Simple classic graph algorithms for simple graph classes.
    Graphs may possess vertex and edge attributes. 'simplegraph' has
    so dependencies and it is written entirely in R, so it is easy to
    install.
Trains neural networks (multilayer perceptrons with one hidden layer) for bi- or multi-class classification.
Simple result caching in R based on R.cache. The global environment is not 
    considered when caching results simplifying moving files between multiple instances 
    of R. Relies on more base functions than R.cache (e.g. cached results are saved using 
    saveRDS() and readRDS()).
When working across multiple machines and, similarly for
    reproducible research, it can be time consuming to ensure that you have
    all of the needed packages installed and loaded and that the correct working
    directory is set. 'simpleSetup' provides simple functions for making these
    tasks more straightforward.
SimpleTable provides a series of methods to conduct
        Bayesian inference and sensitivity analysis for causal effects
        from 2 x 2 and 2 x 2 x K tables when unmeasured confounding is
        present or suspected.
Simplex density, distribution, quantile functions as well as random variable
   	generation of the simplex distribution are given. Regression analysis of proportional data
   	using various kinds of simplex models is available. In addition, GEE method can be applied 
	to longitudinal data to model the correlation. Residual analysis is also involved. Some 
	subroutines are written in C with GNU Scientific Library (GSL) so as to facilitate the 
	computation.
Provides methods to integrate functions over m-dimensional simplices
 in n-dimensional Euclidean space.  There are exact methods for polynomials and
 adaptive methods for integrating an arbitrary function.  Dirichlet probabilities
 are calculated in certain cases.
Pairwise group comparisons are often performed. While there are many packages that can perform these analyses, often it is the case that only a subset of comparisons are desired. 'SimplifyStats' performs pairwise comparisons and returns the results in a tidy fashion.
Tools and methods to simulate populations for surveys based
    on auxiliary data. The tools include model-based methods, calibration and
    combinatorial optimization algorithms. The package was developed with support of
    the International Household Survey Network, DFID Trust Fund TF011722 and funds
    from the World bank.
This package detects instances of Simpson's Paradox in
        datasets. It examines subpopulations in the data, either
        user-defined or by means of cluster analysis, to test whether a
        regression at the level of the group is in the opposite
        direction at the level of subpopulations.
Easy to use interfaces to a number of imputation methods
        that fit in the not-a-pipe operator of the 'magrittr' package.
Calculate power for generalised linear mixed models, using
    simulation. Designed to work with models fit using the 'lme4' package.
    Described in Green and MacLeod, 2016 <doi:10.1111/2041-210X.12504>.
Provides a number of functions to simulate restriction enzyme digestion, library construction and fragments size selection to predict the number of loci expected from most of the Restriction site Associated DNA (RAD) and Genotyping By Sequencing (GBS) approaches. SimRAD estimates the number of loci expected from a particular genome depending on the protocol type and parameters allowing to assess feasibility, multiplexing capacity and the amount of sequencing required.
Functions for performing Bayesian similarity regression,
    and evaluating the probability of association between sets of ontological terms
    and binary response vector. A random model is compared with one in which
    the log odds of a true response is linked to the semantic similarity
    between terms and a latent characteristic ontological profile.
Simulate multivariate linear model data is useful in research and education weather 
    for comparison or create data with specific properties. This package lets user to simulate
    linear model data of wide range of properties with few tuning parameters.
    The package also consist of function to create plots for the simulation
    objects and A shiny app as RStudio gadget. It can be a handy tool for model comparison, 
    testing and many other purposes.
Provides functions to generate and analyze spatially-explicit individual-based multistate movements in rivers,
  heterogeneous and homogeneous spaces. This is done by incorporating landscape bias on local behaviour, based on
  resistance rasters. Although originally conceived and designed to simulate trajectories of species constrained to
  linear habitats/dendritic ecological networks (e.g. river networks), the simulation algorithm is built to be
  highly flexible and can be applied to any (aquatic, semi-aquatic or terrestrial) organism, independently on the
  landscape in which it moves. Thus, the user will be able to use the package to simulate movements either in
  homogeneous landscapes, heterogeneous landscapes (e.g. semi-aquatic animal moving mainly along rivers but also using
  the matrix), or even in highly contrasted landscapes (e.g. fish in a river network). The algorithm and its input
  parameters are the same for all cases, so that results are comparable. Simulated trajectories can then be used as
  mechanistic null models (Potts & Lewis 2014, <DOI:10.1098/rspb.2014.0231>) to test a variety of 'Movement Ecology'
  hypotheses (Nathan et al. 2008, <DOI:10.1073/pnas.0800375105>), including landscape effects (e.g. resources, 
  infrastructures) on animal movement and species site fidelity, or for predictive purposes (e.g. road mortality risk,
  dispersal/connectivity). The package should be relevant to explore a broad spectrum of ecological phenomena, such as
  those at the interface of animal behaviour, management, landscape and movement ecology, disease and invasive species
  spread, and population dynamics.
Routines to simulate and manipulate pedigrees ascertained to
    contain multiple family members affected by a rare disease. 
    Christina Nieuwoudt, Samantha J Jones, Angela Brooks-Wilson, 
    and Jinko Graham (2018) <doi:10.1101/234153>.
Methods to simulate genetic sequence data for pedigrees, with 
    functionality to simulate genetic heterogeneity among pedigrees.
    Christina Nieuwoudt, Angela Brooks-Wilson, 
    and Jinko Graham (2019) <doi:10.1101/534552>.
Tools for setting up ("design"), conducting, and evaluating
  large-scale simulation studies with graphics and tables, including
  parallel computations.
Contains two functions for simulating survival data from piecewise exponential hazards with a proportional hazards adjustment for covariates. The first function SimUNIVPiecewise simulates univariate survival data based on a piecewise exponential hazard, covariate matrix and true regression vector. The second function SimSCRPiecewise semi-competing risks data based on three piecewise exponential hazards, three true regression vectors and three matrices of patient covariates (which can be different or the same). This simulates from the Semi-Markov model of Lee et al (2015) given patient covariates, regression parameters, patient frailties and baseline hazard functions.
Provides an easy framework for Monte Carlo simulation in structural equation modeling, which can be used for various purposes, such as such as model fit evaluation, power analysis, or missing data handling and planning.
RNA sequencing analysis methods are often derived by relying on hypothetical parametric models for read counts that are not likely to be precisely satisfied in practice. Methods are often tested by analyzing data that have been simulated according to the assumed model. This testing strategy can result in an overly optimistic view of the performance of an RNA-seq analysis method. We develop a data-based simulation algorithm for RNA-seq data. The vector of read counts simulated for a given experimental unit has a joint distribution that closely matches the distribution of a source RNA-seq dataset provided by the user. Users control the proportion of genes simulated to be differentially expressed (DE) and can provide a vector of weights to control the distribution of effect sizes. The algorithm requires a matrix of RNA-seq read counts with large sample sizes in at least two treatment groups. Many datasets are available that fit this standard.
Creates simulated data from structural equation models with standardized loading.
Simulates data sets in order to explore modeling techniques or
    better understand data generating processes. The user specifies a set of
    relationships between covariates, and generates data based on these
    specifications. The final data sets can represent data from randomized
    control trials, repeated measure (longitudinal) designs, and cluster
    randomized trials. Missingness can be generated using various
    mechanisms (MCAR, MAR, NMAR).
simSummary is a small utility package which eases the
        process of summarizing simulation results. Simulations often
        produce intermediate results - some focal statistics that need
        to be summarized over several scenarios and many replications.
        This step is in principle easy, but tedious. The package
        simSummary fills this niche by providing a generic way of
        summarizing the focal statistics of simulations. The useR must
        provide properly structured input, holding focal statistics,
        and then the summary step can be performed with one line of
        code, calling the simSummary function.
Simulate survival times from standard parametric survival 
    distributions (exponential, Weibull, Gompertz), 2-component mixture 
    distributions, or a user-defined hazard, log hazard, cumulative hazard,
    or log cumulative hazard function. Baseline covariates can be included 
    under a proportional hazards assumption. 
    Time dependent effects (i.e. non-proportional hazards) can be included by 
    interacting covariates with linear time or a user-defined function of time.
    Clustered event times are also accommodated. 
    The 2-component mixture distributions can allow for a variety of flexible
    baseline hazard functions reflecting those seen in practice. 
    If the user wishes to provide a user-defined 
    hazard or log hazard function then this is possible, and the resulting
    cumulative hazard function does not need to have a closed-form solution. 
    Note that this package is modelled on the 'survsim' package available in 
    the 'Stata' software (see Crowther and Lambert (2012) 
    <http://www.stata-journal.com/sjpdf.html?articlenum=st0275> or 
    Crowther and Lambert (2013) <doi:10.1002/sim.5823>).
Handles datetimes as integers for the usage inside
        Discrete-Event Simulations (DES). The conversion is made using
        the internally generic function as.numeric() of the base
        package. DES is described in Simulation Modeling and Analysis
        by Averill Law and David Kelton (1999) <doi:10.2307/2288169>.
Flexibly simulates a dataset with time-varying covariates with user-specified exchangeable correlation structures across and within clusters. Covariates can be normal or binary and can be static within a cluster or time-varying. Time-varying normal variables can optionally have linear trajectories within each cluster. See ?make_one_dataset for the main wrapper function. See Montez-Rath et al. <arXiv:1709.10074> for methodological details.
Tool for statistical simulations that have two components. 
    One component generates the data and the other one
    analyzes the data. The main aims of the package are the reduction
    of the administrative source code (mainly loops and management code for the
    results) and a simple applicability of the package that allows the user to
    quickly learn how to work with it. Parallel computing is
    also supported. Finally, convenient functions are provided to summarize the
    simulation results.
This package provides simulation process of 4 selection methods in predicting potent compounds
Generates random values from a univariate and multivariate continuous distribution by using kernel density estimation based on a sample. Duong (2017) <doi:10.18637/jss.v021.i07>, Christian P. Robert and George Casella (2010 ISBN:978-1-4419-1575-7) <doi:10.1007/978-1-4419-1576-4>.
A framework for performing simulations such as those common in
    methodological statistics papers.  The design principles of this package
    are described in greater depth in Bien, J. (2016) "The simulator: An Engine
    to Streamline Simulations," which is available at
    <http://faculty.bscb.cornell.edu/~bien/simulator.pdf>.
This is an R implementation of a constrained l1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models (SIMULE). The SIMULE 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 heterogenous data sets, this tool can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(simuleDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Ritambhara Singh, Yanjun Qi (2017) <DOI:10.1007/s10994-017-5635-7>.
Simulation tools for planning Vitamin D studies including
             approximation of power. Individual vitamin D status 
             profiles are simulated, modelling population heterogeneity. 
             Individuals experience random exposures to infectious 		agents. Development of infection considers a heterogeneous treatment effect. This is incorporated into the power approximation.
This package provides routines to perform SIN model selection
  as described in Drton & Perlman (2004, 2008).  The selected models are
  represented in the format of the 'ggm' package, which allows in
  particular parameter estimation in the selected model.
The sinaplot is a data visualization chart suitable for plotting
    any single variable in a multiclass data set. It is an enhanced jitter strip
    chart, where the width of the jitter is controlled by the density
    distribution of the data within each class.
This implements the Brunton et al (2016; PNAS <doi:10.1073/pnas.1517384113>) sparse
    identification algorithm for finding ordinary differential equations for a measured 
    system from raw data (SINDy). The package includes a set of additional tools for 
    working with raw data, with an emphasis on cognitive science applications (Dale 
    and Bhat, in press <doi:10.1016/j.cogsys.2018.06.020>).
Create 'roxygen2' skeleton populated with information scraped from the
         within the function script. Also creates field entries for imports in the
         'DESCRIPTION' and import in the 'NAMESPACE' files. Can be run from the R
         console or through the 'RStudio' 'addin' menu.
Provides R functions for calculating basic effect size indices for 
  single-case designs, including several non-overlap measures and parametric 
  effect size measures, and for estimating the gradual effects model developed 
  by Swan and Pustejovsky (2018) <DOI:10.1080/00273171.2018.1466681>. 
  Standard errors and confidence intervals (based on the assumption that the outcome 
  measurements are mutually independent) are provided for the subset of effect sizes 
  indices with known sampling distributions.
Density, distribution function, quantile function 
	and random generation for the sum of independent non-identical
	binomial distribution with parameters \code{size} and \code{prob}.
Density, distribution function, quantile function, random
    generation and survival function for the Sine Inverse Weibull Distribution as
    defined by SOUZA, L. New Trigonometric Class of Probabilistic Distributions.
    219 p. Thesis (Doctorate in Biometry and Applied Statistics) - Department of
    Statistics and Information, Federal Rural University of Pernambuco, Recife,
    Pernambuco, 2015 (available at <http://www.openthesis.org/documents/New-trigonometric-classes-probabilistic-distributions-602633.html>) and BRITO, C. C. R. Method Distributions generator and
    Probability Distributions Classes. 241 p. Thesis (Doctorate in Biometry and
    Applied Statistics) - Department of Statistics and Information, Federal Rural
    University of Pernambuco, Recife, Pernambuco, 2014 (available upon request).
A platform for experimenting with spatially explicit individual-based vegetation models.
Calculating daily global solar radiation at horizontal surface using several well-known models (i.e. Angstrom-Prescott, Supit-Van Kappel, Hargreaves, Bristow and Campbell, and Mahmood-Hubbard), and model calibration based on ground-truth data, and (3) model auto-calibration. The FAO Penmann-Monteith equation to calculate evapotranspiration is also included.
Provides two main functionalities.
    1 - Given a system of simultaneous equation,
    it decomposes  the matrix of coefficients weighting the endogenous variables 
    into three submatrices: one includes the subset of coefficients that have a causal nature
    in the model, two include the subset of coefficients that have a interdependent nature
    in the model, either at systematic level or induced by the correlation between error terms.
    2 - Given a decomposed model,
    it tests for the significance of the interdependent relationships acting in the system, 
    via Maximum likelihood and Wald test, which can be built starting from the function output.
    For theoretical reference see Faliva (1992) <doi:10.1007/BF02589085> and 
    Faliva and Zoia (1994) <doi:10.1007/BF02589041>.
Allows the user to compare SIRI (Service Interface for Real Time Information) data sets to their GTFS (General Transit Feed Specification) counterparts, a "Request_id" column us needed for the SIRI data frame in order to subset parts of it for use.
Supplementary functions for item response models aiming
    to complement existing R packages. The functionality includes among others
    multidimensional compensatory and noncompensatory IRT models
    (Reckase, 2009, <doi:10.1007/978-0-387-89976-3>), 
    MCMC for hierarchical IRT models and testlet models
    (Fox, 2010, <doi:10.1007/978-1-4419-0742-4>), 
    NOHARM (McDonald, 1982, <doi:10.1177/014662168200600402>), 
    Rasch copula model (Braeken, 2011, <doi:10.1007/s11336-010-9190-4>;
    Schroeders, Robitzsch & Schipolowski, 2014, <doi:10.1111/jedm.12054>),
    faceted and hierarchical rater models (DeCarlo, Kim & Johnson, 2011,
    <doi:10.1111/j.1745-3984.2011.00143.x>),
    ordinal IRT model (ISOP; Scheiblechner, 1995, <doi:10.1007/BF02301417>), 
    DETECT statistic (Stout, Habing, Douglas & Kim, 1996, 
    <doi:10.1177/014662169602000403>), local structural equation modeling 
    (LSEM; Hildebrandt, Luedtke, Robitzsch, Sommer & Wilhelm, 2016,
    <doi:10.1080/00273171.2016.1142856>).
Variable selection techniques are essential tools for model
    selection and estimation in high-dimensional statistical models. Through this
    publicly available package, we provide a unified environment to carry out
    variable selection using iterative sure independence screening (SIS) and all
    of its variants in generalized linear models and the Cox proportional hazards
    model.
Implements the SISAL algorithm by Tikka and Hollmén. It is
        a sequential backward selection algorithm which uses a linear
        model in a cross-validation setting. Starting from the full
        model, one variable at a time is removed based on the
        regression coefficients. From this set of models, a
        parsimonious (sparse) model is found by choosing the model with
        the smallest number of variables among those models where the
        validation error is smaller than a threshold. Also implements
        extensions which explore larger parts of the search space
        and/or use ridge regression instead of ordinary least squares.
An interval fusion procedure for functional data in the
             semiparametric framework of SIR. Standard ridge and sparse SIR are 
             also included in the package.
A tool for the simulation of gene expression profiles for in silico regulatory networks. The package generates gene regulatory networks, which include protein-coding and noncoding genes linked via different types of regulation: regulation of transcription, translation, RNA or protein decay, and post-translational modifications. The effect of genetic mutations on the system behaviour is accounted for via the simulation of genetically different in silico individuals. The ploidy of the system is not restricted to the usual haploid or diploid situations, but is defined by the user. A choice of stochastic simulation algorithms allow us to simulate the expression profiles (RNA and if applicable protein abundance) of the genes in the in silico system for the different in silico individuals. A tutorial explaining how to use the package is available at <https://oliviaab.github.io/sismonr/>. Manuscript in preparation; see also Angelin-Bonnet O., Biggs P.J. and Vignes M. (2018) <doi:10.1109/BIBM.2018.8621131>.
Selects invalid instruments amongst a candidate of potentially bad instruments. The algorithm selects potentially invalid instruments and provides an estimate of the causal effect between exposure and outcome.
Functions for fitting and plotting SITAR (Super
    Imposition by Translation And Rotation) growth curve models. SITAR is
    a shape-invariant model with a regression B-spline mean curve and
    subject-specific random effects on both the measurement and age
    scales.  The model was first described by Lindstrom (1995)
    <doi:10.1002/sim.4780141807> and developed as the SITAR method by Cole
    et al (2010) <doi:10.1093/ije/dyq115>.
Within the area of movement ecology, it can be useful to identify an animal's sites of interest. These will be places that an animal uses a lot and so will spend a proportionately longer time there. The aim of this package is to identify both the position and size of these sites of interest from high resolution (sub-second) positional data.
Provided within are two high quality and fast PPRNGs that may be
    used in an 'OpenMP' parallel environment. In addition, there is a generator
    for one dimensional low-discrepancy sequence. The objective of this library
    to consolidate the distribution of the 'sitmo' (C++98 & C++11), 'threefry' and
    'vandercorput' (C++11-only) engines on CRAN by enabling others to link to the
    header files inside of 'sitmo' instead of including a copy of each engine
    within their individual package. Lastly, the package contains example
    implementations using the 'sitmo' package and three accompanying vignette that
    provide additional information.
Format a number (or a list of numbers) to a string (or a
        list of strings) with SI prefix. Use SI prefixes as constants
        like (4 * milli)^2
Provides extensions for package 'sitree' for allometric variables, growth, mortality, recruitment, management, tree removal and external modifiers functions.
Sensitivity indices with dependent correlated inputs, using a
    method based on PLS regression.
Functions and utilities to perform
    Statistical Analyses in the Six Sigma way.
    Through the DMAIC cycle (Define, Measure, Analyze, Improve, Control),
    you can manage several Quality Management studies:
    Gage R&R, Capability Analysis, Control Charts, Loss Function Analysis,
    etc. Data frames used in the books "Six Sigma with R", Cano et al. (2012) [ISBN 978-1-4614-3652-2]
    and "Quality Control with R", Cano et al. (2015) [ISBN 978-3-319-24046-6], 
    are also included in the package.
This function develops an algorithm for presenting a Bayesian hierarchical model for estimating the sizes of local and national drug injected populations in Bangladesh. The model incorporates multiple commonly used data sources including mapping data, surveys, interventions, capture-recapture data, estimates or guesstimates from organizations, and expert opinion.
Contains functions to estimate morphometric and gonadal size at sexual maturity for organisms, usually fish and invertebrates. It includes methods for classification based on relative growth (using principal components analysis, hierarchical clustering, discriminant analysis), logistic regression (Frequentist or Bayes), parameters estimation and some basic plots.
Calculates and plots the SiZer map for scatterplot data.  A 
  SiZer map is a way of examining when the p-th derivative of a 
  scatterplot-smoother is significantly negative, possibly zero or 
  significantly positive across a range of smoothing bandwidths.
Provides a database-independent JDBC interface.
Collection of functions dealing with labelled data, like reading and 
    writing data between R and other statistical software packages like 'SPSS',
    'SAS' or 'Stata', and working with labelled data. This includes easy ways 
    to get, set or change value and variable label attributes, to convert 
    labelled vectors into factors or numeric (and vice versa), or to deal with 
    multiple declared missing values.
Collection of miscellaneous utility functions, supporting data 
    transformation tasks like recoding, dichotomizing or grouping variables, 
    setting and replacing missing values. The data transformation functions 
    also support labelled data, and all integrate seamlessly into a 
    'tidyverse'-workflow.
Collection of plotting and table output functions for data
    visualization. Results of various statistical analyses (that are commonly used
    in social sciences) can be visualized using this package, including simple and
    cross tabulated frequencies, histograms, box plots, (generalized) linear models,
    mixed effects models, principal component analysis and correlation matrices, 
    cluster analyses, scatter plots, stacked scales, effects plots of regression 
    models (including interaction terms) and much more. This package supports
    labelled data.
Collection of convenient functions for common statistical computations,
             which are not directly provided by R's base or stats packages.
             This package aims at providing, first, shortcuts for statistical
             measures, which otherwise could only be calculated with additional
             effort (like standard errors or root mean squared errors). Second,
             these shortcut functions are generic (if appropriate), and can be
             applied not only to vectors, but also to other objects as well
             (e.g., the Coefficient of Variation can be computed for vectors,
             linear models, or linear mixed models; the r2()-function returns
             the r-squared value for 'lm', 'glm', 'merMod' and other model objects).
             The focus of most functions lies on summary statistics or fit
             measures for regression models, including generalized linear
             models, mixed effects models and Bayesian models. However, some 
             of the functions also deal with other statistical measures, 
             like Cronbach's Alpha, Cramer's V, Phi etc.
Segment-based Kriging methods, including segment-based ordinary 
             Kriging (SOK) and segment-based regression Kriging (SRK) for 
             spatial prediction of line segment spatial data as described in 
             Yongze Song (2018) <doi:10.1109/TITS.2018.2805817>. Includes the 
             spatial prediction and spatial visualisation. The descriptions of 
             the methods and case datasets refer to the citation information 
             below.
Functions for kernel-regression-based association tests including Burden test, SKAT and SKAT-O. These methods aggregate individual SNP score statistics in a SNP set and efficiently compute SNP-set level p-values.
Sparse (Multicategory) Kernel Discriminant Analysis does variable selection for nonparametric classification
A shiny interface and supporting tools to guide users in choosing
    appropriate simulations, setting parameters, calculating summary genetic
    statistics, and organizing data output, all within the R environment. In
    addition to supporting existing forward and reverse-time simulators, new
    simulators can be integrated into the environment relatively easily.
A tool for bootstrapping new packages with useful defaults,
    including a test suite outline that passes checks and helpers for running
    tests, checking test coverage, building vignettes, and more. Package
    skeletons it creates are set up for pushing your package to
    'GitHub' and using other hosted services for building and test automation.
Functions for the Skellam distribution, including: density
    (pmf), cdf, quantiles and regression.
Functions are provided for the density function,
	     distribution function, quantiles and random number
	     generation for the skew hyperbolic
	     t-distribution. There are also functions that fit
	     the distribution to data. There are functions for the
	     mean, variance, skewness, kurtosis and mode of a given
	     distribution and to calculate moments of any order about
	     any centre. To assess goodness of fit, there are
	     functions to generate a Q-Q plot, a P-P plot and a tail plot.
Density, distribution function, quantile function and
        random generation for the skewed t distribution of Fernandez
        and Steel.
A generalization of the statistic used in Friedman's ANOVA method and in Durbin's rank test. This nonparametric statistical test is useful for the data obtained from block designs with missing observations occurring randomly. A resulting p-value is based on the chi-squared distribution and Monte Carlo method.
