A set of tools for empirical analysis of diversity (a number and frequency of different types in population) and similarity (a number and frequency of shared types in two populations) in biological or ecological systems.
Function to test spatial segregation and association based in contingency table analysis of nearest neighbour counts following Dixon (2002) <doi:10.1080/11956860.2002.11682700>. Some 'Fortran' code has been included to the original dixon2002() function of the 'ecespa' package to improve speed.
Implements various decision support tools related to the new product development.
             Subroutines include correlation reliability test, Mahalanobis distance measure for outlier detection, combinatorial search (all possible subset regression), non-parametric efficiency analysis measures: DDF (directional distance function), DEA (data envelopment analysis), HDF (hyperbolic distance function), SBM (slack-based measure), and SF (shortage function), benchmarking, risk analysis, technology adoption model, new product target setting, etc.
Provides functions to facilitate access to the 'DKAN' API (<https://dkan.readthedocs.io/en/latest/apis/index.html>), including the 'DKAN' REST API (metadata), and the 'DKAN' datastore API (data). Includes functions to list, create, retrieve, update, and delete datasets and resources nodes. It also includes functions to search and retrieve data from the 'DKAN' datastore.
Compute diffusion kernels on DNA polymorphisms, including SNP and bi-allelic genotypes.
Provides time series regression models with one predictor using finite distributed lag models, polynomial (Almon) distributed lag models, geometric distributed lag models with Koyck transformation, and autoregressive distributed lag models. It also consists of functions for computation of h-step ahead forecasts from these models. See Baltagi (2011) <doi:10.1007/978-3-642-20059-5> for more information.
An implementation of the differentiable lasso (dlasso) and SCAD (dSCAD) using iterative ridge algorithm. This package allows selecting the tuning parameter by AIC, BIC, GIC and GIC.
Interface for 'Rcpp' users to 'dlib' <http://dlib.net> which is a
    'C++' toolkit containing machine learning algorithms and computer vision tools.
    It is used in a wide range of domains including robotics, embedded devices,
    mobile phones, and large high performance computing environments. This package
    allows R users to use 'dlib' through 'Rcpp'.
Maximum likelihood, Kalman filtering and smoothing, and Bayesian
  analysis of Normal linear State Space models, also known as 
  Dynamic Linear Models
QTL mapping in a mixed model framework with separate
        detection and localization stages. The first stage detects the
        number of QTL on each chromosome based on the genetic variation
        due to grouped markers on the chromosome; the second stage uses
        this information to determine the most likely QTL positions.
        The mixed model can accommodate general fixed and random
        effects, including spatial effects in field trials and pedigree
        effects. Applicable to backcrosses, doubled haploids,
        recombinant inbred lines, F2 intercrosses, and association
        mapping populations.
dlmodeler is a set of user-friendly functions to simplify the state-space modelling, fitting, analysis and forecasting of Generalized Dynamic Linear Models (DLMs). It includes functions to name and extract individual components of a DLM, build classical seasonal time-series models (monthly, quarterly, yearly, etc. with calendar adjustments) and provides a unified interface compatible with other state-space packages including: dlm, FKF and KFAS.
Development, simulation testing, and implementation of management
    procedures for data-limited fisheries 
    (see Carruthers et al (2014) <doi:10.1016/j.fishres.2013.12.014>).
Collection of functions for distributed lag linear and non-linear models.
A collection of tools that support data diagnosis, exploration, and transformation. 
    Data diagnostics provides information and visualization of missing values and outliers and 
    unique and negative values to help you understand the distribution and quality of your data. 
    Data exploration provides information and visualization of the descriptive statistics of 
    univariate variables, normality tests and outliers, correlation of two variables, and 
    relationship between target variable and predictor. Data transformation supports binning 
    for categorizing continuous variables, imputates missing values and outliers, resolving skewness. 
    And it creates automated reports that support these three tasks.
Inference functionalities for distributed-lag linear structural equation models (DLSEMs). DLSEMs are Markovian structural causal models (Pearl, 2000, Chapter 5) <ISBN:978-0-521-89560-6>, where each factor of the joint probability distribution is a distributed-lag linear regression model (Judge et al., 1985, Chapters 9-10) <ISBN: 978-0-471-89530-5>. DLSEMs account for temporal delays in the dependence relationships among the variables and allow to perform dynamic causal inference by assessing causal effects at different time lags. Endpoint-constrained quadratic, quadratic decreasing and gamma lag shapes are available.
Monthly download stats of 'CRAN' and 'Bioconductor' packages.
	     Download stats of 'CRAN' packages is from the 'RStudio' 'CRAN mirror', see <http://cranlogs.r-pkg.org>.
	     'Bioconductor' package download stats is at <https://bioconductor.org/packages/stats/>.
Dynamic model averaging for binary and continuous
        outcomes.
The state-of-the-art algorithms for distance metric learning, including global and local methods such as Relevant Component Analysis, Discriminative Component Analysis, Local Fisher Discriminant Analysis, etc. These distance metric learning methods are widely applied in feature extraction, dimensionality reduction, clustering, classification, information retrieval, and computer vision problems.
Dyadic mixed model analysis with multi-trait responses and
  pedigree-based partitioning of individual variation into a range of
  environmental and genetic variance components for individual and 
  maternal effects.
Implements the daily based Morgan-Morgan-Finney (DMMF) soil erosion model (Choi et al., 2017 <doi:10.3390/w9040278>) for estimating surface runoff and sediment budgets from a field or a catchment on a daily basis.
The framework provides functions to generate ODEs of reaction
    networks, parameter transformations, observation functions, residual functions,
    etc. The framework follows the paradigm that derivative information should be
    used for optimization whenever possible. Therefore, all major functions produce
    and can handle expressions for symbolic derivatives.
A backward selection procedure called delete or merge
        regressors (DMR) combines deleting continuous variables with
        merging levels of factors. The method assumes greedy search
        among linear models with set of constraints of two types:
        either a parameter for a continuous variable is set to zero or
        parameters corresponding to two levels of a factor are
        compared. DMR is a stepwise regression procedure, where in each
        step a new constraint is added according to ranking of the
        hypotheses based on squared t-statistics. As a result a nested
        family of linear models is obtained and the final decision is
        made according to minimization of the generalized information
        criterion (GIC, default BIC). The main function of the package
        is DMR, which is based on hierarchical clustering. Moreover,
        other functions for extensions of DMR method are given, such as
        stepDMR which is based on recalculation of t-statistics in each
        step and function DMR4glm for generalized linear models.
Perform differential analysis for
        methylation array data. Detect differentially
        methylated regions (DMRs) from array M-values. 
        The core is a Non-homogeneous Hidden Markov Model
        for estimating spatial correlation and a novel Constrained 
        Gaussian Mixture Model for modeling the M-value pairs of each individual locus.
Model selection algorithms for regression and classification, where the predictors can be numerical and categorical and the number of regressors exceeds the number of observations. The selected model consists of a subset of numerical regressors and partitions of levels of factors. Aleksandra Maj-Kańska, Piotr Pokarowski and Agnieszka Prochenka (2015) <doi:10.1214/15-EJS1050>. Piotr Pokarowski and Jan Mielniczuk (2015) <http://www.jmlr.org/papers/volume16/pokarowski15a/pokarowski15a.pdf>.
Work within the 'dplyr' workflow to add random variates to your data frame. 
  Variates can be added at any level of an existing column.  Also, bounds can be specified 
  for simulated variates.
This package includes functions and data accompanying the book 
	     "Data Mining with R, learning with case studies" by Luis Torgo, CRC Press 2010.
Functions and data accompanying the second edition of the book  "Data Mining with R, learning with case studies" by Luis Torgo, published by CRC Press.
Package for conducting differential network analysis from
        microarray data.
Generates DNA sequences based on Markov model techniques for matched sequences. This can be generalized to several sequences. The sequences (taxa) are then arranged in an evolutionary tree (phylogenetic tree) depicting how taxa diverge from their common ancestors. This gives the tests and estimation methods for the parameters of different models. Standard phylogenetic methods assume stationarity, homogeneity and reversibility for the Markov processes, and  often impose further restrictions on the parameters.
Computationally efficient tools for comparing all pairs of profiles
    in a DNA database. The expectation and covariance of the summary statistic
    is implemented for fast computing. Routines for estimating proportions of
    close related individuals are available. The use of wildcards (also called F-
    designation) is implemented. Dedicated functions ease plotting the results.
The DnE package involves functions to analyse the distribution of a set of given data. The basic idea of the analysis is chi-squared test. Functions which have the form as "is.xxdistribution" are used to analyse whether the data obeys the xxdistrbution. If you do not know which distribution to judge, use function is.dt().
The focus of the dnet by Fang and Gough (2014) <doi:10.1186/s13073-014-0064-8> is to make sense of omics data (such as gene expression and mutations) from different angles including: integration with molecular networks, enrichments using ontologies, and relevance to gene evolutionary ages. Integration is achieved to identify a gene subnetwork from the whole gene network whose nodes/genes are labelled with informative data (such as the significant levels of differential expression or survival risks). To help make sense of identified gene networks, enrichment analysis is also supported using a wide variety of pre-compiled ontologies and phylostratific gene age information in major organisms including: human, mouse, rat, chicken, C.elegans, fruit fly, zebrafish and arabidopsis. Add-on functionalities are supports for calculating semantic similarity between ontology terms (and between genes) and for calculating network affinity based on random walk; both can be done via high-performance parallel computing.
Provides a modified hierarchical test (Liu (2017) <doi:10.1214/17-AOS1539>) for detecting the structural difference between two Semiparametric Gaussian graphical models. The multiple testing procedure asymptotically controls the false discovery rate (FDR) at a user-specified level. To construct the test statistic, a truncated estimator is used to approximate the transformation functions and two R functions including lassoGGM() and lassoNPN() are provided to compute the lasso estimates of the regression coefficients.
Provides density, distribution function, quantile function and random generation for the split-t distribution, and computes the mean, variance, skewness and kurtosis for the split-t distribution (Li, F, Villani, M. and Kohn, R. (2010) <doi:10.1016/j.jspi.2010.04.031>).
Using Local Moran's I for detection of differential network local consistency.
Discriminant Non-Negative Matrix Factorization aims to extend the Non-negative Matrix Factorization algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. It refers to three article, Zafeiriou, Stefanos, et al. "Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification." Neural Networks, IEEE Transactions on 17.3 (2006): 683-695. Kim, Bo-Kyeong, and Soo-Young Lee. "Spectral Feature Extraction Using dNMF for Emotion Recognition in Vowel Sounds." Neural Information Processing. Springer Berlin Heidelberg, 2013. and Lee, Soo-Young, Hyun-Ah Song, and Shun-ichi Amari. "A new discriminant NMF algorithm and its application to the extraction of subtle emotional differences in speech." Cognitive neurodynamics 6.6 (2012): 525-535.
Functions are provided to fit temporal lag models to dynamic
    networks. The models are build on top of exponential random graph models (ERGM) framework. There are
    functions for simulating or forecasting networks for future time points.
    Stable Multiple Time Step Simulation/Prediction from Lagged Dynamic Network Regression Models. Mallik, Almquist (2017, under review).
Provides Frequentist (EM) and Bayesian  (MCMC) Methods for Inference of  Birth-Death-Immigration Markov Chains.
Example datasets from the book "An Introduction to Generalised Linear Models" (Year: 2018, ISBN:9781138741515) by Dobson and Barnett.
Contains:
    1) Facilities for working with grouped data: 'do' something to data
    stratified 'by' some variables.
    2) LSmeans (least-squares means), general linear contrasts. 
    3) Miscellaneous other utilities.
Allows accessing 'Docker' 'SDK' from 'R' via the 'Docker' 'Python' 'SDK' using the 'reticulate' package.
  This is a very thin wrapper that tries to do very little and get out of the way.
  The user is expected to know how to use the 'reticulate' package to access 'Python' modules, and how the 'Docker' 'Python' 'SDK' works.
Build a Dockerfile straight from your R session. 'dockerfiler' allows 
    you to create step by step a Dockerfile, and provide convenient tools to wrap 
    R code inside this Dockerfile.
Define a command-line interface by just giving it
    a description in the specific format.
A direct approach to optimal designs for copula models based on
    the Fisher information. Provides flexible functions for building joint PDFs,
    evaluating the Fisher information and finding optimal designs. It includes an
    extensible solution to summation and integration called 'nint', functions for
    transforming, plotting and comparing designs, as well as a set of tools for
    common low-level tasks.
Provides the ability to display something analogous to
    Python's docstrings within R.  By allowing the user to document
    their functions as comments at the beginning of their function
    without requiring putting the function into a package we allow
    more users to easily provide documentation for their functions.
    The documentation can be viewed just like any other help files
    for functions provided by packages as well.
Production of R packages from tagged comments introduced within the code 
             and a minimum of additional documentation files.
Have you ever been tempted to create 'roxygen2'-style documentation
    comments for one of your functions that was not part of one of your
    packages (yet)?
    This is exactly what this package is about: running 'roxygen2' on
    (chunks of) a single code file.
Connect to the 'DocuSign' Rest API <https://www.docusign.com/p/RESTAPIGuide/RESTAPIGuide.htm>, 
  which supports embedded signing, and sending of documents.
A set of helper functions for using R Markdown to create documents
    in docx format, especially documents for use in a classroom or workshop
    setting.
Microsoft Word docx files provide an XML structure that is fairly
    straightforward to navigate, especially when it applies to Word tables and
    comments. Tools are provided to determine table count/structure, comment count
    and also to extract/clean tables and comments from Microsoft Word docx documents.
Various sampling plans are able to be compared using evaluations of their OC, AOQ, ATI etc.
Distances on dual-weighted directed graphs using priority-queue
    shortest paths. Weighted directed graphs have weights from A to B which may
    differ from those from B to A. Dual-weighted directed graphs have two sets
    of such weights. A canonical example is a street network to be used for
    routing in which routes are calculated by weighting distances according to
    the type of way and mode of transport, yet lengths of routes must be
    calculated from direct distances.
Detect Differences in rhythmic time series. Using linear
    least squares and the robust semi-parametric rfit() method. Differences in
    harmonic fitting could be detected as well as differences in scale of the
    noise distribution.
Creates full factorial experimental designs and designs based on orthogonal arrays for (industrial) experiments. Provides diverse quality criteria. Provides utility functions for the class design, which is also used by other packages for designed experiments.
'CRAN' package 'DoE.base' and non-'CRAN' packages 'gurobi' and 'Rmosek' are enhanced with functionality for the creation of optimized arrays for experimentation, where optimization is in terms of generalized minimum aberration. It is also possible to optimally extend existing arrays to larger run size. Optimization requires the availability of at least one of the commercial products 'Gurobi' or 'Mosek' (free academic licenses available for both). For installing 'Gurobi' and its R package 'gurobi', follow instructions at <http://www.gurobi.com/downloads/gurobi-optimizer> and <http://www.gurobi.com/documentation/7.5/refman/r_api_overview.html>. For installing 'Mosek' and its R package 'Rmosek', follow instructions at <https://www.mosek.com/downloads/> and <http://docs.mosek.com/8.1/rmosek/install-interface.html>.
Various kinds of designs for
 (industrial) experiments can be created. The package uses, and sometimes enhances,
        design generation routines from other packages. 
        So far, response surface designs from package rsm, latin hypercube
        samples from packages lhs and DiceDesign, and 
        D-optimal designs from package AlgDesign have been implemented.
Rare variant association test integrating variant position information. It aims to identify the presence of clusters of disease-risk variants in specific gene regions. For more details, please read the publication from Persyn et al. (2017)  <doi:10.1371/journal.pone.0179364>.
Provides a '%dopar%' adaptor such that any type of futures can
    be used as backends for the 'foreach' framework.
Provides a parallel backend for the %dopar% function using
        the multicore functionality of the parallel package.
Functions to calculate ADI (Average Dominance Index) and FDI (Frequency-Based Dominance Index). Functions to visualize the Data with Social Network Graphs with Dual
    Directions and Music Notation Graph.
A wrapper on top of the 'Domino Command-Line Client'. It lets you
    run 'Domino' commands (e.g., "run", "upload", "download") directly from your
    R environment. Under the hood, it uses R's system function to run the 'Domino'
    executable, which must be installed as a prerequisite. 'Domino' is a service
    that makes it easy to run your code on scalable hardware, with integrated
    version control and collaboration features designed for analytical workflows
    (see <http://www.dominodatalab.com> for more information).
Provides a parallel backend for the %dopar% function using
        the Rmpi package.
Provides a parallel backend for the %dopar% function using
        the parallel package.
A Redis parallel backend for the %dopar% function
Provides functions to perform
    reproducible parallel foreach loops, using independent
    random streams as generated by L'Ecuyer's combined
    multiple-recursive generator [L'Ecuyer (1999), <DOI:10.1287/opre.47.1.159>].
    It enables to easily convert standard %dopar% loops into
    fully reproducible loops, independently of the number
    of workers, the task scheduling strategy, or the chosen
    parallel environment and associated foreach backend.
The DoseFinding package provides functions for the design and analysis
	     of dose-finding experiments (with focus on pharmaceutical Phase
	     II clinical trials). It provides functions for: multiple contrast
	     tests, fitting non-linear dose-response models (using Bayesian and
	     non-Bayesian estimation), calculating optimal designs and an
	     implementation of the MCPMod methodology.
Provides a parallel backend for the %dopar% function using
        the snow package of Tierney, Rossini, Li, and Sevcikova.
Estimates dose-response relations from summarized dose-response
    data and to combines them according to principles of (multivariate)
    random-effects models.
A small package containing helper utilities for creating function
    for computing statistics.
Renders DOT diagram markup language in R and also provides the possibility to
    export the graphs in PostScript and SVG (Scalable Vector Graphics) formats.
    In addition, it supports literate programming packages such as 'knitr' and
    'rmarkdown'.
Calculation of cluster typicality coefficients as being generated by fuzzy k-means clustering.
Provides .C64(), which is an enhanced version of .C()
    and .Fortran() from the foreign function interface. .C64() supports long
    vectors, arguments of type 64-bit integer, and provides a mechanism to
    avoid unnecessary copies of read-only and write-only arguments. This
    makes it a convenient and fast interface to C/C++ and Fortran code.
Load configuration from a '.env' file, that is
    in the current working directory, into environment variables.
Quick and easy dot-and-whisker plots of regression results.
Contains the discrete nonparametric survivor function estimation algorithm of De Gruttola and Lagakos for doubly interval-censored failure time data and the discrete nonparametric survivor function estimation algorithm of Sun for doubly interval-censored left-truncated failure time data [Victor De Gruttola  & Stephen W. Lagakos (1989) <doi:10.2307/2532030>] [Jianguo Sun (1995) <doi:10.2307/2533008>].
Performs hypothesis tests concerning a regression function in a least-squares model, where the null is a parametric function, and the alternative is the union of large-dimensional convex polyhedral cones. See Bodhisattva Sen and Mary C Meyer (2016) <doi:10.1111/rssb.12178> for more details.
Differential exon usage test for RNA-Seq data via an empirical Bayes shrinkage method for the dispersion parameter the utilizes inclusion-exclusion data to analyze the propensity to skip an exon across groups. The input data consists of two matrices where each row represents an exon and the columns represent the biological samples. The first matrix is the count of the number of reads expressing the exon for each sample. The second matrix is the count of the number of reads that either express the exon or explicitly skip the exon across the samples, a.k.a. the total count matrix. Dividing the two matrices yields proportions representing the propensity to express the exon versus skipping the exon for each sample.
Local linear hazard estimator and its multiplicatively bias correction, including three bandwidth selection methods: best one-sided cross-validation, double one-sided cross-validation, and standard cross-validation.
'Kevin Dowd's' book Measuring Market Risk is a widely read book 
          in the area of risk measurement by students and 
          practitioners alike. As he claims, 'MATLAB' indeed might have been the most 
          suitable language when he originally wrote the functions, but,
          with growing popularity of R it is not entirely 
	  valid. As 'Dowd's' code was not intended to be error free and were mainly 
	  for reference, some functions in this package have inherited those 
	  errors. An attempt will be made in future releases to identify and correct 
	  them. 'Dowd's' original code can be downloaded from www.kevindowd.org/measuring-market-risk/. 
          It should be noted that 'Dowd' offers both
          'MMR2' and 'MMR1' toolboxes. Only 'MMR2' was ported to R. 'MMR2' is more 
          recent version of 'MMR1' toolbox and they both have mostly similar 
          function. The toolbox mainly contains different parametric and non 
	  parametric methods for measurement of market risk as well as 
	  backtesting risk measurement methods.
Provides a wrapper for the download.file function,
    making it possible to download files over HTTPS on Windows, Mac OS X, and
    other Unix-like platforms. The 'RCurl' package provides this functionality
    (and much more) but can be difficult to install because it must be compiled
    with external dependencies. This package has no external dependencies, so
    it is much easier to install.
A set of functions that downscales species occupancy at
    coarse grain sizes to predict species occupancy at fine grain sizes.
Toggles the test and production versions of a large workflow.
A GUI or command-line operated data analysis tool, for
        analyzing time-dependent simulation data in which multiple
        instantaneous or time-lagged relations are assumed. This
        package uses Structural Equation Modeling (the sem package). It
        is aimed to deal with time-dependent data and estimate whether
        a causal diagram fits data from an (agent-based) simulation
        model.
A Scannerless GLR parser/parser generator.  Note that GLR standing for "generalized LR", where L stands for "left-to-right" and
   R stands for "rightmost (derivation)".  For more information see <https://en.wikipedia.org/wiki/GLR_parser>. This parser is based on the Tomita
   (1987) algorithm. (Paper can be found at <http://acl-arc.comp.nus.edu.sg/archives/acl-arc-090501d3/data/pdf/anthology-PDF/J/J87/J87-1004.pdf>).
   The original 'dparser' package documentation can be found at <http://dparser.sourceforge.net/>.  This allows you to add mini-languages to R (like
   RxODE's ODE mini-language Wang, Hallow, and James 2015 <DOI:10.1002/psp4.12052>) or to parse other languages like 'NONMEM' to automatically translate
   them to R code.   To use this in your code, add a LinkingTo dparser in your DESCRIPTION file and instead of using #include <dparse.h> use
   #include <dparser.h>.  This also provides a R-based port of the make_dparser <http://dparser.sourceforge.net/d/make_dparser.cat> command called
   mkdparser().  Additionally you can parse an arbitrary grammar within R using the dparse() function, which works on most OSes and is mainly for grammar
   testing.  The fastest parsing, of course, occurs at the C level, and is suggested.
Beta-binomial Mixture Model is used to infer the pattern from count data.
		It can be used for clustering of RNA methylation sequencing data.
Analysis, visualisation and simulation of digital polymerase chain
    reaction (dPCR). Supports data formats of commerical systems (Bio-Rad QX100 and
    QX200; Fluidigm BioMark) and other systems.
fits the primal graphical lasso, via one-at-a-time
        block-coordinate descent.
Compares distributions with one another in terms of their fit to each sample in a dataset that contains multiple samples, as described in Joo, Aguinis, and Bradley (in press). Users can examine the fit of seven distributions per sample: pure power law, lognormal, exponential, power law with an exponential cutoff, normal, Poisson, and Weibull. Automation features allow the user to compare all distributions for all samples with a single command line, which creates a separate row containing results for each sample until the entire dataset has been analyzed.
Perform tree-ring analyses such as detrending, chronology
        building, and cross dating.  Read and write standard file formats
        used in dendrochronology.
The concordance method is a non-parametric method based on bootstrapping that is used to test the hypothesis that two subsets of time series are similar in terms of mean, variance or both. This method was developed to address a concern within dendroclimatology that young trees may produce a differing climate response to older more established trees. Details of this method are available in  Pirie, M. (2013). The Climate of New Zealand reconstructed from kauri tree rings: Enhancement through the use of novel statistical methodology. PhD. Dissertation, School of Environment and Department of Statistics, University of Auckland, New Zealand. This package also produces a figure with 3 panels, each panel is for a different climate variable. An example of this figure in included in "On the influence of tree size on the climate - growth relationship of New Zealand kauri (Agathis australis): insights from annual, monthly and daily growth patterns. J Wunder, AM Fowler, ER Cook, M Pirie, SPJ McCloskey. Trees 27 (4), 937-948". For further R functions for loading your own dendroclimatology datasets and performing dendrochronology analysis refer to the R package "dplR: Dendrochronology Program Library in R". The concordance procedure is intended to add to the standard dendrochronology techniques provided in "dplR".
A fast, consistent tool for working with data frame like objects,
    both in memory and out of memory.
A 'Teradata' backend for 'dplyr'. It makes it possible to operate
  'Teradata' database <https://www.teradata.com/products-and-services/teradata-database/>
  in the same way as manipulating data frames with 'dplyr'.
An RStudio addin for teaching and learning data manipulation using the 'dplyr' package.
    You can learn each steps of data manipulation by clicking your mouse without coding.
    You can get resultant data (as a 'tibble') and the code for data manipulation.
The package implements a Dirichlet Process Mixture (DPM)
        model for clustering and image segmentation.  The DPM model is
        a Bayesian nonparametric methodology that relies on MCMC
        simulations for exploring mixture models with an unknown number
        of components.  The code implements conjugate models with
        normal structure (conjugate normal-normal DP mixture model).
        The package's applications are oriented towards the
        classification of magnetic resonance images according to tissue
        type or region of interest.
Create, install, and summarise data packages that follow
    the Open Knowledge Foundation's Data Package Protocol.
Functions to perform inference via
        simulation from the posterior distributions for Bayesian
        nonparametric and semiparametric models. Although the name of
        the package was motivated by the Dirichlet Process prior, the
        package considers and will consider other priors on functional
        spaces. So far, DPpackage includes models considering Dirichlet
        Processes, Dependent Dirichlet Processes, Dependent Poisson-
        Dirichlet Processes, Hierarchical Dirichlet Processes, Polya
        Trees, Linear Dependent Tailfree Processes, Mixtures of
        Triangular distributions, Random Bernstein polynomials priors
        and Dependent Bernstein Polynomials.  The package also includes
        models considering Penalized B-Splines. 
        Includes semiparametric models for marginal and conditional
        density estimation, ROC curve analysis, interval censored data,
        binary regression models, generalized linear mixed models, IRT
        type models, and generalized additive models. Also
        contains functions to compute Pseudo-Bayes factors for model
        comparison, and to elicitate the precision parameter of the
        Dirichlet Process. To maximize computational efficiency, the
        actual sampling for each model is done in compiled FORTRAN. The
        functions return objects which can be subsequently analyzed
        with functions provided in the 'coda' package.
Data preprocessing techniques for classification. Functions for normalization, handling of missing values,discretization, outlier detection, feature selection, and data visualization are included.
Provides a generalized method for printing tabular data within 
	the R environment in order to make the process of presenting high quality 
	tabular output seamless for the user.  Output is directed to the R graphics 
	device so that tables can be exported to any file format supported by the 
	graphics device. Utilizes a formula interface to specify the contents of 
	tables often found in manuscripts or business reports.  In addition, formula 
	interface provides inline formatting of the numeric cells of a table and 
	renaming column labels.
Use Dirichlet process Weibull mixture model and dependent Dirichlet process Weibull mixture model for survival data with and without competing risks. Dirichlet process Weibull mixture model is used for data without covariates and dependent Dirichlet process model is used for regression data. The package is designed to handle exact/right-censored/ interval-censored observations without competing risks and exact/right-censored observations for data with competing risks. Inside each cluster of Dirichlet process, we assume a multiplicative effect of covariates as in Cox model and Fine and Gray model. In addition, we provide a wrapper for DPdensity() function from the R package 'DPpackage'. This wrapper automatically uses Low Information Omnibus prior and can model one and two dimensional data with Dirichlet mixture of Gaussian distributions.
Several fast random number generators are provided as C++
  header only libraries: The PCG family by O'Neill (2014
  <https://www.cs.hmc.edu/tr/hmc-cs-2014-0905.pdf>) as well as
  Xoroshiro128+ and Xoshiro256+ by Blackman and Vigna (2018
  <arXiv:1805.01407>). In addition fast functions for generating random
  numbers according to a uniform, normal and exponential distribution
  are included. The latter two use the Ziggurat algorithm originally
  proposed by Marsaglia and Tsang (2000, <doi:10.18637/jss.v005.i08>).
  These functions are exported to R and as a C++ interface and are
  enabled for use with the 64 bit version of the Mersenne-Twister by
  Matsumoto and Nishimura (1998 <doi:10.1145/272991.272995>), the
  default 64 bit generator from the PCG family as well as Xoroshiro128+
  and Xoshiro256+.
Functions, methods, and datasets for fitting dimension
 reduction regression, using slicing (methods SAVE and SIR), Principal
 Hessian Directions (phd, using residuals and the response), and an
 iterative IRE.  Partial methods, that condition on categorical
 predictors are also available.  A variety of tests, and stepwise
 deletion of predictors, is also included.  Also included is
 code for computing permutation tests of dimension.  Adding additional
 methods of estimating dimension is straightforward.
 For documentation, see the vignette in the package.   With version 3.0.4,
 the arguments for dr.step have been modified.
Models the relationship between dose levels and responses in a pharmacological experiment using the 4 Parameter Logistic model. Traditional packages on dose-response modelling such as 'drc' and 'nplr' often draw errors due to convergence failure especially when data have outliers or non-logistic shapes. This package provides robust estimation methods that are less affected by outliers and other initialization methods that work well for data lacking logistic shapes. We provide the bounds on the parameters of the 4PL model that prevent parameter estimates from diverging or converging to zero and base their justification in a statistical principle. These methods are used as remedies to convergence failure problems.  Gadagkar, S. R. and Call, G. B. (2015) <doi:10.1016/j.vascn.2014.08.006> Ritz, C. and Baty, F. and Streibig, J. C. and Gerhard, D. (2015) <doi:10.1371/journal.pone.0146021>.
An R-focused pipeline toolkit
  for reproducible code and high-performance computing.
Creation and use of R Repositories via helper functions 
 to insert packages into a repository, and to add repository information 
 to the current R session. Two primary types of repositories are support:
 gh-pages at GitHub, as well as local repositories on either the same machine
 or a local network. Drat is a recursive acronym: Drat R Archive Template.
Graphical display of R expression, showing the
        interpretation of an expression by R and the various kind of R
        data structure. The steps of the interpretation of an
        expression are obtained through the parsed tree.
We present DRaWR, a network-based method for ranking genes or
    properties related to a given gene set. Such related genes or properties are
    identified from among the nodes of a large, heterogeneous network of biological
    information. Our method involves a random walk with restarts, performed on
    an initial network with multiple node and edge types, preserving more of the
    original, specific property information than current methods that operate
    on homogeneous networks. In this first stage of our algorithm, we find the
    properties that are the most relevant to the given gene set and extract a
    subnetwork of the original network, comprising only the relevant properties. We
    then rerank genes by their similarity to the given gene set, based on a second
    random walk with restarts, performed on the above subnetwork.
Feed longitudinal data into a Bayesian Latent Factor Model to obtain a low-rank representation. Parameters are estimated using a Hamiltonian Monte Carlo algorithm with STAN. See G. Weinrott, B. Fontez, N. Hilgert and S. Holmes, "Bayesian Latent Factor Model for Functional Data Analysis", Actes des JdS 2016.
Analysis of dose-response data is made available through a suite of flexible and versatile model fitting and after-fitting functions.
A penalized/non-penalized implementation for dynamic regression in the presence of autocorrelated residuals (DREGAR) using iterative penalized/ordinary least squares. It applies Mallows CP, AIC, BIC and GCV to select the tuning parameters.
A somewhat outdated package of basic and easy-to-use functions for
  fitting dose-response curves to continuous dose-response data, calculating some
  toxicological parameters and plotting the results. Please consider using
  the more powerful and actively developed 'drc' package.  Functions that are
  fitted are the cumulative density function of the log-normal distribution
  ('probit' fit), of the logistic distribution ('logit' fit), of the Weibull
  distribution ('weibull' fit) and a linear-logistic model ('linlogit' fit),
  derived from the latter, which is used to describe data showing stimulation at
  low doses (hormesis). In addition, functions checking, plotting and retrieving
  dose-response data retrieved from a database accessed via 'RODBC' are included.
  As an alternative to the original fitting methods, the algorithms from the 'drc'
  package can be used.
Fit restricted mean models for the conditional association
  between an exposure and an outcome, given covariates. Three methods
  are implemented: O-estimation, where a nuisance model for the
  association between the covariates and the outcome is used;
  E-estimation where a nuisance model for the association
  between the covariates and the exposure is used, and doubly robust (DR)
  estimation where both nuisance models are used. In DR-estimation,
  the estimates will be consistent when at least one of the nuisance
  models is correctly specified, not necessarily both.
A tidy implementation of equations that correct for instrumental drift in 
    continuous water quality monitoring data.  There are many sources of water quality data 
    including private (ex: YSI instruments) and open source (ex: USGS and NDBC), each of 
    which are susceptible to errors/inaccuracies due to drift. This package allows the 
    user to correct their data using one or two standard reference values in a uniform, 
    reproducible way. The equations implemented are from Hasenmueller (2011)
    <doi:10.7936/K7N014KS>.
Provides a R driver for Apache Drill<https://drill.apache.org>, which could connect to the Apache Drill cluster<https://drill.apache.org/docs/installing-drill-on-the-cluster> or drillbit<https://drill.apache.org/docs/embedded-mode-prerequisites> and get result(in data frame) from the SQL query and check the current configuration status. This link <https://drill.apache.org/docs> contains more information about Apache Drill.
R codes for imputing dropout events. Many statistical methods in cell type identification, visualization and lineage reconstruction do not account for dropout events ('PCAreduce', 'SC3', 'PCA', 't-SNE', 'Monocle', 'TSCAN', etc). 'DrImpute' can improve the performance of such software by imputing dropout events.
This is a connectivity mapping-based drug repurposing tool that identifies drugs that can potentially reverse query disease phenotype or have similar functions with query drugs.
This is a collection of functions for discontinuous regression
	     analysis and image processing.
Contains quality control routines for multiplex immunoassay data, 
    including several approaches for: treating the background noise of the assay,  
    fitting the dose-response curves and estimating the limits of quantification.
Likelihood-based marginal regression and association
        modelling for repeated, or otherwise clustered, categorical
        responses using dependence ratio as a measure of the
        association
Dual empirical likelihood (DEL) inference under semiparametric density ratio models (DRM) in the presence of multiple samples, including population cumulative distribution function estimation, quantile estimation and comparison, density estimation, composite hypothesis testing for DRM parameters which encompasses testing for changes in population distribution functions as a special case, etc.
Drop out analysis for psychologists in a R based web application.
    Shiny is used to visualize and analyze drop outs tailored to the methods of
    online survey methodology. Concept and app presented at the SCIP Conference
    in Long Beach, California.
Generates simulated data representing the LOX/GOX drop testing
    process (also known as impact testing). A simulated process allows for
    accelerated study of test behavior. Functions are provided to simulate
    trials, test series, and groups of test series. Functions for creating plots
    specific to this process are also included. Test attributes and criteria can
    be set arbitrarily. This work is not endorsed by or affiliated with NASA.
    See "ASTM G86-17, Standard Test Method for Determining Ignition Sensitivity
    of Materials to Mechanical Impact in Ambient Liquid Oxygen and Pressurized
    Liquid and Gaseous Oxygen Environments" <doi:10.1520/G0086-17>.
An Implementation of Dimensionality Reduction
    via Regression using Kernel Ridge Regression.
Provides tools for assessing the shape of a dose-response
    curve by testing linearity and non-linearity at user-defined cut-offs. It
    also provides two methods of estimating a threshold dose, or the dose at
    which the dose-response function transitions to significantly increasing:
    bi-linear (based on pkg 'segmented') and smoothed with splines (based on
    pkg 'mgcv').
Targeted minimum loss-based estimators of counterfactual means and
    causal effects that are doubly-robust with respect both to consistency and
    asymptotic normality (Benkeser et al (2017), <doi:10.1093/biomet/asx053>; MJ
    van der Laan (2014), <doi:10.1515/ijb-2012-0038>).
An implementation of a Machine Learning Framework for prediction of new drugs Side Effects.
    Firstly drugs are clustered with respect to their features description and secondly predictions are made, according to Bayesian scores.
    Moreover it can perform protein enrichment considering the proteins clustered together in the first step of the algorithm.
    This last tool is of extreme interest for biologist and drug discovery purposes, given the fact that it can be used either as a validation of the clusters obtained, as well as for the possible discovery of new interactions between certain side effects and non targeted pathways.
    Clustering of the drugs in the feature space can be done using K-Means, PAM or K-Seeds (a novel clustering algorithm proposed by the author).
Perform assessment of synergy/antagonism for drug combinations based
    on the Loewe additivity model, following Harbron's approach (Statistics in
    Medicine, 2010, <doi:10.1002/sim.3916>). The package allows flexible modeling
    of the drug interaction index and supports "2-stage" estimation in addition 
    to "1-stage" estimation, including bootstrap-based confidence intervals.
    The method requires data on the monotherapy responses and the package 
    accommodates both checkerboard and ray designs. Functions are available for 
    graphical exploration of model goodness-of-fit and diagnostics, as well as 
    for synergy/antagonism assessment in 2D and 3D.
The package performs various analyzes of descriptive statistics, including correlations
A collection of Shiny apps that allow for the simulation and
    exploration of various infectious disease transmission dynamics scenarios.
    The purpose of the package is to help individuals learn 
    about infectious disease epidemiology from a dynamical systems perspective.
    All apps include explanations of the underlying models and instructions on
    what to do with the models.
Two discretization-based Monte Carlo algorithms, namely the Fu-Wang algorithm and the Wang-Lee algorithm, are provided for random sample generation from a high dimensional distribution of complex structure.  The normalizing constant of the target distribution needs not to be known.
Calculate posterior modes and credible intervals of parameters of the Dixon-Simon model for subgroup analysis (with binary covariates) in clinical trials.
Tools for multivariate, linear, time-invariant,
	time series models. This includes ARMA and state-space representations,
	and methods for converting between them. It also includes simulation
	methods and several estimation functions. The package has functions 
	for looking at model roots, stability, and forecasts at different 
	horizons. The ARMA model representation is general, so that VAR, VARX, 
	ARIMA, ARMAX, ARIMAX can all be considered to be special cases. Kalman
	filter and smoother estimates can be obtained from the state space
	model, and state-space model reduction techniques are implemented. 
	An introduction and User's Guide is available in a vignette.
An abstract DList class helps storing large list-type objects in a distributed manner. Corresponding high-level functions and methods for handling distributed storage (DStorage) and lists allows for processing such DLists on distributed systems efficiently. In doing so it uses a well defined storage backend implemented based on the DStorage class.
Datasets and functions that facilitate data analysis labs in data science courses and workshops.
Dynamic slicing is a method designed for dependency detection between a categorical variable and a continuous variable. It could be applied for non-parametric hypothesis testing and gene set enrichment analysis.
Density surface modelling of line transect data. A Generalized
    Additive Model-based approach is used to calculate spatially-explicit estimates
    of animal abundance from distance sampling (also presence/absence and strip
    transect) data. Several utility functions are provided for model checking,
    plotting and variance estimation.
An expressive language to facilitate simulation and visualization
    of two-dimensional dynamical systems. The basic elements of the language are
    a model wrapping around a function(x,y) which outputs a list(x
    = xprime, y = yprime), and a range. The language supports three
    types of visual objects: visualizations, features, and backgrounds. Visualizations, including dots and arrows,
    depict the behavior of the dynamical system over the entire range.
    Features display
    user-defined curves and points, and their images under the system.
    Backgrounds define and color regions of interest, such as basins of attraction.
    The language
    can also approximate attractors and their basins through simulation.
Fits inhomogeneous Poisson process spatial models
    to line transect sampling data and provides estimate of
    abundance within a region.
Perform a test of a simple null hypothesis about a 
    directly standardized rate and obtain the matching confidence 
    interval using a choice of methods.
Performs distance sampling simulations.It repeatedly generates
    instances of a user defined population within a given survey region, generates
    realisations of a survey design (currently these must be generated using
    Distance software in advance <http://distancesampling.org/>) and simulates
    the detection process. The data are then analysed so that the results can
    be compared for accuracy and precision across all replications. This will
    allow users to select survey designs which will give them the best accuracy
    and precision given their expectations about population distribution. Any
    uncertainty in population distribution or population parameters can be
    included by running the different survey designs for a number of different
    population descriptions. An example simulation can be found in the help file for
    make.simulation.
Using Dempster-Shafer Theory of Evidence, also called "Theory of Belief Functions". Basic probability assignments, or mass functions, can be defined on the subsets of a set of possible values. Two mass functions can be combined using Dempster's rule of combination. A mass function can be extended to a larger frame. Marginalization, i.e. reduction to a smaller frame can also be done. These features can be combined to analyze small belief networks and take into account situations where information cannot be satisfactorily described by probability distributions.
A collection of functions to estimate parameters of a diffusion model via a D*M analysis. Build in models are: the Ratcliff diffusion model, the RWiener diffusion model, and Linear Ballistic Accumulator models. Custom models functions can be specified as long as they have a density function.
Building discrete-time survival trees and bagged trees based on
    the functionalities of the rpart package. Splitting criterion maximizes the
    likelihood of a covariate-free logistic discrete time hazard model.
Functions for direct surrogate variable analysis, which can identify hidden factors in high-dimensional biomedical data.
Elucidation of human disease similarities has emerged as an active research area, which is highly relevant to etiology, disease classification, and drug repositioning. This package was designed and implemented for identifying disease similarities. It contains five functions which are 'DCEA', 'DCpathway', 'DS', 'comDCGL' and 'comDCGLplot'. In 'DCEA' function, differentially co-expressed genes and differentially co-expressed links are extracted from disease vs. health samples. Then 'DCpathway' function assigns differential co-expression values of pathways to be the average differential co-expression value of their component genes. Then 'DS' employs partial correlation coefficient of pathways as the disease similarity for each disease pairs. And 'DS' contains a permutation process for evaluating the statistical significant of observed disease partial correlation coefficients. At last, 'comDCGL' and 'comDCGLplot' sort out shared differentially co-expressed genes and differentially co-expressed links with regulation information and visualize them.
Data objects in R can be rendered as HTML tables using the
    JavaScript library 'DataTables' (typically via R Markdown or Shiny). The
    'DataTables' library has been included in this R package. The package name
    'DT' is an abbreviation of 'DataTables'.
Towards automation of descriptive frequencies and statistics tables.
Deconvolving cell types from high-throughput gene profiling data.
This package contains functions to compare the accuracy of two binary diagnostic tests in a "paired" study design, i.e. when each test is applied to each subject in the study.
Provides fast methods to work with Merton's distance to default 
  model introduced in Merton (1974) <doi:10.1111/j.1540-6261.1974.tb03058.x>. 
  The methods includes simulation and estimation of the parameters.
This package implements different algorithms for analyzing
        randomly truncated data, one-sided and two-sided (i.e. doubly)
        truncated data. Two real data sets are included.
Non-iterative estimator for the cumulative distribution of a doubly truncated variable. de Uña-Álvarez J. (2018) <doi:10.1007/978-3-319-73848-2_37>.
Diffusion Weighted Imaging (DWI) is a Magnetic Resonance Imaging
             modality, that measures diffusion of water in tissues like the human 
             brain. The package contains R-functions to process diffusion-weighted 
             data. The functionality includes diffusion tensor imaging (DTI),
             diffusion kurtosis imaging (DKI), modeling for high angular resolution 
             diffusion weighted imaging (HARDI) using Q-ball-reconstruction and 
             tensor mixture models, several methods for structural adaptive 
             smoothing including POAS and msPOAS, and a streamline fiber tracking 
             for tensor and tensor mixture models.
             The package provides functionality to manipulate and visualize results 
             in 2D and 3D.
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 Dunnett modified Tukey-Kramer test (a.k.a. the
        T3 Procedure), create two vectors: one for your observations
        and one for the factor level of each observation. The function,
        gl.unequal, provides a means to more conveniently produce a
        factor vector with unequal sample sizes. Next, use the DTK.test
        function to conduct the test and save the output as an object
        to input into the DTK.plot function, which produces a
        confidence interval plot for each of the pairwise comparisons.
        Lastly, the function TK.test conducts the original Tukey-Kramer
        test.
A series of functions which aid in both simulating and
        determining the properties of finite, discrete-time, discrete
        state markov chains.  Two functions (DTMC, MultDTMC) produce n
        iterations of a Markov Chain(s) based on transition
        probabilities and an initial distribution.  The function FPTime
        determines the first passage time into each state.  The
        function statdistr determines the stationary distribution of a
        Markov Chain.
Compute the dynamic threshold panel model suggested by (Stephanie Kremer, Alexander Bick and Dieter Nautz (2013) <doi:10.1007/s00181-012-0553-9>) in which they extended the (Hansen (1999) <doi: 10.1016/S0304-4076(99)00025-1>) original static panel threshold estimation and the Caner and (Hansen (2004) <doi:10.1017/S0266466604205011>) cross-sectional instrumental variable threshold model, where generalized methods of moments type estimators are used.
This implements the data table back-end for 'dplyr' so that you
    can seamlessly use data table and 'dplyr' together.
Estimation and comparison of survival distributions of dynamic treatment regimes (DTRs) from sequentially randomized clinical trials.
Combines various decision tree algorithms, plus both
             linear regression and ensemble methods into one package.
             Allows for the use of both continuous and categorical outcomes.
             An optional feature is to quantify the (in)stability to the
             decision tree methods, indicating when results can be trusted
             and when ensemble methods may be preferential.
Dynamic treatment regimens (DTRs) are sequential decision rules tailored at each stage by time-varying subject-specific features and intermediate outcomes observed in previous stages. This package implements three methods: O-learning (Zhao et. al. 2012,2014), Q-learning (Murphy et. al. 2007; Zhao et.al. 2009) and P-learning (Liu et. al. 2014, 2015) to estimate the optimal DTRs.
Dynamic treatment regime estimation and inference via G-estimation,
    dynamic weighted ordinary least squares (dWOLS) and Q-learning. Inference
    via bootstrap and (for G-estimation) recursive sandwich estimation.
This package provides functions for 1D and 2D Discrete
        Cosine Transform (DCT), Discrete Sine Transform (DST) and
        Discrete Hartley Transform (DHT).
A comprehensive implementation of dynamic time warping (DTW) algorithms in R.  DTW computes the optimal (least cumulative distance) alignment between points of two time series.  Common DTW variants covered include local (slope) and global (window) constraints, subsequence matches, arbitrary distance definitions, normalizations, minimum variance matching, and so on.  Provides cumulative distances, alignments, specialized plot styles, etc.
Time series clustering along with optimized techniques related
    to the Dynamic Time Warping distance and its corresponding lower bounds.
    Implementations of partitional, hierarchical, fuzzy, k-Shape and TADPole
    clustering are available. Functionality can be easily extended with
    custom distance measures and centroid definitions. Implementations of
    DTW barycenter averaging, a distance based on global alignment kernels,
    and the soft-DTW distance and centroid routines are also provided. 
    All included distance functions have custom loops optimized for the 
    calculation of cross-distance matrices, including parallelization support.
    Several cluster validity indices are included.
Provides an implementation of the Time-Weighted Dynamic Time
    Warping (TWDTW) method for land cover mapping using satellite image time series.
    TWDTW is based on the Dynamic Time Warping technique and has achieved high
    accuracy for land cover classification using satellite data. The method is
    based on comparing unclassified satellite image time series with a set of known
    temporal patterns (e.g. phenological cycles associated with the vegetation).
    Using 'dtwSat' the user can build temporal patterns for land cover types, apply
    the TWDTW analysis for satellite datasets, visualize the results of the time
    series analysis, produce land cover maps, create temporal plots for land cover
    change, and compute accuracy assessment metrics.
Functions to analyze multiple choice data using Dual Scaling
Provides an operator for assigning nested components of a list to
  names via a concise, Haskell-like pattern matching syntax. This is especially
  convenient for assigning individual names to the multiple values that a
  function may return in the form of a list, and for extracting deeply nested
  list components.
Programmatic access to the DuckDuckGo Instant Answer API <https://api.duckduckgo.com/api>.
Expands factors, characters and other eligible classes
        into dummy/indicator variables.
Efficiently create dummies of all factors and character vectors in a data frame. Support is included for learning the categories on one data set (e.g., a training set) and deploying them on another (e.g., a test set).
Computes Dunn's test (1964) for stochastic dominance and reports the results among multiple pairwise comparisons after a Kruskal-Wallis test for stochastic dominance among k groups (Kruskal and Wallis, 1952). The interpretation of stochastic dominance requires an assumption that the CDF of one group does not cross the CDF of the other. 'dunn.test' makes k(k-1)/2 multiple pairwise comparisons based on Dunn's z-test-statistic approximations to the actual rank statistics. The null hypothesis for each pairwise comparison is that the probability of observing a randomly selected value from the first group that is larger than a randomly selected value from the second group equals one half; this null hypothesis corresponds to that of the Wilcoxon-Mann-Whitney rank-sum test. Like the rank-sum test, if the data can be assumed to be continuous, and the distributions are assumed identical except for a difference in location, Dunn's test may be understood as a test for median difference. 'dunn.test' accounts for tied ranks.
For the implementation of the step-down or step-up Dunnett testing procedures, the package includes R functions to calculate critical constants and R functions to calculate adjusted P-values of the test statistics. In addition, the package also contains functions to evaluate testing powers and hence the necessary sample sizes specially for the classical problem of comparisons of several treatments with a control.
Inference of population sizes using a binomial likelihood and least informative discrete uniform priors.
Functionality for analyzing dose-volume histograms (DVH)
        in radiation oncology: Read DVH text files, calculate DVH
        metrics as well as generalized equivalent uniform dose (gEUD),
        biologically effective dose (BED), equivalent dose in 2 Gy
        fractions (EQD2), normal tissue complication probability
        (NTCP), and tumor control probability (TCP). Show DVH
        diagrams, check and visualize quality assurance constraints
        for the DVH. Includes web-based graphical user interface.
Faster versions of base R functions (e.g. mean, standard deviation, covariance, weighted mean), mostly written in C++, along with miscellaneous functions for various purposes (e.g. create histogram with fitted probability density function or probability mass function curve, create body mass index groups, assess linearity assumption in logistic regression).
A set of wrapper functions for 'data.world' REST API endpoints <https://apidocs.data.world>.
Solving large scale distance weighted discrimination.
  The main algorithm is a symmetric Gauss-Seidel based alternating direction method of multipliers (ADMM) method. See Lam, X.Y., Marron, J.S., Sun, D.F., and Toh, K.C. (2018) <arXiv:1604.05473> for more details.
Infers networks with hubs using degree weighted Lasso method.
Regression for a discrete response, where the conditional distribution is modelled via a discrete Weibull distribution.
Contains functions for dyadic network analysis.
An R interface to the 'dygraphs' JavaScript charting library
    (a copy of which is included in the package). Provides rich facilities
    for charting time-series data in R, including highly configurable
    series- and axis-display and interactive features like zoom/pan and
    series/point highlighting.
Solves quadratic programming problems using Richard L. Dykstra's cyclic projection algorithm. Routine allows for a combination of equality and inequality constraints. See Dykstra (1983) <doi:10.1080/01621459.1983.10477029> for details.
Add a "Did You Mean" feature to the R interactive. With this
    package, error messages for misspelled input of variable names or package names
    suggest what you really want to do in addition to notification of the mistake.
Time series regression.  The dyn class interfaces ts,
        irts(), zoo() and zooreg() time series classes to lm(), glm(),
        loess(), quantreg::rq(), MASS::rlm(), MCMCpack::MCMCregress(),
        quantreg::rq(), randomForest::randomForest() and other regression
        functions allowing those functions to be used with time series
        including specifications that may contain lags, diffs and
        missing values.
While autoregressive distributed lag models allow for extremely flexible dynamics, interpreting substantive significance of complex lag structures remains difficult. This package is designed to assist users in dynamically simulating and plotting the results of various autoregressive distributed lag models. It also contains post-estimation diagnostics, including a test for cointegration when estimating the error-correction variant of the autoregressive distributed lag model (Pesaran, Shin, and Smith 2001 <doi:10.1002/jae.616>).
The package is aimed at dynamically visualizing probability
    distributions and their moments and all the commonly used distributions are
    included.
Fits localized GP model for dynamic computer experiments via singular value decomposition of the response matrix Y for large N (the number of observations) using the algorithm proposed by Zhang et al. (2018) <arXiv:1611.09488>. The current version only supports 64-bit version of R.
Interactive graphical tool for manipulating graphs
Contains functions that lets you fit dynamic hazard models using 
  state space models. The first implemented model is described in Fahrmeir 
  (1992) <doi:10.1080/01621459.1992.10475232> and Fahrmeir (1994) 
  <doi:10.1093/biomet/81.2.317>. Extensions hereof are available where the  
  Extended Kalman filter is replaced by an unscented Kalman filter and other 
  options including particle filters.
Contains methods for detection of clusters in hierarchical clustering dendrograms.
An implementation of the method proposed in Lund and Hansen (2018) for fitting 3-dimensional dynamical array models.  The implementation is based on the glamlasso package, see Lund et al. (2017) <doi:10.1080/10618600.2017.1279548>, for efficient design matrix free lasso regularized estimation in a generalized linear array model. The implementation uses a block relaxation scheme to fit each individual component in the model using functions from the glamlasso package.
A native R implementation and enhancement of the Dynamic TOPMODEL
    semi-distributed hydrological model. Includes some preprocessing, utility and
    routines for displaying outputs.
Inference by sequential Monte Carlo for 
  dynamic tree regression and classification models
  with hooks provided for sequential design and optimization, 
  fully online learning with drift, variable selection, and 
  sensitivity analysis of inputs.  Illustrative 
  examples from the original dynamic trees paper are facilitated
  by demos in the package; see demo(package="dynaTree").
A GUI to solve dynamic biplots and classical biplot. Try matrices
    of 2-way and 3-way. The GUI can be run in multiple languages.
DynClust is a two-stage procedure for the denoising and clustering of stack of noisy images acquired over time. Clustering only assumes that the data contain an unknown but small number of dynamic features. The method first denoises the signals using local spatial and full temporal information. The clustering step uses the previous output to aggregate voxels based on the knowledge of their spatial neighborhood. Both steps use a single keytool based on the statistical comparison of the difference of two signals with the null signal. No assumption is therefore required on the shape of the signals. The data are assumed to be normally distributed (or at least follow a symmetric distribution) with a known constant variance. Working pixelwise, the method can be time-consuming depending on the size of the data-array but harnesses the power of multicore cpus.
While there are many well-established measures for identifying critical fluctuations and phase transitions, these measures only work with many points of measurement and thus are unreliable when studying short and coarse-grained time series. This package provides a measure for complexity in a time series that does not rely on long time series (Kaiser (2017), <doi:10.17605/OSF.IO/GWTKX>).
Computes dynamical correlation estimates and percentile
        bootstrap confidence intervals for pairs of longitudinal
        responses, including consideration of lags and derivatives.
An anonymization algorithm to resist neighbor label attack in a dynamic network.
Fits semiparametric dynamic frailty models according to the methodology of Putter and van Houwelingen (2015) <doi:10.1093/biostatistics/kxv002>. Intermediate models, where the frailty is piecewise constant on prespecified intervals, are also supported. The frailty process is taken to have a specific auto-correlation structure, and the supported distributions include gamma, inverse Gaussian, power variance family (PVF) and positive stable.
Fit dynamic intervention model using the arima() function.
Dynamic linear models and time series regression.
Demonstrate the results of a statistical model object as a dynamic nomogram in an RStudio panel or web browser. Also, the generic DNbuilder() function in this package provides a simple and straightforward way to build and publish a dynamic nomogram on the web to use the app independent of R. 'DynNom' supports a variety of model objects; lm(), glm(), coxph() models and also ols(), Glm(), lrm(), cph() models in the 'rms' package.
Implements the fast Fourier transform to estimate delays of expression initiation between trajectories to integrate and
    analyse time course omics data.
Computes the first stage GMM estimate of a dynamic linear model with p lags of the dependent variables.
The dynpred package contains functions for dynamic prediction in survival analysis.
A domain-specific language for specifying translating recursions
    into dynamic-programming algorithms. See 
    <https://en.wikipedia.org/wiki/Dynamic_programming> for a description
    of dynamic programming.
Intensive longitudinal data have become increasingly prevalent in
    various scientific disciplines. Many such data sets are noisy, multivariate,
    and multi-subject in nature. The change functions may also be continuous, or
    continuous but interspersed with periods of discontinuities (i.e., showing
    regime switches). The package 'dynr' (Dynamic Modeling in R) is an R package
    that implements a set of computationally efficient algorithms for handling a
    broad class of linear and nonlinear discrete- and continuous-time models with
    regime-switching properties under the constraint of linear Gaussian measurement
    functions. The discrete-time models can generally take on the form of a state-
    space or difference equation model. The continuous-time models are generally
    expressed as a set of ordinary or stochastic differential equations. All
    estimation and computations are performed in C, but users are provided with the
    option to specify the model of interest via a set of simple and easy-to-learn
    model specification functions in R. Model fitting can be performed using single-
    subject time series data or multiple-subject longitudinal data.
Improves the concept of multivariate range boxes, which is highly susceptible for outliers and does not consider the distribution of the data. The package uses dynamic range boxes to overcome these problems.
Dynamic stochastic block model that combines a stochastic block model (SBM) for its static part with independent Markov chains for the evolution of the nodes groups through time, developed in  Matias and Miele (2016) <doi:10.1111/rssb.12200>.
Dynamic simulations and graphical depictions of autoregressive
    relationships.
Functions fitting time-varying coefficient models for interval
    censored and right censored survival data. Three major approaches are
    implemented: 1) Bayesian Cox model with time-independent, time-varying or
    dynamic coefficients for right censored and interval censored data; 2)
    Spline based time-varying coefficient Cox model for right censored data; 3)
    Transformation model with time-varying coefficients for right censored data
    using estimating equations.
Methods to estimate dynamic treatment regimes using Interactive Q-Learning, Q-Learning, weighted learning, and value-search methods based on Augmented Inverse Probability Weighted Estimators and Inverse Probability Weighted Estimators.
Functions for dyadic binary/dichotomous sequence analyses are implemented in this contribution.
             The focus is on estimating actor-partner-interaction models using various approaches, for  
             instances the approach of Bakeman & Gottman's (1997) <DOI:10.1017/cbo9780511527685>,
             generalized multi-level models, and basic Markov models. Moreover, coefficients of one model 
             can be translated into those of the other models. Finally, simulation-based power analyses are
             provided.
A collection of functions designed to estimate and predict skewed spatial processes, and a real data set.
Functions for latent class analysis, short time Fourier
	     transform, fuzzy clustering, support vector machines,
	     shortest path computation, bagged clustering, naive Bayes
	     classifier, ...
Plots of the empirical attainment function for two objectives.
An implementation of multiple-locus association mapping on a genome-wide scale. 'Eagle' can handle inbred or outbred study populations, populations of arbitrary unknown complexity, and data larger than the memory capacity of the computer. Since 'Eagle' is based on linear mixed models, it is best suited to the analysis of data on continuous traits. However, it can tolerate non-normal data. 'Eagle' reports, as its findings, the best set of snp in strongest association with a trait. To perform an analysis, run 'OpenGUI()'. This  opens a web browser to the menu-driven user interface for the input of data, and for performing genome-wide analysis.
Estimator augmentation methods for statistical inference on high-dimensional data, 
    as described in Zhou, Q. (2014) <arXiv:1401.4425v2>
    and Zhou, Q. and Min, S. (2017) <doi:10.1214/17-EJS1309>.
    It provides several simulation-based inference methods: (a) Gaussian and 
    wild multiplier bootstrap for lasso, group lasso, scaled lasso, scaled group
    lasso and their de-biased estimators, (b) importance sampler for approximating
    p-values in these methods, (c) Markov chain Monte Carlo lasso sampler with 
    applications in post-selection inference.
A 'Shiny' web application for energy industry analytics.
      Take an overview of the industry, measure Key Performance Indicators,
      identify changes in the industry over time, and discover new relationships in the data.
Implements a simple, likelihood-based estimation of the reproduction number (R0) using a branching process with a Poisson likelihood. This model requires knowledge of the serial interval distribution, and dates of symptom onsets. Infectiousness is determined by weighting R0 by the probability mass function of the serial interval on the corresponding day. It is a simplified version of the model introduced by Cori et al. (2013) <doi:10.1093/aje/kwt133>.
The Early-Warning-Signals Toolbox provides methods for estimating
    statistical changes in timeseries that can be used for identifying nearby
    critical transitions. Based on Dakos et al (2012) Methods for Detecting
    Early Warnings of Critical Transitions in Time Series Illustrated Using
    Simulated Ecological Data. PLoS ONE 7(7):e41010
Build regression models using the techniques in Friedman's
    papers "Fast MARS" and "Multivariate Adaptive Regression
    Splines" <doi:10.1214/aos/1176347963>.
    (The term "MARS" is trademarked and thus not used in
    the name of the package.)
Downloads a satellite image via Google Maps/Earth (these are
    originally from a variety of aerial photography sources), 
    translates the image into a perceptually uniform color space,
    runs one of a few different clustering algorithms on the colors in the image 
    searching for a user-supplied number of colors,
    and returns the resulting color palette.
Enables launching a series of simulations of a computer code from the R session, and to retrieve the simulation outputs in an appropriate format for post-processing treatments. Five sequential sampling schemes and three coupled-to-MCMC schemes are implemented.
Given the scores from decision makers, the analytic hierarchy process can be conducted easily.
Perform analysis of variance and other important complementary
  analyses.  The functions are easy to use.  Performs analysis in various
  designs, with balanced and unbalanced data.
Allows users to easily read multiple comma separated tables and create a data frame under the same name.
    Is able to read multiple comma separated tables from a local directory, a zip file or a zip file on a remote directory.
Descriptive analysis is essential for publishing medical articles.
  This package provides an easy way to conduct the descriptive analysis.
  1. Both numeric and factor variables can be handled. For numeric variables, normality test will be applied to choose the parametric and nonparametric test.
  2. Both two or more groups can be handled. For groups more than two, the post hoc test will be applied, 'Tukey' for the numeric variables and 'FDR' for the factor variables.
  3. T test, ANOVA or Fisher test can be forced to apply.
Builds format strings that can be used with strptime() and sprintf().
It is a package that can be used to send HTML reports easily.
Contains a few utility functions for working with capture-history data, a function for simulating capture-history data, and a function to fit this data, using a Gibbs sampler.
Utilities for building certain kinds of common matrices and models in 
    the extended structural equation modeling package, 'OpenMx'.
Set of wrappers for the 'ncdf4' package to simplify and extend its reading/writing capabilities into/from multidimensional R arrays.
Easily load and install multiple packages from different sources, 
    including CRAN and GitHub. The libraries function allows you to load or attach 
    multiple packages in the same function call. The packages function will load one 
    or more packages, and install any packages that are not installed on your system 
    (after prompting you). Also included is a from_import function that allows you 
    to import specific functions from a package into the global environment.
Power analysis is used in the estimation of sample sizes for
    experimental designs. Most programs and R packages will only output the highest
    recommended sample size to the user. Often the user input can be complicated
    and computing multiple power analyses for different treatment comparisons can
    be time consuming. This package simplifies the user input and allows the user
    to view all of the sample size recommendations or just the ones they want to see.
    The calculations used to calculate the recommended sample sizes are from the
    'pwr' package.
Query NCBI Entrez and retrieve PubMed records in XML or text format. Process PubMed records by extracting and aggregating data from selected fields. A large number of records can be easily downloaded via this simple-to-use interface to the NCBI PubMed API.
Performs analysis of regression in simple designs with quantitative treatments, 
             including mixed models and non linear models. Plot graphics (equations and data).
The main function, ProtectTable(), performs table suppression according to a 
 frequency rule with a data set as the only required input. Within this function, 
 protectTable(), protectLinkedTables() or runArgusBatchFile() in package 'sdcTable' is called. 
 Lists of level-hierarchy (parameter 'dimList') and other required input to these functions 
 are created automatically. 
 The function, PTgui(), starts a graphical user interface based on the shiny package.
This SVG elements generator can easily generate 
    SVG elements such as rect, line, circle, ellipse, polygon, 
    polyline, text and group. Also, it can combine and 
    output SVG elements into a SVG file.
Set of tools to simplify application of atomic forecast
    verification metrics for (comparative) verification of ensemble forecasts
    to large data sets. The forecast metrics are imported from the
    'SpecsVerification' package, and additional forecast metrics are provided
    with this package. Alternatively, new user-defined forecast scores can be
    implemented using the example scores provided and applied using the
    functionality of this package.
Fitting and testing multi-attribute probabilistic choice
  models, especially the Bradley-Terry-Luce (BTL) model (Bradley &
  Terry, 1952; Luce, 1959), elimination-by-aspects (EBA) models
  (Tversky, 1972), and preference tree (Pretree) models (Tversky &
  Sattath, 1979).
Package implements entropy balancing, a data preprocessing procedure that allows users to reweight a dataset such that the covariate distributions in the reweighted data satisfy a set of user specified moment conditions. This can be useful to create balanced samples in observational studies with a binary treatment where the control group data can be reweighted to match the covariate moments in the treatment group. Entropy balancing can also be used to reweight a survey sample to known characteristics from a target population.
We propose a new sample size calculation method for trial-based
    cost-effectiveness analyses. Our strategy is based on the value of perfect
    information that would remain after the completion of the study.
Empirical Bayes thresholding using the methods developed
    by I. M. Johnstone and B. W. Silverman. The basic problem is to
    estimate a mean vector given a vector of observations of the mean
    vector plus white noise, taking advantage of possible sparsity in
    the mean vector. Within a Bayesian formulation, the elements of
    the mean vector are modelled as having, independently, a
    distribution that is a mixture of an atom of probability at zero
    and a suitable heavy-tailed distribution. The mixing parameter can
    be estimated by a marginal maximum likelihood approach. This leads
    to an adaptive thresholding approach on the original data.
    Extensions of the basic method, in particular to wavelet
    thresholding, are also implemented within the package.
Infer the adjacency matrix of a
	network from time course data using an empirical Bayes
	estimation procedure based on Dynamic Bayesian Networks.
Provides the Empirical Bayesian Elastic Net for handling multicollinearity in generalized linear regression models.  As a special case of the 'EBglmnet'
  package (also available on CRAN), this package encourages a grouping effects to select relevant variables and estimate the corresponding non-zero effects.
Genotyping the population using next generation sequencing data is essentially important for the rare variant detection. In order to distinguish the genomic structural variation from sequencing error, we propose a statistical model which involves the genotype effect through a latent variable to depict the distribution of non-reference allele frequency data among different samples and different genome loci, while decomposing the sequencing error into sample effect and positional effect. An ECM algorithm is implemented to estimate the model parameters, and then the genotypes and SNPs are inferred based on the empirical Bayes method.
Provides empirical Bayesian lasso and elastic net algorithms for variable selection and effect estimation. Key features include sparse variable selection and effect estimation via generalized linear regression models, high dimensionality with p>>n, and significance test for nonzero effects. This package outperforms other popular methods such as lasso and elastic net methods in terms of power of detection, false discovery rate, and power of detecting grouping effects.
Functions for querying the EBI Metagenomics Portal <https://www.ebi.ac.uk/metagenomics/>. The current main focus is on taxa abundance data, but the intention is that this package should evolve into a general purpose package for working with EBI Metagenomics data using R.
Four ensemble-based methods (SMOTEBoost, RUSBoost, UnderBagging, and SMOTEBagging) for class imbalance problem are implemented for binary classification. Such methods adopt ensemble methods and data re-sampling techniques to improve model performance in presence of class imbalance problem. One special feature offers the possibility to choose multiple supervised learning algorithms to build weak learners within ensemble models. References: Nitesh V. Chawla, Aleksandar Lazarevic, Lawrence O. Hall, and Kevin W. Bowyer (2003) <doi:10.1007/978-3-540-39804-2_12>, Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, and Amri Napolitano (2010) <doi:10.1109/TSMCA.2009.2029559>, R. Barandela, J. S. Sanchez, R. M. Valdovinos (2003) <doi:10.1007/s10044-003-0192-z>, Shuo Wang and Xin Yao (2009) <doi:10.1109/CIDM.2009.4938667>, Yoav Freund and Robert E. Schapire (1997) <doi:10.1006/jcss.1997.1504>.
Empirical Bayes ranking applicable to parallel-estimation settings where the estimated parameters are asymptotically unbiased and normal, with known standard errors.  A mixture normal prior for each parameter is estimated using Empirical Bayes methods, subsequentially ranks for each parameter are simulated from the resulting joint posterior over all parameters (The marginal posterior densities for each parameter are assumed independent). Finally, experiments are ordered by expected posterior rank, although computations minimizing other plausible rank-loss functions are also given.
Performs an exact Bayesian segmentation on data and returns the probabilities of breakpoints, an ICL criteria, comparison of change-point location, etc.
Genotyping and SNP calling tool for single-sample next generation sequencing data analysis using an empirical Bayes method.
Provides an interface to the European Central Bank's Statistical
    Data Warehouse API, allowing for programmatic retrieval of a vast quantity
    of statistical data.
Draw sample paths for endpoint-conditioned continuous time Markov chains via modified rejection sampling or uniformization.
Elliptic lambda distribution and lambda option pricing model
    have been evolved into a framework of
    stable-law inspired distributions,
    such as the extended stable lambda distribution for asset return,
    stable count distribution for volatility,
    and Lihn-Laplace process as a leptokurtic extension of Wiener process.
    This package contains functions for the computation of
    density, probability, quantile, random variable, fitting procedures,
    option prices, volatility smile. It also comes with sample financial data,
    and plotting routines.
Computes and plots a transformed empirical CDF (ecdf) as a
    diagnostic for heavy tailed data, specifically data with power law decay on the
    tails.  Routines for annotating the plot, comparing data to a model, fitting a
    nonparametric model, and some multivariate extensions are given.
Some wrappers, functions and data sets for for spatial point pattern analysis (mainly based on 'spatstat'), used in the book "Introduccion al Analisis Espacial de Datos en Ecologia y Ciencias Ambientales: Metodos y Aplicaciones" and in the papers by De la Cruz et al. (2008) <doi:10.1111/j.0906-7590.2008.05299.x> and Olano et al. (2009) <doi:10.1051/forest:2008074>.
Testing the equality of several covariance functions of functional data. Four different methods are implemented: L2-norm with W-S naive, L2-norm with W-S bias-reduced, L2-norm (Zhang 2013) <ISBN:9781439862735>, and sup-norm with resampling (Guo et al. 2017) <arXiv:1609.04232>.
Functions to update data sets in Ecdat and to create,
          manipulate, plot and analyze those and similar data sets.
Embed interactive charts to their Shiny applications. These charts will be generated by ECharts library developed by Baidu (<http://echarts.baidu.com/>). Current version supports line chart, bar chart, pie chart, scatter plot, gauge, word cloud, radar chart, tree map, and heat map.
Easily import multi-frequency acoustic data stored in 'HAC' files (see <http://biblio.uqar.ca/archives/30005500.pdf> for more information on the format), and produce echogram visualisations with predefined or customized color palettes. It is also possible to merge consecutive echograms; mask or delete unwanted echogram areas; model and subtract background noise; and more important, develop, test and interpret different combinations of frequencies in order to perform acoustic filtering of the echogram's data.
An R interface to United States Environmental 
    Protection Agency (EPA) Environmental Compliance 
    History Online ('ECHO') Application Program Interface
    (API). 'ECHO' provides information about EPA permitted 
    facilities, discharges, and other reporting info 
    associated with permitted entities. Data are obtained 
    from <https://echo.epa.gov/>.
Provides a function that quickly computes the fine structure
    isotope patterns of a set of chemical formulas to a given degree of
    accuracy (up to the limit set by errors in floating point arithmetic). A
    data-set comprising the masses and isotopic abundances of individual
    elements is also provided.
Companion package to the paper: An analytic approach for 
    interpretable predictive models in high dimensional data, in the presence of 
    interactions with exposures. Bhatnagar, Yang, Khundrakpam, Evans, Blanchette, Bouchard, Greenwood (2017) <DOI:10.1101/102475>. 
    This package includes an algorithm for clustering high dimensional data that can be affected by an environmental factor.
Functions for easy building of error correction models (ECM) for time series regression.
Implements the Bayesian and likelihood methods proposed 
  in Imai, Lu, and Strauss (2008 <DOI: 10.1093/pan/mpm017>) and 
  (2011 <DOI:10.18637/jss.v042.i05>) for ecological inference in 2 
  by 2 tables as well as the method of bounds introduced by Duncan and 
  Davis (1953).  The package fits both parametric and nonparametric 
  models using either the Expectation-Maximization algorithms (for 
  likelihood models) or the Markov chain Monte Carlo algorithms (for 
  Bayesian models).  For all models, the individual-level data can be 
  directly incorporated into the estimation whenever such data are available.
  Along with in-sample and out-of-sample predictions, the package also
  provides a functionality which allows one to quantify the effect of data
  aggregation on parameter estimation and hypothesis testing under the
  parametric likelihood models.
Dissimilarity-based analysis functions including ordination and Mantel test functions, intended for use with spatial and community data.
The ecoengine ('ecoengine';
    <https://ecoengine.berkeley.edu/>). provides access to more than 5 million georeferenced
    specimen records from the University of California, Berkeley's Natural History
    Museums.
Management and exploratory analysis of spatial data in landscape genetics. Easy integration of information from multiple sources with "ecogen" objects.
Simulates the soil water balance (soil moisture, evapotranspiration, leakage and runoff), rainfall series by using the marked Poisson process and the vegetation growth through the normalized difference vegetation index (NDVI). Please see Souza et al. (2016) <doi:10.1002/hyp.10953>.
This package provides a flexible foundation for scientists, 
  engineers, and policy makers to base teaching exercises as well as for 
  more applied use to model complex eco-hydrological interactions.
Calculates several indices, such as of diversity, fluctuation, etc., and they are used to estimate ecological indicators.
Provides measures to characterize the complexity of classification 
    problems based on the ambiguity and the separation between the classes and 
    the data sparsity and dimensionality of the datasets. This package provides 
    bug fixes, generalizations and implementations of many state of the art 
    measures. The measures are described in the paper: Tin Ho and Mitra Basu 
    (2002) <doi:10.1109/34.990132>.
Figures, data sets and examples from the book "A practical guide to ecological modelling - using R as a simulation platform" by Karline Soetaert and Peter MJ Herman (2009). Springer.
  All figures from chapter x can be generated by "demo(chapx)", where x = 1 to 11. 
  The R-scripts of the model examples discussed in the book are in subdirectory "examples", ordered per chapter.
  Solutions to model projects are in the same subdirectories.
Coalescent-Based Simulation of Ecological Communities as proposed
    by Munoz et al. (2017) <doi:10.13140/RG.2.2.31737.26728>. The package includes
    a tool for estimating parameters of community assembly by using Approximate 
    Bayesian Computation.
Tools for general properties including price, quantity, elasticity, convexity, marginal revenue and manifold of various economics demand systems including Linear, Translog, CES, LES and CREMR.
Randomly generate a wide range of interaction networks with
             specified size, average degree, 'modularity', and topological
             structure. Sample nodes and links from within simulated networks
             randomly, by degree, by module, or by abundance. Simulations
             and sampling routines are implemented in 'FORTRAN', providing
             efficient generation times even for large networks. Basic
             visualization methods also included. Algorithms implemented
             here are described in de Aguiar et al. (2017) <arXiv:1708.01242>.
Tools for using null models to analyse ecological
    networks (e.g. food webs, flower-visitation networks, seed-dispersal
    networks) and detect resource preferences or non-random interactions among
    network nodes. Tools are provided to run null models, test for and plot
    preferences, plot and analyse bipartite networks, and export null model
    results in a form compatible with other network analysis packages. The 
    underlying null model was developed by Agusti et al. (2003) 
    <doi:10.1046/j.1365-294X.2003.02014.x> and the full application to 
    ecological networks by Vaughan et al. (2017) econullnetr: an R package 
    using null models to analyse the structure of ecological networks and 
    identify resource selection. Methods in Ecology & Evolution, in press.
Estimating individual-level covariate-outcome associations 
 using aggregate data ("ecological inference") or a combination of 
 aggregate and individual-level data ("hierarchical related regression").
Creates an R interface to the Bacen <http://api.bcb.gov.br/> and Sidra <http://api.sidra.ibge.gov.br> APIs and IPEA data <http://www.ipeadata.gov.br/Default.aspx>.
Classes and methods for implementing aquatic ecosystem models,
  for running these models, and for visualizing their results.
Given a site by species interaction matrix, users can make inferences about species interactions by performance hypothesis comparing test statistics against a null distribution. The current package provides algorithms and metrics for niche-overlap, body size ratios and species co-occurrence. Users can also integrate their own algorithms and metrics within these frameworks or completely novel null models. Detailed explanations about the underlying assumptions of null model analysis in ecology can be found at http://ecosimr.org.
R interface to the Embedded COnic Solver (ECOS), an efficient
	     and robust C library for convex problems. Conic and equality
	     constraints can be specified in addition to integer and
	     boolean variable constraints for mixed-integer problems. This
	     R interface is inspired by the python interface and has
	     similar calling conventions.
Implements stochastic simulations of community assembly (ecological
    diversification) using customizable ecospace frameworks (functional trait
    spaces). Provides a wrapper to calculate common ecological disparity and
    functional ecology statistical dynamics as a function of species richness.
    Functions are written so they will work in a parallel-computing environment.
A simple approach to using a probit or logit analysis to calculate
        lethal concentration (LC) or time (LT) and the appropriate fiducial 
        confidence limits desired for selected LC or LT for
        ecotoxicology studies (Finney 1971; Wheeler et al. 2006; 
        Robertson et al. 2007). The simplicity of 'ecotox' comes from the 
        syntax it implies within its functions which are similar to functions 
        like glm() and lm(). In addition to the simplicity of the syntax, 
        a comprehensive data frame is produced which gives the user a 
        predicted LC or LT value for the desired level and a suite of important 
        parameters such as fiducial confidence limits and slope.   
        Finney, D.J. (1971, ISBN: 052108041X);
        Wheeler, M.W., Park, R.M., and Bailer, A.J. (2006) <doi:10.1897/05-320R.1>;
        Robertson, J.L., Savin, N.E., Russell, R.M., and Preisler, H.K.
        (2007, ISBN: 0849323312).
Implementation of the EPA's Ecological Exposure Research Division (EERD) tools (discontinued in 1999) for Probit and Trimmed Spearman-Karber Analysis.
    Probit and Spearman-Karber methods from Finney's book "Probit analysis a statistical treatment of the sigmoid response curve" with options for most accurate results or identical results to the book.
    Probit and all the tables from Finney's book (code-generated, not copied) with the generating functions included.
    Control correction: Abbott, Schneider-Orelli, Henderson-Tilton, Sun-Shepard.
    Toxicity scales: Horsfall-Barratt, Archer, Gauhl-Stover, Fullerton-Olsen, etc.
EcoTroph is an approach and software for modelling marine and freshwater ecosystems. It is articulated entirely around trophic levels. EcoTroph's key displays are bivariate plots, with trophic levels as the abscissa, and biomass flows or related quantities as ordinates. Thus, trophic ecosystem functioning can be modelled as a continuous flow of biomass surging up the food web, from lower to higher trophic levels, due to predation and ontogenic processes. Such an approach, wherein species as such disappear, may be viewed as the ultimate stage in the use of the trophic level metric for ecosystem modelling, providing a simplified but potentially useful caricature of ecosystem functioning and impacts of fishing. This version contains catch trophic spectrum analysis (CTSA) function and corrected versions of the mf.diagnosis and create.ETmain functions.
Functions for evaluating and visualizing
  ecological assessment procedures for surface waters
  containing physical, chemical and biological assessments
  in the form of value functions.
Computer simulations of classical ecological models as a
    learning resource.
Implements various procedures for finding 
	     multiple change-points. Two methods make use of dynamic 
	     programming and pruning, with no distributional 
	     assumptions other than the existence of certain absolute 
	     moments in one method. Hierarchical and exact search methods 
	     are included. All methods return the set of estimated change-
	     points as well as other summary information.
Framework for building evolutionary algorithms for both single- and multi-objective continuous or discrete optimization problems. A set of predefined evolutionary building blocks and operators is included. Moreover, the user can easily set up custom objective functions, operators, building blocks and representations sticking to few conventions. The package allows both a black-box approach for standard tasks (plug-and-play style) and a much more flexible white-box approach where the evolutionary cycle is written by hand.
Energy decomposition analysis for measuring contributions of components
             and factors on energy or carbon emission changes, as described in 
             B.W. Ang (2005) <doi:10.1016/j.enpol.2003.10.010>. Includes Log Mean 
             Devisia Index method and multi-year energy decomposition analysis using 
             five methods.
Provides data sets and R Codes for Williams, E.R., Matheson, A.C. and Harwood, C.E. (2002). Experimental Design and Analysis for Tree Improvement, CSIRO Publishing.
Functions useful for exploratory data analysis
    using random forests which can be used to compute multivariate partial
    dependence, observation, class, and variable-wise marginal and joint permutation
    importance as well as observation-specific measures of distance 
    (supervised or unsupervised). All of the aforementioned functions are
    accompanied by 'ggplot2' plotting functions.
This package provides a unified approach for Economic
        Design of Control Charts. The main purpose of this package is
        to find out the optimal parameters to minimize the ECH
        (Expected Cost per Hour) of the process.
Detection of edge points in images based on the difference
        of two asymmetric M-kernel estimators. Linear and circular
        regression clustering based on redescending M-estimators.
        Detection of linear edges in images.
Exploratory and descriptive analysis of event based data. Provides methods for describing and selecting process data, and for preparing event log data for process mining. Builds on the S3-class for event logs implemented in the package 'bupaR'.
This package will allow to connect with email server through Internet Message Access Protocol (IMAP) and extract header information e.g. from, to, cc, subject, date and time. User will supply their email address and password along with other options. Initially this package is developed only for Gmail. To run the functions from this package user have to have IMAP enabled Gmail account.
An implementation of maximum entropy sampling for spatial
  data is provided. An exact branch-and-bound algorithm as well as greedy and
  dual greedy heuristics are included.
Import physiologic data stored in
    the European Data Format (EDF and EDF+) into R.
    Both EDF and EDF+ files are supported. Discontinuous
    EDF+ files are not yet supported.
Functions for reading in data sets of prey and predator isotopic measurements and producing estimates for discrimination factors.
Reads European Data Format files EDF and EDF+, see <http://www.edfplus.info>,
    BioSemi Data Format files BDF, see <http://www.biosemi.com/faq/file_format.htm>,
    and BDF+ files, see <http://www.teuniz.net/edfbrowser/bdfplus%20format%20description.html>.
    The files are read in two steps: first the header is read
    and then the signals (using the header object as a parameter).
Easily creating empirical distribution functions from data: 'dfun', 'pfun',
    'qfun' and 'rfun'.
In the USA, firms file different forms with the U.S. Securities and
    Exchange Commission (SEC) through EDGAR (Electronic Data Gathering, Analysis,
    and Retrieval system). EDGAR automated system collects all the different
    necessary filings and make it publicly available. Secondly, it validates
    collected filings, then perform indexing and accepting of these submitted
    forms. Investors, regulators, and researchers often require these forms for
    various purposes. This package helps in data gathering, management, and 
	visualization in this regard. It downloads SEC EDGAR quarterly master
    indexes, daily master indexes, and filings from SEC.org site and perform 
    sentiment analysis of these filing.
A set of methods to access and parse live filing information from the
    U.S. Securities and Exchange Commission (SEC - <https://sec.gov>) including
    company and fund filings along with all associated metadata.
Generates interactive circle plots with the nodes around the
    circumference and linkages between the connected nodes using hierarchical
    edge bundling via the D3 JavaScript library. See <http://d3js.org/> for more
    information on D3.
Facilitates basic spatial edge correction to point pattern data.
Extends edgeR functionality by improving on exactTest using an unconditional exact test of negative binomial means.
Package EDISON (Estimation of Directed Interactions from
    Sequences Of Non-homogeneous gene expression) runs an MCMC
    simulation to reconstruct networks from time series data, using
    a non-homogeneous, time-varying dynamic Bayesian network.
    Networks segments and changepoints are inferred concurrently,
    and information sharing priors provide a reduction of the
    inference uncertainty.
An 'RStudio' addin for editing a 'data.frame' or a 'tibble'. You can delete, add or update a 'data.frame'
    without coding. You can get resultant data as a 'data.frame'. In the package, modularized 'shiny' app codes are provided. 
    These modules are intended for reuse across applications.
An integrated editing and imputation method for continuous microdata under linear constraints is implemented. It relies on a Bayesian nonparametric hierarchical modeling approach as described in Kim et al. (2015) <DOI:10.1080/01621459.2015.1040881>. In this approach, the joint distribution of the data is estimated by a flexible joint probability model. The generated edit-imputed data are guaranteed to satisfy all imposed edit rules, whose types include ratio edits, balance edits and range restrictions.
Facilitates reading and manipulating (multivariate) data restrictions
    (edit rules) on numerical and categorical data. Rules can be defined with common R syntax
    and parsed to an internal (matrix-like format). Rules can be manipulated with
    variable elimination and value substitution methods, allowing for feasibility checks
    and more. Data can be tested against the rules and erroneous fields can be found based
    on Fellegi and Holt's generalized principle. Rules dependencies can be visualized with 
    using the igraph package.
Perform dynamic model averaging with grid search as in Dangl and Halling (2012) <doi:10.1016/j.jfineco.2012.04.003> using parallel computing.
Implementations of (1) mutual dependence measures and mutual independence tests in 
    Jin, Z., and Matteson, D. S. (2017) <arXiv:1709.02532>;
    (2) independent component analysis methods based on mutual dependence measures in 
    Jin, Z., and Matteson, D. S. (2017) <arXiv:1709.02532> 
    and Pfister, N., et al. (2018) <doi:10.1111/rssb.12235>;
    (3) conditional mean dependence measures and conditional mean independence tests in 
    Shao, X., and Zhang, J. (2014) <doi:10.1080/01621459.2014.887012>,
    Park, T., et al. (2015) <doi:10.1214/15-EJS1047>,
    and Lee, C. E., and Shao, X. (2017) <doi:10.1080/01621459.2016.1240083>.
R client for Empirical Data Platform. More information is at <https://empirical.com>. For support, contact support@empirical.com.
The library contains R-functions to estimate the effective
        dimension reduction space in 'multi-index' regression models.
Reduction methods through slice inverse regression approaches. It mainly designed for illustrating the articles "A graphical tool for selecting the number of slices and the dimension of the model in SIR and SAVE approaches" (Liquet, B., Saracco, J. (2012) <doi:10.1007/s00180-011-0241-9>) and "Comparison of sliced inverse regression approaches for underdetermined cases”.
Provides convenience functions and pre-programmed Stan models
    related to item response theory. Its purpose is to make fitting
    common item response theory models using Stan easy.
Read in and analysis functions for education survey and assessment data from the National Center for Education Statistics (NCES) <https://nces.ed.gov/> including National Assessment of Educational Progress (NAEP) data <https://nces.ed.gov/nationsreportcard/> and data from the International Assessment Database: OECD <http://www.oecd.org/>, including PISA, TALIS, PIAAC; and IEA <http://www.iea.nl/>, including TIMSS, TIMSS Advanced, PIRLS, ICCS, ICILS, and CivEd.
Easily compute education inequality measures and the distribution 
  of educational attainments for any group of countries, using the data set 
  developed in Jorda, V. and Alonso, JM. (2017) <DOI:10.1016/j.worlddev.2016.10.005>. 
  The package offers the possibility to compute not only the Gini index, but 
  also generalized entropy measures for different values of the sensitivity 
  parameter. In particular, the package includes functions to compute the 
  mean log deviation, which is more sensitive to the bottom part of the 
  distribution; the Theil’s entropy measure, equally sensitive to all parts 
  of the distribution; and finally, the GE measure when the sensitivity 
  parameter is set equal to 2, which gives more weight to differences in 
  higher education. The decomposition of these measures in the components 
  between-country and within-country inequality is also provided. Two 
  graphical tools are also provided, to analyse the evolution of the
  distribution of educational attainments: The cumulative distribution 
  function and the Lorenz curve.
Data from the 2013 and 2016 Australian Federal Election (House of
    Representatives) and the 2011 Australian Census. Includes tools for
    visualizing and analysing the data. This package incorporates
    data that is copyright Commonwealth of Australia (Australian
    Electoral Commission) 2016.
Provides tools for analysing education trials. Making different
    methods accessible in a single place is essential for sensitivity analysis
    of education trials, particularly the implication of the different methods in
    analysing simple randomised trials, cluster randomised trials and multisite
    trials.
Analysis and visualization tools for electroencephalography (EEG) data. Includes functions for plotting (a) EEG caps, (b) single- and multi-channel EEG time courses, and (c) EEG spatial maps. Also includes smoothing and Independent Component Analysis functions for EEG data analysis, and a function for simulating event-related potential EEG data.
Contains the example EEG data used in the package eegkit. Also contains code for easily creating larger EEG datasets from the EEG Database on the UCI Machine Learning Repository.
Compute the extended empirical log likelihood ratio (Tsao & Wu, 2014) for the mean and parameters defined by estimating equations.
Read raw EEM data and prepares them for further analysis.
Provides various tools for preprocessing Emission-Excitation-Matrix (EEM) for Parallel Factor Analysis (PARAFAC). Different
  methods are also provided to calculate common metrics such as humification index and fluorescence index.
Collection of convenience functions to make working with
    administrative records easier and more consistent. Includes functions to
    clean strings, and identify cut points. Also includes three example data 
    sets of administrative education records for learning how to process records 
    with errors.
Provides functions to create simulated time series of environmental
    exposures (e.g., temperature, air pollution) and health outcomes for use in
    power analysis and simulation studies in environmental epidemiology. This
    package also provides functions to evaluate the results of simulation studies
    based on these simulated time series. This work was supported by a grant
    from the National Institute of Environmental Health Sciences (R00ES022631) and
    a fellowship from the Colorado State University Programs for Research and
    Scholarly Excellence.
A number of utility function for exploratory factor analysis
    are included in this package. In particular, it computes standard errors for
    parameter estimates and factor correlations under a variety of conditions.
Enhanced False Discovery Rate (EFDR) is a tool to detect anomalies
    in an image. The image is first transformed into the wavelet domain in
    order to decorrelate any noise components, following which the coefficients
    at each resolution are standardised. Statistical tests (in a multiple
    hypothesis testing setting) are then carried out to find the anomalies. The
    power of EFDR exceeds that of standard FDR, which would carry out tests on
    every wavelet coefficient: EFDR choose which wavelets to test based on a
    criterion described in Shen et al. (2002). The package also provides
    elementary tools to interpolate spatially irregular data onto a grid of the
    required size. The work is based on Shen, X., Huang, H.-C., and Cressie, N.
    'Nonparametric hypothesis testing for a spatial signal.' Journal of the
    American Statistical Association 97.460 (2002): 1122-1140.
Variable selection and Bayesian effect fusion for categorical predictors in linear regression models. Effect fusion aims at the question which categories have a similar effect on the response and therefore can be fused to obtain a sparser representation of the model. Effect fusion and variable selection can be obtained either with a prior that has an interpretation as spike and slab prior on the level effect differences or with a sparse finite mixture prior on the level effects. The regression coefficients are estimated with a flat uninformative prior after model selection or model averaged. For posterior inference, an MCMC sampling scheme is used that involves only Gibbs sampling steps.
Use structural equation modeling to estimate average and
    conditional effects of a treatment variable on an outcome variable, taking into
    account multiple continuous and categorical covariates.
Predicts cytoplasmic effector proteins using genomic data by searching for motifs of interest using regular expression searches and hidden Markov models (HMM) based in Haas et al. (2009) <doi:10.1038/nature08358>.
Graphical and tabular effect displays, e.g., of interactions, for 
  various statistical models with linear predictors.
Parametric and nonparametric statistics for single-case design. Regarding nonparametric statistics, the index suggested by Parker, Vannest, Davis and Sauber (2011) <doi:10.1016/j.beth.2010.08.006> was included. It combines both nonoverlap and trend to estimate the effect size of a treatment in a single case design.
Functions to test for changes of a response to a stimulus grouping relative 
  to a background or baseline response.
Notice: The package EffectStars2 provides a more up-to-date implementation of effect stars! EffectStars provides functions to visualize regression models with categorical response as proposed by Tutz and Schauberger (2013) <doi:10.1080/10618600.2012.701379>. The effects of the variables are plotted with star plots in order to allow for an optical impression of the fitted model.
Provides functions for the method of effect stars as proposed by Tutz and Schauberger (2013) <doi:10.1080/10618600.2012.701379>. Effect stars can be used to visualize estimates of parameters corresponding to different groups, for example in multinomial logit models. Beside the main function 'effectstars' there exist methods for special objects, for example for 'vglm' objects from the 'VGAM' package.
In personalized medicine, one wants to know, for a given patient and his or her outcome for a predictor (pre-treatment variable), how likely it is that a treatment will be more beneficial than an alternative treatment. This package allows for the quantification of the predictive causal association (i.e., the association between the predictor variable and the individual causal effect of the treatment) and related metrics. Part of this software has been developed using funding provided from the European Union's 7th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.
An implementation for using efficient initials to compute the
    maximal eigenpair in R. It provides three algorithms to find the efficient
    initials under two cases: the tridiagonal matrix case and the general matrix
    case. Besides, it also provides two algorithms for the next to the maximal eigenpair under
    these two cases.
Fitting a causal loglinear model and calculating the causal effects for a causal loglinear model with the multiplicative interaction or without the multiplicative interaction, obtaining the natural direct, indirect and the total effect. It calculates also the cell effect, which is a new interaction effect.
A collection of functions to compute the standardized 
  effect sizes for experiments (Cohen d, Hedges g, Cliff delta, Vargha-Delaney A). 
  The computation algorithms have been optimized to allow efficient computation even 
  with very large data sets.
The European Eddy Fluxes Database Cluster distributes fluxes of different Green House Gases measured mainly using the eddy covariance technique acquired in sites involved in EU projects but also single sites in Europe, Africa and others continents that decided to share their measurements in the database <http://gaia.agraria.unitus.it>. The package provides two functions to load and row-wise bind CSV files distributed by the database. Currently only L2, L3, and L4 (L=Level), half-hourly and daily (aggregation) files are supported.
Provides a function to check the
  importance of a feature based on a dependent classification
  variable. An ensemble of feature selection methods
  is used to determine the normalized importance value of
  all features. Combining these methods in one function
  (building the cumulative importance values) provides a 
  stable feature selection tool. This selection
  can also be viewed in a barplot using the barplot_fs() function
  and proved using the evaluation function efs_eval().
The binary file format 'netCDF' is developed primarily for climate, ocean and
    meteorological data, and 'efts' is a package to read and write Ensemble Forecast Time 
    Series data in 'netCDF'. 'netCDF' has traditionally been used to store time slices 
    of gridded data, rather than complete time series of point data. 
    'efts' facilitates data handling stored in 'netCDF' files that follow a convention
    devised in the domain of ensemble hydrologic forecasting, but possibly applicable 
    in other domains. 'efts' uses reference class objects to 
    provide a high level interface to read and write such data, wrapping 
    lower level operations performed using 'ncdf4'.
Functions for assigning Clarke or Parkes (Consensus) error grid
    zones to blood glucose values, and for plotting both types of error grids
    in both mg/mL and mmol/L units.
An easy-to-use implementation of the Engle-Granger
  two-step procedure for identifying pairs of cointegrated series.  It is geared towards 
  the analysis of pairs of securities.  Summary and plot functions are provided, 
  and the package is able to fetch closing prices of securities from Yahoo.
  A variety of unit root tests are supported, and an improved unit root test is included.
Miscellaneous functions to help customise 'ggplot2' objects. High-level functions are provided to post-process 'ggplot2' layouts and allow alignment between plot panels, as well as setting panel sizes to fixed values.
An implementation of Bayesian hierarchical models
    for faecal egg count data to assess anthelmintic
    efficacy. Bayesian inference is done via MCMC sampling using Stan.
A small tool for Social Network Analysis, dealing with
        ego-centric network measures, including Burt's effective size
        and aggregate constraint and an import code suitable for a
        large number of adjacency matrices. A free web application is
        also available on http://www.egonet.associazionerospo.org
Estimation of ego-temporal exponential random graph models with two-stage estimation including initialization through k-means clustering on temporal exponential random graph model parameters and EM as per Campbell (2018) <doi:10.7910/DVN/TWHEZ9>.
Statistics and graphics for streamflow history,
    water quality trends, and the statistical modeling algorithm: Weighted
    Regressions on Time, Discharge, and Season (WRTDS).
Collection of functions to evaluate uncertainty of results from
    water quality analysis using the Weighted Regressions on Time Discharge and
    Season (WRTDS) method. This package is an add-on to the EGRET package that
    performs the WRTDS analysis.
Sampling of risk sets in Cox regression, selections in 
        the Lexis diagram, bootstrapping. Parametric proportional 
        hazards fitting with left truncation and right censoring for 
        common families of distributions, piecewise constant hazards, 
        and discrete models. AFT regression for left truncated and 
        right censored data.
Extended and enhanced hierarchical logistic regression models (called Huisman-Olff-Fresco in biology, see Huisman et al. 1993 JVS <doi:10.1111/jvs.12050>) models. Response curves along one-dimensional gradients including no response, monotone, plateau, unimodal and bimodal models.
Process and analyze Electronic Health Record (EHR) data. Frequency and 
    contingency tables for many binary outcomes and a binary exposure variable can be 
    generated more efficiently. Phenome Wide Association Study (PheWAS) performed 
    using EHR data can be analyzed using three commonly used statistical analysis 
    methods: Firth's penalized-likelihood logistic regression; logistic regression 
    with likelihood ratio test; conventional logistic regression with Wald test.
Software accompanying Gary King's book: A Solution to the Ecological Inference Problem. (1997). Princeton University Press. ISBN 978-0691012407.
An R wrapper to allow the user to query categories and Series IDs, and import data, from the EIA's API.
Compares estimates from three ecological inference routines, based on King (1997) <ISBN: 0691012407>, <http://gking.harvard.edu/eicamera/kinroot.html>; King et. al. (2004) <ISBN: 0521542804>, <http://gking.harvard.edu/files/abs/ecinf04-abs.shtml>.
Solves the ``inverse eigenvalue problem'' which is to
        generate a real-valued matrix that has the specified real
        eigenvalue spectrum.  It can generate infinitely many dense
        matrices, symmetric or asymmetric, with the given set of
        eigenvalues.  Algorithm can also generate stochastic and doubly
        stochastic matrices.
This package estimates the parameters of a model for
        symmetric relational data (e.g., the above-diagonal part of a
        square matrix), using a model-based eigenvalue decomposition
        and regression. Missing data is accomodated, and a posterior
        mean for missing data is calculated under the assumption that
        the data are missing at random. The marginal distribution of
        the relational data can be arbitrary, and is fit with an
        ordered probit specification.
Computes confidence intervals for the proportion explained by the first 1,2,k principal components, and computes confidence intervals for each eigenvalue. Both computations are done via nonparametric bootstrap.
Implementation of Efficient Inference of Local Ancestry 
	     using fused quantile regression and k-means classifier
Provides methods for analyzing RxC ecological contingency
        tables using the extreme case analysis, ecological regression,
        and Multinomial-Dirichlet ecological inference models.  Also
        provides tools for manipulating higher-dimension data objects.
Performs a compact genetic algorithm search to reduce errors-in-variables bias in linear regression. The algorithm estimates the regression parameters with lower biases and higher variances but mean-square errors (MSEs) are reduced.
This package allows to use the hybrid Multinomial-Dirichlet-Model
    of Ecological Inference for estimating inner Cells of RxC-Tables. This was
    already implemented in the eiPack-package. eiwild-package now has the
    possibility to use individual level data to support the aggregate level
    data and using different Hyperpriori-Distributions.
Emissions are mass of pollutants released into the atmosphere. Air quality models needs emission data, with spatial and temporal distribution, to represent air pollutant concentrations. 'eixport' creates inputs for the air quality models Weather Research and Forecasting model coupled to Chemistry 'WRF-Chem' Grell et al (2005) <doi:10.1016/j.atmosenv.2005.04.027>, Brazilian Regional Atmospheric Modeling System with a simple photochemical module 'BRAMS-SPM' Freitas et al (2005) <doi:10.1016/j.atmosenv.2005.07.017> and  a line source dispersion model 'RLINE' Snyder et al (2013) <doi:10.1016/j.atmosenv.2013.05.074>.
Functions for estimating catalytic constant and Michaelis-Menten constant
            for enzyme kinetics model using Metropolis-Hasting algorithm within Gibbs 
            sampler based on the Bayesian framework. Additionally, a function to create 
            plot to identify the goodness-of-fit is included.
Empirical likelihood (EL) inference for two-sample problems. The following statistics are included: the difference of two-sample means, smooth Huber estimators, quantile (qdiff) and cumulative distribution functions (ddiff), probability-probability (P-P) and quantile-quantile (Q-Q) plots as well as receiver operating characteristic (ROC) curves.
Functions for computing critical values and implementing the one-sided/two-sided EL tests.
Performs some enhanced variable selection algorithms 
  based on the least absolute shrinkage and selection operator for regression model.
O pacote desponibiliza funções para estimar modelos de componentes 
  não observados e determinar intervenções automaticamente. Com especial
  atenção para a replicação dos modelos utilizados na metodologia de calculo
  do resultado estrutural da SPE/MF.
  The package provides simple ways to estimates general unobserved components models
  and automatically detects intervenctions. It is specially useful to
  replicate Brazilian Ministry of Finance methodology to estimate income-output gap
  elasticities.
Connect to 'Elasticsearch', a 'NoSQL' database built on the 'Java'
    Virtual Machine. Interacts with the 'Elasticsearch' 'HTTP' 'API'
    (<https://www.elastic.co/products/elasticsearch>), including functions for
    setting connection details to 'Elasticsearch' instances, loading bulk data,
    searching for documents with both 'HTTP' query variables and 'JSON' based body
    requests. In addition, 'elastic' provides functions for interacting with 'APIs'
    for 'indices', documents, nodes, clusters, an interface to the cat 'API', and
    more.
Description: Uses k-fold cross-validation and elastic-net regularization to estimate the
              Ising model on binary data. Produces 3D plots of the cost function as a function
              of the tuning parameter in addition to the optimal network structure.
This package provides functions for fitting the entire
        solution path of the Elastic-Net and also provides functions
        for estimating sparse Principal Components. The Lasso solution
        paths can be computed by the same function. First version:
        2005-10.
A lightweight R interface to 'Elasticsearch' - a NoSQL search-engine and 
    column store database (see <https://www.elastic.co/products/elasticsearch> for more 
    information). This package implements a simple Domain-Specific Language (DSL) for indexing, 
    deleting, querying, sorting and aggregating data using 'Elasticsearch'.
This is a bizzare collection of functions written to do
        various sorts of statistical election audits.  There are also
        functions to generate simulated voting data, and simulated
        "truth" so as to do simulations to check charactaristics of
        these methods.
An extension of the elec package intended for use on
        election audits using stratified random samples.  Includes
        functions to obtain conservative and exact p-values, and
        functions that give sample sizes that may make election audits
        more efficient.
Functions to compute state-specific and marginal life expectancies. The computation is based on a fitted continuous-time multi-state model that includes an absorbing death state; see Van den Hout (2017, ISBN:9781466568402). The fitted multi-state model model should be estimated using the 'msm' package using age as the time-scale.
This includes a dataset on the outcomes of the USA presidential elections since 1920, and various predictors, as used in <http://vanderwalresearch.com/blog/15-elections>.
Offers a set of functions to easily download and clean 
    Brazilian electoral data from the Superior Electoral Court website. 
    Among others, the package retrieves data on local and
    federal elections for all positions (city councilor, mayor, state deputy,
    federal deputy, governor, and president) aggregated by
    state, city, and electoral zones.
Highest averages & largest remainders allocating seats methods and
    several party system scores.
    Implemented highest averages allocating seats methods are D'Hondt, Webster,
    Danish, Imperiali, Hill-Huntington, Dean, Modified Sainte-Lague,
    equal proportions and Adams.
    Implemented largest remainders allocating seats methods are Hare, Droop,
    Hangenbach-Bischoff, Imperial, modified Imperial and quotas & remainders.
    The main advantage of this package is that ties are always reported
    and not incorrectly allocated.
    Party system scores provided are competitiveness, concentration,
    effective number of parties, party nationalization score,
    party system nationalization score and volatility.
    References.
    Gallagher (1991) <doi:10.1016/0261-3794(91)90004-C>.
    Norris (2004, ISBN:0-521-82977-1).
    Consejo Nacional Electoral del Ecuador (2014)<http://cne.gob.ec/documents/Estadisticas/Atlas/ATLAS/CAPITULO%206%20web.pdf>.
    Laakso & Taagepera (1979) <http://journals.sagepub.com/doi/pdf/10.1177/001041407901200101>.
    Jones & Mainwaring (2003) <https://kellogg.nd.edu/sites/default/files/old_files/documents/304_0.pdf>.
    Pedersen (1979) <http://janda.org/c24/Readings/Pedersen/Pedersen.htm>.
Aims to facilitate the reduction of elemental microchemistry data from solid-phase LAICPMS analysis (laser ablation inductive coupled plasma mass spectrometry). The 'elementR' package provides a reactive and user friendly interface (based on a 'shiny' application) and a set of 'R6' classes for conducting all steps needed for an optimal data reduction while leaving maximum control for user.
Useful when reading the book above mentioned, in the
        documentation referred to as `the book'.
Several web services are available that provide access to elevation
             data. This package provides access to several of those services and 
             returns elevation data either as a SpatialPointsDataFrame from 
             point elevation services or as a raster object from raster 
             elevation services.  Currently, the package supports access to the
             Mapzen Elevation Service <https://mapzen.com/documentation/elevation/elevation-service/>, 
             Mapzen Terrain Service <https://mapzen.com/documentation/terrain-tiles/>,
             Amazon Web Services Terrain Tiles <https://aws.amazon.com/public-datasets/terrain/> and the USGS
             Elevation Point Query Service <http://ned.usgs.gov/epqs/>.
