER 2.0 is particularly useful for researchers looking to characterize novel coiled-coil sequences. It may also be used to assist in the structural characterization of synthetic coiled-coil sequences. Also included in this package are functions that allows the user to retrain the SCORER 2.0 algorithm using user-defined training data.
Evaluating probabilistic forecasts via proper scoring rules.  scoring implements the beta, power, and pseudospherical families of proper scoring rules, along with ordered versions of the latter two families.  Included among these families are popular rules like the Brier (quadratic) score, logarithmic score, and spherical score.  For two-alternative forecasts, also includes functionality for plotting scores that one would obtain under specific scoring rules.
Dictionary-like reference for computing scoring rules in a wide
    range of situations. Covers both parametric forecast distributions (such as
    mixtures of Gaussians) and distributions generated via simulation.
Division of an ANOVA experiment treatment means into
        homogeneous distinct groups using the clustering method of
        Scott & Knott
An enhancement of the Scott-Knott test (which cluster distributions
    into statistically distinct ranks) that takes effect size into consideration.
Implements the Scout method for regression, described in "Covariance-regularized regression and classification for high-dimensional problems", by Witten and Tibshirani (2008),  Journal of the Royal Statistical Society, Series B 71(3): 615-636.
The package implements different inventory models, the
        bullwhip effect and other supply chain performance variables.
Phase variants within genes using allele counts from single-cell
    RNA-seq data.
Given a word will produce that word's scrabble score. Unlike many naive implementations this package takes into consideration the distribution of letter in scrabble. So a word like 'zzz' will be scored '10' rather than '30'.
Tools for Scraping Data from Web-Based Documents
Routines for a collection of screen-and-clean type
        variable selection procedures, including UPS and GS.
Tools for the analysis of high-dimensional data developed/implemented
  at the group "Statistical Complexity Reduction In Molecular Epidemiology" (SCRIME).
  Main focus is on SNP data. But most of the functions can also be applied to other
  types of categorical data.
Support for using .Rt (transcript) tests
  in the tests directory of a package.  Provides more
  convenience and features than the standard .R/.Rout.save
  tests.  Tests can be run under R CMD check and also
  interactively.  Provides source.pkg() for quickly loading
  code, DLLs, and data from a package for use in an
  edit/compile/test development cycle.
A coalescent simulator that allows the rapid simulation of
    biological sequences under neutral models of evolution. Different to
    other coalescent based simulations, it has an optional approximation
    parameter that allows for high accuracy while maintaining a linear run time
    cost for long sequences. It is optimized for simulating massive data sets as
    produced by Next-Generation Sequencing technologies for up to several thousand
    sequences.
A package for designing single-case phase, alternation and multiple-baseline experiments, as well as for conducting randomization tests on data gathered by means of such designs.
Clean biological occurrence records. Includes functionality
    for cleaning based on various aspects of spatial coordinates,
    unlikely values due to political 'centroids', coordinates based on
    where collections of specimens are held, and more.
scrypt is an R package for working with scrypt. Scrypt is a
    password-based key derivation function created by Colin Percival. The
    algorithm was specifically designed to make it costly to perform
    large-scale custom hardware attacks by requiring large amounts of memory.
Solves convex cone programs via operator splitting. Can solve:
    linear programs (LPs), second-order cone programs (SOCPs), semidefinite programs
    (SDPs), exponential cone programs (ECPs), and power cone programs (PCPs), or
    problems with any combination of those cones.    SCS uses AMD (a set of routines for permuting sparse matrices prior to factorization) and LDL (a sparse LDL' factorization and solve package) from 'SuiteSparse' (<http://www.suitesparse.com>).
Code for describing and manipulating scuba diving profiles 
	(depth-time curves) and decompression models, 
        for calculating the predictions of decompression models,
	for calculating maximum no-decompression time and decompression tables,
	and for performing mixed gas calculations.
A package to make graphical representations of single case data and to transform graphical displays back to raw data. The package also includes tools for visually analyzing single-case data, by displaying central location, variability and trend.
Provides an efficient framework for 
   high-dimensional linear and diagonal discriminant analysis with 
   variable selection.  The classifier is trained using James-Stein-type 
   shrinkage estimators and predictor variables are ranked using 
   correlation-adjusted t-scores (CAT scores).  Variable selection error 
   is controlled using false non-discovery rates or higher criticism.
Functions and Datasets from Lohr, S. (1999), Sampling:
        Design and Analysis, Duxbury.
Test the global null in linear models using marginal approach.
Data from statistical agencies and other institutions are mostly
    confidential. This package can be used for the generation of anonymized
    (micro)data, i.e. for the creation of public- and scientific-use files. In
    addition, various risk estimation methods are included. Note that the package
    'sdcMicroGUI' includes a graphical user interface for various methods in this
    package.
A point and click graphical user interface based on top of the    
    'sdcMicro' package to create anonymized data set. The graphical user    
    interface provides full reproducibility of any result via the script menu    
    in the GUI.
Methods for statistical disclosure control in
    tabular data such as primary and secondary cell suppression are covered in
    this package.
Classes and methods to calculate and evaluate target matrices for
    statistical disclosure control.
Allows for  computing (and by default plotting) different types of serial dependence diagrams.
Compares the evolution of an original network X to an augmented network Y by counting the number of Shortcuts, Detours, Dead Ends (SDDE), equal paths and disconnected nodes.
The semidiscrete decomposition (SDD) approximates a matrix
        as a weighted sum of outer products formed by vectors with
        entries constrained to be in the set {-1, 0, 1}.
Companion package to the book Simulation and Inference for
        Stochastic Differential Equations With R Examples, ISBN
        978-0-387-75838-1, Springer, NY.
Performs two tests to evaluate if the
        experiments are associated and returns a list of interesting
        features common to all the experiments.
Implementation of evolutionary fuzzy systems for the data mining task called
    "subgroup discovery". It also provide a Shiny App
    for make the analysis easier. The algorithms works with data sets provided in
    KEEL, ARFF and CSV format and also with data.frame objects.
An extensible R framework for developing species distribution
    models using individual and community-based approaches, generate ensembles of
    models, evaluate the models, and predict species potential distributions in
    space and time.
Functions provided by this pedagogic package allow to compute models with two popular machine learning approaches, BRT (Boosted Regression Trees) and MaxEnt (Maximum Entropy) applied on sets of marine biological and environmental data. They include the possibility of managing the main parameters for the construction of the models. Classic tools to evaluate model performance are provided (Area Under the Curve, omission rate and confusion matrix, map standard deviation) and are completed with tools to perform null models. The biological dataset includes original occurrences of two species of the class Echinoidea (sea urchins) present on the Kerguelen Plateau and that show contrasted ecological niches. The environmental dataset includes the corresponding statistics for 15 abiotic and biotic descriptors summarized for the Kerguelen Plateau and for different periods in a raster format. The package can be used for practicals to teach and learn the basics of species distribution modelling. Maps of potential distribution can be produced based on the example data included in the package, which brings prior observations of the influence of spatial and temporal heterogeneities on modelling performances. The user can also provide his own datasets to use the modelling functions.
This packages provides a set of tools for post processing the
    outcomes of species distribution modeling exercises. It includes novel
    methods for comparing models and tracking changes in distributions through
    time. It further includes methods for visualizing outcomes, selecting
    thresholds, calculating measures of accuracy and landscape fragmentation
    statistics, etc.. This package was made possible in part by financial
    support from the Australian Research Council & ARC Research Network for
    Earth System Science.
A software package help user to create virtual species for species distribution modelling. It includes
    several methods to help user to create virtual species distribution map.
    Those maps can be used for Species Distribution Modelling (SDM) study. SDM use
    environmental data for sites of occurrence of a species to predict all the sites
    where the environmental conditions are suitable for the species to persist, and
    may be expected to occur.
Fitting discrete Bayesian networks using soft-discretized data.  Soft-discretization is based on mixture of normal distributions.  Also implemented is a supervised Bayesian network learning employing Kullback-Leibler divergence.
Utility functions for scale-dependent and alternative hyperpriors.
The main purpose of this package is to provide saddlepoint
    approximations to some measures of risk, based on the compound Poisson risk
    process that is perturbed by a Brownian motion.  Various approximation
    methods for the probability of ruin are also included.  Furthermore, exact
    values of both the risk measures as well as the probability of ruin are
    available if the individual claims follow a hypo-exponential distribution
    (i. e., if it can be represented as a sum of independent exponentially
    distributed random variables with different rate parameters).
Routines for drawing samples, focusing on spatially balanced algorithms. Draws Halton Lattice (HAL), Balanced Acceptance Samples (BAS), Generalized Random Tesselation Stratified (GRTS), Simple Systematic Samples (SSS) and Simple Random Samples (SRS) from point, line, and polygon resources.  Frames are 'SpatialPoints', 'SpatialLines', or 'SpatialPolygons' objects from package 'sp'.
Implements algorithms to help with scenario discovery - currently only modified version of the the Patient Rule Induction Method.
Formulates a sparse distance weighted discrimination (SDWD) for high-dimensional classification and implements a very fast algorithm for computing its solution path with the L1, the elastic-net, and the adaptive elastic-net penalties.
Calculates parameters of the seawater carbonate system and assists
    the design of ocean acidification perturbation experiments.
Standard error adjusted adaptive lasso (SEA-lasso) is a version of the adaptive lasso, which incorporates OLS standard error to the L1 penalty weight. This method is intended for variable selection under linear regression settings (n > p). This new weight assignment strategy is especially useful when the collinearity of the design matrix is a concern.
Provides functionality for searching / subsetting and slicing named
    objects using 'stringr/i'-style modifiers by case (in)sensitivity,
    regular expressions or fixed expressions; searches uses the standard '['
    operator and allows specification of default search behavior to either the
    search target (named object) and/or the search pattern.
Provides an interface with the Google Search Console,
    formally called Google Webmaster Tools.
This package provides an implementation of the QuadTree
        data structure. It uses this to implement fast k-Nearest
        Neighbor and Rectangular range lookups in 2 dimenions. The
        primary target is high performance interactive graphics.
Capable of deriving seasonal statistics, such as "normals", and
  analysis of seasonal data, such as departures. This package also has
  graphics capabilities for representing seasonal data, including boxplots for
  seasonal parameters, and bars for summed normals. There are many specific
  functions related to climatology, including precipitation normals,
  temperature normals, cumulative precipitation departures and precipitation
  interarrivals. However, this package is designed to represent any
  time-varying parameter with a discernible seasonal signal, such as found
  in hydrology and ecology.
This package conducts Score Equity Assessment (SEA; Dorans, 2004) by calculating and plotting multiple SEA indices as introduced by a variety of authors and summarized by Huggins and Penfield (2012).
Routines for the seasonal analysis of health data,
    including regression models, time-stratified case-crossover,
    plotting functions and residual checks. Thanks to Yuming Guo
    for checking the case-crossover code.
Easy-to-use interface to X-13-ARIMA-SEATS, the
    seasonal adjustment software by the US Census Bureau. It offers full
    access to almost all options and outputs of X-13, including X-11 and SEATS,
    automatic ARIMA model search, outlier detection and support for user
    defined holiday variables, such as Chinese New Year or Indian Diwali. It
    also includes a Shiny-based graphical user interface. Seasonal uses the
    X-13 binaries from the 'x13binary' package.
A model and utilities for analyzing trends in chemical concentrations in streams with a seasonal wave (seawave) and adjustment for streamflow (Q) and other ancillary variables
Displays state-event charts, for graphical presentation of longitudinal data.
Density, distribution function, quantile function, random
    generation and survival function for the Secant Kumaraswamy Weibull Distribution
    as defined by SOUZA, L. New Trigonometric Class of Probabilistic Distributions.
    219 p. Thesis (Doctorate in Biometry and Applied Statistics) - Department of
    Statistics and Information, Federal Rural University of Pernambuco, Recife,
    Pernambuco, 2015 (available at <http://www.openthesis.org/documents/New-trigonometric-classes-probabilistic-distributions-602633.html>) and BRITO, C. C. R. Method Distributions generator and
    Probability Distributions Classes. 241 p. Thesis (Doctorate in Biometry and
    Applied Statistics) - Department of Statistics and Information, Federal Rural
    University of Pernambuco, Recife, Pernambuco, 2014 (available upon request).
SECP package provides functionality for estimating
        parameters of site clusters on 2D & 3D square lattice with
        various lattice sizes, relative fractions of accessible sites
        (occupation probability), iso- & anisotropy, von Neumann &
        Moore (1,d)-neighborhoods
Functions to estimate the density and size of a spatially distributed animal population sampled with an array of passive detectors, such as traps, or by searching polygons or transects. Models incorporating distance-dependent detection are fitted by maximizing the likelihood. Tools are included for data manipulation and model selection.
Tools are provided for designing spatially explicit capture-recapture studies of animal populations. This is primarily a simulation manager for package 'secr'.
Tools for spatially explicit capture-recapture analysis of animal populations in linear habitats, extending package 'secr'
Provides a wrapper to access data from the SeeClickFix
  web API for R. SeeClickFix is a central platform employed by many cities
  that allows citizens to request their city's services. This package
  creates several functions to work with all the built-in calls to the
  SeeClickFix API. Allows users to download service request data from
  numerous locations in easy-to-use dataframe format manipulable in
  standard R functions.
Suite of functions for the simulation, visualisation and analysis of bacterial evolution within- and between-host.
Supports the text book "Data Analysis and Statistics for
        Geography, Environmental Science, and Engineering"
Supports Simulation of Ecological and Environmental Models
Creates SEER (Surveillance, Epidemiology and End Results) and A-bomb data binaries 
             from ASCII sources and provides tools for estimating SEER second cancer risks.
Functions for analysing, manipulating, displaying, editing and synthesizing time waves (particularly sound).  This package processes time analysis (oscillograms and envelopes), spectral content, resonance quality factor, entropy, cross correlation and autocorrelation, zero-crossing, dominant frequency, analytic signal, frequency coherence, 2D and 3D spectrograms and many other analyses.
A package that provides functions for measuring spatial 
        segregation. The methods implemented in this package include 
        White's P index (1983), Morrill's D(adj) (1991), Wong's D(w)
        and D(s) (1993), and Reardon and O'Sullivan's set of spatial 
        segregation measures (2004).
Performs correlation matrix segmentation and applies a test
    procedure to detect highly correlated regions in gene expression.
Contains functions that help to determine event
    boundaries in event segmentation experiments by bootstrapping a critical
    segmentation magnitude under the null hypothesis that all key presses were
    randomly distributed across the experiment. Segmentation magnitude is
    defined as the sum of Gaussians centered at the times of the segmentation
    key presses performed by the participants. Within a participant, the maximum
    of the overlaid Gaussians is used to prevent an excessive influence of a
    single participant on the overall outcome (e.g. if a participant is pressing
    the key multiple times in succession). Further functions are included, such
    as plotting the results.
Given a regression model, segmented `updates' the model by adding one or more segmented (i.e., piecewise-linear) relationships. Several variables with multiple breakpoints are allowed.
Performs a fast exact segmentation on data and allows for use of various cost functions.
A model coupling polygon and point processes for assessing
    risk due to contaminant sources and their impact on exposed individuals.
An implementation of self-exciting point process model for information cascades, which occurs when many people engage in the same acts after observing the actions of others (e.g. post resharings on Facebook or Twitter). It provides functions to estimate the infectiousness of an information cascade and predict its popularity given the observed history. See http://snap.stanford.edu/seismic/ for more information and datasets.
Fast versions of seismic analysis functions that 'roll' over a
             vector of values. See the RcppRoll package for alternative 
             versions of basic statistical functions such as rolling mean, 
             median, etc.
Set of functions that access information about deputies and votings
    in Polish diet from webpage <http://www.sejm.gov.pl/>. The package was developed
    as a result of an internship in MI2 Group - <http://mi2.mini.pw.edu.pl/>,
    Faculty of Mathematics and Information Science, Warsaw University of Technology.
Sejong(http://www.sejong.or.kr/) corpus and
        Hannanum(http://semanticweb.kaist.ac.kr/home/index.php/HanNanum)
        dictionaries for KoNLP
This package implements a novel method for fitting a
        bounded probability distribution to quantiles (for example
        stated by an expert), see Bornkamp and Ickstadt (2009) for
        details.  For this purpose B-splines are used, and the density
        is obtained by penalized least squares based on a Brier entropy
        penalty.  The package provides methods for fitting the
        distribution as well as methods for evaluating the underlying
        density and cdf. In addition methods for plotting the
        distribution, drawing random numbers and calculating quantiles
        of the obtained distribution are provided.
A collection of functions for correcting biased estimates under
    selection (range restriction).
Multi-stage selection is practiced in numerous fields of life and social sciences and particularly in breeding. A special characteristic of multi-stage selection is that candidates are evaluated in successive stages with increasing intensity and effort, and only a fraction of the superior candidates is selected and promoted to the next stage. For the optimum design of such selection programs, the selection gain plays a crucial role. It can be calculated by integration of a truncated multivariate normal (MVN) distribution. While mathematical formulas for calculating the selection gain and the variance among selected candidates were developed long time ago, solutions for numerical calculation were not available. This package can also be used for optimizing multi-stage selection programs for a given total budget and different costs of evaluating the candidates in each stage.
New tools for post-selection inference, for use
    with forward stepwise regression, least angle regression, the
    lasso, and the many means problem. The lasso function implements  Gaussian,  logistic and Cox survival models.
Publication bias, the fact that studies identified for inclusion in a meta analysis do not represent all studies on the topic of interest, is commonly recognized as a threat to the validity of the results of a meta analysis. One way to explicitly model publication bias is via selection models or weighted probability distributions. In this package we provide implementations of several parametric and nonparametric weight functions. The novelty in Rufibach (2011) is the proposal of a non-increasing variant of the nonparametric weight function of Dear & Begg (1992). The new approach potentially offers more insight in the selection process than other methods, but is more flexible than parametric approaches. To maximize the log-likelihood function proposed by Dear & Begg (1992) under a monotonicity constraint we use a differential evolution algorithm proposed by Ardia et al (2010a, b) and implemented in Mullen et al (2009). In addition, we offer a method to compute a confidence interval for the overall effect size theta, adjusted for selection bias as well as a function that computes the simulation-based p-value to assess the null hypothesis of no selection as described in Rufibach (2011, Section 6).
Translates a CSS3 selector into an equivalent XPath
  expression. This allows us to use CSS selectors when working with
  the XML package as it can only evaluate XPath expressions. Also
  provided are convenience functions useful for using CSS selectors on
  XML nodes. This package is a port of the Python package 'cssselect'
  (<https://pythonhosted.org/cssselect/>).
Fit and selects point pattern models based on minimum contrast, AIC and and goodness of fit.
Detection of outliers and influential errors using a latent variable model.
The W3C WebDriver specification defines a way for out-of-process
    programs to remotely instruct the behaviour of web browsers. It is detailed
    at <https://w3c.github.io/webdriver/webdriver-spec.html>. This package provides
    an R client implementing the W3C specification.
Functions using Cohen's effect sizes (Cohen, Jacob. Statistical power analysis for the behavioral sciences. Academic press, 2013) are provided for reliable feature selection in biology data analysis.  In addition to Cohen's effect sizes, p-values are calculated and adjusted from quasi-Poisson GLM, negative binomial GLM and Normal distribution ANOVA.  Significant features (genes, RNAs or proteins) are selected by adjusted p-value and minimum Cohen's effect sizes, calculated to keep certain level of statistical power of biology data analysis given p-value threshold and sample size.
A probability tree allows to compute probabilities of
	     complex events, such as genotype probabilities in intermediate generations of inbreeding
	     through recurrent self-fertilization (selfing). This package implements functionality to compute
	     probability trees for two- and three-marker genotypes in the F2 to F7 selfing
	     generations. The conditional probabilities are derived automatically
	     and in symbolic form. The package also provides functionality to
	     extract and evaluate the relevant probabilities.
Performs a regularization approach to variable selection in the
             model-based clustering and classification frameworks.
             First, the variables are arranged in order with a lasso-like procedure. 
             Second, the method of Maugis, Celeux, and Martin-Magniette (2009, 2011) 
             is adapted to define the role of variables in the two frameworks.
Functions for fitting general linear structural
    equation models (with observed and latent variables) using the RAM approach, 
    and for fitting structural equations in observed-variable models by two-stage least squares.
Outlier and leverage diagnostics for SEM.
Fits a multidimensional scaling (MDS) model for three-way data. It integrates concepts from structural equation models (SEM) by assuming an underlying, latent dissimilarity matrix. The methods uses an alternating estimation procedure in which the unknown symmetric dissimilarity matrix is estimated in a SEM framework while the objects are represented in a low-dimensional space. As a special case it can also handle asymmetric input dissimilarities.
This is an add-on package which provides fourteen
        goodness-of-fit indexes for structural equation models using
        'sem' package.
Package semiArtificial contains methods to generate and evaluate semi-artificial data sets. 
 Based on a given data set different methods learn data properties using machine learning algorithms and
 generate new data with the same properties.
 The package currently includes the following data generator:
  -a RBF network based generator using rbfDDA from RSNNS package,
  -a Random Forest based generator for both classification and regression problems
  -a density forest based generator for unsupervised data
 Data evaluation support tools include:
  -single attribute based statistical evaluation: mean, median, standard deviation, skewness, kurtosis, medcouple, L/RMC, KS test, Hellinger distance
  -evaluation based on clustering using Adjusted Rand Index (ARI) and FM
  -evaluation based on classification performance with various learning models, eg, random forests.
Parametric and semi-parametric analyses of semi-competing risks/univariate survival data.
Provides routines to check identifiability or non-identifiability
    of linear structural equation models as described in Drton, Foygel &
    Sullivant (Ann. Statist., 2011) and Foygel, Draisma & Drton (Ann. Statist.,
    2012). The routines are based on the graphical representation of
    structural equation models by a path diagram/mixed graph.
Functions for fitting multi-state semi-Markov models to longitudinal data. A parametric maximum likelihood estimation method adapted to deal with Exponential, Weibull and Exponentiated Weibull distributions is considered. Right-censoring can be taken into account and both constant and time-varying covariates can be included using a Cox proportional model.
Functions for semiparametric regression analysis, to
        complement the book: Ruppert, D., Wand, M.P. and Carroll, R.J.
        (2003). Semiparametric Regression. Cambridge University Press.
Routines for fitting several classes of copula regression models, with several types of covariate effects, in the presence of associated error equations, endogeneity, non-random sample selection or partial observability.
Routine for fitting continuous or discrete response copula sample selection models with semi-parametric predictors, including linear and nonlinear effects.
Functions to compute Gaussian and p-Gaussian kernels,
        include supervision in these kernels, and perform kernel PCA
        projections.
Conduct tests of difference in fit for mean and covariance
        structure models as in structural equation modeling (SEM)
Path diagrams and visual analysis of various SEM packages' output
Fits structural equation models using partial least
        squares (PLS). The PLS approach is referred to as
        'soft-modeling' technique requiring no distributional
        assumptions on the observed data.
Semiparametric Estimation of Stochastic Frontier Models following a two step procedure: in the first step semiparametric or nonparametric regression techniques are used to relax parametric restrictions of the functional form representing technology and in the second step variance parameters are obtained by pseudolikelihood estimators or by method of moments.
Provides useful tools for structural equation modeling packages.
Package contains a simple SMTP client which provides a
        portable solution for sending email, including attachment, from
        within R.
An implementation of sensitivity analysis in phylogenetic regression models,
    for both linear and logistic phylogenetic regressions. The package is an umbrella
    of statistical and graphical methods that estimate and report different types of
    uncertainty in PGLS models:
    (i) Sampling uncertainty (sample size; influential species and clades).
    (ii) Phylogenetic uncertainty.
    (iii) Data uncertainty (intraspecific variation and measurement error).
A collection of functions for factor screening, global sensitivity analysis and reliability sensitivity analysis. Most of the functions have to be applied on model with scalar output, but several functions support multi-dimensional outputs.
Performs exact or approximate adaptive or nonadaptive Cochran-Mantel-Haenszel-Birch tests and sensitivity analyses for one or two 2x2xk tables in observational studies.
This package performs sensitivity analysis for case-control studies in which some cases may meet a more narrow definition of being a case compared to other cases which only meet a broad definition.  The sensitivity analyses are described in Small, Cheng, Halloran and Rosenbaum (2013, "Case Definition and Sensitivity Analysis", Journal of the American Statistical Association, 1457-1468).  The functions sens.analysis.mh and sens.analysis.aberrant.rank provide sensitivity analyses based on the Mantel-Haenszel test statistic and aberrant rank test statistic as described in Rosenbaum (1991, "Sensitivity Analysis for Matched Case Control Studies", Biometrics); see also Section 1 of Small et al.  The function adaptive.case.test provides adaptive inferences as described in Section 5 of Small et al.  The function adaptive.noether.brown provides a sensitivity analysis for a matched cohort study based on an adaptive test.  The other functions in the package are internal functions.
Sensitivity analysis in observational studies, including evidence factors and amplification, using the permutation distribution of Huber-Maritz M-statistics, including the permutational t-test.
Sensitivity analysis analysis in matched observational studies with multiple controls using weighted M-statistics to increase design sensitivity.
This package provides functions to perform principal stratification sensitivity analyses on datasets.
Functions that facilitate analysis of 
              Sensory as well as Consumer data within a mixed effects model 
              framework. The so-called mixed assessor models, 
              that correct for the scaling effect are implemented.
              The generation of the d-tilde plots forms part of the package.
              The shiny application provides GUI for the functionalities.
Contains the function CUUimpute() which performs model-based clustering and imputation simultaneously.
Provides methods for sensory discrimination methods;
  duotrio, tetrad, triangle, 2-AFC, 3-AFC, A-not A, same-different,
  2-AC and degree-of-difference.
  This enables the calculation of d-primes, standard errors of
  d-primes, sample size and power computations, and
  comparisons of different d-primes. Methods for profile likelihood
  confidence intervals and plotting are included.
Collection of datasets from Sen & Srivastava: "Regression
        Analysis, Theory, Methods and Applications", Springer.  Sources
        for individual data files are more fully documented in the
        book.
Provides access and analytic functions for Sensus data.
Calculate text polarity sentiment at the sentence level
        and optionally aggregate by rows or grouping variable(s).
It combines maximum likelihood estimation of the parameters
    of matrix and 3rd-order tensor normal distributions with unstructured
    factor variance-covariance matrices, two procedures, and unbiased
    modified likelihood ratio testing of simple and double separability
    for variance-covariance structures, two procedures.
Functions to generate separation plots for evaluation of
        model fit.
Potential randomization schemes are prospectively evaluated when
    units are assigned to treatment arms upon entry into the experiment. The schemes
    are evaluated for balance on covariates and on predictability (i.e., how well
    could a site worker guess the treatment of the next unit enrolled).
This is a method for DNA Copy Number Profiling using
        Next-Generation Sequencing. It has new model and test
        statistics based on non-homogeneous Poisson Processes with
        change point models. It uses an adaptation of Circular Binary
        Segmentation. Also included are methods for point-wise Bayesian
        Confidence Interval and model selection method for the
        change-point model. A case and a control sample reads (normal
        and tumor) are required.
A modification of the preventive vaccine efficacy trial design of Gilbert, Grove et al. (2011, Statistical Communications in Infectious Diseases) is implemented, with application generally to individual-randomized clinical trials with multiple active treatment groups and a shared control group, and a study endpoint that is a time-to-event endpoint subject to right-censoring. The design accounts for the issues that the efficacy of the treatment/vaccine groups may take time to accrue while the multiple treatment administrations/vaccinations are given; there is interest in assessing the durability of treatment efficacy over time; and group sequential monitoring of each treatment group for potential harm, non-efficacy/efficacy futility, and high efficacy is warranted. The design divides the trial into two stages of time periods, where each treatment is first evaluated for efficacy in the first stage of follow-up, and, if and only if it shows significant treatment efficacy in stage one, it is evaluated for longer-term durability of efficacy in stage two. The package produces plots and tables describing operating characteristics of a specified design including an unconditional power for intention-to-treat and per-protocol/as-treated analyses; trial duration; probabilities of the different possible trial monitoring outcomes (e.g., stopping early for non-efficacy); unconditional power for comparing treatment efficacies; and distributions of numbers of endpoint events occurring after the treatments/vaccinations are given, useful as input parameters for the design of studies of the association of biomarkers with a clinical outcome (surrogate endpoint problem). The code can be used for a single active treatment versus control design and for a single-stage design.
Provides user friendly methods for the identification of sequence patterns that are statistically significantly associated with a property of the sequence. For instance, SeqFeatR allows to identify viral immune escape mutations for hosts of given HLA types. The underlying statistical method is Fisher's exact test, with appropriate corrections for multiple testing, or Bayes. Patterns may be point mutations or n-tuple of mutations. SeqFeatR offers several ways to visualize the results of the statistical analyses.
The SeqGrapheR package provide interactive GUI for visualization of DNA sequence clusters. Details and principles of usage are described in user manual and (2010 BMC Bioinformatics 11:378). For full functionality installed NCBI blast is required.
Designed for fitting hidden (latent)
    Markov models and mixture hidden Markov models for social
    sequence data and other categorical time series. Also some more restricted
    versions of these type of models are available: Markov models, mixture
    Markov models, and latent class models. The package supports models for one or
    multiple subjects with one or multiple parallel sequences (channels). External
    covariates can be added to explain cluster membership in mixture models. The
    package provides functions for evaluating and comparing models, as well as
    functions for easy plotting of multichannel sequence data and hidden Markov
    models. Models are estimated using maximum likelihood via the EM algorithm and/or
    direct numerical maximization with analytical gradients.
    All main algorithms are written in C++ with support for parallel computation.
Exploratory data analysis and data visualization
  for biological sequence (DNA and protein) data. Includes also
  utilities for sequence data management under the ACNUC system.
A network module-based generalized linear model for differential expression analysis with the count-based sequence data from RNA-Seq.
Computes necessary information to meta analyze region-based
    tests for rare genetic variants (e.g. SKAT, T1) in individual studies, and
    performs meta analysis.
Integrate sequencing data (Variant call format, e.g. VCF or BCF) or meta-analysis results in R.
A program that computes the probability of crossing
        sequential boundaries in a clinical trial. It implements the
        Armitage-McPherson and Rowe Algorithm using the method
        described in Schoenfeld D. (2001)" A simple Algorithm for
        Designing Group Sequential Clinical Trials" Biometrics 27: pp,
        972-974
User inputs a range of values r1 and r2, as well as a
        number of columns v, in the function sq.pe(r1,r2,v). The
        returned statement is a permutation matrix.
This package includes functions for handling DNA
        sequences, especially simulated RFLP and TRFLP pattern based on
        selected restriction enzyme and DNA sequences.
Sequential triangular test for the arithmetic mean in one- and two-
    samples, proportions in one- and two-samples, and the Pearson's correlation
    coefficient.
Provides: 1) By Giving UniProtKB, the Protein Sequence will be returned from UniProt database. 2)By Giving UniProtKB, the Nucleotide Sequence will be returned from EBI database. 3) Amino acid Composition is calculated by four different methods: a) Twenty-two independent categories are considered, with one amino acid for each category. B) Five categories (Nonpolar Aliphatic, Nonpolar Aromatic, Polar Uncharged, Polar Positively Charged, Polar Negatively Charged) are considered according to the standard chemical structures of amino acids. c) Six categories (Nonpolar Aliphatic, Nonpolar Aromatic, Polar Uncharged, Polar Positively Charged, Polar Negatively Charged, Special cases) are considered which Cysteine, Selenocysteine, Glycine and Proline are placed in Special cases group. d)Eight categories are clustered via k-means algorithm on Physicochemical index of amino acids. 4) GC Content: Percentage of nucleotide g and c in sequence. 5) Codon usage: Frequency of occurrence of synonymous codons. 6) Stacking Energy: The NN model for nucleic acids assumes that the stability of a given base pair depends on the identity and orientation of neighboring base pairs. Stacking Energy = DeltaG(total) = Sigma (n(i)*DeltaG(i)) + DeltaG(init) + DeltaG(end) + DeltaG(sym), which DeltaG for i, init and end is obtained by Unified NN free energy parameter. Symmetry of self-complementary duplexes is also included by DeltaG(sym) equals to +0.43 (kcal/mol) if the duplex is self-complementary and zero if it is non-self-complementary. 7) Complement of desired nucleotide sequence. 8) Reverse of desired nucleotide sequence. 9) Reverse-Complement of desired nucleotide sequence. 10) Protein, Gene and Organism of desired UniProt ID. 11) Converting nucleotide sequence to protein sequence. 12) Getting Localization From Gene Ontology Cellular Component inside Uniprot. 13) All related codons of desired amino acid.
Educational package used in R courses to illustrate
       	     object-oriented programming and package
       	     development. Using biological sequences (DNA and RNA) as
       	     a working example.
Functions to calculate exact critical values, statistical power, expected time to				signal and required  sample sizes for  performing exact sequential  analysis. All these				calculations can be done for either Poisson or binomial data, for continuous  or group				sequential analyses, and for different types of rejection boundaries.  In case  of group				sequential  analyses, the group  sizes do not  have to be specified in advance and the				alpha spending can be arbitrarily settled.
Tools to analyze genomic sequencing data from
 paired normal-tumor samples, including cellularity and ploidy estimation; mutation
 and copy number (allele-specific and total copy number) detection, quantification 
 and visualization.
Provides functionality for the use of the internal hardware for
             RS232/RS422/RS485 and any other virtual serial interfaces of the
             computer.
Infrastructure for seriation with an implementation of several
    seriation/sequencing techniques to reorder matrices, dissimilarity
    matrices, and dendrograms. Also provides (optimally) reordered heatmaps,
    color images and clustering visualizations like dissimilarity plots, and
    visual assessment of cluster tendency plots (VAT and iVAT).
Translates antibody levels measured in a (cross-sectional)
    population sample into an estimate of the frequency with which
    seroconversions (infections) occur in the sampled population.
Start an HTTP server in R to serve static files, or dynamic
    documents that can be converted to HTML files (e.g., R Markdown) under a
    given directory.
Structural equation modeling is a powerful statistical approach for the testing of networks of direct and indirect theoretical causal relationships in complex data sets with inter-correlated dependent and independent variables. Here we implement a simple method for spatially explicit structural equation modeling based on the analysis of variance co-variance matrices calculated across a range of lag distances. This method provides readily interpreted plots of the change in path coefficients across scale.
Utility functions for interacting with R processes from
        external programs. This package includes functions to save and
        restore session information (including loaded packages, and
        attached data objects), as well as functions to evaluate
        strings containing R commands and return the printed results or
        an execution transcript.
This initiated as a package companion to the book by C. Q.
        Schneider and C. Wagemann "Set-Theoretic Methods for the Social
        Sciences", Cambridge University Press. It grew to include functions 
	for performing set-theoretic multi-method research, QCA for clustered 
	data, theory evaluation, and Enhanced Standard Analysis. Additionally 
	it includes data to replicate the examples in the book and in the online 
	appendix.
Tests gene expression data from a biological pathway for biologically meaningful differences in the eigenstructure between two classes.  Specifically, it tests the null hypothesis that the two classes' leading eigenvalues and sums of eigenvalues are equal.  A pathway's leading eigenvalue arguably represents the total variability due to variability in pathway activity, while the sum of all its eigenvalues represents the variability due to pathway activity and to other, unregulated causes. Implementation of the method described in Danaher (2015), "Covariance-based analyses of biological pathways".
Implements an algorithm to conduct advanced
    gene set enrichment analysis on the results of genomics experiments.
SetRNG provides utilities to help set and record the setting of
	the seed and the uniform and normal generators used when a random
	experiment is run. The utilities can be used in other functions 
	that do random experiments to simplify recording and/or setting all the 
	necessary information for reproducibility. 
	See the vignette and reference manual for examples.
Data structures and basic operations for ordinary sets,
             generalizations such as fuzzy sets, multisets, and
             fuzzy multisets, customizable sets, and intervals.
Mutators to set attributes of variables, that work well in a pipe
    (much like stats::setNames()).
Provides option settings management that goes
    beyond R's default 'options' function. With this package, users can define
    their own option settings manager holding option names, default values and 
    (if so desired) ranges or sets of allowed option values that will be 
    automatically checked. Settings can then be retrieved, altered and reset 
    to defaults with ease. For R programmers and package developers it offers 
    cloning and merging functionality which allows for conveniently defining 
    global and local options, possibly in a multilevel options hierarchy. See 
    the package vignette for some examples concerning functions, S4 classes, 
    and reference classes. There are convenience functions to reset par() 
    and options() to their 'factory defaults'.
Automatically sets the value of options("width") when the
   terminal emulator is resized. The functions of this package only work if R
   is compiled for Linux systems and it is running interactively in a terminal
   emulator.
This package contains functions for calculating severity
        and generating severity curves. Specifically, the simple case
        of the one-parameter Normal distribution (i.e., with known
        variance) is considered.
This package combines several sighting based estimators of historical extinction, allowing them to be run simultaneously or individually. Code for this package was contributed by Ben Collen, Gene Hunt and Tracy Rout. Additional code was taken from McPherson & Myers (2009).
Stochastic Frontier Analysis
  introduced by Aigner, Lovell and Schmidt (1976)
  and Battese and Coelli (1992, 1995).
Provides a function sfc() to compute the substance flow
    with the input files --- "data" and "model". If sample.size is
    set more than 1, uncertainty analysis will be executed while
    the distributions and parameters are supplied in the file "data".
Gives design points from a sequential full factorial-based
 Latin hypercube design, as described in Duan, Ankenman, Sanchez,
 and Sanchez (2015, Technometrics,
 <doi:10.1080/00401706.2015.1108233>).
The straightforward filtering index (SFINX) identifies true positive
    protein interactions in a fast, user-friendly, and highly accurate way.
    It is not only useful for the filtering of affinity purification -
    mass spectrometry (AP-MS) data, but also for similar types of data
    resulting from other co-complex interactomics technologies, such as TAP-MS,
    Virotrap and BioID. SFINX can also be used via the website interface at
    <http://sfinx.ugent.be>.
Useful utilities ['goodies'] from Seminar fuer Statistik ETH
  Zurich, quite a few related to graphics; many ported from S-plus times.
This package contains a series of tools for analyzing Systems Factorial Technology data.  This includes functions for plotting and statistically testing capacity coefficient functions and survivor interaction contrast functions.
Graph based clustering summaries for spatial point patterns. Includes Connectivity function,
 Cumulative connectivity function and clustering function, plus the triangle/triplet intensity function T.
A fast and flexible set of tools for large scale estimation. It
    features many stochastic gradient methods, built-in models, visualization
    tools, automated hyperparameter tuning, model checking, interval estimation,
    and convergence diagnostics.
An Object-oriented Framework for Geostatistical Modeling in S+ 
  containing functions for variogram estimation, variogram fitting and kriging
  as well as some plot functions. Written entirely in S, therefore works only
  for small data sets in acceptable computing time.
Fit a regularized generalized linear model via penalized
        maximum likelihood.  The model is fit for a path of values of
        the penalty parameter. Fits linear, logistic and Cox models.
RCON(V, E) models (Højsgaard, et al., 2008) are a kind of restriction of the Gaussian Graphical Models defined by a set of equality constraints on the entries of the concentration matrix. 'sglasso' package implements the structured graphical lasso (sglasso) estimator proposed in Abbruzzo et al. (2014) for the weighted l1-penalized RCON(V, E) model. Two cyclic coordinate algorithms are implemented to compute the sglasso estimator, i.e. a cyclic coordinate minimization (CCM) and a cyclic coordinate descent (CCD) algorithm.
Fast generic solver for sparse group lasso optimization
    problems. The loss (objective) function must be defined in a C++ module.
    Use of multiple processors for cross validation and subsampling is supported
    through OpenMP. The optimization problem is solved using a coordinate gradient
    descent algorithm. The algorithm is applicable to a broad class of convex
    loss functions. Convergence of the algorithm is established (see reference).
    Development version is on github, please report package issues on github.
Functions for computing power and boundaries for pre-licensure vaccine trials using the Generalized Likelihood Ratio tests proposed by Shih, Lai, Heyse and Chen
Seven different methods for multiple testing problems. The SGoF-type methods and the BH and BY false discovery rate controlling procedures.
Functions to calculate student growth percentiles and percentile growth projections/trajectories for students using large scale,
        longitudinal assessment data.  Functions use quantile regression to estimate the conditional density associated
        with each student's achievement history.  Percentile growth projections/trajectories are calculated using the coefficient matrices derived from
	the quantile regression analyses and specify what percentile growth is required for students to reach future achievement targets.
Functions for computing sparse generalized principal components, including functions for modeling structured correlation
Data sets utilized by the SGP Package as exemplars for users to conduct their own SGP analyses.
The Sparse Group Partial Least Square package (sgPLS) provides sparse, group, and sparse group versions of partial least square regression models.
The package for Sample Generation by Replacement simulations (SGR; Lombardi & Pastore, 2014; Pastore & Lombardi, 2014). The package can be used to perform fake data analysis according to the sample generation by replacement approach. It includes functions for making simple inferences about discrete/ordinal fake data. The package allows to study the implications of fake data for empirical results.
Provides functions to implement sgRSEA (single-guide RNA Set Enrichment Analysis), which is a robust test for identification of essential genes from genetic screening data using CRISPR (clustered regularly interspaced short palindromic repeats) and Cas9 (CRISPR-associated nuclease 9) system.
Density, distribution function, quantile function and random generation for the skewed generalized t distribution. This package also provides a function that can fit data to the skewed generalized t distribution using maximum likelihood estimation.
Functions for easily manipulating colours and creating colour scales.
Functions for plotting graphical shapes
  such as ellipses, circles, cylinders, arrows, ...
In a scatterplot where the response variable is Gaussian, Poisson or binomial, we consider the case in which the mean function is smooth with a change-point, which is a mode, an inflection point or a jump point. The main routine estimates the mean curve and the change-point as well using shape-restricted B-splines. An optional subroutine delivering a bootstrap confidence interval for the change-point is incorporated in the main routine.
Functions to read and write ESRI shapefiles
An evolving and growing collection of tools for the quantification, assessment, and comparison of planar shape and pattern. The current flagship functionality is in the spatial decomposition of planar shapes using 'ShrinkShape' to incrementally shrink shapes to extinction while computing area, perimeter, and number of parts at each iteration of shrinking. The spectra of results are returned in graphic and tabular formats. Additional utility tools for handling data are provided and this package will be added to as more tools are created, cleaned-up, and documented.
Studies otolith shape variation among fish populations. 
  Otoliths are calcified structures found in the inner ear of teleost fish and their shape has 
  been known to vary among several fish populations and stocks, making them very useful in taxonomy, 
  species identification and to study geographic variations. The package extends previously described 
  software used for otolith shape analysis by allowing the user to automatically extract closed 
  contour outlines from a large number of images, perform smoothing to eliminate pixel noise, 
  choose from conducting either a Fourier or wavelet transform to the outlines and visualize 
  the mean shape. The output of the package are independent Fourier or wavelet coefficients 
  which can be directly imported into a wide range of statistical packages in R. The package 
  might prove useful in studies of any two dimensional objects.
Routines for the statistical analysis of landmark 
  shapes, including Procrustes analysis, graphical displays, principal
  components analysis, permutation and bootstrap tests, thin-plate 
  spline transformation grids and comparing covariance matrices.
Landsat satellites collect important data about global forest conditions. Documentation about Landsat's role in forest disturbance estimation is available at the site <http://landsat.gsfc.nasa.gov/?p=9513>. By constrained quadratic B-splines, this package delivers an optimal shape-restricted trajectory to a time series of Landsat imagery for the purpose of modeling annual forest disturbance dynamics to behave in an ecologically sensible manner assuming one of seven possible "shapes", namely, flat, decreasing, one-jump (decreasing, jump up, decreasing), inverted vee (increasing then decreasing), vee (decreasing then increasing), linear increasing, and double-jump (decreasing, jump up, decreasing, jump up, decreasing). The main routine selects the best shape according to the minimum Bayes information criterion (BIC) or the cone information criterion (CIC), which is defined as the log of the estimated predictive squared error. The package also provides parameters summarizing the temporal pattern including year(s) of inflection, magnitude of change, pre- and post-inflection rates of growth or recovery. In addition, it contains routines for converting a flat map of disturbance agents to time-series disturbance maps and a graphical routine displaying the fitted trajectory of Landsat imagery.
A collection of tools for analyzing significance of trading
    strategies, based on the Sharpe ratio and overfit of the same.
Miscellaneous soil data management, summary, visualization, and conversion utilities to support soil survey.
Hierarchical models for the analysis of species-area 
  relationships (SARs) by combining several data sets and covariates; 
  with a global data set combining individual SAR studies; 
  as described in Solymos and Lele 
  (2012, Global Ecology and Biogeography 21, 109-120).
Provides a computational framework for Bayesian estimation of
    antigen-driven selection in immunoglobulin (Ig) sequences, providing an
    intuitive means of analyzing selection by quantifying the degree of
    selective pressure. Also provides tools to profile mutations in Ig
    sequences, build models of somatic hypermutation (SHM) in Ig sequences,
    and make model-dependent distance comparisons of Ig repertoires.
Implements various methods for eliciting a probability distribution
    for a single parameter from an expert or a group of experts. The expert
    provides a small number of probability judgements, corresponding
    to points on his or her cumulative distribution function. A range of parametric
    distributions can then be fitted and displayed, with feedback provided in the
    form of fitted probabilities and percentiles. A graphical interface for the roulette elicitation
    method is also provided. For multiple experts, a weighted linear pool can be
    calculated. Also includes functions for eliciting beliefs about population distributions.
Makes it incredibly easy to build interactive web
    applications with R. Automatic "reactive" binding between inputs and
    outputs and extensive pre-built widgets make it possible to build
    beautiful, responsive, and powerful applications with minimal effort.
Ace editor bindings to enable a rich text editing environment
    within Shiny.
Provides Bootstrap 2 web components for use with the Shiny
    package. With versions of Shiny prior to 0.11, these Bootstrap 2 components
    were included as part of the package. Later versions of Shiny include
    Bootstrap 3, so the Bootstrap 2 components have been moved into this
    package for those uses who rely on features specific to Bootstrap 2.
Adds additional Twitter Bootstrap components to Shiny.
Create dashboards with 'Shiny'. This package provides
    a theme on top of 'Shiny', making it easy to create attractive dashboards.
Add functionality to create drag and drop div elements in shiny.
Provides functionality for client-side navigation of
    the server side file system in shiny apps. In case the app is running
    locally this gives the user direct access to the file system without the
    need to "download" files to a temporary location. Both file and folder
    selection as well as file saving is available.
Interactive shiny application for analysis of educational tests and their items.
Perform common JavaScript operations in Shiny applications without
    having to know any JavaScript. Examples include: hiding an element, disabling
    an input, resetting an input back to its original value, delaying code execution
    by a few seconds, and many more useful functions.
Provides functions that wrap HTML Bootstrap
    components code to enable the design and layout of informative landing home
    pages for Shiny applications. This can lead to a better user experience for
    the users and writing less HTML for the developer.
Shiny wrappers for the RGL package. This package exposes RGL's
    ability to export WebGL visualization in a shiny-friendly format.
A graphical user interface for interactive Markov chain Monte
    Carlo (MCMC) diagnostics and plots and tables helpful for analyzing a
    posterior sample. The interface is powered by RStudio's Shiny web
    application framework and works with the output of MCMC programs written
    in any programming language (and has extended functionality for Stan models
    fit using the rstan and rstanarm packages).
Themes for use with Shiny. Includes several Bootstrap themes
    from http://bootswatch.com/, which are packaged for use with Shiny
    applications.
Provides a time input widget for Shiny. This widget allows intuitive time input in the
    '[hh]:[mm]:[ss]' or '[hh]:[mm]' (24H) format by using a separate numeric input for each time
    component. The interface with R uses 'DateTimeClasses' objects. See the project page for more
    information and examples.
Browser notifications in 'Shiny' apps, using
    'toastr': <https://github.com/CodeSeven/toastr#readme>.
Exposes bindings to jsTree -- a JavaScript library
    that supports interactive trees -- to enable a rich, editable trees in
    Shiny.
The SHIP-package allows the estimation of various types of
        shrinkage covariance matrices. These types differ in terms of
        the so-called covariance target (to be chosen by the user), the
        highly structured matrix which the standard unbiased sample
        covariance matrix is shrunken towards and which optionally
        incorporates prior biological knowledge extracted from the
        database KEGG. The shrinkage intensity is obtained via an
        analytical procedure.
A statistical method designed to take advantage of population genetics and microevolutionary theory, specifically by testing the association between haplotype sharing length and trait of interest.
Block-diagonal covariance selection for high dimensional Gaussian
    graphical models. The selection procedure is based on the slope heuristics.
An interface to the API of the E-commerce service Shopify
    (http://docs.shopify.com/api)
Analyzes shooting data with respect to group shape,
        precision, and accuracy. This includes graphical methods,
        descriptive statistics, and inference tests using standard,
        but also non-parametric and robust statistical methods.
        Implements distributions for radial error in bivariate normal
        variables. Works with files exported by OnTarget PC/TDS or
        Taran, as well as with custom data files in text format.
        Supports inference from range statistics like extreme spread.
        Includes a set of web-based graphical user interfaces.
Making it easy to use various types of fonts ('TrueType',
    'OpenType', Type 1, web fonts, etc.) in R graphs, and supporting most output
    formats of R graphics including PNG, PDF and SVG. Text glyphs will be converted
    into polygons or raster images, hence after the plot has been created, it no
    longer relies on the font files. No external software such as Ghostscript is
    needed to use this package.
Providing font files that are needed by the 'showtext' package.
Functions for converting network data from a
        SpatialLinesDataFrame object to a "igraph-class" object.
The predictive value of a statistical model can often be improved
    by applying shrinkage methods. This can be achieved, e.g., by regularized
    regression or empirical Bayes approaches. Various types of shrinkage factors can
    also be estimated after a maximum likelihood. While global shrinkage modifies
    all regression coefficients by the same factor, parameterwise shrinkage factors
    differ between regression coefficients. With variables which are either highly
    correlated or associated with regard to contents, such as several columns of a
    design matrix describing a nonlinear effect, parameterwise shrinkage factors are
    not interpretable and a compromise between global and parameterwise shrinkage,
    termed 'joint shrinkage', is a useful extension. A computational shortcut to
    resampling-based shrinkage factor estimation based on DFBETA residuals can be
    applied. Global, parameterwise and joint shrinkage for models fitted by lm(),
    glm(), coxph(), or mfp() is available.
Several shrinkage estimators of the effect-size of a parameter of interest.
Provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in high dimensional settings, that is when the number of variables is larger than the sample size.
Implementation of the shuffle estimator, a non-parametric estimator for signal and noise variance under mild noise correlations.
This package takes data on organism isotopes and fits a
        Bayesian model to their dietary habits based upon a Gaussian
        likelihood with a mixture dirichlet-distributed prior on the
        mean. It also includes SiBER metrics. See siardemo() for an
        example. Version 4.1.2 contains bug fixes to allow more than
        isotope numbers other than 2. Version 4.2 fixes a bug that
        stopped siar working on 64-bit systems
Fits bi-variate ellipses to stable isotope data using Bayesian
    inference with the aim being to describe and compare their isotopic
    niche.
The code computes the structural intervention distance (SID) between a true directed acyclic graph (DAG) and an estimated DAG. Definition and details about the implementation can be found in J. Peters and P. Bühlmann: "Structural intervention distance (SID) for evaluating causal graphs", Neural Computation 27, pages 771-799, 2015.
This package has many purposes: first, it gives to the
        community an R implementation of each known side channel attack
        and countermeasures as well as data to test it, second it
        allows to implement a side channel attack quickly and easily.
Provides function to apply "Subgroup Identification based on Differential Effect Search" (SIDES) method proposed by Lipkovich et al. (2011) <doi:10.1002/sim.4289>.
Evolutionary reconstruction based on substitutions and insertion-deletion (indels) analyses in a distance-based framework.
Functions for making sieve test reports. Sieve test is widely used to obtain particle-size distribution of powders or granular materials.
Print function signatures and find overly complicated code.
SigClust is a statistical method for testing the
        significance of clustering results. SigClust can be applied to
        assess the statistical significance of splitting a data set
        into two clusters. For more than two clusters, SigClust can be
        used iteratively.
Uses logistic regression to model the probability of detection as a function of covariates. 
             This model is then used with observational survey data to estimate population size, while
             accounting for uncertain detection.  See Steinhorst and Samuel (1989).
A collection of tools for estimating the location of a transmitter signal from radio telemetry studies using the maximum likelihood estimation (MLE) approach described in Lenth (1981).
Several different sigmoid functions are implemented, including a wrapper function, SoftMax preprocessing and inverse functions.
A set of signal processing functions originally written for 'Matlab' and 'Octave'.
  Includes filter generation utilities, filtering functions,
  resampling routines, and visualization of filter models. It also
  includes interpolation functions.
Predicts the presence of signal peptides in eukaryotic protein
    using hidden semi-Markov models. The implemented algorithm can be accessed from
    both the command line and GUI.
Perform sign test on one-sample data, which is one of the oldest non-parametric statistical methods. Assume that X comes from a continuous distribution with median = v ( unknown ). Test the null hypothesis H0: median of X v = mu ( mu is the location parameter and is given in the test ) v.s. the alternative hypothesis H1: v > mu ( or v < mu or v != mu ) and calculate the p-value. When the sample size is large, perform the asymptotic sign test. In both ways, calculate the R-estimate of location of X and the distribution free confidence interval for mu.
Pathway Analysis is the process of statistically linking observations on the molecular level to biological processes or pathways on the systems (organism, organ, tissue, cell) level.  
    Traditionally, pathway analysis methods regard pathways as collections of single genes and treat all genes in a pathway as equally informative.  This can lead to identification of spurious (misleading) pathways as statistically significant, since components are often shared amongst pathways. 
    SIGORA seeks to avoid this pitfall by focusing on genes or gene-pairs that are (as a combination) specific to a single pathway.  In relying on such pathway gene-pair signatures (Pathway-GPS), SIGORA inherently uses the status of other genes in the experimental context to identify the most relevant pathways. 
    The current version allows for pathway analysis of human and mouse data sets and contains pre-computed Pathway-GPS data for pathways in the KEGG and Reactome pathway repositories as well as mechanisms for extracting GPS for user supplied repositories.
Takes a phylogenetic tree (of class phylo) and a data frame (or matrix) of corresponding tip labels and p-values, and determines the significance of the branches (families of p-values) and plots the tree with colored branches (corresponding to families) according to their levels of significance.  Also produces a CSV file with the p-values of all of the branches as well as which tips belong to which branches.  Finally, it exports a file that can be opened in FigTree that produces a colored plot (colored branches according to their significance of corresponding groups of p-values) with p-value annotations.
This package calculates ANSI S3.5-1997 Speech Intelligibility Index
   (SII), a standard method for computing the intelligibility of
   speech from acoustical measurements of speech, noise, and hearing
   thresholds. This package includes data frames corresponding to
   Tables 1 - 4 in the ANSI standard as well as a function utilizing
   these tables and user-provided hearing threshold and noise level
   measurements to compute the SII score.  The methods implemented
   here extend the standard computations to allow calculation of SII
   when the measured frequencies do not match those required by the
   standard by applying interpolation to obtain values for the
   required frequencies
   -- 
   Development of this package was funded by the Center for Bioscience
   Education and Technology (CBET) of the Rochester Institute of
   Technology (RIT).
Besides functions for the calculation of similarity and
        multiple plot similarity measures with binary data (for
        instance presence/absence species data) the package contains
        some simple wrapper functions for reshaping species lists into
        matrices and vice versa and some other functions for further
        processing of similarity data (Mantel-like permutation
        procedures) as well as some other useful stuff for vegetation
        analysis.
Package simboot provides estimation of simultaneous bootstrap and asymptotic confidence intervals for diversity indices, namely the Shannon and the Simpson index. Several pre--specified multiple comparison types are available to choose. Further user--defined contrast matrices are applicable. In addition, simboot estimates adjusted as well as unadjusted p--values for two of the three proposed bootstrap methods. Further simboot allows for comparing biological diversities of two or more groups while simultaneously testing a user-defined selection of Hill numbers of orders q, which are considered as appropriate and useful indices for measuring diversity.
A flexible tool for simulating complex longitudinal data using
    structural equations, with emphasis on problems in causal inference.
    Specify interventions and simulate from intervened data generating
    distributions. Define and evaluate treatment-specific means, the average
    treatment effects and coefficients from working marginal structural models.
    User interface designed to facilitate the conduct of transparent and
    reproducible simulation studies, and allows concise expression of complex
    functional dependencies for a large number of time-varying nodes. See the
    package vignette for more information, documentation and examples.
Simultaneous tests and confidence intervals are provided for one-way experimental designs with one or many normally distributed, primary response variables (endpoints). Differences (Hasler and Hothorn, 2011) or ratios (Hasler and Hothorn, 2012) of means can be considered. Various contrasts can be chosen, unbalanced sample sizes are allowed as well as heterogeneous variances (Hasler and Hothorn, 2008) or covariance matrices (Hasler, 2014).
Simulates correlated multinomial responses conditional on a marginal model specification.
Algorithms for the implementation and evaluation of Monte Carlo tests, as well as for their use in multiple testing procedures.
Provides tools to help safely and efficiently organize Monte Carlo simulations in R.
    The package controls the structure and back-end of Monte Carlo simulations
    by utilizing a general generate-analyse-summarise strategy. The functions provided control
    common simulation issues such as re-simulating non-convergent results, support parallel
    back-end and MPI distributed computations, save and restore temporary files,
    aggregate results across independent nodes, and provide native support for debugging.
Provides the functions for simulation and modeling of stochastic differential equations (SDE's) the type Ito and Stratonovich. This package contains many objects, the numerical methods to find the solutions to SDE's (1, 2 and 3-dim), with a possibility for simulates a flows trajectories,with good accuracy. Many theoretical problems on the SDE's have become the object of practical research, as statistical analysis and simulation of solution of SDE's, enabled many searchers in different domains to use these equations to modeling and to analyse practical problems, in financial and actuarial modeling and other areas of application, for example modelling and simulate of dispersion in shallow water using the attractive center (Boukhetala K, 1996).  We hope that the package presented here and the updated survey on the subject might be of help for practitioners, postgraduate and PhD students, and researchers in the field who might want to implement new methods.
An object oriented framework to simulate
  ecological (and other) dynamic systems. It can be used for
  differential equations, individual-based (or agent-based) and other
  models as well. The package helps to organize scenarios (to avoid copy
  and paste) and aims to improve readability and usability of code.
Estimation of function and index vector in single index model with and without shape constraints including different smoothness conditions.
Implementation of the SIMEX-Algorithm by Cook & Stefanski
        and MCSIMEX by Küchenhoff, Mwalili & Lesaffre
Implement of the Simulation-Extrapolation (SIMEX) algorithm for the accelerated failure time (AFT) with covariates subject to measurement error.
A general framework for statistical simulation.
Generate power for the Cox proportional hazards model by simulating survival events data with time dependent exposure status for subjects. A dichotomous exposure variable is considered with a single transition from unexposed to exposed status during the subject's time on study.
Functions to run and assist four
  different similarity measures. The similarity
  measures included are: longest common
  subsequence (LCSS), Frechet distance, edit distance
  and dynamic time warping (DTW). Each of these
  similarity measures can be calculated from two
  n-dimensional trajectories, both in matrix form.
Allows a Simile model saved as a compiled binary to be
  loaded, parameterized, executed and interrogated. This version works 
  with Simile v5.97 on.
Livestock movements are important for the spread of many
    infectious diseases between herds. The package provides an
    efficient and flexible framework for stochastic disease spread
    modelling that integrates within-herd disease dynamics as
    continuous-time Markov chains and livestock movements between
    herds as scheduled events. The core simulation solver is
    implemented in C and uses 'OpenMP' (if available) to divide work
    over multiple processors. The package contains template models and
    can be extended with user defined models.
Provides methods for simulation and analysis of a very general fatigue
			 lifetime model for (metal matrix) composite materials.
A process-oriented and trajectory-based Discrete-Event Simulation
    (DES) package for R. Designed to be a generic framework like 'SimPy' (see 
    <https://simpy.readthedocs.io>) or 'SimJulia' (see <https://github.com/BenLauwens/SimJulia.jl>), 
    it leverages the use of 'Rcpp' to boost the performance, turning DES in R 
    feasible. As a noteworthy characteristic, simmer exploits the concept of 
    trajectory: a common path in the simulation model for entities of the same type.
Fits a stable isotope mixing model via JAGS in R. The package allows for any number of isotopes or sources, as well as concentration dependencies.
Algorithms to create prognostic biomarkers using biological networks.
Simulation of event histories with possibly non-linear baseline hazard rate functions, non-linear (time-varying) covariate effect functions, and dependencies on the past of the history. Random generation of event histories is performed using inversion sampling on the cumulative all-cause hazard rate functions.
Implements the inference of
    co-expression networks based on partial correlation
    coefficients from either steady-state or time-course
    transcriptomic data. Note that with both type of data this
    package can deal with samples collected in different
    experimental conditions and therefore not identically
    distributed. In this particular case, multiple but related
    networks are inferred on one simone run.
Simulate data from a Gaussian graphical model or a Gaussian Bayesian network in two conditions. Given a covariance matrix of a reference condition simulate plausible dysregulations.
Simulates and plots quantities of interest (relative
    hazards, first differences, and hazard ratios) for linear coefficients,
    multiplicative interactions, polynomials, penalised splines, and
    non-proportional hazards, as well as stratified survival curves from Cox
    Proportional Hazard models. It also simulates and plots marginal effects
    for multiplicative interactions.
Simple classic graph algorithms for simple graph classes.
    Graphs may possess vertex and edge attributes. 'simplegraph' has
    so dependencies and it is written entirely in R, so it is easy to
    install.
Trains neural networks (multilayer perceptrons with one hidden layer) for bi- or multi-class classification.
Simple result caching in R based on Henrik
    Bengtsson's R.cache. The global environment is not considered when caching
    results simplifying moving files between multiple instances of R. Relies on
    more base functions than R.cache (e.g. cached results are saved using saveRDS() 
    and readRDS()).
SimpleTable provides a series of methods to conduct
        Bayesian inference and sensitivity analysis for causal effects
        from 2 x 2 and 2 x 2 x K tables when unmeasured confounding is
        present or suspected.
Simplex density, distribution, quantile functions as well as random variable
   	generation of the simplex distribution are given. Regression analysis of proportional data
   	using various kinds of simplex models is available. In addition, GEE method can be applied 
	to longitudinal data to model the correlation. Residual analysis is also involved. Some 
	subroutines are written in C with GNU Scientific Library (GSL) so as to facilitate the 
	computation.
Provides methods to integrate functions over m-dimensional simplices
 in n-dimensional Euclidean space.  There are exact methods for polynomials and
 adaptive methods for integrating an arbitrary function.  Dirichlet probabilities
 are calculated in certain cases.
Basic tools for symbolic expression simplification, e.g. simplify(x*1) => x, or simplify(sin(x)^2+cos(x)^2) => 1. Based on the "Expression v3" (Ev3) 1.0 system by Leo Liberti.
Tools and methods to simulate populations for surveys based
    on auxiliary data. The tools include model-based methods, calibration and
    combinatorial optimization algorithms. The package was developed with support of
    the International Household Survey Network, DFID Trust Fund TF011722 and funds from the World bank.
This package detects instances of Simpson's Paradox in
        datasets. It examines subpopulations in the data, either
        user-defined or by means of cluster analysis, to test whether a
        regression at the level of the group is in the opposite
        direction at the level of subpopulations.
Easy to use interfaces to a number of imputation methods
        that fit in the not-a-pipe operator of the 'magrittr' package.
Calculate power for generalised linear mixed models, using
    simulation. Designed to work with models fit using the 'lme4' package.
Provides a number of functions to simulate restriction enzyme digestion, library construction and fragments size selection to predict the number of loci expected from most of the Restriction site Associated DNA (RAD) and Genotyping By Sequencing (GBS) approaches. SimRAD estimates the number of loci expected from a particular genome depending on the protocol type and parameters allowing to assess feasibility, multiplexing capacity and the amount of sequencing required.
Functions for performing Bayesian `similarity regression',
    specialised for modelling the association between a HPO-encoded phenotype
    and binary genotype. A `no association' model is compared with one in which
    the log odds of having a rare genotype is linked to the semantic similarity
    between patient phenotype and a latent characteristic phenotype. The method
    estimates the probability of an association together with an HPO-coded phenotype
    characteristic of the disease.
Facilitates data simulation from a random regression model where the data properties can be controlled by a few input parameters. The data simulation is based on the concept of relevant latent components and relevant predictors, and was developed for the purpose of testing methods for variable selection for prediction. Included are also functions for designing computer experiments in order to investigate the effects of the data properties on the performance of the tested methods. The design is constructed using the Multi-level Binary Replacement (MBR) design approach which makes it possible to set up fractional designs for multi-factor problems with potentially many levels for each factor.
Provides functions to generate and analyze individual-based spatially-explicit
  simulations of multi-state movements in heterogeneous landscapes, based on "resistance"
  rasters. Although originally conceived and designed to fill the gap of softwares simulating
  spatially-explicit trajectories of species constrained to linear, dendritic habitats
  (e.g., river networks), the simulation algorithm is built to be highly flexible and can be
  applied to any (aquatic, semi-aquatic or terrestrial) organism. Thus, the user will be able
  to use the package to simulate movements either in homogeneous landscapes, heterogeneous
  landscapes (e.g. semi-aquatic animal in a riverscape), or even in highly contrasted
  landscapes (e.g. fish in a river network). The algorithm and its input parameters are
  the same for all cases, so that results are comparable. Simulated trajectories can then
  be used as null models to test e.g. for species site fidelity and other movement ecology
  hypotheses, or to build predictive, mechanistic movement models, among other things. The
  package should thus be relevant to explore a broad spectrum of ecological phenomena, such
  as those at the interface of animal behaviour, landscape, spatial and movement ecology,
  disease and invasive species spread, and population dynamics. This is the first released
  experimental version; do test before using in production.
Tools for setting up ("design"), conducting, and evaluating
  large-scale simulation studies with graphics and tables, including
  parallel computations.
Contains two functions for simulating survival data from piecewise exponential hazards with a proportional hazards adjustment for covariates. The first function SimUNIVPiecewise simulates univariate survival data based on a piecewise exponential hazard, covariate matrix and true regression vector. The second function SimSCRPiecewise semi-competing risks data based on three piecewise exponential hazards, three true regression vectors and three matrices of patient covariates (which can be different or the same). This simulates from the Semi-Markov model of Lee et al (2015) given patient covariates, regression parameters, patient frailties and baseline hazard functions.
Provides an easy framework for Monte Carlo simulation in structural equation modeling, which can be used for various purposes, such as such as model fit evaluation, power analysis, or missing data handling and planning.
RNA sequencing analysis methods are often derived by relying on hypothetical parametric models for read counts that are not likely to be precisely satisfied in practice. Methods are often tested by analyzing data that have been simulated according to the assumed model. This testing strategy can result in an overly optimistic view of the performance of an RNA-seq analysis method. We develop a data-based simulation algorithm for RNA-seq data. The vector of read counts simulated for a given experimental unit has a joint distribution that closely matches the distribution of a source RNA-seq dataset provided by the user. Users control the proportion of genes simulated to be differentially expressed (DE) and can provide a vector of weights to control the distribution of effect sizes. The algorithm requires a matrix of RNA-seq read counts with large sample sizes in at least two treatment groups. Many datasets are available that fit this standard.
Simulates data sets in order to explore modeling techniques or
    better understand data generating processes. The user specifies a set of
    relationships between covariates, and generates data based on these
    specifications. The final data sets can represent data from randomized
    control trials, repeated measure (longitudinal) designs, and cluster
    randomized trials. Missingness can be generated using various
    mechanisms (MCAR, MAR, NMAR).
simSummary is a small utility package which eases the
        process of summarizing simulation results. Simulations often
        produce intermediate results - some focal statistics that need
        to be summarized over several scenarios and many replications.
        This step is in principle easy, but tedious. The package
        simSummary fills this niche by providing a generic way of
        summarizing the focal statistics of simulations. The useR must
        provide properly structured input, holding focal statistics,
        and then the summary step can be performed with one line of
        code, calling the simSummary function.
The simTool package is designed for statistical simulations that
    have two components. One component generates the data and the other one
    analyzes the data. The main aims of the simTool package are the reduction
    of the administrative source code (mainly loops and management code for the
    results) and a simple applicability of the package that allows the user to
    quickly learn how to work with the simTool package. Parallel computing is
    also supported. Finally, convenient functions are provided to summarize the
    simulation results.
This package provides simulation process of 4 selection methods in predicting potent compounds
A framework for performing simulations such as those common in
    methodological statistics papers.  The design principles of this package
    are described in greater depth in Bien, J. (2016) "The simulator: An Engine
    to Streamline Simulations," which is available at
    <http://faculty.bscb.cornell.edu/~bien/simulator.pdf>.
This package performs classical correspondence analysis (CA) and
     simultaneous analysis (SA). Simultaneous analysis is a factorial
     methodology developed for the joint treatment of a set of several
     contingency tables. In SA tables having the same rows, are concatenated 
     row-wise.
     In this version of the package a multiple option have been included for 
     the simultaneous analysis
     of tables having the same columns, concatenated column-wise.
     This way, a MSA allows to perform the analysis of an indicator matrix 
     where the rows represent individuals.
     In this package, functions for computation, summaries
     and graphical visualization in two dimensions are provided, including
     options to display partial and supplementary points.
This package provides routines to perform SIN model selection
  as described in Drton & Perlman (2004, 2008).  The selected models are
  represented in the format of the 'ggm' package, which allows in
  particular parameter estimation in the selected model.
The sinaplot is a data visualization chart suitable for plotting
    any single variable in a multiclass dataset. It is an enhanced jitter strip
    chart, where the width of the jitter is controlled by the density
    distribution of the data within each class.
Density, distribution function, quantile function, random
    generation and survival function for the Sine Inverse Weibull Distribution as
    defined by SOUZA, L. New Trigonometric Class of Probabilistic Distributions.
    219 p. Thesis (Doctorate in Biometry and Applied Statistics) - Department of
    Statistics and Information, Federal Rural University of Pernambuco, Recife,
    Pernambuco, 2015 (available at <http://www.openthesis.org/documents/New-trigonometric-classes-probabilistic-distributions-602633.html>) and BRITO, C. C. R. Method Distributions generator and
    Probability Distributions Classes. 241 p. Thesis (Doctorate in Biometry and
    Applied Statistics) - Department of Statistics and Information, Federal Rural
    University of Pernambuco, Recife, Pernambuco, 2014 (available upon request).
A platform for experimenting with spatially explicit individual-based vegetation models.
Calculating daily global solar radiation at horizontal surface using several well-known models (i.e. Angstrom-Prescott, Supit-Van Kappel, Hargreaves, Bristow and Campbell, and Mahmood-Hubbard), and model calibration based on ground-truth data, and (3) model auto-calibration. The FAO Penmann-Monteith equation to calculate evapotranspiration is also included.
This package fits single-index (quadratic) response surface models.
Supplementary item response theory models to complement existing 
    functions in R, including multidimensional compensatory and
    noncompensatory IRT models, MCMC for hierarchical IRT models and 
    testlet models, NOHARM, Rasch copula model, faceted and 
    hierarchical rater models, ordinal IRT model (ISOP), 
    DETECT statistic, local structural equation modeling (LSEM),
    mean and covariance structure modelling for multivariate normally
    distributed data.
Variable selection techniques are essential tools for model
    selection and estimation in high-dimensional statistical models. Through this
    publicly available package, we provide a unified environment to carry out
    variable selection using iterative sure independence screening (SIS) and all
    of its variants in generalized linear models and the Cox proportional hazards
    model.
Implements the SISAL algorithm by Tikka and Hollmén. It is
        a sequential backward selection algorithm which uses a linear
        model in a cross-validation setting. Starting from the full
        model, one variable at a time is removed based on the
        regression coefficients. From this set of models, a
        parsimonious (sparse) model is found by choosing the model with
        the smallest number of variables among those models where the
        validation error is smaller than a threshold. Also implements
        extensions which explore larger parts of the search space
        and/or use ridge regression instead of ordinary least squares.
SISUS for source partitioning using stable isotopes.
  SISUS reads in a specific Excel-like workbook and performs an IsoSource-type analysis by
    returning a sample of feasible solutions to p in the non-over constrained linear systems, b=Ap.
  Edit \file{sisus_*_template.xls} and input data values and run parameters.
  Run \code{\link{sisus.run}(filename)}. See output in current working directory.
Selects invalid instruments amongst a candidate of potentially bad instruments. The algorithm selects potentially invalid instruments and provides an estimate of the causal effect between exposure and outcome.
Functions for fitting and plotting SITAR (Super Imposition by
    Translation And Rotation) growth curve models. SITAR is a shape-invariant model
    with a regression B-spline mean curve and subject-specific random effects on
    both the measurement and age scales.
Provided within is a high quality and fast PPRNG that is able to be used in an 'OpenMP' parallel
    environment compiled under either C++98 or C++11. The objective of this package release is to consolidate
    the distribution of the 'sitmo' library on CRAN by enabling others to link to the 'sitmo' header file instead 
    of including a copy of 'sitmo' within their individual package. Lastly, the package contains example 
    implementations using 'sitmo' and two accompanying vignette that provide additional information.
Format a number (or a list of numbers) to a string (or a
        list of strings) with SI prefix. Use SI prefixes as constants
        like (4 * milli)^2
Sensitivity indices with dependent correlated inputs, using a
    method based on PLS regression.
Functions and utilities to perform
    Statistical Analyses in the Six Sigma way.
    Through the DMAIC cycle (Define, Measure, Analyze, Improve, Control),
    you can manage several Quality Management studies:
    Gage R&R, Capability Analysis, Control Charts, Loss Function Analysis,
    etc. Data frames used in the books "Six Sigma with R" (Springer, 2012)
    and "Quality Control with R" (Springer, 2015)
    are also included in the package.
This function develops an algorithm for presenting a Bayesian hierarchical model for estimating the sizes of local and national drug injected populations in Bangladesh. The model incorporates multiple commonly used data sources including mapping data, surveys, interventions, capture-recapture data, estimates or guesstimates from organizations, and expert opinion.
Contains functions to estimate morphometric and gonadal size at sexual maturity for organisms, usually fish and invertebrates. It includes methods for classification based on relative growth (using principal components analysis, hierarchical clustering, discriminant analysis), logistic regression (Frequentist or Bayes), parameters estimation and some basic plots.
Calculates and plots the SiZer map for scatterplot data.
        A SiZer map is a way of examining when the p-th derivative of a
        scatterplot-smoother is significantly negative, possibly zero
        or significantly positive across a range of smoothing
        bandwidths.
Provides a database-independent JDBC interface.
Collection of miscellaneous utility functions (especially intended
    for people coming from other statistical software packages like 'SPSS', and/
    or who are new to R), supporting following common tasks: 1) Reading and writing
    data between R and other statistical software packages like 'SPSS', 'SAS' or
    'Stata' and working with labelled data; this includes easy ways to get and set
    label attributes, to convert labelled vectors into factors (and vice versa),
    or to deal with multiple declared missing values etc. 2) Data transformation
    tasks like recoding, dichotomizing or grouping variables, setting and replacing
    missing values. 3) Convenient functions to perform frequently used statistical
    tests, or to calculate various commonly used statistical coefficients.
Collection of plotting and table output functions for data
    visualization. Results of various statistical analyses (that are commonly used
    in social sciences) can be visualized using this package, including simple and
    cross tabulated frequencies, histograms, box plots, (generalized) linear models,
    mixed effects models, PCA and correlation matrices, cluster analyses, scatter
    plots, Likert scales, effects plots of regression models (including interaction
    terms) and much more. This package supports labelled data.
Collection of convenient functions for common statistical computations,
             which are not directly provided by R's base or stats packages.
             This package aims at providing, first, shortcuts for statistical
             measures, which otherwise could only be calculated with additional
             effort (like standard errors or root mean squared errors). Second,
             these shortcut functions are generic (if appropriate), and can be
             applied not only to vectors, but also to other objects as well
             (e.g., the Coefficient of Variation can be computed for vectors,
             linear models, or linear mixed models; the r2()-function returns
             the r-squared value for 'lm', 'glm', 'merMod' or 'lme' objects).
             The focus of most functions lies on summary statistics or fit
             measures for regression models, including generalized linear
             models and mixed effects models. However, some of the functions
             also deal with other statistical measures, like Cronbach's Alpha,
             Cramer's V, Phi etc.
Functions for kernel-regression-based association tests including Burden test, SKAT and SKAT-O. These methods aggregate individual SNP score statistics in a SNP set and efficiently compute SNP-set level p-values.
Computes necessary information to meta analyze SKAT
        statistics in each individual cohort, and then performs the
        meta analysis.
Sparse (Multicategory) Kernel Discriminant Analysis does variable selection for nonparametric classification
A shiny interface and supporting tools to guide users in choosing
    appropriate simulations, setting parameters, calculating summary genetic
    statistics, and organizing data output, all within the R environment. In
    addition to supporting existing forward and reverse-time simulators, new
    simulators can be integrated into the environment relatively easily.