A simple to use summary function that can be used with pipes
    and displays nicely in the console. The default summary statistics may be 
    modified by the user as can the default formatting. Support for data frames 
    and vectors is included, and users can implement their own skim methods for
    specific object types as described in a vignette. Default summaries include
    support for inline spark graphs. Instructions for managing these on 
    specific operating systems are given in the "Using skimr" vignette and the 
    README.
Provides tools for applying Sklar's Omega (Hughes, 2018) <arXiv:1803.02734>
    methodology to nominal, ordinal, interval, or ratio scores. The framework can
    accommodate any number of units, any number of coders, and missingness; and
    can be used to measure agreement with a gold standard, intra-coder agreement,
    and/or inter-coder agreement. Frequentist inference is supported for all levels
    of measurement. Bayesian inference is supported for interval and ratio scores.
Algorithms for solving selective k-means problem,
    which is defined as finding k rows in an m x n matrix such that 
    the sum of each column minimal is minimized. 
    In the scenario when m == n and each cell value in matrix is a 
    valid distance metric, this is equivalent to a k-means problem. 
    The selective k-means extends the k-means problem in the sense 
    that it is possible to have m != n, often the case m < n which 
    implies the search is limited within a small subset of rows. 
    Also, the selective k-means extends the k-means problem in the 
    sense that the instance in row set can be instance not seen in 
    the column set, e.g., select 2 from 3 internet service provider
    (row) for 5 houses (column) such that minimize the overall cost 
    (cell value) - overall cost is the sum of the column minimal of
    the selected 2 service provider.
Algorithms to compute spherical k-means partitions.
  Features several methods, including a genetic and a fixed-point
  algorithm and an interface to the CLUTO vcluster program.
Generates and evaluates D, I, A, Alias, E, T, and G optimal designs. Supports generation and evaluation of split/split-split/.../N-split plot designs. Includes parametric and Monte Carlo power evaluation functions, and supports calculating power for censored responses. Provides a framework to evaluate power using functions provided in other packages or written by the user. Includes a Shiny graphical user interface that displays the underlying code used to create and evaluate the design to improve ease-of-use and make analyses more reproducible.
Provides an alternative to manually process hemispherical pictures. The algorithm processes each picture one by one to determine the proportion of sky pixel. The algorithm uses the Ridler and Calvard method (Ridler and Calvard 1978).
A flexible tool that allows generating bespoke
    air transport statistics for urban studies based on publicly available
    data from the Bureau of Transport Statistics (BTS) in the United States
    <https://www.transtats.bts.gov/databases.asp?Mode_ID=1&Mode_Desc=Aviation&Subject_ID2=0>.
A toolset for data reduction, visualization and analysis in skyscape archaeology, archaeoastronomy and cultural astronomy.
Query and download satellite imagery and climate/atmospheric datasets using the SkyWatch API. 
     Search datasets by wavelength (band), cloud cover, resolution, location, date, etc.
     Get the query results as data frame and as HTML. To learn more about the SkyWatch API, see <https://github.com/skywatchspaceapps/api>.
'Slack' <http://slack.com/> provides a service for teams to
    collaborate by sharing messages, images, links, files and more. Functions are provided
    that make it possible to interact with the 'Slack' platform 'API'. When
    you need to share information or data from R, rather than resort to copy/
    paste in e-mails or other services like 'Skype' <http://www.skype.com/>, you
    can use this package to send well-formatted output from multiple R objects and
    expressions to all teammates at the same time with little effort. You can also
    send images from the current graphics device, R objects, and upload files.
Data structures and algorithms for sparse arrays and matrices,
  based on index arrays and simple triplet representations, respectively.
Estimates the slope and level change present in data after
        removing phase A trend. Represents graphically the original and
        the detrended data.
The skew logistic distribution is a quantile-defined generalisation
 of the logistic distribution (van Staden and King 2015).  Provides random 
 numbers, quantiles, probabilities, densities and density quantiles for the distribution.
 It provides Quantile-Quantile plots and method of L-Moments estimation 
 (including asymptotic standard errors) for the distribution.
Calculates maximum likelihood estimate, exact and asymptotic confidence intervals, and exact and asymptotic goodness of fit p-values for concentration of infectious units from serial limiting dilution assays. This package uses the likelihood equation, exact goodness of fit p-values, and exact confidence intervals described in Meyers et al. (1994) <http://jcm.asm.org/content/32/3/732.full.pdf>. This software is also implemented as a web application through the Shiny R package <https://iupm.shinyapps.io/sldassay/>.
Compute Time series Resistant Smooth 4253H, twice smoothing method.
Use behavioural variables to score activity and infer sleep from bouts of immobility. 
    It is primarily designed to score sleep in fruit flies from Drosophila Activity Monitor (TriKinetics) and Ethoscope data.
    It implements sleep scoring using the "five-minute rule" (Hendricks et al. (2000) <DOI:10.1016/S0896-6273(00)80877-6>),
    activity classification for Ethoscopes (Geissmann et al. (2017) <DOI:10.1371/journal.pbio.2003026>) 
    and a new algorithm to detect when animals are dead.
A tool to interactively explore the
  embeddings created by dimension reduction methods such as 
  Principal Components Analysis (PCA), Multidimensional Scaling (MDS), 
  T-distributed Stochastic Neighbour Embedding (t-SNE),
  Uniform Manifold Approximation and Projection (UMAP) or any other.
Data sets from Ramsey, F.L. and Schafer, D.W. (2002), "The
    Statistical Sleuth: A Course in Methods of Data Analysis (2nd
    ed)", Duxbury.
Data sets from Ramsey, F.L. and Schafer, D.W. (2013), "The
    Statistical Sleuth: A Course in Methods of Data Analysis (3rd
    ed)", Cengage Learning.
Set of tools to find coherent patterns in microarray data using a Bayesian Sparse Latent Factor Model - SLFM; see Duarte and Mayrink (2015) <DOI:10.1007/978-3-319-12454-4_15>. Considerable effort has been put into making slfm fast and memory efficient, turning it an interesting alternative to simpler methods in terms of execution time. It implements versions of the SLFM based on two type of mixtures priors for the loadings: one relying on a degenerate component at zero and the other using a small variance normal distribution for the spike part of the mixture. It also implements additional functions to allow pre-processing procedures for the data and to fit the model for a large number of probesets or genes.
Generate the optimal Latin Hypercube Designs (LHDs) for computer experiments with quantitative factors and the optimal Sliced Latin Hypercube Designs (SLHDs) for computer experiments with both quantitative and qualitative factors. Details of the algorithm can be found in Ba, S., Brenneman, W. A. and Myers, W. R. (2015), "Optimal Sliced Latin Hypercube Designs," Technometrics. Important function in this package is "maximinSLHD".
Provides an implementation of SLICER, an algorithm for inferring cellular trajectories from single cell RNA sequencing data. See Welch, JD, Hartemink AJ, Prins JF (2016) <doi:10.1186/s13059-016-0975-3>.
Create and customize interactive carousels using the 'Slick'
    JavaScript library and the 'htmlwidgets' package. The carousels can contain plots
    produced in R, images, 'iframes', videos and other 'htmlwidgets'.
    These carousels can be used directly from the R console, from 'RStudio', 
    in Shiny apps and R Markdown documents.
This statistical method uses the nearest neighbor algorithm to estimate absolute distances between single cells 
	based on a chosen constellation of surface proteins, with these distances being a measure of the similarity between the 
	two cells being compared. Based on Sen, N., Mukherjee, G., and Arvin, A.M. (2015) <DOI:10.1016/j.ymeth.2015.07.008>.
Create a side-by-side view of raster(image)s with an interactive 
    slider to switch between regions of the images. This can be especially useful
    for image comparison of the same region at different time stamps.
Fits singular linear models to longitudinal data. Singular linear
    models are useful when the number, or timing, of longitudinal observations
    may be informative about the observations themselves. They are described
    in Farewell (2010) <doi:10.1093/biomet/asp068>, and are extensions of the
    linear increments model <doi:10.1111/j.1467-9876.2007.00590.x> to general
    longitudinal data.
Sparse Linear Method(SLIM) predicts ratings and top-n
    recommendations suited for sparse implicit positive feedback systems. SLIM
    is decomposed into multiple elasticnet optimization problems which are solved
    in parallel over multiple cores. The package is based on "SLIM: Sparse Linear
    Methods for Top-N Recommender Systems" by Xia Ning and George Karypis <doi:10.1109/ICDM.2011.134>.
Provides functions for performing common tasks when working with
  slippy map tile service APIs e.g. Google maps, Open Street Map, Mapbox, Stamen,
  among others. Functionality includes converting from latitude and longitude to
  tile numbers, determining tile bounding boxes, and compositing tiles to a
  georeferenced raster image.
A collection of helper functions designed to help
    you to better understand object oriented programming in R,
    particularly using 'S3'.
Efficient procedures for Sorted L1 Penalized Estimation (SLOPE).
    The sorted L1 norm is useful for statistical estimation and testing,
    particularly for variable selection in the linear model.
An implementation of a phylogenetic comparative method. It can fit univariate among-species Ornstein-Uhlenbeck models of phenotypic trait evolution, where the trait evolves towards a primary optimum. The optimum can be modelled as a single parameter, as multiple discrete regimes on the phylogenetic tree, and/or with continuous covariates. See also Hansen (1997) doi:10.2307/2411186, Butler & King (2004) doi:10.1086/426002, Hansen et al. (2008) doi:10.1111/j.1558-5646.2008.00412.x.
A mostly pure-R implementation of the RAKE algorithm (Rose, S., Engel, D., Cramer, N. and Cowley, W. (2010) <doi:10.1002/9780470689646.ch1>), which can be used to extract keywords from documents without any training data.
Interface for creation of 'slp' class smoother objects for 
             use in Generalized Additive Models (as implemented by packages 
             'gam' and 'mgcv').
This is software linked to the book
  'Applied Smoothing Techniques for Data Analysis -
  The Kernel Approach with S-Plus Illustrations' Oxford University Press.
Implementation of the Stochastic Multi-Criteria Acceptability Analysis (SMAA) family of Multiple Criteria Decision Analysis (MCDA) methods.
This package provides a solution path for L1-penalized angle-based classification. Three loss functions are implemented in smac, including the deviance loss in logistic regression, the exponential loss in boosting, and the proximal support vector machine loss.
Implements the following approaches for multidimensional scaling (MDS) based on stress minimization using majorization (smacof): ratio/interval/ordinal/spline MDS on symmetric dissimilarity matrices, MDS with external constraints on the configuration, individual differences scaling (idioscal, indscal), MDS with spherical restrictions, and ratio/interval/ordinal/spline unfolding (circular restrictions, row-conditional). Various tools and extensions like jackknife MDS, bootstrap MDS, permutation tests, MDS biplots, gravity models, unidimensional scaling, drift vectors (asymmetric MDS), classical scaling, and Procrustes are implemented as well.
Statistical methods for analyzing case-control point data.  Methods include the ratio of kernel densities, the difference in K Functions, the spatial scan statistic, and q nearest neighbors of cases.
Inference techniques for Fay Herriot Model.
A statistical disclosure control tool to protect frequency tables in cases where small values are sensitive. The function PLSrounding() performs small count rounding of necessary inner cells so that all small frequencies of cross-classifications to be published (publishable cells) are rounded. This is equivalent to changing micro data since frequencies of unique combinations are changed. Thus, additivity and consistency are guaranteed. The methodology is described in Langsrud and Heldal (2018) <https://www.researchgate.net/publication/327768398>.
Animal movement models including moving-resting process
    with embedded Brownian motion according to
    Yan et al. (2014) <doi:10.1007/s10144-013-0428-8>,
    Pozdnyakov et al. (2017) <doi:10.1007/s11009-017-9547-6>,
    Brownian motion with measurement error according to
    Pozdnyakov et al. (2014) <doi:10.1890/13-0532.1>,
    and moving-resting-handling process with embedded Brownian motion,
    Pozdnyakov et al. (2018) <arXiv:1806.00849>.
Facilitates programmatic access to NASA Soil Moisture Active
    Passive (SMAP) data with R. It includes functions to search for, acquire,
    and extract SMAP data.
Eases data preprocessing tasks, providing a data flow based 
   on a pipe operator which eases cleansing, transformation, oversampling, 
   or instance/feature selection operations.
Exploratory analysis on any input data describing the structure and the relationships present in the data. The package automatically select the variable and does related descriptive statistics. Analyzing information value, weight of evidence, custom tables, summary statistics, graphical techniques will be performed for both numeric and categorical predictors.
Tools for accessing and processing datasets prepared by the Foundation SmarterPoland.pl. Among all: access to API of Google Maps, Central Statistical Office of Poland, MojePanstwo, Eurostat, WHO and other sources.
Sample size calculation to detect dynamic treatment regime (DTR) effects based on change in clinical attachment level (CAL) outcomes from a non-surgical chronic periodontitis treatments study. The experiment is performed under a Sequential Multiple Assignment Randomized Trial (SMART) design. The clustered tooth (sub-unit) level CAL outcomes are skewed, spatially-referenced, and non-randomly missing. The implemented algorithm is available in Xu et al. (2019+) <arXiv:1902.09386>.
A tool for assessing bio-economic feedback in 
  different management scenarios. 'smartR' (Spatial Management and Assessment 
  of demersal Resources for Trawl fisheries) combines information from different
   tasks gathered within the European Data Collection Framework for the fishery
  sector. The 'smartR' package implements the SMART model (Russo et al., 2014 
  <doi:10.1371/journal.pone.0086222>), through the object-oriented 
  programming paradigm, and within this package it is possible to achieve the 
  complete set of analyses required by the SMART approach: from the editing and
   formatting of the raw data; the construction and maintenance of coherent 
  datasets; the numerical and visual inspection of the generated metadata; to 
  the final simulation of management scenarios and the forecast of their 
  effects. The interaction between the user and the application could take 
  place through invocation of methods via the command line or could be entirely
   committed to the graphical user interfaces (GUI).
Addressing the problem of outlier detection from the viewpoint of statistical learning theory. This method is proposed by Yamanishi, K., Takeuchi, J., Williams, G. et al. (2004) <DOI:10.1023/B:DAMI.0000023676.72185.7c>. It learns the probabilistic model (using a finite mixture model) through an on-line unsupervised process. After each datum is input, a score will be given with a high one indicating a high possibility of being a statistical outlier.
A set of tools for determining the necessary sample size
    in order to identify the optimal dynamic treatment regime
    in a sequential, multiple assignment, randomized trial (SMART). 
    Utilizes multiple comparisons with the best methodology 
    to adjust for multiple comparisons.
    Designed for an arbitrary SMART design. Please see Artman (2018) <arXiv:1804.04587> for more details.
Introduces a fast and efficient Surrogate Variable Analysis algorithm that captures variation of unknown sources (batch effects) for high-dimensional data sets. The algorithm is built on the 'irwsva.build' function of the 'sva' package and proposes a revision on it that achieves an order of magnitude faster running time while trading no accuracy loss in return.
Methods for fitting bivariate lines in
    allometry using the major axis (MA) or standardised major axis (SMA), and
    for making inferences about such lines. The available methods of inference
    include confidence intervals and one-sample tests for slope and elevation,
    testing for a common slope or elevation amongst several allometric lines,
    constructing a confidence interval for a common slope or elevation, and
    testing for no shift along a common axis, amongst several samples. 
    See Warton et al. 2012 <doi:10.1111/j.2041-210X.2011.00153.x> for methods description.
A set of functions to build a scoring model from beginning to end, leading the user
	to follow an efficient and organized development process, reducing significantly the time
	spent on data exploration, variable selection, feature engineering, binning and model selection 
	among other recurrent tasks. 
	The package also incorporates monotonic and customized binning, scaling capabilities that 
	transforms logistic coefficients into points for a better business understanding and 
	calculates and visualizes classic performance metrics of a classification model.
particle filtering, auxiliary particle filtering and sequential Monte Carlo algorithms
A canonical correlation based framework for constructing phenotype-specific multi-omics networks by integrating multiple omics data types and a quantitative phenotype of interest.
Implements multiple imputation of missing covariates by
    Substantive Model Compatible Fully Conditional Specification.
    This is a modification of the popular FCS/chained equations
    multiple imputation approach, and allows imputation of missing
    covariate values from models which are compatible with the user
    specified substantive model.
This package is for optimizing non-linear complex
        functions based on Monte Carlo random sampling.
Data Sets for Kumar and Petersen (2012).
    Statistical Methods in Customer Relationship Management,
    Wiley: New York.
An R-package for Estimating Semiparametric PH and AFT
        Mixture Cure Models
Contains data files to accompany Smithson & Merkle (2013), Generalized Linear Models for Categorical and Continuous Limited Dependent Variables.
This package provides similarity among documents.
Symbolic multidimensional scaling for interval-valued dissimilarities. The hypersphere model and the hyperbox model are available.
Fit smoothing-splines mixed-effects models to replicated functional data sets and visualise the results.
Implements statistical methods for analyzing the counts of areal data,
    with a focus on the detection of spatial clusters and clustering.
R functions and data from Chapter 5 of 'Statistical
        Methods for Financial Engineering', by Bruno Remillard, CRC
        Press, (2013).
Provides the filtering algorithms for the state space models on the Stiefel manifold as well as the corresponding sampling algorithms for uniform, vector Langevin-Bingham and matrix Langevin-Bingham distributions on the Stiefel manifold.
Code and data for modelling and simulation of stochastic kinetic biochemical network models. It contains the code and data associated with the second and third editions of the book Stochastic Modelling for Systems Biology, published by Chapman & Hall/CRC Press.
Functions that provide statistical methods for interval censored (grouped) data. The package supports the estimation of linear and linear mixed regression models with interval censored dependent variables. Parameter estimates are obtained by a stochastic expectation maximization algorithm. Furthermore, the package enables the direct (without covariates) estimation of statistical indicators from interval censored data via an iterative kernel density algorithm. Survey and Organisation for Economic Co-operation and Development (OECD) weights can be included into the direct estimation (see, Groß, M., U. Rendtel, T. Schmid, S. Schmon, and N. Tzavidis (2017) <doi:10.1111/rssa.12179>).
This tiny package contains one function smirnov() which
        calculates two scaled taxonomic coefficients, Txy (coefficient
        of similarity) and Txx (coefficient of originality). These two
        characteristics may be used for the analysis of similarities
        between any number of taxonomic groups, and also for assessing
        uniqueness of giving taxon. It is possible to use smirnov()
        output as a distance measure: convert it to distance by
        "as.dist(1 - smirnov(x))".
A collection of functions for statistical computing and data manipulation in R. 
    Includes routines for data ingestion, operating on dataframes and matrices, conversion to and 
    from lists, converting factors, filename manipulation, programming utilities, parallelization, plotting, 
    statistical and mathematical operations, and time series.
Constructs a yield curve by the Smith-Wilson method from a
        table of LIBOR and SWAP rates
Statistical Methods for Inferring Transmissions of Infectious Diseases from deep sequencing data (SMITID).
  It allow sequence-space-time host and viral population data storage, indexation and querying.
Visualisation tools for 'SMITIDstruct' package.
  Allow to visualize host timeline, transmission tree, index diversities
  and variant graph using HTMLwidgets. It mainly using D3JS javascript framework.
Local Correlation Integral (LOCI) method for outlier identification is implemented here. The LOCI method developed here is invented in Breunig, et al. (2000), see <doi:10.1145/342009.335388>.
Performs parametric and non-parametric estimation and simulation for multi-state discrete-time semi-Markov processes. 
	For the parametric estimation, several discrete distributions are considered for the sojourn times: Uniform, Geometric, Poisson, Discrete Weibull and Negative Binomial. The non-parametric estimation concerns the sojourn time distributions, where no assumptions are done on the shape of distributions. 
	Moreover, the estimation can be done on the basis of one or several sample paths, with or without censoring at the beginning or/and at the end of the sample paths. The implemented methods are described in Barbu, V.S., Limnios, N. (2008) <doi:10.1007/978-0-387-73173-5>, Barbu, V.S., Limnios, N. (2008) <doi:10.1080/10485250701261913> and 
	Trevezas, S., Limnios, N. (2011) <doi:10.1080/10485252.2011.555543>. 
	Estimation and simulation of discrete-time k-th order Markov chains are also considered.
Efficient design matrix free procedure for solving a soft maximin problem for  large scale array-tensor structured models. Currently Lasso and SCAD penalized estimation is implemented.
Fit univariate right, left or interval censored regression model under the scale mixture of normal distributions
Fits flexible additive models to data on stream networks, taking account of flow-connectivity of the network.  Models are fitted using penalised least squares.
Provides generators for a high number of both single- and multi-
    objective test functions which are frequently used for the benchmarking of
    (numerical) optimization algorithms. Moreover, it offers a set of convenient
    functions to generate, plot and work with objective functions.
Functions implementing Single Source of Error state space models for purposes of time series analysis and forecasting.
             The package includes Exponential Smoothing (Hyndman et al., 2008, <doi: 10.1007/978-3-540-71918-2>),
             SARIMA (Svetunkov & Boylan, 2019 <doi: 10.1080/00207543.2019.1600764>),
             Complex Exponential Smoothing (Svetunkov & Kourentzes, 2018, <doi: 10.13140/RG.2.2.24986.29123>),
             Simple Moving Average (Svetunkov & Petropoulos, 2018 <doi: 10.1080/00207543.2017.1380326>),
             Vector Exponential Smoothing (de Silva et al., 2010, <doi: 10.1177/1471082X0901000401>) in state space forms,
             several simulation functions and intermittent demand state space models. It also allows dealing with
             intermittent demand based on the iETS framework (Svetunkov & Boylan, 2017, <doi: 10.13140/RG.2.2.35897.06242>).
The implemented method uses for smoothing bivariate thin plate
    splines, bivariate lasso-type regularization, and allows for both period
    and cohort effects. Thus the mortality rates are modelled as the sum of four
    components: a smooth bivariate function of age and time, smooth one-dimensional
    cohort effects, smooth one-dimensional period effects and random errors.
A collection of methods for smoothing numerical data, commencing with a port of the Matlab gaussian window smoothing function. In addition, several functions typically used in smoothing of financial data are included.
Estimation of two-state (survival) models and irreversible illness-
    death models with possibly interval-censored,left-truncated and right-censored
    data. Proportional intensities regression models can be specified to allow for
    covariates effects separately for each transition. We use either a parametric
    approach with Weibull baseline intensities or a semi-parametric approach with
    M-splines approximation of baseline intensities in order to obtain smooth
    estimates of the hazard functions. Parameter estimates are obtained by maximum
    likelihood in the parametric approach and by penalized maximum likelihood in the
    semi-parametric approach.
Provides flexible hazard ratio curves allowing non-linear
        relationships between continuous predictors and survival. To
        better understand the effects that each continuous covariate
        has on the outcome, results are ex pressed in terms of hazard
        ratio curves, taking a specific covariate value as reference.
        Confidence bands for these curves are also derived.
Functions to smooth two-dimensional fields using FFT and the convolution theorem
Some M-estimators for 1-dimensional location (Bisquare, ML
        for the Cauchy distribution, and the estimators from
        application of the smoothing principle introduced in Hampel,
        Hennig and Ronchetti (2011) to the above, the Huber
        M-estimator, and the median, main function is smoothm), and
        Pitman estimator.
Tools for smoothing and tidying spatial features (i.e. lines and 
    polygons) to make them more aesthetically pleasing. Smooth curves, fill 
    holes, and remove small fragments from lines and polygons.
Computes smooth estimations for the Cumulative/Dynamic and Incident/Dynamic ROC curves, in presence of right censorship, based on the bivariate kernel density estimation of the joint distribution function of the Marker and Time-to-event variables.
Contains, as a main contribution, a function to fit
             a regression model with possibly right, left or interval
             censored observations and with the error distribution
             expressed as a mixture of G-splines. Core part
             of the computation is done in compiled C++ written
             using the Scythe Statistical Library Version 0.3.
Given independent and identically distributed observations X(1), ..., X(n) from a Generalized Pareto distribution with shape parameter gamma in [-1,0], offers several estimates to compute estimates of gamma. The estimates are based on the principle of replacing the order statistics by quantiles of a distribution function based on a log--concave density function. This procedure is justified by the fact that the GPD density is log--concave for gamma in [-1,0].