Provides R access to election results data. Wraps elex (https://github.com/newsdev/elex/), a Python package and command line tool for fetching and parsing Associated Press election results.
A tool to draw samples from a Empirical Likelihood Bayesian posterior
   of parameters using Hamiltonian Monte Carlo.
Correlation chart of two set (x and y) of data.  
  Using Quantiles.  Visualize the effect of factor.
Contains various routines for drawing
    ellipses and ellipse-like confidence regions, implementing the plots
    described in Murdoch and Chow (1996), A graphical display of large
    correlation matrices, The American Statistician 50, 178-180. There are
    also routines implementing the profile plots described in Bates and
    Watts (1988), Nonlinear Regression Analysis and its Applications.
A suite of elliptic and related functions including Weierstrass and
 Jacobi forms.  Also includes various tools for manipulating and
 visualizing complex functions.
Training and predict functions for SLFN ( Single
        Hidden-layer Feedforward Neural Networks ) using the ELM
        algorithm. ELM algorithm differs from the traditional
        gradient-based algorithms for very short training times ( it
        doesn't need any iterative tuning, this makes learning time
        very fast ) and there is no need to set any other parameters
        like learning rate, momentum, epochs, etc.
Training and prediction functions are provided for the Extreme Learning Machine algorithm (ELM). The ELM use a Single Hidden Layer Feedforward Neural Network (SLFN) with random generated weights and no gradient-based backpropagation. The training time is very short and the online version allows to update the model using small chunk of the training set at each iteration. The only parameter to tune is the hidden layer size and the learning function.
Perform model based gene clustering for genomics data generated from paired/matched design based on mixture of extended lognormal normal Bayesian hierarchical models (See Li Y, Morrow J, Raby B, Tantisira K, Weiss ST, Huang W, Qiu W. (2017), <doi:10.1371/journal.pone.0174602>).
A flexible framework for calculating Elo ratings and resulting
    rankings of any two-team-per-matchup system (chess, sports leagues, 'Go',
    etc.). This implementation is capable of evaluating a variety of matchups,
    Elo rating updates, and win probabilities, all based on the basic Elo
    rating system.
Allows calculating global scores for characteristics of visual stimuli. Stimuli are presented as sequence of pairwise comparisons ('contests'), during each of which a rater expresses  preference for one stimulus over the other. The algorithm for calculating global scores is based on Elo rating, which updates individual scores after each single pairwise contest. Elo rating is widely used to rank chess players according to their performance. Its core feature is that dyadic contests with expected outcomes lead to smaller changes of participants' scores than outcomes that were unexpected. As such, Elo rating is an efficient tool to rate individual stimuli when a large number of such stimuli are paired against each other in the context of experiments where the goal is to rank stimuli according to some characteristic of interest.
Calculate Elo ratings as means to describe animal dominance hierarchies
elrm implements a Markov Chain Monte Carlo algorithm to
        approximate exact conditional inference for logistic regression
        models. Exact conditional inference is based on the
        distribution of the sufficient statistics for the parameters of
        interest given the sufficient statistics for the remaining
        nuisance parameters. Using model formula notation, users
        specify a logistic model and model terms of interest for exact
        inference.
Flexible implementation of Elston-Stewart algorithm
Empirical likelihood ratio tests for the Yang and Prentice (short/long term hazards ratio) models. 
             Empirical likelihood tests within a Cox model, for parameters defined via 
			 both baseline hazard function and regression parameters.
We propose both a clear analysis strategy and a selection of tools to investigate microarray gene expression data. The most usual and relevant existing R functions were discussed, validated and gathered in an easy-to-use R package (EMA) devoted to gene expression microarray analysis. These functions were improved for ease of use, enhanced visualisation and better interpretation of results.
Do data management functions common in real-time monitoring (also called: ecological momentary assessment, experience sampling, micro-longitudinal) data, including creating power curves for multilevel data, centering on participant means and merging event-level data into momentary data sets where you need the events to correspond to the nearest data point in the momentary data. This is VERY early release software, and more features will be added over time.
Unsupervised, multivariate, binary clustering for meaningful annotation of data, taking into account the uncertainty in the data. A specific constructor for trajectory analysis in movement ecology yields behavioural annotation of trajectories based on estimated local measures of velocity and turning angle, eventually with solar position covariate as a daytime indicator, ("Expectation-Maximization Binary Clustering for Behavioural Annotation").
Tools to analyze the embryo growth and the sexualisation thermal reaction norms.
random walk Metropolis, Metropolis Hasting, parallel tempering, evolutionary Monte Carlo, temperature ladder construction and placement
Computes and visualizes empirical joint distribution of multivariate data with optimized algorithms and multi-thread computation. There is a faster algorithm using dynamic programming to compute the whole empirical joint distribution of a bivariate data. There are optimized algorithms for computing empirical joint CDF function values for other multivariate data. Visualization is focused on bivariate data. Levelplots and wireframes are included.
EM algorithms and several efficient
        initialization methods for model-based clustering of finite
        mixture Gaussian distribution with unstructured dispersion
        in both of unsupervised and semi-supervised learning.
This package carries out empirical mode decomposition and Hilbert spectral
        analysis. For usage of EMD, see Kim and Oh, 2009 (Kim, D and Oh, H.-S. (2009) EMD: A Package for Empirical 
        Mode Decomposition and Hilbert Spectrum, The R Journal, 1, 40-46).
Auxiliary functions and data sets for "Ecological Models and Data", a book presenting maximum likelihood estimation and related topics for ecologists (ISBN 978-0-691-12522-0).
Functions that support estimating, assessing and mapping regional
    disaggregated indicators. So far, estimation methods comprise direct estimation
    and the model-based approach Empirical Best Prediction (see "Small area
    estimation of poverty indicators" by Molina and Rao (2010) <doi:10.1002/cjs.10051>), 
    as well as their precision estimates. The assessment of the used model
    is supported by a summary and diagnostic plots. For a suitable presentation of
    estimates, map plots can be easily created. Furthermore, results can easily be
    exported to excel.
Package providing calculation of Earth Mover's Distance
        (EMD).
Provides basic distribution functions for a mixture model of a Gaussian and exponential distribution.
A toolbox for designing and evaluating predictive models with
    resampling methods. The aim of this package is to provide a simple and
    efficient general framework for working with any type of prediction
    problem, be it classification, regression or survival analysis, that is
    easy to extend and adapt to your specific setting. Some commonly used
    methods for classification, regression and survival analysis are included.
Various Expectation-Maximization (EM) algorithms are implemented for item response theory
				(IRT) models. The current implementation includes IRT models for binary and ordinal
				responses, along with dynamic and hierarchical IRT models with binary responses. The
				latter two models are derived and implemented using variational EM.  Subsequent edits
				also include variational network and text scaling models.
Provides a fast and parallelised calculator to estimate combined wastewater emissions. 
    It supports the planning and design of urban drainage systems, without the requirement of 
    extensive simulation tools. The 'EmiStatR' package implements modular R methods. This enables 
    to add new functionalities through the R framework. Furthermore, 'EmiStatR' was implemented 
    with an interactive user interface with sliders and input data exploration.
Parse and serialize Ecological Metadata Language ('EML', see
    <https://en.wikipedia.org/wiki/Ecological_Metadata_Language> for
    more information) files into S4 objects.
Log-linear modeling is a popular method for the analysis of contingency table data. When the table is sparse, the data can fall on the boundary of the convex support, and we say that "the MLE does not exist" in the sense that some parameters cannot be estimated. However, an extended MLE always exists, and a subset of the original parameters will be estimable. The 'eMLEloglin' package determines which sampling zeros contribute to the non-existence of the MLE. These problematic zero cells can be removed from the contingency table and the model can then be fit (as far as is possible) using the glm() function.
The evolutionary model-based multiresponse approach (EMMA)
        is a novel methodology to process optimisation and product
        improvement. The approach is suitable to contexts in which the
        experimental cost and/or time limit the number of implementable
        trials.
End-member modelling analysis of grain-size data.
This package includes functions to read and write to an
        EMME/2 databank
Obtain estimated marginal means (EMMs) for many linear, generalized 
  linear, and mixed models. Compute contrasts or linear functions of EMMs,
  trends, and comparisons of slopes. Plots and compact letter displays.
  Least-squares means are discussed, and the term "estimated marginal means"
  is suggested, in Searle, Speed, and Milliken (1980) Population marginal means 
  in the linear model: An alternative to least squares means, The American 
  Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>.
Functions to fit finite mixture of multivariate canonical fundamental skew t (FM-CFUST) distributions, random sample generation, 2D and 3D contour plots. See Lee and McLachlan (2017) <doi:10.18637/jss.v083.i03> for more information and examples.
Provides unsupervised selection and clustering of microarray data
    using mixture models. Following the methods described in McLachlan, Bean and
    Peel (2002) <doi:10.1093/bioinformatics/18.3.413> a subset of genes are selected
    based one the likelihood ratio statistic for the test of one versus two
    components when fitting mixtures of t-distributions to the expression data
    for each gene. The dimensionality of this gene subset is further reduced through
    the use of mixtures of factor analyzers, allowing the tissue samples to be
    clustered by fitting mixtures of normal distributions.
EM algorithm for Fitting Mixture of Multivariate Skew Normal and Skew t Distributions. An implementation of the algorithm described in Wang, Ng, and McLachlan (2009) <doi:10.1109/DICTA.2009.88>.
Functions to fit finite mixture of unrestricted multivariate skew t (FM-uMST) model, random sample generation, discriminant analysis, 2D and 3D contour plots
Fit models of modularity to morphological landmarks. Perform model 
  selection on results. Fit models with a single within-module correlation or
  with separate within-module correlations fitted to each module.
The main functions are 'emmreml', and 'emmremlMultiKernel'. 'emmreml' solves a mixed model with known covariance structure using the 'EMMA' algorithm.  'emmremlMultiKernel' is a wrapper for 'emmreml' to handle multiple random components with known covariance structures. The function 'emmremlMultivariate' solves a multivariate gaussian mixed model with known covariance structure using the 'ECM' algorithm.
Collection of building blocks for the design and analysis
        of evolutionary multiobjective optimization algorithms.
An implementation of using emoji and fontawesome for using in both
    base and 'ggplot2' graphics.
Statistical tools for environmental and ecological surveys.
    Simulation-based power and precision analysis; detection probabilities from
    different survey designs; visual fast count estimation.
Fixation and saccade detection in eye movement recordings. This package implements a dispersion-based algorithm (I-DT) proposed by Salvucci & Goldberg (2000) which detects fixation duration and position.
Functions for estimating EMP (Expected Maximum Profit Measure) in Credit Risk Scoring and Customer Churn Prediction, according to Verbraken et al (2013, 2014) <DOI:10.1109/TKDE.2012.50>, <DOI:10.1016/j.ejor.2014.04.001>.
Routines for performing empirical calibration of observational
    study estimates. By using a set of negative control hypotheses we can
    estimate the empirical null distribution of a particular observational
    study setup. This empirical null distribution can be used to compute a
    calibrated p-value, which reflects the probability of observing an
    estimated effect size when the null hypothesis is true taking both random
    and systematic error into account.
Auxiliary functions for the DESeq2 package to simulate read counts according to the null hypothesis (i.e., with empirical sample size factors, per-gene total counts and dispersions, but without effects of predictor variables) and to compute the empirical false discovery rate.
Empirical likelihood ratio tests for means/quantiles/hazards
 	from possibly censored and/or truncated data. Now does regression too.
	This version contains some C code.
Calculates the p-value for a mean-type hypothesis (or multiple mean-type hypotheses) based on two samples with censored data.
Collection of functions for data analysis and
    editing of clinical and epidemiological data.
    Most of them are related to benchmarking with prediction models.
Provides the analysis of variance table including the expected mean squares (EMS) for various types of experimental design. When some variables are random effects or we use special experimental design such as nested design, repeated-measures design, or split-plot design, it is not easy to find the appropriate test, especially denominator for F-statistic which depends on EMS.
Background correction of spectral like data. Handles variations in
  scaling, polynomial baselines, interferents, constituents and replicate variation.
  Parameters for corrections are stored for further analysis, and spectra are corrected
  accordingly.
The package provides functions to carry out a
        Goodness-of-fit test for discrete multivariate data. It is
        tested if a given observation is likely to have occurred under
        the assumption of an ab-initio model. A p-value can be
        calculated using different distance measures between observed
        and expected frequencies. A Monte Carlo method is provided to
        make the package capable of solving high-dimensional problems.
Allows one to estimate the output of a computer program,
 as a function of the input parameters, without actually running it.
 The computer program is assumed to be a Gaussian process, whose
 parameters are estimated using Bayesian techniques that give a PDF of
 expected program output.  This PDF is conditional on a ``training set''
 of runs, each consisting of a point in parameter space and the model
 output at that point.  The emphasis is on complex codes that take
 weeks or months to run, and that have a large number of undetermined
 input parameters; many climate prediction models fall into this
 class.  The emulator essentially determines Bayesian posterior
 estimates of the PDF of the output of a model, conditioned on results
 from previous runs and a user-specified prior linear model.  A vignette is
 provided and the help pages include examples.
Provides the next iteration of the EMU Speech 
    Database Management System (EMU-SDMS) with database management, data 
    extraction, data preparation and data visualization facilities.
Contains logic for the data-driven optimization of annotations via minimization of the entropy of variable group members over discrete variable clusters (see Frost, HR and Moore, JH (2014) <doi:10.1093/bioinformatics/btu110>).
An efficient expectation-maximization algorithm for fitting Bayesian spike-and-slab regularization paths for linear regression. Rockova and George (2014) <doi:10.1080/01621459.2013.869223>.
Provides algorithms for the analysis of ecological networks.
Implements an S3 class for storing 'UTF-8' strings, based on regular character vectors.
    Also contains routines to portably read and write 'UTF-8' encoded text files,
    to convert all strings in an object to 'UTF-8',
    and to create character vectors with various encodings.
Interconverts between ordered lists and compact string notation.  
 Useful for capturing code lists, and pair-wise codes and decodes, for text storage.
 Analogous to factor levels and labels. Generics encode() and decode()
 perform interconversion, while codes() and decodes() extract components of an encoding.
 The function encoded() checks whether something is interpretable as an encoding.
Likelihood-based approaches to estimate linear regression parameters and treatment effects in the presence of endogeneity. Specifically, this package includes James Heckman's classical simultaneous equation models-the sample selection model for outcome selection bias and hybrid model with structural shift for endogenous treatment. For more information, see the seminal paper of Heckman (1978) <DOI:10.3386/w0177> in which the details of these models are provided. This package accommodates repeated measures on subjects with a working independence approach. The hybrid model further accommodates treatment effect modification.
endogMNP is an R package that fits a Bayesian multinomial
        probit model with endogenous selection, which is sometimes
        called an endogenous switching model.  This can be used to
        model discrete choice data when respondents select themselves
        into one of several groups.  This package is based on the MNP
        package by Kosuke Imai and David A. van Dyk.  This package
        modifies their code.
Fit the hierarchical and non-hierarchical Bayesian measurement models proposed by Bullock, Imai, and Shapiro (2011) <DOI:10.1093/pan/mpr031> to analyze endorsement experiments.  Endorsement experiments are a survey methodology for eliciting truthful responses to sensitive questions.  This methodology is helpful when measuring support for socially sensitive political actors such as militant groups.  The model is fitted with a Markov chain Monte Carlo algorithm and produces the output containing draws from the posterior distribution.
Computes the expectation of the number of transmissions and receptions considering an End-to-End transport model with limited number of retransmissions per packet. It provides theoretical results and also estimated values based on Monte Carlo simulations.
E-statistics (energy) tests and statistics for multivariate and univariate inference,
             including distance correlation, one-sample, two-sample, and multi-sample tests for
             comparing multivariate distributions, are implemented. Measuring and testing
             multivariate independence based on distance correlation, partial distance correlation,
             multivariate goodness-of-fit tests, clustering based on energy distance, testing for
             multivariate normality, distance components (disco) for non-parametric analysis of
             structured data, and other energy statistics/methods are implemented.
Provides a function for distribution free control chart based on the change point model, for multivariate statistical process control. The main constituent of the chart is the energy test that focuses on the discrepancy between empirical characteristic functions of two random vectors. This new control chart highlights in three aspects. Firstly, it is distribution free, requiring no knowledge of the random processes. Secondly, this control chart can monitor mean and variance simultaneously. Thirdly it is devised for multivariate time series which is more practical in real data application. Fourthly, it is designed for online detection (Phase II), which is central for real time surveillance of stream data. For more information please refer to O. Okhrin and Y.F. Xu (2017) <https://github.com/YafeiXu/working_paper/raw/master/CPM102.pdf>.
Data published by the United States Federal Energy Regulatory Commission including
    electric company financial data, natural gas company financial data, 
    hydropower plant data, liquified natural gas plant data, oil company financial data
    natural gas company financial data, and natural gas storage field data.
Fully robust versions of the elastic net estimator are introduced for linear and logistic regression, in particular high dimensional data by Kurnaz, Hoffmann and Filzmoser (2017) <DOI:10.1016/j.chemolab.2017.11.017>. The algorithm searches for outlier free subsets on which the classical elastic net estimators can be applied.
Provides a quantitative toolkit to explore elastic net families and to uncover correlates contributing to prediction under a cross-validation framework. Fits linear, binomial (logistic) and multinomial models. Candia J and Tsang JS (2018), (application note under review).
Allow numbers to be presented in an English language
        version, one, two, three, ...
Datasets from Nelson, Coffin and Copeland "Introductory
        Statistics for Engineering Experimentation" (Elsevier, 2003)
        with sample code.
Analyzing English & European soccer results data from 1871-2016.
A set of tools for the analysis of ecological niche of species and calculation of habitat suitability maps.
Automatically partitions data into evaluation bins, executes ecological niche models across a range of settings, and calculates a variety of evaluation statistics.  Current version only implements ENMs with Maxent (Phillips et al. 2006)<doi:10.1016/j.ecolmodel.2005.03.026>.
The ENmisc package contains utility function for different
        purposes: mtapply and mlapply (multivariate version of tapply
        and lapply), wtd.boxplot (a boxplot with weights), and a visual
        interface to restructuring mosaic plots.
An algorithmic framework for measuring feature importance,
    outlier detection, model applicability domain evaluation,
    and ensemble predictive modeling with (sparse)
    partial least squares regressions.
Provides an R interface to all 'Enrichr' databases, a web-based tool for analysing gene sets and returns any enrichment of common annotated biological functions. <http://amp.pharm.mssm.edu/Enrichr/>.
These programs are used for calculating enrichment
        factors, drawing enrichment curves to evaluate virtual
        screening approaches.
Provides the "enrich" method to enrich list-like R objects with new, relevant components. The current version has methods for enriching objects of class 'family', 'link-glm', 'lm', 'glm' and 'betareg'. The resulting objects preserve their class, so all methods associated with them still apply. The package also provides the 'enriched_glm' function that has the same interface as 'glm' but results in objects of class 'enriched_glm'. In addition to the usual components in a `glm` object, 'enriched_glm' objects carry an object-specific simulate method and functions to compute the scores, the observed and expected information matrix, the first-order bias, as well as model densities, probabilities, and quantiles at arbitrary parameter values. The package can also be used to produce customizable source code templates for the structured implementation of methods to compute new components and enrich arbitrary objects.
An implementation of the clustering methods of categorical data
    discussed in Amiri, S., Clarke, B., and Clarke, J. (2015). Clustering categorical 
    data via ensembling dissimilarity matrices.  Preprint <arXiv:1506.07930>.
Extensible S4 classes and methods for batch training of regression and classification algorithms such as Random Forest, Gradient Boosting Machine, Neural Network, Support Vector Machines, K-Nearest Neighbors, Penalized Regression (L1/L2), and Bayesian Additive Regression Trees. These algorithms constitute a set of 'base learners', which can subsequently be combined together to form ensemble predictions. This package provides cross-validation wrappers to allow for downstream application of ensemble integration techniques, including best-error selection. All base learner estimation objects are retained, allowing for repeated prediction calls without the need for re-training. For large problems, an option is provided to save estimation objects to disk, along with prediction methods that utilize these objects. This allows users to train and predict with large ensembles of base learners without being constrained by system RAM.
Bayesian Model Averaging to create probabilistic forecasts
        from ensemble forecasts and weather observations.
Extends the base classes and methods of EnsembleBase package for cross-validation-based integration of base learners. Default implementation calculates average of repeated CV errors, and selects the base learner / configuration with minimum average error. The package takes advantage of the file method provided in EnsembleBase package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. The package can be extended, e.g. by adding variants of the current implementation.
Functions for computing the ensembles of regularized linear
             regression estimators defined in Christidis, Lakshmanan, Smucler and Zamar (2017)
             <arXiv:1712.03561>. The procedure works on top of a given penalized linear regression estimator,
             the Elastic Net in this implementation, by fitting it to possibly overlapping subsets of features, while at the same
             time encouraging diversity among the subsets, to reduce the correlations between the predictions
             that result from each fitted model. The predictions from the models are then aggregated.
Ensemble Model Output Statistics to create probabilistic
        forecasts from ensemble forecasts and weather observations.
Extends the base classes and methods of 'EnsembleBase' package for Principal-Components-Regression-based (PCR) integration of base learners. Default implementation uses cross-validation error to choose the optimal number of PC components for the final predictor. The package takes advantage of the file method provided in 'EnsembleBase' package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. Users and developers can extend the package by extending the generic methods and classes provided in 'EnsembleBase' package as well as this package.
Extending the base classes and methods of EnsembleBase package for Penalized-Regression-based (Ridge and Lasso) integration of base learners. Default implementation uses cross-validation error to choose the optimal lambda (shrinkage parameter) for the final predictor. The package takes advantage of the file method provided in EnsembleBase package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. Users and developers can extend the package by extending the generic methods and classes provided in EnsembleBase package as well as this package.
Data sets for the chapter "Ensemble Postprocessing with R" of the book Stephane Vannitsem, Daniel S. Wilks, and Jakob W. Messner (2017) "Statistical Postprocessing of Ensemble Forecasts", Elsevier, to appear. These data sets contain temperature and precipitation ensemble weather forecasts and corresponding observations at Innsbruck/Austria. Additionally, a demo with the full code of the book chapter is provided.
Functions to use ensembles of several machine learning models
    specified in caret package.
Add simple runtime contracts to R values. These ensure that values
    fulfil certain conditions and will raise appropriate errors if they do not.
Measurement and partitioning of diversity, based on Tsallis entropy, following Marcon and Herault (2015) <doi:10.18637/jss.v067.i08>.
             entropart provides functions to calculate alpha, beta and gamma diversity of communities, including phylogenetic and functional diversity.
             Estimation-bias corrections are available.
This package implements various estimators of entropy, such
  as the shrinkage estimator by Hausser and Strimmer, the maximum likelihood 
  and the Millow-Madow estimator, various Bayesian estimators, and the 
  Chao-Shen estimator.  It also offers an R interface to the NSB estimator.
  Furthermore, it provides functions for estimating Kullback-Leibler divergence,
  chi-squared, mutual information, and chi-squared statistic of independence.
  In addition there are functions for discretizing continuous random variables.
Contains methods for the estimation of Shannon's entropy, variants of Renyi's entropy, mutual information, Kullback-Leibler divergence, and generalized Simpson's indices. The estimators used have a bias that decays exponentially fast.
Rows of two matrices are compared for Shannon entropy,
     coefficient of variation, and expression. P-values can be requested for all metrics.
Small toolbox for data analyses in environmental chemistry and
    ecotoxicology. Provides, e.g., calibration() to calculate calibration curves
    and corresponding limits of detection (LODs) and quantification (LOQs)
    according to German DIN 32645:2008-11. texture() makes it easy to estimate
    soil particle size distributions from hydrometer measurements (ASTM
    D422-63(2007)e2).
Tools for automatic model selection and diagnostics for Climate and Environmental data.  In particular the envcpt() function does automatic model selection between a variety of trend, changepoint and autocorrelation models.  The envcpt() function should be your first port of call.
Prints out information about the R working environment
    (system, R version,loaded and attached packages and versions) from a single
    function "env_doc()".  Optionally adds information on git repository,
    tags, commits and remotes (if available).
A collection of functions for microbial ecology and other
   applications of genomics and metagenomics. Companion package for the
   Enveomics Collection (Rodriguez-R, L.M. and Konstantinidis, K.T., 2016
   <DOI:10.7287/peerj.preprints.1900v1>).
Gas/Liquid Chromatography-Mass Spectrometer(GC/LC-MS) Data Analysis for Environmental Science. This package covered topics such as raw data process, molecular isotope ratio, matrix effects and Short-Chain Chlorinated Paraffins analysis etc. in environmental analysis.
Fast and very memory-efficient calculation of isotope patterns,
    subsequent convolution to theoretical envelopes (profiles) plus valley
    detection and centroidization or intensoid calculation. Batch processing,
    resolution interpolation, wrapper, adduct calculations and molecular
    formula parsing.
Sequential partitioning, clustering and peak detection of
    centroided LC-MS mass spectrometry data (.mzXML). Interactive result and raw
    data plot.
Generation of bioclimatic rasters that will be particularly 
	useful for species distribution modeling.
Methods to perform a Probabilistic Environmental Risk assessment from exposure to toxic substances - i.e. USEPA (1997) <https://www.epa.gov/risk/guiding-principles-monte-carlo-analysis> -.
Functions and datasets to support the book by Nhu D Le and James V Zidek, Springer (2006).
Envelope methodology and aster modeling are combined to provide users with precise estimation of expected Darwinian fitness.
A plot overlying the niche of multiple species is obtained: 1) to determine the niche conditions which favor a higher species richness, 2) to create a box plot with the range of environmental variables of the species, 3) to obtain a list of species in an area of the niche selected by the user and, 4) to estimate niche overlap among the species.
Graphical and statistical analyses of environmental data, with 
  focus on analyzing chemical concentrations and physical parameters, usually in 
  the context of mandated environmental monitoring.  Major environmental 
  statistical methods found in the literature and regulatory guidance documents, 
  with extensive help that explains what these methods do, how to use them, 
  and where to find them in the literature.  Numerous built-in data sets from 
  regulatory guidance documents and environmental statistics literature.  Includes 
  scripts reproducing analyses presented in the book "EnvStats:  An R Package for 
  Environmental Statistics" (Millard, 2013, Springer, ISBN 978-1-4614-8455-4, 
  <http://www.springer.com/book/9781461484554>).
A collection of nice plotting functions directly from a
        data.frame with limited customisation possibilities.
Analyzing censored variables usually requires the use of optimization algorithms. This package provides an alternative algebraic approach to the task of determining the expected value of a random censored variable with a known censoring point. Likewise this approach allows for the determination of the censoring point if the expected value is known. These results are derived under the assumption that the variable follows an Epanechnikov kernel distribution with known mean and range prior to censoring. Statistical functions related to the uncensored Epanechnikov distribution are also provided by this package.
Enables simulation of water piping networks using 'EPANET'.
    The package provides functions from the 'EPANET' programmer's toolkit as R
    functions so that basic or customized simulations can be carried out from R.
    The package uses 'EPANET' version 2.1 from Open Water Analytics
    <https://github.com/OpenWaterAnalytics/EPANET/releases/tag/v2.1>.
Reads water network simulation data in 'Epanet' text-based
    '.inp' and '.rpt' formats into R. Also reads results from 'Epanet-msx'.
    Provides basic summary information and plots.  The README file has a 
    quick introduction. See <http://www2.epa.gov/water-research/epanet> 
    for more information on the Epanet software for modeling
    hydraulic and water quality behavior of water piping systems.
The top-performing ensemble-based Penalized Cox Regression (ePCR) framework developed during the DREAM 9.5 mCRPC Prostate Cancer Challenge <https://www.synapse.org/ProstateCancerChallenge> presented in Guinney J, Wang T, Laajala TD, et al. (2017) <doi:10.1016/S1470-2045(16)30560-5> is provided here-in, together with the corresponding follow-up work. While initially aimed at modeling the most advanced stage of prostate cancer, metastatic Castration-Resistant Prostate Cancer (mCRPC), the modeling framework has subsequently been extended to cover also the non-metastatic form of advanced prostate cancer (CRPC). Readily fitted ensemble-based model S4-objects are provided, and a simulated example dataset based on a real-life cohort is provided from the Turku University Hospital, to illustrate the use of the package. Functionality of the ePCR methodology relies on constructing ensembles of strata in patient cohorts and averaging over them, with each ensemble member consisting of a highly optimized penalized/regularized Cox regression model. Various cross-validation and other modeling schema are provided for constructing novel model objects.
Functions for demographic and epidemiological analysis in
  the Lexis diagram, i.e. register and cohort follow-up data, in
  particular representation, manipulation and simulation of multistate 
  data - the Lexis suite of functions, which includes interfaces to
  'mstate', 'etm' and 'cmprsk' packages. 
  Also contains functions for Age-Period-Cohort and Lee-Carter
  modeling and a function for interval censored data and some useful
  functions for tabulation and plotting, as well as a number of
  epidemiological data sets.
This package contains elementary tools for analysis of
        common epidemiological problems, ranging from sample size
        estimation, through 2x2 contingency table analysis and basic
        measures of agreement (kappa, sensitivity/specificity).
        Appropriate print and summary statements are also written to
        facilitate interpretation wherever possible.  Source code is
        commented throughout to facilitate modification.  The target
        audience includes advanced undergraduate and graduate students
        in epidemiology or biostatistics courses, and clinical
        researchers.
Hierarchical Bayesian models for use in disease 
		freedom and disease prevalence studies, designed 
		with epidemiological applications in mind. The models 
		themselves are in the spirit of those presented in Branscum 
		et al. (2006) (see package documentation for full 
		reference). The helper functions and methods were designed 
		to make implementation and processing of the complex model 
		output relatively simple in application.
A collection of tools for representing epidemiological contact data, composed of case line lists and contacts between cases. Also contains procedures for data handling, interactive graphics, and statistics.
Routines for epidemiological contact tracing
    and visualisation of network of contacts.
Creates simple or stacked epidemic curves for hourly, daily, weekly or monthly outcome data.
The Economic Policy Institute (<http://www.epi.org/>) provides
    researchers, media, and the public with easily accessible, up-to-date, and
    comprehensive historical data on the American labor force. It is compiled
    from Economic Policy Institute analysis of government data sources. Use
    it to research wages, inequality, and other economic indicators over time
    and among demographic groups. Data is usually updated monthly.
Package for data exploration and result presentation. Full 'epicalc' package with data management functions is available at '<http://medipe.psu.ac.th/epicalc>'.
Mathematical models of infectious diseases in humans and animals.
    Both, deterministic and stochastic models can be simulated and plotted.
This package provides tools to quantify transmissibility
        throughout an epidemic from the analysis of time series of
        incidence.
Provides flexible model fitting used in epidemiological data
    analysis by a unified model specification, along with some data manipulation
    functions. This package covers fitting of variety models including Cox
    regression models, linear regression models, Poisson regression models, logistic
    models and others whose likelihood is expressed in negative binomial, gamma and
    Weibull distributions.
Provides tools for simulating from discrete-time individual level models for infectious disease data analysis. This epidemic model class contains spatial and contact-network based models with two disease types: Susceptible-Infectious (SI) and Susceptible-Infectious-Removed (SIR).
Provides tools for simulating from continuous-time individual level models of disease transmission, and carrying out infectious disease data analyses with the same models. The epidemic models considered are distance-based and contact network-based models within Susceptible-Infectious-Removed (SIR) or Susceptible-Infectious-Notified-Removed (SINR) compartmental frameworks.
Tools for simulating mathematical models of infectious disease dynamics. 
    Epidemic model classes include deterministic compartmental models, stochastic 
    agent-based models, and stochastic network models. Network models use the
    robust statistical methods of exponential-family random graph models (ERGMs) 
    from the Statnet suite of software packages in R. Standard templates for epidemic 
    modeling include SI, SIR, and SIS disease types. EpiModel features 
    an easy API for extending these templates to address novel scientific research aims.
A collection of epidemic/network-related tools. Simulates transmission of diseases through contact networks. Performs Bayesian inference on network and epidemic parameters, given epidemic data.
Tools for the analysis of epidemiological data. Contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, and computing confidence intervals around incidence risk and incidence rate estimates. Miscellaneous functions for use in meta-analysis, diagnostic test interpretation, and sample size calculations.
Basic sensitivity analysis of the observed relative risks adjusting
    for unmeasured confounding and misclassification of the
    exposure/outcome, or both. It follows the bias analysis methods and
    examples from the book by Lash T.L, Fox M.P, and Fink A.K.
    "Applying Quantitative Bias Analysis to Epidemiologic Data",
    ('Springer', 2009).
A collection of R functions supporting the text book
  Modern Epidemiology, Second Edition, by Kenneth J.Rothman and Sander Greenland.
  ISBN 13: 978-0781755641  See <http://www.krothman.org/> for more information.
A set of statistical tools for inferring unknown parameters in continuous time processes governed by ordinary differential equations (ODE). Moreover, variance reduction and model selection can be obtained through various implemented penalisation schemes. The package offers two estimation procedures: exact estimation via least squares and a faster approximate estimation via inverse collocation methods. All estimators can handle multiple data sets arising from the same ODE system, but subjected to different interventions.
Produce one-dimensional density estimates using 
 exponential epi-splines. The user may incorporate soft information, by 
 imposing constraints that (i) require unimodality; (ii) require that the 
 density be monotone non-increase or non-decreasing; (iii) put upper bounds
 on first or second moments; (iv) bound the density's values at mesh points;
 (v) require that the estimate be continuous or continuously differentiable;
 and more.
An efficient multi-core package to reconstruct an underlying network of
             genomic signatures of high-dimensional epistatic selection from 
             partially observed genotype data. The phenotype that we consider is viability. 
			 The network captures the conditional dependent short- and long-range linkage 
			 disequilibrium structure of genomes and thus reveals aberrant marker-marker 
			 associations that are due to epistatic selection. We target on high-dimensional
			 genotype data where number of variables (markers) is larger than number of 
			 sample sizes (p >> n). The computations is memory-optimized using the sparse 
			 matrix output.
Provides set of functions aimed at epidemiologists. They include commands for measures of association and impact for case control studies and cohort studies. They may be particularly useful for outbreak investigations and include univariate and stratified analyses. The functions for cohort studies include the CS(), CSTable() and CSInter() commands. The functions for case control studies include the CC(), CCTable() and CCInter() commands.
Constructing an epistemic model such that, for every player i and for every choice c(i) which is optimal, there is one type that expresses common belief in rationality.
Builds contingency tables that cross-tabulate multiple 
    categorical variables and also calculates various summary measures.
    Export to a variety of formats is supported, including: 'HTML', 
    'LaTeX', and 'Excel'.
Tools for training and practicing epidemiologists including methods for two-way and multi-way contingency tables.
A collection of small functions useful for epidemics analysis and infectious disease modelling. This includes computation of basic reproduction numbers from growth rates, generation of hashed labels to anonymise data, and fitting discretised Gamma distributions.
Aim: Adjust the graphical parameters to create nicer
    longitudinal series plots. The default set of graphical parameters is very
    general, and can be improved upon when we are interested in plotting data
    points observed over time. Functions facilitate plotting those kind of
    series, univariate plots, bivariate plots (with vertical axis on both left
    and right hand sides), multivariate plots and plots which allow to examine
    whether a new observation is 'unusual' via construction and visualization
    of prediction intervals around it.
Evaluate a function over a data frame of expressions.
Estimates sparse matrices A or G using fast lasso regression from mRNA transcript levels Y and CNA profiles U. Two models are provided, EPoC A where
      AY + U + R = 0
  and EPoC G where
      Y = GU + E,
  the matrices R and E are so far treated as noise. For details see the reference and the manual page of `lassoshooting'.
Performs analysis of polynomial regression in simple designs with quantitative treatments.
Import data from 'Epidata' XML files '.epx' and convert it to R data structures.
Computation of the EQL for a given family of variance
        functions, Saddlepoint-approximations and related auxiliary
        functions (e.g. Hermite polynomials)
Transitioning from EQS to R for structural equation modeling (SEM)
    is made easier with a set of functions to convert .out files into R code.
    The EQS output can be converted into lavaan syntax and run in the R
    environment.  Other functions parse descriptive statistics and the covariance matrix
    from an EQS .out file.  A heat map plot of a covariance matrix is also
    included.
Analysis of experimental crosses to identify genes (called
        quantitative trait loci, QTLs) contributing to variation in
        quantitative traits.
Tests the equality of two covariance matrices, used in paper "Two sample tests for high dimensional covariance matrices." Li and Chen (2012) <arXiv:1206.0917>.
Functions for examining measurement invariance via equivalence testing along with adjusted RMSEA(root mean square error of approximation; Steiger & Lind, 1980) cutoff values. In particular, a projection-based method is implemented to test the equality of latent factor means across groups without assuming the equality of intercepts.
Contains methods for observed-score linking
  and equating under the single-group, equivalent-groups,
  and nonequivalent-groups with anchor test(s) designs.
  Equating types include identity, mean, linear, general
  linear, equipercentile, circle-arc, and composites of
  these. Equating methods include synthetic, nominal
  weights, Tucker, Levine observed score, Levine true
  score, Braun/Holland, frequency estimation, and chained
  equating. Plotting and summary methods, and methods for
  multivariate presmoothing and bootstrap error estimation
  are also provided.
Computation of direct, chain and average (bisector) equating coefficients with standard errors using Item Response Theory (IRT) methods for dichotomous items. Test scoring can be performed by true score equating and observed score equating methods.
Equating of multiple forms using Item Response Theory (IRT) methods (Battauz M. (2017) <doi:10.1007/s11336-016-9517-x> and Haberman S. J. (2009) <doi:10.1002/j.2333-8504.2009.tb02197.x>).
Provides statistical tests and graphics for assessing tests
        of equivalence.  Such tests have similarity as the alternative
	hypothesis instead of the null.  Sample data sets are included.
Making available in R the complete set of programs accompanying S. Wellek's (2010) monograph
             ''Testing Statistical Hypotheses of Equivalence and Noninferiority. Second Edition''
             (Chapman&Hall/CRC).
Provides an equivalent measure of partial correlation coefficients for high-dimensional Gaussian Graphical Models to learn and visualize the underlying relationships between variables from single or multiple datasets. You can refer to Liang, F., Song, Q. and Qiu, P. (2015) <doi:10.1080/01621459.2015.1012391> for more detail. Based on this method, the package also provides the method for constructing networks for Next Generation Sequencing Data, for jointly estimating multiple Gaussian Graphical Models and constructing directed acyclic graph (Bayesian Network).
Automated compound deconvolution, alignment across samples, and identification of metabolites by spectral library matching in Gas Chromatography - Mass spectrometry (GC-MS) untargeted metabolomics. Outputs a table with compound names, matching scores and the integrated area of the compound for each sample.
Expectile regression is a nice tool for estimating the conditional expectiles of a response variable given a set of covariates. This package implements a regression tree based gradient boosting estimator for nonparametric multiple expectile regression.
Provides a methodology simple and trustworthy for the analysis of extreme values and multiple threshold tests for a generalized Pareto distribution, together
    with an automatic threshold selection algorithm. See del Castillo, J, Daoudi, J and Lockhart, R (2014) <doi:10.1111/sjos.12037>.
Functions, datasets, and sample codes related to the book of 'Empirical Research in Economics: Growing up with R' by Dr. Changyou Sun are included. Marginal effects for binary or ordered choice models can be calculated. Static and dynamic Almost Ideal Demand System (AIDS) models can be estimated. A typical event analysis in finance can be conducted with several functions included.
An integrated set of tools to analyze and simulate networks based on exponential-family random graph models (ERGMs). 'ergm' is a part of the Statnet suite of packages for network analysis.
A set of extensions for the 'ergm' package to fit weighted networks whose edge weights are counts.
Utilities for managing egocentrically sampled network data and a wrapper around the 'ergm' package to facilitate ERGM inference and simulation from such data.
Integrates graphlet statistics based model terms for use in exponential-family random graph models ('ergm') as part of the 'statnet' suite of packages.
A set of extensions for the 'ergm' package to fit weighted networks whose edge weights are ranks.
A template package to demonstrate the use of user-specified statistics for use in "ergm" models as part of the "statnet" suite of packages.
Data for use with the Sage Introduction to Exponential
        Random Graph Modeling text by Jenine K. Harris.  Network data
        set consists of 1283 local health departments and the
        communication links among them along with several attributes.
Fits Rasch models (RM), linear logistic test models (LLTM), rating scale model (RSM), linear rating scale models (LRSM), partial credit models (PCM), and linear partial credit models (LPCM).  Missing values are allowed in the data matrix.  Additional features are the ML estimation of the person parameters, Andersen's LR-test, item-specific Wald test, Martin-Loef-Test, nonparametric Monte-Carlo Tests, itemfit and personfit statistics including infit and outfit measures, various ICC and related plots, automated stepwise item elimination, simulation module for various binary data matrices.
The functions provided in the package ERP are designed for the significance analysis of ERP data in a linear model framework. The possible procedures are either the collection of FDR or FWER controlling methods available in the generic function p.adjust, the same collection combined with a factor modeling of the time dependence among tests (see Sheu, Perthame, Lee and Causeur, 2016) and the Guthrie and Buchwald (1991) test.
A set of user-friendly functions to aid in organizing, plotting
    and analyzing event-related potential (ERP) data.  Provides an easy-to-learn
    method to explore ERP data. Should be useful to those without a background
    in computer programming, and to those who are new to ERPs (or new to the
    more advanced ERP software available). Emphasis has been placed on highly
    automated processes using functions with as few arguments as possible.
    Expects processed (cleaned) data.
This package is dedicated to the analysis of event-related potentials (ERPs). Event-related potentials are the measured brain responses associated with a specific sensory, cognitive, or motor event and are obtained from electroencephalographic (EEG) signal. The erpR package contains a series of functions for importing ERP data, computing traditional ERP measures, exploratory ERP analyses and plotting.
Builds and analyzes error intervals for a particular model predictions assuming different distributions for noise in the data.
Provides environment hooks that obtain errors and warnings which
    occur during the execution of code to automatically search for solutions.
Provides a function to convert existing R functions into "errorized" versions 
    with added logging and handling functionality when encountering errors or warnings. 
    The errorize function accepts an existing R function as its first argument and 
    returns a R function with the exact same arguments and functionality. However, 
    if an error or warning occurs when running that "errorized" R function, it will save a 
    .Rds file to the current working directory with the relevant objects and information 
    required to immediately recreate the error.
Errors in data can be located and removed using validation rules from package 'validate'.
Support for measurement errors in R vectors, matrices and arrays: 
  automatic uncertainty propagation and reporting.
Constructs a 'shiny' app function with interactive displays for summary and analysis of variance regression tables, and parallel coordinate plots of data and residuals.
Implementation of the Edge Selection Algorithm
These functions estimate the latent factors of a given matrix, no matter it is high-dimensional or not. It tries to first estimate the number of factors using bi-cross-validation and then estimate the latent factor matrix and the noise variances. For more information about the method, see Art B. Owen and Jingshu Wang 2015 archived article on factor model (http://arxiv.org/abs/1503.03515).
Tools for fitting the Extended Empirical Saddlepoint (EES) density.
It allows to construct two types of indicators used in the study of
    Electoral Systems and Party Systems starting from electoral results data.
    The Effective Number of Parties (Laakso and Taagepera (1979) <doi:10.1177/001041407901200101>)
    and Electoral Volatility in its three versions (Pedersen (1979) <doi:10.1111/j.1475-6765.1979.tb01267.x>,
    Powell and Tucker (2014) <doi:10.1017/S0007123412000531> and Torcal and Lago (2015, ISBN:9788415260356)).
Implementation of the web-based 'Practical Meta-Analysis Effect Size
    Calculator' from David B. Wilson (<http://www.campbellcollaboration.org/escalc/html/EffectSizeCalculator-Home.php>)
    in R. Based on the input, the effect size can be returned as standardized mean 
    difference, Cohen's f, Hedges' g, Pearson's r or Fisher's
    transformation z, odds ratio or log odds, or eta squared effect size.
The package can identify the dysregulated canonical pathways by investigating the changes of biological relationships of pathways in the context of gene expression data. (1) The ESEA package constructs a background set of edges by extracting pathway structure (e.g. interaction, regulation, modification, and binding etc.) from the seven public databases (KEGG; Reactome; Biocarta; NCI; SPIKE; HumanCyc; Panther) and the edge sets of pathways for each of the above databases. (2) The ESEA package can can quantify the change of correlation between genes for each edge based on gene expression data with cases and controls. (3) The ESEA package uses the weighted Kolmogorov-Smirnov statistic to calculate an edge enrichment score (EES), which reflects the degree to which a given pathway is associated the specific phenotype. (4) The ESEA package can provide the visualization of the results.
The package presents a "Scenarios" class containing
        general parameters, risk parameters and projection results.
        Risk parameters are gathered together into a ParamsScenarios
        sub-object. The general process for using this package is to
        set all needed parameters in a Scenarios object, use the
        customPathsGeneration method to proceed to the projection, then
        use xxx_PriceDistribution() methods to get asset prices.
Toolkit for Monte Carlo simulations of financial assets and
    interest rates models, involved in an Economic Scenario Generator (ESG).
    The underlying simulation loops have been implemented in C++.
Computes shrinkage estimators for regression problems. Selects
    penalty parameter by minimizing bias and variance in the effect estimate, where bias and variance are estimated from the posterior predictive distribution.
Functions for classification and group membership probability estimation are given. 
             The issue of non-informative features in the data is addressed by utilizing the ensemble method. 
             A few optimal models are selected in the ensemble from an initially large set of base k-nearest neighbours (KNN) models, generated on subset of features from the training data.
             A two stage assessment  is applied in selection of optimal models for the ensemble in the training function. 
             The prediction functions for classification and class membership probability estimation returns class outcomes and class membership probability estimates for the test data. 
             The package includes measure of classification error and brier score, for classification and probability estimation tasks respectively.
Misc functions programmed by Eduard Szöcs. 
    Provides read_regnie() to read gridded precipitation data from German Weather 
    Service  (DWD, see <http://www.dwd.de/> for more information).
Support in preparing a raw ESM dataset for statistical analysis. Preparation includes the handling of errors (mostly due to technological reasons) and the generating of new variables that are necessary and/or helpful in meeting the conditions when statistically analyzing ESM data. The functions in 'esmprep' are meant to hierarchically lead from bottom, i.e. the raw (separated) ESM dataset(s), to top, i.e. a single ESM dataset ready for statistical analysis. This hierarchy evolved out of my personal experience in working with ESM data.
Simultaneous modeling of the quantile and the expected shortfall of a response variable given 
    a set of covariates, see Dimitriadis and Bayer (2017) <arXiv:1704.02213>.
Download data from the European Social Survey directly from their website <http://www.europeansocialsurvey.org/>. There are two family of functions that allow you to download and interactively check all countries and rounds available.
Provide an optimal histogram, in the sense of probability density estimation and features detection, by means of multiscale variational inference. For details see Li, Munk, Sieling and Walther (2016) <arXiv:1612.07216>.
Download data from the European Social Survey directly from their website <http://www.europeansocialsurvey.org/>. There are two families of functions that allow you to download and interactively check all countries and rounds available.
Provides an interface to e-Stat API, the one-stop service for official statistics of the Japanese government.
Estimates item and person parameters for the Samejima's Continuous Response Model (CRM), computes item fit residual statistics, draws empirical 3D item category response curves, draws theoretical 3D item category response curves, and generates data under the CRM for simulation studies.
An implementation of sequential testing that uses evidence ratios
    computed from the weights of a set of models. These weights correspond either
    to Akaike weights computed from the Akaike Information Criterion (AIC) or the
    Bayesian Information Criterion (BIC) and following Burnham & Anderson
    (2004, <doi:10.1177/0049124104268644>) recommendations, or to pseudo-BMA
    weights computed from the WAIC or the LOO-IC of models fitted
    with 'brms' and following Yao et al. (2017, <arXiv:1704.02030v3>).
Our method is a variable selection method to select active components in sparse linear mixed models in order to estimate the heritability. The selection allows us to reduce the size of the data sets which improves the accuracy of the estimations. Our package also provides a confidence interval for the estimated heritability.
Provides tools for determining estimability of linear functions 
  of regression coefficients, and 'epredict' methods that handle 
  non-estimable cases correctly. Estimability theory is discussed in
  many linear-models textbooks including Chapter 3 of Monahan, JF (2008), 
  "A Primer on Linear Models", Chapman and Hall (ISBN 978-1-4200-6201-4).
Can be used to simultaneously estimate networks (Gaussian Graphical Models) in data from different groups or classes via Joint Graphical Lasso. Tuning parameters are selected via information criteria (AIC / BIC / eBIC) or crossvalidation.
Fast procedures for small set of commonly-used, design-appropriate estimators with robust standard errors and confidence intervals. Includes estimators for linear regression, regression improving precision of experimental estimates by interacting treatment with centered pre-treatment covariates introduced by Lin (2013) <doi:10.1214/12-AOAS583>, difference-in-means, and Horvitz-Thompson estimation.
This package is intended to speedup the process of
        creating model-comparing tables common in Macroeconomics. The
        function collection stores the estimates of several models and
        formats it to a table of the form estimate starred and std.err.
        below. The default output is LaTeX but output to CSV for later
        editing in a spreadsheet tool is possible as well. It works for
        linear models (lm) and panel models from the "plm"-package
        (plm). Two further implemented functions "descsto" and
        "desctab" enable you to export descriptive statistics of
        data-frames and single variables to LaTeX and CSV.
Estimation of production functions by the Olley-Pakes, Levinsohn-Petrin and Wooldridge methodologies. 
    The package aims to reproduce the results obtained with the Stata's user written opreg <http://www.stata-journal.com/article.html?article=st0145> and levpet <http://www.stata-journal.com/article.html?article=st0060> commands. 
    The first was originally proposed by Olley, G.S. and Pakes, A. (1996) <doi:10.2307/2171831>.
    The second by Levinsohn, J. and Petrin, A. (2003) <doi:10.1111/1467-937X.00246>.
	And the third by Wooldridge (2009) <doi.org/10.1016/j.econlet.2009.04.026>.
This package deals with the estimation of the jump rate for piecewise-deterministic Markov processes (PDMPs), from only one observation of the process within a long time. The main functions provide an estimate of this function. The state space may be discrete or continuous. The associated paper has been published in Scandinavian Journal of Statistics and is given in references. Other functions provide a method to simulate random variables from their (conditional) hazard rate, and then to simulate PDMPs.
An implementation of a most commonly used event study methodology,
    including both parametric and nonparametric tests. It contains variety
    aspects of the rate of return estimation (the core calculation is done in
    C++), as well as three classical for event study market models: mean
    adjusted returns, market adjusted returns and single-index market models.
    There are 6 parametric and 6 nonparametric tests provided, which examine
    cross-sectional daily abnormal return (see the documentation of the
    functions for more information). Parametric tests include tests proposed by 
    Brown and Warner (1980) <DOI:10.1016/0304-405X(80)90002-1>, Brown and Warner
    (1985) <DOI:10.1016/0304-405X(85)90042-X>, Boehmer et al. (1991)
    <DOI:10.1016/0304-405X(91)90032-F>, Patell (1976) <DOI:10.2307/2490543>, and
    Lamb (1995) <DOI:10.2307/253695>. Nonparametric tests covered in estudy2 are
    tests described in Corrado and Zivney (1992) <DOI:10.2307/2331331>,
    McConnell and Muscarella (1985) <DOI:10.1016/0304-405X(85)90006-6>,
    Boehmer et al. (1991) <DOI:10.1016/0304-405X(91)90032-F>, Cowan (1992)
    <DOI:10.1007/BF00939016>, Corrado (1989) <DOI:10.1016/0304-405X(89)90064-0>,
    Campbell and Wasley (1993) <DOI:10.1016/0304-405X(93)90025-7>, Savickas (2003)
    <DOI:10.1111/1475-6803.00052>, Kolari and Pynnonen (2010)
    <DOI:10.1093/rfs/hhq072>. Furthermore, tests for the cumulative
    abnormal returns proposed by Brown and Warner (1985)
    <DOI:10.1016/0304-405X(85)90042-X> and Lamb (1995) <DOI:10.2307/253695>
    are included.
A table function for descriptive statistics in tabular format, using variables in a data.frame. You can create simple or highly customized tables.
Fits the space-time Epidemic Type Aftershock Sequence
    (ETAS) model to earthquake catalogs using a stochastic declustering 
    approach. The ETAS model is a spatio-temporal marked point process
    model and a special case of the Hawkes process. The package is based 
    on a Fortran program by Jiancang Zhuang
    (available at <http://bemlar.ism.ac.jp/zhuang/software.html>),
    which is modified and translated into C++ and C such that it 
    can be called from R. Parallel computing with OpenMP is possible 
    on supported platforms.
Estimation of the components of an ETAS (Epidemic Type Aftershock Sequence) model for earthquake description. Non-parametric background seismicity can be estimated through FLP (Forward Likelihood Predictive), while parametric components are estimated through maximum likelihood. The two estimation steps are alternated until convergence is obtained. For each event the probability of being a background event is estimated and used as a weight for declustering steps. Many options to control the estimation process are present, together with some diagnostic tools. Some descriptive functions for earthquakes catalogs are  present; also plot, print, summary, profile methods are defined for main output (objects of class 'etasclass').
The package allows selecting those treatments of a one-way layout
 being equivalent to a control. Bonferroni adjusted "two one-sided t-tests"
 (TOST) and related simultaneous confidence intervals are given for both
 differences or ratios of means of normally distributed data. For the case of
 equal variances and balanced sample sizes for the treatment groups, the
 single-step procedure of Bofinger and Bofinger (1995) can be chosen. For
 non-normal data, the Wilcoxon test is applied.
Interacts with the open-source, public 'Ethereum' <https://www.ethereum.org/> blockchain.
    It provides a distributed computing platform via smart contracts. This package provides
    functions which interrogate blocks and transactions in the 'Ethereum' blockchain.
Reliable and rapid ethnicity annotation from whole exome sequencing data.
A predictable and pipeable framework for performing ETL 
    (extract-transform-load) operations on publicly-accessible medium-sized data 
    set. This package sets up the method structure and implements generic 
    functions. Packages that depend on this package download specific data sets 
    from the Internet, clean them up, and import them into a local or remote 
    relational database management system.
Provides functions to facilitate the use of the 'ff' package
    in interaction with big data in 'SQL' databases (e.g. in 'Oracle', 'MySQL',
    'PostgreSQL', 'Hive') by allowing easy importing directly into 'ffdf' objects
    using 'DBI', 'RODBC' and 'RJDBC'. Also contains some basic utility functions to
    do fast left outer join merging based on 'match', factorisation of data and a
    basic function for re-coding vectors.
Matrix of transition probabilities for any time-inhomogeneous multistate model with finite state space
Traditional meta-regression based method has been developed for using meta-analysis data, but it faced the challenge of inconsistent estimates. This package purpose a new statistical method to detect epistasis using incomplete information summary, and have proven it not only successfully let consistency of evidence, but also increase the power compared with traditional method (Detailed tutorial is shown in website).
Computes moments of univariate truncated t distribution.
    There is only one exported function, e_trunct(), which should be seen for details.
Client to interact with the 'etcd' 'key-value' data store
    <https://github.com/coreos/etcd>. Functions included for managing
    directories, keys, nodes, and getting statistics.
An eulerian path is a path in a graph which visits every edge exactly once. This package provides methods to handle eulerian paths or cycles.
Generate area-proportional Euler diagrams
    using numerical optimization. An Euler diagram is a generalization of a Venn
    diagram, relaxing the criterion that all interactions need to be
    represented. Diagrams may be fit with ellipses and circles via
    a wide range of inputs and can be visualized in numerous ways.
An R Client for the Europe PubMed Central RESTful Web Service
    (see <https://europepmc.org/RestfulWebService> for more information). It
    gives access to both metadata on life science literature and open access
    full texts. Europe PMC indexes all PubMed content and other literature
    sources including Agricola, a bibliographic database of citations to the
    agricultural literature, or Biological Patents. In addition to bibliographic
    metadata, the client allows users to fetch citations and reference lists.
    Links between life-science literature and other EBI databases, including
    ENA, PDB or ChEMBL are also accessible. No registration or API key is
    required. See the vignettes for usage examples.
This dataset contains population estimates of all European cities 
    with at least 10,000 inhabitants during the period 1500-1800. These data are
    adapted from Jan De Vries, "European Urbanization, 1500-1800" (1984).
Bayesian sample size calculation software and examples for EuroSARC
    clinical trials which utilise Bayesian methodology. These trials rely on
    binomial based endpoints so the majority of programs found here relate to this
    sort of endpoint. Developed as part of the EuroSARC FP7 grant.
Tools to download data from the Eurostat database
    <http://ec.europa.eu/eurostat> together with search and
    manipulation utilities.
Goodness-of-fit tests for selection of r in the r-largest order
    statistics (GEVr) model. Goodness-of-fit tests for threshold selection in the
    Generalized Pareto distribution (GPD). Random number generation and density functions
    for the GEVr distribution. Profile likelihood for return level estimation
    using the GEVr and Generalized Pareto distributions. P-value adjustments for
    sequential, multiple testing error control. Non-stationary fitting of GEVr and
    GPD.
Provides functions for evaluating (time series) model
	estimation methods. These facilitate Monte Carlo experiments of repeated
	simulations and estimations. Also provides methods for
	looking at the distribution of the results from these experiments,
	including model roots (which are an equivalence class invariant).
Parsing and evaluation tools that make it easy to recreate the
    command line behaviour of R.
Provides Some of the most important evaluation measures for evaluating a model. Just by giving the real and predicted class, measures such as accuracy, sensitivity, specificity, ppv, npv, fmeasure, mcc and ... will be returned.
An open source risk analysis toolkit based on the OpenFAIR taxonomy 
  <https://www2.opengroup.org/ogsys/catalog/C13K> and risk assessment standard 
  <https://www2.opengroup.org/ogsys/catalog/C13G>. Empowers an organization to 
  perform a quantifiable, repeatable, and data-driven risk review.
Conducts sensitivity analyses for unmeasured confounding for either an observational study or a meta-analysis of observational studies. For a single observational study, the package reports E-values, defined as the minimum strength of association on the risk ratio scale that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates. One can use one of the evalues.XX() functions to compute E-values for the relevant outcome types. Outcome types include risk ratios, odds ratio with common or rare outcomes, hazard ratios with common or rare outcomes, and standardized differences in outcomes. Optionally, one can use the bias_plot() function to plot the bias factor as a function of two sensitivity parameters. (See VanderWeele & Ding, 2017 [<http://annals.org/aim/article/2643434>] for details.) For a meta-analysis, use the function confounded_meta to compute point estimates and inference for: (1) the proportion of studies with true causal effect sizes more extreme than a specified threshold of scientific importance; and (2) the minimum bias factor and confounding strength required to reduce to less than a specified threshold the proportion of studies with true effect sizes of scientifically significant size. The functions sens_plot() and sens_table() create plots and tables for visualizing these meta-analysis metrics across a range of bias values, and scrape_meta helps scrape study-level data from a published forest plot or summary table to obtain the needed estimates when these are not reported. (See Mathur & VanderWeele [<https://osf.io/jkhfg/>] for details.) Most of the analyses available in this package can also be conducted using web-based graphical interfaces (for a single observational study: <https://mmathur.shinyapps.io/evalue/>; for a meta-analysis: <https://mmathur.shinyapps.io/meta_gui_2/>).
Uses data and constants to calculate potential evapotranspiration (PET) and actual evapotranspiration (AET) from 21 different formulations including Penman, Penman-Monteith FAO 56, Priestley-Taylor and Morton formulations.
Different evidential distance-based classifiers, which provide
    outputs in the form of Dempster-Shafer mass functions. The methods are: the
    evidential K-nearest neighbor rule and the evidential neural network.
Various clustering algorithms that produce a credal partition,
    i.e., a set of Dempster-Shafer mass functions representing the membership of objects
    to clusters. The mass functions quantify the cluster-membership uncertainty of the objects.
    The algorithms are: Evidential c-Means (ECM), Relational Evidential c-Means (RECM),
    Constrained Evidential c-Means (CECM), EVCLUS and EK-NNclus.
Package for combining pieces of evidence
Extends simulation, distribution, quantile and density
        functions to univariate and multivariate parametric extreme
        value distributions, and provides fitting functions which
        calculate maximum likelihood estimates for univariate and
        bivariate maxima models, and for univariate and bivariate
        threshold models.
Provides functions for the bayesian analysis of extreme
        value models, using MCMC methods.
Functions for setting up and analyzing event history data.
Event dataset repository including both real-life and artificial event logs. They can be used in combination with functionalities provided by the 'bupaR' packages 'edeaR', 'processmapR', etc.
Functions for analysis of rate changes in sequential events.
Stores, manipulates, aggregates and otherwise messes with event
    data from KEDS/TABARI or any other extraction tool with similar output
Perform Event Studies from through our <http://EventStudyTools.com> Application Programming Interface, parse the results, visualize it, and / or use the results in further analysis.
Bayesian (and some likelihoodist) functions as alternatives to hypothesis-testing functions in R base using a user interface patterned after those of R's hypothesis testing functions. See McElreath (2016, ISBN: 978-1-4822-5344-3), Gelman and Hill (2007, ISBN: 0-521-68689-X) (new edition in preparation) and Albert (2009, ISBN: 978-0-387-71384-7) for good introductions to Bayesian analysis and Pawitan (2002, ISBN: 0-19-850765-8) for the Likelihood approach.  The functions in the package also make extensive use of graphical displays for data exploration and model comparison.
Integrated Sensitivity Analysis of Evidence Factors in Observational Studies.
Functions for extreme value theory, which may be 
  divided into the following groups; exploratory data analysis, 
  block maxima, peaks over thresholds (univariate and bivariate), 
  point processes, gev/gpd distributions.
The usual distribution functions, maximum likelihood inference and
    model diagnostics for univariate stationary extreme value mixture models
    are provided. Kernel density estimation including various boundary
    corrected kernel density estimation methods and a wide choice of kernels,
    with cross-validation likelihood based bandwidth estimator.
    Reasonable consistency with the base functions in the 'evd' package is
    provided, so that users can safely interchange most code.
Comparative analysis of continuous traits influencing discrete states, and utility tools to facilitate comparative analyses.  Implementations of ABBA/BABA type statistics to test for introgression in genomic data. Wright-Fisher, phylogenetic tree, and statistical distribution Shiny interactive simulations for use in teaching.
Provides functions for covariance matrix comparisons, estimation
    of repeatabilities in measurements and matrices, and general evolutionary
    quantitative genetics tools.
A comprehensive set of tools to illustrate the
    core concepts of evolutionary game theory, such as
    evolutionary stability or various evolutionary dynamics, 
    for teaching and academic research.
An implementation of the evolvability parameters defined in Hansen and Houle (2008).
Evolutionary process simulation using geometric morphometric data. Manipulation of landmark data files (TPS), shape plotting and distances plotting functions.
The EvoPER, Evolutionary Parameter Estimation for 'Repast Simphony'
    Agent-Based framework (<https://repast.github.io/>), provides optimization
    driven parameter estimation methods based on evolutionary computation
    techniques which could be more efficient and require, in some cases,
    fewer model evaluations than other alternatives relying on experimental design.
Uses maximum likelihood to estimate rates of trait evolution across environmental gradients.
Computes extreme value index (EVI) estimate for heavy tailed models by Mean of order p (MOP) 
	     and peaks over random threshold (PORT) Hill methodologies. 
             Besides, also computes moment, generalised Hill and mixed moment estimates for EVI. 
             Compute high quantile or value-at-risk (VaR) based on above EVI estimates.
Commonly used classification and regression tree methods like the CART algorithm
             are recursive partitioning methods that build the model in a forward stepwise search.
	     Although this approach is known to be an efficient heuristic, the results of recursive
	     tree methods are only locally optimal, as splits are chosen to maximize homogeneity at
	     the next step only. An alternative way to search over the parameter space of trees is
	     to use global optimization methods like evolutionary algorithms. The 'evtree' package
	     implements an evolutionary algorithm for learning globally optimal classification and
	     regression trees in R. CPU and memory-intensive tasks are fully computed in C++ while
	     the 'partykit' package is leveraged to represent the resulting trees in R, providing
	     unified infrastructure for summaries, visualizations, and predictions.
Contains a large number of the goodness-of-fit tests for the Exponential and Weibull distributions classified into families: the tests based on the empirical distribution function, the tests based on the probability plot, the tests based on the normalized spacings, the tests based on the Laplace transform and the likelihood based tests.
An implementation of a variety of escalation with overdose control designs introduced by Babb, Rogatko and Zacks (1998) <doi:10.1002/(SICI)1097-0258(19980530)17:10%3C1103::AID-SIM793%3E3.0.CO;2-9>. It calculates the next dose as a clinical trial proceeds as well as performs simulations to obtain operating characteristics.
Performs unconditional exact tests and power calculations for 2x2 contingency tables. Unconditional exact tests are often more powerful than conditional exact tests and asymptotic tests.
Calculates conditional exact tests (Fisher's exact test, Blaker's exact test, or  exact McNemar's test) and unconditional exact tests (including score-based tests on differences in proportions, ratios of proportions, and odds ratios, and Boshcloo's test) with appropriate matching confidence intervals, and provides power and sample size calculations. Gives melded confidence intervals for the binomial case. Gives boundary-optimized rejection region test (Gabriel, et al, 2018, <DOI:10.1002/sim.7579>), an unconditional exact test for the situation where the controls are all expected to fail.
Calculates exact tests and confidence intervals for one-sample binomial and one- or two-sample Poisson cases.
This is a package for exact Confidence Intervals for the
        difference between two independent or dependent proportions.
Monte Carlo and MCMC goodness of fit tests for log-linear
        models
Perform exact fixed effect meta analysis for rare events data without the need of artificial continuity correction.
ExactPath implements an algorithm for exact LASSO
        solution. Two methods are provided to print and visualize the
        whole solution paths. Use ?ExactPath to see an introduction.
        Packages ncvreg and lars are required so that their data sets
        can be used in examples.
Computes exact conditional p-values and quantiles using an
 implementation of the Shift-Algorithm by Streitberg & Roehmel.
Take the examples written in your documentation of functions and 
    use them to create shells (skeletons which must be manually completed by
    the user) of test files to be tested with the 'testthat' package.
Automatic generation of exams based on exercises in Markdown or LaTeX format,
	possibly including R code for dynamic generation of exercise elements.
	Exercise types include single-choice and multiple-choice questions, arithmetic problems,
	string questions, and combinations thereof (cloze). Output formats include standalone
	files (PDF, HTML, Docx, ODT, ...), Moodle XML, QTI 1.2 (for OLAT/OpenOLAT), QTI 2.1,
	Blackboard, ARSnova, and TCExam. In addition to fully customizable PDF exams, a
	standardized PDF format (NOPS) is provided that can be printed, scanned, and automatically evaluated.
Tools for constructing confidence regions for exceedance regions
    and contour lines.
Allows access to data in running instance of Microsoft Excel
    (e. g. 'xl[a1] = xl[b2]*3' and so on). Graphics can be transferred with
    'xl[a1] = current.graphics()'. Additionally there are function for reading/writing 
    'Excel' files - 'xl.read.file'/'xl.save.file'. They are not fast but able to read/write 
    '*.xlsb'-files and password-protected files. There is an Excel workbook with 
    examples of calling R from Excel in the 'doc' folder. It tries to keep things as
    simple as possible - there are no needs in any additional
    installations besides R, only 'VBA' code in the Excel workbook.
    Microsoft Excel is required for this package.
This is an R interface to the
    python package 'excerpts' (<https://pypi.python.org/pypi/excerpts>).
Implementation of a function which calculates the empirical excess mass 
	for given \eqn{\lambda} and given maximal number of modes (excessm()). Offering 
	powerful plot features to visualize empirical excess mass (exmplot()). This 
	includes the possibility of drawing several plots (with different maximal 
	number of modes / cut off values) in a single graph.
Interactive tools to explore topographic-like data
    sets.  Such data sets take the form of a matrix in which the rows and
    columns provide location/frequency information, and the matrix elements
    contain altitude/response information.  Such data is found in cartography,
    2D spectroscopy and chemometrics.  The functions in this package create
    interactive web pages showing the contoured data, possibly with
    slices from the original matrix parallel to each dimension. The interactive
    behavior is created using the 'D3.js' 'JavaScript' library by Mike Bostock.
Functions that compute probabilistic excursion sets, contour credibility regions, contour avoiding regions, and simultaneous confidence bands for latent Gaussian random processes and fields. The package also contains functions that calculate these quantities for models estimated with the INLA package. The main references for excursions are Bolin and Lindgren (2015) <doi:10.1111/rssb.12055> and Bolin and Lindgren (2017) <doi:10.1080/10618600.2016.1228537>. These can be generated by the citation function in R.
Extracts Exchangeable Image File Format (EXIF) metadata, such as camera make and model, ISO speed and the date-time
             the picture was taken on, from JPEG images. Incorporates the 'easyexif' (https://github.com/mayanklahiri/easyexif)
             library.
Reads EXIF data using ExifTool <http://www.sno.phy.queensu.ca/~phil/exiftool/>
    and returns results as a data frame.
    ExifTool is a platform-independent Perl library plus a command-line
    application for reading, writing and editing meta information in a wide variety
    of files. ExifTool supports many different metadata formats including EXIF,
    GPS, IPTC, XMP, JFIF, GeoTIFF, ICC Profile, Photoshop IRB, FlashPix, AFCP and
    ID3, as well as the maker notes of many digital cameras by Canon, Casio, FLIR,
    FujiFilm, GE, HP, JVC/Victor, Kodak, Leaf, Minolta/Konica-Minolta, Motorola, Nikon,
    Nintendo, Olympus/Epson, Panasonic/Leica, Pentax/Asahi, Phase One, Reconyx, Ricoh,
    Samsung, Sanyo, Sigma/Foveon and Sony.
Calls copy number variants (CNVs) from targeted sequence data, typically exome sequencing experiments designed to identify the genetic basis of Mendelian disorders.
For a given metabolic model with well formed Gene-Protein-Reaction (GPR) associations and an expressionSet with their associated gene expression values, this package converts gene expression values to the FBA boundaries for each reaction based in the boolean rules described in its associated GPR.
Provides a shiny-based front end (the 'ExPanD' app) and
    a set of functions for exploratory panel data analysis. Run as a web-based app, 
    'ExPanD' enables users to assess the robustness of empirical evidence without 
    providing them access to the underlying data. You can also use the functions 
    of the package to support your exploratory data analysis workflow. Refer to 
    the vignettes of the package for more information on how to use 'ExPanD' 
    and/or the functions of this package.
Generates feature matrix outputs from R object inputs
    using a variety of expansion functions.  The generated
    feature matrices have applications as inputs
    for a variety of machine learning algorithms.
    The expansion functions are based on coercing the input
    to a matrix, treating the columns as features and
    converting individual columns or combinations into blocks of
    columns.
    Currently these include expansion of columns by
    efficient sparse embedding by vectors of lags,
    quadratic expansion into squares and unique products,
    powers by vectors of degree,
    vectors of orthogonal polynomials functions,
    and block random affine projection transformations (RAPTs).
    The transformations are
    magrittr- and cbind-friendly, and can be used in a
    building block fashion.  For instance, taking the cos() of
    the output of the RAPT transformation generates a
    stationary kernel expansion via Bochner's theorem, and this
    expansion can then be cbind-ed with other features.
    Additionally, there are utilities for replacing features,
    removing rows with NAs,
    creating matrix samples of a given distribution,
    a simple wrapper for LASSO with CV,
    a Freeman-Tukey transform,
    generalizations of the outer function,
    matrix size-preserving discrete difference by row,
    plotting, etc.
Expanding Ploidy and Allele Frequency on Nested Subpopulations (expands) characterizes coexisting subpopulations in a single tumor sample using copy number and allele frequencies derived from exome- or whole genome sequencing input data (<http://www.ncbi.nlm.nih.gov/pubmed/24177718>). The model detects coexisting genotypes by leveraging run-specific tradeoffs between depth of coverage and breadth of coverage. This package predicts the number of clonal expansions, the size of the resulting subpopulations in the tumor bulk, the mutations specific to each subpopulation, tumor purity and phylogeny. The main function runExPANdS() provides the complete functionality needed to predict coexisting subpopulations from single nucleotide variations (SNVs) and associated copy numbers.  The robustness of subpopulation predictions increases with the number of mutations provided. It is recommended that at least 200 mutations are used as input to obtain stable results. Updates in version 2.1 include: (i) new parameter ploidy in runExPANdS.R allows specification of non-diploid background ploidies (e.g. for near-triploid cell lines); (ii) parallel computing option is available. Further documentation and FAQ available at <http://dna-discovery.stanford.edu/software/expands>.
Modular implementation of the Differential Evolution algorithm for
    experimenting with different types of operators.
Package for analysis of simple experimental designs (CRD, RBD and LSD), experiments in double factorial schemes (in CRD and RBD), experiments in a split plot in time schemes (in CRD and RBD), experiments in double factorial schemes with an additional treatment (in CRD and RBD), experiments in triple factorial scheme (in CRD and RBD) and experiments in triple factorial schemes with an additional treatment (in CRD and RBD), performing the analysis of variance and means comparison by fitting regression models until the third power (quantitative treatments) or by a multiple comparison test, Tukey test, test of Student-Newman-Keuls (SNK), Scott-Knott, Duncan test, t test (LSD) and Bonferroni t test (protected LSD)(qualitative treatments); residual analysis (Ferreira, Cavalcanti and Nogueira, 2014) <doi:10.4236/am.2014.519280>.
Pacote destinado a analise de delineamentos experimentais simples (DIC, DBC e DQL), experimentos em esquema de fatorial duplo (em DIC e DBC), experimentos em esquema de parcelas subdivididas no tempo (em DIC e DBC), experimentos em esquema de fatorial duplo com um tratamento adicional (em DIC e DBC), experimentos em esquema de fatorial triplo (em DIC e DBC) e experimentos em esquema de fatorial triplo com um tratamento adicional (em DIC e DBC); realizando a analise de variancia e comparacao de medias pelo ajuste de modelos de regressao ate o terceiro grau (tratamentos quantitativos) ou por testes de comparacao multipla: teste de Tukey, teste de Student-Newman-Keuls (SNK), teste de Scott-Knott, teste de Duncan, teste t (LSD), teste t de Bonferroni (LSD protegido) e teste Bootstrap (tratamentos qualitativos); analise de residuos (Ferreira, Cavalcanti e Nogueira (2014) <doi:10.4236/am.2014.519280>).
Provides various statistical methods for
  designing and analyzing randomized experiments. One functionality
  of the package is the implementation of randomized-block and
  matched-pair designs based on possibly multivariate pre-treatment
  covariates. The package also provides the tools to analyze various
  randomized experiments including cluster randomized experiments,
  two-stage randomized experiments, randomized experiments with 
  noncompliance, and randomized experiments with missing data.
Expert opinion (or judgment) is a body of techniques to
  estimate the distribution of a random variable when data is scarce
  or unavailable. Opinions on the quantiles of the distribution are
  sought from experts in the field and aggregated into a final
  estimate. The package supports aggregation by means of the Cooke,
  Mendel-Sheridan and predefined weights models.
The exponential integrals E_1(x), E_2(x), E_n(x) and
  Ei(x), and the incomplete gamma function G(a, x) defined for
  negative values of its first argument. The package also gives easy
  access to the underlying C routines through an API; see the package
  vignette for details. A test package included in sub-directory
  example_API provides an implementation. C routines derived from the
  GNU Scientific Library <https://www.gnu.org/software/gsl/>.
Generates explanations for classification and regression models and visualizes them.
 Explanations are generated for individual predictions as well as for models as a whole. Two explanation methods
 are included, EXPLAIN and IME. The EXPLAIN method is fast but might miss explanations expressed redundantly
 in the model. The IME method is slower as it samples from all feature subsets.
 For the EXPLAIN method see Robnik-Sikonja and Kononenko (2008) <doi:10.1109/TKDE.2007.190734>, 
 and the IME method is described in Strumbelj and Kononenko (2010, JMLR, vol. 11:1-18).
 All models in package 'CORElearn' are natively supported, for other prediction models a wrapper function is provided 
 and illustrated for models from packages 'randomForest', 'nnet', and 'e1071'.
Shiny interfaces and graphical functions for multivariate analysis results exploration.
Simplifies some complicated and labor intensive processes involved in exploring and explaining data.  Allows you to quickly and efficiently visualize the interaction between variables and simplifies the process of discovering covariation in your data.  Also includes some convenience features designed to remove as much redundant typing as possible.
Computation of the matrix exponential, logarithm, sqrt,
  and related quantities.
An R-interface to the Fortran package Expokit.
ExPosition is for descriptive (i.e., fixed-effects) multivariate analysis with the singular value decomposition.
Calculates the density dependent likelihood of a phylogenetic tree. It takes branching and sampling times as an argument and integrates the likelihood function over the whole tree.
Tools and data to accompany Schlicht, Valcu and Kempenaers "Spatial patterns of extra-pair paternity: beyond paternity gains and losses"
Allows to calculate the probabilities  of occurrences of an event in a 
 great number of repetitions of Bernoulli experiment, through the application of  
 the local and the integral theorem of De Moivre Laplace, and the theorem of Poisson. 
 Gives the possibility to show the results graphically and analytically, and to compare
 the results obtained by the application of the above  theorems with those calculated by
 the direct application of the Binomial formula. Is basically useful for educational
 purposes.
Supervised machine learning has an increasingly important role in data
    analysis. This package introduces a framework for rapidly building and deploying
    supervised machine learning in a high-throughput manner. This package provides a
    user-friendly interface that empowers investigators to execute state-of-the-art
    binary and multi-class classification, as well as regression, with minimal
    programming experience necessary.
Data sets from the book "Forecasting with exponential smoothing: the state space approach" by 
	Hyndman, Koehler, Ord and Snyder (Springer, 2008).
Package provides tabulation functions with support for 'SPSS'-style 
        labels, multiple / nested banners, weights, multiple-response variables 
        and significance testing. There are facilities for nice output of tables 
        in 'knitr', R notebooks, 'Shiny' and 'Jupyter' notebooks. Proper methods 
        for labelled variables add value labels support to base R functions and to 
        some functions from other packages. Additionally, the package offers 
        useful functions for data processing in marketing research / social 
        surveys - popular data transformation functions from 'SPSS' Statistics 
        ('RECODE', 'COUNT', 'COMPUTE', 'DO IF', etc.) and 'Excel' ('COUNTIF', 
        'VLOOKUP', etc.). Package is intended to help people to move data 
        processing from 'Excel'/'SPSS' to R.
Tests for the composite hypothesis of exponentiality
Analysis of experimental results and automatic report generation in both interactive HTML and LaTeX. This package ships with a rich interface for data modeling and built in functions for the rapid application of statistical tests and generation of common plots and tables with publish-ready quality.
Estimation for high conditional quantiles based on quantile regression.
The package provides tools for botanists, plant taxonomists,
    curators of plant genebanks and perhaps other biological collections.
If 'dplyr' is a grammar for data manipulation, 'extdplyr' is like
    a short paragraph written in 'dplyr'. 'extdplyr' extends 'dplyr' and
    'tidyr' verbs to some common "routines" that manipulate data sets. It uses
    the same interface and preserves all the features from 'dplyr', has good 
    performance, and supports various data sources.
If one treated group is matched to one control reservoir in two different ways to produce two sets of treated-control matched pairs, then the two control groups may be entwined, in the sense that some control individuals are in both control groups.  The exterior match is used to compare the two control groups.
This is a package containing the function extfunnel()
        which produces a funnel plot including additional augmentations
        such as statistical significance contours and heterogeneity
        contours.
The package estimates coefficients of extended LASSO penalized linear regression and generalized linear models. Currently lasso and elastic net penalized linear regression and generalized linear models are considered. The package currently utilizes an accurate approximation of L1 penalty and then a modified Jacobi algorithm to estimate the coefficients. There is provision for plotting of the solutions and predictions of coefficients at given values of lambda. The package also contains functions for cross validation to select a suitable lambda value given the data. The package also provides a function for estimation in fused lasso penalized linear regression.
This package tests for differences in minor allele
        frequency between groups and is based on an extra-binomial
        variation model for pooled sequencing data.
Categorical Data Analysis and Visualization.
Density, distribution function, quantile function
    and random generation for a number of univariate
    and multivariate distributions. This package implements the
    following distributions: Bernoulli, beta-binomial, beta-negative
    binomial, beta prime, Bhattacharjee, Birnbaum-Saunders,
    bivariate normal, bivariate Poisson, categorical, Dirichlet,
    Dirichlet-multinomial, discrete gamma, discrete Laplace,
    discrete normal, discrete uniform, discrete Weibull, Frechet,
    gamma-Poisson, generalized extreme value, Gompertz,
    generalized Pareto, Gumbel, half-Cauchy, half-normal, half-t,
    Huber density, inverse chi-squared, inverse-gamma, Kumaraswamy,
    Laplace, location-scale t, logarithmic, Lomax, multivariate
    hypergeometric, multinomial, negative hypergeometric, 
    non-standard beta, normal mixture, Poisson mixture, Pareto,
    power, reparametrized beta, Rayleigh, shifted Gompertz, Skellam,
    slash, triangular, truncated binomial, truncated normal,
    truncated Poisson, Tukey lambda, Wald, zero-inflated binomial,
    zero-inflated negative binomial, zero-inflated Poisson.
Tools to using fonts other than the standard PostScript fonts.
    This package makes it easy to use system TrueType fonts and with PDF or
    PostScript output files, and with bitmap output files in Windows. extrafont
    can also be used with fonts packaged specifically to be used with, such as
    the fontcm package, which has Computer Modern PostScript fonts with math
    symbols. See https://github.com/wch/extrafont for instructions and
    examples.
Package for holding the database for the extrafont package
Classification and regression based on an ensemble of decision trees. The package also provides extensions of ExtraTrees to multi-task learning and quantile regression. Uses Java implementation of the method.
An implementation of Extreme Bounds Analysis (EBA), a global sensitivity analysis that examines the robustness of determinants in regression models. The package supports both Leamer's and Sala-i-Martin's versions of EBA, and allows users to customize all aspects of the analysis.
Extreme value theory, nonparametric kernel estimation, tail
    conditional probabilities, extreme conditional quantile, adaptive estimation,
    quantile regression, survival probabilities.
Functions for performing extreme value analysis.
Code to fit, plot and compare several (extreme value)
    distribution functions. Can also compute (truncated) distribution quantile estimates and
    draw a plot with return periods on a linear scale.
Detect outliers in one-dimensional data.
Estimation of the sample univariate, cross and return time extremograms. The package can also adds empirical confidence bands to each of the extremogram plots via a permutation procedure under the assumption that the data are independent. Finally, the stationary bootstrap allows us to construct credible confidence bands for the extremograms.
It implements the subjectively censored Weibull MLE and censored Weibull mixture methods for the lower quantile estimation. Quantile estimates from these two methods are robust to model misspecification in the lower tail. It also includes functions to evaluation the standard error of the resulting quantile estimates. Also, the methods here can be used to fit the Weibull or Weibull mixture for the Type-I or Type-II right censored data.
Eyelink eye trackers output a horrible mess, typically under
    the form of a '.asc' file. The file in question is an assorted collection of
    messages, events and raw data. This R package will attempt to make sense of it.
Misc function for working with eyetracking data
A set of tools that address tasks along the pipeline from raw
    data to analysis and visualization for eye-tracking data. Offers several
    popular types of analyses, including linear and growth curve time analyses,
    onset-contingent reaction time analyses, as well as several non-parametric
    bootstrapping approaches.
Facilitates easy analysis of factorial experiments, including
    purely within-Ss designs (a.k.a. "repeated measures"), purely between-Ss
    designs, and mixed within-and-between-Ss designs. The functions in this package
    aim to provide simple, intuitive and consistent specification of data analysis
    and visualization. Visualization functions also include design visualization for
    pre-analysis data auditing, and correlation matrix visualization. Finally, this
    package includes functions for non-parametric analysis, including permutation
    tests and bootstrap resampling. The bootstrap function obtains predictions
    either by cell means or by more advanced/powerful mixed effects models, yielding
    predictions and confidence intervals that may be easily visualized at any level
    of the experiment's design.
Because fungicide resistance is an important phenotypic trait for
    fungi and oomycetes, it is necessary to have a standardized method of
    statistically analyzing the Effective Concentration (EC) values. This
    package is designed for those who are not terribly familiar with R to be
    able to analyze and plot an entire set of isolates using the 'drc' package.
This package implements a simplified version of least
        squares, and logistic regression for efficiently selecting the
        significant non-additive interactions between two variables.
An extension of 'knitr' that adds flexibility in several
    ways. One common source of frustration with 'knitr' is that it assumes
    the directory where the source file lives should be the working directory,
    which is often not true. 'ezknitr' addresses this problem by giving you
    complete control over where all the inputs and outputs are, and adds several
    other convenient features to make rendering markdown/HTML documents easier.
ezsim provides a handy way to run simulation and examine its result
Functions that simplify the process of generating print-ready data summary using 'dplyr' syntax.
Frequentist assisted by Bayes (FAB) confidence interval
    construction. See 'Adaptive multigroup confidence intervals with constant
    coverage' by Yu and Hoff <https://arxiv.org/abs/1612.08287>.
Helps you imagine your data before you collect it. Hierarchical data structures
   and correlated data can be easily simulated, either from random number generators or
   by resampling from existing data sources. This package is faster with 'data.table' and
   'mvnfast' installed.
Implementation of the Fast Covariance Estimation for Sparse Functional Data  paper published in Statistics and Computing, Xiao et al. (2018) <doi:10.1007/s11222-017-9744-8>.
Provides an interface to the Facebook API and builds collections of elements that reflects the graph architecture of Facebook.
  See <https://developers.facebook.com/docs/graph-api> for more information.
Provides an interface to the 'Kairos' Face Recognition API <https://kairos.com/face-recognition-api>. The API detects faces in images and returns estimates for demographics like gender, ethnicity and age.
A tool for simulating a variety of spatial individual-based models of
    plant-plant interactions. User-created models can include any number of species, each of
    which can be structured in any number of life-stages, where each life-stage has specific
    death, growth and reproduction rates, as well as specific interaction radius, dispersal
    radius, and interaction effects over each other species/life-stage. Life stages were modeled
    so as to be a stochastic, individual-based version of differential Matrix Population
    Models (Caswell 2001, ISBN:0-87893-096-5). Interactions can be positive
    (facilitation) or negative (competition) and can affect death rates, growth rates or
    reproduction rates. Interactions from multiple numbers are additive, so as to best
    approximate classic population dynamics models such as the logistic model and
    Lotka-Volterra model (Britton 2004, ISBN:9781852335366). All models work in continuous time, implemented as an optimized version
    of the Gillespie algorithm (Gillespie 1976 <doi:10.1016/0021-9991(76)90041-3>) for independent exponential times, and continuous space.
This method tries to explain the gene-wise treatment response ratios in terms of the latent pathways. It uses bayesian sparse factor modeling to infer the loadings (weights) of each pathway on its associated probesets as well as the latent factor activity levels for each treatment.
The package estimates Factor Mixture Analysis via the EM
        algorithm
Perform Maximum Likelihood Factor analysis on a covariance matrix or data matrix.
Some functions of 'ade4' and 'stats' are combined in order to obtain a partition of the rows of a data table, with columns representing variables of scales: quantitative, qualitative or frequency. 
  First, a principal axes method is performed and then, a combination of Ward agglomerative hierarchical classification and K-means is performed, using some of the first coordinates obtained from the previous principal axes method. 
  The function 'kmeansW', a modification of 'kmeans', programmed in C++, is included, in order to permit to have different weights of the elements to be clustered. Some complementary functions and datasets are included.
  See, for example: 
      Lebart, L. and Piron, M. and Morineau, A.  (2006).
      Statistique Exploratoire Multidimensionnelle, Dunod, Paris.
Provides some easy-to-use functions to extract and visualize the
    output of multivariate data analyses, including 'PCA' (Principal Component
    Analysis), 'CA' (Correspondence Analysis), 'MCA' (Multiple Correspondence
    Analysis), 'FAMD' (Factor Analysis of Mixed Data), 'MFA' (Multiple Factor Analysis) and 'HMFA' (Hierarchical Multiple
    Factor Analysis) functions from different R packages. It contains also functions
    for simplifying some clustering analysis steps and provides 'ggplot2' - based
    elegant data visualization.
Brings a set of tools to help and automatically realise the description of principal component analyses (from 'FactoMineR' functions). Detection of existing outliers, identification of the informative components, graphical views and dimensions description are performed threw dedicated functions. The Investigate() function performs all these functions in one, and returns the result as a report document (Word, PDF or HTML).
Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017) <DOI:10.1201/b10345-2>.
Computes factorial A-, D- and E-optimal designs for two-colour cDNA microarray experiments.
Identifies change-points in the common and the idiosyncratic components via factor modelling.
The Merging Path Plot is a methodology for adaptive fusing of k-groups 
    with likelihood-based model selection. This package contains tools for 
    exploration and visualization of k-group dissimilarities. 
    Comparison of k-groups is one of the most important issues
    in exploratory analyses and it has zillions of applications. 
    The traditional approach is to use pairwise post hoc tests
    in order to verify which groups differ significantly. However, 
    this approach fails with a large number of groups in both interpretation 
    and visualization layer.
    The Merging Path Plot solves this problem by using an easy-to-understand 
    description of dissimilarity among groups based on Likelihood Ratio Test (LRT) statistic.
    Work on this package was financially
    supported by the 'NCN Opus grant 2016/21/B/ST6/02176'.
Methods to calculate, print, summarize and plot pairwise differences from GLMs, GLHT or Multinomial Logit models.
Package to fit Bayesian quantile regression models that
        assume a factor structure for at least part of the design
        matrix.
It identifies the factors significantly related to species richness, and their relative contribution, using multiple regressions and support vector machine models. It uses an output file of 'ModestR' (<http://www.ipez.es/ModestR>) with data of richness of the species and environmental variables in a cell size defined by the user. The residuals of the support vector machine model are shown on a map. Negative residuals may be potential areas with undiscovered and/or unregistered species, or areas with decreased species richness due to the negative effect of anthropogenic factors.
Markov chain Monte Carlo (MCMC) sampler for fully Bayesian
             estimation of latent factor stochastic volatility models.
             Sparsity can be achieved through the usage of Normal-Gamma priors
             on the factor loading matrix.
Perform factorial analysis with a menu and draw graphs interactively thanks to 'FactoMineR' and a Shiny application.
Per the Factual.com website, "Factual is a platform where
        anyone can share and mash open, living data on any subject."
        The data is in the form of tables and is accessible via REST
        API.  The factualR package is a thin wrapper around the
        Factual.com API, to make it even easier for people working with
        R to explore Factual.com data sets.
The functions provided in the FADA (Factor Adjusted Discriminant Analysis) package aim at performing supervised classification of high-dimensional and correlated profiles. The procedure combines a decorrelation step based on a  
   factor modeling of the dependence among covariates and a classification method. The available methods are Lasso regularized logistic model
    (see Friedman et al. (2010)), sparse linear discriminant analysis (see
    Clemmensen et al. (2011)), shrinkage linear and diagonal discriminant
    analysis (see M. Ahdesmaki et al. (2010)). More methods of classification can be used on the decorrelated data provided by the package FADA.
Probability distributions that are sometimes useful in hydrology.
Data and functions for the book "Multivariate Statistical 
              Modelling Based on Generalized Linear Models", first edition, by 
              Ludwig Fahrmeir and Gerhard Tutz.  Useful when using the book.
More comfortable interface to work with R data or source files
    in a key-value fashion.
It is used for systematic analysis of decisions based on attributes and its levels.
Use R as a minimal build system. This might come in handy
        if you are developing R packages and can not use a proper build
        system. Stay away if you can (use a proper build system).
Generates fake data from a dataset of different variable types.
    The package contains the functions simulate_dataset and simulate_dataset_ts 
    to simulate time-independent and time-dependent data. It randomly samples 
    character and factor variables from contingency tables and  numeric and 
    ordered factors from a multivariate normal distribution. It currently supports the 
    simulation of stationary and zero-inflated count time series.
This is a method for Allele-specific DNA Copy Number Profiling using Next-Generation Sequencing.  Given the allele-specific coverage at the variant loci, this program segments the genome into regions of homogeneous allele-specific copy number.  It requires, as input, the read counts for each variant allele in a pair of case and control samples. For detection of somatic mutations, the case and control samples can be the tumor and normal sample from the same individual.
This is a method for Allele-specific DNA Copy Number profiling for whole-Exome sequencing data.  Given the allele-specific coverage and site biases at the variant loci, this program segments the genome into regions of homogeneous allele-specific copy number.  It requires, as input, the read counts for each variant allele in a pair of case and control samples, as well as the site biases. For detection of somatic mutations, the case and control samples can be the tumor and normal sample from the same individual.  The implemented method is based on the paper: Chen, H., Jiang, Y., Maxwell, K., Nathanson, K. and Zhang, N. (under review). Allele-specific copy number estimation by whole Exome sequencing.
Functionality provided for conditional simulation, likelihoods and plotting of pedigrees, mostly as a wrapper for 'paramlink'. Users typically start by exporting from the Windows version of 'Familias'.
Simulates age-at-onset traits associated with a segregating major gene in family data 
     obtained from population-based, clinic-based, or multi-stage designs. Appropriate ascertainment 
     correction is utilized to estimate age-dependent penetrance functions either parametrically from 
     the fitted model or nonparametrically from the data. The Expectation and Maximization algorithm 
     can infer missing genotypes and carrier probabilities estimated from family's genotype and
     phenotype information or from a fitted model. Plot functions include pedigrees of simulated 
     families and predicted penetrance curves based on specified parameter values.
An interface to the core Familias functions (www.familias.name), 
 which are programmed in C++.
Fits penalized linear and logistic regression models with pairwise interaction terms.
Estimate parameters of univariate probability distributions 
  with maximum likelihood and Bayesian methods.
FamSKAT-RC is a family-based association kernel test for both 
    rare and common variants. This test is general and several special cases 
    are known as other methods: famSKAT, which only focuses on rare variants 
    in family-based data, SKAT, which focuses on rare variants in 
    population-based data (unrelated individuals), and SKAT-RC, which focuses 
    on both rare and common variants in population-based data. When one 
    applies famSKAT-RC and sets the value of phi to 1, famSKAT-RC becomes 
    famSKAT. When one applies famSKAT-RC and set the value of phi to 1 and the 
    kinship matrix to the identity matrix, famSKAT-RC becomes SKAT. When one 
    applies famSKAT-RC and set the kinship matrix (fullkins) to the identity 
    matrix (and phi is not equal to 1), famSKAT-RC becomes SKAT-RC. We also 
    include a small sample synthetic pedigree to demonstrate the method with.  
    For more details see Saad M and Wijsman EM (2014) <doi:10.1002/gepi.21844>.
The method proposed in this package takes into account the impact of dependence on the multiple testing procedures for high-throughput data as proposed by Friguet et al. (2009). The common information shared by all the variables is modeled by a factor analysis structure. The number of factors considered in the model is chosen to reduce the false discoveries variance in multiple tests. The model parameters are estimated thanks to an EM algorithm. Adjusted tests statistics are derived, as well as the associated p-values. The proportion of true null hypotheses (an important parameter when controlling the false discovery rate) is also estimated from the FAMT model. Graphics are proposed to interpret and describe the factors.
Computes the penalized maximum likelihood estimates of factor loadings and unique variances for various tuning parameters. The pathwise coordinate descent along with EM algorithm is used.  This package also includes a new graphical tool which outputs path diagram, goodness-of-fit indices and model selection criteria for each regularization parameter. The user can change the regularization parameter by manipulating scrollbars, which is helpful to find a suitable value of regularization parameter.
This package contains a collection of R functions to
        perform nonparametric analysis of covariance for regression
        curves or surfaces. Testing the equality or parallelism of
        nonparametric curves or surfaces is equivalent to analysis of
        variance (ANOVA) or analysis of covariance (ANCOVA) for
        one-sample functional data. Three different testing methods are
        available in the package, including one based on L-2 distance,
        one based on an ANOVA statistic, and one based on variance
        estimators.
Provides the function fancycut() which is like cut() except
  you can mix left open and right open intervals with point values,
  intervals that are closed on both ends and intervals that are open on both ends.
Estimation and plotting of a function's FANOVA graph to identify the interaction structure and fitting, prediction and simulation of a Kriging model modified by the identified structure. The interactive function plotManipulate() can only be run on the RStudio IDE with RStudio's package 'manipulate' loaded. RStudio is freely available (www.rstudio.org), and includes package 'manipulate'. The equivalent function plotTk() bases on CRAN Repository packages only.
Visualise sequential distributions using a range of plotting
    styles. Sequential distribution data can be input as either simulations or
    values corresponding to percentiles over time. Plots are added to
    existing graphic devices using the fan function. Users can choose from four
    different styles, including fan chart type plots, where a set of coloured
    polygon, with shadings corresponding to the percentile values are layered
    to represent different uncertainty levels.
Counterparts to R string manipulation functions that account for
   the effects of ANSI text formatting control sequences.
A list of functions to download statistics from FAOSTAT (database
    of the Food and Agricultural Organization of the United Nations) and WDI
    (database of the World Bank), and to perform some harmonization operations.
Tools for detecting and summarize influential cases that
    can affect exploratory and confirmatory factor analysis models as well as
    structural equation models more generally.
Modelizations and previsions functions for
   Functional AutoRegressive processes using
   nonparametric methods: functional kernel,
   estimation of the covariance operator in
   a subspace, ...
Books are "Practical Regression and ANOVA in R" on CRAN, "Linear Models with R" published 1st Ed. August 2004, 2nd Ed. July 2014 by CRC press, ISBN 9781439887332, and "Extending the Linear Model with R" published by CRC press in 1st Ed. December 2005 and 2nd Ed. March 2016, ISBN 9781584884248.
Reads and writes 'ARFF' files. 'ARFF' (Attribute-Relation File Format) files are like 'CSV' files, with a little bit of added meta information in a header and standardized NA values. They are quite often used for machine learning data sets and were introduced for the 'WEKA' machine learning 'Java' toolbox. See <http://weka.wikispaces.com/ARFF> for further info on 'ARFF' and for <http://www.cs.waikato.ac.nz/ml/weka/> for more info on 'WEKA'. 'farff' gets rid of the 'Java' dependency that 'RWeka' enforces, and it is at least a faster reader (for bigger files). It uses 'readr' as parser back-end for the data section of the 'ARFF' file. Consistency with 'RWeka' is tested on 'Github' and 'Travis CI' with hundreds of 'ARFF' files from 'OpenML'. Note that the 'OpenML' package is currently only available from 'Github' at: <https://github.com/openml/openml-r>.
Implements a consistent model selection strategy for high dimensional sparse regression when the covariate dependence can be reduced through factor models. By separating the latent factors from idiosyncratic components, the problem is transformed from model selection with highly correlated covariates to that with weakly correlated variables. It is appropriate for cases where we have many variables compared to the number of samples. Moreover, it implements a robust procedure to estimate distribution parameters wherever possible, hence being suitable for cases when the underlying distribution deviates from Gaussianity. See the paper on the 'FarmSelect' method, Fan et al.(2017) <arXiv:1612.08490>, for detailed description of methods and further references.
Performs robust multiple testing for means in the presence of known and unknown latent factors. It implements a robust procedure to estimate distribution parameters using the Huber's loss function and accounts for strong dependence among coordinates via an approximate factor model. This method is particularly suitable for high dimensional data when there are many variables but only a small number of observations available. Moreover, the method is tailored to cases when the underlying distribution deviates from Gaussian, which is commonly assumed in the literature. Besides the results of hypotheses testing, the estimated underlying factors and diagnostic plots are also output. Multiple comparison correction is done after estimating the proportion of true null hypotheses using the method in Storey (2015) <https://github.com/jdstorey/qvalue>. See the paper on the 'FarmTest' method, Zhou et al.(2017) <https://goo.gl/68SJpd>, for detailed description of methods and further references.
Allow numbers to be presented in an persian language
        version
It contains a function designed to the joint segmentation in the mean of several correlated series. The method is described in the paper X. Collilieux, E. Lebarbier and S. Robin. A factor model approach for the joint segmentation with between-series correlation (2015) <arXiv:1505.05660>.
Provides functions for pricing
	and valuating Asian Options together with tools for analyzing
    and modeling Exponential Brownian Motion (EBM).
This is an R implementation of "A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models" (FASJEM). The FASJEM algorithm can be used to estimate multiple related precision matrices. For instance, it can identify context-specific gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogonous data sets, this tool  can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(fasjem) to learn the basic functions provided by this package. For more details, please see <http://proceedings.mlr.press/v54/wang17e/wang17e.pdf>.
Provides a  collection of functions 
  to manage, to investigate and to analyze data sets of financial 
  assets from different points of view.
The Fourier Amplitude Sensitivity Test (FAST) is a method to determine global sensitivities of a model on parameter changes with relatively few model runs. This package implements this sensitivity analysis method.
A collection of acceleration schemes for proximal gradient methods for estimating penalized regression parameters described in
    Goldstein, Studer, and Baraniuk (2016) <arXiv:1411.3406>. Schemes such as Fast Iterative Shrinkage and Thresholding Algorithm (FISTA) by Beck and Teboulle (2009) <doi:10.1137/080716542> 
    and the adaptive stepsize rule introduced in Wright, Nowak, and Figueiredo (2009) <doi:10.1109/TSP.2009.2016892> are included. You provide the objective function and proximal mappings, and it takes care of the issues like stepsize selection, acceleration, and stopping conditions for you.
Implements Adaboost based on C++ backend code.
             This is blazingly fast and especially useful for large, in memory data sets. 
             The package uses decision trees as weak classifiers. Once the classifiers
             have been trained, they can be used to predict new data. 
             Currently, we support only binary classification tasks.
             The package implements the Adaboost.M1 algorithm and the real
             Adaboost(SAMME.R) algorithm.
Fast and numerically stable estimation of a covariance matrix by banding the Cholesky factor using a modified Gram-Schmidt algorithm implemented in RcppArmadilo. See <http://stat.umn.edu/~molst029> for details on the algorithm.
Provides a method of recovering the precision matrix efficiently 
        and solving for the dantzig selector by applying the parametric 
        simplex method.  The computation is based on a linear optimization
        solver. It also contains a generic LP solver and a parameterized LP 
        solver using parametric simplex method.
This is a two-in-one package which provides interfaces to
        both R and Python. It implements fast hierarchical, agglomerative
        clustering routines. Part of the functionality is designed as drop-in
        replacement for existing routines: linkage() in the SciPy package
        'scipy.cluster.hierarchy', hclust() in R's 'stats' package, and the
        'flashClust' package. It provides the same functionality with the
        benefit of a much faster implementation. Moreover, there are
        memory-saving routines for clustering of vector data, which go beyond
        what the existing packages provide. For information on how to install
        the Python files, see the file INSTALL in the source distribution.
A method which uses the Cochran-Mantel-Haenszel test with significant pattern mining to detect intervals in binary genotype data which are significantly associated with a particular phenotype, while accounting for categorical covariates.
We implement a cocktail algorithm, a good mixture of coordinate decent, the majorization-minimization principle and the strong rule, for computing the solution paths of the elastic net penalized Cox's proportional hazards model. The package is an implementation of Yang, Y. and Zou, H. (2013) DOI: <doi:10.4310/SII.2013.v6.n2.a1>.
Provides an R interface to Bob Jenkin's streaming, 
 non-cryptographic 'SpookyHash' hash algorithm for use in digest-based 
 comparisons of R objects. 'fastdigest' plugs directly into R's internal 
 serialization machinery, allowing digests of all R objects the serialize() 
 function supports, including reference-style objects via custom hooks. Speed is
 high and scales linearly by object size; memory usage is constant and 
 negligible.
Creates dummy columns from columns that have categorical variables (character or factor types). You can also specify which columns to make dummies out of, or which columns to ignore. Also creates dummy rows from character, factor, and Date columns. This package provides a significant speed increase from creating dummy variables through model.matrix().
If there is a need to recognise edges on a raster image or a bitmap or any kind of a matrix, one can find packages
    that does only 90 degrees vectorization. Typically the nature of artefact images is linear and can be vectorized in much more
    efficient way than draw a series of 90 degrees lines. The fasteraster package does recognition of lines using only one pass.
    It also allows to calculate mass and the mass centers for the recognized zones or polygons.
Provides a drop-in replacement for rasterize() from the 'raster'
   package that takes 'sf'-type objects, and is much faster. There is support
   for the main options provided by the rasterize() function, including
   setting the field used and background value, and options for 
   aggregating multi-layer rasters. Uses the scan line algorithm attributed to
   Wylie et al. (1967) <doi:10.1145/1465611.1465619>.
Contains Rcpp and RcppEigen implementations of matrix operations useful for Gaussian process models, such as the inversion of a symmetric Toeplitz matrix, sampling from multivariate normal distributions, evaluation of the log-density of a multivariate normal vector, and Bayesian inference for latent variable Gaussian process models with elliptical slice sampling (Murray, Adams, and MacKay 2010).
Provides functionality to produce graphs of probability density functions and cumulative distribution functions with few keystrokes, allows shading under the curve of the probability density function to illustrate concepts such as p-values and critical values, and fits a simple linear regression line on a scatter plot with the equation as the main title.
The FastHCS algorithm of Schmitt and Vakili (2014) <doi:10.1007/s11222-015-9602-5> for high-dimensional, robust PCA modelling and associated outlier detection and diagnostic tools.
It implements HICA (Hierarchical Independent Component Analysis) algorithm. This approach, obtained through the integration between treelets and Independent Component Analysis, is able to provide a multi-scale non-orthogonal data-driven basis, whose elements have a phenomenological interpretation according to the problem under study.