Functions for the Skellam distribution, including: density
        (pmf), cdf, quantiles and random variates.
Functions are provided for the density function,
        distribution function, quantiles and random number generation
        for the skew hyperbolic t-distribution. There are also
        functions that fit the distribution to data. There are
        functions for the mean, variance, skewness, kurtosis and mode
        of a given distribution and to calculate moments of any order
        about any centre. To assess goodness of fit, there are
        functions to generate a Q-Q plot, a P-P plot and a tail plot.
Density, distribution function, quantile function and
        random generation for the skewed t distribution of Fernandez
        and Steel.
A generalization of the statistic used in Friedman's ANOVA method and in Durbin's rank test. This nonparametric statistical test is useful for the data obtained from block designs with missing observations occurring randomly. A resulting p-value is based on the chi-squared distribution and Monte Carlo method.
Algorithms to compute spherical k-means partitions.
  Features several methods, including a genetic and a fixed-point
  algorithm and an interface to the CLUTO vcluster program.
Provides an alternative to manually process hemispherical pictures. The algorithm processes each picture one by one to determine the proportion of sky pixel. The algorithm uses the Ridler and Calvard method (Ridler and Calvard 1978).
Provides directly interpretable estimated coefficients
    for four models in connection with the two-group straight line
    ANCOVA problem: (A) the full model, which requires the fitting of
    two intercepts and two slopes; (B) a reduced model, which requires
    the fitting of a single intercept and single slope; (C) a reduced
    model, which requires the fitting of two separate intercepts and a
    single, common slope; and (D) a reduced model, which requires the
    fitting of a single, common intercept and two separate slopes.  The
    summary function provides tests of fit for the (null) hypotheses of:
    (1) equivalent data sets, (2) equivalent slopes, and
    (3) equivalent intercepts.
'Slack' <http://slack.com/> provides a service for teams to
    collaborate by sharing messages, images, links, files and more. Functions are provided
    that make it possible to interact with the 'Slack' platform 'API'. When
    you need to share information or data from R, rather than resort to copy/
    paste in e-mails or other services like 'Skype' <http://www.skype.com/>, you
    can use this package to send well-formatted output from multiple R objects and
    expressions to all teammates at the same time with little effort. You can also
    send images from the current graphics device, R objects, and upload files.
Data structures and algorithms for sparse arrays and matrices,
  based on index arrays and simple triplet representations, respectively.
Estimates the slope and level change present in data after
        removing phase A trend. Represents graphically the original and
        the detrended data.
The skew logistic distribution is a quantile-defined generalisation
 of the logistic distribution (van Staden and King 2015).  Provides random 
 numbers, quantiles, probabilities, densities and density quantiles for the distribution.
 It provides Quantile-Quantile plots and method of L-Moments estimation 
 (including asymptotic standard errors) for the distribution.
Calculates maximum likelihood estimate, exact and asymptotic confidence intervals, and exact and asymptotic goodness of fit p-values for infectious units per million (IUPM) from serial limiting dilution assays. This package uses the likelihood equation, exact PGOF, and exact confidence intervals described in Meyers et al. (1994) <http://jcm.asm.org/content/32/3/732.full.pdf>.
Compute Time series Resistant Smooth 4253H, twice smoothing method.
Data sets from Ramsey, F.L. and Schafer, D.W. (2002), "The
    Statistical Sleuth: A Course in Methods of Data Analysis (2nd
    ed)", Duxbury.
Data sets from Ramsey, F.L. and Schafer, D.W. (2013), "The
    Statistical Sleuth: A Course in Methods of Data Analysis (3rd
    ed)", Cengage Learning.
Set of tools to find coherent patterns in microarray data
    using a Bayesian sparse latent factor model (Duarte and Mayrink 2015 -
    http://link.springer.com/chapter/10.1007%2F978-3-319-12454-4_15).
    Considerable effort has been put into making slfm fast and memory efficient,
    turning it an interesting alternative to simpler methods in terms
    of execution time. It implements versions of the SLFM using both type
    of mixtures: using a degenerate distribution or a very concentrated
    normal distribution for the spike part of the mixture. It also implements
    additional functions to help pre-process the data and fit the model
    for a large number of arrays.
Generate the optimal Latin Hypercube Designs (LHDs) for computer experiments with quantitative factors and the optimal Sliced Latin Hypercube Designs (SLHDs) for computer experiments with both quantitative and qualitative factors. Details of the algorithm can be found in Ba, S., Brenneman, W. A. and Myers, W. R. (2015), "Optimal Sliced Latin Hypercube Designs," Technometrics. Important function in this package is "maximinSLHD".
Efficient procedures for Sorted L1 Penalized Estimation (SLOPE).
    The sorted L1 norm is useful for statistical estimation and testing,
    particularly for variable selection in the linear model.
Interface for creation of 'slp' class smoother objects for 
             use in Generalized Additive Models (as implemented by packages 
             'gam' and 'mgcv').
This is software linked to the book
  'Applied Smoothing Techniques for Data Analysis -
  The Kernel Approach with S-Plus Illustrations' Oxford University Press.
Implementation of the Stochastic Multi-Criteria Acceptability Analysis (SMAA) family of Multiple Criteria Decision Analysis (MCDA) methods.
This package provides a solution path for L1-penalized angle-based classification. Three loss functions are implemented in smac, including the deviance loss in logistic regression, the exponential loss in boosting, and the proximal support vector machine loss.
Provides the following approaches for multidimensional scaling (MDS) based on stress minimization by means of majorization (smacof): basic MDS on symmetric dissimilarity matrices,
    MDS with external constraints on the configuration, individual difference scaling (idioscal, indscal, and friends), MDS with spherical restrictions, and unfolding. The MDS type can be ratio, interval, ordinal, and monotone splines. 
    Various tools and extensions like jackknife MDS, permutation tests, gravity models, inverse MDS, unidimensional scaling, classical scaling, and Procrustes.
Various statistical methods for analyzing case-control point data.
    The methods available closely follow those in chapter 6 of Applied Spatial
    Statistics for Public Health Data by Waller and Gotway (2004).
Inference techniques for Fay Herriot Model.
Animal movement models including moving-resting process
 with embedded Brownian motion, Brownian motion with measurement error.
The package "smart" provides a general framework for
        analyzing (including estimation, feature selection and
        prediction) and visualize big data. It integrates several
        novel, efficient and robust data analysis tools, including
        Transelliptical Component Analysis (TCA), Transelliptical
        Correlation Estimation (TCE) and Group Nearest Shrunken
        Centroids (gnsc). We target on high dimensional data
        analysis(usually d >> n), and exploit computationally
        efficiently approaches. Results are organized to be visualized
        properly for users.
Tools for accessing and processing datasets prepared by the Foundation SmarterPoland.pl. Among all: access to API of Google Maps, Central Statistical Office of Poland, MojePanstwo, Eurostat, WHO and other sources.
Addressing the problem of outlier detection from the viewpoint of statistical learning theory. This method is proposed by Yamanishi, K., Takeuchi, J., Williams, G. et al. (2004) <DOI:10.1023/B:DAMI.0000023676.72185.7c>. It learns the probabilistic model (using a finite mixture model) through an on-line unsupervised process. After each datum is input, a score will be given with a high one indicating a high possibility of being a statistical outlier.
This package provides methods of fitting bivariate lines in allometry using the major axis (MA) or standardised major axis (SMA), and for making inferences about such lines. The available methods of inference include confidence intervals and one-sample tests for slope and elevation, testing for a common slope or elevation amongst several allometric lines, constructing a confidence interval for a common slope or elevation, and testing for no shift along a common axis, amongst several samples.
The main purpose of the package is to categorize a numeric variable into bins
    mapped to a binary target variable for its ulterior usage in scoring modeling. 
    This functionality reduces dramatically the time consuming process of finding the optimal 
    cut points for a given numeric variable, quickly calculates the Information Value, either for
    one variable at the time or all at once in one line of code; and also outputs 'SQL' codes, 
    tables, and plots used throughout the development stage.
    The package also allows the user to understand the data via exploratory data analysis 
    in one step, establish customized cut points for numeric characteristics, and run the analysis 
    for categorical variables.
particle filtering, auxiliary particle filtering and sequential Monte Carlo algorithms
Implements multiple imputation of missing covariates by
    Substantive Model Compatible Fully Conditional Specification.
    This is a modification of the popular FCS/chained equations
    multiple imputation approach, and allows imputation of missing
    covariate values from models which are compatible with the user
    specified substantive model.
This package is for optimizing non-linear complex
        functions based on Monte Carlo random sampling.
A package containing functions for genome-wide association
        studies.
Data Sets for Kumar and Petersen (2012).
    Statistical Methods in Customer Relationship Management,
    Wiley: New York.
An R-package for Estimating Semiparametric PH and AFT
        Mixture Cure Models
Provides resampling functions (stratified, residual,
        multinomial, systematic, and branching), measures of weight
        uniformity (coefficient of variation, effective sample size,
        and entropy), and a weight renormalizing function.
Contains data files to accompany Smithson & Merkle (2013), Generalized Linear Models for Categorical and Continuous Limited Dependent Variables.
This package provides similarity among documents.
Symbolic multidimensional scaling for interval-valued dissimilarities. The hypersphere model and the hyperbox model are available.
A package for fitting smoothing-splines mixed-effects models to replicated functional
  data sets.
Provides statistical methods for the analysis of data areal data, with a focus on cluster detection.
R functions and data from Chapter 5 of 'Statistical
        Methods for Financial Engineering', by Bruno Remillard, CRC
        Press, (2013).
This package contains code and data for modelling and simulation of stochastic kinetic biochemical network models. It contains the code and data associated with the second edition of the book Stochastic Modelling for Systems Biology, published by Chapman & Hall/CRC Press, November 2011.
Smooth multivariate interpolation for gridded and scattered data
    with possibly large datasets. The code for scattered data is taken from the
    'SHEPPACK' Fortran package.
This package accompanies Aitkin et al, Statistical
        Modelling in R, OUP, 2009. The package contains some functions
        and datasets used in the text.
This tiny package contains one function smirnov() which
        calculates two scaled taxonomic coefficients, Txy (coefficient
        of similarity) and Txx (coefficient of originality). These two
        characteristics may be used for the analysis of similarities
        between any number of taxonomic groups, and also for assessing
        uniqueness of giving taxon. It is possible to use smirnov()
        output as a distance measure: convert it to distance by
        "as.dist(1 - smirnov(x))".
A collection of functions for statistical computing and data manipulation in R. 
    Includes routines for data ingestion, operating on dataframes and matrices, conversion to and 
    from lists, converting factors, filename manipulation, programming utilities, parallelization, plotting, 
    statistical and mathematical operations, and time series.
Constructs a yield curve by the Smith-Wilson method from a
        table of LIBOR and SWAP rates
The SML package is a collection of statistical machine learning methods.
Fit univariate right, left or interval censored regression model under the scale mixture of normal distributions
Fits flexible additive models to data on stream networks, taking account of flow-connectivity of the network.  Models are fitted using penalised least squares.
Provides generators for a high number of both single- and multi-
    objective test functions which are frequently used for the benchmarking of
    (numerical) optimization algorithms. Moreover, it offers a set of convenient
    functions to generate, plot and work with objective functions.
The set of smoothing functions used for time series analysis and in forecasting.
             Currently the package includes exponential smoothing models and SARIMA in state-space
             form + several simulation functions.
A collection of methods for smoothing numerical data, commencing with a port of the Matlab gaussian window smoothing function. In addition, several functions typically used in smoothing of financial data are included.
SmoothHazard is a package for fitting illness-death (and survival)
    model with possibly interval-censored data for transition toward transient
    state. Left-truncated and right-censored data are also allowed.
    Proportional intensities regression models allow for covariates on each of
    the 3 transitions. We use either a parametric approach with Weibull
    baseline intensities or a semi-parametric approach with M-splines
    approximation of baseline intensities in order to obtain smooth estimates.
    Estimation of parameters is conducted by likelihood maximization in the
    parametric approach or penalized likelihood maximization in the
    semi-parametric approach.
Provides flexible hazard ratio curves allowing non-linear
        relationships between continuous predictors and survival. To
        better understand the effects that each continuous covariate
        has on the outcome, results are ex pressed in terms of hazard
        ratio curves, taking a specific covariate value as reference.
        Confidence bands for these curves are also derived.
Functions to smooth two-dimensional fields using FFT and the convolution theorem
Some M-estimators for 1-dimensional location (Bisquare, ML
        for the Cauchy distribution, and the estimators from
        application of the smoothing principle introduced in Hampel,
        Hennig and Ronchetti (2011) to the above, the Huber
        M-estimator, and the median, main function is smoothm), and
        Pitman estimator.
Contains, as a main contribution, a function to fit
             a regression model with possibly right, left or interval
             censored observations and with the error distribution
             expressed as a mixture of G-splines. Core part
             of the computation is done in compiled C++ written
             using the Scythe Statistical Library Version 0.3.
Given independent and identically distributed observations X(1), ..., X(n) from a Generalized Pareto distribution with shape parameter gamma in [-1,0], offers several estimates to compute estimates of gamma. The estimates are based on the principle of replacing the order statistics by quantiles of a distribution function based on a log--concave density function. This procedure is justified by the fact that the GPD density is log--concave for gamma in [-1,0].
A collection of various oversampling techniques developed from SMOTE is provided. SMOTE is a oversampling technique which synthesizes a new minority instance between a pair of one minority instance and one of its K nearest neighbor. (see <https://www.jair.org/media/953/live-953-2037-jair.pdf> for more information) Other techniques adopt this concept with other criteria in order to generate balanced dataset for class imbalance problem.
This package contains the datasets and a few functions for
        use with the practicals outlined in Appendix A of the book
        Statistical Models (Davison, 2003, Cambridge University Press).
        The practicals themselves can be found at
        http://statwww.epfl.ch/davison/SM/
Computes the studentized midrange distribution (pdf, cdf and quantile) and generates random numbers
Produce small area population estimates by fitting census data to
    survey data.
Datasets used in "Statistical Methods for the Social Sciences"
    (SMSS) by Alan Agresti and Barbara Finlay.
Implements the structural model for variances in order to
        detect differentially expressed genes from gene expression data
Build and manipulate probability distributions of the skew-normal 
  family and some related ones, notably the skew-t family, and provide related
  statistical methods for data fitting and diagnostics, in the univariate and 
  the multivariate case.
A range of tools for social network analysis, including node and graph-level indices, structural distance and covariance methods, structural equivalence detection, network regression, random graph generation, and 2D/3D network visualization.
Run 'Python' code, make function calls, assign and retrieve variables, etc. from R.
    A fork from 'rPython' which uses 'jsonlite', 'Rcpp' and has several fixes and improvements.
Functions for reading and writing Gadget N-body snapshots. The Gadget code is popular in astronomy for running N-body / hydrodynamical cosmological and merger simulations. To find out more about Gadget see the main distribution page at www.mpa-garching.mpg.de/gadget/
Similarity Network Fusion takes multiple views of a network and fuses them together to construct an overall status matrix. The input to our algorithm can be feature vectors, pairwise distances, or pairwise similarities. The learned status matrix can then be used for retrieval, clustering, and classification.
Implementation of robust and non-robust Standard Normal Homogeneity
    Test (SNHT) for changepoint detection. This test statistic is equal sided,
    as proposed in "Homogenization of Radiosonde Temperature Time Series Using
    Innovation Statistics" by Haimberger, L., 2007. However, the statistic contains
    an estimate of sigma^2 in the denominator instead of sigma, which seems to
    be a more appropriate value (based on the paper "Homogenization of Swedish
    temperature data. Part I: Homogeneity test for linear trends." by Alexandersson,
    H., and A. Moberg, 1997).
Implement K-nearest neighbor classifier, weighted nearest neighbor classifier, bagged nearest neighbor classifier, optimal weighted nearest neighbor classifier and stabilized nearest neighbor classifier, and perform model selection via 5 fold cross-validation for them. This package also provides functions for computing the classification error and classification instability of a classification procedure.
Support for simple parallel computing in R.
An R interface to the C libstemmer library that implements
  Porter's word stemming algorithm for collapsing words to a common
  root to aid comparison of vocabulary. Currently supported languages are
  Danish, Dutch, English, Finnish, French, German, Hungarian, Italian,
  Norwegian, Portuguese, Romanian, Russian, Spanish, Swedish
  and Turkish.
Functions for analysis of network objects, which are imported or
    simulated by the package. The non-parametric methods of analysis center
    around snowball and bootstrap sampling.
Usability wrapper around snow for easier development of
        parallel R programs. This package offers e.g. extended error
        checks, and additional functions. All functions work in
        sequential mode, too, if no cluster is present or wished.
        Package is also designed as connector to the cluster management
        tool sfCluster, but can also used without it.
Extension of the snow package supporting fault tolerant and reproducible applications, as well as supporting easy-to-use parallel programming - only one function is needed.
contains several supplementary non-parametric statistics methods including quantile test, Cox-Stuart trend test, runs test, normal score test, kernel PDF and CDF estimation, kernel regression estimation and kernel Kolmogorov-Smirnov test.
This package carries out most common analysis when performing whole genome association studies. These analyses include descriptive statistics and exploratory analysis of missing values, calculation of Hardy-Weinberg equilibrium, analysis of association based on generalized linear models (either for quantitative or binary traits), and analysis of multiple SNPs (haplotype and epistasis analysis). Permutation test and related tests (sum statistic and truncated product) are also implemented. Max-statistic and genetic risk-allele score exact distributions are also possible to be estimated.
Implements classes and methods for large scale SNP enrichment analysis (e.g. SNPs associated with genes expression in a GWAS signal).
A set of functions to create SQL tables of gene and SNP information and compose them into a SNP Set, for example for use with the RSNPset package, or to export to a PLINK set.
This package implements asymptotic methods related to
        maximally selected statistics, with applications to SNP data.
Bivariate Gaussian genotype clustering and calling for Illumina
    microarrays, building on the package 'mclust'. Pronounced snip-em-clust.
Creates plots of p-values using single SNP and/or haplotype data.
    Main features of the package include options to display a linkage
    disequilibrium (LD) plot and the ability to plot multiple datasets
    simultaneously. Plots can be created using global and/or individual
    haplotype p-values along with single SNP p-values. Images are created as
    either PDF/EPS files.
A modification of Breiman and Cutler's classification random forests modified for SNP (Single Nucleotide Polymorphism) data (based on randomForest v4.6-7) to prevent X-chromosome SNP variable importance bias compared to autosomal SNPs by simulating the process of X chromosome inactivation. Classification is based on a forest of trees using random subsets of SNPs and other variables as inputs.
Write snpStats objects to disk in formats suitable for reading by
    snphap, phase, mach, IMPUTE, beagle, and (almost) anything else that
    expects a rectangular format.
This package queries the SNPs data sets and makes plots of
        genes and SNPs.  This package allows users to access these data
        sets once they have been compiled into one file and indexed
        with Tabix (http://samtools.sourceforge.net/tabix.shtml). There
        are functions to retrieve SNPs, subset them for specific
        strains, obtain only SNPs that are polymorphic for a subset of
        strains, plot SNPs and look for certain allele patterns in
        SNPs. We have also added functions to access indels and
        structural variants.
Stochastic Newton Sampler (SNS) is a Metropolis-Hastings-based, Markov Chain Monte Carlo sampler for twice differentiable, log-concave probability density functions (PDFs) where the proposal density function is a multivariate Gaussian resulting from a second-order Taylor-series expansion of log-density around the current point. The mean of the Gaussian proposal is the full Newton-Raphson step from the current point. A Boolean flag allows for switching from SNS to Newton-Raphson optimization (by choosing the mean of proposal function as next point). This can be used during burn-in to get close to the mode of the PDF (which is unique due to concavity). For high-dimensional densities, mixing can be improved via 'state space partitioning' strategy, in which SNS is applied to disjoint subsets of state space, wrapped in a Gibbs cycle. Numerical differentiation is available when analytical expressions for gradient and Hessian are not available. Facilities for validation and numerical differentiation of log-density are provided.
Scan statistics applied in social network data can be used to test the cluster characteristics among a social network.
Contains functions to perform various models and 
	methods for test equating. It currently implements the traditional 
	mean, linear and equipercentile equating methods, as well as the 
	mean-mean, mean-sigma, Haebara and Stocking-Lord IRT linking methods. 
	It also supports newest methods such that local equating, kernel 
	equating (using Gaussian, logistic and uniform kernels) with presmoothing, 
	and IRT parameter linking methods based on asymmetric item characteristic 
	functions. Functions to obtain both standard error of equating (SEE) 
	and standard error of equating difference between two equating 
	functions (SEED) are also implemented for the kernel method of 
	equating.
Allows objects to be stored on disc and automatically
       		recalled into memory, as required, by delayed assignment.
Specific and class specific multiple correspondence analysis on
    survey-like data. Soc.ca is optimized to the needs of the social scientist and
    presents easily interpretable results in near publication ready quality.
A suite of tools for collecting and constructing networks from
    social media data. Provides easy-to-use functions for collecting data across
    popular platforms (Instagram, Facebook, Twitter, and YouTube) and generating
    different types of networks for analysis.
Social media search and analytic tool that takes one or multiple URL(s) and returns the information about the popularity and reach of the URL(s) on social media. The function get_socialmedia() retrieves the number of shares, likes, pins, and hits on Facebook (www.facebook.com), Pinterest (www.pinterest.com), StumbleUpon (www.stumbleupon.com), LinkedIn (www.linkedin.com), and Reddit (www.reddit.com). The package also includes dedicated functions for each social network platform and a function to resolve shortened URLs.
Generates social networks using either of two
    approaches: using either pairwise distances or territorial area intersections.
Provides to sociologists (and related scientists) a toolbox to facilitate the construction of social position indicators from survey data. Social position indicators refer to what is commonly known as social class and social status. There exists in the sociological literature many theoretical conceptualisation and empirical operationalization of social class and social status. This first version of the package offers tools to construct the International Socio-Economic Index of Occupational Status (ISEI) and the Oesch social class schema. It also provides tools to convert several occupational classifications (PCS82, PCS03, and ISCO08) into a common one (ISCO88) to facilitate data harmonisation work, and tools to collapse (i.e. group) modalities of social position indicators.
SOD (Self-Organising-Deltoids) provides multidimensional scaling by gradually reducing the dimensionality of an initial space and using the resulting stress in the configuration to re-arrange nodes. Stress is calculated from the errors in the inter-node distances, and the sum of the stresses at each node is combined to create N-dimensional force vectors that direct the movement of nodes as the dimensionality is iteratively reduced.
Functions, examples and other software related to the book
        "Software for Data Analysis: Programming with R". See
        package?SoDA for an overview.
Variable and interaction selection are essential to classification in high-dimensional setting. In this package, we provide the implementation of SODA procedure, which is a forward-backward algorithm that selects both main and interaction effects under quadratic discriminant analysis and logistic regression model.
To implement two clustering methods, ODC and SODC, for
        clustering datasets using optimal scoring, can also be used as
        an dimension reduction tool.
Bindings to libsodium: a modern, easy-to-use software library for
    encryption, decryption, signatures, password hashing and more. Sodium uses
    curve25519, a state-of-the-art Diffie-Hellman function by Daniel Bernstein,
    which has become very popular after it was discovered that the NSA had
    backdoored Dual EC DRBG.
Este paquete tiene la finalidad de ayudar a aprender de una forma interactiva, teniendo ejemplos y la posibilidad de resolver nuevos al mismo tiempo. Apuntes de clase interactivos.
An extension of sensitivity, specificity, positive and negative
    predictive value to continuous predicted and reference memberships in
    [0, 1].
It contains soft clustering algorithms, in particular approaches derived from rough set theory: Lingras & West original rough k-means, Peters' refined rough k-means, and PI rough k-means. It also contains classic k-means and a corresponding illustrative demo.
Iterative methods for matrix completion that use nuclear-norm regularization. There are two main approaches.The one approach uses iterative soft-thresholded svds to impute the missing values. The second approach uses alternating least squares. Both have an "EM" flavor, in that at each iteration the matrix is completed with the current estimate. For large matrices there is a special sparse-matrix class named "Incomplete" that efficiently handles all computations. The package includes procedures for centering and scaling rows, columns or both, and for computing low-rank SVDs on large sparse centered matrices (i.e. principal components)
Implementation of 'softmax' regression and classification models with multiple layer neural network. It can be used for many tasks like word embedding based document classification, 'MNIST' dataset handwritten digit recognition and so on. Multiple optimization algorithm including 'SGD', 'Adagrad', 'RMSprop', 'Moment', 'NAG', etc are also provided.
Sparsity Oriented Importance Learning (SOIL) provides an objective and informative profile of variable importances for high dimensional regression and classification models.
A collection of functions for reading data from USDA-NCSS soil databases.
Basic and model-based soil physical analyses.
This package provides functions to graphically represent 
	     soil properties. Morphological data gathered in the field 
	     such as horizon boundaries, root abundance and dimensions,
	     skeletal shape, abundance and dimension as well as	
	     meaningful soil color may be represented via the plot
	     function. A lattice-based plot.element function has been 
	     designed to represent depth function of a given variable.
This package contains functions for modeling Soil Organic
        Matter decomposition in terrestrial ecosystems.
Methods and classes for processing and analyzing soil and plant infrared (MIR, alpha-MIR and VISNIR) spectroscopy readings based on the Africa Soil Information Services (AfSIS) project data.
"The Soil Texture Wizard" is a set of R functions designed to produce texture triangles (also called texture plots, texture diagrams, texture ternary plots), classify and transform soil textures data. These functions virtually allows to plot any soil texture triangle (classification) into any triangle geometry (isosceles, right-angled triangles, etc.). This set of function is expected to be useful to people using soil textures data from different soil texture classification or different particle size systems. Many (> 15) texture triangles from all around the world are predefined in the package. A simple text based graphical user interface is provided: soiltexture_gui().
It is a set of R implementations of parametric formulas of soil water
    retention or conductivity curve. At the moment, only Van Genuchten (for
    soil water retention curve) and Mualem (for hydraulic conductivity) were
    implemented. See reference
    \url{http://en.wikipedia.org/wiki/Water_retention_curve}.
Calculation methods of solar radiation and performance of photovoltaic systems from daily and intradaily irradiation data sources.
SOLAR is the standard software program to perform linkage and
    association mappings of the quantitative trait loci (QTLs) in pedigrees of
    arbitrary size and complexity. This package allows the user to exploit the
    variance component methods implemented in SOLAR. It automates such routine
    operations as formatting pedigree and phenotype data. It also parses the model
    output and contains summary and plotting functions for exploration of the
    results. In addition, solarius enables parallel computing of the linkage and
    association analyses, that makes the calculation of genome-wide scans more
    efficient. See <http://solar.txbiomedgenetics.org/> for more information about
    SOLAR.
Calculation of solar zenith and azimuth angles.
Predicted solid earth tide displacements in the meridional,
        zonal and vertical directions. Based on "Solid" from Dennis Milbert, 
        modified from "dehanttideinelMJD" by V. Dehant, S. Mathews, J. Gipson, 
        and C. Bruyninx.
Provides a set of functions for querying and parsing
    data from Solr endpoints (local and remote), including search, faceting,
    highlighting, stats, and 'more like this'.
Provides a set of functions for querying and parsing data
    from 'Solr' (<http://lucene.apache.org/solr>) 'endpoints' (local and 
    remote), including search, 'faceting', 'highlighting', 'stats', and 
    'more like this'. In addition, some functionality is included for 
    creating, deleting, and updating documents in a 'Solr' 'database'.
Self-Organizing Map (with application in gene clustering).
This package provides an R implementation of the Self-Organising Migrating Algorithm, a general-purpose, stochastic optimisation algorithm. The approach is similar to that of genetic algorithms, although it is based on the idea of a series of ``migrations'' by a fixed set of individuals, rather than the development of successive generations. It can be applied to any cost-minimisation problem with a bounded parameter space, and is robust to local minima.
The stochastic (also called on-line) version of the Self-Organising
             Map (SOM) algorithm is provided. Different versions of the 
             algorithm are implemented, for numeric and relational data and for
             contingency tables. The package also contains many plotting 
             features (to help the user interpret the results) and a graphical
             user interface based on shiny.
some Brownian motions simulation functions
This package collects some procedures controlling the
        Generalized Familywise Error Rate.
It's a collection of functions for Multiplicity Correction and Multiple Testing.
Multivariate mixed model solver for multiple random effects allowing the specification of variance covariance structures. ML/REML estimates are obtained using the Average Information (AI), Expectation-Maximization (EM), Newton-Raphson (NR), or Efficient Mixed Model Association (EMMA) algorithms. Designed for genomic prediction and genome wide association studies (GWAS) to include additive, dominance and epistatic relationship structures or other covariance structures in R, but also functional as a regular mixed model program. Multivariate models (multiple responses) can be fitted currently with NR, AI and EMMA algorithms allowing multiple random effects as well. Covariance structures for the residual component is currently supported only for balanced univariate Newton Raphson.
The package provides the plot function som.plot() to
        create high quality visualisations of hexagonal Kohonen maps
        (self-organising maps).
Formulas for calculating sound velocity, water pressure, depth, 
    density, absorption and sonar equations.
Estimate the abundance of cell clones from the
	     distribution of lengths of DNA fragments (as created by
	     sonication, whence `sonicLength').  The algorithm in
	     "Estimating abundances of retroviral insertion sites from
	     DNA fragment length data" by Berry CC, Gillet NA, Melamed
	     A, Gormley N, Bangham CR, Bushman FD. Bioinformatics;
	     2012 Mar 15;28(6):755-62 is implemented.  The
	     experimental setting and estimation details are described
	     in detail there. Briefly, integration of new DNA in a
	     host genome (due to retroviral infection or gene therapy)
	     can be tracked using DNA sequencing, potentially allowing
	     characterization of the abundance of individual cell
	     clones bearing distinct integration sites. The locations
	     of integration sites can be determined by fragmenting the
	     host DNA (via sonication or fragmentase), breaking the
	     newly integrated DNA at a known sequence, amplifying the
	     fragments containing both host and integrated DNA,
	     sequencing those amplicons, then mapping the host
	     sequences to positions on the reference genome. The
	     relative number of fragments containing a given position
	     in the host genome estimates the relative abundance of
	     cells hosting the corresponding integration site, but
	     that number is not available and the count of amplicons
	     per fragment varies widely.  However, the expected number
	     of distinct fragment lengths is a function of the
	     abundance of cells hosting an integration site at a given
	     position and a certain nuisance parameter. The algorithm
	     implicitly estimates that function to estimate the
	     relative abundance.
Collection of different single objective test functions
        useful for benchmarks and algorithm development.
Load Russian economic indicators from the Archive of Economic and Social Data <http://sophist.hse.ru/>.
Provides functions to non-parametrically estimate the off-pulse interval of a source
    function originating from a pulsar. The technique is based on a sequential application of P-values
    obtained from goodness-of-fit tests for the uniform distribution, such as the Kolmogorov-Smirnov,
    Cramer-von Mises, Anderson-Darling and Rayleigh goodness-of-fit tests.
Used to construct the URLs and parameters of 'Socrata Open Data API' <https://dev.socrata.com> calls, using the API's 'SoQL' parameter format. Has method-chained and sensical syntax. Plays well with pipes.
Estimation for longitudinal data following outcome dependent sampling using the sequential offsetted regression technique.  Includes support for binary, count, and continuous data.
SortableHTMLTables writes a data frame to an HTML file
        that contains a sortable table. The sorting is done using the
        jQuery plugin Tablesorter. The appearance is controlled through
        a CSS file and several GIF's.
sortinghat is a classification framework to streamline the
    evaluation of classifiers (classification models and algorithms) and seeks
    to determine the best classifiers on a variety of simulated and benchmark
    data sets. Several error-rate estimators are included to evaluate the
    performance of a classifier. This package is intended to complement the
    well-known 'caret' package.
Algorithms for Finnish open government data.
Search contributed R packages, sort by package.
sos4R is a client for Sensor Observation Services (SOS) as
        specified by the Open Geospatial Consortium (OGC).  It allows
        users to retrieve metadata from SOS web services and to
        interactively create requests for near real-time observation
        data based on the available sensors, phenomena, observations et
        cetera using thematic, temporal and spatial filtering.
Tools to download data from the Sotkanet open data portal.
Basic functions for dealing with wav files and sound samples.
Functions to calculate indices for soundscape ecology and other ecology research that uses audio recordings.
The SoundexBR package provides an algorithm for decoding names
    into phonetic codes, as pronounced in Portuguese. The goal is for
    homophones to be encoded to the same representation so that they can be
    matched despite minor differences in spelling. The algorithm mainly encodes
    consonants; a vowel will not be encoded unless it is the first letter. The
    soundex code resultant consists of a four digits long string composed by
    one letter followed by three numerical digits: the letter is the first
    letter of the name, and the digits encode the remaining consonants.
Construct a ranking of a set of treatments/groups by
    gathering together information coming from several response variables.
    It can be used with both balanced and unbalanced experiments
    (with almost all test statistics) as well as in presence of either
    continuous covariates or a stratifying (categorical) variable.
Analogous to source(), but works when given a Gist URL or
        ID.
Implements a non-parametric source attribution model
    to attribute cases of disease to sources in Bayesian framework with source and
    type effects. Type effects are clustered using a Dirichlet Process. Multiple
    times and locations are supported.
Tools for the reading and tokenization of R code. The
    'sourcetools' package provides both an R and C++ interface for the tokenization
    of R code, and helpers for interacting with the tokenized representation of R
    code.
Genomic and multi-environmental soybean data. Soybean Nested Association Mapping (SoyNAM) project dataset funded by the United Soybean Board (USB), pre-formatted for general analysis and genome-wide association analysis using the NAM package.
Classes and methods for spatial
  data; the classes document where the spatial location information
  resides, for 2D or 3D data. Utility functions are provided, e.g. for
  plotting data as maps, spatial selection, as well as methods for
  retrieving coordinates, for subsetting, print, summary, etc.
Provides methods for generating, exploring and executing seamless Phase II-III designs of Lai, Lavori and Shih using generalized likelihood ratio statistics. Includes pdf and source files that describe the entire R implementation with the relevant mathematical details.
Test the results of any given investment/expense combinations for a series of sliding-window periods of the S&P500 from 1950 to the present.
Implements the Sequential Predictions Algorithm
The package implements the model-based kernel machine
        method for detecting gene-centric gene-gene interactions of Li
        and Cui (2012).
Miscellaneous functions for analysing species association
        and niche overlap.
Partial correlation estimation with joint sparse
        regression model
SPACECAP is a user-friendly software package for
        estimating animal densities using closed model
        capture-recapture sampling based on photographic captures using
        Bayesian spatially-explicit capture-recapture models. This
        approach offers advantage such as: substantially dealing with
        problems posed by individual heterogeneity in capture
        probabilities in conventional capture-recapture analyses. It
        also offers non-asymptotic inferences which are more
        appropriate for small samples of capture data typical of
        photo-capture studies.
undirected graph inference with missing data
This package provides an extension of conditional
        independence (CIG) and directed acyclic graph (DAG) estimation
        to the case where conditional relationships are (non-linear)
        additive models.
Classes and methods for spatio-temporal data, including space-time regular lattices, sparse lattices, irregular data, and trajectories; utility functions for plotting data as map sequences (lattice or animation) or multiple time series; methods for spatial and temporal selection and subsetting, as well as for spatial/temporal/spatio-temporal matching or aggregation, retrieving coordinates, print, summary, etc.
SPACoDi is primarily designed to characterise the
        structure and phylogenetic diversity of communities using
        abundance or presence-absence data of species among community
        plots.
Provides tools to construct and exploit spatially weighted context data. Spatial weights are derived by a Kernel function from a user-defined matrix of distances between contextual units. Spatial weights can then be applied either to precise contextual measures or to aggregate estimates based on micro-level survey data, to compute spatially weighted context data. Available aggregation functions include indicators of central tendency, dispersion, or inter-group variability, and take into account survey design weights. The package further allows combining the resulting spatially weighted context data with individual-level predictor and outcome variables, for the purposes of multilevel modelling. An ad hoc stratified bootstrap resampling procedure generates robust point estimates for multilevel regression coefficients and model fit indicators, and computes confidence intervals adjusted for measurement dependency and measurement error of aggregate estimates. As an additional feature, residual and explained spatial dependency can be estimated for the tested models.
A set of RStudio addins that are designed to be used in
             combination with user-defined RStudio keyboard shortcuts. These
             addins either:
             1) insert text at a cursor position (e.g. insert
             operators %>%, <<-, %$%, etc.),
             2) replace symbols in selected pieces of text (e.g., convert
             backslashes to forward slashes which results in stings like
             "c:\data\" converted into "c:/data/") or
             3) enclose text with special symbols (e.g., converts "bold" into
             "**bold**") which is convenient for editing R Markdown files.
Estimation of various biodiversity indices and related (dis)similarity measures based on individual-based (abundance) data or sampling-unit-based (incidence) data taken from one or multiple communities/assemblages.
Easily implement a variety of simulation models, with a focus on
    spatially explicit agent based models. These include raster-based,
    event-based, and agent-based models. The core simulation components are
    built upon a discrete event simulation framework that facilitates
    modularity, and easily enables the user to include additional functionality
    by running user-built simulation modules. Included are numerous tools to
    visualize raster and other maps. The suggested package `fastshp` can be
    installed with `install.packages("fastshp", repos="http://rforge.net",
    type="source")`.
Implements methods for anticipating the emergence and eradication
    of infectious diseases from surveillance time series. Also provides support
    for computational experiments testing the performance of such methods.
Set of functions for sparse matrix algebra.
  Differences with SparseM/Matrix are: 
  (1) we only support (essentially) one sparse matrix format, 
  (2) based on transparent and simple structure(s), 
  (3) tailored for MCMC calculations within GMRF. 
  (4) S3 and S4 like-"compatible"  ...     and it is fast.
Inference in mixed models, including GLMMs with spatial correlations and models with non-Gaussian random effects (e.g., Beta Binomial, or negative-binomial mixed models). Heteroscedasticity can further be fitted by a linear model. The algorithms are currently various Laplace approximations methods for ML or REML, in particular h-likelihood and penalized-likelihood methods.
Fit the spatial panel data models: the fixed effects, random
    effects and between models.
Carries out a search for an optimal partition in terms of a
    regular Boolean expression
This package performs robust nonparametric tests for rare variants
          association analysis using summation of partition approaches that incorporate
          gene-gene and gene-environmental interactions
We provide an efficient solver for estimating semiparametric generalized linear models.
Implements the sparse clustering methods of Witten and
        Tibshirani (2010): "A framework for feature selection in
        clustering"; published in Journal of the American Statistical
        Association 105(490): 713-726.
Decide which server to connect to,
    based on previous response times, and configuration.
A sparkline is a line chart, without axes and labels.
    Its goal is to show the general shape of changes over time, or
    another quantity. This package is an 'R' implementation
    of the original shell project: <http://zachholman.com/spark/>.
Create sparklines and graphical tables for documents and websites.
Generate syntax for use with the sparklines package for
        LaTeX.
Use SPARQL to pose SELECT or UPDATE queries to an end-point.
Provides functions to estimate kernel-smoothed relative risk functions and perform subsequent inference.
Implements the sparse biclustering proposal of Tan and Witten (2014), Sparse biclustering of transposable data. Journal of Computational and Graphical Statistics  23(4):985-1008.
Fast methods for learning sparse Bayesian networks from high-dimensional data using sparse regularization. Designed to incorporate mixed experimental and observational data with thousands of variables with either continuous or discrete observations.
A set of tools for representing and estimating sparse Bayesian networks from continuous and discrete data.
A collection of sparse and regularized discriminant analysis
    methods intended for small-sample, high-dimensional data sets. The package
    features the High-Dimensional Regularized Discriminant Analysis classifier.
Multidimensional scaling provides a means of uncovering a latent structure underlying observed data, while estimating the number of latent dimensions.  This package presents a means for scaling binary and count data, for example the votes and word counts for legislators.  Future work will include an EM implementation and extend this work to ordinal and continuous data.
SparseGrid is a package to create sparse grids for numerical integration, based on code from www.sparse-grids.de
Estimates Hessian of a scalar-valued function, and returns it
    in a sparse Matrix format. The sparsity pattern must be known in advance. The
    algorithm is especially efficient for hierarchical models with a large number of
    heterogeneous units.
Performs sparse linear discriminant analysis for gaussians and mixture of gaussians models.
Coefficient estimation and model prediction based on the LASSO sparse 
  learning algorithm and its improved versions such as Bolasso, bootstrap ranking 
  LASSO, two-stage hybrid LASSO and others. These LASSO estimation procedures are 
  applied in the fields of variable selection, graphical modeling and ensemble 
  learning. The bagging LASSO model uses a Monte Carlo cross-entropy algorithm to 
  determine the best base-level models and improve predictive performance.
Use RcppEigen to fit least trimmed squares
    regression models with an L1 penalty in order to obtain
    sparse models.
Some basic linear algebra functionality for sparse matrices is
  provided:  including Cholesky decomposition and backsolving as well as 
  standard R subsetting and Kronecker products.
Computes multivariate normal (MVN) densities, and
    samples from MVN distributions, when the covariance or
    precision matrix is sparse.
Sparsenet uses the MC+ penalty of Zhang. It computes the regularization surface over both the family parameter and the tuning parameter by coordinate descent.
Sparse modeling provides a mean selecting a small number of non-zero effects from a large possible number of candidate effects.  This package includes a suite of methods for sparse modeling: estimation via EM or MCMC, approximate confidence intervals with nominal coverage, and diagnostic and summary plots.  The method can implement sparse linear regression and sparse probit regression.  Beyond regression analyses, applications include subgroup analysis, particularly for conjoint experiments, and panel data. Future versions will include extensions to  models with truncated outcomes, propensity score, and instrumental variable analysis.
Sparse-aware maximum likelihood for structural equation models in inferring gene regulatory networks
Wrapper around the 'SVDLIBC' library for (truncated) singular value decomposition of a sparse matrix.
             Currently, only sparse real matrices in Matrix package format are supported.
Fast algorithm for fitting solution paths of sparse linear SVM with lasso or elastic-net regularization generate sparse solutions.
Computes sparse vector autoregressive coefficients and precision 
 matrices for time series chain graphical models.
A wrapper for sparse VAR/VECM time series models estimation 
             using penalties like ENET, SCAD and MCP.
Computer simulations are becoming a popular technique to use in attempts to further our understanding of complex systems. SPARTAN, described in our 2013 publication in PLoS Computational Biology, provides code for four techniques described in available literature which aid the analysis of simulation results, at both single and multiple timepoints in the simulation run. The first technique addresses aleatory uncertainty in the system caused through inherent stochasticity, and determines the number of replicate runs necessary to generate a representative result. The second examines how robust a simulation is to parameter perturbation, through the use of a one-at-a-time parameter analysis technique. Thirdly, a latin hypercube based sensitivity analysis technique is included which can elucidate non-linear effects between parameters and indicate implications of epistemic uncertainty with reference to the system being modelled. Finally, a further sensitivity analysis technique, the extended Fourier Amplitude Sampling Test (eFAST) has been included to partition the variance in simulation results between input parameters, to determine the parameters which have a significant effect on simulation behaviour. Version 1.3 adds support for Netlogo simulations, aiding simulation developers who use Netlogo to build their simulations perform the same analyses. We have also added user support through the group spartan-group[AT]york[DOT]ac[DOT]uk. Version 2.0 added the ability to read all simulations in from a single CSV file in addition to the prescribed folder structure in previous versions.
Fit spatial CAR count regression models using MCMC
Functionality for spatio-temporal modeling of large data sets is provided. A Gaussian process in space and time is defined through a stochastic partial differential equation (SPDE). The SPDE is solved in the spectral space, and after discretizing in time and space, a linear Gaussian state space model is obtained. When doing inference, the main computational difficulty consists in evaluating the likelihood and in sampling from the full conditional of the spectral coefficients, or equivalently, the latent space-time process. In comparison to the traditional approach of using a spatio-temporal covariance function, the spectral SPDE approach is computationally advantageous. This package aims at providing tools for two different modeling approaches. First, the SPDE based spatio-temporal model can be used as a component in a customized hierarchical Bayesian model (HBM). The functions of the package then provide parameterizations of the process part of the model as well as computationally efficient algorithms needed for doing inference with the HBM. Alternatively, the adaptive MCMC algorithm implemented in the package can be used as an algorithm for doing inference without any additional modeling. The MCMC algorithm supports data that follow a Gaussian or a censored distribution with point mass at zero. Covariates can be included in the model through a regression term.
Graphs (or networks) and graph component
        calculations for spatial locations in *D.
Functions for kriging and point pattern analysis.
Perform Spatial Clustering Analysis using Fuzzy Geographically Weighted Clustering. Provide optimization using Gravitational Search Algorithm.
Functions that compute the spatial covariance matrix for the matern and power classes of spatial models, for data that arise on rectangular units.  This code can also be used for the change of support problem and for spatial data that arise on irregularly shaped regions like counties or zipcodes by laying a fine grid of rectangles and aggregating the integrals in a form of Riemann integration.
Utilities to support spatial data manipulation, query, sampling
    and modelling. Functions include models for species population density, download
    utilities for climate and global deforestation spatial products, spatial
    smoothing, multivariate separability, point process model for creating pseudo-
    absences and sub-sampling, polygon and point-distance landscape metrics,
    auto-logistic model, sampling models, cluster optimization and statistical
    exploratory tools.
Methods and data for cluster detection and disease mapping.
Tools for the statistical modelling of spatial extremes using max-stable processes, copula or Bayesian hierarchical models.
Filter matrices or (three dimensional) array data using different convolution kernels.
This package fits a hierarchical spatial model for the generalized extreme value distribution with the option of model averaging over the space of covariates.
Edge-corrected kernel density estimation and binary kernel
        regression estimation for multivariate spatial point process
        data
Network based diffusion analysis (NBDA) allows inference on
             the asocial and social transmission of information.  This may involve
             the social transmission of a particular behaviour such as tool use, for example.
            For the NBDA, the key parameters estimated are the social effect and baseline rate 
            parameters.  The baseline rate parameter gives the rate at which the behaviour
            is first performed (or acquired) asocially amongst the individuals in a given population.
            The social effect parameter quantifies the effect of the social associations amongst 
            the individuals on the rate at which each individual first performs or displays
            the behaviour.  Spatial NBDA involves incorporating spatial information in the analysis.  
            This is done by incorporating social networks derived from 
            spatial point patterns (of the home bases of the individuals under study).  In addition, 
            a spatial covariate such as vegetation cover, or slope may be included in the modelling
            process.
This package contains test and estimates of location, 
             tests of independence, tests of sphericity and several 
             estimates of shape all based on spatial signs, symmetrized 
             signs, ranks and signed ranks.
This package provides tools to assess the association between two spatial processes.
    Currently, three methodologies are implemented: An adapted t-test to perform hypothesis
    testing about the independence between the processes, a suitable nonparametric correlation
    coefficient, and the codispersion coefficient. SpatialPack gives methods to complement
    methodologies that are available in geoR for one spatial process.
Computes spatial position models: Stewart potentials, Reilly
    catchment areas, Huff catchment areas.
Bayesian Estimation of Spatial Probit and Tobit Models.
Summaries for measuring segregation/mingling in multitype spatial point patterns with graph based neighbourhood description. 
     Included indices: Mingling, Shannon, Simpson (also the non-spatial)
     Included functionals: Mingling, Shannon, Simpson, ISAR, MCI.
     Included neighbourhoods: Geometric, k-nearest neighbours, Gabriel, Delaunay.
Provides functions implementing the pairwise M-estimator for
    parametric models for stable tail dependence functions described in "An
    M-estimator of spatial tail dependence"
    (Einmahl, J.H.J., Kiriliouk, A., Krajina, A. and Segers, J., 2014).
    See http://arxiv.org/abs/1403.1975.
Spatial functions meant to enhance the core functionality of the
    package "raster", including a parallel processing engine for use with
    rasters.
Tools for spatial data analysis.  Emphasis on kriging.  Provides functions for prediction and simulation.  Intended to be relatively straightforward, fast, and flexible.
Spatial forecast verification arose from verifying high-resolution forecasts, where coarser-resolution models generally are favored even when a human forecaster finds the higher-resolution model to be considerably better.  Most newly proposed methods, which largely come from image analysis, computer vision, and similar, are available, with more on the way.
Utilities that estimate, predict and cross-validate the
    spatio-temporal model developed for MESA Air.
Provide regularized maximum covariance analysis incorporating smoothness, sparseness and orthogonality of couple patterns by using the alternating direction method of multipliers algorithm. The method can be applied to either regularly or irregularly spaced data.
Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigenfunctions by using the alternating direction method of multipliers algorithm. The method can be applied to either regularly or irregularly spaced data.
Analysis of field trial experiments by modelling spatial trends using two-dimensional Penalised spline (P-spline) models.
Bayesian inference for parametric proportional hazards spatial
    survival models; flexible spatial survival models.
Tools to convert from specific formats to more general forms of 
    spatial data. Using tables to store the actual entities present in spatial
    data provides far greater flexibility, and the functions here deliberately 
    minimize the level of interpretation applied, leaving that for specific 
    applications. Includes round-trip for 'Spatial' classes and long-form 
    tables, analogous to 'ggplot2::fortify'.
Fits univariate and multivariate spatio-temporal
        models with Markov chain Monte Carlo (MCMC).
Provides several Bayesian survival models for spatial/non-spatial survival data: marginal Bayesian Nonparametric models, marginal Bayesian proportional hazards models, generalized accelerated failure time frailty models, and standard semiparametric frailty models within the context of proportional hazards, proportional odds and accelerated failure time.
Evaluation of control charts by means of
        the zero-state, steady-state ARL (Average Run Length) and RL quantiles.
        Setting up control charts for given in-control ARL. The control charts
        under consideration are one- and two-sided EWMA, CUSUM, and
        Shiryaev-Roberts schemes for monitoring the mean of normally
        distributed independent data. ARL calculation
        of the same set of schemes under drift are added. 
        Other charts and parameters are in preparation.
        Further SPC areas will be covered as well
        (sampling plans, capability indices ...).
Calibration of thresholds of control charts such as
    CUSUM charts based on past data, taking estimation error into account.
A new reduced-rank LDA method which works for high dimensional multi-class data.
Spatial coverage sampling and random sampling from compact
        geographical strata created by k-means.
Provides a covariance estimator for multivariate normal
        data that is sparse and positive definite.  Implements the
        majorize-minimize algorithm described in Bien, J., and
        Tibshirani, R. (2011), "Sparse Estimation of a Covariance
        Matrix," Biometrika. 98(4). 807--820.
This package computes the sparse principal component regression. The regularization parameters are optimized by cross-validation.
The Semi Parametric Piecewise Distribution blends the Generalized Pareto Distribution for the tails with a kernel based interior.
A collection of functions to create spatial weights matrix
  objects from polygon contiguities, from point patterns by distance and
  tessellations, for summarizing these objects, and for permitting their
  use in spatial data analysis, including regional aggregation by minimum
  spanning tree; a collection of tests for spatial autocorrelation,
  including global Moran's I, APLE, Geary's C, Hubert/Mantel general cross
  product statistic, Empirical Bayes estimates and Assunção/Reis Index,
  Getis/Ord G and multicoloured join count statistics, local Moran's I
  and Getis/Ord G, saddlepoint approximations  and exact tests for global
  and local Moran's I; and functions for estimating spatial simultaneous
  autoregressive (SAR) lag and error models, impact measures for lag
  models, weighted and unweighted SAR and CAR spatial regression models,
  semi-parametric and Moran eigenvector spatial filtering, GM SAR error
  models, and generalized spatial two stage least squares models.
Methods for 'dplyr' verbs for 'sp' 'Spatial' classes. The basic 
    verbs that modify data attributes, remove or re-arrange rows are supported
    and provide complete 'Spatial' analogues of the input data. The group by
    and summarize workflow returns a non-topological spatial union. There is 
    limited support for joins, with left and inner to copy attributes from 
    another table.
Functions for estimating split-duration regression models and
    various associated generic function methods.
A spatio-dynamic modelling package that focuses on three
    characteristic wetland plant communities in a semiarid Mediterranean
    wetland in response to hydrological pressures from the catchment. The
    package includes the data on watershed hydrological pressure and the
    initial raster maps of plant communities but also allows for random initial
    distribution of plant communities. Ongoing developments of the package
    focus on offering easy to use tools for creating other spatio-dynamic
    models.
Implements stochastic proximity embedding as described by
 Agrafiotis et al. in PNAS, 2002, 99, pg 15869 and J. Comput. Chem., 2003,24, pg 1215
We introduce a novel suite of informatics tools for the quantitative analysis of NMR metabolomic profile data. The core of the processing cascade is a novel peak alignment algorithm, called hierarchical Cluster-based Peak Alignment (CluPA). The algorithm aligns a target spectrum to the reference spectrum in a top-down fashion by building a hierarchical cluster tree from peak lists of reference and target spectra and then dividing the spectra into smaller segments based on the most distant clusters of the tree. To reduce the computational time to estimate the spectral misalignment, the method makes use of Fast Fourier Transformation (FFT) cross-correlation. Since the method returns a high-quality alignment, we can propose a simple methodology to study the variability of the NMR spectra. For each aligned NMR data point the ratio of the between-group and within-group sum of squares (BW-ratio) is calculated to quantify the difference in variability between and within predefined groups of NMR spectra. This differential analysis is related to the calculation of the F-statistic or a one-way ANOVA, but without distributional assumptions. Statistical inference based on the BW-ratio is achieved by bootstrapping the null distribution from the experimental data. Related publication is available at http://www.biomedcentral.com/1471-2105/12/405/.
Alternative to the kernlab::specc function. Includes a spectral clustering implementation, a locally adapted kernel function akin to what is already proposed in kernlab, and an optional procedure that automatically estimates the optimal number of clusters. Several sample data sets are also included.
Utility functions for spectroscopy. 1. Functions to simulate
    spectra for use in teaching or testing. 2. Functions to process files created by
    'LoggerPro' and 'SpectraSuite' software.
SPECIES is an R package for estimation of species richness
        or diversity.
Preparation of species occurrences and distribution data for the use in phylogenetic analyses. SpeciesgeocodeR is built for data cleaning, data exploration and data analysis and especially suited for biogeographical and ecological questions on large datasets. The package includes the easy creation of summary-tables and -graphs and geographical maps, the automatic cleaning of geographic occurrence data, the calculating of coexistence matrices and species ranges (EOO) as well as mapping diversity in geographic areas.
Mixtures using BFGS and analytical derivatives
The package helps to test the specificity of personality trait-outcome (or trait-trait) associations by comparing the observed associations to those obtained using randomly created personality scales.
Provides a set of methods for metabolomics 
	data analysis, including data loading in different formats, 
	pre-processing, metabolite identification, univariate and multivariate 
	data analysis, machine learning and feature selection. Case studies 
	can be found on the website: http://darwin.di.uminho.pt/metabolomics .
A collection of new forecast verification routines for the SPECS FP7 project. The emphasis is on comparative verification of ensemble forecasts.
Fourier and Hilbert transforms are utilized to perform several types
             of spectral analysis on the supplied data. Also fragmented and
             irregularly spaced data can be processed. A user friendly interface
             helps to interpret the results.
Routines for creating, manipulating, and performing 
 Bayesian inference about Gaussian processes in 
 one and two dimensions using the Fourier basis approximation: 
 simulation and plotting of processes, calculation of 
 coefficient variances, calculation of process density, 
 coefficient proposals (for use in MCMC).  It uses R environments to
 store GP objects as references/pointers.
Implements the diffusion map method of dimensionality reduction and spectral method of combining multiple diffusion maps, including creation of the spectra and visualization of maps.
Contains some implementations of Singular Spectrum Analysis (SSA) for the gapfilling and spectral decomposition of time series. It contains the code used by Buttlar et. al. (2014), Nonlinear Processes in Geophysics. In addition, the iterative SSA gapfilling method of Kondrashov and Ghil (2006) is implemented. All SSA calculations are done via the truncated and fast SSA algorithm of Korobeynikov (2010) (package 'Rssa').
Spectra visualization, organizer and data preparation
        from within R or stand-alone. Binary (application) part is 
        installed separately by running spnInstallApp() 
        immediately after installing the package.
From output files obtained from the software ModestR, the relative contribution of factors to explain species distribution is depicted using several plots. A global geographic raster file for each environmental variable may be also obtained with the mean relative contribution, considering all species present in each raster cell, of the factor to explain species distribution. Finally, for each variable it is also possible to compare the frequencies of any variable obtained in the cells where the species is present with the frequencies of the same variable in the cells of the extent.
Fitting linear models and generalized linear models to large data sets by updating algorithms.
Functions for fitting semiparametric regression models for
        panel count survival data.
The package performs estimation and testing of the
        treatment effect in a 2-group randomized clinical trial with a
        quantitative, dichotomous, or right-censored time-to-event
        endpoint. The method improves efficiency by leveraging baseline
        predictors of the endpoint. The inverse probability weighting
        technique of Robins, Rotnitzky, and Zhao (JASA, 1994) is used
        to provide unbiased estimation when the endpoint is missing at
        random.
A set of functions for computing potential evapotranspiration and several widely used drought indices including the Standardized Precipitation-Evapotranspiration Index (SPEI).
Provides some easy-to-use functions to interpolate species range based on species occurrences and to estimate centers of biodiversity.
This package implements spatial error estimation and
        permutation-based variable importance measures for predictive
        models using spatial cross-validation and spatial block
        bootstrap.
Functions to produce a fully fledged 'Spatial' object extent as a
    'SpatialPolygonsDataFrame'.
A set of tools for estimation of various spatial specifications of
    stochastic frontier models.
Interpreted interface between GRASS 6+ geographical 
    information system and R, based on starting R from within the GRASS 
    environment, or running free-standing R in a temporary GRASS location;
    the package provides facilities for using all GRASS commands from the 
    R command line. This package may not be used for GRASS 7, for which
    rgrass7 should be used.
A collection of statistical hypothesis tests and other 
	techniques for identifying certain spatial relationships/phenomena in 
	DNA sequences. In particular, it provides tests and graphical methods for determining 
	whether or not DNA sequences comply with Chargaff's second parity rule 
	or exhibit purine-pyrimidine parity. In addition, there are functions for 
	efficiently simulating discrete state space Markov chains and testing 
	arbitrary symbolic sequences of symbols for the presence of first-order 
	Markovianness.
	Also, it has functions for counting words/k-mers (and cylinder patterns) in 
	arbitrary symbolic sequences. Functions which take a DNA sequence as input 
	can handle sequences stored as SeqFastadna objects from the 'seqinr' package.
Functions for computing geographically weighted
  regressions are provided, based on work by Chris
  Brunsdon, Martin Charlton and Stewart Fotheringham.
Various functions for creating spherical coordinate system plots via extensions to rgl.
Provides several methods to integrate functions over the unit
 sphere and ball in n-dimensional Euclidean space.  Routines for converting to/from
 multivariate polar/spherical coordinates are also provided.
Spherical K-function for point-pattern analysis on the sphere.
This package carries out spherical wavelet transform
        developed by Li (1999) and Oh (1999), and implements wavelet
        thresholding approaches proposed by Oh and Li (2004).
Generalized Method of Moment estimation of Cliff-Ord-type
        spatial autoregressive models with and without heteroskedastic
        innovations
The SNP Panel Identification Assay (SPIA) is a package that enables an accurate determination of cell line identity from the genotype of single nucleotide polymorphisms (SNPs). The SPIA test allows to discern when two cell lines are close enough to be called similar and when they are not. Details about the method are available at "Demichelis et al. (2008) SNP panel identification assay (SPIA): a genetic-based assay for the identification of cell lines. Nucleic Acids Res., 3, 2446-2456".
A package for the analysis of species limits and DNA barcoding data
Fits and simulates data from our predator preferences model, <DOI:10.1007/s10651-016-0341-3>.
Calculate the Standardized Precipitation Index (SPI) for monitoring drought, using Artificial Intelligence techniques (SPIGA) and traditional numerical technique Maximum Likelihood (SPIML).  For more information see: http://drought.unl.edu/monitoringtools/downloadablespiprogram.aspx.
Applies re-sampled kernel density method to detect vote fraud. It estimates the proportion of coarse vote-shares in the observed data relative to the null hypothesis of no fraud.
Spike and slab for prediction and variable selection in
        linear regression models. Uses a generalized elastic net for
        variable selection.
Bayesian variable selection, model choice, and regularized
    estimation for (spatial) generalized additive mixed regression models
    via stochastic search variable selection with spike-and-slab priors.
An optimal weighting strategy to compute
        simulation-efficient shortest probability intervals (spins).
Implements a generative model that uses a
    spike-and-slab like prior distribution obtained by multiplying a
    deterministic binary vector. Such a model allows an EM algorithm,
    optimizing a type-II log-likelihood.
The Splancs package was written as an enhancement to S-Plus for display and analysis of spatial point pattern data; it has been ported to R and is in "maintenance mode".
A complementary package on splines providing functions
    constructing M-spline, I-spline, and integral of B-spline basis.
Some R functions, such as optim(), require a function
  its gradient passed as separate arguments. When these are
  expensive to calculate it may be much faster to calculate
  the function (fn) and gradient (gr) together since they often share
  many calculations (chain rule). This package allows the user
  to pass in a single function that returns both the function
  and gradient, then splits (hence 'splitfngr') them
  so the results can be accessed
  separately. The functions provided allow this to be done with
  any number of functions/values, not just for functions and gradients.
Online data collection tools like Google Forms often export
    multiple-response questions with data concatenated in cells. The
    concat.split (cSplit) family of functions splits such data into 
    separate cells. The package also includes functions to stack groups 
    of columns and to reshape wide data, even when the data are 
    "unbalanced"---something which reshape (from base R) does not handle, 
    and which melt and dcast from reshape2 do not easily handle.
ML and GM estimation and diagnostic testing of econometric models for spatial panel data.
This package provides functions for fitting a Sparse
        Partial Least Squares Regression and Classification
Currently there are many functions in S-PLUS that are
        missing in R. To facilitate the conversion of S-PLUS packages
        to R packages, this package provides some missing S-PLUS
        functionality in R.
A collection of classes and methods for working with
  times and dates. The code was originally available in S-PLUS.
A collection of classes and methods for working with time series.
  The code was originally available in S-PLUS.
Installs SPM12 to the R library directory and has associated functions for fMRI and general imaging utilities, called through MATLAB.
A set of functions is provided for 1) the stratum lengths analysis along a chosen direction, 2) fast estimation of continuous lag spatial Markov chains model parameters and probability computing (also for large data sets), 3) transition probability maps and transiograms drawing, 4) simulation methods for categorical random fields.
Implements the method of general semiparametric maximum likelihood estimation for logistic models in case-mother control-mother designs.
Facilitates the rendering of networks for which nodes have a specific position on a map (cities, participants in a political debate, etc.). Map data and network data are stored together in a single object which handles the match between network nodes and their respective position on the map. The plot method renders both the map and the network data. Several networks can be plot simultaneously. The graphic is highly customisable and the legend is automatically printed. Map data have to be supplied as 'SpatialPolygons' objects (from the 'sp' package) and network data as 'named matrix'.
A programmatic interface to many species occurrence data sources,
    including 'GBIF', 'USGS's' 'BISON', 'iNaturalist', Berkeley 'Ecoinformatics'
    Engine 'eBird', 'AntWeb', and 'iDigBio'. Includes functionality for
    retrieving species occurrence data, and combining that data.
Utilities for use with 'spocc'. Includes functions to
    clean occurrence data, find and remove duplicates, combine data,
    and make maps with base 'R' plots, 'ggplot2', and various interactive
    maps.
SPODT is a spatial partitioning method based on oblique decision trees, in order to classify study area into zones of different risks, determining their boundaries
R implementation of the methods described in "A rank-based
        empirical likelihood approach to two-sample proportional odds
        model and its goodness-of-fit" by Zhong Guan and Cheng Peng,
        Journal of Nonparametric Statistics, to appear.
The aim of this package is to provide infrastructure for
        sports analysis. Anyway, currently it is a selection of data
        sets, functions to fetch sports data, examples, and demos --
        with the ambition to develop bit by bit a set of classes to
        represent general concepts of sports analysis.
The Sequential Parameter Optimization Toolbox provides a set of
    tools for Parameter-tuning, based on modelling techniques, DoE and statistical
    methods.
Sparse arrays interpreted as multivariate polynomials.
The Statistical Package for REliability Data Analysis (SPREDA) implements recently-developed statistical methods for the analysis of reliability data. Modern technological developments, such as sensors and smart chips, allow us to dynamically track product/system usage as well as other environmental variables, such as temperature and humidity. We refer to these variables as dynamic covariates. The package contains functions for the analysis of time-to-event data with dynamic covariates and degradation data with dynamic covariates. The package also contains functions that can be used for analyzing time-to-event data with right censoring, and with left truncation and right censoring. Financial support from NSF and DuPont are acknowledged.
The recent advancement of high-throughput technologies has led to frequent utilization of gene expression and other "omics" data for toxicological, diagnostic or prognostic studies in and clinical applications. Unlike in classical predictions where the number of samples is greater than the number of variables (n>p), the challenge faced with prediction using "omics" data is that the number of parameters greatly exceeds the number of samples (p>>n). To solve this curse of dimensionality problem, several predictive functions have been proposed for direct and probabilistic classification and survival predictions. Nevertheless, these predictive functions have been shown to perform differently across datasets. Comparing predictive functions and choosing the best is computationally intensive and leads to selection bias. Thus, the question which function should one choose for a given dataset is to be ascertained. This package implements the approach proposed by Jong et al., (2016) to address this question.
Calculate species richness functions for rarefaction and
    extrapolation.
SPRINT (Simple Parallel R INTerface) is a parallel
        framework for R. It provides a High Performance Computing (HPC)
        harness which allows R scripts to run on HPC clusters. SPRINT
        contains a library of selected R functions that have been
        parallelized. Functions are named after the original R function
        with the added prefix 'p', i.e. the parallel version of cor()
        in SPRINT is called pcor(). Call to the parallel R functions
        are included directly in standard R scripts.
		SPRINT contains functions for correlation (pcor), partitioning around medoids (ppam), 
		apply (papply), permutation testing (pmaxT), bootstrapping (pboot), random forest (prandomForest), 
		rank product (pRP) and hamming distance (pstringdistmatrix).
The main function of this package builds prognostic models that consider interactions by combining available statistical components. Furthermore, it provides a function for evaluating the relevance of the selected interactions by resampling techniques.
Robust dimension reduction methods for regression and discriminant analysis are implemented that yield estimates with a partial least squares alike interpretability. Partial robust M regression (PRM) is robust to both vertical outliers and leverage points. Sparse partial robust M regression (SPRM) is a related robust method with sparse coefficient estimate, and therefore with intrinsic variable selection. For binary classification related discriminant methods are PRM-DA and SPRM-DA.
R functions to mine sparse models from data.
Perform Wald's Sequential Probability Ratio Test on variables with a Normal, Bernoulli, Exponential and Poisson distribution. Plot acceptance and continuation regions, or create your own with the help of closures.
Methods to optimize spatial sample configurations using spatial simulated annealing. Multiple 
    objective functions are implemented for various purposes, such as variogram estimation, spatial trend 
    estimation, and spatial interpolation. A general purpose spatial simulated annealing function enables the 
    user to define his/her own objective function.
Fits spatial scale (SS) forward stepwise regression, SS incremental forward stagewise regression, SS least angle regression (LARS), and SS lasso models.  All area-level covariates are considered at all available scales to enter a model, but the SS algorithms are constrained to select each area-level covariate at a single spatial scale.
Program uses method of polynomial of variable degrees to interpolate gridded data preserving monotonicity and/or convexity or none. Method is implemented for univariate and bivariate cases. If values of derivatives are provided, spline will fix them,if not program will estimate them numerically. Package written purely in R.
SPSL package provides functionality for labeling of
        percolation clusters on 2D & 3D square lattice with various
        lattice size, relative fraction of accessible sites (occupation
        probability), iso- & anisotropy, von Neumann & Moore
        (1,d)-neighborhood
Produces Slepian smoother basis objects for use in Generalized Additive Models, as estimated by package:mgcv.
This group of functions implements algorithms for design and
    analysis of probability surveys.  The functions are tailored for Generalized
    Random Tessellation Stratified survey designs.
This package collects algorithms related to Sierpinski pedal triangle (SPT).
Fits, spatially predicts, and temporally forecasts space-time data using Gaussian Process (GP): (1) spatially varying coefficient process models and (2) spatio-temporal dynamic linear models.
Implements nonparametric hypothesis tests to check isotropy and
    symmetry properties for two-dimensional spatial data.
spThin is a set of functions that can be used to spatially thin
    species occurrence data. The resulting thinned data can be used in ecological
    modeling, such as ecological niche modeling.
Fits, spatially predicts and temporally forecasts large amounts of space-time data using  [1] Bayesian Gaussian Process (GP) Models, [2] Bayesian Auto-Regressive (AR) Models, and [3] Bayesian Gaussian Predictive Processes (GPP) based AR Models for spatio-temporal big-n problems.
Implements semiparametric transformation model two-phase estimation using calibration weights.
This package provides functions and datasets from the book "Introduction to Scientific Programming and Simulation Using R".
Sparse Quadratic Discriminant Analysis (SQDA) can be performed. In SQDA, the covariance matrix are assumed to be block-diagonal.And, for each block, sparsity assumption is imposed on the covariance matrix. It is useful in high-dimensional setting.
Description: Manipulate R data frames using SQL.
sqliter helps users, mainly data munging practioneers, to organize
    their sql calls in a clean structure. It simplifies the process of
    extracting and transforming data into useful formats.
A rendering tool for parameterized SQL that also translates into
    different SQL dialects.  These dialects include Sql Server, Oracle, 
    PostgreSql, Amazon RedShift, and Microsoft PDW.
This package provides utilities for working with a library of SQL
    files.
Normalization based a subset of negative control probes as
        described in 'Subset quantile normalization using negative
        control features'. Wu Z, Aryee MJ, J Comput Biol. 2010
        Oct;17(10):1385-95 [PMID 20976876].
Algorithms for accelerating the convergence of slow,
        monotone sequences from smooth, contraction mapping such as the
        EM algorithm. It can be used to accelerate any smooth, linearly
        convergent acceleration scheme.  A tutorial style introduction
        to this package is available in a vignette on the CRAN download
        page or, when the package is loaded in an R session, with
        vignette("SQUAREM").
Functions for color-based visualization of multivariate data, i.e. colorgrams or heatmaps.  Lower-level functions  map numeric values to colors, display a matrix as an array of colors, and draw color keys.  Higher-level plotting functions generate a bivariate histogram, a dendrogram aligned with a color-coded matrix, a triangular distance matrix, and more.
A simulation-based tool made to help researchers to become familiar with
    multilevel variations, and to build up sampling designs for their study. 
    This tool has two main objectives: First, it provides an educational tool useful for students, 
    teachers and researchers who want to learn to use mixed-effects models. 
    Users can experience how the mixed-effects model framework can be used to understand 
    distinct biological phenomena by interactively exploring simulated multilevel data. 
    Second, it offers research opportunities to those who are already familiar with 
    mixed-effects models, as it enables the generation of data sets that users may download 
    and use for a range of simulation-based statistical analyses such as power 
    and sensitivity analysis of multilevel and multivariate data.
This package (sra) provides a set of tools to analyse artificial-selection response datasets. The data typically feature for several generations the average value of a trait in a population, the variance of the trait, the population size and the average value of the parents that were chosen to breed. Sra implements two families of models aiming at describing the dynamics of the genetic architecture of the trait during the selection response. The first family relies on purely descriptive (phenomenological) models, based on an autoregressive framework. The second family provides different mechanistic models, accounting e.g. for inbreeding, mutations, genetic and environmental canalization, or epistasis. The parameters underlying the dynamics of the time series are estimated by maximum likelihood. The sra package thus provides (i) a wrapper for the R functions mle() and optim() aiming at fitting in a convenient way a predetermined set of models, and (ii) some functions to plot and analyze the output of the models.
Implementation of the SRCS method for a color-based visualization of the
    results of multiple pairwise tests on a large number of problem configurations, proposed in: 
    I.G. del Amo, D.A. Pelta. SRCS: a technique for comparing multiple algorithms under several
    factors in dynamic optimization problems. In: E. Alba, A. Nakib, P. Siarry
    (Eds.), Metaheuristics for Dynamic Optimization. Series: Studies in
    Computational Intelligence 433, Springer, Berlin/Heidelberg, 2012.
Draws scaled rectangle diagrams to represent a 2^k contingency
    table, for k<=6
This package contains a collection of functions to perform
        nonparametric estimation of receiver operating characteristic
        (ROC) curves for continuous data.
This package implements the SRRS method introduced in Phoa (2013) into a graphical user interface (GUI) R program.
Use piping, verbs like 'group_by' and 'summarize', and other
    'dplyr' inspired syntactic style when calculating summary statistics on survey
    data using functions from the 'survey' package.
Develops a framework for fisheries stock assessment simulation
    testing with Stock Synthesis 3 (SS3).
Procedures for analyzing simultaneous signals, e.g., features that are simultaneously significant in two different studies. Includes methods for detecting simultaneous signals, for identifying them under false discovery rate control, and for leveraging them to improve prediction.
A set of functions to calculate sample size for two-sample difference in means tests. Does adjustments for either nonadherence or variability that comes from using data to estimate parameters.
Provides the spatial sign correlation and the two-stage spatial sign correlation as well as a one-sample test for the correlation coefficient.
ssd calculates the sample size needed to detect the differences between two sets of unordered categorical data.
Functions to visually and statistically analyze single system data.
Allows to map species richness and endemism based on stacked
    species distribution models (SSDM). Individuals SDMs can be created using a
    single or multiple algorithms (ensemble SDMs). For each species, an SDM can
    yield a habitat suitability map, a binary map, a between-algorithm variance
    map, and can assess variable importance, algorithm accuracy, and between-
    algorithm correlation. Methods to stack individual SDMs include summing
    individual probabilities and thresholding then summing. Thresholding can be
    based on a specific evaluation metric or by drawing repeatedly from a Bernoulli
    distribution. The SSDM package also provides a user-friendly interface.
Performs structured OLS (sOLS) and structured SIR (sSIR).
Spatial Stochastic Frontier Analysis (SSFA) is an original method for controlling the spatial heterogeneity in Stochastic Frontier Analysis (SFA) models, for cross-sectional data, by splitting the inefficiency term into three terms: the first one related to spatial peculiarities of the territory in which each single unit operates, the second one related to the specific production features and the third one representing the error term.
Fits complex parametric models using the method proposed by Cox and Kartsonaki (2012) without likelihoods.
This package provides utility functions for system command
    execution, both locally and remotely using ssh/scp. The command
    output is captured and provided to the caller. This functionality is
    intended to streamline calling shell commands from R, retrieving and
    using their output, while instrumenting the calls with appropriate
    error handling. NOTE: this first version is limited to unix with local
    and remote systems running bash as the default shell.
This package contains a set of functions that calculates 
 appropriate sample sizes for one-sample t-tests, two-sample t-tests, 
 and F-tests for microarray experiments based on desired power while 
 controlling for false discovery rates. For all tests, the standard
 deviations (variances) among genes can be assumed fixed or random. 
 This is also true for effect sizes among genes in one-sample and two
 sample experiments. Functions also output a chart of power versus sample
 size, a table of power at different sample sizes, and a table of critical
 test values at different sample sizes.
We propose a procedure for sample size calculation while
    controlling false discovery rate for RNA-seq experimental design. Our
    procedure depends on the Voom method proposed for RNA-seq data analysis
    by Law et al. (2014) <DOI:10.1186/gb-2014-15-2-r29> and the sample size 
    calculation method proposed for microarray experiments by Liu and Hwang 
    (2007) <DOI:10.1093/bioinformatics/btl664>. We develop a set of functions
    that calculates appropriate sample sizes for two-sample t-test for RNA-seq
    experiments with fixed or varied set of parameters. The outputs also contain a
    plot of power versus sample size, a table of power at different sample sizes,
    and a table of critical test values at different sample sizes. 
    To install this package, please use 
    'source("http://bioconductor.org/biocLite.R"); biocLite("ssizeRNA")'.
Semi-supervised learning has attracted the attention of machine learning community because of its high accuracy with less annotating effort compared with supervised learning.The question that semi-supervised learning wants to address is: given a relatively small labeled dataset and a large unlabeled dataset, how to design classification algorithms learning from both ? This package is a collection of some classical semi-supervised learning algorithms in the last few decades.
Performs the EM algorithm for regression models using Skew Scale Mixtures of Normal Distributions.
Package provides a set of tools for robust estimation and inference for models with sample selectivity.
Geostatistical modeling for data on stream networks, including models based on in-stream distance.  Models are created using moving average constructions. Spatial linear models, including covariates, can be fit with ML or REML.  Mapping and other graphical functions are included.