The main function in the package utilizes a windowing function in the form of an exponential weighting function to linear models. The bandwidth and sharpness of the window are controlled by two parameters. Then, a series of tests are used to identify the right parameters of the window (see Charles Kervrann (2004) <doi:10.1007/978-3-540-24672-5_11>).
A collection of various oversampling techniques developed from SMOTE is provided. SMOTE is a oversampling technique which synthesizes a new minority instance between a pair of one minority instance and one of its K nearest neighbor. (see <https://www.jair.org/media/953/live-953-2037-jair.pdf> for more information) Other techniques adopt this concept with other criteria in order to generate balanced dataset for class imbalance problem.
Provides movies to help students to understand statistical 
  concepts.  The 'rpanel' package  <https://cran.r-project.org/package=rpanel> 
  is used to create interactive plots that move to illustrate key statistical 
  ideas and methods.  There are movies to: visualise probability distributions
  (including user-supplied ones); illustrate sampling distributions of the
  sample mean (central limit theorem), the sample maximum (extremal types 
  theorem) and (the Fisher transformation of the) Pearson product moment 
  correlation coefficient; examine the influence of an individual observation 
  in simple linear regression; illustrate key concepts in statistical 
  hypothesis testing. Also provided are dpqr functions for the distribution of 
  the Fisher transformation of the correlation coefficient under sampling from 
  a bivariate normal distribution.
Creates images that are the proper size for social media. Beautiful
    plots, charts and graphs wither and die if they are not shared. Social media 
    is perfect for this but every platform has its own image dimensions. With 
    'smpic' you can easily save your plots with the exact dimensions needed for 
    the different platforms.
Contains the datasets and a few functions for use with 
        the practicals outlined in Appendix A of the book
        Statistical Models (Davison, 2003, Cambridge University Press).
        The practicals themselves can be found at
        <http://statwww.epfl.ch/davison/SM/>.
Computes the studentized midrange distribution (pdf, cdf and quantile) and generates random numbers
Produce small area population estimates by fitting census data to
    survey data.
Datasets used in "Statistical Methods for the Social Sciences"
    (SMSS) by Alan Agresti and Barbara Finlay.
Implementation of the SMuRF algorithm of Devriendt et al. (2018) <arXiv:1810.03136> to fit generalized linear models (GLMs) with multiple types of predictors via regularized maximum likelihood.
Testing the mediation effect of multiple SNPs on an outcome through a mediator.
Implements the structural model for variances in order to
        detect differentially expressed genes from gene expression data
Build and manipulate probability distributions of the skew-normal 
  family and some related ones, notably the skew-t family, and provide related
  statistical methods for data fitting and model diagnostics, in the univariate 
  and the multivariate case.
A range of tools for social network analysis, including node and graph-level indices, structural distance and covariance methods, structural equivalence detection, network regression, random graph generation, and 2D/3D network visualization.
A consistent, flexible and easy to use tool to parse and convert strings into cases like snake or camel among others.
Run 'Python' code, make function calls, assign and retrieve variables, etc. from R.
    A fork from 'rPython' which uses 'jsonlite', 'Rcpp' and has several fixes and improvements.
Plays the game of Snakes and Ladders and has tools for analyses. The tools included allow you to find the average moves to win, frequency of each square, importance of the snakes and the ladders, the most common square and the plotting of the game played.
Compression and decompression with 'Snappy'.
Functions for reading and writing Gadget N-body snapshots. The Gadget code is popular in astronomy for running N-body / hydrodynamical cosmological and merger simulations. To find out more about Gadget see the main distribution page at www.mpa-garching.mpg.de/gadget/
Efficient estimation of multivariate skew-normal distribution in closed form.
Fitting of non-parametric production frontiers for use in efficiency analysis.
    Methods are provided for both a smooth analogue of Data Envelopment Analysis (DEA) and a 
    non-parametric analogue of Stochastic Frontier Analysis (SFA). Frontiers are constructed for 
    multiple inputs and a single output using constrained kernel smoothing as in 
    Racine et al. (2009), which allow for the imposition of monotonicity and concavity constraints 
    on the estimated frontier.
Similarity Network Fusion takes multiple views of a network and fuses them together to construct an overall status matrix. The input to our algorithm can be feature vectors, pairwise distances, or pairwise similarities. The learned status matrix can then be used for retrieval, clustering, and classification.
Implementation of robust and non-robust Standard Normal Homogeneity
    Test (SNHT) for changepoint detection. This test statistic is equal sided,
    as proposed in "Homogenization of Radiosonde Temperature Time Series Using
    Innovation Statistics" by Haimberger, L., 2007. However, the statistic contains
    an estimate of sigma^2 in the denominator instead of sigma, which seems to
    be a more appropriate value (based on the paper "Homogenization of Swedish
    temperature data. Part I: Homogeneity test for linear trends." by Alexandersson,
    H., and A. Moberg, 1997).
Snipping methods optimally removing scattered cells for robust estimation and cluster analysis.
Implement K-nearest neighbor classifier, weighted nearest neighbor classifier, bagged nearest neighbor classifier, optimal weighted nearest neighbor classifier and stabilized nearest neighbor classifier, and perform model selection via 5 fold cross-validation for them. This package also provides functions for computing the classification error and classification instability of a classification procedure.
Solve the problem of over-parameterization in neural networks for genomic selection. Daniel Gianola, Hayrettin OkutEmail, Kent A Weigel and Guilherme JM Rosa (2011) <doi:10.1186/1471-2156-12-87>.
Programmatic interface to the 'SNOTEL' snow data
  (<https://www.wcc.nrcs.usda.gov/snow/>). Provides easy downloads of snow 
  data into your R work space or a local directory. Additional post-processing 
  routines to extract snow season indexes are provided.
Support for simple parallel computing in R.
An R interface to the C 'libstemmer' library that implements
  Porter's word stemming algorithm for collapsing words to a common
  root to aid comparison of vocabulary. Currently supported languages are
  Danish, Dutch, English, Finnish, French, German, Hungarian, Italian,
  Norwegian, Portuguese, Romanian, Russian, Spanish, Swedish
  and Turkish.
Functions for analysis of network objects, which are imported or simulated by the package. The non-parametric methods of analysis center on snowball and bootstrap sampling for estimating functions of network degree distribution. For other parameters of interest, see, e.g., 'bootnet' package.
Usability wrapper around snow for easier development of
        parallel R programs. This package offers e.g. extended error
        checks, and additional functions. All functions work in
        sequential mode, too, if no cluster is present or wished.
        Package is also designed as connector to the cluster management
        tool sfCluster, but can also used without it.
The function generates and plots random snowflakes. Each snowflake is defined by a given diameter, width of the crystal, color, and random seed. Snowflakes are plotted in such way that they always remain round, no matter what the aspect ratio of the plot is. Snowflakes can be created using transparent colors, which creates a more interesting, somewhat realistic, image. Images of the snowflakes can be separately saved as svg files and used in websites as static or animated images.
Extension of the snow package supporting fault tolerant and reproducible applications, as well as supporting easy-to-use parallel programming - only one function is needed. Dynamic cluster size is also available.
Creates plots of p-values using single SNP and/or haplotype data.
    Main features of the package include options to display a linkage
    disequilibrium (LD) plot and the ability to plot multiple datasets
    simultaneously. Plots can be created using global and/or individual
    haplotype p-values along with single SNP p-values. Images are created as
    either PDF/EPS files.
contains several supplementary non-parametric statistics methods including quantile test, Cox-Stuart trend test, runs test, normal score test, kernel PDF and CDF estimation, kernel regression estimation and kernel Kolmogorov-Smirnov test.
This package carries out most common analysis when performing whole genome association studies. These analyses include descriptive statistics and exploratory analysis of missing values, calculation of Hardy-Weinberg equilibrium, analysis of association based on generalized linear models (either for quantitative or binary traits), and analysis of multiple SNPs (haplotype and epistasis analysis). Permutation test and related tests (sum statistic and truncated product) are also implemented. Max-statistic and genetic risk-allele score exact distributions are also possible to be estimated.
Implements classes and methods for large scale SNP enrichment analysis (e.g. SNPs associated with genes expression in a GWAS signal).
Generate knockoffs for genetic data and hidden Markov models.
  For more information, see the website below and the accompanying papers: 
  "Gene hunting with hidden Markov model knockoffs", Sesia et al., Biometrika, 2019, (<doi:10.1093/biomet/asy033>).
  "Multi-resolution localization of causal variants across the genome", Sesia et al., bioRxiv, 2019, (<doi:10.1101/631390>).
A set of functions to create SQL tables of gene and SNP information and compose them into a SNP Set, for example for use with the RSNPset package, or to export to a PLINK set.
Tools for performing variable selection in three-way data using N-PLS 
    in combination with L1 penalization. The N-PLS model (Rasmus Bro, 1996 
    <DOI:10.1002/(SICI)1099-128X(199601)10:1%3C47::AID-CEM400%3E3.0.CO;2-C>) is the 
    natural extension of PLS (Partial Least Squares) to N-way structures, and tries 
    to maximize the covariance between X and Y data arrays. The package also adds
    variable selection through L1 penalization.
This package implements asymptotic methods related to
        maximally selected statistics, with applications to SNP data.
Bivariate Gaussian genotype clustering and calling for Illumina
    microarrays, building on the package 'mclust'. Pronounced snip-em-clust.
Three functions to clean, summarize and prepare genomic datasets to Genome
    Selection and Genome Association analysis and to estimate population genetic parameters.
A modification of Breiman and Cutler's classification random forests modified for SNP (Single Nucleotide Polymorphism) data (based on randomForest v4.6-7) to prevent X-chromosome SNP variable importance bias compared to autosomal SNPs by simulating the process of X chromosome inactivation. Classification is based on a forest of trees using random subsets of SNPs and other variables as inputs.
Write snpStats objects to disk in formats suitable for reading by
    snphap, phase, mach, IMPUTE, beagle, and (almost) anything else that
    expects a rectangular format.
Stochastic Newton Sampler (SNS) is a Metropolis-Hastings-based, Markov Chain Monte Carlo sampler for twice differentiable, log-concave probability density functions (PDFs) where the proposal density function is a multivariate Gaussian resulting from a second-order Taylor-series expansion of log-density around the current point. The mean of the Gaussian proposal is the full Newton-Raphson step from the current point. A Boolean flag allows for switching from SNS to Newton-Raphson optimization (by choosing the mean of proposal function as next point). This can be used during burn-in to get close to the mode of the PDF (which is unique due to concavity). For high-dimensional densities, mixing can be improved via 'state space partitioning' strategy, in which SNS is applied to disjoint subsets of state space, wrapped in a Gibbs cycle. Numerical differentiation is available when analytical expressions for gradient and Hessian are not available. Facilities for validation and numerical differentiation of log-density are provided.
Scan statistics applied in social network data can be used to test the cluster characteristics among a social network.
Allows objects to be stored on disc and automatically
       		recalled into memory, as required, by delayed assignment.
Algorithm to estimate the Sobol indices using a non-parametric fit of the regression curve. The bandwidth is estimated using bootstrap to reduce the finite-sample bias. The package is based on the paper Solís, M. (2018) <arXiv:1803.03333>.
R implementation of S. Joe and F. Y. Kuo(2008)
        <DOI:10.1137/070709359>.
        The implementation is based on the data file new-joe-kuo-6.21201
        <http://web.maths.unsw.edu.au/~fkuo/sobol/>.
Specific and class specific multiple correspondence analysis on
    survey-like data. Soc.ca is optimized to the needs of the social scientist and
    presents easily interpretable results in near publication ready quality.
A set of functions to quantify and visualise 
    social autocorrelation.
Provides methods for sampling contact matrices from diary data for use in infectious disease modelling, as discussed in Mossong et al. (2008) <doi:10.1371/journal.pmed.0050074>.
Generates social networks using either of two
    approaches: using either pairwise distances or territorial area intersections.
Provides to sociologists (and related scientists) a toolbox to facilitate the construction of social position indicators from survey data. Social position indicators refer to what is commonly known as social class and social status. There exists in the sociological literature many theoretical conceptualisation and empirical operationalization of social class and social status. This first version of the package offers tools to construct the International Socio-Economic Index of Occupational Status (ISEI) and the Oesch social class schema. It also provides tools to convert several occupational classifications (PCS82, PCS03, and ISCO08) into a common one (ISCO88) to facilitate data harmonisation work, and tools to collapse (i.e. group) modalities of social position indicators.
Supporting materials for a course and book on data visualization. It contains utility functions for graphs and several sample data sets. See Healy (2019) <ISBN 978-0691181622>.
Functions, examples and other software related to the book
        "Software for Data Analysis: Programming with R". See
        package?SoDA for an overview.
Variable and interaction selection are essential to classification in high-dimensional setting. In this package, we provide the implementation of SODA procedure, which is a forward-backward algorithm that selects both main and interaction effects under logistic regression and quadratic discriminant analysis. We also provide an extension, S-SODA, for dealing with the variable selection problem for semi-parametric models with continuous responses.
To implement two clustering methods, ODC and SODC, for
        clustering datasets using optimal scoring, can also be used as
        an dimension reduction tool.
Bindings to 'libsodium': a modern, easy-to-use software library for
    encryption, decryption, signatures, password hashing and more. Sodium uses
    curve25519, a state-of-the-art Diffie-Hellman function by Daniel Bernstein,
    which has become very popular after it was discovered that the NSA had
    backdoored Dual EC DRBG.
Provides an interface to the 'NoSQL' database 'CouchDB'
    (<http://couchdb.apache.org>). Methods are provided for managing
    databases within 'CouchDB', including creating/deleting/updating/transferring,
    and managing documents within databases. One can connect with a local
    'CouchDB' instance, or a remote 'CouchDB' databases such as 'Cloudant'
    (<https://docs.cloudant.com>). Documents can be inserted directly from
    vectors, lists, data.frames, and 'JSON'. Targeted at 'CouchDB' v2 or
    greater.
Este paquete tiene la finalidad de ayudar a aprender de una forma interactiva, teniendo ejemplos y la posibilidad de resolver nuevos al mismo tiempo. Apuntes de clase interactivos.
Software that leverages the capabilities of Circos by manipulating data, preparing configuration files, and running the Perl-native Circos directly from the R environment with minimal user intervention. Circos is a novel software that addresses the challenges in visualizing genetic data by creating circular ideograms composed of tracks of heatmaps, scatter plots, line plots, histograms, links between common markers, glyphs, text, and etc. Please see <http://www.circos.ca>.
An extension of sensitivity, specificity, positive and negative
    predictive value to continuous predicted and reference memberships in
    [0, 1].
It contains soft clustering algorithms, in particular approaches derived from rough set theory: Lingras & West original rough k-means, Peters' refined rough k-means, and PI rough k-means. It also contains classic k-means and a corresponding illustrative demo.
Iterative methods for matrix completion that use nuclear-norm regularization. There are two main approaches.The one approach uses iterative soft-thresholded svds to impute the missing values. The second approach uses alternating least squares. Both have an "EM" flavor, in that at each iteration the matrix is completed with the current estimate. For large matrices there is a special sparse-matrix class named "Incomplete" that efficiently handles all computations. The package includes procedures for centering and scaling rows, columns or both, and for computing low-rank SVDs on large sparse centered matrices (i.e. principal components)
Implementation of 'softmax' regression and classification models with multiple layer neural network. It can be used for many tasks like word embedding based document classification, 'MNIST' dataset handwritten digit recognition and so on. Multiple optimization algorithm including 'SGD', 'Adagrad', 'RMSprop', 'Moment', 'NAG', etc are also provided.
Performs random forests for soft decision trees for a 
	classification problem.  Current limitations are for a maximum depth of 5
	resulting in 16 terminal nodes.  Some data cleaning is required before
	input.  Final graphic output requires currently requires exporting to 'Microsoft
	Excel' for visualization.  Method based on Irsoy, Yildiz and Alpaydin (2012, ISBN: 978-4-9906441-1-6).
Sparsity Oriented Importance Learning (SOIL) provides a new variable importance measure for high dimensional linear regression and logistic regression from a sparse penalization perspective, by taking into account the variable selection uncertainty via the use of a sensible model weighting. The package is an implementation of Ye, C., Yang, Y., and Yang, Y. (2017+).
A tool for importing, visualizing, and analyzing the soil carbon database created by the Powell Center working group.
A collection of functions for reading data from USDA-NCSS soil databases.
Provides functions for (1) soil water retention (SWC) and unsaturated hydraulic conductivity (Ku) (van Genuchten-Mualem (vGM or vG) [1, 2], Peters-Durner-Iden (PDI) [3, 4, 5], Brooks and Corey (bc) [8]), (2) fitting of parameter for SWC  and/or Ku using Shuffled Complex Evolution (SCE) optimisation and (3) calculation of soil hydraulic properties (Ku and soil water contents) based on the simplified evaporation method (SEM) [6, 7].
    Main references:
    [1] van Genuchten (1980) <doi:10.2136/sssaj1980.03615995004400050002x>, 
    [2] Mualem (1976) <doi:10.1029/WR012i003p00513>, 
    [3] Peters (2013) <doi:10.1002/wrcr.20548>, 
    [4] Iden and Durner (2013) <doi:10.1002/2014WR015937>, 
    [5] Peters (2014) <doi:10.1002/2014WR015937>,
    [6] Wind G. P. (1966),
    [7] Peters and Durner (2008) <doi:10.1016/j.jhydrol.2008.04.016> and
    [8] Brooks and Corey (1964).
Basic and model-based soil physical analyses.
This package contains functions for modeling Soil Organic
        Matter decomposition in terrestrial ecosystems.
"The Soil Texture Wizard" is a set of R functions designed to produce texture triangles (also called texture plots, texture diagrams, texture ternary plots), classify and transform soil textures data. These functions virtually allows to plot any soil texture triangle (classification) into any triangle geometry (isosceles, right-angled triangles, etc.). This set of function is expected to be useful to people using soil textures data from different soil texture classification or different particle size systems. Many (> 15) texture triangles from all around the world are predefined in the package. A simple text based graphical user interface is provided: soiltexture_gui().
It implements parametric formulas of soil
    water retention or conductivity curve. At the moment, only Van Genuchten
    (for soil water retention curve) and Mualem (for hydraulic conductivity)
    were implemented. 
    See reference (<http://en.wikipedia.org/wiki/Water_retention_curve>).
Provides a simple way for utilizing Sojourn methods for
    accelerometer processing, as detailed in Lyden K, Keadle S,
    Staudenmayer J, & Freedson P (2014) <doi:10.1249/MSS.0b013e3182a42a2d>,
    Ellingson LD, Schwabacher IJ, Kim Y, Welk GJ, & Cook DB (2016)
    <doi:10.1249/MSS.0000000000000915>, and Hibbing PR, Ellingson LD,
    Dixon PM, & Welk GJ (2018) <doi:10.1249/MSS.0000000000001486>.
Stores objects (e.g. neural networks) that are needed for
    using Sojourn accelerometer methods. For more information, see
    Lyden K, Keadle S, Staudenmayer J, & Freedson P (2014)
    <doi:10.1249/MSS.0b013e3182a42a2d>, Ellingson LD, Schwabacher IJ,
    Kim Y, Welk GJ, & Cook DB (2016) <doi:10.1249/MSS.0000000000000915>,
    and Hibbing PR, Ellingson LD, Dixon PM, & Welk GJ (2018)
    <doi:10.1249/MSS.0000000000001486>.
Interactively play a game of sokoban ,which has nine game levels.Sokoban is a type of transport puzzle, in which the player pushes boxes or crates around in a warehouse, trying to get them to storage locations.
Calculation methods of solar radiation and performance of photovoltaic systems from daily and intradaily irradiation data sources.
SOLAR is the standard software program to perform linkage and
    association mappings of the quantitative trait loci (QTLs) in pedigrees of
    arbitrary size and complexity. This package allows the user to exploit the
    variance component methods implemented in SOLAR. It automates such routine
    operations as formatting pedigree and phenotype data. It also parses the model
    output and contains summary and plotting functions for exploration of the
    results. In addition, solarius enables parallel computing of the linkage and
    association analyses, that makes the calculation of genome-wide scans more
    efficient. See <http://solar.txbiomedgenetics.org/> for more information about
    SOLAR.
Calculation of solar zenith and azimuth angles.
Provide utilities to work with solar time, 
  i.e. where noon is exactly when sun culminates.
  Provides functions for computing sun position and times of sunrise and sunset.
Isolation forest is anomaly detection method introduced by the paper Isolation based Anomaly Detection (Liu, Ting and Zhou <doi:10.1145/2133360.2133363>).
For surface energy models and estimation of solar positions and components with varying topography, time and locations. The functions calculate solar top-of-atmosphere, open, diffuse and direct components, atmospheric transmittance and diffuse factors, day length, sunrise and sunset, solar azimuth, zenith, altitude, incidence, and hour angles, earth declination angle, equation of time, and solar constant. Details about the methods and equations are explained in Seyednasrollah, Bijan, Mukesh Kumar, and Timothy E. Link. 'On the role of vegetation density on net snow cover radiation at the forest floor.' Journal of Geophysical Research: Atmospheres 118.15 (2013): 8359-8374, <doi:10.1002/jgrd.50575>.
Provides a set of functions for querying and parsing data
    from 'Solr' (<http://lucene.apache.org/solr>) 'endpoints' (local and
    remote), including search, 'faceting', 'highlighting', 'stats', and
    'more like this'. In addition, some functionality is included for
    creating, deleting, and updating documents in a 'Solr' 'database'.
R language bindings for SolveBio's API.
    SolveBio is a biomedical knowledge hub that enables life science
    organizations to collect and harmonize the complex, disparate
    "multi-omic" data essential for today's R&D and BI needs.
    For more information, visit <https://www.solvebio.com>.
Given a symmetric positive definite matrix Q and a non-singular matrix L, find symmetric positive definite solution X such that X = Q + L (X inv) L^T.
        Reference: Benner, P., Faßbender, H. On the Solution of the Rational Matrix Equation.
        Benner, Faßbender (2007) <doi:10.1155/2007/21850>.
Self-Organizing Map (with application in gene clustering).
A topological version of k-NN: An abstract model is build
             as 2-dimensional self-organising map. Samples of unknown
             class are predicted by mapping them on the SOM and analysing
             class membership of neurons in the neighbourhood.
This package provides an R implementation of the Self-Organising Migrating Algorithm, a general-purpose, stochastic optimisation algorithm. The approach is similar to that of genetic algorithms, although it is based on the idea of a series of ``migrations'' by a fixed set of individuals, rather than the development of successive generations. It can be applied to any cost-minimisation problem with a bounded parameter space, and is robust to local minima.
The stochastic (also called on-line) version of the Self-Organising
             Map (SOM) algorithm is provided. Different versions of the
             algorithm are implemented, for numeric and relational data and for
             contingency tables as described, respectively, in Kohonen (2001)
             <isbn:3-540-67921-9>, Olteanu & Villa-Vialaneix (2005)
             <doi:10.1016/j.neucom.2013.11.047> and Cottrell et al (2004)
             <doi:10.1016/j.neunet.2004.07.010>. The package also contains many
             plotting features (to help the user interpret the results) and a
             graphical user interface based on 'shiny'.
some Brownian motions simulation functions
This package collects some procedures controlling the
        Generalized Familywise Error Rate.
It's a collection of functions for Multiplicity Correction and Multiple Testing.
Structural multivariate-univariate linear mixed model solver for multiple random effects and estimation of unknown variance-covariance structures (i.e. heterogeneous and unstructured variance models) (Covarrubias-Pazaran, 2016 <doi:10.1371/journal.pone.0156744>; Maier et al., 2015 <doi:10.1016/j.ajhg.2014.12.006>). ML/REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms. Designed for genomic prediction and genome wide association studies (GWAS), particularly focused in the p > n problem (more coefficients than observations) to include multiple known relationship matrices and estimate complex unknown covariance structures. Spatial models can be fitted using the two-dimensional spline functionality in sommer.