Implementation of FastICA algorithm to perform Independent
        Component Analysis (ICA) and Projection Pursuit.
TrainFastImputation() uses training data to describe a
    multivariate normal distribution that the data approximates or
    can be transformed into approximating and stores this information
    as an object of class 'FastImputationPatterns'. FastImputation()
    function uses this 'FastImputationPatterns' object to impute (make
    a good guess at) missing data in a single line or a whole data frame
    of data.  This approximates the process used by 'Amelia'
    <http://gking.harvard.edu/amelia/> but is much faster when
    filling in values for a single line of data.
This 'Rcpp'-based package implements highly efficient functions for the calculation of the Jonckheere-Terpstra statistic. It can be used for a variety of applications, including feature selection in machine learning problems, or to conduct genome-wide association studies (GWAS) with multiple quantitative phenotypes. The code leverages 'OpenMP' directives for multi-core computing to reduce overall processing time.
A computationally efficient and statistically rigorous fast Kernel Machine method for multi-kernel analysis. The approach is based on a low-rank approximation to the nuisance effect kernel matrices. The algorithm is applicable to continuous, binary, and survival traits and is implemented using the existing single-kernel analysis software 'SKAT' and 'coxKM'. 'coxKM' can be obtained from http://www.hsph.harvard.edu/xlin/software.html.
Compute labels for a test set according to the k-Nearest Neighbors classification. This is a fast way to do k-Nearest Neighbors classification because the distance matrix -between the features of the observations- is an input to the function rather than being calculated in the function itself every time.
Implements a Fellegi-Sunter probabilistic record linkage model that allows for missing data
    and the inclusion of auxiliary information. This includes functionalities to conduct a merge of two datasets under the Fellegi-Sunter model
    using the Expectation-Maximization algorithm. In addition, tools for preparing, adjusting, and summarizing
    data merges are included. The package implements methods described in Enamorado, Fifield,
    and Imai (2017) ''Using a Probabilistic Model to Assist Merging of Large-scale Administrative
    Records'', available at <http://imai.princeton.edu/research/linkage.html>.
Implements the new algorithm for fast computation of M-scatter matrices using a partial Newton-Raphson procedure for several estimators. The algorithm is described in Duembgen, Nordhausen and Schuhmacher (2016) <doi:10.1016/j.jmva.2015.11.009>.
Fast Marching Method (FMM) first developed by Sethian (1996) <http://www.pnas.org/content/93/4/1591.short>, and further extended by including a second-order approximation, the first-arrival rule, additive weights, and non-homogeneous domains following Silva and Steele (2012) <doi:10.1142/S0219525911003293> and Silva and Steele (2014) <doi:10.1016/j.jas.2014.04.021>.
Package providing a fast match() replacement for cases
	that require repeated look-ups. It is slightly faster that R's
	built-in match() function on first match against a table, but
	extremely fast on any subsequent lookup as it keeps the hash
	table in memory.
We present an implementation of the algorithms required to simulate large-scale social networks and retrieve their most relevant metrics.
The FastPCS algorithm of Vakili and Schmitt (2014) <doi:10.1016/j.csda.2013.07.021> for robust estimation of multivariate location and scatter and multivariate outliers detection.
Computes pseudo-observations for survival analysis on right-censored data based on restricted mean survival time.
'FASTQC' is the most widely used tool for evaluating the quality of high throughput sequencing data.  
    It produces, for each sample, an html report and a compressed file containing the raw data. 
    If you have hundreds of samples, you are not going to open up each 'HTML' page. 
    You need some way of looking at these data in aggregate. 
    'fastqcr' Provides helper functions to easily parse, aggregate and analyze 
    'FastQC' reports for large numbers of samples. It provides a convenient solution for building 
    a 'Multi-QC' report, as well as, a 'one-sample' report with result interpretations.
Data sets and utilities to accompany
    "Foundations and Applications of Statistics: an Introduction
    using R" (R Pruim, published by AMS, 2011), a text covering
    topics from probability and mathematical statistics at an advanced
    undergraduate level.  R is integrated throughout, and access to all
    the R code in the book is provided via the snippet function.
Data sets and utilities to accompany the second edition of
    "Foundations and Applications of Statistics: an Introduction
    using R" (R Pruim, published by AMS, 2017), a text covering
    topics from probability and mathematical statistics at an advanced
    undergraduate level.  R is integrated throughout, and access to all
    the R code in the book is provided via the snippet function.
The FastRCS algorithm of Vakili and Schmitt (2014) for robust fit of the multivariable linear regression model and outliers detection.
Learning text representations and text classifiers may rely
  on the same simple and efficient approach. 'fastText' is an open-source, free, 
  lightweight library that allows users to perform both tasks.
  It transforms text into continuous vectors that can later
  be used on many language related task.
  It works on standard, generic hardware (no 'GPU' required).
  It also includes model size reduction feature.
  'fastText' original source code is available 
  at <https://github.com/facebookresearch/fastText>.
Infrastrcture for creating rich, dynamic web content using R scripts while maintaining very fast response time.
An implementation of the fast structural filtering with L0 penalty. It includes an adaptive polynomial estimator by minimizing the least squares error with constraints on the number of breaks in their (k + 1)-st discrete derivative, for a chosen integer k >= 0. It also includes generalized structure sparsity constraint, i.e., graph trend filtering. This package is implemented via the primal dual active set algorithm, which formulates estimates and residuals as primal and dual variables, and utilizes efficient active set selection strategies based on the properties of the primal and dual variables.
Functions for computing spillover measures, especially spillover
    tables and spillover indices, as well as their average, minimal, and maximal
    values.
An interface to the 'fastText' library
	<https://github.com/facebookresearch/fastText>. The package
	can be used for text classification and to learn word vectors.
	The install folder contains the 'PATENTS' file.
	An example how to use 'fastTextR' can be found in the 'README' file.
Fast functions for timestamp
	manipulation that avoid system calls and take shortcuts
	to facilitate operations on very large data.
Performs family-based association tests with a polytomous outcome under 2-locus and 1-locus models
             defined by some design matrix.
Application of 'FateID' allows computation and visualization of cell fate bias for multi-lineage single cell transcriptome data. Herman, J.S., Sagar, Grün D. (2017) <doi:10.1101/218115>.
Kiener distributions K1, K2, K3, K4 and K7 to characterize
    distributions with left and right, symmetric or asymmetric fat tails in market
    finance, neuroscience and other disciplines. Two algorithms to estimate with
    a high accuracy distribution parameters, quantiles, value-at-risk and expected
    shortfall. Include power hyperbolas and power hyperbolic functions.
HTTP error helpers. Methods included for general purpose HTTP 
    error handling, as well as individual methods for every HTTP status
    code, both via status code numbers as well as their descriptive names.
    Supports ability to adjust behavior to stop, message or warning.
    Includes ability to use custom whisker template to have any configuration
    of status code, short description, and verbose message. Currently 
    supports integration with 'crul', 'curl', and 'httr'.
A dataset of favourite numbers, selected from an online poll of over 30,000 people by Alex Bellos
             (http://pages.bloomsbury.com/favouritenumber).
Provides functions and datasets from the book "Forest Analytics with R".
A simple package for Flux Balance Analysis and related
    metabolic modeling techniques. Functions are provided for: parsing
    models in tabular format, converting parsed metabolic models to input
    formats for common linear programming solvers, and
    evaluating and applying gene-protein-reaction mappings. In addition, there
    are wrappers to parse a model, select a solver, find the metabolic fluxes,
    and return the results applied to the original model. Compared to other
    packages in this field, this package puts a much heavier focus on
    providing reusable components that can be used in the design of new
    implementation of new techniques, in particular those that involve large
    parameter sweeps.
Provides a collection of functions to 
    explore and to investigate basic properties of financial returns 
    and related quantities.
    The covered fields include techniques of explorative data analysis
    and the investigation of distributional properties, including
    parameter estimation and hypothesis testing. Even more there are
    several utility functions for data handling and management.
Does family-based gene by environment interaction tests, joint gene, gene-environment interaction test, and a test of a set of genes conditional on another set of genes.
We propose an objective Bayesian algorithm for searching the space of Gaussian directed acyclic graph (DAG) models. The algorithm proposed makes use of moment fractional Bayes factors (MFBF) and thus it is suitable for learning sparse graph. The algorithm is implemented by using Armadillo: an open-source C++ linear algebra library.
Normalizes the data from a file containing the raw values
        of the SNP probes of microarrray data by using the FISH probes
        and their corresponding CNs.
It implements the Nelson-Siegel and the Nelson-Siegel-Svensson
	term structures.
Wrapper functions around the Facebook Marketing 'API' to create, read, update and delete custom audiences, images, campaigns, ad sets, ads and related content.
This package uses time dependent Poisson regression and a record of goals scored in matches to rank teams via estimated attack and defense strengths.  The statistical model is based on Dixon and Coles (1997) Modeling Association Football Scores and Inefficiencies in the Football Betting Market, Applied Statistics, Volume 46, Issue 2, 265-280.  The package has a some webscrapers to assist in the development and updating of a match database.  If the match database contains unconnected clusters (i.e. sets of teams that have only played each other and not played teams from other sets), each cluster is ranked separately relative to the median team strength in the cluster.  The package contains functions for predicting and simulating tournaments and leagues from estimated models. The package allows fitting via the glm(), speedglm(), and glmnet() functions.  The latter allows fast and efficient fitting of very large numbers of teams.  The fitting algorithm will analyze the match data and determine which teams form a cluster (a set of teams where there is a path of matches connecting every team) and fit each cluster separately.
Implements a very fast C++ algorithm to quickly bootstrap receiver
    operating characteristics (ROC) curves and derived performance metrics,
    including the area under the curve (AUC) and the partial area under the curve as well as 
    the true and false positive rate. The analysis of paired receiver operating curves is supported as well,
    so that a comparison of two predictors is possible. You can also plot the
    results and calculate confidence intervals. On a typical desktop computer the time needed for 
    the calculation of 100000 bootstrap replicates given 500 observations requires time on the
    order of magnitude of one second.
Efficient procedures for community detection in network studies, especially for sparse networks with not very obvious community structure. The algorithms impose penalties on the differences of the coordinates which represent the community labels of the nodes.
Collection of pricing by duplication methods for popular structured products ("Zertifikate").
Fatigue Crack Growth in Reliability estimates the distribution
             of material lifetime due to mechanical fatigue efforts. The FCGR
             package provides simultaneous crack growth curves fitting to 
             different specimens in materials under mechanical stress efforts. 
             Linear mixed-effects models (LME) with smoothing B-Splines 
             and the linearized Paris-Erdogan law are applied. Once defined
             the fail for a determined crack length, the distribution function
             of failure times to fatigue is obtained. The density function is
             estimated by applying nonparametric binned kernel density estimate 
             (bkde) and the kernel estimator of the distribution function (kde). 
             The results of Pinheiro and Bates method based on nonlinear 
             mixed-effects regression (nlme) can be also retrieved. The package 
             contains the crack.growth, PLOT.cg, IB.F, and Alea.A (database) functions.
Change point estimation and detection methods for functional data are implemented using dimension reduction via functional principal component analysis and a fully-functional (norm-based) method. Detecting and dating structural breaks for both dependent and independent functional samples is illustrated along with some basic functional data generating processes.
Algorithms for fuzzy clustering, cluster validity indices and plots for cluster validity and visualizing fuzzy clustering results.
Provides a selection of 3 different inference rules (including additionally the clamped types of the referred inference rules) and 4 threshold functions in order to obtain the inference of the FCM (Fuzzy Cognitive Map). Moreover, the 'fcm' package returns a data frame of the concepts' values of each state after the inference procedure. Fuzzy cognitive maps were introduced by Kosko (1986) <doi:10.1002/int.4550010405> providing ideal causal cognition tools for modeling and simulating dynamic systems.
Provides several functions to create and manipulate fuzzy
    cognitive maps. It is based on 'FCMapper' for Excel, distributed at <http://
    www.fcmappers.net/joomla/>, developed by Michael Bachhofer and Martin Wildenberg.
    Maps are inputted as adjacency matrices. Attributes of the maps and the
    equilibrium values of the concepts (including with user-defined constrained
    values) can be calculated. The maps can be graphed with a function that calls
    'igraph'. Multiple maps with shared concepts can be aggregated.
Provides an interface to kernel routines from the FCNN C++ library.
    FCNN is based on a completely new Artificial Neural Network representation that
    offers unmatched efficiency, modularity, and extensibility. FCNN4R provides
    standard teaching (backpropagation, Rprop, simulated annealing, stochastic
    gradient) and pruning algorithms (minimum magnitude, Optimal Brain Surgeon),
    but it is first and foremost an efficient computational engine. Users can
    easily implement their algorithms by taking advantage of fast gradient computing
    routines, as well as network reconstruction functionality (removing weights
    and redundant neurons, reordering inputs, merging networks). Networks can be
    exported to C functions in order to integrate them into virtually any software
    solution.
Provides a  collection of functions to 
	manage, to investigate and to analyze bivariate financial returns by  
	Copulae. Included are the families of Archemedean, Elliptical, 
	Extreme Value, and Empirical Copulae.
Dynamic prediction in functional concurrent regression with an application to child growth. Extends the pffr() function from the 'refund' package to handle the scenario where the functional response and concurrently measured functional predictor are irregularly measured.  Leroux et al. (2017), Statistics in Medicine, <doi:10.1002/sim.7582>.
A fold change rank based method is presented to search for genes with changing
             expression and to detect recurrent chromosomal copy number aberrations. This 
             method may be useful for high-throughput biological data (micro-array, sequencing, ...).
             Probabilities are associated with genes or probes in the data set and there is no
             problem of multiple tests when using this method. For array-based comparative genomic
             hybridization data, segmentation results are obtained by merging the significant
             probes detected.
Automatically suggests a correction when a typo occurs.
FD is a package to compute different multidimensional FD indices. It implements a distance-based framework to measure FD that allows any number and type of functional traits, and can also consider species relative abundances. It also contains other useful tools for functional ecology.
These functions were developed to support functional data
        analysis as described in Ramsay, J. O. and Silverman, B. W.
        (2005) Functional Data Analysis. New York: Springer.  They were
        ported from earlier versions in Matlab and S-PLUS.  An
        introduction appears in Ramsay, J. O., Hooker, Giles, and
        Graves, Spencer (2009) Functional Data Analysis with R and
        Matlab (Springer). The package includes data sets and script
        files working many examples including all but one of the 76
        figures in this latter book.  Matlab versions of the code and
        sample analyses are no longer distributed through CRAN, as they
        were when the book was published.  For those, ftp from
        <http://www.psych.mcgill.ca/misc/fda/downloads/FDAfuns/>
        There you find a set of .zip files containing the functions and
        sample analyses, as well as two .txt files giving instructions for
        installation and some additional information.
        The changes from Version 2.4.1 are fixes of bugs in density.fd and
        removal of functions create.polynomial.basis, polynompen, and 
        polynomial. These were deleted because the monomial basis
        does the same thing and because there were errors in the code.
Routines for exploratory and descriptive analysis of functional data such as depth measurements, atypical curves detection, regression models, supervised classification, unsupervised classification and functional analysis of variance.
An implementation of the methodology described in
    Petersen and Mueller (2016) <doi:10.1214/15-AOS1363> for the functional
    data analysis of samples of density functions.  Densities are first
    transformed to their corresponding log quantile densities, followed by
    ordinary Functional Principal Components Analysis (FPCA).  Transformation
    modes of variation yield improved interpretation of the variability in the
    data as compared to FPCA on the densities themselves.  The standard
    fraction of variance explained (FVE) criterion commonly used for functional
    data is adapted to the transformation setting, also allowing for an
    alternative quantification of variability for density data through the
    Wasserstein metric of optimal transport.
It performs simultaneously clustering and alignment of a multidimensional or unidimensional functional dataset by means of k-mean alignment.
Likelihood based analysis of 1-dimension functional data
        in a mixed-effects model framework. Matrix computation are
        approximated by semi-explicit operator equivalents with linear
        computational complexity.
Performs analysis of variance testing procedures for univariate and multivariate functional data (Cuesta-Albertos and Febrero-Bande (2010) <doi:10.1007/s11749-010-0185-3>, Gorecki and Smaga (2015) <doi:10.1007/s00180-015-0555-0>, Gorecki and Smaga (2017) <doi:10.1080/02664763.2016.1247791>, Zhang et al. (2016) <arXiv:1309.7376v1>).
Provides implementation of various methods of Functional Data
    Analysis (FDA) and Empirical Dynamics. The core of this package is Functional
    Principal Component Analysis (FPCA), a key technique for functional data
    analysis, for sparsely or densely sampled random trajectories and time courses,
    via the Principal Analysis by Conditional Estimation (PACE) algorithm or
    numerical integration. PACE is useful for the analysis of data that have been
    generated by a sample of underlying (but usually not fully observed) random
    trajectories. It does not rely on pre-smoothing of trajectories, which is
    problematic if functional data are sparsely sampled. PACE provides options
    for functional regression and correlation, for Longitudinal Data Analysis,
    the analysis of stochastic processes from samples of realized trajectories,
    and for the analysis of underlying dynamics. The core computational algorithms
    are implemented using the 'Eigen' C++ library for numerical linear algebra and
    'RcppEigen' "glue".
An implementation of regression models with partial differential regularizations, making use of the Finite Element Method. The models efficiently handle data distributed over irregularly shaped domains and can comply with various conditions at the boundaries of the domain. A priori information about the spatial structure of the phenomenon under study can be incorporated in the model via the differential regularization.
Performs alignment, PCA, and modeling of multidimensional and
            unidimensional functions using the square-root velocity framework
            (Srivastava et al., 2011 <arXiv:1103.3817> and
            Tucker et al., 2014 <DOI:10.1016/j.csda.2012.12.001>). This framework
            allows for elastic analysis of functional data through phase and
            amplitude separation.
Implementation of the Interval Testing Procedure for functional data in different frameworks (i.e., one or two-population frameworks, functional linear models) by means of different basis expansions (i.e., B-spline, Fourier, and phase-amplitude Fourier). The current version of the package requires functional data evaluated on a uniform grid; it automatically projects each function on a chosen functional basis; it performs the entire family of multivariate tests; and, finally, it provides the matrix of the p-values of the previous tests and the vector of the corrected p-values. The functional basis, the coupled or uncoupled scenario, and the kind of test can be chosen by the user. The package provides also a plotting function creating a graphical output of the procedure: the p-value heat-map, the plot of the corrected p-values, and the plot of the functional data.
Regression models for functional data, i.e., scalar-on-function,
    function-on-scalar and function-on-function regression models, are fitted
    by a component-wise gradient boosting algorithm.
Provides a variety of tools for the analysis of covariance operators including k-sample tests for equality and classification and clustering methods found in the works of Cabassi et al (2017) <doi:10.1214/17-EJS1347>, Kashlak et al (2017) <arXiv:1604.06310>, Pigoli et al (2014) <doi:10.1093/biomet/asu008>, and Panaretos et al (2010) <doi:10.1198/jasa.2010.tm09239>.
FDG copulas are a class of copulas featuring an interesting balance between flexibility and tractability. This package provides tools to construct, calculate the pairwise dependence coefficients of, simulate from, and fit FDG copulas. The acronym FDG stands for 'one-Factor with Durante Generators', as an FDG copula is a one-factor copula -- that is, the variables are independent given a latent factor -- whose linking copulas belong to the Durante class of bivariate copulas (also referred to as exchangeable Marshall-Olkin or semilinear copulas).
Allows to estimate dynamic model averaging, dynamic model selection and median probability model. The original methods are implemented, as well as, selected further modifications of these methods. In particular the user might choose between recursive moment estimation and exponentially moving average for variance updating. Inclusion probabilities might be modified in a way using 'Google Trends'. The code is written in a way which minimises the computational burden (which is quite an obstacle for dynamic model averaging if many variables are used). For example, this package allows for parallel computations and Occam's window approach. The package is designed in a way that is hoped to be especially useful in economics and finance. Main reference: Raftery, A.E., Karny, M., Ettler, P. (2010) <doi:10.1198/TECH.2009.08104>.
Contains methods of analysis of forest databases, the purpose of the analyzes is to evaluate 
    the quality of the data present in the databases focusing on the dimensions of consistency, punctuality 
    and completeness. Databases can range from forest inventory data to growth model data. The package has 
    methods to work with large volumes of data quickly, in addition in certain analyzes it is possible to 
    generate the graphs for a better understanding of the analysis and reporting of the analyzed analysis.
FDR functions for permutation-based estimators, including pi0 as well as FDR
             confidence intervals. The confidence intervals account for dependencies between
             tests by the incorporation of an overdispersion parameter, which is estimated
             from the permuted data.
It is known that current false discovery rate (FDR) procedures can be very conservative when applied to multiple testing in the discrete paradigm where p-values (and test statistics) have discrete and heterogeneous null distributions. This package implements more powerful weighted or adaptive FDR procedures for FDR control and estimation in the discrete paradigm. The package takes in the original data set rather than just the p-values in order to carry out the adjustments for discreteness and heterogeneity of p-value distributions. The package implements methods for two types of test statistics and their p-values: (a) binomial test on if two independent Poisson distributions have the same means, (b) Fisher's exact test on if the conditional distribution is the same as the marginal distribution for two binomial distributions, or on if two independent binomial distributions have the same probabilities of success.
Tools for FDR problems, including false discovery rate regression.
    See corresponding paper: "False discovery rate regression: application to
    neural synchrony detection in primary visual cortex."  James G. Scott, Ryan
    C. Kelly, Matthew A. Smith, Robert E. Kass.
Defines a collection of functions to compute average power and sample size for studies that use the false discovery rate as the final measure of statistical significance.
Estimate step functions via multiscale inference with controlled false discovery rate (FDR). For details see H. Li, A. Munk and H. Sieling (2016) <doi:10.1214/16-EJS1131>.
Estimates both tail area-based false 
   discovery rates (Fdr) as well as local false discovery rates (fdr) for a 
   variety of null models (p-values, z-scores, correlation coefficients,
   t-scores).  The proportion of null values and the parameters of the null 
   distribution are adaptively estimated from the data.  In addition, the package 
   contains functions for non-parametric density estimation (Grenander estimator), 
   for monotone regression (isotonic regression and antitonic regression with weights),
   for computing the greatest convex minorant (GCM) and the least concave majorant (LCM), 
   for the half-normal and correlation distributions, and for computing
   empirical higher criticism (HC) scores and the corresponding decision threshold.
Perform frequency distribution tables, associated histograms
             and polygons from vector, data.frame and matrix objects for
             numerical and categorical variables.
For each feature, a score is computed that can be useful
        for feature selection. Several random subsets are sampled from
        the input data and for each random subset, various linear
        models are fitted using lars method. A score is assigned to
        each feature based on the tendency of LASSO in including that
        feature in the models.Finally, the average score and the models
        are returned as the output. The features with relatively low
        scores are recommended to be ignored because they can lead to
        overfitting of the model to the training data. Moreover, for
        each random subset, the best set of features in terms of global
        error is returned. They are useful for applying Bolasso, the
        alternative feature selection method that recommends the
        intersection of features subsets.
Read and write feather files, a lightweight binary columnar
    data store designed for maximum speed.
Local inferential feature significance for multivariate kernel density estimation.
Finds modelling features through a detailed analysis of model residuals using 'rpart' classification and regression trees. Scans the residuals of a model across subsets of the data to identify areas where the model prediction differs from the actual target variable. S. Chatterjee, A. S. Hadi (2006) <doi:10.1002/0470055464>.
Feature hashing, also called as the hashing trick, is a method to transform 
  features of a instance to a vector. Thus, it is a method to transform a real dataset to a matrix. 
  Without looking up the indices in an associative array, 
  it applies a hash function to the features and uses their hash values as indices directly.
  The method of feature hashing in this package was proposed in Weinberger et al. (2009) <arXiv:0902.2206>. 
  The hashing algorithm is the murmurhash3 from the 'digest' package. 
  Please see the README in <https://github.com/wush978/FeatureHashing> for more information.
Discretely-sampled function is first smoothed.  Features
        of the smoothed function are then extracted.  Some of the key
        features include mean value, first and second derivatives,
        critical points (i.e. local maxima and minima), curvature of
        cunction at critical points, wiggliness of the function, noise
        in data, and outliers in data.
A collection of functions that would help one to build features based on external data. Very useful for Data Scientists in data to day work. Many functions create features using parallel computation. Since the nitty gritty of parallel computation is hidden under the hood, the user need not worry about creating clusters and shutting them down.
Making the access to the Free Brazilian Repository for Open Soil Data <http://www.ufsm.br/febr/> as easy 
  as possible.
Functions and example datasets for Fechnerian scaling of discrete
  object sets.  User can compute Fechnerian distances among objects representing
  subjective dissimilarities, and other related information.  See
  package?fechner for an overview.
Calculates fishing effort following the DG MARE Ad-Hoc Workshops on Transversal Variables in Zagreb (2015) and Nicosia (2016).
Functions to automate downloading geospatial data available from
    several federated data sources (mainly sources maintained by the US Federal
    government). Currently, the package enables extraction from seven datasets:
    The National Elevation Dataset digital elevation models (1 and 1/3 arc-second;
    USGS); The National Hydrography Dataset (USGS); The Soil Survey Geographic
    (SSURGO) database from the National Cooperative Soil Survey (NCSS), which is
    led by the Natural Resources Conservation Service (NRCS) under the USDA; the
    Global Historical Climatology Network (GHCN), coordinated by National Climatic
    Data Center at NOAA; the Daymet gridded estimates of daily weather parameters 
    for North America, version 3, available from the Oak Ridge National Laboratory's
    Distributed Active Archive Center (DAAC); the International Tree Ring Data Bank; 
    and the National Land Cover Database (NLCD).
Access data from the Federal Register API <https://www.federalregister.gov/developers/api/v1>.
Downloads data from Federal 'RePORTER'
    <https://api.federalreporter.nih.gov/> using the Federal 'RePORTER' API.
    Allows the user to search job projects from different government agencies.
Analysis of fragmented time directionality to investigate feedback in time series. Tools provided by the package allow the analysis of feedback for a single time series and the analysis of feedback for a set of time series collected across a spatial domain.
Efficient estimation of multiple fixed-effects maximum likelihood models
    with, possibly, non-linear in parameters right hand sides. Standard-errors
    can easily be clustered. It also includes tools to seamlessly export (to Latex) 
    the results of various estimations.
Frailty Exponential Random Graph Models estimated through pseudo likelihood with frailty terms estimated using 'Stan' as per Box-Steffensmeier et. al (2017) <doi:10.7910/DVN/K3D1M2>.
    Goodness of fit for Frailty Exponential Random Graph Models is also available, with easy visualizations for comparison to fit Exponential Random Graph Models.
Data from various catalogs of astrophysical gamma-ray sources
    detected by NASA's Large Area Telescope (The Astrophysical Journal, 697, 1071,
    2009 June 1), on board the Fermi gamma-ray satellite. More information on
    Fermi and its data products is available from the Fermi Science Support Center
    (http://fermi.gsfc.nasa.gov/ssc/).
Wind fetch is the unobstructed length of water over which wind can
    blow from a certain direction. The wind fetch is typically calculated for many directions
    around the compass rose for a given location, which can then be incorporated 
    into a larger model (such as the InVEST coastal vulnerability model;
    <http://data.naturalcapitalproject.org/invest-releases/documentation/2_2_0/coastal_vulnerability.html>),
    or simply averaged for a reasonable measure of 
    the overall wind exposure for a specific marine location. The process of calculating
    wind fetch can be extremely time-consuming and tedious, particularly if a large
    number of fetch vectors are required at many locations. The 'fetchR' package
    calculates wind fetch and summarises the information efficiently. There are 
    also plot methods to help visualise the wind exposure at the various
    locations, and methods to output the fetch vectors to a KML file for further
    investigation.
Provides a collection of functions to evaluate
	barrier options, Asian options, binary options, currency 
    translated options, lookback options, multiple asset options
	and multiple exercise options.
Collection of pricing by duplication and Monte Carlo methods for Express Certificates products (also known as Autocallables)
Provides functions for analysing
  and modelling extreme events in financial time Series. The
  topics include: (i) data pre-processing, (ii) explorative 
  data analysis, (iii) peak over threshold modelling, (iv) block
  maxima modelling, (v) estimation of VaR and CVaR, and (vi) the
  computation of the extreme index.
Extends the out of memory vectors of 'ff' with
    statistical functions and other utilities to ease their usage.
Functions, S4 classes/methods and a graphical user interface (GUI) to design surveys to substantiate freedom from disease using a modified hypergeometric function (see Cameron and Baldock, 1997). Herd sensitivities are computed according to sampling strategies "individual sampling" or "limited sampling" (see M. Ziller, T. Selhorst, J. Teuffert, M. Kramer and H. Schlueter, 2002). Methods to compute the a-posteriori alpha-error are implemented. Risk-based targeted sampling is supported.
Force field simulation of interaction of set of points.
        Very useful for placing text labels on graphs, such as
        scatterplots.
This package performs general linear modeling with
        multiple responses (MANCOVA).  An overall p-value for each
        model term is calculated by the 50-50 MANOVA method, which
        handles collinear responses.  Rotation testing is used to
        compute adjusted single response p-values according to
        familywise error rates and false discovery rates.
An implementation of the adaptive forgetting factor scheme described in Bodenham and Adams (2016) <doi:10.1007/s11222-016-9684-8> which adaptively estimates the mean and variance of a stream in order to detect multiple changepoints in streaming data. The implementation is in C++ and uses Rcpp. Additionally, implementations of the fixed forgetting factor scheme from the same paper, as well as the classic CUSUM and EWMA methods, are included.
Create, visualize, and test fast-and-frugal decision trees (FFTs). FFTs are very simple decision trees for
    binary classification problems. FFTs can be preferable to more complex algorithms because they are easy to communicate, require very little information, and are
    robust against overfitting.
Provides a simple and efficient wrapper around the fastest
        Fourier transform in the west (FFTW) <http://www.fftw.org/> library.
Provides a wrapper for several 'FFTW' functions. This package provides access to the two-dimensional 'FFT', the multivariate 'FFT', and the one-dimensional real to complex 'FFT' using the 'FFTW3' library. The package includes the functions fftw() and mvfftw() which are designed to mimic the functionality of the R functions fft() and mvfft(). The 'FFT' functions have a parameter that allows them to not return the redundant complex conjugate when the input is real data.
Bi-variate data fitting is done by two stochastic
        components: the marginal distributions and the dependency
        structure. The dependency structure is modeled through a
        copula. An algorithm was implemented considering seven families
        of copulas (Generalized Archimedean Copulas), the best fitting
        can be obtained looking all copula's options (totally positive
        of order 2 and stochastically increasing models).
This is a package for implementation of Flury-Gautschi
        algorithms.
Provides a collection of functions to 
  analyze and model heteroskedastic behavior in financial time
  series models.
Growth models and forest production require existing data
    manipulation and the creation of new data, structured from basic forest
    inventory data. The purpose of this package is provide functions to support
    these activities.
Exact MLE and Whittle MLE estimation for power law decay models.
Goodness-of-fit test with multiplier or parametric
        bootstrap.
A permutation technique to explore and control for spatial autocorrelation. This package contains low level functions for performing permutations and calculating statistics as well as higher level functions. Higher level functions are an easy to use function for performing spatially restricted permutation tests and summarize and plot results.
Implement algorithms for feature grouping and selection over an undirected graph, solves problems like graph fused lasso, graph OSCAR and so on.
Rapidly create a GUI interface for a function you created by automatically creating widgets for arguments of the function. Automatically parses help routines for context-sensitive help to these arguments. The interface essentially a wrapper to some Tcl/Tk routines to both simplify and facilitate GUI creation. More advanced Tcl/Tk routines/GUI objects can be incorporated into the interface for greater customization for the more experienced.
Impute general multivariate missing data with the fractional hot deck imputation based on Jaekwang Kim (2011) <doi:10.1093/biomet/asq073>.
Functions to compare two or more survival curves with:
       a) The Fleming-Harrington test for right-censored data based on permutations and on counting processes.
       b) An extension of the Fleming-Harrington test for interval-censored data based on a permutation distribution and on a score vector distribution.
Provide functions for forest inventory calculations.
        Common volumetric equations (Smalian, Newton and Huber) as well
        stacking factor and form
Useful functions for fMRI preprocessing.
Algorithms for classical symmetric and deflation-based FastICA, reloaded deflation-based FastICA algorithm and an algorithm for adaptive deflation-based FastICA using multiple nonlinearities. For details, see Miettinen et al. (2014) <doi:10.1109/TSP.2014.2356442> and Miettinen et al. (2017) <doi:10.1016/j.sigpro.2016.08.028>.
For curve, surface and function fitting with an emphasis
 on splines, spatial data and spatial statistics. The major methods
 include cubic, and thin plate splines, Kriging, and compactly supported
 covariance functions for large data sets. The splines and Kriging methods are
 supported by functions that can determine the smoothing parameter
 (nugget and sill variance) and other covariance function parameters by cross
 validation and also by restricted maximum likelihood. For Kriging
 there is an easy to use function that also estimates the correlation
 scale (range parameter).  A major feature is that any covariance function
 implemented in R and following a simple format can be used for
 spatial prediction. There are also many useful functions for plotting
 and working with spatial data as images. This package also contains
 an implementation of sparse matrix methods for large spatial data
 sets and currently requires the sparse matrix (spam) package. Use
 help(fields) to get started and for an overview.  The fields source
 code is deliberately commented and provides useful explanations of
 numerical details as a companion to the manual pages. The commented
 source code can be viewed by expanding  source code version
 and looking in the R subdirectory. The reference for fields can be generated
 by the citation function in R and has DOI <doi:10.5065/D6W957CT>.
Tools for random fields and bridges simulations.
A very flexible framework for building server side logic in R. The 
    framework is unopinionated when it comes to how HTTP requests and WebSocket
    messages are handled and supports all levels of app complexity; from serving
    static content to full-blown dynamic web-apps. Fiery does not hold your hand
    as much as e.g. the shiny package does, but instead sets you free to create
    your web app the way you want.
A simple data package to ease the process of creating choropleths
    in 'ggplot2' with all fifty U.S. states and Washington D.C.,
    including Alaska and Hawaii as insets.
Implements a simple key-value style database where character string keys
  are associated with data values that are stored on the disk. A simple interface is provided for inserting,
  retrieving, and deleting data from the database. Utilities are provided that allow 'filehash' databases to be
  treated much like environments and lists are already used in R. These utilities are provided to encourage
  interactive and exploratory analysis on large datasets. Three different file formats for representing the
  database are currently available and new formats can easily be incorporated by third parties for use in the
  'filehash' framework.
Simple key-value database using SQLite as the backend
Place an exclusive or shared lock on a file. It uses 'LockFile'
    on Windows and 'fcntl' locks on Unix-like systems.
Interface for working with large matrices stored in files,
        not in computer memory. Supports multiple non-character
        data types (double, integer, logical and raw) of
        various sizes (e.g. 8 and 4 byte real values).
        Access to parts of the matrix is done by indexing, 
        exactly as with usual R matrices.
        Supports very large matrices.
        Tested on multi-terabyte matrices.
        Allows for more than 2^32 rows or columns.
        Allows for quick addition of extra columns to a filematrix.
        Cross-platform as the package has R code only.
Create descriptive file names with ease. New file names are
    automatically (but optionally) time stamped and placed in date stamped
    directories. Streamline your analysis pipeline with input and output file
    names that have informative tags and proper file extensions.
Perform chunk processing or split-apply-combine on data in a
    delimited file (example: CSV) and Distributed Dataframes (DDF) across multiple
    cores of a single machine with low memory footprint. These functions are a
    convenient wrapper over the versatile package 'datadr'.
Functions for printing the contents of a folder as columns in a ragged-bottom data.frame and for
  viewing the details (size, time created, time modified, etc.) of a folder's top level contents.
Convenient functions for moving files, deleting directories, 
    and a variety of string operations that facilitate manipulating files 
    and extracting information from strings.
Filling in the missing entries of a partially observed data is one of fundamental problems in various disciplines of mathematical science. For many cases, data at our interests have canonical form of matrix in that the problem is posed upon a matrix with missing values to fill in the entries under preset assumptions and models. We provide a collection of methods from multiple disciplines under Matrix Completion, Imputation, and Inpainting. See Davenport and Romberg (2016) <doi:10.1109/JSTSP.2016.2539100> for an overview of the topic.
Provides a collection of utility functions 
    to download and manage data sets from the Internet or from other 
    sources.
Functions for financial analysis and financial modeling, 
             including batch graphs generation, beta calculation, 
             descriptive statistics, annuity calculation, bond pricing 
             and financial data download.
Time value of money, cash flows and other financial
        functions.
Infrastructure for defining meta-data and
    relationships for financial instruments.
Contains financial math functions and introductory derivative functions included in the Society of Actuaries and Casualty Actuarial Society 'Financial Mathematics' exam, and some topics in the 'Models for Financial Economics' exam.
This package accomplishes two tasks: a) it classifies
        implicit trading activity from quotes in OTC markets using the
        algorithm of Lee and Ready (1991); b) based on information for
        trade initiation, the package computes the probability of
        informed trading of Easley and O'Hara (1987).
Package for time value of money calculation, time series analysis and computational finance.
Parse and create Darwin Core (<http://rs.tdwg.org/dwc/>) Simple
    and Archives. Functionality includes reading and parsing all the
    files in a Darwin Core Archive, including the datasets and metadata;
    read and parse simple Darwin Core files; and validation of Darwin
    Core Archives.
Estimation and regularization for covariance matrix of asset
    returns. For covariance matrix estimation, three major types of factor
    models are included: macroeconomic factor model, fundamental factor model and
    statistical factor model. For covariance matrix regularization, four regularized
    estimators are included: banding, tapering, hard-thresholding and soft-
    thresholding. The tuning parameters of these regularized estimators are selected
    via cross-validation.
Find all root(s) of the equation,including complex
        roots;Find root(s) of the equation by dichotomy.Besides,in
        dichotomy, more than one interval can be given at a time.
The heterogeneous treatment effect estimation procedure 
        proposed by Imai and Ratkovic (2013)<DOI: 10.1214/12-AOAS593>.  
        The proposed method is applicable, for
        example, when selecting a small number of most (or least)
        efficacious treatments from a large number of alternative
        treatments as well as when identifying subsets of the
        population who benefit (or are harmed by) a treatment of
        interest. The method adapts the Support Vector Machine
        classifier by placing separate LASSO constraints over the
        pre-treatment parameters and causal heterogeneity parameters of
        interest. This allows for the qualitative distinction between
        causal and other parameters, thereby making the variable
        selection suitable for the exploration of causal heterogeneity. 	
        The package also contains a class of functions, CausalANOVA, 
        which estimates the average marginal interaction effects (AMIEs)
        by a regularized ANOVA as proposed by Egami and Imai (2016+). 
        It contains a variety of regularization techniques to facilitate 
	analysis of large factorial experiments.
Creates models from all combinations of a list of variables and sorts by minimum IC (information criterion).
Package designed to find an acceptable python binary.
Scans all directories and subdirectories of a path for code snippets, R scripts,
    R Markdown, PDF or text files containing a specific pattern.  Files found can be copied to a new folder.
A tool to explore wide data sets, by detecting, ranking
    and plotting groups of statistically dependent columns.
Statistical tool set for population genetics. The package provides following functions: 1) empirical Bayes estimator of Fst and other measures of genetic differentiation, 2) regression analysis of environmental effects on genetic differentiation using bootstrap method, 3) interfaces to read and manipulate 'GENEPOP' format data files and allele/haplotype frequency format files.
Functions to manipulate binary fingerprints
 of arbitrary length. A fingerprint is represented by an object of S4 class 'fingerprint'
 which is internally represented a vector of integers, such
 that each element represents the position in the fingerprint that is set to 1.
 The bitwise logical functions in R are overridden so that they can be used directly
 with 'fingerprint' objects. A number of distance metrics are also
 available (many contributed by Michael Fadock). Fingerprints 
 can be converted to Euclidean vectors (i.e., points on the unit hypersphere) and
 can also be folded using OR.  Arbitrary fingerprint formats can be handled via line
 handlers. Currently handlers are provided for CDK, MOE and BCI fingerprint data.
Quantifies the provenance of the sediments in a catchment or study area. Based on a comprehensive characterization of the sediment sources and the end sediment mixtures a mixing model algorithm is applied to the sediment mixtures in order to estimate the relative contribution of each potential source. The package includes several statistical methods such as Kruskal-Wallis test, discriminant function analysis ('DFA'), principal component plot ('PCA') to select the optimal subset of tracer properties. The variability within each sediment source is also considered to estimate the statistical distribution of the sources contribution.
Fingertips (<http://fingertips.phe.org.uk/>) contains data for many indicators of public health in England. The underlying data is now more easily accessible by making use of the API.
In the Cramér–Lundberg risk process perturbed by a Wiener
    process, this packages provides approximations to the probability of
    ruin within a finite time horizon.  Currently, there are three methods
    implemented: The first one uses saddlepoint approximation (two
    variants are provided), the second one uses importance sampling and
    the third one is based on the simulation of a dual process.  This last
    method is not very accurate and only given here for completeness.
Download and display company financial data from the U.S. Securities
    and Exchange Commission's EDGAR database. It contains a suite of functions with
    web scraping and XBRL parsing capabilities that allows users to extract data from EDGAR 
    in an automated and scalable manner. See <https://www.sec.gov/edgar/searchedgar/companysearch.html>
    for more information.
A collection of four datasets 
    based around the population dynamics of migratory fish. Datasets 
    contain both basic size information on a per fish basis, as well as
    otolith data that contains a per day record of fish growth history.
    All data in this package was collected by the author, from 
    2015-2016, in the Wellington region of New Zealand.
fisheyeR provides tools for creating Interactive Data
        Visualizations by implementing ideas from Furnas, Munzner,
        Costa and Venturini.
FisHiCal integrates Hi-C and FISH data, offering a modular and easy-to-use tool for chromosomal spatial analysis.
Dataset of 302 measurements of 11 fish species to accompany the
    manuscript "Length-weight relationships of six freshwater fish species
    from lake Kirkkojarvi, Finland".
Fishery science methods and models from published literature and contributions from colleagues.
Fits models to catch and effort data. Single-species models are 1) delta log-normal, 2) Tweedie, or 3) Poisson-gamma (G)LMs.
Functions to predict fish movement parameters plotting leptokurtic fish dispersal kernels (see Radinger and Wolter, 2014: Patterns and predictors of fish dispersal in rivers. Fish and Fisheries. 15:456-473.)
Calculates metabolic rate of fish and other aquatic organisms measured using 
             an intermittent-flow respirometry approach. The tool is used to run a set of 
             graphical QC tests of raw respirometry data, correct it for background 
             respiration and chamber effect, filter and extract target values of absolute 
             and mass-specific metabolic rate. Experimental design should include background 
             respiration tests and measuring of one or two metabolic rate traits. The package 
             allows a user to import raw respirometry data obtained from 'AutoResp' 
             (see <http://www.loligosystems.com> for more information) or other logger 
             software.
Provides functionality for constructing
    statistical models of transcriptomic dynamics in field conditions.
    It further offers the function to predict expression of a gene given 
    the attributes of samples and meteorological data. Nagano, A. J., Sato, 
    Y., Mihara, M., Antonio, B. A., Motoyama, R., Itoh, H., Naganuma, Y., and 
    Izawa, T. (2012). <doi:10.1016/j.cell.2012.10.048>. Iwayama, K., Aisaka, Y., 
    Kutsuna, N., and Nagano, A. J. (2017). <doi:10.1093/bioinformatics/btx049>.
The fit.models function and its associated methods (coefficients,
  print, summary, plot, etc.) were originally provided in the robust package to
  compare robustly and classically fitted model objects. The aim of the fit.models
  package is to separate this fitted model object comparison functionality from
  the robust package and to extend it to support fitting methods (e.g., classical,
  robust, Bayesian, regularized, etc.) more generally.
Comprehensive model building function for identification,
        estimation and diagnostic checking for AR and subset AR models.
        Two types of subset AR models are supported.  One family of
        subset AR models, denoted by ARp, is formed by taking subet of
        the original AR coefficients and in the other, denoted by ARz,
        subsets of the partial autocorrelations are used. The main
        advantage of the ARz model is its applicability to very large
        order models.
Implements fast maximum likelihood algorithm for fitting ARMA time series. Uses S3 methods print, summary, fitted, residuals. Fast exact Gaussian ARMA simulation.
Scrapes data from Fitbit <http://www.fitbit.com>. This does not use the official
    API, but instead uses the API that the web dashboard uses to generate the graphs
    displayed on the dashboard after login at <http://www.fitbit.com>.
A pure R package for decoding activity files written in the FIT ("Flexible and Interoperable Data Transfer") format. A format that is fast becoming the standard for recording running and cycling data. Details of the FIT protocol can be found at <https://www.thisisant.com/resources/fit>.
Extends the fitdistr() function (of the MASS package) with several functions to help the fit of a parametric distribution to non-censored or censored data. Censored data may contain left censored, right censored and interval censored values, with several lower and upper bounds. In addition to maximum likelihood estimation (MLE), the package provides moment matching (MME), quantile matching (QME) and maximum goodness-of-fit estimation (MGE) methods (available only for non-censored data). Weighted versions of MLE, MME and QME are available.
Fits Density Ratio Classes to elicited
        probability-quantile points or intervals
Contains probability mass functions, cumulative mass functions, negative log likelihood value, parameter estimation and modeling data using Binomial Mixture distributions (BMD) (Manoj et al (2013) <doi:10.5539/ijsp.v2n2p24>) and Alternate Binomial distributions (ABD).
Fits Weibull or sigmoidal models to percent loss conductivity (plc) curves as a function of plant water potential, computes confidence intervals of parameter estimates and predictions with bootstrap or parametric methods, and provides convenient plotting methods.
Genotyping assays for bi-allelic markers (e.g. SNPs) produce
	signal intensities for the two alleles. 'fitPoly' assigns genotypes 
	(allele dosages) to a collection of polyploid samples based on these
	signal intensities. 'fitPoly' replaces the older package 'fitTetra' that was
	limited (a.o.) to only tetraploid populations whereas 'fitPoly' accepts any
	ploidy level. Reference: Voorrips RE, Gort G, Vosman B (2011)
	<doi:10.1186/1471-2105-12-172>.
Utilities to read and write files in the FITS (Flexible
  Image Transport System) format, a standard format in astronomy (see
  e.g. <https://en.wikipedia.org/wiki/FITS> for more information).
  Present low-level routines allow: reading, parsing, and modifying
  FITS headers; reading FITS images (multi-dimensional arrays);
  reading FITS binary and ASCII tables; and writing FITS images
  (multi-dimensional arrays).  Higher-level functions allow: reading
  files composed of one or more headers and a single (perhaps
  multidimensional) image or single table; reading tables into
  data frames; generating vectors for image array axes; scaling and
  writing images as 16-bit integers.  Known incompletenesses are
  reading random group extensions, as well as
  bit, complex, and array descriptor data types in binary tables.
Systematic fit of hundreds of theoretical univariate distributions to empirical data via maximum likelihood estimation. Fits are reported and summarized by a data.frame, a csv file or a 'shiny' app (here with additional features like visual representation of fits). All output formats provide assessment of goodness-of-fit by the following methods: Kolmogorov-Smirnov test, Shapiro-Wilks test, Anderson-Darling test.
Package fitTetra contains three functions that can be used
        to assign genotypes to a collection of tetraploid samples based
        on bialleleic marker assays. Functions fitTetra (to fit several
        models for one marker from the data and select the best
        fitting) or saveMarkerModels (calls fitTetra for multiple
        markers and saves the results to files) will probably be the
        most convenient to use. Function CodomMarker offers more
        control and fits one specified model for a given marker.
Wrapper for computing parameters for univariate distributions using MLE. It creates an object that stores d, p, q, r functions as well as parameters and statistics for diagnostics. Currently supports automated fitting from base and actuar packages. A manually fitting distribution fitting function is included to support directly specifying parameters for any distribution from ancillary packages.
Datasets and code published by the data journalism website 
    'FiveThirtyEight' available at <https://github.com/fivethirtyeight/data>. 
    Note that while we received guidance from editors at 'FiveThirtyEight', this 
    package is not officially published by 'FiveThirtyEight'.
For functions that take and return vectors (or scalars), this package provides 8 algorithms for finding fixed point vectors (vectors for which the inputs and outputs to the function are the same vector). These algorithms include Anderson (1965) acceleration <doi:10.1145/321296.321305>, epsilon extrapolation methods (Wynn 1962 <doi:10.2307/2004051>) and minimal polynomial methods (Cabay and Jackson 1976 <doi:10.1137/0713060>).
Distribution functions and test for over-representation of short
    distances in the Liland distribution. Simulation functions are included for
    comparison.
An R client for the "fixer.io" currency conversion and exchange 
  rate API. The API requires registration and some features are only available 
  on paid accounts. The full documentation is available at 
  <https://fixer.io/documentation>.
Several generalized / directional Fixed Sequence Multiple Testing
    Procedures (FSMTPs) are developed for testing a sequence of pre-ordered
    hypotheses while controlling the FWER, FDR and Directional Error (mdFWER).
    All three FWER controlling generalized FSMTPs are designed under arbitrary
    dependence, which allow any number of acceptances. Two FDR controlling
    generalized FSMTPs are respectively designed under arbitrary dependence and
    independence, which allow more but a given number of acceptances. Two mdFWER
    controlling directional FSMTPs are respectively designed under arbitrary
    dependence and independence, which can also make directional decisions based
    on the signs of the test statistics. The main functions for each proposed
    generalized / directional FSMTPs are designed to calculate adjusted p-values
    and critical values, respectively. For users' convenience, the functions also
    provide the output option for printing decision rules.
An implementation of the Fizz Buzz algorithm, as defined e.g. in <https://en.wikipedia.org/wiki/Fizz_buzz>. 
    It provides the standard algorithm with 3 replaced by Fizz and 5 replaced by Buzz, with the option of specifying start 
    and end numbers, step size and the numbers being replaced by fizz and buzz, respectively. This package gives 
    interviewers the optional answer of "I use fizzbuzzR::fizzbuzz()" when interviewing rather than having to write an algorithm
    themselves.
Contains tools and features, which can be used for an Exploratory
    Landscape Analysis (ELA) of single-objective continuous optimization problems.
    Those features are able to quantify rather complex properties, such as the
    global structure, separability, etc., of the optimization problems.
Implements the fused lasso additive model as proposed in Petersen, A., Witten, D., and Simon, N. (2016). Fused Lasso Additive Model. Journal of Computational and Graphical Statistics, 25(4): 1005-1025.
Tools for fluctuations analysis of mutant cells counts.
Provide the implementation of a family of Lasso variants including Dantzig Selector, LAD Lasso, SQRT Lasso, Lq Lasso for estimating high dimensional sparse linear model. We adopt the alternating direction method of multipliers and convert the original optimization problem into a sequential L1 penalized least square minimization problem, which can be efficiently solved by linearization algorithm. A multi-stage screening approach is adopted for further acceleration. Besides the sparse linear model estimation, we also provide the extension of these Lasso variants to sparse Gaussian graphical model estimation including TIGER and CLIME using either L1 or adaptive penalty. Missing values can be tolerated for Dantzig selector and CLIME. The computation is memory-optimized using the sparse matrix output.
Variable selection algorithm for functional linear regression with scalar response variable and mixed scalar/functional predictors.
Fast implementation of hierarchical clustering
Contingency Tables are a pain to work with when you want to run regressions.
  This package takes them, flattens them into a long data frame, so you can more easily analyse them!
  As well, you can calculate other related statistics. All of this is done so in a 'tidy' manner,
  so it should tie in nicely with 'tidyverse' series of packages.
The main function kcca implements a general framework for
        k-centroids cluster analysis supporting arbitrary distance
        measures and centroid computation. Further cluster methods
        include hard competitive learning, neural gas, and QT
        clustering. There are numerous visualization methods for
        cluster results (neighborhood graphs, convex cluster hulls,
        barcharts of centroids, ...), and bootstrap methods for the
        analysis of cluster stability.
Allows for maximum likelihood fitting of cluster-weighted models, a class of mixtures of regression models with random covariates.
Format for converting an R Markdown document to a grid oriented
  dashboard. The dashboard flexibly adapts the size of it's components to the
  containing web page.
Provides tools to work with the Flexible Dirichlet
    distribution. The main features are an E-M algorithm for computing the maximum
    likelihood estimate of the parameter vector and a function based on conditional
    bootstrap to estimate its asymptotic variance-covariance matrix. It contains
    also functions to plot graphs, to generate random observations and to handle
    compositional data.
Standard generalized additive models assume a response function, 
    which induces an assumption on the shape of the distribution of the 
    response. However, miss-specifying the response function results in biased 
    estimates. Therefore in Spiegel et al. (2017) 
    <doi:10.1007/s11222-017-9799-6> we propose to estimate the response function
    jointly with the covariate effects. This package provides the underlying 
    functions to estimate these generalized additive models with flexible 
    response functions. The estimation is based on an iterative algorithm. In 
    the outer loop the response function is estimated, while in the inner loop 
    the covariate effects are determined. For the response function a strictly
    monotone P-spline is used while the covariate effects are estimated based on
    a modified Fisher-Scoring algorithm. Overall the estimation relies on the 
    'mgcv'-package.
Application of the filtered monotonic polynomial (FMP) item response
    model to flexibly fit item response models. The package includes tools that
    allow the item response model to be build on any monotonic transformation of
    the latent trait metric, as described by Feuerstahler (2016) <http://hdl.handle.net/11299/182267>.
A general framework for finite mixtures of regression models
  using the EM algorithm is implemented. The package provides the E-step
  and all data handling, while the M-step can be supplied by the user to
  easily define new models. Existing drivers implement mixtures of standard
  linear models, generalized linear models and model-based clustering.
Model selection tools and 'selfStart' functions to fit parametric curves in 'nls', 'nlsList' and 'nlme' frameworks.
Estimation of flexible parametric models for survival data.
Package for parametric relative survival analyses. It allows to model non-linear and 
        non-proportional effects using splines (B-spline and truncated power basis). It also includes 
        both non proportional and non linear effects of 
			Remontet, L. et al. (2007) <DOI:10.1002/sim.2656> and 
			Mahboubi, A. et al. (2011) <DOI:10.1002/sim.4208>.
Flexible parametric models for time-to-event data,
    including the Royston-Parmar spline model, generalized gamma and
    generalized F distributions.  Any user-defined parametric
    distribution can be fitted, given at least an R function defining
    the probability density or hazard. There are also tools for
    fitting and predicting from fully parametric multi-state models.
Flexible parametric mixture and non-mixture cure models for time-to-event data.
Create pretty tables for 'Microsoft Word', 'Microsoft PowerPoint' and 'HTML' documents. 
  Functions are provided to let users create tables, modify and format their content. 
  It extends package 'officer' that does not contain any feature for customized tabular reporting 
  and can be used with R Markdown documents. It also work within R markdown documents when rendering 
  to HTML and to 'Microsoft Word' documents.
Functions to create and manipulate 
    FIFO (First In First Out), LIFO (Last In First Out), and NINO (Not In or Never Out) 
    stacks in R.
Estimate positions of animal from data collected by solar geolocation archival tags.
FLIM fits linear models for the observed increments in a longitudinal dataset, and imputes missing values according to the models.
It implements many univariate and multivariate permutation (and rotation) tests. Allowed tests: the t one and two samples, ANOVA, linear models, Chi Squared test, rank tests (i.e. Wilcoxon, Mann-Whitney, Kruskal-Wallis), Sign test and McNemar. Test on Linear Models are performed also in presence of covariates (i.e. nuisance parameters). The permutation and the rotation methods to get the null distribution of the test statistics are available. It also implements methods for multiplicity control such as Westfall-Young minP procedure and Closed Testing (Marcus, 1976) and k-FWER. Moreover, it allows to test for fixed effects in mixed effects models.
The lipid scrambling activity of protein extracts and purified
    scramblases is often determined using a fluorescence-based assay involving
    many manual steps. flippant offers an integrated solution for the analysis
    and publication-grade graphical presentation of dithionite scramblase
    assays, as well as a platform for review, dissemination and extension of the
    strategies it employs. The package's name derives from a play on the fact
    that lipid scrambling is also sometimes referred to as 'flipping'.
Provides two robust tests for testing in GLMs,
    by sign-flipping score contributions.
    The tests are often robust against overdispersion,
    heteroscedasticity and, in some cases,
    ignored nuisance variables.
    See Hemerik and Goeman (2017) <doi:10.1007/s11749-017-0571-1>.
Fits the Fused Lasso Latent Feature model, which is used for modeling multi-sample aCGH data to identify regions of copy number variation (CNV).  Produces a set of features that describe the patterns of CNV and a set of weights that describe the composition of each sample.  Also provides functions for choosing the optimal tuning parameters and the appropriate number of features, and for estimating the false discovery rate.
R comes with a suite of utilities for linear algebra with "numeric"
    (double precision) vectors/matrices. However, sometimes single precision (or
    less!) is more than enough for a particular task.  This package extends R's
    linear algebra facilities to include 32-bit float (single precision) data.
    Float vectors/matrices have half the precision of their "numeric"-type but
    are generally faster to numerically operate on, for a performance vs
    accuracy trade-off.  The internal representation is an S4 class, which
    allows us to keep the syntax identical to that of base R's.  Interaction
    between floats and base types for binary operators is generally possible; in
    these cases, type promotion always defaults to the higher precision.  The
    package ships with copies of the single precision 'BLAS' and 'LAPACK', which
    are automatically built in the event they are not available on the system.
Implements synchronization between R processes (spawned by using the "parallel" package for instance) using file locks. Supports both exclusive and shared locking.
Includes several statistical methods for the estimation of parameters and high quantiles of river flow distributions. The focus is on regional estimation based on homogeneity assumptions and computed from multivariate observations (multiple measurement stations).
	For details see Kinsvater et al. (2017) <arXiv:1701.06455>.
Tools to quickly compile taxonomic and distribution data from
    the Brazilian Flora 2020.
Uses multidimensional contingency tables to calculate ecological diversity indices for gated populations.
Flowering is an important life history trait of flowering plants. It has been mainly analyzed with respect to flowering onset and duration of flowering. This tools provide some functions to compute the temporal distribution of an flowering individual related to other population members. fCV() measures the temporal variation in flowering. RIind() measures the rank order of flowering for individual plants within a population. SI(), SI2(), SI3(), and SI4() calculate flowering synchrony with different methods.
Flow field forecasting draws information from an interpolated flow field of the observed time series to incrementally build a forecast.
This framework allows you to design and implement complex
    pipelines, and deploy them on your institution's computing cluster. This has
    been built keeping in mind the needs of bioinformatics workflows. However, it is
    easily extendable to any field where a series of steps (shell commands) are to
    be executed in a (work)flow.
An application to calculate the daily environmental costs of river flow regulation by dams based on García de Jalon et al. 2017 <doi:10.1007/s11269-017-1663-0>.
Selections on flow matrices, statistics on selected flows, map and
    graph visualisations.
Screens daily streamflow time series for temporal trends and
    change-points. This package has been primarily developed for assessing the
    quality of daily streamflow time series. It also contains tools for plotting
    and calculating many different streamflow metrics. The package can be used to
    produce summary screening plots showing change-points and significant temporal
    trends for high flow, low flow, and/or baseflow statistics, or it can be used
    to perform more detailed hydrological time series analyses. The package was
    designed for screening daily streamflow time series from Water Survey Canada
    and the United States Geological Survey but will also work with streamflow time
    series from many other agencies.
This package implements a path algorithm for the Fused
        Lasso Signal Approximator. For more details see the help files
A novel algorithm for solving the subset sum problem with bounded error in multidimensional real domain and its application to the general-purpose knapsack problem.
Use spectrophotometry measurements performed on insects as a way to infer pathogens 
      virulence. Insect movements cause fluctuations in fluorescence signal, and functions are 
      provided to estimate when the insect has died as the moment when variance in autofluorescence 
      signal drops to zero. The package provides functions to obtain this estimate together with 
      functions to import spectrophotometry data from a Biotek microplate reader. Details of the method 
      are given in Parthuisot et al. (2018) <doi:10.1101/297929>.
Contains data sets from Bernard Flury (1997) A First
        Course in Multivariate Statistics, Springer NY
Functions for the calculation of greenhouse gas flux rates 
	from closed chamber concentration measurements. The package follows 
	a modular concept: Fluxes can be calculated in just two simple steps 
	or in several steps if more control in details is wanted. Additionally 
	plot and preparation functions as well as functions for modelling
	gpp and reco are provided.
Compute energy fluxes in trophic networks, from resources to their consumers, and can be applied to systems ranging from simple two-species interactions to highly complex food webs. It implements the approach described in Gauzens et al. (2017) <doi:10.1101/229450> to calculate energy fluxes, which are also used to calculate equilibrium stability.
All data sets from "Forecasting: methods and applications" by
             Makridakis, Wheelwright & Hyndman (Wiley, 3rd ed., 1998).
Implements basic financial market objects like currencies, currency
  pairs, interest rates and interest rate indices. You will be able to use
  Benchmark instances of these objects which have been defined using their most
  common conventions or those defined by International Swap Dealer Association
  (ISDA, <https://www.isda.org>) legal documentation.
Generate cost effective minimally changed run sequences 
              for symmetrical as well as asymmetrical factorial 
              designs.
Implements common date calculations relevant for specifying
  the economic nature of financial market contracts that are typically defined
  by International Swap Dealer Association (ISDA, <http://www2.isda.org>) legal
  documentation. This includes methods to check whether dates are business
  days in certain locales, functions to adjust and shift dates and time length
  (or day counter) calculations.
Provides functions to help in fitting models to data, to
  perform Monte Carlo, sensitivity and identifiability analysis. It is
  intended to work with models be written as a set of differential
  equations that are solved either by an integration routine from
  package 'deSolve', or a steady-state solver from package
  'rootSolve'. However, the methods can also be used with other types of
  functions.
A fast solver for the maximum likelihood estimator (MLE) of a 
  multivariate log-concave probability function. Given a sample X, it estimates 
  a non-parametric density function whose logarithm is a concave function. Many 
  well-known parametric densities belong to that class, among them the normal 
  density, the uniform density, the exponential distribution and many more. This
  package provides functions for the estimation of a log-concave density and
  a mixture of log-concave densities in multiple dimensions. While being similar
  to the package LogConcDEAD, fmlogcondens provides much fast run times for 
  large samples (>= 250 points). As a reference see Fabian Rathke, Christoph 
  Schnörr (2015), <doi:10.1515/auom-2015-0053>.
Estimates Filtered Monotonic Polynomial IRT Models as described by Liang and Browne (2015) <DOI:10.3102/1076998614556816>.
Contains R-functions to perform an fMRI analysis as described in
             Tabelow et al. (2006) <DOI:10.1016/j.neuroimage.2006.06.029>,
             Polzehl et al. (2010) <DOI:10.1016/j.neuroimage.2010.04.241>,
             Tabelow and Polzehl (2011) <DOI:10.18637/jss.v044.i11>.
Methods for performing fMRI quality assurance (QA) measurements of
  test objects. Heavily  based on the fBIRN procedures detailed by Friedman and 
  Glover (2006) <doi:10.1002/jmri.20583>.
Provides parameter estimation as well as variable selection in
    Finite Mixture of Accelerated Failure Time Regression and Finite
    Mixture of Regression Models.
    Furthermore, this package provides Ridge Regression and Elastic Net.
Several utility functions for the book entitled 
	"Practices of Medical and Health Data Analysis using R"
	(Pearson Education Japan, 2007) with Japanese demographic
	data and some demographic analysis related functions.
Fit linear regression models where the random errors follow a finite mixture of of Skew Heavy-Tailed Errors.
This package implements some basic procedures for dealing
        with log maximally skew stable distributions, which are also
        called finite moment log stable distributions.
This package computes posterior residual variances to be
        used in the denominator of a moderated t-statistic from a
        linear model analysis of microarray data.  It is an extension
        of the moderated t-statistic original proposed by Smyth (2004).
        LOESS local regression and empirical Bayesian method are used
        to estimate gene specific prior degrees of freedom and prior
        variance based on average gene intensity level. The posterior
        residual variance in the denominator is a weighted average of
        prior and residual variance and the weights are prior degrees
        of freedom and residual variance degrees of freedom. The
        degrees of freedom of the moderated t-statistic is simply the
        sum of prior and residual variance degrees of freedom.
Provides a  collection of functions 
	to manage, to investigate and to analyze bivariate and multivariate 
	data sets of financial returns.
Cover-tree and kd-tree fast k-nearest neighbor search algorithms and related applications
        including KNN classification, regression and information measures are implemented.
Provides a collection of functions for testing various aspects of
	univariate time series including independence and neglected
	nonlinearities. Further provides functions to investigate the chaotic
	behavior of time series processes and to simulate different types of chaotic
	time series maps.
foba is a package that implements forward, backward, and foba sparse learning algorithms for ridge regression, described in the paper "Adaptive Forward-Backward Greedy Algorithm for Learning Sparse Representations".
Takes a distance matrix and plots it as an 
    interactive graph. One point is focused at the center of the graph,
    around which all other points are plotted in their exact distances as
    given in the distance matrix. All other non-focus points are plotted 
    as best as possible in relation to one another. Double click on any 
    point to choose a new focus point, and hover over points to see their
    ID labels. If color label categories are given, hover over colors in 
    the legend to highlight only those points and click on colors to 
    highlight multiple groups. For more information on the rationale 
    and mathematical background, as well as an interactive introduction,
    see <https://lea-urpa.github.io/focusedMDS.html>.
The CRAN check results in your R terminal.
Defines a compact data format that includes metadata. 
 The function fold() creates the format by converting 
 from data.frame, and unfold() converts back. The predictability
 of the folded format supports reusability of data processing tools,
 while the presence of embedded metadata improves portability, 
 interpretability, and efficiency.
Provides fonts licensed under the 'Bitstream Vera Fonts'
    license for the 'fontquiver' package.
Computer Modern font for use with extrafont package
Provides 'ggplot2' themes based on the 'Hind' fonts.
  'Hind' is an open source 'typeface' supporting the 'Devanagari' and Latin scripts.
  Developed explicitly for use in User Interface design, the 'Hind' font family includes five styles.
  More information about the font can be found at <https://fonts.google.com/specimen/Hind>.
A placeholder for the Liberation fontset intended for the
    `fontquiver` package. This fontset covers the 12 combinations of
    families (sans, serif, mono) and faces (plain, bold, italic, bold
    italic) supported in R graphics devices.
Provides 'ggplot2' themes based on the 'M+' fonts.
  The 'M+' fonts are a font family under a free license. The font family provides
  multilingual glyphs. The fonts provide 'Kana', over 5,000 'Kanji', Basic Latin,
  Latin-1 Supplement, Latin Extended-A, and 'IPA' Extensions glyphs. Most of the Greek,
  Cyrillic, Vietnamese, and extended glyphs and symbols are included too.
  So the fonts are in conformity with ISO-8859-1, 2, 3, 4, 5, 7, 9, 10, 13, 14, 15, 16,
  Windows-1252, T1, and VISCII encoding.
  More information about the fonts can be found at <http://mplus-fonts.osdn.jp/about-en.html>.
Provides a set of fonts with permissive licences. This is
    useful when you want to avoid system fonts to make sure your
    outputs are reproducible.
Calculates twelve commonly-used, basic measures of food
        web network structure from binary, predator-prey matrices:
        species richness, connectance, total number of links, link
        density, number of trophic positions, predator:prey ratio, and
        fraction of carnivores, herbivores, top species and
        intermediate species.  Employs food web language in the code
        and output, translates between a couple of common food web
        formats, can handle food webs consisting of multiple levels,
        and can automate the analysis for a large number of webs. The
        program produces 3-dimensional graphs of high quality that can
        be customized by the user.
Provides functionality for manipulating functions and translating them
    in metaprogramming.
Provides a collection of functions to 
    valuate basic options. This includes the generalized
	Black-Scholes option, options on futures and options on
	commodity futures.
SHE, FORAM Index and ABC Method analyses and custom plot
        functions for community data.
Helpers for reordering factor levels (including moving
    specified levels to front, ordering by first appearance, reversing, and
    randomly shuffling), and tools for modifying factor levels (including
    collapsing rare levels into other, 'anonymising', and manually 'recoding').
Support for the foreach looping construct.  Foreach is an
        idiom that allows for iterating over elements in a collection,
        without the use of an explicit loop counter.  This package in
        particular is intended to be used for its return value, rather
        than for its side effects.  In that sense, it is similar to the
        standard lapply function, but doesn't require the evaluation
        of a function.  Using foreach without side effects also
        facilitates executing the loop in parallel.
Implementation of Forecastable Component Analysis ('ForeCA'),
    including main algorithms and auxiliary function (summary, plotting, etc.) to
    apply 'ForeCA' to multivariate time series data. 'ForeCA' is a novel dimension
    reduction (DR) technique for temporally dependent signals. Contrary to other
    popular DR methods, such as 'PCA' or 'ICA', 'ForeCA' takes time dependency
    explicitly into account and searches for the most ''forecastable'' signal.
    The measure of forecastability is based on the Shannon entropy of the spectral
    density of the transformed signal.
Methods and tools for displaying and analysing
             univariate time series forecasts including exponential smoothing
             via state space models and automatic ARIMA modelling.
Provides geometric- and regression-based forecast
    combination methods under a unified user interface for the packages 'ForecastCombinations'
    and 'GeomComb'. Additionally, updated tools and convenience functions for data pre-processing are available in order to deal with 
    common problems in forecast combination (missingness, collinearity). For method details see Hsiao C, Wan SK (2014). <doi:10.1016/j.jeconom.2013.11.003>, Hansen BE (2007). <doi:10.1111/j.1468-0262.2007.00785.x>,
    Elliott G, Gargano A, Timmermann A (2013). <doi:10.1016/j.jeconom.2013.04.017>, 
    and Clemen RT (1989). <doi:10.1016/0169-2070(89)90012-5>.
Aim: Supports the most frequently used methods to combine forecasts. Among others: Simple average, Ordinary Least Squares, Least Absolute Deviation, Constrained Least Squares, Variance-based, Best Individual model, Complete subset regressions and Information-theoretic (information criteria based).
Create modular models.  Quickly prototype models whose input includes (multiple) time series data.  Create pieces of model use cases separately, and swap out particular models as desired. Create modeling competitions, data processing pipelines, and re-useable models.
Convenient functions for ensemble forecasts in R combining
    approaches from the 'forecast' package. Forecasts generated from auto.arima(), ets(),
    thetam(), nnetar(), stlm(), and tbats() can be combined with equal weights, weights
    based on in-sample errors, or CV weights. Cross validation for time series data
    and user-supplied models and forecasting functions is also supported to evaluate model accuracy.
Methods to compute linear h-step ahead prediction coefficients based
    on localised and iterated Yule-Walker estimates and empirical mean squared
    and absolute prediction errors for the resulting predictors. Also, functions
    to compute autocovariances for AR(p) processes, to simulate tvARMA(p,q) time
    series, and to verify an assumption from Kley et al. (2017),
    Preprint <http://personal.lse.ac.uk/kley/forecastSNSTS.pdf>.
Routines for forecasting univariate time series using Theta Models. Contains several cross-validation routines.
Reading and writing data stored by some versions of
	'Epi Info', 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka',
	and for reading and writing some 'dBase' files.
The statistical evaluation of DNA mixtures, DNA profile
        match probability
Statistical methods and simulation tools for the
        interpretation of forensic DNA mixtures
A tool to create hydroclimate scenarios, stress test systems and visualize system performance in scenario-neutral climate change impact assessments. Scenario-neutral approaches 'stress-test' the performance of a modelled system by applying a wide range of plausible hydroclimate conditions (see Brown & Wilby (2012) <doi:10.1029/2012EO410001> and Prudhomme et al. (2010) <doi:10.1016/j.jhydrol.2010.06.043>). These approaches allow the identification of hydroclimatic variables that affect the vulnerability of a system to hydroclimate variation and change. This tool enables the generation of perturbed time series using a range of approaches including simple scaling of observed time series (e.g. Culley et al. (2016) <doi:10.1002/2015WR018253>) and stochastic simulation of perturbed time series via an inverse approach (see Guo et al. (2018) <doi:10.1016/j.jhydrol.2016.03.025>). It incorporates a number of stochastic weather models to generate hydroclimate variables on a daily basis (e.g. precipitation, temperature, potential evapotranspiration) and allows a variety of different hydroclimate variable properties, herein called attributes, to be perturbed. Options are included for the easy integration of existing system models both internally in R and externally for seamless 'stress-testing'. A suite of visualization options for the results of a scenario-neutral analysis (e.g. plotting performance spaces and overlaying climate projection information) are also included. As further developments in scenario-neutral approaches occur the tool will be updated to incorporate these advances.
Approximate false positive rate control in selection frequency for
    random forest using the methods described by Ender Konukoglu and Melanie Ganz (2015) <arXiv:1410.2838>.
    Methods for calculating the selection frequency threshold at false positive rates
    and selection frequency false positive rate feature selection.
Form visualizations of high dimensional mapping structures of random forests and feature contributions.
Extensive global and small-area estimation procedures for multiphase
    forest inventories under the design-based Monte-Carlo approach are provided.
    The implementation includes estimators for simple and cluster sampling
    published by Daniel Mandallaz in 2007 (<DOI:10.1201/9781584889779>),
    2013 (<DOI:10.1139/cjfr-2012-0381>, <DOI:10.1139/cjfr-2013-0181>,
    <DOI:10.1139/cjfr-2013-0449>, <DOI:10.3929/ethz-a-009990020>)
    and 2016 (<DOI:10.3929/ethz-a-010579388>). It provides point estimates,
    their external- and design-based variances as well as confidence intervals.
    The procedures have also been optimized for the use of remote sensing data
    as auxiliary information.
Produces forest plots using 'ggplot2' from models produced by functions
    such as stats::lm(), stats::glm() and survival::coxph().
A forest plot that allows for
    multiple confidence intervals per row,
    custom fonts for each text element,
    custom confidence intervals,
    text mixed with expressions, and more.
    The aim is to extend the use of forest plots beyond meta-analyses.
    This is a more general version of the original 'rmeta' package's forestplot()
    function and relies heavily on the 'grid' package.
Provides a toolkit for calculating forest and canopy structural complexity metrics from
    terrestrial LiDAR (light detection and ranging). References:  Atkins et al. In Review; Hardiman et al. 2013 <doi:10.3390/f4030537>;
    Parker et al. 2004 <doi:10.1111/j.0021-8901.2004.00925.x>.
Provides tools for analyzing remotely sensed forest data, including functions for detecting treetops from canopy models, outlining tree crowns and generating spatial statistics.
Imputation of missing values in datasets of ordinal variables through a forward imputation algorithm
This package provides estimates of tree volume and biomass from
    Italian NFI models
Implemented, addition, subtracking, multiplication,
        division in formal series rings of any number of variables
        (except division is only to 3 variables). Also are available
        "[" "[<-" operators.
Provides a function tidy_source() to format R source code. Spaces
    and indent will be added to the code automatically, and comments will be
    preserved under certain conditions, so that R code will be more
    human-readable and tidy. There is also a Shiny app as a user interface in
    this package (see tidy_app()).
Provides functions to create formattable vectors and data frames.
    'Formattable' vectors are printed with text formatting, and formattable
    data frames are printed with multiple types of formatting in HTML
    to improve the readability of data presented in tabular form rendered in
    web pages.
Infrastructure for extended formulas with multiple parts on the
             right-hand side and/or multiple responses on the left-hand side
	     (see <DOI:10.18637/jss.v034.i01>).
These utilities facilitate the programmatic manipulations of
    formulas, expressions, calls, assignments and other R language objects. 
    These objects all share the same structure: a left-hand side, operator and 
    right-hand side. This packages provides methods for accessing and 
    modifying this structures as well as extracting and replacing names and 
    symbols from these objects.
Automatically generates wrappers for modelling functions
    that accept data as a data matrix X and a data vector y and produces
    a wrapper that allows users to specify input data with a formula and a data frame.
    In addition to generating formula interfaces, users may also generated wrapper S3
    generics.
A collection of fortunes from the R community.
Anonymized data from surveys conducted by Forwards <http://forwards.github.io/>, the R Foundation task force on women and other under-represented groups. Currently, a single data set of responses to a survey of attendees at useR! 2016 <http://user2016.org/>, the R user conference held at Stanford University, Stanford, California, USA, June 27 - June 30 2016.
Forward Search analysis of time series regressions. Implements the asymptotic theory developed in Johansen and Nielsen (2013, 2014).
A set of analytical tools useful in analysing ecological
        and geographical data sets, both ancient and modern. The
        package includes functions for estimating species richness
        (Chao 1 and 2, ACE, ICE, Jacknife), shared species/beta
        diversity, species area curves and geographic distances and
        areas.
Computes Fourier integrals of functions of one and two variables using the Fast Fourier transform. The Fourier transforms must be evaluated on a regular grid.
Estimate Barton & Lord's (1981) <doi:10.1002/j.2333-8504.1981.tb01255.x> 
    four parameter IRT model with lower and upper asymptotes using Bayesian
    formulation described by Culpepper (2016) <doi:10.1007/s11336-015-9477-6>.
A game for two players: Who gets first four in a row
        (horizontal, vertical or diagonal) wins. Published by Milton
        Bradley, designed by Howard Wexler and Ned Strongin.
Spatio-temporal Fixation Pattern Analysis (FPA) is a new method of analyzing eye 
  movement data, developed by Mr. Jinlu Cao under the supervision of Prof. Chen Hsuan-Chih at 
  The Chinese University of Hong Kong, and Prof. Wang Suiping at the South China Normal 
  Univeristy. The package "fpa" is a R implementation which makes FPA analysis much easier. 
  There are four major functions in the package: ft2fp(), get_pattern(), plot_pattern(), and 
  lineplot(). The function ft2fp() is the core function, which can complete all the preprocessing 
  within moments. The other three functions are supportive functions which visualize the eye 
  fixation patterns.
Various methods for clustering and cluster validation.
  Fixed point clustering. Linear regression clustering. Clustering by 
  merging Gaussian mixture components. Symmetric 
  and asymmetric discriminant projections for visualisation of the 
  separation of groupings. Cluster validation statistics
  for distance based clustering including corrected Rand index. 
  Cluster-wise cluster stability assessment. Methods for estimation of 
  the number of clusters: Calinski-Harabasz, Tibshirani and Walther's 
  prediction strength, Fang and Wang's bootstrap stability. 
  Gaussian/multinomial mixture fitting for mixed 
  continuous/categorical variables. Variable-wise statistics for cluster
  interpretation. DBSCAN clustering. Interface functions for many 
  clustering methods implemented in R, including estimating the number of
  clusters with kmeans, pam and clara. Modality diagnosis for Gaussian
  mixtures. For an overview see package?fpc.
A geometric approach to MLE for functional principal
        components
Compute the two dimension functional principal component scores for a series of two dimension images.
Comparisons of floating point numbers are problematic due to errors
    associated with the binary representation of decimal numbers.
    Despite being aware of these problems, people still use numerical methods
    that fail to account for these and other rounding errors (this pitfall is
    the first to be highlighted in Circle 1 of Burns (2012)
    [The R Inferno](http://www.burns-stat.com/pages/Tutor/R_inferno.pdf)).
    This package provides new relational operators useful for performing
    floating point number comparisons with a set tolerance.
Probabilistic distance clustering (PD-clustering) is an iterative, distribution free, probabilistic clustering method. PD-clustering assigns units to a cluster according to their probability of membership, under the constraint that the product of the probability and the distance of each point to any cluster centre is a constant. PD-clustering is a flexible method that can be used with non-spherical clusters, outliers, or noisy data. Facto PD-clustering (FPDC) is a recently proposed factor clustering method that involves a linear transformation of variables and a cluster optimizing the PD-clustering criterion. It works on high dimensional datasets.
Given the values of sampled units and selection probabilities 
             the desraj function  in the package computes the estimated value of the total
             as well as estimated variance.
Algorithms for detection of outliers based on frequent pattern
    mining. Such algorithms follow the paradigm: if an instance contains more frequent patterns, 
    it means that this data instance is unlikely to be an anomaly (He Zengyou, Xu Xiaofei, Huang 
    Zhexue Joshua, Deng Shengchun (2005) <doi:10.2298/CSIS0501103H>).
    The package implements a list of existing state of the art algorithms as well as other published 
    approaches: FPI, WFPI, FPOF, FPCOF, LFPOF, MFPOF, WCFPOF and WFPOF.
Provides a collection
	of functions to optimize portfolios and to analyze them from
    different points of view.
Returns the noncentrality parameter of the noncentral F
        distribution if probability of type I and type II error,
        degrees of freedom of the numerator and the denominator are
        given.  It may be useful for computing minimal detectable
        differences for general ANOVA models.  This program is
        documented in the paper of A. Baharev, S. Kemeny, On the
        computation of the noncentral F and noncentral beta
        distribution; Statistics and Computing, 2008, 18 (3), 333-340.
All data sets required for the examples and exercises in
        the book "Forecasting: principles and practice" by Rob J
        Hyndman and George Athanasopoulos. All packages required to run
        the examples are also loaded.
All data sets required for the examples and exercises 
  in the book "Forecasting: principles and practice" 
  by Rob J Hyndman and George Athanasopoulos <https://OTexts.org/fpp2/>. 
  All packages required to run the examples are also loaded.
Efficient approximation of first-passage-time densities for diffusion processes based on the First-Passage-Time Location (FPTL) function.
The main goal of this package is drawing the membership function of the fuzzy p-value which is defined as a fuzzy set on the unit interval for three following problems: (1) testing crisp hypotheses based on fuzzy data, see Filzmoser and Viertl (2004) <doi:10.1007/s001840300269>, (2) testing fuzzy hypotheses based on crisp data, see Parchami et al. (2010) <doi:10.1007/s00362-008-0133-4>, and (3) testing fuzzy hypotheses based on fuzzy data, see Parchami et al. (2012) <doi:10.1007/s00362-010-0353-2>. In all cases, the fuzziness of data or / and the fuzziness of the boundary of null fuzzy hypothesis transported via the p-value function and causes to produce the fuzzy p-value. If the p-value is fuzzy, it is more appropriate to consider a fuzzy significance level for the problem. Therefore, the comparison of the fuzzy p-value and the fuzzy significance level is evaluated by a fuzzy ranking method in this package.
Maximum likelihood estimation of the parameters of a
        fractionally differenced ARIMA(p,d,q) model (Haslett and
        Raftery, Appl.Statistics, 1989).
Functions for fitting data to a quiescent growth model,
        i.e. a growth process that involves members of the population
        who stop dividing or propagating.
Stochastic fractal and deterministic chaotic time series
        analysis.
Implements various methods for estimating fractal dimension of time series and 2-dimensional data.
The parameters p and q are estimated with the aid of a randomized Sierpinski Carpet which is built on a [p-p-p-q]-model. Thereby, for three times a simulation with a p-value and once with a q-value is assumed. Hence, these parameters are estimated and displayed. Moreover, functions for simulating random Sierpinski-Carpets with constant and variable probabilities are included.
The basic principle driving fractal generation of time
        series is that data is generated iteratively based on
        increasing levels of resolution.  The initial series is defined
        by a so-called initiator pattern and then generators are used
        to replace each segment of the initial pattern. Regular,
        repeatable patterns can be produced by using the same seed and
        generators.  By using a set of generators, non-repeatable time
        series can be produced.  This technique is the basis of the
        fractal time series process in this package.
This is the package which can help you turn
        numeric,dataframe,matrix into fraction form.
The main function of this package allows numerical vector objects to
  be displayed with their values in vulgar fractional form.  This is convenient if
  patterns can then be more easily detected.  In some cases replacing the components
  of a numeric vector by a rational approximation can also be expected to remove
  some component of round-off error.  The main functions form a re-implementation
  of the functions 'fractions' and 'rational' of the MASS package, but using a
  radically improved programming strategy.
Implements and extends the fragility index calculation for
    dichotomous results as described in Walsh, Srinathan, McAuley,
    Mrkobrada, Levine, Ribic, Molnar, Dattani, Burke, Guyatt,
    Thabane, Walter, Pogue, and Devereaux (2014)
    <DOI:10.1016/j.jclinepi.2013.10.019>.
Performs fragment analysis using genetic data coming from capillary electrophoresis machines. These are files with FSA extension which stands for FASTA-type file, and .txt files from Beckman CEQ 8000 system, both contain DNA fragment intensities read by machinery. In addition to visualization, it performs automatic scoring of SSRs (Sample Sequence Repeats; a type of genetic marker very common across the genome) and other type of PCR markers (standing for Polymerase Chain Reaction) in biparental populations such as F1, F2, BC (backcross), and diversity panels (collection of genetic diversity).
Contains functions for fitting shared frailty models with a semi-parametric
    baseline hazard with the Expectation-Maximization algorithm. Supported data formats 
    include clustered failures with left truncation and recurrent events in gap-time
    or Andersen-Gill format. Several frailty distributions, such as the the gamma, positive stable
    and the Power Variance Family are supported.
Implements the hierarchical likelihood estimation procedures for general frailty models including competing-risk models and joint models.
Simulates and fits semiparametric shared frailty models under a
    wide range of frailty distributions using a consistent and
    asymptotically-normal estimator. Currently supports: gamma, power variance
    function, log-normal, and inverse Gaussian frailty models.
Tools to support sensible statistics for functional response analysis.
Generation of theoretical size distributions of framboidal or sunflower pyrite. 
        The growth mechanisms used are surface and supply controlled and dependent
        or independent of size. The algorithms are fully described in the published work 
        in Mineralogy and Petrology journal:
        "Theoretical growth of framboidal and sunflower pyrite using the R-package frambgrowth"
        The authors Merinero, R., and Cardenes, V. (2018).
        <DOI:10.1007/s00710-017-0535-x>.
Point and interval estimation in dual frame surveys. In contrast
    to classic sampling theory, where only one sampling frame is considered,
    dual frame methodology assumes that there are two frames available for
    sampling and that, overall, they cover the entire target population. Then,
    two probability samples (one from each frame) are drawn and information
    collected is suitably combined to get estimators of the parameter of
    interest.
With no external dependencies and
    support for 335 languages; all languages spoken by
    more than one million speakers. 'Franc' is a port
    of the 'JavaScript' project of the same name,
    see <https://github.com/wooorm/franc>.
Accompanying package of the book 'Financial Risk Modelling
        and Portfolio Optimisation with R', second edition. The data sets used in the book are contained in this package.
This package performs robust inference based on applying
        Fast and Robust Bootstrap on robust estimators. Available
        methods are multivariate regression, PCA and Hotelling tests.
An implementation of various learning algorithms based on fuzzy rule-based systems (FRBSs) for dealing with classification and regression tasks. Moreover, it allows to construct an FRBS model defined by human experts. 
    FRBSs are based on the concept of fuzzy sets, proposed by Zadeh in 1965, which aims at
    representing the reasoning of human experts in a set of IF-THEN rules, to
    handle real-life problems in, e.g., control, prediction and inference, data
    mining, bioinformatics data processing, and robotics. FRBSs are also known
    as fuzzy inference systems and fuzzy models. During the modeling of an
    FRBS, there are two important steps that need to be conducted: structure
    identification and parameter estimation. Nowadays, there exists a wide
    variety of algorithms to generate fuzzy IF-THEN rules automatically from
    numerical data, covering both steps. Approaches that have been used in the
    past are, e.g., heuristic procedures, neuro-fuzzy techniques, clustering
    methods, genetic algorithms, squares methods, etc. Furthermore, in this
    version we provide a universal framework named 'frbsPMML', which is adopted
    from the Predictive Model Markup Language (PMML), for representing FRBS
    models. PMML is an XML-based language to provide a standard for describing
    models produced by data mining and machine learning algorithms. Therefore,
    we are allowed to export and import an FRBS model to/from 'frbsPMML'.
    Finally, this package aims to implement the most widely used standard
    procedures, thus offering a standard package for FRBS modeling to the R
    community.
This package implements the functions associated with Fast
        Regularized Canonical Correlation Analysis.
Despike, ustar filtering, plotting, footprint modelling and general post-processing of a standard EddyPro full output file (LI-COR Inc 2011-2015 <https://www.licor.com/env/products/eddy_covariance/eddypro.html>).
Provides functionality for manipulating elements of the free group (juxtaposition is represented by a plus) including inversion, multiplication by a scalar, group-theoretic power operation, and Tietze forms.  The package is fully vectorized.
This package is for fitting free-knot splines for data
        with one independent variable and one dependent variable.  Four
        free-knot spline algorithms are provided for the case where the
        number of knots is known in advance.  A knot-search algorithm
        is provided for the case where the number of knots is not known
        in advance.  In addition, methods are available to compute the
        fitted values, the residuals, and the coefficients of the
        splines, and to plot the results, along with a method to
        summarize the results.
Provides tools for describing and analysing free sorting data. Main methods are computation of consensus partition and factorial analysis of the dissimilarity matrix between stimuli (using multidimensional scaling approach).
A collections of useful statistical functions used in Columbia
    course W4240/W4400.
Wrapper functions that interface with 'Freesurfer' 
    <https://surfer.nmr.mgh.harvard.edu/>, a powerful and 
    commonly-used 'neuroimaging'
    software, using system commands. The goal is to be able to interface with
    'Freesurfer' completely in R, where you pass R objects of class 'nifti',
    implemented by package 'oro.nifti', and the function executes an 'Freesurfer'
    command and returns an R object of class 'nifti' or necessary output.
Fast regional association analysis of quantitative traits for family-based and population studies.
A collection of functions for linear and non-linear regression modelling. 
	It implements a wrapper for several regression models available in the base and
	contributed packages of R.
Signal compression methods for function-on-function regression with functional response and functional predictors, including linear models with both scalar and functional predictors for a small number of functional predictors, linear models with functional predictors for a large number of functional predictors, stepwise selection for FOF models with two-way interactions, and nonlinear models. References are linked to via the URL below.
Real capture frequencies will be fitted to various distributions which provide the basis of estimating population sizes, their standard error, and symmetric as well as asymmetric confidence intervalls.
Generates a frequency distribution. The frequency
    distribution includes raw frequencies, percentages in each category, and
    cumulative frequencies. The frequency distribution can be stored as a data
    frame.
Implementation of dynamic principal component
    analysis (DPCA), simulation of VAR and VMA processes and frequency domain tools. 
    These frequency domain methods for dimensionality reduction of multivariate time series
    were introduced by David Brillinger in his book Time Series (1974). We follow implementation
    guidelines as described in Hormann, Kidzinski and Hallin (2016),
    Dynamic Functional Principal Component <doi:10.1111/rssb.12076>.
Implementations of functional dynamic principle components analysis. Related graphic tools and frequency domain methods.
  These methods directly use multivariate dynamic principal components implementation,
  following the guidelines from Hormann, Kidzinski and Hallin (2016), Dynamic Functional Principal Component <doi:10.1111/rssb.12076>.
A frequency moving average plot (MAP) is estimated from a
        multinomial data and a continuous covariate. The frequency MAP
        is a moving average estimate of category frequencies, where
        frequency means and posterior bounds are estimated. Comparisons
        of two frequency MAPs as well as odds ratios can be plotted.
New approaches to parallel coordinates plots for
   multivariate data visualization, including applications to clustering,
   outlier hunting and regression diagnostics.  Includes general functions
   for multivariate nonparametric density and regression estimation, 
   using parallel computation.
Tools for generating an informative type of line graph, the frequency profile, 
    which allows single behaviors, multiple behaviors, or the specific behavioral patterns 
    of individual subjects to be graphed from occurrence/nonoccurrence behavioral data.
Provides functions to create frequency tables which display both counts
    and rates.
Accompanies a paper (Barunik, Krehlik (2017) <doi:10.2139/ssrn.2627599>) dedicated to spectral decomposition of connectedness measures and their interpretation. We implement all the developed estimators as well as the historical counterparts. For more information, see the help or GitHub page (<https://github.com/tomaskrehlik/frequencyConnectedness>) for relevant information.
The frequency of a particular data value is the number of times it
    occurs. A frequency table is a table of values with their corresponding
    frequencies. Frequency weights are integer numbers that indicate how many
    cases each case represents. This package provides some functions to work
    with such type of collected data.
Contains a set of utilities for building and testing formula-based models (linear, logistic or COX) for Computer Aided Diagnosis/Prognosis applications. Utilities include data adjustment, univariate analysis, model building, model-validation, longitudinal analysis, reporting and visualization.
Regular and non-regular Fractional Factorial 2-level designs 
        can be created. Furthermore, analysis tools for Fractional
        Factorial designs with 2-level factors are offered (main
        effects and interaction plots for all factors simultaneously,
        cube plot for looking at the simultaneous effects of three
        factors, full or half normal plot, alias structure in a more
        readable format than with the built-in function alias).
This package provides catalogues of resolution IV regular
        fractional factorial designs in 128 runs for up to 33 2-level
        factors. The catalogues are complete, excluding resolution IV
        designs without 5-letter words, because these do not add value
        for a search for clear designs. The previous package version
        1.0 with complete catalogues up to 24 runs (24 runs and a
        namespace added later) can be downloaded from the authors
        website.
Fixed Rank Kriging is a tool for spatial/spatio-temporal modelling
    and prediction with large datasets. The approach, discussed in Cressie and
    Johannesson (2008) <DOI:10.1111/j.1467-9868.2007.00633.x>, decomposes the field, 
    and hence the covariance function, using a fixed set of n basis functions, 
    where n is typically much smaller than the number of data points (or polygons) m. 
    The method naturally allows for non-stationary, anisotropic covariance functions 
    and the use of observations with varying support (with known error variance). The 
    projected field is a key building block of the Spatial Random Effects (SRE) model, 
    on which this package is based. The package FRK provides helper functions to model, 
    fit, and predict using an SRE with relative ease.
Estimation and specification analysis of one- and two-part fractional regression models and calculation of partial effects.
Estimation and specification analysis of fractional regression models with neglected heterogeneity and/or endogenous covariates.
Estimation of panel data regression models for fractional responses.
A collection of R and C++ functions to work with the
        generalized hyperbolic distribution, related distributions and
        their applications in financial risk management and
        quantitative analysis.
Fast computation of moments via 'Rcpp'. Supports computation on
   vectors and matrices, and Monoidal append of moments.
Maximum Likelihood Estimation of
   Stochastic Frontier Production and Cost Functions.
   Two specifications are available:
   the error components specification with time-varying efficiencies
   (Battese and Coelli, 1992)
   and a model specification in which the firm effects are directly 
   influenced by a number of variables (Battese and Coelli, 1995).
It calculates the alpha-quantile and order-m efficiency score in multi-dimension and computes several summaries and representation of the associated frontiers in 2d and 3d.
A cross-platform interface to file system operations, built on
  top of the 'libuv' C library.
A variety of simple fish stock assessment methods.
    Detailed vignettes are available on the fishR website <http://derekogle.com/fishR/>.
The datasets to support the Fish Stock Assessment ('FSA') package.
Automated feature selection using variety of models
        provided by 'caret' package.
        This work was funded by Poland-Singapore bilateral cooperation
        project no 2/3/POL-SIN/2012.
Provides interface to the 'MATLAB' toolbox 'Flexible Statistical Data Analysis
    (FSDA)' which is a comprehensive and computationally efficient
    software package for robust statistics in regression, multivariate
    and categorical data analysis. The current R version implements tools
    for regression: (forward search, S- and MM-estimation, least trimmed
    squares (LTS) and least median of squares (LMS)). The distinctive
    feature of our regression package relies in the possibility of
    monitoring the statistics of interest as function of breakdown point,
    efficiency or subset size, depending on the estimator. This is
    accompanied by a rich set of graphical features, such as dynamic
    brushing, linking, particularly useful for exploratory data analysis.
Functions for selecting attributes from a given dataset. Attribute
    subset selection is the process of identifying and removing as much of the
    irrelevant and redundant information as possible.
'Rcpp' (free of 'Java'/'Weka') implementation of 'FSelector' entropy-based feature selection 
 algorithms based on an MDL discretization (Fayyad U. M., Irani K. B.: Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning.
 In 13'th International Joint Conference on Uncertainly in Artificial Intelligence (IJCAI93), pages 1022-1029, Chambery, France, 1993.) <https://www.ijcai.org/Proceedings/93-2/Papers/022.pdf>
 with a sparse matrix support. It is also equipped with a parallel backend.
Import data of tests and questionnaires from FormScanner. FormScanner is an open source software that converts scanned images to data using optical mark recognition (OMR) and it can be downloaded from <http://sourceforge.net/projects/formscanner/>. The spreadsheet file created by FormScanner is imported in a convenient format to perform the analyses provided by the package. These analyses include the conversion of multiple responses to binary (correct/incorrect) data, the computation of the number of corrected responses for each subject or item, scoring using weights,the computation and the graphical representation of the frequencies of the responses to each item and the report of the responses of a few subjects.
Performs fast detection of interactions in large-scale data using the
    method of random intersection trees introduced in
    Shah, R. D. and Meinshausen, N. (2014) <http://www.jmlr.org/papers/v15/shah14a.html>. 
    The algorithm finds potentially high-order interactions in high-dimensional binary
    two-class classification data, without requiring lower order interactions
    to be informative.  The search is particularly fast when the matrices of
    predictors are sparse.  It can also be used to perform market basket analysis
    when supplied with a single binary data matrix.  Here it will find collections
    of columns which for many rows contain all 1's.
Wrapper functions that interface with 'FSL' 
    <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/>, a powerful and commonly-used 'neuroimaging'
    software, using system commands. The goal is to be able to interface with 'FSL'
    completely in R, where you pass R objects of class 'nifti', implemented by
    package 'oro.nifti', and the function executes an 'FSL' command and returns an R
    object of class 'nifti' if desired.
Fuzzy set ordination is a multivariate analysis used in
        ecology to relate the composition of samples to possible
        explanatory variables.  While differing in theory and method,
        in practice, the use is similar to 'constrained ordination.'
        The package contains plotting and summary functions as well as
        the analyses
Social Relations Analysis with roles ("Family SRM") are computed,
    using a structural equation modeling approach. Groups ranging from three members
    up to an unlimited number of members are supported and the mean structure can
    be computed. Means and variances can be compared between different groups of
    families and between roles.
Multithreaded serialization of compressed data frames using the
    'fst' format. The 'fst' format allows for random access of stored data and
    compression with the LZ4 and ZSTD compressors created by Yann Collet. The ZSTD
    compression library is owned by Facebook Inc.
A program to generate smoothed quantiles for the Fst-heterozygosity distribution. Designed for use with large numbers of loci (e.g., genome-wide SNPs). The best case for analyzing the Fst-heterozygosity distribution is when many populations (>10) have been sampled. See Flanagan & Jones (2017) <doi:10.1093/jhered/esx048>.
Functions for sequencing studies allowing for multiple functional annotation scores. Score type tests and an efficient perturbation method are used for individual gene/large gene-set/genome wide analysis. Only summary statistics are needed.
A wrapper for the API of the Danish Parliament. It makes it 
    possible to get data from the API easily into a data frame. Learn more at 
    <http://www.ft.dk/dokumenter/aabne_data>.
This package was developed partially with funding from the
        NIH Training Program in Biomolecular Technology
        (2-T32-GM08799).
The package contains R-functions to perform the methods in
        nonparametric regression and density estimation, described in
        Davies, P. L. and Kovac, A. (2001) Local Extremes, Runs,
        Strings and Multiresolution (with discussion) Annals of
        Statistics. 29. p1-65 Davies, P. L. and Kovac, A. (2004)
        Densities, Spectral Densities and Modality Annals of
        Statistics. Annals of Statistics. 32. p1093-1136 Kovac, A.
        (2006) Smooth functions and local extreme values. Computational
        Statistics and Data Analysis (to appear) D\"umbgen, L. and
        Kovac, A. (2006) Extensions of smoothing via taut strings
        Davies, P. L. (1995) Data features. Statistica Neerlandica
        49,185-245.
A collection of functions for trading and rebalancing financial
	instruments. It implements various technical indicators to analyse time series such
	as moving averages or stochastic oscillators.
Implementation of Follow The Regularized Leader (FTRL) Proximal algorithm, proposed by McMahan et al. (2013) <doi:10.1145/2487575.2488200>, used for online training of large scale regression models using a mixture of L1 and L2 regularization.
Functions for visualizing, modeling, forecasting and hypothesis testing of functional time series.
Functions for estimating spectral density operator of functional
    time series (FTS) and comparing the spectral density operator of two
    functional time series, in a way that allows detection of differences of
    the spectral density operator in frequencies and along the curve length.
Fuel economy data from the EPA, 1985-2015, conveniently
    packaged for consumption by R users.
This is an evolutionary algorithm for fuzzy systems, a
        genetic algorithm is used to construct a fuzzy system able to
        fit the given training data.  This fuzzy system can then be
        used as a prediction model, it's composed of fuzzy logic rules
        that provide a good lingustic representation.
We facilitate the analysis of full factorial mating designs with mixed-effects models. The observed data functions extract the variance explained by random and fixed effects and provide their significance. We then calculate the additive genetic, nonadditive genetic, and maternal variance components explaining the phenotype. In particular, we integrate nonnormal error structures for estimating these components for nonnormal data types. The resampled data functions are used to produce bootstrap confidence intervals, which can then be plotted using a simple function. This package will facilitate the analyses of full factorial mating designs in R, especially for the analysis of binary, proportion, and/or count data types and for the ability to incorporate additional random and fixed effects and power analyses. The paper associated with the package including worked examples is: Houde ALS, Pitcher TE (2016) <doi:10.1002/ece3.1943>.
Provides a single interface to many sources of full text
    'scholarly' data, including 'Biomed Central', Public Library of
    Science, 'Pubmed Central', 'eLife', 'F1000Research', 'PeerJ',
    'Pensoft', 'Hindawi', 'arXiv' 'preprints', and more. Functionality
    included for searching for articles, downloading full or partial
    text, downloading supplementary materials, converting to various
    data formats.
This is a collection of R games and other funny stuff,
        such as the classical Mine sweeper and sliding puzzles.
A machine learning based approach for fungal species identification using barcode sequence data. The multi-class random forest model has been used for prediction purpose, where the gap-pair compositional feature was used to encode the barcode sequence data. The encoded dataset was used as input for prediction purpose. Though this approach has been developed for fungal species identification in particular, can be used for other species identification as well.
A set of functions, some subset of which I use in every .R file I write. Examples are table2(), which adds useful functionalities to base table (sorting, built-in proportion argument, etc.); lyx.xtable(), which converts xtable() output to a format more easily copy-pasted into LyX; pdf2(), which writes a plot to file while also displaying it in the RStudio plot window; and abbr_to_colClass(), which is a much more concise way of feeding many types to a colClass argument in a data reader.
Statistical hypothesis testing methods for
 model-free functional dependency using asymptotic
 chi-square or exact distributions. Functional chi-squares are
 asymmetric and functionally optimal, unique from other related
 statistics. Tests in this package reveal evidence for causality 
 based on the causality-by-functionality principle. They include
 asymptotic functional chi-square tests, an exact functional
 test, a comparative functional chi-square test, and also a
 comparative chi-square test. The normalized non-constant
 functional chi-square test was used by Best Performer
 NMSUSongLab in HPN-DREAM (DREAM8) Breast Cancer Network
 Inference Challenges. For continuous data, these tests offer an
 advantage over regression analysis when a parametric functional
 form cannot be assumed; for categorical data, they provide a
 novel means to assess directional dependency not possible with
 symmetrical Pearson's chi-square or Fisher's exact tests.
FunCluster performs a functional analysis of microarray
        expression data based on Gene Ontology & KEGG functional
        annotations. From expression data and functional annotations
        FunCluster builds classes of putatively co-regulated biological
        processes through a specially designed clustering procedure.
This packages proposes a model-based clustering algorithm for
    multivariate functional data. The parametric mixture model, based on the
    assumption of normality of the principal components resulting from a
    multivariate functional PCA, is estimated by an EM-like algorithm. The main
    advantage of the proposed algorithm is its ability to take into account the
    dependence among curves.
Analyzes the function calls in an R package and creates a hive plot of the calls, dividing them among functions that only make outgoing calls (sources), functions that have only incoming calls (sinks), and those that have both incoming calls and make outgoing calls (managers).  Function calls can be mapped by their absolute numbers, their normalized absolute numbers, or their rank.  FuncMap should be useful for comparing packages at a high level for their overall design.  Plus, it's just plain fun.  The hive plot concept was developed by Martin Krzywinski (www.hiveplot.com) and inspired this package.  Note: this package is maintained for historical reasons. HiveR is a full package for creating hive plots.
Curry, Compose, and other higher-order functions
R package providing functions to perform gene and gene set network inference.
Extends functional programming in R by
    providing support to the usual higher order functional
    suspects (Map, Reduce, Filter, etc.).
Unified framework to cluster functional data according to one of
    seven models. All models are based on the projection of the curves onto a basis.
    The main function funcit() calls wrapper functions for the existing algorithms,
    so that input parameters are the same. A list is returned with each entry
    representing the same or extended output for the corresponding method. Method
    specific as well as general visualization tools are available.
S4 classes for univariate and multivariate functional data with
    utility functions.
The funFEM algorithm (Bouveyron et al., 2014) allows to cluster functional data by modeling the curves within a common and discriminative functional subspace.
Computes fungible coefficients and Monte Carlo data.  
    Underlying theory for these functions is described in the following publications:
    Waller, N. (2008). Fungible Weights in Multiple Regression. Psychometrika, 73(4), 691-703, <DOI10.1007/s11336-008-9066-z>. 
    Waller, N. & Jones, J. (2009). Locating the Extrema of Fungible Regression Weights. 
    Psychometrika, 74(4), 589-602, <DOI10.1007/s11336-008-9087-7>.
    Waller, N. G. (2016). Fungible Correlation Matrices: 
    A Method for Generating Nonsingular, Singular, and Improper Correlation Matrices for 
    Monte Carlo Research. Multivariate Behavioral Research, 51(4), 554-568, <DOI10.1080/00273171.2016.1178566>. 