R package for computing the spherical smoothing splines
An integrated set of tools to estimate the size of a networked population based on respondent-driven sampling data. The package is part of the "RDS Analyst" suite of packages for the analysis of respondent-driven sampling data.
Takeya Semantic Structure Analysis (TSSA) and Sakai Sequential Relation Analysis (SSRA)
             for polytomous items for examining whether each pair of items has a sequential or equal
             relation. Package includes functions for generating a sequential relation table and a
             treegram to visualize sequential or equal relations between pairs of items.
SSRAT is a computer program for two-dimensional sociometric status
    determination with rating scales. For each person assessed, SSRAT computes
    probability distributions of the total scores for `sympathy' (S),
    `antipathy' (A), `social preference' (P) and `social impact' (I), and
    applies the criteria for sociometric status categorization.
Calculates the power and sample size based on the difference in Restricted Mean Survival Times.
Tools to import survey files
    in the .sss (triple-s) format. The package provides the function
    read.sss() that reads the .asc and .sss files of a
    triple-s survey data file.  The package is experimental
    - feedback, issues and bug reports are welcome.
Fast iterative thresholding sparse SVD, together with an initialization algorithm
Set of tools to fit a semi-parametric regression model suitable for analysis of data sets in which the response variable is continuous, strictly positive, asymmetric and possibly, censored. Under this setup, both the median and the skewness of the response variable distribution are explicitly modeled by using semi-parametric functions, whose non-parametric components may be approximated by natural cubic splines or P-splines. Supported distributions for the model error include log-normal, log-Student-t, log-power-exponential, log-hyperbolic, log-contaminated-normal, log-slash, Birnbaum-Saunders and Birnbaum-Saunders-t distributions.
Implements the "shrinkage t" statistic 
   introduced in Opgen-Rhein and Strimmer (2007) and a shrinkage estimate
   of the "correlation-adjusted t-score" (CAT score) described in
   Zuber and Strimmer (2009).  It also offers a convenient interface 
   to a number of other regularized t-statistics commonly 
   employed in high-dimensional case-control studies.
Density, Probability and Quantile functions, and random number
  generation for (skew) stable distributions, using the parametrizations of
  Nolan.
Estimate the four parameters of stable laws using maximum
             likelihood method, generalised method of moments with
             finite and continuum number of points, iterative
             Koutrouvelis regression and Kogon-McCulloch method.  The
             asymptotic properties of the estimators (covariance
             matrix, confidence intervals) are also provided.
An exploratory and heuristic approach for specification search
    in Structural Equation Modeling. The basic idea is to subsample
    the original data, and for each subset we search the
    optimal models. Here the criteria of an optimal model is defined
    by two objectives: to fit the data well and is simple (parsimonious) model.
    As these objectives are conflicting, we apply NSGA-II to optimize,
    such that to get optimal models for the whole range of model complexities.
    From these optimal models, we observe the model specifications (structures)
    that are both stable and parsimonious, called relevant. At the end we infer
    causal model from these relevant structures.
Resampling procedures to assess the stability of selected variables
    with additional finite sample error control for high-dimensional variable
    selection procedures such as Lasso or boosting
Stacks rectangular datasets on top of each other, possibly
    performing several type coercions along the way. For large datasets,
    depends on the ff package. Provides an aggressive version of
    ffbase::compact for data that may appear be real-typed but is in fact
    int/short/byte. For many purposes plyr::rbind.fill may be more appropriate,
    but for some kinds of survey data, the rules here work better.
Consists of helper functions collected from StackOverflow.com, a 
  question and answer site for professional and enthusiast programmers.
Analyzing a mixture of two-stage logistic regressions with fixed candidate knots. See Bruch, E., F. Feinberg, K. Lee (in press)<DOI:10.1073/pnas.1522494113>.
Provides facilities to implement and run population models of
    stage-structured species...
stam is an evolving package that target on the various
        methods to conduct Spatio-Temporal Analysis and
        Modelling,including Exploratory Spatio-Temporal Analysis and
        Inferred Spatio-Temporal Modelling.
Allows users to calculate pairwise Nei's Genetic Distances (Nei 1972), pairwise Fixation
 Indexes (Fst) (Weir & Cockerham 1984) and also Genomic Relationship matrixes following Yang et al. (2010) in mixed and single
 ploidy populations. Bootstrapping across loci is implemented during Fst calculation to generate confidence intervals and p-values
 around pairwise Fst values. StAMPP utilises SNP genotype data of any ploidy level (with the ability to handle missing data) and is coded to  
 utilise multithreading where available to allow efficient analysis of large datasets. StAMPP is able to handle genotype data from genlight objects 
 allowing integration with other packages such adegenet.
 Please refer to LW Pembleton, NOI Cogan & JW Forster, 2013, Molecular Ecology Resources, 13(5), 946-952. doi:10.1111/1755-0998.12129 for the appropriate citation and user manual. Thank you in advance.
Provides functions for the analysis of
        occupational and environmental data with non-detects. Maximum
        likelihood (ML) methods for censored log-normal data and
        non-parametric methods based on the product limit estimate (PLE)
        for left censored data are used to calculate all of the
        statistics recommended by the American Industrial Hygiene
        Association (AIHA) for the complete data case. Functions for
        the analysis of complete samples using exact methods are also
        provided for the lognormal model. Revised from 2007-11-05
        'survfit~1'.
Standardizes text according to a template; particularly
        useful for country names.
The C++ header files of the Stan project are provided by this package, but it contains no R code, vignettes, or function documentation. There is a shared object containing part of the CVODES library, but it is not accessible from R. StanHeaders is only useful for developers who want to utilize the LinkingTo directive of their package's DESCRIPTION file to build on the Stan library without incurring unnecessary dependencies. The Stan project develops a probabilistic programming language that implements full or approximate Bayesian statistical inference via Markov Chain Monte Carlo or variational methods and implements (optionally penalized) maximum likelihood estimation via optimization. The Stan library includes an advanced automatic differentiation scheme, templated statistical and linear algebra functions that can handle the automatically differentiable scalar types (and doubles, ints, etc.), and a parser for the Stan language. The 'rstan' package provides user-facing R functions to parse, compile, test, estimate, and analyze Stan models.
Functions to analyze neuronal spike trains from a single
        neuron or from several neurons recorded simultaneously.
Produces LaTeX code, HTML/CSS code and ASCII text for well-formatted tables that hold 
    regression analysis results from several models side-by-side, as well as summary
    statistics.
Statistical functions to identify, estimate and diagnose a Space-Time AutoRegressive Moving Average (STARMA) model.
Provides utilities to create or suppress start-up messages.
A Set of Tools for Administering SHared Repositories
Datasets for Stat2 textbook (by Cannon, et. al., published
        by WH Freeman)
A set of tools inspired by 'Stata' to explore data.frames ('summarize',
    'tabulate', 'xtile', 'pctile', 'binscatter', elapsed quarters/month, lead/lag).
Extract statistics from articles and recompute p values.
An implementation of local and global statistical complexity measures (aka Information Theory Quantifiers, ITQ) for time series analysis based on ordinal statistics (Bandt and Pompe (2002) <DOI:10.1103/PhysRevLett.88.174102>). Several distance measures that operate on ordinal pattern distributions, auxiliary functions for ordinal pattern analysis, and generating functions for stochastic and deterministic-chaotic processes for ITQ testing are provided.
This package offers different possibilities to make statistical analysis for Environmental Data.
Support for reading and writing files in StatDataML---an XML-based data exchange format.
Cartogram heatmaps are an alternative to choropleth maps for USA States
    and are based on work by the Washington Post graphics department in their report
    on "The states most threatened by trade". "State bins" preserve as much of the
    geographic placement of the states as possible but has the look and feel of a
    traditional heatmap. Functions are provided that allow for use of a binned,
    discrete scale, a continuous scale or manually specified colors depending on
    what is needed for the underlying data.
Selectively acquire hourly meteorological data from stations located all over the world.
Integration of two data sources referred to the same target population which share a number of common variables (aka data fusion). Some functions can also be used to impute missing values in data sets through hot deck imputation methods. Methods to perform statistical matching when dealing  with data from complex sample surveys are available too.
Offers useful functions to perform day-to-day data manipulation 
    operations, data quality checks and post modelling statistical checks.
    One can effortlessly change class of a number of variables to factor, 
    remove duplicate observations from the data, create deciles of a 
    variable, perform data quality checks for continuous (integer or numeric), 
    categorical (factor) and date variables, and compute goodness of fit 
    measures such as auc for statistical models. The functions are consistent 
    for objects of class 'data.frame' and 'data.table', which is an enhanced 
    'data.frame' implemented in the package 'data.table'.
Functions and datasets to support Mayer Alvo and Philip L.H. Yu (2014). "Statistical Methods for Ranking Data", Springer. In this packages, smaller ranks stand for more preferred items.
A collection of algorithms and functions to aid statistical modeling. Includes growth curve comparisons, limiting dilution analysis (aka ELDA), mixed linear models, heteroscedastic regression, inverse-Gaussian probability calculations, Gauss quadrature and a secure convergence algorithm for nonlinear models.  Includes advanced generalized linear model functions that implement secure convergence, dispersion modeling and Tweedie power-law families.
An integrated set of tools for the representation, visualization, analysis, and simulation of network data. For an introduction, type help(package='statnet').
Non-statistical utilities used by the software developed by the Statnet Project. They may also be of use to others.
A graphical user interface for network modeling with the 'statnet'
    software.
A collection of statistical tools for social network analysis, with strong emphasis on the analysis of discrete powerlaw distributions and statistical hypothesis tests.
A small collection of data on graduate statistics programs from the United States.
Generates a random quotation from a data base of quotes on topics
    in statistics, data visualization and science.
A set of methods to implement Generalized Method of Moments and Maximal
    Likelihood methods for Random Utility Models. These methods are meant to
    provide inference on rank comparison data. These methods accept full,
    partial, and pairwise rankings, and provides methods to break down full or
    partial rankings into their pairwise components. Please see Generalized
    Method-of-Moments for Rank Aggregation from NIPS 2013 for a description of
    some of our methods.
An easy to use tool provides a graphical user interface for quality control based signal correction, integration of metabolomic data from multiple batches, and the comprehensive statistic analysis for non-targeted and targeted approaches.
Provides an implementation of simultaneous tolerance bounds (STB), useful for checking whether a numeric vector fits to a hypothetical null-distribution or not.
             Furthermore, there are functions for computing STB (bands, intervals) for random variates of linear mixed models fitted with package 'VCA'. All kinds of, possibly transformed 
             (studentized, standardized, Pearson-type transformed) random variates (residuals, random effects), can be assessed employing STB-methodology.
Estimates a covariance matrix using Stein's isotonized covariance
    estimator, or a related estimator suggested by Haff.
Contains three main functions including
  stddiff.numeric(), stddiff.binary() and stddiff.category().
  These are used to calculate the standardized difference between two groups.
  It is especially used to evaluate the balance between two groups
  before and after propensity score matching.
Contains functionality for regression standardization. Two general classes of models are allowed; generalized linear models and Cox proportional hazards models.
Functions related to multivariate measures of independence and ICA:
 -estimate independent components by minimizing distance covariance;
 -conduct a test of mutual independence based on distance covariance; 
 -estimate independent components via infomax (a popular method but generally performs poorer than mdcovica, ProDenICA, and/or fastICA, but is useful for comparisons);
 -order indepedent components by skewness;
 -match independent components from multiple estimates;
 -other functions useful in ICA.
steepness is a package that computes steepness as a
        property of dominance hierarchies. Steepness is defined as the
        absolute slope of the straight line fitted to the normalized
        David's scores. The normalized David's scores can be obtained
        on the basis of dyadic dominance indices corrected for chance
        or by means of proportions of wins. Given an observed
        sociomatrix, it computes hierarchy's steepness and estimates
        statistical significance by means of a randomization test.
Routines for computing different types of linear estimators, based on instrumental variables (IVs), including the semi-parametric Stein-like (SPS) estimator, originally introduced by Judge and Mittelhammer (2004)  <DOI:10.1198/016214504000000430>.
A package to manage and display stellar tracks and
        isochrones from Pisa low-mass database. Includes tools for
        isochrones construction and tracks interpolation.
Estimation of the parameters of a spatio-temporal model
        using the EM algorithm, estimation of the parameter standard
        errors using a spatio-temporal parametric bootstrap, spatial
        mapping.
STEPwise Community Assembly Model collection, applying ABC-SMC in order to infer different community assembly processes. Also includes several plotting functions in order to visualize found estimates.
A method to explore the treatment-covariate interactions in survival or generalized 
	linear model (GLM) for continuous, binomial and count data arising from two treatment 
	arms of a clinical trial. A permutation distribution approach to inference is implemented, 
	based on permuting the covariate values within each treatment group.
L2 penalized logistic regression for both continuous and
        discrete predictors, with forward stagewise/forward stepwise
        variable selection procedure.
Allows to fit step-functions to univariate serial data where neither the number of jumps nor their positions is known.
A stepwise approach to identifying recombination
        breakpoints in a sequence alignment.
Collection of stepwise procedures to conduct multiple hypotheses testing. The details of the stepwise algorithm can be found in Romano and Wolf (2007) <DOI:10.1214/009053606000001622> and Hsu, Kuan, and Yen (2014) <DOI:10.1093/jjfinec/nbu014>.
Functions for the collection of 3D points and curves using a stereo camera setup.
Functions implementing the procedure of entropy comparison between two data samples after the renormalization of respective probability distributions with the algorithm designed by Klimontovich (Zeitschrift fur Physik B Condensed Matter. 1987, Volume 66, Issue 1, pp 125-127) and extended by Anishchenko (Proc. SPIE 2098, Computer Simulation in Nonlinear Optics. 1994, pp.130-136).  The package also includes data preparation tools which can also be used separately for various applications.
A set of functions for computing the Standardized Temperature Index (STI).
Functions to build and use an interpolator ("emulator") for time series or 1D regularly spaced data in multidimensional space. The standard usage is for interpolating time-resolved computer model output between model parameter settings. It can also be used for interpolating multivariate data (e.g., oceanographic time-series data, etc.) in space.  There are functions to test the emulator using cross-validation, and to produce contour plots over 2D slices in model input parameter (or physical) space.
This package enables you to estimate a regression trunk model, which is an integration of a regression tree and a multiple regression model.
A consistently well behaved method of interpolation based
        on piecewise rational functions using Stineman's algorithm
Decompose a time series into seasonal, trend, and remainder
    components using an implementation of Seasonal Decomposition of Time
    Series by Loess (STL) that provides several enhancements over the STL
    method in the stats package.  These enhancements include handling missing
    values, providing higher order (quadratic) loess smoothing with automated
    parameter choices, frequency component smoothing beyond the seasonal and
    trend components, and some basic plot methods for diagnostics.
The Structural Topic Model (STM) allows researchers 
  to estimate topic models with document-level covariates. 
  The package also includes tools for model selection, visualization,
  and estimation of topic-covariate regressions.
This visualization allows users to interactively explore the relationships between topics and the covariates estimated from the stm package in R.
Generates an interactive visualization of topic correlations/
    hierarchy in a Structural Topic Model (STM) of Roberts, Stewart, and Tingley.
    The package performs a hierarchical clustering of topics which are then exported
    to a JSON object and visualized using D3.
Analysis by spatio-temporal data using the decomposition in n-dimensional arrays and using median polish technique.
Implementation of the family of generalised age-period-cohort
    stochastic mortality models. This family of models encompasses many models
    proposed in the actuarial and demographic literature including the Lee-Carter
    model and the Cairns-Blake-Dowd model. It includes functions for fitting
    mortality models, analysing their goodness-of-fit and performing mortality
    projections and simulations.
Plots a QQ-Norm Plot with several Gaussian simulations.
Fit a spatial-temporal occupancy models using
    a probit formulation instead of a traditional logit
    model.
This is an R package accompanying the paper "Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles" by Sameer S Bajikar, Christiane Fuchs, Andreas Roller, Fabian J Theis and Kevin A Janes (PNAS 2014, 111(5), E626-635). In this paper, we measure expression profiles from small heterogeneous populations of cells, where each cell is assumed to be from a mixture of lognormal distributions. We perform maximum likelihood estimation in order to infer the mixture ratio and the parameters of these lognormal distributions from the cumulated expression measurements.
Efficient algorithms for fully Bayesian estimation of stochastic volatility (SV) models via Markov chain Monte Carlo (MCMC) methods.
With this package, users can obtain the real-time price and volume information of stocks in China market, as well as the information of the stock index. This package adopted the API from Sina Finance (http://finance.sina.com.cn/).
Download stock data, build single index, constant
        correlation, and multigroup models, and estimate optimal stock
        portfolios. Plotting functions for the portfolio possibilities
        curve and portfolio cloud are included. A function to test a
        portfolio on a data set is also provided.
Provides functions for analyzing historical performance of stocks or other investments. Functions are written in C++ to quickly calculate maximum draw-down, Sharpe ratio, Sterling ratio, and other commonly used metrics of stock performance.
Given a list of substance compositions, a list of
        substances involved in a process, and a list of constraints in
        addition to mass conservation of elementary constituents, the
        package contains functions to build the substance composition
        matrix, to analyze the uniqueness of process stoichiometry, and
        to calculate stoichiometric coefficients if process
        stoichiometry is unique.  (See Reichert, P. and Schuwirth, N.,
        A generic framework for deriving process stoichiometry in
        enviromental models, Environmental Modelling and Software 25,
        1241-1251, 2010 for more details.)
Storm is a distributed real-time computation system. Similar to how
 Hadoop provides a set of general primitives for doing batch processing, Storm
 provides a set of general primitives for doing real-time computation.

    Storm includes a "Multi-Language" (or "Multilang") Protocol to allow
 implementation of Bolts and Spouts in languages other than Java.  This R
 extension provides implementations of utility functions to allow an application
 developer to focus on application-specific functionality rather than Storm/R
 communications plumbing.
Creates and manages simple key-value stores.  These can
    use a variety of approaches for storing the data.  This package
    implements the base methods and support for file system and
    in-memory stores.  A vignette shows how additional drivers can be
    created, and stubs exist for supporting 'Redis' databases.
A toolkit for Reliability Availability and Maintainability (RAM) modeling of industrial process systems.
Can be utilized to select a (test data) calibrated training population in high dimensional prediction problems.
Functionality and data access tools for transport planning,
    including origin-destination analysis, route allocation and modelling travel
    patterns.
Utilities to estimate parameters of the models with survival functions 
             induced by stochastic covariates. Miscellaneous functions for data preparation 
             and simulation are also provided. For more information, see: (i) "Life tables 
             with covariates: Dynamic model for nonlinear analysis of longitudinal data" 
             by Akushevich, I. et al. (2005), Mathematical Population Studies 12(2), 51(80), 
             <DOI:10.1080/08898480590932296>; (ii) "Stochastic model for analysis of 
             longitudinal data on aging and mortality" by Yashin A. et al. (2007), 
             Mathematical Biosciences, 208(2), 538-551, <DOI:10.1016/j.mbs.2006.11.006>;
             (iii) "Health decline, aging and mortality: how are they related?" 
             by Yashin A. et al. (2007), 
             Biogerontology 8(3), 291(302), <DOI:10.1007/s10522-006-9073-3>.
A package for analysing, simulating and displaying space-time point patterns
Implement transformation-based and pixel-based residual
        analysis of spatial-temporal point process models.
Methods for decomposing seasonal data: STR (a Seasonal-Trend decomposition procedure based on Regression) and Robust STR. In some ways, STR is similar to Ridge Regression and Robust STR can be related to LASSO. They allow for multiple seasonal components, multiple linear covariates with constant, flexible and seasonal influence. Seasonal patterns (for both seasonal components and seasonal covariates) can be fractional and flexible over time; moreover they can be either strictly periodic or have a more complex topology. The methods provide confidence intervals for the estimated components. The methods can be used for forecasting.
Regression-based ranking of pathogen strains with respect to their contributions to natural epidemics, using demographic and genetic data sampled in the curse of the epidemics
Functions for the stratigraphic analysis of phylogenetic trees.
A toolkit for analyzing stratified population genetic data.
Univariate stratification of survey populations with a generalization of the 
       Lavallee-Hidiroglou method of stratum construction. The generalized method takes into account 
       a discrepancy between the stratification variable and the survey variable. The determination 
       of the optimal boundaries also incorporate, if desired, an anticipated non-response, a take-all 
       stratum for large units, a take-none stratum for small units, and a certainty stratum to ensure 
       that some specific units are in the sample. The well known cumulative root frequency rule of 
       Dalenius and Hodges and the geometric rule of Gunning and Horgan are also implemented.
Stratified covariate balancing through naturally occurring strata to adjust for confounding and interaction effects. Contains 4 primary functions which perform stratification, sensitivity analysis and return adjusted odds along with naturally occurring strata.
A collection of tools for plotting and analyzing paleontological and geological data distributed through through time in stratigraphic cores or sections. Includes some miscellaneous functions for handling other kinds of palaeontological and paleoecological data.
Provides functions to estimate a strategic selection estimator. A strategic selection estimator is an agent error model in which the two random components are not assumed to be orthogonal. In addition this package provides generic functions to print and plot objects of its class as well as the necessary functions to create tables for LaTeX. There is also a function to create dyadic data sets.
The main function is icweib, which fits a stratified Weibull proportional hazards model for left censored, right censored, interval censored, and non-censored survival data. We parameterize the Weibull regression model so that it allows a stratum-specific baseline hazard function, but where the effects of other covariates are assumed to be constant across strata.
A framework for data stream modeling and associated data mining tasks such as clustering and classification. The development of this package was supported in part by NSF IIS-0948893 and NIH R21HG005912.
I provide functions to calculate Gross Primary Productivity, Net Ecosystem Production, and Ecosystem Respiration from single station diurnal Oxygen curves.
Interface for data stream clustering algorithms implemented in the MOA (Massive Online Analysis) framework.
This package provides a series of functions that allow R users
    to access Twitter's filter, sample, and user streams, and to
    parse the output into data frames.
Functions to assist the process of learning structural
        equation modeling
Forms queries to submit to the Cleveland Federal Reserve Bank web
    site's financial stress index data site.  Provides query functions for both
    the composite stress index and the components data. By default the download
    includes daily time series data starting September 25, 1991.  The functions
    return a class of either type easing or cfsi which contain a list of items
    related to the query and its graphical presentation.  The list includes the
    time series data as an xts object.  The package provides four lattice time
    series plots to render the time series data in a manner similar to the
    bank's own presentation.
Reliability of (normal) stress-strength models and for building two-sided or one-sided confidence intervals according to different approximate procedures.
Implements an approximate string matching version of R's native
    'match' function. Can calculate various string distances based on edits
    (Damerau-Levenshtein, Hamming, Levenshtein, optimal sting alignment), qgrams (q-
    gram, cosine, jaccard distance) or heuristic metrics (Jaro, Jaro-Winkler). An
    implementation of soundex is provided as well. Distances can be computed between
    character vectors while taking proper care of encoding or between integer
    vectors representing generic sequences.
A toolbox for a construction of protein-protein interaction networks through the 'STRING' application programming interface, and an inference of Gaussian networks through 'SIMoNe' and 'WGCNA' approach, from DE genes analysis results and expression data. Additional functions are provided to import automatically networks into an active 'Cytoscape' session.
Allows for fast, correct, consistent, portable,
    as well as convenient character string/text processing in every locale
    and any native encoding. Owing to the use of the ICU library,
    the package provides R users with platform-independent functions
    known to Java, Perl, Python, PHP, and Ruby programmers. Among available
    features there are: pattern searching (e.g., with ICU Java-like regular
    expressions or the Unicode Collation Algorithm), random string generation,
    case mapping, string transliteration, concatenation,
    Unicode normalization, date-time formatting and parsing, etc.
A consistent, simple and easy to use set of wrappers around the
    fantastic 'stringi' package. All function and argument names (and positions)
    are consistent, all functions deal with "NA"'s and zero length vectors
    in the same way, and the output from one function is easy to feed into
    the input of another.
For making Trellis-type conditioning plots without strip labels.
    This is useful for displaying the structure of results from factorial designs
    and other studies when many conditioning variables would clutter the display
    with layers of redundant strip labels. Settings of the variables are encoded by
    layout and spacing in the trellis array and decoded by a separate legend. The
    functionality is implemented by a single S3 generic strucplot() function that
    is a wrapper for the Lattice package's xyplot() function. This allows access to
    all Lattice graphics capabilities in the usual way.
R Codes and Datasets for Stroup, W. W. (2012). Generalized Linear Mixed Models: Modern Concepts, Methods and Applications, CRC Press.
Testing, monitoring and dating structural changes in (linear)
             regression models. strucchange features tests/methods from
	     the generalized fluctuation test framework as well as from
	     the F test (Chow test) framework. This includes methods to
	     fit, plot and test fluctuation processes (e.g., CUSUM, MOSUM,
	     recursive/moving estimates) and F statistics, respectively.
             It is possible to monitor incoming data online using
             fluctuation processes.
             Finally, the breakpoints in regression models with structural
             changes can be estimated together with confidence intervals.
             Emphasis is always given to methods for visualizing the data.
Performs multiple testing corrections that take specific structure of hypotheses into account.
Implements a broad class of latent variable and structural equation models for general pedigree data.
An open source platform for validation and process control.
    Tools to analyse data from internal validation of forensic short tandem
    repeat (STR) kits are provided. The tools are developed to provide
    the necessary data to conform with guidelines for internal validation
    issued by the European Network of Forensic Science Institutes (ENFSI)
    DNA Working Group, and the Scientific Working Group on DNA Analysis Methods
    (SWGDAM). A front-end graphical user interface is provided.
    More information about each function can be found in the
    respective help documentation.
Fit the basic structural time series model by maximum likelihood.
This package defines an S4 class for structural time series models 
  and provides some basic methods to work with it.
Create stubs of functions for use while testing.
A number of functions, supplemented by GUI, to perform various analyses in the field of computational stylistics, authorship attribution, etc.
Calculate empirical subcopula and dependence measures from a given bivariate sample.
The three functions in the package compute the maximum likelihood estimates of the informants' competence scores, tests for two answer keys with known groups, and finds "best" split of the informants into sub-culture groups.
A test for the existence of a subgroup with enhanced treatment effect. And, a sample size calculation procedure for the subgroup detection test.
Produces various measures of expected treatment effect heterogeneity under an assumption of homogeneity across subgroups. Graphical presentations are created to compare these expected differences with the observed differences.
The package implements a convenient procedure for microarray study, which is to do gene selection and classification simultaneously for binary outcomes. Users needn't to tune the parameters and can fix any genes that they desire to keep in the model. The K-folds cross validation results are returned.
Complementary multivariate analysis to the Outlying Mean Index
    analysis to explore niche shift of a community within an Euclidean space, with
    graphical displays.
Routines for identifying metabolic subpathways mediated by microRNAs (miRNAs) through topologically locating miRNAs and genes within reconstructed Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathway graphs embedded by miRNAs. (1) This package can obtain the reconstructed KEGG metabolic pathway graphs with genes and miRNAs as nodes, through converting KEGG metabolic pathways to graphs with genes as nodes and compounds as edges, and then integrating miRNA-target interactions verified by low-throughput experiments from four databases (TarBase, miRecords, mirTarBase and miR2Disease) into converted pathway graphs. (2) This package can locate metabolic subpathways mediated by miRNAs by topologically analyzing the "lenient distance" of miRNAs and genes within reconstructed KEGG metabolic pathway graphs.(3) This package can identify significantly enriched miRNA-mediated metabolic subpathways based on located subpathways by hypergenomic test. (4) This package can support six species for metabolic subpathway identification, such as caenorhabditis elegans, drosophila melanogaster, danio rerio, homo sapiens, mus musculus and rattus norvegicus, and user only need to update interested organism-specific environment variables.
Identify dysfunctional subpathways competitively regulated by lncRNAs through integrating lncRNA-mRNA expression profile and pathway topologies.
The subplex algorithm for unconstrained optimization, developed by Tom Rowan.
Estimation of copula using ranks and subsampling. The main feature of this method is that simulation studies show a low sensitivity to dimension, on realistic cases. Vignette provides some theoretical documentation.
Functions for computing test subscores using methods applicable to classical test theory (CTT) scores. This package enables two sets of subscoring methods within the framework of CTT: (1) Wainer's augmentation method, and (2) Haberman's three subscoring methods. The package also includes the function to compute Proportional Reduction of Mean Squared Errors (PRMSEs) proposed by Haberman's, which are used to examine whether test subscores are of added value.
A collection of functions which (i) assess the quality of variable subsets as surrogates for a full data set, in either an exploratory data analysis or in the context of a multivariate linear model, and (ii) search for subsets which are optimal under various criteria.
Subsemble is a general subset ensemble prediction method, which can be used for small, moderate, or large datasets. Subsemble partitions the full dataset into subsets of observations, fits a specified underlying algorithm on each subset, and uses a unique form of V-fold cross-validation to output a prediction function that combines the subset-specific fits. An oracle result provides a theoretical performance guarantee for Subsemble.
An interface to 'OpenSubspace', an open source framework for
  evaluation and exploration of subspace clustering algorithms in WEKA 
  (see <http://dme.rwth-aachen.de/de/opensubspace> for more
  information).  Also performs visualization.
An interface to 'subspaceMOA', a Framework for the Evaluation of subspace stream clustering algorithms. (see <http://dme.rwth-aachen.de/de/subspacemoa> for more information.)
subtype performs a biclustering procedure on a input
        dataset and assess whether resulting clusters are promising
        subtypes. Note that the R-package rsmooth should be installed
        before implementing subtype. rsmooth can be downloaded from
        http://www.meb.ki.se/~yudpaw.
Substitution matrices are important parameters in protein alignment algorithms. These matrices represent the likelihood that an amino acid will be substituted for another during mutation. This tool allows users to apply predefined and custom matrices and then explore the resulting alignments with interactive visualizations.  SubVis requires the availability of a web browser.
Generates, plays, and solves Sudoku puzzles.  The GUI
        playSudoku() needs package "tkrplot" if you are not on Windows.
Tools for making, retrieving, displaying and solving sudoku games.
    This package is an alternative to the earlier sudoku-solver package,
    'sudoku'.  The present package uses a slightly different algorithm, has a
    simpler coding and presents a few more sugar tools, such as plot and print
    methods.  Solved sudoku games are of some interest in Experimental Design
    as examples of Latin Square designs with additional balance constraints.
This is a package for the subsampling method of robust
        estimation of linear regression models
Generates formatted summary tables for vectors and dataframes.
    Three summarizing functions: 1) 'freq' generates frequency tables
    reporting counts and proportions (including cumulative); 2) 'descr'
    gives common central tendency statistics and measures of dispersion
    for numerical data; 3) 'dfSummary' gives as much information
    as possible about a dataframe and its components in a clear,
    legible table. All three functions can either display plain text tables
    in the console or write markdown, text or html files to disk. An
    additional misc function, 'what.is', extends base 'is' function,
    checking the object against all 'is' functions and gives a variety
    of other information about the object.
Make interactive 'd3.js' sequence sunburst diagrams in R with the
    convenience and infrastructure of an 'htmlwidget'.
Functions for drawing samples according to Sunter's
        sampling design, and for computing first and second order
        inclusion probabilities
Methodology for supervised grouping aka "clustering" of
   potentially many predictor variables, such as genes etc.
Clusters features under the assumption that each cluster has a
 random effect and there is an outcome variable that is related to the random 
 effects by a linear regression. In this way the cluster analysis is 
 ``supervised'' by the outcome variable. An alternate specification is that 
 features in each cluster have the same compound symmetric normal distribution, 
 and the conditional distribution of the outcome given the features
 has the same coefficient for each feature in a cluster.
Biclusters are submatrices in the data matrix which
        satisfy certain conditions of homogeneity. Package contains
        functions for generating robust biclusters with respect to the
        initialization parameters for a given bicluster solution
        contained in a bicluster set in data, the procedure is also
        known as ensemble biclustering. The set of biclusters is
        evaluated based on the similarity of its elements (the
        overlap), and afterwards the hierarchical tree is constructed
        to obtain cut-off points for the classes of robust biclusters.
        The result is a number of robust (or super) biclusters with
        none or low overlap.
A Comprehensive Test Suite for Markov Chain
        Nonconvergence.
Efficient statistical testing and scalable visualization of intersections among multiple sets.
Implements the super learner prediction method and contains a library of prediction algorithms to be used in the super learner.
Witten and Tibshirani (2011) Supervised multidimensional scaling for visualization, classification, and bipartite ranking. Computational Statistics and Data Analysis 55(1): 789-801.
Supervised principal components for regression and
        survival analsysis. Especially useful for high-dimnesional
        data, including microarray data.
Tools for analysing the aggreement of two or more rankings of the same items. Examples are importance rankings of predictor variables and risk predictions of subjects. Benchmarks for agreement are computed based on random permutation and bootstrap.
The Patient Rules Induction Method (PRIM) is typically
  used for "bump hunting" data mining to identify regions with abnormally
  high concentrations of data with large or small values. This package
  extends this methodology so that it can be applied to binary classification
  problems and used for prediction.
Ten distributions supplementing those built into R.
        Inverse Gauss, Kruskal-Wallis, Kendall's Tau, Friedman's chi
        squared, Spearman's rho, maximum F ratio, the Pearson product
        moment correlation coefficiant, Johnson distributions, normal
        scores and generalized hypergeometric distributions. In
        addition two random number generators of George Marsaglia are
        included.
Provides three basic functions that support an implementation of object case (Case 1) best-worst scaling: one for converting a two-level orthogonal main-effect design/balanced incomplete block design into questions; one for creating a data set suitable for analysis; and one for calculating count-based scores.
Provides seven basic functions that support an implementation of choice experiments.
Calculates likelihood based support intervals for
    several common data types including binomial, Poisson, normal, lm(), and
    glm(). For the binomial, Poisson, and normal data likelihood intervals are
    calculated via root finding algorithm. Additional parameters allow the
    user to specify whether they would like to receive a parametric bootstrap
    estimate of the confidence level of said support interval. For lm() and glm(),
    the function returns profile likelihoods for each coefficient in the model.
SURFACE is a data-driven phylogenetic comparative method for fitting stabilizing selection models to continuous trait data, building on the ouch package. The main functions fit a series of Hansen models using stepwise AIC, then identify cases of convergent evolution where multiple lineages have shifted to the same adaptive peak.
In a clinical trial, it frequently occurs that the most credible outcome to evaluate the effectiveness of a new therapy (the true endpoint) is difficult to measure. In such a situation, it can be an effective strategy to replace the true endpoint by a (bio)marker that is easier to measure and that allows for a prediction of the treatment effect on the true endpoint (a surrogate endpoint). The package 'Surrogate' allows for an evaluation of the appropriateness of a candidate surrogate endpoint based on the meta-analytic, information-theoretic, and causal-inference frameworks. Part of this software has been developed using funding provided from the European Union's 7-th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.
suRtex was designed for easy descriptive statistic reporting of categorical survey data (e.g., Likert scales) in LaTeX. suRtex takes a matrix or data frame and produces the LaTeX code necessary for a sideways table creation. Mean, median, standard deviation, and sample size are optional.
Performs inference of several model-free group contrast measures, which include difference/ratio of cumulative incidence rates at given time points, quantiles, and restricted mean survival times (RMST). Two kinds of covariate adjustment procedures (i.e., regression and augmentation) for inference of the metrics based on RMST are also included.
This package provides a function to estimate the AUC, TPR(c),
    FPR(c), PPV(c), and NPV(c) for for a specific timepoint and marker cutoff
    value c using non-parametric and semi-parametric estimators. Standard errors 
    and confidence intervals are also computed. Either analytic or bootstrap 
    standard errors can be computed.
The package provides a variety of functions to estimate
        time-dependent true/false positive rates and AUC curves from a
        set of censored survival data.
Performs inference for C of risk prediction models with
        censored survival data, using the method proposed by Uno et al.
        (2011). Inference for the difference in C between two competing
        prediction models is also implemented.
Estimates correlation coefficients with associated
   confidence limits 
   for bivariate, partially censored survival times. Uses
   the iterative multiple imputation approach proposed
   by Schemper, Kaider, Wakounig and Heinze, Statistics
   in Medicine 2013. Provides a scatterplot function to visualize the bivariate 
   distribution, either on the original time scale or as copula.
Relative survival, AER and SMR based on French death rates
Summary statistics, two-sample tests, rank tests, generalised linear models, cumulative link models, Cox models, loglinear models, and general maximum pseudolikelihood estimation for multistage stratified, cluster-sampled, unequally weighted survey samples. Variances by Taylor series linearisation or replicate weights. Post-stratification, calibration, and raking. Two-phase subsampling designs. Graphics. PPS sampling without replacement. Principal components, factor analysis.
Tools for using different kinds of bootstrap
    for estimating sampling variation using complex survey
    data.
Data obtained from surveys contains information not only about the
    survey responses, but also the survey metadata, e.g. the original survey
    questions and the answer options. The surveydata package makes it easy to
    keep track of this metadata, and to easily extract columns with
    specific questions.
Help generate slides for surveys or experiments.
 The resulted slides allow the subject to respond with the use of the mouse (usual keyboard input is 
 replaced with clicking on a virtual keyboard on the slide). Subjects' responses are saved to the user-
 specified location in the form of R-readable text file. To allow flexibility, each function in 
 this package generates a particular type of slides thus general R function writing skills are 
 required to compile these edited slides.
At present, the only functionality is the calculation of optimal one-sided winsorizing cutoffs. The main function is optimal.onesided.cutoff.bygroup. It calculates the optimal tuning parameter for one-sided winsorisation, and so calculates winsorised values for a variable of interest. See the help file for this function for more details and an example.
Tools for sample survey planning, including sample size calculation, estimation of expected precision for the estimates of totals, and calculation of optimal sample size allocation.
The Gini concentration test for survival data is a nonparametric test based on the Gini index for testing the equality of two survival distributions from the point of view of concentration. The package compares different nonparametric tests (asymptotic Gini test, permutation Gini test, log-rank test, Gray-Tsiatis test and Wilcoxon test) and computes their p-values.
Performs inference for a class of measures to compare
        competing risk prediction models with censored survival data.
        The class includes the integrated discrimination improvement
        index (IDI) and category-less net reclassification index (NRI).
Contains the core survival analysis routines, including
	     definition of Surv objects, 
	     Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models,
	     and parametric accelerated failure time models.
Estimate the regression coefficients and the baseline hazard 
      of proportional hazard Cox models using maximum penalised likelihood.  
      A 'non-parametric' smooth estimate of the baseline hazard function 
      is provided.
Compute time-dependent ROC curve from censored survival
        data using Kaplan-Meier (KM) or Nearest Neighbor Estimation
        (NNE) method of Heagerty, Lumley & Pepe (Biometrics, Vol 56 No
        2, 2000, PP 337-344)
Microarray gene expression data can be analyzed individually or jointly using merging methods or meta-analysis to predict patients' survival and risk assessment.
Three breast cancer gene expression data sets that can be used for package 'survJamda'. This package contains the gene expression and phenotype data of GSE1992, GSE3143 and GSE4335.
Kernel weighting methods for estimation of proportional hazards models with intermittently observed longitudinal covariates.
Contains the function 'ggsurvplot()' for drawing easily beautiful
    and 'ready-to-publish' survival curves with the 'number at risk' table. Other
    functions are also available for visual examinations of 'cox' model assumptions.
A collection of functions to help in the analysis of
    right-censored survival data. These extend the methods available in
    package:survival.
Presmoothed estimators of survival, density, cumulative and non-cumulative hazard functions with right-censored survival data.
Estimation of the prediction accuracy in a unified survival AUC
    approach. Model selection and prediction estimation based on a survival AUC.
    Stepwise model selection, based on several ranking approaches.
Estimation of survival function for recurrent event data
        using Peña-Strawderman-Hollander, Whang-Chang estimators and
        MLE estimation under a Gamma Frailty model.
The main function of this package allows estimation of a Weibull Regression for a right-censored endpoint, one interval-censored covariate, and an arbitrary number of non-censored covariates. Additional functions allow to switch between different parametrizations of Weibull regression used by different R functions, inference for the mean difference of two arbitrarily censored Normal samples, and estimation of canonical parameters from censored samples for several distributional assumptions.
Performs two-sample comparisons using the restricted mean survival time (RMST) as a summary measure of the survival time distribution. Three kinds of between-group contrast metrics (i.e., the difference in RMST, the ratio of RMST and the ratio of the restricted mean time lost (RMTL)) are computed. It performs an ANCOVA-type covariate adjustment as well as unadjusted analyses for those measures.
Simulation of simple and complex survival data including recurrent and multiple events and competing risks.
Conduct asymptotic and empirical power and sample size calculations for Single-Nucleotide Polymorphism (SNP) association studies with right censored time to event outcomes.
The sValues package implements the s-values proposed by Ed. Leamer.
    It provides a context-minimal approach for sensitivity analysis using extreme
    bounds to assess the sturdiness of regression coefficients.
Accurate identification of genes that are truly differentially expressed over two sample varieties, after adjusting for hidden subject-specific effects of residual heterogeneity.
2d and 3d space-varying coefficient models are fitted to
        regular grid data using either a full B-spline tensor product
        approach or a sequential approximation. The latter one is
        computationally more efficient. Resolution increment is
        enabled.
R bindings to SVD and eigensolvers (PROPACK, nuTRLan).
Rapidly construct dialog boxes for your GUI, including an automatic
  function assistant
Reimplementation of the svDialogs dialog boxes in Tcl/Tk
Visualize singular value decompositions (SVD), principal component analysis (PCA), factor analysis (FA) and related methods.
Some visualization tools based on Singular Value Decomposition
A graphics device for R that produces 'Scalable Vector Graphics'.
  'svglite' is a fork of the older 'RSvgDevice' package.
This 'htmlwidget' provides pan and zoom interactivity to R
    graphics, including 'base', 'lattice', and 'ggplot2'. The interactivity is
    provided through the 'svg-pan-zoom.js' library. Various options to the widget
    can tailor the pan and zoom experience to nearly any user desire.
Creates 3D animated, interactive visualizations in Scalable Vector Graphics (SVG) format that can be viewed in a web browser.
Implements a simple HTTP server allowing to connect GUI clients to R
Function for the GUI API to interact with external IDE/code editors
Functions to manage the GUI client, like Komodo with the
  SciViews-K extension
Solve large-scale regularised linear/kernel classification by using ADMM and IADMM algorithms. This package provides linear L2-regularised primal classification (both ADMM and IADMM are available), kernel L2-regularised dual classification (IADMM) as well as L1-regularised primal classification (both ADMM and IADMM are available).
Supporting functions for the GUI API (various utility functions)
Implements the SVM-Maj algorithm to train data with
        Support Vector Machine, this algorithm uses two efficient
        updates, one for linear kernel and one for the nonlinear
        kernel.
Causal effect estimation in observational data often requires identifying a set of untreated observations that are comparable to some treated group of interest.  This package provides a suite of functions for identifying such a set of observations and for implementing standard and new diagnostics tools.  The primary function, svmmatch(), uses support vector machines to identify a region of common support between treatment and control groups.  A sensitivity analysis, balance checking, and assessment of the region of overlap between treated and control groups is included.  The Bayesian implementation allows for recovery of uncertainty estimates for the treatment effect and all other parameters.
Computes the entire regularization path for the two-class svm classifier
		with essentially the same cost as a single SVM fit.
Various tools for semantic vector spaces, such as
    correspondence analysis (simple, multiple and discriminant), latent
    semantic analysis, probabilistic latent semantic analysis, non-negative
    matrix factorization, latent class analysis and EM clustering. Furthermore,
    there are specialized distance measures, plotting functions and some helper
    functions.
Implements a simple socket server allowing to connect GUI clients to R
Supporting functions for the GUI API (Sweave functions)
Set of tools aimed at wrapping some of the functionalities
  of the packages tools, utils and codetools into a nicer format so
  that an IDE can use them
A complete unit test system and functions to implement its GUI part
High level management of widgets, windows and other graphical resources.
Builds, evaluates and validates a nomogram with survey data and right-censored outcomes.
This package deals with data which stem from survey designs including plausible values. This package has been created to handle data from Large Scale Assessments like PISA, PIAAC etc. which use  complex survey designs to draw the sample and plausible values to report person related estimates. Various functions/statistics (mean, quantile, GLM etc.) are provided to handle this kind of data.
The package contains functions to connect the structure of
        the data with the information on the samples. Three types of
        associations are covered: 1. linear model of principal
        components. 2. hierarchical clustering analysis. 3.
        distribution of features-sample annotation associations.
        Additionally, the inter-relation between sample annotations can
        be analyzed. Simple methods are provided for the correction of
        batch effects and removal of principal components.
Three ensemble learning algorithms based on support vector machines. 
    They all train support vector machines on subset of data and combine the result.
The Soil and Water Assessment Tool is a river basin or
        watershed scale model developed by Dr. Jeff Arnold for the
        USDA-ARS.
Provides utilities for defining R / Rd as "language" for TeX-package "listings" 
        and for including R / Rd source file (snippets) copied from R-forge in its most 
		recent version (or another URL) thereby avoiding inconsistencies between vignette 
		and documented source code.
Structural handling of identity numbers used in the Swedish
    administration such as personal identity numbers ('personnummer') and
    organizational identity numbers ('organisationsnummer').
Collection of conversion, analytical, geodesic, mapping, and
    plotting functions. Used to support packages and code written by
    researchers at the Southwest Fisheries Science Center of the National
    Oceanic and Atmospheric Administration.
Use the R console as an interactive learning
    environment. Users receive immediate feedback as they are guided through
    self-paced lessons in data science and R programming.
A set of tools for writing and sharing interactive courses
    to be used with swirl.
Ozone, NOx (= Sum of Nitrogenmonoxide and
        Nitrogendioxide), Nitrogenmonoxide, ambient temperature, dew
        point, wind speed and wind direction at 3 sites around lake of
        Lucerne in Central Switzerland in 30 min time resolution for
        year 2004.
Functions for estimating the parameters from the latent
  state process and the functions corresponding to the J states as
  proposed by De Souza and Heckman (2013).
Provides an abstraction for managing, installing,
    and switching between sets of installed R packages. This allows users to
    maintain multiple package libraries simultaneously, e.g. to maintain
    strict, package-version-specific reproducibility of many analyses, or
    work within a development/production release paradigm. Introduces a 
    generalized package installation process which supports multiple repository 
    and non-repository sources and tracks package provenance.
Provides a simple plugin to the switchr
	     framework which allows users to publish manifests of packages - or of specific versions thereof - as  single-file GitHub repositories (Gists). These manifest files can then be used as remote seeds (see switchr documentation) when creating new package libraries.
Tools for retrieving, organizing, and analyzing environmental
    data from the System Wide Monitoring Program of the National Estuarine
    Research Reserve System.  These tools address common challenges
    associated with continuous time series data for environmental decision
    making.
This Systems Biology Library for R implements algorithms for constraint based analyses of metabolic networks (e.g. flux-balance analysis (FBA), minimization of metabolic adjustment (MOMA), regulatory on/off minimization (ROOM), robustness analysis and flux variability analysis). Most of the current LP/MILP solvers are supported via additional packages.
An implementation of a cost constrained flux balance analysis technique (i.e. MetabOlic Modeling with ENzyme kineTics (MOMENT)).
             MOMENT uses enzyme kinetic data and enzyme molecular weights to constrain flux balance analysis(FBA) and it is described in 
			 Adadi, R., Volkmer, B., Milo, R., Heinemann, M., & Shlomi, T. (2012). Prediction of Microbial Growth Rate versus Biomass 
			 Yield by a Metabolic Network with Kinetic Parameters, 8(7). doi:10.1371/journal.pcbi.1002575. 
             This package also implements an improvement of MOMENT that considers multi-functional enzymes. 
			 FBA is a mathematical technique to find fluxes in metabolic models at steady state. 
			 It is described in Orth, J.D., Thiele, I. and Palsson, B.O.  What is flux balance analysis? Nat. Biotech. 28, 245-248(2010).
Implement cycle-Free flux balance analysis, flux variability, and Random Sampling of solution space. Flux balance analysis is a technique to find fluxes in metabolic models at steady state. It is described in Orth, J.D., Thiele, I. and Palsson, B.O. What is flux balance analysis? Nat. Biotech. 28, 245-248 (2010).
Implements dynamic FBA technique proposed by Varma et al 1994.
Three different approaches to use gene expression data (or protein measurements) for improving FBA predictions.
'SBML' (Systems Biology Markup Language) integration in 'sybil'. Many constraint based metabolic models are published in 'SBML' format ('*.xml'). Herewith is the ability to read and check 'SBML' files in 'sybil' provided.
Symbolic data analysis methods: importing/ exporting data from ASSO XML Files, distance calculation for symbolic data (Ichino-Yaguchi, de Carvalho measure), zoom star plot, 3d interval plot, multidimensional scaling for symbolic interval data, dynamic clustering based on distance matrix, HINoV method for symbolic data, Ichino's feature selection method, principal component analysis for symbolic interval data, decision trees for symbolic data based on optimal split with bagging, boosting and random forest approach (+visualization), kernel discriminant analysis for symbolic data, Kohonen's self-organizing maps for symbolic, replication and profiling, artificial symbolic data generation.
Package that implements various symbol plots (bars,
        profiles, stars, Chernoff faces, color icons, stick figures).
Symbolic central and non-central moments of the multivariate normal distribution. Computes a standard representation, LateX code, and values at specified mean and covariance matrices.
A collection of functions required for genomic prediction which were developed within the Synbreed project for synergistic plant and animal breeding (www.synbreed.tum.de). This covers data processing, data visualization, and analysis. All functions are embedded within the framework of a single, unified data object. The implementation is flexible with respect to a wide range of data formats in plant and animal breeding. This research was funded by the German Federal Ministry of Education and Research (BMBF) within the AgroClustEr Synbreed - Synergistic plant and animal breeding (FKZ 0315528A).
Data sets for the 'synbreed' package with three data sets from cattle, maize and mice to illustrate the functions in the 'synbreed' R package. All data sets are stored in the gpData format introduced in the 'synbreed'
 package. This research was funded by the German Federal Ministry of Education and Research (BMBF) within the AgroClustEr Synbreed - Synergistic plant and animal breeding (FKZ 0315528A).
Methods for computing spatial, temporal, and spatiotemporal
    statistics including: empirical univariate, bivariate and multivariate
    variograms; fitting variogram models; phase locking and synchrony analysis;
    generating autocorrelated and cross-correlated matrices.
This package carries out synchrosqueezed wavelet transform. The package is a translation of MATLAB Synchrosqueezing Toolbox, version 1.1 originally developed by Eugene Brevdo (2012). The C code for curve_ext was authored by Jianfeng Lu, and translated to Fortran by Dongik Jang. Synchrosqueezing is based on the papers: [1] Daubechies, I., Lu, J. and Wu, H. T. (2011) Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis, 30. 243-261. [2] Thakur, G., Brevdo, E., Fukar, N. S. and Wu, H-T. (2013) The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications. Signal Processing, 93, 1079-1094.
The function 'syncSubsample' subsamples temporal data of different entities so that the result only contains synchronal events. The function 'mci' calculates the Movement Coordination Index (MCI, see reference on help page for function 'mci') of a data set created with the function 'syncSubsample'.
Analysis of metacommunities based on functional traits and phylogeny of the community components. The functions that are offered here implement for the R environment methods that have been available in the SYNCSA application written in C++ (by Valerio Pillar, available at http://ecoqua.ecologia.ufrgs.br/ecoqua/SYNCSA.html).
This package provides programmatic access to
    The Synergizer service for translating between sets of
    biological identifiers.
Efficient implementations for all the popular synergy scoring models for drug combinations, including HSA, Loewe, Bliss and ZIP and visualization of the synergy scores as either a two-dimensional or a three-dimensional interaction surface over the dose matrix.
Infers sympatry matrices from distributional data and analyzes them in order to identify groups of species cohesively connected.
Framework to perform synthetic likelihood inference
    for models where the likelihood function is unavailable or
    intractable.
It implements various estimators of mutual information, such as
	the maximum likelihood and the Millow-Madow estimator, various Bayesian
	estimators, the shrinkage estimator, and the Chao-Shen estimator. It also
	offers wrappers to the kNN and kernel density estimators. Furthermore, it
	provides various index of performance evaluation such as precision, recall,
	FPR, F-Score, ROC-PR Curves and so on. Lastly, it provides a brand new way
	of generating synthetic RNA-Seq Network with known dependence structure.
Implements the synthetic control group method for comparative case studies as described in Abadie and Gardeazabal (2003) and Abadie, Diamond, and Hainmueller (2010, 2011, 2014). The synthetic control method allows for effect estimation in settings where a single unit (a state, country, firm, etc.) is exposed to an event or intervention. It provides a data-driven procedure to construct synthetic control units based on a weighted combination of comparison units that approximates the characteristics of the unit that is exposed to the intervention. A combination of comparison units often provides a better comparison for the unit exposed to the intervention than any comparison unit alone.
Firstly provides a wrapper to library(acs) to access curated set
  of American Community Survey (ACS) base tables which may be of interest
  to many researchers. Secondly, it builds synthetic micro-datasets of ACS data
  at any specified geographic level with 10 default individual attributes. Thirdly,
  provides functionality for data-extensibility of micro-datasets; allowing users
  to both add data attributes and marginalize undesired attributes. And
  finally, the package also conducts spatial microsimulation modeling (SMSM)
  via simulated annealing.  SMSM is conducted in parallel by default.
A tool for producing synthetic versions of microdata containing confidential information so that they are safe to be released to users for exploratory analysis. The key objective of generating synthetic data is to replace sensitive original values with synthetic ones causing minimal distortion of the statistical information contained in the data set. Variables, which can be categorical or continuous, are synthesised one-by-one using sequential modelling. Replacements are generated by drawing from conditional distributions fitted to the original data using parametric or classification and regression trees models. Data are synthesised via the function syn() which can be largely automated, if default settings are used, or with methods defined by the user. Optional parameters can be used to influence the disclosure risk and the analytical quality of the synthesised data.
Using FreeType to load system fonts
    and Google Fonts (https://www.google.com/fonts) into R.
    It is supposed to support other packages such as 'R2SWF'
    and 'showtext'.
Fitting simultaneous
 systems of linear and nonlinear equations using Ordinary Least
 Squares (OLS), Weighted Least Squares (WLS), Seemingly Unrelated
 Regressions (SUR), Two-Stage Least Squares (2SLS), Weighted
 Two-Stage Least Squares (W2SLS), and Three-Stage Least Squares (3SLS).
A toolbox for systemic risk based on liabilities matrices. Contains
    a Gibbs sampler for liabilities matrices where only row and column sums of the
    liabilities matrix as well as some other fixed entries are observed. Includes models 
    for power law distribution on the degree distribution.
Extracts sentiment and sentiment-derived plot arcs
    from text using three sentiment dictionaries conveniently
    packaged for consumption by R users.  Implemented dictionaries include
    "syuzhet" (default) developed in the Nebraska Literary Lab
    "afinn" developed by Finn {\AA}rup Nielsen, "bing" developed by Minqing Hu
    and Bing Liu, and "nrc" developed by Mohammad, Saif M. and Turney, Peter D.
    Applicable references are available in README.md and in the documentation
    for the "get_sentiment" function.  The package also provides a hack for
    implementing Stanford's coreNLP sentiment parser. The package provides
    several methods for plot arc normalization.
Contains functions for applying the T^2-test for equivalence.
    The T^2-test for equivalence is a multivariate two-sample equivalence test. 
    Distance measure of the test is the Mahalanobis distance.
    For multivariate normally distributed data the T^2-test for equivalence 
    is exact and UMPI.
    The function T2EQ() implements the T^2-test for equivalence 
    according to Wellek (2010) <DOI:10.1201/ebk1439808184>.
    The function T2EQ.dissolution.profiles.hoffelder() implements a variant 
    of the T^2-test for equivalence according to Hoffelder (2016) 
    <http://www.ecv.de/suse_item.php?suseId=Z|pi|8430> for the 
    equivalence comparison of highly variable dissolution profiles.
Contains functions for generating tables for statistical reports written in Microsoft Word or LaTeX. There are functions for I-by-J frequency tables, comparison of means or medians across levels of a categorical variable, and summarizing fitted generalized linear models, generalized estimating equations, and Cox proportional hazards regression. Functions are available to handle data simple random samples or survey data. The package is intended to make it easier for researchers to translate results from statistical analyses in R to their reports or manuscripts.
Sometimes you need to split your data and work on the two chunks independently before bringing them back together. 'Taber' allows you to do that with its two functions.
Some functions are included in this package for writing tables in Excel format suitable for distribution.
Table 1 is the classical way to describe the patients in a
    clinical study. The amount of splits in the data in such a table is
    limited. Table1Heatmap draws a heatmap of all crosstables that can be
    generated with the data. Users can choose between showing the actual
    crosstables or direction of effect of associations, and highlight
    associations by number of patients or p-values.
A collection of time-saving wrappers for producing and exporting
    summary tables commonly used in scientific articles, to .xls/.xlsx multi-tab spreadsheets, while controlling spreadsheet layout. Powered by 'XLConnect'/'rJava' utilities.
A tool to easily create and style HTML tables, which are compatible with shiny.
Provides two classes extending 'data.table' class. Simple 'tableList' class wraps 'data.table' and any additional structures together. More complex 'tableMatrix' class combines strengths of 'data.table' and 'matrix'. See <http://github.com/InferenceTechnologies/tableMatrix> for more information and examples.
Provides a user friendly interface to generation of booktab style
  tables using xtable.
Creates "Table 1", i.e., description of baseline patient
    characteristics, which is essential in every medical research.
    Supports both continuous and categorical variables, as well as
    p-values and standardized mean differences. Weighted data are
    supported via the survey package. See github for a screencast.
    tableone was inspired by descriptive statistics functions in
    Deducer , a Java-based GUI package by Ian Fellows. This package
    does not require GUI or Java, and intended for command-line users.
Computes and displays complex tables of summary statistics.
TableToLongForm automatically converts hierarchical Tables intended for a human reader into a simple LongForm Dataframe that is machine readable.
A tableplot is a visualisation of a (large)
    dataset with a dozen of variables, both numeric and
    categorical. Each column represents a variable and each
    row bin is an aggregate of a certain number of records.
    Numeric variables are visualized as bar charts, and
    categorical variables as stacked bar charts. Missing
    values are taken into account. Also supports large ffdf
    datasets from the ff package.
A tableplot is a visualisation of a (large)
    dataset with a dozen of variables, both numeric and
    categorical. This package contains an interactive version of
    tableplot working in your browser.
R based tabu search algorithm for binary configurations
Contains functions for the easy display of statistical tests as well as
    some convenience functions for recoding or data cleanup. It is meant to ease existing workflows
    with packages like 'sjPlot', 'dplyr', and 'ggplot2'. The primary components are the functions
    prefixed with 'tadaa_', which are built to work in an interactive environment, but also print
    tidy markdown tables powered by 'pixiedust' for the creation of 'RMarkdown' reports.
Provides functions implementing minimal distance estimation methods for parametric tail dependence models.
Set of tools to estimate the probability in the upper tail of the aggregate loss distribution using different methods: Panjer recursion, Monte Carlo simulations, Markov bound, Cantelli bound, Moment bound, and Chernoff bound.
Includes marginal maximum likelihood estimation of uni- and 
    multidimensional item response models (Rasch, 2PL, 3PL, 
    Generalized Partial Credit, Multi Facets,
    Nominal Item Response, Structured Latent Class Analysis,
    Mixture Distribution IRT Models, Located Latent Class Models)
    and joint maximum likelihood estimation for models
    from the Rasch family. 
    Latent regression models and plausible value imputation are 
    also supported.
Density, distribution function, quantile function, random
    generation and survival function for the Tangent Burr Type XII Distribution as
    defined by SOUZA, L. New Trigonometric Class of Probabilistic Distributions.
    219 p. Thesis (Doctorate in Biometry and Applied Statistics) - Department of
    Statistics and Information, Federal Rural University of Pernambuco, Recife,
    Pernambuco, 2015 (available at <http://www.openthesis.org/documents/New-trigonometric-classes-probabilistic-distributions-602633.html>) and BRITO, C. C. R. Method Distributions generator and
    Probability Distributions Classes. 241 p. Thesis (Doctorate in Biometry and
    Applied Statistics) - Department of Statistics 
    University of Pernambuco, Recife, Pernambuco, 2014 (available upon request).
A two-stage regression method that can be used when various input data types are correlated, for example gene expression and methylation in drug response prediction. In the first stage it uses the upstream features (such as methylation) to predict the response variable (such as drug response), and in the second stage it uses the downstream features (such as gene expression) to predict the residuals of the first stage. In our manuscript (Aben et al., 2016, in press), we show that using TANDEM prevents the model from being dominated by gene expression and that the features selected by TANDEM are more interpretable.
Functions for performing analysis of variance on time
        course microarray data
Implementation of functions for fitting taper curves (a semiparametric linear mixed effects taper model) to diameter measurements along stems. Further functions are provided to estimate the uncertainty around the predicted curves, to calculate timber volume (also by sections) and marginal (e.g., upper) diameters. For cases where tree
  heights are not measured, methods for estimating
  additional variance in volume predictions resulting from uncertainties in
  tree height models (tariffs) are provided.  The example data include the taper curve parameters for Norway spruce used in the 3rd German NFI fitted to 380 trees and a subset of section-wise diameter measurements of these trees. The functions implemented here are detailed in the following publication: Kublin, E., Breidenbach, J., Kaendler, G. (2013) A flexible stem taper and volume prediction method based on mixed-effects B-spline regression, Eur J For Res, 132:983-997.
Manager of tick-by-tick transaction data that performs 'cleaning', 'aggregation' and 'import' in an efficient and fast way. The package engine, written in C++, exploits the 'zlib' and 'gzstream' libraries to handle gzipped data without need to uncompress them. 'Cleaning' and 'aggregation' are performed according to Brownlees and Gallo (2006). Currently, TAQMNGR processes raw data from WRDS (Wharton Research Data Service).
Implement the Tariff algorithm for coding cause-of-death from verbal autopsies. The Tariff method was originally proposed in James et al (2011) <DOI:10.1186/1478-7954-9-31> and later refined as Tariff 2.0 in Serina, et al. (2015) <DOI:10.1186/s12916-015-0527-9>. Note that this package was not developed by authors affiliated with the Institute for Health Metrics and Evaluation and thus unintentional discrepancies may exist between the this implementation and the implementation available from IHME.
A collection of various utility and convenience functions.
Evaluates traveltimes and ray paths using predefined Earth
        (or other planet) models.  Includes phase plotting routines.
        The IASP91 and AK135 Earth models are included, and most
        important arrival phases can be evaluated.
Computes the t* statistic corresponding to the tau* population
          coefficient introduced by Bergsma and Dassios (2014) <DOI:10.3150/13-BEJ514>
          and does so in O(n^2*log(n)) time following the algorithm of Weihs,
          Drton, and Leung (2016) <DOI:10.1007/s00180-015-0639-x>. Also allows for
          independence testing using the asymptotic distribution of t* as described by
          Nandy, Weihs, and Drton (2016) <http://arxiv.org/abs/1602.04387>.
Portfolio optimization typically requires an estimate of a covariance matrix of asset returns. There are many approaches for constructing such a covariance matrix, some using the sample covariance matrix as a starting point. This package provides implementations for two such methods: random matrix theory and shrinkage estimation. Each method attempts to clean or remove noise related to the sampling process from the sample covariance matrix.
Base library of types for tawny and related packages
Interacts with a suite of web 'APIs' for taxonomic tasks,
    such as verifying species names, and getting taxonomic 'hierarchies'.
Automated standardization of taxonomic names and removal of orthographic errors in plant species names using 'The Plant List' website (www.theplantlist.org).
Solves Teitz and Bart's p-median problem - given a set of
    points attempts to find subset of size p such that summed distances of any
    point in the set to the nearest point in p is minimised.  Although
    generally effective, this algorithm does not guarantee that a globally
    optimal subset is found.
This package provides functions to assist researchers
        working in the field of tuberculosis diagnostics.  Functions
        for the interpretation of two popular interferon-gamma release
        assays are provided, and additional functionality is planned.
Our method introduces mathematically well-defined measures for tightness of branches in a hierarchical tree. Statistical significance of the findings is determined, for all branches of the tree, by performing permutation tests, optionally with generalized Pareto p-value estimation.
Functions to perform the reliability/survival
  analysis using a parametric Transform-both-sides (TBS) model.
Automatically downloads and processes TCGA genomics and clinical data into a format convenient for statistical analyses in the R environment.
The Cancer Genome Atlas (TCGA) is a program aimed at improving our understanding of Cancer Biology. Several TCGA Datasets are available online. 'TCGAretriever' helps accessing and downloading TCGA data hosted on 'cBioPortal' via its Web Interface (see <http://www.cbioportal.org/web_api.jsp> for more information). Features of 'TCGAretriever' include: 1) it is very simple to use (get all the TCGA data you need with a few lines of code); 2) performance (smooth and reliable data download via 'httr'); 3) it is tailored for downloading large volumes of data.
Implementation of TcGSA, a method for analyzing longitudinal
    gene-expression data at the gene set level.
Gene set analysis of longitudinal RNA-seq data with variance component
    score test accounting for data heteroscedasticity through precision weights.
A series of additional Tcl commands and Tk widgets with style
  and various functions (under Windows: DDE exchange, access to the
  registry and icon manipulation) to supplement the tcltk package
A set of tools for processing and modeling high-throughput and
    high-content chemical screening data. The package was developed for the
    the chemical screening data generated by the US EPA ToxCast program, but
    can be used for diverse chemical screening efforts.
Platform for the advanced analysis of T cell receptor and
    Immunoglobulin repertoires data and visualisation of the analysis results.
Tools for the statistical analysis of persistent homology and for density clustering. For that, this package provides an R interface for the efficient algorithms of the C++ libraries GUDHI, Dionysus, and PHAT.
Topological Data Analysis using Mapper (discrete Morse theory).
    Generate a 1-dimensional simplicial complex from a filter 
    function defined on the data:  1. Define a filter function (lens) on the 
    data.  2. Perform clustering within within each level set and generate 
    one node (vertex) for each cluster.  3. For each pair of clusters in 
    adjacent level sets with a nonempty intersection, generate one edge 
    between vertices.  The function mapper1D uses a filter function with
    codomain R, while the the function mapper2D uses a filter function with
    codomain R^2.
A boosted Tweedie compound Poisson model using the gradient boosting. It is capable of fitting a flexible nonlinear Tweedie compound Poisson model (or a gamma model) and capturing interactions among predictors.
The Time-Delay Correlation algorithm (TDCor) reconstructs the topology of a gene regulatory network (GRN) from time-series transcriptomic data.  The algorithm is described in details in Lavenus et al., Plant Cell, 2015.  It was initially developed to infer the topology of the GRN controlling lateral root formation in Arabidopsis thaliana.  The time-series transcriptomic dataset which was used in this study is included in the package to illustrate how to use it.
Deconvolution of instrument responses from seismic traces and seismogram lists from RSEIS.  Includes pre-calculated instrument responses for several common instruments.
Tuned Data Mining in R (TDMR) performs a complete tuning of a data
    mining task.
Implementation of target diagrams using 'lattice' and 'ggplot2' graphics. Target diagrams provide a graphical overview of the respective contributions of the unbiased RMSE and MBE to the total RMSE (Jolliff, J. et al., 2009. "Summary Diagrams for Coupled Hydrodynamic-Ecosystem Model Skill Assessment." Journal of Marine Systems 76: 64–82.)
Compute time-dependent ROC curve from censored survival data using
    nonparametric weight adjustments.
Transmission/disequilibrium tests for extended marker haplotypes
Demonstration functions that can be used in a classroom to demonstrate statistical concepts, or on your own to better understand the concepts or the programming.
Allows the user to draw probabilistic samples and make inferences from a finite population based on several sampling designs.
Can fit neural networks with up to two hidden layer and two different error functions. Also able to handle a weight decay. But just able to compute one output neuron and very slow.
TED performs Turbulence time series Event Detection and classification.
For fitting multiple linear regressions, the ordinary least squares approach is sensitive to outliers and/or violations of model assumptions. The trimmed elemental estimators are more robust to such situations. This package contains functions for computing the trimmed elemental estimates, as well as for creating the bias-corrected and accelerated bootstrap confidence intervals based on elemental regressions.
Fits mixtures of multivariate t-distributions (with eigen-decomposed covariance structure) via the expectation conditional-maximization algorithm under a clustering or classification paradigm.
R wrapper around the Telegram Bot API (http://core.telegram.org/bots/api) to access Telegram's messaging facilities with ease (e.g. you send messages, images, files from R to your smartphone).
Using The Free Evocation of Words Technique method with some functions, this package will make a
    social representation and other analysis. The Free Evocation of Words Technique consists of collecting a number of words evoked by a subject facing exposure to an inducer term. The purpose of this technique is to understand the relationships created between words evoked by the individual and the inducer term. This technique is included in the theory of social representations, therefore, on the information transmitted by an individual, seeks to create a profile that define a social group.
This is the data companion package to the package tempcycles.
    This package includes the metadata, linear, and cycling parameters from
    "Recent geographic convergence in diurnal and annual temperature cycling
    flattens global thermal profiles", Wang & Dillon, Nature Climate Change,
    4, 988-992 (2014). doi:10.1038/nclimate2378.
Temporal disaggregation methods are used to disaggregate and
    interpolate a low frequency time series to a higher frequency series, where
    either the sum, the average, the first or the last value of the resulting
    high frequency series is consistent with the low frequency series. Temporal
    disaggregation can be performed with or without one or more high frequency
    indicator series. Contains the methods of Chow-Lin, Santos-Silva-Cardoso, 
    Fernandez, Litterman, Denton and Denton-Cholette.
Analysis and visualization of data from temporal sensory methods, including for temporal check-all-that-apply (TCATA) and temporal dominance of sensations (TDS).
The tensor product of two arrays is notionally an outer
        product of the arrays collapsed in specific extents by summing
        along the appropriate diagonals.
The package provides convenience functions for advance
        linear algebra with tensors and computation with datasets of
        tensors on a higher level abstraction. It includes Einstein and
        Riemann summing conventions, dragging, co- and contravariate
        indices, parallel computations on sequences of tensors.
Contains several utility functions for manipulating tensor-valued data (centering, multiplication from a single mode etc.) and the implementations of the following blind source separation methods for tensor-valued data: 'tPCA', 'tFOBI', 'tJADE', 'tgFOBI', 'tgJADE' and 'tSOBI'.
A collection of functions for Kronecker structured covariance
    estimation and testing under the array normal model. For estimation,
    maximum likelihood and Bayesian equivariant estimation procedures are
    implemented. For testing, a likelihood ratio testing procedure is
    available. This package also contains additional functions for manipulating
    and decomposing tensor data sets. This work was partially supported by NSF
    grant DMS-1505136.
The TEQR package contains software to calculate the operating characteristics for the TEQR and the ACT designs.The TEQR (toxicity equivalence range) design is a toxicity based cumulative cohort design with added safety rules. The ACT (Activity constrained for toxicity) design  is also a cumulative cohort design with additional safety rules. The unique feature of this design is that dose is escalated based on lack of activity rather than on lack of toxicity and is de-escalated only if an unacceptable level of toxicity is experienced.
This package is for the comparison of various types of A+B
        escalation rules for dose finding trials.
An integrated set of extensions to the 'ergm' package to analyze and simulate network evolution based on exponential-family random graph models (ERGM). 'tergm' is a part of the 'statnet' suite of packages for network analysis.
The package offers a wide range of functions for term
        structure estimation based on static and dynamic coupon bond
        and yield data sets. The implementation focuses on the cubic
        splines approach of McCulloch (1971, 1975) and the Nelson and
        Siegel (1987) method with extensions by Svensson (1994),
        Diebold and Li (2006) and De Pooter (2007). We propose a
        weighted constrained optimization procedure with analytical
        gradients and a globally optimal start parameter search
        algorithm. Extensive summary statistics and plots are provided
        to compare the results of the different estimation methods.
        Several demos are available using data from European government
        bonds and yields.
A suite of functions for visualising ternary probabilistic
        forecasts.
Simulation of reconstructed phylogenetic trees under tree-wide time-heterogeneous birth-death processes and estimation of diversification parameters under the same model. Speciation and extinction rates can be any function of time and mass-extinction events at specific times can be provided. Trees can be simulated either conditioned on the number of species, the time of the process, or both. Additionally, the likelihood equations are implemented for convenience and can be used for Maximum Likelihood (ML) estimation and Bayesian inference.
Functions for imputing missing item responses for dichotomous and
    polytomous test and assessment data. This package enables missing imputation
    methods that are suitable for test and assessment data, including: listwise (LW)
    deletion, treating as incorrect (IN), person mean imputation (PM), item mean
    imputation (IM), two-way imputation (TW), logistic regression imputation (LR),
    and EM imputation.
tester allows you to test characteristics of common R objects.
Provides a bootstrap test which decides whether two dose response curves can be assumed as equal concerning their maximum absolute deviation. A plenty of choices for the model types are available, which can be found in the 'DoseFinding' package, which is used for the fitting of the models.
Provides two convenience functions assert() and test_pkg() to
    facilitate testing R packages.
GUI for entering test items and obtaining raw
   and transformed scores. The results are shown on the
   console and can be saved to a tabular text file for further
   statistical analysis. The user can define his own tests and
   scoring procedures through a GUI.
These are weighted tests type logrank for recurrent events.
A unit testing system designed to be fun, flexible and easy to
    set up.
Statistical extreme value modelling of threshold excesses,
  maxima and multivariate extremes.  Univariate models for threshold
  excesses and maxima are the Generalised Pareto, and Generalised
  Extreme Value model respectively.  These models may be fitted by
  using maximum (optionally penalised-)likelihood, or Bayesian
  estimation, and both classes of models may be fitted with covariates
  in any/all model parameters.  Model diagnostics support the fitting
  process. Graphical output for visualising fitted models and return
  level estimates is provided.  For serially dependent sequences, the
  intervals declustering algorithm of Ferro and Segers is provided,
  with diagnostic support to aid selection of threshold and declustering
  horizon.  Multivariate modelling is performed via the conditional
  approach of Heffernan and Tawn, with graphical tools for threshold
  selection and to diagnose estimation convergence.
Analysis and visualization of community dynamics in microbial
             ecology experiments (that use sequence count data) using the
             truncated Poisson lognormal distribution.
TExPosition is an extension of ExPosition for two table analyses, specifically, discriminant analyses.
Converts coefficients, standard errors, significance stars, and goodness-of-fit statistics of statistical models into LaTeX tables or HTML tables/MS Word documents or to nicely formatted screen output for the R console for easy model comparison. A list of several models can be combined in a single table. The output is highly customizable. New model types can be easily implemented.
Very fast and memory-friendly tools for text vectorization and
    state-of-the-art word embeddings (GloVe). This package provides a
    source-agnostic streaming API, which allows researchers to perform analysis
    of collections of documents which are much larger than available RAM. All
    core functions are parallelized to benefit from multicore machines.
Multinomial [inverse] regression inference for text documents and associated attributes. Provides fast sparse multinomial logistic regression for phrase counts.  A minimalist partial least squares routine is also included.  Note that the topic modeling capability of textir is now a separate package, maptpx.
An aid for text mining in R, with a syntax that
    should be familiar to experienced R users. Provides a wrapper for several 
    topic models that take similarly-formatted input and give similarly-formatted
    output. Has additional functionality for analyzing and diagnostics for
    topic models.
Statistical exploration of textual corpora using several methods
    from French 'Textometrie' (new name of 'Lexicometrie') and French 'Data Analysis' schools.
    It includes methods for exploring irregularity of distribution of lexicon features across
    text sets or parts of texts (Specificity analysis); multi-dimensional exploration (Factorial analysis), etc. 
    Those methods are used in the TXM software.
Function for sparse regression on raw text, regressing a labeling
    vector onto a feature space consisting of all possible phrases.
Tools for measuring similarity among documents and detecting
    passages which have been reused. Implements shingled n-gram, skip n-gram,
    and other tokenizers; similarity/dissimilarity functions; pairwise
    comparisons; minhash and locality sensitive hashing algorithms; and a
    version of the Smith-Waterman local alignment algorithm suitable for
    natural language.
The TFDEA algorithm for technology forecasts when future products
   will be introduced based upon their features.
   It also includes DEA (Data Envelopment Analysis) functions including extensions dealing with
   with infeasibility.
   In addition it includes some standard technology forecasting data sets.
Statistical interpretation of forensic glass transfer
        (Simulation of the probability distribution of recovered glass
        fragments).
In putative Transcription Factor Binding Sites (TFBSs) 
             identification from sequence/alignments,
             we are interested in the significance of certain match score.
             TFMPvalue provides the accurate calculation of P-value with 
             score threshold for Position Weight Matrices, 
             or the score with given P-value. 
             This package is an interface to code originally made available by 
             Helene Touzet and Jean-Stephane Varre, 2007, 
             Algorithms Mol Biol:2, 15.
Utilities for simple manipulation and quick  	
	plotting of time series data. These utilities use the tframe package
	which provides a programming kernel for time series. Extensions to
	tframe provided in tframePlus can also be used. See the Guide vignette
	for examples.