The package provides the plot function som.plot() to
        create high quality visualisations of hexagonal Kohonen maps
        (self-organising maps).
Application of the Self-Organizing Maps technique for spatial classification of time series. The package uses spatial data, point or gridded, to create clusters with similar characteristics. The clusters can be further refined to a smaller number of regions by hierarchical clustering and their spatial dependencies can be presented as complex networks. Thus, meaningful maps can be created, representing the regional heterogeneity of a single variable. More information and an example of implementation can be found in Markonis and Strnad (2019).
Formulas for calculating sound velocity, water pressure, depth, 
    density, absorption and sonar equations.
Simulates the cultural evolution of quantitative traits of bird song. 'SongEvo' is an individual- (agent-) based model. 'SongEvo' is spatially-explicit and can be parameterized with, and tested against, measured song data. Functions are available for model implementation, sensitivity analyses, parameter optimization, model validation, and hypothesis testing.
Estimate the abundance of cell clones from the
	     distribution of lengths of DNA fragments (as created by
	     sonication, whence `sonicLength').  The algorithm in
	     "Estimating abundances of retroviral insertion sites from
	     DNA fragment length data" by Berry CC, Gillet NA, Melamed
	     A, Gormley N, Bangham CR, Bushman FD. Bioinformatics;
	     2012 Mar 15;28(6):755-62 is implemented.  The
	     experimental setting and estimation details are described
	     in detail there. Briefly, integration of new DNA in a
	     host genome (due to retroviral infection or gene therapy)
	     can be tracked using DNA sequencing, potentially allowing
	     characterization of the abundance of individual cell
	     clones bearing distinct integration sites. The locations
	     of integration sites can be determined by fragmenting the
	     host DNA (via sonication or fragmentase), breaking the
	     newly integrated DNA at a known sequence, amplifying the
	     fragments containing both host and integrated DNA,
	     sequencing those amplicons, then mapping the host
	     sequences to positions on the reference genome. The
	     relative number of fragments containing a given position
	     in the host genome estimates the relative abundance of
	     cells hosting the corresponding integration site, but
	     that number is not available and the count of amplicons
	     per fragment varies widely.  However, the expected number
	     of distinct fragment lengths is a function of the
	     abundance of cells hosting an integration site at a given
	     position and a certain nuisance parameter. The algorithm
	     implicitly estimates that function to estimate the
	     relative abundance.
Sonification (or audification) is the process of representing data by sounds in the audible range. This package provides the R function sonify() that transforms univariate data, sampled at regular or irregular intervals, into a continuous sound with time-varying frequency. The ups and downs in frequency represent the ups and downs in the data. Sonify provides a substitute for R's plot function to simplify data analysis for the visually impaired.
Collection of different single objective test functions
        useful for benchmarks and algorithm development.
Load Russian economic indicators from the Archive of Economic and Social Data <http://sophist.hse.ru/>.
Provides functions to non-parametrically estimate the off-pulse interval of a source
    function originating from a pulsar. The technique is based on a sequential application of P-values
    obtained from goodness-of-fit tests for the uniform distribution, such as the Kolmogorov-Smirnov,
    Cramer-von Mises, Anderson-Darling and Rayleigh goodness-of-fit tests.
Computes sequential A-, MV-, D- and E-optimal or near-optimal block and row-column designs for two-colour cDNA microarray experiments using the linear fixed effects and mixed effects models where the interest is in a comparison of all possible elementary treatment contrasts. The package also provides an optional method of using the graphical user interface (GUI) R package 'tcltk' to ensure that it is user friendly.
Used to construct the URLs and parameters of 'Socrata Open Data API' <https://dev.socrata.com> calls, using the API's 'SoQL' parameter format. Has method-chained and sensical syntax. Plays well with pipes.
Estimation for longitudinal data following outcome dependent sampling using the sequential offsetted regression technique.  Includes support for binary, count, and continuous data.  The first regression is a logistic regression, which uses a known ratio (the probability of being sampled given that the subject/observation was referred divided by the probability of being sampled given that the subject/observation was no referred) as an offset to estimate the probability of being referred given outcome and covariates.  The second regression uses this estimated probability to calculate the mean population response given covariates.
Provides tools to efficiently analyze and visualize laboratory data from aqueous static adsorption experiments. The package provides functions to plot Langmuir, Freundlich, and Temkin isotherms and functions to determine the statistical conformity of data points to the Langmuir, Freundlich, and Temkin adsorption models through statistical characterization of the isothermic least squares regressions lines. Scientific Reference: Dada, A.O, Olalekan, A., Olatunya, A. (2012) <doi:10.9790/5736-0313845>.
SortableHTMLTables writes a data frame to an HTML file
        that contains a sortable table. The sorting is done using the
        jQuery plugin Tablesorter. The appearance is controlled through
        a CSS file and several GIF's.
Implements the estimation and inference methods for sorted causal effects and 
    classification analysis as in Chernozhukov, Fernandez-Val and Luo (2018) <doi:10.3982/ECTA14415>.
Algorithms for Finnish open government data.
Search contributed R packages, sort by package.
A client for Sensor Observation Services (SOS, see 
        <https://www.opengeospatial.org/standards/sos>) as specified by the 
        Open Geospatial Consortium (OGC). With the package users can
        retrieve (meta)data from SOS instances and interactively 
        create requests for near real-time observation
        data based on the available sensors, phenomena, observations etc.
        using thematic, temporal, and spatial filtering.
Access data from the sotkanet open data portal
        <https://www.sotkanet.fi/sotkanet/fi/index>.
The President of the United States is constitutionally obligated to provide
  a report known as the 'State of the Union'. The report summarizes the current challenges
  facing the country and the president's upcoming legislative agenda. While historically
  the State of the Union was often a written document, in recent decades it has always
  taken the form of an oral address to a joint session of the United States Congress.
  This package provides the raw text from every such address with the intention of
  being used for meaningful examples of text analysis in R. The corpus is well suited
  to the task as it is historically important, includes material intended to be read
  and material intended to be spoken, and it falls in the public domain. As the corpus
  spans over two centuries it is also a good test of how well various methods hold up
  to the idiosyncrasies of historical texts. Associated data about each address, such
  as the year, president, party, and format, are also included.
Basic functions for dealing with wav files and sound samples.
Functions to calculate indices for soundscape ecology and other ecology research that uses audio recordings.
The SoundexBR package provides an algorithm for decoding names
    into phonetic codes, as pronounced in Portuguese. The goal is for
    homophones to be encoded to the same representation so that they can be
    matched despite minor differences in spelling. The algorithm mainly encodes
    consonants; a vowel will not be encoded unless it is the first letter. The
    soundex code resultant consists of a four digits long string composed by
    one letter followed by three numerical digits: the letter is the first
    letter of the name, and the digits encode the remaining consonants.
Tools for sound synthesis and acoustic analysis.
    Performs parametric synthesis of sounds with harmonic and noise components
    such as animal vocalizations or human voice. Also includes tools for
    spectral analysis, pitch tracking, audio segmentation, self-similarity
    matrices, morphing, etc.
Construct a ranking of a set of treatments/groups by
    gathering together information coming from several response variables.
    It can be used with both balanced and unbalanced experiments
    (with almost all test statistics) as well as in presence of either
    continuous covariates or a stratifying (categorical) variable.
Analogous to source(), but works when given a Gist URL or
        ID.
Implements a non-parametric source attribution model to attribute
    cases of disease to sources in Bayesian framework with source and type effects.
    Type effects are clustered using a Dirichlet Process. Multiple times and
    locations are supported.
The algorithm pursues the identification of the set of variables driving the differences in two different experimental conditions (i.e., the primary genes) within a graphical model context. It uses the idea of simultaneously looking for the differences between two multivariate normal distributions in all marginal and conditional distributions associated with a decomposable graph, which represents the pathway under exam. The implementation accommodates genomics specific issues (low sample size and multiple testing issues) and provides a number of functions offering numerical and visual summaries to help the user interpret the obtained results. In order to use the (optional) 'Cytoscape' functionalities, the suggested 'r2cytoscape' package must be installed from the 'GitHub' repository ('devtools::install_github('cytoscape/r2cytoscape')').
Tools for the reading and tokenization of R code. The
    'sourcetools' package provides both an R and C++ interface for the tokenization
    of R code, and helpers for interacting with the tokenized representation of R
    code.
Genomic and multi-environmental soybean data. Soybean Nested
    Association Mapping (SoyNAM) project dataset funded by the United Soybean Board
    (USB). See <doi.org/10.1534/g3.118.200332> and <doi.org/10.1534/g3.117.300300>. 
    BLUP function formats data for genome-wide prediction and association analysis.
Classes and methods for spatial
  data; the classes document where the spatial location information
  resides, for 2D or 3D data. Utility functions are provided, e.g. for
  plotting data as maps, spatial selection, as well as methods for
  retrieving coordinates, for subsetting, print, summary, etc.
Provides methods for generating, exploring and executing seamless Phase II-III designs of Lai, Lavori and Shih using generalized likelihood ratio statistics. Includes pdf and source files that describe the entire R implementation with the relevant mathematical details.
The package implements the model-based kernel machine
        method for detecting gene-centric gene-gene interactions of Li
        and Cui (2012).
Miscellaneous functions for analysing species association
        and niche overlap.
Genomic Region Detection via Spatial Convex Clustering. See <https://arxiv.org/abs/1611.04696> for details.
Partial correlation estimation with joint sparse
        regression model
SPACECAP is a user-friendly software package for
        estimating animal densities using closed model
        capture-recapture sampling based on photographic captures using
        Bayesian spatially-explicit capture-recapture models. This
        approach offers advantage such as: substantially dealing with
        problems posed by individual heterogeneity in capture
        probabilities in conventional capture-recapture analyses. It
        also offers non-asymptotic inferences which are more
        appropriate for small samples of capture data typical of
        photo-capture studies.
This package provides an extension of conditional
        independence (CIG) and directed acyclic graph (DAG) estimation
        to the case where conditional relationships are (non-linear)
        additive models.
Latent space models for multivariate networks (multiplex) estimated via MCMC algorithm. See D Angelo et al. (2018) <arXiv:1803.07166> and D Angelo et al. (2018) <arXiv:1807.03874>.
Standard RGB spaces included are sRGB, 'Adobe' RGB, 'ProPhoto' RGB, BT.709, and others.  User-defined RGB spaces are also possible.  There is partial support for ACES Color workflows.
Functions for converting among CIE XYZ, xyY, Lab, and Luv.
      The XYZs of some standard illuminants and some standard linear chromatic adaptation transforms (CATs) are included.
      Compute CCT and the Planckian and daylight loci.  Three different standard color difference metrics are included.
Classes and methods for spatio-temporal data, including space-time regular lattices, sparse lattices, irregular data, and trajectories; utility functions for plotting data as map sequences (lattice or animation) or multiple time series; methods for spatial and temporal selection and subsetting, as well as for spatial/temporal/spatio-temporal matching or aggregation, retrieving coordinates, print, summary, etc.
SPACoDi is primarily designed to characterise the
        structure and phylogenetic diversity of communities using
        abundance or presence-absence data of species among community
        plots.
Provides tools to construct and exploit spatially weighted context data. Spatial weights are derived by a Kernel function from a user-defined matrix of distances between contextual units. Spatial weights can then be applied either to precise contextual measures or to aggregate estimates based on micro-level survey data, to compute spatially weighted context data. Available aggregation functions include indicators of central tendency, dispersion, or inter-group variability, and take into account survey design weights. The package further allows combining the resulting spatially weighted context data with individual-level predictor and outcome variables, for the purposes of multilevel modelling. An ad hoc stratified bootstrap resampling procedure generates robust point estimates for multilevel regression coefficients and model fit indicators, and computes confidence intervals adjusted for measurement dependency and measurement error of aggregate estimates. As an additional feature, residual and explained spatial dependency can be estimated for the tested models.
An R wrapper to the 'Python' 'spaCy' 'NLP' library,
    from <http://spacy.io>.
Provides easy to use functions to create all-sky grid plots of widely used astronomical coordinate systems (equatorial, ecliptic, galactic) and scatter plots of data on any of these systems including on-the-fly system conversion. It supports any type of spherical projection to the plane defined by the 'mapproj' package.
A set of RStudio addins that are designed to be used in
             combination with user-defined RStudio keyboard shortcuts. These
             addins either:
             1) insert text at a cursor position (e.g. insert
             operators %>%, <<-, %$%, etc.),
             2) replace symbols in selected pieces of text (e.g., convert
             backslashes to forward slashes which results in stings like
             "c:\data\" converted into "c:/data/") or
             3) enclose text with special symbols (e.g., converts "bold" into
             "**bold**") which is convenient for editing R Markdown files.
Estimation of various biodiversity indices and related (dis)similarity measures based on individual-based (abundance) data or sampling-unit-based (incidence) data taken from one or multiple communities/assemblages.
Metapackage for implementing a variety of event-based models, with
    a focus on spatially explicit models. These include raster-based,
    event-based, and agent-based models. The core simulation components
    (provided by 'SpaDES.core') are built upon a discrete event simulation (DES;
    see Matloff (2011) ch 7.8.3 <https://nostarch.com/artofr.htm>)
    framework that facilitates modularity, and easily enables the user to
    include additional functionality by running user-built simulation modules
    (see also 'SpaDES.tools'). Included are numerous tools to visualize rasters
    and other maps (via 'quickPlot'), and caching methods for reproducible
    simulations (via 'reproducible'). Additional functionality is provided by
    the 'SpaDES.addins' and 'SpaDES.shiny' packages.
Provides 'RStudio' addins for 'SpaDES' packages and 'SpaDES' module
    development. See '?SpaDES.addins' for an overview of the tools provided.
Provide the core discrete event simulation (DES) framework for
    implementing spatially explicit simulation models. The core DES components 
    facilitate modularity, and easily enable the user to include additional
    functionality by running user-built simulation modules. Now includes (experimental)
    conditional scheduling.
Provides GIS and map utilities, plus additional modeling tools for
    developing cellular automata, dynamic raster models,  and agent based models
    in 'SpaDES'.
    Included are various methods for spatial spreading, spatial agents, GIS
    operations, random map generation, and others.
    See '?SpaDES.tools' for an categorized overview of these additional tools.
Implements methods for anticipating the emergence and eradication
    of infectious diseases from surveillance time series. Also provides support
    for computational experiments testing the performance of such methods.
A utility package containing some simple tools to design and generate density functions on bounded regions in space and space-time, and simulate iid data therefrom. See Davies & Hazelton (2010) <doi:10.1002/sim.3995> for example.
Set of functions for sparse matrix algebra.
    Differences with other sparse matrix packages are:
    (1) we only support (essentially) one sparse matrix format,
    (2) based on transparent and simple structure(s),
    (3) tailored for MCMC calculations within G(M)RF.
    (4) and it is fast and scalable (with the extension package spam64).
Provides the Fortran code of the R package 'spam'
    with 64-bit integers. Loading this package together with the R package
    spam enables the sparse matrix class spam to handle huge sparse matrices
    with more than 2^31-1 non-zero elements.
Inference based on mixed-effect models, including generalized linear mixed models with spatial correlations and models with non-Gaussian random effects (e.g., Beta). Both classical geostatistical models, and Markov random field models on irregular grids, can be fitted. Variation in residual variance (heteroscedasticity) can itself be represented by a generalized linear mixed model. Various approximations of likelihood or restricted likelihood are implemented, in particular h-likelihood (Lee and Nelder 2001 <doi:10.1093/biomet/88.4.987>) and Laplace approximation.
Fit the spatial panel data models: the fixed effects, random
    effects and between models.
Character vector to numerical translation in Euros from Spanish
    spelled monetary quantities. Reverse translation from integer to Spanish.
    Upper limit is up to the millions range. Geocoding via Cadastral web site.
Tools for reading, visualising and processing Magnetic Resonance
    Spectroscopy data.
This package performs robust nonparametric tests for rare variants
          association analysis using summation of partition approaches that incorporate
          gene-gene and gene-environmental interactions
We provide an efficient solver for estimating semiparametric generalized linear models.
Implements the sparse clustering methods of Witten and
        Tibshirani (2010): "A framework for feature selection in
        clustering"; published in Journal of the American Statistical
        Association 105(490): 713-726.
Read in 'SAS' Data ('.sas7bdat' Files) into 'Apache Spark' from R. 'Apache Spark' is an open source cluster computing framework available at <http://spark.apache.org>. This R package uses the 'spark-sas7bdat' 'Spark' package (<https://spark-packages.org/package/saurfang/spark-sas7bdat>) to import and process 'SAS' data in parallel using 'Spark'. Hereby allowing to execute 'dplyr' statements in parallel on top of 'SAS' data.
Load Avro Files into 'Apache Spark' using 'sparklyr'. This
    allows to read files from 'Apache Avro' <https://avro.apache.org/>.
A 'sparklyr' extension package providing an integration with Google 'BigQuery'.
  It supports direct import/export where records are directly streamed from/to 'BigQuery'.
  In addition, data may be imported/exported via intermediate data extracts on Google 'Cloud Storage'.
Include interactive sparkline charts
    <http://omnipotent.net/jquery.sparkline> in 
    all R contexts with the convenience of 'htmlwidgets'.
R interface to Apache Spark, a fast and general engine for big data
    processing, see <http://spark.apache.org>. This package supports connecting to
    local and remote Apache Spark clusters, provides a 'dplyr' compatible back-end,
    and provides an interface to Spark's built-in machine learning algorithms.
A 'sparklyr' extension adding the capability to work easily with nested data.
Provides an R Front end for 'Apache Spark' <https://spark.apache.org>.
Create sparklines and graphical tables for documents and websites.
Generate syntax for use with the sparklines package for
        LaTeX.
A 'sparklyr' extension that enables reading and writing 'TensorFlow'
  TFRecord files via 'Apache Spark'.
Load WARC (Web ARChive) files into Apache Spark using 'sparklyr'. This
    allows to read files from the Common Crawl project <http://commoncrawl.org/>.
A 'sparklyr' <https://spark.rstudio.com/> extension that provides an 
  interface for 'XGBoost' <https://github.com/dmlc/xgboost> on 'Apache Spark'. 'XGBoost' is an 
  optimized distributed gradient boosting library.
Use SPARQL to pose SELECT or UPDATE queries to an end-point.
Provides functions to estimate kernel-smoothed spatial and spatio-temporal densities and relative risk functions, and perform subsequent inference. Methodological details can be found in the accompanying tutorial: Davies et al. (2018) <DOI:10.1002/sim.7577>.
Implements the sparse biclustering proposal of Tan and Witten (2014), Sparse biclustering of transposable data. Journal of Computational and Graphical Statistics  23(4):985-1008.
Fast methods for learning sparse Bayesian networks from high-dimensional data using sparse regularization, as described in Aragam, Gu, and Zhou (2017) <arXiv:1703.04025>. Designed to handle mixed experimental and observational data with thousands of variables with either continuous or discrete observations.
A set of tools for representing and estimating sparse Bayesian networks from continuous and discrete data, as described in Aragam, Gu, and Zhou (2017) <arXiv:1703.04025>.
Implements the algorithm described in 
    Barron, M., Zhang, S. and Li, J. 2017, "A sparse differential
    clustering algorithm for tracing cell type changes via single-cell
    RNA-sequencing data", Nucleic Acids Research, gkx1113,
    <doi:10.1093/nar/gkx1113>. This algorithm clusters samples from two
    different populations, links the clusters across the conditions and
    identifies marker genes for these changes. The package was designed for
    scRNA-Seq data but is also applicable to many other data types, just
    replace cells with samples and genes with variables. The package also
    contains functions for estimating the parameters for SparseDC as outlined
    in the paper. We recommend that users further select their marker genes
    using the magnitude of the cluster centers.
Computation of sparse eigenvectors of a matrix (aka sparse PCA)
    with running time 2-3 orders of magnitude lower than existing methods and
    better final performance in terms of recovery of sparsity pattern and 
    estimation of numerical values. Can handle covariance matrices as well as 
    data matrices with real or complex-valued entries. Different levels of 
    sparsity can be specified for each individual ordered eigenvector and the 
    method is robust in parameter selection. See vignette for a detailed 
    documentation and comparison, with several illustrative examples. 
    The package is based on the paper:
    K. Benidis, Y. Sun, P. Babu, and D. P. Palomar (2016). "Orthogonal Sparse PCA 
    and Covariance Estimation via Procrustes Reformulation," IEEE Transactions on 
    Signal Processing <doi:10.1109/TSP.2016.2605073>.
Multidimensional scaling provides a means of uncovering a latent structure underlying observed data, while estimating the number of latent dimensions.  This package presents a means for scaling binary and count data, for example the votes and word counts for legislators.  Future work will include an EM implementation and extend this work to ordinal and continuous data.
Estimation of functional linear mixed models for irregularly or
    sparsely sampled data based on functional principal component analysis.
SparseGrid is a package to create sparse grids for numerical integration, based on code from www.sparse-grids.de
Estimates Hessian of a scalar-valued function, and returns it
    in a sparse Matrix format. The sparsity pattern must be known in advance. The
    algorithm is especially efficient for hierarchical models with a large number of
    heterogeneous units.  See Braun, M. (2017) <doi:10.18637/jss.v082.i10>.
Computation of sparse portfolios for financial index tracking, i.e., joint
    selection of a subset of the assets that compose the index and computation
    of their relative weights (capital allocation). The level of sparsity of the
    portfolios, i.e., the number of selected assets, is controlled through a
    regularization parameter. Different tracking measures are available, namely,
    the empirical tracking error (ETE), downside risk (DR), Huber empirical
    tracking error (HETE), and Huber downside risk (HDR). See vignette for a
    detailed documentation and comparison, with several illustrative examples.
    The package is based on the paper:
    K. Benidis, Y. Feng, and D. P. Palomar, "Sparse Portfolios for High-Dimensional
    Financial Index Tracking," IEEE Trans. on Signal Processing, vol. 66, no. 1,
    pp. 155-170, Jan. 2018. <doi:10.1109/TSP.2017.2762286>.
Creates a wrapper for the 'SuiteSparse' routines 
    that execute the Takahashi equations. These equations compute the
    elements of the inverse of a sparse matrix at locations where the
    its Cholesky factor is structurally non-zero. The resulting matrix is known as a 
	sparse inverse subset. Some helper functions are also implemented. 
	Support for spam matrices is currently limited and will be implemented 
	in the future. See Rue and Martino (2007) <doi:10.1016/j.jspi.2006.07.016> 
	and Zammit-Mangion and Rougier (2018) <doi:10.1016/j.csda.2018.02.001> for the 
	application of these equations to statistics.
Performs sparse linear discriminant analysis for Gaussians and mixture of Gaussian models.
Models the nonnegative entries of a rectangular adjacency matrix using a sparse latent position model, as illustrated in Rastelli, R. (2018) "The Sparse Latent Position Model for nonnegative weighted networks" <arXiv:1808.09262>.
Use RcppEigen to fit least trimmed squares
    regression models with an L1 penalty in order to obtain
    sparse models.
Some basic linear algebra functionality for sparse matrices is
  provided:  including Cholesky decomposition and backsolving as well as 
  standard R subsetting and Kronecker products.
The 'sparseMatEst' package provides functions for estimating sparse 
  covariance and precision matrices with error control. A false positive
  rate is fixed corresponding to the probability of falsely including a matrix 
  entry in the support of the estimator.  It uses the binary search
  method outlined in Kashlak and Kong (2019) <arXiv:1705.02679> and in 
  Kashlak (2019) <arXiv:1903.10988>.
Implements the algorithm described in
    Barron, M., and Li, J. (Not yet published). This algorithm clusters
    samples from multiple ordered populations, links the clusters across the
    conditions and identifies marker genes for these changes. The package was
    designed for scRNA-Seq data but is also applicable to many other data
    types, just replace cells with samples and genes with variables. The
    package also contains functions for estimating the parameters for SparseMDC
    as outlined in the paper. We recommend that users further select their
    marker genes using the magnitude of the cluster centers.