    Jones, J. A. & Waller, N. G. (2015). The normal-theory and asymptotic distribution-free (ADF) 
    covariance matrix of standardized regression coefficients: theoretical extensions 
    and finite sample behavior. Psychometrika, 80, 365-378, <DOI10.1007/s11336-013-9380-y>.
The funHDDC algorithm (Bouveyron and Jacques, 2001, Schmutz et al., 2017) allows to cluster functional univariate or multivariate data by modeling each group within a specific functional subspace.
Provides four addons for analyzing trends and
    unit roots in financial time series: (i) functions for the density
    and probability of the augmented Dickey-Fuller Test, (ii) functions 
    for the density and probability of MacKinnon's unit root test 
    statistics, (iii) reimplementations for the ADF and MacKinnon
    Test, and (iv) an 'urca' Unit Root Test Interface for Pfaff's
    unit root test suite.
A tool for fitting latent Dirichlet allocation models to genomic annotation data.
Around 10% of almost any predictive modeling project is spent in predictive modeling, 'funModeling' and the book Data Science Live Book (<http://livebook.datascienceheroes.com/>) are intended to cover remaining 90%: data preparation, profiling, selecting best variables 'dataViz', assessing model performance and other functions.
A set of simplified functions for creating funnel plots for proportion data. This package supports user defined benchmarks, confidence limits and estimation methods (i.e. exact or approximate) based on Spiegelhalter (2005) <doi:10.1002/sim.1970>. Additional routines for returning scored unit level data according to a set of specifications is also implemented for convenience. Specifically, both a categorical and a continuous score variable is returned to the sample data frame, which identifies which observations are deemed extreme or in control. Typically, such variables are useful as stratifications or covariates in further exploratory analyses. Lastly, the plotting routine returns a base funnel plot ('ggplot2'), which can also be tailored.
A small utility which wraps Rscript and provides access to all R
    functions from the shell.
Computes functional rarity indices as proposed by Violle et al.
    (2017) <doi:10.1016/j.tree.2017.02.002>. Various indices can be computed
    using both regional and local information. Functional Rarity combines both
    the functional aspect of rarity as well as the extent aspect of rarity.
    'funrar' is presented in Grenié et al. (2017) <doi:10.1111/ddi.12629>.
Performs functional regression, and some related
    approaches, for intensive longitudinal data (see the book by Walls & Schafer, 
    2006, Models for Intensive Longitudinal Data, Oxford) when such data is not
    necessarily observed on an equally spaced grid of times.  The
    approach generally follows the ideas of Goldsmith, Bobb, Crainiceanu,
    Caffo, and Reich (2011)<DOI:10.1198/jcgs.2010.10007> and the approach taken in their sample code, but
    with some modifications to make it more feasible to use with long rather
    than wide, non-rectangular longitudinal datasets with unequal and
    potentially random measurement times.  It also allows easy plotting of the
    correlation between the smoothed covariate and the outcome as a function of
    time, which can add additional insights on how to interpret a functional
    regression.  Additionally, it also provides several permutation tests for
    the significance of the functional predictor.  The heuristic interpretation
    of ``time'' is used to describe the index of the functional predictor, but
    the same methods can equally be used for another unidimensional continuous
    index, such as space along a north-south axis.  The development of this
    package was part of a research project supported by Award R03 CA171809-01
    from the National Cancer Institute and Award P50 DA010075 from the National
    Institute on Drug Abuse. The content is solely the responsibility of the
    authors and does not necessarily represent the official views of the
    National Institute on Drug Abuse, the National Cancer Institute, or the
    National Institutes of Health.
Computes the functional tangential angle pseudo-depth and its robustified version from the paper by Kuhnt and Rehage (2016). See Kuhnt, S.; Rehage, A. (2016): An angle-based multivariate functional pseudo-depth for shape outlier detection, JMVA 146, 325-340, <doi:10.1016/j.jmva.2015.10.016> for details.
Includes non-parametric estimators and tests for time series analysis. The functions are to test for presence of possibly non-monotonic trends and for synchronism of trends in multiple time series, using modern bootstrap techniques and robust non-parametric difference-based estimators.
Contains three main functions (i.e., three pieces of furniture):
    table1() which produces a well-formatted table of descriptives common as Table 1
    in research articles, tableC() which produces a well-formatted table of correlations,
    tableF() which provides frequency counts,
    and washer() which is helpful in cleaning up the data. In addition,
    long() and wide(), built on the stats::reshape(), can help reformat the data.
    These furniture-themed functions are designed to simplify common tasks in 
    quantitative analysis. Other data summary and cleaning tools are also available.
Implementations of the family of map() functions from 'purrr' that can be resolved using any 'future'-supported backend, e.g. parallel on the local machine or distributed on a compute cluster.
Efficient procedures for community detection in network studies, especially for sparse networks. The algorithms impose penalties on the differences of the coordinates which represent the community labels of the nodes.
Enables high-dimensional penalized regression across heterogeneous 
    subgroups. Fusion penalties are used to share information about the linear 
    parameters across subgroups. The underlying model is described 
    in detail in Dondelinger and Mukherjee (2017) <arXiv:1611.00953>.
Provides the Big Merge Tracker and COSCI algorithms for convex clustering and 
    feature screening using L1 fusion penalty. See Radchenko, P. and Mukherjee, G. (2017) <doi:10.1111/rssb.12226> and 
    T.Banerjee et al. (2017) <doi:10.1016/j.jmva.2017.08.001> for more details.
The fusion learning method uses a model selection algorithm to learn from multiple data sets across different experimental platforms through group penalization. The responses of interest may include a mix of discrete and continuous variables. The responses may share the same set of predictors, however, the models and parameters differ across different platforms. Integrating information from different data sets can enhance the power of model selection. Package is based on Xin Gao, Raymond J. Carroll (2017) <arXiv:1610.00667v1>.
This utility package provides polymorphism over common operations and is now subsumed by lambda.tools.
Provides a simple yet powerful logging utility. Based loosely on
    log4j, futile.logger takes advantage of R idioms to make logging a
    convenient and easy to use replacement for cat and print statements.
A collection of functions for manipulating matrices and generating
    ensembles of random matrices. Used primarily to identify the cutoff point
    for the noise portion of the eigenvalue spectrum.
A scoped options management framework. Used in other packages.
Provides dispatching implementations suitable for
        functional programming paradigms. The framework provides a
        mechanism for attaching guards to functions similar to Erlang,
        while also providing the safety of assertions reminiscent of
        Eiffel.
Randomized clinical trials commonly follow participants for a time-to-event efficacy endpoint for a fixed period of time. Consequently, at the time when the last enrolled participant completes their follow-up, the number of observed endpoints is a random variable. Assuming data collected through an interim timepoint, simulation-based estimation and inferential procedures in the standard right-censored failure time analysis framework are conducted for the distribution of the number of endpoints--in total as well as by treatment arm--at the end of the follow-up period. The future (i.e., yet unobserved) enrollment, endpoint, and dropout times are generated according to mechanisms specified in the simTrial() function in the 'seqDesign' package. A Bayesian model for the endpoint rate, offering the option to specify a robust mixture prior distribution, is used for generating future data (see the vignette for details).
The purpose of this package is to provide a lightweight and
    unified Future API for sequential and parallel processing of R
    expression via futures.  The simplest way to evaluate an expression
    in parallel is to use `x %<-% { expression }` with `plan(multiprocess)`.
    This package implements sequential, multicore, multisession, and
    cluster futures.  With these, R expressions can be evaluated on the
    local machine, in parallel a set of local machines, or distributed
    on a mix of local and remote machines.
    Extensions to this package implement additional backends for
    processing futures via compute cluster schedulers etc.
    Because of its unified API, there is no need to modify any code in order
    switch from sequential on the local machine to, say, distributed
    processing on a remote compute cluster.
    Another strength of this package is that global variables and functions
    are automatically identified and exported as needed, making it
    straightforward to tweak existing code to make use of futures.
Implementations of lapply(), sapply(), tapply(), vapply() and friends that can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster.  These future_*apply() functions come with the same pros and cons as the corresponding base-R *apply() functions but with the additional feature of being able to be processed via the future framework.
Implementation of the Future API on top of the 'BatchJobs' package.
    This allows you to process futures, as defined by the 'future' package,
    in parallel out of the box, not only on your local machine or ad-hoc
    cluster of machines, but also via high-performance compute ('HPC') job
    schedulers such as 'LSF', 'OpenLava', 'Slurm', 'SGE', and 'TORQUE' / 'PBS',
    e.g. 'y <- future_lapply(files, FUN = process)'.
Implementation of the Future API on top of the 'batchtools' package.
    This allows you to process futures, as defined by the 'future' package,
    in parallel out of the box, not only on your local machine or ad-hoc
    cluster of machines, but also via high-performance compute ('HPC') job
    schedulers such as 'LSF', 'OpenLava', 'Slurm', 'SGE', and 'TORQUE' / 'PBS',
    e.g. 'y <- future_lapply(files, FUN = process)'.
Implementation of the Future API on top of the 'callr' package.  This allows you to process futures, as defined by the 'future' package, in parallel out of the box, on your local (Linux, macOS, Windows, ...) machine.  Contrary to backends relying on the 'parallel' package (e.g. 'future::multisession'), the 'callr' backend provided here can run more than 125 parallel R processes.
Inputs a directory of climate projection files and, for each,
    identifies and characterizes heat waves for specified study locations. The
    definition used to identify heat waves can be customized. Heat wave
    characterizations include several metrics of heat wave length, intensity,
    and timing in the year. The heat waves that are identified can be
    explored using a function to apply user-created functions across all
    generated heat wave files.This work was supported in part by grants from
    the National Institute of Environmental Health Sciences (R00ES022631), the
    National Science Foundation (1331399), and the Colorado State University
    Vice President for Research.
Test function arguments with a wide array of inputs, and produce
  reports summarizing messages, warnings, errors, and returned values.
The main goal of this package is drawing the membership function of the fuzzy p-value which is defined as a fuzzy set on the unit interval for three following problems: (1) testing crisp hypotheses based on fuzzy data, (2) testing fuzzy hypotheses based on crisp data, and (3) testing fuzzy hypotheses based on fuzzy data. In all cases, the fuzziness of data or/and the fuzziness of the boundary of null fuzzy hypothesis transported via the p-value function and causes to produce the fuzzy p-value. If the p-value is fuzzy, it is more appropriate to consider a fuzzy significance level for the problem. Therefore, the comparison of the fuzzy p-value and the fuzzy significance level is evaluated by a fuzzy ranking method in this package.
Calculation of AHP (Analytic Hierarchy Process -
    <http://en.wikipedia.org/wiki/Analytic_hierarchy_process>)
    with classic and fuzzy weights based on Saaty's pairwise
    comparison method for determination of weights.
Exact calculation of fuzzy decision rules for multiple
	testing. Choose to control FDR (false discovery rate) using the
	Benjamini and Hochberg method, or FWER (family wise error rate)
	using the Bonferroni method. Kulinsakaya and Lewin (2007).
Fuzzy forests, a new algorithm based on random forests,
    is designed to reduce the bias seen in random forest feature selection
    caused by the presence of correlated features.  Fuzzy forests uses
    recursive feature elimination random forests to select
    features from separate blocks of correlated features where the
    correlation within each block of features is high
    and the correlation between blocks of features is low.
    One final random forest is fit using the surviving features.
    This package fits random forests using the 'randomForest' package and
    allows for easy use of 'WGCNA' to split features into distinct blocks.
Join tables together based not on whether columns
  match exactly, but whether they are similar by some comparison.
  Implementations include string distance and regular expression
  matching.
Provides methods to solve Fuzzy Linear Programming Problems with
             fuzzy constraints (following different approaches proposed by 
             Verdegay, Zimmermann, Werners and Tanaka), fuzzy costs, and fuzzy
             technological matrix.
Implementation of several MCDM methods for fuzzy data (triangular
    fuzzy numbers) for decision making problems. The methods that are implemented in
    this package are Fuzzy TOPSIS (with two normalization procedures), Fuzzy VIKOR,
    Fuzzy Multi-MOORA and Fuzzy WASPAS. In addition, function MetaRanking() calculates
    a new ranking from the sum of the rankings calculated, as well as an aggregated ranking.
S4 classes and methods
    to deal with fuzzy numbers. They allow for computing any arithmetic
    operations (e.g., by using the Zadeh extension principle),
    performing approximation of arbitrary FNs by trapezoidal and piecewise
    linear FNs, preparing plots of FNs for publications, computing
    possibility and necessity values for comparisons, etc.
One can easily draw the membership function of f(x,y) by package 'FuzzyNumbers.Ext.2' in which f(.,.) is supposed monotone and x and y are two fuzzy numbers. This work is possible using function f2apply() which is an extension of function fapply() from Package 'FuzzyNumbers' for two-variable monotone functions. Moreover, this package has the ability of computing the core, support and alpha-cuts of the fuzzy-valued final result.
Design and simulate fuzzy logic systems using Type 1 Fuzzy Logic.
    This toolkit includes with graphical user interface (GUI) and an adaptive neuro-
    fuzzy inference system (ANFIS). This toolkit is a continuation from the previous
    package ('FuzzyToolkitUoN'). Produced by the Intelligent Modelling & Analysis
    Group, University of Nottingham.
Does fuzzy tests and confidence intervals (following Geyer
    and Meeden, Statistical Science, 2005, <doi:10.1214/088342305000000340>)
    for sign test and Wilcoxon signed rank and rank sum tests.
Estimators for fuzzy linear regression. The functions estimates parameters of 
    fuzzy linear regression models with crisp or fuzzy independent variables (triangular 
    fuzzy numbers are supported). Implements multiple methods for parameter estimation and 
    algebraic operations with triangular fuzzy numbers. Includes functions for 
    summarising, printing and plotting the model fit. Calculates predictions from the 
    model. 
    Diamond (1988) <doi:10.1016/0020-0255(88)90047-3>
	Hung & Yang (2006) <doi:10.1016/j.fss.2006.08.004>
	Lee & Tanaka (1999) <doi:10.15807/jorsj.42.98>
	Nasrabadi, Nasrabadi & Nasrabady (2005) <doi:10.1016/j.amc.2004.02.008>
	Tanaka, Hayashi & Watada (1989) <doi:10.1016/0377-2217(89)90431-1>.
An implementation of a method for computing fuzzy numbers representing stationary probabilities of an unknown Markov chain,
        from which a sequence of observations along time has been obtained. The algorithm is based on the proposal presented by James Buckley 
        in his book on Fuzzy probabilities (Springer, 2005), chapter 6. Package 'FuzzyNumbers' is used to represent the output probabilities.
The aim of the package is to provide some basic functions
 for doing statistics with trapezoidal fuzzy numbers. In particular,
  the package contains several functions for simulating trapezoidal 
  fuzzy numbers, as well as for calculating some central tendency 
  measures (mean and two types of median), some scale measures 
  (variance, ADD, MDD, Sn, Qn, Tn and some M-estimators) and 
  one diversity index and one inequality index. Moreover, 
  functions for calculating the 1-norm distance, the mid/spr 
  distance and the (phi,theta)-wabl/ldev/rdev distance between 
  fuzzy numbers are included, and a function to calculate the 
  value phi-wabl given a sample of trapezoidal fuzzy numbers.
A custom framework for working with Type 1 Fuzzy Logic,
        produced by the University of Nottingham IMA Group.
Fuzzy string matching implementation of the 'fuzzywuzzy' <https://github.com/seatgeek/fuzzywuzzy> 'python' package. It uses the Levenshtein Distance <https://en.wikipedia.org/wiki/Levenshtein_distance> to calculate the differences between sequences.
fwdmsa performs the Forward Search for Mokken scale
        analysis. It detects outliers, it produces several types of
        diagnostic plots.
A simple method
    to select the best model or best subset of variables using
    different types of data (binary, Gaussian or Poisson) and
    applying it in different contexts (parametric or non-parametric).
Provides three functions to calculate the outputs of the two main components of the Canadian Forest Fire Danger Rating System (CFFDRS): the Fire Weather Index (FWI) System and the Fire Behaviour Prediction (FBP) System.
Simulates a population under the Fisher-Wright model (fixed or stochastic population size) with a one-step neutral mutation process (stepwise mutation model, logistic mutation model and exponential mutation model supported). The stochastic population sizes are random Poisson distributed and different kinds of population growth are supported. For the stepwise mutation model, it is possible to specify locus and direction specific mutation rate (in terms of upwards and downwards mutation rate). Intermediate generations can be saved in order to study e.g. drift.
Exchange rate regression and structural change tools
             for estimating, testing, dating, and monitoring
	     (de facto) exchange rate regimes.
Create and maintain delayed-data packages (ddp's).  Data stored in
  a ddp are available on demand, but do not take up memory until requested.
  You attach a ddp with g.data.attach(), then read from it and assign to it in
  a manner similar to S-PLUS, except that you must run g.data.save() to
  actually commit to disk.
G1DBN performs DBN inference using 1st order conditional
        dependencies.
For a given metabolic network, this package finds the gaps (metabolites not produced or not consumed in any other reaction), and fills it from the stoichiometric reactions of a reference metabolic reconstruction using a weighting function. Also the option to download all the set of gene-associated stoichiometric reactions for a specific organism from the KEGG database <http://www.genome.jp/kegg/> is available.
Full descriptive statistics, physical description of sediment,
            metric or phi sieves.
Flexible general-purpose toolbox implementing genetic algorithms (GAs) for stochastic optimisation. Binary, real-valued, and permutation representations are available to optimize a fitness function, i.e. a function provided by users depending on their objective function. Several genetic operators are available and can be combined to explore the best settings for the current task. Furthermore, users can define new genetic operators and easily evaluate their performances. Local search using general-purpose optimisation algorithms can be applied stochastically to exploit interesting regions. GAs can be run sequentially or in parallel, using an explicit master-slave parallelisation or a coarse-grain islands approach.
Scale abbreviation using Genetic Algorithms that maximally capture the variance in the original data.
Generalized subspace pattern mining in data arrays, using a genetic algorithm framework.
This package analyses complex ANOVA models with any
        combination of orthogonal/nested and fixed/random factors, as
        described by Underwood (1997). There are two restrictions: (i)
        data must be balanced; (ii) fixed nested factors are not
        allowed. Homogeneity of variances is checked using Cochran's C
        test and 'a posteriori' comparisons of means are done using
        Student-Newman-Keuls (SNK) procedure.
Sparse large Directed Acyclic Graphs learning with a combination of a convex program and a tailored genetic algorithm (see Champion et al. (2017) <https://hal.archives-ouvertes.fr/hal-01172745v2/document>).
In this package we consider Gaussian Diffusion processes and smooth thresholds. After evaluating the mean of the process to check the subthreshold regimen hypothesis, the FPT density function is reconstructed via the numerical quadrature of the integral equation in (Buonocore 1987); first passage times are also generated by the method in (Buonocore 2014) and results are compared. The timestep of the simulations can iteratively be refined. User should provide the functional form for the drift and the infinitesimal variance in the script 'userfunc.R' and for the threshold in the script 'userthresh.R'. All the parameters required by the implementation are to be set in the script 'userparam.R'. Example scripts for common drifts and thresholds are given.
Manipulate, assemble, export <http://www.gadm.org> shapefiles. Create choropleth, heatmaps, dots plot, proportional dots and more.
A group of sample points are evaluated against a
        user-defined expression, the sample points are lists of
        parameters with values that may be substituted into that
        expression. The genetic algorithm attempts to make the result
        of the expression as low as possible (usually this would be the
        sum of residuals squared).
Tools for using genetic markers, stable isotope data, and habitat
    suitability data to calculate posterior probabilities of breeding origin of
    migrating birds.
Constructs gains tables and lift charts for prediction algorithms. Gains tables and lift charts are commonly used in direct marketing applications.  The method is described in Drozdenko and Drake (2002), "Optimal Database Marketing", Chapter 11.
GAIPE implements graphical representation of accuracy in
        parameter estimation (AIPE) on RMSEA for sample size planning
        in structural equation modeling. Sample sizes suggested by
        RMSEA with AIPE method and power analysis approach can also be
        obtained separately using the provided functions.
Includes the ga.lts() function that estimates
        LTS (Least Trimmed Squares) parameters using genetic algorithms
        and C-steps. ga.lts() constructs a genetic algorithm to form a
        basic subset and iterates C-steps as defined in Rousseeuw and
        van-Driessen (2006) to calculate the cost value of the LTS
        criterion. OLS (Ordinary Least Squares) regression is known to
        be sensitive to outliers. A single outlying observation can
        change the values of estimated parameters. LTS is a resistant
        estimator even the number of outliers is up to half of the
        data. This package is for estimating the LTS parameters with
        lower bias and variance in a reasonable time. Version >=1.3 
        includes the function medmad for fast outlier detection in
        linear regression.
Functions for fitting and working with generalized
		additive models, as described in chapter 7 of "Statistical Models in
		S" (Chambers and Hastie (eds), 1991), and "Generalized Additive
		Models" (Hastie and Tibshirani, 1990).
Data sets and scripts used in the book "Generalized
        Additive Models: An Introduction with R", Wood (2006) CRC.
Fits unimodal and multimodal gambin distributions to species-abundance distributions
    from ecological data, as in in Matthews et al. (2014) <DOI:10.1111/ecog.00861>. 
    'gambin' is short for 'gamma-binomial'. The main function is fit_abundances(), which estimates 
    the 'alpha' parameter(s) of the gambin distribution using maximum likelihood. Functions are 
    also provided to generate the gambin distribution and for calculating likelihood statistics.
This package provides routines for fitting generalized
        linear and and generalized additive models by likelihood based
        boosting, using penalized B-splines
Boosting models for fitting generalized additive models for
  location, shape and scale ('GAMLSS') to potentially high dimensional
  data.
Provides features to use function gamboost() from  package mboost for estimation of multistate models
Functions and data are provided that support a course that emphasizes statistical
  issues of inference and generalizability.  Attention is restricted to a relatively small
  number of methods, often (misleadingly in my view) referred to as algorithms.
Implementation of various inference and simulation tools to
    apply generalized additive models to bivariate dependence structures and
    non-simplified vine copulas.
Implements the GAMbag, GAMrsm and GAMens ensemble
    classifiers for binary classification (De Bock et al., 2010) <doi:10.1016/j.csda.2009.12.013>. The ensembles
    implement Bagging (Breiman, 1996) <doi:10.1023/A:1010933404324>, the Random Subspace Method (Ho, 1998) <doi:10.1109/34.709601>
    , or both, and use Hastie and Tibshirani's (1990, ISBN:978-0412343902) generalized additive models (GAMs)
    as base classifiers. Once an ensemble classifier has been trained, it can
    be used for predictions on new data. A function for cross validation is also
    included.
Provides estimation and analysis functions for
    strategic statistical models.
Finds adaptive strategies for sequential symmetric 
    games using a genetic algorithm. Currently, any symmetric two by two matrix
    is allowed, and strategies can remember the history of an opponent's play
    from the previous three rounds of moves in iterated interactions between
    players. The genetic algorithm returns a list of adaptive strategies given
    payoffs, and the mean fitness of strategies in each generation.
Implementation of a common set of punctual solutions  for Cooperative Game Theory.
Many situations can be modeled as game theoretic situations. Some procedures are included in this package to calculate the most important allocations rules in Game Theory: Shapley value, Owen value or nucleolus, among other. First, we must define as an argument the value of the unions of the envolved agents with the characteristic function.
The gamma lasso algorithm provides regularization paths corresponding to a range of non-convex cost functions between L0 and L1 norms.  As much as possible, usage for this package is analogous to that for the glmnet package (which does the same thing for penalization between L1 and L2 norms).  For details see: Taddy (2017 JCGS), One-Step Estimator Paths for Concave Regularization, <arXiv:1308.5623>.
Functions for fitting the Generalized Additive Models for Location Scale and Shape introduced by Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The models use a distributional regression approach where all the parameters of the conditional distribution of the response variable are modelled using explanatory variables.
Interface for extra smooth functions including tensor products, neural networks and decision trees.
This is an add-on package to GAMLSS. The purpose of this
        package is to allow users to fit interval response variables in
        GAMLSS models. The main function gen.cens() generates a
        censored version of an existing GAMLSS family distribution.
Data used as examples to demonstrate GAMLSS models.
Demos for smoothing and gamlss.family distributions.
A set of distributions  which can be used  for modelling the response variables in Generalized Additive Models for Location Scale and Shape, Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>.  The distributions can be continuous, discrete or mixed  distributions. Extra distributions can be created, by transforming, any continuous distribution defined on the real line,  to  a distribution defined on ranges 0 to infinity  or  0 to 1, by using a "log" or a "logit" transformation respectively.
The main purpose of this package is to allow fitting of
        mixture distributions with GAMLSS models.
This is an add on package to GAMLSS. It allows one extra
        method for fitting GAMLSS models. The main function nlgamlss()
        can fit any parametric (up to four parameter) GAMLSS
        distribution.
It allows us to fit Gaussian Markov Random Field within the
    Generalized Additive Models for Location Scale and Shape algorithms.
This is an add on package to GAMLSS. The purpose of this
        package is to allow users to defined truncated distributions in
        GAMLSS models. The main function gen.trun() generates truncated
        version of an existing GAMLSS family distribution.
Extra functions for GAMLSS and others models.
Density, distribution function, quantile function and random generation for the bimodal skew symmetric normal distribution of Hassan and El-Bassiouni (2016) <doi:10.1080/03610926.2014.882950>.
Estimate generalized additive mixed models via a version of
         function gamm() from 'mgcv', using 'lme4' for estimation.
To compare nonlinear curves and surface estimations between groups using semiparametric methods for cross-sectional and longitudinal dataset.
This package performs gamma regression, where both mean and shape parameters follows lineal regression structures.
Uses a slice sampling-based Markov chain Monte Carlo to
        conduct Bayesian fitting and inference for generalized additive
        mixed models.  Generalized linear mixed models and generalized 
        additive models are also handled as special cases of generalized
        additive mixed models. The methodology and software is described 
        in Pham, T.H. and Wand, M.P. (2018). Australian and New Zealand
        Journal of Statistics (in press).
Robust regression via gamma-divergence with L1, elastic net and ridge.
To calculate the relative risk (RR) for the generalized additive model.
Using overlap grouped-lasso penalties, 'gamsel' selects whether a term in a 'gam' is nonzero, linear, or a non-linear spline (up to a specified max df per variable). It fits the entire regularization path on a grid of values for the overall penalty lambda, both for gaussian and binomial families.
This package implements a network-based gene weighting
        algorithm for pathways, as well as a gene-weighted gene set
        analysis approach for microarray data pathway analysis.
This is a dataset package for GANPA, which implements a
        network-based gene weighting approach to pathway analysis. This
        package includes data useful for GANPA, such as a functional
        association network, pathways, an expression dataset and
        multi-subunit proteins.
Performs a Genetic Algorithm Optimization, given a
        real-based or permutation-based function and the associated
        search space.
It is designed as an integrated package for genetic data
        analysis of both population and family data. Currently, it
        contains functions for sample size calculations of both
        population-based and family-based designs, probability of
        familial disease aggregation, kinship calculation, statistics
        in linkage analysis, and association analysis involving genetic
        markers including haplotype analysis with or without environmental
        covariates.
Datasets associated with the 'gap' package. Currently,
             it includes an example data for regional association
             plot (CDKN), Manhattan plots with (hr1420, mhtdata) and
             without (w4) gene annotations.
Methodology that combines feature selection, model tuning, and parsimonious model selection with Genetic Algorithms (GA) proposed in {Martinez-de-Pison} (2015) <DOI:10.1016/j.asoc.2015.06.012>. To this objective, a novel GA selection procedure is introduced based on separate cost and complexity evaluations.
Tools to fill missing values in satellite data and to develop new
    gap-fill algorithms. The methods are tailored to data (images) observed
    at equally-spaced points in time. The package is illustrated with MODIS
    NDVI data.
The gap encodes the distance between clusters and improves
    interpretation of cluster heatmaps. The gaps can be of the same
    distance based on a height threshold to cut the dendrogram. Another
    option is to vary the size of gaps based on the distance between
    clusters.
An excerpt of the data available at Gapminder.org. For each of 142
    countries, the package provides values for life expectancy, GDP per capita,
    and population, every five years, from 1952 to 2007.
The functions included are used to obtain initial authentication with Google Analytics as well as simple and organized data retrieval from the API. Allows for retrieval from multiple profiles at once.
Simulate, estimate and forecast using univariate and multivariate GAS models 
  as described in Ardia et al. (2016) <https://ssrn.com/abstract=2825380>.
Provides a genetic algorithm for finding variable
    subsets in high dimensional data with high prediction performance. The
    genetic algorithm can use ordinary least squares (OLS) regression models or
    partial least squares (PLS) regression models to evaluate the prediction
    power of variable subsets. By supporting different cross-validation
    schemes, the user can fine-tune the tradeoff between speed and quality of
    the solution.
Functions for greenhouse gas flux calculation from chamber
    measurements.
Fits gastric emptying time series from MRI or scintigraphic measurements
   using nonlinear mixed-model population fits with 'nlme' and Bayesian methods with 
   Stan; computes derived parameters such as t50 and AUC.
Manipulation of genetic data (SNPs). Computation of GRM and dominance matrix, LD, heritability with efficient algorithms for linear mixed model (AIREML) .
Allows user to choose/gate a region on the plot and returns points
    within it.
Fits a Gaussian process model to data. Gaussian processes
 are commonly used in computer experiments to fit an interpolating model.
 The model is stored as an 'R6' object and can be easily updated with new 
 data. There are options to run in parallel (not for Windows), and 'Rcpp'
 has been used to speed up calculations. Other R packages that perform
 similar calculations include 'laGP', 'DiceKriging', 'GPfit', and 'mlegp'.
A collection difference measures for multivariate Gaussian
        probability density functions, such as the Euclidea mean, the
        Mahalanobis distance, the Kullback-Leibler divergence, the
        J-Coefficient, the Minkowski L2-distance, the Chi-square
        divergence and the Hellinger Coefficient.
Display a random fact about Carl Friedrich Gauss
 based the on collection curated by Mike Cavers via the
 <http://gaussfacts.com> site.
A collection of functions to perform Gaussian quadrature
        with different weight functions corresponding to the orthogonal
        polynomials in package orthopolynom.  Examples verify the
        orthogonality and inner products of the polynomials.
Eye-tracking data must be transformed into fixations and saccades before it can be analyzed. This package provides a non-parametric speed-based approach to do this on a trial basis. The method is especially useful when there are large differences in data quality, as the thresholds are adjusted accordingly. The same pre-processing procedure can be applied to all participants, while accounting for individual differences in data quality.
A collection of algorithms and functions 
  for fitting data to a generalized lambda distribution 
  via moment matching methods, and generalized 
  bootstrapping.
Package GB2 explores the Generalized Beta distribution of the second kind. Density, cumulative distribution function, quantiles and moments of the distributions are given. Functions for the full log-likelihood, the profile log-likelihood and the scores are provided. Formulas for various indicators of inequality and poverty under the GB2 are implemented. The GB2 is fitted by the methods of maximum pseudo-likelihood estimation using the full and profile log-likelihood, and non-linear least squares estimation of the model parameters. Various plots for the visualization and analysis of the results are provided. Variance estimation of the parameters is provided for the method of maximum pseudo-likelihood estimation. A mixture distribution based on the compounding property of the GB2 is presented (denoted as "compound" in the documentation). This mixture distribution is based on the discretization of the distribution of the underlying random scale parameter. The discretization can be left or right tail. Density, cumulative distribution function, moments and quantiles for the mixture distribution are provided. The compound mixture distribution is fitted using the method of maximum pseudo-likelihood estimation. The fit can also incorporate the use of auxiliary information. In this new version of the package, the mixture case is complemented with new functions for variance estimation by linearization and comparative density plots.
Supplies a set of functions to interface with General Bikeshare 
    Feed Specification .json feeds in R, allowing users to save and accumulate 
    tidy .rds datasets for specified cities/bikeshare programs.
Offers the Generalized Berk-Jones (GBJ) test for set-based inference in genetic
	association studies. The GBJ is designed as an alternative to tests such as Berk-Jones (BJ),
    Higher Criticism (HC), Generalized Higher Criticism (GHC), Minimum p-value (minP), and Sequence
    Kernel Association Test (SKAT). All of these other methods (except for SKAT) are also implemented 
    in this package, and we additionally provide an omnibus test (OMNI) which integrates information from each of the tests.
    The GBJ has been shown to outperform other tests in genetic association studies when signals
    are correlated and moderately sparse. Please see the vignette for a quickstart guide.
An implementation of extensions to Freund and
        Schapire's AdaBoost algorithm and Friedman's gradient boosting
        machine. Includes regression methods for least squares,
        absolute loss, t-distribution loss, quantile regression,
        logistic, multinomial logistic, Poisson, Cox proportional
        hazards partial likelihood, AdaBoost exponential loss,
        Huberized hinge loss, and Learning to Rank measures
        (LambdaMart).
Writes SAS code to get predicted values from every tree of a gbm.object.
Basic infrastructure and several algorithms for 1d-4d bin packing
    problem. This package provides a set of c-level classes and solvers for
    1d-4d bin packing problem, and an r-level solver for 4d bin packing problem,
    which is a wrapper over the c-level 4d bin packing problem solver.
    The 4d bin packing problem solver aims to solve bin packing problem, a.k.a
    container loading problem, with an additional constraint on weight.
    Given a set of rectangular-shaped items, and a set of rectangular-shaped bins
    with weight limit, the solver looks for an orthogonal packing solution
    such that minimizes the number of bins and maximize volume utilization.
    Each rectangular-shaped item i = 1, .. , n is characterized by length l_i,
    depth d_i, height h_i, and weight w_i, and each rectangular-shaped bin
    j = 1, .. , m is specified similarly by length l_j, depth d_j, height h_j,
    and weight limit w_j.
    The item can be rotated into any orthogonal direction, and no further
    restrictions implied.
Provides utilities for processing Rd objects and files.
        Extract argument descriptions and other parts of the help pages
        of functions.
Functions for inference of ploidy from (Genotyping-by-sequencing) GBS data, including a function to infer allelic ratios and allelic proportions in a Bayesian framework.
An implementation of hyperparameter optimization for Gradient
    Boosted Trees on binary classification and regression problems. The current
    version provides two optimization methods: Bayesian optimization and random
    search. Instead of giving the single best model, the final output is an 
    ensemble of Gradient Boosted Trees constructed via the method of ensemble 
    selection.
Simulate real and complex numbers from distributions of their
       magnitude and arguments. Optionally, the magnitudes and/or arguments may
       be fixed in almost arbitrary ways. Plot density and distribution
       functions with automatic selection of suitable regions.  Small programming
       utilities: check if an object is identical to NA, count positional
       arguments in a call, set intersection of more than two sets, check if an
       argument is unnamed.
Many inherited biases and effects exists in RNA-seq due to both biological and technical effects. We observed the biological variance of testing target transcripts can influence the yield of sequencing reads, which might indicate a resource competition existing in RNA-seq. We developed this package to capture the bias depending on local sequence and perform the correction of this type of bias by borrowing information from spike-in measurement.
Aligns peak based on peak retention times and matches homologous peaks
    across samples. The underlying alignment procedure comprises three sequential steps.
    (1) Full alignment of samples by linear transformation of retention times to 
    maximise similarity among homologous peaks (2) Partial alignment of peaks within 
    a user-defined retention time window to cluster homologous peaks (3) Merging rows
    that are likely representing homologous substances (i.e. no sample shows peaks in 
    both rows and the rows have similar retention time means). The algorithm is described in detail
    in Ottensmann et al. submitted <doi:10.1101/110494>.
These are two-sample tests for categorical data utilizing similarity information among the categories.  They are useful when there is underlying structure on the categories.
'GPU'/CPU Benchmarking on Debian-package based systems
 This package benchmarks performance of a few standard linear algebra
 operations (such as a matrix product and QR, SVD and LU decompositions)
 across a number of different 'BLAS' libraries as well as a 'GPU' implementation.
 To do so, it takes advantage of the ability to 'plug and play' different
 'BLAS' implementations easily on a Debian and/or Ubuntu system.  The current
 version supports
  - 'Reference BLAS' ('refblas') which are un-accelerated as a baseline
  - Atlas which are tuned but typically configure single-threaded
  - Atlas39 which are tuned and configured for multi-threaded mode
  - 'Goto Blas' which are accelerated and multi-threaded
  - 'Intel MKL' which is a commercial accelerated and multithreaded version.
 As for 'GPU' computing, we use the CRAN package
  - 'gputools'
 For 'Goto Blas', the 'gotoblas2-helper' script from the ISM in Tokyo can be
 used. For 'Intel MKL' we use the Revolution R packages from Ubuntu 9.10.
Contains the Global Charcoal database data. Data include charcoal
    series (age, depth, charcoal quantity, associated units and methods) and
    information on sedimentary sites (localisation, depositional environment, biome,
    etc.) as well as publications informations. Since 4.0.0 the GCD mirrors the online SQL database at <http://paleofire.org>.
A generalized coordinate descent (GCD) algorithm for computing the solution path of the hybrid Huberized support vector machine (HHSVM) and its generalization, including the LASSO and elastic net (adaptive) penalized least squares, logistic regression, HHSVM, squared hinge loss SVM and expectile regression.
Generalized competing event model based on Cox PH model and Fine-Gray model.
    This function is designed to develop optimized risk-stratification methods for competing
    risks data, such as described in:
    1. Carmona R, Gulaya S, Murphy JD, Rose BS, Wu J, Noticewala S,McHale MT, Yashar CM, Vaida F,
    and Mell LK (2014) <DOI:10.1016/j.ijrobp.2014.03.047>.
    2. Carmona R, Zakeri K, Green G, Hwang L, Gulaya S, Xu B, Verma R, Williamson CW, Triplett DP, Rose
    BS, Shen H, Vaida F, Murphy JD, and Mell LK (2016) <DOI:10.1200/JCO.2015.65.0739>.
    3. Lunn, Mary, and Don McNeil (1995) <DOI:10.2307/2532940>.
Scrapes Google Citation pages and creates data frames of 
    citations over time.
Provides a variety of functions to analyze and model
    geostatistical count data with Gaussian copulas, including
 1) data simulation and visualization; 
 2) correlation structure assessment (here also known as the Normal To Anything); 
 3) calculate multivariate normal rectangle probabilities; 
 4) likelihood inference and parallel prediction at predictive locations.
Orders panels in scatterplot matrices and parallel
        coordinate displays by some merit index. Package contains
        various indices of merit, ordering functions, and enhanced
        versions of pairs and parcoord which color panels according to
        their merit level.
Likelihood inference in Gaussian copula marginal
        regression models.
Estimates a sparse inverse covariance matrix from a convex
    pseudo-likelihood function with L1 penalty
This package contains data sets used in the book "R
        Graphics Cookbook" by Winston Chang, published by O'Reilly
        Media.
Analyze the default risk of credit portfolios. Commonly known models, 
		like CreditRisk+ or the CreditMetrics model are implemented in their very basic settings.
		The portfolio loss distribution can be achieved either by simulation or analytically 
		in case of the classic CreditRisk+ model. Models are only implemented to respect losses
		caused by defaults, i.e. migration risk is not included. The package structure is kept
		flexible especially with respect to distributional assumptions in order to quantify the
		sensitivity of risk figures with respect to several assumptions. Therefore the package
		can be used to determine the credit risk of a given portfolio as well as to quantify
		model sensitivities.
Geographical detectors for measuring spatial stratified heterogeneity,
             as described in Jinfeng Wang (2010) <doi:10.1080/13658810802443457> and 
             Jinfeng Wang (2016) <doi:10.1016/j.ecolind.2016.02.052>. Includes the
             optimal discretization of continuous data, four primary functions of 
             geographical detectors, comparison of size effects of spatial unit and 
             the visualizations of results. The descriptions of the package, methods
             and case datasets refer to the citation information below.
Datasets used in the book 'Graphical Data Analysis with R' (Antony Unwin, CRC Press 2015).
Wrappers for the Geospatial Data Abstraction Library (GDAL)
    Utilities.
Various R programming tools for data manipulation, including:
   - medical unit conversions ('ConvertMedUnits', 'MedUnits'),
   - combining objects ('bindData', 'cbindX', 'combine', 'interleave'),
   - character vector operations ('centerText', 'startsWith', 'trim'),
   - factor manipulation ('levels', 'reorder.factor', 'mapLevels'),
   - obtaining information about R objects ('object.size', 'elem', 'env',
     'humanReadable', 'is.what', 'll', 'keep', 'ls.funs',
     'Args','nPairs', 'nobs'),
   - manipulating MS-Excel formatted files ('read.xls',
     'installXLSXsupport', 'sheetCount', 'xlsFormats'),
   - generating fixed-width format files ('write.fwf'),
   - extricating components of date & time objects ('getYear', 'getMonth',
     'getDay', 'getHour', 'getMin', 'getSec'),
   - operations on columns of data frames  ('matchcols', 'rename.vars'),
   - matrix operations ('unmatrix', 'upperTriangle', 'lowerTriangle'),
   - operations on vectors ('case', 'unknownToNA', 'duplicated2', 'trimSum'),
   - operations on data frames ('frameApply', 'wideByFactor'),
   - value of last evaluated expression ('ans'), and
   - wrapper for 'sample' that ensures consistent behavior for both
     scalar and vector arguments ('resample').
Contains functions for 'specific' MCA (Multiple Correspondence Analysis), 
	'class specific' MCA, computing and plotting structuring factors and concentration ellipses, 
	Multiple Factor Analysis, 'standardized' MCA, inductive tests and others tools for Geometric Data Analysis. It also provides functions
	for the translation of logit models coefficients into percentages, weighted contingency tables and an association 
        measure - i.e. Percentages of Maximum Deviation from Independence (PEM).
The GDELT data set is over 60 GB now and growing 100 MB a month.
    The number of source articles has increased over time and unevenly across
    countries. This package makes it easy to download a subset of that data,
    then normalize that data to facilitate valid time series analysis.
Diffusion anisotropy has been used to characterize
 white matter neuronal pathways in the human brain, and infer global
 connectivity in the central nervous system. The package implements
 algorithms to estimate and visualize the orientation of neuronal
 pathways in model-free methods (q-space imaging methods).
 For estimating fibre orientations two methods have been
 implemented.  One method implements fibre orientation detection
 through local maxima extraction. A second more robust method
 is based on directional statistical clustering of ODF voxel data.
 Fibre orientations in multiple fibre voxels are estimated using
 a mixture of von Mises-Fisher (vMF) distributions. This statistical
 estimation procedure is used to resolve crossing fibre
 configurations.
 Reconstruction of orientation distribution function (ODF)
 profiles may be performed using the standard generalized
 q-sampling imaging (GQI) approach, Garyfallidis' GQI (GQI2)
 approach, or Aganj's variant of the Q-ball imaging (CSA-QBI)
 approach. Procedures for the visualization of RGB-maps,
 line-maps and glyph-maps of real diffusion magnetic resonance
 imaging (dMRI) data-sets are included in the package.
A set of psychometric tools for cognitive diagnosis modeling for both dichotomous and polytomous responses. Various cognitive diagnosis models can be estimated, include the generalized deterministic inputs, noisy and gate (G-DINA) model by de la Torre (2011) <DOI:10.1007/s11336-011-9207-7>, the sequential G-DINA model by Ma and de la Torre (2016) <DOI:10.1111/bmsp.12070>, and many other models they subsume. Joint attribute distribution can be independent, saturated, higher-order, loglinear smoothed or structured. Q-matrix validation, item and model fit statistics, model comparison at test and item level and differential item functioning can also be conducted. A graphical user interface is also provided.
Calculate distances and routes on geographic grids.
Manage and analyze high-dimensional SNP data from chips with multiple densities.
To address the problem of insecurity of UDP-based DNS requests,
    Google Public DNS offers DNS resolution over an encrypted HTTPS
    connection. DNS-over-HTTPS greatly enhances privacy and security
    between a client and a recursive resolver, and complements DNSSEC
    to provide end-to-end authenticated DNS lookups. Functions that enable
    querying individual requests that bulk requests that return detailed
    responses and bulk requests are both provided. Support for reverse
    lookups is also provided. See <https://developers.google.com/speed/public-dns/docs/dns-over-https> 
    for more information.
Functions to compute the Generalized Dynamic Principal Components
    introduced in Peña and Yohai (2016) <DOI:10.1080/01621459.2015.1072542>.
Contains a function called gds() which accepts three input
    parameters like lower limits, upper limits and the frequencies of the
    corresponding classes. The gds() function calculate and return the values
    of mean ('gmean'), median ('gmedian'), mode ('gmode'), variance ('gvar'), standard
    deviation ('gstdev'), coefficient of variance ('gcv'), quartiles ('gq1', 'gq2', 'gq3'),
    inter-quartile range ('gIQR'), skewness ('g1'), and kurtosis ('g2') which facilitate
    effective data analysis. For skewness and kurtosis calculations we use moments.
Useful tools for writing vector graphics devices.
Implements common geostatistical methods in a clean,
    straightforward, efficient manner. A quasi reboot of the SpatialTools R package.
Geometrically Designed Spline ('GeDS') Regression is a non-parametric geometrically 
  motivated method for fitting variable knots spline predictor models in one or two independent 
  variables, in the context of generalized (non-)linear models. 'GeDS' estimates the number and 
  position of the knots and the order of the spline, assuming the response variable has a 
  distribution from the exponential family. A description of the method can be found in
  Kaishev et al. (2016) <doi:10.1007/s00180-015-0621-7> and Dimitrova et al. (2017) <https://openaccess.city.ac.uk/18460>.
Fit joint mean-covariance models for longitudinal data within the 
    framework of (weighted) generalised estimating equations (GEE/WGEE). The 
    models and their components are represented using S4 classes and methods. 
    The core computational algorithms are implemented using the 'Armadillo' C++ 
    library for numerical linear algebra and 'RcppArmadillo' glue.
Provides adaptive association tests for SNP level, gene level and pathway level analyses.
Features the marginal parametric and semi-parametric proportional hazards mixture cure models for analyzing clustered survival data with a possible cure fraction. A reference is Yi Niu and Yingwei Peng (2014) <doi:10.1016/j.jmva.2013.09.003>.
GEE estimation of the parameters in mean structures with possible
    correlation between the outcomes. User-specified mean link and variance
    functions are allowed, along with observation weighting. The "M" in the name
    "geeM" is meant to emphasize the use of the Matrix package, which allows for an
    implementation based fully in R.
Causal mediation analysis for a single exposure/treatment and a
    single mediator, both allowed to be either continuous or binary. The package
    implements the difference method and provide point and interval estimates as
    well as testing for the natural direct and indirect effects and the mediation
    proportion.
Generalized estimating equations solver for parameters in
        mean, scale, and correlation structures, through mean link,
        scale link, and correlation link. Can also handle clustered
        categorical responses.
Generalized estimating equations with the original sandwich variance estimator proposed by Liang and Zeger (1986), and eight types of more recent modified variance estimators for improving the finite small-sample performance.
Provides a general, flexible framework for estimating parameters
    and empirical sandwich variance estimator from a set of unbiased estimating
    equations (i.e., M-estimation in the vein of Stefanski & Boos (2002)
    <doi:10.1198/000313002753631330>). Also provides an API to compute finite-sample
    variance corrections.
Functions to compute generalized eigenvalues and eigenvectors,
             the generalized Schur decomposition and
             the generalized Singular Value Decomposition of a matrix pair,
             using Lapack routines.
Methods for fitting macroevolutionary models to phylogenetic trees.
First major functionality is to compute the bias in misspecified linear gene-environment interaction models. The most 
	generalized function for this objective is GE_bias().  However GE_bias() requires specification of many
	higher order moments of covariates in the model.  If users are unsure about how to calculate/estimate
	these higher order moments, it may be easier to use GE_bias_normal_squaredmis().  This function places
	many more assumptions on the covariates (most notably that they are all jointly generated from a multivariate
	normal distribution) and is thus able to automatically calculate many of the higher order moments automatically,
	necessitating only that the user specify some covariances.  There are also functions to solve for the bias 
	through simulation and non-linear equation solvers, these can be used to check your work. Second major functionality
	is to implement the Bootstrap Inference with Correct Sandwich (BICS) testing procedure, which we have found to provide better finite-sample
	performance than other inference procedures for testing GxE interaction.  More details on these functions 
	are available in Sun, Carroll, Christiani, and Lin, "Testing for Gene-Environment Interaction Under Exposure Misspecification"
	(In Revision).
Implements several extensions of the elastic net regularization
    scheme. These extensions include individual feature penalties for the L1 term,
    feature-feature penalties for the L2 term, as well as translation coefficients
    for the latter.
Reads data files from the 'Gem infrasound logger' for analysis and converts to segy format (which is convenient for reading with traditional seismic analysis software). The Gem infrasound logger is an in-development low-cost, lightweight, low-power instrument for recording infrasound in field campaigns; email the maintainer for more information.
Simulate and analyze multistate models with general hazard
    functions. gems provides functionality for the preparation of hazard functions
    and parameters, simulation from a general multistate model and predicting future
    events. The multistate model is not required to be a Markov model and may take
    the history of previous events into account. In the basic version, it allows
    to simulate from transition-specific hazard function, whose parameters are
    multivariable normally distributed.
Network meta-analyses (mixed treatment comparisons) in the Bayesian
    framework using JAGS. Includes methods to assess heterogeneity and
    inconsistency, and a number of standard visualizations.
a package for genome-wide association analysis between 
             quantitative or binary traits and single-nucleotide
             polymorphisms (SNPs).
GenABEL.data package consists of a data set used by GenABEL functions
R based genetic algorithm for binary and floating point
        chromosomes.
Defines classes and methods that can be used
  to implement genetic algorithms for feature selection.  The idea is
  that we want to select a fixed number of features to combine into a
  linear classifier that can predict a binary outcome, and can use a
  genetic algorithm heuristically to select an optimal set of features.
genasis package contains methods for air pollution assessment. Concerned on persistent organic pollutants, the package allows to compute trends of their concentrations, compare different datasets and estimate relations between values from active and passive air samplers.
Provides the necessary functions to identify and extract a selection of already available barcode constructs (Cornils, K. et al. (2014) <doi:10.1093/nar/gku081>) and freely choosable barcode designs from next generation sequence (NGS) data. Furthermore, it offers the possibility to account for sequence errors, the calculation of barcode similarities and provides a variety of visualisation tools (Thielecke, L. et al. (2017) <doi:10.1038/srep43249>).
A set of functions to generate and format results from statistical
    analyses of a wide range of high throughput experiments that can then 
    be uploaded into the 'BART' (Bio-Statistical Analysis Reporting Tool) 'shiny' 
    app <https://github.com/jcardenas14/BART>. The app provides users with tools 
    to visualize and efficiently sift through large amounts of data and results.
Density, distribution function, quantile function and random generation for the Generalized Binomial Distribution. Functions to compute the Clopper-Pearson Confidence Interval and the required sample size. Enhanced model for burn-in studies, where failures are tackled by countermeasures.
Engines for cross-validation of many types of regression and class prediction models are provided. These engines include built-in support for 'glmnet', 'lars', 'plus', 'MASS', 'rpart', 'C50' and 'randomforest'. It is easy for the user to add other regression or classification algorithms. The 'parallel' package is used to improve speed. Several data generation algorithms for problems in regression and classification are provided.
Set of functions to create datasets using a correlation matrix.
Encodes gender based on names and dates of birth using historical
    datasets. By using these datasets instead of lists of male and female names,
    this package is able to more accurately guess the gender of a name, and it
    is able to report the probability that a name was male or female.
A method to predict and report gender from Brazilian first names
    using the Brazilian Institute of Geography and Statistics' Census data.
Utilizes the 'genderize.io' Application Programming Interface 
    to predict gender from first names extracted from a text vector. 
    The accuracy of prediction could be controlled by two parameters: 
    counts of a first name in the database and probability of prediction.
Computes the probability density function (pdf), cumulative distribution function (cdf), quantile function (qf) and generates random values (rg) for the following general models : mixture models, composite models, folded models, skewed symmetric models and arc tan models.
Segmentation and classification procedures for data from the 'Activinsights GENEActiv' <https://www.activinsights.com/products/geneactiv/> accelerometer that provides the user with a model to guess behaviour from test data where behaviour is missing.
    Includes a step counting algorithm, a function to create segmented data with custom features and a function to use recursive partitioning provided in the function rpart() of the 'rpart' package to create classification models.
Functions and analytics for GENEA-compatible accelerometer
        data into R objects. See topic 'GENEAread' for an introduction
        to the package.
Creates visualisations in two and three dimensions of simulated
    data based on detected segments or raw accelerometer data.
Functions for clustering time-course gene expression and reconstructing of gene regulatory network based on Dynamic Bayesian Network.
The GeneCycle package implements the approaches of Wichert
        et al. (2004), Ahdesmaki et al. (2005) and Ahdesmaki et al.
        (2007) for detecting periodically expressed genes from gene
        expression time series data.
This package implements several generalized F-statistics.
        The current version includes a generalized F-statistic based on
        the flexible isotonic/monotonic regression or order restricted
        hypothesis testing.
"geneListPie" package is for mapping a gene list to
        function categories defined in GOSlim or Kegg. The results can
        be plotted as a pie chart to provide a quick view of the genes
        distribution of the gene list among the function categories.
        The gene list must contain a list of gene symbols. The package
        contains a set of pre-processed gene sets obtained from Gene
        Ontology and MSigDB including human, mouse, rat and yeast. To
        provide a high level concise view, only GO slim and kegg are
        provided. The gene sets are regulared updated. User can also
        use customized gene sets. User can use the R Pie() or Pie3D()
        function for plotting the pie chart. Users can also choose to
        output the gene function mapping results and use external
        software such as Excel(R) for ploting.
Using simple input, this package creates plots of gene models. Users can create plots of alternatively spliced gene variants and the positions of mutations and other gene features.
Analyzes gene expression
  (time series) data with focus on the inference of gene networks.
  In particular, GeneNet implements the methods of Schaefer and 
  Strimmer (2005a,b,c) and Opgen-Rhein and Strimmer (2006, 2007)
  for learning large-scale gene association networks (including
  assignment of putative directions).
Belief propagation methods in genotype-phenotype networks (Conditional Gaussian and Discrete Bayesian Networks) to propagate phenotypic evidence through the network.
Functions for Genetic Epi Methods Developed at MSKCC
Makes the Genepop software available in R. This software implements a mixture of traditional population genetic methods and some more focused developments: it computes exact tests for Hardy-Weinberg equilibrium, for population differentiation and for genotypic disequilibrium among pairs of loci; it computes estimates of F-statistics, null allele frequencies, allele size-based statistics for microsatellites, etc.; and it performs analyses of isolation by distance from pairwise comparisons of individuals or population samples.
Since causal paths from data are important for all sciences, the
     package provides sophisticated functions. The idea is simply 
     that if X causes Y (path: X to Y) then non-deterministic variation in X
     is more "original or independent" than similar variation in Y.  We compare 
     two flipped kernel regressions: X=f(Y, Z) and Y=g(X,Z), where Z are control 
     variables. Our first two criteria compare absolute cross products
     of regressor values and residuals (Cr1) and 
     absolute residuals (Cr2), are both quantified by stochastic dominance of four 
     orders (SD1 to SD4). Our third criterion (Cr3) expects X to be better able 
     to predict Y than the other way around using generalized partial correlation 
     If |r*(x|y)|> |r*(y|x)| it suggests that y is more likely
    the "kernel cause" of x. The usual partial correlations are generalized with 
    a new non-symmetric matrix developed here.
    Partial correlations help asses effect of x on y after removing the effect of a
    set of variables. 
    The package provides additional tools for causal assessment,
    for printing the causal directions in a clear, comprehensive compact summary form,
    for matrix algebra, for "outlier detection", and for numerical integration by the
    trapezoidal rule, stochastic dominance, etc. 
    The package has functions for bootstrap-based statistical inference and one 
    for a heuristic t-test.
Functions to assess the goodness of fit of binary, multinomial and ordinal logistic models.
	Included are the Hosmer-Lemeshow tests (binary, multinomial and ordinal) and the Lipsitz and 
	Pulkstenis-Robinson tests (ordinal).
Functions for the hyperbolic and related distributions.
  Density, distribution and quantile functions and random number generation
  are provided for the hyperbolic distribution, the generalized hyperbolic
        distribution, the generalized inverse Gaussian distribution and
        the skew-Laplace distribution. Additional functionality is
        provided for the hyperbolic distribution, normal inverse
	Gaussian distribution and generalized inverse Gaussian distribution,
	including fitting of these distributions to data. Linear models with
        hyperbolic errors may be fitted using hyperblmFit.
Projections are common dimensionality reduction methods, which represent high-dimensional data in a two-dimensional space. However, when restricting the output space to two dimensions, which results in a two dimensional scatter plot (projection) of the data, low dimensional similarities do not represent high dimensional distances coercively [Thrun, 2018]. This could lead to a misleading interpretation of the underlying structures [Thrun, 2018]. By means of the 3D topographic map the generalized Umatrix is able to depict errors of these two-dimensional scatter plots. The package is based on the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9>.
Perform the Blinder-Oaxaca decomposition for generalized linear 
   model with bootstrapped standard errors. The twofold and threefold 
   decomposition are given, even the generalized linear model output in each group.
Allows users to quickly and easily generate fake data containing
    Personally Identifiable Information (PII) through convenience functions.
GeneReg is an R package for inferring time delay gene
        regulatory network using time course gene expression profiles.
        The main idea of time delay linear model is to fit a linear
        regression model using a set of putative regulators to estimate
        the transcription pattern of a specific target gene.
A tool for finding an ensemble gene-signature by a steepest ascending algorithm partially supervised by survival time data.
Genome-wide association study (GWAS) performed with SLOPE,
    short for Sorted L-One Penalized Estimation, a
    method for estimating the vector of coefficients in linear model.
    In the first step of GWAS, SNPs are clumped according to their correlations and
    distances. Then, SLOPE is performed on data where each clump has
    one representative.
Classes and methods for handling genetic data. Includes
        classes to represent genotypes and haplotypes at single markers
        up to multiple markers on multiple chromosomes. Function
        include allele frequencies, flagging homo/heterozygotes,
        flagging carriers of certain alleles, estimating and testing
        for Hardy-Weinberg disequilibrium, estimating and testing for
        linkage disequilibrium, ...
Finds subsets of sets of genotypes with a high Heterozygosity, and Mean of Transformed Kinships (MTK), measures that can indicate a subset would be beneficial for rare-trait discovery and genome-wide association scanning, respectively.
Implementation of spatial graph-theoretic genetic gravity models.
    The model framework is applicable for other types of spatial flow questions.
    Includes functions for constructing spatial graphs, sampling and summarizing
    associated raster variables and building unconstrained and singly constrained
    gravity models.
Two methods based on the Forward Imputation approach are implemented for the imputation of quantitative missing data. One method alternates Nearest Neighbour Imputation and Principal Component Analysis (function 'ForImp.PCA'), the other uses Nearest Neighbour Imputation with the Mahalanobis distance (function 'ForImp.Mahala').
A new hierarchical clustering linkage criterion:
    the Genie algorithm links two clusters in such a way that a chosen
    economic inequity measure (e.g., the Gini index) of the cluster
    sizes does not increase drastically above a given threshold. Benchmarks
    indicate a high practical usefulness of the introduced method:
    it most often outperforms the Ward or average linkage in terms of
    the clustering quality while retaining the single linkage speed,
    see (Gagolewski et al. 2016a <DOI:10.1016/j.ins.2016.05.003>,
    2016b <DOI:10.1007/978-3-319-45656-0_16>)
    for more details.
Provides tools to interact nicely with the 'Genius' API <https://docs.genius.com/>. Search hosted content, extract different types of associated metadata and retrieve lyrics with ease.
This package computes the solution path for generalized lasso problems. Important use cases are the fused lasso over an arbitrary graph, and trend fitting of any given polynomial order. Specialized implementations for the latter two subproblems are given to improve stability and speed.
Genealogical data analysis including descriptive statistics (e.g., kinship and inbreeding coefficients) and gene-dropping simulations.
Provides basic distribution functions for a generalized logistic distribution proposed by Rathie and Swamee (2006) <https://www.rroij.com/open-access/on-new-generalized-logistic-distributions-and-applicationsbarreto-fhs-mota-jma-and-rathie-pn-.pdf>. It also has an interactive 'RStudio' plot for better guessing dynamically of initial values for ease of included optimization and simulating.
Implements the Mode Oriented Stochastic Search (MOSS) algorithm as well as a simple moving window approach to look for combinations of SNPs that are associated with a response.
This is a genMOSS package with additional datafile preprocessing functions. Performs genome-wide analysis of dense SNP array data using the mode oriented stochastic search (MOSS) algorithm in a case-control design. The MOSS algorithm is a Bayesian variable selection procedure that is applicable to GWAS data. It identifies combinations of the best predictive SNPs associated with the response. It also performs a hierarchical log-linear model search to identify the most relevant associations among the resulting subsets of SNPs. This package also includes preprocessing of the data from Plink format to the format required by the MOSS algorithm.
Using the DNA sequence and gene annotation files provided in 
            'ENSEMBL' <https://www.ensembl.org/index.html>, 
            the functions implemented in the package try to find the DNA sequences and 
            protein sequences of any given genomic loci, and to find the genomic coordinates 
            and protein sequences of any given protein locations, which are the frequent
            tasks in the analysis of genomic and proteomic data.
Evaluates likelihood ratio tests for alleged ancestry. Implements the methods of Tvedebrink et al (2018) <doi:10.1016/j.tpb.2017.12.004>.
Plot values of markers(SNPs, expression, genes, RNA,...) for all chromosomes.
Local structure in genomic data often induces dependence between observations taken at different genomic locations.  Ignoring this dependence leads to underestimation of the standard error of parameter estimates.  This package uses block bootstrapping to estimate asymptotically correct standard errors of parameters from any standard generalised linear model that may be fit by the glm() function.
Implements the genomic mating approach in the recently published article: Akdemir, D., & Sanchez, J. I. (2016). Efficient Breeding by Genomic Mating. Frontiers in Genetics, 7. <DOI:10.3389/fgene.2016.00210>.
Circular genomic permutation approach uses GWAS results to establish the significance of pathway/gene-set associations whilst accounting for genomic structure. All SNPs in the GWAS are placed in a 'circular genome' according to their location. Then the complete set of SNP association p-values are permuted by rotation with respect to the SNPs' genomic locations. Two testing frameworks are available: permutations at the gene level, and permutations at the SNP level. The permutation at the gene level uses fisher's combination test to calculate a single gene p-value, followed by the hypergeometric test. The SNP count methodology maps each SNP to pathways/gene-sets and calculates the proportion of SNPs for the real and the permutated datasets above a pre-defined threshold. Genomicper requires a matrix of GWAS association p-values. The SNPs annotation and pathways annotations can be performed within the package or provided by the user.
A loose collection of tools for the analysis of expression and genotype data, currently with the main focus on (e)QTL and MDR analysis.
Draws gene or genome maps and comparisons between these, in a 
   publication-grade manner. Starting from simple, common files, it will 
   draw postscript or PDF files that can be sent as such to journals.
A gaussian copula based procedure for generating samples from discrete random variables with prescribed correlation matrix and marginal distributions.
We implement a common genotyping workflow with a standardized software interface. 'genotypeR' designs genotyping markers from vcf files, outputs markers for multiplexing suitability on various platforms (Sequenom and Illumina GoldenGate), and provides various QA/QC and analysis functions. We developed this package to analyze data in Stevison LS, SA Sefick, CA Rushton, and RM Graze. 2017. Invited Review: Recombination rate plasticity: revealing mechanisms by design. Philosophical Transactions Royal Society London B Biol Sci 372:1-14. <DOI:10.1098/rstb.2016.0459>, and have published it here Sefick, S.A., M.A. Castronova, and L.S. Stevison. 2017. GENOTYPER: An integrated R packages for single nuleotide polymorphism genotype marker design and data analysis. Methods in Ecology and Evolution 9: 1318-1323. <DOI: 10.1111/2041-210X.12965>.
Provides a very interesting solution for
    handling segmentation variables in complex statistical methodology. It
    contains en extended version of the "Pathmox" algorithm (Lamberti, Sanchez
    and Aluja,(2016)<doi:10.1002/asmb.2168>) in the context of
    Partial Least Squares Path Modeling including the F-block
    test (to detect the responsible latent endogenous equations of the
    difference), the F-coefficient (to detect the path coefficients responsible
    of the difference) and the "invariance" test (to realize a comparison
    between the sub-models' latent variables). Furthermore, the package
    contains a generalized version of the "Pathmox" algorithm to approach
    different methodologies: linear regression and least absolute regression
    models.
The genridge package introduces generalizations of the standard univariate
	ridge trace plot used in ridge regression and related methods.  These graphical methods
	show both bias (actually, shrinkage) and precision, by plotting the covariance ellipsoids of the estimated
	coefficients, rather than just the estimates themselves.  2D and 3D plotting methods are provided,
	both in the space of the predictor variables and in the transformed space of the PCA/SVD of the
	predictors.
Performs search for global minimum of a very complex non-linear
    objective function with a very large number of optima.
Generalized ensemble methods allowing arbitrary underlying
        models to be used. Currently only bagging is supported.
Define and compute with generalized spherical distributions - multivariate probability
 laws that are specified by a star shaped contour (directional behavior) and a radial component.
Generation of survival data with one (binary)
        time-dependent covariate.  Generation of survival data arising
        from a progressive illness-death model.
The GenSVM classifier is a generalized multiclass support vector
	machine (SVM). This classifier aims to find decision boundaries that
	separate the classes with as wide a margin as possible. In GenSVM, the
	loss function is very flexible in the way that misclassifications are
	penalized.  This allows the user to tune the classifier to the dataset
	at hand and potentially obtain higher classification accuracy than
	alternative multiclass SVMs.  Moreover, this flexibility means that
	GenSVM has a number of other multiclass SVMs as special cases. One of
	the other advantages of GenSVM is that it is trained in the primal
	space, allowing the use of warm starts during optimization.  This
	means that for common tasks such as cross validation or repeated model
	fitting, GenSVM can be trained very quickly. Based on: G.J.J. van den
	Burg and P.J.F. Groenen (2018) <http://www.jmlr.org/papers/v17/14-526.html>.
Defines window or bin boundaries for the analysis of genomic data.
    Boundaries are based on the inflection points of a cubic smoothing spline
    fitted to the raw data. Along with defining boundaries, a technique to
    evaluate results obtained from unequally-sized windows is provided.
    Applications are particularly pertinent for, though not limited to, genome
    scans for selection based on variability between populations (e.g. using
    Wright's fixations index, Fst, which measures variability in subpopulations
    relative to the total population).
Split 'geospatial' objects into pieces. Includes
    support for some spatial object inputs, 'Well-Known Text', and
    'GeoJSON'.
Functions to fit geostatistical data. The data can be
        continuous, binary or count data and the models implemented are
        flexible. Conjugate priors are assumed on some parameters while
        inference on the other parameters can be done through a full
        Bayesian analysis of by empirical Bayes methods.
Make geographic box plot as detailed in Willmott et al. (2007).
This package provides a variety of functions to analyze and model geostatistical count data with generalized linear spatial models, including
 1) simulate and visualize the data;
 2) posterior sampling with robust MCMC algorithms (in serial or parallel way);
 3) perform prediction for unsampled locations;
 4) conduct Bayesian model checking procedure to evaluate the goodness of fitting;
 5) conduct transformed residual checking procedure.
 In the package, seamlessly embedded C++ programs and parallel computing techniques are implemented to speed up the computing processes.
Given expression data this package calculate a multivariate
    geometrical characterization of the differential expression and can also
    perform gene-set enrichment.
Visualizes two-dimensional geoelectric resistivity measurement profiles in three dimensions.
Provides geofaceting functionality for 'ggplot2'. Geofaceting arranges a sequence of plots of data for different geographical entities into a grid that preserves some of the geographical orientation.
Kriging based methods are used for predicting functional data 
             (curves) with spatial dependence.
A model building procedure to build parsimonious geoadditive model from a large number of covariates. Continuous, binary and ordered categorical responses are supported. The model building is based on component wise gradient boosting with linear effects, smoothing splines and a smooth spatial surface to model spatial autocorrelation. The resulting covariate set after gradient boosting is further reduced through backward elimination and aggregation of factor levels. The package provides a model based bootstrap method to simulate prediction intervals for point predictions. A test data set of a soil mapping case study in Berne (Switzerland) is provided.
Quantification of the effect of geographic versus environmental isolation on genetic differentiation
Turn irregular polygons (such as geographical regions) into regular or hexagonal grids. This package enables the generation of regular (square) and hexagonal grids through the package 'sp' and then assigns the content of the existing polygons to the new grid using the Hungarian algorithm, Kuhn (1955) (<doi:10.1007/978-3-540-68279-0_2>). This prevents the need for manual generation of hexagonal grids or regular grids that are supposed to reflect existing geography.
Provides tools to encode lat/long pairs into geohashes, decode those geohashes,
             and identify their neighbours.
Classes for 'GeoJSON' to make working with 'GeoJSON' easier.
    Includes S3 classes for 'GeoJSON' classes with brief summary output,
    and a few methods such as extracting and adding bounding boxes,
    properties, and coordinate reference systems; linting through
    the 'geojsonlint' package; and serializing to/from 'Geobuf' binary
    'GeoJSON' format.
Convert data to 'GeoJSON' or 'TopoJSON' from various R classes,
    including vectors, lists, data frames, shape files, and spatial classes.
    'geojsonio' does not aim to replace packages like 'sp', 'rgdal', 'rgeos',
    but rather aims to be a high level client to simplify conversions of data
    from and to 'GeoJSON' and 'TopoJSON'.
Tools for linting 'GeoJSON'. Includes tools for interacting with the
    online tool <http://geojsonlint.com>, the 'Javascript' library 'geojsonhint'
    (<https://www.npmjs.com/package/geojsonhint>), and validating against a
    'GeoJSON' schema via the 'Javascript' library
    (<https://www.npmjs.com/package/is-my-json-valid>). Some tools work locally
    while others require an internet connection.
Includes functions for processing GeoJson objects <https://en.wikipedia.org/wiki/GeoJSON> relying on 'RFC 7946' <https://tools.ietf.org/pdf/rfc7946.pdf>. The geojson encoding is based on 'json11', a tiny JSON library for 'C++11' <https://github.com/dropbox/json11>. Furthermore, the source code is exported in R through the 'Rcpp' and 'RcppArmadillo' packages.
Converts Between GeoJSON and simple feature objects.
Processes gridded datasets found on the U.S. Geological Survey
    Geo Data Portal web application or elsewhere, using a web-enabled workflow
    that eliminates the need to download and store large datasets that are reliably
    hosted on the Internet. The package provides access to several data subset and
    summarization algorithms that are available on remote web processing servers.
Provides basic functions for global
    positioning based on light intensity measurements over time.
    Positioning process includes the determination of sun events, a
    discrimination of residency and movement periods, the
    calibration of period-specific data and, finally, the
    calculation of positions.
Set of routines for making Map Projections (forward and inverse), Topographic Maps, Perspective plots, Geological Maps, geological map symbols, geological databases, interactive plotting and selection of focus regions.
Set of data for use in package GEOmap.  Includes world
        map, USA map, Coso map, Japan Map, ETOPO5
Provides eigenvector-based (geometric) forecast
    combination methods; also includes simple approaches (simple average,
    median, trimmed and winsorized mean, inverse rank method) and regression-based
    combination. Tools for data pre-processing are available in order to deal with 
    common problems in forecast combination (missingness, collinearity).
The Genomic Observatory Metadatabase (GeOMe Database) is an open access repository for
    geographic and ecological metadata associated with sequenced samples. This package is used to retrieve
    GeOMe data for analysis. See <http://www.geome-db.org> for more information regarding GeOMe.
Geospatial data integration framework that merges raster, spatial polygon, and (dynamic) spatial points data into a spatial (panel) data frame at any geographical resolution.
Provides facilities to handle reading and writing of geographic metadata 
 defined with OGC/ISO 19115, 11119 and 19110 geographic information metadata standards,
 and encoded using the ISO 19139 (XML) standard. It includes also a facility to check
 the validity of ISO 19139 XML encoded metadata.
Makes the qhull library (www.qhull.org)
    available in R, in a similar manner as in Octave and MATLAB. Qhull
    computes convex hulls, Delaunay triangulations, halfspace
    intersections about a point, Voronoi diagrams, furthest-site
    Delaunay triangulations, and furthest-site Voronoi diagrams. It
    runs in 2-d, 3-d, 4-d, and higher dimensions. It implements the
    Quickhull algorithm for computing the convex hull. Qhull does not
    support constrained Delaunay triangulations, or mesh generation of
    non-convex objects, but the package does include some R functions
    that allow for this. Currently the package only gives access to
    Delaunay triangulation and convex hull computation.
Network visualization in the 'ggplot2' framework. Network
    functionality is provided in a single 'ggplot2' layer by calling the geom 'net'.
    Layouts are calculated using the 'sna' package, example networks are included.
Utilizes methods of the 'PyMongo' 'Python' library to initialize, insert and query 'GeoJson' data (see <https://api.mongodb.com/python/current/#> for more information on 'PyMongo'). Furthermore, it allows the user to validate 'GeoJson' objects and to use the console for 'MongoDB' (bulk) commands. The 'reticulate' package provides the 'R' interface to 'Python' modules, classes and functions.
Read, manipulate, and digitize landmark data, generate shape
    variables via Procrustes analysis for points, curves and surfaces, perform
    shape analyses, and provide graphical depictions of shapes and patterns of
    shape variation.
Code for querying the web service at www.geonames.org
Provides an R interface to the 'GeoNetwork' API (<https://geonetwork-opensource.org/#api>) allowing to upload and publish metadata in a 'GeoNetwork' web-application and exposte it to OGC CSW.
Tools for doing calculations and manipulations on 'GeoJSON',
    a 'geospatial' data interchange format (<https://tools.ietf.org/html/rfc7946>).
    'GeoJSON' is also valid 'JSON'.
A wrapper for the Geoparser.io API version 0.4.0 (see <https://geoparser.io/>), which is a web service
    that identifies places mentioned in text, disambiguates those places, and
    returns detailed data about the places found in the text. Basic, limited
    API access is free with paid plans to accommodate larger workloads.
Codes for analyzing various problems of geophysics, continuum mechanics and gravity models.
Geostatistical analysis including traditional, likelihood-based and Bayesian methods.
Calculates and analyzes six measures of geographic range from a set of longitudinal and latitudinal occurrence data. Measures included are minimum convex hull area, minimum spanning tree distance, longitudinal range, latitudinal range, maximum pairwise great circle distance, and number of X by X degree cells occupied.
Functions for inference in generalised linear spatial models. The posterior and predictive inference is based on Markov chain Monte Carlo methods. Package geoRglm is an extension to the package geoR, which must be installed first.
Provides functions for efficiently fitting linear models with 
         spatially correlated errors by robust and Gaussian (Restricted) 
         Maximum Likelihood and for computing robust and customary point 
         and block external-drift Kriging predictions, along with utility
         functions for variogram modelling in ad hoc geostatistical analyses, 
         model building, model evaluation by cross-validation, (conditional) 
         simulation of Gaussian processes, unbiased back-transformation of 
         Kriging predictions of log-transformed data.
Provides an R interface to the GeoServer REST API, allowing to upload 
 and publish data in a GeoServer web-application and expose data to OGC Web-Services. 
 The package currently supports all CRUD (Create,Read,Update,Delete) operations
 on GeoServer workspaces, namespaces, datastores (stores of vector data), featuretypes,
 layers, styles, as well as vector data upload operations. For more information about 
 the GeoServer REST API, see <http://docs.geoserver.org/stable/en/user/rest/>.
Function for adding the geological timescale to bivariate plots.
Generates distance matrices from shape files and represents spatially weighted multilevel analysis results (see 'spacom')
Provides S4 classes and data import, preprocessing, graphing, 
    manipulation and export methods for geo-Spectral datasets (datasets with space/time/spectral 
    dimensions). These type of data are frequently collected within earth observation projects 
    (remote sensing, spectroscopy, bio-optical oceanography, mining, agricultural, atmospheric, 
    environmental or similar branch of science).
Spherical trigonometry for geographic applications. That is, compute distances and related measures for angular (longitude/latitude) locations.
Estimation of the variogram through trimmed mean, radial basis 
        functions (optimization, prediction and cross-validation), summary
        statistics from cross-validation, pocket plot, and design of
        optimal sampling networks through sequential and simultaneous
        points methods.
Spatio-temporal: Inverse Distance Weighting (IDW) and radial basis functions; optimization, prediction, summary statistics from leave-one-out cross-validation, adjusting distance-based linear regression model and generation of the principal coordinates of a new individual from Gower's distance.
Geostatistical modelling facilities using Raster and SpatialPoints
    objects are provided. Non-Gaussian models are fit using INLA, and Gaussian
    geostatistical models use Maximum Likelihood Estimation.  For details see Brown (2015) <doi:10.18637/jss.v063.i12>.
A compilation of functions for performing calculations and
    creating plots that commonly arise in geotechnical engineering and soil
    mechanics. The types of calculations that are currently included are:
    (1) phase diagrams and index parameters, (2) grain-size distributions, 
    (3) plasticity, (4) soil classification, (5) compaction, (6) groundwater,
    (7) subsurface stresses (geostatic and induced), (8) Mohr circle analyses,
    (9) consolidation settlement and rate, (10) shear strength, (11) bearing
    capacity, (12) lateral earth pressures, (13) slope stability, and (14)
    subsurface explorations. Geotechnical engineering students, educators,
    researchers, and practitioners will find this package useful.
The basic idea of this package is provides some tools to help the researcher to work with geostatistics. Initially, we present a collection of functions that allow the researchers to deal with spatial data using bootstrap procedure.There are five methods available and two ways to display them: bootstrap confidence interval - provides a two-sided bootstrap confidence interval; bootstrap plot - a graphic with the original variogram and each of the B bootstrap variograms.
It analyzes raster maps and other information as input/output
    files from the Hydrological Distributed Model GEOtop. It contains functions
    and methods to import maps and other keywords from geotop.inpts file. Some
    examples with simulation cases of GEOtop 2.0 are presented in the package.
    Any information about the GEOtop Distributed Hydrological Model source code
    is available on https://github.com/geotopmodel or
    https://github.com/se27xx/GEOtop. Technical details about the model are
    available in Endrizzi et al, 2014
    (http://www.geosci-model-dev.net/7/2831/2014/gmd-7-2831-2014.html).
A zoning method and a numerical criterion for zoning quality are available in this package.
   The zoning method is based on a numerical criterion that evaluates the zoning quality.
   This criterion quantifies simultaneously how zones are heterogeneous on
   the whole map and how neighbouring zones are similar. This approach allows comparison
   between maps either with different zones or different labels, which is of importance
   for zone delineation algorithms aiming at maximizing inter-zone variability. An optimisation
   procedure provides the user with the best zonings thanks to contour delineation for a given map.
Geometric objects defined in 'geozoo' can be simulated or displayed in the R package 'tourr'.
Encode and decode the Google Encoded Polyline Algorithm Format (<https://developers.google.com/maps/documentation/utilities/polylinealgorithm>).
Estimation and diagnosis of the convergence of Generalized
    Exponential Random Graph Models via Gibbs sampling or Metropolis
    Hastings with exponential down weighting.
Different types of seed indexes, rates and visualization techniques
    are used to provide a robust approach for germination data analysis. The package
    aims to make available germination seed indexes and graphical functions to
    analyze germination seed data.
Provides functions to compute various germination indices such as
    germinability, median germination time, mean germination time, mean germination rate, speed of
    germination, Timson's index, germination value, coefficient of uniformity of
    germination, uncertainty of germination process, synchrony of germination
    etc. from germination count data. Includes functions for fitting cumulative
    seed germination curves using four-parameter hill function and computation
    of associated parameters. See the vignette for more, including full list of
    citations for the methods implemented.
Fit a variety of component-based structural equation models.
Implements the gene-based segregation test(GESE) and the weighted GESE test for identifying genes with causal variants of large effects for family-based sequencing data. The methods are described in Qiao, D. Lange, C., Laird, N.M., Won, S., Hersh, C.P., et al. (2017). <DOI:10.1002/gepi.22037>. Gene-based segregation method for identifying rare variants for family-based sequencing studies. Genet Epidemiol 41(4):309-319. More details can be found at <http://scholar.harvard.edu/dqiao/gese>.
Provides programmatic access to the GESIS - Leibniz-Institute for
    the Social Sciences Data Catalogue/Datenbestandkatalog (DBK), which
    maintains a large repository of data sets related to the social sciences.
    See <http://www.gesis.org> for more information.
The Gene Expression State Transformation (GESTr) models
        the states of expression of genes across a compendium of
        samples in order to provide a universal scale of gene
        expression for all genes. TranSAM is a modification of the SAM
        approach designed to utilise GESTr-transformed gene expression
        data.
Provides functions that automate downloading and importing
    University of East Anglia Climate Research Unit (CRU) CL v. 2.0 climatology
    data into R, facilitates the calculation of minimum temperature and maximum
    temperature and formats the data into a tidy data frame as a tibble or a 
    list of raster stack objects for use in an R session.  CRU CL v. 2.0 data 
    are a gridded climatology of 1961-1990 monthly means released in 2002 and
    cover all land areas (excluding Antarctica) at 10 arcminutes
    (0.1666667 degree) resolution.  For more information see the description of
    the data provided by the University of East Anglia Climate Research Unit,
    <https://crudata.uea.ac.uk/cru/data/hrg/tmc/readme.txt>.
Reads annual financial reports including assets, liabilities, dividends history, stockholder composition and much more from Bovespa's DFP, FRE and FCA systems <http://www.bmfbovespa.com.br/en_us/products/listed-equities-and-derivatives/equities/listed-companies.htm>.
 These are web based interfaces for all financial reports of companies traded at Bovespa. The package is specially designed for large scale data importation, keeping a tabular (long) structure for easier processing.
Downloads and aggregates high frequency trading data for Brazilian instruments directly from Bovespa ftp site <ftp://ftp.bmf.com.br/MarketData/>.
Reads quarterly and annual financial reports including assets, liabilities, income and cash flow statements from Bovespa's ITR (informacoes trimestrais) system <http://www.bmfbovespa.com.br/en_us/products/listed-equities-and-derivatives/equities/listed-companies.htm>.
 The ITR/DFP system is a web based interface for all financial reports of companies traded at Bovespa. The package is especially designed for large scale data importation, keeping a tabular (long) structure for easier processing.
Get Landsat 8 Data from Amazon Web Services ('AWS')
    public data sets (<https://registry.opendata.aws/landsat-8/>).
    Includes functions for listing images and fetching them, and handles
    caching to prevent unnecessary additional requests.
A simple API for downloading and reading xml data directly from Lattes <http://lattes.cnpq.br/>.
Hydrologic models often require users to collect and format input meteorological data. This package contains functions for sourcing, formatting, and
    editing meteorological data for hydrologic models.
Quantifying systematic heterogeneity in meta-analysis using R.
    The M statistic aggregates heterogeneity information across multiple
    variants to, identify systematic heterogeneity patterns and their direction
    of effect in meta-analysis. It's primary use is to identify outlier studies,
    which either show "null" effects or consistently show stronger or weaker
    genetic effects than average across, the panel of variants examined in a
    GWAS meta-analysis. In contrast to conventional heterogeneity metrics
    (Q-statistic, I-squared and tau-squared) which measure random heterogeneity
    at individual variants, M measures systematic (non-random)
    heterogeneity across multiple independently associated variants. Systematic
    heterogeneity can arise in a meta-analysis due to differences in the study
    characteristics of participating studies. Some of the differences may
    include: ancestry, allele frequencies, phenotype definition, age-of-disease
    onset, family-history, gender, linkage disequilibrium and quality control
    thresholds. See <https://magosil86.github.io/getmstatistic/> for statistical
    statistical theory, documentation and examples.
Package designed to be used with Rscript to write
    ``#!'' shebang scripts that accept short and long flags/options.
    Many users will prefer using instead the packages optparse or argparse
    which add extra features like automatically generated help option and usage,
    support for default values, positional argument support, etc.
This is yet another command-line argument parser which wraps the 
    powerful Perl module Getopt::Long and with some adaptation for easier use
	in R. It also provides a simple way for variable interpolation in R.
A micro-package for reading "passwords", i.e.  reading
    user input with masking, so that the input is not displayed as it 
    is typed.  Currently we have support for 'RStudio', the command line
    (every OS), and any platform where 'tcltk' is present.
The GetR package calculates Guttman error trees, which can
        be used to find homogeneous subgroups regarding Guttman errors.
Automated General-to-Specific (GETS) modelling of the mean and variance of a regression, and indicator saturation methods for detecting and testing for structural breaks in the mean.
Facilitates easy import of analysis ready World Health Organisation Tuberculosis data and provides plotting functions for exploratory data analysis.
Downloads and aggregates data for Brazilian government issued bonds directly from the website of Tesouro Direto <http://www.tesouro.fazenda.gov.br/tesouro-direto-balanco-e-estatisticas>.
A function to retrieve the system timezone on Unix systems
 which has been found to find an answer when 'Sys.timezone()' has failed.
 It is based on an answer by Duane McCully posted on 'StackOverflow', and
 adapted to be callable from R. The package also builds on Windows, but
 just returns NULL.
Implements a flexible nonlinear modelling framework for nonstationary generalized extreme value analysis in hydroclimatology following Cannon (2010) <doi:10.1002/hyp.7506>.
Package for simulation and estimation of ARMA-GARCH/APARCH models with GEV and stable distributions.
Perform statistical tests to unveil genomic clusters,
        produces garphical interpretations of the statistical results
        in pdf files, perform a Gene Ontology analysis and produces
        graphic results in pdf files
Factor analysis implementation for multiple data sources, i.e., for groups of variables. The whole data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The model group factor analysis (GFA) is inferred with Gibbs sampling, and it has been presented originally by Virtanen et al. (2012), and extended in Klami et al. (2015) <DOI:10.1109/TNNLS.2014.2376974> and Bunte et al. (2016) <DOI:10.1093/bioinformatics/btw207>; for details, see the citation info.
The gfcanalysis package supports analyses using the Global
    Forest Change dataset released by Hansen et al. gfcanalysis was
    written for the Tropical Ecology Assessment and Monitoring (TEAM) Network
    (http://www.teamnetwork.org). For additional details on the Global Forest
    Change dataset, see: Hansen, M. et al. 2013. "High-Resolution Global Maps
    of 21st-Century Forest Cover Change." Science 342 (15 November): 850-53.
    The forest change data and more information on the product is available at
    http://earthenginepartners.appspot.com.
Implemented are the Wald-type statistic,
    a permuted version thereof as well as the ANOVA-type statistic
    for general factorial designs, even with non-normal error terms
    and/or heteroscedastic variances, for crossed designs with an
    arbitrary number of factors and nested designs with up to three factors.
Focuses on data collecting, analyzing and visualization in green finance and environmental 
  risk research and analysis. Main function includes environmental data collecting from 
  official websites such as MEP (Ministry of Environmental Protection of China, <http://www.mep.gov.cn>), water 
  related projects identification and environmental data visualization.
Compute bivariate dependence measures and perform bivariate competing risks analysis under the generalized Farlie-Gumbel-Morgenstern (FGM) copula. See Shih and Emura (2016) <doi:10.1007/s00362-016-0865-5> and Shih and Emura (2018) <doi:10.1007/s00180-018-0804-0> for details.
Software to implement methodology to preform automatic response category
  combinations in multinomial logistic regression. There are functions for both cross validation
  and AIC for model selection.  The method provides regression coefficient estimates
  that may be useful for better understanding the true probability distribution of
  multinomial logistic regression when category probabilities are similar. These methods are not
  recommended for a large number of predictor variables.
Extra geoms and scales for 'ggplot2', including geom_cloud(),
  a Normal density cloud replacement for errorbars;
  transforms ssqrt_trans and pseudolog10_trans, which are loglike but 
  appropriate for negative data; interp_trans() and warp_trans() which
  provide scale transforms based on interpolation;
  and an infix compose operator for scale transforms.
Alluvial diagrams encompass a variety of charts that use x-splines
    (alluvia and flows), sometimes augmented with stacked bars (lodes or
    strata), to visualize incidence structures derived from several data types,
    including categorical repeated measures data and multi-dimensional frequency
    data. This package contains stat and geom layers that interpret multiple
    data formats compatible with this framework while hewing to the principles
    of tidy data and the grammar of graphics.
The R package 'ggplot2' is a plotting system based on the grammar of graphics.
    'GGally' extends 'ggplot2' by adding several functions
    to reduce the complexity of combining geometric objects with transformed data.
    Some of these functions include a pairwise plot matrix, a two group pairwise plot
    matrix, a parallel coordinates plot, a survival plot, and several functions to
    plot networks.
A compendium of new geometries, coordinate systems, statistical 
    transformations, scales and fonts for 'ggplot2', including splines, 1d and 2d densities, 
    univariate average shifted histograms, a new map coordinate system based on the 
    'PROJ.4'-library along with geom_cartogram() that mimics the original functionality of 
    geom_map(), formatters for "bytes", a stat_stepribbon() function, increased 'plotly'
    compatibility and the 'StateFace' open source font 'ProPublica'. Further new 
    functionality includes lollipop charts, dumbbell charts, the ability to encircle
    points and coordinate-system-based text annotations.
Provides two methods of plotting categorical scatter plots such
    that the arrangement of points within a category reflects the density of
    data at that region, and avoids over-plotting.
Saves a 'ggplot' object into multiple files, each with a layer 
    added incrementally. Generally to be used in presentation slides.
    Flexible enough to allow different file types for the final complete plot,
    and intermediate builds.
Provides a Chernoff face geom for 'ggplot2'. Maps multivariate data
    to human-like faces.
A flexible interface for ggplot2::theme(), potentially saving 50% of your typing.
The 'ggcorrplot' package can be used to visualize easily a
    correlation matrix using 'ggplot2'. It provides a solution for reordering the
    correlation matrix and displays the significance level on the plot. It also
    includes a function for computing a matrix of correlation p-values.
Tidy, analyze, and plot directed acyclic graphs (DAGs). 'ggdag' is 
    built on top of 'dagitty', an R package that uses the 'DAGitty' web tool 
    (<http://dagitty.net>) for creating and analyzing DAGs. 'ggdag' makes it 
    easy to tidy and plot 'dagitty' objects using 'ggplot2' and 'ggraph', as 
    well as common analytic and graphical functions, such as determining 
    adjustment sets and node relationships.
This is a set of tools for dendrograms and
    tree plots using 'ggplot2'.  The 'ggplot2' philosophy is to
    clearly separate data from the presentation.
    Unfortunately the plot method for dendrograms plots
    directly to a plot device without exposing the data.
    The 'ggdendro' package resolves this by making available
    functions that extract the dendrogram plot data. The package
    provides implementations for tree, rpart, as well as diana and agnes
    cluster diagrams.
A fast engine for computing hierarchical Bayesian model
    implemented in the Dynamic Model of Choice.
Create biplots for GGE (genotype plus genotype-by-environment) and
    GGB (genotype plus genotype-by-block-of-environments) models.
Description: A GUI with which to construct and interact with GGE biplots.
Genotype plus genotype-by-environment (GGE) biplots rendered using 'ggplot2'. Provides a command line interface to all of the functionality contained within 'GGEBiplotGUI'.
Interactively edit 'ggplot2' layer and theme aesthetics definitions.
Compute marginal effects at the mean or average marginal effects from 
             statistical models and returns the result as tidy data frames. These 
             data frames are ready to use with the 'ggplot2'-package.
             Marginal effects can be calculated for many different models. Interaction
             terms, splines and polynomial terms are also supported. The two main
             functions are ggpredict() and ggaverage(), however, there are
             some convenient wrapper-functions especially for polynomials or
             interactions. There is a generic plot()-method to plot the results
             using 'ggplot2'.
Methods for searching through genealogical data and displaying the results. Plotting algorithms assist with data exploration and publication-quality image generation. Includes interactive genealogy visualization tools. Provides parsing and calculation methods for variables in descendant branches of interest. Uses the Grammar of Graphics.
Extension of 'ggplot2' providing layers, scales and preprocessing functions
    useful to represent behavioural variables that are recorded over multiple animals and days.
Collection of functions and layers to enhance 'ggplot2'. The 
    flagship function is 'ggMarginal()', which can be used to add marginal
    histograms/boxplots/density plots to 'ggplot2' scatterplots.
Extending facet_grid(...) from 'ggplot2', 'ggFacetSample' provides a simple interface to
  sample from the values of large facet dimensions: facet_sample(..., sample_n, seed).
Implements the functionality of the 'fanplot' package as 'geoms' 
  for 'ggplot2'. Designed for summarising MCMC samples from a posterior 
  distribution, where a visualisation is desired for several values of a 
  continuous covariate. Increasing posterior intervals of the sampled quantity 
  are mapped to a continuous colour scale.
Provides 'ggplot2' geoms to fit text into a box by growing, shrinking
    or wrapping the text.
The aim of 'ggplot2' is to aid in visual data investigations. This
    focus has led to a lack of facilities for composing specialised plots.
    'ggforce' aims to be a collection of mainly new stats and geoms that fills
    this gap. All additional functionality is aimed to come through the official
    extension system so using 'ggforce' should be a stable experience.
Provides a formula interface to 'ggplot2' graphics.
Unified plotting tools for statistics commonly used, such as GLM,
    time series, PCA families, clustering and survival analysis. The package offers
    a single plotting interface for these analysis results and plots in a unified
    style using 'ggplot2'.
Provides a 'ggplot2' geom and helper functions for drawing gene
  arrow maps.
Creates a reproducible 'ggplot2' object by storing the data and calls.
Utilities for Creating Guitar Tablature using tidyverse packages.
Reproduce the halfnorm() function found in the 'faraway' package 
    using the 'ggplot2' API.
Make it easier to explore data with highlights.
Supports image files and graphic objects to be visualized in
    'ggplot2' graphic system.
A tool to process and analyse data collected with wearable raw acceleration sensors as described in van Hees and colleagues (2014) <doi: 10.1152/japplphysiol.00421.2014> and (2015) <doi: 10.1371/journal.pone.0142533>. The package has been developed and tested for binary data from 'GENEActiv' <https://www.activinsights.com/> and GENEA devices (not for sale), .csv-export data from  'Actigraph' <http://actigraphcorp.com> devices, and .cwa and .wav-format data from 'Axivity' <https://axivity.com/product/ax3>. These devices are currently widely used in research on human daily physical activity.
Create interactive 'ggplot2' graphics using 'htmlwidgets'.
Collection of functions to enhance 'ggplot2' and 'ggiraph'. Provides functions for exploratory plots.
    All plot can be a 'static' plot or an 'interactive' plot using 'ggiraph'.
Joyplots provide a convenient way of visualizing changes in distributions over time or space. This package enables the creation of such plots in 'ggplot2'.
A unified algorithm, blockwise-majorization-descent (BMD), for efficiently computing the solution paths of the group-lasso penalized least squares, logistic regression, Huberized SVM and squared SVM. The package is an implementation of Yang, Y. and Zou, H. (2015) DOI: <doi:10.1007/s11222-014-9498-5>.
Visualize sequences in (modified) logo plots. The design choices
    used by these logo plots allow sequencing data to be more easily analyzed.
    Because it is integrated into the 'ggplot2' geom framework, these logo plots
    support native features such as faceting.
Pass a data frame and mapping aesthetics to ggloop() in order
    to create a list of 'ggplot2' plots. The way x-y and dots are paired together
    is controlled by the remapping arguments. Geoms, themes, facets, and other
    features can be added with the special %L+% (L-plus) operator.
Provides statistical transformations for plotting empirical 
    ordinary Lorenz curve (Lorenz 1905) <doi:10.2307/2276207> and 
    generalized Lorenz curve (Shorrocks 1983) <doi:10.2307/2554117>.
Functions and datasets for maximum likelihood fitting of some classes of graphical Markov models.
A collection of functions to visualize spatial data and models
    on top of static maps from various online sources (e.g Google Maps and Stamen
    Maps). It includes tools common to those tasks, including functions for
    geolocation and routing.
Tools for assessing and diagnosing convergence of
    Markov Chain Monte Carlo simulations, as well as for graphically display
    results from full MCMC analysis. The package also facilitates the graphical
    interpretation of models by providing flexible functions to plot the
    results against observed variables.
The Gaussian graphical model is a widely used tool for learning gene regulatory networks with high-dimensional gene expression data. For many real problems, the data are heterogeneous, which may contain some subgroups or come from different resources. This package provide a Gaussian Graphical Mixture Model (GGMM) for the heterogeneous data. You can refer to Jia, B. and Liang, F. (2018) at <arXiv:1805.02547> for detail.
Mosaic plots in the 'ggplot2' framework. Mosaic plot functionality
    is provided in a single 'ggplot2' layer by calling the geom 'mosaic'.
Estimation of partial correlation matrix using ridge penalty followed by thresholding and reestimation. Under multivariate Gaussian assumption, the matrix constitutes an Gaussian graphical model (GGM).
Graph estimation in Gaussian Graphical Models. The main functions return the adjacency matrix of an undirected graph estimated from a data matrix.
Create plots that combine a phylogeny and frequency dynamics.
    Phylogenetic input can be a generic adjacency matrix or a tree of class "phylo".
    Inspired by similar plots in publications of the labs of RE Lenski and JE
    Barrick. Named for HJ Muller (who popularised such plots) and H Wickham (whose
    code this package exploits).
Geometries to plot network objects with 'ggplot2'.
Create hammock plots, parallel sets, and common angle plots
    with 'ggplot2'.
A system for 'declaratively' creating graphics,
    based on "The Grammar of Graphics". You provide the data, tell 'ggplot2'
    how to map variables to aesthetics, what graphical primitives to use,
    and it takes care of the details.
A dataset about movies. This was previously contained in ggplot2,
   but has been moved its own package to reduce the download size of ggplot2.
An 'RStudio' addin for teaching and learning making plot using the 'ggplot2' package.
    You can learn each steps of making plot by clicking your mouse without coding.
    You can get resultant code for the plot.
Easily explore data by creating ggplots through a (shiny-)GUI. R-code to recreate graph provided.
Convert plot function call (using expression or formula) to 'grob' or 'ggplot' object that compatible to the 'grid' and 'ggplot2' ecosystem. With this package, we are able to e.g. using 'cowplot' to align plots produced by 'base' graphics, 'grid', 'lattice', 'vcd' etc. by converting them to 'ggplot' objects.
Extensions to 'ggplot2' respecting the grammar of graphics
    paradigm. Provides new statistics to locate and tag peaks and valleys in 2D
    plots, a statistics to add a label with the equation of a polynomial fitted
    with lm(), or R^2 or adjusted R^2 or information criteria for any model
    fitted with function lm(). Additional statistics give access to functions
    in package 'broom'. Provides a function for flexibly converting time
    series to data frames suitable for plotting with ggplot(). In addition
    provides statistics and ggplot geometries useful for diagnosing what data
    are passed to compute_group() and compute_panel() functions and to
    geometries.
A 'ggplot2' extension for implementing parliament charts and several other useful visualizations.
Tools for working with polygons with holes in 'ggplot2', with a
    new 'geom' for drawing a 'polypath' applying the 'evenodd' or 'winding'
    rules.
'ggplot2' is an excellent and flexible package for elegant data
    visualization in R. However the default generated plots requires some formatting
    before we can send them for publication. Furthermore, to customize a 'ggplot',
    the syntax is opaque and this raises the level of difficulty for researchers
    with no advanced R programming skills. 'ggpubr' provides some easy-to-use
    functions for creating and customizing 'ggplot2'- based publication ready plots.
Automatically performs desired statistical tests (e.g. wilcox.test(), t.test()) to compare between groups, 
    and adds the resulting p-values to the plot with an annotation bar.
    Visualizing group differences are frequently performed by boxplots, bar plots, etc.
    Statistical test results are often needed to be annotated on these plots. 
    This package provides a convenient function that works on 'ggplot2' objects, 
    performs the desired statistical test between groups of interest and annotates the test results on the plot.
Plot single and faceted type quality control charts
  for 'ggplot'.
A quantile-quantile plot can be used to compare a sample of p-values 
  to the uniform distribution.  But when the dataset is big (i.e. > 1e4 p-values),
  plotting the quantile-quantile plot can be slow.  geom_QQ uses all the data to
  calculate the quantiles, but thins it out in a way that focuses on points near zero 
  before plotting to speed up plotting and decrease file size, when vector graphics are stored.
Quickly and easily perform exploratory data analysis by uploading your
     data as a 'csv' file. Start generating insights using 'ggplot2' plots and
     tables with descriptive stats, all using an easy-to-use point and click 
     'Shiny' interface.
An extension of 'ggplot2' to provide quiver plots to visualise vector fields. 
    This functionality is implemented using a geom to produce a new graphical layer, which
    allows aesthetic options. This layer can be overlaid on a map to improve visualisation
    of mapped data.
Graphic elements for exploring Random Forests using the 'randomForest' or
    'randomForestSRC' package for survival, regression and classification forests and
    'ggplot2' package plotting.
The grammar of graphics as implemented in ggplot2 is a poor fit for
    graph and network visualizations due to its reliance on tabular data input.
    ggraph is an extension of the ggplot2 API tailored to graph visualizations
    and provides the same flexible approach to building up plots layer by layer.
Intended for both technical and non-technical users to create
    interactive data visualizations through a web browser GUI without writing any
    code.
Provides text and label geoms for 'ggplot2' that help to avoid
    overlapping text labels. Labels repel away from each other and away from the
    data points.
Ridgeline plots provide a convenient way of visualizing changes in distributions over time or space. This package enables the creation of such plots in 'ggplot2'.
A collection of 'ggplot2' color palettes inspired by
    plots in scientific journals, data visualization libraries,
    science fiction movies, and TV shows.
Provides 'ggplot2' 'stats' that estimate seasonally adjusted series 
    and rolling summaries such as rolling average on the fly for time series.
The extensive range of functions provided by this package makes it possible to draw highly versatile sequence logos. Features include, but not limited to, modifying colour schemes and fonts used to draw the logo, generating multiple logo plots, and aiding the visualisation with annotations. Sequence logos can easily be combined with other plots 'ggplot2' plots.
Enrich your 'ggplots' with group-wise comparisons.
  This package provides an easy way to indicate if two groups are significantly different.
  Commonly this is shown by a bracket on top connecting the groups of interest which itself is annotated with the level of significance (NS, *, **, ***).
  The package provides a single layer (geom_signif()) that takes the groups for comparison and the test (t.test(), wilcox.text() etc.) as arguments and adds the annotation
  to the plot.
Adds north symbols (18 options) and scale bars in kilometers to
    maps in geographic or metric coordinates created with 'ggplot2' or 'ggmap'.
Spatial data plus the power of the ggplot2 framework means easier mapping when input 
  data are already in the form of Spatial* objects.
Additional annotations, and stats for plotting "light"
    spectra with 'ggplot2', together with specializations of ggplot()
    and plot() methods for spectral data stored in objects of the classes
    defined in package 'photobiology' and a plot() method for objects of
    class "waveband", also defined in package 'photobiology'. Part of the 
    'r4photobiology' suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
A 'ggplot2' extension that provides flipped components:
    horizontal versions of 'Stats' and 'Geoms', and vertical versions
    of 'Positions'.
Extension of 'ggplot2', 'ggstatsplot' creates graphics with details from statistical tests 
  (parametric, non-parametric, or robust) included in the plots themselves. It is targeted primarily at 
  behavioral sciences community to provide a one-line code to generate information-rich plots for 
  statistical analysis of continuous (violin plots, scatterplots, histograms) or categorical (pie charts) data.
Offers various swiss maps as data frames and 'ggplot2' objects and gives the
    possibility to add layers of data on the maps. Data are publicly available
    from the swiss federal statistical office.
Extends the functionality of 'ggplot2', providing the capability
    to plot ternary diagrams for (subset of) the 'ggplot2' geometries. Additionally,
    'ggtern' has implemented several NEW geometries which are unavailable to the
    standard 'ggplot2' release. For further examples and documentation, please
    proceed to the 'ggtern' website.
Rstudio add-in that delivers a graphical interface for editing 'ggplot2' theme elements.
Some extra themes, geoms, and scales for 'ggplot2'.
    Provides 'ggplot2' themes and scales that replicate the look of plots
    by Edward Tufte, Stephen Few, 'Fivethirtyeight', 'The Economist', 'Stata',
    'Excel', and 'The Wall Street Journal', among others.
    Provides 'geoms' for Tufte's box plot and range frame.
An implementation of the generalized graded unfolding model (GGUM) in R, see Roberts, Donoghue, and Laughlin (2000) <doi:10.1177/01466216000241001>). It allows to simulate data sets based on the GGUM. It fits the GGUM and the GUM, and it retrieves item and person parameter estimates. Several plotting functions are available (item and test information functions; item and test characteristic curves; item category response curves). Additionally, there are some functions that facilitate the communication between R and 'GGUM2004'. Finally, a model-fit checking utility, MODFIT(), is also available.
A collection of datasets for the upcoming book "Graficas versatiles con ggplot: Analisis visuales de datos", by Raymond L. Tremblay and Julian Hernandez-Serano.
An implementation of an interactive grammar of graphics, taking the
    best parts of 'ggplot2', combining them with the reactive framework of
    'shiny' and drawing web graphics using 'vega'.
Minimal client to access the 'GitHub' 'API'.
Colour palettes inspired by Studio Ghibli films, ported to R for your enjoyment.
The GHQ function can be used to obtain the quadrature points and weights to approximate an integral in two or more dimensions. This function uses the pruning approach to eliminate that points that do not contribute to the approximation of the integral and increases computational cost. The advantage to conducting this elimination of points is the decrease in the number of times that the function of interest is evaluated. This advantage is crucial in mixed models in which we must address several integrations within an iterative process to obtain model parameters.