A kernel of functions for programming 
	time series methods in a way that is relatively independently of the 
	representation of time. Also provides plotting, time windowing, 
	and some
	other utility functions which are specifically intended for time series.
	See the Guide distributed as a vignette, or ?tframe.Intro for more
	details. (User utilities are in package tfplot.)
Extensions and additional 'tframe' utilities.
Connects R to TrueFX(tm) for free streaming real-time and
        historical tick-by-tick market data for dealable interbank
        foreign exchange rates with millisecond detail.
Deconvolving thermoluminescence glow curves according to the general-order 
        empirical expression or the semi-analytical expression derived from the one trap-
        one recombination (OTOR) model using a modified Levenberg-Marquardt algorithm. 
        It provides the possibility of setting constraints or fixing any of parameters. 
        It offers an interactive way to initialize parameters by clicking with a mouse 
        on a plot at positions where peak maxima should be located. The optimal estimate 
        is obtained by "trial-and-error". It also provides routines for simulating 
        first-order, second-order, and general-order glow peaks (curves).
Density, distribution function, quantile function and random generation for the Truncated Generalised Gamma Distribution (also in log10(x) and ln(x) space).
Use Gibbs sampler with Polya-Gamma data augmentation to fit logistic and probit regression under independent Student-t priors (including Cauchy priors and normal priors as special cases).
Bayesian nonstationary, semiparametric nonlinear regression 
 and design by treed Gaussian processes (GPs) with jumps to the limiting 
 linear model (LLM).  Special cases also implemented include Bayesian 
 linear models, CART, treed linear models, stationary separable and 
 isotropic GPs, and GP single-index models.  Provides 1-d and 2-d plotting functions 
 (with projection and slice capabilities) and tree drawing, designed for 
 visualization of tgp-class output.  Sensitivity analysis and 
 multi-resolution models are supported. Sequential experimental 
 design and adaptive sampling functions are also provided, including ALM, 
 ALC, and expected improvement.  The latter supports derivative-free
 optimization of noisy black-box functions.
Functions to compute and plot tracheidograms
Contains data sets used in other packages Torsten Hothorn
  maintains.
A collection of functions and routines for inputting thermal
    image video files, plotting and converting binary raw data into estimates of
    temperature.  First published 2015-03-26.  Written primarily for research purposes
    in biological applications of thermal images.  v1 included the base calculations 
    for converting thermal image binary values to temperatures. v2 included additional
    equations for providing heat transfer calculations and an import function for thermal
    image files.
Temperature measurement data, equations and methods for thermocouples,
    wire RTD, thermistors, IC thermometers, bimetallic strips and the ITS-90.
Methods and tools for generating forecasts at different temporal
    frequencies using a hierarchical time series approach.
This package interacts directly with the Thinknum API to offer data
    in a number of formats usable in R
Design and analyze three-arm non-inferiority or superiority trials
    which follow a gold-standard design, i.e. trials with an experimental treatment,
    an active, and a placebo control.
This package implements a thresholded version of the EEBoost
    algorithm described in [Wolfson (2011, JASA)]. EEBoost is a general-purpose
    method for variable selection which can be applied whenever inference would
    be based on an estimating equation. The package currently implements
    variable selection based on the Generalized Estimating Equations, but can
    also accommodate user-provided estimating functions. Thresholded EEBoost is
    a generalization which allows multiple variables to enter the model at each
    boosting step.
Implements the Maximum Likelihood estimator for baseline, placebo, and treatment groups (three-group) experiments with non-compliance proposed by Gerber, Green, Kaplan, and Kern (2010).
Create interactive 3D scatter plots, network plots, and
    globes using the 'three.js' visualization library ("http://threejs.org").
Component analysis for three-way data arrays by means of Candecomp/Parafac, Tucker3, Tucker2 and Tucker1 models.
A connector to the 'What3Words' (http://what3words.com/) service, which represents each 3m by 3m square on earth
             with a unique trio of English-language words.
Fit a threshold regression model based on the first-hitting-time of a boundary by the sample path of a Wiener diffusion process. The threshold regression methodology is well suited to applications involving survival and time-to-event data.
Fit a threshold regression model for interval-censored data based on the first-hitting-time of a boundary by the sample path of a Wiener diffusion process. The threshold regression methodology is well suited to applications involving survival and time-to-event data.
Point and interval estimations of optimum thresholds for continuous diagnostic tests (two- and three- state settings).
Fit the Simultaneous Systems of Linear Equations using Three-stage Least Squares.
Provides a 'tbl_df' class that offers better checking and
    printing capabilities than traditional data frames.
Functions to execute orders in backtesting using tick data. A testing platform was established by the four major execution functions, namely 'LimitBuy', 'LimitSell', 'MarketBuy' and 'MarketSell', which enclosed all tedious aspects (such as queueing for order executions and calculate actual executed volumes) for order execution using tick data. Such that one can focus on the logic of strategies, rather than its execution.
This package provides the timing functions 'tic' and 'toc' that
    can be nested. One can record all timings while a complex script is
    running, and examine the values later. It is also possible to instrument
    the timing calls with custom callbacks. In addition, this package provides
    class 'Stack', implemented as a vector, and class 'List', implemented as a
    list, both of which support operations 'push', 'pop', 'first', 'last' and
    'clear'.
Utilities to generate wiki reports in TiddlyWiki format.
Tidal analysis of evenly spaced observed time series (time step 1 to 60 min) with or
    without shorter gaps.
    The analysis should preferably cover an observation period of at least 19 years.
    For shorter periods low frequency constituents are not taken into account, in accordance with the Rayleigh-Criterion.
    The main objective of this package is to synthesize or predict a tidal time series.
Implements harmonic analysis of tidal and sea-level data.
  Over 400 harmonic tidal constituents can be estimated, all with 
  daily nodal corrections. Time-varying mean sea-levels can also
  be used.
Calculate Characteristics of Quasi-Periodic Time Series, e.g. Estuarine Water Levels.
Tide analysis and prediction of predominantly semi-diurnal tides
    with two high waters and two low waters during one lunar day (~24.842 hours,
    ~1.035 days). The analysis should preferably cover an observation period of at
    least 19 years. For shorter periods, for example, the nodal cycle can not be
    taken into account, which particularly affects the height calculation. The main
    objective of this package is to produce tide tables.
An easy and consistent way to turn 'JSON' into tidy data frames
    that are natural to work with in 'dplyr', 'ggplot2' and other tools.
An evolution of 'reshape2'. It's designed specifically for data
    tidying (not general reshaping or aggregating) and works well with
    'dplyr' data pipelines.
Text mining for word processing and sentiment analysis using
    'dplyr', 'ggplot2', and other tidy tools.
The 'tidyverse' is a set of packages that work in harmony
    because they share common data representations and 'API' design. This
    package is designed to make it easy to install and load multiple
    'tidyverse' packages in a single step. Learn more about the 'tidyverse'
    at <https://github.com/hadley/tidyverse>.
This package provides an easy and simple way to read, write and display bitmap images stored in the TIFF format. It can read and write both files and in-memory raw vectors.
Temporally resolved groups of typical differences (errors) between two time series are determined and visualized
Pre-process for discrete time series data set which is not continuous at the column
    of 'date'. Refilling records of missing 'date' and other columns to the hollow data set so that
    final data set is able to be dealt with time series analysis.
A collection of data sets and functions that are useful in the
    teaching of statistics at an elementary level to students who may have
    little or no previous experience with the command line.  The functions for
    elementary inferential procedures follow a uniform interface for user
    input.  Some of the functions are instructional applets that can only be
    run on the R Studio integrated development environment with package
    'manipulate' installed.  Other instructional applets are Shiny apps
    that may be run locally. In teaching the package is used alongside of
    package 'mosaic', 'mosaicData' and 'abd', which are therefore listed as
    dependencies.
Infers the V genotype of an individual from immunoglobulin (Ig)
    repertoire-sequencing (Rep-Seq) data, including detection of any novel
    alleles. This information is then used to correct existing V allele calls
    from among the sample sequences.
This package contains functions for tight clustering
        Algorithm.
Download TIGER/Line shapefiles from the United States Census Bureau
    and load into R as 'SpatialDataFrame' objects.
Provides a graphics output device for R that records plots
        in a LaTeX-friendly format. The device transforms plotting
        commands issued by R functions into LaTeX code blocks. When
        included in a LaTeX document, these blocks are interpreted with
        the help of 'TikZ'---a graphics package for TeX and friends
        written by Till Tantau. Using the 'tikzDevice', the text of R
        plots can contain LaTeX commands such as mathematical formula.
        The device also allows arbitrary LaTeX code to be inserted into
        the output stream.
Methods and classes to build HMMs
        that are suitable for the analysis of ChIP-chip data. The
        provided parameter estimation methods include the Baum-Welch
        algorithm and Viterbi training as well as a combination of
        both.
This package is intended for processing the output from
        functional gene tiling DNA microarray experiments. It produces
        hybridization pattern plots for each gene on the array, and
        statistics for each gene including mean probe intensity, median
        probe intensity, bright probe fraction, bright segment length
        dependent score, bright probe mean intensity, and bright probe
        median intensity. Output is generated in order of bright
        segment length dependent score in both a latex/eps format and
        tab-delimited text file. The package works in two modes: single
        array, and comparison of two arrays. Array comparison includes
        array comparison statistics: median of logarithm of one array
        probe divided by its counterpart on the other array, median
        absolute deviation of that value, and the binomial test to see
        whether the genes are equally abundant in both arrays.
Implements an algorithm for variable selection in high-dimensional linear regression using the "tilted correlation", a new way of measuring the contribution of each variable to the response which takes into account high correlations among the variables in a data-driven way.
Cox proportional hazard and competing risk regression analyses can be performed with time-to-event data as covariates.
Environment for teaching 
	"Financial Engineering and Computational Finance".
	Managing chronological and calendar objects.
We provide a toolbox to estimate the time delay between the brightness time series of gravitationally lensed quasar images via Bayesian and profile likelihood approaches. The model is based on a state-space representation for  irregularly observed time series data generated from a latent continuous-time Ornstein-Uhlenbeck process. Our Bayesian method adopts scientifically motivated hyper-prior distributions and a Metropolis-Hastings within Gibbs sampler, producing posterior samples of the model parameters that include the time delay. A profile likelihood of the time delay is a simple approximation to the marginal posterior distribution of the time delay. Both Bayesian and profile likelihood approaches complement each other, producing almost identical results; the Bayesian way is more principled but the profile likelihood is easier to implement.
Provides an easy-to-use wrapper to Rprof() and
        summaryRprof() in the profiling of both function calls and
        whole blocks of code.
An easy tool for plotting annotated timelines, grouped timelines, and exploratory graphics (boxplot/histogram/density plot/scatter plot/line plot). Filter, summarize date data by duration and convert to calendar units.
Implements the Time Machine, a simulation approach for stochastic
             trees.
Methods for incorporating time into network analysis. Construction of time-ordered networks (temporal graphs). Shortest-time and shortest-path-length analyses. Resource spread calculations. Data resampling and rarefaction for null model construction. Reduction to time-aggregated networks with variable window sizes; application of common descriptive statistics to these networks. Vector clock latencies. Plotting functionalities.
Extract useful time components of a date object, such as
        day of week, weekend, holiday, day of month, etc, and put it in
        a data frame.  This can be used to create many predictor
        variables out of a single time variable, which can then be used
        in a regression or decision tree.  Also includes function
        plotCalendarHeatmap which draws a calendar and overlays a
        heatmap based on values.
Programs for Martinussen and Scheike (2006), `Dynamic Regression
    Models for Survival Data', Springer Verlag.  Plus more recent developments.
    Additive survival model, semiparametric proportional odds model, fast cumulative
    residuals, excess risk models and more. Flexible competing risks regression
    including GOF-tests. Two-stage frailty modelling. 
    PLS for the additive risk model. Lasso in the 'ahaz' package.
Estimation of time-dependent ROC curve and area under time dependent ROC curve (AUC) in the presence of censored data, with or without competing risks. Confidence intervals of AUCs and tests for comparing AUCs of two rival markers measured on the same subjects can be computed, using the iid-representation of the AUC estimator. Plot functions for time-dependent ROC curves and AUC curves are provided. Time-dependent Positive Predictive Values (PPV) and Negative Predictive Values (NPV) can also be computed.
Computes bootstrap CI for the sample ACF and periodogram
Uses a negative binomial mixed-effects (NBME) model to detect nonparallel differential expression(NPDE) genes and parallel differential expression(PDE) genes in the time course RNA-seq data.
Environment for teaching 
	"Financial Engineering and Computational Finance".
	Managing financial time series objects.
Store and organize a large amount of low frequency time series data. 
    The package was designed to manage a large catalog of official statistics which are
    typically published on monthly, quarterly or yearly basis. Thus timeseriesdb is
    optimized to handle a large number of lower frequency time series as opposed to a
    smaller amount of high frequency time series such as real time data from measuring devices.
    Hence timeseriesdb provides the opportunity to store extensive multi-lingual
    meta information. The package also provides a web GUI to explore the underlying
    PostgreSQL database interactively.
Objects to manipulate sequential and seasonal time series. Sequential time series based on time instants and time durations are handled. Both can be regularly or unevenly spaced (overlapping durations are allowed). Only POSIX* format are used for dates and times. The following classes are provided : 'POSIXcti', 'POSIXctp', 'TimeIntervalDataFrame', 'TimeInstantDataFrame', 'SubtimeDataFrame' ; methods to switch from a class to another and to modify the time support of series (hourly time series to daily time series for instance) are also defined. Tools provided can be used for instance to handle environmental monitoring data (not always produced on a regular time base).
A interface to the TimeTree of Life Webpage (www.timetree.org). TimeTree is a public database for information on the evolutionary timescale of life. This package includes functions for searching divergence time for  taxa or all nodes of a phylogeny.
Create rich and fully interactive timeline visualizations.
    Timelines can be included in Shiny apps and R markdown documents, or viewed
    from the R console and RStudio Viewer. 'timevis' includes an extensive API
    to manipulate a timeline after creation, and supports getting data out of
    the visualization into R. Based on the 'vis.js' Timeline module and the
    'htmlwidgets' R package.
Date sequence, relative date calculations, and date manipulation with business days
  and holidays.  Works with Date and POSIXt classes.
Prediction and ranking of drug combinations based on their drug-target interaction profiles and single-drug sensitivities in a given cancer cell line or patient-derived sample.
A problem-solving environment (PSE) for fitting
         separable nonlinear models to measurements arising in physics
         and chemistry experiments; has been extensively applied to
         time-resolved spectroscopy and FLIM-FRET data.
Functions for statistical analysis, prediction and control of time series.
A universal non-uniform random number generator
  for quite arbitrary distributions with piecewise twice
  differentiable densities.
Non-parametric resampling-based inference tests for TExPosition.
Assist performing tip-dating of phylogenetic trees with BEAST.
TIPOM is based on a methodology that was developed in the
  1960s by Bernardino Bagolini. The basic idea is to use
  the three simple dimensions of length, width and thickness
  of each lithic artefact to classify them in discrete
  groups and infer their function.
Using the idea of "tipping point" (proposed in
   Gregory Campbell, Gene Pennello and Lilly Yue(2011)
   <DOI:10.1080/10543406.2011.550094>) to visualize  the results
   of sensitivity analysis for missing data, the package provides
   a set of functions to list out all the possible combinations
   of the values of missing data in two treatment arms, calculate
   corresponding estimated treatment effects and p values and draw
   a colored heat-map to visualize them. It could deal with randomized
   experiments with a binary outcome or a continuous outcome. In addition,
   the package provides a visualized method to compare various imputation
   methods by adding the rectangles or convex hulls on the basic plot.
Functions and S3 classes for time indexes and time indexed
        series, which are compatible with FAME frequencies.
GUI to analyze mass spectrometric data on the relative
        abundance of two substances from a titration series.
Uses indicator species scores across binary partitions of
  a sample set to detect congruence in taxon-specific changes of abundance
  and occurrence frequency along an environmental gradient as evidence of
  an ecological community threshold.  Relevant references include: Baker,
  ME and RS King. 2010. A new method for detecting and interpreting
  biodiversity and ecological community thresholds.  Methods in Ecology and
  Evolution 1(1): 25:37. King, RS and ME Baker. 2010. Considerations for
  identifying and interpreting ecological community thresholds.  Journal
  of the North American Benthological Association 29(3):998-1008. Baker ME
  and RS King. 2013. Of TITAN and straw men: an appeal for greater
  understanding of community data. Freshwater Science 32(2):489-506.
This data set provides information on the fate of passengers on
    the fatal maiden voyage of the ocean liner "Titanic", summarized according
    to economic status (class), sex, age and survival. Whereas the base R
    Titanic data found by calling data("Titanic") is an array resulting from
    cross-tabulating 2201 observations, these data sets are the individual
    non-aggregated observations and formatted in a machine learning context
    with a training sample, a testing sample, and two additional data sets
    that can be used for deeper machine learning analysis. These data sets
    are also the data sets downloaded from the Kaggle competition and thus
    lowers the barrier to entry for users new to R or machine learing.
A collection of functions to plot acid/base titration 
    curves (pH vs. volume of titrant), complexation titration curves 
    (pMetal vs. volume of EDTA), redox titration curves (potential 
    vs.volume of titrant), and precipitation titration curves (either 
    pAnalyte or pTitrant vs. volume of titrant). Options include the 
    titration of mixtures, the ability to overlay two or more 
    titration curves, and the ability to show equivalence points.
Pairwise evolutionary distance estimation between protein sequences with the TKF91 and TKF92 model, which consider all the possible paths of transforming from one sequence to another.
simple mechanism for placing R graphics in a Tk widget
Contains functions for training and applying two-level random forest and hidden Markov models for human behavior classification from raw tri-axial accelerometer and/or GPS data. Includes functions for training a two-level model, applying the model to data, and computing performance.
A series of function to make thermoluminescence dating using the MAAD or the SAR protocol.
    This package completes the R package "Luminescence."
TLE implements a general framework for robust fitting of
        finite mixture models. Parameter estimation is performed using
        the EM algorithm.
Computation of effects under linear, logistic and Poisson regression models with transformed variables. Logarithm and power transformations are allowed. Effects can be displayed both numerically and graphically in both the original and the transformed space of the variables.
Fit a linear mixed effects model for censored data with
        Student-t or normal distributions. The errors are assumed
        independent and identically distributed.
Functions for two level normal models as described in
        Everson and Morris (2000). J. R. Statist. Soc. B, 62 prt 2,
        pp.399--412.
A framework for text mining applications within R.
Thematic maps are geographical maps in which spatial data distributions are visualized. This package offers a flexible, layer-based, and easy to use approach to create thematic maps, such as choropleths and bubble maps.
With this tool, a user should be able to quickly implement complex
    random effect models through simple C++ templates. The package combines
    CppAD (C++ automatic differentiation), Eigen (templated matrix-vector
    library) and CHOLMOD (sparse matrix routines available from R) to obtain an
    efficient implementation of the applied Laplace approximation with exact
    derivatives. Key features are: Automatic sparseness detection, parallelism
    through BLAS and parallel user templates.
Provides an R-interface to the TMDb API (see TMDb API on <http://docs.themoviedb.apiary.io/#>). The Movie Database (TMDb) is a popular user editable database for movies and TV shows (see <https://www.themoviedb.org>).
Random number generation of truncated multivariate Gaussian distributions using Hamiltonian Monte Carlo. The truncation is defined using linear and/or quadratic polynomials.
Miscellaneous data and utility functions
    for manipulating data and your R environment.
tmle implements targeted maximum likelihood estimation, first described in van der Laan and Rubin, 2006 (Targeted Maximum Likelihood Learning, The International Journal of biostatistics, 2(1), 2006.  This version adds the tmleMSM function to the package, for estimating the parameters of a marginal structural model (MSM) for a binary point treatment effect.  The tmle function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes.  Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. Effect estimation stratified by a binary mediating variable is also available. The population mean is calculated when there is missingness, and no variation in the treatment assignment.  An ID argument can be used to identify repeated measures. Default settings call SuperLearner to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.
Estimation of average causal effects for single time point
    interventions in network-dependent data (e.g., in the presence of spillover
    and/or interference). Supports arbitrary interventions (static or
    stochastic). Implemented estimation algorithms are the targeted maximum
    likelihood estimation (TMLE), the inverse-probability-of-treatment (IPTW)
    estimator and the parametric G-computation formula estimator. Asymptotically
    correct influence-curve-based confidence intervals are constructed for the
    TMLE and IPTW. The data are assumed to consist of rows of unit-specific
    observations, each row i represented by variables (F.i,W.i,A.i,Y.i), where
    F.i is a vector of friend IDs of unit i (i's network), W.i is a vector of 
    i's baseline covariates, A.i is i's exposure (can be binary, categorical or
    continuous) and Y.i is i's binary outcome. Exposure A.i depends on 
    (multivariate) user-specified baseline summary measure(s) sW.i, where sW.i
    is any function of i's baseline covariates W.i and the baseline covariates
    of i's friends in F.i. Outcome Y.i depends on sW.i and (multivariate)
    user-specified summary measure(s) sA.i, where sA.i is any function of i's
    baseline covariates and exposure (W.i,A.i) and the baseline covariates and
    exposures of i's friends. The summary measures are defined with functions
    def.sW and def.sA. See ?'tmlenet-package' for a general overview.
Targeted minimum loss estimation (TMLE) of a non-parametric variable importance measure of a continuous exposure 'X' on an outcome 'Y', taking baseline covariates 'W' into account.
Methods and feature set definitions for feature or gene set
  enrichment analysis in transcriptional and metabolic profiling data.
  Package includes tests for enrichment based on ranked lists of features,
  functions for visualisation and multivariate functional analysis.
This package provides a tm Source to create corpora from
  a corpus prepared in the format used by the Alceste application (i.e.
  a single text file with inline meta-data). It is able to import both
  text contents and meta-data (starred) variables.
A plug-in for the text mining framework tm to support text mining 
             in a distributed way. The package provides a convenient interface for
             handling distributed corpus objects based on distributed list objects.
Provides a 'tm' Source to create corpora from
  articles exported from the 'Europresse' content provider as
  HTML files. It is able to read both text content and meta-data
  information (including source, date, title, author and pages).
This package provides a tm Source to create corpora from
  articles exported from the Dow Jones Factiva content provider as
  XML or HTML files. It is able to read both text content and meta-data
  information (including source, date, title, author, subject,
  geographical coverage, company, industry, and various
  provider-specific fields).
Provides a 'tm' Source to create corpora from
  articles exported from the 'LexisNexis' content provider as
  HTML files. It is able to read both text content and meta-data
  information (including source, date, title, author and pages).
  Note that the file format is highly unstable: there is no warranty
  that this package will work for your corpus, and you may have
  to adjust the code to adapt it to your particular format.
A plug-in for the tm text mining framework providing mail handling
  functionality.
Facilitate text retrieval from feed
    formats like XML (RSS, ATOM) and JSON. Also direct retrieval from
    HTML is supported. As most (news) feeds only incorporate small
    fractions of the original text tm.plugin.webmining even retrieves
    and extracts the text of the original text source.
Trauma Mortality prediction for ICD-9, ICD-10, and AIS lexicons in
    long or wide format based on Dr. Alan Cook's tmpm mortality model.
Importance sampling from the truncated multivariate normal using the GHK (Geweke-Hajivassiliou-Keane) simulator.
  Unlike Gibbs sampling which can get stuck in one truncation sub-region depending on initial values, this package allows 
  truncation based on disjoint regions that are created by truncation of absolute values. The GHK algorithm uses simple Cholesky
  transformation followed by recursive simulation of univariate truncated normals hence there are also no convergence issues. 
  Importance sample is returned along with sampling weights, based on which, one can calculate integrals over truncated regions
  for multivariate normals.
Random number generation for the truncated multivariate normal and Student t distribution. 
  Computes probabilities, quantiles and densities, 
  including one-dimensional and bivariate marginal densities. Computes first and second moments (i.e. mean and covariance matrix) for the double-truncated multinormal case.
Temporal and cross-sectional network autocorrelation models (TNAM).
R package for analyzing weighted, two-mode, and longitudinal networks.
A consistent set of tools to perform in-database analytics
    on Teradata Aster Big Data Discovery Platform. toaster (a.k.a 'to Aster')
    embraces simple 2-step approach: compute in Aster - visualize and analyze
    in R. Its `compute` functions use combination of parallel SQL, SQL-MR and
    SQL-GR executing in Aster database - highly scalable parallel
    and distributed analytical platform. Then `create` functions visualize
    results with boxplots, scatterplots, histograms, heatmaps, word clouds,
    maps, networks, or slope graphs. Advanced options such as faceting, coloring,
    labeling, and others are supported with most visualizations.
Construction of the Total Operating Characteristic (TOC) Curve and the Receiver (aka Relative) Operating Characteristic (ROC) Curve for spatial and non-spatial data. The TOC method is a modification of the ROC method which measures the ability of an index variable to diagnose either presence or absence of a characteristic. The diagnosis depends on whether the value of an index variable is above a threshold. Each threshold generates a two-by-two contingency table, which contains four entries: hits (H), misses (M), false alarms (FA), and correct rejections (CR). While ROC shows for each threshold only two ratios, H/(H + M) and FA/(FA + CR), TOC reveals the size of every entry in the contingency table for each threshold (Pontius Jr., R.G., Si, K. 2014. The total operating characteristic to measure diagnostic ability for multiple thresholds. Int. J. Geogr. Inf. Sci. 28 (3), 570-583).
Convert natural language text into tokens. The tokenizers have a
    consistent interface and are compatible with Unicode, thanks to being built
    on the 'stringi' package. Includes tokenizers for shingled n-grams, skip
    n-grams, words, word stems, sentences, paragraphs, characters, lines, and
    regular expressions.
Imports the fundamental definitions and utilities of the Time Oriented Language (TOL), focused on time series analysis and stochastic processes, and provides the basis for the integration of TOL in R. See <https://www.tol-project.org> for more information about the TOL project.
Statistical tolerance limits provide the limits between which we can expect to find a specified proportion of a sampled population with a given level of confidence.  This package provides functions for estimating tolerance limits for various distributions.  Plotting is also available for tolerance limits of continuous random variables.
Function for converting cardinal to ordinal numbers by adding a language specific ordinal indicator (http://en.wikipedia.org/wiki/Ordinal_indicator) to the number.
Provides an interface to the C code for Latent Dirichlet
	     Allocation (LDA) models and Correlated Topics Models
	     (CTM) by David M. Blei and co-authors and the C++ code
	     for fitting LDA models using Gibbs sampling by Xuan-Hieu
	     Phan and co-authors.
For multiple ranked input lists (full or partial) representing the same set of N objects, the package TopKLists offers (1) statistical inference on the lengths of informative top-k lists, (2) stochastic aggregation of full or partial lists, and (3) graphical tools for the statistical exploration of input lists, and for the visualization of aggregation results.
Set of hydrological functions including an R
        implementation of the hydrological model TOPMODEL, which is
        based on the 1995 FORTRAN version by Keith Beven. From version
        0.7.0, the package is put into maintenance mode. New functions
        for hydrological analysis are now developed as part of the
        RHydro package. RHydro can be found on R-forge and is built on
        a set of dedicated S4 classes.
Using Gaussian graphical models we propose a novel approach to
             perform pathway analysis using gene expression. Given the
             structure of a graph (a pathway) we introduce two statistical
             tests to compare the mean and the concentration matrices between
             two groups. Specifically, these tests can be performed on the
             graph and on its connected components (cliques).
Evaluation of alternatives based on multiple criteria using TOPSIS method.
Fit an Instrumental Variables Two Stage Least Squares model
Quantifies how balanced a phylogenetic tree is, using the Total Cophenetic Index
  - per A. Mir, F. Rossello, L. A. Rotger (2013), A new balance index for phylogenetic trees.
  Math. Biosci. 241, 125-136 <DOI:10.1016/j.mbs.2012.10.005>.
Implements geodesic interpolation and basis
    generation functions that allow you to create new tour
    methods from R.
The GUI allows user to control the tour by checkboxes for
        the variable selection, slider for the speed, and toggle boxes
        for pause.
Provides methods to use bootstrapping to quantify uncertainty
    in fitting 'ToxCast' concentration response data. Data is stored in memory,
    written to file, or stored in 'MySQL' or 'MongoDB' databases.
Calculates sample size and dose allocation for binary toxicity 
 tests, using the Fish Embryo Toxicity Test as example. 
 An optimal test design is obtained by running 
 (i) spoD (calculate the number of individuals to test under control 
 conditions), (ii) setD (estimate the minimal sample size per treatment
 given the users precision requirements) and (iii) doseD (construct 
 an individual dose scheme).
Tools for estimating and inferring two-way partial area under receiver operating characteristic curves (two-way pAUC), partial area under receiver operating characteristic curves (pAUC), and partial area under ordinal dominance curves (pODC). Methods includes Mann-Whitney statistic and Jackknife, etc. Plots of regions under corresponding curves can also be generated.
This package implements the greedy approximation for tree
        preserving embedding.
We described a novel Topology-based pathway enrichment analysis (TPEA), which integrated the global position of the nodes and the topological property of the pathways in KEGG Database.
Estimation of transition probabilities for the illness-death model. Both the Aalen-Johansen estimator for a Markov model and a novel non-Markovian estimator (de Una-Alvarez and Meira-Machado, 2015) are included.
Estimation of transition probabilities for the
        illness-death model and or the three-state progressive model.
Regression models for temporal process responses with
        time-varying coefficient.
Plant ecologists often need to collect "traits" data 
	     about plant species which are often scattered among various 
	     databases: TR8 contains a set of tools which take care of
	     automatically retrieving some of those functional traits data
	     for plant species from publicly available databases (Biolflor, 
	     The Ecological Flora of the British Isles, LEDA traitbase, Ellenberg 
	     values for Italian Flora, Mycorrhizal intensity databases, Catminat, BROT,
	     PLANTS, Jepson Flora Project).
	     The TR8 name, inspired by "car plates" jokes, was chosen since
	     it both reminds of the main object of the package and is
	     extremely short to type.
Contains functions to standardize tracheid profiles
    using the traditional method (Vaganov) and a new method to standardize
    tracheidograms based on the relative position of tracheids within tree rings.
Automatically stores objects in files on disk
  so that files are rewritten when objects are changed, and
  so that objects are accessible but do not occupy memory
  until they are accessed. Keeps track of times when objects
  are created and modified, and caches some basic
  characteristics of objects to allow for fast summaries of
  objects.  Also provides a command history mechanism that
  saves the last command to a history file after each
  command completes.
The aim of this package is to provide infrastructure for handling running and cycling
    data from GPS-enabled tracking devices. After extraction and appropriate
    manipulation of the training or competition attributes, the data are placed
    into session-based and unit-aware data objects of class trackeRdata (S3 class). The
    information in the resulting data objects can then be visualised, summarised,
    and analysed through corresponding flexible and extensible methods.
Reconstructs animal tracks from magnetometer, accelerometer, depth and optional speed data.  Designed primarily using data from Wildlife Computers Daily Diary tags deployed on northern fur seals.
Functions for working with magnetic resonance images. Analyze,
    NIfTI-1, NIfTI-2 and MGH format images can be read and written; DICOM files
    can only be read.
Tree Ring Analysis of Disturbance Events in R (TRADER) package provides only one way for disturbance reconstruction from tree-ring data.
Contains trades from the five major assets classes and also functionality to use pricing curves, rating tables, CSAs and add-on tables. The implementation follows an object oriented logic whereby each trade inherits from more abstract classes while also the curves/tables are objects. There is a lot of functionality focusing on the counterparty credit risk calculations however the package can be used for trading applications in general.
An interface for creating GUIs modeled in part after the
    traits UI module for python. The basic design takes advantage of
    the model-view-controller design pattern. The interface makes basic
    dialogs quite easy to produce.
Species trait data from many different sources, including
    sequence data from 'NCBI', plant trait data from 'BETYdb', invasive species
    data from the Global Invasive Species Database and 'EOL', 'Traitbank' data
    from 'EOL', Coral traits data from http://coraltraits.org, 'nativity' status
    ('Flora Europaea' or 'ITIS'), and 'Birdlife' International.
Implements a predictive model of community assembly called 'Traitspace' (Laughlin et al. 2012, Ecology Letters). Traitspace is a hierarchical Bayesian model that translates the theory of trait-based environmental filtering into a statistical model that incorporates intraspecific trait variation to predict the relative abundances and the distributions of species. 
	The package includes functions to plot the predicted and the observed values. It also includes functions to compare the predicted values against the observed values using a variety of different distance measures and to implement permutation tests to test their statistical significance.
Implements the three-step procedure proposed by Leffondree et al. (2004) to identify clusters of individual longitudinal trajectories. The procedure involves (1) calculating 24 measures describing the features of the trajectories; (2) using factor analysis to select a subset of the 24 measures and (3) using cluster analysis to identify clusters of trajectories, and classify each individual trajectory in one of the clusters.
Classes and methods for trajectory data, with nested classes for individual trips, and collections for different entities. Methods include selection, generalization, aggregation, intersection, and plotting.
Toolbox for the manipulation, description and rendering of sequences, and more generally the mining of sequence data in the field of social sciences. Although the toolbox is primarily intended for analyzing state or event sequences that describe life courses such as family formation histories or professional careers, its features also apply to many other kinds of categorical sequence data. It accepts many different sequence representations as input and provides tools for converting sequences from one format to another. It offers several functions for describing and rendering sequences, for computing distances between sequences with different metrics (among which optimal matching), original dissimilarity-based analysis tools, and simple functions for extracting the most frequent subsequences and identifying the most discriminating ones among them. A user's guide can be found on the TraMineR web page.
Collection of ancillary functions and utilities to be used in conjunction with the TraMineR package for sequence data exploration. Most of the functions are in test phase, lack systematic consistency check of the arguments and are subject to changes. Once fully checked, some of the functions of this collection could be included in a next release of TraMineR.
Matching terminal restriction fragment length
        polymorphism ('TRFLP') profiles between unknown samples and a
        database of known samples.  TRAMPR facilitates analysis of
        many unknown profiles at once, and provides tools for working
        directly with electrophoresis output through to generating
        summaries suitable for community analyses with R's rich set of
        statistical functions.  TRAMPR also resolves the issues of
        multiple 'TRFLP' profiles within a species, and shared 'TRFLP'
        profiles across species.
Transcribes audio to text with the HP IDOL API. Includes functions to upload files, 
	     retrieve transcriptions, and monitor jobs.
Estimates the transfer entropy from one time series to another, where each time series consists of continuous random variables. The transfer entropy is an extension of mutual information which takes into account the direction of information flow, under the assumption that the underlying processes can be described by a Markov model. Two estimation methods are provided. The first calculates transfer entropy as the difference of mutual information. Mutual information is estimated using the Kraskov method, which builds on a nearest-neighbor framework (see package references). The second estimation method estimate transfer entropy via the a generalized correlation sum.
Bindings for the Google Translate API v2
translateR provides easy access to the Google and Microsoft APIs. The package is easy to use with the related R package "stm" for the estimation of multilingual topic models.
Package with translated commands of SPSS. The usage is oriented
    on the handling of SPSS-Syntax. Mainly the package has two purposes:
    It facilitates SPSS-Users to change over to R and aids migration projects from SPSS to R.
R  version 2.1.0 and later support Korean translations of program messages.  The continuous efforts have been made by <http://developer.r-project.org/TranslationTeams.html>   The R Documentation files are licensed under the General Public License, version 2 or 3.   This means that the pilot project to translate them into Korean has permission to reproduce them and translate them.  This work is done with GNU 'gettext' utilities.  The portable object template is updated a weekly basis or whenever changes are necessary.  Comments and corrections via email to the maintainer is of course most welcome.   In order to voluntarily participate in or offer your help with this translation, please contact the maintainer.   To check the change and progress of Korean translation, please visit <http://www.openstatistics.net>.
A unified estimation procedure for the analysis of censored data using linear transformation models.
Implementation of two transportation problem algorithms. 
             1. North West Corner Method 
             2. Minimum Cost Method or Least cost method.
             For more technical details about the algorithms please refer below URLs.
             <http://www.universalteacherpublications.com/univ/ebooks/or/Ch5/nw.htm>.
             <http://personal.maths.surrey.ac.uk/st/J.F/chapter7.pdf>.
Solve optimal transport problems in statistics. Compute Wasserstein metrics (a.k.a. Kantorovitch, Fortet--Mourier, Mallows, Earth Mover's, or minimal L_p metrics), return the corresponding transference plans, and display them graphically. Objects that can be compared include grey-scale images, point patterns, and mass vectors.
A structural transformation model for a latent, quasi-independent
  truncation time as a function of the observed dependent truncation
  time and the event time, and an unknown dependence parameter. 
  The dependence parameter is chosen to minimize the conditional
  Kendall's tau. The marginal distribution for the
  truncation time and the event time are completely left unspecified.
The trapezoid package provides dtrapezoid, ptrapezoid,
        qtrapezoid, and rtrapezoid functions for the trapezoidal
        distribution.
Transmission Ratio Distortion (TRD) is a genetic phenomenon where the two alleles from either parent are not transmitted to the offspring at the expected 1:1 ratio under Mendelian inheritance, leading to spurious signals in genetic association studies. Functions in this package are developed to account for this phenomenon using loglinear model and Transmission Disequilibrium Test (TDT). Some population information can also be calculated.
Evaluate the performance of markers used to guide treatment selection decisions. Package includes functions to plot risk curves, evaluate marker performance, assess model calibration, and compare markers.
Utilities to investigate sensitivity to unmeasured confounding in parametric models with either binary or continuous treatment.
Interface to the API for 'TreeBASE' (http://treebase.org)
    from 'R.' 'TreeBASE' is a repository of user-submitted phylogenetic
    trees (of species, population, or genes) and the data used to create
    them.
User-friendly analysis of hierarchical multinomial processing tree (MPT) models that are often used in cognitive psychology. Implements the latent-trait MPT approach (Klauer, 2010) <DOI:10.1007/s11336-009-9141-0> and the beta-MPT approach (Smith & Batchelder, 2010) <DOI:10.1016/j.jmp.2009.06.007> to model heterogeneity of participants. MPT models are conveniently specified by an .eqn-file as used by other MPT software and data are provided by a .csv-file or directly in R. Models are either fitted by calling JAGS or by an MPT-tailored Gibbs sampler in C++ (only for nonhierarchical and beta MPT models). Provides tests of heterogeneity and MPT-tailored summaries and plotting functions.
Bootstrapped response and correlation functions,
    seasonal correlations and evaluation of reconstruction
    skills for use in dendroclimatology and dendroecology.
Create a measure of inter-point dissimilarity useful 
 for clustering mixed data, and, optionally, perform the clustering.
The centre of mass is a crucial data for arborists in order to
    consolidate a tree using steel or dynamic cables. Given field-recorded data
    on branchiness of a tree, the package: (i) computes and plots the centre of
    mass of the tree itself, (ii) computes branches slenderness coefficient in
    order to aid the arborist identify potentially dangerous branches, and
    (iii) computes the force acting on a ground plinth and its best position
    relating to the tree centre of mass, should the tree need to be stabilized
    by a steel cable.
Hidden Factor graph models generalise Hidden Markov Models to tree structured data. The distinctive feature of 'treeHFM' is that it learns a transition matrix for first order (sequential) and for second order (splitting) events. It can be applied to all discrete and continuous data that is structured as a binary tree. In the case of continuous observations, 'treeHFM' has Gaussian distributions as emissions.
Treelets provides a novel construction of multi-scale bases that extends
        wavelets to non-smooth signals. It returns a multi-scale orthonormal 
	basis, where the final computed basis functions are supported 
	on nested clusters in a hierarchical tree.  Both the tree and
	the basis, which are constructed simultaneously, reflect the 
	internal structure of the data.
S4 class and methods for efficient phylogenetic tree manipulation
    for simulating evolution, running phylogenetic statistics and plotting.
A treemap is a space-filling visualization of hierarchical
    structures. This package offers great flexibility to draw treemaps.
(i) For a given species phylogeny on present day data which is calibrated to calendar-time, a method for estimating maximum likelihood speciation and extinction processes is provided. The method allows for non-constant rates. Rates may change (1) as a function of time, i.e. rate shifts at specified times or mass extinction events (likelihood implemented as LikShifts, optimization as bd.shifts.optim and visualized as bd.shifts.plot) or (2) as a function of the number of species, i.e. density-dependence (likelihood implemented as LikDD and optimization as bd.densdep.optim) or (3) extinction rate may be a function of species age (likelihood implemented as LikAge and optimization as bd.age.optim.matlab). Note that the methods take into account the whole phylogeny, in particular it accounts for the "pull of the present" effect. (1-3) can take into account incomplete species sampling, as long as each species has the same probability of being sampled. For a given phylogeny on higher taxa (i.e. all but one species per taxa are missing), where the number of species is known within each higher taxa, speciation and extinction rates can be estimated under model (1) (implemented within LikShifts and bd.shifts.optim with groups !=0). (ii) For a given phylogeny with sequentially sampled tips, e.g. a virus phylogeny, rates can be estimated under a model where rates vary across time using bdsky.stt.optim based on likelihood LikShiftsSTT (extending LikShifts and bd.shifts.optim). Furthermore, rates may vary as a function of host types using LikTypesSTT (multitype branching process extending functions in R package diversitree). This function can furthermore calculate the likelihood under an epidemiological model where infected individuals are first exposed and then infectious.
An implementation of permutation tests in R, supporting both exact and asymptotic K sample test of data locations. The p value of exact tests is found using tree algorithms. Tree algorithms treat permutations of input data as tree nodes and perform constraint depth-first searches for permutations that fall into the critical region of a test systematically. Pruning of tree search and optimisations at C level enable exact tests for certain large data sets.
Matches phylogenetic trees and trait data, and
    allows simultaneous manipulation of the tree and data using 'dplyr'.
Tools for the exploration of distributions of phylogenetic trees.
    This package includes a shiny interface which can be started from R using
    'treescapeServer()'.
Simulation methods for phylogenetic trees where (i) all tips are sampled at one time point or (ii) tips are sampled sequentially through time. (i) For sampling at one time point, simulations are performed under a constant rate birth-death process, conditioned on having a fixed number of final tips (sim.bd.taxa()), or a fixed age (sim.bd.age()), or a fixed age and number of tips (sim.bd.taxa.age()). When conditioning on the number of final tips, the method allows for shifts in rates and mass extinction events during the birth-death process (sim.rateshift.taxa()). The function sim.bd.age() (and sim.rateshift.taxa() without extinction) allow the speciation rate to change in a density-dependent way. The LTT plots of the simulations can be displayed using LTT.plot(), LTT.plot.gen() and LTT.average.root(). TreeSim further samples trees with n final tips from a set of trees generated by the common sampling algorithm stopping when a fixed number m>>n of tips is first reached (sim.gsa.taxa()). This latter method is appropriate for m-tip trees generated under a big class of models (details in the sim.gsa.taxa() man page). For incomplete phylogeny, the missing speciation events can be added through simulations (corsim()). (ii) sim.rateshifts.taxa() is generalized to sim.bdsky.stt() for serially sampled trees, where the trees are conditioned on either the number of sampled tips or the age. Furthermore, for a multitype-branching process with sequential sampling, trees on a fixed number of tips can be simulated using sim.bdtypes.stt.taxa(). This function further allows to simulate under epidemiological models with an exposed class. The function sim.genespeciestree() simulates coalescent gene trees within birth-death species trees, and sim.genetree() simulates coalescent gene trees.
The package is a flexible simulation tool for phylogenetic trees under a general model. It is possible to assume any probability distribution for the waiting time until speciation and extinction independently. Thus, TreeSimGM allows to simulate stochastic phylogenetic trees using any probability distribution and parameters for speciation and  extinction. The speciation modes have all binary splits and are: (i) symmetric, where for every speciation event new waiting times until speciation and extinction are drawn for both daughter lineages; and (ii) asymmetric, where a speciation event results in one species with new waiting times, and another that carries the extinction time and age of its ancestor. Those two modes were inspired by allopatric and peripatric speciation respectively.Both models (symmetric and asymmetric) were created and implemented so that different processes (distributions) for speciation and extinction could be independently and explicitly specified. It is also possible to have an implicit extinction process by setting the extinction rate to zero.
An implementation of TreeThresh, a locally adaptive version of EbayesThresh.
An extension of Trellis Display that enables creation,
    organization, and interactive viewing of multi-panel displays created
    against potentially very large data sets.  The dynamic viewer tiles
    panels of a display across the screen in a web browser and allows the
    user to interactively page through the panels and sort and filter them
    based on "cognostic" metrics computed for each panel.  Panels can be
    created using many of R's plotting capabilities, including base R
    graphics, 'lattice', 'ggplot2', and many 'htmlwidgets'.  Conditioning is
    handled through the 'datadr' package, which enables 'Trelliscope' displays
    with potentially millions of panels to be created against terabytes of
    data on systems like 'Hadoop'.  While designed to scale, 'Trelliscope'
    displays can also be very useful for small data sets.
The analysis of environmental data often requires
	     the detection of trends and change-points. 
	     This package provides the Mann-Kendall Trend Test,
             seasonal Mann-Kendall Test,
             correlated seasonal Mann-Kendall Test,
             partial Mann-Kendall Trend test,
	     (Seasonal) Sen's slope, partial correlation trend test and
	     change-point test after Pettitt.
Estimation of causal odds ratio given trends in exposure prevalence
    and outcome frequencies of stratified data.
functions and examples in Sample Size Calculation in
        Clinical Research.
Provides the "r, q, p, and d" distribution functions for the triangle distribution.
Tribology test devices such as pin-on-disk, ball-on-disk machines and profilometers give important data for tribological analysis. trib is built to analyze and visualize that data into human understandable form. It has functions for finding coefficient of friction, Friction Force vs Distance graphs and calculating Wear Track Volume and Wear Track plotting.
'Radix trees', or 'tries', are key-value data structures optimised for efficient lookups, similar in purpose
             to hash tables. 'triebeard' provides an implementation of 'radix trees' for use in R programming and in
             developing packages with 'Rcpp'.
The package contains routines to 1) project unity-summed
        triples to unit-square doubles and vice versa, 2) make a grid
        of unity-summed triples paired to doubles, 3) evaluate a
        function over the grid and 4) make simple plots including
        ternary contour plots over a grid of values.
Propensity score matching for non-binary treatments.
Provides various commonly-used response time trimming
    methods, including the recursive / moving-criterion methods reported by
    Van Selst and Jolicoeur (1994). By passing trimming functions raw data files,
    the package will return trimmed data ready for inferential testing.
Creates point and probability forecasts from the trees in a random forest using a trimmed opinion pool.
The package contains functions that 1) estimates
        gene-environment interaction between a SNP and a continuous
        non-genetic attribute by fitting a generalized additive model
        to case-parent trio data, 2) produces graphical displays of
        estimated interaction, 3) performs permutation test of
        gene-environment interaction; 4) simulates informative
        case-parent trios.
Functions for accessing and manipulating spatial data for animal
    tracking, with straightforward coercion from and to other formats. Filter
    for speed and create time spent maps from animal track data.
A constrained two-dimensional Delaunay triangulation package
  providing both triangulation and generation of voronoi mosaics of 
  irregular spaced data.
Data handling and estimation functions for animal movement
    estimation from archival or satellite tags. Helper functions are included
    for making image summaries binned by time interval from Markov Chain Monte Carlo
    simulations.
Social Relation Model (SRM) analyses for single or multiple
    round-robin groups are performed. These analyses are either based on one
    manifest variable, one latent construct measured by two manifest variables,
    two manifest variables and their bivariate relations, or two latent
    constructs each measured by two manifest variables. Within-group t-tests
    for variance components and covariances are provided for single groups.
    For multiple groups two types of significance tests are provided:
    between-groups t-tests (as in SOREMO) and enhanced standard errors based on
    Lashley and Bond (1997) <DOI:10.1037/1082-989X.2.3.278>. Handling for missing values is provided.
A new bioinformatic tool for comparing transcriptomes of two biological samples from the same or different species. The mapping is conducted based on the overlap of the associated genes of different samples. More examples and detailed explanations are available in the vignette.
Fish stock assessment methods and fisheries models based on the FAO
	Manual "Introduction to tropical fish stock assessment" by P. Sparre and
	S.C. Venema <http://www.fao.org/docrep/W5449E/W5449E00.htm>.  Focus is the
	analysis of length-frequency data and data poor fisheries.
Class definitions and constructors for pseudo-vectors containing
    all permutations, combinations and subsets of objects taken from a vector.
    Simplifies working with structures commonly encountered in combinatorics.
Functions and datasets for the reader of the book "The R Software: Fundamentals of Programming and Statistical Analysis".
An implementation of the TrueSkill algorithm (Herbrich,
        R., Minka, T. and Grapel, T) in R; a Bayesian skill rating
        system with inference by approximate message passing on a
        factor graph. Used by Xbox to rank gamers and identify
        appropriate matches.
        http://research.microsoft.com/en-us/projects/trueskill/default.aspx
        Current version allows for one player per team. Will update as
        time permits.  Requires R version 3.0 as it is written with
        Reference Classes.  URL:
        https://github.com/bhoung/trueskill-in-r Acknowledgements to
        Doug Zongker and Heungsub Lee for their python implementations
        of the algorithm and for the liberal reuse of Doug's code
        comments (@dougz and @sublee on github).
A collection of functions to deal with the truncated univariate and multivariate normal distributions.
A collection of tools to evaluate probability density
        functions, cumulative distribution functions, quantile functions
        and random numbers for truncated random variables.  These functions are
        provided to also compute the expected value and variance. Nadarajah
        and Kotz (2006) developed most of the functions. QQ plots can be produced.
        All the probability functions in the stats, stats4 and evd
        packages are automatically available for truncation..
Goodness-of-fit tests and some adjusted exploratory tools
        allowing for left truncated data
r/d/p/q functions for the truncated normal distribution
Estimation of models for truncated Gaussian variables by maximum likelihood.
Semi-parametric estimation of truncated linear regression models
Does local optimization using two derivatives and trust regions.
    Guaranteed to converge to local minimum of objective function.
Trust region algorithm for nonlinear optimization.  Efficient when the Hessian of the objective function is sparse (i.e., relatively few nonzero cross-partial derivatives).
Contains R functions and datasets detailed in the book
        "Time Series Analysis with Applications in R (second edition)"
        by Jonathan Cryer and Kung-Sik Chan
Tsallis distribution also known as the q-exponential family distribution. Provide distribution d, p, q, r functions, fitting and testing functions. Project initiated by Paul Higbie and based on Cosma Shalizi's code.
The tsbridge package contains a collection of R functions that can be used to estimate normalising constants using the bridge sampler of Meng and Wong (1996). The functions can be applied to calculate posterior model probabilities for a variety of time series Bayesian models, where parameters are estimated using BUGS, and models themselves are created using the tsbugs package.
Different estimates are provided to solve the blind source separation problem for time series with stochastic volatility.
The tsbugs package contains a collection of R functions
        that can be used to create time series BUGS models of various
        order. Included are function to create BUGS with non-constant
        variance such stochastic volatility models and random variance
        shift models.
Performs the two-sample comparisons using the following exact test procedures: the exact likelihood-ratio test (LRT) for equality of two normal populations proposed in Zhang et al. (2012); the combined test based on the LRT and Shapiro-Wilk test for normality via the Bonferroni correction technique; the newly proposed density-based empirical likelihood (DBEL) ratio test. To calculate p-values of the DBEL procedures, three procedures are used: (a) the traditional Monte Carlo (MC) method implemented in C++, (b) a new interpolation method based on regression techniques to operate with tabulated critical values of the test statistic; (c) a Bayesian type method that uses the tabulated critical values as the prior information and  MC generated DBEL-test-statistic's values as data.
This package contains a set of measures of dissimilarity between time series to perform time series clustering. Metrics based on raw data, on generating models and on the forecast behavior are implemented. Some additional utilities related to time series clustering are also provided, such as clustering algorithms and cluster evaluation metrics.
Utilities for comparing the equality of series on two databases.
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
Likelihood-based methods for model fitting and assessment, prediction and intervention analysis of count time series following generalized linear models are provided. Models with the identity and with the logarithmic link function are allowed. The conditional distribution can be Poisson or Negative Binomial.
Illustrates the various 'TSdbi' packages with a vignette using time series 
	data from several sources. The vignette also illustrates some simple time series
	manipulation and plotting using packages 'tframe' and 'tfplot'.
Provides a common interface to time series databases. The
	objective is to define a standard interface so users can retrieve time 
	series data from various sources with a simple, common, set of 
	commands, and so programs can be written to be portable with respect 
	to the data source. The SQL implementations also provide a database 
	table design, so users needing to set up a time series database 
	have a reasonably complete way to do this easily. The interface 
	provides for a variety of options with respect to the representation 
	of time series in R. The interface, and the SQL implementations, also
	handle vintages of time series data (sometime called editions or 
	realtime data). There is also a (not yet well tested) mechanism to
	handle multilingual data documentation.
	Comprehensive examples of all the TS* packages is provided in the
	vignette Guide.pdf with the TSdata package.
A set of commonly used distance measures and some additional functions which, although initially not designed for this purpose, can be used to measure the dissimilarity between time series. These measures can be used to perform clustering, classification or other data mining tasks which require the definition of a distance measure between time series.
Implements nonlinear autoregressive (AR) time series models. For univariate series, a non-parametric approach is available through additive nonlinear AR. Parametric modeling and testing for regime switching dynamics is available when the transition is either direct (TAR: threshold AR) or smooth (STAR: smooth transition AR, LSTAR). For multivariate series, one can estimate a range of TVAR or threshold cointegration TVECM models with two or three regimes. Tests can be conducted for TVAR as well as for TVECM (Hansen and Seo 2002 and Seo 2006).
Time series analysis and computational finance.
Routines for the analysis of nonlinear time series. This
        work is largely inspired by the TISEAN project, by Rainer
        Hegger, Holger Kantz and Thomas Schreiber:
        http://www.mpipks-dresden.mpg.de/~tisean/
Implements an Entropy measure of dependence based on the Bhattacharya-Hellinger-Matusita distance. Can be used as a (nonlinear) autocorrelation/crosscorrelation function for continuous and categorical time series. The package includes tests for serial dependence and nonlinearity based on it. Some routines have a parallel version that can be used in a multicore/cluster environment. The package makes use of S4 classes.
Extraction of Factors from Multivariate Time Series. See ?00tsfa-Intro for more details.
A 'fame' interface for 'TSdbi'.
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
two-stage procedure for comparing hazard rate functions
        which may or may not cross each other
Functions for analysing and forecasting intermittent demand/slow moving items time series.
Implementations of a number of functions used to mine numeric time-series data. It covers the implementation of SAX transformation, univariate motif discovery (based on the random projection method), multivariate motif discovery (based on graph clustering), and several functions used for the ease of visualizing the motifs discovered. The details of SAX transformation can be found in J. Lin. E. Keogh, L. Wei, S. Lonardi, Experiencing SAX: A novel symbolic representation of time series, Data Mining and Knowledge Discovery 15 (2) (2007) 107-144. Details on univariate motif discovery method implemented can be found in B. Chiu, E. Keogh, S. Lonardi, Probabilistic discovery of time series motifs, ACM SIGKDD, Washington, DC, USA, 2003, pp. 493-498. Details on the multivariate motif discovery method implemented can be found in A. Vahdatpour, N. Amini, M. Sarrafzadeh, Towards unsupervised activity discovery using multi-dimensional motif detection in time series, IJCAI 2009 21st International Joint Conference on Artificial Intelligence.
Methods to 
	retrieve data from several different sources. This include historical
	quote data from 'Yahoo' and 'Oanda', economic data from 'FRED', and
	'xls' and 'csv' data from different sources.
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
Tools for specifying time series regression models
A 'MySQL' interface for 'TSdbi'.
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
Temporal SNA tools for continuous- and discrete-time longitudinal networks having vertex, edge, and attribute dynamics stored in the 'networkDynamic' format. This work was supported by grant R01HD68395 from the National Institute of Health.
A "pure R" implementation of the t-SNE algorithm.
An ODBC interface for 'TSdbi'.
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
Detection of outliers in time series following the 
    Chen and Liu (1993) procedure. Innovational outliers, additive outliers, 
    level shifts, temporary changes and seasonal level shifts are considered.
Basic infrastructure and some algorithms for the traveling
    salesperson problem (also traveling salesman problem; TSP).
    The package provides some simple algorithms and
    an interface to the Concorde TSP solver and its implementation of the
    Chained-Lin-Kernighan heuristic. Concorde
    itself is not included in the package and has to be obtained separately
    from http://www.math.uwaterloo.ca/tsp/concorde.html.
Adjusts for correlations among the rows and columns via
        the Transposable Sphering Algorithm when conducting large-scale
        inference on the rows of a data matrix.
Prediction intervals for ARIMA and structural time series
    models using importance sampling approach with uninformative priors for model
    parameters, leading to more accurate coverage probabilities in frequentist
    sense. Instead of sampling the future observations and hidden states of the
    state space representation of the model, only model parameters are sampled,
    and the method is based solving the equations corresponding to the conditional
    coverage probability of the prediction intervals. This makes method relatively
    fast compared to for example MCMC methods, and standard errors of prediction
    limits can also be computed straightforwardly.
Instance feature calculation and evolutionary instance generation
    for the traveling salesman problem. Also contains code to "morph" two TSP
    instances into each other. And the possibility to conveniently run a couple
    of solvers on TSP instances.
A 'PostgreSQL' interface for 'TSdbi'.
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
Functions for time series prediction and accuracy assessment using automatic ARIMA modelling. The generated ARIMA models and its yielded prediction errors are intended to be used as baseline for evaluating the practical value of other time series prediction methods and creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used.
Methods to 
	retrieve data in the Statistical Data and Metadata Exchange ('SDMX') format
	from several database. (For example,
	'EuroStat', the European Central Bank, the Organisation for Economic 
	Co-operation and Development, the 'Unesco' Institute for Statistics,
	and the International Labor Organization.)
	This is a wrapper for package 'RJSDMX'.
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
Standard SQL query functions used by 
	SQL plugins packages for the 'TSdbi' interface to time series databases. 
	It will mainly be used by other packages rather than directly by end
	users. The one exception is the function 'TSquery' which can be used to
	construct a time series from a database containing observations over
	time (e.g. balance statements for multiple years), but where the database
	is not specifically designed to store time series (as with other 
	'TSdbi' SQL plugin packages).
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
An 'SQLite' interface for 'TSdbi'.
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
A ternary search tree is a type of prefix tree with up to three children and the ability for incremental string search. The package uses this ability for word auto-completion and spell checking. Includes a dataset with the 10001 most frequent English words.
Interactive laboratory of Time Series based in Box-Jenkins methodology.
This interface was created to develop a standard procedure 
 to analyse temporal trend in the framework of the OSPAR convention.
 The analysis process run through 4 successive steps : 1) manipulate your data, 2)
 select the parameters you want to analyse, 3) build your regulated 
 time series, 4) perform diagnosis and analysis and 5) read the results. 
 Statistical analysis call other package function such as Kendall tests
 or cusum() function.
U.S. Department of the Treasury, Alcohol and Tobacco Tax and
    Trade Bureau (TTB) collects data and reports on monthly beer
    industry production and operations. This data package includes
    a collection of 10 years (2006 - 2015) worth of data on materials
    used at U.S. breweries in pounds reported by the Brewer's Report
    of Operations and the Quarterly Brewer's Report of Operations
    forms, ready for data analysis. This package also includes historical
    tax rates on distilled spirits, wine, beer, champagne, and tobacco
    products as individual data sets.
The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response
 following stimulation. A common observation in this type of data is that some genes respond with quick,
 transient dynamics, while other genes change their expression slowly over time. The existing methods for the
 detection of significant expression dynamics often fail when the expression dynamics show a large
 heterogeneity, and often cannot cope with irregular and sparse measurements. The method proposed here is
 specifically designed for the analysis of perturbation responses. It combines different scores to capture fast and
 transient dynamics as well as slow expression changes, and deals with low replicate numbers and irregular
 sampling times. The results are given in the form of tables linked to figures. These allow to quickly recognize
 the relevance of detection, to identify possible false positives and to distinguish between changes in the early
 and later expression. An extension of the method allows the analysis of the expression dynamics of functional
 groups of genes, providing a quick overview of the cellular response. The performance of this package was
 tested on microarray data derived from lung cancer cells stimulated with epidermal growth factor.
 See publication "TTCA: An R package for the identification of differentially expressed genes in time course microarray data".
C source code and R wrappers for the tth/ttm TeX to 
  HTML/MathML translators.
Computing the first two moments of the truncated multivariate t (TMVT) distribution under the double truncation. Appling the slice sampling algorithm to generate random variates from the TMVT distribution.
Functions and data to construct technical trading rules with R.
Time-Temperature Superposition analysis is often applied to frequency modulated data obtained by Dynamic
		Mechanic Analysis (DMA) and Rheometry in the analytical chemistry and physics
		areas. These techniques provide estimates of material mechanical properties
		(such as moduli) at different temperatures in a wider range of time. This
		package provides the Time-Temperature superposition Master Curve at a referred
		temperature by the three methods: the two wider used methods, Arrhenius based
		methods and WLF, and the newer methodology based on derivatives procedure.
		The Master Curve is smoothed by B-splines basis. The package output is composed
		of plots of experimental data, horizontal and vertical shifts, TTS data, and TTS
		data fitted using B-splines with bootstrap confidence intervals.
This package utilizes training and testing samples to filter out uninformative DNA methylation sites. Surrogate variables (SVs) of DNA methylation are included in the filtering process to explain unknown factor effects.
Implementation of Time to Target plot based on the work 
             of Ribeiro and Rosseti (2015) <DOI:10.1007/s11590-014-0760-8>, 
             that describe a numerical method that gives the probability of 
             an algorithm A finds a solution at least as good as a given 
             target value in smaller computation time than algorithm B.
This package makes Travel To Work Area from a commuting flow data frame.
Get comments posted on YouTube videos, information on how many 
    times a video has been liked, search for videos with particular content, and 
    much more. You can also scrape captions from a few videos. To learn more about
    the YouTube API, see <https://developers.google.com/youtube/v3/>.
Provides R Markdown output formats to use Tufte styles for PDF and HTML output.
Custom template and output formats for use with rmarkdown. Produce
    Edward Tufte-style handouts in html formats with full support for rmarkdown
    features
Perform the conventional Tukey test from aov and aovlist
        objects
Provides an R-interface to the Tumblr web API (see Tumblr v2 API on https://www.tumblr.com/docs/en/api/v2). Tumblr is a microblogging platform and social networking website (https://www.tumblr.com).
A tool to obtain tumor growth rates from clinical trial patient data.  Output includes individual and summary data for tumor growth rate estimates as well as optional plots of the observed and predicted tumor quantity over time.
Generic methods for parameter tuning of classification algorithms using multiple scoring functions
Analyze music and speech, extract features like MFCCs, handle wave files and their representation in various ways, read mp3, read midi, perform steps of a transcription, ...
        Also contains functions ported from the 'rastamat' 'Matlab' package.
Functions to find all matches or non-matches, orphans, and
    duplicate or other replicated elements.
Algorithms for accelerating the convergence of slow,
        monotone sequences from smooth, contraction mapping such as the
        EM and MM algorithms. It can be used to accelerate any smooth,
        linearly convergent acceleration scheme.  A tutorial style
        introduction to this package is available in a vignette on the
        CRAN download page or, when the package is loaded in an R
        session, with vignette("turboEM").
Package for analyzing TURF (Total Unduplicated Reach and Frequency) data in R. 
    No looping in TURF algorithm results in fast processing times. Allows for individual-level 
	weights, depth specification, and user-truncated combination set(s). Allows user to substitute 
	Monte Carlo simulated combination set(s) after set(s) exceed a user-specified limit.
Package designed for working with vectors and lists of vectors,
    mainly for turning them into other indexed data structures.
An implementation of turtle graphics
    (http://en.wikipedia.org/wiki/Turtle_graphics) in R.
    Turtle graphics comes from Papert's language Logo and has
    been used to teach concepts of computer programming.
DataCamp Light (<https://github.com/datacamp/datacamp-light>) is a light-weight implementation of the DataCamp UI,
  that allows you to embed interactive exercises inside HTML documents. The tutorial package makes it easy to create these
  HTML files from R Markdown files. An extension to knitr, tutorial detects appropriately formatted code chunks and replaces them
  with DataCamp Light readable chunks in the resulting HTML file.
The model, developed at the Vienna University of Technology, is a lumped conceptual rainfall-runoff model, following the structure of the HBV model. 
             The model runs on a daily or shorter time step and consists of a snow routine, a soil moisture routine and a flow routing routine. 
             See Parajka, J., R. Merz, G. Bloeschl (2007) Uncertainty and multiple objective calibration in regional water balance modelling: case study in 320 Austrian catchments, Hydrological Processes, 21, 435-446.
Total Variation Denoising is a regularized denoising method which
    effectively removes noise from piecewise constant signals whilst preserving
    edges.  This package contains a C++ implementation of Condat's very fast 1D
    squared error loss TVD algorithm.  Additional methods and loss functions may
    be added in future versions.
Functions for managing cashflows and interest rate curves.
Provides functions for propensity score
        estimating and weighting, nonresponse weighting, and diagnosis
        of the weights.  This package was originally developed by Drs.
        Ridgeway, McCaffrey, and Morral. Burgette, Griffin and
        McCaffrey updated the package during 2011-2016.
Maximum likelihood computations for Tweedie families.
In order to create smooth animation between states of data,
    tweening is necessary. This package provides a range of functions for
    creating tweened data that plugs right into functions such as gg_animate()
    from the 'gganimate' package. Furthermore it adds a number of vectorized
    interpolaters for common R data types such as numeric, date and colour.
This is an improved implementation of the package 'StreamR' to
    capture tweets and store it into R, SQLite, 'postGIS' data base or GIS format. The package
    performs a description of harvested data and performs space time exploratory analysis.
Twiddler is an interactive tool that automatically creates
        a Tcl/Tk GUI for manipulating variables in any R expression.
        See the documentation of the function twiddle to get started.
Provides an interface to the Twitter web API.
This package implements the nonparametric test of equality
        between two copulas proposed by Remillard and Scaillet in their
        2009 JMVA paper.
Estimation of gene-treatment interactions in randomized clinical trials exploiting gene-treatment independence.
This program computes thresholds and power for a two-stage
        genome-wide association study. It follows the methods described
        in Skol AD, Scott, LJ, Abecasis GR, Boehnke M (2006) Nature
        Genetics doi:10.1038/ng1706 and in the "CaTS" computer program
        (http://www.sph.umich.edu/csg/abecasis/CaTS/)
Implements a variety of non-parametric methods for computing one-stage and two-stage confidence intervals, as well as point estimates of threshold values.
Conditional logistic regression with longitudinal follow up and
    individual-level random coefficients: A stable and efficient
    two-step estimation method.
Provides functions to produce rudimentary ascii graphics
        directly in the terminal window. Provides a basic plotting
        function (and equivalents of curve, density, acf and barplot)
        as well as a boxplot function.
Despite there being a section in RFC 7231
    <https://tools.ietf.org/html/rfc7231#section-5.5.3> defining a suggested
    structure for 'User-Agent' headers this data is notoriously difficult
    to parse consistently. A function is provided that will take in user agent
    strings and return structured R objects. This is a 'V8'-backed package
    based on the 'ua-parser' project <https://github.com/ua-parser>.
Two Phase I designs are implemented in the package: the classical 3+3 and the Continual Reassessment Method. Simulations tools are also available to estimate the operating characteristics of the methods with several user-dependent options.
Provides a set of functions that can be used to obtain better predictive performance on cost-sensitive and cost/benefits tasks (for both regression and classification). This includes re-sampling approaches that modify the original data set biasing it towards the user preferences.
This package contains latex, knitr and R Markdown templates that
    adhere to the UC Berkeley Graduate Division's thesis guidelines. The
    templates are located in the inst/ directory.
An algorithm for general-purpose unconstrained non-linear optimization.
             The algorithm is of quasi-Newton type with BFGS updating of the inverse
             Hessian and soft line search with a trust region type monitoring of the
             input to the line search algorithm. The interface of 'ucminf' is
             designed for easy interchange with 'optim'.
A client for the Urban Dictionary <http://www.urbandictionary.com/> API.
Provides simple bindings to Unidata's udunits library.
Construct a Hidden Markov Model with states learnt by unsupervised classification.
Allows users to access live UK gas market information via National Grid's API.
A generic reference Bayesian analysis of unidimensional mixtures of Gaussian distributions obtained by a location-scale parameterisation of the model is implemented. Included functions can be applied to produce a Bayesian analysis of Gaussian mixtures with an arbitrary number of components, with no need to define the prior distribution.
Does uniformly most powerful (UMP) and uniformly most
    powerful unbiased (UMPU) tests.  At present only distribution implemented
    is binomial distribution.  Also does fuzzy tests and confidence intervals
    (following Geyer and Meeden, Statistical Science, 2005) for the binomial
    distribution (one-tailed procedures based on UMP test and two-tailed
    procedures based on UMPU test).
Create, run, and report structural equation and twin models quickly.
    If you're just starting out, try typing '?umx'.
A dataset is said to be unbalanced when the class of interest (minority class) is much rarer than normal behaviour (majority class). The cost of missing a minority class is typically much higher that missing a majority class. Most learning systems are not prepared to cope with unbalanced data and several techniques have been proposed. This package implements some of most well-known techniques and propose a racing algorithm to select adaptively the most appropriate strategy for a given unbalanced task.
The package implements top-down and bottom-up algorithms
        for nonparametric function estimation in Gaussian noise using
        Unbalanced Haar wavelets.
Provides a basic (random) data, grids, 6 models of uncertainty, 3 automatic interpolations (idw, spline, kriging), variogram and basic data visualization.
Consensus clustering by the unification of clustering results from multiple datasets using external specifications.
Computes a universal numeric fingerprint (UNF) for an R data
    object. UNF is a cryptographic hash or signature that can be used to uniquely
    identify (a version of) a rectangular dataset, or a subset thereof. UNF can
    be used, in tandem with a DOI, to form a persistent citation to a versioned
    dataset.
Stereological unfolding as implemented in this package consists in
    the estimation of the joint size-shape-orientation distribution of spheroidal
    shaped particles based on the same measured quantities of corresponding planar
    section profiles. A single trivariate discretized version of the (stereological)
    integral equation in the case of prolate and oblate spheroids is solved
    numerically by the EM algorithm. The estimation of diameter distribution of
    spheres from planar sections (Wicksell's corpuscle problem) is also implemented.
    Further, the package provides routines for the simulation of a Poisson germ-
    grain process with either spheroids, spherocylinders or spheres as grains together
    with functions for planar sections. For the purpose of exact simulation a bivariate size-shape
    distribution is implemented.
Taking a list of genes, the package collates together the summary
    information about those genes from the publicly available resources at Uniprot
    and NCBI. Additionally, the package is able to collate publication information
    from a search of the NCBI Pubmed database.
Nonparametric estimation of a unimodal or U-shape covariate effect under additive hazards model.
Data from Unicode 8.0.0 and related utilities.
Univarate shrinkage prediction for survival analysis using
        in the Cox model.. Especially useful for high-dimensional data,
        including microarray data.
Goodness-of-fit tests for the uniform distribution.
For each string in a set of strings, determine a unique tag that is a substring of fixed size k unique to that string, if it has one. If no such unique substring exists, the least frequent substring is used. If multiple unique substrings exist, the lexicographically smallest substring is used. This lexicographically smallest substring of size k is called the "UniqTag" of that string.
An alternative implementation and extension (grpDuplicated) of base::duplicated.matrix, base:anyDuplicated.matrix and base::unique.matrix for matrices of atomic mode, avoiding the time consuming collapse of the matrix into a character vector.
Univariate spline regression. It is possible to add the shape constraint of unimodality and predefined or
	self-defined penalties on the B-spline coefficients.
United is a software tool which can be downloaded at the following
    website <http://www.schroepl.net/pbm/software/united/>. In general, it is
    a virtual manager game for football teams. This package contains helpful
    functions for determining an optimal formation for a virtual match in
    United. E.g. knowing that the opponent has a strong defensive it is
    advisable to beat him in the midfield. Furthermore, this package contains
    functions for computing the optimal usage of hardness in a game.
Support for measurement units in R vectors; automatic propagation, conversion, derivation and simplification of units; raising errors in case of unit incompatibility. Compatible with difftime. Uses the UNIDATA udunits library and unit database for unit conversion and compatibility checking.
Concise TAP (http://testanything.org/) compliant unit testing package. Authored tests can be run using CMD check with minimal implementation overhead.
Fits hierarchical models of animal abundance and occurrence to data collected using survey methods such as point counts, site occupancy sampling, distance sampling, removal sampling, and double observer sampling. Parameters governing the state and observation processes can be modeled as functions of covariates.
A collection of utilities for biodiversity data.
  Includes the simulation of ecological drift under Hubbell's Unified
  Neutral Theory of Biodiversity, and the calculation of various
  diagnostics such as Preston curves.  Now includes functionality
  provided by Francois Munoz and Andrea Manica.
Historical voting data of the United Nations General Assembly. This
    includes votes for each country in each roll call, as well as descriptions and
    topic classifications for each vote.
This package contains a collection of functions which 
        implement data classification. It uses unlabeled data to obtain 
        parameter estimates of models. The functions can be implemented 
        over a number of models with the best model selected and 
        displayed.
An integrated package for building and testing uplift models
An implementation of the UPMASK method for performing membership
    assignment in stellar clusters in R. It is prepared to use photometry and
    spatial positions, but it can take into account other types of data. The
    method is able to take into account arbitrary error models, and it is
    unsupervised, data-driven, physical-model-free and relies on as few
    assumptions as possible. The approach followed for membership assessment is
    based on an iterative process, principal component analysis, a clustering
    algorithm and a kernel density estimation.
Creates visualizations of intersecting sets using a novel matrix
    design, along with visualizations of several common set, element and attribute
    related tasks.
Provide a set of wrappers to call all the endpoints of UptimeRobot API
  which includes various kind of ping, keep-alive and speed tests.
  See <https://uptimerobot.com/> for more information.
Unit root and cointegration tests encountered in applied 
 econometric analysis are implemented.
Allows using different URL shortening services, which also provide
    expanding and analytic functions. Specifically developed for 'Bit.ly', 'Goo.gl'
    (both OAuth2) and 'is.gd' (no API key). Others can be added by request.
A toolkit for handling URLs that so far includes functions for URL
    encoding and decoding, parsing, and parameter extraction. All functions are
    designed to be both fast and entirely vectorised. It is intended to be
    useful for people dealing with web-related datasets, such as server-side
    logs, although may be useful for other situations involving large sets of
    URLs.
Seasonal unit roots and seasonal stability tests.
    P-values based on response surface regressions are available for both tests.
    P-values based on bootstrap are available for seasonal unit root tests.
    A parallel implementation of the bootstrap method requires a CUDA capable GPU 
    with compute capability >= 3.0, otherwise a debugging version fully coded in R is used.
The boundaries for geographical units in the United States of
    America contained in this package include state, county, congressional
    district, and zip code tabulation area. Contemporary boundaries are provided
    by the U.S. Census Bureau (public domain). Historical boundaries for the
    years from 1629 to 2000 are provided form the Newberry Library's 'Atlas of
    Historical County Boundaries' (licensed CC BY-NC-SA). Additional high
    resolution data is provided in the 'USAboundariesData' package;
    this package provides an interface to access that data.
This package contains functions to read cancer data from SEER (http://seer.cancer.gov/) and IARC (http://www.iarc.fr) to create datasets at the county level based on US census information.
US Census 2000 Designated Places shapefiles and additional
        demographic data from the SF1 100 percent files. This data set
        contains polygon files in lat/lon coordinates and the
        corresponding demographic data for a number of different
        variables.
US 2000 Census Tract shapefiles and additional demographic
        data from the SF1 100 percent files. This data set contains
        polygon files in lat/lon coordinates and the corresponding
        demographic data for a number of different variables.
US Census 2010 shape files and additional demographic data
        from the SF1 100 percent files. This package contains a number
        of helper functions for the UScensus2010blk,
        UScensus2010blkgrp, UScensus2010tract, UScensus2010cdp
        packages.
This is a framework that aims to provide methods and tools for assessing the impact of different sources of uncertainties (e.g.positional uncertainty) on performance of species distribution models (SDMs).)
A set of little functions that have been found useful to do little
    odds and ends such as plotting the results of K-means clustering, substituting
    special text characters, viewing parts of a data.frame, constructing formulas
    from text and building design and response matrices.
Contains a number of functions that serve
  two goals: first, make R more accessible to people migrating from
  SPSS by adding a number of functions that behave roughly like their
  SPSS equivalents; and second, make a number of slightly more
  advanced functions more user friendly to relatively novice users.
A map of the USA from the United States Geological Survey (USGS).
    Irucka worked with this data set while a Cherokee Nation Technology
    Solutions (CNTS) USGS Contractor and/or USGS employee. It is replaced by
    'states2k'.
A collection of data sets to accompany the
    textbook "Using R for Introductory Statistics," second
    edition.