Implements the routines and algorithms developed and analysed in "Multiple systems estimation for Sparse Capture Data: Inferential Challenges when there are Non-Overlapping Lists" Chan, L, Silverman, B. W., Vincent, K (2019) <arXiv:1902.05156>. This package explicitly handles situations where there are pairs of lists which have no observed individuals in common.
Computes multivariate normal (MVN) densities, and
    samples from MVN distributions, when the covariance or
    precision matrix is sparse.
Efficient procedure for fitting regularization paths between L1 and L0, using the MC+ penalty of Zhang, C.H. (2010)<doi:10.1214/09-AOS729>. Implements the methodology described in Mazumder, Friedman and Hastie (2011) <DOI: 10.1198/jasa.2011.tm09738>. Sparsenet computes the regularization surface over both the family parameter and the tuning parameter by coordinate descent.
Sparse principal component analysis (SPCA) attempts to find sparse weight vectors (loadings), i.e., a weight vector with only a few 'active' (nonzero) values. This approach provides better interpretability for the principal components in high-dimensional data settings. This is, because the principal components are formed as a linear combination of only a few of the original variables. This package provides efficient routines to compute SPCA. Specifically, a variable projection solver is used to compute the sparse solution. In addition, a fast randomized accelerated SPCA routine and a robust SPCA routine is provided. Robust SPCA allows to capture grossly corrupted entries in the data. The methods are discussed in detail by N. Benjamin Erichson et al. (2018) <arXiv:1804.00341>.
Provides interface to 'sparsepp' - fast, memory efficient hash map. 
    It is derived from Google's excellent 'sparsehash' implementation.
    We believe 'sparsepp' provides an unparalleled combination of performance and memory usage, 
    and will outperform your compiler's unordered_map on both counts. 
    Only Google's 'dense_hash_map' is consistently faster, at the cost of much greater 
    memory usage (especially when the final size of the map is not known in advance).
Sparse modeling provides a mean selecting a small number of non-zero effects from a large possible number of candidate effects.  This package includes a suite of methods for sparse modeling: estimation via EM or MCMC, approximate confidence intervals with nominal coverage, and diagnostic and summary plots.  The method can implement sparse linear regression and sparse probit regression.  Beyond regression analyses, applications include subgroup analysis, particularly for conjoint experiments, and panel data. Future versions will include extensions to  models with truncated outcomes, propensity score, and instrumental variable analysis.
Sparse-aware maximum likelihood for structural equation models in inferring gene regulatory networks
Implements the SparseStep model for solving regression
    problems with a sparsity constraint on the parameters. The SparseStep 
    regression model was proposed in Van den Burg, Groenen, and Alfons (2017) 
    <https://arxiv.org/abs/1701.06967>. In the model, a regularization term is 
    added to the regression problem which approximates the counting norm of 
    the parameters.  By iteratively improving the approximation a sparse 
    solution to the regression problem can be obtained.  In this package both 
    the standard SparseStep algorithm is implemented as well as a path 
    algorithm which uses golden section search to determine solutions with 
    different values for the regularization parameter.
Wrapper around the 'SVDLIBC' library for (truncated) singular value decomposition of a sparse matrix.
             Currently, only sparse real matrices in Matrix package format are supported.
Fast algorithm for fitting solution paths of sparse SVM models with lasso or elastic-net regularization.
Computes sparse vector autoregressive coefficients and precision 
 matrices for time series chain graphical models.
A wrapper for sparse VAR/VECM time series models estimation
             using penalties like ENET, SCAD and MCP.
Fast 'SVMlight' reader and writer. 
    'SVMlight' is most commonly used format for storing 
    sparse matrices (possibly with some target variable) on disk.
    For additional information about 'SVMlight' format see <http://svmlight.joachims.org/>.
The Stratified-Petersen Analysis System (SPAS) is designed
    to estimate abundance in two-sample capture-recapture experiments 
    where the capture and recaptures are stratified. This is a generalization
    of the simple Lincoln-Petersen estimator.
    Strata may be defined in time or in space or both, 
    and the s strata in which marking takes place 
    may differ from the t strata in which recoveries take place.
    When s=t, SPAS reduces to the method described by 
    Darroch (1961) <https://www.jstor.org/stable/2332748>.
    When s<t, SPAS implements the methods described in
    Plante, Rivest, and Tremblay (1988) 
    <https://www.jstor.org/stable/2533994>.
    Schwarz and Taylor (1998) <doi:10.1139/f97-238> describe
    the use of SPAS in estimating return of salmon stratified by
    time and geography. 
    A related package, BTSPAS, deals with temporal stratification where 
    a spline is used to model the distribution of the population 
    over time as it passes the second capture location.
    This is the R-version of the (now obsolete) standalone Windows 
    program available at <http://www.cs.umanitoba.ca/~popan/spas/spas_home.html>.
Functionality for spatio-temporal modeling of large data sets is provided. A Gaussian process in space and time is defined through a stochastic partial differential equation (SPDE). The SPDE is solved in the spectral space, and after discretizing in time and space, a linear Gaussian state space model is obtained. When doing inference, the main computational difficulty consists in evaluating the likelihood and in sampling from the full conditional of the spectral coefficients, or equivalently, the latent space-time process. In comparison to the traditional approach of using a spatio-temporal covariance function, the spectral SPDE approach is computationally advantageous. This package aims at providing tools for two different modeling approaches. First, the SPDE based spatio-temporal model can be used as a component in a customized hierarchical Bayesian model (HBM). The functions of the package then provide parameterizations of the process part of the model as well as computationally efficient algorithms needed for doing inference with the HBM. Alternatively, the adaptive MCMC algorithm implemented in the package can be used as an algorithm for doing inference without any additional modeling. The MCMC algorithm supports data that follow a Gaussian or a censored distribution with point mass at zero. Covariates can be included in the model through a regression term.
The heterogeneity of spatial data presenting a finite number of categories can be measured via computation of spatial entropy. Functions are available for the computation of the main entropy and spatial entropy measures in the literature. They include the traditional version of Shannon's entropy, Batty's spatial entropy, O'Neill's entropy, Li and Reynolds' contagion index, Karlstrom and Ceccato's entropy, Leibovici's entropy, Parresol and Edwards' entropy and Altieri's entropy. References for all measures can be found under the topic 'SpatEntropy'. The package is able to work with lattice and point data.
Performs score test using saddlepoint approximation to estimate the null distribution. Also prepares summary statistics for meta-analysis and performs meta-analysis to combine multiple association results. For the latest version, please check <https://github.com/leeshawn/SPAtest>.
Graphs (or networks) and graph component
        calculations for spatial locations in 1D, 2D, 3D etc.
Functions for kriging and point pattern analysis.
This package fits a hierarchical spatial model for the generalized extreme value distribution with the option of model averaging over the space of covariates.
Spatial functions meant to enhance the core functionality of the
    package "raster", including a parallel processing engine for use with
    rasters.
Provides a set of spatial accessibility measures from a set of locations 
             (demand) to another set of locations (supply). It aims, among others, 
             to support research on spatial accessibility to health care facilities. 
             Includes the locations and some characteristics of major public hospitals in Greece.
Creates offensive and defensive shot charts for teams, 
    players and seasons, and more comprehensively for spatial 
    analysis of NBA data. Includes data from the 2016-17 NBA 
    season extracted from <http://stats.nba.com>.
Perform Spatial Clustering Analysis using Fuzzy Geographically Weighted Clustering. Provide optimization using Gravitational Search Algorithm.
Functions that compute the spatial covariance matrix for the matern and power classes of spatial models, for data that arise on rectangular units.  This code can also be used for the change of support problem and for spatial data that arise on irregularly shaped regions like counties or zipcodes by laying a fine grid of rectangles and aggregating the integrals in a form of Riemann integration.
Utilities to support spatial data manipulation, query, sampling
    and modelling. Functions include models for species population density, download
    utilities for climate and global deforestation spatial products, spatial
    smoothing, multivariate separability, point process model for creating pseudo-
    absences and sub-sampling, polygon and point-distance landscape metrics,
    auto-logistic model, sampling models, cluster optimization, statistical
    exploratory tools and raster-based metrics.
Methods and data for cluster detection and disease mapping.
Runs a Shiny web application that allows to visualize spatial and spatio-temporal disease data, estimate disease risk and detect clusters. The application allows to fit Bayesian disease models to obtain risk estimates and their uncertainty by using the 'R-INLA' package, <http://www.r-inla.org>, and to detect clusters by using the scan statistics implemented in 'SaTScan', <https://www.satscan.org>. The application allows user interaction and creates interactive visualizations such as maps supporting padding and zooming and tables that allow for filtering. It also enables the generation of reports containing the analyses performed.
Tools for the statistical modelling of spatial extremes using max-stable processes, copula or Bayesian hierarchical models. More precisely, this package allows (conditional) simulations from various parametric max-stable models, analysis of the extremal spatial dependence, the fitting of such processes using composite likelihoods or least square (simple max-stable processes only), model checking and selection and prediction. Other approaches (although not completely in agreement with the extreme value theory) are available such as the use of (spatial) copula and Bayesian hierarchical models assuming the so-called conditional assumptions. The latter approaches is handled through an (efficient) Gibbs sampler. Some key references: Davison et al. (2012) <doi:10.1214/11-STS376>, Padoan et al. (2010) <doi:10.1198/jasa.2009.tm08577>, Dombry et al. (2013) <doi:10.1093/biomet/ass067>.
Filter matrices or (three dimensional) array data using different convolution kernels.
Spatial floor simulation with exponential/Gaussian variance-covariance function (isotropic), with specification of distance function, nugget, sill, range. The methodology follows Nole A.C. Cressie (2015) <doi:10.1002/9781119115151>. The original release is 2017-08-29.
Provision of the S4 SpatialGraph class built on top of objects provided by 'igraph' and 'sp' packages, and associated utilities. See the documentation of the SpatialGraph-class within this package for further description. An example of how from a few points one can arrive to a SpatialGraph is provided in the function sl2sg().
Edge-corrected kernel density estimation and binary kernel
        regression estimation for multivariate spatial point process
        data. For details, see Diggle, P.J., Zheng, P. and Durr, P. A. (2005)
        <doi:10.1111/j.1467-9876.2005.05373.x>.
Implements a spatial extension of the random forest algorithm 
             (Georganos et al. (2019) <doi:10.1080/10106049.2019.1595177>). 
             Future updates include more local machine learning methods 
             as well as a geographically weighted random forest.
Network based diffusion analysis (NBDA) allows inference on
             the asocial and social transmission of information.  This may involve
             the social transmission of a particular behaviour such as tool use, for example.
            For the NBDA, the key parameters estimated are the social effect and baseline rate 
            parameters.  The baseline rate parameter gives the rate at which the behaviour
            is first performed (or acquired) asocially amongst the individuals in a given population.
            The social effect parameter quantifies the effect of the social associations amongst 
            the individuals on the rate at which each individual first performs or displays
            the behaviour.  Spatial NBDA involves incorporating spatial information in the analysis.  
            This is done by incorporating social networks derived from 
            spatial point patterns (of the home bases of the individuals under study).  In addition, 
            a spatial covariate such as vegetation cover, or slope may be included in the modelling
            process.
Test and estimates of location, tests of independence, tests of sphericity and several estimates of shape all based on spatial signs, symmetrized signs, ranks and signed ranks. For details, see Oja and Randles (2004) <doi:10.1214/088342304000000558> and Oja (2010) <doi:10.1007/978-1-4419-0468-3>.
Tools to assess the association between two spatial processes. Currently,
  four methodologies are implemented: A modified t-test to perform hypothesis testing
  about the independence between the processes, a suitable nonparametric correlation
  coefficient, the codispersion coefficient, and an F test for assessing the multiple
  correlation between one spatial process and several others. SpatialPack gives methods
  to complement methodologies that are available in geoR for one spatial process.
Computes spatial position models: Stewart potentials, Reilly
    catchment areas, Huff catchment areas.
Bayesian Estimation of Spatial Probit and Tobit Models.
A collection of all the estimation functions for spatial cross-sectional models (on lattice/areal data using spatial weights matrices) contained up to now in 'spdep', 'sphet' and 'spse'. These model fitting functions include maximum likelihood methods for cross-sectional models proposed by 'Cliff' and 'Ord' (1973, ISBN:0850860369) and (1981, ISBN:0850860814), fitting methods initially described by 'Ord' (1975) <doi:10.1080/01621459.1975.10480272>. The models are further described by 'Anselin' (1988) <doi:10.1007/978-94-015-7799-1>. Spatial two stage least squares and spatial general method of moment models initially proposed by 'Kelejian' and 'Prucha' (1998) <doi:10.1023/A:1007707430416> and (1999) <doi:10.1111/1468-2354.00027> are provided. Impact methods and MCMC fitting methods proposed by 'LeSage' and 'Pace' (2009) <doi:10.1201/9781420064254> are implemented for the family of cross-sectional spatial regression models. Methods for fitting the log determinant term in maximum likelihood and MCMC fitting are compared by 'Bivand et al.' (2013) <doi:10.1111/gean.12008>, and model fitting methods by 'Bivand' and 'Piras' (2015) <doi:10.18637/jss.v063.i18>; both of these articles include extensive lists of references. 'spatialreg' >= 1.1-* correspond to 'spdep' >= 1.1-1, in which the model fitting functions are deprecated and pass through to 'spatialreg', but will mask those in 'spatialreg'. From versions 1.2-*, the functions will be made defunct in 'spdep'.
Methods for determining spatial risk, in particular calculating the maximum value of insured fire risk policies of all buildings that are partly or fully located within circle of a radius of 200m.
Summaries for measuring segregation/mingling in multitype spatial
    point patterns with graph based neighbourhood description. Included indices:
    Mingling, Shannon, Simpson (also the non-spatial) Included functionals:
    Mingling, Shannon, Simpson, ISAR, MCI. Included neighbourhoods: Geometric, k-
    nearest neighbours, Gabriel, Delaunay. Dixon's test.
Provides functions implementing the pairwise M-estimator for
    parametric models for stable tail dependence functions described in "An
    M-estimator of spatial tail dependence"
    (Einmahl, J.H.J., Kiriliouk, A., Krajina, A. and Segers, J., 2014).
    See http://arxiv.org/abs/1403.1975.
Tools for spatial data analysis.  Emphasis on kriging.  Provides functions for prediction and simulation.  Intended to be relatively straightforward, fast, and flexible.
Perform variable selection for the spatial Poisson regression model under the adaptive elastic net penalty. Spatial count data with covariates is the input. We use a spatial Poisson regression model to link the spatial counts and covariates. For maximization of the likelihood under adaptive elastic net penalty, we implemented the penalized quasi-likelihood (PQL) and the approximate penalized loglikelihood (APL) methods. The proposed methods can automatically select important covariates, while adjusting for possible spatial correlations among the responses. More details are available in Xie et al. (2018, <arXiv:1809.06418>). The package also contains the Lyme disease dataset, which consists of the disease case data from 2006 to 2011, and demographic data and land cover data in Virginia. The Lyme disease case data were collected by the Virginia Department of Health. The demographic data (e.g., population density, median income, and average age) are from the 2010 census. Land cover data were obtained from the Multi-Resolution Land Cover Consortium for 2006.
Spatial forecast verification arose from verifying high-resolution forecasts, where coarser-resolution models generally are favored even when a human forecaster finds the higher-resolution model to be considerably better.  Most newly proposed methods, which largely come from image analysis, computer vision, and similar, are available, with more on the way.
Tools to compute and assess significance of early-warnings signals (EWS) of ecosystem degradation on raster data sets. EWS are metrics derived from the observed spatial structure of an ecosystem -- e.g. spatial autocorrelation -- that increase before an ecosystem undergoes a non-linear transition (Genin et al. (2018) <doi:10.1111/2041-210X.13058>).
Many packages use 'htmlwidgets' <https://CRAN.R-project.org/package=htmlwidgets>
    for interactive plotting of spatial data.
    This package provides functions for converting R objects, such as simple features, 
    into structures suitable for use in 'htmlwidgets' mapping libraries.
Mixture model is used to achieve the clustering goal. Each component is itself a mixture model of polynomial autoregressive regressions whose the logistic weights consider the spatial and temporal information.
Utilities that estimate, predict and cross-validate the
    spatio-temporal model developed for MESA Air.
Provide regularized maximum covariance analysis incorporating smoothness,
  sparseness and orthogonality of couple patterns by using the alternating direction method
  of multipliers algorithm. The method can be applied to either regularly or irregularly
  spaced data,  including 1D, 2D, and 3D (Wang and Huang, 2017 <doi:10.1002/env.2481>).
Provide regularized principal component analysis incorporating smoothness,
  sparseness and orthogonality of eigenfunctions by using the alternating direction method of
  multipliers algorithm (Wang and Huang, 2017, <DOI:10.1080/10618600.2016.1157483>). The method can be applied to either regularly or irregularly spaced
  data, including 1D, 2D, and 3D.
Analysis of field trial experiments by modelling spatial trends using two-dimensional Penalised spline (P-spline) models.
Detects spatial and temporal groups in GPS relocations. 
    It can be used to convert GPS relocations to 
    gambit-of-the-group format to build proximity-based social networks. 
    In addition, the randomizations function provides data-stream 
    randomization methods suitable for GPS data.
Contains all the datasets for the 'spatstat' package.
Extension to the 'spatstat' package, enabling the user
	     to fit point process models to point pattern data
	     by local composite likelihood ('geographically weighted
	     regression').
Contains utility functions for the 'spatstat' package
             which may also be useful for other purposes.
Bayesian inference for parametric proportional hazards spatial
    survival models; flexible spatial survival models.
Tools to convert from specific formats to more general forms of 
    spatial data. Using tables to store the actual entities present in spatial
    data provides flexibility, and the functions here deliberately 
    minimize the level of interpretation applied, leaving that for specific 
    applications. Includes support for simple features,  round-trip for 'Spatial' classes and long-form 
    tables, analogous to 'ggplot2::fortify'. There is also a more 'normal form' representation
    that decomposes simple features and their kin to tables of objects, parts, and unique coordinates.
Fits univariate and multivariate spatio-temporal
        random effects models for point-referenced data using Markov chain Monte Carlo (MCMC). Details are given in Finley, Banerjee, and Gelfand (2015) <doi:10.18637/jss.v063.i13> and Finley, Banerjee, and Cook (2014) <doi:10.1111/2041-210X.12189>.
Provides several Bayesian survival models for spatial/non-spatial survival data: proportional hazards (PH), accelerated failure time (AFT), proportional odds (PO), and accelerated hazards (AH), a super model that includes PH, AFT, PO and AH as special cases, Bayesian nonparametric nonproportional hazards (LDDPM), generalized accelerated failure time (GAFT), and spatially smoothed Polya tree density estimation. The spatial dependence is modeled via frailties under PH, AFT, PO, AH and GAFT, and via copulas under LDDPM and PH. Model choice is carried out via the logarithm of the pseudo marginal likelihood (LPML), the deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC).
Selection of spatially balanced samples. In particular, the implemented sampling designs allow to select probability samples spread over the population of interest, in any dimension and using any distance function (e.g. Euclidean distance, Manhattan distance). For more details, Benedetti R and Piersimoni F (2017) <doi:10.1002/bimj.201600194> and Benedetti R and Piersimoni F (2017) <arXiv:1710.09116>. The implementation has been done in C++ through the use of 'Rcpp' and 'RcppArmadillo'.
Evaluation of control charts by means of
        the zero-state, steady-state ARL (Average Run Length) and RL quantiles.
        Setting up control charts for given in-control ARL. The control charts
        under consideration are one- and two-sided EWMA, CUSUM, and
        Shiryaev-Roberts schemes for monitoring the mean or variance of normally
        distributed independent data. ARL calculation of the same set of schemes under drift (in the mean) are added.
        Eventually, all ARL measures for the multivariate EWMA (MEWMA) are provided.
Provides statistical process control tools for stochastic 
    textured surfaces. The current version supports the following tools:
    (1) generic modeling of stochastic textured surfaces.
    (2) local defect monitoring and diagnostics in stochastic 
        textured surfaces, which was proposed by Bui and Apley (2018a) 
        <doi:10.1080/00401706.2017.1302362>.
    (3) global change monitoring in the nature of stochastic 
        textured surfaces, which was proposed by Bui and Apley (2018b) 
        <doi:10.1080/00224065.2018.1507559>.
    (4) computation of dissimilarity matrix of stochastic textured 
        surface images, which was proposed by Bui and Apley (2019b)
        <doi:10.1016/j.csda.2019.01.019>.
Calibration of thresholds of control charts such as
    CUSUM charts based on past data, taking estimation error into account.
A new reduced-rank LDA method which works for high dimensional multi-class data.
Implements the SPCAvRP algorithm, developed and analysed in "Sparse principal component analysis via random projections" Gataric, M., Wang, T. and Samworth, R. J. (2018) <arXiv:1712.05630>. The algorithm is based on the aggregation of eigenvector information from carefully-selected random projections of the sample covariance matrix.
Programs to find the sample size or power of studies using the 
    Sequential Parallel Comparison Design (SPCD) and programs to analyze 
    such studies. This is a clinical trial design where 
    patients initially on placebo who did not respond are re-randomized between 
    placebo and active drug in a second phase and the results of the two phases are 
    pooled. The method of analyzing binary data with this design is described in 
    Fava,Evins, Dorer and Schoenfeld(2003) <doi:10.1159/000069738>, and the 
    method of analyzing continuous data is described in Chen, Yang, 
    Hung and Wang (2011) <doi:10.1016/j.cct.2011.04.006>.
Spatial coverage sampling and random sampling from compact
        geographical strata created by k-means. See Walvoort et al. (2010) <doi:10.1016/j.cageo.2010.04.005> for details.
Provides a covariance estimator for multivariate normal
        data that is sparse and positive definite.  Implements the
        majorize-minimize algorithm described in Bien, J., and
        Tibshirani, R. (2011), "Sparse Estimation of a Covariance
        Matrix," Biometrika. 98(4). 807--820.
Implements a spatially varying change point model with 
    unique intercepts, slopes, variance intercepts and slopes, and 
    change points at each location. Inference is within the 
    Bayesian setting using Markov chain Monte Carlo (MCMC). The 
    response variable can be modeled as Gaussian (no nugget), 
    probit or Tobit link and the five spatially varying parameter
    are modeled jointly using a multivariate conditional 
    autoregressive (MCAR) prior. The MCAR is a unique process that
    allows for a dissimilarity metric to dictate the local spatial 
    dependencies. Full details of the package can be found in the accompanying vignette.
    Furthermore, the details of the package can be found in the corresponding paper on arXiv
    by Berchuck et al (2018): "A spatially varying change points model for monitoring glaucoma 
    progression using visual field data", <arXiv:1811.11038>.
The sparse principal component regression is computed. The regularization parameters are optimized by cross-validation.
The Semi Parametric Piecewise Distribution blends the Generalized Pareto Distribution for the tails with a kernel based interior.
Diverse spatial datasets for demonstrating, benchmarking and teaching spatial data analysis. 
    It includes R data of class sf (defined by the package 'sf'), Spatial ('sp'), and nb ('spdep').
    Unlike other spatial data packages such as 'rnaturalearth' and 'maps', 
    it also contains data stored in a range of file formats including GeoJSON, ESRI Shapefile and GeoPackage. 
    Some of the datasets are designed to illustrate specific analysis techniques.
    cycle_hire() and cycle_hire_osm(), for example, is designed to illustrate point pattern analysis techniques.