Detailed functionality for working
        with the univariate and multivariate Generalized Hyperbolic
        distribution and its special cases (Hyperbolic (hyp), Normal
        Inverse Gaussian (NIG), Variance Gamma (VG), skewed Student-t
        and Gaussian distribution). Especially, it contains fitting
        procedures, an AIC-based model selection routine, and functions
        for the computation of density, quantile, probability, random
        variates, expected shortfall and some portfolio optimization
        and plotting routines as well as the likelihood ratio test. In
        addition, it contains the Generalized Inverse Gaussian
        distribution.
Build a map of path-based geometry, this is a simple description of the number
 of parts in an object and their basic structure. Translation and restructuring operations for 
 planar shapes and other hierarchical types require a data model with a record of the underlying
 relationships between elements. The gibble() function creates a geometry map, a simple record of 
 the underlying structure in path-based hierarchical types. There are methods for the planar shape 
 types in the 'sf' and 'sp' packages.
This package provides two generic functions for performing
        Markov chain sampling in a naive way for a user-defined target
        distribution, which involves only continuous variables. The
        function "gibbs_met" performs Gibbs sampling with each
        1-dimensional distribution sampled with Metropolis update using
        Gaussian proposal distribution centered at the previous state.
        The function "met_gaussian" updates the whole state with
        Metropolis method using independent Gaussian proposal
        distribution centered at the previous state. The sampling is
        carried out without considering any special tricks for
        improving efficiency. This package is aimed at only routine
        applications of MCMC in moderate-dimensional problems.
Gibbs sampler for one-way linear mixed-effects models
        (ANOVA, ANCOVA) with homoscedasticity of errors and uniform
        priors.
Functions to read in the geometry format under the 
    'Neuroimaging' 'Informatics' Technology Initiative ('NIfTI'), called 
    'GIFTI' <https://www.nitrc.org/projects/gifti/>. 
    These files contain surfaces of brain imaging data.
Generator and density function for the
  Generalized Inverse Gaussian (GIG) distribution.
GillespieSSA provides a simple to use, intuitive, and
        extensible interface to several stochastic simulation
        algorithms for generating simulated trajectories of finite
        population continuous-time model. Currently it implements
        Gillespie's exact stochastic simulation algorithm (Direct
        method) and several approximate methods (Explicit tau-leap,
        Binomial tau-leap, and Optimized tau-leap). The package also
        contains a library of template models that can be run as demo
        models and can easily be customized and extended. Currently the
        following models are included, decaying-dimerization reaction
        set, linear chain system, logistic growth model, Lotka
        predator-prey model, Rosenzweig-MacArthur predator-prey model,
        Kermack-McKendrick SIR model, and a metapopulation SIRS model.
Implements the generalized integration model, which integrates individual-level data and summary statistics under a generalized linear model framework. It supports continuous and binary outcomes to be modeled by the linear and logistic regression models.
Automated identification and estimation of group- and
    individual-level relations in time series data from within a structural
    equation modeling framework.
Supplemental tools for the 'gimme' R package. It contains an interactive graphical user interface, allowing for the flexible specification of a variety of both basic and advanced options. It will expand to include a variety of tools for navigating output.
This is a set of functions to retrieve information about GIMMS
    NDVI3g files currently available online; download (and re-arrange, in the 
    case of NDVI3g.v0) the half-monthly data sets from 
    <https://ecocast.arc.nasa.gov/data/pub/gimms/>; import downloaded files from 
    ENVI binary (NDVI3g.v0) or NetCDF format (NDVI3g.v1) directly into R based 
    on the widespread 'raster' package; conduct quality control; and generate 
    monthly composites (e.g., maximum values) from the half-monthly input data. 
    As a special gimmick, a method is included to conveniently apply the 
    Mann-Kendall trend test upon 'Raster*' images, optionally featuring 
    trend-free pre-whitening to account for lag-1 autocorrelation.
Performs image segmentation in fruit or seeds pictures in order to measure physical features in a high-throughput manner for genome-wide association (GWAS) and genomic selection programs.
Gini-based coefficients and plot of the ordinary and generalized curve of maximum inequality in the presence of weighted and negative attributes.
Maximum likelihood estimation under relational models, with or without the overall effect.
An interface to the 'API' of 'Giphy', a popular index-based search 
    engine for 'GIFs' and animated stickers (see <http://giphy.com/faq> and 
    <https://github.com/Giphy/GiphyAPI> for more information about 'Giphy' and 
    its 'API') . This package also provides a 'RStudio Addin', which can help 
    users easily search and download 'GIFs' and insert them to a 'rmarkdown' 
    presentation.
Allows calculation on, and
 sampling from Gibbs Random Fields, and more precisely general 
 homogeneous Potts model. The primary tool is the exact computation of
 the intractable normalising constant for small rectangular lattices. 
 Beside the latter function, it contains method that give exact sample from the likelihood
 for small enough rectangular lattices or approximate sample from the 
 likelihood using MCMC samplers for large lattices.
Supply classes and methods to represent and manipulate
        graphs
Some mapping and spatial data manipulation tools - in particular
    drawing choropleth maps with nice looking legends,  and aggregation of point
    data to polygons.
Work with 'GitHub' 'gists' from 'R' (e.g., 
    <http://en.wikipedia.org/wiki/GitHub#Gist>, 
    <https://help.github.com/articles/about-gists/>). A 'gist'
    is simply one or more files with code/text/images/etc. This package allows
    the user to create new 'gists', update 'gists' with new files, rename files,
    delete files, get and delete 'gists', star and 'un-star' 'gists', fork 'gists',
    open a 'gist' in your default browser, get embed code for a 'gist', list
    'gist' 'commits', and get rate limit information when 'authenticated'. Some
    requests require authentication and some do not. 'Gists' website: 
    <https://gist.github.com/>.
Interface to the 'libgit2' library, which is a pure C
    implementation of the 'Git' core methods. Provides access to 'Git'
    repositories to extract data and running some basic 'Git'
    commands.
An Rstudio addin for version control that allows users to clone
    repositories, create and delete branches, and sync forks on GitHub, GitLab, etc.
    Furthermore, the addin uses the GitLab API to allow instructors to create
    forks and merge requests for all students/teams with one click of a button.
Provides an helpful way to install packages hosted on GitHub.
Provides R functions to access the API of the project and
    repository management web application gitlab. For many common tasks (repository
    file access, issue assignment and status, commenting) convenience wrappers
    are provided, and in addition the full API can be used by specifying request
    locations. Gitlab is open-source software and can be self-hosted or used on
    gitlab.com.
The goal of this package is to allow users to robustly
    and quickly grid and quantify sizes of pinned colonies in plate images.
    gitter works by first finding the grid of colonies from a preprocessed image and then locating the bounds of each colony separately.
    It includes several image pre-processing techniques, such
    as autorotation of plates, noise removal, contrast adjustment and image
    resizing.
Functions to assess the calibration of logistic regression models with the GiViTI (Gruppo Italiano per la Valutazione degli interventi in Terapia Intensiva, Italian Group for the Evaluation of the Interventions in Intensive Care Units - see <http://www.giviti.marionegri.it/>) approach. The approach consists in a graphical tool, namely the GiViTI calibration belt, and in the associated statistical test. These tools can be used both to evaluate the internal calibration (i.e. the goodness of fit) and to assess the validity of an externally developed model.
`Giza' offers a simple way of creating multiple pyramid
        plots in one graphics window, exploiting the power of the
        lattice package. It is a handy way of visualizing longitudinal
        grouped (i.e.: age- and education-structured) data.
Analyzes joint attribute data (e.g., species abundance) that are combinations of continuous and discrete data with Gibbs sampling.  Full model and computation details are described in Clark et al. (2018) <doi.org/10.1002/ecm.1241>.
Routines for fitting various joint regression models, with several types of covariate effects, in the presence of associated error equations, endogeneity, non-random sample selection or partial observability.
Functions for the g-and-k and generalised g-and-h distributions.
Implementations of the treatment effect estimators for hybrid (self-selection) experiments, as developed by Brian J. Gaines and James H. Kuklinski, (2011), "Experimental Estimation of Heterogeneous Treatment Effects Related to Self-Selection," American Journal of Political Science 55(3): 724-736.
Imports the 'gkmSVM' v2.0 functionalities into R <http://www.beerlab.org/gkmsvm/>
    It also uses the 'kernlab' library (separate R package by different authors) for various SVM algorithms.
A fully distributed glacier surface mass balance model developed for the simulation of accumulation and ablation processes on debris-free as well as debris-covered glaciers.
Functions capable of performing  efficient design matrix free  penalized estimation in large scale 2 and 3-dimensional generalized linear array model framework. The generic glamlasso() function solves the penalized maximum likelihood estimation (PMLE) problem in a pure generalized linear array model (GLAM) as well as in a GLAM containing a non-tensor component. Currently Lasso or Smoothly Clipped Absolute Deviation (SCAD) penalized estimation is possible for the followings models: The Gaussian model with identity link, the Binomial model with logit link, the Poisson model with log link and the Gamma model with log link. Furthermore this package also contains two  functions  that can be used to fit special cases of GLAMs, see glamlassoRR() and glamlassoS(). The procedure underlying these functions is based on the gdpg algorithm from Lund et al. (2017) <doi:10.1080/10618600.2017.1279548>.
Functions are provided for estimation, testing, diagnostic checking and forecasting of generalized linear autoregressive moving average (GLARMA) models for discrete valued time series with regression variables.  These are a class of observation driven non-linear non-Gaussian state space models. The state vector consists of a linear regression component plus an observation driven component consisting of an autoregressive-moving average (ARMA) filter of past predictive residuals. Currently three distributions (Poisson, negative binomial and binomial) can be used for the response series. Three options (Pearson, score-type and unscaled) for the residuals in the observation driven component are available. Estimation is via maximum likelihood (conditional on initializing values for the ARMA process) optimized using Fisher scoring or Newton Raphson iterative methods. Likelihood ratio and Wald tests for the observation driven component allow testing for serial dependence in generalized linear model settings. Graphical diagnostics including model fits, autocorrelation functions and probability integral transform residuals are included in the package. Several standard data sets are included in the package.
Interacts with the 'Glassdoor' API
    <https://www.glassdoor.com/developer/index.htm>. Allows the user to 
    search job statistics, employer statistics, and job progression, 
    where 'Glassdoor' provides a breakdown of other jobs a person did 
    after their current one.
Analyses response times and accuracies from psychological experiments with the linear ballistic accumulator (LBA) model from Brown and Heathcote (2008). The LBA model is optionally fitted with explanatory variables on the parameters such as the drift rate, the boundary and the starting point parameters. A log-link function on the linear predictors can be used to ensure that parameters remain positive when needed.
Enables calculation of image textures derived from grey-level
    co-occurrence matrices (GLCMs). Supports processing images that cannot
    fit in memory.
The  generalised lambda distribution, or Tukey lambda distribution, provides a wide variety of shapes with one functional form.   
  This package provides random numbers, quantiles, probabilities, densities and density quantiles for four different parameterisations of the distribution. 
  It provides the density function, distribution function, and Quantile-Quantile plots.  
  It implements a variety of estimation methods for the distribution, including diagnostic plots.  
  Estimation methods include the starship (all 4 parameterisations) and a number of methods for only the FKML parameterisation.  
  These include maximum likelihood, maximum product of spacings, Titterington's method, Moments, L-Moments, Trimmed L-Moments and Distributional Least Absolutes.
The fitting algorithms considered in this package have two major objectives. One is to provide a smoothing device to fit distributions to data using the weight and unweighted discretised approach based on the bin width of the histogram. The other is to provide a definitive fit to the data set using the maximum likelihood and quantile matching estimation. Other methods such as moment matching, starship method, L moment matching are also provided. Diagnostics on goodness of fit can be done via qqplots, KS-resample tests and comparing mean, variance, skewness and kurtosis of the data with the fitted distribution.
Owing to the rich shapes of Generalised Lambda Distributions (GLDs), GLD standard/quantile regression is a competitive flexible model compared to standard/quantile regression.  The proposed method has some major advantages: 1) it provides a reference line which is very robust to outliers with the attractive property of zero mean residuals and 2) it gives a unified, elegant quantile regression model from the reference line with smooth regression coefficients across different quantiles. The goodness of fit of the proposed model can be assessed via QQ plots and Kolmogorov-Smirnov tests and data driven smooth test, to ensure the appropriateness of the statistical inference under consideration. Statistical distributions of coefficients of the GLD regression line are obtained using simulation, and interval estimates are obtained directly from simulated data.
Fits a generalized linear density ratio model (GLDRM).
    A GLDRM is a semiparametric generalized linear model.
    In contrast to a GLM, which assumes a particular exponential family distribution, 
    the GLDRM uses a semiparametric likelihood to estimate the reference distribution. 
    The reference distribution may be any discrete, continuous, or mixed exponential 
    family distribution. The model parameters, which include both the regression 
    coefficients and the cdf of the unspecified reference distribution, are estimated 
    by maximizing a semiparametric likelihood. Regression coefficients are estimated 
    with no loss of efficiency, i.e. the asymptotic variance is the same as if the 
    true exponential family distribution were known.
    Huang (2014) <doi:10.1080/01621459.2013.824892>.
    Huang and Rathouz (2012) <doi:10.1093/biomet/asr075>.
    Rathouz and Gao (2008) <doi:10.1093/biostatistics/kxn030>.
Functions evaluate global and individual tests for direct effects in Mendelian randomization studies.
Group-Lasso INTERaction-NET. Fits linear pairwise-interaction models that satisfy strong hierarchy: if an interaction coefficient is estimated to be nonzero, then its two associated main effects also have nonzero estimated coefficients. Accommodates categorical variables (factors) with arbitrary numbers of levels, continuous variables, and combinations thereof. Implements the machinery described in the paper "Learning interactions via hierarchical group-lasso regularization" (JCGS 2015, Volume 24, Issue 3). Michael Lim & Trevor Hastie (2015) <DOI:10.1080/10618600.2014.938812>.
Routines for log-linear models of incomplete contingency tables,
             including some latent class models, via EM and Fisher scoring
             approaches.  Allows bootstrapping.
Generalized linear latent variable model (gllvm) for analyzing multivariate data. 
      Estimation is performed using either Laplace approximation or variational approximation method implemented via TMB (Kristensen et al., (2016), <doi:10.18637/jss.v070.i05>). 
      Details for gllvm, see Hui et al. (2015) <doi:10.1111/2041-210X.12236> 
      and (2017) <doi:10.1080/10618600.2016.1164708> and Niku et al. (2017) 
      <doi:10.1007/s13253-017-0304-7>.
Provides two functions that generate source code implementing the predict 
  function of fitted glm objects. In this version, code can be generated for either 'C' 
  or 'Java'. The idea is to provide a tool for the easy and fast deployment of glm 
  predictive models into production. The source code generated by this package implements 
  two function/methods. One of such functions implements the equivalent to 
  predict(type="response"), while the second implements predict(type="link"). Source code 
  is written to disk as a .c or .java file in the specified path. In the case of c, an .h 
  file is also generated.
Functions to calculate predicted values and the difference between
    the two cases with confidence interval for glm(), glm.nb(), polr() and multinom().
Fits generalized linear models using the same model specification as glm in the stats package, but with a modified default fitting method that provides greater stability for models that may fail to converge using glm.
Several tests for high dimensional generalized linear models have been proposed recently. In this package, we implemented a new test called adaptive  sum of powered score (aSPU) for high dimensional generalized linear models, which is often more powerful than the existing methods in a wide scenarios. We also implemented permutation based version of several existing methods for research purpose. We recommend users use the aSPU test for their real testing problem. You can learn more about the tests implemented in the package via the following papers: 1. Pan, W., Kim, J., Zhang, Y., Shen, X. and Wei, P. (2014) <DOI:10.1534/genetics.114.165035> A powerful and adaptive association test for rare variants, Genetics, 197(4). 2. Guo, B., and Chen, S. X. (2016) <DOI:10.1111/rssb.12152>. Tests for high dimensional generalized linear models. Journal of the Royal Statistical Society: Series B. 3. Goeman, J. J., Van Houwelingen, H. C., and Finos, L. (2011) <DOI:10.1093/biomet/asr016>. Testing against a high-dimensional alternative in the generalized linear model: asymptotic type I error control. Biometrika, 98(2).
Find all hierarchical models of specified generalized linear
    model with information criterion (AIC, BIC, or AICc) within specified
    cutoff of minimum value.  Alternatively, find all such graphical models.
    Use branch and bound algorithm so we do not have to fit all models.
Implements the Bayesian paradigm
    for fractional polynomials in generalized linear
    models, described by Held, Gravestock, Sabanes Bove (2015) <doi:10.1214/14-STS510>.
    See package 'bfp' for the treatment of normal
    models.
Fits generalized linear models where the parameters are
        subject to linear constraints. The model is specified by giving
        a symbolic description of the linear predictor, a description
        of the error distribution, and a matrix of constraints on the
        parameters.
This package contains functions to perform generalized
        linear mixed Dirichlet models using posterior simulation.
Recursive partitioning based on (generalized) linear mixed models
    (GLMMs) combining lmer()/glmer() from lme4 and lmtree()/glmtree() from partykit.
We propose to use sparse regression model to achieve variable selection while accounting for graph-constraints among coefficients. Different linear combination of a sparsity penalty(L1) and a smoothness(MCP) penalty has been used, which induces both sparsity of the solution and certain smoothness on the linear coefficients.
Efficient algorithms for fitting regularization paths for
        linear or logistic regression models penalized by LEP.
Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approximation. Then maximizes the likelihood approximation to return maximum likelihood estimates, observed Fisher information, and other model information.
Automates running Generalised Linear Mixed Models, including
    spatial models, with WinBUGS, OpenBUGS and JAGS. Models are specified with
    formulas, with the package writings model files, arranging unbalanced data
    in ragged arrays, and creating starting values. The model is re-parameterized,
    and functions are provided for converting model outputs to the original
    parameterization.
A variable selection approach for generalized linear mixed models by L1-penalized estimation is provided.
Binomial and Poisson regression for clustered data, fixed
        and random effects with bootstrapping.
Generalized Linear Mixed Model (GLMM) for Binary Randomized Response Data.
    Includes Cauchit, Compl. Log-Log, Logistic, and Probit link functions for Bernoulli Distributed RR data.
    RR Designs: Warner, Forced Response, Unrelated Question, Kuk, Crosswise, and Triangular.
Conduct inference about generalized linear mixed models, with a
    choice about which method to use to approximate the likelihood. In addition
    to the Laplace and adaptive Gaussian quadrature approximations, which are
    borrowed from 'lme4', the likelihood may be approximated by the sequential
    reduction approximation, or an importance sampling approximation. These
    methods provide an accurate approximation to the likelihood in some
    situations where it is not possible to use adaptive Gaussian quadrature.
Fit linear and generalized linear mixed models with various
    extensions, including zero-inflation. The models are fitted using maximum
    likelihood estimation via 'TMB' (Template Model Builder). Random effects are
    assumed to be Gaussian on the scale of the linear predictor and are integrated
    out using the Laplace approximation. Gradients are calculated using automatic
    differentiation.
Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial regression. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper linked to via the URL below.
Penalized methods are useful for fitting over-parameterized models. This package includes functions for restructuring an ordinal 
 response dataset for fitting continuation ratio models for datasets  where the number of covariates exceeds the sample size or when   
 there is collinearity among the covariates. The glmnet fitting algorithm is used to fit the continuation ratio model after data restructuring.
Provides a formula interface for the 'glmnet' package for
    elasticnet regression, a method for cross-validating the alpha parameter,
    and other quality-of-life tools.
A path-following algorithm for L1 regularized generalized linear models and Cox proportional hazards model.
Provides a function for fitting a penalized constrained continuation ratio model using the glmpath algorithm and methods for extracting coefficient estimates, predicted class, class probabilities, and plots.
Data sets from the book Generalized Linear Models with Examples in R by Dunn and Smyth.
It provides an extremely efficient procedure for fitting the entire truncated lasso regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. The algorithm uses the difference of convex technique. The detail of the algorithm is described in Shen, Pan and Zhu (2012) <doi:10.1080/01621459.2011.645783>. The package is inherited from a popular R package 'glmnet' and many functions in 'glmnet' can be directly used in 'glmtlp'. You can learn more details by the online manual (<http://wuchong.org/glmtlp.html>).
Automated model selection and model-averaging. Provides a
        wrapper for glm and other functions, automatically generating
        all possible models (under constraints set by the user) with
        the specified response and explanatory variables, and finding
        the best models in terms of some Information Criterion (AIC,
        AICc or BIC). Can handle very large numbers of candidate
        models. Features a Genetic Algorithm to find the best models
        when an exhaustive screening of the candidates is not feasible.
Variable selection deviation (VSD) measures and instability tests for high-dimensional model selection methods such as LASSO, SCAD and MCP, etc., to decide whether the sparse patterns identified by those methods are reliable.
Extended techniques for generalized linear models (GLMs), especially for binary responses,
             including parametric links and heteroskedastic latent variables.
'globalboosttest' implements a permutation-based testing
        procedure to globally test the (additional) predictive value of
        a large set of predictors given that a small set of predictors
        is already available. Currently, 'globalboosttest' supports
        binary outcomes (via logistic regression) and survival outcomes
        (via Cox regression). It is based on boosting regression as
        implemented in the package 'mboost'.
permutation based global test with deviance as test statistic
Initial metabolic networks often inaccurately predict in-silico growth or non-growth if compared to in-vivo data. This package refines metabolic network models by making networks changes (i.e., removing, adding, changing reversibility of reactions; adding and removing biomass metabolites) and simultaneously matching sets of experimental growth and non-growth data (e.g., KO-mutants, mutants grown under different media conditions,...)
Implementation of three different Gene set analysis (GSA) algorithms for combining the individual pvalues of a set of genetic variats (SNPs) in a gene level pvalue. The implementation includes the selection of the best inheritance model for each SNP.
It provides more controls on the option values such as validation
    and filtering on the values, making options invisible or private.
This package makes available 50 objective functions for benchmarking the performance of global optimization algorithms
Identifies global ("unknown" or "free") objects in R expressions
    by code inspection using various strategies, e.g. conservative or liberal.
    The objective of this package is to make it as simple as possible to
    identify global objects for the purpose of exporting them in distributed
    compute environments.
Basic functions for plotting 2D and 3D views of a sphere, by default the Earth with its major coastline, and additional lines and points.
Tools for the generalized logistic distribution (Type I,
             also known as skew-logistic distribution), encompassing
	     basic distribution functions (p, q, d, r, score), maximum
	     likelihood estimation, and structural change methods.
R Interface to C API of GLPK, needs GLPK Version >= 4.42
Functions to conduct four generalized logrank tests and a score test under a proportional hazards model
Likelihood ratio tests for genome-wide association and genome-wide linkage analysis under heterogeneity.
Performs linear regression with correlated predictors, responses and correlated measurement errors in predictors and responses, correcting for biased caused by these.
An implementation of interpreted string literals, inspired by
  Python's Literal String Interpolation <https://www.python.org/dev/peps/pep-0498/> and Docstrings
  <https://www.python.org/dev/peps/pep-0257/> and Julia's Triple-Quoted String Literals
  <https://docs.julialang.org/en/stable/manual/strings/#triple-quoted-string-literals>.
Performs Granger mediation analysis (GMA) for time series. This package includes a single level GMA model and a two-level GMA model, for time series with hierarchically nested structure. The single level GMA model for the time series of a single participant performs the causal mediation analysis which integrates the structural equation modeling and the Granger causality frameworks. A vector autoregressive model of order p is employed to account for the spatiotemporal dependencies in the data. Meanwhile, the model introduces the unmeasured confounding effect through a nonzero correlation parameter. Under the two-level model, by leveraging the variabilities across participants, the parameters are identifiable and consistently estimated based on a full conditional likelihood or a two-stage method. See Zhao, Y., & Luo, X. (2017), Granger Mediation Analysis of Multiple Time Series with an Application to fMRI, <arXiv:1709.05328> for details.
Performs genomic mediation
    analysis with adaptive confounding adjustment (GMAC) proposed by Yang et al. (2017) <doi:10.1101/078683>. It implements large scale
    mediation analysis and adaptively selects potential confounding variables to
    adjust for each mediation test from a pool of candidate confounders. The package
    is tailored for but not limited to genomic mediation analysis (e.g., cis-gene
    mediating trans-gene regulation pattern where an eQTL, its cis-linking gene
    transcript, and its trans-gene transcript play the roles as treatment, mediator
    and the outcome, respectively), restricting to scenarios with the presence of
    cis-association (i.e., treatment-mediator association) and random eQTL (i.e.,
    treatment).
An interface to the Gmail RESTful API.  Allows access to your
    Gmail messages, threads, drafts and labels.
Get distance and travel time between two points from Google Maps.
    Four possible modes of transportation (bicycling, walking, driving and
    public transportation).
Unsupervised Clustering and Meta-analysis using Gaussian Mixture
    Copula Models.
Functions and a graphical user interface for graphical described
    multiple test procedures.
GMD is a package for non-parametric distance measurement between
    two discrete frequency distributions.
A template for a geometallurgical database and a fast and easy
    interface for accessing it is provided in this package.
Group method of data handling (GMDH) - type neural network algorithm is the heuristic self-organization method for modelling the complex systems. In this package, GMDH-type neural network algorithms are applied to make short term forecasting for a univariate time series.
Performs binary classification via Group Method of Data Handling (GMDH) - type neural network algorithm. There exist two main algorithms available in GMDH() and dceGMDH() functions. GMDH() performs classification via GMDH algorithm for a binary response and returns important variables. dceGMDH() performs classification via diverse classifiers ensemble based on GMDH (dce-GMDH) algorithm. Also, the package produces a well-formatted table of descriptives for a binary response. Moreover, it produces confusion matrix and related statistics and scatter plot (2D and 3D) with classification labels of binary classes to assess the prediction performance. All 'GMDH2' functions are designed for a binary response. See Dag O. and Yozgatligil C. (2016, ISSN:2073-4859) and Kondo T. and Ueno J. (2016, ISSN:1349-4198) for the details of GMDH algorithms.
Fast algorithms  for robust estimation with large samples of multivariate observations. Estimation of the geometric median, robust k-Gmedian clustering, and robust PCA based on the Gmedian covariation matrix.
Current version of this R package conducts mediation path analysis for multiple mediators in two stages.
An implementation of an all-in-one function for a wide range of meta-analysis problems. It contains three functions. The gmeta() function unifies all standard meta-analysis methods and also several newly developed ones under a framework of combining confidence distributions (CDs). Specifically, the package can perform classical p-value combination methods (such as methods of Fisher, Stouffer, Tippett, etc.), fit meta-analysis fixed-effect and random-effects models, and synthesizes 2x2 tables. Furthermore, it can perform robust meta-analysis, which provides protection against model-misspecifications, and limits the impact of any unknown outlying studies. In addition, the package implements two exact meta-analysis methods from synthesizing 2x2 tables with rare events (e.g., zero total event). The np.gmeta() function summarizes information obtained from multiple studies and makes inference for study-level parameters with no distributional assumption. Specifically, it can construct confidence intervals for unknown, fixed study-level parameters via confidence distribution. Furthermore, it can perform  estimation via asymptotic confidence distribution whether tie or near tie condition exist or not. The plot.gmeta() function to visualize individual and combined CDs through extended forest plots is also available. Compared to version 2.2-6, version 2.3-0 contains a new function np.gmeta().
Some methods for the inference and clustering of univariate and 
             multivariate functional data, using a generalization of Mahalanobis
             distance, along with some functions useful for the analysis of functional data.
             For further details, see Martino A., Ghiglietti, A., Ieva, F. and Paganoni A. M. (2017) <arXiv:1708.00386>.
Tools for making the descriptive "Table 1" used in medical
    articles, a transition plot for showing changes between categories, 
    flow charts by extending the grid package, a method for
    variable selection based on the SVD, Bézier lines with arrows complementing the
    ones in the 'grid' package, and more.
It is a complete suite to estimate models based on moment
        conditions. It includes the two step Generalized method of
        moments (Hansen 1982; <doi:10.2307/1912775>), the iterated GMM and continuous
        updated estimator (Hansen, Eaton and Yaron 1996; <doi:10.2307/1392442>) and several
        methods that belong to the Generalized Empirical Likelihood
        family of estimators (Smith 1997; <doi:10.1111/j.0013-0133.1997.174.x>,
        Kitamura 1997; <doi:10.1214/aos/1069362388>, Newey and Smith 2004; <doi:10.1111/j.1468-0262.2004.00482.x>,
	and Anatolyev 2005 <doi:10.1111/j.1468-0262.2005.00601.x>).
Likelihood-based Boosting for Generalized mixed models
An implementation of maximum simulated likelihood method for the
    estimation of multinomial logit models with random coefficients.
    Specifically, it allows estimating models with continuous heterogeneity
    such as the mixed multinomial logit and the generalized multinomial logit.
    It also allows estimating models with discrete heterogeneity such as the
    latent class and the mixed-mixed multinomial logit model.
Various R programming tools for model fitting.
Make 2D plots of the polyeder of a LP or IP problem, including
    integer points and iso profit curve. Can also make a plot of a bi-objective
    criterion space.
Multiple Precision Arithmetic (big integers and rationals,
 prime number tests, matrix computation), "arithmetic without limitations"
 using the C library GMP (GNU Multiple Precision Arithmetic).
Integrates game theory and ecological theory to construct 
    social-ecological models that simulate the management of populations and 
    stakeholder actions. These models build off of a previously developed 
    management strategy evaluation (MSE) framework to simulate all aspects of 
    management: population dynamics, manager observation of populations, manager
    decision making, and stakeholder responses to management decisions. The 
    newly developed generalised management strategy evaluation (GMSE) 
    framework uses genetic algorithms to mimic the decision-making process of 
    managers and stakeholders under conditions of change, uncertainty, and 
    conflict. Simulations can be run using gmse(), gmse_apply(), and
    gmse_gui() functions.
Interface between the GMT map-making software and R, enabling the
  user to manipulate geographic data within R and call GMT commands to draw and
  annotate maps in postscript format. The gmt package is about interactive data
  analysis, rapidly visualizing subsets and summaries of geographic data, while
  performing statistical analysis in the R console.
Generalized Mann-Whitney type tests based on probabilistic
        indices and new diagnostic plots.
Provide functions to compute standard and generalized Nash Equilibria. Optimization methods are available nonsmooth reformulation, fixed-point formulation, minimization problem and constrained-equation reformulation.
Computes the test statistic and p-value of the Cramer-von Mises and Anderson-Darling test for some continuous distribution functions proposed by Chen and Balakrishnan (1995) <http://asq.org/qic/display-item/index.html?item=11407>. In addition to our classic distribution functions here, we  calculate the Goodness of Fit (GoF) test to dataset which follows the extreme value distribution function, without remembering the formula of distribution/density functions.
A variety of functions to fit linear and nonlinear
    regression with a large selection of distributions.
Functions to specify and fit generalized nonlinear models, including models with multiplicative interaction terms such as the UNIDIFF model from sociology and the AMMI model from crop science, and many others.  Over-parameterized representations of models are used throughout; functions are provided for inference on estimable parameter combinations, as well as standard methods for diagnostics etc.
This package performs generalized non-negative matrix factorization based on Renyi divergence.
Functions for obtaining generalized normal/exponential power distribution probabilities, quantiles, densities and random deviates. The generalized normal/exponential power distribution was introduced by Subbotin (1923) and rediscovered by Nadarajah (2005). The parametrization given by Nadarajah (2005) <doi:10.1080/02664760500079464> is used.
Read data files readable by 'gnumeric' into 'R'. Can read
        whole sheet or a range, from several file formats, including
        the native format of 'gnumeric'. Reading is done by using
        'ssconvert' (a file converter utility included in the 'gnumeric'
        distribution <http://projects.gnome.org/gnumeric/>) to convert
        the requested part to CSV. From 'gnumeric' files (but not other
        formats) can list sheet names and sheet sizes or read all
        sheets.
A collection of functions to solve weighted and lexicographical
        goal programming problems as specified by Lee (1972) and Ignizio (1976).
A collection of functions useful in (vegetation) community analyses and ordinations, mainly to facilitate plotting and interpretation. Includes automatic species selection for ordination diagrams, species response curves and rank-abundance curves.
Implementation of model-checking techniques for generalized linear
    models and linear structural equation models based on cumulative residuals
Harness the power of 'quanteda', 'data.table' & 'stringi' to quickly generate 'tm' DocumentTermMatrix
             and TermDocumentMatrix data structures.
Several GoF tests for Copulae are provided. A new hybrid test is implemented which supports all of the individual tests. Estimation methods for the margins are provided. All the tests support parameter estimation and predefined values. The parameters are estimated by pseudo maximum likelihood but if it fails the estimation switches automatically to inversion of Kendall's tau. All the tests support automatized parallelization of the bootstrapping tasks.
Tests of goodness-of-fit based on a kernel smoothing of the data.
Consider a goodness-of-fit(GOF) problem of testing whether a random sample comes from one sample location-scale model where location and scale parameters are unknown. It is well known that Khmaladze martingale transformation method provides asymptotic distribution free test for the GOF problem. This package contains one function: KhmaladzeTrans(). In this version, KhmaladzeTrans() provides test statistic and critical value of GOF test for normal, Cauchy, logistic, Gamma, Weibull, Gumbel, and Rayleigh distributions.
Goodness-of-fit metrics, such as R-Squared, RMSE, etc., share a sensitivity to noise, dependent on the degrees of freedom.  Some metrics, such as R-Squared, decrease with increasing dof and some, such as RMSE, increase with increasing dof.  This package calculates the noise baseline (ceiling) by random sampling, calculating the metric’s value for each sample and counting the number of samples below a desired level, 95% by default. If one’s measure is above (below) the calculation corresponding to the desired level, then the measurement is distinguishable from noise.  In addition, the ratio of the measurement to the calculated level provides a way to compare measurements of different degrees of freedom.
Goodness-of-fit tests for  gamma, inverse Gaussian, log-normal, 'Weibull', 'Frechet', Gumbel, normal, multivariate normal, Cauchy, Laplace or double exponential, exponential and generalized Pareto distributions. Parameter estimators for gamma, inverse Gaussian and generalized Pareto distributions.
Cramer-Von Mises and Anderson-Darling tests of goodness-of-fit
	     for continuous univariate distributions, using
	     efficient algorithms.
Extension of 'gof' package to survival models.
Easy and flexible interface for manipulating go game (weiqi, baduk) data.
    The package features a reader function for SGF (smart go format) text files,
    and a set of plotting functions that draw go board images.
Accounting for genes' functional-non-equivalence within pathways in classical Gene-set-analysis.
Implementation of the GO-GARCH model class
A tool for identifying multiple word key terms in free text with application to Gene Ontology labels.
A zero-inflated quasi-Poisson factor model to display similarity between samples visually in a low (2 or 3) dimensional space.
Association analysis between categorical
    variables using the Goodman and Kruskal tau measure. This asymmetric association
    measure allows the detection of asymmetric relations between categorical
    variables (e.g., one variable obtained by re-grouping another).
Give advice about good practices when building R packages.
    Advice includes functions and syntax to avoid, package structure,
    code complexity, code formatting, etc.
Interact with the Google Analytics 
  APIs <https://developers.google.com/analytics/>, including 
  the Core Reporting API (v3 and v4), Management API, and Multi-Channel
  Funnel API.
Create R functions that interact with OAuth2 Google APIs 
    <https://developers.google.com/apis-explorer/> easily,
    with auto-refresh and Shiny compatibility.
Interact with Google Cloud Storage <https://cloud.google.com/storage/> 
  API in R. Part of the 'cloudyr' <https://cloudyr.github.io/> project.
Interact with the 'Google Compute Engine' API in R. Lets you create, 
  start and stop instances in the 'Google Cloud'.  Support for preconfigured instances, 
  with templates for common R needs.
GET and POST data to Google Forms; an API to Google Forms,
    allowing users to POST data securely to Google Forms without needing authentication or permissioning.
Allows you to retrieve information from the 'Google Knowledge Graph' API <https://www.google.com/intl/bn/insidesearch/features/search/knowledge.html> and process it in R in various forms. The 'Knowledge Graph Search' API lets you find entities in the 'Google Knowledge Graph'. The API uses standard 'schema.org' types and is compliant with the 'JSON-LD' specification.
Call 'Google Cloud' machine learning APIs for text and speech tasks.
  Call the 'Cloud Translation' API <https://cloud.google.com/translate/> for detection 
  and translation of text, the 'Natural Language' API <https://cloud.google.com/natural-language/> to 
  analyse text for sentiment, entities or syntax or the 'Cloud Speech' API 
  <https://cloud.google.com/speech/> to transcribe sound files to text.
Encodes simple feature ('sf') objects and coordinates, and decodes polylines 
    using the 'Google' polyline encoding algorithm (<https://developers.google.com/maps/documentation/utilities/polylinealgorithm>).
Allows printing documents from R through 'Google Cloud Print' API.
  See <https://developers.google.com/cloud-print/docs/overview> for more information
  about 'Google Cloud Print'.
Provides a collection of functions to set up 'Google Public Data Explorer'
  <https://www.google.com/publicdata/> data visualization tool with your own data,
  building automatically the corresponding DataSet Publishing Language file, or
  DSPL (XML), metadata file jointly with the CSV files. All zip-up and ready to
  be published in 'Public Data Explorer'.
R interface to Google's chart tools, allowing users
    to create interactive charts based on data frames. Charts
    are displayed locally via the R HTTP help server. A modern
    browser with an Internet connection is required and for some
    charts a Flash player. The data remains local and is not
    uploaded to Google.
Provides a mechanism to plot a 'Google Map' from 'R' and overlay
    it with shapes and markers. Also provides access to 'Google Maps' APIs,
    including places, directions, roads, distances, geocoding, elevation and
    timezone.
Implementation of multilayered visualizations for enhanced
    graphical representation of functional analysis data. It combines and integrates
    omics data derived from expression and functional annotation enrichment
    analyses. Its plotting functions have been developed with an hierarchical
    structure in mind: starting from a general overview to identify the most
    enriched categories (modified bar plot, bubble plot) to a more detailed one
    displaying different types of relevant information for the molecules in a given
    set of categories (circle plot, chord plot, cluster plot, Venn diagram, heatmap).
Generalized Odds Rate Mixture Cure (GORMC) model is a flexible model of fitting survival data with a cure fraction, including the Proportional Hazards Mixture Cure (PHMC) model and the Proportional Odds Mixture Cure Model as special cases. This package fit the GORMC model with interval censored data.
Generalized Order-Restricted Information Criterion (GORIC) value for a set of hypotheses in multivariate regression models.
This package purposes to deal with public survey data of
        Japanese government via their Application Programming Interface
        (http://statdb.nstac.go.jp/)
Compute Gower's distance (or similarity) coefficient between records. Compute 
    the top-n matches between records. Core algorithms are executed in parallel on systems
    supporting OpenMP.
Produces a generalized pairs (gpairs) plot.
Gaussian process regression models, a.k.a. Kriging models, are
    applied to global multi-objective optimization of black-box functions.
    Multi-objective Expected Improvement and Step-wise Uncertainty Reduction
    sequential infill criteria are available. A quantification of uncertainty
    on Pareto fronts is provided using conditional simulations.
Gradient Projection Algorithm Rotation for Factor Analysis. See ?GPArotation.Intro for more details.
Functions that compute the distribution functions for the Generalized Poisson Binomial distribution, which provides the cdf, pmf, quantile function, and random number generation for the distribution.
A generalized polynomial chaos expansion of a model taking as input independent random variables is achieved. A statistical and a global sensitivity analysis of the model are also carried out.
This package implements guided principal components analysis for the detection of batch effects in high-throughput data.
General polygon clipping routines for R based on Alan
        Murta's C library
Provides a function to detect Differential Item Functioning (DIF) in Generalized Partial Credit Models (GPCM) and special cases of the GPCM. A joint model is set up where DIF is explicitly parametrized and penalized likelihood estimation is used for parameter selection. The big advantage of the method called GPCMlasso is that several variables can be treated simultaneously and that both continuous and categorical variables can be used to detect DIF.
This package implements an extension of principal component analysis (PCA) tailored to handle multiple data tables. It can handle Big Data in the sense that the variation in massive data can be described by intervals [a, b] and multiple tables.
Functions implement collocation-inference for
    stochastic process driven by distributed delay differential equations.
    They also provide tools for selecting the lags for distributed delay
    using shrinkage methods, estimating time-varying coefficients,
    and tools for inference and prediction.
This package computes the bootstrap goodness-of-fit test
        for the generalized Pareto distribution by Villasenor-Alva and
        Gonzalez-Estrada (2009). The null hypothesis includes heavy and
        non-heavy tailed gPd's. A function for fitting the gPd to data
        using the parameter estimation methods proposed in the same
        article is also provided.
Use functional regression as the mean structure and Gaussian Process as the covariance structure.
A computationally stable approach of fitting a Gaussian Process (GP) model to a deterministic simulator.
Bindings to GnuPG for working with OpenGPG (RFC4880) cryptographic methods.
    Includes utilities for public key encryption, creating and verifying digital signatures,
    and managing your local keyring. Note that some functionality depends on the version of 
    GnuPG that is installed on the system. On Windows this package can be used together with
    'GPG4Win' which provides a GUI for managing keys and entering passphrases.
Sequential strategies for finding a game equilibrium are proposed in a black-box setting (expensive pay-off evaluations, no derivatives). The algorithm handles noiseless or noisy evaluations. Two acquisition functions are available. Graphical outputs can be generated automatically.
Functions for reordering
    input locations, finding ordered nearest neighbors
    (with help from 'FNN' package), grouping operations, 
    approximate likelihood evaluations, profile likelihoods,
    Gaussian process predictions, and conditional simulations.
    Covariance functions for spatial and spatial-temporal data
    on Euclidean domains and spheres are provided. The original 
    approximation is due to Vecchia (1988) 
    <http://www.jstor.org/stable/2345768>, and the reordering and
    grouping methods are from Guinness (2018)
    <doi:10.1080/00401706.2018.1437476>.
Implementation of a generalized pair hidden Markov chain model (GPHMM) that
  can be used to compute the probability of alignment between two sequences
  of nucleotides (e.g., a reference sequence and a noisy sequenced read).
  The model can be trained on a dataset where the noisy sequenced reads are known
  to have been sequenced from known reference sequences. If no training sets are
  available default parameters can be used.
Collection of datasets as prepared by Profs. A.P. Gore, S.A. Paranjape, and M.B. Kulkarni of Department of Statistics, Poona University, India. With their permission, first letter of their names forms the name of this package, the package has been built by me and made available for the benefit of R users. This collection requires a rich class of models and can be a very useful building block for a beginner.
Provides functions for estimating a generalized partial
	     linear model, a semiparametric variant of the generalized linear model
	     (GLM) which replaces the linear predictor by the sum of a linear
	     and a nonparametric function.
Various R programming tools for plotting data, including:
  - calculating and plotting locally smoothed summary function as
    ('bandplot', 'wapply'),
  - enhanced versions of standard plots ('barplot2', 'boxplot2',
    'heatmap.2', 'smartlegend'),
  - manipulating colors ('col2hex', 'colorpanel', 'redgreen',
    'greenred', 'bluered', 'redblue', 'rich.colors'),
  - calculating and plotting two-dimensional data summaries ('ci2d',
    'hist2d'),
  - enhanced regression diagnostic plots ('lmplot2', 'residplot'),
  - formula-enabled interface to 'stats::lowess' function ('lowess'),
  - displaying textual data in plots ('textplot', 'sinkplot'),
  - plotting a matrix where each cell contains a dot whose size
    reflects the relative magnitude of the elements ('balloonplot'),
  - plotting "Venn" diagrams ('venn'),
  - displaying Open-Office style plots ('ooplot'),
  - plotting multiple data on same region, with separate axes
    ('overplot'),
  - plotting means and confidence intervals ('plotCI', 'plotmeans'),
  - spacing points in an x-y plot so they don't overlap ('space').
Combining a generalized linear model with an additional tree part 
          on the same scale. A four-step procedure is proposed to fit the model and test 
          the joint effect of the selected tree part while adjusting on confounding factors. 
          We also proposed an ensemble procedure based on the bagging to improve prediction 
          accuracy and computed several scores of importance for variable selection.
Provides a general and efficient tool for fitting a response surface to datasets via Gaussian processes. The dataset can have multiple responses. The package is based on the work of Bostanabad, R., Kearney, T., Tao, S., Apley, D. W. & Chen, W. Leveraging the nugget parameter for efficient Gaussian process modeling (2017) <doi:10.1002/nme.5751>.
This package contains tools for studying genotype-phenotype (GP) maps for bi-allelic loci underlying quantitative phenotypes. The 0.1 version is released in connection with the publication of Gjuvsland et al. (2003) and implements basic line plots and the monotonicity measures for GP maps presented in the paper. Reference: Gjuvsland AB, Wang Y, Plahte E and Omholt SW (2013) Monotonicity is a key feature of genotype-phenotype maps. Front. Genet. 4:216. doi: 10.3389/fgene.2013.00216 [\href{http://www.frontiersin.org/Journal/10.3389/fgene.2013.00216/full}{link}]
Platform dedicated to the Global Modelling technique. Its aim is to
    obtain ordinary differential equations of polynomial form directly from
    time series. It can be applied to single or multiple time series under various
    conditions of noise, time series lengths, sampling, etc. This platform
    is developped at the Centre d'Etudes Spatiales de la Biosphere (CESBIO), UMR 5126
    UPS/CNRS/CNES/IRD, 18 av. Edouard Belin, 31401 TOULOUSE, FRANCE.
    The developments were funded by the French program Les Enveloppes
    Fluides et l'Environnement (LEFE, MANU, projets GloMo, SpatioGloMo and MoMu).
    The French program Defi InFiNiTi (CNRS) and PNTS are also acknowledged
    (projects Crops'IChaos and Musc & SlowFast).
Implementation of the Fourier method to estimate aquatic gross primary production from high frequency oxygen data, described in Cox, et al (2015) <doi:10.1002/lom3.10046> and Cox, et al (2017) <doi:10.5194/bg-2017-81>.
This package provides a minimalistic functionality necessary to apply Gaussian Process in R. They provide a selection of functionalities of GPML Matlab library.
Implements a Gaussian process (GP)-based ranking method
    which can be used to rank multiple time series according to their
    temporal activity levels. An example is the case when expression
    levels of all genes are measured over a time course and the main
    concern is to identify the most active genes, i.e. genes which
    show significant non-random variation in their expression levels.
    This is achieved by computing Bayes factors for each time series
    by comparing the marginal likelihoods under time-dependent and
    time-independent GP models. Additional variance information from
    pre-processing of the observations is incorporated into the GP
    models, which makes the ranking more robust against model
    overfitting. The package supports exporting the results to
    'tigreBrowser' for visualisation, filtering or ranking.
Functional enrichment analysis, gene identifier conversion and
    mapping homologous genes across related organisms via the 'g:Profiler' toolkit
    (<https://biit.cs.ut.ee/gprofiler/>).
The gptk package implements a general-purpose toolkit for Gaussian
  process regression with a variety of covariance functions (e.g. RBF, Mattern, polynomial, etc).
  Based on a MATLAB implementation by Neil D. Lawrence. See inst/doc/index.html for more details.
Provides GPU enabled functions for R objects in a simple and
    approachable manner.  New gpu* and vcl* classes have been provided to
    wrap typical R objects (e.g. vector, matrix), in both host and device
    spaces, to mirror typical R syntax without the need to know OpenCL.
An EM algorithm, Karl et al. (2013) <doi:10.1016/j.csda.2012.10.004>, is used to estimate the generalized, variable, and complete persistence models, Mariano et al. (2010) <doi:10.3102/1076998609346967>. These are multiple-membership linear mixed models with teachers modeled as "G-side" effects and students modeled with either "G-side" or "R-side" effects.
Server implementation of 'GraphQL' <http://facebook.github.io/graphql/>,
    a query language created by Facebook for describing data requirements on complex application
    data models.  Visit <http://graphql.org> to learn more about 'GraphQL'.
Genomic biology is not limited to the confines of the canonical B-
    forming DNA duplex, but includes over ten different types of other secondary
    structures that are collectively termed non-B DNA structures. Of these non-B
    DNA structures, the G-quadruplexes are highly stable four-stranded structures
    that are recognized by distinct subsets of nuclear factors. This package
    provide functions for predicting intramolecular G quadruplexes. In addition, 
    functions for predicting other intramolecular nonB DNA structures are included.
Use the graph-constrained estimation (Grace) procedure (Zhao and Shojaie, 2016 <doi:10.1111/biom.12418>) to estimate graph-guided linear regression coefficients and use the Grace/GraceI/GraceR tests to perform graph-guided hypothesis tests on the association between the response and the predictors.
An implementation of various learning algorithms based on Gradient Descent for dealing with regression tasks. 
	The variants of gradient descent algorithm are :
	Mini-Batch Gradient Descent (MBGD), which is an optimization to use training data partially to reduce the computation load.
	Stochastic Gradient Descent (SGD), which is an optimization to use a random data in learning to reduce the computation load drastically.
	Stochastic Average Gradient (SAG), which is a SGD-based algorithm to minimize stochastic step to average.
	Momentum Gradient Descent (MGD), which is an optimization to speed-up gradient descent learning.
	Accelerated Gradient Descent (AGD), which is an optimization to accelerate gradient descent learning.
	Adagrad, which is a gradient-descent-based algorithm that accumulate previous cost to do adaptive learning.
	Adadelta, which is a gradient-descent-based algorithm that use hessian approximation to do adaptive learning.
	RMSprop, which is a gradient-descent-based algorithm that combine Adagrad and Adadelta adaptive learning ability.
	Adam, which is a gradient-descent-based algorithm that mean and variance moment to do adaptive learning.
	Stochastic Variance Reduce Gradient (SVRG), which is an optimization SGD-based algorithm to accelerates the process toward converging by reducing the gradient.
	Semi Stochastic Gradient Descent (SSGD),which is a SGD-based algorithm that combine GD and SGD to accelerates the process toward converging by choosing one of the gradients at a time.
	Stochastic Recursive Gradient Algorithm (SARAH), which is an optimization algorithm similarly SVRG to accelerates the process toward converging by accumulated stochastic information.
	Stochastic Recursive Gradient Algorithm+ (SARAHPlus), which is a SARAH practical variant algorithm to accelerates the process toward converging provides a possibility of earlier termination.
Provides functions for matching student-answers to teacher answers for a variety of data types.
Widget for an interactive selection and modification of a color gradient. 'gradientPickerD3' allows addition, removement and replacement of color ticks. List of numeric values will automatically translate in their corresponding tick position within the numeric range. App returns a data.frame containing tick values, colors and the positions in percent (0.0 to 1.0) for each color tick in the gradient. The original JS 'jquery-gradient-picker' was implemented by Matt Crinklaw-Vogt (nick: tantaman) <https://github.com/tantaman/>. Widget and JS modifications were done by CD. Peikert.
Functions to fit, visualise and compare Gaussian random field species distribution models.
Probability propagation in graphical independence networks, also
    known as Bayesian networks or probabilistic expert systems.
A native R implementation of grammatical evolution (GE). GE facilitates the discovery of programs that can achieve a desired goal. This is done by performing an evolutionary optimisation over a population of R expressions generated via a user-defined context-free grammar (CFG) and cost function.
Represents metagenomic samples on the Euclidean space to examine similarity amongst samples by studying clusters in the model. Given the matrix of metagenomic counts for samples, this package (1) quantifies dissimilarity between samples using Kendall's tau-distance, (2) constructs multidimensional models of different dimension, and (3) plots the models for visualization and comparison.
Repository based tools for department and analysis level
    reproducibility. 'GRANBase' allows creation of custom branched, continuous
    integration-ready R repositories, including incremental testing of only packages
    which have changed versions since the last repository build.
Provides the classes and methods for GRANRepository
    objects that are used within the 'GRAN' build framework for R packages.
    This is primarily used by the 'GRANBase' package and repositories
    that are created by it.
Gene-Ranking Analysis of Pathway Expression (GRAPE) is a tool for
   summarizing the consensus behavior of biological pathways in the form of a
   template, and for quantifying the extent to which individual samples deviate
   from the template. GRAPE templates are based only on the relative rankings
   of the genes within the pathway and can be used for classification of tissue
   types or disease subtypes. GRAPE can be used to represent gene-expression
   samples as vectors of pathway scores, where each pathway score indicates the
   departure from a given collection of reference samples. The resulting pathway-
   space representation can be used as the feature set for various applications,
   including survival analysis and drug-response prediction.
   ----------------------------------------------------------------------------------------------
       GRAPE is a generalization and extension of DIRAC, originally implemented in:
   Eddy, J.A., et al. (2010) <DOI:10.1371/journal.pcbi.1000792>. As a result, some
   of the software below may have been previously published in Matlab by the DIRAC
   authors and can be found at:
   https://price.systemsbiology.org/pricelab-resources/software/.
Turn arbitrary functions into binary operators.
gRapfa is for modelling discrete longitudinal data using acyclic probabilistic finite automata (APFA). The package contains functions for constructing APFA models from a given data using penalized likelihood methods. For graphical display of APFA models, gRapfa depends on 'igraph package'. gRapfa also contains an interface function to Beagle software that implements an efficient model selection algorithm.
A multi-platform user interface for drawing highly customizable graphs in R. It aims to be a valuable help to quickly draw publishable graphs without any knowledge of R commands. Six kinds of graph are available: histogram, box-and-whisker plot, bar plot, pie chart, curve and scatter plot.
Set of functions for step-wise generation of (weighted) graphs. Aimed for research in the field of single- and multi-objective combinatorial optimization. Graphs are generated adding nodes, edges and weights. Each step may be repeated multiple times with different predefined and custom generators resulting in high flexibility regarding the graph topology and structure of edge weights.
A Network Implementation of Fuzzy Sets: Build Network Objects from Multivariate Flat Files. For more information on fuzzy sets, refer to: Zadeh, L.A. (1964) <DOI:10.1016/S0019-9958(65)90241-X>.
A 'sparklyr' <https://spark.rstudio.com/> extension that provides an R 
  interface for 'GraphFrames' <https://graphframes.github.io/>. 'GraphFrames' is a package
  for 'Apache Spark' that provides a DataFrame-based API for working with graphs. Functionality
  includes motif finding and common graph algorithms, such as PageRank and Breadth-first
  search.
Estimates within and between time point interactions in experience sampling data, using the Graphical VAR model in combination with LASSO and EBIC.
Functions to generate
             graphics files, compare them with "model" files,
             and report the results, including visual and textual
             diffs of any differences.
A fast C++ implementation for computing various graph kernels including (1) simple kernels between vertex and/or edge label histograms, (2) graphlet kernels, (3) random walk kernels (popular baselines), and (4) the Weisfeiler-Lehman graph kernel (state-of-the-art).
Efficient methods for constructing confidence intervals of monotone
    graph invariants, as well as testing for monotone graph properties. Many
    packages are available to estimate precision matrices, this package serves as a
    tool to extract structural properties from their induced graphs. By iteratively
    bootstrapping on only the relevant edge set, we are able to obtain the optimal
    interval size.
Provides a not-so-comprehensive list of methods for estimating graphon,
    a symmetric measurable function, from a single or multiple of observed networks. 
    For a detailed introduction on graphon and popular estimation techniques, 
    see the paper by Orbanz, P. and Roy, D.M.(2014) <doi:10.1109/TPAMI.2014.2334607>.
    It also contains several auxiliary functions for generating sample networks
    using various network models and graphons.
Histogram principal components analysis is the generalization of the PCA. Histogram data are adapted to design complex and big data which histograms used as variables (big data adapter). Functions implemented provides numerical and graphical tools of an extension of PCA. Sun Makosso Kallyth (2016) <doi:10.1002/sam.11270>. Sun Makosso Kallyth and Edwin Diday (2012) <doi:10.1007/s11634-012-0108-0>.
Bindings to the 'libgraphqlparser' C++ library. Currently parses
    GraphQL and exports the AST in JSON format.
Multiple scan statistic with variable window for one dimension data and scan statistic based on connected components in 2D or 3D.
Allows building an edge table from data frame of tweets, 
  also provides function to build nodes and another create a temporal graph.
Optimizing a function F(U), where U is a semi-orthogonal matrix and F is invariant under an orthogonal transformation of U
Create graticule lines and labels for maps. Control the creation
    of lines by setting their placement (at particular meridians and parallels)
    and extent (along parallels and meridians). Labels are created independently of
    lines.
Utilities for costing and evaluating Australian tax policy, including high-performance tax and transfer calculators,
    a fast method of projecting tax collections, and an interface to common indices from the Australian Bureau of Statistics. 
    Written to support Grattan Institute's Australian Perspectives program. For access to the 'taxstats' package, please run
    install.packages("taxstats", repos = "https://hughparsonage.github.io/drat/", type = "source").
    N.B. The 'taxstats' package is approximately 50 MB.
The 'gRbase' package provides general features
    which are used by other graphical modelling packages, in particular
    by the packages 'gRain', 'gRim' and 'gRc'.
    'gRbase' contains several data sets relevant for use in connection with
    graphical models. Almost all data sets used in the book Graphical
    Models with R (2012) are contained in 'gRbase'.
    'gRbase' implements several graph algorithms (based mainly on
    representing graphs as adjacency matrices - either in the form
    of a standard matrix or a sparse matrix). Some graph
    algorithms are:
    (i) maximum cardinality search (for marked and unmarked graphs).
    (ii) moralize.
    (iii) triangulate.
    (iv) junction tree.
    'gRbase' facilitates array operations,
    'gRbase' implements functions for testing for conditional independence.
    'gRbase' illustrates how hierarchical log-linear models may be
    implemented and describes concept of graphical meta
    data. These features, however, are not maintained anymore and
    remains in 'gRbase' only because there exists a paper describing
    these facilities: A Common Platform for Graphical Models in R:
    The 'gRbase' Package, Journal of Statistical Software, Vol 14,
    No 17, 2005.
    NOTICE Proper functionality of 'gRbase' requires that the packages graph,
    'Rgraphviz' and 'RBGL' are installed from 'bioconductor'; for
    installation instructions please refer to the web page given below.
Estimation, model selection and other aspects of
        statistical inference in Graphical Gaussian models with edge
        and vertex symmetries (Graphical Gaussian models with colours).
We implement two main functions.
    The first function uses a given grouped and/or
    right-censored grouping scheme and empirical data to infer parameters,
    and implements chi-square goodness-of-fit tests.
    The second function searches for the global optimal grouping
    scheme of grouped and/or right-censored count responses in surveys.
Provides algorithms for detection of spatial patterns from oceanographic data using image processing methods based on Gradient Recognition.
Summarises a collection of partitions into a single optimal partition. The objective function is the expected posterior loss, and the minimisation is performed through a greedy algorithm described in Rastelli, R. and Friel, N. (2016) "Optimal Bayesian estimators for latent variable cluster models" <arXiv:1607.02325>.
Computes experimental designs for a
    two-arm experiment with covariates by greedily optimizing a
    balance objective function. This optimization provides lower
    variance for the treatment effect estimator (and higher power) 
    while preserving a design that is close to complete randomization.
    We return all iterations of the designs for use in a permutation test.
Performs clustering on the nodes of an undirected binary dynamic network, by maximising the exact integrated complete likelihood. The greedy algorithm used is described in Rastelli, R. (2017) "Exact integrated completed likelihood maximisation in a stochastic block transition model for dynamic networks" <arXiv:1710.03551>.
Methods for manipulating regression models and for describing these in a style adapted for medical journals. 
  Contains functions for generating an HTML table with crude and adjusted estimates, plotting hazard ratio, plotting model 
  estimates and confidence intervals using forest plots, extending this to comparing multiple models in a single forest plots. 
  In addition to the descriptive methods, there are add-ons for the robust covariance matrix provided by the 'sandwich'
  package, a function for adding non-linearities to a model, and a wrapper around the 'Epi' package's Lexis() functions for
  time-splitting a dataset when modeling non-proportional hazards in Cox regressions.
Contains many functions useful for
    monitoring and reporting the results of clinical trials and other
    experiments in which treatments are compared.  LaTeX is
    used to typeset the resulting reports, recommended to be in the
    context of 'knitr'. The 'Hmisc', 'ggplot2', and 'lattice' packages are used
    by 'greport' for high-level graphics.
Write statistical models in R and fit them by MCMC on CPUs and GPUs, using Google TensorFlow (see <https://goldingn.github.io/greta> for more information).
Convert 'Ensembl' gene identifiers from Genotype-Tissue
    Expression (GTEx) data to identifiers in other annotation systems,
    including 'Entrez', 'HGNC', and 'UniProt'.
Implements model selection and combinations via information criteria based on
             the values of partial correlations. This allows, for example, solving "fat regression"
             problems, where the number of variables is much larger than the number of observations.
             This is driven by the research on information criteria, which is well discussed in
             Burnham & Anderson (2002) <doi:10.1007/b97636>, and currently developed further by
             Ivan Svetunkov and Yves Sagaert (working paper in progress). Models developed in the
             package are tailored specifically for forecasting purposes. So as a results there are
             several methods that allow producing forecasts from these models and visualising them.
Allows one to classify patients into low, intermediate, and high risk groups for disease progression based on a continuous marker that is associated with progression-free survival. It uses a latent class model to link the marker and survival outcome and produces two cutoffs for the marker to divide patients into three groups. See the References section for more details.
A pluggable package for forest-based statistical estimation and inference.
    GRF currently provides methods for non-parametric least-squares regression,
    quantile regression, and treatment effect estimation (optionally using instrumental
    variables). This package is currently in beta, and we expect to make continual
    improvements to its performance and usability.
The functionality of this package has been deprecated;
    please use the Grid2Polygons function in the inlmisc package instead.
    Converts a spatial object from class sp::SpatialGridDataFrame to
    sp::SpatialPolygonsDataFrame.
Functions for drawing scene trees representing 
              scenes that have been drawn using grid graphics.
Provides a number of user-level functions to work with "grid"
    graphics, notably to arrange multiple grid-based plots on a page, and draw
    tables.
Functions to convert a page of plots drawn with the 
  'graphics' package into identical output drawn with the 'grid' package.
  The result looks like the original 'graphics'-based plot, but consists
  of 'grid' grobs and viewports that can then be manipulated with 
  'grid' functions (e.g., edit grobs and revisit viewports).
Functions for drawing node-and-edge graphs that have been 
              laid out by graphviz.  This provides an alternative 
              rendering to that provided by the 'Rgraphviz' package, with
              two main advantages:  the rendering provided by 'gridGraphviz'
              should be more similar to what 'graphviz' itself would draw;
              and rendering with 'grid' allows for post-hoc customisations
              using the named viewports and grobs that 'gridGraphviz'
              produces.
Multi-stage cluster surveys of households are commonly performed by 
	governments and programmes to monitor population-level demographic, social, 
	economic, and health outcomes. Generally, communities are sampled from 
	subpopulations (strata) in a first stage, and then households are listed and 
	sampled in a second stage. In this typical two-stage design, sampled communities 
	are the Primary Sampling Units (PSUs) and households are the Secondary Sampling 
	Units (SSUs). Census data typically serve as the sample frame from which PSUs 
	are selected. However, if census data are outdated inaccurate, or too 
	geographically course, gridded population data (such as <http://www.worldpop.org.uk>) 
	can be used as a sample frame instead. GridSample generates PSUs from gridded 
	population data according to user-specified complex survey design characteristics 
	and household sample size. In gridded population sampling, like census sampling, 
	PSUs are selected within each stratum using a serpentine sampling method, and can 
	be oversampled in urban or rural areas to ensure a minimum sample size in each of 
	these important sub-domains. Furthermore, because grid cells are uniform in size 
	and shape, gridded population sampling allows for samples to be representative of
	both the population and of space, which is not possible with a census sample frame.
Simulation tool to facilitate determination of
    required sample size to achieve category saturation
    for studies using multiple repertory grids in conjunction with
    content analysis.
Functions to export graphics drawn with package grid to SVG
  format.  Additional functions provide access to SVG features that
  are not available in standard R graphics, such as hyperlinks, 
  animation, filters, masks, clipping paths, and gradient and pattern fills.
The function GriegSmith accepts either quadrat count data,
        a point process object(ppp) or a matrix of x and y coordinates.
        The function calculates a nested analysis of variance and
        simulation envelopes.
Provides the following types of models: Models for for contingency
    tables (i.e. log-linear models) Graphical Gaussian models for multivariate
    normal data (i.e. covariance selection models) Mixed interaction models.
Functions for converting, importing, and drawing PostScript 
              pictures in R plots.
Functions for importing external vector images and
  drawing them as part of 'R' plots.  This package is different from the
  'grImport' package because, where that package imports 'PostScript'
  format images, this package imports 'SVG' format images.  Furthermore,
  this package imports a specific subset of 'SVG', so external images
  must be preprocessed using a package like 'rsvg' to produce 'SVG'
  that this package can import.  'SVG' features that are not supported
  by 'R' graphics, e.g., gradient fills, can be imported and then
  exported via the 'gridSVG' package.
The program GRNN implements the algorithm proposed by
        Specht (1991).
Workbench for testing genomic regression accuracy on (optionally noisy) phenotypes.
Robust multiple or multivariate linear regression, nonparametric regression on orthogonal components, classical or robust partial least squares models.
The package was developed to fit fit many growth curves
        obtained under different conditions in order to derive a
        conclusive dose-response curve, for instance for a compound
        that potentially affects growth. grofit fits data to different
        parametric models (function gcFitModel) and in addition
        provides a model free spline fit (function gcFitSpline) to
        circumvent systematic errors that might occur within
        application of parametric methods.
Computing Gromov-Hausdorff type l^p distances for labeled metric spaces. These distances were introduced in V.Liebscher, Gromov meets Phylogenetics - new Animals for the Zoo of Metrics on Tree Space. preprint  arXiv:1504.05795,  for phylogenetic trees but may apply to much more situations.
Subsetting methods for balanced cross-validation, time series windowing,
    and general grouping and splitting of data.
Regression models for grouped and coarse data, under the
        Coarsened At Random assumption.
These 'Rcpp'-based functions compute the efficient score statistics for grouped time-to-event data (Prentice and Gloeckler, 1978), with the optional inclusion of baseline covariates. Functions for estimating the parameter of interest and nuisance parameters, including baseline hazards, using maximum likelihood are also provided. A parallel set of functions allow for the incorporation of family structure of related individuals (e.g., trios). Note that the current implementation of the frailty model (Ripatti and Palmgren, 2000) is sensitive to departures from model assumptions, and should be considered experimental. For these data, the exact proportional-hazards-model-based likelihood is computed by evaluating multiple variable integration. The integration is accomplished using the 'Cuba' library (Hahn, 2005), and the source files are included in this package. The maximization process is carried out using Brent's algorithm, with the C++ code file from John Burkardt and John Denker (Brent, 2002).
An implementation of the GroupRemMap penalty for fitting regularized multivariate response regression models under the high-dimension-low-sample-size setting. When the predictors naturally fall into groups, the GroupRemMap penalty encourages procedure to select groups of predictors, while control for the overall sparsity of the final model.
A graphical user interface to compute group sequential designs
    based on normally distributed test statistics, particularly critical
    boundaries, power, drift, and confidence intervals of such designs. All
    computations are based on the alpha spending approach by Lan-DeMets with
    various alpha spending functions being available to choose among.
Group subset selection for linear regression models is provided in this package. Given response variable, and explanatory variables, which are organised in groups, group subset selection selects a small number of groups to explain response variable linearly using least squares.
Contains functions for a two-stage multiple testing procedure for grouped hypothesis, aiming at controlling both the total posterior false discovery rate and within-group false discovery rate.
Functional denoising and functional ANOVA through wavelet-domain 
  Markov groves. Fore more details see: Ma L. and Soriano J. (2016) 
  Efficient functional ANOVA through wavelet-domain Markov groves. 
  <arXiv:1602.03990v2 [stat.ME]>.
Employs a non-parametric formulation for by-subject random effect
    parameters to borrow strength over a constrained number of repeated
    measurement waves in a fashion that permits multiple effects per subject.
    One class of models employs a Dirichlet process (DP) prior for the subject
    random effects and includes an additional set of random effects that
    utilize a different grouping factor and are mapped back to clients through
    a multiple membership weight matrix; e.g. treatment(s) exposure or dosage.
    A second class of models employs a dependent DP (DDP) prior for the subject
    random effects that directly incorporates the multiple membership pattern.
Estimates a collection of time-indexed functions under
    either of Gaussian process (GP) or intrinsic Gaussian Markov
    random field (iGMRF) prior formulations where a Dirichlet process
    mixture allows sub-groupings of the functions to share the same
    covariance or precision parameters.  The GP and iGMRF formulations
    both support any number of additive covariance or precision terms,
    respectively, expressing either or both of multiple trend and
    seasonality.
Functions for fitting various normal theory (growth
    curve) and elliptically-contoured repeated measurements models
    with ARMA and random effects dependence.
This is a simple package that fits the logistic equation to
    microbial growth curve data (e.g., repeated absorbance measurements
    taken from a plate reader over time). From this fit, a variety of
    metrics are provided, including the maximum growth rate,
    the doubling time, the carrying capacity, the area under the logistic
    curve, and the time to the inflection point.
A compilation of nonlinear growth models used in many areas
A nonparametric empirical Bayes method for recovering
        gradients (or growth velocities) from observations of smooth
        functions (e.g., growth curves) at isolated time points.
A collection of methods to determine growth rates from
    experimental data, in particular from batch experiments and
    plate reader trials.
Fits user specified (GLM-) models with Group Lasso penalty
Efficient algorithms for fitting the regularization path of
 linear or logistic regression models with grouped penalties.  This
 includes group selection methods such as group lasso, group MCP, and
 group SCAD as well as bi-level selection methods such as the group
 exponential lasso, the composite MCP, and the group bridge.
Fit the regularization path of linear, logistic or Cox models with 
	overlapping grouped covariates based on the latent group lasso approach. Latent 
	group MCP/SCAD as well as bi-level selection methods, namely the group exponential 
	lasso and the composite MCP are also available. This package serves as an 
	extension of R package 'grpreg' (by Dr. Patrick Breheny <patrick-breheny@uiowa.edu>)
	for grouped variable selection involving overlaps between groups.
Group SLOPE is a penalized linear regression method that is used
    for adaptive selection of groups of significant predictors in a
    high-dimensional linear model.
    The Group SLOPE method can control the (group) false discovery rate at a
    user-specified level (i.e., control the expected proportion of irrelevant
    among all selected groups of predictors).
Contains the tools to screen grouped variables, and select screened grouped variables afterwards. The main function grpss() can perform the grouped variables screening as well as selection for ultra-high dimensional data with group structure. The screening step is primarily used to reduce the dimensions of data so that the selection procedure can easily handle the moderate or low dimensions instead of ultra-high dimensions.
Methods include converting series of event names to strings, finding common patterns
    in a group of strings, discovering featured patterns when comparing two groups of strings as well
    as the number and starting position of each pattern in each string, obtaining transition matrix, 
    computing transition entropy, statistically comparing the difference between two groups of strings,
    and clustering string groups. Event names can be any action names or labels such as events in log
    files or areas of interest (AOIs) in eye tracking research.
Alternative implementations of some base R functions, including sort, order, and match.  Functions are simplified but can be faster or have other advantages.
Computational resources for test proposed by Gibbons, Ross, Shanken (1989)<DOI:10.2307/1913625>.
	It also has the functions for the power analysis and the choice of the optimal level of significance.
	The optimal level is determined by minimizing the expected loss from hypothesis testing.
Functions to generate and analyze data for psychology experiments based on the General Recognition Theory.
Offers functions for the comparison of Gutenberg-Richter 
	b-values. Several functions in GRTo are helpful for the assessment of the
	quality of seismicity catalogs.
GSAgm is an R package that completes a self-contained gene set analysis (GSA) for RNA-seq and SNP data using the Gamma Method.
This package contains utility functions used by the Genome Analysis Toolkit (GATK) to load tables and plot data. The GATK is a toolkit for variant discovery in high-throughput sequencing data.
Computation of Quantitative Trait Loci hits in the selected gene set. Performing gene set validation with Quantitative Trait Loci information. Performing gene set enrichment analysis with available Quantitative Trait Loci data and computation of statistical significance value from gene set analysis. Obtaining the list of Quantitative Trait Loci hit genes along with their overlapped Quantitative Trait Loci names.
Write SARIMA models in (finite) AR representation and simulate 
	generalized multiplicative seasonal autoregressive moving average (time) series 
	with Normal / Gaussian, Poisson or negative binomial distribution.
Group Sequential Operating Characteristics for Clinical,
        Bayesian two-arm Trials with known Sigma and Normal Endpoints.
Method proposed in "Simultaneous Sparse Dictionary Learning and Pruning" ( Qu and Wang (2016) <arXiv:1605.07870>) is implemented. The idea is to conduct a linear decomposition of a signal using a few atoms of a learned and usually over-completed dictionary instead of a pre-defined basis. A proper size of the to-be-learned dictionary is determining at the same time during the procedure. Application includes image denoising and image inpainting.
Design and analysis of group sequential designs with negative
    binomial outcomes, as described by T Muetze, E Glimm, H Schmidli, T Friede (2017) <arXiv:1707.04612>.
Derives group sequential designs and describes their properties.
Robust Estimation of Multivariate Location and Scatter in the
        Presence of Cellwise and Casewise Contamination and Missing Data.
R-interface to C++ implementation of the rank/score permutation based GSEA test (Subramanian et al 2005 <doi: 10.1073/pnas.0506580102>).
Provides function to apply "Group sequential enrichment design incorporating subgroup selection" (GSED) method proposed by Magnusson and Turnbull (2013) <doi:10.1002/sim.5738>.
Using an approach based on similarity graph to estimate change-point(s) and the corresponding p-values.  Can be applied to any type of data (high-dimensional, non-Euclidean, etc.) as long as a reasonable similarity measure is available.
Conducts a semi-gSEM statistical analysis (semi-supervised generalized structural equation modeling) on a data frame of coincident observations of multiple predictive or intermediate variables and a final continuous, outcome variable, via two functions sgSEMp1() and sgSEMp2(), representing fittings based on two statistical principles. Principle 1 determines all sensible univariate relationships in the spirit of the Markovian process. The relationship between each pair of variables, including predictors and the final outcome variable, is determined with the Markovian property that the value of the current predictor is sufficient in relating to the next level variable, i.e., the relationship is independent of the specific value of the preceding-level variables to the current predictor, given the current value. Principle 2 resembles the multiple regression principle in the way multiple predictors are considered simultaneously. Specifically, the relationship of the first-level predictors (such as Time and irradiance etc) to the outcome variable (such as, module degradation or yellowing)  is fit by a supervised additive model. Then each significant intermediate variable is taken as the new outcome variable and the other variables (except the final outcome variable) as the predictors in investigating the next-level multivariate relationship by a supervised additive model. This fitting process is continued until all sensible models are investigated.
calculate equivalence and futility boundaries based on the exact bivariate $t$ test statistics for group sequential designs in studies with equivalence hypotheses.
gsg (gam selection gradients) provides a unified approach to the regression analysis of selection from longitudinal data collected from natural populations.
Simple package to download Google Sheets using just the sharing
    link. Spreadsheets can be downloaded as a data frame, or as plain text to parse
    manually. Google Sheets is the new name for Google Docs Spreadsheets.
Global Soil Information Facilities - tools (standards and functions) and sample datasets for global soil mapping.
Family based association test via GEE Kernel Machine score test
An R wrapper for the special functions and quasi random number
 generators of the Gnu Scientific Library
 (http://www.gnu.org/software/gsl/).  See gsl-package.Rd for details of 
 overall package organization, and Misc.Rd for some functions that are
 widely used in the package, and some tips on installation.
Contains published data sets for global benthic d18O data for 
    0-5.3 Myr <doi:10.1029/2004PA001071> and global sea levels based 
    on marine sediment core data for 0-800 ka <doi:10.5194/cp-12-1-2016>.
Implementation of a Bayesian approach for estimating a mixture of gamma distributions in which the mixing occurs over the shape parameter. This family provides a flexible and novel approach for modeling heavy-tailed distributions, it is computationally efficient, and it only requires to specify a prior distribution for a single parameter.
Tools rewritten in C for various smoothing tasks
Estimating trait heritability and handling overfitting. This package includes a collection of functions for (1) estimating genetic variance-covariances and calculate trait heritability; and (2) handling overfitting by calculating the variance components and the heritability through cross validation.
Provides automated downloading, parsing, cleaning, unit conversion
    and formatting of Global Surface Summary of the Day (GSOD) weather data from
    the from the USA National Centers for Environmental Information (NCEI) for
    use in R.  Units are converted from from United States Customary System
    (USCS) units to International System of Units (SI).  Stations may be 
    individually checked for number of missing days defined by the user, where
    stations with too many missing observations are omitted.  Only stations with
    valid reported latitude and longitude values are permitted in the final 
    data.  Additional useful elements, saturation vapour pressure (es), actual 
    vapour pressure (ea) and relative humidity are calculated from the original 
    data and included in the final data set.  The resulting data include station
    identification information, state, country, latitude, longitude, elevation,
    weather observations and associated flags.  Data may be automatically saved 
    to disk. File output may be returned as a comma-separated values (CSV) or
    GeoPackage (GPKG) file.  Additional data are included with this R package: a
    list of elevation values for stations between -60 and 60 degrees latitude 
    derived from the Shuttle Radar Topography Measuring Mission (SRTM).  For
    information on the GSOD data from NCEI, please see the GSOD readme.txt file
    available from, <http://www1.ncdc.noaa.gov/pub/data/gsod/readme.txt>.
Approaches a group sparse solution of an underdetermined linear system. It implements the proximal gradient algorithm to solve a lower regularization model of group sparse learning. For details, please refer to the paper "Y. Hu, C. Li, K. Meng, J. Qin and X. Yang. Group sparse optimization via l_{p,q} regularization. Journal of Machine Learning Research, to appear, 2017".
A gate-keeping procedure to test a primary and a secondary endpoint in a group sequential design with multiple interim looks. Computations related to group sequential primary and secondary boundaries. Refined secondary boundaries are calculated for a gate-keeping test on a primary and a secondary endpoint in a group sequential design with multiple interim looks. The choices include both the standard boundaries and the boundaries using error spending functions. Version 1.0.0 was released on April 12, 2017. See Tamhane et al. (2017+) "A gatekeeping procedure to test a primary and a secondary endpoint in a group sequential design with multiple interim looks", Biometrics, to appear.
A comprehensive package for structural multivariate
        function estimation using smoothing splines.
A collection of routines for the estimation of copula density
        and 2-D hazard function using smoothing splines.
We propose a fully efficient sieve maximum likelihood method to estimate genotype-specific distribution of time-to-event outcomes under a nonparametric model. We can handle missing genotypes in pedigrees.  We estimate the time-dependent hazard ratio between two genetic mutation groups using B-splines, while applying nonparametric maximum likelihood estimation to the reference baseline hazard function.  The estimators are calculated via an expectation-maximization algorithm.
Variogram modelling; simple, ordinary and universal point or block (co)kriging; spatio-temporal kriging; sequential Gaussian or indicator (co)simulation; variogram and variogram map plotting utility functions.
The gsubfn function is like gsub but can take a replacement 
   function or certain other objects instead of the replacement string.
   Matches and back references are input to the replacement function and 
   replaced by the function output.   gsubfn can be used to split strings 
   based on content rather than delimiters and for quasi-perl-style string 
   interpolation. The package also has facilities for translating formulas 
   to functions and allowing such formulas in function calls instead of 
   functions.  This can be used with R functions such as apply, sapply,
   lapply, optim, integrate, xyplot, Filter and any other function that 
   expects another function as an input argument or functions like cat
   or sql calls that may involve strings where substitution is desirable.
   There is also a facility for returning multiple objects from functions
   and a version of transform that allows the RHS to refer to LHS used in
   the same transform.
Provides an interface to the Gibbs 'SeaWater' ('TEOS-10') C
    library, version 3.05-4 (commit '5b4d959e54031f9e972f3e863f63e67fa4f5bfec', dated 2017-08-07,
    available at <https://github.com/TEOS-10/GSW-C>, which stems from
    'Matlab' and other code written by members of Working Group 127 of
    'SCOR'/'IAPSO' (Scientific Committee on Oceanic Research /
    International Association for the Physical Sciences of the Oceans).
Estimation of the cutpoint defined by the Generalized Symmetry point in a binary classification setting based on a continuous diagnostic test or marker. Two methods have been implemented to construct confidence intervals for this optimal cutpoint, one based on the Generalized Pivotal Quantity and the other based on Empirical Likelihood. Numerical and graphical outputs for these two methods are easily obtained.
Provides causal inference with interactive fixed-effect models. It imputes counterfactuals for each treated unit using control group information based on a linear interactive fixed effects model that incorporates unit-specific intercepts interacted with time-varying coefficients. This method generalizes the synthetic control method to the case of multiple treated units and variable treatment periods, and improves efficiency and interpretability. This version supports unbalanced panels. Reference: Yiqing Xu (2017) <doi:10.1017/pan.2016.2>.
Provides tools to measure the reliability of an Information Retrieval test collection.
  It allows users to estimate reliability using Generalizability Theory and map those estimates onto
  well-known indicators such as Kendall tau correlation or sensitivity.
Tools to make it easier to work with "tables" of 'grobs'.
This package provides functions to calculate the
        efficiencies (expected tests per unit) of hierarchical and
        matrix group testing procedures.  Efficiencies can be
        calculated in the presense of correlated responses under
        multiple arrangements of clustesrs.  Efficiencies can also be
        evaluated in the presense of test error.
Generalized Turnbull's estimator proposed by Dehghan and Duchesne
    (2011).
Four graph-based tests are provided for testing whether two samples are from the same distribution.  It works for both continuous data and discrete data.
Estimates variance components, generalizability coefficients,
    universe scores, and standard errors when observed scores contain variation from
    one or more measurement facets (e.g., items and raters).
Functions to assist in R programming, including:
  - assist in developing, updating, and maintaining R and R packages ('ask', 'checkRVersion',
    'getDependencies', 'keywords', 'scat'),
  - calculate the logit and inverse logit transformations ('logit', 'inv.logit'),
  - test if a value is missing, empty or contains only NA and NULL values ('invalid'),
  - manipulate R's .Last function ('addLast'),
  - define macros ('defmacro'),
  - detect odd and even integers ('odd', 'even'),
  - convert strings containing non-ASCII characters (like single quotes) to plain ASCII ('ASCIIfy'),
  - perform a binary search ('binsearch'),
  - sort strings containing both numeric and character components ('mixedsort'),
  - create a factor variable from the quantiles of a continuous variable ('quantcut'),
  - enumerate permutations and combinations ('combinations', 'permutation'),
  - calculate and convert between fold-change and log-ratio ('foldchange',
    'logratio2foldchange', 'foldchange2logratio'),
  - calculate probabilities and generate random numbers from Dirichlet distributions
    ('rdirichlet', 'ddirichlet'),
  - apply a function over adjacent subsets of a vector ('running'),
  - modify the TCP\_NODELAY ('de-Nagle') flag for socket objects,
  - efficient 'rbind' of data frames, even if the column names don't match ('smartbind'),
  - generate significance stars from p-values ('stars.pval'),
  - convert characters to/from ASCII codes.
In hierarchical time series (HTS) forecasting, the hierarchical relation between multiple time series is exploited to make better forecasts. This hierarchical relation implies one or more aggregate consistency constraints that the series are known to satisfy. Many existing approaches, like for example bottom-up or top-down forecasting, therefore attempt to achieve this goal in a way that guarantees that the forecasts will also be aggregate consistent. This package provides with an implementation of the Game-Theoretically OPtimal (GTOP) reconciliation method proposed in van Erven and Cugliari (2015), which is guaranteed to only improve any given set of forecasts. This opens up new possibilities  for constructing the forecasts. For example, it is not necessary to assume that bottom-level forecasts are unbiased, and aggregate forecasts may be constructed by regressing both on bottom-level forecasts and on other covariates that may only be available at the aggregate level.
An interface for retrieving and displaying the information returned
    online by Google Trends is provided. Trends (number of hits) over the time as
    well as geographic representation of the results can be displayed.
Quantitative trait loci mapping and genome wide association analysis are used to find candidate molecular marker or region associated with phenotype based on linkage analysis and linkage disequilibrium. Gene expression quantitative trait loci mapping is used to find candidate molecular marker or region associated with gene expression. In this package, we applied the method in Liu W. (2011) <doi:10.1007/s00122-011-1631-7> and Gusev A. (2016) <doi:10.1038/ng.3506> to genome and transcriptome wide association study, which is aimed at revealing the association relationship between phenotype and molecular markers, expression levels, molecular markers nested within different related expression effect and expression effect nested within different related molecular marker effect. F test based on full and reduced model are performed to obtain p value or likelihood ratio statistic. The best linear model can be obtained by stepwise regression analysis.
Assorted tools for genetic association analyses.  The
        current focus is on implementing (either exactly or
        approximately) regression analyses using summary statistics
        instead of using subject-specific data.  So far, functions
        exist to support multi-SNP risk score analyses, multi-SNP
        conditional regression analyses, and multi-phenotype analyses,
        using summary statistics.  There are helper functions for
        reading and manipulating subject-specific genotype data, which
        provide a platform for calculating the summary statistics, or
        for using R to conduct other analyses not supported by specific
        GWAS analysis tools.
Provides an interface to the Open Platform's Content API of the Guardian Media Group. It retrieves content from news outlets The Observer, The Guardian, and guardian.co.uk from 1999 to current day.
This package comprises maps of France in 1830, multivariate data from A.-M. Guerry and others, and statistical and 
	graphic methods related to Guerry's "Moral Statistics of France". The goal is to facilitate the exploration and
	development of statistical and graphic methods for multivariate data in a geo-spatial context of historical interest.
Adjust Estimates of Learning for Guessing. The package provides 
    standard guessing correction, and a latent class model that leverages
    informative pre-post transitions. For details of the latent class model,
    see <http://gsood.com/research/papers/guess.pdf>.
A nice GUI for financial DErivatives in R.
A graphical user interface for the R package Gems.  
    Apart from the functionality of Gems package in the Graphical User interface, GUIgems
    allows adding states to a defined model, merging states for the analysis and plotting 
    progression paths between states based on the simulated cohort.
    There is also a module in the GUIgems which allows to compare costs and QALYs between different cohorts.
A collection of sampling formulas for the unified neutral model of biogeography and biodiversity. Alongside the sampling formulas, it includes methods to perform maximum likelihood optimization of the sampling formulas, methods to generate data given the neutral model, and methods to estimate the expected species abundance distribution. Sampling formulas included in the GUILDS package are the Etienne Sampling Formula (Etienne 2005), the guild sampling formula, where guilds are assumed to differ in dispersal ability (Janzen et al. 2015), and  the guilds sampling formula conditioned on guild size (Janzen et al. 2015).
Show graphically the results of profiling R functions by tracking their execution time.
Provides probability functions (cumulative distribution and density functions), simulation function (Gumbel copula multivariate simulation) and estimation functions (Maximum Likelihood Estimation, Inference For Margins, Moment Based Estimation and Canonical Maximum Likelihood).
Generalized UniFrac distances for comparing microbial communities. Permutational multivariate analysis of variance using multiple distance matrices.
Statistical analysis of monthly background checks of gun purchases for the New York Times 
 story "What Drives Gun Sales: Terrorism, Obama and Calls for Restrictions" at 
 <http://www.nytimes.com/interactive/2015/12/10/us/gun-sales-terrorism-obama-restrictions.html?> is provided.
Download and process public domain works in the Project
    Gutenberg collection <http://www.gutenberg.org/>. Includes metadata for
    all Project Gutenberg works, so that they can be searched and retrieved.
Given exposure and survival time series as well as parameter values, GUTS allows for the fast calculation of the survival probabilities as well as the logarithm of the corresponding likelihood.
Several tools for Global Value Chain ('GVC') analysis are
    implemented.
Generalized structured regression models with regularized categorical effects, categorical effect modifiers, continuous effects and smooth effects.
Functionalities to compute model based genetic components i.e. genotypic variance, phenotypic variance and heritability for given traits of different genotypes from replicated data using methodology explained by Burton, G. W. & Devane, E. H. (1953) (<doi:10.2134/agronj1953.00021962004500100005x>) and Allard, R.W. (2010, ISBN:8126524154).
Methods from the paper: Pena, EA and Slate, EH, "Global Validation of Linear Model Assumptions," J. American Statistical Association, 101(473):341-354, 2006.
Functions for genome-wide association/interaction analysis and rare variant analysis on a continuous/dichotomous trait using family data, and for making genome-wide p-value plot and QQ plot.
This package contains a function to do exact
        Hardy-Weinburg testing (using Fisher's test) for SNP genotypes
        as typically obtained in a Genome Wide Association Study
        (GWAS).
Performs variable selection with data from Genome-wide association studies (GWAS) combining, in an iterative variable selection framework, the computational efficiency of the screen-and-select approach based on some association learning and the parsimonious uncertainty quantification provided by the use of nonlocal priors, as described in Sanyal et al. (2018) [submitted].
This is a Shiny application intended to provide better understanding of how geometrically-weighted degree terms function in exponential random graph models of networks. It contains just one user function, gwdegree(), which launches the Shiny application.
This package provides functions to calculate the
        significance threshold for controlling the type I error rate in
        mixed-model association mapping analyses.
Performs Geographically Weighted Fractal Analysis (GWFA) to calculate the local fractal dimension of a set of points. GWFA mixes the Sandbox multifractal algorithm and the Geographically Weighted Regression. Unlike fractal box-counting algorithm, the sandbox algorithm avoids border effects because the boxes are adjusted on the set of points. The Geographically Weighted approach consists in applying a kernel that describes the way the neighbourhood of each estimated point is taken into account to estimate its fractal dimension. GWFA can be used to discriminate built patterns of a city, a region, or a whole country.
Based on calculations of 758 women this package calculates
        positive predictive values (PPV) and negative predictive values
        (NPV) for inadequate and excessive gestational weight gain
        (GWG) for different prevalences for different BMI categories.
gWidgets provides a toolkit-independent API for building interactive GUIs. At least one of the 'gWidgetsXXX packages', such as gWidgetstcltk, needs to be installed. Some icons are on loan from the scigraphica project http://scigraphica.sourceforge.net.
Re-implementation of the 'gWidgets' API. The API is defined in this
    package. A second, toolkit-specific package is required to use it. There
    are three in development: 'gWidgets2RGtk2', 'gWidgets2Qt', and 'gWidgets2tcltk'.
Implements the 'gWidgets2' API for 'RGtk2.'
Port of the 'gWidgets2' API for the 'tcltk' package.
Port of the gWidgets API to the RGtk2 toolkit.
Port of the gWidgets API to the tcltk package. Requires Tk 8.5 or greater.
Fit a geographically weighted logistic elastic net regression. Detailed explanations can be found in Yoneoka et al. (2016): New algorithm for constructing area-based index with geographical heterogeneities and variable selection: An application to gastric cancer screening <doi:10.1038/srep26582>.
In GWmodel, we introduce techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. GWmodel includes functions to calibrate: GW summary statistics, GW principal components analysis, GW discriminant analysis and various forms of GW regression; some of which are provided in basic and robust (outlier resistant) forms.
Fits Weighted Quantile Sum (WQS) regressions for continuous or binomial outcomes.
Statistical functions to fit, validate and describe a Generalized
    Waring Regression Model (GWRM).
Computes the sample probability value (p-value)
    for the estimated coefficient from a standard
    genome-wide univariate regression.  It computes the exact
    finite-sample p-value under the assumption that the measured
    phenotype (the dependent variable in the regression) has
    a known Bernoulli-normal mixture distribution.
    Finite-sample genome-wide regression p-values (Gwrpv) with
    a non-normally distributed phenotype (Gregory Connor and
    Michael O'Neill, bioRxiv 204727 <doi:10.1101/204727>).
Fits geographically weighted regression (GWR) models and
        has tools to diagnose and remediate collinearity in the GWR
        models. Also fits geographically weighted ridge regression
        (GWRR) and geographically weighted lasso (GWL) models.
Shiny application for the analysis of groundwater monitoring data, designed to work with simple time-series data for solute concentration and ground water elevation, but can also plot non-aqueous phase liquid (NAPL) thickness if required. Also provides the import of a site basemap in GIS shapefile format.
The correlations and linkage disequilibrium between tests can vary as a function of minor allele frequency thresholds used to filter variants, and also varies with different choices of test statistic for region-based tests. Appropriate genome-wide significance thresholds can be estimated empirically through permutation on only a small proportion of the whole genome.
Quantifying and testing gene-by-measured-environment interaction in behavior genetic designs.
OpenAI Gym is a open-source Python toolkit for developing and comparing
    reinforcement learning algorithms. This is a wrapper for the OpenAI Gym API,
    and enables access to an ever-growing variety of environments.
    For more details on OpenAI Gym, please see here: <https://github.com/openai/gym>.
    For more details on the OpenAI Gym API specification, please see here:
    <https://github.com/openai/gym-http-api>.
SNP-set association testing for censored phenotypes in the presence of intrafamilial correlation.
R scripting functionality for 'H2O', the open source
  math engine for big data that computes parallel distributed
  machine learning algorithms such as generalized linear models,
  gradient boosting machines, random forests, and neural networks
  (deep learning) within various cluster environments.
Interface to 'H2O4GPU' <https://github.com/h2oai/h2o4gpu>, a collection of 'GPU' solvers for machine learning algorithms.
S4 Interface to the 'HDF5' library supporting fast storage and
    retrieval of R-objects like vectors, matrices and arrays to binary files in
    a language independent format. The 'HDF5' format can therefore be used as
    an alternative to R's save/load mechanism. Since h5 is able to access only
    subsets of stored data it can also handle data sets which do not fit into
    memory.
A Haar-Fisz algorithm for Poisson intensity estimation
Package provides the estimation of the structure and the parameters, sampling methods and structural plots of Hierarchical Archimedean Copulae (HAC).
Provides a framework for writing map/reduce scripts for
        use in Hadoop Streaming. Also facilitates operating on data in
        a streaming fashion, without Hadoop.
Read data from the City of Portland's 'HYDRA' <http://or.water.usgs.gov/precip/> rainfall datasets within R.
Various functions and algorithms are provided here for solving optimal matching tasks in the context of preclinical cancer studies. Further, various helper and plotting functions are provided for unsupervised and supervised machine learning as well as longitudinal mixed-effects modeling of tumor growth response patterns.
An S4 implementation of Eq. (3) and Eq. (7) by David J. Hand and 
    Robert J. Till (2001) <DOI:10.1023/A:1010920819831>.
Provides an R routine for a Cramer-von Mises type two sample test which is based on empirical Hankel transforms of the non-negative sample variables. The test is non-parametric and not distribution free. The exact value of the test statistic for univariate data as well as the p-value and the critical value are computed.
Provides functions to download data from the 
  <http://www.data.parliament.uk/> APIs. Because of the structure of the API, 
  there is a named function for each type of available data for ease of use, 
  as well as some functions designed to retrieve specific pieces of commonly 
  used data. Functions for each new API will be added as and when they become
  available.
Functions to perform ROR for sequence-based association
        analysis
The following R functions are used for inference of trait
        associations with haplotypes and other covariates in
        generalized linear models.  The functions are developed
        primarily for data collected in cohort or cross-sectional
        studies. They can accommodate uncertain haplotype phase and
        handle missing genotypes at some SNPs.
The multi-locus stepwise regression (MSR) combines the advantages of stepwise regression and haplotype-based analysis. The MSR can be used to identify informative combinations of single nucleotide polymorphisms (SNPs) from unlinked SNPs (allele combinations) or SNPs within a chromosomal region (haplotypes).
The package provides a set of functions whose aim is to
        propose 4 methods of QTL detection. HAPimLD is an
        interval-mapping method designed for unrelated individuals with
        no family information that makes use of linkage disequilibrium.
        HAPimLDL is an interval-mapping method for design of half-sib
        families. It combines linkage analysis and linkage
        disequilibrium. HaploMax is based on an analysis of variance
        with a dose haplotype effect. HaploMaxHS is based on an
        analysis of variance with a sire effect and a dose haplotype
        effect in half-sib family design.  Fundings for the package
        development were provided to the LDLmapQTL project by the ANR
        GENANIMAL program and APIS-GENE.
Performs genetic association analyses of case-parent triad (trio) data with multiple markers. It can also incorporate complete or incomplete control triads, for instance independent control children. Estimation is based on haplotypes, for instance SNP haplotypes, even though phase is not known from the genetic data. 'Haplin' estimates relative risk (RR + conf.int.) and p-value associated with each haplotype. It uses maximum likelihood estimation to make optimal use of data from triads with missing genotypic data, for instance if some SNPs has not been typed for some individuals. 'Haplin' also allows estimation of effects of maternal haplotypes and parent-of-origin effects, particularly appropriate in perinatal epidemiology. 'Haplin' allows special models, like X-inactivation, to be fitted on the X-chromosome. A GxE analysis allows testing interactions between environment and all estimated genetic effects. The models were originally described in Gjessing, HK and Lie, RT (2006) <doi:10.1111/j.1529-8817.2005.00218.x>.
'haplo.ccs' estimates haplotype and covariate relative
        risks in case-control data by weighted logistic regression.
        Diplotype probabilities, which are estimated by EM computation
        with progressive insertion of loci, are utilized as weights.
Routines for the analysis of indirectly measured haplotypes. The statistical methods assume that all subjects are unrelated and that haplotypes are ambiguous (due to unknown linkage phase of the genetic markers). The main functions are: haplo.em(), haplo.glm(), haplo.score(), and haplo.power(); all of which have detailed examples in the vignette.
A set of utilities for querying 
             'HaploReg' <http://archive.broadinstitute.org/mammals/haploreg/haploreg.php> 
             and 'RegulomeDB' <http://www.regulomedb.org> web-based tools. The package connects to 
             'HaploReg' or 'RegulomeDB', searches and downloads results, without 
             opening web pages, directly from R environment. 
             Results are stored in a data frame that can be directly used in various 
             kinds of downstream analyses.
Reconstruction of founder haplotype blocks from time series data.
Simulate haplotypes through meioses. Allows specification
        of population parameters.
Function to  identify  haplotypes
  within QTL (Quantitative Trait Loci). One haplotype is a combination of SNP
  (Single Nucleotide Polymorphisms) within the QTL. This function groups
  together all individuals of a population with the same haplotype.
  Each group contains individual with the same allele in each SNP,
  whether or not missing data. Thus, haplotyper groups individuals,
  that to be imputed, have a non-zero probability of having the same alleles
  in the entire sequence of SNP's. Moreover, haplotyper calculates such
  probability from relative frequencies.
Provides S4 classes and methods for reading and manipulating aligned DNA sequences, supporting an indel coding methods (only simple indel coding method is available in the current version), showing base substitutions and indels, calculating absolute pairwise distances between DNA sequences, and inferring haplotypes from DNA sequences or user provided absolute character difference matrix.  This package also includes S4 classes and methods for estimating genealogical relationships among haplotypes using statistical parsimony.
Software creation is a pretty big deal!  A repository's initial commit date can be thought of as its birthday.  Next time, drop in and wish a developer (any Github username) a happy birthday of their repo(s).  Or maybe just toast to the upcoming anniversary of your own software!  The software life cycle is too short not to celebrate!
Package for haplotype-based genotype simulations. Haplotypes are
        generated such that their allele frequencies and linkage
        disequilibrium coefficients match those estimated from an input
        data set.
Contains tools for exploring Hardy-Weinberg equilibrium (Hardy, 1908; Weinberg, 1908) for bi and multi-allelic genetic marker data. All classical tests (chi-square, exact, likelihood-ratio and permutation tests) with bi-allelic variants are included in the package, as well as functions for power computation and for the simulation of marker data under equilibrium and disequilibrium. Routines for dealing with markers on the X-chromosome are included, including Bayesian procedures. Some exact and permutation procedures also work with multi-allelic variants. Special test procedures that jointly address Hardy-Weinberg equilibrium and equality of allele frequencies in both sexes are supplied, for the bi and multi-allelic case. Functions for testing equilibrium in the presence of missing data by using multiple imputation are also provided. Implements several graphics for exploring the equilibrium status of a large set of bi-allelic markers: ternary plots with acceptance regions, log-ratio plots and Q-Q plots.
The harmonic mean p-value (HMP) test simply and instantly combines p-values and corrects for multiple testing while controlling the family-wise error rate in a way that is more powerful than common alternatives including Bonferroni and Simes procedures, more stringent than controlling the false discovery rate, and is robust to positive correlations between tests and unequal weights. It is a multi-level test in the sense that a superset of one or more significant tests is almost certain to be significant and conversely when the superset is non-significant, the constituent tests are almost certain to be non-significant. It is based on MAMML (model averaging by mean maximum likelihood), a frequentist analogue to Bayesian model averaging, and is theoretically grounded in generalized central limit theorem.
Fits the first harmonics in a Fourier expansion to one or more time series. Trend elimination can be performed.  Computed values include estimates of amplitudes and phases, as well as confidence intervals and p-values for the null hypothesis of Gaussian noise.
Harriet was Charles Darwin's pet tortoise (possibly). 'harrietr'
    implements some function to manipulate distance matrices and phylogenetic trees
    to make it easier to plot with 'ggplot2' and to manipulate using 'tidyverse'
    tools.
The goal of 'HARtools' is to provide a simple set of functions
    to read/parse, write and visualise HTTP Archive ('HAR') files in R.
Aimed at applying the Harvest classification tree algorithm, modified algorithm of classic classification tree.The harvested tree has advantage of deleting redundant rules in trees, leading to a simplify and more efficient tree model.It was firstly used in drug discovery field, but it also performs well in other kinds of data, especially when the region of a class is disconnected. This package also improves the basic harvest classification tree algorithm by extending the field of data of algorithm to both continuous and categorical variables. To learn more about the harvest classification tree algorithm, you can go to http://www.stat.ubc.ca/Research/TechReports/techreports/220.pdf for more information.
Functions for easy and reproducible simulation.
This package implements a data structure similar to hashes
        in Perl and dictionaries in Python but with a purposefully R
        flavor.  For objects of appreciable size, access using hashes
        outperforms native named lists and vectors.
This package provides common non-cryptographic hash
        functions for R. For example, SpookyHash, Murmur3Hash, Google
        CityHash.
An R port of the hashids library.  hashids generates YouTube-like hashes from integers or vector of integers.  Hashes generated from integers are relatively short, unique and non-seqential.  hashids can be used to generate unique ids for URLs and hide database row numbers from the user.  By default hashids will avoid generating common English cursewords by preventing certain letters being next to each other.  hashids are not one-way: it is easy to encode an integer to a hashid and decode a hashid back into an integer.
Provides a hash table class for fast
    key-value storage of atomic vector types.
Apply the SuperFastHash algorithm to any R object. Hash whole R objects or, 
     for vectors or lists, hash R objects to obtain a set of hash values that is stored 
     in a structure equivalent to the input.
Drawing Hasse diagram - visualization of transitive reduction of a finite partially ordered set.
Import foreign statistical formats into R via the embedded
    'ReadStat' C library, <https://github.com/WizardMac/ReadStat>.
The package allows to simulate Hawkes process both in univariate and multivariate settings. It gives functions to compute different moments of the number of jumps of the process on a given interval, such as mean, variance or autocorrelation of process jumps on time intervals separated by a lag.
Provides a set of functions to estimate haziness of an image based on RGB bands. It returns a haze factor, varying from 0 to 1, a metric for fogginess and cloudiness. The package also presents additional functions to estimate brightness, darkness and contrast rasters of the RGB image. This package can be used for several applications such as inference of weather quality data and performing environmental studies from interpreting digital images.
Damage Functions (DFs), also known as
    Vulnerability Functions, associate the physical damage
    to a building or a structure (and also its contents and
    inventory) from natural disasters to financial damage.
    The Federal Emergency Management Agency (FEMA) in USA
    developed several thousand DFs and these serve as a
    benchmark in natural catastrophe modeling, both in
    academia and industry. However, these DFs and their
    documentation are buried within the HAZUS software are
    not easily accessible for analysis and visualization.
    This package provides more than 1300 raw DFs used by FEMA's
    HAZUS software and also functionality to extract and
    visualize DFs specific to the flood hazard. The vignette
    included with this package demonstrates its use.
Fit an array of decision-making tasks with computational models in
    a hierarchical Bayesian framework. Can perform hierarchical Bayesian analysis of
    various computational models with a single line of coding.
Implements our hierarchical Bayesian design for randomized clinical trials.
Convenient and efficient functions for performing 2-level hierarchical Bayesian regression analysis for multi-group data. The lowest level may belong to the generalized linear model (GLM) family while the prior level, which effects pooling, allows for linear regression on lower level covariates. Constraints on all or part of the parameter set maybe specified with ease. A rich set of methods is included to visualize and analyze results.
Calculate expected relative risk and proportion protected assuming normally distributed log10 transformed antibody dose for several component vaccine. Uses Hill models for each component which are combined under Bliss independence.
A package for building hierarchical block matrices from association matrices and for performing multi-scale analysis. It specifically targets chromatin contact maps, generated from high-throughput chromosome conformation capture data, such as 5C and Hi-C, and provides methods for detecting movements and for computing chain hierarchy and region communicability across scales.
Contains functions for fitting hierarchical versions of
        EVSD, UVSD, DPSD, DPSD with d' restricted to be positive, and
        our gamma signal detection model to recognition memory
        confidence-ratings data.
Hi-C dataset processing algorithm introduced by Chao He (2016) <doi.org/10.1101/083576>.
   The optimized and flexible pipeline for analyzing the
   folding of whole chromosome and interactions between some
   specific sites from the Hi-C raw sequencing reads to the
   partially processed datasets. The other complex genetic and
   epigenetic datasets from public sources such as GWAS, ENCODE
   consortiums etc. will also easily be integrated into HBP,
   hence the final output results of HBP could provide a
   comprehensive in-depth understanding for the specific
   chromatin interactions, potential molecular mechanisms and
   biological significance. We believe that HBP is a reliable
   tool for the rapidly analysis of Hi-C data and will be very
   useful for a wide range of researchers, particularly those
   who lack of background in computational biology.