Implements mixtures of unrestricted skew-t factor analyzer models via the EM algorithm.
The Universal Scalability Law is a model to predict hardware and
    software scalability. It uses system capacity as a function of load to
    forecast the scalability for the system.
Forms a query to submit for US Treasury yield curve data, posting
    this query to the US Treasury web site's data feed service.  By default the
    download includes data yield data for 12 products from January 1, 1990,
    some of which are NA during this span.  The caller can pass parameters to
    limit the query to a certain year or year and month, but the full download
    is not especially large.  The download data from the service is in XML
    format.  The package's main function transforms that XML data into a numeric 
    data frame with treasury product items (constant maturity yields for 12 kinds 
    of bills, notes, and bonds) as columns and dates as row names. The function 
    returns a list which includes an item for this data frame as well as query-related
    values for reference and the update date from the service.
Construct and plot objective hierarchies and associated value and utility functions. 
  Evaluate the values and utilities and visualize the results as colored objective hierarchies or tables. 
  Visualize uncertainty by plotting median and quantile intervals within the nodes of objective hierarchies.
  Get numerical results of the evaluations in standard R data types for further processing.
Multi-label learning methods and others utilities to support multi-
  label classification in R.
Tools for generating and handling of UUIDs (Universally Unique Identifiers).
A method for estimating log-normalizing constants (or free
        energies) and expectations from multiple distributions (such as
        multiple generalized ensembles).
An R interface to Google's open source JavaScript engine.
    V8 is written in C++ and implements ECMAScript as specified in ECMA-262,
    5th edition. In addition, this package implements typed arrays as
    specified in ECMA 6 used for high-performance computing and libraries
    compiled with 'emscripten'.
A likelihood framework for estimating the effective
        coverage of vaccination programs using cross-sectional surveys
        combined with administrative data.
Non-Domestic VAERS vaccine data for 01/01/2016 - 06/14/2016. If
    you want to explore the full VAERS data for 1990 - Present (data, symptoms,
    and vaccines), then check out the 'vaersND' package from the URL below. The
    URL and BugReports below correspond to the 'vaersND' package, of which
    'vaersNDvax' is a small subset (2016 only). 'vaersND' is not hosted on CRAN
    due to the large size of the data set. To install the Suggested 'vaers' and
    'vaersND' packages, use the following R code:
    'devtools::install_git("https://gitlab.com/iembry/vaers.git",
    build_vignettes = TRUE)' and
    'devtools::install_git("https://gitlab.com/iembry/vaersND.git",
    build_vignettes = TRUE)'. "VAERS is a national vaccine safety
    surveillance program co-sponsored by the US Centers for Disease Control and
    Prevention (CDC) and the US Food and Drug Administration (FDA). VAERS is a
    post-marketing safety surveillance program, collecting information about
    adverse events (possible side effects) that occur after the administration
    of vaccines licensed for use in the United States." For more information
    about the data, visit <https://vaers.hhs.gov/index>. For information about
    vaccination/immunization hazards, visit
    <http://www.questionuniverse.com/rethink.html/#vaccine>.
US VAERS vaccine data for 01/01/2016 - 06/14/2016. If you want to
    explore the full VAERS data for 1990 - Present (data, symptoms, and
    vaccines), then check out the 'vaers' package from the URL below. The URL
    and BugReports below correspond to the 'vaers' package, of which 'vaersvax'
    is a small subset (2016 only). 'vaers' is not hosted on CRAN due to the
    large size of the data set. To install the Suggested 'vaers' and 'vaersND'
    packages, use the following R code:
    'devtools::install_git("https://gitlab.com/iembry/vaers.git",
    build_vignettes = TRUE)' and
    'devtools::install_git("https://gitlab.com/iembry/vaersND.git",
    build_vignettes = TRUE)'. "VAERS is a national vaccine safety
    surveillance program co-sponsored by the US Centers for Disease Control and
    Prevention (CDC) and the US Food and Drug Administration (FDA). VAERS is a
    post-marketing safety surveillance program, collecting information about
    adverse events (possible side effects) that occur after the administration
    of vaccines licensed for use in the United States." For more information
    about the data, visit <https://vaers.hhs.gov/index>. For information about
    vaccination/immunization hazards, visit
    <http://www.questionuniverse.com/rethink.html/#vaccine>.
Methods and tools for analysing and validating the outputs
    and modelled functions of artificial neural networks (ANNs) in terms
    of predictive, replicative and structural validity. Also provides a
    method for fitting feed-forward ANNs with a single hidden layer.
Declare data validation rules and data quality indicators; confront
    data with them and analyze or visualize the results. The package supports
    rules that are per-field, in-record, cross-record or cross-dataset. Rules
    can be automatically analyzed for rule type and connectivity.
An implementation of statistical tests for the validation of rating systems as described in the ECB Working paper ''Advances in multivariate back-testing for credit risk underestimation'', by F. Coppens, M. Mayer, L. Millischer, F.  Resch, S. Sauer, K. Schulze (ECB WP series, forthcoming).
Historical results for the state of Virginia lottery draw games. Data were downloaded from https://www.valottery.com/.
Fast algorithms for fitting Bayesian variable selection models and computing Bayes factors, in which the outcome (or response variable) is modeled using a linear regression or a logistic regression. The algorithms are based on the variational approximations described in "Scalable variational inference for Bayesian variable selection in regression, and its accuracy in genetic association studies" (P. Carbonetto & M. Stephens, 2012, <DOI:10.1214/12-BA703>). This software has been applied to large data sets with over a million variables and thousands of samples.
Variance component models: REML estimation, testing fixed effect contrasts through Satterthwaite or Kenward-Roger methods, testing the nullity of variance components through (linear or quadratic) score tests or likelihood ratio tests.
Generation of domain variables, linearisation of several nonlinear population statistics (the ratio of two totals, weighted income percentile,  relative median income ratio,  at-risk-of-poverty rate, at-risk-of-poverty threshold, Gini coefficient, gender pay gap, the aggregate replacement ratio, the relative median income ratio, median income below at-risk-of-poverty gap, income quintile share ratio, relative median at-risk-of-poverty gap), computation of regression residuals in case of weight calibration, variance estimation of sample surveys by the ultimate cluster method (Hansen, Hurwitz and Madow, 1953), variance estimation for longitudinal, cross-sectional measures and measures of change for single and multistage stage cluster sampling designs (Berger, Y.G.). Several other precision measures are derived - standard error, the coefficient of variation, the margin of error, confidence interval, design effect.
Computes Value at risk and expected shortfall, two most popular measures of financial risk, for over one hundred parametric distributions,  including all commonly known distributions.  Also computed are the corresponding probability density function and cumulative distribution function.
Estimation, Hypothesis Testing, Prediction for Stationary Vector Autoregressive Models
Captures and compiles the genes and variants related to a disease, a phenotype or a clinical feature based on the public databases including HPO (Human Phenotype Ontology, <http://human-phenotype-ontology.github.io/about.html>), Orphanet <http://www.orpha.net/consor/cgi-bin/index.php>, OMIM (Online Mendelian Inheritance in Man, <http://www.omim.org>), ClinVar <http://www.ncbi.nlm.nih.gov/clinvar>, and UniProt (Universal Protein Resource, <http://www.uniprot.org>) and PubMed abstracts. HPO provides a standardized vocabulary of phenotypic abnormalities encountered in human disease. HPO currently contains approximately 11,000 terms and over 115,000 annotations to hereditary diseases. Orphanet is the reference portal for information on rare diseases and orphan drugs, whose aim is to help improve the diagnosis, care and treatment of patients with rare diseases. OMIM is a continuously updated catalog of human genes and genetic disorders and traits, with particular focus on the molecular relationship between genetic variation and phenotypic expression. ClinVar is a freely accessible, public archive of reports of the relationships among human variations and phenotypes, with supporting evidence. UniProt focuses on amino acid altering variants imported from Ensembl Variation databases. For Homo sapiens, the variants including human polymorphisms and disease mutations in the UniProt are manually curated  from UniProtKB/Swiss-Prot. Additionally, PubMed provides the primary and latest source of the information. Text mining was employed to capture the information from PubMed abstracts.
Variables are the fundamental parts of each programming language but handling them might be frustrating for programmers from time to time. This package contains some functions to help user (especially data explorers) to make more sense of their variables and take the most out of variables as well as their hardware. These functions are written, collected and crafted over some years of experience in statistical data analysis and for each of them there was a need. Functions in this package are suppose to be efficient and easy to use, hence they will be frequently updated to make them more convenient.
Presence of interaction between a SNP and another SNP (or another factor) can result 
	in heterogeneity of variance between the genotypes of an interacting SNP. 
	Detecting such heterogeneity gives prior knowledge for constructing a genetic model 
	underlying complex trait.
Abstract descriptions of (yet) unobserved variables.
Implements a screening procedure proposed by Wanghuan Chu, Runze Li
    and Matthew Reimherr (2016) <DOI:10.1214/16-AOAS912> for varying coefficient
    longitudinal models with ultra-high dimensional predictors . The effect of each
    predictor is allowed to vary over time, approximated by a low-dimensional B-spline.
    Within-subject correlation is handled using a generalized estimation equation
    approach with structure specified by the user. Variance is allowed to change
    over time, also approximated by a B-spline.
Uses a Bayesian model to
    estimate the variability in a repeated
    measure outcome and use that as an outcome or a predictor
    in a second stage model.
This package provides functions for the variance gamma
        distributions. Density, distribution and quantile functions.
        Functions for random number generation and fitting of the
        variance gamma to data. Also, functions for computing moments
        of the variance gamma distribution of any order about any
        location. In addition, there are functions for checking the
        validity of parameters and to interchange different sets of
        parameterizatons for the variance gamma distribution.
Estimation, lag selection, diagnostic testing, forecasting, causality analysis, forecast error variance decomposition and impulse response functions of VAR models and estimation of SVAR and SVEC models.
An algorithm which identifies the morphometric features that significantly discriminate two taxa and validates the morphological distinctness between them via a Monte-Carlo test, polar coordinates and overlap of the area under the density curve.
Uses a finite mixture model for performing the cluster analysis with variable selection of continuous data by assuming independence between classes. The package deals dataset with missing values by assuming that values are missing at random. The one-dimensional marginals of the components follow Gaussian distributions for facilitating both model interpretation and model selection. The variable selection is led by the Maximum Integrated Complete-Data Likelihood criterion. The maximum likelihood inference is done by an EM algorithm for the selected model. This package also performs the imputation of  missing values.
Variable selection from random forests using both
        backwards variable elimination (for the selection of small sets
        of non-redundant variables) and selection based on the
        importance spectrum (somewhat similar to scree plots; for the
        selection of large, potentially highly-correlated variables).
        Main applications in high-dimensional data (e.g., microarray
        data, and other genomics and proteomics applications).
Provides routines for identifying structural shocks in vector autoregressions (VARs) using sign restrictions.
Computes a portfolio of European options that replicates
        the cost of capturing the realised variance of an equity index.
Efficient algorithm for solving discrete mixture
		regression model for rare variant association analysis.
		Uses variational Bayes algorithm to efficiently search over
		model space.  Outputs an approximate likelihood ratio test
		as well as variant level posterior probabilities of
		association.
Fit and simulate latent position and cluster
        models for network data, using a fast Variational Bayes approximation.
Variational algorithms and methods for fitting mixture
        models. Main functions are varbayes, vbcomp, vbconstr, mppca,
        mmppca and gmmkmsock.
Efficient algorithm for solving ultra-sparse
		regularized regression models using a variational
		Bayes algorithm with a spike (l0) prior.  Algorithm
		is solved on a path, with coordinate updates, and is
		capable of generating very sparse models.  There are
		very general model diagnostics for controling type-1
		error included in this package.
ANOVA and REML estimation of linear mixed models is implemented, once following
 Searle et al. (1991, ANOVA for unbalanced data), once making use of the 'lme4' package.
 The primary objective of this package is to perform a variance component analysis (VCA)
 according to CLSI EP05-A3 guideline "Evaluation of Precision of Quantitative Measurement
 Procedures" (2014). There are plotting methods for visualization of an experimental design,
 plotting random effects and residuals. For ANOVA type estimation two methods for computing
 ANOVA mean squares are implemented (SWEEP and quadratic forms). The covariance matrix of 
 variance components can be derived, which is used in estimating confidence intervals. Linear
 hypotheses of fixed effects and LS means can be computed. LS means can be computed at specific
 values of covariables and with custom weighting schemes for factor variables. See ?VCA for a
 more comprehensive description of the features.
Visualization techniques, data sets, summary and inference
        procedures aimed particularly at categorical data. Special
        emphasis is given to highly extensible grid graphics. The
        package was package was originally inspired by the book 
	"Visualizing Categorical Data" by Michael Friendly and is 
	now the main support package for a new book, 
	"Discrete Data Analysis with R" by Michael Friendly and 
	David Meyer (2015).
Provides additional data sets, methods and documentation to complement the 'vcd' package for Visualizing Categorical Data
    and the 'gnm' package for Generalized Nonlinear Models.
	In particular, 'vcdExtra' extends mosaic, assoc and sieve plots from 'vcd' to handle 'glm()' and 'gnm()' models and
	adds a 3D version in 'mosaic3d'.  Additionally, methods are provided for comparing and visualizing lists of
	'glm' and 'loglm' objects. This package is now a support package for the book, "Discrete Data Analysis with R" by
  Michael Friendly and David Meyer.
Facilitates easy manipulation of variant call format (VCF) data.
    Functions are provided to rapidly read from and write to VCF files.
    Once VCF data is read into R a parser function extracts matrices of data.
    This information can then be used for quality control or other purposes.
    Additional functions provide visualization of genomic data.
    Once processing is complete data may be written to a VCF file (*.vcf.gz).
    It also may be converted into other popular R objects (e.g., genlight, DNAbin).
    VcfR provides a link between VCF data and familiar R software.
Recursive partitioning for varying coefficient generalized linear models and ordinal linear mixed models. Special features are coefficient-wise partitioning, non-varying coefficients and partitioning of time-varying variables in longitudinal regression.
Multicategory Vertex Discriminant Analysis: A novel supervised multicategory classification method
Analyze Peptide Array Data and characterize peptide
    sequence space. Allows for high level visualization of global signal, Quality control based
    on replicate correlation and/or relative Kd, calculation of peptide Length/Charge/Kd parameters,
    Hits selection based on RFU Signal, and amino acid composition/basic motif recognition with RFU
    signal weighting. Basic signal trends can be used to generate peptides that follow the observed
    compositional trends.
Facilities for constructing variance dispersion graphs, fraction-
    of-design-space plots and similar graphics for exploring the properties of
    experimental designs. The design region is explored via random sampling, which
    allows for more flexibility than traditional variance dispersion graphs. A
    formula interface is leveraged to provide access to complex model formulae.
    Graphics can be constructed simultaneously for multiple experimental designs
    and/or multiple model formulae. Instead of using pointwise optimization to
    find the minimum and maximum scaled prediction variance curves, which can be
    inaccurate and time consuming, this package uses quantile regression as an
    alternative.
Uses a modification of the published FORTRAN code in "A Computer Program for Generating Variance Dispersion Graphs" by G. Vining, Journal of Quality Technology, Vol. 25 No. 1 January 1993, to produce variance dispersion graphs. Also produces fraction of design space plots, and contains data frames for several minimal run response surface designs.
Functions for creating variance dispersion graphs, fraction of design space plots, and contour plots of scaled prediction variances for second-order response surface designs in spherical and cuboidal regions. Also, some standard response surface designs can be generated.
This provides web-based visual data-mining tools by adding
    interactive functions to 'ggplot2' graphics. Brushing and linking between the
    multiple plots is one of the main feature of this package. Currently scatter
    plots, histograms, parallel coordinate plots and choropleth maps are supported.
A package for applying affine and similarity transformations on vector spatial data (sp objects). Transformations can be defined from control points or directly from parameters. If redundant control points are provided Least Squares is applied allowing to obtain residuals and RMSE.
The base 'sets' tools follow the algebraic definition
 that each element of a set must be unique. 
 Since it's often helpful to compare all elements of two vectors,
 this toolset treats every element as unique for counting purposes.
 For ease of use, all functions in vecsets have an argument
 'multiple' which, when set to FALSE,
 reverts them to the base::set tools functionality.
A 2D statistical analysis is performed, both numerical and graphical, of a set of vectors. Since a vector has two components (module and azimuth) vector analysis is performed in three stages: modules are analyzed by means of linear statistics, azimuths are analyzed by circular statistics, and the joint analysis of modules and azimuths is done using density maps that allow detecting another distribution properties (i.e. anisotropy) and outliers. Tests and circular statistic parameters have associated a full range of graphing: histograms, maps of distributions, point maps, vector maps, density maps, distribution modules and azimuths.
This package performs a 3D statistical analysis, both numerical and graphical, of a set of vectors. Since a vector has three components (a module and two angles) vectorial analysis is performed in two stages: modules are analyzed by means of linear statistics and orientations are analyzed by spherical statistics. Tests and spherical statistic parameters are accompanied by a graphs as: density maps, distribution modules and angles. The tests, spherical statistic parameters and graphs allow us detecting another distribution properties (i.e. anisotropy) and outliers.
The 'Vega-Lite' 'JavaScript' framework provides a higher-level grammar
    for visual analysis, akin to 'ggplot' or 'Tableau', that generates complete 'Vega'
    specifications. Functions exist which enable building a valid 'spec' from scratch
    or importing a previously created 'spec' file. Functions also exist to export 'spec'
    files and to generate code which will enable plots to be embedded in properly
    configured web pages. The default behavior is to generate an 'htmlwidget'.
Ordination methods, diversity analysis and other
  functions for community and vegetation ecologists.
Static and dynamic 3D plots to be used with ordination
    results and in diversity analysis, especially with the vegan package.
Contains functions used to perform fuzzy clustering of vegetation data under different models. It also includes functions to measure community dissimilarity on the basis of structure and composition.
Handling of vegetation data from Turboveg (<http://www.synbiosys.alterra.nl/turboveg/>) and other sources (<http://www.vegetweb.de>). Taxonomic harmonization given appropriate taxonomic lists (e.g. the German taxonomical standard list "GermanSL", <http://www.botanik.uni-greifswald.de/GermanSL.html>).
This package computes diversity for community data sets
        using the methods outlined by Jost (2006, 2007). While there
        are differing opinions on the ideal way to calculate diversity
        (e.g. Magurran 2004), this method offers the advantage of
        providing diversity numbers equivalents, independent alpha and
        beta diversities, and the ability to incorporate 'order' (q) as
        a continuous measure of the importance of rare species in the
        metrics. The functions provided in this package largely
        correspond with the equations offered by Jost in the cited
        papers. The package computes alpha diversities, beta
        diversities, gamma diversities, and similarity indices.
        Confidence intervals for diversity measures are calculated
        using a bootstrap method described by Chao et al. (2008).  For
        datasets with many samples (sites, plots), sim.table creates
        tables of all pairwise comparisons possible, and for grouped
        samples sim.groups calculates pairwise combinations of within-
        and between-group comparisons.
C++ accelerated raster manipulation and extraction.
A set of functions for generating HTML to
    embed hosted video in your R Markdown documents or Shiny apps.
Draws and displays Venn diagrams up to 7 sets, and any Boolean union of set intersections.
A set of functions to generate high-resolution Venn and Euler plots. Includes handling for several special cases, including two-case scaling, and extensive customization of plot shape and structure.
Calculates and displays Venn and Euler Diagrams
Utilities for verifying discrete, continuous and probabilistic forecasts, and forecasts expressed as parametric distributions are included.
Installs specified versions of R packages hosted on CRAN and
    provides functions to list available versions and the versions of currently
    installed packages. These tools can be used to help make R projects and
    packages more reproducible. 'versions' fits in the narrow gap between
    the 'devtools' install_version() function and the 'checkpoint' package.
    devtools::install_version() installs a stated package version from source files
    stored on the CRAN archives. However CRAN does not store binary versions of
    packages so Windows users need to have RTools installed and Windows and OSX
    users get longer installation times. 'checkpoint' uses the Revolution Analytics
    MRAN server to install packages (from source or binary) as they were available
    on a given date. It also provides a helpful interface to detect the packages
    in use in a directory and install all of those packages for a given date.
    'checkpoint' doesn't provide install.packages-like functionality however, and
    that's what 'versions' aims to do, by querying MRAN. As MRAN only goes back to
    2014-09-17, 'versions' can't install packages archived before this date.
When given a description of a polyhedral set by a system of linear inequalities Ax <= b, produce the list of the vertices of the set.
Creates Vertex Similarity matrix of an undirected graph based
  on the method stated by E. A. Leicht, Petter Holme, AND M. E. J. Newman in
  their paper <DOI:10.1103/PhysRevE.73.026120>.
Integrated data management library that offers a variety of tools concerning the loading and manipulation of environmental data available from different Venezuelan governmental sources. Facilities are provided to plot temporal and spatial data as well as understand the health of a collection of meteorological data.
R Codes and Datasets for Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998). Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
An implementation of about 6 major classes of
    statistical regression models. At the heart of it are the
    vector generalized linear and additive model (VGLM/VGAM)
    classes, and the book "Vector Generalized Linear and
    Additive Models: With an Implementation in R" (Yee, 2015)
    <DOI:10.1007/978-1-4939-2818-7>
    gives details of the statistical framework and VGAM package.
    Currently only fixed-effects models are implemented,
    i.e., no random-effects models. Many (150+) models and
    distributions are estimated by maximum likelihood estimation
    (MLE) or penalized MLE, using Fisher scoring. VGLMs can be
    loosely thought of as multivariate GLMs. VGAMs are data-driven
    VGLMs (i.e., with smoothing). The other classes are RR-VGLMs
    (reduced-rank VGLMs), quadratic RR-VGLMs, reduced-rank VGAMs,
    RCIMs (row-column interaction models)---these classes perform
    constrained and unconstrained quadratic ordination (CQO/UQO)
    models in ecology, as well as constrained additive ordination
    (CAO). Note that these functions are subject to change;
    see the NEWS and ChangeLog files for latest changes.
Data sets to accompany the VGAM package and
	the book "Vector Generalized Linear and
	Additive Models: With an Implementation in R" (Yee, 2015)
	<DOI:10.1007/978-1-4939-2818-7>.
	These are used to illustrate vector generalized
	linear and additive models (VGLMs/VGAMs), and associated models
	(Reduced-Rank VGLMs, Quadratic RR-VGLMs, Row-Column
	Interaction Models, and constrained and unconstrained ordination
	models in ecology).
This package provides an implementation of Linear discriminant analysis and quadratic discriminant analysis that works fine in very high dimension (when there are many more variables than observations).
The "Vertical and Horizontal Inheritance Consistence Analysis" method is described in the following publication: "VHICA: a new method to discriminate between vertical and horizontal transposon transfer: application to the mariner family within Drosophila" by G. Wallau. et al. (2016) <DOI:10.1093/molbev/msv341>. The purpose of the method is to detect horizontal transfers of transposable elements, by contrasting the divergence of transposable element sequences with that of regular genes.
It will take the vectors of motion for two videos
	(coming from a variant of shotdetect code allowing to store
	detailed motion vectors in JSON format, for instance) and it
	will look for comparing taking out the common chunk.
	Then, provided you have some image's hashes it will compare
	their signature in order to make up the decision about
	chunk similarity of two video files.
        ShotDetect is a free software which detects shots and scenes
        from a video (http://johmathe.name/shotdetect.html).
Converts Vietnam's provinces' names and ID
    across different formats. Handles diacritics and different spellings.
This package implements a fast regression algorithm for
        building linear model for large data as defined in the paper
        "VIF-Regression: A Fast Regression Algorithm for Large Data
        (2011), Journal of the American Statistical Association, Vol.
        106, No. 493: 232-247" by Dongyu Lin, Dean P. Foster, and Lyle
        H. Ungar.
Contains a function to support the following paper:
    Xiaoping Shi, Xiang-Sheng Wang, Dongwei Wei, Yuehua Wu (2016), <DOI:10.1007/s00180-015-0587-5>,
    A sequential multiple change-point detection procedure via VIF regression,
    Computational Statistics, 31(2): 671-691.
Conducts linear regression using variational Bayesian inference, particularly optimized for genome-wide association mapping and whole-genome prediction which use a number of DNA markers as the explanatory variables. Provides seven regression models which select the important variables (i.e., the variables related to response variables) among the given explanatory variables in different ways (i.e., model structures).
New tools for the visualization of missing         
    and/or imputed values are introduced, which can be used for exploring the data and the         
    structure of the missing and/or imputed values. Depending on this structure         
    of the missing values, the corresponding methods may help to identify the         
    mechanism generating the missing values and allows to explore the data         
    including missing values. In addition, the quality of imputation can be         
    visually explored using various univariate, bivariate, multiple and         
    multivariate plot methods. A graphical user interface available in the         
    separate package VIMGUI allows an easy handling of the implemented plot         
    methods.
This package introduces new tools for the visualization of missing
    and/or imputed values, which can be used for exploring the data and the
    structure of the missing and/or imputed values. Depending on this structure
    of the missing values, the corresponding methods may help to identify the
    mechanism generating the missings and allows to explore the data including
    missing values. In addition, the quality of imputation can be visually
    explored using various univariate, bivariate, multiple and multivariate
    plot methods. A graphical user interface allows an easy handling of the
    implemented plot methods.
Provides tools for the statistical analysis of vine copula models.
    The package includes tools for parameter estimation, model selection,
    simulation, goodness-of-fit tests, and visualization. Tools for estimation,
    selection and exploratory data analysis of bivariate copula models are also
    provided.
Implementation of the vine graphical model for building
    high-dimensional probability distributions as a factorization of
    bivariate copulas and marginal density functions. This package
    provides S4 classes for vines (C-vines and D-vines) and methods
    for inference, goodness-of-fit tests, density/distribution
    function evaluation, and simulation.
A lattice violin-plot is overlayed with the arithmetic
        mean and standard deviation.
A violin plot is a combination of a box plot and a kernel density plot.
viopoints draws one dimensional scatter plots with jitter
        using kernel density estimates in a similar way to violin
        plots.
Generate a violin point plot, a combination of a violin/histogram
    plot and a scatter plot by offsetting points within a category based on their
    density using quasirandom noise.
Port of the new 'matplotlib' color maps ('viridis' - the default
    -, 'magma', 'plasma' and 'inferno') to 'R'. 'matplotlib' <http://matplotlib.org/
    > is a popular plotting library for 'python'. These color maps are designed
    in such a way that they will analytically be perfectly perceptually-uniform,
    both in regular form and also when converted to black-and-white. They are
    also designed to be perceived by readers with the most common form of color
    blindness.
Port of the new 'matplotlib' color maps ('viridis' - the default
    -, 'magma', 'plasma' and 'inferno') to 'R'. 'matplotlib' <http://matplotlib.org/
    > is a popular plotting library for 'python'. These color maps are designed
    in such a way that they will analytically be perfectly perceptually-uniform,
    both in regular form and also when converted to black-and-white. They are
    also designed to be perceived by readers with the most common form of color
    blindness. This is the 'lite' version of the more complete 'viridis' package
    that can be found at <https://cran.r-project.org/package=viridis>.
Provides a framework for generating virtual species distributions, 
  a procedure increasingly used in ecology to improve species distribution models.
  This package integrates the existing methodological approaches with the 
  objective of generating virtual species distributions with increased 
  ecological realism.
Use VirusTotal, a Google service that analyzes files and URLs 
    for viruses, worms, trojans etc., provides category of the content hosted by a 
    domain from a variety of prominent services, provides passive DNS information,
    among other things. See <http://www.virustotal.com> for more information.
A graphical tool designed to visualize and to give an overview of behavioural observations realized on individuals or groups. Visualization of raw data during experimental observations of the realization of a procedure. It graphically presents an overview of individuals and group actions usually acquired from timestamps during video recorded sessions. Options of the package allow adding graphical information as statistical indicators (mean, standard deviation, quantile or statistical test) but also for each action green or black zones providing visual information about the accuracy of the realized actions.
Provides an R interface to the 'vis.js' JavaScript charting
    library. It allows an interactive visualization of networks.
Provides a convenient interface for constructing plots to visualize the fit of regression models arising from a wide variety of models in R ('lm', 'glm', 'coxph', 'rlm', 'gam', 'locfit', 'randomForest', etc.)
A collection of tools for analyzing the field of vision. It provides a framework for development and use of innovative methods for visualization, statistical analysis, and clinical interpretation of visual-field loss and its change over time. It is intended to be a tool for collaborative research.
Graphs the pdf or pmf and highlights what area or probability is present in user defined locations. Visualize is able to provide lower tail, bounded, upper tail, and two tail calculations. Supports strict and equal to inequalities.  Also provided on the graph is the mean and variance of the distribution.
Displays multivariate data, based on Sammon's nonlinear mapping.
Implements the novel testing approach by Janitza et al.(2015)
    <http://nbn-resolving.de/urn/resolver.pl?urn=nbn:de:bvb:19-epub-25587-4>
    for the permutation variable importance measure in a random forest and the
    PIMP-algorithm by Altmann et al.(2010) <doi:10.1093/bioinformatics/btq134>.
    Janitza et al.(2015) <http://nbn-resolving.de/urn/resolver.pl?urn=nbn:de:bvb:19-epub-25587-4>
    do not use the "standard" permutation variable
    importance but the cross-validated permutation variable
    importance for the novel test approach. The cross-validated
    permutation variable importance is not based on the out-of-bag
    observations but uses a similar strategy which is inspired by
    the cross-validation procedure. The novel test approach can be
    applied for classification trees as well as for regression
    trees. However, the use of the novel testing approach has not
    been tested for regression trees so far, so this routine is
    meant for the expert user only and its current state is rather
    experimental.
Provides fitting routines for four versions of the
        Vitality family of mortality models.
Provides individual- and aggregate-level graphical depictions of
    patterns of treatment and response in patient registries, and a graphical
    tool for examining potential for confounding in analyses of observational
    data.
Using frequency matrices, very low frequency variants (VLFs) are assessed for amino acid and nucleotide sequences.  The VLFs are then compared to see if they occur in only one member of a species, singleton VLFs, or if they occur in multiple members of a species, shared VLFs.  The amino acid and nucleotide VLFs are then compared to see if they are concordant with one another.  Amino acid VLFs are also assessed to determine if they lead to a change in amino acid residue type, and potential changes to protein structures.
Functions, Classes & Methods for estimation, prediction, and
  simulation (bootstrap) of Variable Length Markov Chain ('VLMC') Models.
The tools you need to process, analyze, combine, integrate and
    plot your fishery data: the georeferenced dataset from the Vessel Monitoring
    System (VMS), from the Automatic Information System (AIS) or other tracking
    devices, as well as the catches or landings dataset from the Logbook or Vessel
    Register. Package 'vmsbase' is equipped by Viewer Tools to visually inspect data
    at different steps of the analyses and to produce effective outputs for reports
    and scientific publications. Viewers are conceived to show the VMS pings, to
    visualize single or multiple tracks for fishing vessels, or to represent the VMS
    data on Google Viewer, so that the user can produce easy to interpret and more
    realistic visualization of both fishing effort and effort behaviour. Package
    'vmsbase' represents the implementation of several R routines which have been
    developed by the "Tor Vergata" University of Rome Team involved in the Italian
    National Program for the Data Collection Framework for Fisheries Data between
    2009-2012.
Using V-algorithm and Newton-Raphson method to obtain multiple-objective optimal design for estimating the shape of dose-response, the ED50 (the dose producing an effect midway between the expected responses at the extreme doses) and the MED (the minimum effective dose level) for the 2,3,4-parameter logistic models and for evaluating its efficiencies for the three objectives.
Voss package provides functionality for generating
        realizations of a fractal Brownian function on uniform 1D & 2D
        grid with classic and generic versions of the Voss algorithm
        (random sequential additions)
Calculates voter transitions comparing two elections, using the function solve.QP() in package 'quadprog'.
Procedures for the manipulation, normalization, and plotting of phonetic and sociophonetic vowel formant data.  vowels is the backend for the NORM website.
Parametric and semiparametric inference for massively parallel
    models, i.e., a large number of models with common design matrix, as often
    occurs with brain imaging data.
Functions for the mass-univariate voxelwise analysis of medical imaging data that follows the NIfTI <http://nifti.nimh.nih.gov> format.
Tools for tree crown structure description based on T-LiDAR data voxelisation
Variable Penalty Dynamic Time Warping for aligning GC-MS chromatograms to a master signal and more. With the appropriate penalty this method performs good alignment without altering the shape of peaks in GC-MS data.
In recognition that there are many factors (genetic loci, macro-genetic factors such as sex,
  and environmental factors) that influence the environmental variation, the 'vqtl' package conducts genome
  scans that accommodate and target these factors.  The main functions of this package, scanonevar() and
  scanonevar.perm() take as input a cross object from the popular 'qtl' package.
Estimation of varying regression segments and a change point in
    2-segment regression models with heteroscedastic variances, and with or
    without a smoothness constraint at the change point.
vrmlgen creates 3D scatter and bar plots, visualizations of 3D meshes, parametric functions and height maps in web-formats like the Virtual Reality Markup Language (VRML, filetype .wrl) and the LiveGraphics3D format.
This is a package to visualize risk prediction models.  For each
    predictor, a color bar represents the contribution to the linear predictor
    or latent variable.  A conversion from the linear predictor to the
    estimated risk or survival is also given.  (Cumulative) contribution charts
    enable to visualize how the estimated risk for one particular observation
    is obtained by the model. Several options allow to choose different color
    maps, and to select the zero level of the contributions.  The package is
    able to deal with 'glm', 'coxph', 'mfp', 'multinom' and 'ksvm' objects.  For 'ksvm'
    objects, the visualization is not always exact.  Functions providing tools
    to indicate the accuracy of the approximation are provided in addition to
    the visualization.
A collection of statistical tests for martingale difference hypothesis
Performs variable selection/feature reduction under a clustering or classification framework. In particular, it can be used in an automated fashion using mixture model-based methods (tEIGEN and MCLUST are currently supported).
Calculates the enrichment of associated variant set (AVS) for an array of genomic regions. The AVS is the collection of disjoint LD blocks computed from a list of disease associated SNPs and their linked (LD) SNPs. VSE generates a null distribution of matched random variant sets (MRVSs) from 1000 Genome Project Phase III data that are identical to AVS, LD block by block. It then computes the enrichment of AVS intersecting with user provided genomic features (e.g., histone marks or transcription factor binding sites) compared with the null distribution.
Three steps variable selection procedure based on random forests.
    Initially developed to handle high dimensional data (for which number of
    variables largely exceeds number of observations), the package is very
    versatile and can treat most dimensions of data, for regression and
    supervised classification problems. First step is dedicated to eliminate
    irrelevant variables from the dataset. Second step aims to select all
    variables related to the response for interpretation purpose. Third step
    refines the selection by eliminating redundancy in the set of variables
    selected by the second step, for prediction purpose.
Designed to facilitate the assimilation, analysis and synthesis of animal location and movement data collected by the VEMCO suite of acoustic transmitters and receivers. As well as database and geographic information capabilities the principal feature of VTrack is the qualification and identification of ecologically relevant events from the acoustic detection and sensor data. This procedure condenses the acoustic detection database by orders of magnitude, greatly enhancing the synthesis of acoustic detection data.
A data.frame processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner.
    Prepares variables so that data has fewer exceptional cases, making
    it easier to safely use models in production. Common problems 'vtreat' defends
    against: Inf, NA, too many categorical levels, rare categorical levels, new
    categorical levels (levels seen during application, but not during training).
    'vtreat::prepare' should be used as you would use 'model.matrix'.
Contains functions for visualization univariate data: ccdplot and qddplot.
Gaze data from the Visual World Paradigm requires significant
    preprocessing prior to plotting and analyzing the data. This package 
    provides functions for preparing visual world eye-tracking data for 
    statistical analysis and plotting. It can prepare data for linear 
    analyses (e.g., ANOVA, Gaussian-family LMER, Gaussian-family GAMM) as
    well as logistic analyses (e.g., binomial-family LMER and binomial-family GAMM).
    Additionally, it contains various plotting functions for creating grand average and
    conditional average plots. See the vignette for samples of the functionality.
    Currently, the functions in this package are designed for handling data
    collected with SR Research Eyelink eye trackers using Sample Reports created
    in SR Research Data Viewer; however, in subsequent releases we would like 
    to add functionality for data collected with other systems.
Functions and data for use in visual word recognition research: 
	 Computation of neighbors (Hamming and Levenshtein
	 distances), average distances to neighbors (e.g., OLD20),
	 and Coltheart's N. Also includes the LD1NN algorithm to
	 detect bias in the composition of a lexical decision task. Most of
	 the functions support parallel execution. Supplies wordlists for 
	 several languages. Uses the string distance functions from the stringdist package by Mark van der Loo.
Set of functions that improves the graphical presentations of the functions 'wave.correlation' and 'spin.correlation' (waveslim package, Whitcher 2012) and the 'wave.multiple.correlation' and 'wave.multiple.cross.correlation' (wavemulcor package, Fernandez-Macho 2012b). The plot outputs (heatmaps) can be displayed in the screen or can be saved as PNG or JPG images or as PDF or EPS formats. The W2CWM2C package also helps to handle the (input data) multivariate time series easily as a list of N elements (times series) and provides a multivariate data set (dataexample) to exemplify its use. A description of the package was published in Computing in Science & Engineering (Volume:16, Issue: 6) on Sep. 09, 2014, doi:10.1109/MCSE.2014.96.
R interface to a W3C Markup Validation service.
  See <http://validator.w3.org/> for more information.
Multivariate weather generator for daily climate variables based 
    on weather-states using a Markov chain for modeling the succession of weather states. 
    Conditionally to the weather states, the multivariate variables are modeled using the family 
    of Complete Skew-Normal distributions. Parameters are estimated on measured series. Data must 
    include the variable 'Rain' and can accept as many other variables as desired.
waffect (pronounced 'double-u affect' for 'weighted
        affectation') is a package to simulate phenotypic (case or
        control) datasets under a disease model H1 such that the total
        number of cases is constant across all the simulations (the
        constrain in the title). The package also makes it possible to
        generate phenotypes in the case of more than two classes, so
        that the number of phenotypes belonging to each class is
        constant across all the simulations. waffect is used to assess
        empirically the statistical power of Genome Wide Association
        studies.
Square pie charts (a.k.a. waffle charts) can be used
    to communicate parts of a whole for categorical quantities. To emulate the
    percentage view of a pie chart, a 10x10 grid should be used with each square
    representing 1% of the total. Modern uses of waffle charts do not
    necessarily adhere to this rule and can be created with a grid of any
    rectangular shape. Best practices suggest keeping the number of categories
    small, just as should be done when creating pie charts. Tools are provided
    to create waffle charts as well as stitch them together, and to use glyphs
    for isotype pictogram creation.
Fit the fixed effect panel data model with heteroskedasticity and
    autocorrelation correction.
Generates random data sets including: data.frames, lists,
    and vectors.
Consider the intersection of two spaces: the complete solution space
             to Ax = b and the N-simplex. The intersection of these two spaces is 
             a non-negative convex polytope. The package walkr samples from this 
             intersection using two Monte-Carlo Markov Chain (MCMC) methods: 
             hit-and-run and Dikin walk. walkr also provide tools to examine sample 
             quality.
A collection of functions to perform the Application
        Programming Interface (API) calls associated with the Walk
        Score website (www.walkscore.com) within the R environment.
        These functions can be used to query the Walk Score and Transit
        Score database for a wide variety of information using R
        scripts. This package includes the simple Walk Score and
        Transit Score API calls, which return the scores associated
        with an input location, as well as calls which return some data
        used to calculate the scores. These functions are especially
        useful for mass data collection and gathering Walk Score and
        Transit Score values for large lists of locations.
The 'libmagic' library provides functions to determine
    'MIME' type and other metadata from files through their "magic"
    attributes. This is useful when you do not wish to rely solely on
    the honesty of a user or the extension on a file name. It also
    incorporates other metadata from the mime-db database
    <https://github.com/jshttp/mime-db>.
Offers three overarching categories of functions to obtain bird
    vocalization recordings from the web, manage sound files, and facilitate
    (bio)acoustic analysis in R.
This estimates precise weaning ages
	for a given skeletal population
	by analyzing the stable nitrogen isotope ratios of them.
	Bone collagen turnover rates estimated anew and
	the approximate Bayesian computation (ABC)
	were adopted in this package.
Helpful tools for data processing and visualisation of results of the hydrological model WASIM-ETH.
Tools and functions to calculate actual evapotranspiration
    using surface energy balance models.
This package imports U.S. Geological Survey (USGS) daily hydrologic data from USGS web services, plots the data, addresses some common data problems, and calculates and plots anomalies.
Provides support for creating waterfall charts in R
    using both traditional base and lattice graphics.
There seems to be no simple way to create 'waterfall charts' in 'ggplot2' currently. This package contains a single function (waterfall) that draws simply a waterfall chart as a 'ggplot2' object. Some flexibility in the appearance is available.
Lets you connect to any of the Consortium of Universities for the Advancement
    of Hydrologic Sciences, Inc. ('CUAHSI') Water Data Center 'WaterOneFlow' web services
    and read any 'WaterML' hydrological time series data file. To see list of available
    web services, see <http://hiscentral.cuahsi.org>. All versions of 'WaterML'
    (1.0, 1.1 and 2.0) and both types of the web service protocol ('SOAP' and 'REST') are supported.
    The package has six data download functions: GetServices(): show all public web
    services from the HIS Central Catalog. HISCentral_GetSites() and HISCentral_GetSeriesCatalog():
    search for sites or time series from the HIS Central catalog based on geographic bounding box,
    server, or keyword. GetVariables(): Show a data.frame with all variables on the server.
    GetSites(): Show a data.frame with all sites on the server. GetSiteInfo(): Show what variables,
    methods and quality control levels are available at the specific site. GetValues(): Given a site
    code, variable code, start time and end time, fetch a data.frame of all the observation time
    series data values. The GetValues() function can also parse 'WaterML' data from a custom URL or
    from a local file. The package also has five data upload functions:
    AddSites(), AddVariables(), AddMethods(), AddSources(), and AddValues().
    These functions can be used for uploading data to a 'HydroServer Lite' Observations
    Data Model ('ODM') database via the 'JSON' data upload web service interface.
Methods for watersheds aggregation and spatial drainage network analysis.
Wrap-around Time Series (WATS) plots for interrupted time series
    designs with seasonal patterns.
Computes Bayesian wavelet shrinkage credible intervals
Makes available code necessary to reproduce figures and
        tables in papers on the WaveD method for wavelet deconvolution
        of noisy signals as presented in The WaveD Transform in R,
        Journal of Statistical Software Volume 21, No. 3, 2007.
Wavelet analysis and reconstruction of time series, cross-wavelets and phase-difference (with filtering options), significance with simulation algorithms.
This package contains functions for computing and plotting
        discrete wavelet transforms (DWT) and maximal overlap discrete
        wavelet transforms (MODWT), as well as their inverses.
        Additionally, it contains functionality for computing and
        plotting wavelet transform filters that are used in the above
        decompositions as well as multiresolution analyses.
Wavelet routines that calculate single sets of wavelet
        multiple correlations and cross-correlations out of n variables
        (either 1D time series, 2D images or 3D arrays). They can later
        be plotted in single graphs, as an alternative to trying to
        make sense out of several sets of wavelet correlations or
        wavelet cross-correlations. The code is based on the
        calculation, at each wavelet scale, of the square root of the
        coefficient of determination in a linear combination of
        variables for which such coefficient of determination is a
        maximum. The code provided here is based on the
        wave.correlation routine in Brandon Whitcher's waveslim R
        package Version: 1.6.4, which in turn is based on wavelet
        methodology developed in Percival and Walden (2000); Gencay,
        Selcuk and Whitcher (2001) and others.
For multivariate datasets, this function enables the estimation of missing data using the Weighted AVERage of all possible Regressions using the data available.
Basic wavelet routines for time series (1D), image (2D) 
  and array (3D) analysis.  The code provided here is based on
  wavelet methodology developed in Percival and Walden (2000);
  Gencay, Selcuk and Whitcher (2001); the dual-tree complex wavelet
  transform (DTCWT) from Kingsbury (1999, 2001) as implemented by
  Selesnick; and Hilbert wavelet pairs (Selesnick 2001, 2002).  All
  figures in chapters 4-7 of GSW (2001) are reproducible using this 
  package and R code available at the book website(s) below.
Performs 1, 2 and 3D real and complex-valued wavelet transforms,
	nondecimated transforms, wavelet packet transforms, nondecimated
	wavelet packet transforms, multiple wavelet transforms,
	complex-valued wavelet transforms, wavelet shrinkage for
	various kinds of data, locally stationary wavelet time series,
	nonstationary multiscale transfer function modeling, density
	estimation.
Supplies bootstrap alternatives to traditional hypothesis-test
  and confidence-interval procedures such as one-sample and two-sample
  inferences for means, medians, standard deviations, and proportions; simple
  linear regression; and more. Suitable for general audiences, including
  individual and group users, introductory statistics courses, and more advanced
  statistics courses that desire an introduction to bootstrap methods.
Provides efficient implementation of the Wild Binary Segmentation and Binary
    Segmentation algorithms for estimation of the number and locations of
    multiple change-points in the piecewise constant function plus Gaussian
    noise model.
Tools for searching and downloading data and statistics from
  the World Bank Data API (<http://data.worldbank.org/developers/api-overview>)
  and the World Bank Data Catalog API (<http://data.worldbank.org/developers/data-catalog-api>).
Implements detection for the number and locations of
    the change-points in a time series using the Wild Binary Segmentation and
    the Locally Stationary Wavelet model.
SOMs can be useful tools to group patterns containing several peaks. If peaks do not always occur at exactly the same position, classical distance measures cannot be used. This package provides SOM technology using the weighted crosscorrelation (WCC) distance.
WCE implements a flexible method for modeling cumulative effects of time-varying exposures, weighted according to their relative proximity in time, and represented by time-dependent covariates. The current implementation estimates the weight function in the Cox proportional hazards model. The function that assigns weights to doses taken in the past is estimated using cubic regression splines.
Calculates Pearson, Spearman, polychoric, and polyserial correlation coefficients, in weighted or unweighted form. The package implements tetrachoric correlation as a special case of the polychoric and biserial correlation as a specific case of the polyserial.
The package contains the WCQ method for detection of QTL
        effects in a small mapping population. It also contains
        implementation of the Chen-Qin two-sample and one-sample test
        of means.
Search, extract and format data from the World Bank's
    World Development Indicators
Functions that help in fetching weather data from
    websites. Given a location and a date range, these functions help fetch
    weather data (temperature, pressure etc.) for any weather related analysis.
Functions to convert between weather metrics, including conversions
    for metrics of temperature, air moisture, wind speed, and precipitation.
    This package also includes functions to calculate the heat index from
    air temperature and air moisture.
Handle instant weather forecasts and geographical information. It combines multiple sources of information to obtain instant weather forecasts.
Chemical information from around the web. This package interacts
    with a suite of web APIs for chemical information.
Lossless webp images are 26% smaller in size compared to PNG. Lossy
    webp images are 25-34% smaller in size compared to JPEG. This package reads
    and writes webp images into a 3 (rgb) or 4 (rgba) channel bitmap array using
    conventions from the 'jpeg' and 'png' packages.
R is used by a vast array of people for a vast array of purposes
    - including web analytics. This package contains functions for consuming and
    munging various common forms of request log, including the Common and Combined
    Web Log formats and various Amazon access logs.
Takes screenshots of web pages, including Shiny applications.
A Stata-style `webuse()` function for importing named datasets from Stata's online collection.
Utility functions for developing web applications. Includes parsers
    for application/x-www-form-urlencoded as well as multipart/form-data
    and examples of using the parser with either httpuv or rhttpd.
Uses Protovis to provide web graphics for R (exposes most
        low-level functions).  Package is still under active
        development and shouldn't be considered stable until version
        0.1.  Currently uses a web browser to process JavaScript,
        although future version will process JavaScript directly and
        return the SVG output.  Also does not properly support discrete
        labels (e.g. with histograms) or statistical functions.  See
        website for more details.
Provides functions to create factor variables with contrasts based on weighted effect coding. In weighted effect coding the estimates from a first order regression model show the deviations per group from the sample mean. This is especially useful when a researcher has no directional hypotheses and uses a sample from a population in which the number of observation per group is different The package also provides functionality for interactions between two factor variables based on weighted effect coding. Please note that this is a beta version: while functional, it does not follow all R conventions.
The WeightedCluster library provides functions to cluster states sequences and weighted data. These functionalities include aggregating replicated cases, an optimized weighted PAM algorithm, function computing cluster quality measures for a range of clustering solutions and miscellaneous functions to plot clustering solutions of state sequences.
Weighted descriptive statistics is the discipline of quantitatively describing the main features of real-valued fuzzy data which usually given from a fuzzy population. One can summarize this special kind of fuzzy data numerically or graphically using this package. To interpret some of the properties of one or several sets of real-valued fuzzy data, numerically summarize is possible by some weighted statistics which are designed in this package such as mean, variance, covariance and correlation coefficent. Also, graphically interpretation can be given by weighted histogram and weighted scatter plot using this package to describe properties of real-valued fuzzy data set.
This packages contains the Weighted Portmanteau Tests as
        described in "New Weighted Portmanteau Statistics for Time
        Series Goodness-of-Fit Testing' accepted for publication by the
        Journal of the American Statistical Association.
Has functions to implement the weighted scores method and CL1 information criteria as an intermediate step for variable/correlation selection for longitudinal categorical and count data in Nikoloulopoulos, Joe and Chaganty (2011, Biostatistics, 12: 653-665) and Nikoloulopoulos (2015a,2015b).
Set of functions for estimating the Vevea and Hedges (1995)
    weight-function model in R. By specifying arguments, users can
    also estimate the modified model described in Vevea and Woods (2005), which
    may be more practical with small datasets. Users can also specify moderators
    to estimate a linear model. The package functionality allows users to easily
    extract the results of these analyses as R objects for other uses. In addition,
    the package includes a function to launch both models as a Shiny application.
    Although the Shiny application is also available online, this function allows
    users to launch it locally if they choose.
Provides a variety of functions for producing simple weighted statistics, such as weighted Pearson's correlations, partial correlations, Chi-Squared statistics, histograms, and t-tests.  Also now includes some software for quickly recoding survey data and plotting point estimates from interaction terms in regressions (and multiply imputed regressions).  Future versions of the package will be more closely integrated with "anesrake" and additional weighting tools and will provide the option to find weighting benchmarks and weight data using a variety of methodologies.  NOTE: Weighted partial correlation calculations temporarily pulled to address a bug.
The weightTAPSPACK subsets The American Panel Survey (TAPS) data by outcome and covariates, models the attrition rates, imputes data for attrited individuals, and finds weights for analysis.
Provides computational support for flow over weirs, such as sharp-crested, broad-crested, and embankments. Initially, the package supports broad- and sharp-crested weirs.
Convert 'WKT' to 'GeoJSON' and 'GeoJSON' to 'WKT'. Functions
    included for converting between 'GeoJSON' to 'WKT', creating both
    'GeoJSON' features, and non-features, creating WKT from R objects
    (e.g., lists, data.frames, vectors), and linting 'WKT'.
Palettes generated mostly from Wes Anderson movies
This R package provides a computationally efficient way
	     of fitting weighted linear fixed effects estimators for
	     causal inference with various weighting schemes.  Imai
	     and Kim (2012) show that weighted linear fixed effects
	     estimators can be used to estimate the average treatment
	     effects under different identification strategies.  This
	     includes stratified randomized experiments, matching and
	     stratification for observational studies, first
	     differencing, and difference-in-differences.  The package
	     also provides various robust standard errors and a
	     specification test for standard linear fixed effects
	     estimators.
Implementation of Weighted Fast Greedy algorithm for community detection in networks with mixed types of attributes.
Provides a large English words list and tools to find words by patterns. In particular, anagram finder and scrabble word finder.
Integrates sophisticated mixed modelling methods with a whole genome approach to detecting significant QTL in linkage maps.
Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data. Includes functions for rudimentary data cleaning, construction of correlation networks, module identification, summarization, and relating of variables and modules to sample traits. Also includes a number of utility functions for data manipulation and visualization.
Non parametric alternative to Kolmogorov-Smirnov based
    standard GSEA testing.
Inferences about counterfactuals are essential for prediction,
      answering what if questions, and estimating causal effects.
      However, when the counterfactuals posed are too far from the data at
      hand, conclusions drawn from well-specified statistical analyses
      become based largely on speculation hidden in convenient modeling
      assumptions that few would be willing to defend.  Unfortunately,
      standard statistical approaches assume the veracity of the model
      rather than revealing the degree of model-dependence, which makes this
      problem hard to detect.  WhatIf offers easy-to-apply methods to
      evaluate counterfactuals that do not require sensitivity testing over
      specified classes of models.  If an analysis fails the tests offered
      here, then we know that substantive inferences will be sensitive to at
      least some modeling choices that are not based on empirical evidence,
      no matter what method of inference one chooses to use.  WhatIf
      implements the methods for evaluating counterfactuals discussed in
      Gary King and Langche Zeng, 2006, "The Dangers of Extreme
      Counterfactuals," Political Analysis 14 (2); and Gary 
      King and Langche Zeng, 2007, "When Can History Be Our Guide?  The 
      Pitfalls of Counterfactual Inference," International Studies 
      Quarterly 51 (March).
logicless templating, reuse templates in many programming
        languages including R
Shinohara et. al introduced Whitestripe, an 
        intensity-based normalization of T1 and T2 images, where normal
        appearing white matter performs well, but requires segmentation.
        This method performs white matter mean and standard deviation
        estimates on data that has been rigidly-registered to the MNI
        template and uses histogram-based methods.
Provides programmatic access to the World Health Organization API.
Look up the username and full name of the current user,
    the current user's email address and 'GitHub' username,
    using various sources of system and configuration information.
Retrieve data from the 'Whoapi' (https://whoapi.com) store of
    domain information, including a domain's geographic location, registration
    status and search prominence.
Provides very fast access to whole genome, population scale variation data
	from VCF files and sequence data from FASTA-formatted files.
	It also reads in alignments from FASTA, Phylip, MAF and other file formats.
	Provides easy-to-use interfaces to genome annotation from UCSC and Bioconductor and gene ontology data
	from AmiGO and is capable to read, modify and write PLINK .PED-format pedigree files.
Fit, forecast, predict massive spacio-temporal data
Extends the glmnet and relaxnet packages with polynomial basis expansions. Basis expansion is applied to the predictors and a subset of the basis functions is chosen using relaxnet. Predictors may be screened using correlation or t-tests. Screening is done separately within cross-validation folds. Cross-validation may be used to select the order of basis expansion and alpha, the elastic net tuning parameter.
The german Wikibook "GNU R" introduces R to new users.
        This package is a collection of functions and datas used in the
        german WikiBook "GNU R"
An API client for the Wikidata <http://wikidata.org/> store of
    semantic data.
Provides an interface to the Wikipedia web application programming
    interface (API), using internet connexion.Three functions provide details for
    a specific Wikipedia page : all links that are present, all pages that link
    to, all the contributions (revisions for main pages, and discussions for talk
    pages). Two functions provide details for a specific user : all contributions,
    and general information (as name, gender, rights or groups). It provides
    additional information compared to others packages, as WikipediR. It does not
    need login. The multiplex network that can be constructed from the results
    of the functions of WikipediaR can be modeled as Stochastic Block Model as in
    Barbillon P., Donnet, S., Lazega E., and Bar-Hen A. : Stochastic Block Models
    for Multiplex networks: an application to networks of researchers, ArXiv
    1501.06444, http://arxiv.org/abs/1501.06444.
Public attention is an interesting field of study. The
    internet not only allows to access information in no time on
    virtually any subject but via page access statistics gathered
    by website authors the subject of attention as well can be
    studied. For the omnipresent Wikipedia those access statistics
    are made available via 'http://stats.grok.se' a server
    providing the information as file dumps as well as as web API.
    This package provides an easy to use, consistent and traffic
    minimizing approach to make those data accessible within R.
A wrapper for the MediaWiki API, aimed particularly at the
    Wikimedia 'production' wikis, such as Wikipedia. It can be used to retrieve
    page text, information about users or the history of pages, and elements of
    the category tree.
MediaWiki is  wiki platform. Providing the infrastructure of Wikipedia, it also offers very sophisticated archiving functionalities.
  This package is built to store these wiki's archives to R object - data-frame, lists, vector and variables. All data are downloaded
  with the help of MediaWiki REST API. For instance, you can get all revisions made by a contributor - contrib_list(), all the revisions 
  of a page page_revisions(), or create corpus of contributors - corpus_contrib_create() - and pages corpus_page_create(). Then, you can 
  make these corpus rich of data about contributors or pages - corpus_contrib_data() or corpus_page_data().
This package provides functions to perform fast variable
        selection based on the Wilcoxon rank sum test in the
        cross-validation or Monte-Carlo cross-validation settings, for
        use in microarray-based binary classification.
Dynamic interaction refers to spatial-temporal associations in the
    movements of two (or more) animals. This package provides tools for
    calculating a suite of indices used for quantifying dynamic interaction
    with wildlife telemetry data. For more information on each of the methods
    employed see the references within. The package draws heavily on the
    classes and methods developed in the 'adehabitat' packages.
Provides insight into how the best hand for a poker game changes based on the game dealt, players who stay in until the showdown and wildcards added to the base game.  At this time the package does not support player tactics, so draw poker variants are not included.
Analysing convergent evolution using the Wheatsheaf index.
Helps for interfacing with the operating system
    particularly for Windows.
Data sets from the World Input Output Database, for the years 1995-2011.
Perform the Wild Scale-Enhanced (WiSE) bootstrap.  Specifically, the user may supply a single or multiple equally-spaced time series and use the WiSE bootstrap to select a wavelet-smoothed model.  Conversely, a pre-selected smooth level may also be specified for the time series.  Quantities such as the bootstrap sample of wavelet coefficients, smoothed bootstrap samples, and specific hypothesis testing and confidence region results of the wavelet coefficients may be obtained.  Additional functions are available to the user which help format the time series before analysis.  This methodology is recommended to aid in model selection and signal extraction.
    Note:  This package specifically uses wavelet bases in the WiSE bootstrap methodology, but the theoretical construct is much more versatile.
A set of functions to run code 'with' safely and temporarily
    modified global state. Many of these functions were originally a part of the
    'devtools' package, this provides a simple package with limited dependencies
    to provide access to these functions.
Utility functions to convert between the 'Spatial' classes
    specified by the package 'sp', and the well-known binary '(WKB)'
    representation for geometry specified by the Open Geospatial Consortium.
    Supports 'Spatial' objects of class 'SpatialPoints',
    'SpatialPointsDataFrame', 'SpatialLines', 'SpatialLinesDataFrame',
    'SpatialPolygons', and 'SpatialPolygonsDataFrame'. Supports 'WKB' geometry
    types 'Point', 'LineString', 'Polygon', 'MultiPoint', 'MultiLineString', and
    'MultiPolygon'. Includes extensions to enable creation of maps with
    'TIBCO Spotfire'.
Approach to the robustness via Weighted Likelihood.
A GUI R implementation of hierarchical Bayesian models of working memory, used for analyzing change detection data.
Distance discriminant analysis method is one of
        classification methods according to multiindex performance
        parameters.However,the traditional Mahalanobis distance
        discriminant method treats with the importance of all
        parameters equally,and exaggerates the role of parameters which
        changes a little.The weighted Mahalanobis distance is used in
        discriminant analysis method to distinguish the importance of
        each parameter.In the concrete application,firstly based on the
        principal component analysis scheme,a new group of parameters
        and their corresponding percent contributions of the parameters
        are calculated ,and the weighted matrix is regarded as the
        diagonal matrix of the contributions rates.Setting data to
        standardization,then the weighted Mahalanobis distance can be
        calculated.Besides the methods metioned above,bayes method is
        also given.
Analyzing the texture of an image from a multifractal wavelet leader analysis.
Software to book Wavelet Methods for Time Series Analysis,
        Donald B. Percival and Andrew T. Walden, Cambridge University
        Press, 2000.
Estimates Poole and Rosenthal W-NOMINATE scores from roll
        call votes supplied though a 'rollcall' object from package
        'pscl'.
Shows the relationship between an independent and dependent variable through Weight of Evidence and Information Value.
For a given Sentence-Aligned Parallel Corpus, it aligns words for each sentence pair. It considers many-to-one and symmetrization alignments. Moreover, it evaluates word alignment based on this package and some other software or method. It also builds an automatic dictionary of two languages based on given parallel corpus.
Tools for connecting to Wordbank, an open repository for
    developmental vocabulary data.
A fast visualization tool for creating wordcloud
    by using wordcloud2.js.
Matches words in one file with words in another file and shows index(row number) for the matches
An interface to WordNet using the Jawbone Java API to WordNet.
  WordNet (<http://wordnet.princeton.edu/>) is a large lexical database of
  English.  Nouns, verbs, adjectives and adverbs are grouped into sets of
  cognitive synonyms (synsets), each expressing a distinct concept.  Synsets
  are interlinked by means of conceptual-semantic and lexical relations.
  Please note that WordNet(R) is a registered tradename.  Princeton
  University makes WordNet available to research and commercial users
  free of charge provided the terms of their license
  (<http://wordnet.princeton.edu/wordnet/license/>) are followed, and
  proper reference is made to the project using an appropriate
  citation (<http://wordnet.princeton.edu/wordnet/citing-wordnet/>).
This package collects several classical word pools used
        most often to provide lists of words in psychological studies
        of learning and memory.
An interactive laboratory for research on distributional semantic models ('DSM',
             see <https://en.wikipedia.org/wiki/Distributional_semantics> for more information).
Implementing weighted predictiveness curve to visualize the marker-by-treatment relationship and measure the performance of biomarkers for guiding treatment decision.
Supplies permutation-test alternatives to traditional hypothesis-test
  procedures such as two-sample tests for means, medians, and standard deviations;
  correlation tests; tests for homogeneity and independence; and more. Suitable for
  general audiences, including individual and group users, introductory statistics
  courses, and more advanced statistics courses that desire an introduction to
  permutation tests.
Data from the United Nation's World Population Prospects 2008
Data from the United Nation's World Population Prospects
        2010
Data from the United Nation's World Population Prospects 2012
Provides data from the United Nation's World Population Prospects 2015.
Explore data in the 'wpp2015' (or 2012, 2010) package using a 'shiny' interface.
Fits weighted quantile sum regression models, calculates weighted quantile sum index and estimated component weights.
A wrapper around Michel Scheffers's libassp (Advanced
    Speech Signal Processor). The libassp library aims at providing
    functionality for handling speech signal files in most common audio formats
    and for performing analyses common in phonetic science/speech science. This
    includes the calculation of formants, fundamental frequency, root mean
    square, auto correlation, a variety of spectral analyses, zero crossing
    rate, filtering etc. This wrapper provides R with a large subset of
    libassp's signal processing functions and provides them to the user in a
    (hopefully) user-friendly manner.
A powerful yet simple graphical tool available in the field of psychometrics is the Wright Map (also known as item maps or item-person maps), which presents the location of both respondents and items on the same scale. Wright Maps are commonly used to present the results of dichotomous or polytomous item response models. The 'WrightMap' package provides functions to create these plots from item parameters and person estimates stored as R objects. Although the package can be used in conjunction with any software used to estimate the IRT model (e.g. 'TAM', 'mirt', 'eRm' or 'IRToys' in 'R', or 'Stata', 'Mplus', etc.),  'WrightMap' features special integration with 'ConQuest' to facilitate reading and plotting its output directly.The 'wrightMap' function creates Wright Maps based on person estimates and item parameters produced by an item response analysis. The 'CQmodel' function reads output files created using 'ConQuest' software and creates a set of data frames for easy data manipulation, bundled in a 'CQmodel' object. The 'wrightMap' function can take a 'CQmodel' object as input or it can be used to create Wright Maps directly from data frames of person and item parameters.
Function for writing a SNNS pattern file from a data.frame
        or matrix.
Cross-platform Perl based R function to create Excel 2003 (XLS) and Excel 2007 (XLSX)
             files from one or more data frames. Each data frame will be
             written to a separate named worksheet in the Excel spreadsheet.
             The worksheet name will be the name of the data frame it contains
             or can be specified by the user.
A collection of robust statistical methods based on Wilcox' WRS functions. It implements robust t-tests (independent and dependent samples), robust ANOVA (including between-within subject designs), quantile ANOVA, robust correlation, robust mediation, and nonparametric ANCOVA models based on robust location measures.
Contains functions for working with the Worldwide Reference System
    (WRS) 1 and 2 systems used by NASA. WRS-1 applies to Landsat 1-3, WRS-2
    applies to Landsat 4-8. The package has functions for retrieving a given
    path and row as a polygon, and for retrieving the path(s) and row(s)
    containing a given raster or vector.
Contains the Worldwide Reference System (WRS) 1 and 2 polygon
    data from NASA, for use by the wrspathrow package. WRS-1 and WRS-2
    Shape files courtesy of the U.S. Geological Survey, from
    http://landsat.usgs.gov/tools_wrs-2_shapefile.php
A collection of implementations of classical and novel
        algorithms for weighted sampling without replacement.
Includes performance measurements and results of repeated
    experiment runs (for correctness checks) for code in the
    'wrswoR' package.
This open-source software package enables researchers to predict
    individual ethnicity using his/her surname, geolocation, and other attributes
    such as gender and age. The method utilizes the Bayes' rule to compute the
    posterior probability of each racial category for any given individual voter.
    The package implements methods described in Imai and Khanna (2016) <DOI:10.1093/pan/mpw001>.
Entropy weighted k-means (ewkm) is a weighted subspace
        clustering algorithm that is well suited to very high
        dimensional data. Weights are calculated as the importance of
        a variable with regard to cluster membership.  The two-level
        variable weighting clustering algorithm tw-k-means (twkm)
        introduces two types of weights, the weights on individual
        variables and the weights on variable groups, and they are
        calculated during the clustering process.  The feature group
        weighted k-means (fgkm) extends this concept by grouping
        features and weighting the group in addition to weighting
        individual features.
A parallel implementation of Weighted Subspace Random
    Forest.  The Weighted Subspace Random Forest algorithm was
    proposed in the International Journal of Data Warehousing and
    Mining by Baoxun Xu, Joshua Zhexue Huang, Graham Williams, Qiang
    Wang, and Yunming Ye (2012) <DOI:10.4018/jdwm.2012040103>.  The
    algorithm can classify very high-dimensional data with random
    forests built using small subspaces.  A novel variable weighting
    method is used for variable subspace selection in place of the
    traditional random variable sampling.This new approach is
    particularly useful in building models from high-dimensional data.
We propose weighted SVM methods with penalization form. By
        adding weights to loss term, we can build up weighted SVM
        easily and examine classification algorithm properties under
        weighted SVM. Through comparing each of test error rates, we
        conclude that our Weighted SVM with boosting has predominant
        properties than the standard SVM have, as a whole.
Estimation and regression modeling of subdistribution functions for competing risks data with adjustment for covariate-dependent censoring, as described in He et al. (2015).
Perform the calculation of W-test, diagnostic checking, calculate minor allele frequency (MAF) and odds ratio.
Allows form managers to download entries from their respondents
    using Wufoo JSON API. Additionally, the Wufoo reports - when public - can be
    also acquired programmatically. Note that building new forms within this package
    is not supported.
Methods to calculate and interpret climate change signals and time series from climate multi-model ensembles. Climate model output in binary 'NetCDF' format is read in and aggregated over a specified region to a data.frame for statistical analysis. Global Circulation Models, as the 'CMIP5' simulations, can be read in the same way as Regional Climate Models, as e.g. the 'CORDEX' or 'ENSEMBLES' simulations. The package has been developed at the 'Wegener Center for Climate and Global Change' at the University of Graz, Austria.
Book is "Linear Mixed Models: A Practical Guide Using
        Statistical Software" published in 2006 by Chapman Hall / CRC
        Press
A wrapper function and GUI for the X12 binaries
A graphical user interface for the x12 package
The US Census provides a seasonal adjustment program now
 called 'X-13ARIMA-SEATS' building on both earlier Census programs called
 X-11 and X-12 as well as the SEATS program by the Bank of Spain. Census
 offers both source and binary versions -- which this package integrates
 for use by other R packages.
Functions to extract business financial information from
  an Extensible Business Reporting Language ('XBRL') instance file and the
  associated collection of files that defines its 'Discoverable' Taxonomy
  Set ('DTS').
Provides a tool for extracting information (entities and relations between them) in text datasets. It also emphasizes the results exploration with graphical displays. It is a rule-based system and works with hand-made dictionaries and local grammars defined by users. X.ent uses parsing with Perl functions and JavaScript to define user preferences through a browser and R to display and support analysis of the results extracted. Local grammars are defined and compiled with the tool Unitex, a tool developed by University Paris Est that supports multiple languages. See ?xconfig for an introduction.
Extensions of Exponential Random Graph Models (ERGM): Temporal Exponential Random Graph Models (TERGM), Generalized Exponential Random Graph Models (GERGM), Temporal Network Autocorrelation Models (TNAM), and Relational Event Models (REM).
Datasets and definitions of generic functions used in dependencies of the 'xergm' package.
Extreme Gradient Boosting, which is an 
    efficient implementation of gradient boosting framework. 
    This package is its R interface. The package includes efficient 
    linear model solver and tree learning algorithms. The package can automatically 
    do parallel computation on a single machine which could be more than 10 times faster
    than existing gradient boosting packages. It supports various
    objective functions, including regression, classification and ranking. The
    package is made to be extensible, so that users are also allowed to define
    their own objectives easily.
Interface to the XGobi and XGvis programs for graphical
        data analysis.
The central goal of XGR is to provide a data interpretation system. It is designed to make a user-defined gene or SNP list (or genomic regions) more interpretable by comprehensively utilising ontology annotations and interaction networks to reveal relationships and enhance opportunities for biological discovery. XGR is unique in supporting a broad range of ontologies (including knowledge of biological and molecular functions, pathways, diseases and phenotypes - in both human and mouse) and different types of networks (including functional, physical and pathway interactions). There are two core functionalities of XGR. The first is to provide basic infrastructures for easy access to built-in ontologies and networks. The second is to support data interpretations via 1) enrichment analysis using either built-in or custom ontologies, 2) similarity analysis for calculating semantic similarity between genes (or SNPs) based on their ontology annotation profiles, 3) network analysis for identification of gene networks given a query list of (significant) genes or SNPs, and 4) annotation analysis for interpreting genomic regions using co-localised functional genomic annotations (such as open chromatin, epigenetic marks and TF binding sites) and using nearby gene annotations (by ontologies). Together with its web app, XGR aims to provide a user-friendly tool for exploring genomic relations at the gene, SNP and genomic region level.
R scripts for plotting and assessing XHMM whole-exome-sequencing-based CNV calls. XHMM (eXome Hidden Markov Model) is a C++ software package (http://atgu.mgh.harvard.edu/xhmm) written to call copy number variation (CNV) from next-generation sequencing projects, where exome capture was used (or targeted sequencing, more generally). This R package enables the user to visualize both the PCA normalization performed by XHMM and the CNVs it has called.
Conduct the likelihood ratio tests for Hardy-Weinberg equilibrium at marker loci on the X chromosome.
Provides a simple XML tree parser/generator. It includes functions
    to read XML files into R objects, get information out of and into nodes, and
    write R objects back to XML code. It's not as powerful as the 'XML' package
    and doesn't aim to be, but for simple XML handling it could be useful. It was
    originally developed for the R GUI and IDE RKWard (https://rkward.kde.org), to
    make plugin development easier.
Plotting ggplot2 graphs using the XKCD style.
The XKCD color survey asked participants to name colours. Randall Munroe published the top thousand(roughly) names and their sRGB hex values. This package lets you use them.
Provides comprehensive functionality to read, write and format Excel data.
Provides external JAR dependencies for the XLConnect package.
Provide R functions to read/write/format Excel 2007 and Excel 97/2000/XP/2003 file formats.
The xlsxjars package collects all the external jars
  required for the xlxs package. This release corresponds to POI
  3.10.1.
Extract Excel files from folder.
    Also display extracted data and compute a summary of it.
    Based on the 'readxl' package.
A toolbox for meta-analysis. This package includes a collection of functions for (1) implementing robust multivariate meta-analysis of continuous or binary outcomes; and (2) testing and correcting of publication bias in multivariate meta-analysis settings.
This is Xiaobei's miscellaneous classes and functions useful when
    developing R packages, particularly for OOP using R Reference Class.
Many approaches for both reading and
        creating XML (and HTML) documents (including DTDs), both local
        and accessible via HTTP or FTP.  Also offers access to an
        'XPath' "interpreter".
Work with XML files using a simple, consistent interface. Built on
    top of the 'libxml2' C library.
XML2R is a framework that reduces the effort required to transform
    XML content into number of tables while preserving parent to child
    relationships.
Convert the output of 'utils::getParseData()' to an 'XML'
  tree, that is searchable and easier to manipulate in general.
Fit Markov Networks to a wide range of high-throughput genomics data.
Tests whether a set of counts fit a given expected ratio. For
    example, a genetic cross might be expected to produce four types in the
    relative frequencies of 9:3:3:1. To see whether a set of observed counts
    fits this expectation, one can examine all possible outcomes with xmulti() or a
    random sample of them with xmonte() and find the probability of an observation
    deviating from the expectation by at least as much as the observed. As a
    measure of deviation from the expected, one can use the log-likelihood
    ratio, the multinomial probability, or the classic chi-square statistic. A
    histogram of the test statistic can also be plotted and compared with the
    asymptotic curve.
Analysis of crossover interference in experimental crosses,
    particularly regarding the gamma model.
Xpose is collection of functions to be used as a model 
  building aid for non-linear mixed effects (population) analysis 
  using NONMEM. It facilitates data set checkout, exploration and 
  visualization, model diagnostics, candidate covariate identification 
  and model comparison.
Support for interfaces from R to other languages,
    built around a class for evaluators and a combination of functions, classes and
    methods for communication. Will be used through a specific language interface
    package. Described in the book "Extending R".
A Julia interface structured according to the general
	     form described in package XR and in the book "Extending R".
A Python interface structured according to the general
    form described in package XR and in the book "Extending R".
A hierarchical Bayesian approach to assess functional impact of mutations on gene expression in cancer. Given a patient-gene matrix encoding the presence/absence of a mutation, a patient-gene expression matrix encoding continuous value expression data, and a graph structure encoding whether two genes are known to be functionally related, xseq outputs: a) the probability that a recurrently mutated gene g influences gene expression across the population of patients; 
  and b) the probability that an individual mutation in gene g in an individual patient m influences expression within that patient.
This is the tool set for crystallographer to design and analyze crystallization experiments, especially for ribosome from Mycobacterium tuberculosis.
Can be used for coloring output in terminals.
	It was developed for the standard Ubuntu terminal but should be compatible
	with any terminal using xterm or ANSI escape sequences. If run in windows,
	RStudio, or any other platform not supporting such escape sequences it
	gracefully passes on any output without modifying it.
Provide for uniform handling of R's different time-based data classes by extending zoo, maximizing native format information preservation and allowing for user level customization and extension, while simplifying cross-class interoperability.
Calculates a number of valuation adjustments including CVA, DVA,
    FBA, FCA, MVA and KVA. A two-way margin agreement has been implemented. For
    the KVA calculation three regulatory frameworks are supported: CEM, SA-CCR and
    IMM. The probability of default is implied through the credit spreads curve.
    Currently, only IRSwaps are supported.
An implementation of Item Response Theory (IRT) in R, comprising of five modules:(1) common and utility functions, (2) estimation/calibration procedures, (3) computerized adaptive testing (CAT) framework, (4) automated test assembly (ATA) framework, and (5) multistage testing (MST) framework. See documentation at https://github.com/xluo11/xxIRT/blob/master/README.md.
A method for creating vertical histograms sharing a y-axis using
    base graphics.
This package provides an alternative canonical
        correlation/redundancy analysis function, with associated
        print, plot, and summary methods.  A method for generating
        helio plots is also included.
Performs nearest neighbor-based imputation using one or more alternative 
 approaches to processing multivariate data. These include methods based on canonical 
 correlation analysis, canonical correspondence analysis, and a multivariate adaptation 
 of the random forest classification and regression techniques of Leo Breiman and Adele 
 Cutler. Additional methods are also offered. The package includes functions for 
 comparing the results from running alternative techniques, detecting imputation targets 
 that are notably distant from reference observations, detecting and correcting 
 for bias, bootstrapping and building ensemble imputations, and mapping results.
This is a simple wrapper for the yakmo K-Means library (developed by Naoki Yoshinaga, see http://www.tkl.iis.u-tokyo.ac.jp/~ynaga/yakmo/). It performs fast and robust (orthogonal) K-Means.
This collection of data exploration tools was developed at
    Yale University for the graphical exploration of complex
    multivariate data; barcode and gpairs now have their own
    packages.  The new big.read.table() provided here may be
    useful for large files when only a subset is needed.
This package implements the libyaml YAML 1.1 parser and emitter (http://pyyaml.org/wiki/LibYAML) for R.