A collection of functions to create spatial weights matrix
  objects from polygon 'contiguities', from point patterns by distance and
  tessellations, for summarizing these objects, and for permitting their
  use in spatial data analysis, including regional aggregation by minimum
  spanning tree; a collection of tests for spatial 'autocorrelation',
  including global 'Morans I' and 'Gearys C' proposed by 'Cliff' and 'Ord'
  (1973, ISBN: 0850860369) and (1981, ISBN: 0850860814), 'Hubert/Mantel'
  general cross product statistic, Empirical Bayes estimates and
  'Assunção/Reis' (1999) <doi:10.1002/(SICI)1097-0258(19990830)18:16%3C2147::AID-SIM179%3E3.0.CO;2-I> Index, 'Getis/Ord' G ('Getis' and 'Ord' 1992)
  <doi:10.1111/j.1538-4632.1992.tb00261.x> and multicoloured
  join count statistics, 'APLE' ('Li 'et al.' )
  <doi:10.1111/j.1538-4632.2007.00708.x>, local 'Moran's I'
  ('Anselin' 1995) <doi:10.1111/j.1538-4632.1995.tb00338.x> and
  'Getis/Ord' G ('Ord' and 'Getis' 1995)
  <doi:10.1111/j.1538-4632.1995.tb00912.x>,
  'saddlepoint' approximations ('Tiefelsdorf' 2002)
  <doi:10.1111/j.1538-4632.2002.tb01084.x> and exact tests
  for global and local 'Moran's I' ('Bivand et al.' 2009)
  <doi:10.1016/j.csda.2008.07.021> and 'LOSH' local indicators
  of spatial heteroscedasticity ('Ord' and 'Getis')
  <doi:10.1007/s00168-011-0492-y>. The implementation of most of
  the measures is described in 'Bivand' and 'Wong' (2018)
  <doi:10.1007/s11749-018-0599-x>.
  'spdep' >= 1.1-1 corresponds to 'spatialreg' >= 1.1-1, in which the model
  fitting functions are deprecated and pass through to 'spatialreg', but
  will mask those in 'spatialreg'. From versions 1.2-1, the functions will
  be made defunct in 'spdep'.
  For now 'spatialreg' only has functions from 'spdep', where they are shown
  as deprecated. 'spatialreg' only loads the namespace of 'spdep'; if you
  attach 'spdep', the same functions in the other package will be masked.
  Some feed through adequately, others do not.
Spatial downscaling of climate data (Global Circulation Models/Regional Climate Models) using quantile-quantile bias correction technique.
Methods for 'dplyr' verbs for 'sp' 'Spatial' classes. The basic 
    verbs that modify data attributes, remove or re-arrange rows are supported
    and provide complete 'Spatial' analogues of the input data. The group by
    and summarize work flow returns a non-topological spatial union. There is 
    limited support for joins, with left and inner to copy attributes from 
    another table.
An implementation of split-population duration regression models. 
    Unlike regular duration models, split-population duration models are
    mixture models that accommodate the presence of a sub-population that is 
    not at risk for failure, e.g. cancer patients who have been cured by 
    treatment. This package implements Weibull and Loglogistic forms for the 
    duration component, and focuses on data with time-varying covariates. 
    These models were originally formulated in Boag (1949) 
    <http://www.jstor.org/stable/2983694> and Berkson and Gage (1952) 
    <http://www.jstor.org/stable/2281318>, and extended in Schmidt and Witte 
    (1989) <doi:10.1016/0304-4076(89)90034-1>.
A spatio-dynamic modelling package that focuses on three
    characteristic wetland plant communities in a semiarid Mediterranean
    wetland in response to hydrological pressures from the catchment. The
    package includes the data on watershed hydrological pressure and the
    initial raster maps of plant communities but also allows for random initial
    distribution of plant communities. For more detailed info see: Martinez-Lopez et al. (2015) <doi:10.1016/j.ecolmodel.2014.11.024>.
Implements stochastic proximity embedding as described by
 Agrafiotis et al. in PNAS, 2002, 99, pg 15869 and J. Comput. Chem., 2003,24, pg 1215
Makes Nuclear Magnetic Resonance spectroscopy (NMR spectroscopy) data analysis as easy as possible by only requiring a small set of functions to perform an entire analysis. 'speaq' offers the possibility of raw spectra alignment and quantitation but also an analysis based on features whereby the spectra are converted to peaks which are then grouped and turned into features. These features can be processed with any number of statistical tools either included in 'speaq' or available elsewhere on CRAN. More details can be found in Vu et al. (2011) <doi:10.1186/1471-2105-12-405> and Beirnaert et al. (2018) <doi:10.1371/journal.pcbi.1006018>.
Functions for conducting jackknife Euclidean / empirical likelihood inference for Spearman's rho (de Carvalho and Marques (2012) <10.1080/10920277.2012.10597644>).
Creates a data specification that describes the columns of a 
 table (data.frame). Provides methods to read, write, and update the 
 specification. Checks whether a table matches its specification. See
 specification.data.frame(),read.spec(), write.spec(), as.csv.spec(),
 respecify.character(), and %matches%.data.frame().
Alternative to the kernlab::specc function. Includes a spectral clustering implementation, a locally adapted kernel function akin to what is already proposed in kernlab, and an optional procedure that automatically estimates the optimal number of clusters. Several sample data sets are also included.
Calculate change point based on spectral clustering with the option to automatically calculate the number of clusters if this information is not available.
Utility functions for spectroscopy. 1. Functions to simulate
    spectra for use in teaching or testing. 2. Functions to process files created by
    'LoggerPro' and 'SpectraSuite' software.
SPECIES is an R package for estimation of species richness
        or diversity.
Fitting Mixtures to Species distributions using BFGS and analytical derivatives.
A set of functions for obtaining positional parameters and magnitude difference between components of binary and multiple stellar systems from series of speckle images.
A collection of forecast verification routines developed for the SPECS
    FP7 project. The emphasis is on comparative verification of ensemble forecasts of weather and climate.
Stores and eases the manipulation of spectra and associated data,
    with dedicated classes for spatial and soil-related data.
Fourier and Hilbert transforms are utilized to perform several
    types of spectral analysis on the supplied data. Fragmented and irregularly
    spaced data can be processed in terms of Lomb-Scargle method. Both,
    FFT as well as LOMB methods take multivariate data. A user friendly
    interface helps to interpret the results.
Contains some implementations of Singular Spectrum Analysis (SSA) for the gapfilling and spectral decomposition of time series. It contains the code used by Buttlar et. al. (2014), Nonlinear Processes in Geophysics. In addition, the iterative SSA gapfilling method of Kondrashov and Ghil (2006) is implemented. All SSA calculations are done via the truncated and fast SSA algorithm of Korobeynikov (2010) (package 'Rssa').
Infrared, near-infrared and Raman spectroscopic data measured during chemical reactions, provide structural fingerprints by which molecules can be identified and quantified. The application of these spectroscopic techniques as inline process analytical tools (PAT), provides the (pharma-)chemical industry with novel tools, allowing to monitor their chemical processes, resulting in a better process understanding through insight in reaction rates, mechanistics, stability, etc.
    Data can be read into R via the generic spc-format, which is generally supported by spectrometer vendor software. Versatile pre-processing functions are available to perform baseline correction by linking to the 'baseline' package; noise reduction via the 'signal' package; as well as time alignment, normalization, differentiation, integration and interpolation. Implementation based on the S4 object system allows storing a pre-processing pipeline as part of a spectral data object, and easily transferring it to other datasets. Interactive plotting tools are provided based on the 'plotly' package. 
    Non-negative matrix factorization (NMF) has been implemented to perform multivariate analyses on individual spectral datasets or on multiple datasets at once. NMF provides a parts-based representation of the spectral data in terms of spectral signatures of the chemical compounds and their relative proportions.
    The functionality to read in spc-files was adapted from the 'hyperSpec' package.
Routines for creating, manipulating, and performing 
 Bayesian inference about Gaussian processes in 
 one and two dimensions using the Fourier basis approximation: 
 simulation and plotting of processes, calculation of 
 coefficient variances, calculation of process density, 
 coefficient proposals (for use in MCMC).  It uses R environments to
 store GP objects as references/pointers.
Block coordinate descent estimators to learn k-component, bipartite,
    and k-component bipartite graphs from data by imposing spectral constraints
    on the eigenvalues and eigenvectors of the Laplacian and adjacency matrices.
    Those estimators leverages spectral properties of the graphical models as a
    prior information, which turn out to play key roles in unsupervised machine
    learning tasks such as clustering and community detection.  This package is
    based on the paper "A Unified Framework for Structured Graph Learning via
    Spectral Constraints" by S. Kumar et al (2019) <arXiv:1904.09792>.
Implements the diffusion map method of dimensionality reduction and spectral method of combining multiple diffusion maps, including creation of the spectra and visualization of maps.
Spectra viewer, organizer, data preparation and property blocks
        from within R or stand-alone. Binary (application) part is 
        installed separately using spnInstallApp() from spectrino package.
Input/Output, processing and visualization of spectra taken with different spectrometers, including SVC (Spectra Vista), ASD and PSR (Spectral Evolution). Implements an S3 class 'spectra' that other packages can build on. Provides methods to access, plot, manipulate, splice sensor overlap, vector normalize and smooth spectra.
A self-tuning spectral clustering method for single or multi-view data. 'Spectrum' uses a new type of adaptive density aware kernel that strengthens local connections in the graph. It uses a tensor product graph data integration and diffusion procedure to integrate different data sources and reduce noise. 'Spectrum' uses either the eigengap or multimodality gap heuristics to determine the number of clusters. 'Spectrum' was developed for clustering complex single and multi-omic data. However, the method is sufficiently flexible so that a wide range of Gaussian and non-Gaussian structures can be clustered with automatic selection of K.
From output files obtained from the software 'ModestR', the relative contribution of factors to explain species distribution is depicted using several plots. A global geographic raster file for each environmental variable may be also obtained with the mean relative contribution, considering all species present in each raster cell, of the factor to explain species distribution. Finally, for each variable it is also possible to compare the frequencies of any variable obtained in the cells where the species is present with the frequencies of the same variable in the cells of the extent.
Fitting linear models and generalized linear models to large data sets by updating algorithms.
Functions for fitting semiparametric regression models for
        panel count survival data. An overview of the package can be found 
        in Wang and Yan (2011) <doi:10.1016/j.cmpb.2010.10.005> and
	Chiou et al. (2018) <doi:10.1111/insr.12271>.
The package performs estimation and testing of the
        treatment effect in a 2-group randomized clinical trial with a
        quantitative, dichotomous, or right-censored time-to-event
        endpoint. The method improves efficiency by leveraging baseline
        predictors of the endpoint. The inverse probability weighting
        technique of Robins, Rotnitzky, and Zhao (JASA, 1994) is used
        to provide unbiased estimation when the endpoint is missing at
        random.
A set of functions for computing potential evapotranspiration and several widely used drought indices including the Standardized Precipitation-Evapotranspiration Index (SPEI).
Corrects the spelling of a given word in English 
    using a modification of Peter Norvig's spell correct 
    algorithm (see <http://norvig.com/spell-correct.html>) 
    which handles up to three edits. The algorithm tries to 
    find the spelling with maximum probability of intended
    correction out of all possible candidate corrections from
    the original word.
Spell checking common document formats including latex, markdown, manual pages,
    and description files. Includes utilities to automate checking of documentation and 
    vignettes as a unit test during 'R CMD check'. Both British and American English are 
    supported out of the box and other languages can be added. In addition, packages may
    define a 'wordlist' to allow custom terminology without having to abuse punctuation.
This implementation of the Empirical Mode Decomposition (EMD) works in 2 dimensions simultaneously, and 
    can be applied on spatial data. It can handle both gridded or un-gridded datasets.
Provides some easy-to-use functions to interpolate species range based on species occurrences and to estimate centers of biodiversity.
Implements spatial error estimation and 
   permutation-based variable importance measures for predictive models using 
   spatial cross-validation and spatial block bootstrap.
Functions to produce a fully fledged 'geo-spatial' object extent as a
    'SpatialPolygonsDataFrame'. Also included are functions to generate polygons
    from raster data using 'quadmesh' techniques, a round number buffered extent, and
    general spatial-extent and 'raster-like' extent helpers missing from the originating
    packages. Some latitude-based tools for polar maps are included.
A set of tools for estimation of various spatial specifications of
    stochastic frontier models.
An implementation of feature selection and ranking via simultaneous perturbation
    stochastic approximation (SPSA-FSR) based on works by V. Aksakalli and M. Malekipirbazari 
    (2015) <arXiv:1508.07630> and Zeren D. Yenice and et al. (2018) <arXiv:1804.05589>.
    The SPSA-FSR algorithm searches for a locally optimal set of features that yield the best 
    predictive performance using a specified error measure such as mean squared error (for 
    regression problems) and accuracy rate (for classification problems). This package requires 
    an object of class 'task' and an object of class 'Learner' from the 'mlr' package.
Functions to manipulate, pre-process and analyze spectra in the mid-infrared region. The pre-processing of the mid-infrared spectra is a transcendental step in the spectral analysis. Preprocessing of the spectra includes smoothing, offset, baseline correction, and normalization,  is performed before the analysis of the spectra and is essential to obtain conclusive results in subsequent quantitative or qualitative analysis. This package was supported by FONDECYT 3150630, and CIPA Conicyt-Regional R08C1002 is gratefully acknowledged.
A collection of functions to deal with spatial and spatiotemporal autoregressive conditional heteroscedasticity (spatial ARCH and GARCH models) by Otto, Schmid, Garthoff (2018, Spatial Statistics) <arXiv:1609.00711>: simulation of spatial ARCH-type processes (spARCH, exponential spARCH, complex spARCH); quasi-maximum-likelihood estimation of the parameters of spARCH models and spatial autoregressive models with spARCH disturbances, diagnostic checks, visualizations.
Interpreted interface between GRASS 6+ geographical 
    information system and R, based on starting R from within the GRASS 
    environment, or running free-standing R in a temporary GRASS location;
    the package provides facilities for using all GRASS commands from the 
    R command line. This package may not be used for GRASS 7, for which
    rgrass7 should be used.
A collection of statistical hypothesis tests and other 
	techniques for identifying certain spatial relationships/phenomena in 
	DNA sequences. In particular, it provides tests and graphical methods for determining 
	whether or not DNA sequences comply with Chargaff's second parity rule 
	or exhibit purine-pyrimidine parity. In addition, there are functions for 
	efficiently simulating discrete state space Markov chains and testing 
	arbitrary symbolic sequences of symbols for the presence of first-order 
	Markovianness.
	Also, it has functions for counting words/k-mers (and cylinder patterns) in 
	arbitrary symbolic sequences. Functions which take a DNA sequence as input 
	can handle sequences stored as SeqFastadna objects from the 'seqinr' package.
Functions for computing geographically weighted
  regressions are provided, based on work by Chris
  Brunsdon, Martin Charlton and Stewart Fotheringham.
Various functions for creating spherical coordinate system plots via extensions to rgl.
Provides several methods to integrate functions over the unit
 sphere and ball in n-dimensional Euclidean space.  Routines for converting to/from
 multivariate polar/spherical coordinates are also provided.
Spherical K-function for point-pattern analysis on the sphere.
Generalized Method of Moment estimation of Cliff-Ord-type spatial autoregressive models with and without  Heteroscedasticity.
The SNP Panel Identification Assay (SPIA) is a package that enables an accurate determination of cell line identity from the genotype of single nucleotide polymorphisms (SNPs). The SPIA test allows to discern when two cell lines are close enough to be called similar and when they are not. Details about the method are available at "Demichelis et al. (2008) SNP panel identification assay (SPIA): a genetic-based assay for the identification of cell lines. Nucleic Acids Res., 3, 2446-2456".
Analysis of species limits and DNA barcoding data. Included are functions for generating important summary statistics from DNA barcode data, assessing specimen identification efficacy, testing and optimizing divergence threshold limits, assessment of diagnostic nucleotides, and calculation of the probability of reciprocal monophyly. Additionally, a sliding window function offers opportunities to analyse information across a gene, often used for marker design in degraded DNA studies. Further information on the package has been published in Brown et al (2012) <doi:10.1111/j.1755-0998.2011.03108.x>.
The 'Robots Exclusion Protocol' <http://www.robotstxt.org/orig.html> documents
    a set of standards for allowing or excluding robot/spider crawling of different areas of
    site content. Tools are provided which wrap The 'rep-cpp' <https://github.com/seomoz/rep-cpp>
    C++ library for processing these 'robots.txt' files.
Fits and simulates data from our predator preferences model, <DOI:10.1007/s10651-016-0341-3>.
Calculate the Standardized Precipitation Index (SPI) for monitoring drought, using Artificial Intelligence techniques (SPIGA) and traditional numerical technique Maximum Likelihood (SPIML).  For more information see: http://drought.unl.edu/monitoringtools/downloadablespiprogram.aspx.
Applies re-sampled kernel density method to detect vote fraud. It estimates the proportion of coarse vote-shares in the observed data relative to the null hypothesis of no fraud.
Spike and slab for prediction and variable selection in
        linear regression models. Uses a generalized elastic net for
        variable selection.
Bayesian variable selection, model choice, and regularized
    estimation for (spatial) generalized additive mixed regression models
    via stochastic search variable selection with spike-and-slab priors.
An optimal weighting strategy to compute
        simulation-efficient shortest probability intervals (spins).
Calculates constant structure parameters of endocrine homeostatic systems from equilibrium hormone concentrations. Methods and equations have been described in Dietrich et al. (2012) <doi:10.1155/2012/351864> and Dietrich et al. (2016) <doi:10.3389/fendo.2016.00057>.
Many complex diseases are known to be affected by the interactions between 
   genetic variants and environmental exposures beyond the main genetic and environmental 
   effects. Existing Bayesian methods for gene-environment (G×E) interaction studies are 
   challenged by the high-dimensional nature of the study and the complexity of environmental 
   influences. We have developed a novel and powerful semi-parametric Bayesian variable 
   selection method that can accommodate linear and nonlinear G×E interactions simultaneously 
   (Ren et al. (2019) <arXiv:1906.01057>). Furthermore, the proposed method can conduct 
   structural identification by distinguishing nonlinear interactions from main effects only 
   case within Bayesian framework. Spike-and-slab priors are incorporated on both individual 
   and group level to shrink coefficients corresponding to irrelevant main and interaction 
   effects to zero exactly. The Markov chain Monte Carlo algorithms of the proposed and 
   alternative methods are  efficiently implemented in C++.
Functions for spatial methods based on generalized estimating equations (GEE) and
  wavelet-revised methods (WRM), functions for scaling by wavelet multiresolution regression (WMRR),
  conducting multi-model inference, and stepwise model selection. Further, contains functions 
  for spatially corrected model accuracy measures.
Generates the path for manual tours
  ['Cook' & 'Buja' (1997) <doi:10.2307/1390747>]. Tours are generally available in the
  'tourr' package ['Wickham' 'et' 'al.' (2011) <doi:10.18637/jss.v040.i02>]. 
  The grand tour is an algorithm that shows all possible projections given sufficient time. 
  Guided uses projection pursuit to steer the tour towards interesting projections. 
  The 'spinifex' package implements manual control, where the contribution of a selected variable 
  can be adjusted between -1 to 1, to examine the sensitivity of structure in the data
  to that variable. 
  The result is an animation where the variable is toured into and out of the projection 
  completely, which can be rendered using the 'gganimate' and 'plotly' packages.
Implements a generative model that uses a
    spike-and-slab like prior distribution obtained by multiplying a
    deterministic binary vector. Such a model allows an EM algorithm,
    optimizing a type-II log-likelihood.
The Splancs package was written as an enhancement to S-Plus for display and analysis of spatial point pattern data; it has been ported to R and is in "maintenance mode".
'Splash' <https://github.com/scrapinghub/splash> is a 'JavaScript' rendering service.
    It is a lightweight web browser with an 'HTTP' API, implemented in 'Python' using 'Twisted' 
    and 'QT' and provides some of the core functionality of the 'RSelenium' or 'seleniumPipes'
    R packages in a lightweight footprint. Some of 'Splash' features include the ability to process 
    multiple web pages in parallel; retrieving 'HTML' results and/or take screen shots; disabling 
    images or use 'Adblock Plus' rules to make rendering faster; executing custom 'JavaScript' in 
    page context; getting detailed rendering info in 'HAR' format.
Constructs B-splines and its integral, monotone splines
    (M-splines) and its integral (I-splines), convex splines (C-splines),
    and their derivatives of given order. Piecewise constant basis is
    allowed for B-splines and M-splines. See
    De Boor (1978) <doi:10.1002/zamm.19800600129>,
    Ramsay (1988) <doi:10.1214/ss/1177012761>, and
    Meyer (2008) <doi:10.1214/08-AOAS167> for more information.
Builds regression trees and random forests for longitudinal or functional data using a spline projection method. Implements and extends the work of Yu and Lambert (1999) <doi:10.1080/10618600.1999.10474847>. This method allows trees and forests to be built while considering either level and shape or only shape of response trajectories.
An implementation of the majorization-minimization (MM) algorithm introduced by
    Xu, Chi, Yang, and Lange (2017) <arXiv:1612.05614> for solving multi-set split feasibility problems. In the multi-set split feasibility problem, we seek to find a point x
    in the intersection of multiple closed sets and whose image under a mapping also must fall in the intersection of several closed sets.
Some R functions, such as optim(), require a function
  its gradient passed as separate arguments. When these are
  expensive to calculate it may be much faster to calculate
  the function (fn) and gradient (gr) together since they often share
  many calculations (chain rule). This package allows the user
  to pass in a single function that returns both the function
  and gradient, then splits (hence 'splitfngr') them
  so the results can be accessed
  separately. The functions provided allow this to be done with
  any number of functions/values, not just for functions and gradients.
Estimate the internal consistency of your tasks with a permutation based split-half reliability approach.
    Unofficial release name: "Fight Milk - re-brand".
Calculates scores and estimates bootstrapped split-half reliabilities for reaction time tasks and questionnaires. The 'splithalfr' can be extended with custom scoring algorithms for user-provided datasets. For more information, see Parsons, Kruijt, & Fox (2018) <doi:10.31234/osf.io/6ka9z>.
Functions for computing split regularized estimators defined in Christidis, Lakshmanan, 
             Smucler and Zamar (2019) <arXiv:1712.03561>. The approach fits linear regression models that
             split the set of covariates into groups. The optimal split of the variables into groups and the 
             regularized estimation of the regression coefficients are performed by minimizing  an objective 
             function that encourages sparsity within each group and diversity among them. 
             The estimated coefficients are then pooled together to form the final fit.
Allows to produce and use classification trees with probability (soft) splits.
Online data collection tools like Google Forms often export
    multiple-response questions with data concatenated in cells. The
    concat.split (cSplit) family of functions splits such data into separate 
    cells. The package also includes functions to stack groups of columns and 
    to reshape wide data, even when the data are "unbalanced"---something 
    which reshape (from base R) does not handle, and which melt and dcast from 
    reshape2 do not easily handle.
ML and GM estimation and diagnostic testing of econometric models for spatial panel data.
Automates common plotting tasks to ease data exploration.
  Makes density plots (potentially overlaid on histograms),
  scatter plots with prediction lines, or bar or line plots with error bars.
  For each type, y, or x and y variables can be plotted at levels of other variables,
  all with minimal specification.
Provides functions for fitting a sparse
        partial least squares (SPLS) regression and classification
        (Chun and Keles (2010) <doi:10.1111/j.1467-9868.2009.00723.x>).
Currently there are many functions in S-PLUS that are
        missing in R. To facilitate the conversion of S-PLUS packages
        to R packages, this package provides some missing S-PLUS
        functionality in R.
A collection of classes and methods for working with
  times and dates. The code was originally available in S-PLUS.
A collection of classes and methods for working with time series.
  The code was originally available in S-PLUS.
Introduction to some novel accurate hybrid methods of geostatistical and machine learning methods for spatial predictive modelling. It contains two commonly used geostatistical methods, two machine learning methods, four hybrid methods and two averaging methods. For each method, two functions are provided. One function is for assessing the predictive errors and accuracy of the method based on cross-validation. The other one is for generating spatial predictions using the method. For details please see: Li, J., Potter, A., Huang, Z., Daniell, J. J. and Heap, A. (2010) <https:www.ga.gov.au/metadata-gateway/metadata/record/gcat_71407>
  Li, J., Heap, A. D., Potter, A., Huang, Z. and Daniell, J. (2011) <doi:10.1016/j.csr.2011.05.015>
  Li, J., Heap, A. D., Potter, A. and Daniell, J. (2011) <doi:10.1016/j.envsoft.2011.07.004>
  Li, J., Potter, A., Huang, Z. and Heap, A. (2012) <https:www.ga.gov.au/metadata-gateway/metadata/record/74030>.