Functions to compute small area estimates based on a basic
        area or unit-level model. The model is fit using restricted
        maximum likelihood, or in a hierarchical Bayesian way. In the
        latter case numerical integration is used to average over the
        posterior density for the between-area variance.  The output
        includes the model fit, small area estimates and corresponding
        MSEs, as well as some model selection measures. Additional
        functions provide means to compute aggregate estimates and
        MSEs, to minimally adjust the small area estimates to
        benchmarks at a higher aggregation level, and to graphically
        compare different sets of small area estimates.
This package fits Hierarchical Bayesian space-Time models for Gaussian data. Furthermore, its functions have been implemented for analysing the fitting qualities of those models.
A new diagnostic check for model adequacy in regression
        and generalized linear models is implemented.
This package calculates the interval estimates for the parameters of 
 linear models heteroscedastic regression using bootstrap - (Wild Bootstrap) and double
 bootstrap-t (Wild Bootstrap). It is also possible to calculate confidence intervals using
 the percentile bootstrap and percentile bootstrap double. It is possible to calculate
 consistent estimates of the covariance matrix of the parameters of linear regression models
 with heteroskedasticity of unknown form. The package also provides function to calculate
 consistently the covariance matrix of the parameters of linear models with heteroskedasticity
 of unknown form.
Estimation of parameters in 3-segment (i.e. 2 change-point)
    regression models with heteroscedastic variances is provided based on both
    likelihood and hybrid Bayesian approaches, with and without continuity
    constraints at the change points.
Functions to perform dimensionality reduction for classification if the covariance matrices of the classes are unequal.
Fits regression models on high dimensional data to estimate coefficients and use bootstrap method to obtain confidence intervals. Choices for regression models are Lasso, Lasso+OLS, Lasso partial ridge, Lasso+OLS partial ridge.
Discriminant analysis and data clustering methods for high
    dimensional data, based on the assumption that high-dimensional data live in
    different subspaces with low dimensionality proposing a new parametrization of
    the Gaussian mixture model which combines the ideas of dimension reduction and
    constraints on the model.
Determine the sample size requirement to achieve the target probability of correct classification (PCC) for studies employing high-dimensional features.  The package implements functions to 1) determine the asymptotic feasibility of the classification problem; 2) compute the upper bounds of the PCC for any linear classifier; 3) estimate the PCC of three design methods given design assumptions; 4) determine the sample size requirement to achieve the target PCC for three design methods.
Cross-validated linear discriminant calculations determine
  the optimum number of features. Test and training scores from
  successive cross-validation steps determine, via a principal
  components calculation, a low-dimensional global space onto which test
  scores are projected, in order to plot them. Further functions are
  included that are intended for didactic use. The  package implements,
  and extends, methods described in J.H. Maindonald and C.J. Burden (2005)
  <https://journal.austms.org.au/V46/CTAC2004/Main/home.html>.
A flexible and hierarchical framework for comparing categorical map composition and configuration (spatial pattern) along spatial, thematic, or external grouping variables.  Comparisons are based on measures of mutual information between thematic classes (colours) and location (spatial partitioning).  Results are returned in textual, tabular, and graphical forms.
'HDF5' is a data model, library and file format for storing 
    and managing large amounts of data. This package provides a nearly
    feature complete, object oriented  wrapper for the 'HDF5' API
    <https://support.hdfgroup.org/HDF5/doc/RM/RM_H5Front.html> using R6 classes. 
    Additionally, functionality is added so that 'HDF5' objects behave very 
    similar to their corresponding R counterparts.
Test the significance of coefficients in high dimensional generalized linear models.
Implementation of multiple approaches to perform inference in high-dimensional models.
A generic function and a set of methods to calculate highest density intervals for a variety of classes of objects which can specify a probability density distribution, including MCMC output, fitted density objects, and functions.
Mimics the lm() function found in the package stats to fit
        high dimensional regression models with point estimates,
        standard errors, and p-values. Methods for printing and
        summarizing the results are given.
Implementation of selected high-dimensional statistical and
    econometric methods for estimation and inference. Efficient estimators and
    uniformly valid confidence intervals for various low-dimensional causal/
    structural parameters are provided which appear in high-dimensional
    approximately sparse models. Including functions for fitting heteroscedastic
    robust Lasso regressions with non-Gaussian errors and for instrumental variable
    (IV) and treatment effect estimation in a high-dimensional setting. Moreover,
    the methods enable valid post-selection inference and rely on a theoretically
    grounded, data-driven choice of the penalty.
    Chernozhukov, Hansen, Spindler (2016) <arXiv:1603.01700>.
Penalized regression for generalized linear models for
  measurement error problems (aka. errors-in-variables). The package
  contains a version of the lasso (L1-penalization) which corrects
  for measurement error (Sorensen et al. (2015) <doi:10.5705/ss.2013.180>). 
  It also contains an implementation of the Generalized Matrix Uncertainty 
  Selector, which is a version the (Generalized) Dantzig Selector for the 
  case of measurement error (Sorensen et al. (2018) <doi:10.1080/10618600.2018.1425626>).
Creates nomogram visualizations for penalized Cox regression
    models, with the support of reproducible survival model building,
    validation, calibration, and comparison for high-dimensional data.
An implementation of an algorithm for outlier detection that can handle a) data with a mixed categorical and continuous variables, b) many columns of data, c) many rows of data, d) outliers that mask other outliers, and e) both unidimensional and multidimensional datasets. Unlike ad hoc methods found in many machine learning papers, HDoutliers is based on a distributional model that uses probabilities to determine outliers. See <https://www.cs.uic.edu/~wilkinson/Publications/outliers.pdf>.
In high-dimensional settings:
	Estimate the number of distant spikes based on the Generalized Spiked Population (GSP) model.
	Estimate the population eigenvalues, angles between the sample and population eigenvectors, correlations between the sample and population PC scores, and the asymptotic shrinkage factors.
	Adjust the shrinkage bias in the predicted PC scores.
Algorithms for lasso and fused-lasso problems: implementation of
    the lars algorithm for lasso and fusion penalization and EM-based
    algorithms for (logistic) lasso  and fused-lasso penalization.
Computation of highest density regions in one and two dimensions, kernel estimation of univariate density functions conditional on one covariate,and multimodal regression.
Functions for calculating the hazard discrimination summary and its
    standard errors, as described in Liang and Heagerty (2016) <doi:10.1111/biom.12628>.
High dimensional testing procedures on mean, covariance and white noises.
This package implements a iteratively reweighed least square (IRLS) strategy that incorporates a blockwise majorization decent (BMD) method, for efficiently computing the solution paths of the (grouped) lasso and the (grouped) elastic net for the Tweedie model.
A machine learning toolbox tailored to healthcare data.
Timeseries analysis is conducted using Korean mortality and environmental variables
The heatex package calculates heat storage in the body and
        the components of heat exchange (conductive, convective,
        radiative, and evaporative) between the body and the
        environment during physical activity based on the principles of
        partitional calorimetry. The program enables heat exchange
        calculations for a range of environmental conditions when
        wearing various clothing ensembles.
Allows heatmap matrix to have non-identical X- and
        Y-dimensions.  Allows multiple tracks of annotation for
        RowSideColors and ColSideColors.
An improved heatmap package. Completely
    compatible with the original R function 'heatmap',
    and provides more powerful and convenient features.
Generates a fit plot for diagnosing misspecification in models of
    binary dependent variables, and calculates the related heatmap fit
    statistic described in Esarey and Pierce (2012) <DOI:10.1093/pan/mps026>.
Create interactive cluster 'heatmaps' that can be saved as a stand-
    alone HTML file, embedded in 'R Markdown' documents or in a 'Shiny' app, and
    available in the 'RStudio' viewer pane. Hover the mouse pointer over a cell to
    show details or drag a rectangle to zoom. A 'heatmap' is a popular graphical
    method for visualizing high-dimensional data, in which a table of numbers
    are encoded as a grid of colored cells. The rows and columns of the matrix
    are ordered to highlight patterns and are often accompanied by 'dendrograms'.
    'Heatmaps' are used in many fields for visualizing observations, correlations,
    missing values patterns, and more. Interactive 'heatmaps' allow the inspection
    of specific value by hovering the mouse over a cell, as well as zooming into
    a region of the 'heatmap' by dragging a rectangle around the relevant area.
    This work is based on the 'ggplot2' and 'plotly.js' engine. It produces
    similar 'heatmaps' as 'heatmap.2' or 'd3heatmap', with the advantage of speed
    ('plotly.js' is able to handle larger size matrix), the ability to zoom from
    the 'dendrogram' panes, and the placing of factor variables in the sides of the
    'heatmap'.
Functions to perform robust estimation considering heavy-tailed distributions.
    Currently, the package includes routines for linear regression, linear mixed-effect
    models, multivariate location and scatter estimation, multivariate regression,
    penalized splines, random variate generation and some support functions.
An implementation of the modelling and
    reporting features described in reference textbooks
    and guidelines: deterministic and probabilistic
    sensitivity analysis, heterogeneity analysis, time
    dependency on state-time and model-time (semi-Markov
    and non-homogeneous Markov models), etc.
Calculates Healthy Eating Index (HEI) scores for National Health and Nutrition Examination Survey (NHANES) data sets to facilitate analysis of
    demographic and dietary differences. For more information on the HEI metric, refer to Guenther et al. (2014) <doi:10.3945/jn.113.183079>.
Decode elements of the Australian Higher Education Information Management System (HEIMS) data for clarity and performance. HEIMS is the record system of the Department of Education, Australia to record enrolments and completions in Australia's higher education system, as well as a range of relevant information. For more information, including the source of the data dictionary, see <http://heimshelp.education.gov.au/sites/heimshelp/dictionary/pages/data-element-dictionary>.
Create pretty visualizations of alpha-helical
    peptide sequences in R. The amino acid type (polar, hydrophobic, etc.) is
    denoted by color. This package allows creation of helical wheels and (soon)
    Wenxiang diagrams. Future versions aim to include increased
    customization of the two existing visualizations, as well as a
    visualization for the helical net diagram.
Base R's default setting for 'stringsAsFactors' within
    'data.frame()' and 'as.data.frame()' is supposedly the most often complained
    about piece of code in the R infrastructure. The 'hellno' package provides
    an explicit solution without changing R itself or having to mess around with
    options. It tries to solve this problem by providing alternative
    'data.frame()' and 'as.data.frame()' functions that are in fact simple
    wrappers around base R's 'data.frame()' and 'as.data.frame()' with
    'stringsAsFactors' option set to 'HELLNO' ( which in turn equals FALSE )
    by default.
A dummy package to demonstrate how to interface to a jar
        file that resides inside an R package.
Contains many functions useful for managing 'NetCDF' files (see <http://en.wikipedia.org/wiki/NetCDF>), get tide levels on any point of the globe, get moon phase and time for sun rise and fall, analyse and reconstruct periodic time series of temperature with irregular sinusoidal pattern, show scales and wind rose in plot with change of color of text, Metropolis-Hastings algorithm for Bayesian MCMC analysis, plot graphs or boxplot with error bars, search files in disk by there names or their content, read the contents of all files from a folder at one time.
Tools for accessing various open data sources in the Helsinki
    region in Finland. Current data sources include
    the Real Estate Department (<http://ptp.hel.fi/avoindata/>),
    Service Map API (<http://api.hel.fi/servicemap/v1/>),
    Linked Events API (<http://api.hel.fi/linkedevents/v0.1/>),
    Helsinki Region Infoshare statistics API (<https://dev.hel.fi/stats/>).
An implementation of Hierarchical Ensemble Methods for Directed Acyclic Graphs (DAGs). The 'HEMDAG' package can be used to enhance the predictions of virtually any flat learning methods, by taking into account the hierarchical nature of the classes of a bio-ontology. 'HEMDAG' is specifically designed for exploiting the hierarchical relationships of DAG-structured taxonomies, such as the Human Phenotype Ontology (HPO) or the Gene Ontology (GO), but it can be also safely applied to tree-structured taxonomies (as FunCat), since trees are DAGs. 'HEMDAG' scale nicely both in terms of the complexity of the taxonomy and in the cardinality of the examples. (Marco Notaro, Max Schubach, Peter N. Robinson and Giorgio Valentini (2017) <doi:10.1186/s12859-017-1854-y>).
Provides HE plot and other functions for visualizing hypothesis
    tests in multivariate linear models. HE plots represent sums-of-squares-and-
    products matrices for linear hypotheses and for error using ellipses (in two
    dimensions) and ellipsoids (in three dimensions). The related 'candisc' package
    provides visualizations in a reduced-rank canonical discriminant space when
    there are more than a few response variables.
Constructs paths to your project's files.
    The 'here()' function uses a reasonable heuristics to find your project's
    files, based on the current working directory at the time when the package
    is loaded. Use it as a drop-in replacement for 'file.path()', it will always
    locate the files relative to your project root.
Hierarchical exponential-family random graph models with local dependence.
Implements marker-based estimation of heritability when observations on genetically identical replicates are available. These can be either observations on individual plants or plot-level data in a field trial. Heritability can then be estimated using a mixed model for the individual plant or plot data. For comparison, also mixed-model based estimation using genotypic means and estimation of repeatability with ANOVA are implemented. For illustration the package contains several datasets for the model species Arabidopsis thaliana.
Statistical framework to analyze heritability of gene expression 
    based on next-generation sequencing data and simulating sequencing reads. 
    Variance partition coefficients (VPC) are computed using linear mixed effects 
    and generalized linear mixed effects models. Compound Poisson and negative 
    binomial models are included.
Probability functions and other utilities for the generalized Hermite distribution.
Functionality for developing and analyzing the output of health-economic 
    simulation models. Contains random sampling functions for conducting probabilistic  
    sensitivity analyses (Claxton et al. 2005) <doi:10.1002/hec.985> and 
    individual patient simulations (Brennan et al. 2006) <doi:10.1002/hec.1148>. 
    Individualized cost-effectiveness analysis (Basu and Meltzer 2007, Ioannidis and Garber 2011) 
    <doi:10.1177/0272989X06297393>, <doi:10.1371/journal.pmed.1001058> 
    can be performed on simulation output and used to 
    summarize a probabilistic sensitivity analysis at the subgroup or individual 
    level. Core functions are written in C++ to facilitate computationally 
    intensive modeling.
An implementation of White's Test for Heteroskedasticity
        as outlined in Doornik (1996).
Performs Gaussian process regression with heteroskedastic noise following Binois, M., Gramacy, R., Ludkovski, M. (2016) <arXiv:1611.05902>. The input dependent noise is modeled as another Gaussian process. Replicated observations are encouraged as they yield computational savings. Sequential design procedures based on the integrated mean square prediction error and lookahead heuristics are provided, and notably fast update functions when adding new observations.
Assess the presence of statistical heterogeneity and quantify its impact in the context of meta-analysis. It includes test for heterogeneity as well as other statistical measures (R_b, I^2, R_I).
Functions for the fitting and summarizing of heteroscedastic t-regression.
Implements various heuristics like Take The Best and
    unit-weight linear, which do two-alternative choice: which of
    two objects will have a higher criterion?  Also offers functions
    to assess performance, e.g. percent correct across all row pairs
    in a data set and finding row pairs where models disagree.
    New models can be added by implementing a fit and predict function--
    see vignette.
Binning and plotting functions for hexagonal bins.
Helper functions for creating reproducible hexagon sticker purely in R.
Display hexagonally binned scatterplots for multi-class data, using coloured triangles to show class proportions.
Functions to view files in raw binary form like in a hex editor.  Additional functions to specify and read arbitrary binary formats.
A data only package containing commercial domestic flights that
    departed Houston (IAH and HOU) in 2011.
High-dimensional additive models as introduced by Meier,
        van der Geer and Buehlmann (2009).
Implements the hub graphical lasso and hub covariance graph proposal by Tan, KM., London, P., Mohan, K., Lee, S-I., Fazel, M., and Witten, D. (2014). Learning graphical models with hubs. To appear in Journal of Machine Learning Research. arXiv.org/pdf/1402.7349.pdf.
Procedures for fitting hierarchical generalized linear models (HGLM). It can be used for linear mixed models and generalized linear mixed models with random effects for a variety of links and a variety of distributions for both the outcomes and the random effects. Fixed effects can also be fitted in the dispersion part of the mean model.
This data-only package was created for distributing data used in the examples of the hglm package.
The holonomic gradient method (HGM, hgm) gives a way to evaluate normalization
  constants of unnormalized probability distributions by utilizing holonomic 
  systems of differential or difference equations. The holonomic gradient descent (HGD, hgd) gives a method
  to find maximal likelihood estimates by utilizing the HGM.
Contains functions for
 identifying and correcting HGNC gene symbols which have been converted
 to date format by Excel, for reversibly converting between HGNC
 symbols and valid R names, identifying invalid HGNC symbols and
 correcting synonyms and outdated symbols which can be mapped to an
 official symbol.
Support software for Statistical Analysis and Data Display (Second Edition, Springer, ISBN 978-1-4939-2121-8, 2015) and (First Edition, Springer, ISBN 0-387-40270-5, 2004) by Richard M. Heiberger and Burt Holland.  This contemporary presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis.  The second edition includes redesigned graphics and additional chapters. The authors emphasize how to construct and interpret graphs, discuss principles of graphical design, and show how accompanying traditional tabular results are used to confirm the visual impressions derived directly from the graphs. Many of the graphical formats are novel and appear here for the first time in print.  All chapters have exercises.  All functions introduced in the book are in the package.  R code for all examples, both graphs and tables, in the book is included in the scripts directory of the package.
Heller-Heller-Gorfine ('HHG') tests are a set of powerful statistical
    tests of multivariate k-sample homogeneity and independence. For the univariate
    case, the package also offers implementations of the 'MinP DDP' and 'MinP ADP'
    tests, which are consistent against all continuous alternatives but are
    distribution-free, and are thus much faster to apply.
Meyer and Held (2017) <doi:10.1093/biostatistics/kxw051> present an
    age-structured spatio-temporal model for infectious disease counts. The
    approach is illustrated in a case study on norovirus gastroenteritis in
    Berlin, 2011-2015, by age group, city district and week, using additional
    contact data from the POLYMOD survey. This package contains the data and
    code to reproduce the results from the paper, see 'demo("hhh4contacts")'.
Based on the aggregated shares retained by individual firms or actors within a market or space, the Herfindahl-Hirschman Index (HHI) measures the level of concentration in the market or space. It is often used as a measure of competition, where 0 equals perfect competition amongst firms or actors and 10,000 equals perfect monopoly. This package allows for intuitive and straightforward computation of the HHI, requiring placement of objects directly into the function, including the data frame first, followed by the name of the vector (or variable) corresponding with the market shares in quotation marks. The package also includes a plot function for quick visual display of HHI time series across any measure of time (year, quarter, month, etc.) Suggested citation of the HHI: Rhoades, Stephen A. (1993, "The herfindahl-hirschman index." Federal Reserve Bulletin 79: 188).
Builds on the EMD package to provide additional tools for empirical mode decomposition (EMD) and Hilbert spectral analysis. It also implements the ensemble empirical decomposition (EEMD) and the complete ensemble empirical mode decomposition (CEEMD) methods to avoid mode mixing and intermittency problems found in EMD analysis.  The package comes with several plotting methods that can be used to view intrinsic mode functions, the HHT spectrum, and the Fourier spectrum.
Simulation from distributions supported by nested
        hyperplanes, using the algorithm described in Petris &
        Tardella, "A geometric approach to transdimensional Markov
        chain Monte Carlo", Canadian Journal of Statistics, v.31, n.4,
        (2003).  Also random direction multivariate Adaptive Rejection
        Metropolis Sampling.
Utilising the 'Have I been pwned?' API (see <https://haveibeenpwned.com/API/v2>
    for more information), check whether email addresses and/or user names have been present
    in a publicly disclosed data breach.
Here we propose a model to systematically analyze the roles of architectural proteins and functional elements in blocking long-range contacts between loci. The proposed model does not rely on topologically associating domain (TAD) mapping from Hi-C data. Instead of testing the enrichment or influence of protein binding at TAD borders, the model directly estimates the blocking effect of proteins on long-range contacts between flanking loci, making the model intuitive and biologically meaningful.
We propose a multiple logistic regression model to assess the influences of genomic features such as DNA-binding proteins and functional elements on topological domain borders.
We propose a generalized linear regression with higher-order interaction terms to assess the influences of genomic features such as DNA-binding proteins and functional elements on long-range contacts from Hi-C experiments.
A tool for Hierarchical Climate Regionalization applicable to any correlation-based clustering.
             It adds several features and a new clustering method (called, 'regional' linkage) to hierarchical 
             clustering in R ('hclust' function in 'stats' library): data regridding, coarsening spatial resolution,
             geographic masking (by continents, regions, or countries), data filtering by mean and/or variance 
             thresholds, data preprocessing (detrending, standardization, and PCA), faster correlation function
             with preliminary big data support, different clustering methods, hybrid hierarchical clustering, 
             multi-variate clustering (MVC), cluster validation, and visualization of region maps.
This package allows you to detect domains in HiC data by rephrasing this problem as a two-dimensional segmentation issue.
Computes the ACMIF test and Bonferroni-adjusted p-value of interaction in two-factor studies.  Produces corresponding interaction plot and analysis of variance tables and p-values from several other tests of non-additivity.
Contains functions for the analysis of Discrete Time Hidden Markov Models, Markov Modulated GLMs and the Markov Modulated Poisson Process. It includes functions for simulation, parameter estimation, and the Viterbi algorithm. See the topic "HiddenMarkov" for an introduction to the package, and "Change Log" for a list of recent changes. The algorithms are based of those of Walter Zucchini.
Performs linear discriminant analysis in high dimensional
        problems based on reliable covariance estimators for problems
        with (many) more variables than observations. Includes routines
        for classifier training, prediction, cross-validation and
        variable selection.
Variance partition of a multivariate data set
Functions for performing hierarchical analysis of distance
    sampling data, with ability to use an areal spatial ICAR model on
    top of user supplied covariates to get at variation in abundance
    intensity.  The detection model can be specified as a function of
    observer and individual covariates, where a parametric model is
    supposed for the population level distribution of covariate values.
    The model uses data augmentation and a reversible jump MCMC
    algorithm to sample animals that were never observed.  Also
    included is the ability to include point independence (increasing
    correlation multiple observer's observations as a function of
    distance, with independence assumed for distance=0 or first
    distance bin), as well as the ability to model species
    misclassification rates using a multinomial logit formulation on data
    from double observers.  New in version 2.1 is the ability to
    include zero inflation, but this is only recommended for cases where
    sample sizes and spatial coverage of the survey are high.
Pure set data visualization approaches are often limited in
    scalability due to the combinatorial explosion of distinct set families as
    the number of sets under investigation increases. hierarchicalSets applies
    a set centric hierarchical clustering of the sets under investigation and
    uses this hierarchy as a basis for a range of scalable visual
    representations. hierarchicalSets is especially well suited for collections
    of sets that describe comparable comparable entities as it relies on the
    sets to have a meaningful relational structure.
Implementation of the convex banding procedure (using a
    hierarchical group lasso penalty) for covariance estimation that is
    introduced in Bien, Bunea, Xiao (2015) Convex Banding of the Covariance
    Matrix. Accepted for publication in JASA.
Hierarchical group-wise partitioning of phenotypic diversity into 
    within-group (alpha), among-group (beta), and pooled-total (gamma) 
    components using Hill numbers.
    Turnover and overlap are also calculated as standardized alternatives to
    beta diversity. Hierarchical bootstrapping is used to approximate 
    uncertainty around each diversity component.
Allows the estimation of hierarchical F-statistics from haploid or diploid genetic data 
    with any numbers  of levels in the hierarchy, following the algorithm of Yang (Evolution, 1998, 52(4):950-956; 
    <DOI:10.2307/2411227>. Functions are also given to test via randomisations the significance of each F and variance components,  
	using the likelihood-ratio statistics G.
Fits sparse interaction models for continuous and binary responses subject to the strong (or weak) hierarchy restriction that an interaction between two variables only be included if both (or at least one of) the variables is included as a main effect.  For more details, see Bien, J., Taylor, J., Tibshirani, R., (2013) "A Lasso for Hierarchical Interactions." Annals of Statistics. 41(3). 1111-1141.
HierO is a graphical user interface (GUI) tool for calculating optimal statistical power and sample size for hierarchical data structure. HierO constructs a user defined sample size optimization problem to GAMS (General Algebraic Modeling System)form and uses Rneos package to send the problem to NEOS server for solving.
Implementation of the convex hierarchical testing (CHT) procedure
    introduced in Bien, Simon, and Tibshirani (2015) Convex Hierarchical Testing
    of Interactions. Annals of Applied Statistics. Vol. 9, No. 1, 27-42.
Uses likelihood to estimate ancestry and heterozygosity.
        Evaluates simple hybrid classifications (parentals, F1, F2,
        backcrosses). Estimates genomic clines.
A wrapper for the 'Highcharts' library including
  shortcut functions to plot R objects. 'Highcharts' 
  <http://www.highcharts.com/> is a charting library offering
  numerous chart types with a simple configuration syntax.
Performs the generalized component test from Gregory et al (2015), as well as the tests from Chen and Qin (2010), Srivastava and Kubokawa (2013), and Cai, Liu, and Xia (2014) for equality of two population mean vectors when the length of the vectors exceeds the sample size.
Three high-dimensional outlier detection algorithms and a outlier unification scheme are implemented in this package. The angle-based outlier detection (ABOD) algorithm is based on the work of Kriegel, Schubert, and Zimek [2008]. The subspace outlier detection (SOD) algorithm is based on the work of Kriegel, Kroger, Schubert, and Zimek [2009]. The feature bagging-based outlier detection (FBOD) algorithm is based on the work of Lazarevic and Kumar [2005]. The outlier unification scheme is based on the work of Kriegel, Kroger, Schubert, and Zimek [2011].
Provide functionality to manage, clean and match highfrequency
    trades and quotes data, calculate various liquidity measures, estimate and
    forecast volatility, detect price jumps and investigate microstructure noise and intraday
    periodicity.
Syntax highlighter for R code based 
	on the results of the R parser. Rendering in HTML and latex 
	markup. Custom Sweave driver performing syntax highlighting 
	of R code chunks.
A tool to format R markdown with CSS ids for HTML output. 
    The tool may be most helpful for those using markdown to create reproducible
    documents. The biggest limitations in formatting is the knowledge of CSS
    by the document authors.
Provides various tests for comparing high-dimensional mean vectors in two sample populations.
Provides syntax highlighting for R source code. Currently it
    supports LaTeX and HTML output. Source code of other languages is supported
    via Andre Simon's highlight package (http://www.andre-simon.de).
Functions for determining and evaluating high-risk zones and
    simulating and thinning point process data, as described in 'Determining
    high risk zones using point process methodology - Realization by building
    an R package' Seibold (2012) <http://highriskzone.r-forge.r-project.org/Bachelorarbeit.pdf> 
    and 'Determining high-risk zones for unexploded World War II bombs by using point 
    process methodology', Mahling et al. (2013) <doi:10.1111/j.1467-9876.2012.01055.x>.
Can be used to carry out extraction, normalization, quality control (QC), candidate hits identification and visualization for plate based essays, in drug discovery. This project was funded by the Division of Allergy, Immunology, and Transplantation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under contract No. HHSN272201400054C entitled "Adjuvant Discovery For Vaccines Against West Nile Virus and Influenza", awarded to Duke University and lead by Drs. Herman Staats and Soman Abraham.
Implements the method developed by Cao and Kosorok (2011) for the significance analysis of thousands of features in high-dimensional biological studies. It is an asymptotically valid data-driven procedure to find critical values for rejection regions controlling the k-familywise error rate, false discovery rate, and the tail probability of false discovery proportion.
Implements the Hierarchical Incremental GRAdient Descent (HiGrad) algorithm,
    a first-order algorithm for finding the minimizer of a function in online learning just like stochastic gradient descent (SGD).
    In addition, this method attaches a confidence interval to assess the uncertainty of its predictions.
    See Su and Zhu (2018) <arXiv:1802.04876> for details.
Generate occupancy patterns based on arrival and departure timestamps
Estimation of heritability with confidence intervals in linear mixed models.
Allows to estimate and test high-dimensional mediation effects based on sure independent screening and minimax concave penalty techniques. A joint significance test is used for mediation effect. Haixiang Zhang, Yinan Zheng, Zhou Zhang, Tao Gao, Brian Joyce, Grace Yoon, Wei Zhang, Joel Schwartz, Allan Just, Elena Colicino, Pantel Vokonas, Lihui Zhao, Jinchi Lv, Andrea Baccarelli, Lifang Hou, Lei Liu (2016) <doi:10.1093/bioinformatics/btw351>.
H(x) is the h-index for the past x years. Here, the h(x) of a scientist/department/etc. can be calculated using the exported excel file from a Web of Science citation report of a search. Also calculated is the year of first publication, total number of publications, and sum of times cited for the specified period. Therefore, for h-10: the date of first publication, total number of publications, and sum of times cited in the past 10 years are calculated. Note: the excel file has to first be saved in a .csv format.
Hypergeometric Intersection distributions are a broad group of distributions that describe the probability of picking intersections when drawing independently from two (or more) urns containing variable numbers of balls belonging to the same n categories.
Based on hierarchical modeling, this package provides a
        few practical functions to find and present the optimal designs
        for a pooled NGS design.
Identifying labeled compounds in a 13C-tracer experiment in non-targeted fashion is a cumbersome process. This package facilitates such type of analyses
    by providing high level quality control plots, deconvoluting and evaluating spectra and performing a multitude of tests in an automatic fashion.
    The main idea is to use changing intensity ratios of ion pairs from peak list generated with 'xcms' as candidates and evaluate those against
    base peak chromatograms and spectra information within the raw measurement data automatically.
Implementation of a class of hierarchical item response
  theory (IRT) models where both the mean and the variance of latent preferences
  (ability parameters) may depend on observed covariates. The current
  implementation includes both the two-parameter latent trait model for binary data and the
  graded response model for ordinal data. Both are fitted via the Expectation-Maximization (EM)
  algorithm. Asymptotic standard errors are derived from the observed information
  matrix.
Sets up and executes a HiSSE model (Hidden State Speciation and Extinction) on a phylogeny and character sets to test for hidden shifts in trait dependent rates of diversification. Beaulieu and O'Meara (2016) <doi:10.1093/sysbio/syw022>.
The 'HistData' package provides a collection of small data sets
    that are interesting and important in the history of statistics and data
    visualization. The goal of the package is to make these available, both for
    instructional use and for historical research. Some of these present interesting
    challenges for graphics or analysis in R.
In the framework of Symbolic Data Analysis, a relatively new
    approach to the statistical analysis of multi-valued data, we consider
    histogram-valued data, i.e., data described by univariate histograms. The
    methods and the basic statistics for histogram-valued data are mainly based
    on the L2 Wasserstein metric between distributions, i.e., the Euclidean metric
    between quantile functions. The package contains unsupervised classification
    techniques, least square regression and tools for histogram-valued data and for
    histogram time series. An introducing paper is Irpino A. Verde R. (2015) <doi:10.1007/s11634-014-0176-4>.
Using the MDL principle, it is possible to estimate
	parameters for a histogram-like model. The package contains
	the implementation of such an estimation method.
Automatic construction of regular and irregular histograms as described in Rozenholc/Mildenberger/Gather (2010).
Provides a number of utility functions useful for manipulating large histograms.  This includes methods to trim, subset, merge buckets, merge histograms, convert to CDF, and calculate information loss due to binning.  It also provides a protocol buffer representations of the default R histogram class to allow histograms over large data sets to be computed and manipulated in a MapReduce environment.
These sample data sets are intended for historians
    learning R. They include population, institutional, religious,
    military, and prosopographical data suitable for mapping,
    quantitative analysis, and network analysis.
Automatically tracks and makes programmatically available code
    evaluation history in R sessions and dynamic documents.
Hierarchical inference testing (HIT) for (generalized) linear models with 
    correlated covariates applicable to high-dimensional settings.
The "Hit and Run" Markov Chain Monte Carlo method for sampling uniformly from convex shapes defined by linear constraints, and the "Shake and Bake" method for sampling from the boundary of such shapes. Includes specialized functions for sampling normalized weights with arbitrary linear constraints.
The functions in this package produce a complete set of mortality rates as a function of a combination of HIV prevalence and either life expectancy at birth (e0), child mortality (5q0), or child mortality with adult mortality (45q15)
Hadoop InteractiVE facilitates distributed 
             computing via the MapReduce paradigm through R and Hadoop. An easy to use 
             interface to Hadoop, the Hadoop Distributed File System (HDFS), 
	     and Hadoop Streaming is provided.
Creates and plots 2D and 3D hive plots. Hive plots are a unique method of displaying networks of many types in which node properties are mapped to axes using meaningful properties rather than being arbitrarily positioned.  The hive plot concept was invented by Martin Krzywinski at the Genome Science Center (www.hiveplot.net/).  Keywords: networks, food webs, linnet, systems biology, bioinformatics.
This is a collection of functions for converting coordinates between WGS84UTM, WGS84GEO, HK80UTM, HK80GEO and HK1980GRID Coordinate Systems used in Hong Kong SAR, based on the algorithms described in Explanatory Notes on Geodetic Datums in Hong Kong by Survey and Mapping Office Lands Department, Hong Kong Government (1995).
Implements an ensemble algorithm for clustering combining a k-means and a hierarchical clustering approach.
Several procedures around a particular hierarchical model for extreme value: the HKEVP of Reich and Shaby (2012) <DOI:10.1214/12-AOAS591>. Simulation, estimation and spatial extrapolation of this model are available for extreme value data. A special case of this process is also handled: the Latent Variable Model of Davison et al. (2012) <DOI:10.1214/11-STS376>.
A set of functions helps to retrieve data from HKEX (Hong Kong Stock Exchange), see <https://www.hkex.com.hk/> for more information. In addition, a function generates insert SQL statements from a dataframe.
Methods to make inference about the Hurst-Kolmogorov and the AR(1) process.
A suite of diagnostic tools for hierarchical
    (multilevel) linear models. The tools include
    not only leverage and traditional deletion diagnostics (Cook's
    distance, covratio, covtrace, and MDFFITS) but also 
    convenience functions and graphics for residual analysis. Models
    can be fit using either lmer in the 'lme4' package or lme in the 'nlme' package,
    but only two-level models fit using lme are currently supported.
Implements Hierarchical Latent Space Network Model (HLSM) for ensemble of networks as described in Sweet et. al. (2012). <DOI:10.3102/1076998612458702>.
Utilities for reading data from the Human Mortality Database (<www.mortality.org>), Human Fertility Database (<www.humanfertility.org>), and similar databases from the web or locally into an R session as data.frame objects. These are the two most widely used sources of demographic data to study basic demographic change, trends, and develop new demographic methods. Other supported databases at this time include the Human Fertility Collection (<http://www.fertilitydata.org/>), The Japanese Mortality Database (<http://www.ipss.go.jp/p-toukei/JMD>), and the Canadian Human Mortality Database (<http://www.bdlc.umontreal.ca/chmd/>). Arguments and data are standardized.
Scalar performance metrics, including the H-measure, based
        on classification scores for several classifiers applied to the
        same dataset.
Runs single level and multilevel imputation models. The user just has to pass the data to the main function and, optionally, his analysis model. Basically the package then translates this analysis model into commands to impute the data according to it with functions from 'mice', 'MCMCglmm' or routines build for this package.
Contains many functions useful for data
	analysis, high-level graphics, utility operations, functions for
	computing sample size and power, importing and annotating datasets,
	imputing missing values, advanced table making, variable clustering,
	character string manipulation, conversion of R objects to LaTeX and html code,
	and recoding variables.
Easy to use library to setup, apply and make inference
        with discrete time and discrete space Hidden Markov Models
Fits hidden Markov models with discrete non-parametric 
        observation distributions to data sets.  Simulates data
	from such models.  Finds most probable underlying hidden
	states, the most probable sequences of such states, and the
	log likelihood of a collection of observations given the
	parameters of the model.
The package includes the functions designed to analyse continuous observations processes with the Hidden Markov Model approach. They include Baum-Welch and Viterbi algorithms and additional visualisation functions. The observations are assumed to have Gaussian distribution and to be weakly stationary processes. The package was created for analyses of financial time series, but can also be applied to any continuous observations processes.
Consider the linear mixed model with normal random effects. A typical method to solve Henderson's Mixed Model Equations (HMME) is recursive estimation of the fixed effects and random effects. We provide a fast, stable, and scalable solver to the HMME without computing matrix inverse. See Kim (2017) <arXiv:1710.09663> for more details.
Functions for specifying and fitting marginal models for contingency tables proposed 
	by Bergsma and Rudas (2002) here called hierarchical multinomial marginal models (hmmm) and their extensions presented by Bartolucci et al. 
	(2007); multinomial Poisson homogeneous (mph) models and homogeneous linear predictor (hlp) models for contingency
 	tables proposed by Lang (2004) and (2005); hidden Markov models where the distribution of the observed variables 
	is described by a marginal model. 
	Inequality constraints on the parameters are allowed and can be tested.
Improved analysis of marine animal movement data by implementing
    a state-space hidden Markov model (HMM) to improve position estimates. Position
    estimates are derived by comparing electronic tag data (from tags deployed on
    marine animals, typically fish) to three-dimensional oceanographic data.
Using Dirichlet-Multinomial distribution to provide several functions for formal hypothesis testing, power and sample size calculations for human microbiome experiments.
Tools to model, compare, and visualize populations of taxonomic tree objects.
Statistical analysis of static chamber concentration data for trace gas flux estimation.
Implements an S3 class for storing and formatting time-of-day
    values, based on the 'difftime' class.
Perform association test between a group of variable and the outcome.
Hierarchical and single-level non-negative matrix factorization. Several NMF algorithms are available.
Generates (half-)normal plots with simulation envelopes using different diagnostics from a range of different fitted models. A few example datasets are included.
Performs likelihood-based inference for a wide range of regression models. Provides higher-order approximations for inference based on extensions  of saddlepoint type arguments as discussed in the book Applied Asymptotics: Case Studies in Small-Sample Statistics by Brazzale, Davison, and Reid (2007).
Cross-species identification of novel gene candidates using the NCBI web service is provided. Further, sets of miRNA target genes can be identified by using the targetscan.org API.
Suite of tools for managing cached files, targeting
    use in other R packages. Uses 'rappdirs' for cross-platform paths.
    Provides utilities to manage cache directories, including targeting
    files by path or by key; cached directories can be compressed and
    uncompressed easily to save disk space.
Implementation of higher order generalized singular value decomposition (HO GSVD). Based on Ponnapalli, Saunders, etal (2011) <doi:10.1371/journal.pone.0028072>.
Simulates hands and tournaments of Texas Holdem, 
	the most popular form of poker.
	For examples of probability problems involving Texas Holdem 
	and a brief explanation of the game see Schoenberg, F. (2011).
	An Introduction to Probability with Texas Hold'em Examples.
	Taylor and Francis, New York, ISBN-13: 978-1439827680.
Contains trading holiday and half-day data that is automatically registered when loaded.
Performs a homogeneity analysis (multiple correspondence analysis) and various extensions. Rank restrictions on the category quantifications can be imposed (nonlinear PCA). The categories are transformed by means of optimal scaling with options for nominal, ordinal, and numerical scale levels (for rank-1 restrictions). Variables can be grouped into sets, in order to emulate regression analysis and canonical correlation analysis.
A collection of functions useful for the analysis of
    building physics experiments.
A simple implementation of doughnut plots - pie charts with a blank center. The package is named after Homer Simpson - arguably the best-known lover of doughnuts.
Provides methods for closed testing using Simes local tests. In particular, calculates adjusted p-values for Hommel's multiple testing method, and provides lower confidence bounds for true discovery proportions. A robust but more conservative variant of the closed testing procedure that does not require the assumption of Simes inequality is also implemented. The methods have been described in detail in Meijer et al (2016) <arXiv:1611.06739>.
A wrapper for the homologene database by the National Center for
    Biotechnology Information ('NCBI'). It allows searching for gene homologs across 
    species. Data in this package can be found at <ftp://ftp.ncbi.nih.gov/pub/HomoloGene/build68/>.
Homomorphic computations in R for privacy-preserving applications. Currently only
             the Paillier Scheme is implemented.
A theoretical model to simulate  radical polymerization. Material, energy and population balances are integrated for batch, semi batch and continuous process in a ideally mixed reactor. Limitations: single monomer (i.e.homo polymer), one phase (organic, aqueous). Datasets with chemical-physical data for the most common monomers is included. Some background in polymer science is suggested for its use. Graphical interface for a quick and friendly use is available.
A collection of homogeneity tests described in: Viglione
        A., Laio F., Claps P. (2007) ``A comparison of homogeneity
        tests for regional frequency analysis'', Water Resources
        Research, 43, W03428, doi:10.1029/2006WR005095.  More on
        Regional Frequency Analysis can be found in package nsRFA.
Computes the expectation of the number of transmissions and receptions considering a Hop-by-Hop transport model with limited number of retransmissions per packet. It provides the theoretical results shown in Palma et. al.(2016) <DOI:10.1109/TLA.2016.7555237> and also estimated values based on Monte Carlo simulations.
Calculates horizon elevation angle and sky view factor from a digital terrain model.
Supplement for the book "Handbook of Regression Methods" by D. S. Young.  Some datasets used in the book are included and documented.  Wrapper functions are included that simplify the examples in the textbook, such as code for constructing a regressogram and expanding ANOVA tables to reflect the total sum of squares.
A stand-alone function that generates a user specified number of random datasets and computes eigenvalues using the random datasets (i.e., implements Horn's [1965, Psychometrika] parallel analysis). Users then compare the resulting eigenvalues (the mean or the specified percentile) from the random datasets (i.e., eigenvalues resulting from noise) to the eigenvalues generated with the user's data. Can be used for both principal components analysis (PCA) and common/exploratory factor analysis (EFA). The output table shows how large eigenvalues can be as a result of merely using randomly generated datasets. If the user's own dataset has  actual eigenvalues greater than the corresponding eigenvalues, that lends support to retain that factor/component. In other words, if the i(th) eigenvalue from the actual data was larger than the percentile of the (i)th eigenvalue generated using randomly generated data, empirical support is provided to retain that factor/component. 
    Horn, J. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 32, 179-185.
Implementation of the HorseRule model a flexible tree based Bayesian regression method for linear and nonlinear regression described in Nalenz & Villani (2017) <arXiv:1702.05008>.
Contains functions for applying the horseshoe prior to high-
    dimensional linear regression, yielding the posterior mean and credible
    intervals, amongst other things. The key parameter tau can be equipped with
    a prior or estimated via maximum marginal likelihood estimation (MMLE).
    The main function, horseshoe, is for linear regression. In addition, there
    are functions specifically for the sparse normal means problem, allowing
    for faster computation of for example the posterior mean and posterior
    variance. Finally, there is a function available to perform variable
    selection, using either a form of thresholding, or credible intervals.
Performs multiple hot-deck imputation of categorical and continuous variables in a data frame.
Hot deck imputation methods to resolve missing data.
A set of R functions which implements Hotelling's T^2 test and some variants of it. Functions are also included for Aitchison's additive log ratio and centred log ratio transformations.
Contains data for software hotspot analysis, along with a function performing the analysis itself.
Calculates a hot spot cutoff and associated analyses for
        statistical populations
Monthly median home listing, sale price per square foot, and number of units sold for 2984 counties in the contiguous United States From 2008 to January 2016.  Additional data sets containing geographical information and links to Wikipedia are also included.
When testing multiple hypotheses simultaneously, this
        package provides functionality to calculate a lower bound for
        the number of correct rejections (as a function of the number
        of rejected hypotheses), which holds simultaneously -with high
        probability- for all possible number of rejections.  As a
        special case, a lower bound for the total number of false null
        hypotheses can be inferred.  Dependent test statistics can be
        handled for multiple tests of associations. For independent
        test statistics, it is sufficient to provide a list of
        p-values.
This package provides all the functions necessary to
        estimate the 8 parameters of the Heligman Pollard mortality
        model using a Bayesian Melding procedure with IMIS as well as
        to convert those parameters into age-specifc probabilities of
        death and a corresponding life table
Probabilistic models describing the behavior 
	of workload and queue on a High Performance Cluster and computing GRID 
	under FIFO service discipline basing on modified Kiefer-Wolfowitz 
	recursion. Also sample data for inter-arrival times, service times, 
	number of cores per task and waiting times of HPC of Karelian 
	Research Centre are included, measurements took place from 06/03/2009 to 02/30/2011.
	Functions provided to import/export workload traces in Standard Workload Format (swf).
	Stability condition of the model may be verified either exactly, or approximately.
Collection of functions for manipulating sets of HPO terms and
    plotting them with a various options.
This package contains some miscellaneous convenience functions, 
	to create a matrix of dummy columns from a factor, 
	to determine whether x lies in range [a,b], 
	to add a rectangular bracket to an existing plot, 
	and to convert frequencies between Hz, semitones, mel and Bark. 
	This package also contains an example data set of a stratified sample
	of 80 talkers of Dutch.
Efficient algorithms for fitting regularization paths for lasso or elastic-net penalized regression models with Huber loss, quantile loss or squared loss.
Manipulate, analyze, and visualize people data in meaningful ways.
    This package is meant for practitioners of Human Resources analytics (often 
    referred to as talent analytics or people analytics).
A compilation of extra 'ggplot2' themes, scales and utilities, including a 
    spell check function for plot label fields and an overall emphasis on typography. 
    A copy of the 'Google' font 'Roboto Condensed' <https://github.com/google/roboto/> 
    is also included along with a copy of the 'IBM' 'Plex Sans' <https://github.com/IBM/type>
    and 'Titillium Web' <https://fonts.google.com/specimen/Titillium+Web> fonts
    are also included to support their respective typography-oriented themes.
Methods for testing main and interaction effects in possibly
    high-dimensional parametric or nonparametric repeated measures in factorial designs.
    The observations of the subjects are assumed to be multivariate normal if using the parametric test.
    The nonparametric version tests with regard to nonparametric relative effects (based on pseudo-ranks).
    It is possible to use up to 2 whole- and 3 subplot factors.
Offers tools and modelling approaches for binomial data with overdispersion, with particular interest in Health Related Quality of Life (HRQoL) questionnaires regression analysis.
A Hierarchical Spatial Autoregressive Model (HSAR), based on a Bayesian Markov Chain Monte Carlo (MCMC) algorithm ( Dong and Harris (2014) <doi:10.1111/gean.12049> ). The creation of this package was supported by the Economic and Social Research Council (ESRC) through the Applied Quantitative Methods Network: Phase II, grant number ES/K006460/1.
Functions, data sets, analyses and examples from the book 
  ''A Handbook of Statistical Analyses Using R'' (Brian S. Everitt and Torsten
  Hothorn, Chapman & Hall/CRC, 2006). The first chapter
  of the book, which is entitled ''An Introduction to R'', 
  is completely included in this package, for all other chapters,
  a vignette containing all data analyses is available.
Functions, data sets, analyses and examples from the 
  second edition of the book 
  ''A Handbook of Statistical Analyses Using R'' (Brian S. Everitt and Torsten
  Hothorn, Chapman & Hall/CRC, 2008). The first chapter
  of the book, which is entitled ''An Introduction to R'', 
  is completely included in this package, for all other chapters,
  a vignette containing all data analyses is available. In addition,
  the package contains Sweave code for producing slides for selected
  chapters (see HSAUR2/inst/slides).
Functions, data sets, analyses and examples from the 
  third edition of the book 
  ''A Handbook of Statistical Analyses Using R'' (Torsten Hothorn and Brian S.
  Everitt, Chapman & Hall/CRC, 2014). The first chapter
  of the book, which is entitled ''An Introduction to R'', 
  is completely included in this package, for all other chapters,
  a vignette containing all data analyses is available. In addition,
  Sweave source code for slides of selected chapters is included in 
  this package (see HSAUR3/inst/slides). The publishers web page is 
  '<http://www.crcpress.com/product/isbn/9781482204582>'.
Transformation of reflectance spectra, calculation of vegetation indices and red edge parameters, spectral resampling for hyperspectral remote sensing, simulation of reflectance and transmittance using the leaf reflectance model PROSPECT and the canopy reflectance model PROSAIL.
hSDM is an R package for estimating parameters of hierarchical Bayesian species distribution models. Such models allow interpreting the observations (occurrence and abundance of a species) as a result of several hierarchical processes including ecological processes (habitat suitability, spatial dependence and anthropogenic disturbance) and observation processes (species detectability). Hierarchical species distribution models are essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results.
Canonical correlation analysis that extracts nonlinear
        correlation through the use of Hilbert Schmidt Independence
        Criterion and Centered Kernel Target Alignment.
Implementation of the block coordinate descent procedure for
    solving the proximal function of latent group Lasso, highlighted by
    decomposing a DAG into several non-overlapping path graphs, and getting
    closed-form solution for each path graph. The procedure was introduced
    as Algorithm 4 in Yan and Bien (2017) <doi:10.1214/17-STS622>
    "Hierarchical Sparse Modeling: A Choice of Two Group Lasso Formulations", and the
    closed-form solution for each path graph is solved in Algorithm 3 of
    that paper.
A package for computation of hidden semi markov models
Identification of recombination events, haplotype reconstruction, sire imputation and pedigree reconstruction using half-sib family SNP data
A data mining tool for discovering subgroups of patients and genes that simultaneously display unusual levels of variability compared to other genes and patients. Based on sparse singular value decomposition (SSVD), the method can detect both mean and variance biclusters in the presence of heterogeneous residual variance.
Provides bindings to the National Geodetic Survey (NGS) Horizontal
 Time Dependent Positioning (HTDP) utility, v3.2.5, written by Richard Snay,
 Chris Pearson, and Jarir Saleh of NGS. HTDP is a utility that allows users to
 transform positional coordinates across time and between spatial reference
 frames. See <https://www.ngs.noaa.gov/TOOLS/Htdp/Htdp.shtml> for more
 information.
Convert a html document to simple plain texts by removing all html tags.  This package utilizes regular expressions to strip off html tags. It also offers gettxt() and browse() function, which enables you to get or browse texts at a certain web page.
HTML tables are a valuable data source but extracting and recasting
    these data into a useful format can be tedious. This package allows to collect
    structured information from HTML tables. It is similar to readHTMLTable()
    of the XML package but provides three major advantages. First, the function
    automatically expands row and column spans in the header and body cells.
    Second, users are given more control over the identification of header and body
    rows which will end up in the R table, including semantic header information
    that appear throughout the body. Third, the function preprocesses table code,
    corrects common types of malformations, removes unneeded parts and so helps to
    alleviate the need for tedious post-processing.
Tables with state-of-the-art layout elements such as row spanners,
    column spanners, table spanners, zebra striping, and more. While allowing
    advanced layout, the underlying css-structure is simple in order to maximize
    compatibility with word processors such as 'MS Word' or 'LibreOffice'. The package
    also contains a few text formatting functions that help outputting text
    compatible with HTML/'LaTeX'.
HTML documents can be beautiful and pristine. They can also be
    wretched, evil, malformed demon-spawn. Now, you can tidy up that HTML and XHTML
    before processing it with your favorite angle-bracket crunching tools, going beyond
    the limited tidying that 'libxml2' affords in the 'XML' and 'xml2' packages and
    taming even the ugliest HTML code generated by the likes of Google Docs and Microsoft
    Word. It's also possible to use the functions provided to format or "pretty print"
    HTML content as it is being tidied. Utilities are also included that make it  
    possible to view formatted and "pretty printed" HTML/XML
    content from HTML/XML document objects, nodes, node sets and plain character HTML/XML
    using 'vkbeautify' (by Vadim Kiryukhin) and 'highlight.js' (by Ivan Sagalaev).
    Also (optionally) enables filtering of nodes via XPath or viewing an HTML/XML document
    in "tree" view using 'xml-viewer' (by Julian Gruber). See
    <https://github.com/vkiryukhin/vkBeautify> and 
    <https://github.com/juliangruber/xml-viewer> for more information about 'vkbeautify'
    and 'xml-viewer', respectively.
Facilitates automated HTML report creation, in particular
        framed HTML pages and dynamically sortable tables.
A framework for creating HTML widgets that render in various
    contexts including the R console, 'R Markdown' documents, and 'Shiny'
    web applications.
Historical regression trees are an extension of standard trees, 
	producing a non-parametric estimate of how the response depends on 
	all of its prior realizations as well as that of any time-varying predictor 
	variables. The method applies equally to regularly as well as irregularly 
	sampled data. The package implements random forest and boosting ensembles 
	based on historical regression trees, suitable for longitudinal data. 
	Standard error estimation and Z-score variable importance is also implemented.
Provides methods for analysing and forecasting hierarchical and 
    grouped time series. The available forecast methods include bottom-up,
    top-down, optimal combination reconciliation (Hyndman et al. 2011) 
    <doi:10.1016/j.csda.2011.03.006>, and trace minimization reconciliation
    (Wickramasuriya et al. 2018) <doi:10.1080/01621459.2018.1448825>.
A Poisson mixture model is implemented to cluster genes from high-
    throughput transcriptome sequencing (RNA-seq) data. Parameter estimation is
    performed using either the EM or CEM algorithm, and the slope heuristics are
    used for model selection (i.e., to choose the number of clusters).
Functions for analyzing high throughput sequencing 
    stable isotope probing (HTS-SIP) data.
    Analyses include high resolution stable isotope probing (HR-SIP),
    multi-window high resolution stable isotope probing (MW-HR-SIP), 
    and quantitative stable isotope probing (q-SIP).
Functions and data tables for simulation and statistical analysis of chemical toxicokinetics ("TK") using data obtained from relatively high throughput, in vitro studies. Both physiologically-based ("PBTK") and empirical (e.g., one compartment) "TK" models can be parameterized for several hundred chemicals and multiple species. These models are solved efficiently, often using compiled (C-based) code. A Monte Carlo sampler is included for simulating biological variability and measurement limitations. Functions are also provided for exporting "PBTK" models to "SBML" and "JARNAC" for use with other simulation software. These functions and data provide a set of tools for in vitro-in vivo extrapolation ("IVIVE") of high throughput screening data (e.g., ToxCast) to real-world exposures via reverse dosimetry (also known as "RTK").
In order to improve performance for HTTP API clients, 'httpcache'
    provides simple tools for caching and invalidating cache. It includes the
    HTTP verb functions GET, PUT, PATCH, POST, and DELETE, which are drop-in
    replacements for those in the 'httr' package. These functions are cache-aware
    and provide default settings for cache invalidation suitable for RESTful
    APIs; the package also enables custom cache-management strategies.
    Finally, 'httpcache' includes a basic logging framework to facilitate the
    measurement of HTTP request time and cache performance.
Find and explain the meaning of 'HTTP' status codes.
    Functions included for searching for codes by full or partial number,
    by message, and get appropriate dog and cat images for many
    status codes.
A suite of functions to ping 'URLs' and to time
    'HTTP' 'requests'. Designed to work with 'httr'.
HTTP Request protocols. Implements the GET, POST and multipart POST request.
Testing and documenting code that communicates with remote servers
    can be painful. Dealing with authentication, server state,
    and other complications can make testing seem too costly to
    bother with. But it doesn't need to be that hard. This package enables one
    to test all of the logic on the R sides of the API in your package without
    requiring access to the remote service. Importantly, it provides three
    contexts that mock the network connection in different ways, as well as
    testing functions to assert that HTTP requests were---or were
    not---made. It also allows one to safely record real API responses to use as
    test fixture. The ability to save responses and load them offline also
    enables one to write vignettes and other dynamic documents that can be
    distributed without access to a live server.
Provides low-level socket and protocol support for handling
    HTTP and WebSocket requests directly from within R. It is primarily
    intended as a building block for other packages, rather than making it
    particularly easy to create complete web applications using httpuv alone.
    httpuv is built on top of the libuv and http-parser C libraries, both of
    which were developed by Joyent, Inc. (See LICENSE file for libuv and
    http-parser license information.)
Useful tools for working with HTTP organised by HTTP verbs
    (GET(), POST(), etc). Configuration functions make it easy to control
    additional request components (authenticate(), add_headers() and so on).
Creating effective colour palettes for figures is 
    challenging. This package generates and plot palettes of optimally 
    distinct colours in perceptually uniform colour space, based on 
    'iwanthue' <http://tools.medialab.sciences-po.fr/iwanthue/>. 
    This is done through k-means clustering of CIE Lab colour space, 
    according to user-selected constraints on hue, chroma, and 
    lightness.
Provides a general framework for
        high-dimensional undirected graph estimation. It integrates
        data preprocessing, neighborhood screening, graph estimation,
        and model selection techniques into a pipeline. In
        preprocessing stage, the nonparanormal(npn) transformation is
        applied to help relax the normality assumption. In the graph
        estimation stage, the graph structure is estimated by
        Meinshausen-Buhlmann graph estimation or the graphical lasso,
        and both methods can be further accelerated by the lossy
        screening rule preselecting the neighborhood of each variable
        by correlation thresholding. We target on high-dimensional data
        analysis usually d >> n, and the computation is
        memory-optimized using the sparse matrix output. We also
        provide a computationally efficient approach, correlation
        thresholding graph estimation. Three
        regularization/thresholding parameter selection methods are
        included in this package: (1)stability approach for
        regularization selection (2) rotation information criterion (3)
        extended Bayesian information criterion which is only available
        for the graphical lasso.
Tools for computing HUM (Hypervolume Under the Manifold) value to estimate features ability
             to discriminate the class labels, visualizing the ROC curve for two or three class labels.
Format quantities of time or bytes into human-friendly strings.
Human names are complicated and nonstandard things. Humaniformat,
    which is based on Anthony Ettinger's 'humanparser' project (https://github.com/
    chovy/humanparser) provides functions for parsing human names, making a best-
    guess attempt to distinguish sub-components such as prefixes, suffixes, middle
    names and salutations.
An almost direct port of the 'python' 'humanize' package <https://github.com/jmoiron/humanize>.
  This package contains utilities to convert values into human readable forms.
Generates high-entropy integer synthetic populations from marginal and (optionally) seed data using quasirandom sampling, 
  in arbitrary dimensionality (Smith, Lovelace and Birkin (2017) <doi:10.18564/jasss.3550>). 
  The package also provides an implementation of the Iterative Proportional Fitting (IPF) algorithm (Zaloznik (2011) <doi:10.13140/2.1.2480.9923>).
Vapor pressure, absolute humidity, specific humidity, and mixing ratio are commonly used water vapor measures in meteorology. This R package provides functions for calculating saturation vapor pressure (hPa), partial water vapor pressure (Pa), absolute humidity (kg/m^3), specific humidity (kg/kg), and mixing ratio (kg/kg) from temperature (K) and relative humidity (%).
Low level spell checker and morphological analyzer based on the 
    famous 'hunspell' library <https://hunspell.github.io>. The package can analyze
    or check individual words as well as parse text, latex, html or xml documents.
    For a more user-friendly interface use the 'spelling' package which builds on
    this package to automate checking of files, documentation and vignettes in all
    common formats.
Scraped dataset of the Hurricane Research Division's Hurricane 
    Re-Analysis Project known as HURDAT. Storm details are available for most 
    known hurricanes and tropical storms for the Atlantic and northeastern 
    Pacific ocean (northwestern hemisphere). See <http://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html> 
    for more information.
When considering count data, it is often the case that many more zero counts than would be expected of some given distribution are observed. It is well established that data such as this can be reliably modelled using zero-inflated or hurdle distributions, both of which may be applied using the functions in this package. Bayesian analysis methods are used to best model problematic count data that cannot be fit to any typical distribution. The package functions are flexible and versatile, and can be applied to varying count distributions, parameter estimation with or without explanatory variable information, and are able to allow for multiple hurdles as it is also not uncommon that count data have an abundance of large-number observations which would be considered outliers of the typical distribution. In lieu of throwing out data or misspecifying the typical distribution, these extreme observations can be applied to a second, extreme distribution. With the given functions of this package, such a two-hurdle model may be easily specified in order to best manage data that is both zero-inflated and over-dispersed.
Allows users to create time series of tropical storm
    exposure histories for chosen counties for a number of hazard metrics (wind, 
    rain, distance from the storm, etc.). This package interacts with data available 
    through the 'hurricaneexposuredata' package, which is available in a 'drat'
    repository. To access this data package, run 'install.packages("hurricaneexposuredata", 
    repos = "https://geanders.github.io/drat/", type = "source")'. The size of the 
    'hurricaneexposuredata' package is approximately 25 MB. This work was supported in 
    part by grants from the National Institute of Environmental Health Sciences 
    (R00ES022631), the National Science Foundation (1331399), and a NASA Applied 
    Sciences Program/Public Health Program Grant (NNX09AV81G).
Provides utility functions for, and drawing on, the 'data.table' package. The package also collates useful miscellaneous functions extending base R not available elsewhere. The name is a portmanteau of 'utils' and the author.
Like 'xtable', creates styled tables. Export to HTML, LaTeX, 'Word', 
  'Excel', 'PowerPoint' and other formats. Simple, modern interface to manipulate 
  borders, size, position, captions, colours, text styles and number formatting.
  Includes  a 'huxreg' function for creation of regression tables, and 'quick_*' 
  one-liners to print data to a new document.
HW.pval calculates plain and fully conditional
        root-mean-square, chi-square, and log likelihood-ratio P-values
        for the user-provided genotypic counts to be consistent with
        the Hardy-Weinberg equilibrium model.  For further information
        on the Hardy-Weinberg equilibrium model and the pseudocode,
        refer to the paper "Testing Hardy-Weinberg equilibrium with a
        simple root-mean-square statistic" by Rachel Ward.
Fits models for genotypic disequilibria, as described in
  Huttley and Wilson (2000), Weir (1996) and Weir and Wilson (1986).
  Contrast terms are available that account for first order interactions
  between loci.  Also implements, for a single locus in a single
  population, a conditional exact test for Hardy-Weinberg equilibrium.
Estimation and testing of HWE using Bayesian methods.
        Three models are currently considered: HWE, a model
        parameterized in terms of the allele frequencies and a single
        inbreeding coefficient f, and the saturated model. Testing is
        based on Bayes factors.
General (multi-allelic) Hardy-Weinberg equilibrium problem from an objective Bayesian testing standpoint. This aim is achieved through the identification of a class of priors specifically designed for this testing problem. A class of intrinsic priors under the full model is considered. This class is indexed by a tuning quantity, the training sample size, as discussed in Consonni, Moreno and Venturini (2010). These priors are objective, satisfy Savage's continuity condition and have proved to behave extremely well for many statistical testing problems.
Easy-to-use and versatile functions to output R objects in
        HTML format
Provides methods to test whether time series is consistent
	with white noise.
Tests whether a set of genotype counts fits the HW expectations.
    Exact tests performed by an efficient algorithm. Included test statistics
    are likelihood ratio, probability, U-score and Pearson's X2.
Functions to build and deploy a hybrid ensemble consisting of eight different sub-ensembles: bagged logistic regressions, random forest, stochastic boosting, kernel factory, bagged neural networks, bagged support vector machines, rotation forest, and bagged k-nearest neighbors. Functions to cross-validate the hybrid ensemble and plot and summarize the results are also provided. There is also a function to assess the importance of the predictors.
A hybrid method of feature selection which combines both filter and wrapper methods. The first level involves feature reduction based on some of the important filter methods while the second level involves feature subset selection as in a wrapper method. Comparative analysis with the existing feature selection packages shows this package results in higher classification accuracy, reduced processing time and improved data handling capacity.
Hybrid hierarchical clustering via mutual clusters.  A mutual cluster is a set of points closer to each other than to all other points.  Mutual clusters are used to enrich top-down hierarchical clustering.
This package is an R implementation of the Hybrid Monte
        Carlo and Multipoint Hybrid Monte Carlo sampling techniques
        described in Liu (2001): "Monte Carlo Strategies in Computing".
Simulates stochastic hybrid models for transmission of infectious
    diseases in dynamic networks. It is a metapopulation model in which each
    node in the network is a sub-population and disease spreads within nodes
    and among them, combining two approaches: stochastic simulation algorithm
    or its approximations (Gillespie DT (2007)
    <doi:10.1146/annurev.physchem.58.032806.104637>) and individual-based
    approach, respectively. Movement among nodes are data based and can be
    irregular. Equations that models spread within nodes are customizable and
    there are two link types among nodes: migration and influence (commuting).
Facilities for easy implementation of hybrid Bayesian networks
    using R. Bayesian networks are directed acyclic graphs representing joint
    probability distributions, where each node represents a random variable and
    each edge represents conditionality. The full joint distribution is therefore
    factorized as a product of conditional densities, where each node is assumed
    to be independent of its non-descendents given information on its parent nodes.
    Since exact, closed-form algorithms are computationally burdensome for inference
    within hybrid networks that contain a combination of continuous and discrete
    nodes, particle-based approximation techniques like Markov Chain Monte Carlo
    are popular. We provide a user-friendly interface to constructing these networks
    and running inference using the 'rjags' package. Econometric analyses (maximum
    expected utility under competing policies, value of information) involving
    decision and utility nodes are also supported.
Package providing tools for hydrological applications and models developed for regional analysis in Northwestern Italy.
Contains one function for drawing Piper diagrams (also
    called Piper-Hill diagrams) of water analyses for major ions.
S3 functions implementing both statistical and graphical goodness-of-fit measures between observed and simulated values, mainly oriented to be used during the calibration, validation, and application of hydrological models. Missing values in observed and/or simulated values can be removed before computations. Comments / questions / collaboration of any kind are very welcomed.
Tools to link geographic data with hydrologic network, including lakes, streams and rivers. 
    Includes automated download of U.S. National Hydrography Network and other hydrolayers.
This package is version 2 of HydroMe v.1 package. It
        estimates the parameters in infiltration and water retention
        models by curve-fitting method. The models considered are those
        that are commonly used in soil science. It has new models for
        water retention characteristic curve and debugging of errors in
        HydroMe v.1
This package implements a state-of-the-art version of the Particle Swarm Optimisation (PSO) algorithm (SPSO-2011 and SPSO-2007 capable). hydroPSO can be used as a replacement of the 'optim' R function for (global) optimization of non-smooth and non-linear functions. However, the main focus of hydroPSO is the calibration of environmental and other real-world models that need to be executed from the system console. hydroPSO is model-independent, allowing the user to easily interface any computer simulation model with the calibration engine (PSO). hydroPSO  communicates with the model through the model's own input and output files, without requiring access to the model's source code. Several PSO variants and controlling options are included to fine-tune the performance of the calibration engine to different calibration problems. An advanced sensitivity analysis function together with user-friendly plotting summaries facilitate the interpretation and assessment of the calibration results. hydroPSO is parallel-capable, to alleviate the computational burden of complex models with "long" execution time. Bugs reports/comments/questions are very welcomed (in English, Spanish or Italian).
R interface to the Greek National Data Bank for Hydrological and 
    Meteorological Information <http://www.hydroscope.gr/>. It covers 
    Hydroscope's data sources and provides functions to transliterate, 
    translate and download them into tidy dataframes (tibbles).
Calculates a suite of hydrologic indices for daily time series data that are widely used in hydrology and stream ecology.
S3 functions for management, analysis, interpolation and plotting of time series used in hydrology and related environmental sciences. In particular, this package is highly oriented to hydrological modelling tasks. The focus of this package has been put in providing a collection of tools useful for the daily work of hydrologists (although an effort was made to optimise each function as much as possible, functionality has had priority over speed). Bugs / comments / questions / collaboration of any kind are very welcomed, and in particular, datasets that can be included in this package for academic purposes.
Focuses on data processing and visualization in hydrology and
    climate forecasting. Main function includes data extraction, data downscaling,
    data resampling, gap filler of precipitation, bias correction of forecasting
    data, flexible time series plot, and spatial map generation. It is a good pre-
    processing and post-processing tool for hydrological and hydraulic modellers.
Includes two main high level codes for hyperplane fitting (hyper.fit) and visualising (hyper.plot2d / hyper.plot3d). In simple terms this allows the user to produce robust 1D linear fits for 2D x vs y type data, and robust 2D plane fits to 3D x vs y vs z type data. This hyperplane fitting works generically for any N-1 hyperplane model being fit to a N dimension dataset. All fits include intrinsic scatter in the generative model orthogonal to the hyperplane.
A suite of routines for the hyperdirichlet distribution; supersedes the hyperdirichlet package for most purposes.
This package provides functions for the hyperbolic and
        related distributions. Density, distribution and quantile
        functions and random number generation are provided for the
        hyperbolic distribution, the generalized hyperbolic
        distribution, the generalized inverse Gaussian distribution and
        the skew-Laplace distribution. Additional functionality is
        provided for the hyperbolic distribution, including fitting of
        the hyperbolic to data.
Provides methods for organizing data in a hypercube
  (i.e. a multi-dimensional cube). Cubes are generated from molten data frames.
  Each cube can be manipulated with five operations: rotation (changeDimensionOrder()),
  dicing and slicing (add.selection(), remove.selection()), drilling down (add.aggregation()),
  and rolling up (remove.aggregation()).
Given a high-dimensional dataset that typically represents a cytometry dataset, and a subset of the datapoints, this algorithm outputs an hyperrectangle so that datapoints within the hyperrectangle best correspond to the specified subset. In essence, this allows the conversion of clustering algorithms' outputs to gating strategies outputs. For more details see Etienne Becht, Yannick Simoni, Elaine Coustan-Smith, Maximilien Evrard, Yang Cheng, Lai Guan Ng, Dario Campana and Evan Newell (2018) <doi:10.1101/278796>.
Performs (exact) hypergeometric tests on IxJ and 2x2x2 contingency tables using parallelized C code.
The Gaussian hypergeometric function for complex numbers.
Implements an algorithm for efficient and exact calculation of hypergeometric 
    and binomial probabilities using Chebyshev polynomials, while other algorithm use an 
    approximation when N is large. A useful applications is also considered in this package 
    for the construction of attribute sampling plans which is an important field of statistical
    quality control. The quantile, and the confidence limit for the attribute sampling plan are
    also implemented in this package. The hypergeometric distribution can be represented in 
    terms of Chebyshev polynomials. This representation is particularly useful in the calculation
    of exact values of hypergeometric variables.
Machine learning supervised method to learn rare genomic features in imbalanced genetic data sets. This method can be also applied to classify or rank examples characterized by a high imbalance between the minority and majority class. hyperSMURF adopts a hyper-ensemble (ensemble of ensembles) approach, undersampling of the majority class and oversampling of the minority class to learn highly imbalanced data.
Comfortable ways to work with hyperspectral data sets.
    I.e. spatially or time-resolved spectra, or spectra with any other kind
    of information associated with each of the spectra. The spectra can be data
    as obtained in XRF, UV/VIS, Fluorescence, AES, NIR, IR, Raman, NMR, MS,
    etc. More generally, any data that is recorded over a discretized variable,
    e.g. absorbance = f (wavelength), stored as a vector of absorbance values
    for discrete wavelengths is suitable.
Estimates the shape and volume of high-dimensional datasets and performs set operations: intersection / overlap, union, unique components, inclusion test, and hole detection. Uses stochastic geometry approach to high-dimensional kernel density estimation, support vector machine delineation, and convex hull generation. Applications include modeling trait and niche hypervolumes and species distribution modeling.
Identifying hyphenation points in strings can be useful for both
    text processing and display functions. The 'Hunspell' hyphenation library
    <https://github.com/hunspell/hyphen> provides tools to perform hyphenation
    using custom language rule dictionaries. Many hyphenation rules
    dictionaries are included. Words can be hyphenated directly or split into
    hyphenated component strings for further processing.
A Bay Area high level phylogenetic analysis package mostly
        using the birth-death process. Analysis of species tree
        branching times and simulation of species trees under a number
        of different time variable birth-death processes.  Analysis of
        gene tree species tree reconciliations and simulations of gene
        trees in species trees.
A Multi-Hypothesis CSV Parser. Stresses your computer not you.
Compute the confidence interval of the population mean
        with one sample or of the difference of population means of two
        samples from normal distributions or t-distributions.Compute
        the confidence interval of population variance with one sample
        or of the difference of population variances of two samples by
        chi-square tests.Test for population mean or the differences of
        two normal samples under normality with the given null
        hypothesis H0,which depends on the user,so that he can know if
        he can reject H0 or not at the significance level alpha.Do the
        chi-square tests with one or two samples which have multinomial
        distributions by using an approximate chi-square distribution
        when n is large enough.
Interact with the application programming interface for the web
    annotation service 'Hypothes.is' (See <http://hypothes.is> for more
    information.) Allows users to download data about public annotations, and
    create, retrieve, update, and delete their own annotations.
Methods for addressing uncertainty in risk assessments using hybrid representations of uncertainty (probability distributions, fuzzy numbers, intervals, probability distributions with imprecise parameters). The uncertainty propagation procedure combines random sampling using Monte Carlo method with fuzzy interval analysis of Baudrit et al. (2007) <doi:10.1109/TFUZZ.2006.876720>. The sensitivity analysis is based on the pinching method of Ferson and Tucker (2006) <doi:10.1016/j.ress.2005.11.052>.
Fit, summarize and plot sinusoidal hysteretic processes using:
    two-step simple harmonic least squares, ellipse-specific non-linear least
    squares, the direct method, geometric least squares or linear least squares.
A collection of tools for modeling the shape of 1 dimensional clines.
In randomized-controlled trials, interim analyses are often planned for possible early 
   trial termination to claim superiority or futility of a new therapy. Blinded data also have 
   information about the potential treatment difference between the groups. We developed a blinded 
   data monitoring tool that enables investigators to predict whether they observe such an unblinded 
   interim analysis results that supports early termination of the trial. Investigators may skip 
   some of the planned interim analyses if an early termination is unlikely. 
   This tool will provide reference information about N: Sample size at interim analysis, and T: Total 
   number of responders at interim analysis for decision on performing an interim analysis.
Calculate false ring proportions from data frames of intra annual 
  density fluctuations.
Synthesizes information across collaborating research. Created specifically for Integrative Analysis of Longitudinal Studies of Aging (IALSA).
Functions for Water and Steam Properties based on the IAPWS Formulation
    1995 for the Thermodynamic Properties of Ordinary Water Substance for General and
    Scientific Use and on the releases for viscosity, conductivity, surface tension and
    melting pressure.
Calculate AIC's and AICc's of unimodal model (one normal distribution) and bimodal model(a mixture of two normal distributions) which fit the distribution of indices of asymmetry (IAS), and plot their density, to help determine IAS distribution is unimodal or bimodal.
Implements the standard D-Scoring algorithm
    (Greenwald, Banaji, & Nosek, 2003) for Implicit Association Test (IAT)
    data and includes plotting capabilities for exploring raw IAT data.
Quickly score raw data outputted from an Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) <doi:10.1037/0022-3514.74.6.1464>. IAT scores are calculated as specified by Greenwald, Nosek, and Banaji (2003) <doi:10.1037/0022-3514.85.2.197>.    The output of this function is a data frame that consists of four rows containing the following information: (1) the overall IAT effect size for the participant's dataset, (2) the effect size calculated for odd trials only, (3) the effect size calculated for even trials only, and (4) the proportion of trials with reaction times under 300ms (which is important for exclusion purposes). Items (2) and (3) allow for a measure of the internal consistency of the IAT. Specifically, you can use the subsetted IAT effect sizes for odd and even trials to calculate Cronbach's alpha across participants in the sample.    The input function consists of three arguments. First, indicate the name of the dataset to be analyzed. This is the only required input. Second, indicate the number of trials in your entire IAT (the default is set to 220, which is typical for most IATs). Last, indicate whether congruent trials (e.g., flowers and pleasant) or incongruent trials (e.g., guns and pleasant) were presented first for this participant (the default is set to congruent).    Data files should consist of six columns organized in order as follows: Block (0-6), trial (0-19 for training blocks, 0-39 for test blocks), category (dependent on your IAT), the type of item within that category (dependent on your IAT), a dummy variable indicating whether the participant was correct or incorrect on that trial (0=correct, 1=incorrect), and the participant’s reaction time (in milliseconds).    A sample dataset (titled 'sampledata') is included in this package to practice with.
This minimalist package is designed to quickly score raw data outputted from an Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) <doi:10.1037/0022-3514.74.6.1464>. IAT scores are calculated as specified by Greenwald, Nosek, and Banaji (2003) <doi:10.1037/0022-3514.85.2.197>. Outputted values can be interpreted as effect sizes. The input function consists of three arguments. First, indicate the name of the dataset to be analyzed. This is the only required input. Second, indicate the number of trials in your entire IAT (the default is set to 219, which is typical for most IATs). Last, indicate whether congruent trials (e.g., flowers and pleasant) or incongruent trials (e.g., guns and pleasant) were presented first for this participant (the default is set to congruent). The script will tell you how long it took to run the code, the effect size for the participant, and whether that participant should be excluded based on the criteria outlined by Greenwald et al. (2003). Data files should consist of six columns organized in order as follows: Block (0-6), trial (0-19 for training blocks, 0-39 for test blocks), category (dependent on your IAT), the type of item within that category (dependent on your IAT), a dummy variable indicating whether the participant was correct or incorrect on that trial (0=correct, 1=incorrect), and the participant’s reaction time (in milliseconds). Three sample datasets are included in this package (labeled 'IAT', 'TooFastIAT', and 'BriefIAT') to practice with.
Compute several variations of the Implicit Association Test (IAT) scores, including the D scores (Greenwald, Nosek, Banaji, 2003, <doi:10.1037/0022-3514.85.2.197>) and the new scores that were developed using robust statistics (Richetin, Costantini, Perugini, and Schonbrodt, 2015, <doi:10.1371/journal.pone.0129601>).
Bayesian integrative models of gene expression and comparative genomic hybridization data. The package provides inference on copy number variations and their association with gene expression.
Implements the item based collaborative filtering (IBCF) method for continues phenotypes in the context of plant breeding where data are collected for various traits that were studied in various environments proposed by Montesinos-López et al. (2017) <doi:10.1534/g3.117.300309>.
This package contains several utility functions related to incomplete block designs. The package contains function to generate efficient incomplete block designs with given numbers of treatments, blocks and block size. The package also contains function to generate an incomplete block design with specified concurrence matrix. There are functions to generate balanced treatment incomplete block designs and incomplete block designs for test versus control treatments comparisons with specified concurrence matrix. Package also allows performing analysis of variance of data and computing least square means of factors from experiments using a connected incomplete block design. Tests of hypothesis of treatment contrasts in incomplete block design set up is supported.
Functions to analyze, plot, and store the output of running IBD_Haplo software package. More information regarding IBD_Haplo can be found at http://www.stat.washington.edu/thompson/Genepi/pangaea.shtml.
Convert "label", "lexicographic", "jacquard" and "vec", full state description vector. All conversions are done to and from "label", as used in IBD_Haplo. More information regarding IBD_Haplo can be found at http://www.stat.washington.edu/thompson/Genepi/pangaea.shtml.
A method to test genetic linkage with covariates by
        regression methods with response IBD sharing for relative
        pairs.  Account for correlations of IBD statistics and
        covariates for relative pairs within the same pedigree.
Simulation of segments shared identical-by-descent (IBD) by 
    pedigree members. Using sex specific recombination rates along the human
    genome (Kong et. al (2010) <doi:10.1038/nature09525>), phased chromosomes
    are simulated for all pedigree members, either by unconditional gene 
    dropping or conditional on a specified IBD pattern. Additional functions 
    provide summaries and further analysis of the simulated genomes.
A data-driven and adaptive hierarchical-scale decomposition method for irregular-lattice field (represented by polygons).
Some basic functions to implement belief functions including:
    transformation between belief functions using the method introduced by
    Philippe Smets (arXiv:1304.1122 [cs.AI]), evidence combination, evidence
    discounting, decision-making, and constructing masses. Currently, thirteen
    combination rules and five decision rules are supported. It can also be
    used to generate different types of random masses when working on belief
    combination and conflict management.
Implementation of an incremental model construction method called IBHM which
            stands for Incrementally Built Heterogeneous Model. The method is designed for solving
            real number approximation problems in a highly automated fashion.
Implementation of some (simple) Individual Based Models and methods
  to create new ones, particularly for population dynamics models (reproduction, 
  mortality and movement). The basic operations for the simulations are 
  implemented in Rcpp for speed.
It provides a generic set of tools for initializing a synthetic
         population with each individual in specific disease states, and
         making transitions between those disease states according to the rates
         calculated on each timestep. The new version 1.0.0 has C++ code 
         integration to make the functions run faster. It has also a higher level
         function to actually run the transitions for the number of timesteps
         that users specify. Additional functions will follow for changing
         attributes on demographic, health belief and movement.
Functionality required to efficiently use R with IBM(R) Db2(R) 
        Warehouse offerings (formerly IBM dashDB(R)) and IBM Db2 for z/OS(R) in 
        conjunction with IBM Db2 Analytics Accelerator for z/OS. 
        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 parallel processing in the underlying database.
The package implements a general algorithm to obtain
        iterated bootstrap tests and confidence sets for a
        p-dimensional parameter based on the unstudentized version of
        the Rao statistic.
Multivariate smoothing using iterative bias reduction with kernel, thin plate splines, Duchon splines or low rank splines.
Provides native R access to Interactive Brokers Trader Workstation API.
Fit a bagging survival tree on a mixture of population (susceptible and nonsusceptible)
             using either a pseudo R2 criterion or an adjusted Logrank criterion. The predictor is 
             evaluated using the Out Of Bag Integrated Brier Score (IBS) and several scores of importance
             are computed for variable selection. The thresholds values for variable selection are 
             computed using a nonparametric permutation test.
Implements inequality constrained inference. This includes parameter estimation in normal (linear) models under linear equality and inequality constraints, as well as normal likelihood ratio tests involving inequality-constrained hypotheses. For inequality-constrained linear models, averaging over R-squared for different orderings of regressors is also included.
Implementation of the classifier described in the paper 'Genome-driven integrated classification of breast cancer validated in over 7,500 samples' (Ali HR et al., Genome Biology 2014). It uses copy number and/or expression form breast cancer data, trains a pamr classifier (Tibshirani et al.) with the features available and predicts the iC10 group.
Training datasets for iC10; which implements the classifier described in the paper 'Genome-driven integrated classification of breast cancer validated in over 7,500 samples' (Ali HR et al., Genome Biology 2014). It uses copy number and/or expression form breast cancer data, trains a pamr classifier (Tibshirani et al.) with the features available and predicts the iC10 group. Genomic annotation for the training dataset has been obtained from Mark Dunning's lluminaHumanv3.db package.
Lorenz and concentration curves; Atkinson, Generalized
        entropy and SGini indices (with decomposition)
Calculation of IC50 values, automatic drawing of
        dose-response curves and validation of compound screens on 96-
        and 384-well plates.
Independent Component Analysis (ICA) using various algorithms: FastICA, Information-Maximization (Infomax), and Joint Approximate Diagonalization of Eigenmatrices (JADE).
Imperialist Competitive Algorithm (ICA)
         <http://en.wikipedia.org/wiki/Imperialist_competitive_algorithm>
         is a computational method that is used to solve optimization
	 problems of different types and it is the mathematical model
	 and the computer simulation of human social evolution.  
         The package provides a minimum value for the cost function
	 and the best value for the optimization variables by
	 Imperialist Competitive Algorithm. 
         Users can easily define their own objective function
	 depending on the problem at hand. 
         This version has been successfully applied to solve
	 optimization problems, for continuous functions.
Provides R functions which facilitate the estimation of ICA mixture models. We have developed and implemented the NSMM-ICA algorithm that currently integrates npEM and Fast-ICA for non-parametric estimation of ICA mixture models (Zhu, X., & Hunter, D.R., 2016 <DOI:10.1080/10485252.2016.1225049> and Zhu, X., & Hunter, D.R., 2015 <arXiv:1510.08178>). This is a new tool for unsupervised clustering.
Finds optimal designs for nonlinear models using a metaheuristic algorithm called imperialist competitive algorithm ICA. See, for details, Masoudi et al. (2017) <doi:10.1016/j.csda.2016.06.014>.
Implements mixed ICA/PCA model for blind source separation, potentially with inclusion of Gaussian sources
Provides user-friendly tools for calibration in survey sampling.
    The package is production-oriented, and its interface is inspired by the famous
    popular macro 'Calmar' for SAS, so that 'Calmar' users can quickly get used to
    'icarus'. In addition to calibration (with linear, raking and logit methods),
    'icarus' features functions for calibration on tight bounds and penalized
    calibration.
Contains functions to fit Bayesian semiparametric regression survival models (proportional hazards model, proportional odds model, and probit model) to interval-censored time-to-event data.
Assist in the estimation of the Intraclass Correlation Coefficient (ICC) from variance components of a one-way analysis of variance and also estimate the number of individuals or groups necessary to obtain an ICC estimate with a desired confidence interval width.
Provides functions to calculate the requisite sample size for studies where ICC is 
    the primary outcome. Can also be used for calculation of power. In both cases it
    allows the user to test the impact of changing input variables by calculating the outcome
    for several different values of input variables. Based off the work of Zou.
    Zou, G. Y. (2012). Sample size formulas for estimating intraclass correlation coefficients with
    precision and assurance. Statistics in medicine, 31(29), 3972-3981.
Calibration and risk-set calibration methods for fitting Cox proportional hazard model when a binary covariate is measured intermittently. Methods include functions to fit calibration models from interval-censored data and modified partial likelihood for the proportional hazard model, Nevo et al. (2018+) <arXiv:1801.01529>.
A function and vignettes for computing an intraclass correlation
    described in Aguinis & Culpepper (2015) <doi:10.1177/1094428114563618>.
    This package quantifies the share of variance in a dependent variable that
    is attributed to group heterogeneity in slopes.
Assists in generating binary clustered data, estimates of Intracluster Correlation coefficient (ICC) for binary response in 16 different methods, and 5 different types of confidence intervals.
Calculate comorbidities, Charlson scores, perform
    fast and accurate validation, conversion, manipulation, filtering and
    comparison of ICD-9 and ICD-10 codes. This package enables a work flow
    from raw lists of ICD codes in hospital databases to
    comorbidities. ICD-9 and ICD-10 comorbidity mappings from Quan (Deyo
    and Elixhauser versions), Elixhauser and AHRQ included.  Common
    ambiguities and code formats are handled.
Data from the United States Center for Medicare and Medicaid
    Services (CMS) is included in this package. There are ICD-9 and ICD-10
    diagnostic and procedure codes, and lists of the chapter and sub-chapter 
    headings and the ranges of ICD codes they encompass. There are also two 
    sample datasets. These data are used by the 'icd' package for finding
    comorbidities.
Provides an estimator for generalized linear models with incomplete
    data for discrete covariates. The estimation is based on the EM algorithm by the
    method of weights by Ibrahim (1990) <DOI:10.2307/2290013>.
Kernel Estimators for Interval-Censored Data
Implements Individual Conditional Expectation (ICE) plots, a tool for visualizing the model estimated by any supervised learning algorithm. ICE plots refine Friedman's partial dependence plot by graphing the functional relationship between the predicted response and a covariate of interest for individual observations. Specifically, ICE plots highlight the variation in the fitted values across the range of a covariate of interest, suggesting where and to what extent they may exist.
Tools for modeling and correcting biases in sea ice predictions obtained from dynamical models. This package depends on the 'ncdf4' and 'rgeos' R packages. These packages respectively require installing externally from R Unidata's 'NetCDF' library and Geometry Engine - Open Source ('GEOS'). (See the 'rgeos' and 'ncdf4' packages for details.) References, Bivand and Rundel (2017) <https://CRAN.R-project.org/package=rgeos>, Open Source Geospatial Foundation (2017) <https://trac.osgeo.org/geos>, Pierce (2017) <https://CRAN.R-project.org/package=ncdf4>, Unidata (2017) <https://www.unidata.ucar.edu/software/netcdf/>.
Given two unbiased samples of patient level data on cost and effectiveness
  for a pair of treatments, make head-to-head treatment comparisons by (i) generating the
  bivariate bootstrap resampling distribution of ICE uncertainty for a specified value of
  the shadow price of health, lambda, (ii) form the wedge-shaped ICE confidence region with
  specified confidence fraction within [0.50, 0.99] that is equivariant with respect to
  changes in lambda, (iii) color the bootstrap outcomes within the above confidence wedge
  with economic preferences from an ICE map with specified values of lambda, beta and gamma
  parameters, (iv) display VAGR and ALICE acceptability curves, and (v) illustrate variation
  in ICE preferences by displaying potentially non-linear indifference(iso-preference) curves
  from an ICE map with specified values of lambda, beta and either gamma or eta parameters.
Regression models for interval censored data. Currently supports
    Cox-PH, proportional odds, and accelerated failure time models. Allows for
    semi and fully parametric models (parametric only for accelerated failure
    time models) and Bayesian parametric models. Includes functions for easy visual
    diagnostics of model fits and imputation of censored data.
Contains datasets and several smaller functions suitable for analysis of interval-censored data. The package complements the book Bogaerts, Komárek and Lesaffre (2017). "Survival Analysis with Interval-Censored Data: A Practical Approach." ISBN 978-1-4200-7747-6, published by Chapman and Hall/CRC. Full R code related to the examples presented in the book can be found at <https://ibiostat.be/online-resources/icbook/supplemental>. Packages mentioned in the "Suggests" section are used in those examples.
We consider studies in which information from error-prone
    diagnostic tests or self-reports are gathered sequentially to determine the
    occurrence of a silent event. Using a likelihood-based approach
    incorporating the proportional hazards assumption, we provide functions to
    estimate the survival distribution and covariate effects. We also provide
    functions for power and sample size calculations for this setting.
Functions that are related to the ICES advisory process.
R interface to access the web services of the ICES (International
             Council for the Exploration of the Sea) DATRAS trawl survey
             database <https://datras.ices.dk/WebServices/Webservices.aspx>.
R interface to access the web services of the ICES Stock Assessment
             Graphs database <http://sg.ices.dk>.
Functions to support the ICES Transparent Assessment Framework
  <http://taf.ices.dk> to organize data, methods, and results used in ICES
  assessments. ICES is an organization facilitating international collaboration
  in marine science.
R interface to access the RECO POX web services of the ICES (International
             Council for the Exploration of the Sea) Vocabularies database <http://vocab.ices.dk/services/POX.aspx>.
ICGE is a package that helps to estimate the number of real clusters in data as well as to identify atypical units. The underlying methods are based on distances rather than on unit x variables.
Generalized Odds Rate Hazards (GORH) model is a flexible model of fitting survival data, including the Proportional Hazards (PH) model and the Proportional Odds (PO) Model as special cases. This package fit the GORH model with interval censored data.
A GUI designed to support the analysis of financial-economic time
    series data.
Integrative clustering of multiple genomic data types
        using a joint latent variable model.
Carries out empirical Bayes variable selection via ICM/M algorithm. The basic problem is to fit high-dimensional regression which most coefficients are assumed to be zero. This package allows incorporating the Ising prior to capture structure of predictors in the modeling process. The current version of this package can handle the normal, binary logistic, and Cox's regression (Pungpapong et. al. (2015) <doi:10.1214/15-EJS1034>, Pungpapong et. al. (2017) <arXiv:1707.08298>).
Employs triangular tessellation to refine icosahedra
    defined in 3d space. The procedures can be set to provide a grid with a
    custom resolution. Both the primary triangular and their inverted penta-
    hexagonal grids are available for implementation. Additional functions
    are provided to position points (latitude-longitude data) on the grids,
    to allow 2D and 3D plotting, use raster data and shapefiles.
Reproducible, programmatic retrieval of datasets from the
    Inter-university Consortium for Political and Social Research archive.
Provides functions to compute and plot Krippendorff's inter-coder 
    reliability coefficient alpha and bootstrapped uncertainty estimates 
    (Krippendorff 2004, ISBN:0761915443). The bootstrap routines are set up to
    make use of parallel threads where supported.
Algorithms to construct confidence intervals for the ranks of centers mu_1,...,mu_n based on an independent Gaussian sample using multiple testing techniques.
Implements a modification to the Random Survival Forests algorithm for obtaining variable importance in high dimensional datasets. The proposed algorithm is appropriate for settings in which a silent event is observed through sequentially administered, error-prone self-reports or laboratory based diagnostic tests.  The modified algorithm incorporates a formal likelihood framework that accommodates sequentially administered, error-prone self-reports or laboratory based diagnostic tests. The original Random Survival Forests algorithm is modified by the introduction of a new splitting criterion based on a likelihood ratio test statistic.
Implementation of Tyler, Critchley, Duembgen and Oja's (JRSS B, 2009,
            <doi:10.1111/j.1467-9868.2009.00706.x>) and Oja, Sirkia and Eriksson's
            (AJS, 2006, <http://www.ajs.or.at/index.php/ajs/article/view/vol35,%20no2%263%20-%207>) method of two different
            scatter matrices to obtain an invariant coordinate system or independent
            components, depending on the underlying assumptions.
Tools for multivariate nonparametrics, as location tests based on marginal ranks, spatial median and spatial signs computation, Hotelling's T-test, estimates of shape are implemented.
Multivariate outlier detection is performed using invariant coordinates where the package offers different methods to choose the appropriate components.
Performs Invariant Coordinate Selection (ICS) (Tyler, Critchley, Duembgen and Oja (2009) <doi:10.1111/j.1467-9868.2009.00706.x>) and especially ICS for multivariate outlier detection with application to quality control (Archimbaud, Nordhausen, Ruiz-Gazen (2016) <arXiv:1612.06118>) using a shiny app.
Currently using the proportional hazards (PH) model. More methods under other semiparametric regression models will be included in later versions.
Provides the necessary tools to estimate average treatment effects with an instrumental variable by re-weighting observations using a model of compliance.
For different linear dimension reduction methods like principal components analysis (PCA), independent components analysis (ICA) and supervised linear dimension reduction tests and estimates for the number of interesting components (ICs) are provided.
Functions for computing the global and local Gaussian density estimates based on the ICV bandwidth. See the article of Savchuk, O.Y., Hart, J.D., Sheather, S.J. (2010). Indirect cross-validation for density estimation. Journal of the American Statistical Association, 105(489), 415-423 <doi:10.1198/jasa.2010.tm08532>.
Computes and tests individual (species, phylogenetic and functional) diversity-area relationships, i.e., how species-, phylogenetic- and functional-diversity varies with spatial scale around the individuals of some species in a community. See applications of these methods in Wiegand et al. (2007) <doi:10.1073/pnas.0705621104> or Chacon-Labella et al. (2016) <doi:10.1007/s00442-016-3547-z>.
Use R to make requests to the US Census Bureau's International Data Base API.
             Results are returned as R data frames.  For more information about the IDB API, visit
             <http://www.census.gov/data/developers/data-sets/international-database.html>.
The digits of the old version (before 2000 year) of 'Chinese ID Card Number' is 15, this package aims to update to the current version of 18 digits. Besides, this package can help check whether the given 'ID' is right or not.
The Integro-Difference Equation model is a linear, dynamical model used to model
   phenomena that evolve in space and in time; see, for example, Cressie and Wikle (2011,
   ISBN:978-0-471-69274-4) or Dewar et al. (2009) <doi:10.1109/TSP.2008.2005091>. At the
   heart of the model is the kernel, which dictates how the process evolves from one time
   point to the next. Both process and parameter reduction are used to facilitate computation,
   and spatially-varying kernels are allowed. Data used to estimate the parameters are assumed
   to be readings of the process corrupted by Gaussian measurement error. Parameters are fitted
   by maximum likelihood, and estimation is carried out using an evolution algorithm.
Offers item-response theory (IRT) ideal-point scaling/dimension reduction methods that incorporate additional response categories and missing/censored values, including absences and abstentions, for roll call voting data (or any other kind of binary or ordinal item-response theory data). Full and approximate Bayesian inference is done via the 'Stan' engine (www.mc-stan.org).
Generates efficient designs for discrete choice experiments based on the multinomial logit model, and individually adapted designs for the mixed multinomial logit model. The generated designs can be presented on screen and choice data can be gathered using a shiny application. Crabbe M, Akinc D and Vandebroek M (2014) <doi:10.1016/j.trb.2013.11.008>.
In randomized studies involving severely ill patients, functional
    outcomes are often unobserved due to missed clinic visits, premature
    withdrawal or death. It is well known that if these unobserved functional
    outcomes are not handled properly, biased treatment comparisons can be
    produced. In this package, we implement a procedure for comparing treatments
    that is based on the composite endpoint of both the functional outcome and
    survival. The procedure was proposed in Wang et al. (2016) <doi:10.1111/biom.12594>.
    It considers missing data imputation with a sensitivity
    analysis strategy to handle the unobserved functional outcomes not due to
    death.
Interactive dendrogram that enables the user to select and
    color clusters, to zoom and pan the dendrogram, and to visualize
    the clustered data not only in a built-in heat map, but also in
    'GGobi' interactive plots and user-supplied plots. 
    This is a backport of Qt-based 'idendro' 
    (<https://github.com/tsieger/idendro>) to base R graphics and 
    Tcl/Tk GUI.
Calculate identity coefficients, based on Mark Abney's C
        code.
An implementation of various methods for estimating intrinsic
    dimension of vector-valued dataset or distance matrix. Most methods implemented
    are based on different notion of fractal dimension such as the capacity
    dimension, the box-counting dimension, and the information dimension.
Provides efficient implementation of the Isolate-Detect
    methodology for the consistent estimation of the number and location of multiple 
    change-points in one-dimensional data sequences from the "deterministic 
    + noise" model. For details on the Isolate-Detect methodology, please see Anastasiou and
    Fryzlewicz (2018) <https://docs.wixstatic.com/ugd/24cdcc_6a0866c574654163b8255e272bc0001b.pdf>.
    Currently implemented scenarios are: piecewise-constant signal with Gaussian
    noise, piecewise-constant signal with heavy-tailed noise, continuous piecewise-linear 
    signal with Gaussian noise, continuous piecewise-linear signal with heavy-tailed noise.
Intensity-duration-frequency (IDF) curves are a widely used analysis-tool in hydrology to assess extreme values of precipitation [e.g. Mailhot et al., 2007, <doi:10.1016/j.jhydrol.2007.09.019>]. The package 'IDF' provides a function to read precipitation data from German weather service (DWD) 'webwerdis' <http://www.dwd.de/EN/ourservices/webwerdis/webwerdis.html> files and Berlin station data from 'Stadtmessnetz' <http://www.geo.fu-berlin.de/en/met/service/stadtmessnetz/index.html> files, and additionally IDF parameters can be estimated also from a given data.frame containing a precipitation time series. The data is aggregated to given levels yearly intensity maxima are calculated either for the whole year or given months. From these intensity maxima IDF parameters are estimated on the basis of a duration-dependent generalised extreme value distribution [Koutsoyannis et al., 1998, <doi:10.1016/S0022-1694(98)00097-3>]. IDF curves based on these estimated parameters can be plotted.
Fits covariate dependent partial correlation matrices for integrative models to identify differential networks between two groups. The method is described in Ha et. al., (2015) <doi:10.1093/bioinformatics/btv406>.
Incremental Multiple Correspondence Analysis and Principal
    Component Analysis.
Contains techniques for mining large and high-dimensional data
    sets by using the concept of Intrinsic Dimension (ID). Here the ID is
    not necessarily integer. It is extended to fractal dimensions. And
    the Morisita estimator is used for the ID estimation, but other
    tools are included as well.
Modeling of regression effects for transition probabilities in a 
   progressive illness-death model. Azarang, Scheike, and de Una-Alvarez (2017) <doi:10.1002/sim.7245>.
Integrate molecular profiles to discover candidate oncogenic drivers.
The IDPmisc package contains different high-level graphics
        functions for displaying large datasets, displaying circular
        data in a very flexible way, finding local maxima, brewing
        color ramps, drawing nice arrows, zooming 2D-plots, creating
        figures with differently colored margin and plot region.  In
        addition, the package contains auxiliary functions for data
        manipulation like omitting observations with irregular values
        or selecting data by logical vectors, which include NAs. Other
        functions are especially useful in spectroscopy and analyses of
        environmental data: robust baseline fitting, finding peaks in
        spectra.
Functions to perform robust
		 nonparametric survival analysis with right censored 
		 data using a prior near-ignorant Dirichlet Process.
		 Mangili, F., Benavoli, A., de Campos, C.P., Zaffalon, M. (2015) <doi:10.1002/bimj.201500062>.
This is a package for estimating the copula mixture model and plotting correspondence curves in "Measuring reproducibility of high-throughput experiments" (2011), Annals of Applied Statistics, Vol. 5, No. 3, 1752-1779, by Li, Brown, Huang, and Bickel
Generate random or human readable and pronounceable identifiers.
Implements various novel and standard
    clustering statistics and other analyses useful for understanding the
    spread of infectious disease.
Convert files to and from IDX format to vectors, matrices and arrays.
    IDX is a very simple file format designed for storing vectors and multidimensional matrices in
    binary format. The format is described on the website from Yann LeCun
    <http://yann.lecun.com/exdb/mnist/>.
iDynoMiCS is a computer program, developed by an international team of researchers, whose purpose is to model and simulate microbial communities in an individual-based way. It is described in detail in the paper "iDynoMiCS: next-generation individual-based modelling of biofilms" by Lardon et al, published in Environmental Microbiology in 2011. The simulation produces results in XML file format, describing the state of each species in each timestep (agent_State), a summary of the species statistics for a timepoint (agent_Sum), the state of each solute grid in each timestep (env_State) and a summary of the solutes for a timestep (env_Sum). This R package provides a means of reading this XML data into R such that the simulation response can be statistically analysed. iDynoMiCS is available from the website iDynoMiCS.org, where a full tutorial on using both the simulation and this R package is provided.
A collection of Irucka Embry's miscellaneous USGS functions
    (processing .exp and .psf files, statistical error functions,
    "+" dyadic operator for use with NA, creating ADAPS and QW
    spreadsheet files, calculating saturated enthalpy). Irucka created these
    functions while a Cherokee Nation Technology Solutions (CNTS) United States
    Geological Survey (USGS) Contractor and/or USGS employee.
Functions for single-variant and region-based tests with external control samples. These methods use external study samples as control samples with adjusting for possible batch effects.
A pair of functions for getting and setting the IEEE
        rounding mode for floating point computations.
A collection of Irucka Embry's miscellaneous functions
    (Engineering Economics, Civil & Environmental/Water Resources Engineering,
    Geometry, Statistics, GNU Octave length functions, Trigonometric functions
    in degrees, etc.).
Miscellaneous data sets [Engineering Economics, Environmental/
    Water Resources Engineering, US Presidential Elections].
The eclectic collection includes the following written pieces:
    "The War Prayer" by Mark Twain, "War Is A Racket" by Major General
    Smedley Butler, "The Mask of Anarchy: Written on the Occasion of the
    Massacre at Manchester" by Percy Bysshe Shelley, "Connect the D.O.T.S." by
    Obiora Embry, "Untitled: Climate Strange" by Irucka Ajani Embry, and
    "Untitled: Us versus Them or People Screwing over Other People (as we all
    live on one Earth and there is no "us versus them" in the actual Ultimate
    Reality}" by Irucka Ajani Embry.
The package performes Independent Factor Analysis
This package implements a Bayesian sparse factor model for the joint analysis of paired datasets, one is the gene expression dataset and the other is the drug sensitivity profiles, measured across the same panel of samples, e.g., cell lines. Prior knowledge about gene-pathway associations can be easily incorporated in the model to aid the inference of drug-pathway associations.
Tools, tutorials, and demos of Item Factor Analysis using 'OpenMx'.
Provides utility functions to deal with Italian fiscal
    code ('codice fiscale').
A suite for identifying causal models using relative concordances and identifying causal polymorphisms in case-control genetic association data, especially with large controls re-sequenced data.
With this package you can draw fractals using iterated function systems.
A dataset of the top colours of photos from Instagram 
 taken in 2014 in the city of Vancouver, British Columbia, Canada.
 It consists of: top colour and counts data. This data was
 obtained using the Instagram API. Instagram is a web photo 
 sharing service. It can be found at: <https://instagram.com>.
 The Instagram API is documented at: <https://instagram.com/developer/>.
A collection of statistical tests for genetic association studies.
Implements Bayesian linear regression, normal means estimation, and variable selection using the inverse gamma-gamma prior, as introduced by Bai and Ghosh (2018) <arXiv:1710.04369>.
Provides function to read data from the 'Igor Pro' data analysis
    program by Wavemetrics. The data formats supported are 'Igor' packed 
    experiment format (pxp) and 'Igor' binary wave (ibw). See: 
    http://www.wavemetrics.com/ for details. Also includes functions to load 
    special pxp files produced by the 'Igor Pro' 'Neuromatic' and 'Nclamp' 
    packages for recording and analysing neuronal data. See 
    http://www.neuromatic.thinkrandom.com/ for details.
Creates a Gaussian process model using the specified package. 
    Makes it easy to try different packages in same code, only the
    package argument needs to be changed.
    It is essentially a wrapper for the other Gaussian process
    software packages.
Routines for simple graphs and network analysis. It can
  handle large graphs very well and provides functions for generating random
  and regular graphs, graph visualization, centrality methods and much more.
A small collection of various network data sets,
    to use with the 'igraph' package: the Enron email network, various food webs,
    interactions in the immunoglobulin protein, the karate club network,
    Koenigsberg's bridges, visuotactile brain areas of the macaque monkey,
    UK faculty friendship network, domestic US flights network, etc.
Using 'shiny' to demo 'igraph' package makes learning graph theory easy and fun.
This program facilitates exporting igraph graphs to the
        SoNIA file format
To integrate multiple GSEA studies, we propose a hybrid strategy,
    iGSEA-AT, for choosing random effects (RE) versus fixed effect (FE) models,
    with an attempt to achieve the potential maximum statistical efficiency as 
    well as stability in performance in various practical situations. In addition
    to iGSEA-AT, this package also provides options to perform integrative GSEA
    with testing based on a FE model (iGSEA-FE) and testing based on a RE model
    (iGSEA-RE). The approaches account for different set sizes when testing a
    database of gene sets. The function is easy to use, and the three approaches
    can be applied to both binary and continuous phenotypes.