Yield curve or zero-coupon prices interpolation and extrapolation using the Nelson-Siegel, Svensson, Smith-Wilson models, and Hermite cubic splines.
Calculates cryptic scores for genes using the ratio
    (Cheung et al., 2008), the 3' enrichment method
    (DeGennaro et al., 2013) and the probabilistic
    method. It also provide	methods to estimates
    cryptic transcription start sites.
The purpose of this package is to provide methods to interpret multiple
  linear regression and canonical correlation results including beta weights,structure coefficients, 
  validity coefficients, product measures, relative weights, all-possible-subsets regression,
  dominance analysis, commonality analysis, and adjusted effect sizes.
Deploy, maintain, and invoke models via the Yhat
    REST API.
Modelling the yield curve with some parametric models.
        The models implemented are: Nelson-Siegel, Diebold-Li and
        Svensson.  The package also includes the data of the term
        structure of interest rate of Federal Reserve Bank and European
        Central Bank.
The clustering by k-means of using the target variable.
             To determine the number of clusters with the variance of 
             the target variable in the cluster.
Analyzing performances of cricketers and cricket teams
             based on 'yaml' match data from Cricsheet <http://cricsheet.org>.
An R implementation of Yplant, combined with the QuasiMC
    raytracer. Calculate radiation absorption, transmission and scattering,
    photosynthesis and transpiration of virtual 3D plants.
Inference procedures accommodate a flexible range of hazard ratio patterns with a two-sample semi-parametric model. This model contains the proportional hazards model and the proportional odds model as sub-models, and accommodates non-proportional hazards situations to the extreme of having crossing hazards and crossing survivor functions. Overall, this package has four major functions: 1) the parameter estimation, namely short-term and long-term hazard ratio parameters; 2) 95 percent and 90 percent point-wise confidence intervals and simultaneous confidence bands for the hazard ratio function; 3) p-value of the adaptive weighted log-rank test; 4) p-values of two lack-of-fit tests for the model. See the included "read_me_first.pdf" for brief instructions. In this version (1.1), there is no need to sort the data before applying this package.
Simple method for comparison of gene
        expression generated across different experiments, and on
        different platforms; that does not require global
        renormalization, and is not restricted to comparison of
        identical probes. YuGene works on a range of microarray dataset
        distributions, such as between manufacturers. The resulting
        output allows direct comparisons of gene expression between
        experiments and experimental platforms.
Simulation and Inference for Stochastic Differential Equations.
Yummly.com is one of the world's largest and most powerful recipe search sites and this package aims to provide R bindings for publicly available Yummly.com Recipe API (https://developer.yummly.com/).
Implements principled methods to impute multivariate left-censored data and zeros in compositional data sets.
R package accompanying the book Working with dynamic
        models for agriculture and environment, by Daniel Wallach
        (INRA), David Makowski (INRA), James W. Jones (U.of Florida),
        Francois Brun (ACTA), in preparation for June 2013.
Implements the estimation of local (and global) association measures: Ducher's Z, pointwise mutual information and normalized pointwise mutual information. The significance of local (and global) association is accessed using p-values estimated by permutations. Finally, using local association subgroup analysis, it identifies if the association between variables is dependent on the value of another variable.
A framework that brings together an abundance of common
    statistical models found across packages into a unified interface, and
    provides a common architecture for estimation and interpretation, as well
    as bridging functions to absorb increasingly more models into the
    collective library.  Zelig allows each individual package, for each
    statistical model, to be accessed by a common uniformly structured call and
    set of arguments.  Moreover, Zelig automates all the surrounding building
    blocks of a statistical work-flow --procedures and algorithms that may be
    essential to one user's application but which the original package
    developer did not use in their own research and might not themselves
    support. These include bootstrapping, jackknifing, and re-weighting of data.
    In particular, Zelig automatically generates predicted and simulated
    quantities of interest (such as relative risk ratios, average treatment
    effects, first differences and predicted and expected values) to interpret
    and visualize complex models.
Add-on package for Zelig 5. Enables the use of a variety of logit
    and probit regressions.
Add-on package for Zelig 5. Enables the use of a variety of
    ecological inference models.
This package provides an R wrapper for the Zendesk API
Functions to compute compositional turnover using zeta-diversity, the number of species shared by multiple assemblages. The package includes a Zeta.order() function to compute zeta-diversity for a specific number of assemblages, and a Zeta.decline() function to compute zeta-diversity for a range of numbers of assemblages. It also includes additional functions to explain how zeta-diversity varies with distance and with differences of environmental variables between assemblages, using linear regressions and generalized additive models.
Provides MCMC algorithms for the analysis of
        zero-inflated count models. The case of stochastic search
        variable selection (SVS) is also considered.  All MCMC samplers
        are coded in C++ for improved efficiency. A data set
        considering the demand for health care is provided.
Zillow, an online real estate company, provides real estate and
    mortgage data for the United States through a REST API. The ZillowR package
    provides an R function for each API service, making it easy to make API
    calls and process the response into convenient, R-friendly data structures.
    See <http://www.zillow.com/howto/api/APIOverview.htm> for the Zillow API
    Documentation.
Fits observation-driven and parameter-driven models for zero-inflated time series
This package contains a database of city, state, latitude,
        and longitude information for U.S. ZIP codes from the
        CivicSpace Database (August 2004) augmented by Daniel Coven's
        federalgovernmentzipcodes.us web site (updated January 22,
        2012). Previous versions of this package (before 1.0) were
        based solely on the CivicSpace data, so an original version of
        the CivicSpace database is also included.
Statistical models and utilities for the analysis of word
        frequency distributions.  The utilities include functions for
        loading, manipulating and visualizing word frequency data and
        vocabulary growth curves.  The package also implements several
        statistical models for the distribution of word frequencies in
        a population.  (The name of this library derives from the most
        famous word frequency distribution, Zipf's law.)
Calculate and plot scattering matrix coefficients for plane waves at interface.
Fits beta regression and zero-or-one inflated beta regression and obtains Bayesian Inference of the model via the Markov Chain Monte Carlo approach implemented in JAGS.
An S3 class with methods for totally ordered indexed
             observations. It is particularly aimed at irregular time series
             of numeric vectors/matrices and factors. zoo's key design goals
             are independence of a particular index/date/time class and
             consistency with ts and base R by providing methods to extend
             standard generics.
The analysis and inference of faunal remains recovered from
    archaeological sites concerns the field of zooarchaeology. The zooaRch package
    provides analytical tools to make inferences on zooarchaeological data.
    Functions in this package allow users to read, manipulate, visualize, and
    analyze zooarchaeological data.
ZooImage is a free (open source) solution for analyzing
        digital images of zooplankton. In combination with ImageJ, a
        free image analysis system, it processes digital images,
        measures individuals, trains for automatic classification of
        taxa, and finally, measures zooplankton samples (abundances,
        total and partial size spectra or biomasses, etc.)
zm(), called with any active plot allow to enter an
    interactive session to zoom/navigate any plot. The development
    version, as well as binary releases can be found at
    https://github.com/cbarbu/R-package-zoom
Reproducible and remixable species distribution modelling. The
    package reads user submitted modules from an online repository, runs full
    SDM workflows and returns output that is fully reproducible.
Combines a forecast of a time series, using the function forecast(), with the dynamic plots from dygraphs.
Makes zebra-striped tables (tables with alternating row colors)
    in LaTeX and HTML formats easily from a data.frame, matrix, lm, aov, anova,
    glm, coxph, nls, fitdistr, mytable and cbind.mytable objects.
The zyp package contains an efficient implementation of Sen's slope method (Sen, 1968) plus implementation of Xuebin Zhang's (Zhang, 1999) and Yue-Pilon's (Yue, 2002) prewhitening approaches to determining trends in climate data.
Please send comments and bug reports to
ismrp@jasp.ism.ac.jp
Please send questions and comments to etiennead@gmail.com.
Please send questions and comments regarding NScluster to
ismrp@jasp.ism.ac.jp
Please send questions and comments regarding pbdR to
RBigData@gmail.com
Please send questions and comments regarding Rcpp to
rcpp-devel@lists.r-forge.r-project.org
Please send questions and comments regarding RInside to
rcpp-devel@lists.r-forge.r-project.org
Please send questions and comments regarding SAPP to
ismrp@jasp.ism.ac.jp
Please send questions and comments regarding timsac to
ismrp@jasp.ism.ac.jp
Dane R. Van Domelen <vandomed@gmail.com>
Renata T C Yokota <Renata.Yokota@wiv-isp.be>
Pedro Rafael Diniz Marinho <pedro.rafael.marinho@gmail.com>
Carlos J. Gil Bellosta <cgb@datanalytics.com>
Achmad Fauzi Bagus F <fauzi.pandya@gmail.com>
Sy Han (Steven) Chiou  <schiou@d.umn.edu>
P Lafaye de Micheaux <lafaye@dms.umontreal.ca>
Robin K. S. Hankin <hankin.robin@gmail.com>
Lafaye de Micheaux Pierre <lafaye@dms.umontreal.ca>
Joao Vitor Dias Monteiro <monte092@umn.edu>
Ricardo de Matos Simoes <ricardo_dematossimoes@dfci.harvard.edu>
Karel Van den Meersche <karel.van_den_meersche@cirad.fr>
Jose Antonio Garcia Bueno <joseagbueno@gmail.com>
Anderson Rodrigo da Silva <anderson.agro@hotmail.com>
Ana Belen Nieto Librero <ananieto@usal.es>
David Alejandro Martell Juarez <alex91599@gmail.com>
A. Jonathan R. Godfrey <a.j.godfrey@massey.ac.nz>
L. A. van der Ark <L.A.vanderArk@uva.nl>
Francesca Marta Lilja Di Lascio <marta.dilascio@unibz.it>
K. Gerald van den Boogaart <support@boogaart.de>
P. Lafaye de Micheaux <lafaye@dms.umontreal.ca>
Dennis J. J. Walvoort <dennis.Walvoort@wur.nl>
Rafael S. de Souza <rafael.2706@gmail.com>
Sjoerd van der Spoel <R@sjoerdvanderspoel.nl>
Ernst van der Maaten <ernst.vandermaaten@gmail.com>
Eduardo L. T. Conceicao <mail@eduardoconceicao.org>
Marcelino de la Cruz Rot <marcelino.delacruz@upm.es>
Don van den Bergh <donvdbergh@hotmail.com>
Alexandre Adalardo de Oliveira <ecovirtualpackage@gmail.com>
Tomas J. Aragon Developer <aragon@berkeley.edu>
Manuel Oviedo de la Fuente <manuel.oviedo@usc.es>
Clayton Vieira Fraga Filho <forestgrowthsoftware@gmail.com>
Eduardo San Miguel Martin
<eduardo.san.miguel.martin@gmail.com>
Shaiful Anuar Abu Bakar <saab@um.edu.my>
Vincent T van Hees <vincentvanhees@gmail.com>
Gabriel Coelho Goncalves de Abreu <abreu_ga@yahoo.com.br>
Geraldine Van der Auwera <vdauwera@broadinstitute.org>
Rafael de Andrade Moral <rafael_moral@yahoo.com.br>
"Markus Loecher, Berlin School of Economics and Law (BSEL)" <markus.loecher@gmail.com>
Sai Thein Than Tun <theinthantun.sai@gmail.com>
Alejandro Quintela del Rio <aquintela@udc.es>
Jorge Luis Ojeda Cabrera <jojeda@unizar.es>
Eufrasio de A. Lima Neto <eufrasio@de.ufpb.br>
Camilo Antonio Suarez Bolanos <caasuarezbo@unal.edu.co>
Ricardo Jorge de Almeida Queiroz Filho <rjaqfcin@gmail.com>
Jan van der Laan <djvanderlaan@unrealizedtime.nl>
"Antoine Tremblay, Dalhousie University" <trea26@gmail.com>
"Robin K. S. Hankin" <hankin.robin@gmail.com>
Elizabeth Holmes - NOAA Federal <eli.holmes@noaa.gov>
Fernando Ferraz do Nascimento <fernandofn@ufpi.edu.br>
Antonio Hermes M. da Silva-Junior <hermes@ccet.ufrn.br>
Alysha M De Livera <alyshad@unimelb.edu.au>
Stephen L R Ellison <s.ellison@lgc.co.uk>
Cherise R. Chin Fatt <cherise.chinfatt@utdallas.edu>
Marcelino de la Cruz <marcelino.delacruz@upm.es>
Julio Cesar Hernandez Sanchez <juliocesar_avila@usal.es>
Pierre Lafaye de Micheaux <lafaye@dms.umontreal.ca>
Rune Haubo Bojesen Christensen <rune.haubo@gmail.com>
Euloge Clovis Kenne Pagui <kenne@stat.unipd.it>
Joaquin Bienvenido Ordieres Mere <j.ordieres@upm.es>
ACHIEPO Odilon Yapo M. <kingodilon@gmail.com>
Marieke van der Maaten-Theunissen <marieketheunissen@gmail.com>
Peter J. van der Most <p.j.van.der.most@umcg.nl>
"Shingo Yamamoto (gloops, Inc.)" <si.yamamoto@gloops.com>
Vito M. R. Muggeo <vito.muggeo@unipa.it>
Achmad Fauzi Bagus F <uzi.fauz@gmail.com>
Sy Han (Steven) Chiou <schiou@hsph.harvard.edu>
Raymond K. W. Wong <raymondkww.dev@gmail.com>
María del Carmen Calatrava Moreno
<mc.calatrava.moreno@gmail.com>
Titus von der Malsburg <malsburg@posteo.de>
Rutger M. van den Bor <r.m.vandenbor@umcutrecht.nl>
Marcelino de la Cruz <marcelino.delacruz@urjc.es>
Jakson Alves de Aquino <jalvesaq@gmail.com>
Prof. Juan David Velasquez H. <jdvelasq@unal.edu.co>
Willem Robert van Hage <willem.van.hage@synerscope.com>
Frederick Kin Hing Phoa <fredphoa@stat.sinica.edu.tw>
Stephen A Sefick Jr. <ssefick@gmail.com>
Daniel A. Dos Santos <pseudalopex_79@yahoo.com>
Jesus Maria Rodriguez Rodriguez <jesusmro@ono.com>
Gabriel Henrique Oliveira Assuncao <gabrielhenryoliver@hotmail.com>
K. Gerald van den Boogaart <boogaart@math.tu-freiberg.de>
Antonio Fabio Di Narzo <antonio.fabio@gmail.com>
Angel Garcia de la Garza <agarciadlg@gmail.com>
Joerg Schlarmann (aka Produnis) <schlarmann@produnis.de>
Olivier M. F. Martin <olivmrtn@gmail.com>
Maintainers are not available to give advice on using a package
they did not author.
Several parts are still preliminary and may be changed in the
future. This typically includes function and argument names, as
well as defaults for arguments and return values.
Several parts are still preliminary and may be changed in the
future. this typically includes function and argument names, as
well as defaults for arguments and return values.
SEVERAL PARTS ARE STILL PRELIMINARY AND MAY BE CHANGED IN THE
FUTURE. THIS TYPICALLY INCLUDES FUNCTION AND ARGUMENT NAMES, AS
WELL AS DEFAULTS FOR ARGUMENTS AND RETURN VALUES.
The header file blaswrap.h, f2c.h and fgsg.h are from the
VisualStudio library created by Julie Langou.
This software comes with NO warranty! It is NOT intended to be
used in clinical applications! For evaluation only!
Package stable is available at http://www.robustanalysis.com
For convenience, the package includes the necessary header files
for the Eigen distribution. (This is all that is needed to use
that functionality.) You can use an alternative installation of
Eigen on your system or the one we provide. The license for the
Eigen code is very permissive and allows us to distribute it
with this package. See http://eigen.tuxfamily.org/index.php?
title=Main_Page and also the License section on that page.
In order to use this package you need your personal Project Vote
Smart API key. To get an API-key you need to sign up for access
to PVS' API (see http://votesmart.org/share/api for details).
Whenever you are using pvsR make sure that your personal PVS
API key is saved as a character string in the variable pvs.key
(pvs.key <- "yourkey"). If there is no valid PVS API key saved
in the variable pvs.key the functions in this package won't
work. This program is free software; you can redistribute it
and/or modify it under the terms of the GNU General Public
License as published by the Free Software Foundation; either
version 2 of the License, or (at your option) any later
version. This program is distributed in the hope that it will
be useful, but WITHOUT ANY WARRANTY; without even the implied
warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the GNU General Public License for more details.
More information about the system requirements can be found in
the README file.
For details and derivation, see Braumoeller, Bear F. "Guarding
Against False Positives in Qualitative Comparative Analysis."
Forthcoming, Political Analysis.
This will be used generally to communicate with HTTP servers and
specifically in the SOAP package, HTML forms and for accessing
data on Web sites that require more state information than
simply URL access (e.g. cookies).
NOTICE regarding the transfer of data from Retrosheet: The
information used here was obtained free of charge from and is
copyrighted by Retrosheet. Interested parties may contact
Retrosheet at "www.retrosheet.org".
See http://www.json.org/JSON_checker/test.zip for tests. We now
use code from the libjson project
(http://libjson.sourceforge.net).
The files configure.in, cleanup, Makevars.in and Makevars.win are
based on the same files found in RPostgreSQL. I thank Dirk
Eddelbuettel for giving the go ahead to reuse their build
setup. Copyright of the unmodified protions remains with the
original authors. The files in src/libpq/ are part of
PostgreSQL. Please refer to http://www.postgresql.org/ for
their copyright and license.
The name "Scrabble" is a trademark of Hasbro, Inc. in the United
States and Canada; elsewhere, it is a trademark of Mattel.
This is experimental software distributed free of charge and
comes with no warranty!  Exercise professional caution.
In version 2.4.0 of this package, a new approach to garbage
collection has been implemented and it is experimental. You can
disable it via the configuration option --enable-nodegc=no.
However, you are encouraged to use this and report problems as
the results are beneficial and being able to produce any errors
should they occur will be very helpful.
2015-01-03 19:56:29 UTC; Steven Harris Ranney
2012-06-11 14:36:21 UTC; Sophie Baillargeon
2010-01-31 13:29:31 UTC; Jens Henrik Badsberg
2015-02-26 17:45:29 UTC; Sophie Baillargeon
2015-08-23 02:08:23 UTC; Fernando de Villar
2016-06-21 08:12:20 UTC; LARMARANGE Joseph
2016-02-23 18:10:11 UTC; LARMARANGE Joseph
2014-08-28 18:37:46 UTC; Sophie Baillargeon
2016-08-15 00:57:22 UTC; Sebastian Calonico
2016-07-26 19:41:07 UTC; Timothy Schwinghamer
2014-10-17 18:39:03 UTC; Christiane Dargatz
2014-12-04 17:04:02 UTC; Sophie Baillargeon
2016-03-19 23:54:53 UTC; Sophie Baillargeon
Image Magick (for reading non PPM format), dcraw
(for reading RAW images).
Optionally Latte-integrale, Bertini, and Macaulay2.
Cygwin is required for each of the above for Windows users. See
INSTALL file for details.
ImageMagick (http://imagemagick.org) or
GraphicsMagick (http://www.graphicsmagick.org) or LyX
(http://www.lyx.org) for saveGIF(); (PDF)LaTeX for saveLatex();
SWF Tools (http://swftools.org) for saveSWF(); FFmpeg
(http://ffmpeg.org) or avconv (https://libav.org/avconv.html)
for saveVideo()
Java JDK 1.2 or higher (for JRI/REngine JDK 1.4 or
higher), GNU make
Requires external zip utility, available in path.
On windows, it's sufficient to install RTools.
JAGS (>= 3.4.0) (see
http://mcmc-jags.sourceforge.net)
Preferred genomic operations engine: BEDtools,
BEDOPS and Tabix.
OpenBUGS (>= 3.2.2), hence Windows or Linux
MAGeCK (=0.51, from
http://sourceforge.net/p/mageck/wiki/Home/), bowtie2
(http://bowtie-bio.sourceforge.net/bowtie2/index.shtml)
OpenCL library; double-precision AMD or Nvidia GPU;
GNU make
xclip (http://sourceforge.net/projects/xclip/) or
xsel (http://www.vergenet.net/~conrad/software/xsel/) for
accessing the X11 clipboard
Java (>= 7.0); Stanford CoreNLP
<http://nlp.stanford.edu/software/corenlp.shtml> (>= 3.5.2)
couchDB instance to connect to and work with.
nvcc (release >= 3.1) (NVIDIA Cuda Compiler
driver); Linux operating system; GNU make.
libcurl: libcurl-devel (rpm) or
libcurl4-openssl-dev (deb).
xclip <http://sourceforge.net/projects/xclip/> or
xsel <http://www.vergenet.net/~conrad/software/xsel/> for
accessing the X11 clipboard
none, one or more of JAGS (>= 3.0.0), WinBUGS (>=
1.4), OpenBUGS (>= 3.2.2)
GLPK library package (e.g., libglpk-dev on
Debian/Ubuntu)
Xft (only needed for platforms other than OSX and
Windows)
Python (>= 2.7 or >= 3.5) and elex package
Debian or Ubuntu system with access to Goto Blas,
Intel MKL, Atlas development build as well as a Nvidia GPU with
CUDA support
zlib headers and library. OpenSSL headers and
library. LibSSH2 (optional on non-Windows) to enable the SSH
transport.
Nvidia's CUDA toolkit (>= release 5.0) and Nvidia
GPU with compute capibility >=2.0
C++11 (supporting at least std=c++0x), OpenCL
shared library (provided by an SDK such as AMD/NVIDIA) and
OpenCL headers including the C++ header file (provided by
Khronos if not by SDK)
libhdf5 (>= 1.8.12) with C++ interface
(--enable-cxx=yes) and v18 API enabled
BLAS/LAPACK libraries, PLASMA library , NVIDIA CUDA
toolkit, MAGMA library
Apache Hadoop >= 2.6.0
(https://hadoop.apache.org/releases.html#Download); Obsolete:
Hadoop core >= 0.19.1 and <= 1.0.3 or CDH3
(http://www.cloudera.com); standard unix tools (e.g., chmod)
ImageMagick (http://imagemagick.org) or
GraphicsMagick (http://www.graphicsmagick.org)
A C library with the fesetround/fegetround
functions.
OpenBabel (>= 2.3.1) with headers.
http://openbabel.org
Package vignettes based on R Markdown v2 require
Pandoc (http://pandoc.org). The function rst2pdf() and
vignettes based on reStructuredText require rst2pdf
(https://github.com/rst2pdf/rst2pdf).
ImageJ (>=1.48), ij.jar (see
http://imagej.nih.gov/ij/), Java (>=1.6.0)
Docker (see
<https://docs.docker.com/engine/installation/>), Rabix (see
<https://github.com/rabix/rabix>)
JAGS (>= 3.0.0) (see
http://mcmc-jags.sourceforge.net)
At least trial version of LocalSolver to be
downloaded from http://www.localsolver.com/download.html
ImageMagick++: ImageMagick-c++-devel (rpm) or
libmagick++-dev (deb)
ADMB version 11 (http://admb-project.org/) for
use.admb=TRUE; see readme.txt
Maxent.jar software, Java Runtime Environment (JRE)
JAGS (>= 4.1) for the script version, both JAGS and
GTK+ for the GUI version. For install instructions, see README
file.
MonetDB, available from http://www.monetdb.org or
MonetDBLite R package
jags (>= 4.0.0) (see
http://mcmc-jags.sourceforge.net)
A valid account MUST be registered with Microsoft's
Cognitive Services website
<https://www.microsoft.com/cognitive-services/> in order to
obtain a (free) API key. Without an API key, this package will
not work properly.
pandoc, ipython (>= 3.0) or jupyter,
ipython/jupyter notebook optional dependencies, pywin32 (on
windows)
A compiler with C++11 support for the thread
library, Requires Rtools >= 33 (i.e. R >= 3.3.0) to build on
Windows.
pandoc (http://johnmacfarlane.net/pandoc) for
exporting markdown files to other formats.
For the MERLIN wrapper, MERLIN
(www.sph.umich.edu/csg/abecasis/merlin) must be installed and
pointed to in the PATH environment variable.
The 'Japanese.Rnw' vignette requires uplatex and
dvipdfmx.
OpenMPI (>= 1.5.4) on Solaris, Linux, Mac, and
FreeBSD. MS-MPI (Microsoft HPC Pack 2012) or MPICH2 (>=
1.4.1p1) on Windows.
OpenMPI (>= 1.5.4) on Solaris, Linux, Mac, and
FreeBSD. MS-MPI (Microsoft HPC Pack 2012 R2 MS-MPI
Redistributable Package) on Windows.
OpenMPI (>= 1.5.4) on Solaris, Linux, Mac, and
FreeBSD. MS-MPI (Microsoft MPI v7.1 (SDK) and Microsoft HPC
Pack 2012 R2 MS-MPI Redistributable Package) on Windows.
OpenMPI (>= 1.5.4) on Solaris, Linux and Mac.
(Parallel) HDF5 and (Parallel) NetCDF4 (4.1 or later)
libraries. No MPI library required on Windows.
OpenMPI (>= 1.5.4) on Solaris, Linux, Mac, and
FreeBSD. No MPI library required on Windows yet.
Linux, Mac OSX, and Windows, or ZeroMQ library >=
4.0.4. Solaris 10 needs ZeroMQ library 4.0.7 and OpenCSW.
Poppler C++ interface library and headers
Phoenix NLME with Phoenix Modeling Language (PML)
license
For Windows users, Rtools (see
http://cran.r-project.org/bin/windows/Rtools/).
Python 2.7.x or Python 3.2.x-3.4.x, NumPy (Python
scientific computing package)
pandoc (>= 1.12.3) -
http://johnmacfarlane.net/pandoc
JAGS (>= 3.2.0) (see
http://mcmc-jags.sourceforge.net)
proj 4.4.6 or higher (http://proj.maptools.org/)
Pasting to clipboard requires the xclip command on
BSD and Linux or the pbcopy command on Mac OS.
Python (>= 2.7.0) with header files and shared
library (UNIX) / static library (Windows)
Qt4 libraries and headers (http://qt.nokia.com),
cmake (http://www.cmake.org)
OpenBugs for functions bugs() and openbugs() or
WinBUGS 1.4 for function bugs()
linux (>= 3.0), libapparmor-dev (optional)
A valid Bloomberg installation. The API headers and
dynamic library are downloaded from
<https://github.com/Rblp/blp> during the build step. See
<http://bloomberg.github.io/blpapi-docs/cpp/3.8> as as
<http://www.bloomberglabs.com/api/> for API documentation. A
compiler recent enough for (at least partial) C++11 support;
g++-4.6.* or later should be sufficient and g++-4.9.* or later
is preferred.
GMP (GNU MP bignum library from http://gmplib.org/)
A Debian-based system with the libapt-pkg-dev
package
A 64-bit POSIX OS such as Linux or OS X with IANA
time zone data in /usr/share/zoneinfo as well as a C++11
compiler.
A POSIX system. Currently Linux and OS X are known
to work.
A C++11 compiler. Version 4.6.* of g++ (as
currently in Rtools) is insufficient; versions 4.8.*, 4.9.* or
later will be fine.
Octave (>= 3.2.4) and its development files
GNU make, Windows: cmd.exe and cscript.exe,
Solaris: g++ is required
The hiredis library (eg via package libhiredis-dev
on Debian/Ubuntu, hiredis-devel on Fedora/RedHat, or directly
from source from https://github.com/redis/hiredis) is required.
MsgPack support is activated if the (optional) RcppMsgPack
package is detected; it can be installed from the ghrr drat
(see Additional_repositories below).
DieHarder library (>= 3.31.1) from
http://www.phy.duke.edu/~rgb/General/dieharder.php, GNU GSL for
the GSL random-number generators
MOSEK (http://www.mosek.com) and MOSEK License for
use of Rmosek, optional use of the pogs optimizer may require
CUDA/GPU accoutrements.
Subversion for install_svn, git for install_git
for building from source: GDAL >= 1.6.3, library
from http://trac.osgeo.org/gdal/wiki/DownloadSource and PROJ.4
(proj >= 4.4.9) from http://download.osgeo.org/proj/; GDAL OSX
frameworks built by William Kyngesburye at
http://www.kyngchaos.com/ may be used for source installs on
OSX.
GEOS (>= 3.2.0); for building from source: GEOS
from http://trac.osgeo.org/geos/; GEOS OSX frameworks built by
William Kyngesburye at http://www.kyngchaos.com/ may be used
for source installs on OSX.
OpenGL, GLU Library, XQuartz (on OSX), zlib
(optional), libpng (>=1.2.9, optional), FreeType (optional)
R built as a shared or static library, 'MPI'
library.
Java (>= 1.5), zlib headers and library.
Oracle Java 7,Apache Kafka 2.8.0-0.8.1.1
notepad.exe, mark.exe (>= 8.0) (or mark32.exe and
mark64.exe) and rel_32.exe (see README.txt)
libmariadb-client-dev | libmariadb-client-lgpl-dev
| libmysqlclient-dev (deb), mariadb-devel (rpm), mariadb |
mysql-connector-c (brew), mysql56_dev (csw)
Oracle Instant Client or Oracle Database Client
Poppler Glib interface headers and libraries
(<http://poppler.freedesktop.org/>) [Debian/Ubuntu:
libpoppler-glib-dev, Fedora: poppler-glib-devel]
C++11, GNU make, Windows: cmd.exe and cscript.exe
ProtoBuf libraries and compiler version 2.2.0 or
later; version 3.0.0 or later is supported as well. On
Debian/Ubuntu these can be installed as libprotoc-dev and
libprotobuf-dev, while on Fedora/CentOS protobuf-devel and
protobuf-compiler are needed.
The 'curl' binary has to be in the path (and this
is checked at package startup), a user API key (which can be
obtained from http://www.pushbullet.com), and one or more
devices to push messages to which may be any one of an (Android
or iOS) phone, the (Chrome or Firefox, or Opera or Safari)
browser or (Windows or Mac) desktop application provided the
corresponding Pushbullet 'app' has been installed on any one of
these.
Windows, Python (>= 2.4.0), ArcGIS (>= 9.2)
Python (>= 2.7) and Python headers and libraries
(See the INSTALL file)
QuantLib library (>= 1.7.0) from
http://quantlib.org, Boost library from http://www.boost.org
need the RDRAND instruction on Intel CPU. and C
compiler must be able to compile a GNU-style in-line assembler.
NW RFC SDK downloaded from http://service.sap.com
Tcl/Tk (>= 8.5), Tktable (>= 2.9, optional)
The Boost 'program_options' library
(http://boost.org) is required.
Python (>=2.6) in path and NumPy module installed,
ghostscript (>=9.0)
ZeroMQ >= 3.0.0 libraries and headers (see
<http://www.zeromq.org/>; Debian packages libzmq3, libzmq3-dev,
Fedora packages zeromq3, zeromq3-devel)
GLPK library, including -dev or -devel part
zlib headers and libraries, optionally also bzip2
and POSIX-compliant regex functions.
(PDF)LaTeX (http://www.latex-project.org/) with
'pdfpages' package for studentGrowthPlot option in visualizeSGP
to bind together student growth plots into school catalogs
little-endian processor required for some functions
(see README)
Komodo Edit (http://www.openkomodo.com), SciViews-K
(http://www.sciviews.org/SciViews-K)
Antiword (<http://www.winfield.demon.nl/>) for
reading MS Word files, pdfinfo and pdftotext from Poppler
(<http://poppler.freedesktop.org/>) for reading PDF
Teradata Aster Database 5.20 (AD5.20) or higher,
Teradata Aster Analytical Foundation 5.20 (AA5.20) or higher.
PARI/GP >= 2.3.0 [strongly recommended for
logkda()]
nvcc (release >= 7.1) (NVIDIA Cuda Compiler
driver); GNU make
V8: libv8-dev (deb), v8-devel (rpm), v8-3.14 (arch)
libmagic (>= 5.14) for Unix/Linux/macOS; Rtools
3.3+ for Windows
PhantomJS (http://phantomjs.org) for taking
screenshots, ImageMagick (http://www.imagemagick.org) or
GraphicsMagick (http://www.graphicsmagick.org) and OptiPNG
(http://optipng.sourceforge.net) for manipulating images.
Java (>= 5.0); WordNet database files (direct
download:
<http://wordnetcode.princeton.edu/3.0/WNdb-3.0.tar.gz>; Debian
and Fedora package: wordnet)
Perl (>= 5.0), Unitex (>= 3.0
http://www-igm.univ-mlv.fr/~unitex/)
The standalone program xgobi must be installed
additionally, see file README, or INSTALL.windows under Windows
libxml2: libxml2-dev (deb), libxml2-devel (rpm)
Accurate, Adaptable, and Accessible Error Metrics for Predictive
Models
Access to Abbyy Optical Character Recognition (OCR) API
Tools for Approximate Bayesian Computation (ABC)
Data Only: Tools for Approximate Bayesian Computation (ABC)
ABCDE_FBA: A-Biologist-Can-Do-Everything of Flux Balance
Analysis with this package
Implementation of Artificial Bee Colony (ABC) Optimization
Approximate Bayesian Computational Model for Estimating P2
Approximate Bayesian Computation via Random Forests
Modelling Multivariate Data with Additive Bayesian Networks
Improved False Positive Control of Gene-Permuting GSEA with
Absolute Filtering
Abundant regression and high-dimensional principal fitted
components
Abrupt Change-Point or Aberration Detection in Point Series
Functions for Processing and Analyzing Accelerometer Data
Functions for Processing Minute-to-Minute Accelerometer Data
Missing Value Imputation for Accelerometer Data
Creation and Evaluation of Acceptance Sampling Plans
Data Quality Visualization Tools for Partially Accruing Data
Tools for Autoregressive Conditional Duration Models
Optimal Bayesian Experimental Design using the ACE Algorithm
ace() and avas() for selecting regression transformations
Estimating Dynamic Heritability and Twin Model Comparison
Align-and-Count Method comparisons of RFLP data
Implements ACME Estimator of Bird and Bat Mortality by Wind
Turbines
Affymetrix SNP Probe-Summarization using Non-Negative Matrix
Factorization
Modelling dependence with multivariate Archimax (or any
user-defined continuous) copulas
Convenience functions for analytical Customer Relationship
Management
A Cost-Minimal Regular Spanning Subgraph with TreeClust
Download, Manipulate, and Present American Community Survey and
Decennial Data from the US Census
Gene Enrichment Analysis from ACSN Maps or GMT Files
Algorithmic Complexity for Short Strings
Data Only: Algorithmic Complexity of Short Strings (Computed via
Coding Theorem Method)
A Companion Package for the Book "A Course in Statistics with R"
Asymptotic Classification Theory for Cognitive Diagnosis
Actuarial Functions and Heavy Tailed Distributions
The R Package Ada for Stochastic Boosting
Applies Multiclass AdaBoost.M1, SAMME and Bagging
Discrete and Global Optimization Routines
A Class of Adaptive Elastic Net Methods for Censored Data
Adaptive Semiparametric Regression with Simultaneous Confidence
Bands
Implementation of a generic adaptive Monte Carlo Markov Chain
sampler
Binomial and Multinomial Additive Hazards Models
Fit Additive Hazards Models for Survival Analysis
Discover and Install Useful RStudio Addins
Additivity Tests in the Two Way Anova with Single Sub-class
Numbers
A Package for Analysis of Accelerated Destructive Degradation
Test Data
Analysis of Ecological Data : Exploratory and Euclidean Methods
in Environmental Sciences
Exploratory Analysis of Genetic and Genomic Data
An S4 Lattice-Based Package for the Representation of
Multivariate Data
Analysis of Habitat Selection by Animals
adephylo: exploratory analyses for the phylogenetic comparative
method
Adequacy of Probabilistic Models and General Purpose
Optimization
calculate ad hoc distance thresholds for DNA barcoding
identification
An Interpretation of the ADM method - automated detection
algorithm
Adaptive Mixture of Student-t distributions
Admixture Graph Manipulation and Fitting
Regularized Model with Selecting the Number of Non-Zeros
Fast Clustering Using Adaptive Density Peak Detection
Upper Clopper-Pearson Confidence Limits for Burn-in Studies
under Additional Available Information
Wavelet Analysis of Genomic Data from Admixed Populations
Change Point Analysis in ARIMA Forecasting
Download and Process AEMO Price and Demand Data
Model-Based Estimation of Confounder-Adjusted Attributable
Fractions
Hybrid Simulation and Genome Scan for Dominant Markers
Accelerated Failure Time Model with Generalized Estimating
Equations
Aggregate Numeric, Date and Categorical Variables by an ID
Aggregation Operators and Preordered Sets
Statistical Tools for Measuring Agreement
Statistical Procedures for Agricultural Research
Calculate Agreement or Consensus in Ordered Rating Scales
Estimation in Adaptive Group Sequential Trials
Miscellaneous plotting and utility functions
Regularization for semiparametric additive hazards regression
Fast Searching for Multiple Keywords in Multiple Texts
Estimation and Testing of Average Hazard Ratios
Model Selection and Multimodel Inference Based on (Q)AIC(c)
Estimation of Box-Cox Power Transformation Parameter
Tools for reading AIDA (http://aida.freehep.org/) files into R
Interpolation of Irregularly and Regularly Spaced Data
Adaptive Kmeans algorithm based on threshold
Immunoglobulin Clonal Lineage and Diversity Analysis
Quantile Regression Using Asymmetric Laplace Distribution
Estimating Absolute Protein Quantities from Label-Free LC-MS/MS
Proteomics Data
Haplotype Two Snips Out of a Paired Group of Patients
Allows you to Easily Interact with the Algorithmia Platform
Comparison of Alternative Multiple Sequence Alignments
Alignment of Phonetic Sequences Using the 'ALINE' Algorithm
Generate Age-Length Keys for fish populations
Allele Imputation and Haplotype Reconstruction from Pedigree
Databases
Identifying unique multilocus genotypes where genotyping error
and missing data may be present
Allele Retention, Inbreeding, and Demography
A fast, unbiased and exact allelic exact test
Computes all of a word's possible spellings
R Client for the Lagotto Altmetrics Platform
Generalization of the Convex Hull of a Sample of Points in the
Plane
Obtain Alpha-Outlier Regions for Well-Known Probability
Distributions
Implementation of the 3D Alpha-Shape for the Reconstruction of
3D Sets from a Point Cloud
Data to accompany Applied Linear Regression 3rd edition
Data to accompany Applied Linear Regression 4rd edition
Multivariate Curve Resolution Alternating Least Squares
(MCR-ALS)
Accelerated line search algorithm for simultaneous orthogonal
transformation of several positive definite symmetric matrices
to nearly diagonal form
Companion to Applied Linear Statistical Models
Optimal Experimental Designs for Accelerated Life Testing
Compare Gene Expressions from 2-Treatment RNA-Seq Experiments
European Commission Annual Macro-Economic (AMECO) Database
Adaptive Management of Epidemiological Interventions
Additive and Multiplicative Effects Models for Networks and
Relational Data
This package includes pricing function for selected American
call options with underlying assets that generate payouts
A Multidirectional Optimum Ecotope-Based Algorithm
Generates Samples or Complete List of Brazilian IBGE (Instituto
Brasileiro De Geografia e Estatistica) Census Households,
Geocoding it by Google Maps
Statistical Analysis of Amplicon Data of the Same Sample to
Identify Artefacts
Simple and Canonical Correspondence Analysis
Analogue and Weighted Averaging Methods for Palaeoecology
Additional Functions for Use with the Analogue Package
Model Layer for Automatic Data Analysis via CSV File
Interpretation
Functions for analysis of fMRI datasets stored in the ANALYZE or
NIFTI format
Bidirectional Connector to Anametrix API
Assigning Ancestry Based on Population References
Statistical analysis of surveys with anchoring vignettes
An extended Bayesian Learning Technique developed by Dr. Geoff
Webb
Adaptive Neuro Fuzzy Inference System in R
Simulate a Bus Route Creel Survey of Anglers
Animal track reconstruction for high frequency 2-dimensional
(2D) or 3-dimensional (3D) movement data
A Gallery of Animations in Statistics and Utilities to Create
Animations
AnnotLists: A tool to annotate multiple lists from a specific
annotation file
Anonymize Data Containing Personally Identifiable Information
Statistical Methods for Anthropometric Data
Anything to 'POSIXct' or 'Date' Converter
Analysis of Overdispersed Data using S3 methods
aoristic analysis with spatial output (kml)
Format Outputs of Statistical Tests According to APA Guidelines
Process the Apache Web Server Access Log Files
Create American Psychological Association (APA) Style Tables
Phylogenetic Methods for Multiple Gene Data
Datasets from Hosmer, Lemeshow and Sturdivant, "Applied Logistic
Regression" (3rd ed.)
Another Plot PACKage: stem.leaf, bagplot, faces, spin3R,
plotsummary, plothulls, and some slider functions
Pre- and Postprocessing for AP-MS data analysis based on
spectral counts
Compute Appell's F1 hypergeometric function
Approximate Path for Penalized Likelihood Estimators
Functions and Data Sets for 'Applied Predictive Modeling'
Amyloid Propensity Prediction Neural Network
Bayesian prediction of complex computer codes
Datasets from Draper and Smith "Applied Regression Analysis"
(3rd Ed., 1998)
Tools for the a Priori Comparison of Regression Modelling
Strategies
Amdahl's Profiler, Directed Optimization Made Easy
General Utility Functions for the 'Agricultural Production
Systems Simulator'
Edit, Run and Evaluate APSIM Simulations Easily Using R
apsrtable model-output formatter for social science
Average Positive Predictive Values (AP) for Binary Outcomes and
Censored Event Times
Functions to help display air quality model output and
monitoring data
Interface methods to use with an ActiveQuant Master Server
Integrated Development Toolbox for Aquatic Chemical Model
Generation
Segmentation of an autoregressive Gaussian process of order 1
Rigorous Data Reduction and Error Propagation of Ar40 / Ar39
Data
Fitting Univariate Censored Linear Regression Model with
Autoregressive Errors
Example Datasets from Archaeological Research
Plant Root System Architecture Analysis Using DART and RSML
Files
Tools for Storing, Restoring and Searching for R Objects
Tools for Archiving, Managing and Sharing R Objects via GitHub
Activated Region Fitting, fMRI data analysis (3D)
Fractional ARIMA (and Other Long Memory) Time Series Modeling
Arfima-MLM Estimation For Repeated Cross-Sectional Data
Command line optional and positional argument parser
Improved Communication to Users with Respect to Problems in
Function Arguments
Data Analysis Using Regression and Multilevel/Hierarchical
Models
Analysis of Large Affymetrix Microarray Data Sets
A Probe-Level Data File Format Used by 'aroma.affymetrix'
[deprecated]
Copy-Number Analysis of Large Microarray Data Sets
Core Methods and Classes Used by 'aroma.*' Packages Part of the
Aroma Framework
Tools for Simulating Direct Behavioral Observation Recording
Procedures Based on Alternating Renewal Processes
Apply a Function to a Margin of an Array
Aligned Rank Transform for Nonparametric Factorial Analysis
Time-Varying DBN Inference with the ARTIVA (Auto Regressive TIme
VArying) Model
Gene and Pathway p-values computed using the Adaptive Rank
Truncated Product
Mining Association Rules and Frequent Itemsets
Classification Based on Association Rules
Mining NB-Frequent Itemsets and NB-Precise Rules
Visualizing Association Rules and Frequent Itemsets
Data Sets for "Applied Survival Analysis Using R""
A Collection of Statistical Tools for Biologists
Export R objects to several markup languages
Assign Random Colors to Unique Items in a Vector
Simulations for Adaptive Seamless Designs
Linkage Map Construction using the MSTmap Algorithm
Animal Social Network Inference and Permutations for Ecologists
A collection of functions for estimating centrographic
statistics and computational geometries for spatial point
patterns
Bayesian Inference on Causal Genetic Variants using Affected
Sib-Pairs Data
A General Framework for Multivariate Analysis with Optimal
Scaling
Augments the Use of 'ASReml-R' in Fitting Mixed Models
Readable Check Functions to Ensure Code Integrity
A Lightweight Core of the 'assertive' Package
Assertions to Check Properties of Strings
Assertions to Check Properties of Dates and Times
Assertions to Check Properties of Matrices
Assertions to Check Properties of Models
Assertions to Check Properties of Numbers
Assertions to Check Properties of Variables
Assertive Programming for R Analysis Pipelines
Optimal pricing of assets with fixed expiry date
A Suite of R Functions Implementing Spline Smoothing Techniques
Adaptive Subgroup Selection in Group Sequential Trials
Calculate the Assortativity Coefficient of Weighted and Binary
Networks
Statistical Tests for Genetic Association Studies
A Computational Tool for Astrochronology
Astronomy: time and position functions, misc. utilities
Applied Statistical Time Series Analysis
Average Shifted Visual Predictive Checks
Asymmetric Linkage Disequilibrium (ALD) for Polymorphic Genetic
Data
The Slide-Vector Model for Multidimensional Scaling of
Asymmetric Data
Regression Analysis of Sparse Asynchronous Longitudinal Data
Calculate Power Utilizing Asymptotic Likelihood Ratio Methods
Inference for Average Treatment Effects using Covariate
Balancing
A GTK GUI for teaching basic concepts in statistical inference,
and doing elementary bayesian tests
Automatically Tuned Markov Chain Monte Carlo
Acoustic Traveltime Calculations for 1-D Atmospheric Models
Support Querying Axibase Time-Series Database
Threshold independent performance measures for probabilistic
classifiers
Variable Selection with Random Forest and the Area Under the
Curve
Approximate Unconditional and Permutation Tests
Sparse Autoencoder for Automatic Learning of Representative
Features from Unlabeled Data
Display Multiple Images with Scaled Colors
Automated Hierarchical Multiple Regression with Assumptions
Checking
Partial Least Squares Regression with Backward Selection of
Predictors
Minimum Distance Estimation in Autoregressive Model
Performs Specification Search in Structural Equation Models
Automated Vector Autoregression Models and Networks
Calculation of Average Population Attributable Fractions and
Confidence Intervals
Client for the Amazon Alexa Web Information Services API
Class and Methods Definitions for Packages 'aws', 'adimpro',
'fmri', 'dwi'
Interface with Azure Machine Learning Datasets, Experiments and
Web Services
Adaptive differential evolution, b6e6rl variant
Bayesian Bacterial Growth Curve Analysis in R
Bayesian Bootstrap Predictive Mean Matching - Multiple and
Single Imputation for Discrete Data
Bubble Chart to Compare Biological Annotations by using DAVID
Modeling Framework for Cellular Communities in their
Environments
Bayesian Analysis of Computer Code Output (BACCO)
Bayesian Augmented Control for Clinical Trials
Reimplementations of Functions Introduced Since R-3.0.0
Learning Causal Cyclic Graphs from Unknown Shift Interventions
Exploring Portfolio-Based Conjectures About Financial
Instruments
Choice of the Hyperparameter Omega in the Bayesian Adjustment
for Confounding (BAC) Algorithm
Create Badges for 'Travis', 'Repostatus' 'Codecov.io' Etc in
Github Readme
Bayesian Average Error approach to Sample Size Determination
Ensemble bagging and boosting classifiers
Balance Check for Multiple Covariates in Matched Observational
Studies
Balanced and Spatially Balanced Sampling
Functions and Datasets for Books by Jeff Gill
Bayesian Meta-Analysis of Diagnostic Test Data
Analysis and Visualization of Macroevolutionary Dynamics on
Phylogenetic Trees
Functions for simple A/B split test and multi-armed bandit
analysis
Batch Effect Removal and Addon Normalization (in Phenotype
Prediction using Gene Data)
Bayesian Prediction of Stochastic Processes
Species Identification using DNA Barcodes
Bayesian Model Averaging using Bayesian Adaptive Sampling
Infrastructure for Computing with Basis Functions
Recovering a Basic Space from Issue Scales
Age-Specific Survival Analysis from Incomplete
Capture-Recapture/Recovery Data
Big Data Statistical Analysis for High-Dimensional Models
Batching Routines in Parallel and Passing Command-Line Arguments
to R
Statistical Experiments on Batch Computing Clusters
Consistent Batch Means Estimation of Monte Carlo Standard Errors
Convert Categorical Representations of Logicals to Actual
Logicals
An R Package for Data Reduction by Battery Reduction
Bayesian Feature Allocation Model for Tumor Heterogeneity
Bayesian Estimation of Mixtures of Multivariate Bernoulli
Distributions
Miscellaneous Functions for Bioinformatics and Bayesian
Statistics
An Implementation of Rubin's (1981) Bayesian Bootstrap
Power Calculations and Bayesian Analysis of Count Distributions
and FECRT Data using MCMC
Bayesian Analysis of Censored Regression Models Under Scale
Mixture of Skew Normal Distributions
Functions and Datasets for the book "Bayesian Data Analysis"
The Bayesian Dynamic Conditional Correlation GARCH Model
Graphical User Interface for bayesTFR, bayesLife and bayesPop
Computation of Bayes Factors for Common Designs
Bayesian Estimation of the GARCH(1,1) Model with Student-t
Innovations
Scalable Rejection Sampling for Bayesian Hierarchical Models
Bayesian Analysis of Generalized Elliptical Semi-Parametric
Models and Flexible Measurement Error Models
Bayesian Regression Model with Mixture of Two Scaled Inverse Chi
Square as Hyperprior
Bayesian Melding of GPS and DR Path for Animal Tracking
Bayesian Beta regression: joint mean and precision modeling
Designing Bayesian Multi-Arm Multi-Stage Studies
Mixtures-of-Experts Markov Chain Clustering and Dirichlet
Multinomial Clustering
Default Bayesian Hypothesis Tests for Correlation, Partial
Correlation, and Mediation
Bayesian Mixture Survival Models using Additive
Mixture-of-Weibull Hazards, with Lasso Shrinkage and
Stratification
BayesNI: Bayesian Testing Procedure for Noninferiority with
Binary Endpoints
Hierarchical Bayesian Model for a Hazard Function
Hierarchical Bayesian analysis of ecological count data
Bayesian Analysis of Small Area Estimation
Computation of Bayes factors for interrupted time-series designs
MCMC Sampling of Bayesian Linear Models via Summary Statistics
Bayesian Survival Regression with Flexible Error and Random
Effects Distributions
Bayesian thresholds mixed-effects models for categorical data
Bayes Factors, Model Choice and Variable Selection in Linear
Models
R Utilities Accompanying the Software Package BayesX
R Package Distribution of the BayesX C++ Sources
Bayesian analysis of haplotype association using Markov Chain
Monte Carlo
R Functions for Bayesian Hazard Rate Estimation
R Package for Baylor University Educational Psychology
Quantitative Courses
Bayesian Fitting of Ornstein-Uhlenbeck Models to Phylogenies
Bayesian Analysis of Significantly Mutated Genes in Cancer
On Bayesian analysis of Threshold autoregressive model (BAYSTAR)
Solving and Optimizing Large-Scale Nonlinear Systems
Bayesian bandwidth estimation and semi-metric selection for the
functional kernel regression with unknown error density
Bayesian bandwidth estimation for multivariate kernel regression
with Gaussian error
Bayesian Estimation of Incoherent Neutron Scattering Backgrounds
Miscellaneous Helper Functions for B. Bischl
Tools for General Maximum Likelihood Estimation
Gene Regulatory Network Inference with Bc3net
Bias-Corrected Bayesian Classification with Selected Features
Business Cycle Dating and Plotting Tools
Bayesian composition estimator: estimating sample (taxonomic)
composition from biomarker data
The Bayesian Causal Effect Estimation Algorithm
Bayesian Models for Cost-Effectiveness Analysis in the Presence
of Structural Zero Costs
Radiocarbon Dating, Age-Depth Modelling, Relative Sea Level Rate
Estimation, and Non-Parametric Phase Modelling
Bayesian Palaeoclimate Reconstruction from Pollen Data
Bayesian Hierarchical Clustering Using Spike and Slab Models
Bayesian Analysis of Change Point Problems
Behavioral change point analysis of animal movement
Bayesian Multiple Changepoint Detection Using Metadata
Advanced Analysis of B Cell Receptor Repertoire Data
Bayesian Continual Reassessment Method for Phase I
Dose-Escalation Trials
Cross-Validation for the SVD (Bi-Cross-Validation)
Bayesian Graph Selection Based on Birth-Death MCMC Approach
Optimisation of Bayesian Decision Problems
Inference and design for predictive values in binary diagnostic
tests
Remove Weekends and Holidays from ggplot2 Axes
Routines for Block Diagonal Symmetric matrices
Bayesian Inference for Discrete Weibull Regression
Analysis of Illumina BeadArray SNP data including MSV markers
Visualization via Beanplots (like Boxplot/Stripchart/Violin
Plot)
Bayesian Estimate of Age-specific Nest Survival Probabilities
Bayesian Analysis of Heterogeneous Treatment Effect
Quantifies effects of geo/eco distance on genetic
differentiation
Matrices Backed by Binary PED Files (PLINK)
Genomic Region Processing using Tools Such as BEDtools, BEDOPS
and Tabix
Easily Play Notification Sounds on any Platform
The Bee Swarm Plot, an Alternative to Stripchart
Download Historical Data from the Belgrade Stock Exchange
28 benchmark densities from Berlinet/Devroye (1994)
Benchmark and Frontier Analysis Using DEA and SFA
Benford Analysis for Data Validation and Forensic Analytics
Statistical Tests for Evaluating Conformity to Benford's Law
Bent-Cable Regression for Independent Data or Autoregressive
Time Series
Bayesian analysis for exponential random graph models
Function Collection Related to Plotting and Hydrology
Bessel -- Bessel Functions Computations and Approximations
Bayesian Estimation Supersedes the t-Test
Mini Games from Adventures of Beta and Bit
Detecting rare variants for quantitative traits using nuclear
families
Partitioning beta diversity into turnover and nestedness
components
Functions to incorporate taxonomic uncertainty on multivariate
analyses of ecological data
Simulation, estimation and forecasting of Beta-Skew-t-EGARCH
models
Bayesian Evaluation of Variant Involvement in Mendelian
Disorders
Breaks For Additive Season and Trend (BFAST)
Binary Generalized Extreme Value Additive Models
Bayesian Estimation in Generalized Linear Models
Example BGM Files for the Atlantis Ecosystem Model
Gaussian Mixture Modeling Algorithms And The Belief-based
Mixture Modeling
Markov Beta and Gamma Processes for Modeling Hazard Rates
Block Gibbs Sampler with Incomplete Multinomial Distribution
Useful Functions for Box, Hunter and Hunter II
Bias correction of the parameter estimates of the beta
regression model
Bibliometric and Co-Citation Analysis Tool
Reading Mass Spectrometric Search Results
Calculates the bidimensional regression between two 2D
configurations
Inferences for bi- and trifactorial trial designs
Tools for Survey Statistics in Educational Assessment
BLAS routines for native R matrices and big.matrix objects
Utilities for 'big.matrix' Objects from Package 'bigmemory'
Case-Control Analysis of Multi-Allelic Loci
Scalable Least-Angle Regression and Lasso
Big Lasso: Extending Lasso Model Fitting to Big Data in R
bounded memory linear and generalized linear models
Manage Massive Matrices with Shared Memory and Memory-Mapped
Files
A shared resource interface for Bigmemory Project packages
PCA, Transpose and Multicore Functionality for 'big.matrix'
Objects
Interface with Google BigQuery with Shiny Compatibility
Big Quadratic Inverse Covariance Estimation
Generalized Linear Models (GLM) for Large Data Sets
An Interface to Google's 'BigQuery' 'API'
Generalized Ridge Regression (with special advantage for p >> n
cases)
Table, Apply, and Split Functionality for Matrix and
'big.matrix' Objects
Top Scoring Pair based methods for classification
Dimension Reduction Methods for Multivariate Time Series
Power for SNP analyses using silver standard cases
Estimates Mixture Models for Case-Control Data
Variable Selection for Binary Data Using the EM Algorithm
Mean and Variance Modeling of Binary Data
Multi-Class Discriminant Analysis using Binary Predictors
Methods for Analyzing Binned Income Data
Evaluation and experimental design for binomial group testing
Asymptotic simultaneous confidence intervals for many-to-one
comparisons of proportions
Data Generation with Binary and Continuous Non-Normal Components
Simultaneous Generation of Multivariate Binary and Normal
Variates
Binomial Confidence Intervals For Several Parameterizations
'Taxonomic' Specification and Parsing Methods
Generates binomial random numbers via the coupling from the past
algorithm
Efficient MCMC for Binomial Logit Models
Confidence intervals and sample size determination for a
binomial proportion under simple random sampling and pooled
sampling
Performing diagnostics on binomial regression models
Concurrent Generation of Binary, Ordinal and Continuous Data
Cut Numeric Values into Evenly Distributed Groups
Exact Binary Sequential Designs and Analysis
Data Preprocessing, Binning for Classification and Regression
Feature Selection and Classification with the Embedded
Validation Procedures for Biomedical Data Analysis
Package for Community Ecology and Suitability Analysis
Multilevel Modeling of Dendroclimatical Fluctuations
Biodiversity Assessment Using Functional Tools
Process Biogas Data and Predict Biogas Production
Point Data Quality Assessment and Coordinate Conversion
BioGeography with Bayesian (and Likelihood) Evolutionary
Analysis in R Scripts
Simulation of Dynamic Microbial Inactivation
Predict environmental conditions from biological observations
An interface package (beta) for the BIOM file format
Find Biomarkers in Two-Class Discrimination Problems
Functional Annotation and Biological Data Retrieval with R
Ensemble Platform for Species Distribution Modeling
Utilities for the BIOM (Biological Observation Matrix) Format
Visualization of Overlapping Results with Heatmap
Simulation of deterministic and stochastic biochemical reaction
networks using Petri Nets
Search and Download Papers from the bioRxiv Preprint Server
From BIOlogical Sequences to MultiDimensional Scaling
Tools for Electromyogram Signals (EMG) Analysis
Calculation of Freshwater Biotic Indices
Tools for Biometry and Applied Statistics in Agricultural
Science
Visualising Bipartite Networks and Calculating Some (Ecological)
Indices
Bootstrap on Classical Biplots and Clustering Disjoint Biplot
BIPOD (Bayesian Inference for Partially Observed diffusions)
Methods to Analyse Ring Re-Encounter Data
Tools to find bad commits with git bisect
Toolkit to Identify Candidate Synthetic Lethality
Bayesian Isotonic Regression with Bernstein Polynomials
A S3 Class for Vectors of 64bit Integers
Binarization and Trinarization of One-Dimensional Data
Bayesian Inference of Transmission Routes Using Genome Sequences
Estimating the Parameters of Some Bivariate Distributions
Sample size calculations for bivariate longitudinal data
Generation of Bivariate Uniform Data and Its Relation to
Bivariate Binary Data
Conduct Univariate and Bivariate Wavelet Analyses
Functions to compute the biweight mean vector and covariance &
correlation matrices
Business Days Calculations and Utilities
Black Box Optimization and Exploration of Parameter Space
Plots (Slightly Extended) Bland-Altman Plots
Calculate and Analyze Blau Status for Measuring Social Distance
Black-Litterman and Copula Opinion Pooling Frameworks
A New Link Function that Blends Two Specified Link Functions
Analyze biotic homogenization of landscapes
Data Exploration with Multiple Machine Learning Algorithms
Fitting block ERGM given the block structure on social networks
Binomial linear and linear-expit regression
Data Files and Functions Accompanying the Book "Bayesian Data
Analysis in Ecology using R, BUGS and Stan"
Coclustering Package for Binary, Categorical, Contingency and
Continuous Data-Sets
blockmatrix: Tools to solve algebraic systems with partitioned
matrices
Creates strings that show a text message in 8 by 8 block letters
An R package for Generalized and classical blockmodeling of
valued networks
Latent and Stochastic Block Model Estimation by a 'V-EM'
Algorithm
Randomization for block random clinical trials
Nested and Crossed Block Designs for Unstructured Treatment Sets
Block, Assign, and Diagnose Potential Interference in Randomized
Experiments
Statistical Comparisons with Distance-Function Based Permutation
Tests
Request Data from the U.S. Bureau of Labor Statistics API
An API Wrapper for the Bureau of Labor Statistics (BLS)
Benchmark dose analysis for dose-response data
Mediation analysis with missing data using bootstrap
Bayesian Sampling for Stick-Breaking Mixtures
The pseudo-likelihood method for pairwise binary markov networks
Bundle Methods for Regularized Risk Minimization Package
Bayesian Models for Rare Variant Association Analysis
Data Generator based on Bayesian Network Model
Bayesian Network Structure Learning, Parameter Learning and
Inference
Bagged k-Nearest Neighbors Survival Prediction
Bayesian Estimation for Normal Heteroscedastic Nonlinear
Regression Models
Ferguson-Klass Type Algorithm for Posterior Normalized Random
Measures
A Bayesian Nonparametric Algorithm for Time Series Clustering
Bayesian Non- and Semi-Parametric Model Fitting
Spatial Implementation of Bayesian Networks and Mapping
Bayesian Network Structure Learning from Data with Missing
Values
Bayesian Output Analysis Program (BOA) for MCMC
Board Games and Tools for Building Board Games
Profilling of Peripheral Blood Mononuclear Cells using CyTOF
Bacterium and Virus Analysis of Orthologous Groups (BOG) is a
Package for Identifying Differentially Regulated Genes in the
Light of Gene Functions
Interface to the Boilerpipe Java Library
Bayesian Optimal INterval (BOIN) Design for Single-Agent and
Drug- Combination Phase I Clinical Trials
Functions for Elementary Bayesian Inference
An Improved Multiple Testing Procedure for Controlling False
Discovery Rates
Construction, Simulation and Analysis of Boolean Networks
Boosted Multivariate Trees for Longitudinal Data
A modular framework to bag or boost any estimation procedure
Optimized GWAS cohort subset selection for resequencing studies
Bootstrap Functions (Originally by Angelo Canty for S)
Bootstrapped Confidence Intervals for (Negative) Likelihood
Ratio Tests
Bootstrap-MRMR Technique for Informative Gene Selection
Bootstrap Methods for Various Network Estimation Routines
Bootstrap Prediction Intervals and Bias-Corrected Forecasting
Bootstrapped Response and Correlation Functions
A bootstrap test for the probability of ruin in the classical
risk process
Bootstrap Subsamplings of Sparse Partial Least Squares -
Discriminant Analysis for Classification and Signature
Identification
Functions for the Book "An Introduction to the Bootstrap"
Fast, Exact Bootstrap Principal Component Analysis for High
Dimensional Data
Robust Performance Hypothesis Testing with the Sharpe Ratio
Bootstrap Tolerance Levels for Credit Scoring Validation
Statistics
Test Stationarity using Bootstrap Wavelet Packet Tests
Bayesian Ordination and Regression AnaLysis
Wrapper Algorithm for All Relevant Feature Selection
a Bunch of Structure and Sequence Analysis
Analytic Solutions for (ground-water) Boussinesq Equation
Downloads Box Office Information for Given Dates (How Much Each
Movie Earned in Theaters)
Double Box Plot for Two-Axes Correlation
Biplot of Multivariate Data Based on Principal Components
Analysis
Beta Product Confidence Procedure for Right Censored Data
bPeaks: an intuitive peak-calling strategy to detect
transcription factor binding sites from ChIP-seq data in small
eukaryotic genomes
Bayesian Phylogeographic and Ecological Clustering
Visualize Combined Action Response Surfaces and Report BRAID
Analyses
Fitting Dose Response with the BRAID Combined Action Model
Helper functions to misc3d and rgl packages for brain imaging
Basic wavelet analysis of multivariate time series with a
visualisation and parametrisation using graph theory
Simulation and Estimation for Branching Processes
Bootstrapped Robustness Assessment for Qualitative Comparative
Analysis
Species Richness Estimation and Modeling
An R Package for Multiple Break-Point Detection via the
Cross-Entropy Method
Templating Framework for Report Generation
Extracting Usable Data from the Grad Cafe Results Search
Bias reduction in binomial-response generalized linear models
Brier score decomposition of probabilistic forecasts for binary
events
An Implementation of the Bridge Distribution with Logit-Link as
in Wang and Louis (2003)
Bayesian Regularization for Feed-Forward Neural Networks
Convert Statistical Analysis Objects into Tidy Data Frames
A Compression Format Optimized for the Web
Bayesian Inference on the Ratio of Two Poisson Rates
Interface to the 'OpenBUGS' MCMC Software
Statistical methods for safety assessment in agricultural field
trials
Bayesian State-Space Models for Animal Movement
Bayesian Survival Model with Lasso Shrinkage Using Generalized
Weibull Regression
Nonparametric Smoothing of the Hazard Function
Bayes Screening and Model Discrimination
bspmma: Bayesian Semiparametric Models for Meta-Analysis
Asymptotic Covariance Matrices of Some BSS Mixing and Unmixing
Matrix Estimates
Birnbaum-Saunders Model Based on Skew-Normal Distribution
Temporal Exponential Random Graph Models by Bootstrapped
Pseudolikelihood
Estimates univariate function via Bayesian trend filtering
Modelling Heterogeneity in Paired Comparison Data
Training and Analysing Asynchronous Boolean Models
Buckley-James Regression for Survival Data with High-Dimensional
Covariates
Analyze Bunching in a Kink or Notch Setting
Markov model for bursty behavior in streams
Bayesian Analysis of a Vector Autoregressive Model with
Stochastic Volatility and Time-Varying Parameters
A Simple alternative to proportional Venn diagrams
The Stark-Parker algorithm for bounded-variable least squares
Solvers for Boundary Value Problems of Differential Equations
Bayesian Variant Selection: Bayesian Model Uncertainty
Techniques for Genetic Association Studies
Baumgartner Weiss Schindler Test of Equal Distributions
Extended Inference for Lasso and Elastic-Net Regularized Cox and
Generalized Linear Models
Infering large-scale gene networks with C3NET
C5.0 Decision Trees and Rule-Based Models
Simple, Multiple and Joint Correspondence Analysis
Bootstrap Confidence Regions for Correspondence Analysis
Classification Accuracy and Consistency under Item Response
Theory
Conditional Density Estimation Network Construction and
Evaluation
This package performs the CADF unit root test proposed in Hansen
(1995)
Conditional Akaike information criterion for lme4
R graphics device using cairo graphics library for creating
high-quality bitmap (PNG, JPEG, TIFF), vector (PDF, SVG,
PostScript) and display (X11 and Win32) output
Calculations for All Common Subsequences
Multiple imputation using MICE and Random Forest
Automated Parameter Estimation for Complex (Ecological) Models
Calibration of Scatterplot and Biplot Axes
Weighting and Estimation for Panel Data with Non-Response
Bayesian calibration of complex computer codes
Improved Allele-Specific Copy Number of SNP Microarrays for
Downstream Segmentation
Finite Mixture Models and Meta-Analysis Tools - Based on C.A.MAN
Camera Trap Data Management and Preparation of Occupancy and
Spatial Capture-Recapture Analyses
Estimation of Temporal Ordering of Cancer Abnormalities
Visualizing Generalized Canonical Discriminant and Canonical
Correlation Analysis
Accessing Intra-Tumor Heterogeneity and Tracking Longitudinal
and Spatial Clonal Evolutionary History by Next-Generation
Sequencing
Directly Extracts Complete CANSIM Data Tables
Combined Analysis of Pleiotropy and Epistasis
Comparative Analyses of Phylogenetics and Evolution in R
Numbers Figures and Creates Simple Captions
CAlibrating Penalities Using Slope HEuristics
Estimates population size from non-invasive sampling
Spatial Generalised Linear Mixed Models for Areal Unit Data
Data Used in the Vignettes Accompanying the CARBayes and
CARBayesST Packages
Spatio-Temporal Generalised Linear Mixed Models for Areal Unit
Data
Estimation of the Number of Fatalities from Carcass Searches
Identification of Cardinal Dates in Ecological Time Series
Cardiovascular Safety Exposure-Response Modeling in Early-Phase
Clinical Studies
High-Dimensional Regression and CAR Score Variable Selection
Statistical package for population size estimation in
capture-recapture models
Estimation of caribou abundance based on large scale
aggregations monitored by radio telemetry
Functions for Statistics Classes at Carleton College
Ecological Quality Ratios Calculation and Plot
A Collection of Database, Data Structure, Visualization, and
Utility Functions for R
Build Common Tables of Summary Statistics for Reports
CRISPR AnalyzeR for Pooled CRISPR Screens
Reparameterized and marginalized posterior sampling for
conditional autoregressive models, OpenCL implementation
Censored Autoregressive Model with Exogenous Covariates
Identify Similar Cases for Qualitative Case Studies
Analysis of categorical-variable datasets with missing values
Categorical Data Analysis Program Package
Fishery Stock Assessment by Generalized Depletion Models
High Dimensional Factor Analysis and Confounder Adjusted Testing
and Estimation
Community Assembly by Traits: Individuals and Beyond
An R Package for Simulating IRT-Based Computerized Adaptive
Tests
Tools: moving window statistics, GIF, Base64, ROC AUC, etc
Procedures to Generate Patterns under Computerized Adaptive
Testing
Special models for categorical variables
Tools for Estimating Causal Dose Response Functions
Deriving Expressions of Joint Interventional Distributions and
Transport Formulas in Causal Models
Methods for Estimating Causal Effects from Observational Data
Estimation of Causal Effects with Generalized Additive Models
Selection Bias Approach to Sensitivity Analysis for Causal
Effects
Analysis utilities for binary classification and Causata users
Statistics Netherlands (CBS) Open Data API Client
Bayesian Canonical Correlation Analysis and Group Factor
Analysis
(Robust) Canonical Correlation Analysis via Projection Pursuit
Concordance Correlation Coefficient for Repeated (and
Non-Repeated) Measures
Combined Cluster and Discriminant Analysis
CCDr Algorithm for Learning Sparse Gaussian Bayesian Networks
Cox Model for Case-Cohort Data with Stratified
Subcohort-Selection
Convex Clustering Methods and Clustering Indexes
Simulate Congruence Class Model for Networks
Significance Tests for Canonical Correlation Analysis (CCA)
One and two locus GWAS of binary phenotype with
case-control-population design
Cultural Consensus Theory applications to data
Coupled-Dipole Approximation for Electromagnetic Scattering by
Three-Dimensional Clusters of Sub-Wavelength Particles
Retrieve U.S. Flu Season Data from the CDC FluView Portal
Conditional Distance Correlation and Its Related Feature
Screening Method
Evaluate Differentially Private Algorithms for Publishing
Cumulative Distribution Functions
Quantile Regression for Random Variables on the Unit Interval
Coordinate Descent Algorithms for Lasso Penalized L1, L2, and
Logistic Regression
Tools to Download and Work with USDA Cropscape Data
Components of Canadian Monetary and Credit Aggregates
Phylogenetically Classifies Retention Mechanisms of Duplicate
Genes from Gene Expression Data
Constrained Dual Scaling for Detecting Response Styles
Statistical Inference of C- And D-Vine Copulas
Benchmark for the CEC 2005 Special Session on Real-Parameter
Optimization
Benchmark functions for the Special Session and Competition on
Real-Parameter Single Objective Optimization at CEC-2013
Collection of Common Astronomical Conversion Routines and
Functions
Translate Spreadsheet Cell Ranges to Rows and Columns
Functions to fit cell volume distributions and thereby estimate
cell growth rates and division times
Censored Linear Mixture Regression Models
Fits Normal and Student-t Censored Regression Model
Changes United States Census Geographic Code into Name of
Location
Cross-Entropy R Package for Optimization
Discriminant Analysis of Time Series in the Presence of
Within-Group Spectral Variability
Context Driven Exploratory Projection Pursuit
Outlier Detection Using the Iterated RMCD Method of Cerioli
(2010)
Covariance Estimation Regularized by Nuclear Norm Penalties
Cause-Specific Framework for Competing-Risk Analysis
Estimate quantiles using any order Cornish-Fisher expansion
Canadian Forest Fire Danger Rating System
Compare Groups, Analytically and Graphically
Calculate AUC-type measure when gold standard is continuous and
the corresponding optimal linear combination of variables with
respect to it
R-Based API for Accessing the MSKCC Cancer Genomics Data Server
(CGDS)
Microarray CGH analysis using the Smith-Waterman algorithm
Detecting hot spot on CGH array data with fused lasso
regression
Segmentation Methods for Array CGH Analysis
Statistical Methods and Models for Claims Reserving in General
Insurance
Methods for Nonparametric Changepoint Detection
Change Point Estimation for Clustered Signals
Markov Model for the Online Multi-Channel Attribution Problem
Shiny Web Application for the Multichannel Attribution Problem
Charge Transfer Rates and Charge Carrier Mobilities
Install Packages from Snapshots on the Checkpoint Server for
Reproducibility
Analysis and Visualisation of Ecological Communities
Calibration Functions for Analytical Chemistry
Multivariate Statistical Analysis in Chemometrics
Chemometrics with R - Multivariate Data Analysis in the Natural
Sciences and Life Sciences
Multiple Testing Methods for Exploratory Research
Closest History Flow Field Forecasting for Bivariate Time Series
Calculation of standard deviation scores adduced from different
growth standards
Statistical Methods for Phenology Analysis in Temperate Fruit
Trees
Toolkit of Helper Functions to Pre-Process Amplification Data
Nonparametric Methods for Identifying Differential Enrichment
Regions with ChIP-Seq Data
Chemical Thermodynamics and Activity Diagrams
Simplify the Creation of Choropleth Maps in R
Contains an Administrative-Level-1 Map of the World
Contains maps used by the choroplethr package
Interface to Chromosome Counts Database API
Analysis of chromosomal interactions data (correction,
segmentation and comparison)
Chronological Objects which can Handle Dates and Times
Chunkwise Text-File Processing for 'dplyr'
Generative Models for Complex Networks with Conditionally
Independent Dyadic Structure
Weighted summary of cumulative incidence functions
Co-Information based N-Order Epistasis Detector and Visualizer
Statistical Analysis of Circular Data using Nonnegative
Trigonometric Sums (NNTS) Models
Detection of Outliers in Circular-Circular Regression
Circular Statistics, from "Topics in circular Statistics" (2001)
CIT Breast Cancer Molecular SubTypes Prediction
CIT Colon Cancer Molecular SubTypes Prediction
An RStudio Addin to Insert BibTex Citation in Rmarkdown
Documents
RStudio Add-in to Insert Markdown Citations
Visualization of structure and contents of a database
Client for the Comprehensive Knowledge Archive Network ('CKAN')
'API'
Optimal and Fast Univariate k-Means Clustering
C++ implementations of phylogenetic cladogenesis calculations
Time Series Modeling for Climate Change Proxies
Construct Graphs of S4 Class Hierarchies
Explore classification models in high dimensions
Classification Accuracy and Consistency under IRT models
Robust multivariate classification using highly optimised SVM
ensembles
Cleaning Geometries from Spatial Objects
Simultaneous Variables Clustering and Regression
Model-Based Clustering of Categorical Sequences
Analyzes Clickstreams Based on Markov Chains
Easily Download and Visualise Climate Data from CliFlo
Censoring Data and Likelihood-Based Correlation Estimation
Climate Tools (Series Homogenization and Derived Products)
Climate Classification According to Several Indices
Constrained L1-minimization for Inverse (covariance) Matrix
Estimation
Statistical Methods for Climate Sciences
Clinical Trial Design and Data Analysis Functions
Read and Write from the System Clipboard
Constrained Inference for Linear Mixed Effects Models
Fitting exact conditional logistic regression with lasso and
elastic net penalties
Lasso Estimation of Conditional Logistic Regression Models
Central Limit Theorem Experiments (Theoretical and Simulation)
Cluster-Robust (Sandwich) Variance Estimators with Small-Sample
Corrections
Clustering Method Based on Local Shrinking
Clustering and Visualization of Mixed-Type Data
Wilcoxon Rank Sum Test for Clustered Data
"Finding Groups in Data": Cluster Analysis Extended Rousseeuw et
al.