Installs 'SPM12' to the R library directory and has 
    associated functions for 'fMRI' and general imaging utilities, 
    called through 'MATLAB'.
Build custom Europe SpatialPolygonsDataFrame, if you don't know what is a SpatialPolygonsDataFrame see SpatialPolygons() in 'sp', by example for mapLayout() in 'antaresViz'. Antares is a powerful software developed by RTE to simulate and study electric power systems (more information about 'Antares' here: <https://antares.rte-france.com>).
A set of functions is provided for 1) the stratum lengths analysis along a chosen direction, 2) fast estimation of continuous lag spatial Markov chains model parameters and probability computing (also for large data sets), 3) transition probability maps and transiograms drawing, 4) simulation methods for categorical random fields.
Implements the method of general semiparametric maximum likelihood estimation for logistic models in case-mother control-mother designs.
Functions for estimating Moran's eigenvector-based
    spatial regression models.
    For details see Murakami (2018) <arXiv:1703.04467>.
Facilitates the rendering of networks for which nodes have a specific position on a map (cities, participants in a political debate, etc.). Map data and network data are stored together in a single object which handles the match between network nodes and their respective position on the map. The plot method renders both the map and the network data. Several networks can be plot simultaneously. The graphic is highly customisable and the legend is automatically printed. Map data have to be supplied as 'SpatialPolygons' objects (from the 'sp' package) and network data as 'named matrix'.
Launches a Shiny application that allows users to carry out some of the steps that are required to curate both a linear network object based on a road structure and a point pattern that lies on such a network, becoming two previous steps to the performance of a spatial statistics analysis.
Non-negative Matrix Factorization(NMF) is a powerful tool for identifying the key features of microbial communities and a dimension-reduction method. When we are interested in the differences between the structures of two groups of communities, supervised NMF(Yun Cai, Hong Gu and Tobby Kenney (2017),<doi:10.1186/s40168-017-0323-1>) provides a better way to do this, while retaining all the advantages of NMF -- such as interpretability, and being based on a simple biological intuition.
Scale invariant version of the original PNN proposed by Specht (1990) <doi:10.1016/0893-6080(90)90049-q> with the added functionality of allowing for smoothing along multiple dimensions while accounting for covariances within the data set. It is written in the R statistical programming language. Given a data set with categorical variables, we use this algorithm to estimate the probabilities of a new observation vector belonging to a specific category. This type of neural network provides the benefits of fast training time relative to backpropagation and statistical generalization with only a small set of known observations.
Fits Gaussian univariate Bayesian spatial regression models for large datasets using Nearest Neighbor Gaussian Processes (NNGP) detailed in Datta, Banerjee, Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091> and Finley, Datta, Cook, Morton, Andersen, and Banerjee (2017) <arXiv:1702.00434v2>.
A programmatic interface to many species occurrence data sources,
    including Global Biodiversity Information Facility ('GBIF'), 'USGSs'
    Biodiversity Information Serving Our Nation ('BISON'), 'iNaturalist',
    Berkeley 'Ecoinformatics' Engine, 'eBird', Integrated Digitized
    'Biocollections' ('iDigBio'), 'VertNet', Ocean 'Biogeographic' Information
    System ('OBIS'), and Atlas of Living Australia ('ALA'). Includes
    functionality for retrieving species occurrence data, and combining
    those data.
SPODT is a spatial partitioning method based on oblique decision trees, in order to classify study area into zones of different risks, determining their boundaries
Convert text (and text in R objects) to Mocking SpongeBob case 
             <https://knowyourmeme.com/memes/mocking-spongebob> and show them 
             off in fun ways. 
             CoNVErT TexT (AnD TeXt In r ObJeCtS) To MOCkINg SpoNgebOb CAsE
             <https://knowyourmeme.com/memes/mocking-spongebob> aND shOw tHem 
             OFf IN Fun WayS.
Transform a Movie into a Synthetic Picture. A frame every 10 seconds is summarized into one colour, then every generated colors are stacked together.
R implementation of the methods described in "A rank-based
        empirical likelihood approach to two-sample proportional odds
        model and its goodness-of-fit" by Zhong Guan and Cheng Peng,
        Journal of Nonparametric Statistics, to appear.
Calculates ratings for two-player or 
  multi-player challenges. Methods included in package such as are able to 
  estimate ratings (players strengths) and their evolution in time, also able to 
  predict output of challenge. Algorithms are based on Bayesian Approximation 
  Method, and they don't involve any matrix inversions nor likelihood estimation. 
  Parameters are updated sequentially, and computation doesn't require any 
  additional RAM to make estimation feasible. Additionally, base of the package 
  is written in C++ what makes sport computation even faster. Methods used in the 
  package refers to Mark E. Glickman (1999) <http://www.glicko.net/research/glicko.pdf>; 
  Mark E. Glickman (2001) <doi:10.1080/02664760120059219>; 
  Ruby C. Weng, Chih-Jen Lin (2011) <http://jmlr.csail.mit.edu/papers/volume12/weng11a/weng11a.pdf>; 
  W. Penny, Stephen J. Roberts (1999) <doi:10.1109/IJCNN.1999.832603>.
The aim of this package is to provide infrastructure for
        sports analysis. Anyway, currently it is a selection of data
        sets, functions to fetch sports data, examples, and demos --
        with the ambition to develop bit by bit a set of classes to
        represent general concepts of sports analysis.
A set of tools for model based optimization and tuning of
    algorithms. It includes surrogate models, optimizers and design of experiment
    approaches. The main interface is spot, which uses sequentially updated
    surrogate models for the purpose of efficient optimization. The main goal is
    to ease the burden of objective function evaluations, when a single evaluation
    requires a significant amount of resources.
A graphical user interface for the Sequential Parameter Optimization Toolbox (package 'SPOT').
    It includes a quick, graphical setup for spot, interactive 3D plots, export possibilities and more.
An R wrapper for pulling data from the 'Spotify' Web API 
  <http://developer.spotify.com/web-api> in bulk. It allows you to enter 
  an artist's name and retrieve their entire discography in seconds, along 
  with audio features and lyrics from Genius Lyrics <https://www.genius.com>. 
  You can also pull song and playlist information for a given 'Spotify' user.
Analysis of ChIP-seq and other functional sequencing data [Kharchenko PV (2008) <DOI:10.1038/nbt.1508>].
We propose a novel two-step procedure to combine epidemiological
    data obtained from diverse sources with the aim to quantify risk factors
    affecting the probability that an individual develops certain disease such as
    cancer. See  Hui Huang, Xiaomei Ma, Rasmus Waagepetersen, Theodore R. Holford,
    Rong Wang, Harvey Risch, Lloyd Mueller & Yongtao Guan (2014) A New Estimation Approach
    for Combining Epidemiological Data From Multiple Sources, Journal of the American Statistical
    Association, 109:505, 11-23,  <doi:10.1080/01621459.2013.870904>.
Implements classes and methods for modeling spatial point patterns using inhomogeneous Poisson point processes, where the intensity surface is assumed to be analogous to a finite additive mixture of normal components and the number of components is a finite, fixed or random integer. Extensions to the marked inhomogeneous Poisson point processes case are also presented. We provide an extensive suite of R functions that can be used to simulate, visualize and model point patterns, estimate the parameters of the models, assess convergence of the algorithms and perform model selection and checking in the proposed modeling context.
Sparse arrays interpreted as multivariate polynomials.
The notion of spreading activation is a prevalent metaphor in the cognitive sciences. 
    This package provides the tools for cognitive scientists and psychologists to conduct computer simulations 
    that implement spreading activation in a network representation. The algorithmic method implemented in 'spreadr' 
    subroutines follows the approach described in Vitevitch, Ercal, and Adagarla (2011, Frontiers), 
    who viewed activation as a fixed cognitive resource that could spread among nodes that were connected to 
    each other via edges or connections (i.e., a network). See Vitevitch, M. S., Ercal, G., & Adagarla, B. (2011). 
    Simulating retrieval from a highly clustered network: Implications for spoken word recognition. 
    Frontiers in Psychology, 2, 369. <doi:10.3389/fpsyg.2011.00369>.
The Statistical Package for REliability Data Analysis (SPREDA) implements recently-developed statistical methods for the analysis of reliability data. Modern technological developments, such as sensors and smart chips, allow us to dynamically track product/system usage as well as other environmental variables, such as temperature and humidity. We refer to these variables as dynamic covariates. The package contains functions for the analysis of time-to-event data with dynamic covariates and degradation data with dynamic covariates. The package also contains functions that can be used for analyzing time-to-event data with right censoring, and with left truncation and right censoring. Financial support from NSF and DuPont are acknowledged.
The recent advancement of high-throughput technologies has led to frequent utilization of gene expression and other "omics" data for toxicological, diagnostic or prognostic studies in and clinical applications. Unlike in classical predictions where the number of samples is greater than the number of variables (n>p), the challenge faced with prediction using "omics" data is that the number of parameters greatly exceeds the number of samples (p>>n). To solve this curse of dimensionality problem, several predictive functions have been proposed for direct and probabilistic classification and survival predictions. Nevertheless, these predictive functions have been shown to perform differently across datasets. Comparing predictive functions and choosing the best is computationally intensive and leads to selection bias. Thus, the question which function should one choose for a given dataset is to be ascertained. This package implements the approach proposed by Jong et al., (2016) to address this question.
Provides a function for the estimation of parameters 
		in a binary regression with the skew-probit link function. 
		Naive MLE, Jeffrey type of prior and Cauchy prior type of penalization are implemented,
		as described in DongHyuk Lee and Samiran Sinha (2019+) <doi:10.1080/00949655.2019.1590579>.
Calculate species richness functions for rarefaction and
    extrapolation.
The main function of this package builds prognostic models that consider interactions by combining available statistical components. Furthermore, it provides a function for evaluating the relevance of the selected interactions by resampling techniques.
Robust dimension reduction methods for regression and discriminant analysis are implemented that yield estimates with a partial least squares alike interpretability. Partial robust M regression (PRM) is robust to both vertical outliers and leverage points. Sparse partial robust M regression (SPRM) is a related robust method with sparse coefficient estimate, and therefore with intrinsic variable selection. For binary classification related discriminant methods are PRM-DA and SPRM-DA.
R functions to mine sparse models from data.
Perform Wald's Sequential Probability Ratio Test on variables with a Normal, Bernoulli, Exponential and Poisson distribution. Plot acceptance and continuation regions, or create your own with the help of closures.
Methods to optimize sample configurations using spatial simulated annealing. Multiple objective 
    functions are implemented for various purposes, such as variogram estimation, spatial trend estimation 
    and spatial interpolation. A general purpose spatial simulated annealing function enables the user to 
    define his/her own objective function. Solutions for augmenting existing sample configurations and solving
    multi-objective optimization problems are available as well.
Fits spatial scale (SS) forward stepwise regression, SS incremental forward stagewise regression, SS least angle regression (LARS), and SS lasso models.  All area-level covariates are considered at all available scales to enter a model, but the SS algorithms are constrained to select each area-level covariate at a single spatial scale.
Program uses method of polynomial of variable degrees to interpolate gridded data preserving monotonicity and/or convexity or none. Method is implemented for univariate and bivariate cases. If values of derivatives are provided, spline will fix them,if not program will estimate them numerically. Package written purely in R.
Provides basic functionality for labeling 
        iso- & anisotropic percolation clusters on 2D & 3D square lattices 
        with various lattice sizes, occupation probabilities, von Neumann & 
        Moore (1,d)-neighborhoods, and random variables weighting 
        the percolation lattice sites.
A collection of functions to test and estimate Seemingly 
    Unrelated Regression (usually called SUR) models, with spatial structure, by maximum 
    likelihood and three-stage least squares. The package estimates the 
    most common spatial specifications, that is, SUR with Spatial Lag of 
    X regressors (called SUR-SLX), SUR with Spatial Lag Model (called SUR-SLM), 
    SUR with Spatial Error Model (called SUR-SEM), SUR with Spatial Durbin Model (called SUR-SDM), 
    SUR with Spatial Durbin Error Model (called SUR-SDEM), 
    SUR with Spatial Autoregressive terms and Spatial Autoregressive 
    Disturbances (called SUR-SARAR) and SUR with Spatially Independent Model (called SUR-SIM).
    The methodology of these models can be found in next references
    Mur, J., Lopez, F., and Herrera, M. (2010) <doi:10.1080/17421772.2010.516443> 
    Lopez, F.A., Mur, J., and Angulo, A. (2014) <doi:10.1007/s00168-014-0624-2>.
These functions provide procedures for selecting sites for spatial
    surveys using spatially balanced algorithms applied to discrete points,
    linear networks, or polygons. The probability survey designs available
    include independent random samples, stratified random samples, and unequal
    probability random samples (categorical or probability proportional to
    size).  Design-based estimation based on the results from surveys is
    available for estimating totals, means, quantiles, CDFs, and linear models.
    The analyses rely on package survey for most results.  Variance estimation
    options include a local neighborhood variance estimator that is appropriate
    for spatially-balanced survey designs.  A reference for the survey design
    portion of the package is: D. L. Stevens, Jr. and A. R. Olsen (2004),
    "Spatially-balanced sampling of natural resources.", Journal of the American
    Statistical Association 99(465): 262-278, <DOI:10.1198/016214504000000250>.
    Additional helpful references for this package are A. R. Olsen, T. M.
    Kincaid, and Q. Payton (2012) and T. M. Kincaid and A. R. Olsen (2012), both
    of which are chapters in the book "Design and Analysis of Long-Term
    Ecological Monitoring Studies" (R. A. Gitzen, J. J. Millspaugh, A. B.
    Cooper, and D. S. Licht (eds.), Cambridge University Press, New York,
    <Online ISBN:9781139022422>).
A collection of algorithms related to Sierpinski 
  pedal triangle (SPT).
Fits, spatially predicts, and temporally forecasts space-time data using Gaussian Process (GP): (1) spatially varying coefficient process models and (2) spatio-temporal dynamic linear models. Bakar et al., (2016) <doi:10.1080/00949655.2015.1038267>. Bakar et al., (2015) <doi:10.1111/rssc.12103>.
The approach of constrained spatiotemporal mixed models is to make reliable estimation of air pollutant concentrations at high spatiotemporal
    resolution (Li, L., Zhang, J., Meng, X., Fang, Y., Ge, Y., Wang, J., Wang, C., Wu, J., Kan, H. (2018) <doi.org/10.1016/j.rse.2018.09.001>; 
    Li, L., Lurmann, F., Habre, R., Urman, R., Rappaport, E., Ritz, B., Chen, J., Gilliland, F., Wu, J., (2017) <doi:10.1021/acs.est.7b01864>).
    This package is an extensive tool for this modeling approach with support of block Kriging (Goovaerts, P. (1997) <http://www.gbv.de/dms/goettingen/229148123.pdf>)
    and uses the PM2.5 modeling as examples. It provides the following functionality:
    (1) Extraction of covariates from the satellite images such as GeoTiff and NC4 raster;
    (2) Generation of temporal basis functions to simulate the seasonal trends in the study regions;
    (3) Generation of the regional monthly or yearly means of air pollutant concentration;
    (4) Generation of Thiessen polygons and spatial effect modeling;
    (5) Ensemble modeling for spatiotemporal mixed models, supporting multi-core parallel computing;
    (6) Integrated predictions with or without weights of the model's performance, supporting multi-core parallel computing;
    (7) Constrained optimization to interpolate the missing values;
    (8) Generation of the grid surfaces of air pollutant concentration estimates at high resolution;
    (9) Block Kriging for regional mean estimation at multiple scales.
Implements nonparametric hypothesis tests to check isotropy and
    symmetry properties for two-dimensional spatial data.
spThin is a set of functions that can be used to spatially thin
    species occurrence data. The resulting thinned data can be used in ecological
    modeling, such as ecological niche modeling.
Fits, spatially predicts and temporally forecasts large amounts of space-time data using  [1] Bayesian Gaussian Process (GP) Models, [2] Bayesian Auto-Regressive (AR) Models, and [3] Bayesian Gaussian Predictive Processes (GPP) based AR Models for spatio-temporal big-n problems. Bakar and Sahu (2015) <doi:10.18637/jss.v063.i15>.
Implements semiparametric transformation model two-phase estimation using calibration weights. The method in Fong and Gilbert (2015) Calibration weighted estimation of semiparametric transformation models for two-phase sampling. Statistics in Medicine <DOI:10.1002/sim.6439>.
Uncertainty propagation analysis in spatial environmental  modelling following methodology
         described in Heuvelink et al. (2007) <doi:10.1080/13658810601063951> 
         and Brown and Heuvelink (2007) <doi:10.1016/j.cageo.2006.06.015>. The package provides functions
         for examining the uncertainty propagation starting from input data and model parameters,
         via the environmental model onto model outputs. The functions include uncertainty model specification,
         stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques.
         Uncertain variables are described by probability distributions. Both numerical and categorical data types are handled.
         Spatial auto-correlation within an attribute and cross-correlation between attributes is accommodated for.
         The MC realizations may be used as input to the environmental models called from R, or externally.
Provides functions and datasets from Jones, O.D., R. Maillardet, and A.P. Robinson.  2014.  An Introduction to Scientific Programming and Simulation, Using R. 2nd Ed. Chapman And Hall/CRC.
A set of tools for the peak filtering of mass spectrometry
  imaging data (MSI or IMS) based on spatial distribution of signal. Given a 
  region-of-interest (ROI), representing the spatial region where the informative
  signal is expected to be localized, a series of filters determine which peak
  signals are characterized by an implausible spatial distribution. The filters
  reduce the dataset dimensionality and increase its information vs noise ratio,
  improving the quality of the unsupervised analysis results, reducing data
  dimensionality and simplifying the chemical interpretation.
Data on the Spy vs. Spy comic strip of Mad magazine, created and
   written by Antonio Prohias.
Methodology: Remove one observation. Training the rest of data that are sampled without replacement and given this observation's input, predict the response back. Replicate this N times and for each response, take a sample from these replicates with replacement. Average each responses of sample and again replicate this step N time for each observation. Approximate these N new responses by using bootstrap method and generate another N responses y'. Training these y' and predict to have N responses of each testing observation. The average of N is the final prediction. Each observation will do the same.
Sparse Quadratic Discriminant Analysis (SQDA) can be performed. In SQDA, the covariance matrix are assumed to be block-diagonal.And, for each block, sparsity assumption is imposed on the covariance matrix. It is useful in high-dimensional setting.
The sqldf() function is typically passed a single argument which 
	is an SQL select statement where the table names are ordinary R data 
	frame names.  sqldf() transparently sets up a database, imports the 
	data frames into that database, performs the SQL select or other
	statement and returns the result using a heuristic to determine which 
	class to assign to each column of the returned data frame.  The sqldf() 
	or read.csv.sql() functions can also be used to read filtered files 
	into R even if the original files are larger than R itself can handle.
	'RSQLite', 'RH2', 'RMySQL' and 'RPostgreSQL' backends are supported.
sqliter helps users, mainly data munging practioneers, to organize
    their sql calls in a clean structure. It simplifies the process of
    extracting and transforming data into useful formats.
A rendering tool for parameterized SQL that also translates into
  different SQL dialects.  These dialects include 'Microsoft Sql Server', 'Oracle', 
  'PostgreSql', 'Amazon RedShift', 'Apache Impala', 'IBM Netezza', 'Google BigQuery', 'Microsoft PDW', and 'SQLite'.
Provides utilities for generating SQL queries (particularly CREATE
    TABLE statements) from R model objects. The most important use case is
    generating SQL to score a generalized linear model or related model
    represented as an R object, in which case the package handles parsing
    formula operators and including the model's response function.
This package provides utilities for working with a library of SQL
    files.
Normalization based a subset of negative control probes as
        described in 'Subset quantile normalization using negative
        control features'. Wu Z, Aryee MJ, J Comput Biol. 2010
        Oct;17(10):1385-95 [PMID 20976876].
Facilitates interaction with 'ODBC' data sources.
Algorithms for accelerating the convergence of slow,
        monotone sequences from smooth, contraction mapping such as the
        EM algorithm. It can be used to accelerate any smooth, linearly
        convergent acceleration scheme.  A tutorial style introduction
        to this package is available in a vignette on the CRAN download
        page or, when the package is loaded in an R session, with
        vignette("SQUAREM").
Functions for color-based visualization of multivariate data, i.e. colorgrams or heatmaps.  Lower-level functions  map numeric values to colors, display a matrix as an array of colors, and draw color keys.  Higher-level plotting functions generate a bivariate histogram, a dendrogram aligned with a color-coded matrix, a triangular distance matrix, and more.
A simulation-based tool made to help researchers to become familiar with
    multilevel variations, and to build up sampling designs for their study. 
    This tool has two main objectives: First, it provides an educational tool useful for students, 
    teachers and researchers who want to learn to use mixed-effects models. 
    Users can experience how the mixed-effects model framework can be used to understand 
    distinct biological phenomena by interactively exploring simulated multilevel data. 
    Second, it offers research opportunities to those who are already familiar with 
    mixed-effects models, as it enables the generation of data sets that users may download 
    and use for a range of simulation-based statistical analyses such as power 
    and sensitivity analysis of multilevel and multivariate data.
This package (sra) provides a set of tools to analyse artificial-selection response datasets. The data typically feature for several generations the average value of a trait in a population, the variance of the trait, the population size and the average value of the parents that were chosen to breed. Sra implements two families of models aiming at describing the dynamics of the genetic architecture of the trait during the selection response. The first family relies on purely descriptive (phenomenological) models, based on an autoregressive framework. The second family provides different mechanistic models, accounting e.g. for inbreeding, mutations, genetic and environmental canalization, or epistasis. The parameters underlying the dynamics of the time series are estimated by maximum likelihood. The sra package thus provides (i) a wrapper for the R functions mle() and optim() aiming at fitting in a convenient way a predetermined set of models, and (ii) some functions to plot and analyze the output of the models.
Implementation of the SRCS method for a color-based visualization of the
    results of multiple pairwise tests on a large number of problem configurations, proposed in: 
    I.G. del Amo, D.A. Pelta. SRCS: a technique for comparing multiple algorithms under several
    factors in dynamic optimization problems. In: E. Alba, A. Nakib, P. Siarry
    (Eds.), Metaheuristics for Dynamic Optimization. Series: Studies in
    Computational Intelligence 433, Springer, Berlin/Heidelberg, 2012.
Sparse redundancy analysis
	for high dimensional (biomedical) data. Directional multivariate analysis 
	to express the maximum variance in the predicted data set by a linear 
	combination of variables of the predictive data set. Implemented in a 
	partial least squares framework, for more details see Csala et al. (2017) <doi:10.1093/bioinformatics/btx374>.
Provides functionality for structural equation modeling for
    the social relations model (Warner, Kenny, & Soto, 1979,
    <doi:10.1037/0022-3514.37.10.1742>). Maximum likelihood
    estimation (Gill & Swartz, 2001, <doi:10.2307/3316080>;
    Nestler, 2018, <doi:10.3102/1076998617741106>) and
    least squares estimation is supported (Bond & Malloy, 2018,
    <doi:10.1016/B978-0-12-811967-9.00014-X>).
This package contains a collection of functions to perform
        nonparametric estimation of receiver operating characteristic
        (ROC) curves for continuous data.
Performs the change-point detection in regression coefficients of linear model 
    by partitioning the regression coefficients into two classes of smoothness. The change-point and
    the regression coefficients are jointly estimated.
This package implements the SRRS method introduced in Phoa (2013) into a graphical user interface (GUI) R program.
Srt file is a common subtitle format for videos, it contains subtitle and when the subtitle showed.
    This package is for ealign time of srt file, and also change color, style and position of subtitle in videos,
	the srt file will be read as a vector into R, and can be write into srt file after modified using this package.
Use piping, verbs like 'group_by' and 'summarize', and other
    'dplyr' inspired syntactic style when calculating summary statistics on survey
    data using functions from the 'survey' package.