Make complex, interactive heatmaps. 'iheatmapr' includes a modular 
    system for iteratively building up complex heatmaps, as well as the 
    iheatmap() function for making relatively standard heatmaps.
Density, distribution function, quantile function and random generation for the inverse hyperbolic sine distribution. This package also provides a function that can fit data to the inverse hyperbolic sine distribution using maximum likelihood estimation.
Simulate an inhomogeneous self-exciting process (IHSEP), or Hawkes process, with a given (possibly time-varying) baseline intensity and an excitation function. Calculate the likelihood of an IHSEP with given baseline intensity and excitation functions for an (increasing) sequence of event times. Calculate the point process residuals (integral transforms of the original event times). Calculate the mean intensity process.
Efficient algorithms for fitting linear / logistic regression model with Independently Interpretable Lasso.
  Takada, M., Suzuki, T., & Fujisawa, H. (2018). Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables. AISTATS.
  <http://proceedings.mlr.press/v84/takada18a/takada18a.pdf>.
These datasets and functions accompany Wolfe and Schneider (2017) - Intuitive Introductory Statistics (ISBN:  978-3-319-56070-0) <http://www.springer.com/us/book/9783319560700>. They are used in the examples throughout the text and in the end-of-chapter exercises. The datasets are meant to cover a broad range of topics in order to appeal to the diverse set of interests and backgrounds typically present in an introductory Statistics class.
Integrative framework for the simultaneous estimation of interactions from different class of data.
Correctly import TIFF files that were saved from 'ImageJ' and write 
    TIFF files than can be correctly read by 'ImageJ' 
    <https://imagej.nih.gov/ij/>. Full support for TIFF files with floating 
    point (real-numbered) pixels. Also supports text image I/O.
The package offers an S4 infrastructure to store climatological
  station data of various temporal aggregation scales. In-built quality
  control and homogeneity tests follow the methodology from the European
  Climate Assessment & Dataset project. Wrappers for climate indices
  defined by the Expert Team on Climate Change Detection and Indices
  (ETCCDI), a quick summary of important climate statistics and climate
  diagram plots provide a fast overview of climatological
  characteristics of the station.
Fitting a class of Lee-Carter mortality models using iterative fitting algorithm.
It performs interlaboratory studies (ILS) to detect those
    laboratories that provide non-consistent results when comparing to others. It
    permits to work simultaneously with various testing materials, from standard
    univariate, and functional data analysis (FDA) perspectives. The univariate
    approach based on ASTM E691-08 consist of estimating the Mandel's h and
    k statistics to identify those laboratories that provide more significant
    different results, testing also the presence of outliers by Cochran and Grubbs
    tests, Analysis of variance (ANOVA) techniques are provided (F and Tuckey
    tests) to test differences in means corresponding to different laboratories per
    each material. Taking into account the functional nature of data retrieved in
    analytical chemistry, applied physics and engineering (spectra, thermograms,
    etc.). ILS package provides a FDA approach for finding the Mandel's k and h
    statistics distribution by smoothing bootstrap resampling.
Compute moments of images and perform reconstruction from
        moments.
Extracts traits from imaging data produced using a Lemna-Tec Scananalyzer 
    (see <http://www.lemnatec.com/products/hardware-solutions/scanalyzer-3d> for more 
    information). Growth rates between successive imagings are obtained and those for 
    a nominated set of intervals can also be calculated. Profile or longitudinal plots 
    of the traits and growth rates can be produced. These allow one to check for 
    anomalous data and to explore growth patterns in the data.
Fast image processing for images in up to 4 dimensions (two spatial
    dimensions, one time/depth dimension, one colour dimension). Provides most
    traditional image processing tools (filtering, morphology, transformations,
    etc.) as well as various functions for easily analysing image data using R. The
    package wraps 'CImg', <http://cimg.eu>, a simple, modern C++ library for image
    processing.
Simulate general insurance policies, losses and loss emergence. The package contemplates 
  deterministic and stochastic policy retention and growth scenarios. Retention and growth rates are percentages relative
  to the expiring portfolio. Claims are simulated for each policy. This is accomplished either be assuming a frequency
  distribution per development lag or by generating random wait times until claim emergence and settlement. Loss simulation 
  uses standard loss distributions for claim amounts.
Provides fast application of image filters to data matrices,
    using R and C++ algorithms.
The pearl oyster, Pinctada margaritifera (Linnaeus, 1758), represents the second economic resource of French Polynesia. It is one of the only bivalves expressing a large varied range of inner shell color, & by correlation, of pearl color. This phenotypic variability is partly under genetic control, but also under environmental influence. With ImaginR, it's now possible to delimit the color phenotype of the pearl oyster's inner shell and to characterize their color variations (by the HSV color code system) with pictures.
This is an Automatic Item Generator for Psychological Testing. It is recommended for research purposes only.
Zoom in and out of maps or any supplied lines or points,
        with control for color, poly fill, and aspect.
Class imbalance usually damages the performance of classifiers. Thus, it is
             important to treat data before applying a classifier algorithm. This package
             includes recent resampling algorithms in the literature: (Barua et al. 2014)
             <doi:10.1109/tkde.2012.232>; (Das et al. 2015) <doi:10.1109/tkde.2014.2324567>,
             (Zhang et al. 2014) <doi:10.1016/j.inffus.2013.12.003>; (Gao et al. 2014)
             <doi:10.1016/j.neucom.2014.02.006>; (Almogahed et al. 2014)
             <doi:10.1007/s00500-014-1484-5>. It also includes an useful interface to
             perform oversampling.
Provides API access to the <http://imdbapi.net> which maintains metadata
             about movies, games and television shows through a public API.
Implements likelihood based methods for mediation analysis.
Search, extract and formulate IMF's datasets.
Explore and download data from the International Monetary Fund's
    data API <http://data.imf.org/>.
A complete API client for the image hosting service Imgur.com, including the an imgur graphics device, enabling the easy upload and sharing of plots.
Provides flexible Bayesian estimation of Infinite Mixtures of Infinite Factor Analysers and related models, for nonparametrically clustering high-dimensional data, introduced by Murphy et al. (2017) <arXiv:1701.07010v4>. The IMIFA model conducts Bayesian nonparametric model-based clustering with factor analytic covariance structures without recourse to model selection criteria to choose the number of clusters or cluster-specific latent factors, mostly via efficient Gibbs updates. Model-specific diagnostic tools are also provided, as well as many options for plotting results, conducting posterior inference on parameters of interest, and quantifying uncertainty.
IMIS algorithm draws samples from the posterior
        distribution. The user has to define the following R functions
        in advance: prior(x) calculates prior density of x,
        likelihood(x) calculates the likelihood of x, and
        sample.prior(n) draws n samples from the prior distribution.
Interpretability methods to analyze the behavior and predictions of
 any machine learning model.
 Implemented methods are:
 Feature importance described by Fisher et al. (2018) <arXiv:1801.01489>,
 partial dependence plots described by Friedman (2001) <http://www.jstor.org/stable/2699986>,
 individual conditional expectation ('ice') plots described by Goldstein et al. (2013) <doi:10.1080/10618600.2014.907095>,
 local models (variant of 'lime') described by Ribeiro et. al (2016) <arXiv:1602.04938>,
 the Shapley Value described by Strumbelj et. al (2014) <doi:10.1007/s10115-013-0679-x>, 
 feature interactions described by Friedman et. al <doi:10.1214/07-AOAS148> and
 tree surrogate models.
Implements some item response models for multiple
    ratings, including the hierarchical rater model, 
    conditional maximum likelihood estimation of linear 
    logistic partial credit model and a wrapper function
    to the commercial FACETS program. See Robitzsch and
    Steinfeld (2018) for a description of the functionality
    of the package.
Contains functions for evaluating & comparing the performance of Binary classification models. Functions can be called either statically or interactively (as Shiny Apps).
Functions to analyse missing value mechanisms and to impute data sets in the context of bottom-up MS-based proteomics.
Implement a multivariate analysis of the impact of items to identify a bias in the questionnaire validation of Likert-type scale variables. The items requires considering a null value (category doesn't have tendency). Offering frequency, importance and impact of the items.
In this package, you can find two functions proposed in
        Ding, Geng and Zhou (2011) to estimate direct and indirect
        causal effects with randomization and multiple-component
        intervention using instrumental variable method.
'SQL' back-end to 'dplyr' for Apache Impala, the massively
    parallel processing query engine for Apache 'Hadoop'. Impala enables
    low-latency 'SQL' queries on data stored in the 'Hadoop' Distributed
    File System '(HDFS)', Apache 'HBase', Apache 'Kudu', Amazon Simple 
    Storage Service '(S3)', Microsoft Azure Data Lake Store '(ADLS)', 
    and Dell 'EMC' 'Isilon'. See <https://impala.apache.org> for more
    information about Impala.
Tools performing an imprecise inference for estimating the parameter of Poisson sampling model.   Extended the original work done in the PhD thesis of Lee (2014).   The theory of imprecise probabilities introduced by Peter Walley in 1991 becomes the basis of this inferential framework.
This is an alternative mechanism for importing
  objects from packages. The syntax allows for importing multiple objects
  from a package with a single command in an expressive way. The import
  package bridges some of the gap between using library (or require) and
  direct (single-object) imports. Furthermore the imported objects are not
  placed in the current environment. It is also possible to import
  objects from stand-alone .R files. For more information, refer to
  the package vignette.
Enables 'Python'-like importing/loading of packages or functions
    with aliasing to prevent namespace conflicts.
Import and export data from the most common statistical formats by using 
	     R functions that guarantee the least loss of the data information, giving special
	     attention to the date variables and the labelled ones.
A minimum distance estimator is calculated for an
        imprecise probability model. The imprecise probability model
        consists of upper coherent previsions whose credal sets are
        given by a finite number of constraints on the expectations.
        The parameter set is finite. The estimator chooses that
        parameter such that the empirical measure lies next to the
        corresponding credal set with respect to the total variation
        norm.
The package contains a collection of functions for left-censored missing data imputation. Left-censoring is a special case of missing not at random (MNAR)  mechanism that generates non-responses in proteomics experiments. The package also contains functions to artificially generate peptide/protein expression data (log-transformed) as random draws from a multivariate Gaussian distribution as well as a function to generate missing data (both randomly and non-randomly). For comparison reasons, the package also contains several wrapper functions for the imputation of non-responses that are missing at random. * New functionality has been added: a hybrid method that allows the imputation of missing values in a more complex scenario where the missing data are both MAR and MNAR.
This package provides various approaches to handling
        missing values for the MDR analysis to identify gene-gene
        interactions using biallelic marker data in genetic association
        studies
Compute missing values on a training data set and impute them on a new data set. Current available options are median/mode and random forest.
Implements imputation methods using EM and Data Augmentation for
    multinomial data following the work of Schafer 1997 <ISBN: 978-0-412-04061-0>.
Imputes the missing values in time series data with PSF algorithm based approach.
    The details about PSF algorithm are available at:
    <https://cran.r-project.org/package=PSF>.
General imputation framework based on
    variable selection methods including regularisation methods,
    tree-based models and dimension reduction methods.
Provides new imputation methods for the 'mice' package based on generalized additive models for location, scale, and shape (GAMLSS) as described in de Jong, van Buuren and Spiess <doi:10.1080/03610918.2014.911894>.
Provides a test bench for the comparison of missing data imputation 
    methods in uni-variate time series. Imputation methods are compared using 
    different error metrics. Proposed imputation methods and alternative error 
    metrics can be used.
Imputation (replacement) of missing values 
             in univariate time series. 
             Offers several imputation functions
             and missing data plots. 
             Available imputation algorithms include: 
            'Mean', 'LOCF', 'Interpolation', 
            'Moving Average', 'Seasonal Decomposition', 
            'Kalman Smoothing on Structural Time Series models',
            'Kalman Smoothing on ARIMA models'.
Method brings less bias and more efficient estimates for AFT models.
Do Multi-Reader, Multi-Case (MRMC) analyses of data from imaging studies where clinicians (readers)
  evaluate patient images (cases). What does this mean? ... Many imaging studies are designed
  so that every reader reads every case in all modalities, a fully-crossed study. In this case, the
  data is cross-correlated, and we consider the readers and cases to be cross-correlated random effects.
  An MRMC analysis accounts for the variability and correlations from the readers and cases when
  estimating variances, confidence intervals, and p-values. The functions in this package can treat
  arbitrary study designs and studies with missing data, not just fully-crossed study designs.
  The initial package analyzes the reader-average area under the receiver operating characteristic (ROC)
  curve with U-statistics according to Gallas, Bandos, Samuelson,
  and Wagner 2009 <doi:10.1080/03610920802610084>.
  Additional functions analyze other endpoints with U-statistics (binary performance
  and score differences) following the work by Gallas, Pennello, and Myers 2007
  <doi:10.1364/JOSAA.24.000B70>.
  Package development and documentation is at <https://github.com/DIDSR/iMRMC/tree/master>.
Implementation of the information matrix test for generalized partial credit models.
Graphical User Interface (GUI) to some of the functions in the package extRemes version >= 2.0 are included.
Partitioning clustering algorithms divide data sets into k subsets or partitions so-called clusters. They require some initialization procedures for starting to partition the data sets. Initialization of cluster prototypes is one of such kind of procedures for most of the partitioning algorithms. Cluster prototypes are the data elements, i.e. centroids or medoids, representing the clusters in a data set. In order to initialize cluster prototypes, the package 'inaparc' contains a set of the functions that are the implementations of several linear time-complexity and loglinear time-complexity methods in addition to some novel techniques. Initialization of fuzzy membership degrees matrices is another important task for starting the probabilistic and possibilistic partitioning algorithms. In order to initialize membership degrees matrices required by these algorithms, a number of functions based on some traditional and novel initialization techniques are also available in the package 'inaparc'.
Fits mixtures models for longitudinal data. Appropriate when the data are counts and when the correlation structure is assumed to be AR(1).
A framework for analysing inbreeding and heterozygosity-fitness
    correlations (HFCs) based on microsatellite and SNP markers.
Specific functions are provided for rounding real weights to integers and performing an integer programming algorithm for calibration problems. They are useful for census-weights adjustments, or for performing linear regression with integer parameters.
Handle data in formats used by cancer centers in Sweden, both from 'INCA' 
  (the current register platform, (see <http://www.incanet.se> for more information) and 
  by the older register platform 'Rockan' (used in the Western and Northern part 
  of the country). 
  All variables are coerced to suitable classes based on their 
  format. 
  Dates (from various formats such as with missing month or day, with or 
  without century prefix or with just a week number) are all recognized as
  dates and coerced to the ISO 8601 standard (Y-m-d).
  Boolean variables (internally stored either as 0/1 or "True"/"False"/blanks 
  when exported) are coerced to logical. 
  Variable names ending in '_Beskrivning' and '_Varde' will be character, 
  and 'PERSNR' will be coerced (if possible) to a valid personal identification 
  number 'pin' (by the 'sweidnumbr' package).
  The package also allow the user to interactively choose if a variable should 
  be coerced into a potential format even though not all of its values might 
  conform to the recognized pattern.
  It also contain a caching mechanism in order to temporarily store data sets 
  with its newly decided formats in order to not rerun the identification 
  process each time. 
  And finally, it also include a mechanism to aid the documentation process 
  connected to projects build on data from 'INCA'.
Data analysis, normalisation and differential expression for Translatome studies by microarrays (T Sbarrato et al. RNA. 2017 Aug 25; <DOI:10.1261/rna.060525.116>).
Implements incremental calculation of the DTW (Dynamic Time Warping) distance of two vectors, which is specifically useful for life data streams. Further the calculation of the global cost matrix is implemented in C++ to be faster. The Sakoe Chiba band is also implemented. The calculation of DTW is less functional then the one of dtw(), however much faster. For details about DTW see the original paper "Dynamic programming algorithm optimization for spoken word recognition" by Sakoe and Chiba (1978) <DOI:10.1109/TASSP.1978.1163055>. Any feedback is highly appreciated!
An efficient and incremental approach for calculating 
    the differences in orbit counts when performing single edge modifications 
    in a network. Calculating the differences in orbit counts is much more efficient than
    recalculating all orbit counts from scratch for each time point.
Provides functions and classes to compute, handle and visualise incidence from dated events for a defined time interval. Dates can be provided in various standard formats. The class 'incidence' is used to store computed incidence and can be easily manipulated, subsetted, and plotted. In addition, log-linear models can be fitted to 'incidence' objects using 'fit'. This package is part of the RECON (<http://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.
Suite of functions to study animal incubation.
    At the core of incR 
    lies an algorithm that allows for the scoring of 
    incubation behaviour. Additionally, several functions 
    extract biologically relevant metrics of incubation such as off-bout number 
    and off-bout duration - for a review of avian incubation studies, 
    see Nests, Eggs, and Incubation: New ideas about avian reproduction (2015) 
    edited by D. Charles Deeming and S. James Reynolds <doi:10.1093/acprof:oso/9780198718666.001.0001>.
Tools for estimating incidence from biomarker data in cross-
    sectional surveys, and for calibrating tests for recent infection. 
    Implements and extends the method of Kassanjee et al. (2012)
    <doi:10.1097/EDE.0b013e3182576c07>.
Package to import data generated by the Incucyte Zoom from Essen Biosciences and use this to fit dose response curves using the drc package.
Genome-wide gene insertion and deletion rates can be modelled in a maximum 
    likelihood framework with the additional flexibility of modelling potential missing 
    data using the models included within. These models simultaneously estimate insertion 
    and deletion (indel) rates of gene families and proportions of "missing" data for 
    (multiple) taxa of interest. The likelihood framework is utilized for parameter 
    estimation. A phylogenetic tree of the taxa and gene presence/absence patterns 
    (with data ordered by the tips of the tree) are required. See Dang et al.
    (2016) <doi:10.1534/genetics.116.191973> for more details.
Functions for testing mutual independence between many
        numerical random vectors or serial independence of a
        multivariate stationary sequence. The proposed test works when
        some or all of the marginal distributions are singular with
        respect to Lebesgue measure.
Implementations of the weighted Kozachenko-Leonenko entropy estimator and independence tests based on this estimator, (Kozachenko and Leonenko (1987) <http://mi.mathnet.ru/eng/ppi797>). Also includes a goodness-of-fit test for a linear model which is an independence test between covariates and errors.
Computes bilateral and multilateral index numbers. 
    It has support for several standard bilateral indices as well as
    the GEKS multilateral index number methods 
    (see Ivancic, Diewert and Fox (2011) <doi:10.1016/j.jeconom.2010.09.003>) . 
    It also supports updating of GEKS indexes using several splicing methods.
Calculate Indian Income Tax liability for Financial years of Individual resident aged below 60 years,Senior Citizen,Super Senior Citizen, Firm, Local Authority, Any Non Resident Individual / Hindu Undivided Family / Association of Persons /Body of Individuals / Artificial Judicial Person, Co-operative Society.
Functions to assess the strength and statistical significance of the relationship between species occurrence/abundance and groups of sites. Also includes functions to measure species niche breadth using resource categories.
Functions are provided to facilitate prior elicitation for Bayesian generalised linear models using independent conditional means priors. The package supports the elicitation of multivariate normal priors for generalised linear models. The approach can be applied to indirect elicitation for a generalised linear model that is linear in the parameters. The package is designed such that the facilitator executes functions within the R console during the elicitation session to provide graphical and numerical feedback at each design point. Various methodologies for eliciting fractiles (equivalently, percentiles or quantiles) are supported, including versions of the approach of Hosack et al. (2017) <doi:10.1016/j.ress.2017.06.011>. For example, experts may be asked to provide central credible intervals that correspond to a certain probability. Or experts may be allowed to vary the probability allocated to the central credible interval for each design point. Additionally, a median may or may not be elicited.
Several parametric and non-parametric tests  and measures to check independence between two or more (homogeneous or nonhomogeneous) point processes  in time are provided. Tools for simulating point processes in one dimension with different types of dependence are also implemented.
Provides functions to download and parse information from INEGI
    (Official Mexican statistics agency). To learn more about the API, see <http://www.inegi.org.mx/desarrolladores/default.aspx>.
Inequality, concentration, and poverty measures. Lorenz curves (empirical and theoretical).
Provides simple functions to compute and plot two
    types (sample-size- and coverage-based) rarefaction and extrapolation of species
    diversity (Hill numbers) for individual-based (abundance) data or sampling-unit-
    based (incidence) data.
Interpolation and extrapolation for phylogenetic diversity.
This package contains functions to perform calculations of
        the infine-dimensional model (IDM) and to produce 95%
        confidence intervals around the model elements through
        bootstrapping.
The objective of this package is to perform inference using an expressive statistical grammar that coheres with the tidy design framework.
Collection of functions to extract inferential values
        (point estimates, confidence intervals, p-values, etc) of a
        fitted model object into a matrix-like object that can be used
        for table/report generation; transform point estimates via the
        delta method.
The InferenceSMR package provides functions to make
        inference about the standardized mortality ratio (SMR) when
        evaluating the effect of a screening program. The package is
        based on methods described in Sasieni (2003) and Talbot et al.
        (2011).
Provides methods for estimating causal effects in the presence of interference described in  B. Saul and M. Hugdens (2017) <doi:10.18637/jss.v082.i02>. Currently it implements the inverse-probability weighted (IPW) estimators proposed by E.J. Tchetgen Tchetgen and T.J. Vanderweele (2012) <doi:10.1177/0962280210386779>.
Select set of parametric and non-parametric statistical tests. 'inferr' builds upon the solid set of
    statistical tests provided in 'stats' package by including additional data types as inputs, expanding and
    restructuring the test results. The tests included are t tests, variance tests, proportion tests, chi square tests, Levene's test, McNemar Test, Cochran's Q test and Runs test.
The proportion of cancer cells in solid tumor sample, known as the tumor purity, has adverse impact on a variety of data analyses if not properly accounted for. We develop 'InfiniumPurify', which is a comprehensive R package for estimating and accounting for tumor purity based on DNA methylation Infinium 450k array data. 'InfiniumPurify' provides functionalities for tumor purity estimation. In addition, it can perform differential methylation detection and tumor sample clustering with the consideration of tumor purities.
Provides access to core inflation functions. Four different core inflation 
 functions are provided. The well known trimmed means, exclusion and double weighing methods, 
 alongside the new Triple Filter method introduced in Ferreira et al. (2016) <https://goo.gl/UYLhcj>.
Implementation of methods Extremum Surface Estimator (ESE) and 
 Extremum Distance Estimator (EDE) to identify the inflection point of a curve.
Provides a collection of tools for
        detecting influential cases in generalized mixed effects
        models. It analyses models that were estimated using 'lme4'. The
        basic rationale behind identifying influential data is that
        when single units are omitted from the data, models
        based on these data should not produce substantially different
        estimates. To standardize the assessment of how influential a
        (single group of) observation(s) is, several measures of
        influence are common practice, such as Cook's Distance. 
        In addition, we provide a measure of percentage change of the fixed point 
        estimates and a simple procedure to detect changing levels of significance.
A set of tools for evaluating several measures of case influence for structural equation models.
Provides functionality to compute various node centrality measures on networks.
    Included are functions to compute betweenness centrality (by utilizing Madduri and Bader's
    SNAP library), implementations of Burt's constraint and effective
    network size (ENS) metrics, Borgatti's algorithm to identify key players, and Valente's
    bridging metric. On Unix systems, the betweenness, Key Players, and
    bridging implementations are parallelized with OpenMP, which may run
    faster on systems which have OpenMP configured.
An R interface to the InfluxDB time series database <https://www.influxdata.com>. This package allows you to fetch and write time series data from/to an InfluxDB server. Additionally, handy wrappers for the Influx Query Language (IQL) to manage and explore a remote database are provided.
The main purpose of this package is to generate the structure of the analysis of variance 
            (ANOVA) table of the two-phase experiments. The user only need to input the design and the 
              relationships of the random and fixed factors using the Wilkinson-Rogers' syntax, 
              this package can then quickly generate the structure of the ANOVA table with the 
              coefficients of the variance components for the expected mean squares.
              Thus, the balanced incomplete block design and provides the efficiency
              factors of the fixed effects can also be studied and compared much easily.
Performs exploratory data analysis and variable screening for
    binary classification models using weight-of-evidence (WOE) and information
    value (IV). In order to make the package as efficient as possible, aggregations
    are done in data.table and creation of WOE vectors can be distributed across
    multiple cores. The package also supports exploration for uplift models (NWOE
    and NIV).
Provides companion function for analysing the performance of
    classification models. Also, provides function to optimise probability cut-
    off score based on used specified objectives, Plot 'ROC' Curve in 'ggplot2',
    'AUROC', 'IV', 'WOE' Calculation, 'KS Statistic' etc to aid accuracy improvement
    in binary classification models.
Multiple Imputation for Informative Censoring.
    This package implements two methods. Gamma Imputation
    from Jackson et al. (2014) <DOI:10.1002/sim.6274> and Risk Score Imputation
    from Hsu et al. (2009) <DOI:10.1002/sim.3480>.
Aids in creating sequence statistics for Butts's 'relevent' software.
This package implements various measures of information theory based on several entropy estimators.
Estimates the probability of informed trading (PIN) initially introduced by Easley et. al. (1996) <doi:10.1111/j.1540-6261.1996.tb04074.x> . Contribution of the package is that it uses likelihood factorizations of Easley et. al. (2010) <doi:10.1017/S0022109010000074> (EHO factorization) and Lin and Ke (2011) <doi:10.1016/j.finmar.2011.03.001> (LK factorization). Moreover, the package uses different estimation algorithms. Specifically, the grid-search algorithm proposed by Yan and Zhang (2012) <doi:10.1016/j.jbankfin.2011.08.003> , hierarchical agglomerative clustering approach proposed by Gan et. al. (2015) <doi:10.1080/14697688.2015.1023336> and later extended by Ersan and Alici (2016) <doi:10.1016/j.intfin.2016.04.001> .
Takes a data frame containing latitude and longitude coordinates and downloads images from map servers to determine their file size as a proxy of infrastructure
Facilitates the generation of input files for infraFDTD and processes snapshot output. infraFDTD is a finite-difference model written by Keehoon Kim for simulating infrasound that considers topography and a 1-D atmosphere (see Kim et al., 2015 <doi:10.1002/2015GL064466>).
This light-weight templating package provides flexible and 
    user-friendly value substitution in string templates or template files
    (e.g. SQL files) at runtime.
Implements functions for simulation-based inference. In particular, implements functions to perform likelihood inference from data summaries whose distributions are simulated (Rousset et al. 2017 <doi:10.1111/1755-0998.12627>).
Calculation of information utility (i.e., Lindley
        information) quantities for item response models.
Parse simple '.ini' configuration files to an structured list. Users
  can manipulate this resulting list with lapply() functions. This same
  structured list can be used to write back to file after modifications.
R dependency injection framework. Dependency injection allows
    a program design to follow the dependency inversion principle. The user
    delegates to external code (the injector) the responsibility of providing its
    dependencies. This separates the responsibilities of use and construction.
Calculate the injury severity score (ISS)
    based on the dictionary in 'ICDPIC' from <https://ideas.repec.org/c/boc/bocode/s457028.html>. The original code was written in 
    'STATA 11'. The original 'STATA' code was written by David Clark, 
    Turner Osler and David Hahn. I implement the same logic for easier access.
    Ref: David E. Clark & Turner M. Osler & David R. Hahn, 2009. 
    "ICDPIC: Stata module to provide methods for translating International 
    Classification of Diseases (Ninth Revision) diagnosis codes into standard injury 
    categories and/or scores," Statistical Software Components S457028, 
    Boston College Department of Economics, revised 29 Oct 2010.
Fit Spatial Econometrics models using Bayesian model averaging 
  on models fitted with INLA. The INLA package can be obtained from 
  <http://www.r-inla.org>.
Facilitates spatial modeling using integrated nested Laplace approximation via the 
  INLA package (<http://www.r-inla.org>). Additionally, implements a log Gaussian Cox process likelihood for 
  modeling univariate and spatial point processes based on ecological survey data. See Yuan Yuan, 
  Fabian E. Bachl, Finn Lindgren, David L. Borchers, Janine B. Illian, Stephen T. Buckland, Havard Rue, 
  Tim Gerrodette (2017), <arXiv:1604.06013>.
Functionality to dynamically define R functions and S4 methods
 with 'inlined' C, C++ or Fortran code supporting the .C and .Call calling
 conventions.
Generates Rd files from R source code with comments.
 The main features of the default syntax are that
 (1) docs are defined in comments near the relevant code,
 (2) function argument names are not repeated in comments, and
 (3) examples are defined in R code, not comments.
 It is also easy to define a new syntax.
A collection of functions for creating high-level graphics,
    performing raster-based analysis, processing MODFLOW-based models,
    subset selection using a genetic algorithm, etc.
    Used to support packages and scripts written by researchers at the
    United States Geological Survey (USGS)
    Idaho National Laboratory (INL) Project Office.
Integrated joint analysis of multiple platform genomic data across biological gene sets or pathways using powerful variance-component based testing procedures.
A set of functions to analyse and compare texts, using classical 
  text mining	functions, as well as those from theoretical ecology.
Non-parametric resampling-based inference tests for ExPosition.
Provides a bioinformatics pipeline for DNA meta-barcoding analysis, including functions for sequence parsing, demultiplexing, quality filtering and probabilistic taxon assignment with informatic sequence classification trees. See Wilkinson et al (2018) <doi:10.7287/peerj.preprints.26812v1>.
insideRODE package includes buildin functions from
        deSolve, compiled functions from compiler, and C/FORTRAN code
        interfaces to nlme. It includes nlmLSODA, nlmODE,
        nlmVODE,nlmLSODE for general purpose; cfLSODA,cfLSODE, cfODE,
        cfVODE call C/FORTRAN compiled dll functions.ver2.0 add
        sink()function into example it helps to directly combine
        c/fortran source code in R files. Finally, with new compiler
        package, we generated compiled functions: nlmODEcp, nlmVODEcp,
        nlmLSODEcp,nlmLSODAcp and cpODE, cpLSODA, cpLSODE, cpVODE. They
        will help to increase speed.
Computes individual causes of death and population cause-specific mortality fractions using the 'InSilicoVA' algorithm from McCormick et al. (2016) <DOI:10.1080/01621459.2016.1152191>. It uses data derived from verbal autopsy (VA) interviews, in a format similar to the input of the widely used 'InterVA4' method. This package provides general model fitting and customization for 'InSilicoVA' algorithm and basic graphical visualization of the output.
Functions to compute insolation on complex terrain.
Provides a data-driven projection-based method for estimating changepoints in high-dimensional time series. Multiple changepoints are estimated using a (wild) binary segmentation scheme.
Check one column or multiple columns of a dataframe
    using the preset basic checks or your own functions. Enables
    checks without knowledge of lapply() or sapply().
A method to infer modules of co-expressed genes and the
    dependencies among the modules from multiple expression datasets that may
    contain different sets of genes. Please refer to: Extracting a low-dimensional
    description of multiple gene expression datasets reveals a potential driver for
    tumor-associated stroma in ovarian cancer, Safiye Celik, Benjamin A. Logsdon,
    Stephanie Battle, Charles W. Drescher, Mara Rendi, R. David Hawkins and Su-In
    Lee (2016) <DOI:10.1186/s13073-016-0319-7>.
The function `install_load` checks the local R library(ies) to see
    if the required package(s) is/are installed or not. If the package(s)
    is/are not installed, then the package(s) will be installed along with
    the required dependency(ies). This function pulls source or
    binary packages from the Rstudio-sponsored CRAN mirror and/or
    the USGS GRAN Repository. Lastly, the chosen package(s)
    is/are loaded. The function `load_package` simply loads the provided
    packages.
R is great for installing software.  Through the 'installr'
    package you can automate the updating of R (on Windows, using updateR())
    and install new software. Software installation is initiated through a
    GUI (just run installr()), or through functions such as: install.Rtools(),
    install.pandoc(), install.git(), and many more. The updateR() command
    performs the following: finding the latest R version, downloading it,
    running the installer, deleting the installation file, copy and updating
    old packages to the new R installation.
Provides an interface to the Instagram API <https://instagram.com/
    developer/>, which allows R users to download public pictures filtered by
    hashtag, popularity, user or location, and to access public users' profile data.
Insurance datasets, which are often used in claims severity and claims frequency modelling. It helps testing new regression models in those problems, such as GLM, GLMM, HGLM, non-linear mixed models etc. Most of the data sets are applied in the project "Mixed models in ratemaking" supported by grant NN 111461540 from Polish National Science Center.
Provides classes and methods for automated
    spatial interpolation.
Includes additional functionality for spatial interpolation in the intamap package, such as bias correction and network optimization.
Semiparametric regression models for the cumulative incidence function with interval-censored competing risks data as described in Bakoyannis, Yu, & Yiannoutsos (2017) <doi:10.1002/sim.7350>. The main function fits the proportional subdistribution hazards model (Fine-Gray model), the proportional odds model, and other models that belong to the class of semiparametric generalized odds rate transformation models.
The kernel of this 'Rcpp' based package is an efficient implementation of the generalized gradient projection method for spline function based constrained maximum likelihood estimator for interval censored survival data (Wu, Yuan; Zhang, Ying. Partially monotone tensor spline estimation of the joint distribution function with bivariate current status data. Ann. Statist. 40, 2012, 1609-1636 <doi:10.1214/12-AOS1016>). The key function computes the density function of the joint distribution of event time and the marker and returns the receiver operating characteristic (ROC) curve for the interval censored survival data as well as area under the curve (AUC).
Several integrative data methods in which information of objects from different data sources can be combined are included in the IntClust package. As a single data source is limited in its point of view, this provides more insight and the opportunity to investigate how the variables are interconnected. Clustering techniques are to be applied to the combined information. For now, only agglomerative hierarchical clustering is implemented. Further, differential gene expression and pathway analysis can be conducted on the clusters. Plotting functions are available to visualize and compare results of the different methods.
Provides a systematic framework for
 integrating multiple modalities of assays profiled on the same set of
 samples. The goal is to identify genes that are altered in cancer
 either marginally or consistently across different assays. The
 heterogeneity among different platforms and different samples are
 automatically adjusted so that the overall alteration magnitude can
 be accurately inferred. See Tong and Coombes (2012) 
 <doi:10.1093/bioinformatics/bts561>.
Offers modeling the association between gene-expression and bioassay data, taking care of the effect due to a fingerprint feature and helps with several plots to better understand the analysis.
An implementation of a framework for drug sensitivity prediction from various genetic characterizations using ensemble approaches. Random Forests or Multivariate Random Forest predictive models can be generated from each genetic characterization that are then combined using a Least Square Regression approach. It also provides options for the use of different error estimation approaches of Leave-one-out, Bootstrap, N-fold cross validation and 0.632+Bootstrap along with generation of prediction confidence interval using Jackknife-after-Bootstrap approach.
This package searches for marginal interactions in a
        binary response model. Interact uses permutation methods to
        estimate false discovery rates for these marginal interactions
        and has some, limited visualization capabilities
Implements the procedures suggested in Esarey and Sumner (2017) <URL:https://goo.gl/riqX7Q> for controlling the false discovery rate or familywise error rate when constructing marginal effects plots for models with interaction terms.
An extension of the package 'igraph'. This package create
        possibly to work with 'igraph' objects interactively.
A shiny application for designing adaptive clinical trials. For
    more details, see: http://arxiv.org/abs/1404.0734
Chi-square tests are computed with corrections.
Implementations of semiparametric cure rate estimators for interval
    censored data in R. The algorithms are based on the promotion time and
    frailty models, all for interval censoring. For the frailty model,
    there is also a implementation contemplating clustered data.
Graphical User Interface allowing to determine the concentration in the sample in CFU per mL or in number of copies per mL provided to qPCR results after with or without PMA treatment. This package is simply to use because no knowledge in R commands is necessary. A graphic represents the standard curve, and a table containing the result for each sample is created.
Computes large sample confidence intervals of Liu and Hudgens (2014), exact confidence intervals of Tchetgen Tchetgen and VanderWeele (2012), and exact confidence intervals of Rigdon and Hudgens (2014) for treatment effects on a binary outcome in two-stage randomized experiments with interference.
Performs diagnostic tests of multiplicative interaction models and plots non-linear marginal effects of a treatment on an outcome across different values of a moderator.
Computes interference color tables and plots customized Michel-Levy or Raith-Sorensen charts. Automatic interpretation of polarized-light microscopy images is still under development and will come soon.
Functions implemented in this package allow to coerce (i.e.
	convert) network data between classes provided by other R packages.
	Currently supported classes are those defined in packages: network and
	igraph.
Allows the simulation of both the recruitment and treatment phase of a clinical trial. Based on these simulations, the timing of interim analyses can be assessed.
Interlinearized glossed texts (IGT) are used in descriptive linguistics for
 representing a morphological analysis of a text through a morpheme-by-morpheme gloss.
 'InterlineaR' provide a set of functions that targets several popular formats of IGT
 ('SIL Toolbox', 'EMELD XML') and that turns an IGT into a set of data frames following
 a relational model (the tables represent the different linguistic units: texts,
 sentences, word, morphems).
 The same pieces of software ('SIL FLEX', 'SIL Toolbox') typically produce dictionaries
 of the morphemes used in the glosses. 'InterlineaR' provide a function for turning
 the LIFT XML dictionary format into a set of data frames following a relational model
 in order to represent the dictionary entries, the sense(s) attached to the entries,
 the example(s) attached to senses, etc.
Search the Internet Archive, retrieve metadata, and download
    files.
Bivariate data interpolation on regular and irregular
  grids, either linear or using splines are the main part of this
  package.  It is intended to provide FOSS replacement functions for
  the ACM licensed akima::interp and tripack::tri.mesh functions.
  Currently the piecewise linear interpolation part of akima::interp
  (and also akima::interpp) is implemented in interp::interp, this
  corresponds to the call akima::interp(..., linear=TRUE) which is the
  default setting and covers most of akima::interp use cases in
  depending packages.  A re-implementation of Akimas spline
  interpolation (akima::interp(..., linear=FALSE)) is currently under
  development and will complete this package in a later
  version. Estimators for partial derivatives are already available,
  these are a prerequisite for the spline interpolation.  The basic
  part is currently a GPLed triangulation algorithm (sweep hull
  algorithm by David Sinclair) providing the starting point for the
  piecewise linear interpolator. As side effect this algorithm is also
  used to provide replacements for the basic functions of the tripack
  package which also suffer from the ACM restrictions.  All functions
  are designed to be backward compatible with their akima / tripack
  counterparts.
Plots the conditional coefficients ("marginal effects") of
    variables included in multiplicative interaction terms.
A package for numerical encoding as well as for linear and
        non-linear interpolation of amino acid sequences.
Hourly interpolation of daily minimum and maximum temperature
    series. Carries out interpolation on multiple series ad once. Requires some
    hourly series for calibration (alternatively can use default calibration
    table).
Annotate and interpret deconvoluted mass spectra (mass*intensity pairs) from high resolution mass spectrometry devices.
Compute permutation- based performance measures and create partial
    dependence plots for (cross-validated) 'randomForest' and 'ada' models.
Provides an R version of the 'InterVA4' software (<http://www.interva.net>) for coding cause of death from verbal autopsies. It also provides simple graphical representation of individual and population level statistics.
Provides an R version of the 'InterVA5' software (<http://www.interva.net>) for coding cause of death from verbal autopsies. It also provides simple graphical representation of individual and population level statistics.
Functions to fit nonparametric survival curves, plot them, and perform logrank or Wilcoxon type tests.
Tools for working with and comparing sets of points and intervals.
This package allows a dynamic Bayesian network to be inferred from microarray timecourse data with interventions (inhibitors).
This package calculates the integrated likelihood numerically. Given the Likelihood function and the prior function, this package integrates out the nuisance parameters by Metropolis-Hastings (MCMC) Algorithm.
For tree ensembles such as random forests, regularized random forests and gradient boosted trees, this package provides functions for: extracting, measuring and pruning rules; selecting a compact rule set; summarizing rules into a learner; calculating frequent variable interactions; formatting rules in latex code.
Estimating interval regression models.  Supports both common and observation-specific boundaries.
Performs Goodness of Fit for regression models 
  using Integrated Regression method. Works for several 
  different fitting techniques.
A variety of methods for estimating intrinsic dimension of data sets (i.e the manifold or Hausdorff dimension of the support of the distribution that generated the data) as reviewed in Johnsson, K. (2016, ISBN:978-91-7623-921-6) and Johnsson, K., Soneson, C. and Fontes, M. (2015) <doi:10.1109/TPAMI.2014.2343220>. Furthermore, to evaluate the performance of these estimators, functions for generating data sets with given intrinsic dimensions are provided.
introgress provides functions for analyzing introgression
        of genotypes between divergent, hybridizing lineages, including
        estimating genomic clines from multi-locus genotype data and
        testing for deviations from neutral expectations. Functions are
        also provided for maximum likelihood estimation of molecular
        hybrid index and graphical analysis.
Evaluating if values 
  of vectors are within different open/closed intervals
  (`x %[]% c(a, b)`), or if two closed
  intervals overlap (`c(a1, b1) %[]o[]% c(a2, b2)`).
  Operators for negation and directional relations also implemented.
Calculates event rates and compares means and variances of groups of interval data corrected for missed arrival observations.
Provides tools for importing, merging, and analysing data from 
  international assessment studies (TIMSS, PIRLS, PISA. ICILS, and PIAAC).
Interface to popular R functions with formulas and data,
  such as 'lm', so they can be included painlessly in data
  science pipelines implemented by 'magrittr'
  with the operator %>%.
Enum-type representation of vectors and representation
  of intervals, including a method of coercing variables in data frames.
Confidence intervals for causal effects, using data collected in different experimental or environmental conditions. Hidden variables can be included in the model with a more experimental version.
The correction is achieved under the assumption that non-migrating cells of the essay approximately form a quadratic flow profile due to frictional effects, compare law of Hagen-Poiseuille for flow in a tube. The script fits a conical plane to give xyz-coordinates of the cells. It outputs the number of migrated cells and the new corrected coordinates.
Determination of the optimal policy in inventory problems from a game-theoretic perspective.
Functions to facilitate inverse estimation (e.g., calibration) in
    linear, generalized linear, nonlinear, and (linear) mixed-effects models. A
    generic function is also provided for plotting fitted regression models with
    or without confidence/prediction bands that may be of use to the general
    user.
Light weight implementation of the standard distribution
  functions for the inverse gamma distribution, wrapping those for the gamma
  distribution in the stats package.
invGauss fits the (randomized drift) inverse Gaussian distribution to survival data. The model is described in Aalen OO, Borgan O, Gjessing HK. Survival and Event History Analysis. A Process Point of View. Springer, 2008. It is based on describing time to event as the barrier hitting time of a Wiener process, where drift towards the barrier has been randomized with a Gaussian distribution. The model allows covariates to influence starting values of the Wiener process and/or average drift towards a barrier, with a user-defined choice of link functions.
Provides two functions for the numerical inversion of Laplace-Transformed functions, returning the value of the standard (time) domain function at a specified value.  The first algorithm is the first optimum contour algorithm described by Evans and Chung (2000)[1].
    The second algorithm uses the Bromwich contour as per the definition of the inverse Laplace Transform.  The latter is unstable for numerical inversion and mainly included for comparison or interest.  There are also some additional functions provided for utility, including plotting and some simple Laplace Transform examples, for which there are known analytical solutions.  Polar-cartesian conversion functions are included in this package and are used by the inversion functions.
    [1] Evans & Chung, 2000: Laplace transform inversions using optimal contours in the complex plane; International Journal of Computer Mathematics v73 pp531-543.
One function to read files. One function to write files. One
    function to direct plots to screen or file. Automatic file format inference
    and directory structure creation.
Method for the calculation of copy numbers and calling of copy number alterations. The algorithm uses coverage data from amplicon sequencing of a sample cohort as input. The method includes significance assessment, correction for multiple testing and does not depend on normal DNA controls. Budczies (2016 Mar 15) <doi:10.18632/oncotarget.7451>.
Two methods for calculation of the number of required flows for semiconductor sequencing: 1. Using a simulation, the number of flows can be calculated for a concrete list of amplicons. 2. An exact combinatorial model is evaluated to calculate the number of flows for a random ensemble of sequences.
Provides icons from the 'Ionicons' icon pack (<http://ionicons.com/>). 
  Functions are provided to get icons as png files or as raw matrices. This is useful 
  when you want to embed raster icons in a report or a graphic.
Provides item exclusion procedure, which is a formal method to 
    test 'Indifference Of iNdicator' (ION). When a latent personality 
    trait-outcome association is assumed, then the association strength 
    should not depend on which subset of indicators (i.e. items) has been 
    chosen to reflect the trait. Personality traits are  often measured 
    (reflected) by  a sum-score of a certain set of indicators. 
    Item exclusion procedure randomly excludes items from a sum-score and 
    tests, whether the sum-score - outcome correlation changes. ION has been 
    achieved, when any item can be excluded from the sum-score without the 
    sum-score - outcome correlation substantially changing . For more details, 
    see Vainik, Mottus et. al, (2015) "Are Trait-Outcome Associations Caused
    by Scales or Particular Items? Example Analysis of Personality Facets and
    BMI",European Journal of Personality  DOI: <10.1002/per.2009> .
Collection of functions for IO Psychologists.
Density, spectral density, and regression estimation using infinite
    order flat-top kernels.
Pre-processing tasks related to working with Eurostat's symmetric input-output
    tables and provide basic input-output economics calculations. The package is 
    a part of rOpenGov <http://ropengov.github.io/> to open source open government initiatives.
Basic I/O tools for streaming and data parsing.
This package provides functions to estimate an IPD linear
        mixed effects model for a continuous outcome and any
        categorical covariate from study summary statistics. There are
        also functions for estimating the power of a
        treatment-covariate interaction test in an individual patient
        data meta-analysis from aggregate data.
Functions are provided to interpolate geo-referenced point data via
    Inverse Path Distance Weighting. Useful for coastal marine applications where
    barriers in the landscape preclude interpolation with Euclidean distances.
Access data from IPEA (Instituto de Pesquisa Economica Aplicada) hosted
             at <http://www.ipeadata.gov.br>. It gets metadata and values for all series on 
             the website.
Calculates the RMS intrinsic and parameter-effects curvatures of a nonlinear regression model.
The core of the package is cvr2.ipflasso(), an extension of glmnet to be used when the (large) set of available predictors is partitioned into several modalities which potentially differ with respect to their information content in terms of prediction. For example, in biomedical applications patient outcome such as survival time or response to therapy may have to be predicted based on, say, mRNA data, miRNA data, methylation data, CNV data, clinical data, etc. The clinical predictors are on average often much more important for outcome prediction than the mRNA data. The ipflasso method takes this problem into account by using different penalty parameters for predictors from different modalities. The ratio between the different penalty parameters can be chosen by cross-validation.
A fast (C) implementation of the iterative proportional fitting
    procedure. Based on corresponding code from the networkTomography package.
Algorithms and utility functions for indoor positioning using fingerprinting techniques. 
    These functions are designed for manipulation of RSSI (Received Signal Strength Intensity) data 
    sets, estimation of positions,comparison of the performance of different models, and graphical 
    visualization of data. Machine learning algorithms and methods such as k-nearest neighbors or
    probabilistic fingerprinting are implemented in this package to perform analysis
    and estimations over RSSI data sets.
IPMpack takes demographic vital rates and (optionally) environmental data to build integral projection models.  A number of functional forms for growth and survival can be incorporated, as well as a range of reproductive strategies.  The package also includes a suite of diagnostic routines, provides classic matrix model output (e.g., lambda, elasticities, sensitivities), and produces post-hoc metrics (e.g., passage time and life expectancy).
Computes IPM for assessing variable importance for random forests. See details at I. Epifanio (2017) <DOI:10.1186/s12859-017-1650-8>.
Improved predictive models by indirect classification and
  bagging for classification, regression and survival problems 
  as well as resampling based estimators of prediction error.
Provides methods to perform and analyse I-prior regression models.
    Estimation is done either via direct optimisation of the log-likelihood or 
    an EM algorithm.
This package provides functions that wrap popular phylogenetic software for sequence alignment, masking of sequence alignments, and estimation of phylogenies and ancestral character states.
Tools for identifying the origins of IP addresses. Includes functions for converting IP addresses
  to country names, location details (region, city, zip, latitude, longitude), IP codes, binary values, as well
  as a function for plotting IP locations on a world map. This product includes IP2Location LITE data available
  from <http://www.ip2location.com> and is is available by Creative Commons Attribution-ShareAlike 4.0 Interational
  license (CC-BY-SA 4.0).
A toolkit for manipulating, validating and testing 'IP' addresses and
    ranges, along with datasets relating to 'IP' addresses. Tools are also provided
    to map 'IPv4' blocks to country codes. While it primarily has support for the 'IPv4'
    address space, more extensive 'IPv6' support is intended.
An easy way to import census, survey and geographic data provided by 'IPUMS'
    into R plus tools to help use the associated metadata to make analysis easier. 'IPUMS'
    data describing 1.4 billion individuals drawn from over 750 censuses and surveys is
    available free of charge from our website <https://ipums.org>.
Functions to estimate the probability to receive the observed treatment, based on
    individual characteristics. The inverse of these probabilities can be used as weights when
	estimating causal effects from observational data via marginal structural models. Both point
	treatment situations and longitudinal studies can be analysed. The same functions can be used to
	correct for informative censoring.
An implementation of the correction methods proposed by Shu and Yi (2017) <doi:10.1177/0962280217743777> for the inverse probability weighting (IPW) estimation of average treatment effect (ATE) with misclassified outcomes. Logistic regression model is assumed for treatment model for all implemented correction methods, and is assumed for the outcome model for the implemented doubly robust correction method.
In observational studies, the presence of confounding factors is common and the comparison of different groups of subjects requires adjustment. In this package, we propose simple functions to estimate adjusted survival curves and log-rank test based on inverse probability weighting (IPW).
Builds statistical control charts with exact limits for
        univariate and multivariate cases.
Estimate an optimal dynamic treatment regime using Interactive Q-learning.
Iterated race is an extension of the Iterated F-race method for
             the automatic configuration of optimization algorithms, that is,
             (offline) tuning their parameters by finding the most appropriate
             settings given a set of instances of an optimization problem.
Provides a flexible integrative algorithm that allows information from prior data, such as protein protein interactions and gene knock-down, to be jointly considered for gene regulatory network inference.
A R interface to setup, run and read IRATE model runs to assess band recovery (conventional tagging) data (i.e. age-dependent or independent fishing and natural mortality rates).
Provides implementation of various correlation coefficients of common use in
  Information Retrieval. In particular, it includes Kendall (1970, isbn:0852641990) tau coefficient
  as well as tau_a and tau_b for the treatment of ties. It also includes Yilmaz et al. (2008)
  <doi:10.1145/1390334.1390435> tauAP correlation coefficient, and versions tauAP_a and tauAP_b
  developed by Urbano and Marrero (2017) <doi:10.1145/3121050.3121106> to cope with ties.
An interface to the rich display capabilities of 'Jupyter' front-ends (e.g. 'Jupyter Notebook') <https://jupyter.org>.
    Designed to be used from a running 'IRkernel' session <https://irkernel.github.io>.
"iRefR" allows the user to load any version of the consolidated protein interaction database "iRefIndex" and perform tasks such as: selecting databases, pmids, experimental methods, searching for specific proteins, separate binary interactions from complexes and polymers, generate complexes according to an algorithm that looks after possible binary-represented complexes, make general database statistics and create network graphs, among others.
Contains some important regression methods for interval-valued variables. For each method, it is available the fitted values, residuals and some goodness-of-fit measures.
This package calculates intraclass correlation coefficient (ICC) for assessing reproducibility of interval-censored data with two repeated measurements. ICC is estimated by maximum likelihood from model with one fixed and one random effect (both intercepts). Help in model checking (normality of subjects' means and residuals) is provided.
Iteratively grows feature weighted random forests and finds high-order feature interactions in a stable fashion.
This data package contains the boards' compositions of companies quoted in the Irish Stock Exchange at the end of each year from 2003 to 2013. The data have been first analysed in Friel, N., Rastelli, R., Wyse, J. and Raftery, A.E. (2016) <DOI:10.1073/pnas.1606295113>.
Classes and functions for metrics calculation as part of the
             'IRIS DMC MUSTANG' project. The functionality in this package 
             builds upon the base classes of the 'IRISSeismic' package.
             Metrics include basic statistics as well as higher level
             'health' metrics that can help identify problematic seismometers.
Provides classes and methods for seismic data analysis. The
             base classes and methods are inspired by the python code found in
             the 'ObsPy' python toolbox <https://github.com/obsypy/obspy>. Additional classes and 
             methods support data returned by web services provided by the 'IRIS DMC'
             <http://service.iris.edu/>.
Fast and memory efficient methods for truncated singular value
    decomposition and principal components analysis of large sparse and dense matrices.
Missing data are frequently encountered in high-dimensional data analysis, but they are usually difficult to deal with using standard algorithms, such as the EM algorithm and its variants. This package provides a general algorithm, the so-called Imputation Regularized Optimization (IRO) algorithm, for high-dimensional missing data problems. You can refer to Liang, F., Jia, B., Xue, J., Li, Q. and Luo, Y. (2018) at <arXiv:1802.02251> for detail.
Coefficients of Interrater Reliability and Agreement for
        quantitative, ordinal and nominal data: ICC, Finn-Coefficient,
        Robinson'A, Kendall's W, Cohen's Kappa, ...
Provides coefficients of interrater reliability, that are generalized to cope with randomly incomplete (i.e. unbalanced) datasets without any imputation of missing values or any (row-wise or column-wise) omissions of actually available data. Applied to complete (balanced) datasets, these generalizations yield the same results as the common procedures, namely the Intraclass Correlation according to McGraw & Wong (1996) <doi:10.1037/1082-989X.1.1.30> and the Coefficient of Concordance according to Kendall & Babington Smith (1939) <doi:10.1214/aoms/1177732186>.
Includes a collection of shiny applications to demonstrate
  or to explore fundamental item response theory (IRT) concepts
  such as estimation, scoring, and multidimensional IRT models.
A collection of functions useful in learning and practicing IRT,
    which can be combined into larger programs. Provides basic CTT analysis,
    a simple common interface to the estimation of item
    parameters in IRT models for binary responses with three different programs
    (ICL, BILOG-MG, and ltm), ability estimation (MLE, BME, EAP, WLE, plausible 
    values), item and person fit statistics, scaling methods (MM, MS, Stocking-Lord,
    and the complete Hebaera method), and a rich array of parametric and 
    non-parametric (kernel) plots. Estimates and plots Haberman's interaction model
    when all items are dichotomously scored.
An implementation of the IRT paradigm for the scoring of different
    instruments measuring latent traits (a.k.a Abilities) and estimating item
    parameters for a variety of models, this package is highly optimized using
    Rcpp and carefully written R for the rest of the package, it aims to expand IRT
    applications to those applications that require faster and more robust estimation
    procedures. See the IRTpp documentation and github site for more information and
    examples.
Multidimensional Person Item Response Theory probability distributions
Helper functions and example data sets accompanying De
        Boeck, P. and Partchev, I. (2012) IRTrees: Tree-Based Item
        Response Models of the GLMM Family, Journal of Statistical
        Software - Code Snippets, 48(1), 1-28.
Estimation of reliability coefficients for ability estimates and sum scores from item response theory models as defined in Cheng, Y., Yuan, K.-H. and Liu, C. (2012) <doi:10.1177/0013164411407315> and Kim, S. and Feldt, L. S. (2010) <doi:10.1007/s12564-009-9062-8>. The package supports the 3-PL and generalized partial credit models and includes estimates of the standard errors of the reliability coefficient estimators, derived in Andersson, B. and Xin, T. (2018) <doi:10.1177/0013164417713570>.
Interactive shiny application for running Item Response Theory
    analysis. Provides graphics for characteristic and information curves.
The ISA is a biclustering algorithm that finds modules 
  in an input matrix. A module or bicluster is a block of the
  reordered input matrix.
Selection of features for sparse regression estimation (like the LASSO). Selection of blocks of features when the regression parameter is sparse and constant by blocks (like the Fused-LASSO). Application to cgh arrays.
describes a set of operations for symbolic data type based
        on interval-valued. The operations are processing of punctuals
        variables to interval variables, construction of a 3D graphic
        interval, linear regression interval and interval descriptive
        statistics such as mean, median, variance, standard deviation
        and mode.
Provides datasets for Introduction to Statistical Data Analysis for the Life Sciences
Functions for predicting and mapping potential and realized distributions of invasive species within the invaded range.
Tools for parsing 'NOAA' Integrated Surface Data ('ISD') files,
    described at <https://www.ncdc.noaa.gov/isd>. Data includes for example,
    wind speed and direction, temperature, cloud data, sea level pressure,
    and more. Includes data from approximately 35,000 stations worldwide,
    though best coverage is in North America/Europe/Australia. Data is stored
    as variable length ASCII character strings, with most fields optional.
    Included are tools for parsing entire files, or individual lines of data.
This network estimation procedure eLasso, which is based on the Ising model, combines l1-regularized logistic regression with model selection based on the Extended Bayesian Information Criterion (EBIC). EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary data.
Classical Ising Model is a land mark system in statistical physics.The model explains the physics of spin glasses and magnetic materials, and cooperative phenomenon in general, for example phase transitions and neural networks.This package provides utilities to simulate one dimensional Ising Model with Metropolis and Glauber Monte Carlo with single flip dynamics in periodic boundary conditions. Utility functions for exact solutions are provided.
Sample states from the Ising model and compute the probability of states. Sampling can be done for any number of nodes, but due to the intractibility of the Ising model the distribution can only be computed up to ~10 nodes.
Tools to develop stochastic models based on the Theory of Island
    Biogeography (TIB) of MacArthur and Wilson (1967) <DOI:10.1023/A:1016393430551>
    and extensions. The package implements methods to estimate colonization and
    extinction rates (including environmental variables) given presence-absence
    data, simulate community assembly, and perform model selection.
We provide the collection of data-sets used in the book 'An Introduction to Statistical Learning with Applications in R'.
The development of ISM was made by Warfield in 1974. 
      ISM is the process of collaborating distinct or related essentials into a simplified and an organized format. Hence, ISM is a methodology that seeks the interrelationships among the various elements considered and endows with a hierarchical and multilevel structure.
      To run this package user needs to provide a matrix (VAXO) converted into 0's and 1's.
      Warfield,J.N. (1974) <doi:10.1109/TSMC.1974.5408524>
      Warfield,J.N. (1974, E-ISSN:2168-2909).
Functions to support the computations carried out in
  `An Introduction to Statistical Modeling of Extreme Values' by
  Stuart Coles. The functions may be divided into the following 
  groups; maxima/minima, order statistics, peaks over thresholds
  and point processes.
The current version provides functions to compute, print and summarize the Index of Sensitivity to Nonignorability (ISNI) in the generalized linear model for independent data, and in the marginal multivariate Gaussian model and the linear mixed model for longitudinal/clustered data. It allows for arbitrary patterns of missingness in the regression outcomes  caused by dropout and/or intermittent missingness. One can compute the sensitivity index without estimating any nonignorable models or positing specific magnitude of nonignorability. Thus ISNI provides a simple quantitative assessment of how robust the standard estimates assuming missing at random  is with respect to the assumption of ignorability. For a tutorial, download at <http://huixie.people.uic.edu/Research/ISNI_R_tutorial.pdf>.	For more details, see Troxel Ma and Heitjan (2004) and Xie and Heitjan (2004) <doi:10.1191/1740774504cn005oa> and Ma Troxel and Heitjan (2005) <doi:10.1002/sim.2107> and  Xie (2008) <doi:10.1002/sim.3117> and  Xie (2012) <doi:10.1016/j.csda.2010.11.021> and Xie and Qian (2012) <doi:10.1002/jae.1157>.
Check if an 'externalptr' is a null pointer.
    R does currently not have a native function for that purpose.
    This package contains a C function that returns TRUE in case of a null pointer.
Linear order and unimodal order (univariate)
	     isotonic regression; bivariate isotonic regression
	     with linear order on both variables.
Some functions for confidence intervals for current status data based on transformations and bootstrap.
A bunch of functions to deal with circular data under order restrictions.
ISO language, territory, currency, script and character codes.
  Provides ISO 639 language codes, ISO 3166 territory codes, ISO 4217
  currency codes, ISO 15924 script codes, and the ISO 8859 character codes
  as well as the UN M.49 area codes.
Offers framework for testing for monotonic relationship between gene expression and doses in a microarray experiment. Several testing procedures including the global likelihood-ratio test (Bartholomew, 1961), Williams (1971, 1972), Marcus (1976), M (Hu et al. 2005) and the modified M (Lin et al. 2007) are used to test for the monotonic trend in gene expression with respect to doses. BH (Benjamini and Hochberg 1995) and BY (Benjamini and Yekutieli 2004) FDR controlling procedures are applied to adjust the raw p-values obtained from the permutations.
Isopam clustering algorithm and utilities. 
 Isopam optimizes clusters and optionally cluster numbers in 
 a brute force style and aims at an optimum separation  
 by all or some descriptors (typically species).
The function calculates the isotopic pattern (fine
        structures) for a given chemical formula.
Nonparametric estimation of an isotonic covariate effect for proportional hazards model.
Plots U-Pb data on Wetherill and Tera-Wasserburg concordia diagrams. Calculates concordia and discordia ages. Performs linear regression of measurements with correlated errors using 'York', 'Titterington' and 'Ludwig' approaches. Generates Kernel Density Estimates (KDEs) and Cumulative Age Distributions (CADs). Produces Multidimensional Scaling (MDS) configurations and Shepard plots of multi-sample detrital datasets using the Kolmogorov-Smirnov distance as a dissimilarity measure. Calculates 40Ar/39Ar ages, isochrons, and age spectra. Computes weighted means accounting for overdispersion. Calculates U-Th-He (single grain and central) ages, logratio plots and ternary diagrams. Processes fission track data using the external detector method and LA-ICP-MS, calculates central ages and plots fission track and other data on radial (a.k.a. 'Galbraith') plots. Constructs total Pb-U, Pb-Pb, Re-Os, Sm-Nd, Lu-Hf, Rb-Sr and 230Th-U isochrons as well as 230Th-U evolution plots.
Deconvolution of mixed tumour profiles into normal and cancer for each patient, using 
	the ISOpure algorithm in Quon et al. Genome Medicine, 2013 5:29. Deconvolution requires 
	mixed tumour profiles and a set of unmatched "basis" normal profiles.
Building isoscapes using mixed models and inferring the geographic origin of 
  organisms based on their isotopic ratios. This package is essentially a 
  simplified interface to several other packages. It uses 'spaMM' for fitting 
  and predicting isoscapes, and assigning an organism's origin depending on its 
  isotopic ratio. 'IsoriX' also relies heavily on the package 'rasterVis' for 
  plotting the maps using lattice.
IsoSpec is a fine structure calculator used for obtaining the most
    probable masses of a chemical compound given the frequencies of the composing
    isotopes and their masses. It finds the smallest set of isotopologues with
    a given probability. The probability is assumed to be that of the product of
    multinomial distributions, each corresponding to one particular element and
    parametrized by the frequencies of finding these elements in nature. These
    numbers are supplied by IUPAC - the International Union of Pure and Applied
    Chemistry.
Contains two main functions: one for
        solving general isotone regression problems using the
        pool-adjacent-violators algorithm (PAVA); another one provides
        a framework for active set methods for isotone optimization
        problems with arbitrary order restrictions. Various types of
        loss functions are prespecified.
Given a response y and a one- or two-dimensional predictor, the isotonic regression estimator is calculated with the usual orderings.
Estimates diet contributions from isotopic sources using JAGS.
    Includes estimation of concentration dependence and measurement error.
This is an substitute for the %V and %u formats which are
        not implemented on Windows. In addition, the package offers
        functions to convert from standard calender format yyyy-mm-dd
        to and from ISO 8601 week format yyyy-Www-d.
Accomplish high performance simulations in quantitative genetics. 
    The molecular genetic components are represented by R6/C++ classes and methods. 
    Mimic the meiosis recombination and de novo genetic variability by means a 
    count-location process (Karlin & Liberman, 1978) <doi:10.1073/pnas.75.12.6332>. 
    The core computational algorithm is implemented using 'Boost' dynamic bitsets 
    (Schaling, 2014) [ISBN:978-1937434366]. A mix between low and high level interfaces 
    provides great flexibility and allows user defined extensions and a wide range of 
    applications.
Performs multivariate normal imputation through iterative sequential 
  regression.  Conditional dependency structure between imputed variables can be 
  specified a priori to accelerate imputation.
Independent Surrogate Variable Analysis is an algorithm
        for feature selection in the presence of potential confounding
        factors (see Teschendorff AE et al 2011, <doi: 10.1093/bioinformatics/btr171>).
Data sets and scripts for text examples and exercises in 
  P. Dalgaard (2008), `Introductory Statistics with R', 2nd ed., Springer Verlag, ISBN 978-0387790534.
Provides two record linkage data sets on the Italian Survey on Household and Wealth, 2008 and 2010, a sample survey conducted by the Bank of Italy every two years. The 2010 survey covered 13,702 individuals, while the 2008 survey covered 13,734 individuals. The following categorical variables are included in this data set: year of birth, working status, employment status, branch of activity, town size, geographical area of birth, sex, whether or not Italian national, and highest educational level obtained. Unique identifiers are available to assess the accuracy of one’s method. Please see Steorts (2015) <DOI:10.1214/15-BA965SI> to find more details about the data set.
Functions for analyzing multiple choice items. These analyses include the convertion of student response into binaty data (correct/incorrect), the computation of the number of corrected responses and grade for each subject, the calculation of item difficulty and discrimination, the computation of the frecuency and point-biserial correlation for each distractor and the graphical analysis of each item.
Three methods for Individual Tree Crowns (ITCs) delineation on remote sensing data: one is based on LiDAR data in x,y,z format and one on imagery data in raster format.
Iterates through a phylogenetic tree to identify regions
        of rate variation using the parametric rate comparison test.
Support for iterators, which allow a programmer to traverse
        through all the elements of a vector, list, or other collection
        of data.
The iterLap (iterated Laplace approximation) algorithm approximates a
             general (possibly non-normalized) probability density on R^p, by repeated
             Laplace approximations to the difference between current approximation 
             and true density (on log scale). The final approximation is a mixture of
             multivariate normal distributions and might be used for example as a
             proposal distribution for importance sampling (eg in Bayesian applications). 
             The algorithm can be seen as a computational generalization of the Laplace 
             approximation suitable for skew or multimodal densities.
Iterator for generating permutations and combinations. They
    can be either drawn with or without replacement, or with distinct/ non-distinct 
    items (multiset). The generated sequences are in lexicographical order 
    (dictionary order). The algorithms to generate permutations and combinations are 
    memory efficient. These iterative algorithms enable users to process all sequences 
    without putting all results in the memory at the same time. 
    The algorithms are written in C/C++ for faster performance. Note: 'iterpc' is no longer
    being maintained. Users are recommended to switch to 'arrangements'.
Various tools for creating iterators, many patterned after
        functions in the Python itertools module, and others patterned
        after functions in the 'snow' package.
A port of Python's excellent itertools module to R for efficient
    looping.
Individual tree model is an instrument to support the decision with
    regard to forest management. This package provides functions that let you work
    with data for this model. Also other support functions and extension related to
    this model are available.
Functions to clean and process international trade data into an international trade network (ITN) are provided. It then provides a set a functions to undertake analysis and plots of the ITN (extract the backbone, centrality, blockmodels, clustering). Examining the key players in the ITN and regional trade patterns.
Infers a topology of relationships between different datasets, such as multi-omics and phenotypic data recorded on the same samples. We based this methodology on the RV coefficient (Robert & Escoufier, 1976, <doi:10.2307/2347233>), a measure of matrix correlation, which we have extended for partial matrix correlations and binary data (Aben et al., 2018, <doi:10.1101/293993>).
This package is based on the code of the rpart package.
        It extends rpart by adding additional splitting methods
        emphasizing interpretable/parsimonious trees.  Unless indicated
        otherwise, it is safe to assume that all functions herein are
        extensions of or copied directly from similar or nearly
        identical rpart methods.  As such, the authors of rpart are
        authors of this package as well.  However, please direct any
        error reports or other questions about itree to the maintainer
        of this package; they are welcome and appreciated.
Sequential advantage selection (SAS, Fan, Lu and Song, 2016) <arXiv:1405.5239> 
    and penalized A-learning (PAL, Shi, et al., 2018) methods are implement for selecting important variables 
	involved in optimal individualized (dynamic) treatment regime in both single-stage or multi-stage studies.
GAMM (Generalized Additive Mixed Modeling; Lin & Zhang, 1999)
    as implemented in the R package 'mgcv' (Wood, S.N., 2006; 2011) is a nonlinear
    regression analysis which is particularly useful for time course data such as
    EEG, pupil dilation, gaze data (eye tracking), and articulography recordings,
    but also for behavioral data such as reaction times and response data. As time
    course measures are sensitive to autocorrelation problems, GAMMs implements
    methods to reduce the autocorrelation problems. This package includes functions
    for the evaluation of GAMM models (e.g., model comparisons, determining regions
    of significance, inspection of autocorrelational structure in residuals)
    and interpreting of GAMMs (e.g., visualization of complex interactions, and
    contrasts).
Provides functions for modeling and forecasting time series data. Forecasting is based on the innovations algorithm. A description of the innovations algorithm can be found in the textbook "Introduction to Time Series and Forecasting" by Peter J. Brockwell and Richard A. Davis. <http://www.springer.com/us/book/9781475777505>.
To enable 'iOS' App Developers to access iTunes App Store Ratings and Reviews using R to extract Basic App Information and Reviews submitted by their App users, Since Apple Store does not provide this straightforward.
This package includes functions to incorporate
        uncertainties in estimated propensity scores and provide
        adjusted standard errors for making valid causal inference.
This package allows one incorporate instrument and covariate uncertainty into instrumental variable regression.
Fit an Instrumental least square dummy variable model
GMM estimation of triangular systems using heteroscedasticity based instrumental variables as in Lewbel (2012)
Contains functions for carrying out instrumental variable
    estimation of causal effects, including power analysis, sensitivity analysis,
    and diagnostics.
This package contains functions for carrying out instrumental variable estimation of causal effects and power analyses for instrumental variable studies.
Fit the instrumental panel data models: the fixed effects, random
    effects and between models.
Compute the instrumental variables probit model using  the Amemiya's Generalized Least Squares estimators (Amemiya, Takeshi, (1978) <doi: 10.2307/1911443>).
Allows you to create an evidence factor (EX analysis) in an 
	instrumental variables regression model. Additionally, performs Sensitivity 
	analysis for OLS analysis, 2SLS analysis and EX analysis with interpretable 
	plotting and printing features.
Contains tools for instrumental variables estimation. Currently, only non-parametric bounds and two-stage least squares are implemented, but more methods will be added in the future. Balke, A. and Pearl, J. (1997) <doi:10.2307/2965583>, Vansteelandt S., Bowden J., Babanezhad M., Goetghebeur E. (2011) <doi:10.1214/11-STS360>.
Improved methods based on inverse probability weighting
        and outcome regression for causal inference and missing data
        problems
A powerful system for estimating an improved wavelet-based index
    of magnetic storm activity, storm activity preindex (from individual station) and SQ variations.
    It also serves as a flexible visualization tool.
An estimation method that can use computer simulations to
    approximate maximum-likelihood estimates even when the likelihood function can not
    be evaluated directly. It can be applied whenever it is feasible to conduct many
    simulations, but works best when the data is approximately Poisson distributed.
    It was originally designed for demographic inference in evolutionary
    biology. It has optional support for conducting coalescent simulation using
    the 'coala' package.
Computing the original and modified jackknife estimates of Kaplan-Meier estimators.
Significance test for association between variables
	and their estimated latent variables.
	Latent variables may be estimated by principal component analysis (PCA),
	logistic factor analysis (LFA), and other techniques.
Uses the Jaccard similarity index to account for population
    structure in sequencing studies. This method was specifically
    designed to detect population stratification based on rare variants, hence it
    will be especially useful in rare variant analysis.
Cardoso's JADE algorithm as well as his functions for joint diagonalization are ported to R. Also several other blind source separation (BSS) methods, like AMUSE and SOBI, and some criteria for performance evaluation of BSS algorithms, are given. The package is described in Miettinen, Nordhausen and Taskinen (2017) <doi:10.18637/jss.v076.i02>.
A set of wrappers around 'rjags' functions to run Bayesian analyses in 'JAGS' (specifically, via 'libjags').  A single function call can control adaptive, burn-in, and sampling MCMC phases, with MCMC chains run in sequence or in parallel. Posterior distributions are automatically summarized (with the ability to exclude some monitored nodes if desired) and functions are available to generate figures based on the posteriors (e.g., predictive check plots, traceplots). Function inputs, argument syntax, and output format are nearly identical to the 'R2WinBUGS'/'R2OpenBUGS' packages to allow easy switching between MCMC samplers.
Implements a novel score test that measures 1) the overall shift in the gene expression due to genotype (additive genetic effect), and 2) group-specific changes in gene expression due to genotype (interaction effect) in a mixed-effects model framework.
Analyze and visualize results of studies performed with the
             analysis tools in 'JAMES', a modern object-oriented Java
             framework for discrete optimization using local search
             metaheuristics (see http://www.jamesframework.org).
Full texts for Jane Austen's 6 completed novels, ready for text
    analysis. These novels are "Sense and Sensibility", "Pride and Prejudice",
    "Mansfield Park", "Emma", "Northanger Abbey", and "Persuasion".
The main janitor functions can: perfectly format data.frame column
    names; provide quick counts of variable combinations (i.e., frequency
    tables and crosstabs); and isolate duplicate records. Other janitor functions
    nicely format the tabulation results. These tabulate-and-report functions
    approximate popular features of SPSS and Microsoft Excel. This package
    follows the principles of the "tidyverse" and works well with the pipe function
    %>%. janitor was built with beginning-to-intermediate R users in mind and is
    optimized for user-friendliness. Advanced R users can already do everything
    covered here, but with janitor they can do it faster and save their thinking for
    the fun stuff.
Client for the Directory of Open Access Journals ('DOAJ')
    (<https://doaj.org/>). 'API' documentation at
    <https://doaj.org/api/v1/docs>. Methods included for working with
    all 'DOAJ' 'API' routes: fetch article information by identifier,
    search for articles, fetch journal information by identifier,
    and search for journals.
R modules for JASPAR data processing and visualization
Graphics device routing all graphics commands to a Java
        program. The actual functionality of the JavaGD depends on the
        Java-side implementation. Simple AWT and Swing implementations
        are included.
Collection of several tools and helper functions used across the other packages of J. Buttlar ('ncdf.tools' and 'spectral.methods').
Provides a gridded classification of weather types by applying the Jenkinson and Collison classification. For a given region (it can be either local region or the whole map),it computes at each grid the 11 weather 
             types during the period considered for the analysis. See Otero et al., (2017) <doi:10.1007/s00382-017-3705-y> for more information.
Contains a selection of color palettes and 'ggplot2' themes designed by the package author.
Black-Scholes model [Black (1973) <doi:10.1086/260062>] is important to calculate option prices in the stock market and a variety of improved models are studied. In this package, I propose methods in order to calculate both Black-Scholes model and Jump diffusion model [Kou (2002) <doi:10.1287/mnsc.48.8.1086.166>] by Monte Carlo methods. This package can be used for computational finance.
Simplifies and extends data exchange between 'R' and 'Java'.
On Affymetrix gene expression microarrays, a single gene may be measured by multiple probe sets. This can present a mild conundrum when attempting to evaluate a gene "signature" that is defined by gene names rather than by specific probe sets. This package provides a one-to-one mapping from gene to "best" probe set for four Affymetrix human gene expression microarrays: hgu95av2, hgu133a, hgu133plus2, and u133x3p. This package also includes the pre-calculated probe set quality scores that were used to define the mapping.
The Just Finance and Econometrics ('JFE') provides a 'tcltk' based interface to global assets selection and portfolio optimization. 'JFE' aims to provide a simple GUI that allows a user to quickly load data from a .RData (.rda) file, explore the data and evaluate financial models. Invoked as JFE(), 'JFE' exports a number of utility functions for visualizing assets price (e.g. technical charting) and returns, selecting assets by performance index (based on the package 'PerformanceAnalytics') and backtesting specific portfolio profiles (based on the package 'fPortfolio').
Fits two different joint generalized estimating equation models to multivariate longitudinal data.
The Joint Graphical Lasso is a generalized method for
        estimating Gaussian graphical models/ sparse inverse covariance
        matrices/ biological networks on multiple classes of data.  We
        solve JGL under two penalty functions: The Fused Graphical
        Lasso (FGL), which employs a fused penalty to encourage inverse
        covariance matrices to be similar across classes, and the Group
        Graphical Lasso (GGL), which encourages similar network
        structure between classes.  FGL is recommended over GGL for
        most applications.
Java GUI for R - cross-platform, universal and unified Graphical User Interface for R. For full functionality on Windows and Mac OS X JGR requires a start application which depends on your OS.
Chinese text segmentation, keyword extraction and speech tagging
    For R.
jiebaR is a package for Chinese text segmentation, keyword extraction
   and speech tagging. This package provides the data files required by jiebaR.
Shared parameter models for the joint modeling of longitudinal and time-to-event data.
Shared parameter models for the joint modeling of longitudinal and time-to-event data using MCMC; Dimitris Rizopoulos (2016) <doi:10.18637/jss.v072.i07>.
Fit joint mean-covariance models for longitudinal data. The models
    and their components are represented using S4 classes and methods. The core
    computational algorithms are implemented using the 'Armadillo' C++ library
    for numerical linear algebra and 'RcppArmadillo' glue.
Fit joint models of continuous or ordinal longitudinal data and time-to-event data with competing risks. For a detailed information, see Robert Elashoff, Gang li and Ning Li (2016, ISBN:9781439807828) ; Robert M. Elashoff,Gang Li and Ning Li (2008) <doi:10.1111/j.1541-0420.2007.00952.x> ; Ning Li, Robert Elashoff, Gang Li and Jeffrey Saver (2010) <doi:10.1002/sim.3798> .
Joint mean and dispersion effects models fit the mean and dispersion parameters of a response variable by two separate linear models, the mean and dispersion submodels, simultaneously. It also allows the users to choose either the deviance or the Pearson residuals as the response variable of the dispersion submodel. Furthermore, the package provides the possibility to nest the submodels in one another, if one of the parameters has significant explanatory power on the other. Wu & Li (2016) <doi:10.1016/j.csda.2016.04.015>.
Performs power calculations for joint modeling of longitudinal and survival data with k-th order trajectories when the variance-covariance matrix, Sigma_theta, is unknown.
The main purpose of this package is to make it easy for userR's to interact with 'jMetrik' an open source application for psychometric analysis. For example it allows useR's to write data frames to file in a format that can be used by 'jMetrik'. It also allows useR's to read *.jmetrik files (e.g. output from an analysis) for follow-up analysis in R. The *.jmetrik format is a flat file that includes a multiline header and the data as comma separated values. The header includes metadata about the file and one row per variable with the following information in each row: variable name, data type, item scoring, special data codes, and variable label.
Implements time series z-normalization, SAX, HOT-SAX, VSM, SAX-VSM, RePair, and RRA
    algorithms facilitating time series motif (i.e., recurrent pattern), discord (i.e., anomaly),
    and characteristic pattern discovery along with interpretable time series classification.
Performs a permutation test on the difference between two location parameters, a permutation correlation test, a permutation F-test, the Siegel-Tukey test, a ratio mean deviance test.  Also performs some graphing techniques, such as for confidence intervals, vector addition, and Fourier analysis; and includes functions related to the Laplace (double exponential) and triangular distributions.  Performs power calculations for the binomial test.
A suite of common statistical methods such as descriptives,
    t-tests, ANOVAs, regression, correlation matrices, proportion tests,
    contingency tables, and factor analysis. This package is also useable from
    the 'jamovi' statistical spreadsheet (see <https://www.jamovi.org> for more
    information).
A framework for creating rich interactive analyses for the jamovi
    platform (see <https://www.jamovi.org> for more information).
Computing and plotting joint confidence regions and intervals. Regions include classical ellipsoids, minimum-volume or minimum-length regions, and an empirical Bayes region. Intervals include the TOST procedure with ordinary or expanded intervals and a fixed-sequence procedure. Such regions and intervals are useful e.g., for the assessment of multi-parameter (bio-)equivalence. Joint confidence regions for the mean and variance of a normal distribution are available as well.
RE.Johnson performs the Johnson Transformation to increase the normality.
Analysis of repeated measurements and time-to-event data via random
    effects joint models. Fits the joint models proposed by Henderson and colleagues
    <doi:10.1093/biostatistics/1.4.465> (single event time) and by Williamson and
    colleagues (2008) <doi:10.1002/sim.3451> (competing risks events time) to a
    single continuous repeated measure. The time-to-event data is modelled using a 
    (cause-specific) Cox proportional hazards regression model with time-varying 
    covariates. The longitudinal outcome is modelled using a linear mixed effects
    model. The association is captured by a latent Gaussian process. The model is 
    estimated using am Expectation Maximization algorithm. Some plotting functions 
    and the variogram are also included. This project is funded by the Medical 
    Research Council (Grant numbers G0400615 and MR/M013227/1).
Fits joint models of the type proposed by Henderson and colleagues 
    (2000) <doi:10.1093/biostatistics/1.4.465>, but extends to the multi-study, 
    meta-analytic case. Functions for meta-analysis of a single longitudinal and 
    a single  time-to-event outcome from multiple studies using joint models.  
    Options to produce plots for multi study joint data, to pool joint model 
    fits from 'JM' and 'joineR' packages in a two stage meta-analysis, and to 
    model multi-study joint data in a one stage meta-analysis.
Fits the joint model proposed by Henderson and colleagues (2000) 
    <doi:10.1093/biostatistics/1.4.465>, but extended to the case of multiple 
    continuous longitudinal measures. The time-to-event data is modelled using a 
    Cox proportional hazards regression model with time-varying covariates. The 
    multiple longitudinal outcomes are modelled using a multivariate version of the 
    Laird and Ware linear mixed model. The association is captured by a multivariate
    latent Gaussian process. The model is estimated using a Monte Carlo Expectation 
    Maximization algorithm. This project is funded by the Medical Research Council 
    (Grant number MR/M013227/1).
Perform the Cox regression and dynamic prediction methods under
 the joint frailty-copula model between tumour progression and death for meta-analysis.
 A penalized likelihood is employed for estimating model parameters, where the baseline hazard functions are approximated by smoothing splines.
 The methods are applicable for meta-analytic data combining several studies.
 The methods can analyze data having information on both terminal event time (e.g., time-to-death) and non-terminal event time (e.g., time-to-tumour progression).
 See Emura et al. (2015) <doi:10.1177/0962280215604510> and
 Emura et al. (2017) <doi:10.1177/0962280216688032> for details.
 Survival data from ovarian cancer patients are also available.
Provides joint analysis and imputation of linear regression models,
    generalized linear regression models or linear mixed models with incomplete
    (covariate) data in the Bayesian framework.
    The package performs some preprocessing of the data and creates a 'JAGS'
    model, which will then automatically be passed to 'JAGS' 
    <http://mcmc-jags.sourceforge.net> with the help of 
    the package 'rjags'.
    It also provides summary and plotting functions for the output.
Different algorithms to perform approximate joint diagonalization
	of a finite set of square matrices. Depending on the algorithm,
	orthogonal or non-orthogonal diagonalizer is found. These algorithms
	are particularly useful in the context of blind source separation.
Joint fit of a semiparametric regression model for longitudinal responses and a semiparametric transformation model for time-to-event data.
Fits univariate and joint N-mixture models for data on two unmarked site-associated species. Includes functions to estimate latent abundances through empirical Bayes methods.
A bivariate integration method to estimate risk caused by two extreme and dependent forcing variables.
A joint regression model for mixed correlated binary and
        continuous responses is presented. In this model binary
        response can be dependent on the continuous response. With this
        model, the dependence between responses can be taken into
        account by the correlation between errors in the models for
        binary and continuous responses.
Methods for fast segmentation of multivariate
    signals into piecewise constant profiles and for generating realistic
    copy-number profiles. A typical application is the joint segmentation of total
    DNA copy numbers and allelic ratios obtained from Single Nucleotide Polymorphism
    (SNP) microarrays in cancer studies. The methods are described in Pierre-Jean, 
    Rigaill and Neuvial (2015) <doi:10.1093/bib/bbu026>.
Performs Joins and Minus Queries on 'Excel' Files
    fulljoinXL() Merges all rows of 2 'Excel' files based upon a common column in the files.
    innerjoinXL() Merges all rows from base file and join file when the join condition is met.
    leftjoinXL() Merges all rows from the base file, and all rows from the join file
    if the join condition is met.
    rightjoinXL() Merges all rows from the join file, and all rows from the base file if the join
    condition is met.
    minusXL() Performs 2 operations source-minus-target and target-minus-source
    If the files are identical all output files will be empty.
    Choose two 'Excel' files via a dialog box, and then follow prompts at the console to
    choose a base or source file and columns to merge or minus on.
Similarly to Schafer's package 'pan', 'jomo' is a package for multilevel joint modelling multiple imputation (Carpenter and Kenward, 2013) <doi: 10.1002/9781119942283>.
 Novel aspects of 'jomo' are the possibility of handling binary and categorical data through latent normal variables, the option to use cluster-specific covariance matrices and to impute compatibly with the substantive model.
JOP is a tool for simultaneous optimization of multiple
        responses and visualization of the results. The visualization
        is done by the joint optimization plot introduced by Kuhnt and
        Erdbruegge (2004).
Implementation of some unit and area level EBLUP estimators as well as the estimators of their MSE also under heteroscedasticity. The package further documents the publications Breidenbach and Astrup (2012) <DOI:10.1007/s10342-012-0596-7>, Breidenbach et al. (2016) <DOI:10.1016/j.rse.2015.07.026> and Breidenbach et al. (2018 in press). The vignette further explains the use of the implemented functions.
Read and write JSON Web Keys (JWK, rfc7517), generate and verify JSON
    Web Signatures (JWS, rfc7515) and encode/decode JSON Web Tokens (JWT, rfc7519).
    These standards provide modern signing and encryption formats that are the basis
    for services like OAuth 2.0 or LetsEncrypt and are natively supported by browsers
    via the JavaScript WebCryptoAPI.
Implements under/oversampling for probability estimation.  To be
    used with machine learning methods such as AdaBoost, random forests, etc.
This package provides an easy and simple way to read, write and display bitmap images stored in the JPEG format. It can read and write both files and in-memory raw vectors.
A Joint PENalty Estimation of Covariance and Inverse Covariance Matrices.
Helpful functions for using mesh code (80km to 125m) data in Japan. Visualize mesh code using 'ggplot2' and 'leaflet', etc.
Utilizing the data that Japanese administration area provided 
    by the National Land Numerical Information download service (<http://nlftp.mlit.go.jp/ksj/index.html>). 
    This package provide map data is based on the Digital Map 25000 (Map Image) published 
    by Geospatial Information Authority of Japan (Approval No.603FY2017 information usage <http://www.gsi.go.jp>).
Functions, methods, and datasets for cancer survival
        analysis, including the proportional hazard relative survival
        model, the join point relative survival model.
Client for 'jq', a 'JSON' processor (<https://stedolan.github.io/jq/>), 
    written in C. 'jq' allows the following with 'JSON' data: index into, parse, 
    do calculations, cut up and filter, change key names and values, perform 
    conditionals and comparisons, and more.
Simultaneous estimation of multiple related networks.
These functions calculate the taxonomic measures presented in Miranda-Esquivel (2016). 
   The package introduces Jack-knife resampling in evolutionary distinctiveness prioritization analysis, 
   as a way to evaluate the support of the ranking in area prioritization, and the persistence of a given area 
   in a conservation analysis.
   The algorithm is described in: Miranda-Esquivel, D (2016) <DOI:10.1007/978-3-319-22461-9_11>.
Implements the basic financial analysis
    functions similar to (but not identical to) what
    is available in most spreadsheet software. This
    includes finding the IRR and NPV of regularly
    spaced cash flows and annuities. Bond pricing and
    YTM calculations are included. In addition, Black
    Scholes option pricing and Greeks are also
    provided.
A set of utilities for working with JavaScript syntax in R.
    Includes tools to parse, tokenize, compile, validate, reformat, optimize 
    and analyze JavaScript code.
Maximum likelihood estimation for the semiparametric joint modeling of survival and longitudinal data.
This package enables users to access MongoDB by running queries
    and returning their results in R data frames. Usually, data in MongoDB is
    only available in the form of a JSON document. jSonarR uses data
    processing and conversion capabilities in the jSonar Analytics Platform
    and the JSON Studio Gateway (http://www.jsonstudio.com), to convert it to
    a tabular format which is easy to use with existing R packages.
JSON-LD is a light-weight syntax for expressing linked data. It is primarily
    intended for web-based programming environments, interoperable web services and for 
    storing linked data in JSON-based databases. This package provides bindings to the 
    JavaScript library for converting, expanding and compacting JSON-LD documents.
A fast JSON parser and generator optimized for statistical data
    and the web. Started out as a fork of 'RJSONIO', but has been completely
    rewritten in recent versions. The package offers flexible, robust, high
    performance tools for working with JSON in R and is particularly powerful
    for building pipelines and interacting with a web API. The implementation is
    based on the mapping described in the vignette (Ooms, 2014). In addition to
    converting JSON data from/to R objects, 'jsonlite' contains functions to
    stream, validate, and prettify JSON data. The unit tests included with the
    package verify that all edge cases are encoded and decoded consistently for
    use with dynamic data in systems and applications.
Uses the node library 'is-my-json-valid' to validate 'JSON' against
    a 'JSON' schema.
Provides a high-level integration for the 'Java' platform that makes 'Java' objects easy to use from within 'R'; provides a unified interface to integrate 'R' with several programming languages; and features extensive data exchange between 'R' and 'Java'. The 'jsr223'-supported programming languages include 'Groovy', 'JavaScript', 'JRuby' ('Ruby'), 'Jython' ('Python'), and 'Kotlin'. Any of these languages can use and extend 'Java' classes in natural syntax. Furthermore, solutions developed in any of the 'jsr223'-supported languages are also accessible to 'R' developers. The 'jsr223' package also features callbacks, script compiling, and string interpolation. In all, 'jsr223' significantly extends the computing capabilities of the 'R' software environment.
Create and customize interactive trees using the 'jQuery' 'jsTree' <https://www.jstree.com/>
    plugin library and the 'htmlwidgets' package. These trees can be used
    directly from the R console, from 'RStudio', in Shiny apps and R Markdown documents.
The core of this 'Rcpp' based package is a function to compute standardized Jonckheere-Terpstra test statistics for large numbers of dependent and independent variables, e.g., genome-wide analysis. It implements 'OpenMP', allowing the option of computing on multiple threads. Supporting functions are also provided to calculate p-values and summarize results.
This is a collection of tools that the author (Jacob) has written
  for the purpose of more efficiently understanding and sharing the results of
  (primarily) regression analyses. There are a number of functions focused
  specifically on the interpretation and presentation of interactions.
  Just about everything supports models from the survey package.
Transforming univariate non-normal data to normality using Johnson 
  families of distributions. Johnson family is a comprehensive distribution 
  family that accommodates many kinds of non-normal distributions. A bunch of 
  distributions with various parameters will be fit and the corresponding 
  p-values under a user-specified normality test will be given. The final 
  transformation will be the one with the largest p-value under the given 
  normality test.
jug is a web framework aimed at easily building APIs. It is mostly aimed at exposing R functions, 
    models and visualizations to third-parties by way of http requests.
Generates image data for fractals (Julia and Mandelbrot
        sets) on the complex plane in the given region and resolution.
Provides an R interface to 'Julia',
    which is a high-level, high-performance dynamic programming language
    for numerical computing, see <https://julialang.org/> for more information.
    It provides a high-level interface as well as a low-level interface.
    Using the high level interface, you could call any 'Julia' function just like
    any R function with automatic type conversion. Using the low level interface,
    you could deal with C-level SEXP directly while enjoying the convenience of
    using a high-level programming language like 'Julia'.
A fast simulation on stochastic volatility model, with jump tests, p-values pooling, and FDR adjustments.
Individual based simulations of hybridizing populations, where the accumulation of junctions is tracked. Furthermore, mathematical equations are provided to verify simulation outcomes. Both simulations and mathematical equations are based on Janzen (2018) <doi:10.1101/058107>.
Provides a full implementation of the 'Jupyter' <http://jupyter.org/> messaging protocol in C++ by leveraging 'Rcpp' and 'Xeus' <https://github.com/QuantStack/xeus>.
             'Jupyter' supplies an interactive computing environment and a messaging protocol defined over 'ZeroMQ' for multiple programming languages. This package implements
             the 'Jupyter' kernel interface so that 'R' is exposed to this interactive computing environment. 'ZeroMQ' functionality is provided by the 'pbdZMQ' package.
             'Xeus' is a C++ library that facilitates the implementation of kernels for 'Jupyter'. Additionally, 'Xeus' provides an interface to libraries that exist in the 'Jupyter' ecosystem for building widgets,
             plotting, and more <https://blog.jupyter.org/interactive-workflows-for-c-with-jupyter-fe9b54227d92>. 'JuniperKernel' uses 'Xeus' as a library for the 'Jupyter' messaging protocol.
The 'Junar' API is a commercial platform to organize and publish data
    <http://www.junar.com>. It has been used in a number of national and local
    government Open Data initiatives in Latin America and the USA. This package
    is a wrapper to make it easier to access data made public through the 'Junar'
    API.
Many method to compute, predict and back-test VaR. For more detail, see the report: Value at Risk <researchgate.net>.
A collection of miscellaneous tools and functions,
    such as tools to generate descriptive statistics tables,
    format output, visualize relations among variables or check
    distributions.
This is a set of simple utilities for various data manipulation and testing tasks.
    The goal is to use base tools well, without bringing in many
    dependencies. Main areas of interest are semi-automated data frame manipulation, such as
    converting factors in multiple binary indicator columns. There are testing
    functions which provide 'testthat' expectations to permute arguments to
    function calls. There are functions and data to test extreme numbers, dates,
    and bad input of various kinds which should allow testing failure and corner
    cases, which can be used for fuzzing your functions. The test suite has many examples of usage.
Build complex HTML or 'LaTeX' tables using 'kable()' from 'knitr' 
    and the piping syntax from 'magrittr'. Function 'kable()' is a light weight 
    table generator coming from 'knitr'. This package simplifies the way to 
    manipulate the HTML or 'LaTeX' codes generated by 'kable()' and allows 
    users to construct complex tables and customize styles using a readable 
    syntax.
Implementation of various kernel adaptive methods in nonparametric curve
    estimation like density estimation as introduced in Stute and Srihera (2011)
    <doi:10.1016/j.spl.2011.01.013> and Eichner and Stute (2013)
    <doi:10.1016/j.jspi.2012.03.011> for pointwise estimation, and like regression
    as described in Eichner and Stute (2012) <doi:10.1080/10485252.2012.760737>.
Implements methods for clustering mixed-type data,
  specifically combinations of continuous and nominal data. Special attention
  is paid to the often-overlooked problem of equitably balancing the
  contribution of the continuous and categorical variables. This package
  implements KAMILA clustering, a novel method for clustering
  mixed-type data in the spirit of k-means clustering. It does not require
  dummy coding of variables, and is efficient enough to scale to rather large
  data sets. Also implemented is Modha-Spangler clustering, which uses a
  brute-force strategy to maximize the cluster separation simultaneously in the
  continuous and categorical variables.
Methods to extract information on pathways, genes and SNPs from
    online databases. It provides functions for data preparation and evaluation
    of genetic influence on a binary outcome using the logistic kernel machine
    test (LKMT). Three different kernel functions are offered to analyze genotype
    information in this variance component test: A linear kernel, a size-adjusted
    kernel and a network based kernel.
Identify and sort genes overexpressed and associated to transmembrane protein in Affymetrix expression set or any other results of microarray experiment.
Computes the Kantorovich distance between two probability measures on a finite set.
Tests the homogeneity of intraclass kappa statistics obtained from independent studies or a stratified study with binary results. It is desired to compare the kappa statistics obtained in multi-center studies or in a single stratified study to give a common or summary kappa using all available information. If the homogeneity test of these kappa statistics is not rejected, then it is possible to make inferences over a single kappa statistic that summarizes all the studies. Jun-mo Nam (2003) <doi:10.1111/j.0006-341X.2003.00118.x> Jun-mo Nam (2005) <doi:10.1002/sim.2321>Mousumi Banerjee, Michelle Capozzoli, Laura McSweeney,Debajyoti Sinha (1999) <doi:10.2307/3315487> Allan Donner, Michael Eliasziw, Neil Klar (1996) <doi:10.2307/2533154>.
Offers a graphical user interface for the evaluation of inter-rater agreement with Cohen's and Fleiss' Kappa. The calculation of kappa statistics is done using the R package 'irr', so that 'KappaGUI' is essentially a Shiny front-end for 'irr'.
S4 tool box for capacity (or non-additive measure, fuzzy measure) and integral manipulation in a finite setting. It contains routines for handling various types of set functions such as games or capacities. It can be used to compute several non-additive integrals: the Choquet integral, the Sugeno integral, and the symmetric and asymmetric Choquet integrals. An analysis of capacities in terms of decision behavior can be performed through the computation of various indices such as the Shapley value, the interaction index, the orness degree, etc. The well-known Möbius transform, as well as other equivalent representations of set functions can also be computed. Kappalab further contains seven capacity identification routines: three least squares based approaches, a method based on linear programming, a maximum entropy like method based on variance minimization, a minimum distance approach and an unsupervised approach based on parametric entropies. The functions contained in Kappalab can for instance be used in the framework of multicriteria decision making or cooperative game theory.
This package contains basic tools for the purpose of
        sample size estimation in studies of interobserver/interrater
        agreement (reliability).  This package contains sample size
        estimation functions for both the power-based and confidence
        interval-based methods, with binary or multinomial outcomes and
        two through six raters.
this package allows to quantify the congruence between two patchy
    mosaics or landscapes. This "vectorial Kappa" approach extends the
    principle of Cohen's Kappa index by calculating areas of intersected
    patches between two mosaics rather than agreement between pixels. It
    provides an exact alternative for patchy mosaics when a Kappa index is
    needed.
This package provides some routines to conduct the K-adaptive parititioning (kaps) algorithm for survival data. A function kaps is an implementation version of our algorithm.
Attempts to remove vocals from a stereo '.wav' recording of a song.
An R graphical user interface for microclimate time series, with an emphasis on underground environments. 'KarsTS' provides linear and nonlinear methods, including recurrence analysis (Marwan et al. (2007) <10.1016/j.physrep.2006.11.001>) and filling methods (Moffat et al. (2007) <doi:10.1016/j.agrformet.2007.08.011>), as well as tools to manipulate easily time series and gap sets.
Many data science problems reduce to operations on very tall,
    skinny matrices.  However, sometimes these matrices can be so tall that they
    are difficult to work with, or do not even fit into main memory.  One
    strategy to deal with such objects is to distribute their rows across
    several processors.  To this end, we offer an 'S4' class for tall, skinny,
    distributed matrices, called the 'shaq'.  We also provide many useful
    numerical methods and statistics operations for operating on these
    distributed objects.  The naming is a bit "tongue-in-cheek", with the class
    a play on the fact that 'Shaquille' 'ONeal' ('Shaq') is very tall, and he
    starred in the film 'Kazaam'.
Create, Simulate, Fit, Solve k-Cube Thurstonian IRT Models
Provides an efficient implementation of univariate local polynomial
    kernel density estimators that can handle bounded and discrete data. See 
    Geenens (2014) <arXiv:1303.4121>, 
    Geenens and Wang (2018) <arXiv:1602.04862>, 
    Nagler (2018a) <arXiv:1704.07457>, 
    Nagler (2018b) <arXiv:1705.05431>.
Provides fast implementations of kernel smoothing techniques for
    bivariate copula densities, in particular density estimation and resampling,
    see Nagler (2018) <doi:10.18637/jss.v084.i07>.
Handles univariate non-parametric density estimation with 
    parametric starts and asymmetric kernels in a simple and flexible way. 
    Kernel density estimation with parametric starts involves fitting a
    parametric density to the data before making a correction with kernel 
    density estimation, see Hjort & Glad (1995) <doi:10.1214/aos/1176324627>.
    Asymmetric kernels make kernel density estimation more efficient on bounded
    intervals such as (0, 1) and the positive half-line. Supported asymmetric 
    kernels are the gamma kernel of Chen (2000) <doi:10.1023/A:1004165218295>,
    the beta kernel of Chen (1999) <doi:10.1016/S0167-9473(99)00010-9>, and the
    copula kernel of Jones & Henderson (2007) <doi:10.1093/biomet/asm068>.
    User-supplied kernels, parametric starts, and bandwidths are supported.
A non-parametric method for identifying potential
    outlying observations in a collection of phylogenetic trees based
    on the methods of Owen and Provan (2011). Such discordant trees
    may indicate problems with sequence annotation or tree
    reconstruction, or they may represent interesting biological
    phenomena, such as horizontal gene transfers.
Implements the vine copula based kernel density estimator of
    Nagler and Czado (2016) <doi:10.1016/j.jmva.2016.07.003>. The estimator does
    not suffer from the curse of dimensionality and is therefore well suited for
    high-dimensional applications.
Density, distribution function, quantile function and random generation
             for the K-distribution. A plotting function that plots data on Weibull
             paper and another function to draw additional lines. See results from package in T Lamont-Smith (2018), submitted J. R. Stat. Soc.
Provides various tools for working with multidimensional
  data in R and C++, including extremely fast nearest-neighbor- and range-
  queries without the overhead of linked tree nodes.
Smoothing techniques and computing bandwidth selectors of the nth derivative of a probability density for one-dimensional data.
Provides arrays with flexible control over dimension dropping when subscripting.
Collection of utility functions used in the KEHRA project (see http://www.brunel.ac.uk/ife/britishcouncil). It refers to the multidimensional analysis of air pollution, weather and health data.
Uses Bessel functions to calculate the 
  fundamental and complementary analytic solutions to the
  Kelvin differential equation.
Computes the Kendall rank correlation and Mann-Kendall
        trend test. See documentation for use of block bootstrap when
        there is autocorrelation.
Calculate the kernel-smoothed nonparametric estimator for the exposure distribution in presence of detection limits.
Demo and datasets accompanying the book "Knowledge Synthesis: From Experimental Network to Meta-Analysis" (Makowski, Piraux and Brun, in preparation for December 2017).
Implements the kernel method of test equating using the CB, EG, SG, NEAT CE/PSE and NEC designs, supporting gaussian, logistic and uniform kernels and unsmoothed and pre-smoothed input data.
Interface to 'Keras' <https://keras.io>, a high-level neural
  networks 'API'. 'Keras' was developed with a focus on enabling fast experimentation,
  supports both convolution based networks and recurrent networks (as well as
  combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices.
Adds a high-level interface for 'keras' neural nets. kms() fits neural net and accepts R formulas to aid data munging and hyperparameter selection. kms() can optionally accept a compiled keras_sequential_model() from 'keras'. 
    kms() accepts a number of parameters (like loss and optimizer) and splits the data into sparse test and training matrices. kms() returns a single object with predictions, a confusion matrix, and function call details.
Provides a consistent interface to the 'Keras' Deep Learning Library
  directly from within R. 'Keras' provides specifications for describing dense
  neural networks, convolution neural networks (CNN) and recurrent neural networks
  (RNN) running on top of either 'TensorFlow' or 'Theano'. Type conversions between
  Python and R are automatically handled correctly, even when the default
  choices would otherwise lead to errors. Includes complete R documentation
  and many working examples.
Nonparametric kernel distribution function estimation is
        performed. Three automatic bandwidth selection methods for
        nonparametric kernel distribution function estimation are
        implemented: the plug-in of Altman and Leger, the plug-in of
        Polansky and Baker, and the modified cross-validation of
        Bowman, Hall and Prvan. The exceedance function, the mean
        return period and the return level are also computed.
An efficient algorithm inspired by majorization-minimization principle for solving the entire solution path of a flexible nonparametric expectile regression estimator constructed in a reproducing kernel Hilbert space.
Gaussian Process models with customised covariance kernels.
Contains functions for estimation and model selection of kernel
    deep stacking networks.
A novel implementation that solves the linear distance weighted discrimination and the kernel distance weighted discrimination.
Smoothed bootstrap and functions for random generation from
             univariate and multivariate kernel densities. It does not
             estimate kernel densities.
Binary classification based on an ensemble of kernel machines ("Ballings, M. and Van den Poel, D. (2013), Kernel Factory: An Ensemble of Kernel Machines. Expert Systems With Applications, 40(8), 2904-2913"). Kernel factory is an ensemble method where each base classifier (random forest) is fit on the kernel matrix of a subset of the training data.
In self-reported or anonymised data the user often encounters
    heaped data, i.e. data which are rounded (to a possibly different degree
    of coarseness). While this is mostly a minor problem in parametric density
    estimation the bias can be very large for non-parametric methods such as kernel
    density estimation. This package implements a partly Bayesian algorithm treating
    the true unknown values as additional parameters and estimates the rounding
    parameters to give a corrected kernel density estimate. It supports various
    standard bandwidth selection methods. Varying rounding probabilities (depending
    on the true value) and asymmetric rounding is estimable as well: Gross, M. and Rendtel, U. (2016) (<doi:10.1093/jssam/smw011>).
    Additionally, bivariate non-parametric density estimation for rounded data, Gross, M. et al. (2016) (<doi:10.1111/rssa.12179>),
    as well as data aggregated on areas is supported.
Extends the simple k-nearest neighbors algorithm by incorporating numerous kernel functions and a variety of distance metrics. The package takes advantage of 'RcppArmadillo' to speed up the calculation of distances between observations.