Location and Visualization of Clustered Somatic Mutations
Explore clustering interactively using R and GGobi
Random Cluster Generation (with Specified Degree of Separation)
Identifying clusters in genomics data by recursive partitioning
Optimal Distance-Based Clustering for Multidimensional Data with
Sequential Constraint
Integrative Clustering for Heterogeneous Biomedical Datasets
Power calculations for cluster-randomized and cluster-randomized
crossover trials
Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans and
K-Medoids Clustering
Reproducibility of gene expression clusters
Calculate Cluster-Robust p-Values and Confidence Intervals
Searching for Optimal Clustering Procedure for a Data Set
Assessment of Stability of Individual Objects or Clusters in
Partitioning Solutions
Clustering of Observations with Geographical Constraints
k-Prototypes Clustering for Mixed Variable-Type Data
Variable Selection in Clustering by Mixture Models for Discrete
Data
Methods for joint dimension reduction and clustering
Clustering of Variables Around Latent Variables
Variable Selection for Model-Based Clustering
Covariance Matrix Adapting Evolutionary Strategy
Covariance Matrix Adaptation Evolution Strategy
Computational Methods for Numerical Analysis
Control Charts for Conway-Maxwell-Poisson Distribution
Subdistribution Analysis of Competing Risks
Analysis of Competing Risks Using Quantile Regressions
Misc Functions of the Center for the Mathematical Research
The Complex Multivariate Gaussian Distribution
A Package for Coincidence Analysis (CNA)
Canonical Non-Symmetrical Correspondence Analysis in R
Maximum Likelihood Estimation of a Log-Concave Density Function
Pre-process DNA Copy Number (CN) Data for Detection of CN Events
Association Analysis of CNV Data and Imputed SNPs
Example data sets for association analysis of CNV data
A Collection of Functions to Help with Analysis of Coarsely
Observed Data
COnstrained B-Splines (Sparse Matrix Based)
COmbat CO-Normalization Using conTrols (COCONUT)
Extracts Shared Variation in Collections of Datasets Using
Regression Models
Statistical Comparisons of Two or more Alpha Coefficients
Output Analysis and Diagnostics for MCMC
Markov chain Monte Carlo burn-in based on "bridge" statistics
Automated C Code Generation for Use with the 'deSolve' and
'bvpSolve' Packages
Alternative Factor Coding Matrices for Linear Model Formulae
Robust Coefficient Alpha and Omega with Missing and Non-Normal
Data
Species coexistence modeling and analysis
Conditional Inference Procedures in a Permutation Test Framework
Simultaneous Confidence Interval for Multinomial Proportion
Consistent Monitoring of Stationarity and Cointegrating
Relationships
Parameter Estimation and Inference in a Cointegrating Regression
Collocation Inference for Dynamic Systems
Auxiliary Package for Package 'CollapsABEL'
Colocalisation tests of two genetic traits
Implementation of Colored Independent Component Analysis and
Spatial Colored Independent Component Analysis
Plot and add custom coloring to Venn diagrams for 2-dimensional,
3-dimensional and 4-dimensional data
Color Calculations with Emphasis on Spectral Data
Tools for colors in a Hue-Saturation-Value (HSV) color model
A Colour Picker Widget for Shiny Apps, RStudio, R-markdown, and
'htmlwidgets'
Synergy/Antagonism Analyses of Drug Combinations
Estimation of Optimal Portfolio Weights by Combining Simple
Portfolio Strategies
Construction Methods of some Series of PBIB Designs
Exact Test from the Combinations of Mutually Exclusive
Alterations (CoMEt) Algorithm
Computational Methods for Immune Cell-Type Subsets
Print Nicely Formatted Comments for Use in Script Files
Bindings to the CommonMark Reference Implementation
Extract and plot common trends from a cointegration system.
Calculate P-value for Johansen Statistics
Comparative Phylogeographic Analysis using the Community Tree
Framework
Creates empty plots with compact axis notation
Compare Two Correlated C Indices with Right-censored Survival
Outcome
Interface to Diverse Estimation Methods of Causal Networks
Do a Git Style Diff of the Rows Between Two Dataframes with
Similar Structure
comparison of medical forms in CDISC ODM format
Correct for Verification Bias in Diagnostic Accuracy & Agreement
Multivariate likelihood ratio calculation and evaluation
Event-specific incidence rates for competing risks data
Tools for Retrieval and Storage of Functional Genomics Data
Conway-Maxwell-Poisson GLM and distribution functions
Numerically Evaluate Integrals and Derivatives (also Higher
Order) of Vector- And Complex-Valued Functions
Robust Linear Regression with Compositional Data as Covariates
Regression Estimation Based on the Compound Covariate Method
Under the Cox Proportional Hazard Model
Distribution function of quadratic forms in normal variables
Composite-Likelihood Based Analysis of Random Fields
Human-Friendly Text from Unknown Strings
Optimisation with Continuous Convex Piecewise (Linear and
Quadratic) Functions
Approximate conditional inference for logistic and loglinear
models
Parameter estimation in conditional GEE for recurrent event gap
times
Conditional Density Estimation with Neural Network Conditional
Mixtures
Condition Number Regularized Covariance Estimation
Estimation of the Conditional Survival Function for Ordered
Multivariate Failure Time Data
Conditional Visualization for Statistical Models
Primal or Dual Cone Projections with Routines for Constrained
Regression
Confidence Estimation of Environmental State Classifications
Manage Environment Specific Configuration Values
Conformal Prediction for Regression and Classification
Configural Frequencies Analysis Using Log-Linear Modeling
Algorithms for Fitting Circles, Ellipses and Conics Based on the
Work by Prof. Nikolai Chernov
A package to check the cancellation axioms of conjoint
measurement
A Tool for Reproducible Research by Converting 'LaTeX' Files
Generated by R Sweave to Rich Text Format Files
Tools for working with connectivity matrices
Conover-Iman Test of Multiple Comparisons Using Rank Sums
Partial Linear Least-Squares Regression using Constrained
Splines
Compute the Median Ranking(s) According to the Kemeny's
Axiomatic Approach
Constrained, Covariance-Matching Constrained and Universal Point
or Block Kriging
Model-Based Clustering and Classification with the Multivariate
Contaminated Normal Distribution
Contouring of Non-Regular Three-Dimensional Data
Median Comparison for Two-Sample Right-Censored Survival Data
Quantifies and assesses the significance of convergent evolution
Income Concentration Analysis with Complex Survey Samples
Probabilistic Species Co-Occurrence Analysis in R
Co-Operation: Fast Covariance, Correlation, and Cosine
Similarity Operations
Cooperative aspects of optimal trees in weighted graphs
General Bivariate Copula Theory and Many Utility Functions
Fitting the copCAR Regression Model for Discrete Areal Data
Coverage Probability Excursion (CoPE) Sets
Copula Based Bivariate Beta-Binomial Model for Diagnostic Test
Accuracy Studies
Estimation of Distribution Algorithms Based on Copulas
Estimation and Statistical Process Control Under Copula-Based
Time Series Models
Bivariate Copula Based Regression Models
Copula Mixed Effect Models for Bivariate and Trivariate
Meta-Analysis of Diagnostic Test Accuracy Studies
Computing Statistical Indices to Detect Answer Copying on
Multiple-Choice Tests
Baseline Correction for Copy Number Data from Cancer Samples
Collection of Rudimentary Bioinformatics Tools
Generate correlated count random variables
Community Estimation in G-Models via CORD
Classification, Regression and Feature Evaluation
Core Functionality for Simulating Quantities of Interest from
Generalised Linear Models
TDT for compound heterozygous and recessive models
The Clustering of Regression Models Method
Efficient Estimation of Covariance and (Partial) Correlation
Statistics and data sets for corpus frequency data
Implementation of 2D Correlation Analysis
Nonparametrics with Clustered Binary and Multinomial Data
Correct Overloaded Peaks from GC-APCI-MS Data
Linear Regression Based on Linear Structure Between Covariates
Functions and Methods for Correspondence Regression
A collection of functions for graphing correlation matrices
Estimate Correlations Between Repeatedly Measured Endpoints
(E.g., Reliability) Based on Linear Mixed-Effects Models
Modeling Correlational Magnitude Transformations in
Discretization Contexts
Tools for processing data after a Genome Wide Association Study
Tools for estimating and predicting the cosinor model
Functions for cosmological distances, times, luminosities, etc
Photometric redshift estimation using generalized linear models
Fit Regularized Nonparametric Regression Models Using COSSO
Penalty
Connect to and Work with CouchDB Databases
Flexible Univariate Count Models Based on Renewal Processes
Mean and Variance Modeling of Count Data
Convert from UTC to Local Time for United States Counties
A Residue-Residue Contact Detecting Method
Local Polynomial Regression of State Dependent Covariates in
State-Space Models
Brownian Motion Processes for 'nlme'-Models
Latent Class Models with Covariate Effects on Underlying and
Measured Variables
A simultaneous regression model for the mean and covariance
Robust Covariance Estimation via Nearest Neighbor Cleaning
Model-Free Covariate Selection in High Dimensions
Tests for Determining if the Covariance Structure of
2-Dimensional Data is Separable
Computes covariance test for adaptive linear modelling
Streamlined Plot Theme and Plot Annotations for 'ggplot2'
Messages, Warnings, Strings with Ascii Animals
Cox models by likelihood based boosting for a single survival
endpoint or competing risks
Cox-Type Models for Interval-Censored Data
Cox Regression (Proportional Hazards Model) with Multiple Causes
and Mixed Effects
Confirmatory Path Analysis through the d-sep tests
Methods to perform Common Principal Component Analysis (CPCA)
Concordance Probability Estimates in Survival Analysis
Association Between Methylation and a Phenotype of Interest
Parallelized Genomic Prediction and GWAS
CpG Filtering Method Based on Intra-class Correlation
Coefficients
Find the maximum likelihood estimator of the shape constrained
hazard baseline and the effect parameters in the Cox
proportional hazards model
Sequential and Batch Change Detection Using Parametric and
Nonparametric Methods
Correction of the pvalue after multiple coding
Conditional probability function of a competing event
Quantile, Composite Quantile Regression and Regularized Versions
Conditional Maximum Likelihood for Quadratic Exponential Models
for Binary Panel Data
Power Calculation for Weighted Log-Rank Tests in Cure Rate
Models
Probabilistic damage tolerance analysis for fatigue cracking of
metallic aerospace structures
Multivariate nonparametric Cramer-Test for the
two-sample-problem
Download Logs from the 'RStudio' 'CRAN' Mirror
Fit Continuous-Time Correlated Random Walk Models to Animal
Movement Data
Categorical Randomized Block Data Analysis
Censored Regression with Conditional Heteroscedasticity
Functions for calculating the CreditMetrics risk model
Analyze Cricketers Based on ESPN Cricinfo Statsguru
Group-Based Modelling of Longitudinal Data
Statistical Methods for Crime Series Linkage
Continual Reassessment Method (CRM) for Phase I Clinical Trials
CCMN and other noRMalizatioN methods for metabolomics data
Object-Oriented Implementation of CRM Designs
Downloads and Builds datasets for Climate Reference Network
A Partial Clustering Algorithm with Automatic Estimation of the
Number of Clusters and Identification of Outliers
Analysis and Search of Crossover Designs
Confidence intervals for crossover points of two simple
regression lines
Cross-Recurrence Quantification Analysis for Categorical and
Continuous Time-Series
Penalized Variable Selection in Competing Risks Regression
Competing risks regression for Stratified and Clustered data
Stepwise Covariate Selection for the Fine & Gray Competing Risks
Regression Model
Nonlinear Regression Parameters Estimation by 'CRS4HC' and
'CRS4HCe'
Doubly Robust Inverse Probability Weighted Augmented GEE
Estimator
Sample Size Estimation Functions for Cluster Randomized Trials
Interface to Climatic Research Unit Time-Series Version 3.21
Data
Identifying cryptic relatedness in genetic association studies
Statistical Tests for Cryptographic Randomness
Functions for Conditional Simulation in Regression-Scale Models
Covariate-adjusted Skewed Functional Model (cSFM)
Correlates of State Policy Data Set in R
Methods to Test Hypotheses on the Distribution of Spatial Point
Processes
csSAM - cell-specific Significance Analysis of Microarrays
Substantive significance testing for regression estimates and
marginal effects
Import and Export CSV Data with a YAML Metadata Header
A Text Mining Toolkit for Chinese Document
Estimating the Parameters of a Continuous-Time Markov Chain from
Discrete-Time Data
Modeling Animal Movement with Continuous-Time Discrete-Space
Markov Chains
Censored and Truncated Quantile Regression
Continuous Time Structural Equation Modelling
A Class of Mixture Models for Ordinal Data
Rule- and Instance-Based Regression Modeling
CUDA Parallel Implementation of a Bayesian Multilevel Model for
fMRI Data Analysis
Data sets for the examples used in the package "cudaBayesreg"
Charles's Utility Function using Formula
Analyze Multivariate Phenotypes by Combining Univariate Results
Extends ddply to allow calculation of cumulative quantities
Change point detection in genomic sequences
Tools to Transform 'cURL' Command-Line Calls to 'httr' Requests
The package will formally test two curves represented by
discrete data sets to be statistically equal or not when the
errors of the two curves were assumed either equal or not using
the tube formula to calculate the tail probabilities
Cusp-Catastrophe Model Fitting Using Maximum Likelihood
Customized Training for Lasso and Elastic-Net Regularized
Generalized Linear Models
Compute Decision Interval and Average Run Length for CUSUM
Charts
CUTOFF: A Spatio-temporal Imputation Method
An R package to perform LPUE standardization and stock
assessment of the English Channel cuttlefish stock using a
two-stage biomass model
Cross-Validated Area Under the ROC Curve Confidence Intervals
Estimation of the Calibration Equation with Error-in
Observations
Penalized Logistic Regression Model using Majorization
Minimization by Coordinate Descent (MMCD) Algorithm
Calculate the predictive squared correlation coefficient
Fast Cross-Validation via Sequential Testing
Cross-validation tools for regression models
Regularized Estimators of Covariance Matrices with CV Tuning
Miscellaneous Functions for Math, Plotting, Printing,
Statistics, Strings, and Tools
extend cxxfunction by saving the dynamic shared objects
Cyclic Coordinate Descent for Logistic, Poisson and Survival
Analysis
Query 'Team Cymru' 'IP' Address, Autonomous System Number
('ASN'), Border Gateway Protocol ('BGP'), Bogon and 'Malware'
Hash Data Services
Cycle and Phase Identification for mastication data
Agent-Based Model for Taenia_solium Transmission and Control
Predicting Causal Direction from Dependency Features
Interactive Heat Maps Using 'htmlwidgets' and 'D3.js'
Tools for creating D3 JavaScript network, tree, dendrogram, and
Sankey graphs from R
Data Analysis and Graphics Data and Functions
Data Sets and Functions, for Demonstrations with Expression
Arrays and Gene Sequences
Data Sets and Functions, supplementary to DAAG
Three-Way Data Analysis Through Densities
Functions Useful in the Design and ANOVA of Experiments
Design and Analysis of Experiments with R
Learning directed acyclic graphs (DAGs) through bootstrap
aggregating
Graphical Analysis of Structural Causal Models
Diagnostic accuracy of classification models
Dynamical Assembly of Islands by Speciation, Immigration and
Extinction
DALY Calculator - A GUI for stochastic DALY calculation in R
Dynamic Assembly Model of Colonization, Local Extinction and
Speciation
Dams in the United States from the National Inventory of Dams
(NID)
Dandelion Plot for R-Mode Exploratory Factor Analysis
Package for Deep Architectures and Restricted Boltzmann Machines
Statistical Tools to Analyze Your Darts Game
Interactive Data Visualization with D3.js
File-Based Access to Large Matrices Stored on HDD in Binary
Format
Tools for Easily Combining and Cleaning Data Sets
Divide and Recombine for Large, Complex Data
dataframes2xls writes data frames to xls files
Estimating Finite State Machine Models from Data
A system for mapping foreign objects to R variables and
environments
Easily Source Publicly Available Data on Derivatives
A Flexible Container to Transport and Manipulate Data and
Associated Resources
Performs variable level data quality checks and generates
summary statistics
Retrieval Functions for USGS and EPA Hydrologic and Water
Quality Data
An Implementation of a SAS-Style Data Step
General Purpose Hierarchical Data Structure
Support functions for packages VBmix, semisupKernelPCA, and
patchPlot
Data and Workspace Browser for Terminals
Deconvolution Analysis Tool for Dynamic Contrast Enhanced MRI
Functions for "Data Analysis in Vegetation Ecology"
Discriminant Analysis with Additional Information
Discrete Bayesian Additive Regression Trees Sampler
Provides a graphical user interface to connect with databases
that use MySQL
Goodness-of-fit and two sample comparison tests using sample
entropy
Detecting Breakpoints and Estimating Segments in Trend
methods of distance-based gene set functional enrichment
analysis
'DBHYDRO' Hydrologic and Water Quality Data
Discrete Beta Kernel Graduation of Mortality Data
Compute the NPMLE of distribution from doubly censored data
Distance-Based Measures of Spatial Structures
Density Based Clustering of Applications with Noise (DBSCAN) and
Related Algorithms
Analyzing Dichotomous Choice Contingent Valuation Data
A Package for Image Analysis of DCE-MRI (S4 Implementation)
Data Cloud Geometry (DCG): Using Random Walks to Find Community
Structure in Social Network Analysis
Differential Co-expression Analysis and Differential Regulation
Analysis of Gene Expression Microarray Data
Analysis of Ontologies and Protein Domain Annotations
Claims Reserving under the Double Chain Ladder Model
Data Cloning and MCMC Tools for Maximum Likelihood Methods
Functions for the Detection of Spatial Clusters of Diseases
Hierarchical Models Made Easy with Data Cloning
Attribute profile estimation using Diagnostic Classification
Models and MCMC
List Linear n-Peptide Constraints for Overlapping Protein
Regions
Distance Covariance and Correlation for Time Series Analysis
Conventional Cross-validation statistics for climate-growth
model
Depth-Based Classification and Calculation of Data Depth
Variance Stabilization by Data-Driven Haar-Fisz (for
Microarrays)
Analysis and Visualization of Droplet Digital PCR in R and on
the Web
Learning Bayesian Networks with Mixed Variables
Deconvolution density estimation with adaptive methods for a
variable prone to measurement error
Bayesian Inference for Differential Equations
DEComposition of Indirect and Direct Effects
Quantitative Support of Decision Making under Uncertainty
Differential Co-Expression and Differential Expression Analysis
Decode Coded Variables to Plain Text (and Vice Versa)
Deconvolution Estimation in Measurement Error Models
Identifies Signatures Present in a Tumor Sample
Additional dialogs and functions for Deducer
Deductive Correction, Deductive Imputation, and Deterministic
Correction
Data Correction and Imputation Using Deductive Methods
An Implementation of Deep Neural Network for Regression and
Classification
Dirichlet-multinomial Evolutionary Event Profile Randomization
(DEEPR) test
Integration of One-Dimensional Functions with Double Exponential
Formulas
Detection of differentially expressed genes
Models for Skewed Count Distributions Relevant to Networks
Statistical Functions for the Delaporte Distribution
Delaunay Triangulation and Dirichlet (Voronoi) Tessellation
Estimates Pseudotimes for Single Cell Expression Data
Estimation of Multivariate Densities Using Adaptive Partitions
Identification of dichotomous differential item functioning
(DIF) using Angoff's Delta Plot method
Functions to Calculate Relatedness on Diploid Genetic Data
Evaluating the genetic differentiation between populations based
on Gst and D values
Differential Expression from Multiple Indicators
Deming, Thiel-Sen and Passing-Bablock Regression
Forecasting mortality, fertility, migration and population data
Kernel Density Estimation for Demonstration Purposes
DEvelopment (of Multi-Linear QSPR/QSAR) MOdels VAlidated using
Test Set
Faster Dendrogram Manipulation using 'Rcpp'
Extra functions to cut, label and colour dendrogram clusters
Dendrogram seriation: ordering for visualisation
Modular Leaf Ordering Methods for Dendrogram Nodes
Parameter Estimates and Real-Time Prediction of a Single Dengue
Outbreak
Statistical Analyses of De Novo Genetic Variants
Visualization of Multivariate Functions, Sets, and Data
Clustering by Fast Search and Find of Density Peaks
Density.T.HoldOut: Non-combinatorial T-estimation Hold-Out for
density estimation
Dens-Based Method for Partial Correlation Estimation in Large
Scale Brain Networks
Density strips and other methods for compactly illustrating
distributions
Global Optimization by Differential Evolution
Differential Evolution Optimization in Pure R
Statistical Inference for Parametric and Semiparametric Models
Based on Dependently Truncated Data
Dependent Mixture Models - Hidden Markov Models of GLMs and
Other Distributions in S4
Depth functions tools for multivariate analysis
Statistical Depth Functions for Multivariate Analysis
Functions and R Code to Accompany Derivatives Markets
Seasonal Adjustment by Frequency Analysis
An Interface to the DescribeDisplay GGobi Plugin
Describe Data in R Using Common Descriptive Statistics
Optimal deseasonalization for geophysical time series using AR
fitting
Computational tool for designing genetical genomics experiments
Finding Optimal Block Designs for a Generalised Linear Mixed
Model
Construction of Optimally Matched Samples for Randomized
Experiments and Observational Studies that are Balanced and
Representative by Design
Desirability Functions for Ranking, Selecting, and Integrating
Data
Desirability Function Optimization and Ranking
Bagging with Distance-based Regression for Differential Gene
Expression Analyses
Solvers for Initial Value Problems of Differential Equations
(ODE, DAE, DDE)
Estimation of Diagonal Elements of Sparse Precision-Matrices
Plotting Field Plans for Agricultural Experiments
Analyzing Wildlife Data with Detection Error
Detect Data Containing Personally Identifiable Information
Implementation of the DetMCD Algorithm (Robust and Deterministic
Estimation of Location and Scatter)
Suite of Deterministic and Robust Algorithms for Linear
Regression
Start the detrendeR Graphical User Interface (GUI)
A computer program to detect markers responding to selection
Data sets from Devore's "Prob and Stat for Eng (7th ed)"
Quantify Relationship Between Developmental Rate and Temperature
in Ectotherms
Tools to Make Developing R Packages Easier
Phase I/II Adaptive Dose-Finding Design for Combination Studies
Dose-finding by the continual reassessment method
Explore data.frames by plotting NA and classes of each variable
Differential Functioning of Items and Tests
Phase I/II Adaptive Dose-Finding Design for MTA
A Bayesian Dose-Finding Design using Pharmacokinetics(PK) for
Phase I Clinical Trials
Capture-Recapture Estimation using Bayesian Model Averaging
Differential Geometric LARS (dgLARS) method
Simulating Data for PLS Mode B Structural Models
Residual Diagnostics for Hierarchical (Multi-Level / Mixed)
Regression Models
Double Hierarchical Generalized Linear Models
Independence Testing via Hilbert Schmidt Independence Criterion
Block Diagonal Extraction or Replacement
Functions for visualising simple graphs (networks), plotting
flow diagrams
Create Graph Diagrams and Flowcharts Using R
Export DiagrammeR Graphviz Graphs as SVG
Diagnostic test summary measures for three ordinal groups
Calculate probabilities of various dice-rolling events
Construction and Evaluation of Metamodels
Kriging Methods for Computer Experiments
Kriging-Based Optimization for Computer Experiments
Plot methods for computer experiments design and surrogate
Dictionaries for reading microdata surveys from IBGE
A Simple Implementation and Demonstration of Gradient Boosting
Defines some physical constants and dielectric functions
commonly used in optics, plasmonics
R Interface to the ZEIT ONLINE Content API
Detection of Differential Item Functioning (DIF) in Rasch Models
by Boosting Techniques
Difference of Two Precision Matrices Estimation
Analyzing and Visualizing Differential Correlation Networks in
Biological Data
Calculates Confidence Intervals for two Dependent Proportions
Functions from the book Solving Differential Equations in R
Metrics of Difference for Comparing Pairs of Maps
Diffusion IRT Models for Response and Response Time Data
Display Differences Between Two Files using Codediff Library
Baseline identification and peak decomposition for x-ray
diffractograms
Inference and Analysis for Generalized Quadratic Diffusions
Inference and Analysis for Diffusion Processes via Data
Imputation and Method of Lines
Inference and Analysis for Jump Generalized Quadratic Diffusions
A Penalty Approach to Differential Item Functioning in Rasch
Models
Detection of Dichotomous Differential Item Functioning (DIF) by
Non-Linear Regression Function
Collection of Methods to Detect Dichotomous Differential Item
Functioning (DIF)
Item Focused Trees for the Identification of Items in
Differential Item Functioning
Create Compact Hash Digests of R Objects
An application programming interface for generating null models
of social contacts based on individuals' space use
Data-Informed Link Strength. Combine multiple-relationship
networks into a single weighted network. Impute (fill-in)
missing network links
DIME (Differential Identification using Mixture Ensemble)
DiNAMIC A Method To Analyze Recurrent DNA Copy Number
Aberrations in Tumors
Hartigan's Dip Test Statistic for Unimodality - Corrected
Bayesian Clustering of Multivariate Data Under the
Dirichlet-Process Prior
Direction Analysis for Pathways and Kinases
Discrete Laplace Mixture Inference using the EM Algorithm
DiscML: An R package for estimating evolutionary rates of
discrete characters using maximum likelihood
Coordinate-Descent Algorithm for Discrete Data
Multiple testing procedures for discrete test statistics
Create and Manipulate Discrete Random Variables
Discrete Weibull Distributions (Type 1 and 3)
Data preprocessing, discretization for classification
Discrimination of Alternative Reproductive Tactics (ARTs)
Tools of the Trade for Discriminant Analysis
Modelling Spatial Variation in Disease Risk for Areal Data
Calculate Dissimilarity Matrix for Dataset with Mixed Attributes
A Pipeline to Analyze Resistance and Tolerance from Drug Disk
Diffusion Assays
2D Hausdorff and Simplex Dispersion Orderings
Disparity Filter Algorithm for Weighted Networks
Create Disposable R Packages for Testing
Utilities for making pairwise comparisons of multivariate data
Distance Sampling Detection Function and Abundance Estimation
Calculates Study Size Required for Distance Sampling
DiSTATIS Three Way Metric Multidimensional Scaling
Computations over Distributed Data without Aggregation
Method Functions for Confidence Intervals and to Distill
Information from an Object
Object Oriented Implementation of Distributions
Documentation for 'distr' Family of R Packages
S4 Classes for Elliptically Contoured Distributions
Object Oriented Implementation of Probability Models
Distribution Classes for Distributions from Rmetrics
Simulation Classes Based on Package 'distr'
Extensions of Package 'distr' for Teaching
Stochastics/Statistics in Secondary School
Estimation and Testing Classes Based on Package 'distr'
Provides tools for quality checks of georeferenced plant species
accessions
Comparative Phylogenetic Analyses of Diversification
A Comprehensive, General Purpose Population Genetics Analysis
Package
Building Diversity Models from Multiple Species Occupancy Models
Functions for re-sampling a community matrix to compute
diversity indices at different sampling levels
Tools for Analysis of Diversity and Similarity in Biological
Systems
Nearest Neighbour Contingency Table Analysis
Distance Measure Based Judgment and Learning
Implementation of Differentiable Lasso Penalty in Linear Models
Bayesian and Likelihood Analysis of Dynamic Linear Models
Distributed-Lag Structural Equation Modelling with Second-Order
Polynomial Lag Shapes
Dynamic Modeling and Parameter Estimation in ODE Models
Delete or Merge Regressors for linear model selection
DMR Detection by Non-Homogeneous Hidden Markov Model from
Methylation Array Data
Functions and data for "Data Mining with R"
Functions and Data for the Second Edition of "Data Mining with
R"
Tools for Analysing Forensic Genetic DNA Data
Integrative Analysis of Omics Data in Terms of Network,
Evolution and Ontology
Analysis of Discretely Observed Linear
Birth-and-Death(-and-Immigration) Markov Chains
Groupwise Statistics, LSmeans, Linear Contrasts, Utilities
Extract Data Tables and Comments from Microsoft Word Documents
Functions for Acceptance Sampling Ideas originated by H.F. Dodge
Full Factorials, Orthogonal Arrays and Base Utilities for DoE
Packages
Wrapper package for design of experiments functionality
Foreach Parallel Adaptor using the Future API of the 'future'
Package
ADI (Average Dominance Index), Social Network Graphs with Dual
Directions, and Music Notation Graph
R Console Bindings for the 'Domino Command-Line Client'
Foreach parallel adaptor for the Rmpi package
Foreach Parallel Adaptor for the 'parallel' Package
Foreach parallel adapter for the rredis package
Generic Reproducible Parallel Backend for foreach Loops
Planning and Analyzing Dose Finding Experiments
Foreach Parallel Adaptor for the 'snow' Package
Dot-and-Whisker Plots of Regression Results
Test against parametric regression function
Differential Exon Usage Test for RNA-Seq Data via Empirical
Bayes Shrinkage of the Dispersion Parameter
Local Linear Hazard Estimation with Do-Validated and
Cross-Validated Bandwidths
Functions Ported from 'MMR2' Toolbox Offered in Kevin Dowd's
Book Measuring Market Risk
Dirichlet Process Mixture model simulation for clustering and
image segmentation
Data Pre-Processing and Visualization Functions for
Classification
Methods for Dimension Reduction for Regression
Data Representation: Bayesian Approach That's Sparse
Regularized Estimation of Dynamic Linear Regression in the
Presence of Autocorrelated Residuals (DREGAR)
Doubly Robust Generalized Estimating Equations
Discontinuous Regression and Image Processing
Regression and association models for repeated categorical data
Dual Empirical Likelihood Inference under Density Ratio Models
in the Presence of Multiple Samples
Analyze Drop Out of an Experiment or Survey
Dose-Response Modeling with Smoothing Splines
Implementation of a Machine Learning Framework for Predicting
Drugs Side Effects
Discretization-Based Direct Random Sample Generation
Bayesian subgroup analysis in clinical trials
Dynamic Systems Estimation (Time Series Package)
Density Surface Modelling of Distance Sampling Data
Spatial Modelling for Distance Sampling Data
Analyze Two Choice Reaction Time Data with the D*M Method
Recursive Partitioning for Discrete-Time Survival Trees
Exploring Disease Similarity in Terms of Dysfunctional
Regulatory Mechanisms
A Wrapper of the JavaScript Library 'DataTables'
Simplifying Descriptive Frequencies and Statistics
Comparison of Binary Diagnostic Tests in a Paired Study Design
Analysis of Diffusion Weighted Imaging (DWI) Data
Dunnett-Tukey-Kramer Pairwise Multiple Comparison Test Adjusted
for Unequal Variances and Unequal Sample Sizes
Suite of functions related to discrete-time discrete-state
Markov Chains
Estimation and Comparison of Dynamic Treatment Regimes
Learning Algorithms for Dynamic Treatment Regimes
DTR Estimation and Inference via G-Estimation, Dynamic WOLS, and
Q-Learning
Time Series Clustering Along with Optimizations for the Dynamic
Time Warping Distance
Time-Weighted Dynamic Time Warping for Satellite Image Time
Series Analysis
Dual Scaling Analysis of Multiple Choice Data
Create dummy/indicator variables flexibly and efficiently
Automatic Creation of Dummies with Support for Predictive
Modeling
Software implementation of step-down and step-up Dunnett test
procedures
Dunn's Test of Multiple Comparisons Using Rank Sums
Bayesian inference from count data using discrete uniform priors
Discrete variations of a fractional Brownian motion
Analyze Dose-Volume Histograms and Check Constraints
Parametric Regression for Discrete Response
Interface to 'Dygraphs' Interactive Time Series Charting Library
Dynamically visualized probability distributions and their
moments
Methods for Detection of Clusters in Hierarchical Clustering
Dendrograms
Implementation of the Dynamic TOPMODEL Hydrological Model
Full Interactive GUI for Dynamic Biplot in R
Denoising and clustering for dynamical image sequence (2D or
3D)+T
Dynamic Nomograms for Linear, Generalized Linear and
Proportional Hazard Models
Companion Package to "Dynamic Prediction in Clinical Survival
Analysis"
Dynamic Simulations of Autoregressive Relationships
Methods for Estimating Dynamic Treatment Regimes
Misc Functions of the Department of Statistics, Probability
Theory Group (Formerly: E1071), TU Wien
Plots of the Empirical Attainment Function
Dynamic Web Analytics for the Energy Industry
Early Warning Signals Toolbox for Detecting Critical Transitions
in Timeseries
Derive a Color Palette from a Particular Location on Earth
Efficient Approximate Bayesian Computation Sampling Schemes
Analysis of variance and other important complementary analyzes
Utility functions for working with mark-recapture data
Sample Size Estimation for Experimental Designs
Search and Retrieve Scientific Publication Records from Pubmed
Evaluation of stratified genome-wide association meta-analysis
results
Ensemble Forecast Verification for Large Data Sets
Entropy reweighting to create balanced samples
Empirical Bayes Thresholding and Related Methods
Empirical Bayes Estimation of Dynamic Bayesian Networks
Genotyping and SNP Detection using Next Generation Sequencing
Data
Empirical Bayesian Lasso and Elastic Net Methods for Generalized
Linear Models
Genotyping and SNP calling using single-sample next generation
sequencing data
Programmatic Access to the European Central Bank's Statistical
Data Warehouse (SDW)
Simulation from Endpoint-Conditioned Continuous Time Markov
Chains
Elliptic Distribution and Lambda Option Pricing Model
Functions for Spatial Point Pattern Analysis
Embedding Interactive Charts Generated with ECharts Library into
Shiny Applications
Efficient calculation of fine structure isotope patterns via
Fourier transforms of simplex-based elemental models
Dissimilarity-based functions for ecological analysis
Programmatic Interface to the API Serving UC Berkeley's Natural
History Data
Spatial Analysis of Phenotypic, Genotypic and Environmental Data
A community modeling foundation for Eco-Hydrology
"A practical guide to ecological modelling - using R as a
simulation platform"
General Analysis of Various Economics Demand Systems
Ecological Regression using Aggregate and Individual Data
Simulating Community Assembly and Ecological Diversification
Using Ecospace Frameworks
Procedures for Ecological Assessment of Surface Waters
Non-Parametric Multiple Change-Point Analysis of Multivariate
Data
Exploratory and Descriptive Event-Based Data Analysis
Read Data from European Data Format (EDF and EDF+) Files
More Powerful Unconditional Testing of Negative Binomial Means
for Digital Gene Expression Data
Network Reconstruction and Changepoint Detection
Simultaneous Edit-Imputation for Continuous Microdata
Parsing, Applying, and Manipulating Data Cleaning Rules
Estimation of the Effective Dimension Reduction ('EDR') Space
Provides tools for dimension reduction methods
Exploring Election and Census Highly Informative Data Nationally
for Australia
Tools for analysis and classification of electroencephalography
(EEG) data
Read and Preprocess Fluorescence Excitation-Emission Matrix
(EEM) Data
Tools for Pre-Processing Emission-Excitation-Matrix (EEM)
Fluorescence Data
Convenience Functions for Education Data
Wavelet-Based Enhanced FDR for Signal Detection in Noisy Images
Effect Displays for Linear, Generalized Linear, and Other Models
Test changes of a grouped response relative to baseline
Visualization of Categorical Response Models
The Causal Effects for a Causal Loglinear Model
Hierarchical Modelling of Faecal Egg Counts
Tool for ego-centric measures in Social Network Analysis
Exploration and Graphics for RivEr Trends (EGRET)
Exploration and Graphics for RivEr Trends (EGRET) Confidence
Intervals
R Wrapper for the Energy Information Administration (EIA) API
Generates (dense) matrices that have a given set of eigenvalues
Semiparametric factor and regression models for symmetric
relational data
Computes confidence intervals for principal components
eiPack: Ecological Inference and Higher-Dimension Data
Management
An algorithm for reducing errors-in-variable bias in simple
linear regression
Ecological Inference with individual and aggregate data
Enhanced Least Absolute Shrinkage and Selection Operator
Regression Model
General Purpose Interface to 'Elasticsearch'
Ising Network Estimation using Elastic Net and k-Fold
Cross-Validation
Elastic-Net for Sparse Estimation and Sparse PCA
Collection of functions for statistical election audits
Functions for election audits using stratified random samples
R Functions to Download and Clean Brazilian Electoral Data
A Shiny Application for Reducing Elemental LA-ICPMS Data from
Solid Structures
Data Sets, Functions and Examples from the Book: "The Elements
of Statistical Learning, Data Mining, Inference, and
Prediction" by Trevor Hastie, Robert Tibshirani and Jerome
Friedman
Load Associated Press Election Results with Elex
Functions for drawing ellipses and ellipse-like confidence
regions
Implementation of ELM (Extreme Learning Machine ) algorithm for
SLFN ( Single Hidden Layer Feedforward Neural Networks )
Preference Rating for Visual Stimuli Based on Elo Ratings
Animal Dominance Hierarchies by Elo Rating
Empirical Likelihood Analysis for the Cox Model and
Yang-Prentice (2005) Model
Tools to Analyze the Thermal Reaction Norm of Embryo Growth
Evolutionary Monte Carlo (EMC) algorithm
EM Algorithm for Model-Based Clustering of Finite Mixture
Gaussian Distribution
Empirical Mode Decomposition and Hilbert Spectral Analysis
Support Functions and Data for "Ecological Models and Data"
Exponentially Modified Gaussian (EMG) Distribution
Evaluation of Modeling without Information Leakage
EM Algorithms for Estimating Item Response Theory Models
Estimation of Wastewater Emissions in Combined Sewer Systems
Contrasts in mixed effects for EMMIX model with random effects
The EM Algorithm and Skew Mixture Distribution
Fitting Unrestricted Multivariate Skew t Mixture Models
A Maximum Likelihood Approach to the Analysis of Modularity
Fitting Mixed Models with Known Covariance Structures
Eye Movement Analysis Package for Fixation and Saccade Detection
Expected Maximum Profit Classification Performance Measure
Routines for Performing Empirical Calibration of Observational
Study Estimates
Simulation-Based False Discovery Rate in RNA-Seq
Empirical Likelihood Ratio for Censored/Truncated Data
Empirical Likelihood Ratio Test for Two Samples with Censored
Data
Exact Multinomial Test: Goodness-of-Fit Test for Discrete
Multivariate data
Main Package of the EMU Speech Database Management System
Entropy Minimization over Variable Clusters (EMVC)
R Package for Fitting Multinomial Probit Models with Endogenous
Selection
R Package for Analyzing Endorsement Experiments
Transmissions and Receptions in an End to End Network
E-Statistics: Multivariate Inference via the Energy of Data
Data Published by the United States Federal Energy Regulatory
Commission
Data sets from "Introductory Statistics for Engineering
Experimentation"
English and European Soccer Results 1871-2016
Automated Runs and Evaluations of Ecological Niche Models
Ensemble Partial Least Squares Regression
An R Package for the Analysis of Multiple ChIP-Seq Data
Enrichment assessment of virtual screening approaches
Methods to Enrich R Objects with Extra Components
Extensible Package for Parallel, Batch Training of Base Learners
for Ensemble Modeling
Probabilistic Forecasting using Ensembles and Bayesian Model
Averaging
Extensible Package for Cross-Validation-Based Integration of
Base Learners
Extensible Package for Principal-Component-Regression-Based
Heterogeneous Ensemble Meta-Learning
Extensible Classes and Methods for Penalized-Regression-Based
Integration of Base Learners
Entropy Partitioning to Measure Diversity
Estimation of Entropy, Mutual Information and Related Quantities
Estimation of Entropy and Related Quantities
Tools for Exploring Differential Shannon Entropy, Differential
Coefficient of Variation and Differential Expression
Isotope Pattern, Profile and Centroid Calculation for Mass
Spectrometry
Peak Picking for High Resolution Mass Spectrometry Data
Statistical Analysis of Environmental Space-Time Processes
Package for Environmental Statistics, Including US EPA Guidance
Statistical Functions for the Censored and Uncensored
Epanechnikov Distribution
Gaussian Approximation of Bayesian Binary Regression Models
A Package for Statistical Analysis in Epidemiology
Elementary Epidemiological Functions for Epidemiology and
Biostatistics
Implements Hierarchical Bayesian Models for Epidemiological
Applications
Epidemiological Tool for Contact Tracing
EpiEstim: a package to estimate time varying reproduction
numbers from epidemic curves
Flexible Modelling Functions for Epidemiological Data Analysis
Mathematical Modeling of Infectious Disease
Tools for the Analysis of Epidemiological Data
Basic Sensitivity Analysis of Epidemiological Results
Density Estimation with Soft Information by Exponential
Epi-splines
Conversion Between Epidemiological Weeks and Calendar Dates
EPoC (Endogenous Perturbation analysis of Cancer)
EQS Output Conversion to lavaan Functions
Tools for analyzing eQTL experiments: A complementary to Karl
Broman's 'qtl' package for genome-wide analysis
Direct, Chain and Average Equating Coefficients with Standard
Errors Using IRT Methods
Provides Tests and Graphics for Assessing Tests of Equivalence
Automated Spectral Deconvolution, Alignment, and Metabolite
Identification in GC/MS-Based Untargeted Metabolomics
Nonparametric Multiple Expectile Regression via ER-Boost
Fit, Simulate and Diagnose Exponential-Family Models for
Networks
Fit, Simulate and Diagnose Exponential-Family Models for
Networks with Count Edges
Fit, Simulate and Diagnose Exponential-Family Random Graph
Models to Egocentrically Sampled Network Data
ERG Modeling Based on Graphlet Properties
Local Health Department network data set
Fit, Simulate and Diagnose Exponential-Family Models for
Rank-Order Relational Data
User-specified terms for the statnet suite of packages
Significance Analysis of Event-Related Potentials Data
Event-Related Potential (ERP) Data Exploration Made Easy
Event-related potentials (ERP) analysis, graphics and utility
functions
Estimate Number of Latent Factors and Factor Matrix for Factor
Analysis
Effect Size Computation for Meta Analysis
ESEA: Discovering the Dysregulated Pathways based on Edge Set
Enrichment Analysis
Toolkit for the simulation of financial assets and interest
rates models
Ensemble of Subset of K-Nearest Neighbours Classifiers for
Classification and Class Membership Probability Estimation
Calibrating Parameters for the Samejima's Continuous IRT Model
Estimation of Heritability in High Dimensional Sparse Linear
Mixed Models using Variable Selection
Tools for Assessing Estimability of Linear Predictions
Modeling Earthquake Data Using ETAS Model
Mixed FLP and ML Estimation of ETAS Space-Time Point Processes
Extract-Transform-Load Framework for Medium Data
Utility Functions to Execute Standard Extract/Transform/Load
Operations (using Package 'ff') on Large Data
Computes Moments of Univariate Truncated t Distribution
Client for 'etcd', a 'Key-value' Database
eulerian: A package to find eulerian paths from graphs
R Interface to the Europe PubMed Central RESTful Web Service
Bayesian Single Arm Sample Size Calculation Software
Extreme Value Analysis with Goodness-of-Fit Testing
Parsing and Evaluation Tools that Provide More Details than the
Default
Collection of Model Evaluation Measure Functions
Modelling Actual, Potential and Reference Crop
Evapotranspiration
Functions for Extreme Value Distributions
Bayesian Analysis in Extreme Value Theory
A Powerful Tool to Quickly Compare Huge Lists and Draw Venn
Diagrams
Reporting Tools for Sensitivity Analysis of Evidence Factors in
Observational Studies
Extreme Value Mixture Modelling, Threshold Estimation and
Boundary Corrected Kernel Density Estimation
Comparative and Population Genetic Analyses
Tools for Evolutionary Quantitative Genetics
Evolutionary Parameter Estimation for 'Repast Simphony' Models
Mean of order p, peaks over random threshold Hill and high
quantile estimates
Evolutionary Learning of Globally Optimal Trees
Goodness-of-Fit Tests for the Exponential and Two-Parameter
Weibull Distributions
Exact Conditional Tests and Confidence Intervals for 2x2 tables
Exact P-Values and Matching Confidence Intervals for Simple
Discrete Parametric Cases
Inductive Confidence Intervals for the difference between two
proportions
Monte Carlo Exact Tests for Log-linear models
Exact solution paths for regularized LASSO regressions with L_1
penalty
Exact Distributions for Rank and Permutation Tests
Confidence regions for exceedance sets and contour lines
Convenient Data Exchange with Microsoft Excel
Excursion Sets and Contour Credibility Regions for Random Fields
Calls Copy Number Variants from Targeted Sequence Data
Expanding Ploidy and Allele-Frequency on Nested Subpopulations
Modular Differential Evolution for Experimenting with Operators
experiment: R package for designing and analyzing randomized
experiments
Modeling without data using expert opinion
Explanation of Predictions for Classification and Regression
Models
Interactive Interfaces for Results Exploration
Exploratory analysis with the singular value decomposition
Calculate density dependent likelihood of a phylogenetic tree
Spatial analysis of extra-pair paternity
Data Sets from "Forecasting with Exponential Smoothing"
Some Useful Functions from Spreadsheets and 'SPSS' Statistics
Fast, Reliable and Elegant Reproducible Research
Convenience Functions for Botanists to Create Specimens Indices
Extending the Range of Functions for Probability Distributions
Maximum penalized likelihood estimation with extended lasso
penalty
Extra-binomial approach for pooled sequencing data
Categorical Data Analysis and Visualization
Additional Univariate and Multivariate Distributions
Extremely Randomized Trees (ExtraTrees) Method for
Classification and Regression
Estimation of Extreme Conditional Quantiles and Probabilities
Extreme Value Statistics and Quantile Estimation
Estimation of Extreme Value Dependence for Time Series Data
Estimate Lower Extreme Quantile with the Censored Weibull MLE
and Censored Weibull Mixture
Easy Analysis and Visualization of Factorial Experiments
Easy Interface to Effective Concentration Calculations
selects significant non-additive interaction between two
variables using fast GLM implementation
Avoid the Typical Working Directory Pain When Using 'knitr'
provide an easy to use framework to conduct simulation
Fast Covariance Estimation for Sparse Functional Data
Bayesian Sparse Factor Analysis model for the inference of
pathways responsive to drug treatment
Combination of Factorial Methods and Cluster Analysis
Extract and Visualize the Results of Multivariate Data Analyses
Multivariate Exploratory Data Analysis and Data Mining
Graphical Presentation of Simple Contrasts
Bayesian quantile regression factor models
Bayesian Estimation of (Sparse) Latent Factor Stochastic
Volatility Models
Perform Factorial Analysis from 'FactoMineR' with a Shiny
Application
Scores the FACT and FACIT Family of Patient-Reported Outcome
Measures
thin wrapper for the Factual.com server API
Variable Selection for Supervised Classification in High
Dimension
Distributions that are Sometimes Used in Hydrology
Data from the Book "Multivariate Statistical Modelling Based on
Generalized Linear Models", First Edition, by Ludwig Fahrmeir
and Gerhard Tutz
Faisal Conjoint Model: A New Approach to Conjoint Analysis
Simulates Data from a Data Frame of Different Variable Types
Finding Allele-Specific Copy Number in Next-Generation
Sequencing Data
Finding Allele-Specific Copy Number in Whole-Exome Sequencing
Data
Simulation of Time-to-Event Family Data and Penetrance
Estimation
Probabilities for Pedigrees Given DNA Data
A Convex Formulation for Modeling Interactions with Strong
Heredity
Maximum Likelihood and Bayesian Estimation of Univariate
Probability Distributions
Factor Analysis for Multiple Testing (FAMT) : simultaneous tests
under dependence in high-dimensional data
Penalized Likelihood Factor Analysis via Nonconvex Penalty
Building Kriging Models from FANOVA Graphs
Visualisation of Sequential Probability Distributions Using Fan
Charts
Download Data from the FAOSTAT Database of the Food and
Agricultural Organization (FAO) of the United Nations
Influential Case Detection Methods for Factor Analysis and
Structural Equation Models
Modelization for Functional AutoRegressive Processes
Functions and Datasets for Books by Julian Faraway
Rmetrics - Analysing and Modelling Financial Assets
Implementation of the Fourier Amplitude Sensitivity Test (FAST)
Fast Estimation of a Covariance Matrix by Banding the Cholesky
Factor
A Fast Solver for Parameterized LP Problems, Constrained L1
Minimization Approach to Sparse Precision Matrix Estimation and
Dantzig Selector
Fast Hierarchical Clustering Routines for R and Python
Significant Interval Discovery with Categorical Covariates
Lasso and elastic-net penalized Cox's regression in high
dimensions models using the cocktail algorithm
Fast, Low Memory-Footprint Digests of R Objects
Raster Image Processing and Vector Recognition
Fast Rcpp implementation of Gauss-Hermite quadrature
Efficiently Using Gaussian Processes with Rcpp and RcppEigen
Fast Drawing and Shading of Graphs of Statistical Distributions
Robust Algorithm for Principal Component Analysis
Hierarchical Independent Component Analysis: a Multi-Scale
Sparse Non-Orthogonal Data-Driven Basis
FastICA Algorithms to perform ICA and Projection Pursuit
Learn from Training Data then Quickly Fill in Missing Data
A Fast Multiple-Kernel Method Based on a Low-Rank Approximation
Fast Computation of Multivariate M-estimators
FastPCS Robust Fit of Multivariate Location and Scatter
Foundations and Applications of Statistics Using R
Fits the FastRCS Robust Multivariable Linear Regression Model
Fast Interactive Framework for Web Scripting Using R
Fast Utility Function for Time Parsing and Conversion
Two-Locus Family-Based Association Test with Polytomic Outcome
Kiener Distributions and Fat Tails in Finance
Functions and Datasets for "Forest Analytics with R"
Gene by Environment Interaction and Conditional Gene Tests for
Nuclear Families
Algorithm for searching the space of Gaussian directed acyclic
graphical models through moment fractional Bayes factors
FISH Based Normalization and Copy Number inference of SNP
microarray data
Analyzing and Managing Facebook Ads from R
Association Football (Soccer) Ranking via Poisson Regression
Fast Algorithms to Bootstrap Receiver Operating Characteristics
Curves
Basics of Certificates and Structured Products Valuation
Rmetrics - Bivariate Dependence Structures with Copulae
A Method to Search for Differentially Expressed Genes and to
Detect Recurrent Chromosomal Copy Number Aberrations
Measuring functional diversity (FD) from multiple traits, and
other tools for functional ecology
Functional Data Analysis: K-Mean Alignment
Functional data analysis in a mixed model framework
Functional Data Analysis and Empirical Dynamics
Functional Data Analysis and Partial Differential Equations;
Statistical Analysis of Functional and Spatial Data, Based on
Regression with Partial Differential Regularizations
Interval Testing Procedure for Functional Data
Functional Data Analysis and Utilities for Statistical Computing
Multivariate Dependence with FDG Copulas
Permutation-based FDR Point and Confidence Interval Estimation
False Discovery Rate Procedure Under Discrete Null Distributions
Compute Sample Size that Meets Requirements for Average Power
and FDR
Estimation of (Local) False Discovery Rates and Higher Criticism
Frequency Distribution Tables, Histograms and Polygons
Local Inferential Feature Significance for Multivariate Kernel
Density Estimation
Creates a Model Matrix via Feature Hashing with a Formula
Interface
Feature Extraction for Discretely-Sampled Functional Data
Fechnerian Scaling of Discrete Object Sets
Functions to Automate Downloading Geospatial Data Available from
Several Federated Data Sources
Client Package for the U.S. Federal Register API
Fixed Effects Nonlinear Maximum Likelihood Models
fExpressCertificates - Structured Products Valuation for
ExpressCertificates/Autocallables
Rmetrics - Extreme Financial Market Data
memory-efficient storage of large data on disk and fast access
functions
Basic Statistical Functions for Package 'ff'
Force field simulation for a set of points
Generate, Visualise, and Compare Fast and Frugal Decision Trees
Wrapper for FFTW3: Includes 1-D, Univariate and Multivariate,
and 2-D Transform
Rmetrics - Autoregressive Conditional Heteroskedastic Modelling
Fractional Gaussian Noise and power law decay time series model
fitting
Feature Grouping and Selection Over an Undirected Graph
Tests for Right and Interval-Censored Survival Data Based on the
Fleming-Harrington Class
Provide functions for forest inventory calculations
Classical, Reloaded and Adaptive FastICA Algorithms
A Lightweight and Flexible Web Framework
File-Backed Matrix Class with Convenient Read and Write Access
Effective File Navigation from the R Console
Rmetrics - Economic and Financial Data Import
Financial Instrument Model Infrastructure for R
Classifies implicit trading activity from market quotes and
computes the probability of informed trading
Time Value of Money, Time Series Analysis and Computational
Finance
Covariance Matrix Estimation and Regularization for Finance
Find all root(s) of the equation and Find root(s) of the
equation by dichotomy
Python tools to find an acceptable python binary
Functions to operate on binary fingerprint data
Computation of the probability of ruin within a finite time
horizon
Financial Data from U.S. Securities and Exchange Commission
Companion to Tsay (2005) Analysis of Financial Time Series
Fisheye and Hyperbolic-space-alike Interactive Visualization
Tools in R
Iterative FISH-based Calibration of Hi-C Data
Dataset of Measurements of Fish Species at Kirkkojarvi Lake,
Finland
Fits Poisson-sum-of-Gammas GLMs, Tweedie GLMs, and delta
log-normal mdoels
Transcriptomic Dynamics Models in Field Conditions
FitARMA: Fit ARMA or ARIMA using fast MLE algorithm
Help to Fit of a Parametric Distribution to Non-Censored or
Censored Data
FITS (Flexible Image Transport System) utilities
fitTetra is an R package for assigning tetraploid genotype
scores
Feature-Based Landscape Analysis of Continuous and Constraint
Optimization Problems
Fits Piecewise Constant Models with Data-Adaptive Knots
Implementation of optimal hierarchical clustering
R Markdown Format for Flexible Dashboards
Flexible Parametric Models for Censored and Truncated Data
Flexible Parametric Survival and Multi-State Models
Tools for Interacting with the Brazilian Flora 2020
Cytometric Diversity Indices from 'FlowJo' Workspaces
Tools for characterizing flowering traits
Forecasts future values of a univariate time series
Streamlining Design and Deployment of Complex Workflows
Daily Streamflow Trend and Change Point Screening
Path algorithm for the general Fused Lasso Signal Approximator
Multithreaded Fixed Size Subset Sum with Bounded Error in
Multidimensional Real Domain
Flux rate calculation from dynamic closed chamber measurements
Data Sets from "Forecasting: Methods and Applications" by
Makridakis, Wheelwright & Hyndman (1998)
A Flexible Modelling Environment for Inverse Modelling,
Sensitivity, Identifiability and Monte Carlo Analysis
Filtered Monotonic Polynomial IRT Models
Variable Selection in Finite Mixture of AFT Regression and FMR
Functions for Medical Statistics Book with some Demographic Data
Regression Models with Finite Mixtures of Skew Heavy-Tailed
Errors
Variance estimation of FMT method (Fully Moderated t-statistic)
Rmetrics - Analysing and Modeling Multivariate Financial Return
Distributions
Fast Nearest Neighbor Search Algorithms and Applications
Nonlinear and Chaotic Time Series Modelling
visualisation and analysis of food web networks
Rmetrics - Pricing and Evaluating Basic Options
Foraminifera and Community Ecology Analyses
Tools for Working with Categorical Variables (Factors)
Provides Foreach Looping Construct for R
Forecasting Functions for Time Series and Linear Models
Convenient Functions for Ensemble Time Series Forecasts
Floating-Point Genetic Algorithms with Statistical Forecast
Based Inheritance Operator
Read Data Stored by Minitab, S, SAS, SPSS, Stata, Systat, Weka,
dBase, ...
Statistical Methods in Forensic Genetics
Statistical tools for the interpretation of forensic DNA
mixtures
Visualizes Random Forests with Feature Contributions
Design-Based Global and Small-Area Estimations for Multiphase
Forest Inventories
Advanced Forest Plot Using 'grid' Graphics
Imputation of Missing Values Through a Forward Imputation
Algorithm
Functions from the 2nd Italian Forest Inventory (INFC)
Elementary arithemtic in formal series rings
Utilities for Formulas, Expressions, Calls and Other Objects
Palaeoecological and Palaeogeographical Analysis Tools
Bayesian 4 Parameter Item Response Model
Restricted MLE for Functional Principal Components Analysis
Two Dimensional Functional Principal Component Analysis
Reliable Comparison of Floating Point Numbers
Rmetrics - Portfolio Selection and Optimization
Computing the noncentrality parameter of the noncentral F
distribution
Data for "Forecasting: principles and practice"
Approximation of First-Passage-Time Densities for Diffusion
Processes
Fractionally differenced ARIMA aka ARFIMA(p,d,q) models
Fraction Proliferation via a Quiescent Growth Model
Fractal Time Series Modeling and Analysis
Estimation of Parameters p and q for Randomized Sierpinski
Carpet for [p-p-p-q]-Model
Generate fractal time series with non-normal returns
distribution
General Frailty Models: Shared, Joint and Nested Frailty Models
with Prediction
General Semiparametric Shared Frailty Model
Financial Risk Modelling and Portfolio Optimisation with R
Fuzzy Rule-Based Systems for Classification and Regression Tasks
Fast Regularized Canonical Correlation Analysis
Post-Processing EddyPro Full Output File
Statistical algorithms used in common data mining course
Rmetrics - Regression Based Decision and Prediction
FREQ: Estimate population size from capture frequencies
Frequency Domain Analysis for Multivariate Time Series
Frequency Moving Average Plots (MAP) of Multinomial Data by a
Continuous Covariate
Frequency Profiles Computing and Plotting
Feature Selection Algorithms for Computer Aided Diagnosis
Fractional Factorial designs with 2-level factors
Catalogues of resolution IV 128 run 2-level fractional
factorials up to 33 factors that do have 5-letter words
Regression Analysis of Fractional Responses
Regression Analysis of Fractional Responses Under Unobserved
Heterogeneity
Regression Analysis of Panel Fractional Responses
The Generalized Hyperbolic Distribution, Related Distributions
and Their Applications in Finance
Simple Fisheries Stock Assessment Methods
Data to Support Fish Stock Assessment ('FSA') Package
Automated Feature Selection from 'caret'
Import and Analysis of OMR Data from FormScanner
Wrapper Functions for FSL ('FMRIB' Software Library) from
Functional MRI of the Brain ('FMRIB')
Social Relations Analyses with Roles ("Family SRM")
Programs for Analyzing Fourier Transform-Ion Cyclotron Resonance
Mass Spectrometry Data
Features and Strings for Nonparametric Regression
R interface to tslib (a time series library in c++)
Spectral Density Estimation and Comparison for Functional Time
Series
FUzzy GEnetic, a machine learning algorithm to construct
prediction model based on fuzzy logic
Full Text of 'Scholarly' Articles Across Many Data Sources
Chi-Square and Exact Tests for Non-Parametric Functional
Dependencies
Functional Profiling of Microarray Expression Data
A package for functional data clustering
An algorithm for gene and gene set network inference
Clustering in the Discriminative Functional Subspace
Fungible Coefficients and Monte Carlo Functions
Model-based clustering in group-specific functional subspaces
Title: Learn Data Science Through the "Data Science Live Book"
Simple Utility Providing Terminal Access to all R Functions
Functional Regression for Irregularly Timed Data
Community Detection via Fused Principal Component Analysis
A Tiny Utility Providing Polymorphic Operations
Random Matrix Generation and Manipulation
A framework for working in a functional programming paradigm in
R
Find, Characterize, and Explore Heat Waves in Climate
Projections
Exact calculation of fuzzy decision rules for multiple testing
Join Tables Together on Inexact Matching
Multi-Criteria Decision Making Methods for Fuzzy Data
Fuzzy Rank Tests and Confidence Intervals
Fuzzy Stationary Probabilities from a Sequence of Observations
of an Unknown Markov Chain
Forward search for Mokken scale analysis
Selecting Variables in Regression Models
Fire Weather Index System and Fire Behaviour Prediction System
Calculations
A package performing Dynamic Bayesian Network inference
Grain-Size Statistics and Description of Sediment
A genetic algorithm approach to determine stratum boundaries and
sample sizes of each stratum in stratified sampling
Abbreviating Items Measures using Genetic Algorithms
Framework for Generalized Subspace Pattern Mining
GAD: Analysis of variance from general principles
First Passage Time Simulation for Gaussian Diffusion Processes
Graphical Extension with Accuracy in Parameter Estimation
(GAIPE)
Genetic algorithms and C-steps based LTS (Least Trimmed Squares)
estimation
Fit the GamBin Model to Species Abundance Distributions
Generalized linear and additive models by likelihood based
boosting
Estimating multistate models using gamboost()
Functions and Data for a Course on Modern Regression and
Classification
Applies GAMbag, GAMrsm and GAMens Ensemble Classifiers for
Binary Classification
Statistical Estimation of Game-Theoretic Models
Tools for Calculating Allocations in Game Theory
Generalised Additive Models for Location Scale and Shape
Fitting an Interval Response Variable Using `gamlss.family'
Distributions
Distributions to be Used for GAMLSS Modelling
Fitting Mixture Distributions with GAMLSS
Fitting non linear parametric GAMLSS models
Generating and Fitting Truncated `gamlss.family' Distributions
Generalized additive mixed models using mgcv and lme4
classic gamma regression: joint modeling of mean and shape
parameters
Generalized additive mixed model analysis via slice sampling
Fit Regularization Path for Generalized Additive Models
Gene Association Network-based Pathway Analysis
Genetic Algorithm optimization for real-based and
permutation-based problems
Functions for Drawing Gapped Cluster Heatmap with ggplot2
Authorize and Request Google Analytics Data
Genetic Algorithm (GA) for Variable Selection from
High-Dimensional Data
Genetic Data Manipulation (Quality Control, GRM and LD
Computations, PCA), Linear and Logistic Mixed Models (AIREML
Algorithm), Association Testing
Difference measures for multivariate Gaussian probability
density functions
The Greatest Mathematician Since Antiquity
Collection of functions for Gaussian quadrature
Generalize Lambda Distribution and Generalized Bootstrapping
Generalized Beta Distribution of the Second Kind: Properties,
Likelihood, Estimation
Utilities for processing Rd objects and files
Simulation of Real and Complex Numbers and Small Programming
Utilities
Guided Correction Approach for Inherited bias (GCAI.bias)
Graph-based two-sample tests for categorical data
GPU/CPU Benchmarking in Debian-based systems
LASSO and (adaptive) Elastic-Net penalized least squares,
logistic regression, HHSVM and squared hinge loss SVM using a
fast GCD algorithm
Concord method for Graphical Model Selection
Datasets for the Book Graphical Data Analysis with R
Wrappers for the Geospatial Data Abstraction Library (GDAL)
Utilities
Various R Programming Tools for Data Manipulation
A toolbox for the analysis of categorical data in social
sciences, and especially Geometric Data Analysis
Download, slice, and normalize GDELT data
Generalized Diffusion Magnetic Resonance Imaging
Distances and Routes on Geographical Grids
Functions for Generalized Dissimilarity Modeling
Adaptive Association Tests for Multiple Phenotypes using
Generalized Estimating Equations (GEE)
Modified Variance Estimators for Generalized Estimating
Equations
Calculate Generalized Eigenvalues, the Generalized Schur
Decomposition and the Generalized Singular Value Decomposition
of a Matrix Pair with Lapack
File Conversion for 'Gem Infrasound Logger'
Network Meta-Analysis Using Bayesian Methods
Package contains data which is used by GenABEL example and test
functions
Global ENvironmental ASsessment Information System (GENASIS)
computational tools
Clopper-Pearson Confidence Interval and Generalized Binomial
Distribution
Predict Gender from Names Using Historical Data
Identification of Periodically Expressed Genes
Bayesian calculation of gene-specific FST from genomic SNP data
Detection of Structure from Multilocus Genetic Data
Profiling a gene list into GOslim or KEGG function pie
Belief Propagation in Genotype-Phenotype Networks
Genetic Epidemiology Design and Inference
Generalized Correlations and Initial Causal Path
Goodness of Fit Tests for Logistic Regression Models
Blinder-Oaxaca Decomposition for Generalized Linear Model
Generate Data Containing Fake Personally Identifiable
Information
Construct time delay gene regulatory network
Genome-Wide Association Study with SLOPE
Identify Favorable Subsets of Germplasm Collections
Collection of Genetic Data Analysis Tools
Spatial Graph-Theoretic Genetic Gravity Modelling
The Forward Imputation: A Sequential Distance-Based Approach for
Imputing Missing Data
A New, Fast, and Outlier Resistant Hierarchical Clustering
Algorithm
Functions for generating and manipulating binned kernel density
estimates
Path algorithm for generalized lasso problems
Functions for the Bayesian Analysis of GWAS Data
Application of MOSS algorithm to genome-wide association study
(GWAS)
'Plot genome wide values for all chromosomes'
Circular Genomic Permutation using Gwas p-Values of Association
Collection of Tools for Genomic Data Analysis
Plot Publication-Grade Gene and Genome Maps
Simulation of Discrete Random Variables with Given Correlation
Matrix and Marginal Distributions
Generalized PATHMOX Algorithm for PLS-PM, LS and LAD Regression
Generalized Ridge Trace Plots for Ridge Regression
R Functions for Generalized Simulated Annealing
Spline Based Window Boundaries for Genomic Analyses
Draw and Annotate Maps, Especially Charts of the North Atlantic
Analysis of Geostatistical Data using Bayes and Empirical Bayes
Methods
Analysis and Modeling for Geostatistical Count Data
A geometrical Approach to Differential expression and gene-set
enrichment
3D-Visualization of Geoelectric Resistivity Measurement Profiles
Spatial Prediction for Function Value Data
Quantification of the effect of geographic versus environmental
isolation on genetic differentiation
Tools for Geohash Creation and Manipulation
Convert Data from and to 'geoJSON' or 'topoJSON'
Web-Processing of Large Gridded Datasets
Data for topographic and Geologic Mapping
Network Visualization in the 'ggplot2' Framework
Geometric Morphometric Analyses of 2D/3D Landmark Data
Interface to www.geonames.org web service
Interface to the Geoparser.io API for Identifying and
Disambiguating Places Mentioned in Text
Geophysics, Continuum Mechanics, Mogi Models, Gravity
A Package for Generalised Linear Spatial Models
Robust Geostatistical Analysis of Spatial Data
Facilitate Generating of Distance Matrices Used in Package
'spacom' and Plotting Data on Maps
Geostatistical Analysis and Design of Optimal Spatial Sampling
Networks
Spatio-Temporal; Inverse Distance Weighting and Radial Basis
Functions with Distance-Based Regression
Geostatistical Modelling with Likelihood and Bayes
An R Plug-in for the Distributed Hydrological Model GEOtop
Interactive exploratory spatial data analysis
Google Encoded Polyline Algorithm Format
Estimation and Fit Diagnostics for Generalized Exponential
Random Graph Models
Generalized Structured Component Analysis (GSCA)
Download and Aggregate High Frequency Trading Data from Bovespa
Get Landsat 8 Data from Amazon Public Data Sets
Get Meteorological Data for Hydrologic Models
Parsing Command-Line Arguments and Variable Interpolation
GetR: Calculate Guttman error trees in R
General-to-Specific (GETS) Modelling and Indicator Saturation
Methods
Get Data for Brazilian Bonds (Tesouro Direto)
Learning Optimization and Machine Learning for Statistics
GEV Conditional Density Estimation Network
ARMA-GARCH/APARCH Models with GEV and Stable Distributions
A visual, intuitive, easy to use software giving access to a new
type of information buried into your microarray data
Tools for Working with Hansen et al. Global Forest Change
Dataset
Extra Coordinate Systems, Geoms and Statistical Transformations
for 'ggplot2'
Categorical Scatter (Violin Point) Plots
Visualization of a Correlation Matrix using 'ggplot2'
Create Dendrograms and Tree Diagrams Using 'ggplot2'
Visualization Tools for Genealogical Data
Add Marginal Histograms to 'ggplot2', and More 'ggplot2'
Enhancements
Data Visualization Tools for Statistical Analysis Results
Capture the Spirit of Your 'ggplot2' Calls
Make 'ggplot2' Graphics Interactive Using 'htmlwidgets'
Group Lasso Penalized Learning Using A Unified BMD Algorithm
Tools for Analyzing MCMC Simulations from Bayesian Inference
Gaussian Graphical Models Using Ridge Penalty Followed by
Thresholding and Reestimation
Geometries to Plot Networks with 'ggplot2'
Variations of Parallel Coordinate Plots for Categorical Data
An Implementation of the Grammar of Graphics
Polygons with Holes for the Grammar of Graphics
Allows Interactive Visualization of Data Through a Web Browser
GUI
Repulsive Text and Label Geoms for 'ggplot2'
Scientific Journal and Sci-Fi Themed Color Palettes for
'ggplot2'
'stats' for Seasonal Adjustment on the Fly with 'ggplot2'
North Symbols and Scale Bars for Maps Created with 'ggplot2' or
'ggmap'
Extensions to 'ggplot2' for Radiation Spectra
Offers Various Swiss Maps as ggplot2 Objects
An Extension to 'ggplot2', for the Creation of Ternary Diagrams
Extra Themes, Scales and Geoms for 'ggplot2'
A Package on Generalized Hyperbolic Distribution and Its Special
Cases
Gene Set Uncertainty in Enrichment Analysis
Gibbs Sampler for One-Way Mixed-Effects ANOVA and ANCOVA Models
Naive Gibbs Sampling with Metropolis Steps
Random Variate Generator for the GIG Distribution
Gillespie's Stochastic Simulation Algorithm (SSA)
Group Iterative Multiple Model Estimation
Computing the Gini-Based Coefficients for Weighted and Negative
Attributes
Generalized Iterative Proportional Fitting for Relational Models
The giRaph package for graph representation in R
Rstudio Addin for Version Control and Assignment Management
using Git
A Helpful Way to Install R Packages Hosted on GitHub
Quantification of Pinned Microbial Cultures
Constructing panels of population pyramid plots based on lattice
Gaines and Kuklinski (2011) Estimators for Hybrid Experiments
Penalization in Large Scale Generalized Linear Array Models
Generalized Linear Autoregressive Moving Average Models
Graphical lasso- estimation of Gaussian graphical models
General Linear Ballistic Accumulator Models
Calculate Textures from Grey-Level Co-Occurrence Matrices
(GLCMs)
Estimation and Use of the Generalised (Tukey) Lambda
Distribution
Fitting Single and Mixture of Generalised Lambda Distributions
(RS and FMKL) using Various Methods
An Asymmetry-Steepness Parameterization of the Generalized
Lambda Distribution
Fit GLD Regression Model and GLD Quantile Regression Model to
Empirical Data
Learning Interactions via Hierarchical Group-Lasso
Regularization
All Hierarchical or Graphical Models for Generalized Linear
Model
Bayesian Fractional Polynomials for GLMs
Fitting Generalized Linear Models Subject to Constraints
Distributed 'glm' for Big Data using 'ddR' API
Graph-Constrained Regularization for Sparse Generalized Linear
Models
Fit GLM with LEP-based penalized maximum likelihood
Generalized Linear Mixed Models via Monte Carlo Likelihood
Approximation
Generalised Linear Mixed Models and Spatial Models with WinBUGS,
BRugs, or OpenBUGS
Gauss-Seidel Generalized Linear Mixed Model solver
Variable Selection for Generalized Linear Mixed Models by
L1-Penalized Estimation
Generalized linear models with clustering
Generalized Linear Mixed Model (GLMM) for Binary Randomized
Response Data
Lasso and Elastic-Net Regularized Generalized Linear Models
Fit a penalized constrained continuation ratio model for
predicting an ordinal response
L1 Regularization Path for Generalized Linear Models and Cox
Proportional Hazards Model
Fit a penalized continuation ratio model for predicting an
ordinal response
Model selection and multimodel inference made easy
Variable Selection Deviation Measures and Instability Tests for
High-Dimensional Generalized Linear Models
Testing the additional predictive value of high-dimensional data
Bi-Level Optimization of Metabolic Network Models
Global Gene-Set Analysis for Association Studies
Generate Functions to Get or Set Global Options
Objective functions for benchmarking the performance of global
optimization algorithms
Identify Global Objects in R Expressions
Plot 2D and 3D Views of the Earth, Including Major Coastline
Fitting and Testing Generalized Logistic Distributions
Generalized Logrank Tests for Interval-censored Failure Time
Data
Generalized Least Squares with Measurement Error
Distance and Travel Time Between Two Points from Google Maps
Fast Estimation of Gaussian Mixture Copula Models
Graph Based Multiple Comparison Procedures
Generalized Minimum Distance of distributions
Accessing a Geometallurgical Database with R
Short Term Forecasting via GMDH-Type Neural Network Algorithms
Geometric Median, k-Median Clustering and Robust Median PCA
Meta-Analysis via a Unified Framework of Confidence Distribution
Descriptive Statistics, Transition Plots, and More
Generalized Method of Moments and Generalized Empirical
Likelihood
Multinomial Logit Models with Random Parameters
Various R Programming Tools for Model Fitting
Interface between GMT Map-Making Software and R
Computation of Generalized Nash Equilibria
Generalized Non-negative Matrix Factorization Based on Renyi
Divergence
Read Data from Files Readable by Gnumeric
Weighted and lexicographical goal programming and optimization
Functions for Community Data and Ordinations
Model-diagnostics based on cumulative residuals
Testing Goodness-of-Fit with the Kernel Density Estimator
Khmaladze Martingale Transformation Goodness-of-Fit Test
Tests of Fit for some Probability Distributions
Classical Goodness-of-Fit Tests for Univariate Distributions
Generalized Orthogonal GARCH (GO-GARCH) models
Association Analysis for Categorical Variables
Easy Authentication with Google OAuth2 API
Collect Data Programmatically by POST Methods to Google Forms
Working with Google Public Data Explorer DSPL Metadata Files
Visualization of Functional Analysis Data
Fit Generalized Odds Rate Mixture Cure Model with Interval
Censored Data
functions to get public survey data in Japan
Gaussian Processes for Pareto Front Estimation and Optimization
Batch Effect Detection via Guided Principal Components Analysis
GPCSIV, Generalized Principal Component of Symbolic Interval
variables
General Profiling Method for Delay Differential Equation
Bootstrap goodness-of-fit test for the generalized Pareto
distribution
Apply Gaussian Process in Functional data analysis
Generalized Partial Linear Models (GPLM)
Various R Programming Tools for Plotting Data
Generalized Partially Linear Tree-Based Regression Model
Analysing and plotting genotype-phenotype maps
A Minimalistic package to apply Gaussian Process in R
gpseq: Using the generalized Poisson distribution to model
sequence read counts from high throughput sequencing
experiments
Maximum Likelihood Estimation of Multiple Membership Mixed
Models Used in Value-Added Modeling
Graph-Constrained Estimation and Hypothesis Tests
Species distribution modelling using latent Gaussian random
fields
Graphical Representation and Modeling of Metagenomic Reads
Creating Continuously Integrated Package Repositories from
Manifests
Graphical Analysis of Variance Using ggplot2
Gene-Ranking Analysis of Pathway Expression
Efficient selection of undirected graphical models for
high-dimensional datasets
A Multi-Platform GUI for Drawing Customizable Graphs in R
Graphical VAR for Experience Sampling Data
Cluster Detection with Hypothesis Free Scan Statistic
Inference in Graphical Gaussian Models with Edge and Vertex
Symmetries
Gene ID Mapping for Genotype-Tissue Expression (GTEx) Data
Fit a Grey-Zone Model with Survival Data
Miscellaneous Functions for "Grid" Graphics
Redraw Base Graphics Using 'grid' Graphics
A Simulation Tool to Determine the Required Sample Size for
Repertory Grid Studies
Uses Grieg-Smith method on 2 dimentional spatial data
Generalized Regression on Orthogonal Components
The package was developed to fit fit many growth curves obtained
under different conditions
Gromov-Hausdorff Type Distances for Labeled Metric Spaces
Regression Analysis of Grouped and Coarse Data
Regularized Multivariate Regression for Identifying Master
Predictors Using the GroupRemMap Penalty
A GUI-Based Program to Compute Probabilities Regarding Group
Sequential Designs
Multiple Testing Procedure for Grouped Hypotheses
Bayesian Semi and Nonparametric Growth Curve Models that
Additionally Include Multiple Membership Random Effects
Bayesian Non-Parametric Dependent Models for Time-Indexed
Functional Data
Simple Metrics to Summarize Growth Curves
Bayesian reconstruction of growth velocity
Estimate Growth Rates from Experimental Data
Fitting user specified models with Group Lasso penalty
Concave 1-norm and 2-norm group penalty in linear and logistic
regression
Regularization Paths for Regression Models with Grouped
Covariates
Penalized Regression Models with Overlapping Grouped Covariates
Alternative Implementations of Base R Functions
GRS Test for Portfolio Efficiency and Its Statistical Power
Analysis
Tools for the Analysis of Gutenberg-Richter Distributions of
Earthquake Magnitudes
Gene Set Analysis using the Gamma Method
Two functions for Generalized SARIMA time series simulation
Robust Estimation in the Presence of Cellwise and Casewise
Contamination and Missing Data
Semi-Supervised Generalized Structural Equation Modeling
Group Sequential Design in Equivalence Studies
Download Google Sheets Using Just the URL
Download, Clean and Generate New Variables from GSOD Data
Genome Structure Rearrangement Calling in Genomes with High
Synteny
Copula Density and 2-D Hazard Estimation using Smoothing Splines
Spatial and Spatio-Temporal Geostatistical Modelling, Prediction
and Simulation
Utilities for strings and function arguments
Estimation of the Generalized Symmetry Point, an Optimal
Cutpoint in Continuous Diagnostic Tests
Calculate efficiencies of group testing algorithms with
correlated responses
Game-Theoretically OPtimal (GTOP) Reconciliation Method
R Functions to Perform and Display Google Trends Queries
Maps, data and methods related to Guerry (1833) "Moral
Statistics of France"
Adjust Estimates of Learning for Guessing
Implementation of Sampling Formulas for the Unified Neutral
Model of Biodiversity and Biogeography, with or without Guild
Structure
Statistical Analysis of Monthly Background Checks of Gun
Purchases
Download and Process Public Domain Works from Project Gutenberg
Fast Calculation of the Likelihood of a Stochastic Survival
Model
Regularized Categorical Effects/Categorical Effect
Modifiers/Continuous/Smooth Effects in GLMs
Global Validation of Linear Models Assumptions
Genome-Wide Association/Interaction Analysis and Rare Variant
Analysis with Family Data
Exact Hardy-Weinburg testing for Genome Wide Association Studies
A Shiny App to Aid Interpretation of Geometrically-Weighted
Degree Estimates in Exponential Random Graph Models
Controlling the genome-wide type I error rate in association
mapping experiments
Calculation of probabilities for inadequate and excessive
gestational weight gain
gWidgets API for building toolkit-independent, interactive GUIs
Rewrite of gWidgets API for Simplified GUI Construction
Implementation of gWidgets2 for the RGtk2 Package
Toolkit Implementation of gWidgets2 for tcltk
Toolkit implementation of gWidgets for RGtk2
Toolkit implementation of gWidgets for tcltk package
Geographically Weighted Logistic Elastic Net Regression
Generalized Waring Regression Model for Count Data
Fits geographically weighted regression models with diagnostic
tools
Estimating Genome-Wide Significance for Whole Genome Sequencing
Studies, Either Single SNP Tests or Region-Based Tests
Maximum Likelihood Estimation for Gene-by-Measured Environment
Interaction Models
Software to perform Haar Fisz transforms
Estimation, Simulation and Visualization of Hierarchical
Archimedean Copulae (HAC)
Utilities for using R scripts in Hadoop streaming
Hierarchical Optimal Matching and Machine Learning Toolbox
Univariate non-parametric two-sample test based on empirical
Hankel transforms
Inference of Trait Associations with SNP Haplotypes and Other
Attributes using the EM Algorithm
Analyzing Case-Parent Triad and/or Case-Control Data with SNP
Haplotypes
Estimate Haplotype Relative Risks in Case-Control Data
Statistical Analysis of Haplotypes with Traits and Covariates
when Linkage Phase is Ambiguous
Tool for Clustering Genotypes in Haplotypes
Haplotype Inference and Statistical Analysis of Genetic
Variation
Statistical Tests and Graphics for Hardy-Weinberg Equilibrium
Harmonic Regression to One or more Time Series
Full feature implementation of hash/associated
arrays/dictionaries
A collection of non-cryptographic hash functions
Generate Short Unique YouTube-Like IDs (Hashes) from Integers
Hawkes process simulation and calibration toolkit
Damage functions from FEMA's HAZUS software for use in modeling
financial losses from natural disasters
Hierarchical Bayesian Modeling of Decision-Making Tasks
Hierarchical Bayesian Regression for GLMs
Hill/Bliss Independence Model for Combination Vaccines
Hierarchical Bayesian Analysis of Recognition Memory
Hierarchical Bayesian Small Area Estimation
Hierarchical Bayesian Space-Time models for Gaussian space-time
data
Interval estimation for the parameters of linear models with
heteroskedasticity (Wild Bootstrap)
Change Point Estimation for Regression with Heteroscedastic Data
High Dimensional Supervised Classification and Clustering
Sample Size Calculation for High Dimensional Classification
Study
Use known groups in high-dimensional data to derive scores for
plots
Hierarchical DECOmposition of Entropy for Categorical Map
Comparisons
Tests for High Dimensional Generalized Linear Models
Statistical Analysis Tools for High Dimension Molecular Data
(HDMD)
Nomograms for High-Dimensional Cox Models
Leland Wilkinson's Algorithm for Detecting Multidimensional
Outliers
Principal Component Analysis in High-Dimensional Data
Interface to the UNDR Human Development Report API
Highest density regions and conditional density estimation
The Lasso for the Tweedie's Compound Poisson Model Using an
IRLS-BMD Algorithm
Health Effects of Air Pollution and Temperature (HEAT)
Heat exchange calculations during physical activity
Fit Statistic for Binary Dependent Variable Models
Robust Estimation Using Heavy-Tailed Distributions
Providing 'stringsAsFactors=FALSE' Variants of 'data.frame()'
and 'as.data.frame()'
Visualizing Hypothesis Tests in Multivariate Linear Models
Hierarchical Exponential-Family Random Graph Models
Marker-Based Estimation of Heritability Using Individual Plant
or Plot Data
Heuristics Including Take the Best and Unit-Weight Linear
Holonomic Gradient Method and Gradient Descent
Handy Functions for Working With HGNC Gene Symbols and
Affymetrix Probeset Identifiers
Statistical Analysis and Data Display: Heiberger and Holland
Heller-Heller-Gorfine Tests of Independence and Equality of
Distributions
The Hilbert-Huang Transform: Tools and Methods
Simulation from distributions supported by nested hyperplanes
Bindings for the 'HaveIBeenPwned.com' Data Breach API
Multiple Logistic Regression for 3D Chromatin Domain Border
Analysis
The All-Configurations, Maximum-Interaction F-Test for Hidden
Additivity
Inference on High Dimensional Max-Stable Distributions
Functions For Performing Hierarchical Analysis of Distance
Sampling Data
Set Data Visualization Using Hierarchies
Hierarchical Multiplicative Partitioning of Complex Phenotypes
Analysis of Dynamics Hierarchy Formation
Estimation and Tests of Hierarchical F-Statistics
A graphical user interface for calculating power and sample size
for hierarchical data
Convex Hierarchical Testing of Interactions
Two-Sample Tests for Equality of Means in High Dimension
Outlier Detection Algorithms for High-Dimensional Data
Two-Sample Tests for High-Dimensional Mean Vectors
Determining and Evaluating High-Risk Zones
High Throughput Screening for Plate Based Essays
Simultaneous Critical Values for t-Tests in Very High Dimensions
Estimation of Heritability in Linear Mixed Models
H-Index Calculator using Data from a Web of Science (WoS)
Citation Report
Tools for hypothesis testing based on Hypergeometric
Intersection distributions
Hierarchical Semiparametric Regression of Test Statistics
Data Sets from the History of Statistics and Data Visualization
Construction of regular and irregular histograms with different
options for automatic choice of bins
"Hit and Run" and "Shake and Bake" for Sampling Uniformly from
Convex Shapes
HIV calibrated model life tables for countries with generalized
HIV epidemics
Conversion Tools for HK80 Geographical Coordinate System
Ensemble Clustering using K Means and Hierarchical Clustering
Spatial Extreme Value Analysis with the Hierarchical Model of
Reich and Shaby (2012)
API to Retrieve Data from Hong Kong Stock Exchange
Diagnostic Tools for Hierarchical (Multilevel) Linear Models
Hierarchical Latent Space Network Model (HLSM)
The H-measure and other scalar classification performance
metrics
Hidden Markov Model for Continuous Observations Processes
Hidden Markov Models with Discrete Non-Parametric Observation
Distributions
Analysing accelerometer data using hidden Markov models
Hypothesis Testing and Power Calculations for Comparing
Metagenomic Samples from HMP
Statistical Object Oriented Data Analysis of RDP-Based Taxonomic
Trees from Human Microbiome Data
Flux Estimation with Static Chamber Data
Group Association Test using a Hidden Markov Model
Half-Normal Plots with Simulation Envelopes
Collect and Retrieve Annotation Data for Various Genomic Data
Using Different Web Services
Holiday and Half-Day Data, for Use with the 'TimeWarp' Package
Theoretical Model to Simulate Radical Polymerization
Homogeneity tests for Regional Frequency Analysis
Transmissions and Receptions in a Hop by Hop Network
Horn's (1965) Test to Determine the Number of Components/Factors
Hot Deck Imputation Methods for Missing Data
A lower bound for the number of correct rejections
Heligman Pollard mortality model parameter estimation using
Bayesian Melding with Incremental Mixture Importance Sampling
High Performance Cluster Models Based on Kiefer-Wolfowitz
Recursion
Miscellaneous convenience functions and dataset
Regularization Paths for Lasso or Elastic-Net Penalized Huber
Loss Regression and Quantile Regression
Hierarchical Spatial Autoregressive Model (HSAR)
A Handbook of Statistical Analyses Using R (1st Edition)
A Handbook of Statistical Analyses Using R (2nd Edition)
A Handbook of Statistical Analyses Using R (3rd Edition)
Manage, Analyse and Simulate Hyperspectral Data
hierarchical Bayesian species distribution models
Canonical Correlation Analysis based on Kernel Independence
Measures
A Path-Based BCD for Proximal Function of Latent Group Lasso
Phasing, Pedigree Reconstruction, Sire Imputation and
Recombination Events Identification of Half-sib Families Using
SNP Data
Meta-Analysis of Diagnostic Test Accuracy when Reference Test is
Imperfect
Clean Up or Pretty Print Gnarly HTML and XHTML
Facilitates Automated HTML Report Creation
Clustering High-Throughput Transcriptome Sequencing (HTS) Data
compute HUM value and visualize ROC curves
Simplify Analysis and Annotation of Human Microarray Datasets
An R Package for Calculating Water Vapor Measures from
Temperature and Relative Humidity
High-Performance Stemmer, Tokenizer, and Spell Checker for R
Models and Tests for Departure from Hardy-Weinberg Equilibrium
and Independence Between Loci
Bayesian investigation of Hardy-Weinberg Equilibrium via
estimation and testing
Objective Bayesian Testing for the Hardy-Weinberg Equilibrium
Problem
Testing Hardy-Weinberg Equilibrium for Multiallelic Genes
HTML Writer - Outputs R objects in HTML format
hwriterPlus: Extending the hwriter Package
Exact Tests for Hardy-Weinberg Proportions
Build, Deploy and Evaluate Hybrid Ensembles
Implementation of the Hybrid Monte Carlo and Multipoint Hybrid
Monte Carlo sampling techniques
Stochastic Hybrid Models in Dynamic Networks
Hybrid Bayesian Networks Using R and JAGS
Tools and models for hydrological applications
Groundwater Data Presentation and Interpretation
Goodness-of-fit functions for comparison of simulated and
observed hydrological time series
R codes for estimating water retention and infiltration model
parameters using experimental data
Particle Swarm Optimisation, with focus on Environmental Models
Hydrologic Indices for Daily Time Series Data
Time series management, analysis and interpolation for
hydrological modelling
A generalization of the Dirichlet distribution
Generic N-Dimensional Hyperplane Fitting with Heteroscedastic
Covariant Errors and Intrinsic Scatter
Hyper-Ensemble Smote Undersampled Random Forests
Work with Hyperspectral Data, i.e. Spectra + Meta Information
(Spatial, Time, Concentration, ...)