Kernel-based machine learning methods for classification,
        regression, clustering, novelty detection, quantile regression
        and dimensionality reduction.  Among other methods 'kernlab'
        includes Support Vector Machines, Spectral Clustering, Kernel
        PCA, Gaussian Processes and a QP solver.
Provides wind energy practitioners with an effective machine learning-based
    tool that estimates a multivariate power curve and predicts the wind power output
    for a specific environmental condition.
Kernel Machine Score Test for Pathway Analysis in the Presence of Semi-Competing Risks.
Functions for kernel smoothing (and density estimation)
  corresponding to the book: 
  Wand, M.P. and Jones, M.C. (1995) "Kernel Smoothing".
This package fits nonparametric item and option characteristic curves using kernel smoothing. It allows for optimal selection of the smoothing bandwidth using cross-validation and a variety of exploratory plotting tools.
Implements functions from 'EXPOKIT' 
			 (<https://www.maths.uq.edu.au/expokit/>) to calculate 
			 matrix exponentials, Sidje RB, (1998) <doi:10.1145/285861.285868>. 
			 Includes functions for small dense matrices along with functions 
			 for large sparse matrices. The functions for large sparse matrices 
			 implement Krylov subspace methods which help minimise the 
			 computational complexity for matrix exponentials. 'Kexpmv' can be 
			 utilised to calculate both the matrix exponential in isolation 
			 along with the product of the matrix exponential and a vector.
Tools for keeping track of information, named "keys", about rows of
    data frame like objects. This is done by creating special attribute "keys"
    which is updated after every change in rows (subsetting, ordering, etc.).
    This package is designed to work tightly with 'dplyr' package.
Computes group centrality scores and identifies the most central group of players in a network.
Wait for a single key press at the 'R' prompt.
    This works in terminals, but does not currently work
    in the 'Windows' 'GUI', the 'OS X' 'GUI' ('R.app'),
    in 'Emacs' 'ESS', in an 'Emacs' shell buffer or in
    'R Studio'. In these cases 'keypress' stops with an
    error message.
Platform independent 'API' to access the operating system's
    credential store. Currently supports: 'Keychain' on 'macOS', Credential
    Store on 'Windows', the Secret Service 'API' on 'Linux', and a simple,
    platform independent store implemented with environment variables.
    Additional storage back-ends can be added easily.
Decrypts passwords stored in the Gnome Keyring, macOS Keychain and
    strings encrypted with the Windows Data Protection API.
State space modelling is an efficient and flexible framework for 
    statistical inference of a broad class of time series and other data. KFAS 
    includes computationally efficient functions for Kalman filtering, smoothing, 
    forecasting, and simulation of multivariate exponential family state space models, 
    with observations from Gaussian, Poisson, binomial, negative binomial, and gamma 
    distributions. See the paper by Helske (2017) <doi:10.18637/jss.v078.i10> for details.
Kernel Fisher Discriminant Analysis (KFDA) is performed using Kernel Principal Component Analysis (KPCA) and Fisher Discriminant Analysis (FDA).
    There are some similar packages. First, 'lfda' is a package that performs Local Fisher Discriminant Analysis (LFDA) and performs other functions.
    In particular, 'lfda' seems to be impossible to test because it needs the label information of the data in the function argument. Also, the 'ks' package has a limited dimension, which makes it difficult to analyze properly.
    This package is a simple and practical package for KFDA based on the paper of Yang, J., Jin, Z., Yang, J. Y., Zhang, D., and Frangi, A. F. (2004) <DOI:10.1016/j.patcog.2003.10.015>.
A streamlined cross-referencing system for R Markdown documents
    generated with 'knitr'. R Markdown is  an authoring format for generating
    dynamic content from R. 'kfigr' provides a hook for anchoring code
    chunks and a function to cross-reference document elements generated from
    said chunks, e.g. figures and tables.
Naive implementation of the Kalman filter, smoother and disturbance 
  smoother for state space models.
Aids in identifying the Koeppen-Geiger (KG) climatic zone for 
    a given location. The Koeppen-Geiger climate zones were first published in 1884, as a system
    to classify regions of the earth by their relative heat and humidity through the year, for 
    the benefit of human health, plant and agriculture and other human activity [1]. This climate
    zone classification system, applicable to all of the earths surface, has continued to be 
    developed by scientists up to the present day.  Recently one of use (FZ) has published updated,
    higher accuracy KG climate zone definitions [2]. In this package we use these updated 
    high-resolution maps as the data source [3]. We provide functions that return the KG climate zone 
    for a given longitude and lattitude, or for a given United States zip code. In addition
    the CZUncertainty() function will check climate zones nearby to check if the given location
    is near a climate zone boundary. In addition an interactive shiny app is provided to define 
    the KG climate zone for a given longitude and lattitude, or United States zip code. 
    Digital data, as well as animated maps, showing the shift of the climate zones are provided 
    on the following website <http://koeppen-geiger.vu-wien.ac.at>.
    This work was supported by the DOE-EERE SunShot award DE-EE-0007140.
     [1] W. Koeppen, (2011) <doi:10.1127/0941-2948/2011/105>.
     [2] F. Rubel and M. Kottek, (2010) <doi:10.1127/0941-2948/2010/0430>.
     [3] F. Rubel, K. Brugger, K. Haslinger, and I. Auer, (2016) <doi:10.1127/metz/2016/0816>.
The kernel ridge regression and the gradient matching algorithm proposed in Niu et al. (2016) <http://jmlr.org/proceedings/papers/v48/niu16.html> and the warping algorithm proposed in Niu et al. (2017) <DOI:10.1007/s00180-017-0753-z> are implemented for parameter inference in differential equations. Four schemes are provided for improving parameter estimation in odes by using the odes regularisation and warping.
Restore underlining numeric data from rating history graph of 
    KGS (an online platform of the game of go, <http://www.gokgs.com/>). 
    A shiny application is also provided.
A collection of useful functions not found anywhere else,
        mainly for programming: Pretty intervals, generalized lagged
        differences, checking containment in an interval, and an
        alternative interface to assign().
Analysis of kin-cohort studies. kin.cohort provides estimates of age-specific 
 cumulative risk of a disease for carriers and noncarriers of a mutation. The cohorts are
 retrospectively built from relatives of probands for whom the genotype is known. Currently 
 the method of moments and marginal maximum likelihood are implemented. Confidence intervals 
 are calculated from bootstrap samples.
 Most of the code is a translation from previous 'MATLAB' code by N. Chatterjee.
Data cleaning, processing, visualisation and analysis for manual (Goldmann) and automated (Octopus 900) kinetic visual field data.
The FOCUS Kinetics Report first published in 2006 describes mathematical
  models and recommends statistical methods for the evaluation of 
  chemical degradation data. This package implements fitting the kinetic
  models suitable for observations of the decline of a single chemical 
  compound (no metabolite formation/decline or multi-compartment kinetics).
  Please note that no warranty is implied for correctness of results or
  fitness for a particular purpose.  'kinfit' is maintained, but not
  actively developed at the moment. Please check the 'mkin' package for an
  actively developed package for kinetic evaluations of degradation data.
Routines to handle family data with a pedigree object. The initial purpose was to create correlation structures that describe 
 family relationships such as kinship and identity-by-descent, which
 can be used to model family data in mixed effects models, such as in the 
 coxme function.  Also includes a tool for pedigree drawing which is 
 focused on producing compact layouts without intervention.  Recent additions
 include utilities to trim the pedigree object with various criteria, and 
 kinship for the X chromosome.
Multi-modal magnetic resonance imaging ('MRI')
    data from the 'Kirby21' 'reproducibility' study
    <https://www.nitrc.org/projects/multimodal/>, including functional
    and structural imaging.
Functional magnetic resonance imaging ('fMRI') data from the
        'Kirby21' 'reproducibility' study
        <doi:10.1016/j.neuroimage.2010.11.047>.
Structural T1 magnetic resonance imaging ('MRI') data from the
        'Kirby21' 'reproducibility' study
        <doi:10.1016/j.neuroimage.2010.11.047>.
Simulating species migration and range dynamics under stable or changing environmental conditions based on a simple, raster-based, stochastic migration model. Providing accessibility for considering species migration in niche-based species distribution models.
Provides tools to calculate the theoretical hydrodynamic response
    of an aquifer undergoing harmonic straining or pressurization. There are
    two classes of models here: (1) for sealed wells, based on the model of
    Kitagawa et al (2011), and (2) for open wells, based on the models of
    Cooper et al (1965), Hsieh et al (1987), Rojstaczer (1988), and Liu et al
    (1989). These models treat strain (or aquifer head) as an input to the
    physical system, and fluid-pressure (or water height) as the output. The
    applicable frequency band of these models is characteristic of seismic
    waves, atmospheric pressure fluctuations, and solid earth tides.
Weighted k-Nearest Neighbors for Classification, Regression and Clustering.
Miscellaneous functions for classification and visualization,
	 e.g. regularized discriminant analysis, sknn() kernel-density naive Bayes, 
	 an interface to 'svmlight' and stepclass() wrapper variable selection 
   	 for supervised classification, partimat() visualization of classification rules 
         and shardsplot() of cluster results as well as kmodes() clustering for categorical data, 
	 corclust() variable clustering, variable extraction from different variable clustering models 
         and weight of evidence preprocessing.
A set of functions designed to quickly generate results of a
    multiple choice test. Generates detailed global results, lists for
    anonymous feedback and personalised result feedback (in LaTeX and/or PDF
    format), as well as item statistics like Cronbach's alpha or disciminatory
    power. klausuR also includes a plugin for the R GUI and IDE RKWard,
    providing dialogs for its basic features. To use them, install RKWard from
    http://rkward.sf.net (plugins are detected automatically). Due to some
    restrictions on CRAN, the full package sources are only available from the
    project homepage.
The package implements efficient ways to evaluate and
        solve equations of the form Ax=b, where A is a kronecker
        product of matrices.  Functions to solve least squares problems
        of this type are also included.
Computes various confidence intervals for the Kaplan-Meier
        estimator, namely: Petos CI, Rothman CI, CI's based on
        Greenwoods variance, Thomas and Grunkemeier CI and the
        simultaneous confidence bands by Nair and Hall and Wellner.
Computes and plots an exact nonparametric band for any user-specified level of confidence from a single-sample survfit object
K-means implementation is based on "Yinyang K-Means: A Drop-In Replacement of 
	the Classic K-Means with Consistent Speedup". While it introduces some overhead and many 
	conditional clauses which are bad for CUDA, it still shows 1.6-2x speedup against the Lloyd 
	algorithm. K-nearest neighbors employ the same triangle inequality idea and require 
	precalculated centroids and cluster assignments, similar to the flattened ball tree.
Compute p-values of metabolite differential expression analysis using the kernel-based approach.
A simple and fast distance-based k-medoids clustering algorithm from
  Park and Jun (2009) <doi:10.1016/j.eswa.2008.01.039>. Calculate distances for mixed
  variable data such as Gower (1971) <doi:10.2307/2528823>,
  Wishart (2003) <doi:10.1007/978-3-642-55721-7_23>, Podani (1999) <doi:10.2307/1224438>,
  Huang (1997) <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.94.9984&rep=rep1&type=pdf>, and
  Harikumar and PV (2015) <doi:10.1016/j.procs.2015.10.077>.
  Cluster validation applies bootstrap procedure producing a heatmap with a flexible
  reordering matrix algorithm such as complete, ward, or average linkages.
Contains tools for rapidly computing distance matrices 
    and clustering large sequence datasets using fast alignment-free 
    k-mer counting and recursive k-means partitioning. 
    See Vinga and Almeida (2003) <doi:10.1093/bioinformatics/btg005> 
    for a review of k-mer counting methods and applications for 
    biological sequence analysis.
Gene based association test between a group of SNPs and traits
    including familial (both continuous and binary) traits, multivariate
    continuous (both independent and familial) traits, longitudinal continuous
    traits and survival traits. Part of methods were previously published in
    Maity et al (2012) <doi:10.1002/gepi.21663>, Chen et al (2013)
    <doi:10.1002/gepi.21703>, Chen et al (2014) <doi:10.1002/gepi.21791>, Yan
    et al (2015) <doi:10.1159/000375409>, Yan et al (2015)
    <doi:10.1534/genetics.115.178590> and Yan et al (2015)
    <doi:10.1159/000445057>.
Performs a Kaplan-Meier multiple imputation to recover the missing potential censoring information from competing risks events, so that standard right-censored methods could be applied to the imputed data sets to perform analyses of the cumulative incidence functions (Allignol and Beyersmann, 2010 <doi:10.1093/biostatistics/kxq018>).
An implementation of k-means specifically design
        to cluster longitudinal data. It provides facilities to deal with missing
        value, compute several quality criterion (Calinski and Harabatz,
        Ray and Turie, Davies and Bouldin, BIC, ...) and propose a graphical
        interface for choosing the 'best' number of clusters.
An implementation of k-means specifically design
        to cluster joint trajectories (longitudinal data on
        several variable-trajectories).
        Like 'kml', it provides facilities to deal with missing
        value, compute several quality criterion (Calinski and Harabatz,
        Ray and Turie, Davies and Bouldin, BIC,...) and propose a graphical
        interface for choosing the 'best' number of clusters. In addition, the 3D graph
        representing the mean joint-trajectories of each cluster can be exported through
        LaTeX in a 3D dynamic rotating PDF graph.
'kmlcov' Cluster longitudinal data using the likelihood as a
    metric of distance. The generalised linear model allow the user to
    introduce covariates with different level effects (2 levels).
K-means for longitudinal data using shape-respecting distance and shape-respecting means.
An implementation of the 'k-means--' algorithm proposed by Chawla and Gionis, 2013 in their paper, "k-means-- : A unified approach to clustering and outlier detection. SIAM International Conference on Data Mining (SDM13)", and using 'ordering' described by Howe, 2013 in the thesis, "Clustering and anomaly detection in tropical cyclones". Useful for creating (potentially) tighter clusters than standard k-means and simultaneously finding outliers inexpensively in multidimensional space.
Data sets and functions for Klein and Moeschberger (1997),
        "Survival Analysis, Techniques for Censored and Truncated
        Data", Springer.
The sampl.mcmc() function creates samples of the feasible region of a knapsack problem with both equalities and inequalities constraints.
Provides the ability to create dynamic citations
    in which the bibliographic information is pulled from the web rather
    than having to be entered into a local database such as 'bibtex' ahead of
    time. The package is primarily aimed at authoring in the R 'markdown'
    format, and can provide outputs for web-based authoring such as linked
    text for inline citations.  Cite using a 'DOI', URL, or
    'bibtex' file key.  See the package URL for details.
Provides several helper functions for working with 'knitr' and 'LaTeX'.
  It includes 'xTab' for creating traditional 'LaTeX' tables, 'lTab' for generating
  'longtable' environments, and 'sTab' for generating a 'supertabular' environment.
  Additionally, this package contains a knitr_setup() function which fixes a
  well-known bug in 'knitr', which distorts the 'results="asis"' command when used
  in conjunction with user-defined commands; and a com command (<<com=TRUE>>=)
  which renders the output from 'knitr' as a 'LaTeX' command.
Provides a general-purpose tool for dynamic report generation in R
    using Literate Programming techniques.
A framework to create Bootstrap <http://getbootstrap.com/> HTML reports from 'knitr'
    'rmarkdown'.
Provides a progress bar similar to 'dplyr' that can write progress out to a 
    variety of locations, including stdout(), stderr(), or from file(). Useful when using 'knitr' or 'rmarkdown',
    and you still want to see progress of calculations in the terminal.
Scale categorical variables in such a way as
        to make NN classification as accurate as possible. The code also
        handles continuous variables and prior probabilities, and does
        intelligent variable selection and estimation of both error rates
        and the right number of NN's.
Multi-distance based k-Nearest Neighbors Classification
        with K Threshold Value Check and Same K_i Problem Dealing,
        Missing Observations Filling
This package provides the implementation of an exact formula of the
    ith nearest neighbour distance distribution and implementations of tests of
    independence based on that formula. Furthermore the package provides a
    general framework to benchmark tests of independence.
The knockoff filter is a general procedure for controlling the false discovery rate (FDR)
  when performing variable selection. 
  For more information, see the website below and the accompanying paper: Candes et al., 
  "Panning for Gold: Model-free Knockoffs for High-dimensional Controlled Variable Selection", 
  2016, <arXiv:1610.02351>.
Using this package, users can access to the largest collection of public data and statistics on the Internet featuring about 2.5 billion time series from thousands of sources collected in 'Knoema' repository and use rich R calculations in order to analyze the data. Because data in 'Knoema' is time series data, 'Knoema' function offers data in a number of formats usable in R such as 'ts', 'xts' or 'zoo'. For more information about 'Knoema' API go to <https://knoema.com/dev/docs>.
The k-means 'NUMA' Optimized Routine library or 'knor' is a highly optimized and fast library for computing k-means in parallel with accelerations for Non-Uniform Memory Access ('NUMA') architectures.
Makes nice pictures of knots using Bezier curves and
  numerical optimization.
It uses species accumulation curves and diverse estimators to assess, at the same time, the levels of survey coverage in multiple geographic cells of a size defined by the user or polygons. It also enables the geographical depiction of observed species richness, survey effort and completeness values including a background with administrative areas.
Retrieve data from 'kintone' (<https://www.kintone.com/>) via its API.
    'kintone' is an enterprise application platform.
An unsupervised and semi-supervised learning algorithm that performs feature extraction from noisy and high-dimensional data.
Read Swiss time series data from the 'KOF Datenservice' API, <https://datenservice.kof.ethz.ch>. The API provides macroeconomic survey data, business cycle and further macro economic time series about Switzerland. The package itself is a set of wrappers around the 'KOF Datenservice' API. The 'kofdata' package is able to consume public information as well as data that requires an API token.
Function kofnGA uses a genetic algorithm to choose a subset of a 
 fixed size k from the integers 1:n, such that a user-supplied objective function 
 is minimized at that subset.  The selection step is done by tournament selection 
 based on ranks, and elitism may be used to retain a portion of the best solutions 
 from one generation to the next.
Rank-based tests for enrichment of KOG (euKaryotic Orthologous Groups) classes with up- or down-regulated genes based on a continuous measure. The meta-analysis is based on correlation of KOG delta-ranks across datasets (delta-rank is the difference between mean rank of genes belonging to a KOG class and mean rank of all other genes). With binary measure (1 or 0 to indicate significant and non-significant genes), one-tailed Fisher's exact test for over-representation of each KOG class among significant genes will be performed.
Functions to train self-organising maps (SOMs). Also interrogation of the maps and prediction using trained maps are supported. The name of the package refers to Teuvo Kohonen, the inventor of the SOM.
Provides an interface to 'Kokudo Suuchi' API, the GIS data service of the Japanese government.
    See <http://nlftp.mlit.go.jp/ksj-e/index.html> for more information.
Provides an alternative, more efficient evaluation of extreme
  probabilities of Kolmogorov's goodness-of-fit measure, Dn, when compared to
  the original implementation of Wang, Marsaglia, and Tsang. These
  probabilities are used in Kolmogorov-Smirnov tests when comparing two
  samples.
R Markdown templates based on the 'KOMA-Script' classes for
  LaTeX, additionally offering cross-referencing via the 'bookdown' package.
An R Markdown template for writing beautiful yet versatile letters,
  using the 'KOMA-Script' letter class 'scrlttr2' and an adaptation of the
  'pandoc-letter' template. 'scrlttr2' provides layouts for many different
  window envelope types and the possibility to define your own.
Statistical methods that quantify the conditions necessary to alter
    inferences, also known as sensitivity analysis, are becoming increasingly
    important to a variety of quantitative sciences. A series of recent works,
    including Frank (2000) <doi:10.1177/0049124100029002001> and Frank et al.
    (2013) <doi:10.3102/0162373713493129> extend previous sensitivity analyses
    by considering the characteristics of omitted variables or unobserved cases
    that would change an inference if such variables or cases were observed. These
    analyses generate statements such as "an omitted variable would have to be
    correlated at xx with the predictor of interest (e.g., treatment) and outcome
    to invalidate an inference of a treatment effect". Or "one would have to replace
    pp percent of the observed data with null hypothesis cases to invalidate the
    inference". We implement these recent developments of sensitivity analysis and
    provide modules to calculate these two robustness indices and generate such
    statements in R. In particular, the functions konfound(), pkonfound() and 
    mkonfound() allow users to calculate the robustness of inferences for a user's 
    own model, a single published study and multiple studies respectively.
POS Tagger and Morphological Analyzer for Korean text based research. It provides tools for corpus linguistics research such as Keystroke converter, Hangul automata, Concordance, and Mutual Information. It also provides a convenient interface for users to apply, edit and add morphological dictionary selectively.
A set of tools to analyze texts. Includes, amongst others, functions for automatic language detection, hyphenation,
               several indices of lexical diversity (e.g., type token ratio, HD-D/vocd-D, MTLD) and readability (e.g., Flesch, SMOG,
               LIX, Dale-Chall). Basic import functions for language corpora are also provided, to enable frequency analyses (supports
               Celex and Leipzig Corpora Collection file formats) and measures like tf-idf. Support for additional languages can be
               added on-the-fly or by plugin packages. Note: For full functionality a local installation of TreeTagger is recommended.
               'koRpus' also includes a plugin for the R GUI and IDE RKWard, providing graphical dialogs for its basic features. The
               respective R package 'rkward' cannot be installed directly from a repository, as it is a part of RKWard. To make full
               use of this feature, please install RKWard from <https://rkward.kde.org> (plugins are detected automatically). Due to
               some restrictions on CRAN, the full package sources are only available from the project homepage. To ask for help,
               report bugs, request features, or discuss the development of the package, please subscribe to the koRpus-dev mailing
               list (<http://korpusml.reaktanz.de>).
Adds support for the English language to the 'koRpus'
        package. To ask for help, report bugs, suggest feature
        improvements, or discuss the global development of the package,
        please consider subscribing to the koRpus-dev mailing list
        (<http://korpusml.reaktanz.de>).
Consider linear regression model and autoregressive model of
    order q where errors in the linear regression model and innovations in the
    autoregression model are independent and symmetrically distributed. Hira L. Koul
    (1986) <DOI:10.1214/aos/1176350059> proposed a nonparametric minimum distance
    estimation method by minimizing L2-type distance between certain weighted
    residual empirical processes. He also proposed a simpler version of the loss
    function by using symmetry of the integrating measure in the distance. This
    package contains three functions: KoulLrMde(), KoulArMde(), and Koul2StageMde().
    The former two provide minimum distance estimators for linear regression model
    and autoregression model, respectively, where both are based on Koul's method.
    These two functions take much less time for the computation than those based
    on parametric minimum distance estimation methods. Koul2StageMde() provides
    estimators for regression and autoregressive coefficients of linear regression
    model with autoregressive errors through minimum distant method of two stages.
    The new version is written in Rcpp and dramatically reduces computational time.
Cubic spline fitting along with knot selection, includes support for additional variables.
Kernel PC (kPC) algorithm for causal structure learning and causal inference using graphical models. kPC is a version of PC algorithm that uses kernel based independence criteria in order to be able to deal with non-linear relationships and non-Gaussian noise.
The input argument k which is the number of clusters is needed to start all of the partitioning clustering algorithms. In unsupervised learning applications, an optimal value of this argument is widely determined by using the internal validity indexes. Since these indexes suggest a k value which is computed on the clustering results after several runs of a clustering algorithm they are computationally expensive. On the contrary, 'kpeaks' enables to estimate k before running any clustering algorithm. It is based on a simple novel technique using the descriptive statistics of peak counts of the features in a data set.
Functions that implement the known population median test.
The kpodclustr package implements the k-POD method for clustering
    partially observed data.
Implements a quantified approach to the Kraljic Matrix (Kraljic, 1983, <https://hbr.org/1983/09/purchasing-must-become-supply-management>)
    for strategically analyzing a firm’s purchasing portfolio. It combines multi-objective decision analysis to measure purchasing characteristics and
    uses this information to place products and services within the Kraljic Matrix.
Provides basic functions for Continuation-Passing Style development.
Implements different methods for spatial statistics, in particular focused in
    Kriging based models. We count with different implemented models, simple, ordinary and 
    universal forms of Kriging, co-Kriging and regression Kriging models. Includes, multivariate 
    sensitivity analysis under an approximation designed over reproducing kernel Hilbert spaces and 
    computation of Sobol indexes under this framework.
Simple and highly optimized ordinary kriging algorithm to plot geographical data
Criteria and algorithms for sequentially estimating level sets of a multivariate numerical function, possibly observed with noise.
Provides useful functions which are needed for bioinformatic analysis such as calculating linear principal components from numeric data and Single-nucleotide polymorphism (SNP) dataset, calculating fixation index (Fst) using Hudson method, creating scatter plots in 3 views, handling with PLINK binary file format, detecting rough structures and outliers using unsupervised clustering, and calculating matrix multiplication in the faster way for big data.
Package implements Kernel-based Regularized Least Squares (KRLS), a machine learning method to fit multidimensional functions y=f(x) for regression and classification problems without relying on linearity or additivity assumptions. KRLS finds the best fitting function by minimizing the squared loss of a Tikhonov regularization problem, using Gaussian kernels as radial basis functions. For further details see Hainmueller and Hazlett (2014).
Implements several methods for testing the variance component parameter in regression models that contain kernel-based random effects, including a maximum of adjusted scores test. Several kernels are supported, including a profile hidden Markov model mutual information kernel for protein sequence.
Solves kernel ridge regression, within the the mixed model framework, for the linear, polynomial, Gaussian, Laplacian and ANOVA kernels. The model components (i.e. fixed and random effects) and variance parameters are estimated using the expectation-maximization (EM) algorithm. All the estimated components and parameters, e.g. BLUP of dual variables and BLUP of random predictor effects for the linear kernel (also known as RR-BLUP), are available. The kernel ridge mixed model (KRMM) is described in Jacquin L, Cao T-V and Ahmadi N (2016) A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice. Front. Genet. 7:145. <doi:10.3389/fgene.2016.00145>.
Kernel smoothers for univariate and multivariate data, including densities, density derivatives, cumulative distributions, clustering, classification, density ridges, significant modal regions, and two-sample hypothesis tests. Duong (2017) <doi:10.18637/jss.v021.i07>.
Compares k samples using the Anderson-Darling test, Kruskal-Wallis type tests 
	with different rank score criteria, Steel's multiple comparison test, and the 
        Jonckheere-Terpstra (JT) test. It computes asymptotic, simulated or (limited) exact 
        P-values, all valid under randomization, with or without ties, or conditionally 
        under random sampling from populations, given the observed tie pattern.  Except for 
        Steel's test and the JT test it also combines these tests across several blocks of 
	samples.  Also analyzed are 2 x t contingency tables and their blocked combinations 
	using the Kruskal-Wallis criterion.  Steel's test is inverted to provide simultaneous 
	confidence bounds for shift parameters.  A plotting function compares tail probabilities
	obtained under asymptotic approximation with those obtained via simulation or exact 
	calculations.
Predicts a protein's residue contact map, based on the estimation of the corresponding knob-socket list. For more details, please refer to our paper:  Q. Li, D. B. Dahl, M. Vannucci, H. Joo, J. W. Tsai (2016), KScons: A Bayesian Approach for Protein Residue Contact Prediction using the Knob-socket Model of Protein Tertiary Structure, Bioinformatics, 32(24): 3774-3781 <doi:10.1093/bioinformatics/btw553>.
Implements the Lilliefors-corrected Kolmogorov-Smirnov test for use
    in goodness-of-fit tests, suitable when population parameters are unknown and
    must be estimated by sample statistics. P-values are estimated by simulation.
    Can be used with a variety of continuous distributions, including normal,
    lognormal, univariate mixtures of normals, uniform, loguniform, exponential,
    gamma, and Weibull distributions. Functions to generate random numbers and
    calculate density, distribution, and quantile functions are provided for use
    with the log uniform and mixture distributions.
An adaptation of Kernelized Stein Discrepancy, this package provides a goodness-of-fit test of whether a given i.i.d. sample is drawn from a given distribution. It works for any distribution once its score function (the derivative of log-density) can be provided. This method is based on "A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluation" by Liu, Lee, and Jordan, available at <http://arxiv.org/abs/1602.03253>.
Infers relative kinase activity from phosphoproteomics data
    using the method described by Casado et al. (2013) <doi:10.1126/scisignal.2003573>.
Selection of k in k-means clustering based on Pham et al. paper
    ``Selection of k in k-means clustering''.
Computes a p-value of the one-sample two-sided (or one-sided, as a special case) Kolmogorov-Smirnov (KS) statistic, for any fixed critical level, and an arbitrary, possibly large sample size for a pre-specified purely discrete, mixed or continuous cumulative distribution function (cdf) under the null hypothesis. If a data sample is supplied, 'KSgeneral' computes the p-value corresponding to the value of the KS test statistic computed based on the user provided data sample. The package 'KSgeneral' implements a novel, accurate and efficient method named Exact-KS-FFT, expressing the p-value as a double-boundary non-crossing probability for a homogeneous Poisson process, which is then efficiently computed using Fast Fourier Transform (FFT). The package can also be used to compute and plot the complementary cdf of the KS statistic which is known to depend on the hypothesized distribution when the latter is discontinuous (i.e. purely discrete or mixed).
Using this package you can combine known kinase substrate relationships with experimental data and determine active kinases and their substrates.
Knowledge space theory by Doignon and Falmagne (1999) 
   <doi:10.1007/978-3-642-58625-5> is a set- and order-theoretical
   framework, which proposes mathematical formalisms to operationalize 
   knowledge structures in a particular domain. The 'kst' package provides 
   basic functionalities to generate, handle, and manipulate knowledge 
   structures and knowledge spaces.
Knowledge space theory by Doignon and Falmagne (1999) 
   <doi:10.1007/978-3-642-58625-5> is a set- and order-theoretical
   framework which proposes mathematical formalisms to operationalize 
   knowledge structures in a particular domain.  The 'kstIO' package 
   provides basic functionalities to read and write KST data from/to files
   to be used together with the 'kst', 'pks' or 'DAKS' packages.
Provides a function called COTUCKER3() (Co-Inertia Analysis
    + Tucker3 method) which performs a Co-Tucker3 analysis of two sequences of
    matrices, as well as other functions called PCA() (Principal Component Analysis)
    and BGA() (Between-Groups Analysis), which perform analysis of one matrix,
    COIA() (Co-Inertia Analysis), which performs analysis of two matrices, PTA()
    (Partial Triadic Analysis) and TUCKER3(), which perform analysis of a sequence
    of matrices, and BGCOIA() (Between-Groups Co-Inertia Analysis), STATICO()
    (STATIS method + Co-Inertia Analysis), COSTATIS() (Co-Inertia Analysis + STATIS
    method), which also perform analysis of two sequences of matrices.
This function is designed for use with an arbitrary set of equations with
 an arbitrary set of unknowns.
 The user selects "fixed" values for enough unknowns to leave as many variables as
 there are equations, which in most cases means the system is properly
 defined and a unique solution exists. The function, the fixed values
 and initial values for the remaining unknowns are fed to a nonlinear backsolver.  
 The original version of "TK!Solver" was the inspiration for this function.
These functions compute the k best pairs of genes used to classify samples based on the relative rank of the genes expression within each profile. A score based on the sensitivity and the specificity is calculated for every possible pair. The k pairs with the highest score will be selected with the restriction that a gene can appear in at most one pair. The value of k is either set as a parameter chosen by the user or computed through crossvalidation. Other functions related to the k-TSP are also available, for example the functions prediction, summary, plot, etc. can be found in the package.
Provides various functions and data sets from experiments at the Faculty of Life Sciences, University of Copenhagen. This package will be discontinued and archived, and the functions and datasets will be maintained and updated in the MESS package
Tools for data importation, recoding, and inspection that
    are used at the University of Kansas Center for Research Methods
    and Data Analysis. There are functions to create new project
    folders, R code templates, create uniquely named output
    directories, and to quickly obtain a visual summary for each
    variable in a data frame.  The main feature here is the systematic
    implementation of the "variable key" framework for data
    importation and recoding.  We are eager to have community feedback
    about the variable key and the vignette about it.
The format KVH is a lightweight format that can be read/written both by humans and machines.
   It can be useful in situations where XML or alike formats seem to be an overkill.
   We provide an ability to parse KVH files in R pretty fast due to 'Rcpp' use.
Calculation groundwater mounding beneath an infiltration basin based on the Hantush (1967) equation (http://doi.org/10.1029/WR003i001p00227). The correct implementation is shown with a verification example based on a USGS report (page 25, http://pubs.usgs.gov/sir/2010/5102/support/sir2010-5102.pdf).
Helper functions for creating formatted summary of regression models, writing publication-ready tables to latex files, and running Monte Carlo experiments.
Time Series Analysis including break detection, spectral analysis, KZ Fourier Transforms.
Separation of wave motions in different scales and directions based on
     Kolmogorov-Zurbenko Periodograms and Kolmogorov-Zurbenko Fourier Transform.
A colletion of functions to implement Kolmogorov-Zurbenko
        Fourier transform based periodograms and smoothing methods
A spatial smoothing algorithm based on convolutions of finite rectangular kernels that provides sharp resolution in the presence of high levels of noise.
An efficient procedure for feature selection for generalized linear models with L0 penalty, including linear, logistic, Poisson, gamma, inverse Gaussian regression. Adaptive ridge algorithms are used to fit the models.
LINCS L1000 is a high-throughput technology that allows the gene expression measurement in a large number of assays. However, to fit the measurements of ~1000 genes in the ~500 color channels of LINCS L1000, every two landmark genes are designed to share a single channel. Thus, a deconvolution step is required to infer the expression values of each gene. Any errors in this step can be propagated adversely to the downstream analyses. We present a LINCS L1000 data peak calling R package l1kdeconv based on a new outlier detection method and an aggregate Gaussian mixture model. Upon the remove of outliers and the borrowing information among similar samples, l1kdeconv shows more stable and better performance than methods commonly used in LINCS L1000 data deconvolution.
L1 estimation for linear regression, density, distribution function,
    quantile function and random number generation for univariate and multivariate
    Laplace distribution.
Efficient implementation of Friedman's boosting algorithm with
    l2-loss function and coordinate direction (design matrix columns) basis
    functions.
A variety of ordination and community analyses
   useful in analysis of data sets in community ecology.  
   Includes many of the common ordination methods, with 
   graphical routines to facilitate their interpretation, 
   as well as several novel analyses.
The Bayesian estimation of mixture models (and more general hidden Markov models) suffers from the label switching phenomenon, making the MCMC output non-identifiable. This package can be used in order to deal with this problem using various relabelling algorithms.
Support labeled loop and escape from nested loop
Provides a range of axis labeling algorithms
Work with labelled data imported from
    'SPSS' or 'Stata' with 'haven' or 'foreign'.
An implementation of distance-based ranking algorithms to predict rankings of labels.  Two common algorithms are included: the naive Bayes and the nearest neighbor algorithms.
Labels are a common construct in statistical software providing a 
  human readable description of a variable. While variable names are succinct,
  quick to type, and follow a language's naming conventions, labels may 
  be more illustrative and may use plain text and spaces. R does not provide
  native support for labels. Some packages, however, have made this feature
  available.  Most notably, the 'Hmisc' package provides labelling methods
  for a number of different object. Due to design decisions, these methods
  are not all exported, and so are unavailable for use in package development.
  The 'labelVector' package supports labels for atomic vectors in a light-weight
  design that is suitable for use in other packages.
Produces Labour Market Areas from commuting flows available at elementary territorial units. It provides tools for automatic tuning based on spatial contiguity. It also allows for statistical analyses and visualisation of the new functional geography.
Insieme di funzioni di supporto al volume "Laboratorio di
        Statistica con R", Iacus-Masarotto, MacGraw-Hill Italia, 2006.
        This package contains sets of functions defined in "Laboratorio
        di Statistica con R", Iacus-Masarotto, MacGraw-Hill Italia,
        2006. Function names and docs are in italian as well.
Contains data sets to accompany the book: Lazic SE
    (2016). "Experimental Design for Laboratory Biologists: Maximising Information
    and Improving Reproducibility". Cambridge University Press.
Estimation of indicators on social exclusion and poverty, as well
    as Pareto tail modeling for empirical income distributions.
The package contains functions to compare and group means.
Methods for fast access to large ASCII files.  Currently the
    following file formats are supported: comma separated format (CSV) and fixed
    width format. It is assumed that the files are too large to fit into memory,
    although the package can also be used to efficiently access files that do
    fit into memory. Methods are provided to access and process files blockwise.
    Furthermore, an opened file can be accessed as one would an ordinary
    data.frame. The LaF vignette gives an overview of the functionality
    provided.
Provides classes and methods for lagged objects.
Client for programmatic access to the Lake
    Multi-scaled Geospatial and Temporal database <https://lagoslakes.org>, with functions
    for accessing lake water quality and ecological context data for the US.
Performs approximate GP regression for large computer experiments and spatial datasets.  The approximation is based on finding small local designs for prediction (independently) at particular inputs. OpenMP and SNOW parallelization are supported for prediction over a vast out-of-sample testing set; GPU acceleration is also supported for an important subroutine.  OpenMP and GPU features may require special compilation.  An interface to lower-level (full) GP inference and prediction is also provided, as are associated wrapper routines for blackbox optimization under mixed equality and inequality constraints via an augmented Lagrangian scheme, and for large scale computer model calibration.
Model-based linear model trees adjusting for spatial correlation using a
             simultaneous autoregressive spatial lag.
Provides the tables from the 'Sean Lahman Baseball Database' as
    a set of R data.frames. It uses the data on pitching, hitting and fielding
    performance and other tables from 1871 through 2015, as recorded in the 2016
    version of the database.
A collection of tools for the calculation of freewater metabolism.
Lake morphometry metrics are used by limnologists to understand,
    among other things, the ecological processes in a lake. Traditionally, these
    metrics are calculated by hand, with planimeters, and increasingly with
    commercial GIS products. All of these methods work; however, they are either
    outdated, difficult to reproduce, or require expensive licenses to use. The
    'lakemorpho' package provides the tools to calculate a typical suite
    of these metrics from an input elevation model and lake polygon. The metrics
    currently supported are: fetch, major axis, minor axis, major/minor axis 
    ratio, maximum length, maximum width, mean width, maximum depth, mean depth, 
    shoreline development, shoreline length, surface area, and volume.
Lake temperature records, metadata, and climate drivers for 291 global lakes during the time period 1985-2009. Temperature observations were collected using satellite and in situ methods. Climatic drivers and geomorphometric characteristics were also compiled and are included for each lake. Data are part of the associated publication from the Global Lake Temperature Collaboration project (http://www.laketemperature.org). See citation('laketemps') for dataset attribution.
Contains some procedures for latent variable modelling with a 
    particular focus on multilevel data.
    The 'LAM' package contains mean and covariance structure modelling
    for multivariate normally distributed data (mlnormal()),
    a general Metropolis-Hastings algorithm (amh()) and penalized
    maximum likelihood estimation (pmle()).
A language extension to efficiently write functional programs in R. Syntax extensions include multi-part function definitions, pattern matching, guard statements, built-in (optional) type safety.
Provides tools that manipulate and transform data using methods
    and techniques consistent with functional programming. The idea is that
    through the use of these tools, a program can be reasoned about insomuch
    that the implementation can be proven to be equivalent to the mathematical
    model.
Currently the package includes 14 methods for calculating internal
    consistency reliability but is still growing.  The package allows users
    access to whichever reliability estimator is deemed most appropriate for
    their situation.
Lambert W x F distributions are a generalized framework to analyze
    skewed, heavy-tailed data. It is based on an input/output system, where the
    output random variable (RV) Y is a non-linearly transformed version of an input
    RV X ~ F with similar properties as X, but slightly skewed (heavy-tailed).
    The transformed RV Y has a Lambert W x F distribution. This package contains
    functions to model and analyze skewed, heavy-tailed data the Lambert Way:
    simulate random samples, estimate parameters, compute quantiles, and plot/
    print results nicely. Probably the most important function is 'Gaussianize',
    which works similarly to 'scale', but actually makes the data Gaussian.
    A do-it-yourself toolkit allows users to define their own Lambert W x
    'MyFavoriteDistribution' and use it in their analysis right away.
Implements both real-valued branches of the Lambert-W function, also known as the product logarithm, without the need for installing the entire GSL.
Using Liquid Association for Network Dynamics Detection.
Provides functions to estimate survival and a treatment effect using a landmark estimation approach.
This package includes functions for landmark prediction of a survival outcome incorporating covariate and short-term event information. For more information about landmark prediction please see: Parast, Layla, Su-Chun Cheng, and Tianxi Cai. Incorporating short-term outcome information to predict long-term survival with discrete markers. Biometrical Journal 53.2 (2011): 294-307.
Processing of Landsat or other multispectral satellite
        imagery. Includes relative normalization, image-based
        radiometric correction, and topographic correction options.
Functions for converted Landsat 8 multispectral satellite imagery
    rescaled to the top of atmosphere (TOA) reflectance, radiance
    and/or at satellite brightness temperature using radiometric
    rescaling coefficients provided in the metadata file (MTL file).
Simulates categorical maps on actual geographical realms, starting from either empty landscapes or landscapes provided by the user (e.g. land use maps). Allows to tweak or create landscapes while retaining a high degree of control on its features, without the hassle of specifying each location attribute. In this it differs from other tools which generate null or neutral landscapes in a theoretical space. The basic algorithm currently implemented uses a simple agent style/cellular automata growth model, with no rules (apart from areas of exclusion) and von Neumann neighbourhood (four cells, aka Rook case). Outputs are raster dataset exportable to any common GIS format.
Provides utility functions to complete tasks involved in most
    landscape analysis. It includes functions to coerce raster data to the
    common tibble format and vice versa, it helps with flexible reclassification
    tasks of raster data and it provides a function to merge multiple raster.
    Furthermore, 'landscapetools' helps landscape scientists to visualize their
    data by providing optional themes and utility functions to plot single
    landscapes, rasterstacks, -bricks and lists of raster.
A spatio-temporal stochastic model to assess resistance deployment strategies 
    against plant pathogens. The model is based on stochastic geometry for describing the 
    landscape and the resistant hosts, a dispersal kernel for the dissemination of the 
    pathogen, and a SEIR (Susceptible-Exposed-Infectious-Removed) architecture to simulate 
    plant response to disease.
    Loup Rimbaud, Julien Papaïx, Jean-François Rey, Luke G Barrett, 
    Peter H Thrall (2018) <doi:10.1101/260836>.
Estimate drift and diffusion functions from time series and
    generate synthetic time series from given drift and diffusion coefficients.
Improve your text analysis with languagelayer
        <https://languagelayer.com>, a powerful language detection
        API.
Data sets exemplifying statistical methods, and some
        facilitatory utility functions used in "Analyzing Linguistic
        Data: A practical introduction to statistics using R",
        Cambridge University Press, 2008.
An implementation of the Language Server Protocol for R. The Language Server protocol is used by an editor client to integrate features like auto completion. See <https://microsoft.github.io/language-server-protocol> for details.
R interface for working with nanometer scale secondary ion mass 
    spectrometry (NanoSIMS) data exported from Look at NanoSIMS.
Solves the problem of Laplace deconvolution with noisy discrete
    non-equally spaced observations on a finite time interval based on expansions
    of the convolution kernel, the unknown function and the observed signal over
    Laguerre functions basis. It implements the methodology proposed in the paper
    "Laplace deconvolution on the basis of time domain data and its application to
    Dynamic Contrast Enhanced imaging" by F. Comte, C-A. Cuenod, M. Pensky and Y.
    Rozenholc in ArXiv (http://arxiv.org/abs/1405.7107).
Provides a complete environment for Bayesian inference using a variety of different samplers (see ?LaplacesDemon for an overview). The README describes the history of the package development process.
This package is intended for researchers studying historical labour relations (see http://www.historyoflabourrelations.org). The package allows for easy access of excel files in the standard defined by the Global Collaboratory on the History of Labour Relations. The package also allows for visualisation of labour relations according to the Collaboratory's format.
Provides instrumental variable estimation of treatment effects when both the endogenous treatment and its instrument are binary. Applicable to both binary and continuous outcomes.
Functions to write or append a R list to a file, as well as read, remove, modify elements from it without restoring the whole list.
Implements the largeVis algorithm (see Tang, et al. (2016)  <DOI:10.1145/2872427.2883041>) for visualizing very large high-dimensional datasets. Also very fast search for approximate nearest neighbors; outlier detection; and optimized implementations of the HDBSCAN*, DBSCAN and OPTICS clustering algorithms; plotting functions for visualizing the above.
Efficient procedures for fitting an entire lasso sequence
        with the cost of a single least squares fit. Least angle
        regression and infinitesimal forward stagewise regression are
        related to the lasso, as described in the paper below.
Routines and documentation for solving regression problems
  while imposing an L1 constraint on the estimates, based on
  the algorithm of Osborne et al. (1998)
Implementation of the algorithm introduced in Shah, R. D.
    (2016) <http://www.jmlr.org/papers/volume17/13-515/13-515.pdf>.
    Data with thousands of predictors can be handled. The algorithm
    performs sequential Lasso fits on design matrices containing
    increasing sets of candidate interactions. Previous fits are used to greatly
    speed up subsequent fits so the algorithm is very efficient.
Estimate the p-values for predictors x against target variable y in lasso regression, using the regularization strength when each predictor enters the active set of regularization path for the first time as the statistic. This is based on the assumption that predictors (of the same variance) that (first) become active earlier tend to be more significant. Three null distributions are supported: normal and spherical, which are computed separately for each predictor and analytically under approximation, which aims at efficiency and accuracy for small p-values.
Use the lasso regression method to perform approximate inference
    in high dimensions, by penalizing the effects of nuisance parameters.
L1 regularized regression (Lasso) solver using the Cyclic
        Coordinate Descent algorithm aka Lasso Shooting is fast. This
        implementation can choose which coefficients to penalize. It
        support coefficient-specific penalities and it can take X'X and
        X'y instead of X and y.
Estimate the sufficient dimension reduction space using sparsed sliced inverse regression via Lasso (Lasso-SIR) introduced in Lin, Zhao, and Liu (2017) <arxiv:1611.06655>. The Lasso-SIR is consistent and achieve the optimal convergence rate under certain sparsity conditions for the multiple index models.
A graph
  proposed by Rosenbaum is useful
  for checking some properties of various
  sorts of latent scale, this program generates commands
  to obtain the graph using 'dot' from 'graphviz'.
Fit and simulate latent position and cluster models for statistical networks.
Executes arbitrary R or C functions some time after the current
    time, after the R execution stack has emptied.
Calculates and plots Handedness index (HI), absolute HI, mean HI and z-score which are commonly used indexes for the study of hand preference (laterality) in non-human primates.
Parses and converts LaTeX math formulas to R's plotmath
    expressions, used to enter mathematical formulas and symbols to be rendered as
    text, axis labels, etc. throughout R's plotting system.
Converts table-like objects to stand-alone PDF or PNG.
 Can be used to embed tables and arbitrary content in PDF or Word
 documents. Provides a low-level R interface for creating 'LaTeX'
 code, e.g. command() and a high-level interface for creating PDF
 documents, e.g. as.pdf.data.frame(). Extensive customization is
 available via mid-level functions, e.g. as.tabular(). See also 
 'package?latexpdf'. Support for PNG is experimental; see
 'as.png.data.frame'. Adapted from 'metrumrg' 
 <http://r-forge.r-project.org/R/?group_id=1215>.
 Requires a compatible installation of 'pdflatex',
 e.g. <https://miktex.org/>.
A powerful and elegant high-level data visualization
  system inspired by Trellis graphics, with an emphasis on
  multivariate data. Lattice is sufficient for typical graphics needs,
  and is also flexible enough to handle most nonstandard requirements.
  See ?Lattice for an introduction.
This package contains functions that compute the
  lattice-based density estimator of Barry and McIntyre, which
  accounts for point processes in two-dimensional regions with 
  irregular boundaries and holes.  The package also implements
  two-dimensional non-parametric regression for similar regions.
Building on the infrastructure provided by the lattice
	     package, this package provides several new high-level
	     functions and methods, as well as additional utilities
	     such as panel and axis annotation functions.
Methods for the interpolation of large spatial
  datasets. This package follows a "fixed rank Kriging" approach using
  a large number of basis functions and provides spatial estimates
  that are comparable to standard families of covariance functions.
  Using a large number of basis functions allows for estimates that
  can come close to interpolating the observations (a spatial model
  with a small nugget variance.)  Moreover, the covariance model for
  this method can approximate the Matern covariance family but also
  allows for a multi-resolution model and supports efficient
  computation of the profile likelihood for estimating covariance
  parameters. This is accomplished through compactly supported basis
  functions and a Markov random field model for the basis
  coefficients. These features lead to sparse matrices for the
  computations and this package makes of the R spam package for this.
  An extension of this version over previous ones ( < 5.4 ) is the
  support for different geometries besides a rectangular domain. The
  Markov random field approach combined with a basis function
  representation makes the implementation of different geometries
  simple where only a few specific functions need to be added with
  most of the computation and evaluation done by generic routines that
  have been tuned to be efficient.  One benefit of this package's
  model/approach is the facility to do unconditional and conditional
  simulation of the field for large numbers of arbitrary points. There
  is also the flexibility for estimating non-stationary covariances
  and also the case when the observations are a linear combination
  (e.g. an integral) of the spatial process.  Included are generic
  methods for prediction, standard errors for prediction, plotting of
  the estimated surface and conditional and unconditional simulation.
A general implementation of Structural Equation Models
	with latent variables (MLE, 2SLS, and composite likelihood
	estimators) with both continuous, censored, and ordinal
	outcomes (Holst and Budtz-Joergensen (2013)
	<doi:10.1007/s00180-012-0344-y>). Mixture latent variable models and non-linear latent variable models (two-stage)...		
	The package also provides methods for graph exploration (d-separation,
	back-door criterion), simulation of general non-linear latent variable
	models, and estimation of influence functions for a broad range of
	statistical models.
Lava plugin allowing combinations of left and right censored and
    binary outcomes.
Fit a variety of latent variable models, including confirmatory
   factor analysis, structural equation modeling and latent growth curve models.
Interactive shiny application for working with different kinds of
    latent variable analysis, with the 'lavaan' package. Graphical output for models
    are provided and different estimators are supported.
Fit structural equation models (SEM) including factor analysis,
    multivariate regression models with latent variables and many other latent
    variable models while correcting estimates, standard errors, and
    chi-square-derived fit measures for a complex sampling design. 
    Incorporate clustering, stratification, sampling weights, and 
    finite population corrections into a SEM analysis.
    Wrapper around packages lavaan and survey.
Plots path diagrams from models in lavaan using the plotting
    functionality from the DiagrammeR package. DiagrammeR provides nice path diagrams 
    via Graphviz, and these functions make it easy to generate these diagrams from a
    lavaan path model without having to write the DOT language graph specification.
Tools for model specification in the latent variable framework
    (add-on to the 'lava' package). The package contains three main functionalities:
    Wald tests/F-tests with improved control of the type 1 error in small samples,
    adjustment for multiple comparisons when searching for local dependencies,
    and adjustment for multiple comparisons when doing inference for multiple latent variable models.
The lava estimation is a new technique to recover signals that is the sum of a sparse and dense signals. The post-lava method corrects the shrinkage bias of lava. For more information on the lava estimation, see Chernozhukov, Hansen, and Liao (2017) <doi:10.1214/16-AOS1434>.
Client for 'Turfjs' (<http://turfjs.org>) for
    'geospatial' analysis. The package revolves around using 'GeoJSON'
    data. Functions are included for creating 'GeoJSON' data objects,
    measuring aspects of 'GeoJSON', and combining, transforming,
    and creating random 'GeoJSON' data objects.
Statistical tests widely utilized in biostatistics, public policy, and law. Along with the well-known tests for equality of means and variances, randomness, 
 measures of relative variability etc, the package contains new robust tests of symmetry, 
 omnibus and directional tests of normality, and their graphical counterparts such as 
 Robust QQ plot; a robust trend tests for variances etc. All implemented tests and methods 
 are illustrated by simulations and real-life examples from legal statistics, economics, 
 and biostatistics.
By combining constant, linear, and quadratic local models,
        lazy estimates the value of an unknown multivariate function on
        the basis of a set of possibly noisy samples of the function
        itself.  This implementation of lazy learning automatically
        adjusts the bandwidth on a query-by-query basis through a
        leave-one-out cross-validation.
Supplies a LazyData facility for packages which have data
		sets but do not provide LazyData: true.  A single function is
		is included, requireData, which is a drop-in replacement for
		base::require, but carrying the additional
		functionality. By default, it suppresses package
		startup messages as well.  See argument 'reallyQuitely'.
An alternative approach to non-standard evaluation using
    formulas. Provides a full implementation of LISP style 'quasiquotation',
    making it easier to generate code with other code.
An R Markdown html document format that provides the ability to lazily
    load plot outputs as the user scrolls over them.  This is useful for large R
    Markdown documents with many plots, as it allows for a fast initial page load and
    defers loading of individual graphics to the time that the user navigates near them.
Helper functions to build SQL statements
    for dbGetQuery or dbSendQuery under program control.
    They are intended to increase speed of coding and
    to reduce coding errors. Arguments are carefully checked,
    in particular SQL identifiers such as names of tables or columns.
    More patterns will be added as required.
Provides the functionality to write LaTeX code from within R
    without having to learn LaTeX.  Functionality also exists to create HTML
    and Markdown code.  While the functionality still exists to write
    complete documents with lazyWeave, it is generally easier to do so with
    with markdown and knitr.  lazyWeave's main strength now is the ability
    to design custom and complex tables for reporting results.
Latent budget analysis is a method for the analysis of a two-way
    contingency table with an exploratory variable and a response variable. It is
    specially designed for compositional data.
A wrapper built around the libLBFGS optimization library by Naoaki Okazaki. The lbfgs package implements both the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) and the Orthant-Wise Quasi-Newton Limited-Memory (OWL-QN) optimization algorithms. The L-BFGS algorithm solves the problem of minimizing an objective, given its gradient, by iteratively computing approximations of the inverse Hessian matrix. The OWL-QN algorithm finds the optimum of an objective plus the L1-norm of the problem's parameters. The package offers a fast and memory-efficient implementation of these optimization routines, which is particularly suited for high-dimensional problems.
Interfacing to Nocedal et al. L-BFGS-B.3.0 (2011) limited
	memory BFGS minimizer with bounds on parameters.
The package offers various length-bias corrections to
        survival curve estimation.
Maximum likelihood estimation of log-binomial regression with special functionality when the MLE is on the boundary of the parameter space.
Simulate expected equilibrium length composition, yield-per-recruit, and
    the spawning potential ratio (SPR) using the length-based SPR (LBSPR) model. Fit the LBSPR
    model to length data to estimate  selectivity, relative apical fishing mortality, and
    the spawning potential ratio for data-limited fisheries.
    See Hordyk et al (2016) <doi:10.1139/cjfas-2015-0422> for more information about the
    LBSPR assessment method.
Performs model fitting and significance estimation for Localised Co-Dependency between pairs of features of a numeric dataset.
This package performs a Latent Class Analysis of
        phenotypic measurements in pedigrees and a model selection
        based on one of two methods: likelihood-based cross-validation
        and Bayesian Information Criterion. It computes also individual
        and triplet child-parents weights in a pedigree using an
        upward-downward algorithm. It takes into account the familial
        dependence defined by the pedigree structure by considering
        that a class of a child depends on his parents classes via
        triplet-transition probabilities of the classes. The package
        handles the case where measurements are available on all
        subjects and the case where measurements are available only on
        symptomatic (i.e. affected) subjects. Distributions for
        discrete (or ordinal) and continuous data are currently
        implemented. The package can deal with missing data.
Variable selection for latent class analysis for model-based clustering of multivariate categorical data. The package implements a general framework for selecting the subset of variables with relevant clustering information and discard those that are redundant and/or not informative. The variable selection method is based on the approach of Fop et al. (2017) <doi:10.1214/17-AOAS1061> and Dean and Raftery (2010) <doi:10.1007/s10463-009-0258-9>. Different algorithms are available to perform the selection: stepwise, swap-stepwise and evolutionary stochastic search. Concomitant covariates used to predict the class membership probabilities can also be included in the latent class analysis model. The selection procedure can be run in parallel on multiple cores machines.
Local Discrimination via Latent Class Models
Baseline correction, normalization and linear combination fitting (LCF) 
    of X-ray absorption near edge structure (XANES) spectra.
    The package includes data loading of .xmu files exported from 'ATHENA' (Ravel and Newville, 2005) <doi:10.1107/S0909049505012719>. 
    Loaded spectra can be background corrected and all standards can be fitted at once.
    Two linear combination fitting functions can be used:
    (1) fit_athena(): Simply fitting combinations of standards as in ATHENA, 
    (2) fit_float(): Fitting all standards with changing baseline correction and edge-step normalization parameters.
This package contains (1) event-related brain potential data recorded from 10 participants at electrodes Fz, Cz, Pz, and Oz (0--300 ms) in the context of Antoine Tremblay's PhD thesis (Tremblay, 2009); (2) ERP amplitudes at electrode Fz restricted to the 100 to 175 millisecond time window; and (3) plotting data generated from a linear mixed-effects model.
The core of this 'Rcpp' based package is several functions to estimate the baseline hazard, frailty variance, and fixed effect parameter for a discrete-time shared frailty model with random effects. The functions are designed to analyze grouped time-to-event data accounting for family structure of related individuals (i.e., trios). The core functions include two processes: (1) evaluate the multivariable integration to compute the exact proportional hazards model based likelihood and (2) estimate the desired parameters using maximum likelihood estimation. The integration is evaluated by the 'Cuhre' algorithm from the 'Cuba' library (Hahn, T., Cuba-a library for multidimensional numerical integration, Comput. Phys. Commun. 168, 2005, 78-95 <doi:10.1016/j.cpc.2005.01.010>), and the source files of the 'Cuhre' function are included in this package. The maximization process is carried out using Brent's algorithm, with the 'C++' code file from John Burkardt and John Denker (Brent, R.,Algorithms for Minimization without Derivatives, Dover, 2002, ISBN 0-486-41998-3).
Bayesian population size estimation using non parametric latent-class models.
Estimation of various extensions of the mixed models including latent class mixed models, joint latent latent class mixed models and mixed models for curvilinear univariate or multivariate longitudinal outcomes using a maximum likelihood estimation method.
Collections of functions allowing random number generations and
    estimation of Liouville copulas.
Longitudinal genomics data and survival outcome are common in biomedical studies. It is of great interest to select genes related to the survival outcome. LCox is a computationally efficient tool for selecting genes related to the survival outcome using the longitudinal genomics data. LCox is powerful to detect different forms of dependence between the longitudinal biomarkers and the survival outcome.
Provides researchers and educators with easy-to-learn user friendly tools for calculating 
    key spatial statistics and to apply simple as well as advanced methods of spatial analysis in real data. 
    These include: Local Pearson and Geographically Weighted Pearson Correlation Coefficients, 
    Spatial Inequality Measures (Gini, Spatial Gini, LQ, Focal LQ), Spatial Autocorrelation 
    (Global and Local Moran's I), several Geographically Weighted Regression techniques and other 
    Spatial Analysis tools (other geographically weighted statistics). This package also contains functions for 
    measuring the significance of each statistic calculated, mainly based on Monte Carlo simulations.
Analysis of stock data ups and downs trend, the stock technical analysis indicators function have trend line, reversal pattern and market trend.
Implements latent Dirichlet allocation (LDA)
	     and related models.  This includes (but is not limited
	     to) sLDA, corrLDA, and the mixed-membership stochastic
	     blockmodel.  Inference for all of these models is
	     implemented via a fast collapsed Gibbs sampler written
	     in C.  Utility functions for reading/writing data
	     typically used in topic models, as well as tools for
	     examining posterior distributions are also included.
Select statistically similar research groups by backward selection using various robust algorithms, including a heuristic based on linear discriminant analysis, multiple heuristics based on the test statistic, and parallelized exhaustive search.
Tools for working with vectors and data sets that are too large
    to keep in memory. Extends the basic functionality provided in the 'lvec'
    package. Provides basis statistical functionality of 'lvec' objects, such
    as arithmetic operations and calculating means and sums. Also implements
    'data.frame'-like objects storing its data in 'lvec' objects.
For this first version only metrics to estimate the best fitting
    number of topics are implemented.
Tools to create an interactive web-based visualization of a
    topic model that has been fit to a corpus of text data using
    Latent Dirichlet Allocation (LDA). Given the estimated parameters of
    the topic model, it computes various summary statistics as input to
    an interactive visualization built with D3.js that is accessed via
    a browser. The goal is to help users interpret the topics in their
    LDA topic model.
Flexible procedures to compute local density-based outlier scores for ranking outliers.
  Both exact and approximate nearest neighbor search can be implemented, while also accommodating
  multiple neighborhood sizes and four different local density-based methods. It allows for
  referencing a random subsample of the input data or a user specified reference data set
  to compute outlier scores against, so both unsupervised and semi-supervised outlier
  detection can be implemented.
Computations related to group sequential boundaries.
             Includes calculation of bounds using the Lan-DeMets
             alpha spending function approach.
Four measures of linkage disequilibrium are provided: the usual r^2
        measure, the r^2_S measure (r^2 corrected by the structure
        sample), the r^2_V (r^2 corrected by the relatedness of
        genotyped individuals), the r^2_VS measure (r^2 corrected by
        both the relatedness of genotyped individuals and the structure
        of the sample).
Produces a graphical display, as a heat map, of measures
        of pairwise linkage disequilibria between SNPs. Users may
        optionally include the physical locations or genetic map
        distances of each SNP on the plot.
Hidden Markov Model (HMM) based on symmetric lambda distribution
    framework is implemented for the study of return time-series in the financial
    market. Major features in the S&P500 index, such as regime identification,
    volatility clustering, and anti-correlation between return and volatility,
    can be extracted from HMM cleanly. Univariate symmetric lambda distribution
    is essentially a location-scale family of exponential power distribution.
    Such distribution is suitable for describing highly leptokurtic time series
    obtained from the financial market. It provides a theoretically solid foundation
    to explore such data where the normal distribution is not adequate. The HMM
    implementation follows closely the book: "Hidden Markov Models for Time Series",
    by Zucchini, MacDonald, Langrock (2016).
ldlasso requires data be of class gwaa.data from the the
        package GenABEL
this package provides functions for Finding Locally
        D-optimal designs for Logistic, Negative Binomial, Poisson,
        Michaelis-Menten, Exponential, Log-Linear, Emax, Richards,
        Weibull and Inverse Quadratic regression models and also
        functions for auto-constructing Fisher information matrix and
        Frechet derivative based on some input variables and without
        user-interfere.
Implementation of most popular approaches to PD (probability of default) calibration: Quasi Moment Matching algorithm (D. Tasche), algorithm proposed by M. van der Burgt, K. Pluto and D. Tasche's most prudent estimation methodology.
Functions, methods, and data sets for fitting likelihood-based dimension reduction in regression, using principal fitted components (pfc), likelihood acquired directions (lad), covariance reducing models (core).
Linear dimension reduction subspaces can be uniquely defined using orthogonal projection matrices. This package provides tools to compute distances between such subspaces and to compute the average subspace. For details see Liski, E.Nordhausen K., Oja H., Ruiz-Gazen A. (2016) Combining Linear Dimension Reduction Subspaces <doi:10.1007/978-81-322-3643-6_7>.
Exact tests for Linkage Disequilibrium (LD) and Hardy-Weinberg Equilibrium (HWE). - 2-sided LD tests based on different measures of LD (Kulinskaya and Lewin 2008) - 1-sided Fisher's exact test for LD - 2-sided Haldane test for HWE (Wiggington 2005) - 1-sided test for inbreeding - conditional p-values proposed in Kulinskaya (2008) to overcome the problems of asymetric distributions (for both LD and HWE)
The algorithm Leabra (local error driven and associative
    biologically realistic algorithm) allows for the construction of artificial
    neural networks that are biologically realistic and balance supervised and
    unsupervised learning within a single framework. This package is based on
    the 'MATLAB' version by Sergio Verduzco-Flores, which in turn was based on
    the description of the algorithm by Randall O'Reilly (1996)
    <ftp://grey.colorado.edu/pub/oreilly/thesis/oreilly_thesis.all.pdf>. For
    more general (not 'R' specific) information on the algorithm Leabra see
    <https://grey.colorado.edu/emergent/index.php/Leabra>.
The leader clustering algorithm provides
 a means for clustering a set of data points. Unlike many other clustering
 algorithms it does not require the user to specify the number of clusters,
 but instead requires the approximate radius of a cluster as its primary
 tuning parameter. The package provides a fast implementation of this
 algorithm in n-dimensions using Lp-distances (with special cases for p=1,2,
 and infinity) as well as for spatial data using the Haversine
 formula, which takes latitude/longitude pairs as inputs and clusters
 based on great circle distances.
An interface for the image processing program 'ImageJ', which
    allows a rapid digital image analysis for particle sizes. This package includes
    function to write an 'ImageJ' macro which is optimized for a leaf area analysis by
    default.
Create and customize interactive maps using the 'Leaflet'
    JavaScript library and the 'htmlwidgets' package. These maps can be used
    directly from the R console, from 'RStudio', in Shiny applications and R Markdown
    documents.
An add-on package to the 'leaflet' package, which provides bindings for 'ESRI' services. This package allows a user to add 'ESRI' provided services such as 'MapService', 'ImageMapService', 'TiledMapService' etc. to a 'leaflet' map.
The 'leaflet' JavaScript library provides many plugins some of which
    are available in the core 'leaflet' package, but there are many more. It is not
    possible to support them all in the core 'leaflet' package. This package serves
    as an add-on to the 'leaflet' package by providing extra functionality via 'leaflet'
    plugins.
Add and modify small charts on an interactive map created with 
    package 'leaflet'. These charts can be used to represent at same time multiple 
    variables on a single map.
An R gallery for China and other geojson choropleth map in leaflet. Contains the geojson data for provinces, cities in China.
Display your spatial data on interactive web-maps using the open-source JavaScript library Leaflet. 'leafletR' provides basic web-mapping functionality to combine vector data and online map tiles from different sources. See <http://leafletjs.com> for more information on Leaflet.
Implementation of trigonometric functions to calculate the exposure of flat, tilted surfaces, such as leaves and slopes, to direct solar radiation. It implements the equations in A.G. Escribano-Rocafort, A. Ventre-Lespiaucq, C. Granado-Yela, et al. (2014) <doi:10.1111/2041-210X.12141> in a few user-friendly R functions. All functions handle data obtained with 'Ahmes' 1.0 for Android, as well as more traditional data sources (compass, protractor, inclinometer). The main function (star()) calculates the potential exposure of flat, tilted surfaces to direct solar radiation (silhouette to area ratio, STAR). It is equivalent to the ratio of the leaf projected area to total leaf area, but instead of using area data it uses spatial position angles, such as pitch, roll and course, and information on the geographical coordinates, hour, and date. The package includes additional functions to recalculate STAR with custom settings of location and time, to calculate the tilt angle of a surface, and the minimum angle between two non-orthogonal planes.
Implements the method described in "Network-based analysis of omics data: The LEAN method" [Gwinner Boulday (2016) <DOI:10.1093/bioinformatics/btw676>]
 Given a protein interaction network and a list of p-values describing a measure of interest (as e.g. differential gene expression) this method
 computes an enrichment p-value for the protein neighborhood of each gene and compares it to a background distribution of randomly drawn p-values.
 The resulting scores are corrected for multiple testing and significant hits are returned in tabular format.
Advances in sequencing technology now allow researchers to capture the expression profiles of individual cells. Several algorithms have been developed to attempt to account for these effects by determining a cell's so-called `pseudotime', or relative biological state of transition. By applying these algorithms to single-cell sequencing data, we can sort cells into their pseudotemporal ordering based on gene expression. LEAP (Lag-based Expression Association for Pseudotime-series) then applies a time-series inspired lag-based correlation analysis to reveal linearly dependent genetic associations.
Contains LEAPFrOG Gradient Optimisation and Expectation Maximisation functions for inferring parental admixture proportions from an offspring with SNP genotypes.
These functions take a gene expression value matrix, a
        primary covariate vector, an additional known covariates
        matrix.  A two stage analysis is applied to counter the effects
        of latent variables on the rankings of hypotheses.  The
        estimation and adjustment of latent effects are proposed by
        Sun, Zhang and Owen (2011).  "leapp" is developed in the
        context of microarray experiments, but may be used as a general
        tool for high throughput data sets where dependence may be
        involved.
Regression subset selection, including exhaustive search.
Provides methods for estimating borders of uniform distribution on
  the interval (one-dimensional) and on the elliptical domain (two-dimensional)
  under measurement errors. For one-dimensional case, it also estimates the
  length of underlying uniform domain and tests the hypothesized length against
  two-sided or one-sided alternatives. For two-dimensional case, it estimates
  the area of underlying uniform domain. It works with numerical inputs as well
  as with pictures in JPG format.
A collection of functions helpful in learning the basic tenets of Bayesian statistical inference.  It contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions.  It contains MCMC algorithms for summarizing posterior distributions defined by the user.  It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.
Contains some functions to learn and teach basic plane Geometry at undergraduate level with the aim of being helpful to young students with little programming skills.
Implements common learning curve production functions. It incorporates
    Crawford's and Wright's learning curve functions to compute unit and cumulative 
    block estimates for time (or cost) of units along with an aggregate learning 
    curve. It also provides delta and error functions and some basic learning curve 
    plotting functions.along with functions to compute aggregated learning curves, 
    error rates, and to visualize learning curves.
Crabs in the English channel, deer skulls, English
    monarchs, half-caste Manga characters, Jamaican cities,
    Shakespeare's The Tempest, drugged up cyclists and sexually
    transmitted diseases.
Implementations of several basic neural network concepts in R, as based on posts on \url{http://qua.st/}.
Create interactive tutorials using R Markdown. Use a combination 
  of narrative, figures, videos, exercises, and quizzes to create self-paced
  tutorials for learning about R and R packages.
Datasets used in the book "Learn R ...as you learnt your mother
    tongue" by Pedro J. Aphalo (2017) <https://leanpub.com/learnr>.
Allows students to use R as an interactive educational environment
    for statistical concepts, ranging from p-values to confidence intervals
	to stability in time series.
Functions to download data from the SIE, which is the Statistical Information System (Sistema de Información Estadística) in the Statistical Portal of the Government of Castilla y León (Spain) <https://estadistica.jcyl.es>.
Utilizing phylogenetic and functional information for the analyses of ecological datasets. The analyses include methods for quantifying the phylogenetic and functional diversity of assemblages.
Constructs genotype x environment interaction (GxE) models where
    G is a weighted sum of genetic variants (genetic score) and E is a weighted
    sum of environments (environmental score) using the alternating optimization algorithm 
    by Jolicoeur-Martineau et al. (2017) <arXiv:1703.08111>. This approach has greatly 
    enhanced predictive power over traditional GxE models which include only a single 
    genetic variant and a single environmental exposure. Although this approach was 
    originally made for GxE modelling, it is flexible and does not require the use of 
    genetic and environmental variables. It can also handle more than 2 latent variables 
    (rather than just G and E) and 3-way interactions or more. The LEGIT model produces 
    highly interpretable results and is very parameter-efficient thus it can even be 
    used with small sample sizes (n < 250). Tools to determine the type of interaction
    (vantage sensitivity, diathesis-stress or differential susceptibility), with any 
    number of genetic variants or environments, are available <arXiv:1712.04058>.
Estimate the slope and intercept of a bivariate
	linear relationship by calculating a posterior density
	that is invariant to interchange and scaling of the
	coordinates.
Functions and datasets for the
        reader of the book "Le logiciel R: Maitriser le langage,
        Effectuer des analyse statistiques". The documentation and help
        pages are written in french.
Functions for working with legends and axis lines of 'ggplot2',
    facets that repeat axis lines on all panels, and some 'knitr' extensions.
Functions to access Lending Club's API and assist the investor manage 
	their account. Lending Club is a peer-to-peer lending service where loans are 
	broken up into $25 notes that investors buy with the expectation of earning a 
	return on the interest. You can learn more about the API here:
	<http://www.lendingclub.com/developers/lc-api.action>.
Generates quotes from 'Lero Lero', a database for meaningless sentences filled with corporate buzzwords, intended to be used as corporate lorem ipsum (see <http://www.lerolero.com/> for more information). Unfortunately, quotes are currently portuguese-only.
Each function accomplishes the work of several or more standard R functions. For example, two function calls, Read() and CountAll(), read the data and generate summary statistics for all variables in the data frame, plus histograms and bar charts as appropriate.  Other functions provide for descriptive statistics, a comprehensive regression analysis, analysis of variance and t-test, plotting including the introduced here Violin/Box/Scatter plot for a numerical variable, bar chart, histogram, box plot, density curves, calibrated power curve, reading multiple data formats with the same function call, variable labels, color themes, Trellis graphics and a built-in help system. A confirmatory factor analysis of multiple indicator measurement models is available, as are pedagogical routines for data simulation such as for the Central Limit Theorem. Compatible with 'RStudio' and 'knitr' including generation of R markdown instructions for interpretative output.
This package contains some simple objects and functions to do 
             statistics using linear models and a Bayesian framework.
R functions for handling, processing, and analyzing geographic
    data on species' distributions and environmental variables.
Utilities for formatting strings and character
    vectors to for capitalization, word break and white space. Supported formats
    are: snake_case, spine-case, camelCase, PascalCase, Title Case, UPPERCASE,
    lowercase, Sentence case or combinations thereof. 'lettercase' strives to
    provide a simple, consistent, intuitive and high performing interface.
A collection of lexical hash tables, dictionaries, and word lists.
Provides easy access for sentiment lexicons for those who want to do text analysis in Portuguese texts.
    As of now, two Portuguese lexicons are available: 'SentiLex-PT02' and 'OpLexicon' (v2.1 and v3.0).
My PhD supervisor once told me that everyone doing newspaper
 analysis starts by writing code to read in files from the 'LexisNexis'
 newspaper archive (retrieved e.g., from <http://www.nexis.com/> or any of the 
 partner sites). However, while this is a nice exercise I do recommend, not
 everyone has the time. This package takes TXT files downloaded from the
 newspaper archive of 'LexisNexis' in Since this package takes in TXT files
 which are unstructured in the sense that beginning and end of an article are
 not clearly indicated, the main function lnt_read() relies on certain keywords
 that signal to R where an article begins, ends and where meta-data is stored.
 lnt_checkFiles() thus tests if all keywords are in place. Every article in every
 TXT file should start with "X of X DOCUMENTS" and end with "LANGUAGE:". The
 end of the metadata is usually indicated by "LENGTH:". Some measures were
 taken to eliminate problems but where these keywords appear inside an article
 or headline, test1 or test2 from the lnt_checkFiles() will return FALSE and
 lnt_read() will not be able to do its job. In these cases, it is recommended to
 slightly alter the TXT files, e.g. by changing a headline to "language: never
 stop learning new ones" instead of "LANGUAGE: never stop learning new
 ones"---as "language:" without capital letters is not picked up by the
 functions.
Functions to plot Lexis Diagrams for Demographic purposes.
An R implementation of the LexRank algorithm described by G. Erkan and D. R. Radev (2004) <DOI:10.1613/jair.1523>.
Provides an extension to factors called 'lfactor' that are similar
    to factors but allows users to refer to 'lfactor' levels by either the level or
    the label.
Functions for performing and visualizing Local Fisher Discriminant
    Analysis(LFDA), Kernel Fisher Discriminant Analysis(KLFDA), and Semi-supervised
    Local Fisher Discriminant Analysis(SELF).
Suite of R functions for the estimation of the local false discovery rate (LFDR) using Type II maximum likelihood estimation (MLE).
New empirical Bayes methods aiming at analyzing the association of single nucleotide polymorphisms (SNPs) to some particular disease are implemented in this package. The package uses local false discovery rate (LFDR) estimates of SNPs within a sample population defined as a  "reference class" and discovers if SNPs are associated with the corresponding disease. Although SNPs are used throughout this document, other biological data such as protein data and other gene data can be used. Karimnezhad, Ali and Bickel, D. R. (2016) <http://hdl.handle.net/10393/34889>.
Transforms away factors with many levels prior to doing an OLS. 
  Useful for estimating linear models with multiple group fixed effects, and for
  estimating linear models which uses factors with many levels as pure control variables.
  Includes support for instrumental variables, conditional F statistics for weak instruments,
  robust and multi-way clustered standard errors, as well as limited mobility bias correction.
Various algorithms related to linguistic fuzzy logic: mining for linguistic fuzzy association
    rules, composition of fuzzy relations, performing perception-based logical deduction (PbLD), 
    and forecasting time-series using fuzzy rule-based ensemble (FRBE).
The "Manual on Low-flow Estimation and Prediction", published by
    the World Meteorological Organisation (WMO), gives a comprehensive summary on
    how to analyse stream flow data focusing on low-flows. This packages provides
    functions to compute the described statistics and produces plots similar to the
    ones in the manual.
An implementation of locally Gaussian distributions. It provides methods for
    implementing the locally Gaussian density estimator (LGDE) by Otneim and Tjøstheim (2017a) 
    <doi:10.1007/s11222-016-9706-6>, as well as the corresponding estimator for conditional 
    density functions by Otneim and Tjøstheim (2017b) <doi:10.1007/s11222-017-9732-z>.
Tools for linear grouping analysis. Three user-level
        functions: gap, rlga and lga.
Simulation and estimation of univariate and multivariate log-GARCH models. The main functions of the package are: lgarchSim(), mlgarchSim(), lgarch() and mlgarch(). The first two functions simulate from a univariate and a multivariate log-GARCH model, respectively, whereas the latter two estimate a univariate and multivariate log-GARCH model, respectively.
Spatial and spatio-temporal modelling of point patterns using the
    log-Gaussian Cox process. Bayesian inference for spatial, spatiotemporal,
    multivariate and aggregated point processes using Markov chain Monte Carlo.
Functions for genome-wide association studies (GWAS)/gene-environment-wide interaction studies (GEWIS) with longitudinal outcomes and exposures. He et al. (2017) "Set-Based Tests for Gene-Environment Interaction in Longitudinal Studies" and He et al. (2017) "Rare-variant association tests in longitudinal studies, with an application to the Multi-Ethnic Study of Atherosclerosis (MESA)".
Functions for the longitudinal genetic random field method (He et al., 2015, <doi:10.1111/biom.12310>) to test the association between a longitudinally measured quantitative outcome and a set of genetic variants in a gene/region.
A very simple implementation of a class for 
	     longitudinal data.
Fits sex-specific life-history models for fish and other taxa where some of the individuals have unknown sex.
Provides a number of methods for creating and augmenting Latin Hypercube Samples.
R interface to the open-source, ANSI C library 'libamtrack' (http://libamtrack.dkfz.org). 'libamtrack' provides computational routines for the prediction of detector response and radiobiological efficiency in heavy charged particle beams. It is designed for research in proton and ion dosimetry and radiotherapy. 'libamtrack' also includes many auxiliary physics routines for proton and ion beams. Original package and C-to-R conversion routines developed by Felix A. Klein.
Basic infrastructure for linear test statistics and permutation
  inference in the framework of Strasser and Weber (1999) <http://epub.wu.ac.at/102/>. 
  This package must not be used by end-users. CRAN package 'coin' implements all 
  user interfaces and is ready to be used by anyone.
A wrapper around the 'LIBLINEAR' C/C++ library for machine
    learning (available at
    <http://www.csie.ntu.edu.tw/~cjlin/liblinear>). 'LIBLINEAR' is
    a simple library for solving large-scale regularized linear
    classification and regression. It currently supports
    L2-regularized classification (such as logistic regression,
    L2-loss linear SVM and L1-loss linear SVM) as well as
    L1-regularized classification (such as L2-loss linear SVM and
    logistic regression) and L2-regularized support vector
    regression (with L1- or L2-loss). The main features of
    LiblineaR include multi-class classification (one-vs-the rest,
    and Crammer & Singer method), cross validation for model
    selection, probability estimates (logistic regression only) or
    weights for unbalanced data. The estimation of the models is
    particularly fast as compared to other libraries.
Solving the linear SVM problem with coordinate descent
    is very efficient and is implemented in one of the most often used packages,
    'LIBLINEAR' (available at http://www.csie.ntu.edu.tw/~cjlin/liblinear).
    It has been shown that the uniform selection of coordinates can be
    accelerated by using an online adaptation of coordinate frequencies (ACF).
    This package implements ACF and is based on 'LIBLINEAR' as well as
    the 'LiblineaR' package (<https://cran.r-project.org/package=LiblineaR>).
    It currently supports L2-regularized L1-loss as well as L2-loss linear SVM.
    Similar to 'LIBLINEAR' multi-class classification (one-vs-the rest, and
    Crammer & Singer method) and cross validation for model selection is
    supported. The training of the models based on ACF is much faster than
    standard 'LIBLINEAR' on many problems.
Efficient procedures for fitting the regularization path
    for linear, binomial, multinomial, Ising and Potts models with lasso,
    group lasso or column lasso(only for multinomial) penalty.
    The package uses Linearized Bregman Algorithm to solve the
    regularization path through iterations. Bregman Inverse Scale Space Differential
    Inclusion solver is also provided for linear model with lasso penalty.
Generate a local library copy with relevant packages. 
    All packages currently found within the search path - except base packages - 
    will be copied to the directory provided and can be used later on with the 
    .libPaths() function.
Handle 'PharmML' (Pharmacometrics Markup Language) standard output (SO) XML files.
    SO files can be created, read, manipulated and written through a
    data binding from the XML structure to a tree structure of R objects.
Tools for fast and accurate evaluation of skew stable distributions (CDF, PDF and quantile functions), random number generation and parameter estimation.
Estimates predictive states from spatio-temporal data and
    consequently can provide provably optimal forecasts.
    Currently this implementation
    supports an N-dimensional spatial grid observed over equally spaced time
    intervals. E.g. a video is a 2D spatial systems observed over time. This
    package implements mixed LICORS, has plotting tools (for (1+1)D and (2+1)D
    systems), and methods for optimal forecasting.  Due to memory limitations
    it is recommend to only analyze (1+1)D systems.
This package presents a general method for assessing the
        local influence of minor perturbations of case weight for the
        linear regression models.
Airborne LiDAR (Light Detection and Ranging) interface for data
    manipulation and visualization. Read/write 'las' and 'laz' files, computation
    of metrics in area based approach, point filtering, artificial point reduction,
    classification from geographic data, normalization, individual tree segmentation
    and other manipulations.
Classes and methods that allow the user to manage life table,
    actuarial tables (also multiple decrements tables). Moreover, functions to easily
    perform demographic, financial and actuarial mathematics on life contingencies
    insurances calculations are contained therein.
Provides in built datasets and three functions.
  These functions are mobility_index, nonStanTest and linkedLives.  The mobility_index
  function facilitates the calculation of lifecourse fluidity, whilst the nonStanTest and the 
  linkedLives functions allow the user to determine the probability that the observed sequence data 
  was due to chance.  The linkedLives function acknowledges the fact that some individuals may
  have identical sequences.
  The datasets available provide sequence data on marital status(maritalData) 
  and mobility (mydata) for a selected group of individuals from the British Household Panel Study
  (BHPS).  In addition, personal and house ID's for 100 individuals are provided in a 
  third dataset (myHouseID) from the BHPS.
Likelihood-based estimation of individual growth and sexual maturity models for organisms, usually fish and invertebrates. It includes methods for data organization, plotting standard exploratory and analytical plots, predictions.
Provides a framework for combining self-data (exercise, sleep, etc.) from multiple sources (fitbit, Apple Health), creating visualizations, and experimenting on onself.
Functions supplied in this package will implement
        discriminant analysis to select an appropriate life table
        family, select an appropriate alpha level based on a desired
        life expectancy at birth, produce a model mortality pattern
        based on family and level as well as plot the results.
Compute the top decile lift and plot the lift curve. Cumulative lift curves are also supported.
Implementations of Hurst exponent estimators based on the relationship between wavelet lifting scales and wavelet energy.
Persistent reproducible reporting by containerization of R Markdown documents.
The purpose of this package is to enable usage of lightningviz server to be accessible from R. The
   server itself can be found at http://lightning-viz.org/ and is required to work with this package. Package
   by itself cannot and will not create any visualizations.
Lights Out is a puzzle game consisting of a grid of lights
    that are either on or off. Pressing any light will toggle it and its
    adjacent lights. The goal of the game is to switch all the lights off. This
    package provides an interface to play the game on different board sizes,
    both through the command line or with a visual application. Puzzles can
    also be solved using the automatic solver included. View a demo
    online at http://daattali.com/shiny/lightsout/.
A Poisson Subordinated Distribution to capture major
        leptokurtic features in log-return time series of financial
        data.
Tools for maximum likelihood estimation of parameters 
  of scientific models.
Functions for computing the r and r* statistics for inference on an arbitrary scalar function of model parameters, plus some code for the (modified) profile likelihood.
Provides likelihood functions as defined by Fisher (1922)
    <doi:10.1098/rsta.1922.0009> and a function that creates likelihood 
    functions from density functions. The functions are meant to aid in 
    education of likelihood based methods.
Tools to determine DNA profile Weight of Evidence. 
            For further information see the 'likeLTD' guide provided, 
            Balding, D.J. (2013) <DOI:10.1073/pnas.1219739110>,
	    or Steele, C.D. et al. (2016) <DOI:10.1515/sagmb-2016-0038>.
An approach to analyzing Likert response items, with an emphasis on visualizations. 
    The stacked bar plot is the preferred method for presenting Likert results. Tabular results
    are also implemented along with density plots to assist researchers in determining whether 
    Likert responses can be used quantitatively instead of qualitatively. See the likert(), 
    summary.likert(), and plot.likert() functions to get started.
Functions that read and solve linear inverse problems (food web problems, linear programming problems).
  These problems find solutions to linear or quadratic functions:
  min or max (f(x)), where f(x) = ||Ax-b||^2 or f(x) = sum(ai*xi)
  subject to equality constraints Ex=f and inequality constraints Gx>=h. Uses package limSolve.
When building complex models, it is often difficult to explain why
    the model should be trusted. While global measures such as accuracy are
    useful, they cannot be used for explaining why a model made a specific
    prediction. 'lime' (a port of the 'lime' 'Python' package) is a method for
    explaining the outcome of black box models by fitting a local model around
    the point in question an perturbations of this point. The approach is
    described in more detail in the article by Ribeiro et al. (2016) 
    <arXiv:1602.04938>.
Values below a specified limit of detection are stacked in
        rows in order to reduce overplotting and create a clear
        graphical representation of your data.
Functions that (1) find the minimum/maximum of a linear or quadratic function:
  min or max (f(x)), where f(x) = ||Ax-b||^2 or f(x) = sum(a_i*x_i)
  subject to equality constraints Ex=f and/or inequality constraints Gx>=h,
  (2) sample an underdetermined- or overdetermined system Ex=f subject to Gx>=h, and if applicable Ax~=b,      
  (3) solve a linear system Ax=B for the unknown x. It includes banded and tridiagonal linear systems. 
  The package calls Fortran functions from 'LINPACK'.
Short for 'linear binning', the linbin package provides functions
    for manipulating, binning, and plotting linearly referenced data. Although
    developed for data collected on river networks, it can be used with any interval
    or point data referenced to a 1-dimensional coordinate system. Flexible bin
    generation and batch processing makes it easy to compute and visualize variables
    at multiple scales, useful for identifying patterns within and between variables
    and investigating the influence of scale of observation on data interpretation.
Estimate and confidence/credible intervals for an unknown
    regressor x0 given an observed y0.
L-systems or Lindenmayer systems are parallel rewriting systems which can
    be used to simulate biological forms and certain kinds of fractals.
    Briefly, in an L-system a series of symbols in a string are replaced
    iteratively according to rules to give a more complex string. Eventually,
    the symbols are translated into turtle graphics for plotting. Wikipedia has
    a very good introduction: en.wikipedia.org/wiki/L-system This package
    provides basic functions for exploring L-systems.
Provides a set of streamlined functions that allow
    easy generation of linear regression diagnostic plots necessarily 
    for checking linear model assumptions.
    This package is meant for easy scheming of linear 
    regression diagnostics, while preserving merits of 
    "The Grammar of Graphics" as implemented in 'ggplot2'.
    See the 'ggplot2' website for more information regarding the
    specific capability of graphics.
Computes  the  probability density,  the  cumulative  distribution, the quantile and
   the hazard rate functions and generates random deviates from the discrete and continuous
   Lindley  distribution  as  well  as  for 19 of  its  modifications. It  also  generates censored
   random deviates from any probability  distribution available in R.
Provides tools to manipulate formulas, such as getting x, y or contrasts from the model/formula, and functions to evaluate and check the marginal effects of a linear model.
It is a linear algorithm to simulate quantiles of multiscale statistics 
            under hull hypothesis for multiscale change-point segmentation. 
            The reference is in preparation.
Consider linear regression model Y = Xb + error where the distribution function of errors is unknown, but errors are independent and symmetrically distributed. The package contains a function named LRMDE which takes Y and X as input and returns minimum distance estimator of parameter b in the model.
Create maps made of lines. The package contains two functions: linemap() and getgrid(). linemap() displays a map made of lines using a data frame of gridded data. getgrid() transforms a set of polygons (sf objects) into a suitable data frame for linemap().
Fits a linear excess relative risk model by maximum likelihood, possibly including several variables and allowing for lagged exposures.
Tools for detecting and correcting sample mix-ups between two sets
    of measurements, such as between gene expression data on two tissues.
Provides R with the Glottolog database <http://glottolog.org> and some more abilities for purposes of linguistic mapping. The Glottolog database contains the catalogue of languages of the world. This package helps researchers to make a linguistic maps, using philosophy of the Cross-Linguistic Linked Data project <http://clld.org/>, which allows for while at the same time facilitating uniform access to the data across publications. A tutorial for this package is available on GitHub pages <https://ropensci.github.io/lingtypology/> and package vignette. Maps created by this package can be used both for the investigation and linguistic teaching. In addition, package provides an ability to download data from typological databases such as WALS, AUTOTYP and others.
Functions to simplify the linking of open source GIS and remote sensing related command line interfaces.
Produces high resolution, publication ready linkage maps
    and quantitative trait loci maps. Input can be output from 'R/qtl',
    simple text or comma delimited files. Output is currently
    a portable document file.
Link communities reveal the nested and overlapping structure in networks, and uncover the key nodes that form connections to multiple communities. linkcomm provides a set of tools for generating, visualizing, and analysing link communities in networks of arbitrary size and type. The linkcomm package also includes tools for generating, visualizing, and analysing Overlapping Cluster Generator (OCG) communities.
A class that links matrix-like objects (nodes) by rows or by
    columns while behaving similarly to a base R matrix. Very large matrices
    are supported if the nodes are memory-mapped objects.
A linkage information based method for imputing missing diploid genotypes
Creates kinematic and static force models of 3D levers and linkage mechanisms, with particular application to the fields of engineering and biomechanics.
A 'LaTeX' Letter class for 'rmarkdown', using the
 'pandoc-letter' template adapted for use with 'markdown'.
This package implements the methodology of
        Likelihood-based Imprecise Regression (LIR) for the case of
        linear regression with interval data.
This package can be used to solve Linear Programming /
        Linear Optimization problems by using the simplex algorithm.
Interactive visualization of effects, response functions 
    and marginal effects for different kinds of regression models. In this version 
    linear regression models, generalized linear models, generalized additive
    models and linear mixed-effects models are supported.  
    Major features are the interactive approach and the handling of the effects of categorical covariates: 
    if two or more factors are used as covariates every combination of the levels of each 
    factor is treated separately. The automatic calculation of 
    marginal effects and a number of possibilities to customize the graphical output 
    are useful features as well.
Estimate the mean of a Gaussian vector, by choosing among a large collection of estimators. In particular it solves the problem of variable selection by choosing the best predictor among  predictors emanating from different methods as lasso, elastic-net, adaptive lasso, pls, randomForest. Moreover, it can be applied for choosing the tuning parameter in a Gauss-lasso procedure.
Variable elimination (Gaussian elimination, Fourier-Motzkin elimination), 
    Moore-Penrose pseudoinverse, reduction to reduced row echelon form, value substitution,  
    projecting a vector on the convex polytope described by a system of (in)equations, 
    simplify systems by removing spurious columns and rows and collapse implied equalities, 
    test if a matrix is totally unimodular, compute variable ranges implied by linear
    (in)equalities.
Checks adherence to a given style, syntax errors and possible
    semantic issues.  Supports on the fly checking of R code edited with 'RStudio IDE', 'Emacs',
    'Vim', 'Sublime Text' and 'Atom'.
Provides three classes: Queue, PriorityQueue and Stack. Queue is just a
    "plain vanilla" FIFO queue; PriorityQueue orders items according to priority. Stack implements LIFO.
Support vector machines (SVMs) and related kernel-based learning
    algorithms are a well-known class of machine learning algorithms, for
    non-parametric classification and regression.
    liquidSVM is an implementation of SVMs whose key features are:
    fully integrated hyper-parameter selection,
    extreme speed on both small and large data sets,
    full flexibility for experts, and
    inclusion of a variety of different learning scenarios:
    multi-class classification, ROC, and Neyman-Pearson learning, and
    least-squares, quantile, and expectile regression.
Performs Bayesian linear regression and forecasting in astronomy. The method accounts for heteroscedastic errors in both the independent and the dependent variables, intrinsic scatters (in both variables) and scatter correlation, time evolution of slopes, normalization, scatters, Malmquist and Eddington bias, upper limits and break of linearity. The posterior distribution of the regression parameters is sampled with a Gibbs method exploiting the JAGS library.
Though SRFI-1 scopes both list-processing and higher-order
        programming, we'll save some list-orthogonal functions for the
        `functional' package; this is freely a mixture of
        implementation and API.
This is an unofficial package aimed at automating the
        import of LISREL output in R.  This package or its maintainer
        is not in any way affiliated with the creators of LISREL and
        SSI, Inc.
Allows researchers to conduct multivariate
    statistical analyses of survey data with list experiments. This
    survey methodology is also known as the item count technique or
    the unmatched count technique and is an alternative to the commonly
    used randomized response method. The package implements the methods
    developed by Imai (2011) <doi:10.1198/jasa.2011.ap10415>, 
    Blair and Imai (2012) <doi:10.1093/pan/mpr048>, 
    Blair, Imai, and Lyall (2013) <doi:10.1111/ajps.12086>, 
    Imai, Park, and Greene (2014) <doi:10.1093/pan/mpu017>,
    Aronow, Coppock, Crawford, and Green (2015) <doi:10.1093/jssam/smu023>, 
    and Chou, Imai, and Rosenfeld (2016) 
    <http://imai.princeton.edu/research/files/auxiliary.pdf>. 
    This includes a Bayesian MCMC implementation of regression for the 
    standard and multiple sensitive item list experiment designs and a 
    random effects setup, a Bayesian MCMC hierarchical regression model 
    with up to three hierarchical groups, the combined list experiment 
    and endorsement experiment regression model, a joint model of the 
    list experiment that enables the analysis of the list experiment as 
    a predictor in outcome regression models, and a method for combining 
    list experiments with direct questions. In addition, the package
    implements the statistical test that is designed to detect
    certain failures of list experiments, and a placebo test
    for the list experiment using data from direct questions.
A toolbox for R arrays. Flexibly split, bind, reshape, modify, 
    subset and name arrays.
Construction of list-based rules, i.e. a list of if-then clauses, to estimate the optimal dynamic treatment regime.
List environments are environments that have list-like properties.  For instance, the elements of a list environment are ordered and can be accessed and iterated over using index subsetting, e.g. 'x <- listenv(a = 1, b = 2); for (i in seq_along(x)) x[[i]] <- x[[i]] ^ 2; y <- as.list(x)'.
Tests for independence between X and Y computed from a paired sample (x1,y1),...(xn,yn) of (X,Y), using one of the following statistics (a) the Longest Increasing Subsequence (Ln), (b) JLn, a Jackknife version of Ln or (c) JLMn, a Jackknife version of the longest monotonic subsequence. This family of tests can be applied under the assumption of continuity of X and Y.
A lightweight utility for converting lists to tidy data frames.
R lists, especially nested lists, can be very difficult to
    visualize or represent. Sometimes 'str()' is not enough, so this suite of
    htmlwidgets is designed to help see, understand, and maybe even modify your R
    lists.  The function 'reactjson()' requires a package
    'reactR' that can be installed from CRAN or <https://github.com/timelyportfolio/reactR>.
Provides a function that, as an alternative to base::list, allows
    default values to be inherited from another list.
Temporary and permanent message queues for R. Built on top of
    'SQLite' databases. 'SQLite' provides locking, and makes it possible
    to detect crashed consumers. Crashed jobs can be automatically marked
    as "failed", or put in the queue again, potentially a limited number of times.
A scripting and command-line front-end
 is provided by 'r' (aka 'littler') as a lightweight binary wrapper around
 the GNU R language and environment for statistical computing and graphics.
 While R can be used in batch mode, the r binary adds full support for
 both 'shebang'-style scripting (i.e. using a  hash-mark-exclamation-path
 expression as the first line in scripts) as well as command-line use in
 standard Unix pipelines. In other words, r provides the R language without
 the environment.
Linear Liu regression coefficient's estimation and testing with different Liu related measures such as MSE, R-squared etc.
  REFERENCES
  i.   Akdeniz and Kaciranlar (1995) <doi:10.1080/03610929508831585>
  ii.  Druilhet and Mom (2008) <doi:10.1016/j.jmva.2006.06.011>
  iii. Imdadullah, Aslam, and Saima (2017)
  iv.  Liu (1993) <doi:10.1080/03610929308831027>
  v.   Liu (2001) <doi:10.1016/j.jspi.2010.05.030>.
Interpretability of complex machine learning models is a growing concern.
    This package helps to understand key factors that drive the 
    decision made by complicated predictive model (so called black box model). 
    This is achieved through local approximations that are either based on 
    additive regression like model or CART like model that allows for 
    higher interactions. The methodology is based on Tulio Ribeiro, Singh, Guestrin (2016) <doi:10.1145/2939672.2939778>.
    More details can be found in Staniak, Biecek (2018) <arXiv:1804.01955>.
Provides a wrapper around LiveChat's API. The R functions allow for
    one to extract chat sessions, raw text of chats between agents and customers and
    events.
Fits and tests logistic joinpoint models.
Provides functionality to train and evaluate algorithm selection models for portfolios.
LLE is a non-linear algorithm for mapping high-dimensional
        data into a lower dimensional (intrinsic) space. This package
        provides the main functions to performs the LLE alogrithm
        including some enhancements like subset selection, calculation
        of the intrinsic dimension etc.
Applies local log-linear capture-recapture models (LLLMs) for
    closed populations, as described in the doctoral thesis of Zachary Kurtz.
    The method is relevant when there are 3-5 capture occasions, with auxiliary
    covariates available for all capture occasions.  As part of estimating the
    number of missing population units, the method estimates the "rate of
    missingness" as it varies over the covariate space.  In addition,
    user-friendly functions are provided to recreate (approximately) the method
    of Zwane and van der Heijden (2004), which applied the VGAM package
    in a way that is closely related to LLLMs.
Fits the Logit Leaf Model, makes predictions and visualizes the output. (De Caigny et al., (2018) <DOI:10.1016/j.ejor.2018.02.009>).
Density, distribution function, quantile function and random generation for the L-Logistic distribution with parameters m and b. The parameter m is the median of the distribution.
Analyses experimental data from liquid-liquid phase diagrams and
    provide a simple way to obtain its parameters and a simplified report. Designed
    initially to analyse Aqueous Two-Phases Systems, the package will include (every
    other update) new functions in order to comprise useful tools in liquid-liquid
    analysis.
Adds standardized regression coefficients to objects created by lm. Also extends the S3 methods print, summary and coef with additional boolean argument standardized.
Exact significance tests for a changepoint in linear or multiple linear regression.  
  Confidence regions with exact coverage probabilities for the changepoint.  The method is from
  Knowles, Siegmund and Zhang (1991) <doi:10.1093/biomet/78.1.15>.
Fit linear and generalized linear mixed-effects models.
    The models and their components are represented using S4 classes and
    methods.  The core computational algorithms are implemented using the
    'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".
This package includes a function to fit a linear
        mixed-effects model in the formulation described in Laird and
        Ware (1982) but allowing for censored normal responses. In this
        version, the with-in group errors are assumed independent and
        identically distributed.
Performs Genome-Wide Association analysis for diverse populations and
             for multi-environment and multi-trait analysis using linear mixed models.
Performs QTL mapping analysis for balanced and for
  multi-environment and multi-trait analysis using mixed models.
  Balanced population, single trait, single environment QTL mapping is
  performed through marker-regression (Haley and Knott (1992) <DOI:10.1038/hdy.1992.131>,
  Martinez and Curnow (1992) <DOI:10.1007/BF00222330>,
  while multi-environment and multi-trait QTL
  mapping is performed through linear mixed models.
  These functions could use any of the following populations: double haploid,
  F2, recombinant inbred lines, back-cross, and 4-way crosses.
  Performs a Single Marker Analysis, a Single Interval Mapping,
  or a Composite Interval Mapping analysis, and then constructs a final model
  with all of the relevant QTL.
The functions in this package implement the safety monitoring procedures proposed in the paper titled "Detection of unusual increases in MRI lesion counts in individual multiple sclerosis patients" by Zhao, Y., Li, D.K.B., Petkau, A.J., Riddehough, A., Traboulsee, A., published in Journal of the American Statistical Association in 2013. The procedure first models longitudinally collected count variables with a negative binomial mixed-effect regression model. To account for the correlation among repeated measures from the same patient, the model has subject-specific random intercept, which can be modelled with a gamma or log-normal distributions. One can also choose the semi-parametric option which does not assume any distribution for the random effect. These mixed-effect models could be useful beyond the application of the safety monitoring. The maximum likelihood methods are used to estimate the unknown fixed effect parameters of the model. Based on the fitted model, the personalized activity index is computed for each patient. Lastly, this package is companion to R package lmeNBBayes, which contains the functions to compute the Personalized Activity Index in Bayesian framework.
The functions in this package implement the safety monitoring procedures proposed in the paper titled "A flexible mixed effect negative binomial regression model for detecting unusual increases in MRI lesion counts in individual multiple sclerosis patients" by Kondo, Y., Zhao, Y. and Petkau, A.J. The procedure first models longitudinally collected count variables with a negative binomial mixed-effect regression model. To account for the correlation among repeated measures from the same patient, the model has subject-specific random intercept, which is modelled with the infinite mixture of Beta distributions, very flexible distribution that theoretically allows any form. The package also has the option of a single beta distribution for random effects. These mixed-effect models could be useful beyond the application of the safety monitoring. The inference is based on MCMC samples and this package contains a Gibbs sampler to sample from the posterior distribution of the negative binomial mixed-effect regression model. Based on the fitted model, the personalized activity index is computed for each patient. Lastly, this package is companion to R package lmeNB, which contains the functions to compute the Personalized Activity Index in the frequentist framework.
Contains functions to estimate model parameters and filter, smooth and forecast random effects coefficients for mixed models with stationary and non-stationary stochastic processes under multivariate normal and t response distributions, diagnostic checks, bootstrap standard error calculation, etc.
The main function of the package is to perform backward selection of fixed effects, forward fitting of the random effects, and post-hoc analysis using parallel capabilities. Other functionality includes the computation of ANOVAs with upper- or lower-bound p-values and R-squared values for each model term, model criticism plots, data trimming on model residuals, and data visualization. The data to run examples is contained in package LCF_data.
Bootstrap routines for nested linear mixed effects models fit using
    either 'lme4' or 'nlme'. The provided 'bootstrap()' function implements the
    parametric, semi-parametric (i.e., CGR), residual, cases, and random effect
    block (REB) bootstrap procedures.
Provides p-values in type I, II or III anova and summary tables
    for lmer model fits (cf. lme4) via Satterthwaite's degrees of freedom method. A
    Kenward-Roger method is also available via the pbkrtest package. Model selection
    methods include step, drop1 and anova-like tables for random effects (ranova).
    Methods for Least-Square means (LS-means) and tests of linear contrasts of fixed
    effects are also available.
Add smoothing spline modelling capability to nlme. Fit
        smoothing spline terms in Gaussian linear and nonlinear
        mixed-effects models
Fit certain versions of the Latent Markov model for longitudinal categorical data.
Test zero variance components in linear mixed models and 
  test additivity in nonparametric regression 
  using the restricted likelihood ratio test and the generalized F-test.
  Details can be found at Zhang et al (2016) <doi:10.1002/cjs.11295>.
This R package provide methods for estimation and statistical
               inference on directional and fluctuating selection in age-structured
               populations.
We present a method based on filtering algorithms to estimate the parameters of linear regressions, i.e. the coefficients and the variance of the error term. The proposed algorithm makes use of Particle Filters following Ristic, B., Arulampalam, S., Gordon, N. (2004, ISBN: 158053631X) resampling methods.
Functions for different purposes related to Forest biometrics, including illustrative graphics, numerical computation, modeling height-diameter relationships, prediction of tree volumes, and modelling of diameter distributions. Datasets on tree height-diameter relationship,  light response of moss photosynthesis, and productivity of stump-lifting machines are included.
Some improved procedures for linear mixed models.
Fits (Gaussian) linear mixed-effects models
        for high-dimensional data (n<<p) using the linear mixed model elastic-net penalty.
This package fits (gaussian) linear mixed-effects models
        for high-dimensional data (n<<p) using a Lasso-type approach
        for the fixed-effects parameter.
Fit right censored Multiple Ordinal Tobit (MOT) model.
Embarrassingly Parallel Linear Mixed Model calculations spread across local cores which repeat until convergence.
Linear Mixed effect Model Splines ('lmms') implements linear mixed
    effect model splines for modelling and differential expression for highly
    dimensional data sets: investNoise() for quality control and filterNoise() for
    removing non-informative trajectories; lmmSpline() to model time course expression
    profiles and lmmsDE() performs differential expression analysis to identify
    differential expression between groups, time and/or group x time interaction.