High-Dimensional Kernel Density Estimation and Geometry
Operations
Tools to Hyphenate Strings Using the 'Hunspell' Hyphenation
Library
Macroevolutionary phylogentic analysis of species trees and gene
trees
Confidence Intervals and Tests of Statistical Hypotheses
Wrapper for the 'Hypothes.is' Web Annotation Service
Tools for Modeling Rate-Dependent Hysteretic Processes and
Ellipses
Synthesizing Information Across Collaborating Research
Thermophysical Properties of Water and Steam
Model Selection for Index of Asymmetry Distribution
Cleaning and Visualizing Implicit Association Test (IAT) Data
Implicit Association Test Scores Using Robust Statistics
Integrative Bayesian Analysis of Transcriptomic and CGH Data
Functions for the Analysis of IBD Haplo Output
Convert Between Different IBD-State Labelling Schemes
Regression Methods for IBD Linkage With Covariates
Simulation of Chromosomal Regions Shared by Family Members
Irregular-lattice based ensemble empirical mode decomposition
Toolkits to Develop Individual-Based Models in Infectious
Disease
Iboot: iterated bootstrap tests and confidence sets
R API to Interactive Brokers Trader Workstation
An Interface to WinBUGS/OpenBUGS/JAGS by gWidgets
A Copy Number and Expression-Based Classifier for Breast Tumours
Inequality and Concentration Indices and Curves
Standardized high-throughput evaluation of cell-based compound
screens
Independent Components Analysis (ICA) based artifact correction
Imperialist Competitive Algorithm for Optimal Designs
Calibrates and Reweights Units in Samples
Bayesian Semiparametric Models for Interval-Censored Data
Facilitating Estimation of the Intraclass Correlation
Coefficient
Multilevel model intraclass correlation for slope heterogeneity
Calculation of Sample Size and Power for ICC
Tools for Working with ICD-9 and ICD-10 Codes, and Finding
Comorbidities
Tools for Working with ICD-9 Codes, and Finding Comorbidities
EM by the Method of Weights for Incomplete Categorical Data in
Generlized Linear Models
Individual Conditional Expectation Plot Toolbox
Incremental Cost-Effectiveness (ICE) Statistical Inference from
Two Unbiased Samples
Regression Models for Interval Censored Data
Study Design and Data Analysis in the Presence of Error-Prone
Diagnostic Tests and Self-Reported Outcomes
Stock Assessment Graphs Database Web Services
Estimation of number of clusters and identification of atypical
units
Fit Generalized Odds Rate Hazards Model with Interval Censored
Data
Inequality constrained inference in linear normal situations
A Button-Based GUI for Financial and Economic Data Analysis
Integrative clustering of multiple genomic data types
Reproducible Data Retrieval from the ICPSR Archive
A Modified Random Survival Forest Algorithm
Tools for Exploring Multivariate Data via ICS/ICA
A package for semiparametric regression analysis of
interval-censored data
R Interface to the US Census Bureau International Data Base API
Inference in Randomized Controlled Trials with Death and
Missingness
Jacquard Condensed Coefficients of Identity
Integrated Discovery of Oncogenic Signatures
Utilities of Institute of Data Analyses and Process Design
(www.idp.zhaw.ch)
Imprecise Dirichlet Process for Survival Analysis
Estimate Global Clustering in Infectious Disease
Identify Turtles by their Plastral Biometries
R Analysis package for iDynoMiCS Simulation Results
Irucka Embry's Miscellaneous USGS Functions
Irucka Embry's Miscellaneous USGS Data Collection
Functions to set and get the IEEE rounding mode
Irucka Embry's Miscellaneous Data Collection
Irucka Embry's Miscellaneous Text Collection
An integrative factor analysis model for drug-pathway
association inference
Toolkit for Item Factor Analysis with 'OpenMx'
Italian Fiscal Code ('Codice Fiscale') Utilities
Statistical Tests and Utilities for Genetic Association
Read Binary Files Saved by 'Igor Pro' (Including 'Neuromatic'
Data)
A Collection of Network Data Sets for the 'igraph' Package
Convert iGraph graps to SoNIA .son files
Instagram 2014 Vancouver Top Colour Dataset
Consistency, Homogeneity and Summary Statistics of
Climatological Data
Improved Laplace Approximation for Integrals of Unimodal
Functions
Lee-Carter Mortality Models using Iterative Fitting Algorithms
Aids in Processing and Plotting Data from a Lemna-Tec
Scananalyzer
Image Processing Library Based on 'CImg'
R Interface for International Monetary Fund(IMF) Data API
Download Data from the International Monetary Fund's Data API
Item Response Models for Multiple Ratings
Identifying Causal Effect for Multi-Component Intervention Using
Instrumental Variable Method
Imprecise Inferential Framework for Poisson Sampling Model
Minimum distance estimation in an imprecise probability model
A collection of methods for left-censored missing data
imputation
The Multifactor Dimensionality Reduction (MDR) Analysis for
Incomplete Data
Impute Missing Values in a Predictive Context
Imputation Methods for Multivariate Multinomial Data
Impute Missing Data in Time Series Data with PSF Based Method
Test Bench for Missing Data Imputing Models/Methods Comparison
Imputing the Last Largest Censored Observation(s) Under Weighted
Least Squares
Mixture models for longitudinal count data
Analysing Inbreeding Based on Genetic Markers
Dose Response Curves from Incucyte Proliferation Assays
Insertion Deletion Analysis While Accounting for Possible
Missing Data
Nonparametric tests of independence between random vectors
Relationship Between Species and Groups of Sites
Tests of Independence Between Point Processes in Time
Integrate INEGI’s (Mexican Stats Office) API with R
Measuring Inequality, Concentration, and Poverty
Interpolation and Extrapolation for Species Diversity
Infine-dimensional model (IDM) to analyse phenotypic variation
in growth trajectories
Functions to extract inferential values of a fitted model object
Inference about the standardized mortality ratio when evaluating
the effect of a screening program on survival
Methods for Causal Inference with Interference
Tools for Detecting Influential Data in Mixed Effects Models
Software Tools to Quantify Structural Importance of Nodes in a
Network
Case Influence in Structural Equation Models
Information Decomposition of Two-phase Experiments
Data Exploration with Information Theory (Weight-of-Evidence and
Information Value)
Performance Analysis and Companion Functions for Binary
Classification Models
Multiple Imputation for Informative Censoring
Sequence Statistics for Relational Event Models
Functions to Inline C, C++, Fortran Function Calls from R
Convert inline comments to documentation
Miscellaneous Functions for the USGS INL Project Office
Analyse Text Documents Using Ecological Tools
insideRODE includes buildin functions with deSolve solver and
C/FORTRAN interfaces to nlme, together with compiled codes
Probabilistic Verbal Autopsy Coding with 'InSilicoVA' Algorithm
High-Dimensional Changepoint Estimation via Sparse Projection
Check, Install and Load CRAN & USGS GRAN Packages
Using R to Install Stuff (Such As: R, Rtools, RStudio, Git, and
More!)
A Collection of Insurance Datasets Useful in Risk Classification
in Non-life Insurance
procedures for automated interpolation - methods only to be used
interactively, not included in intamap package
Joint Modelling of the Gene-Expression and Bioassay Data, Taking
Care of the Effect Due to a Fingerprint Feature
Integrated Prediction using Univariate and Multivariate Random
Forests
Tests for marginal interactions in a 2 class response model
Calculates Critical Test Statistics to Control False Discovery
and Familywise Error Rates in Marginal Effects Plots
interactive network analysis and visualization
Compute Chi-Square Measures with Corrections
Cure Rate Estimators for Interval Censored Data
Exact Confidence Intervals in the Presence of Interference
Coercion Routines for Network Data Objects
Plot the Effects of Variables in Interaction Terms
Hourly interpolation of multiple temperature daily series
Interpreting High Resolution GC-APCI-MS Spectra
Binary Classifier and Regression Model Interpretation Functions
Simulation of Inter-Related Genomic Datasets
Weighted Logrank Tests and NPMLE for interval censored data
Tools for Working with Points and Intervals
Interventional Inference for Dynamic Bayesian Networks
Numerical Integration for Integrated Likelihood
Integrative Clustering of Multiple Genomic Dataset
linear programming solver by the interior point method and
graphically (two dimensions)
methods for analyzing introgression between divergent lineages
Interface to Popular R Functions for Data Science Pipelines
Threshold regression that fits the (randomized drift) inverse
Gaussian distribution to survival data
A Unified Framework for Input-Output Operations in R
Calling Copy Number Alterations in Amplicon Sequencing Data
Calculate the Number of Required Flows for Semiconductor
Sequencing
Functions for smoothing with infinite order flat-top kernels
Tools for subgroup analyses with multiple trial data using
aggregate statistics
Spatial Interpolation by Inverse Path Distance Weighting
Fast Implementation of the Iterative Proportional Fitting
Procedure in C
Builds and analyses Integral Projection Models (IPMs)
Interfaces to Phylogenetic Software in R
Introduction to Probability and Statistics Using R
Convert IP Addresses to Country Names or Full Location with
Geoplotting
Manipulate, Validate and Resolve 'IP' Addresses
Generate Chemical Strings (SMILES) with the Inverse QSPR Model
Integrative Random Forest for Gene Regulatory Network Inference
Regression Methods for Interval-Valued Variables
Classes and Methods for Seismic Data Analysis
Fast Truncated SVD, PCA and Symmetric Eigendecomposition for
Large Dense and Sparse Matrices
Various Coefficients of Interrater Reliability and Agreement
Simple interface to the estimation and plotting of IRT models
Estimating IRT Parameters using the IRT Methodology
Utilities and Probability Distributions Related to
Multidimensional Person Item Response Models
Estimation of Tree-Based Item Response Models
Iterative Selection of Blocks of Features - ISBF
Provides datasets for Introduction to Statistical Data Analysis
for the Life Sciences
Fitting Ising Models Using the ELasso Method
Sampling Methods and Distribution Functions for the Ising Model
Stochastic Island Biogeography Theory Made Easy
Data for An Introduction to Statistical Learning with
Applications in R
An Introduction to Statistical Modeling of Extreme Values
Functions to Perform Isotonic Regression
Confidence intervals for current status data based on
transformations and bootstrap
Order-Restricted Inference for Microarray Experiments
Calculation of isotopic pattern for a given molecular formula
Statistical Toolbox for Radiometric Geochronology
Isoscape Computation and Inference of Spatial Origins using
Mixed Models
Active Set and Generalized PAVA for Isotone Optimization
Penalized Isotonic Regression in one and two dimensions
Week of the year and weekday according to ISO 8601
Approximate probability densities by iterated Laplace
Approximations
Efficient Iterator for Permutations and Combinations
itertools2: Functions creating iterators for efficient looping
Tools for classification and regression trees, with an emphasis
on interpretability
Interpreting Time Series and Autocorrelated Data Using GAMMs
Time series analysis package for students
Incorporating Uncertainties in Propensity Scores
Bayesian Instrumental Variable Estimation and Model
Determination via Conditional Bayes Factors
Instrumental fixed effect panel data model
Uses heteroscedasticity to estimate mismeasured and endogenous
regressor models
Statistical Inference and Sensitivity Analysis for Instrumental
Variables Model
Improved methods for causal inference and missing data problems
Simulation-Based Maximum Likelihood Parameter Estimation
Jackknife Estimates of Kaplan-Meier Estimators or Integrals
Statistical Inference of Variables Driving Systematic Variation
Jaccard Index for Population Structure Identification
Blind Source Separation Methods Based on Joint Diagonalization
and Some BSS Performance Criteria
A Wrapper Around 'rjags' to Streamline 'JAGS' Analyses
Joint Analysis of Genotype and Group-Specific Variability Using
a Novel Score Test Approach to Map Expression Quantitative
Trait Loci (eQTL)
Analysis Tools for the 'JAMES' Framework
R modules for JASPAR databases: a collection of transcription
factor DNA-binding preferences, modeled as matrices
Misc Small Tools and Helper Functions for Other Code of J.
Buttlar
One-to-One Gene-Probeset Mapping for Affymetrix Human
Microarrays
Joint Generalized Estimating Equation Solver
Performs the Joint Graphical Lasso for sparse inverse covariance
estimation on multiple classes
Chinese Text Segmentation Data for jiebaR Package
Joint Modeling of Longitudinal and Survival Data
Joint Modeling of Longitudinal and Time-to-Event Data under a
Bayesian Approach
Joint Mean-Covariance Models using 'Armadillo' and S4
Joint Modeling of Longitudinal and Survival Data - Power
Calculation
Time Series Analysis Toolkit Based on Symbolic Aggregate
Dicretization, i.e. SAX
Permutation Tests for Nonparametric Statistics
Joint modelling of repeated measurements and time-to-event data
Penalized Likelihood Estimation and Dynamic Prediction under the
Joint Frailty-Copula Models Between Tumour Progression and
Death for Meta-Analysis
Joint Approximate Diagonalization of a set of square matrices
Semiparametric Joint Models for Longitudinal and Counting
Processes
Risk estimation using the joint probability method
Joint Modelling of Mixed Correlated Binary and Continuous
Responses : A Latent Variable Approach
Perform Joins or Minus Queries on 'Excel' Files
Multilevel Joint Modelling Multiple Imputation
Functions for some Unit-Level Small Area Estimators and their
Variances
Javascript Object Signing and Encryption
Implements Under/Oversampling for Probability Estimation
Covariance and Inverse Covariance Matrix Estimation Using Joint
Penalty
Methods for population-based cancer survival analysis
Joint Random Forest (JRF) for the Simultaneous Estimation of
Multiple Related Networks
Jack-Knife Support for Evolutionary Distinctiveness Indices I
and W
Basic Finance; NPV/IRR/Annuities/Bond-Pricing; Black Scholes
Semiparametric Joint Modeling of Survival and Longitudinal Data
A Robust, High Performance JSON Parser and Generator for R
Package to identify and sort genes overexpressed
Kantorovich Distance Between Probability Measures
Non-Additive Measure and Integral Manipulation Functions
Sample Size Estimation Functions for Studies of Interobserver
Agreement
Calculates "vectorial Kappa", an index of congruence between
patchy mosaics
K-Adaptive Partitioning for Survival data
Kinetic analysis and visualization of DCE-MRI data
Kernel Smoothing for Bivariate Copula Densities
Nonparametric method for identifying discordant phylogenetic
trees
Kernel Estimator and Bandwidth Selection for Density and Its
Derivatives
Arrays with Better Control over Dimension Dropping
Collect, Assemble and Model Air Pollution, Weather and Health
Data
Calculate Solutions to the Kelvin Differential Equation using
Bessel Functions
Kendall rank correlation and Mann-Kendall trend test
Nonparametric kernel estimation of the distribution function.
Bandwidth selection and estimation of related functions
Expectile Regression in Reproducing Kernel Hilbert Space
Distance Weighted Discrimination (DWD) and Kernel Methods
Kernel Factory: An Ensemble of Kernel Machines
Kernel Density Estimation for Heaped and Rounded Data
Kernel Machine Score Test for Semi-Competing Risks
Functions for Kernel Smoothing Supporting Wand & Jones (1995)
Kalman Filter and Smoother for Exponential Family State Space
Models
Integrated Code Chunk Anchoring and Referencing for R Markdown
Documents
Kalman Filter, Smoother and Disturbance Smoother
Framework for the Analysis of Kinetic Visual Field Data
Routines for Fitting Kinetic Models to Chemical Degradation Data
An Implementation of 'kinn' Algorithm, a Graph Based Regression
Model
a Keep It Simple Species Migration Model
Spectral response of water wells to harmonic strain and pressure
Linear equations with Kronecker structure
Kaplan-Meier Estimator with Constraints for Right Censored Data
-- a Recursive Computational Algorithm
Confidence intervals for the Kaplan-Meier estimator
Kaplan-Meier Simultaneous Confidence Band for the Survivor
Function
Distributed k-Means for Big Data using 'ddR' API
Kaplan-Meier multiple imputation for the analysis of cumulative
incidence functions in the competing risks setting
Clustering longitudinal data using the likelihood as a metric of
distance
K-Means for Longitudinal Data using Shape-Respecting Distance
K-Means with Simultaneous Outlier Detection
Data sets from Klein and Moeschberger (1997), Survival Analysis
A General-Purpose Package for Dynamic Report Generation in R
Nearest-neighbor Classification with Categorical Variables
Knockoff Filter for Controlling the False Discovery Rate
Tools for the provision of scientific fisheries management
advice
Knowledge discovery by accuracy maximization
A Genetic Algorithm for Fixed-Size Subset Selection
Functional Summary and Meta-Analysis of Gene Expression Data
Supervised and Unsupervised Self-Organising Maps
An Improved Evaluation of Kolmogorov's Distribution
Koul's Minimum Distance Estimation in Linear Regression and
Autoregression Model
Cubic Spline Fitting with Knot Selection
Method for Clustering Partially Observed Data
Kriging-based Inversion for Deterministic and Noisy Computer
Experiments
Kernel-based Regularized Least squares (KRLS)
K-Sample Rank Tests and their Combinations
Goodness-of-Fit Tests using Kernelized Stein Discrepancy
Identify Kinase Substrate Relationships Using Dynamic Data
Configurable function for solving families of nonlinear
equations
k-Top Scoring Pairs for Microarray Classification
Datasets and functions from the (now non-existing) Faculty of
Life Sciences, University of Copenhagen
Calculation of Groundwater Mounding Beneath an Infiltration
Basin
Utility Functions by Youyi, Krisz and Others
Multi-Scale Motions Separation with Kolmogorov-Zurbenko
Periodogram Signals
Kolmogorov-Zurbenko Fourier Transform and Applications
Kolmogorov-Zurbenko Spatial Smoothing and Applications
Sparse Generalized Linear Model with L0 Approximation for
Feature Selection
l2boost - Friedman's boosting algorithm for regularized linear
regression
Ordination and Multivariate Analysis for Ecology
Relabelling MCMC Outputs of Mixture Models
Convert labels or tables to a dendrogram
Libreria Del Laboratorio Di Statistica Con R
Data Sets for the Book "Experimental Design for Laboratory
Biologists"
Estimation of indicators on social exclusion and poverty
Duncan test, Tukey test and Scott-Knott test
Local Approximate Gaussian Process Regression
Tools for the Analysis of Ecosystem Metabolism
Lake Temperatures Collected by Situ and Satellite Methods from
1985-2009
Collection of Internal Consistency Reliability Coefficients
Modeling Data with Functional Programming
Tools for Modeling Data with Functional Programming
Probabilistic Models to Analyze and Gaussianize Heavy-Tailed,
Skewed Data
Liquid Association for Network Dynamics Detection
Landmark Estimation of Survival and Treatment Effect
Landmark Prediction of a Survival Outcome
Radiometric and topographic correction of satellite imagery
Landsat 8 Imagery Rescaled to Reflectance, Radiance and/or
Temperature
Langevin Analysis in One and Two Dimensions
Data sets and functions with "Analyzing Linguistic Data: A
practical introduction to statistics"
Laplace Deconvolution with Noisy Discrete Non-Equally Spaced
Observations on a Finite Time Interval
Complete Environment for Bayesian Inference
Local Average Response Functions for Instrumental Variable
Estimation of Treatment Effects
Serialization Interface for Large List Objects
High-Quality Visualizations of Large, High-Dimensional Datasets
Least Angle Regression, Lasso and Forward Stagewise
Likelihood Analysis of Speciation/Extinction Rates from
Phylogenies
Modelling Interactions in High-Dimensional Data with
Backtracking
High-Dimensional Inference with the Penalized Score Test
L1 regularized regression (Lasso) solver using the Cyclic
Coordinate Descent algorithm aka Lasso Shooting
Draws Diagrams Useful for Checking Latent Scales
Latent Position and Cluster Models for Statistical Networks
Functions to Calculate Common Laterality Statistics in
Primatology
Density estimation and nonparametric regression on irregular
regions
Extra Graphical Utilities Based on Lattice
Multiresolution Kriging Based on Markov Random Fields
Complex Survey Structural Equation Modeling (SEM)
Client for 'Turfjs' for 'Geospatial' Analysis
Tools for Biostatistics, Public Policy, and Law
Latent Budget Analysis for Compositional Data
Limited Memory BFGS Minimizer with Bounds on Parameters
Length-biased correction to survival curve estimation
Latent Class Analysis (LCA) with familial dependence in extended
pedigrees
Data sets for package ``LMERConvenienceFunctions''
Extended Mixed Models Using Latent Classes and Latent Processes
Local Correlation, Spatial Inequalities, Geographically Weighted
Regression and Other Tools
Collapsed Gibbs Sampling Methods for Topic Models
Selection of Statistically Similar Research Groups
Interactive Visualization of Topic Models
Lan-DeMets Method for Group Sequential Boundaries
Linkage disequilibrium corrected by the structure and the
relatedness
Graphical Display of Pairwise Linkage Disequilibria Between SNPs
LD LASSO Regression for SNP Association Study
Finding Locally D-optimal optimal designs for some nonlinear and
generalized linear models
Methods for likelihood-based dimension reduction in regression
Linear Dependence Statistics for High-Dimensional Data
Exact tests for Linkage Disequilibrium and Hardy-Weinberg
Equilibrium
Analysis and Visualization of Plant Leaf Angle Distributions
Rapid Digital Image Analysis of Leaf Area
Create Interactive Web Maps with the JavaScript 'Leaflet'
Library
Interactive Web-Maps Based on the Leaflet JavaScript Library
Constructing Gene Co-Expression Networks for Single-Cell
RNA-Sequencing Data Using Pseudotime Ordering
Likelihood Estimation of Admixture in Parents From Offspring
Genotypes
latent effect adjustment after primary projection
Functions for Learning Bayesian Inference
An Implementation of Crawford's and Wright's Learning Curve
Production Functions
Data and functions to accompany the book "Learning R"
An Interactive Environment for Learning Statistics
Phylogenetic and Functional Analyses for Ecology
Functions and datasets to accompany the book "Le logiciel R:
Maitriser le langage, Effectuer des analyses statistiques"
(french)
Tools for Data Handling and Analysis in Macroecology
Utilities for Formatting Strings with Consistent Capitalization,
Word Breaks and White Space
Plot Lexis Diagrams for Demographic Purposes
Extractive Summarization of Text with the LexRank Algorithm
Estimation of the Local False Discovery Rates by Type II Maximum
Likelihood Estimation
Calculation of Low Flow Statistics for Daily Stream Flow Data
Tools for linear grouping analysis (LGA)
Simulation and Estimation of Log-GARCH Models
Tests for Genetic Association/Gene-Environment Interaction in
Longitudinal Gene-Environment-Wide Interaction Studies
Set-Based Tests for Genetic Association in Longitudinal Studies
A set of methods for longitudinal data objects
Computational Routines for Proton and Ion Radiotherapy
Linear Predictive Models Based on the LIBLINEAR C/C++ Library
Linear Classification with Online Adaptation of Coordinate
Frequencies
Linearized Bregman Algorithms for Generalized Linear Models
Light Cone Reconstruction of States - Predictive State
Estimation From Spatio-Temporal Data
Sensitivity Analysis for Case Weight in Normal Linear Regression
Financial and Actuarial Mathematics for Life Contingencies
Two-Parameter HMD Model Life Table System
Compute the Top Decile Lift and Plot the Lift Curve
Wavelet Lifting Estimators of the Hurst Exponent for Regularly
and Irregularly Sampled Time Series
Tools for Communication with Lightning-Viz Server
Implementation of the 'Lights Out' Puzzle Game
Methods for Maximum Likelihood Estimation
Functions to Analyze and Visualize Likert Type Items
Linear Inverse Model examples and solution methods
Jitter/CI Plot with Ordered Points Below the Limit of Detection
Binning and Plotting of Linearly Referenced Data
Static Univariate Frequentist and Bayesian Linear Calibration
Functions to Explore L-Systems (Lindenmayer Systems)
The Lindley Distribution and Its Modifications
Minimum Distance Estimation in Linear Regression Model
Manipulate Formulas and Evaluate Marginal Effects
Tools for Generating, Visualizing, and Analysing Link
Communities in Networks
Linkage information based genotype imputation method
Interactive Interpretation of Linear Regression Models
Manipulation of Linear Systems of (in)Equalities
Implements Queue, PriorityQueue and Stack Classes
Fitting lasso penalised additive isotone models
Statistical Methods for the Item Count Technique and List
Experiment
Tests of independence based on the Longest Increasing
Subsequence
'htmlwidget' for Interactive Views of R Lists
Leveraging Learning to Automatically Manage Algorithms
Local Log-linear Models for Capture-Recapture
Add Standardized Regression Coefficients to lm-Objects
Linear Mixed-Effects Models using 'Eigen' and S4
Linear Mixed-Effects Models with Censored Responses
Linear Mixed Effects Models for Genome-Wide Association Studies
Linear Mixed Effects Models for QTL Mapping for Multienvironment
and Multitrait Analysis
Compute the Personalized Activity Index Based on a Negative
Binomial Model
Compute the Personalized Activity Index Based on a Flexible
Bayesian Negative Binomial Model
Linear Mixed Effects Models with Non-Stationary Stochastic
Processes
Model Selection and Post-hoc Analysis for (G)LMER Models
Bootstrap Methods for Nested Linear Mixed-Effects Models
Add smoothing spline modelling capability to nlme
Latent Markov Models with and without Covariates
Testing for a subset of variance components in linear mixed
models
Functions for estimation and inference of selection in
age-structured populations
Linear Mixed Effect Model Splines for Modelling and Analysis of
Time Course Data
L-Moments, Censored L-Moments, Trimmed L-Moments, L-Comoments,
and Many Distributions
Regional frequency analysis using L-moments
Predicting Modulator and Functional/Survival Analysis
Identifying the Pathways Regulated by LncRNA Sets of Interest
Prioritizing Candidate LncRNAs Based on a Composite Multi-Level
Network
Functions for Performing Local FDR Estimation when Null and
Alternative are Poisson
Local Regression, Likelihood and Density Estimation
Test of Stationarity and Localized Autocovariance
Datasets for "Statistics: UnLocking the Power of Data"
Datasets for 'Statistics: Unlocking the Power of Data'
Genome-Wide Association Analysis of a Biomarker Accounting for
Limit of Detection
A simple logging system for R, based on log4j
Relative Risk Regression Using the Log-Binomial Model
Log-concave Density Estimation in Arbitrary Dimensions
Maximum likelihood estimation of a log-concave density based on
censored data
Estimate a Log-Concave Probability Density from Iid Observations
Compute MLE of Log-Concave Density on R with Fixed Mode, and
Perform Inference for the Mode
Estimate a Log-Concave Probability Mass Function from Discrete
i.i.d. Observations
CoxPH Model with Log Concave Baseline Distribution
LOGICOIL: multi-state prediction of coiled-coil oligomeric
state
Firth's bias reduced logistic regression
Diagnostic Tests for Models with a Binomial Response
Functions, Data and Code for Binary and Binomial Data
Fitting l2-regularized logit choice models via generalized
gradient descent
Infer network based on binary arrays using regularized logistic
regression
Functions for the logitnormal distribution
Double log normal distribution functions
Log-Multiplicative Models, Including Association Models
Logrank Test for Aggregated Survival Data
Kernel Regression Smoothing with Local or Global Plug-in
Bandwidth
Package for Plotting Categorical Longitudinal and Time-Series
Data
Model-Based Clustering and Classification for Longitudinal Data
Statistics for Long-Memory Processes (Jan Beran) -- Data and
Functions
Sample Size Calculations for Longitudinal Data
Expand Short URLs Using the 'URL-Expander' API
Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian
Models
loop decomposition of weighted directed graphs for life cycle
analysis, providing flexbile network plotting methods, and
analyzing food chain properties in ecology
A Collection of Tools to Conduct Levins' Loop Analysis
Uses an Archive to Amend Previous Stages of a Pipe using Current
Output
Logistic Ordinal Regression Differential Item Functioning using
IRT
LOw Rand and sparsE Covariance matrix estimation
Missing Morphometric Data Simulation and Estimation
Runs Lotka's Law which is One of the Special Applications of
Zipf's Law
Lassoed principal components for testing significance of
features
Local polynomial estimators of intensity function or its
derivatives
Linear Parametric Models Applied to Hydrological Series
Local Polynomial Estimator in Measurement Error Models
Merging linkage maps by linear programming
Modeler for linear programs (LP) and mixed integer linear
programs (MILP)
Linear Predictor Score, for Binary Inference from Multiple
Continuous Variables
Interface to 'Lp_solve' v. 5.5 to Solve Linear/Integer Programs
R Interface to 'lp_solve' Version 5.5.2.0
Learned Pattern Similarity and Representation for Time Series
LP Nonparametric Approach to Non-Gaussian Non-Linear Time Series
Modelling
Dose Response Signal Detection under Model Uncertainty
Sources an R "Module" with Caching & Encapsulation, Returning
Exported Vars
Different Types of Estimators to Deal with Multicollinearity
A Likelihood Ratio Test Accounting for Genetic Heterogeneity
Locally stationary two-dimensional wavelet process estimation
scheme
A Multiscale Test of Spatial Stationarity for LS2W processes
Applied Latent Semantic Analysis (LSA) Functions
Datensätze und Übungsmaterial zu Schreiner und Breit (2016);
beta-version
Least-Squares Bilinear Clustering for Three-Way Data
Local Statistical Complexity - Automatic Pattern Discovery in
Spatio-Temporal Data
Solving Least Squares Problems under Equality/Inequality
Constraints
American options pricing with Least Squares Monte Carlo method
Lysate and Secretome Peptide Feature Plotter
Companion to "Learning Statistics with R"
the accelerated failure time model to right censored data based
on least-squares principle
Simulation-Based Bayesian Inference for Latent Traits of Item
Response Models
Longitudinal Targeted Maximum Likelihood Estimation
LTPD and AOQL Plans for Acceptance Sampling Inspection by
Variables
Survival Trees to Fit Left-Truncated and Right-Censored Survival
Data
Ridge and Liu Estimates based on LTS (Least Trimmed Squares)
Method
Likelihood inference from case-control data Under Covariate
Assumptions (LUCA)
Printing Floating Point Numbers in a Human-Friendly Format
Comprehensive Luminescence Dating Data Analysis
Lunar Phase & Distance, Seasons and Other Environmental Factors
A Latent Variable Model for Mixed Continuous and Ordinal
Responses
An Automated Approach to Evaluating Dose-Effect Experiments
Following Litchfield and Wilcoxon (1949)
Accurate Estimation of Clonal Coincidences and Abundances from
Biological Replicates
Data from the M4 Time Series Forecasting Competition
Binary and Multiclass Boosting Algorithms
Machina Time Series Generation and Backtesting
Retrieve Data from MacLeish Field Station
Class prediction based on microarray data and clinical
parameters
Automatic Differentiation of Multivariate Operations
A Flexible Microarray Data Simulation Model
Data Class and Tools for Handling Spatial-Temporal Data
Pretty Scientific Plotting with Minor-Tick and Log Minor-Tick
Support
Magnetic Laplacian Matrix and Magnetic Eigenmap Visualization
A Graphical User Interface for Microarray Data Analysis
Creates an empty package framework for the LCFD format
Quantitative Analysis of Mass Spectrometry Data
A wrapper around the Java machine learning tool MALLET
Calculation of Minimum Averaged Mean Squared Error (MAMSE)
weights
Matrix Analysis and Normalization by Concordant Information
Enhancement
Interactive Q-Q and Manhattan Plots Using 'plotly.js'
Access and Process Data and Documents of the Manifesto Project
Manly Mixture Modeling and Model-Based Clustering
Analysis of Multivariate Data and Repeated Measures Designs
Multiple testing procedures of Cox (2011) and Wang and Cox
(2007)
Mapping Markers to the Nearest Genomic Feature
Mapping Smoothed Effect Estimates from Individual-Level Data
Geographic Display of Survey Data Statistics
Tools for Reading and Handling Spatial Objects
Mapping, pruning, and graphing tree models
Interactive Viewing of Spatial Objects in R
Multivariate Autoregressive Modeling for Analysis of Community
Time-Series Data
Multiplicative AR(1) with Seasonal Processes
Estimation of Meiotic Recombination Rates Using Marey Maps
Approximate marginal inference for regression-scale models
Mark-Recapture Analysis for Survival and Abundance Estimation
Markov Chain Models for Phylogenetic Trees
Easy Handling Discrete Time Markov Chains
Statistical Significance of the Markowitz Portfolio
Multivariate Analysis Based on Relative Likelihoods
Import, Plot and Analyze Bathymetric and Topographic Data
An algorithm for least-squares curve fitting
Mandallaz' Model-Assisted Small Area Estimators
Build Data Structures for Common Statistical Analysis
Support Functions and Datasets for Venables and Ripley's MASS
Body Mass Estimation Equations for Vertebrates
ML and MCMC Methods for Pedigree Reconstruction and Analysis
Model-Averaged Tail Area Wald (MATA-Wald) Confidence Interval
Multivariate and Propensity Score Matching with Balance
Optimization
Computation of the Balance - Sample Size Frontier in Matching
Methods for Causal Inference
MatchIt: Nonparametric Preprocessing for Parametric Casual
Inference
Combining Matching and Linear Regression for Causal Inference
Optimal Multilevel Matching using a Network Algorithm
A Code Converter from the Matlab/Octave Language to R
Tools to Assess Mating Potential in Space and Time
Measuring Association and Testing Independence Efficiently
An Interface for MATLAB using System Calls
Matrix Functions for Teaching and Learning Linear Algebra and
Multivariate Statistics
Sparse and Dense Matrix Classes and Methods
Collection of functions for matrix calculations
Matrix eQTL: Ultra fast eQTL analysis via large matrix
operations
Normalized Laplacian Matrix and Laplacian Map
Modelling with Sparse And Dense Matrices
Matrix-Based Partial Least Squares Estimation
Functions that Apply to Rows and Columns of Matrices (and to
Vectors)
Data.frame-Like Operations on Sparse and Dense Matrix Objects
Modern Calibration Functions for the Modern Analog Technique
(MAT)
Three methods to identify differentially expressed gene sets,
ordinary least square test, Multivariate Analysis Of Variance
test with n contrasts and Random forest
Exact max-type Cochran-Armitage trend test(CATT)
Low-memory Multinomial Logistic Regression with Support for Text
Classification
Selecting the Best Set of Relevant Environmental Variables along
with the Optimal Regularization Multiplier for Maxent Niche
Modeling
Maximum Likelihood Estimation and Related Tools
Model species distributions by estimating the probability of
occurrence using presence-only data
Orthogonal Data Projections with Maximal Skewness
Mazama Science Spatial Data Download and Utility Functions
Maxwell Boltzmann Bose Einstein Fermi Dirac Distribution and
Destruction Rate Modelling
Moment-Based Estimation for Hierarchical Models
Nucleic Acid Melting Curve Analysis on Microbead Surfaces with R
Model Based Multifactor Dimensionality Reduction
Multivariate Bayesian Sparse Group Selection with Spike and Slab
Access Data from the Massachusetts Bay Transit Authority (MBTA)
Web API
Multivariate Bayesian Variable Selection Method Exploiting
Dependence among Outcomes
Tools for Two-Dimensional Monte-Carlo Simulations
Translates information from netcdf files with MC2 output into
inter-PVT transitions
Matrix Computation Based Identification Of Prime Implicants
Functions to Support the Multicriteria Decision Aiding Process
Multi-Criteria Decision Making Methods for Crisp Data
Maximum Likelihood Estimation for Generalized Linear Mixed
Models
Machine Coded Genetic Algorithms for Real-Valued Optimization
Problems
Warnes and Raftery's MCGibbsit MCMC diagnostic
Multivariate Covariance Generalized Linear Models
Global optimization using Monte Carlo and Quasi Monte Carlo
simulation
Monte Carlo hypothesis tests with Sequential Stopping
Multiplicative Competitive Interaction (MCI) Model
IRT models for multiple choice items (mcIRT)
Estimating Conditional Auto-Regression (CAR) Models using Monte
Carlo Likelihood Methods
Gaussian Mixture Modelling for Model-Based Clustering,
Classification, and Density Estimation
Posterior Distribution of Extreme Value Models in R
Bayesian Analysis of Multivariate Counts Data in DNA
Metabarcoding and Ecology
Multiple Criteria Optimization Algorithms and Related Functions
Conjoint Analysis through Averaging of Multiple Analyses
Estimating Preferential Attachment from a Single Network
Snapshot by Markov Chain Monte Carlo
Multiple Comparisons Using Normal Approximation
A Simplified Interface for Running Commands on Parallel
Processes
A Monte Carlo permutation method for multiple test correlation
Design and Analysis of Dose-Finding Studies (see also
DoseFinding package)
Functions for Monte Carlo Methods with R
Selecting Bandwidth for Kernel Density Estimator with Minimum
Distance Method
Mixture and Flexible Discriminant Analysis
Multivariate Data Analysis for Chemometrics
Measuring the Difference Between Two Empirical Distributions
Multivariate Double Hierarchical Generalized Linear Models
Multi-Dimensional MA Normalization for Plate Effect
Detect gene-gene interactions using multifactor dimensionality
reduction
Improved Score Tests for Generalized Linear Models
Functions for Analysis of Data with General Recognition Theory
Complement to 'Modern Data Science with R'
A Computationally Efficient Mean Shift Implementation
Detection and Control of Tree-Ring Widths on Scanned Image
Sections
Maximum Entropy Bootstrap for Time Series
Bayesian Methods for Identifying the Most Harmful Medication
Errors
Flexible Mediation Analysis Using Natural Effect Models
Medical Risk and Comorbidity Tools for ICD-9-CM Data
A max-margin supervised Sparse Topical Coding Model
Multivariate Data Handling in Ecology and Biogeography
Multivariate Data Handling with S4 Classes and Sparse Matrices
Multiscale Clustering of Geometrical Network
Interactive model exploration using GGobi
Spatial pattern detection in genetic distance data using Moran's
Eigenvector Maps
Tools for Management of Survey Data and the Presentation of
Analysis Results
In-Memory Caching for Repeated Computations
Data sets from Mixed-effects Models in S
Tools for Analyzing Mixed Effect Regression Models
Meta-Analysis for Diagnostic Test Studies
An Interactive Visualisation of Meta-Analysis as a Physical
Weighing Machine
Meta-analysis of genome-wide SNP association results
Probabilistic latent variable models for metabolomic data
Normalize Metabolomic Data using QC Signal
Tools for Parsing, Manipulating, and Graphing Hierarchical Data
Analysis of the 'Elements of Metacommunity Structure'
Evaluate Periodicity in Large Scale Data
MetaDE: Microarray meta-analysis for differentially expressed
gene detection
Metapopulation simulations for conserving salmon through
portfolio optimization
Fused Lasso Approach in Regression Coefficient Clustering
Comprehensive Research Synthesis Tools for Systematic Reviews
and Meta-Analysis
Inference in Meta Analysis and Meta Regression
Metaheuristic Optimization Framework for Preprocessing
Combinations
An Implementation of Flower Pollination Algorithm in R
MetaIntegrator- Meta-Analysis of Gene Expression Data
Landscape and Range Expansion Simulation
Likelihood Inference in Meta-Analysis and Meta-Regression Models
Diagnostic and prognostic meta analysis (metamisc)
Bayesian Mixture Analysis for Metagenomic Community Profiling
Meta-Analysis of Studies with Non Statistically-Significant
Unreported Effects
Perform the Meta-Analysis for Pathway Enrichment Analysis (MAPE)
MetaPCA: Meta-analysis in the Dimension Reduction of Genomic
data
Creates CrossHairs Plots for Meta-Analyses
MetaQC: Objective Quality Control and Inclusion/Exclusion
Criteria for Genomic Meta-Analysis
Meta-Analysis using Structural Equation Modeling
Advanced Statistical Methods to Model and Adjust for Bias in
Meta-Analysis
Meta Analysis for SNP-Set (Sequence) Kernel Association Test
Meta-analysis for Classification Data and Correction to
Imperfect Reference
Spatio-Temporal Analysis and Mapping of Meteorological
Observations
Fitting and Plotting Tools for the Maximum Entropy Theory of
Ecology (METE)
Meth27QC: sample quality analysis, and sample control analysis
Functions for Analysis of Agreement in Method Comparison Studies
Full Bayesian Partition Model for Identifying Differentially
Methylated Loci
Detection of Differentially Methylated Regions using
MethylCap-Seq Data
Statistical Methods for Normalizing Metabolomics Data
Separate Metabolites into Likely Measurement Artifacts and True
Metabolites
Create Interactive Charts with the JavaScript 'MetricsGraphics'
Library
Test Metric and Null Model Statistical Performance
GUI Tool for Estimating Sample Sizes for Metabolomic Experiments
A Fixed Memeory Moving Expanding Window Average
Multivariate Functional Principal Component Analysis for Data
Observed on Different Dimensional Domains
Marginal Effects, Odds Ratios and Incidence Rate Ratios for GLMs
Simulating, Estimating and Diagnosing MGARCH (BEKK and mGJR)
Processes
Mixed GAM Computation Vehicle with GCV/AIC/REML Smoothness
Estimation
Structural Pursuit Over Multiple Undirected Graphs
Multivariate Response Generalized Linear Models
Model Averaging for Multivariate GLM with Null Models
Estimating Time-Varying Mixed Graphical Models
mgpd: Functions for multivariate generalized Pareto distribution
(MGPD of Type II)
Graphing map attributes and non-map variables in R
API Client for the MG-RAST Server of the US DOE KBase
Gene set analysis based on GSZ-scoring function and asymptotic
p-value
General Markov Chain Monte Carlo for Bayesian Inference using
adaptive Metropolis-Hastings sampling
Minimum Hellinger Distance Test for Normality
Inference for Hidden Markov and Semi-Markov Models
Multiple Hypothesis Testing for Variable Selection in
High-Dimensional Linear Models
Bayesian Model Selection in Logistic Regression for the
Detection of Adverse Drug Reactions
Missing Data Imputation and Model Checking
Multivariate Imputation by Chained Equations
Some Additional Multiple Imputation Functions, Especially for
'mice'
Demand Analysis with the Almost Ideal Demand System (AIDS)
Analysis with the Constant Elasticity of Substitution (CES)
function
Symmetric Normalized Quadratic Profit Function
Multivariate Independent Comparison of Observations
Promotion Time Cure Model with Mis-Measured Covariates
Methods for Taxonomic Classification of Prokaryotes
Utilities for Official Spanish Microdata
Use R Graphics Files as Microplots (Sparklines) in Tables in
LaTeX or HTML Files, and Output Data.frames to Org-Mode
Multiple Imputation by Distance Aided Donor Selection
Multiple Comparisons Procedures Based on Studentized Midrange
and Range Distributions
Implementing dispersal into species distribution models
Methods for the Indirect Estimation of Bilateral Migration
Graphical User Interface to the 'mi' Package
Multiple Imputation for Interval Censored Data
Model Implied Instrumental Variable (MIIV) Estimation of
Structural Equation Models
MIcrosimulation Lung Cancer (MILC) model
Multiple Imputation for Multivariate Binary Outcome
Multiple-Instance Logistic Regression with LASSO Penalty
Mixture summary method for multiple imputation
Data sets included in Utts and Heckard's Mind on Statistics
Maximal Information-Based Nonparametric Exploration R Package
for Variable Analysis
Implementation of the Well-Known Game to Clear Bombs from a
Given Field (Matrix)
Create a Mini Version of CRAN Containing Only Selected Packages
R Interface to the Levenberg-Marquardt Nonlinear Least-Squares
Algorithm Found in MINPACK, Plus Support for Bounds
Gene region-level testing procedure for SNP data, using the min
P test resampling approach
Derivative-free optimization algorithms by quadratic
approximation
An R Package for Linear Mixed Model Analyses
MINimal VALidation for Stoichiometric Reactions
Multidimensional Iterative Proportional Fitting and Alternative
Models
Mutant Identification through Probabilistic High throughput
Enabled NOrmalization
Computerized Adaptive Testing with Multidimensional Item
Response Theory
Miscellaneous Useful Functions Including LaTeX Tables, Kalman
Filtering and Development Tools
Computation of Generalized Linear Models with Misclassified
Covariates Using Side Information
Miscellaneous Functions for the Correlation Coefficient
Microbiome Based Sum of Powered Score (MiSPU) Tests
'Correctly Analyse Disease Recurrence with Missing at Risk
Information using Population Mortality'
Nonparametric Missing Value Imputation using Random Forest
Handling Missing Values with Multivariate Data Analysis
Testing Homoscedasticity, Multivariate Normality, and Missing
Completely at Random
Mixed effects Score Test for continuous outcomes
Data Sets, Functions and Examples from the Book: "Modern
Industrial Statistics" by Kenett, Zacks and Amberti
Methods in Structural Reliability Analysis
Mixture of Student t Distributions using Importance Sampling and
Expectation Maximization
Tools for Multiple Imputation in Multilevel Modeling
Tools for multiple imputation of missing data
Estimation/Multiple Imputation for Mixed Categorical and
Continuous Data
Multivariate Normal Mixture Models and Mixtures of Generalized
Linear Mixed Models Including Model Based Clustering
Mixed effects cumulative link and logistic regression models
Missing Data Imputation for Continuous and Categorical Data
using Nonparametric Bayesian Joint Models
Tools for Discrete Multivariate Mixed Membership Models
Estimation Methods for Stochastic Differential Mixed Effects
Models
Design and Analysis of Mixture Experiments
Evaluation of the FIM in NLMEMs using MCMC
Model Based Clustering, Classification and Discriminant Analysis
Using the Mixture of Generalized Hyperbolic Distributions
Mixed Model ANOVA and Statistics for Education
Functions to fit mixtures of regressions
A Random-Forest-Based Approach for Imputing Clustered Incomplete
Data
Forensic Genetics DNA Mixture Separation
Simulating Data to Study Performance of Clustering Algorithms
Fitting Finite Mixture of Scale Mixture of Skew-Normal
Distributions
DE Analysis of RNA-Seq Data by Mixtures of NB
Tools for Analyzing Finite Mixture Models
Curve Fitting and Mixture Toxicity Assessment
Mixture Models for Clustering and Classification
Multi-species sIZE spectrum modelling in R
2D and 3D movement-based kernel density estimates (MKDEs)
Kinetic Evaluation of Chemical Degradation Data
Efficient multi-level k-circulant supersaturated designs
Latent Class Item Response Theory (LC-IRT) Models under
Within-Item Multidimensionality
Machine Learning-based Differential Network Analysis of
Transcriptome Data
Exploratory Data Analysis and Manipulation of Multi-Label Data
Sets
R Ultimate Multilabel Dataset Repository
Machine learning algorithms with unified interface and confusion
matrices
Computation of the MLE for bivariate (interval) censored data
Maximum Likelihood Estimates of Gaussian Processes
Independent Component Analysis using Maximum Likelihood
ML estimation under multivariate linear mixed models with
missing values
Examples from Multilevel Modelling Software Review
Bayesian Model Averaging for Multinomial Logit Models
Multi-Label Prediction Using Gibbs Sampling (and Classifier
Chains)
A package for Multilinear Regression Model Population Analysis
Use the MLS Junk Generator Algorithm to Generate a Stream of
Pseudo-Random Numbers
Most Likely Transformations: Documentation and Regression Tests
Single-Sample Classifier of Medulloblastoma Subtypes for
Medulloblastoma Patient Samples, Mouse Models, and Cell Lines
Gene Expression Datasets for the 'MM2S' Package
Mathematical Morphology in Any Number of Dimensions
Multivariate Multinomial Distribution Approximation and
Imputation for Incomplete Data
Estimation of Multinormal Mixture Distribution
an R package for analyzing multivariate longitudinal data with
multivariate marginal models
Multivariate marginal models with shared regression parameters
Multi-Marker Molecular Signature for Treatment-specific Subgroup
Identification
Modern Measures of Population Differentiation
Various Similarity and Distance Metrics for Marked Point
Processes
Markov Modulated Poisson Process for Unsupervised Event
Detection in Time Series of Counts
Model-Based Clustering and Classification with Mixtures of
Modified t Factor Analyzers
Convert Dates to MMWR Day, Week, and Year
Multivariate Nonparametric Methods. An Approach Based on Spatial
Signs and Ranks
Multivariate Normal Distributions with Power Integrand
The Multivariate Normal and t Distributions
R Package for Fitting the Multinomial Probit Model
Multi-objective optimization for collecting cluster alternatives
Extraction of Bands from MODIS Surface Reflectance Product MOD09
NRT
Multiscale Analysis for Density Functions
Model-free estimation of a psychometric function
Modeling and Map Production using Random Forest and Stochastic
Gradient Boosting
A Model Object Framework for Regression Analysis
Modelling Functions that Work with the Pipe
Tools and Classes for Statistical Models
Find the Modes and Assess the Modality of Complex and Mixture
Distributions, Especially with Big Datasets
R tools for processing Level 2 Cloud Mask products from MODIS
Community Structure Detection via Modularity Maximization
Modelling temperature effects using time series data
Multiobjective Genetic Algorithm for Variable Selection in
Regression
Dental Surface Complexity Measurement Tools
Moment-Matching Methods for Weighted Sums of Chi-Squared Random
Variables
Moments, cumulants, skewness, kurtosis and related tests
A Simple Graphical Representation of the Relative Occurrence and
Co-Occurrence of Events
Accesses the Monkeylearn API for Text Classifiers and Extractors
Monotone Multi-Layer Perceptron Neural Network
Geometric Morphometric Analysis of Monogenean Anchors
Estimation for Multivariate Normal and Student-t Data with
Monotone Missingness
Allows to Explore Monophyly (or Lack of it) of Taxonomic Groups
in a Phylogeny
Functions and Datasets for the Book by Keon-Woong Moon
MOPSOCD: Multi-objective Particle Swarm Optimization with
Crowding Distance
Check Time Homogeneity and Markov Chain Order
Color schemes compatible with red-green color perception
difficulties
Calculations and Visualisations Related to Geometric
Morphometrics
MOdelling Tools for Reproduction and Survival Data in
Ecotoxicology
Simple raw data generator based on user-specified summary
statistics
Smoothing and Forecasting Poisson Counts with P-Splines
Project MOSAIC Statistics and Mathematics Teaching Utilities
Learning Hybrid Bayesian Networks using Mixtures of Truncated
Basis Functions
Mountain Plots, Folded Empirical Cumulative Distribution Plots
Mouse-Tracking Measures from Trajectory Data
Visualizing and Analyzing Animal Track Data
Animal Movement Modelling using Hidden Markov Models
Mixtures of von Mises-Fisher Distributions
Multi-Patient Analysis of Genomic Markers
Multinomial Probit Bayesian Additive Regression Trees
Multivariate Process Capability Indices (MPCI)
Data sets and scripts for Modeling Psychophysical Data in R
The package can implement the network-based metabolite pathway
identification of pathways
Modified Profile Likelihood Estimation for Weibull Shape and
Regression Parameters
Graphical Model Stability and Variable Selection Procedures
Automating Mplus Model Estimation and Interpretation
Symbolic Computation and More with Multivariate Polynomials
Mposterior: R package for Robust and Scalable Bayes via a Median
of Subset Posterior Measures
Statistics for analysing multiple simultaneous point processes
on the real line
Modeling Phylogenetic Signals using Eigenvector Maps
Analyze Multinomial Processing Tree Models
Data Sets from Montgomery, Peck and Vining's Book
Metabolomic Quantitative Trait Locus Mapping
Inferences for ratios of coefficients in the general linear
model
Methods for Analyzing Multiple Response Categorical Variables
(MRCVs)
Fits regression models when the outcome is partially missing
Marine Regions Data from 'Marineregions.org'
Multi-Resolution Estimation of the Hazard Rate
Modified Rand Index (1 and 2.1 and 2.2) and Modified Adjusted
Rand Index (1 and 2.1 and 2.2)
An R package for conditional maximum likelihood estimation in
mixed Rasch models
Multilocus Random Mixed Linear Model for Genome-Wide Association
Study and Linkage Analysis
Multivariate Regression Models for Reserving
R package for parallelized mRMR ensemble feature selection
Minimal Clinically Important Difference and Response Shift
Effect for Health-Related Quality of Life
Multi-Resolution Scanning for Cross-Sample Differences
Multinomial Response Models with Structured Penalties
A multiplicative-regression model for relative survival
Multiresolution time warping for functional data
Statistical analysis for Methylation-sensitive Amplification
Polymorphism data
Draw Diagrams (mis)Representing the Results of Mass Spec
Experiments
Multiscale Bernstein Polynomials for Densities
Markov-Switching, Bayesian, Vector Autoregression Models
Multivariate Synthetic Control Method Using Time Series
Combine Data from Positive and Negative Ionization Mode Finding
Common Entities
R Client for the Microsoft Cognitive Services Text Analytics
REST API
R Client for the Microsoft Cognitive Services Web Language Model
REST API
Metabolite set enrichment analysis for factor loading in
principal component analysis
Preprocessing of Gas Chromatography-Mass Spectrometry (GC-MS)
data
Data and Functions for the Book Modern Statistical Graphics
High Dimensional Multiclass Classification Using Sparse Group
Lasso
A library to serialize or unserialize data in MessagePack format
Degrees of freedom of elastic net, adaptive lasso and
generalized elastic net
Assess Tumor Microsatellite Instability with a Decision Tree
Classifier from Exome Somatic Mutations
Improved Techniques to Estimate Trend, Velocity and Acceleration
from Sea Level Records
Multi-State Markov and Hidden Markov Models in Continuous Time
Functions and Datasets for "Methods of Statistical Model
Estimation"
Building Augmented Data to Run Multi-State Models with 'msm'
Package
Datasets and Functions used in Multivariate Statistics: Old
School by John Marden
Morse-Smale Approximation, Regression and Visualization
Package for Calculation of ARMSS, Local MSSS and Global MSSS
Nonparametric Estimation for Multistate Models
Data Preparation, Estimation and Prediction in Multi-State
Models
Read Data from Delimited 'MTConnect' Data Files and Perform some
Analysis
All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS)
and Estimating Multivariate Volatility Models
Multivariate time series data imputation
Munich Franz-Josef-Strauss Airport Pattern Analysis
Data on Freedom of Information Act Requests
Hazard Function Estimation in Survival Analysis
Exploring Data with Tree Data Structures
Optimal Allocation in Stratified Sampling
Simultaneous Inference in General Parametric Models
Multiple Equivalence Tests and Simultaneous Confidence Intervals
GEE Solver for Correlated Nominal or Ordinal Multinomial
Responses
Finite Difference Method for Multi-Asset Option Valuation
Quantitative Linkage Analysis Tools using the Variance
Components Approach
Multiple Copy Number Variation Detection
Permutations of Multisets in Cool-Lex Order
Multivariate Analysis and Visualization for Biological Data
Read Fixed Width Format Files Containing Lines of Different Type
Multidimensional Latent Class Item Response Theory Models
Capture-Mark-Recapture Analysis using Multiple Non-Invasive
Marks
Meta-analysis of Multivariate Genome Wide Association Studies
Regression analysis of overdispersed correlated count data
Simultaneous confidence intervals for multinomial proportions
according to the method by Sison and Glaz
Robust Estimation of Overdispersed Multinomial Regression Models
Generation of Multivariate Ordinal Variates
Infrastructure to Assemble Multi-Panel Figures (from Grobs)
A Package to Test for Pleiotropic Effects
Variable Importance Analysis with Population Intervention Models
Weighted Cox-Regression for Nested Case-Control Data
Algebraic Tools for the Analysis of Multiple Social Networks
Data Manipulation with Parallelism and Shared Memory Matrices
Calculate Multivariate Richness via UTC and sUTC
Bias, Precision, and Power for Multi-Level Random Regressions
Clustering a Dataset using Multi-SOM Algorithm
MultiSV: an R package for identification of structural
variations in multiple populations based on whole genome
resequencing
Multivariate Random Forest for Linearly Related Output Features
Estimation of Multivariate Long-Memory Models Parameters
Common Multiple Testing Procedures and Gatekeeping Procedures
Metabolomics Univariate and Multivariate Analysis
Interpolate Munsell Renotation Data from Hue/Chroma to CIE/sRGB
Murphy Diagrams for Forecast Comparisons
Conversion of Nuclear Magnetic Resonance spectrum in audio file
A Graphical User Interface for the MuToss Project
MultiClass Visualizable Classification using Combination of
Projections
An Introduction to Applied Multivariate Analysis with R
Statistical Methods for Analysing Multivariate Abundance Data
Modelling Multivariate Binary Data with Blocks of Specific
One-Factor Distribution
Workspace organization, code and documentation editing, package
prep and editing, etc
Multivariate Variance Components Tests for Multilevel Data
Wavelet analysis of multiple time series
Multivariate Generalized Linear Mixed Models for Ranking Sports
Teams
Influence Measures and Diagnostic Plots for Multivariate Linear
Models
Multivariate Linear Model with Analytic p-Values
Multivariate Meshes and Histograms in Arbitrary Dimensions
Multivariate and Univariate Meta-Analysis and Meta-Regression
Multivariate Comparative Tools for Fitting Evolutionary Models
to Morphometric Data
Nelson-Aalen estimator of the cumulative hazard in multistate
models
Moving Grid Adjustment in Plant Breeding Field Trials
ML estimation for multivariate normal data with missing values
Normality test for multivariate variables
Generate New Multivariate Normal Samples from Permutations
Goodness of Fit Tests for Multivariate Normality
Multivariate outlier detection based on robust methods
Multivariate Peaks-over-Threshold Modelling for Spatial Extreme
Events
Orthant Probability of the Multivariate Normal Distribution
Mean and Variance of Truncated Normal Distribution
Generalized Shapiro-Wilk test for multivariate normality
Multivariate Stochastic Linear Ornstein-Uhlenbeck Models for
Phylogenetic Comparative Hypotheses
Bayesian Inference for the Multivariate Skew-t Model
Estimation and Testing for the Multivariate t-Distribution
Causal Inference in Spatiotemporal Event Data
Multichannel Wavelet Deconvolution with Additive Long Memory
Noise
Penalized Moving-Window Lasso Method for Genome-Wide Association
Studies
Efficient mixed-level k-circulant supersaturated designs
Integrative immunoinformatics for Mycobacterial diseases in R
platform
Automatic Correlation and Regression Test in a Data Frame
Interface to Myrrix. Myrrix is a complete, real-time, scalable
clustering and recommender system, evolved from Apache Mahout
External jars required for package Myrrix
Regression Models for Monozygotic Twin Data
Wraps 'libnabo', a Fast K Nearest Neighbour Library for Low
Dimensions
Nondetects And Data Analysis for environmental data
Provide namespace managment functions not (yet) present in base
R
Tools for Nanoparticle Simulation and Calculation of PDF and
Total Scattering Structure Function
Normalize NanoString miRNA and mRNA Data
Performs the Processing and Normalisation of Nanostring miRNA
and mRNA Data
Collection of datasets from the ASA 2006 data expo
NeuroAnatomy Toolbox for Analysis of 3D Image Data
NeuroAnatomy Toolbox ('nat') Extension for Assessing Neuron
Similarity and Clustering
NeuroAnatomy Toolbox ('nat') Extension for Handling Template
Brains
File System Utility Functions for 'NeuroAnatomy Toolbox'
Maximum Likelihood method in estimating effective population
size from genetic data
Bayes Classifier for Verbal Autopsy Data
Determining the Best Number of Clusters in a Data Set
Vignette Engine Wrapping IPython Notebooks
NBD-Dirichlet Model of Consumer Buying Behavior for Marketing
Research
Functions for Optimal Non-Bipartite Matching
Negative Binomial Models for RNA-Sequencing Data
NCA Calculation and Population PK Model Diagnosis
Interface to Unidata netCDF (Version 4 or Earlier) Format Data
Files
Helper functions for use with the ncdf4 package
Miscellaneous Functions for Creating Adaptive Functions and
Scripts
Regularization Paths for SCAD and MCP Penalized Regression
Models
Wicked-Fast Streaming 'JSON' ('ndjson') Reader
Efficient Network Enrichment Analysis Test
Calculating Effective Sizes Based on Known Demographic
Parameters of a Population
Estimating the Number of Essential Genes in a Genome
Access to the Neotoma Paleoecological Database Through R
Network Assisted Algorithm for Epigenetic Studies Using Mean and
Variance Combined Signals
Survival Analysis for Cohorts with Missing Covariate Information
Mann-Whitney-Wilcoxon Test for Nested Ranks
Inference of Species Associations from Co-Occurrence Data
netClass: An R Package for Network-Based Biomarker Discovery
Statistical Modeling with Network Cohesion
Network Analysis for Diffusion of Innovations
Network Generator for Combinatorial Graph Problems
Estimating network indices, including trophic structure of
foodwebs in R
Network Meta-Analysis using Frequentist Methods
Origin Estimation for Propagation Processes on Complex Networks
Permutation Testing Network Module Preservation Across Datasets
Network Strengths and Weaknesses Analysis
Statistical Comparison of Two Networks Based on Three Invariance
Measures
Tools for using Network Reporting Estimators
Simulate Bipartite Graphs with Fixed Marginals Through
Sequential Importance Sampling
Visualization and Analysis Tools for Neural Networks
Data Structures and Handling for Neuroimaging Data
Functions to Generate fMRI Data Including Activated Data, Noise
Data and Resting State Data
Computes Pdf, Cdf, Quantile and Random Numbers, Measures of
Inference for 19 General Families of Distributions
Parallel Analysis and Non Graphical Solutions to the Cattell
Scree Test
Numerical Formal Concept Analysis for Systematic Clustering
A Simple General Purpose N-Gram Tokenizer
Fitting the Centered Autologistic and Sparse Spatial Generalized
Linear Mixed Models for Areal Data
Data from the US National Health and Nutrition Examination Study
Non-hierarchical evolutionary multi-objective tree learner to
perform cost-sensitive classification
Compiling the NHL Real Time Scoring System Database for easy use
in R
Bayesian NHMM Modeling (Multiple Time Series)
Non-Homogeneous Markov Switching Autoregressive Models
Modelling and Validation of Non Homogeneous Poisson Processes
(Niche) (R)egion and Niche (Over)lap Metrics for
Multidimensional Ecological Niches
Flexible BUGS-Compatible System for Hierarchical Statistical
Modeling and Algorithm Development
Fast and Accurate Trisomy Prediction in Non-Invasive Prenatal
Testing
Nonlinear least squares examples from NIST
Fundamental Physical Constants and Unit Conversions from NIST
Noninferiority Tests with Variable Margins
Solve Nonlinear Optimization with Nonlinear Constraints
Linear and Nonlinear Mixed Effects Models
Non-linear mixed-effects modelling in nlme using differential
equations
Datasets and utility functions enhancing functionality of nlme
package
Functions for Nonlinear Least Squares Solutions
Nonlinear Network Reconstruction and Clustering Based on DCOL
(Distance Based on Conditional Ordered List)
Higher Order Inference for Nonlinear Heteroscedastic Models
Non-Linear Relative Risk Estimation and Plotting
Fitting Structural Equation Mixture Models
Non-Linear Shrinkage Estimation of Population Eigenvalues and
Covariance Matrices
Nonlinear regression in predictive microbiology
Fitting nonlinear models with scale mixture of skew-normal
distributions
Non-Linear Model Fitting of Time Distribution of Biological
Phenomena
Utilities and Kinship Information for Research with the NLSY
A nondecimated lifting transform for signal denoising
Wald Test of Nonlinear Restrictions and Nonlinear CI
Algorithms and Framework for Nonnegative Matrix Factorization
(NMF)
Non-Negative Matrix Factorization (NMF) using CUDA
Normalized Mutual Information of Community Structure in Network
Numerical Methods and Optimization in Finance
Feed-Forward Neural Networks and Multinomial Log-Linear Models
Prediction Intervals of Multi-Layer Neural Networks
Non-Negative Lasso and Elastic Net Penalized Generalized Linear
Models
Fast and Versatile Non-Negative Matrix Factorization
The Lawson-Hanson algorithm for non-negative least squares
(NNLS)
Calculate Design Parameters for Biomarker Validation Studies
Node Harvest for Regression and Classification
Compares the Distribution of Sister Clades Through a Phylogeny
Implementation of the Natural and Orthogonal InterAction (NOIA)
model
Estimation of Intrinsic and Extrinsic Noise from Single-Cell
Data
Label Noise Filters for Data Preprocessing in Classification
Biplot representations of categorical data
U.S. Census Regional and Demographic Data
Causal Inference in the Presence of Treatment Noncompliance
Under the Binary Instrumental Variable Model
Loading NONMEM Output Files and Simulate with Parameter
Uncertainty
Nonparametric Methods for Measuring Efficiency and Productivity
Model Checking and Variable Selection in Nonparametric
Regression
Stratification and matching by the propensity score
Detecting Isotope, Adduct and Homologue Relations in LC-MS Data
Quantized simulation data of isotope pattern centroids
Normal (1-d) Mixture Models (S3 Classes and Methods)
Dataset for Climate Analysis with Data from the Nordic Region
Analysis of multivariate normal datasets with missing values
Analysis of Incomplete Multivariate Data under a Normal Model
Routines for Exponential Power Distribution
Evaluates complex erf, erfc, Faddeeva, and density of sum of
Gaussian and Student's t
Generation of Multivariate Data with Arbitrary Marginals
Neyman Smooth Tests of Normality for the Errors of ARMA Models
Send push notifications to your smartphone via pushover.net
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NOVEL Integration of the Sample and Thresholded (NOVELIST)
Correlation and Covariance Estimators
Nonparametric kernel smoothing methods for mixed data types
Non-Parametric Measures of Actigraphy Data
Multiple Comparisons and Simultaneous Confidence Intervals
Nonparametric Analysis of Longitudinal Data in Factorial
Experiments
Non-Parametric Bayesian Multiple Imputation for Categorical Data
Nonparametric Combination of Hypothesis Tests
Nonparametric Methods for Cognitive Diagnosis
Some Nonparametric Tests for Change-Point Detection in Possibly
Multivariate Observations
Normalised prediction distribution errors for nonlinear
mixed-effect models
Bayesian Nonparametrics for Automatic Gating of Flow-Cytometry
Data
Sample Size Calculation for the Proportional Hazards Mixture
Cure Model
Nonparametric Interaction Tests for Factorial Designs with
Repeated Measures
Plotting linkage and association results
Type-Specific Failure Rate and Hazard Rate on Competing Risks
Data
Nonparametric maximum likelihood estimation for random effect
models
tertiary probabilistic model in predictive microbiology for use
in food manufacture
Nonparametric Comparison of Multivariate Samples
Nonparametric Partially-Balanced Incomplete Block Design
Analysis
Nonparametric Estimation of Regression Models with
Factor-by-Curve Interactions
Kernel-Based Nonparametric ROC Regression Modelling
Convenience Functions and Tests for Working With the Net
Promoter Score (NPS)
Nonparametric and Stochastic Efficiency and Productivity
Analysis
Nonparametric Smoothing for contaminated data using
Simulation-Extrapolation
Package for Nonparametric Statistical Methods using R
Generalization of Hewitt's Seasonality Test