Develops a framework for fisheries stock assessment simulation
    testing with Stock Synthesis 3 (SS3) as described in Anderson et al.
    (2014) <doi:10.1371/journal.pone.0092725>.
Procedures for analyzing simultaneous signals, e.g., features that are simultaneously significant in two different studies. Includes methods for detecting simultaneous signals, for identifying them under false discovery rate control, and for leveraging them to improve prediction.
A set of functions to calculate sample size for two-sample difference in means tests. Does adjustments for either nonadherence or variability that comes from using data to estimate parameters.
Functions used by other packages from Statistics Norway are gathered. General data manipulation functions, and functions for hierarchical computations are included. The hierarchy specification functions are useful within statistical disclosure control.
Provides a collection of self-labeled techniques for semi-supervised 
    classification. In semi-supervised classification, both labeled and unlabeled
    data are used to train a classifier. This learning paradigm has obtained promising
    results, specifically in the presence of a reduced set of labeled examples. 
    This package implements a collection of self-labeled techniques to construct a
    classification model. This family of techniques enlarges the original labeled set 
	using the most confident predictions to classify unlabeled data. The techniques 
	implemented can be applied to classification problems in several domains by the 
	specification of a supervised base classifier. At low ratios of labeled data, it 
	can be shown to perform better than classical supervised classifiers.
Provides the spatial sign correlation and the two-stage spatial sign correlation as well as a one-sample test for the correlation coefficient.
ssd calculates the sample size needed to detect the differences between two sets of unordered categorical data.
Functions to visually and statistically analyze single system data.
Allows to map species richness and endemism based on stacked
    species distribution models (SSDM). Individuals SDMs can be created using a
    single or multiple algorithms (ensemble SDMs). For each species, an SDM can
    yield a habitat suitability map, a binary map, a between-algorithm variance
    map, and can assess variable importance, algorithm accuracy, and between-
    algorithm correlation. Methods to stack individual SDMs include summing
    individual probabilities and thresholding then summing. Thresholding can be
    based on a specific evaluation metric or by drawing repeatedly from a Bernoulli
    distribution. The SSDM package also provides a user-friendly interface.
Performs structured OLS (sOLS) and structured SIR (sSIR).
Species sensitivity distributions are cumulative probability 
  distributions which are fitted to toxicity concentrations for multiple species.
  The ssdtools package uses Maximum Likelihood to fit
  log-normal, log-logistic, log-Gumbel, Gompertz, gamma or Weibull distributions.
  Multiple distributions can be averaged using Information Criteria.
  Confidence intervals can be calculated for the fitted cumulative distribution 
  function or specific hazard concentrations (percentiles).
  Confidence intervals are currently produced by bootstrapping.
Computes the optimal sample size for various 2-group designs (e.g., when comparing the means of two groups assuming equal variances, unequal variances, or comparing proportions) when the aim is to maximize the rewards over the full decision procedure of a) running a trial (with the computed sample size), and b) subsequently administering the winning treatment to the remaining N-n units in the population. Sample sizes and expected rewards for standard t- and z- tests are also provided.
Spatial Stochastic Frontier Analysis (SSFA) is an original method for controlling the spatial heterogeneity in Stochastic Frontier Analysis (SFA) models, for cross-sectional data, by splitting the inefficiency term into three terms: the first one related to spatial peculiarities of the territory in which each single unit operates, the second one related to the specific production features and the third one representing the error term.
Fits complex parametric models using the method proposed by Cox and Kartsonaki (2012) without likelihoods.
Bayesian estimation for undirected graphical models using spike-and-slab priors. The package handles continuous, discrete, and mixed data. To speed up the computations, the computationally intensive tasks of the package are implemented in C++ in parallel using OpenMP.
Connect to a remote server over SSH to transfer files via SCP, 
    setup a secure tunnel, or run a command or script on the host while 
    streaming stdout and stderr directly to the client.
This package provides utility functions for system command
    execution, both locally and remotely using ssh/scp. The command
    output is captured and provided to the caller. This functionality is
    intended to streamline calling shell commands from R, retrieving and
    using their output, while instrumenting the calls with appropriate
    error handling. NOTE: this first version is limited to unix with local
    and remote systems running bash as the default shell.
This package contains a set of functions that calculates 
 appropriate sample sizes for one-sample t-tests, two-sample t-tests, 
 and F-tests for microarray experiments based on desired power while 
 controlling for false discovery rates. For all tests, the standard
 deviations (variances) among genes can be assumed fixed or random. 
 This is also true for effect sizes among genes in one-sample and two
 sample experiments. Functions also output a chart of power versus sample
 size, a table of power at different sample sizes, and a table of critical
 test values at different sample sizes.
We propose a procedure for sample size calculation while
    controlling false discovery rate for RNA-seq experimental design. Our
    procedure depends on the Voom method proposed for RNA-seq data analysis
    by Law et al. (2014) <DOI:10.1186/gb-2014-15-2-r29> and the sample size 
    calculation method proposed for microarray experiments by Liu and Hwang 
    (2007) <DOI:10.1093/bioinformatics/btl664>. We develop a set of functions
    that calculates appropriate sample sizes for two-sample t-test for RNA-seq
    experiments with fixed or varied set of parameters. The outputs also contain a
    plot of power versus sample size, a table of power at different sample sizes,
    and a table of critical test values at different sample sizes. 
    To install this package, please use 
    'source("http://bioconductor.org/biocLite.R"); biocLite("ssizeRNA")'.
Efficient coordinate ascent algorithm for fitting regularization paths for linear models penalized by Spike-and-Slab LASSO of Rockova and George (2018) <doi:10.1080/01621459.2016.1260469>.
Creates an S4 class "SSM" and defines functions for fitting smooth
    supersaturated models, a polynomial model with spline-like behaviour.
    Functions are defined for the computation of Sobol indices for sensitivity
    analysis and plotting the main effects using FANOVA methods. It also
    implements the estimation of the SSM metamodel error using a GP model with
    a variety of defined correlation functions.
Performs the EM algorithm for regression models using Skew Scale Mixtures of Normal Distributions.
Estimates previously compiled state-space modeling for mouse-tracking experiments using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation.
Package provides a set of tools for robust estimation and inference for models with sample selectivity.
It provides the density and random number generator for the Scale-Shape Mixtures of Skew-Normal Distributions proposed by Jamalizadeh and Lin (2016) <doi:10.1007/s00180-016-0691-1>.
Spatial statistical modeling and prediction for data on stream networks, including models based on in-stream distance (Ver Hoef, J.M. and Peterson, E.E., 2010. <DOI:10.1198/jasa.2009.ap08248>.) Models are created using moving average constructions. Spatial linear models, including explanatory variables, can be fit with (restricted) maximum likelihood.  Mapping and other graphical functions are included.
Soft-margin support vector machines (SVMs) are a common class of classification models. The training of SVMs usually requires that the data be available all at once in a single batch, however the Stochastic majorization-minimization (SMM) algorithm framework allows for the training of SVMs on streamed data instead Nguyen, Jones & McLachlan(2018)<doi:10.1007/s42081-018-0001-y>. This package utilizes the SMM framework to provide functions for training SVMs with hinge loss, squared-hinge loss, and logistic loss.
R package for computing the spherical smoothing splines
Estimate the size of a networked population based on
    respondent-driven sampling data. The package is part of the "RDS Analyst"
    suite of packages for the analysis of respondent-driven sampling data.
    See Handcock, Gile and Mar (2014) <doi:10.1214/14-EJS923> and
    Handcock, Gile and Mar (2015) <doi:10.1111/biom.12255>.
Takeya Semantic Structure Analysis (TSSA) and Sakai Sequential Relation Analysis (SSRA)
             for polytomous items for examining whether each pair of items has a sequential or equal
             relation. Package includes functions for generating a sequential relation table and a
             treegram to visualize sequential or equal relations between pairs of items.
A set of functions for two-dimensional sociometric status
    determination with rating scales. For each person assessed, SSrat computes
    probability distributions of the total scores for `Sympathy' (S),
    `Antipathy' (A), social `Preference' (P) and social `Impact' (I), and
    applies a set of criteria for sociometric status categorization.
Provides the necessary sample size for a longitudinal study with binary outcome in order to attain a pre-specified power while strictly maintaining the Type I error rate. 
 Kapur K, Bhaumik R, Tang XC, Hur K, Reda DJ, Bhaumik D (2014) <doi:10.1002/sim.6203>.
Calculates the power and sample size based on the difference in Restricted Mean Survival Time.
Tools to import survey files
    in the .sss (triple-s) format. The package provides the function
    read.sss() that reads the .asc (or .csv) and .sss files of a
    triple-s survey data file.
Imports Variant Calling Format file into R. It can detect
             whether a sample contains contaminant from the same species.
             In the first stage of the approach, a change-point detection
             method is used to identify copy number variations for filtering.
             Next, features are extracted from the data for a support vector
             machine model. For log-likelihood calculation, the deviation
             parameter is estimated by maximum likelihood method. Using
             a radial basis function kernel support vector machine, the
             contamination of a sample can be detected.
Generates and predicts a set of linearly stacked Random Forest models using bootstrap sampling. Individual datasets may be heterogeneous (not all samples have full sets of features). Contains support for parallelization but the user should register their cores before running. This is an extension of the method found in Matlock (2018) <doi:10.1186/s12859-018-2060-2>.
Framework for the implementation of solvency related computations based on 
             standard models for the Swiss Solvency Test (SST), a risk-based capital standard for Swiss 
             insurance companies. Allows Monte Carlo simulation of market risk, some insurance risks and 
             their aggregation. Additional toolbox for preprocessing computations. Convenient 'shiny' GUI 
             combined with a parser for an input 'excel' (.xlsx) template to simplify model configuration, 
             data fill-in and results visualization.
Fast iterative thresholding sparse SVD, together with an initialization algorithm
Set of tools to fit a semi-parametric regression model suitable for analysis of data sets in which the response variable is continuous, strictly positive, asymmetric and possibly, censored. Under this setup, both the median and the skewness of the response variable distribution are explicitly modeled by using semi-parametric functions, whose non-parametric components may be approximated by natural cubic splines or P-splines. Supported distributions for the model error include log-normal, log-Student-t, log-power-exponential, log-hyperbolic, log-contaminated-normal, log-slash, Birnbaum-Saunders and Birnbaum-Saunders-t distributions.
Implements the "shrinkage t" statistic 
   introduced in Opgen-Rhein and Strimmer (2007) and a shrinkage estimate
   of the "correlation-adjusted t-score" (CAT score) described in
   Zuber and Strimmer (2009).  It also offers a convenient interface 
   to a number of other regularized t-statistics commonly 
   employed in high-dimensional case-control studies.
Functionalities to perform Stability Analysis of Genotype by Environment Interaction (GEI) to identify superior and stable genotypes under diverse environments. It performs Eberhart & Russel's ANOVA (1966) (<doi:10.2135/cropsci1966.0011183X000600010011x>), Finlay and Wilkinson (1963) Joint Linear Regression (<doi:10.1071/AR9630742>), Wricke (1962, 1964) Ecovalence, Shukla's stability variance parameter (1972) (<doi:10.1038/hdy.1972.87>)  and Kang's (1991) (<doi:10.2134/agronj1991.00021962008300010037x>) simultaneous selection for high yielding and stable parameter.
Density, distribution, quantile and hazard functions of a
    stable variate; generalized regression models for the parameters
    of a stable distribution. See the README for how to make equivalent calls
    to those of 'stabledist'. See github for Journal article.
Density, Probability and Quantile functions, and random number
  generation for (skew) stable distributions, using the parametrizations of
  Nolan.
Estimate the four parameters of stable laws using maximum
             likelihood method, generalised method of moments with
             finite and continuum number of points, iterative
             Koutrouvelis regression and Kogon-McCulloch method.  The
             asymptotic properties of the estimators (covariance
             matrix, confidence intervals) are also provided.
Graphical and computational methods that can be used to assess the 
  stability of results from supervised statistical learning.
An exploratory and heuristic approach for specification search in
    Structural Equation Modeling. The basic idea is to subsample the original data
    and then search for optimal models on each subset. Optimality is defined through
    two objectives: model fit and parsimony. As these objectives are conflicting,
    we apply a multi-objective optimization methods, specifically NSGA-II, to obtain
    optimal models for the whole range of model complexities. From these optimal
    models, we consider only the relevant model specifications (structures), i.e.,
    those that are both stable (occur frequently) and parsimonious and use those to
    infer a causal model.
An implementation of many measures for the assessment of the stability
    of feature selection. Both simple measures and measures which take into account
    the similarities between features are available, see Bommert et al. (2017) <doi:10.1155/2017/7907163>.
Efficient regression for heavy-tailed and skewed data following a stable distribution. Generalized regression where the skewness and tail parameter of residuals are dependent on regressors is also available. Includes fast calculation of stable densities. Calculation of densities is based on efficient numerical methods from Ament and O'Neil (2017) <doi:10.1007/s11222-017-9725-y>. Parts of the code have been ported to C from Ament's 'Matlab' code available at <https://gitlab.com/s_ament/qastable>.
Resampling procedures to assess the stability of selected variables
    with additional finite sample error control for high-dimensional variable
    selection procedures such as Lasso or boosting. Both, standard stability
    selection (Meinshausen & Buhlmann, 2010, <doi:10.1111/j.1467-9868.2010.00740.x>) 
    and complementary pairs stability selection with improved error bounds 
    (Shah & Samworth, 2013, <doi:10.1111/j.1467-9868.2011.01034.x>) are
    implemented. The package can be combined with arbitrary user specified
    variable selection approaches.
Stacks rectangular datasets on top of each other, possibly
    performing several type coercions along the way. For large datasets,
    depends on the ff package. Provides an aggressive version of
    ffbase::compact for data that may appear be real-typed but is in fact
    int/short/byte. For many purposes plyr::rbind.fill may be more appropriate,
    but for some kinds of survey data, the rules here work better.
Helper functions collected from StackOverflow.com, a
    question and answer site for professional and enthusiast programmers.
Graphical outputs and treatment for a database of fish pass
    monitoring. It is a part of the 'STACOMI' open source project developed in
    France by the French Agency for Biodiversity (AFB) institute to centralize
    data obtained by fish pass monitoring. This version is available in French and
    English. See <http://stacomir.r-forge.r-project.org/> for more information on
    'STACOMI'.
S4 class wrappers for the 'ODBC' connection, also provides some 
    utilities to paste small datasets to clipboard, rename columns. It is used by the package 'stacomiR' for
    connections to the database. Development versions of 'stacomiR' are available in R-forge.
Analyzing a mixture of two-stage logistic regressions with fixed candidate knots. See Bruch, E., F. Feinberg, K. Lee (in press)<DOI:10.1073/pnas.1522494113>.
Provides facilities to implement and run population models of
    stage-structured species...
Proposes an original instrument for measuring stakeholder influence on the development of an infrastructure project that is carried through by a municipality, drawing on stakeholder classifications (Mitchell, Agle, & Wood, 1997) and input-output modelling (Hester & Adams, 2013). Mitchell R., Agle B.R., & Wood D.J. <doi:10.2307/259247> Hester, P.T., & Adams, K.M. (2013) <doi:10.1016/j.procs.2013.09.282>.
stam is an evolving package that target on the various
        methods to conduct Spatio-Temporal Analysis and
        Modelling,including Exploratory Spatio-Temporal Analysis and
        Inferred Spatio-Temporal Modelling.
Allows users to calculate pairwise Nei's Genetic Distances (Nei 1972), pairwise Fixation
 Indexes (Fst) (Weir & Cockerham 1984) and also Genomic Relationship matrixes following Yang et al. (2010) in mixed and single
 ploidy populations. Bootstrapping across loci is implemented during Fst calculation to generate confidence intervals and p-values
 around pairwise Fst values. StAMPP utilises SNP genotype data of any ploidy level (with the ability to handle missing data) and is coded to  
 utilise multithreading where available to allow efficient analysis of large datasets. StAMPP is able to handle genotype data from genlight objects 
 allowing integration with other packages such adegenet.
 Please refer to LW Pembleton, NOI Cogan & JW Forster, 2013, Molecular Ecology Resources, 13(5), 946-952. <doi:10.1111/1755-0998.12129> for the appropriate citation and user manual. Thank you in advance.
Perform spatial temporal analysis of moving polygons; a
    longstanding analysis problem in Geographic Information Systems. Facilitates
    directional analysis, shape analysis, and some other simple functionality for
    examining spatial-temporal patterns of moving polygons.
Provides functions for the analysis of
        occupational and environmental data with non-detects. Maximum
        likelihood (ML) methods for censored log-normal data and
        non-parametric methods based on the product limit estimate (PLE)
        for left censored data are used to calculate all of the
        statistics recommended by the American Industrial Hygiene
        Association (AIHA) for the complete data case. Functions for
        the analysis of complete samples using exact methods are also
        provided for the lognormal model. Revised from 2007-11-05
        'survfit~1'.
Tools which allow regression variables to be placed on similar
    scales, offering computational benefits as well as easing interpretation of
    regression output.
Standardizes text according to a template; particularly
        useful for country names.
The C++ header files of the Stan project are provided by this package, but it contains no R code or function documentation. There is a shared object containing part of the 'CVODES' library, but it is not accessible from R. 'StanHeaders' is only useful for developers who want to utilize the 'LinkingTo' directive of their package's DESCRIPTION file to build on the Stan library without incurring unnecessary dependencies. The Stan project develops a probabilistic programming language that implements full or approximate Bayesian statistical inference via Markov Chain Monte Carlo or 'variational' methods and implements (optionally penalized) maximum likelihood estimation via optimization. The Stan library includes an advanced automatic differentiation scheme, 'templated' statistical and linear algebra functions that can handle the automatically 'differentiable' scalar types (and doubles, 'ints', etc.), and a parser for the Stan language. The 'rstan' package provides user-facing R functions to parse, compile, test, estimate, and analyze Stan models.
An implementation of Simultaneous Truth and 
    Performance Level Estimation (STAPLE) <doi:10.1109/TMI.2004.828354>.  This
    method is used when there are multiple raters for an object, typically an
    image, and this method fuses these ratings into one rating.  It uses an
    expectation-maximization method to estimate this rating and the individual
    specificity/sensitivity for each rater.
Provides function to manipulate PDF files: 
    fill out PDF forms;
    merge multiple PDF files into one; 
    remove selected pages from a file;
    rename multiple files in a directory;
    rotate entire pdf document; 
    rotate selected pages of a pdf file;
    Select pages from a file;
    splits single input PDF document into individual pages;
    splits single input PDF document into parts from given points.
Functions to analyze neuronal spike trains from a single
        neuron or from several neurons recorded simultaneously.
This is a user-friendly way to run a parallel factor (PARAFAC) analysis (Harshman, 1971) <doi:10.1121/1.1977523> on excitation emission matrix (EEM) data from dissolved organic matter (DOM) samples (Murphy et al., 2013) <doi:10.1039/c3ay41160e>. The analysis includes profound methods for model validation. Some additional functions allow the calculation of absorbance slope parameters and create beautiful plots.
Produces LaTeX code, HTML/CSS code and ASCII text for well-formatted tables that hold 
    regression analysis results from several models side-by-side, as well as summary
    statistics.
Statistical functions to identify, estimate and diagnose a Space-Time AutoRegressive Moving Average (STARMA) model.
Data structures and methods for  manipulating output of genetic population structure clustering algorithms.
  'starmie' can parse output from 'STRUCTURE' (see <https://pritchardlab.stanford.edu/structure.html> for details) or
  'ADMIXTURE' (see <https://www.genetics.ucla.edu/software/admixture/> for details). 'starmie' performs model selection via
  information criterion, and provides functions for MCMC diagnostics, correcting label switching and visualisation of admixture coefficients.
Reading, manipulating, writing and plotting
    spatiotemporal arrays (raster and vector data cubes) in 'R', using 'GDAL'
    bindings provided by 'sf', and 'NetCDF' bindings by 'ncmeta' and 'RNetCDF'.
The package is used for calibrating the design parameters for single-to-double arm transition design proposed by Shi and Yin (2017). The calibration is performed via numerical enumeration to find the optimal design that satisfies the constraints on the type I and II error rates.
Tool to automatically fetch, transform and arrange subsets of multidimensional data sets (collections of files) stored in local and/or remote file systems or servers, using multicore capabilities where possible. The tool provides an interface to perceive a collection of data sets as a single large multidimensional data array, and enables the user to request for automatic retrieval, processing and arrangement of subsets of the large array. Wrapper functions to add support for custom file formats can be plugged in/out, making the tool suitable for any research field where large multidimensional data sets are involved.
Contains functions for estimating the STARTS model of
    Kenny and Zautra (1995, 2001) <DOI:10.1037/0022-006X.63.1.52>,
    <DOI:10.1037/10409-008>. Penalized maximum likelihood
    estimation and Markov Chain Monte Carlo estimation are
    also provided, see Luedtke, Robitzsch and Wagner (2018) 
    <DOI:10.1037/met0000155>.
Adds support for R startup configuration via '.Renviron.d' and '.Rprofile.d' directories in addition to '.Renviron' and '.Rprofile' files.  This makes it possible to keep private / secret environment variables separate from other environment variables.  It also makes it easier to share specific startup settings by simply copying a file to a directory.
Provides utilities to create or suppress start-up messages.
An interactive document on  the topic of basic statistical analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://jarvisatharva.shinyapps.io/StatisticsPrimer/>.
Datasets for the textbook Stat2: Modeling with Regression and ANOVA (second edition). 
    The package also includes data for the first edition, Stat2: Building Models for a World of Data
    and a few functions for plotting diagnostics.
A set of tools inspired by 'Stata' to explore data.frames ('summarize',
    'tabulate', 'xtile', 'pctile', 'binscatter', elapsed quarters/month, lead/lag).
A front end for the statistical analyses involved in the tier II endocrine 
	disruptor screening program. The analyses available to this package are: 
	Rao-Scott adjusted Cochran-Armitage test for trend By Slices (RSCABS), 
	a Standard Cochran-Armitage test for trend By Slices (SCABS), 
	mixed effects Cox proportional model, Jonckheere-Terpstra step down trend test 
	Dunn test, one way ANOVA, weighted ANOVA, mixed effects ANOVA, repeated 
	measures ANOVA, and Dunnett test.
Extract statistics from articles and recompute p values.
Several tools are provided for the statistical analysis of environmental data.
Support for reading and writing files in StatDataML---an XML-based data exchange format.
Cartogram heatmaps are an alternative to choropleth maps for USA States
    and are based on work by the Washington Post graphics department in their report
    on "The states most threatened by trade". "State bins" preserve as much of the
    geographic placement of the states as possible but has the look and feel of a
    traditional heatmap. Functions are provided that allow for use of a binned,
    discrete scale, a continuous scale or manually specified colors depending on
    what is needed for the underlying data.
Create panel data consisting of independent states from 1816 to
    the present. The package includes the Gleditsch & Ward (G&W) and Correlates
    of War (COW) lists of independent states, as well as helper functions for 
    working with state panel data and standardizing other data sources to 
    create country-year/month/etc. data.
Contains statistical methods to analyze graphs, such as graph parameter estimation, model selection based on the
              GIC (Graph Information Criterion), statistical tests to discriminate two or more populations of graphs (ANOGVA
              -Analysis of Graph Variability), correlation between graphs, and clustering of graphs.
Templates, guides, and scripts for
    writing documents in 'LaTeX' and 'R markdown' to produce
    guides, slides, and reports. Special care is taken to
    illustrate use of templates and customization opportunities.
    Challenges and opportunities of 'HTML' output from 'R markdown'
    receive special attention. Includes several vignettes
    to assist new users of literate programming.
A collection of miscellaneous statistical functions for 
    probability distributions: dbern(), pbern(), qbern(), rbern() for 
    the Bernoulli distribution, and distr2name(), name2distr() for 
    distribution names; 
    probability density estimation: densityfun(); 
    most frequent value estimation: mfv(), mfv1(); 
    calculation of the Hellinger distance: hellinger(); 
    use of classical kernels: kernelfun(), kernel_properties(); 
    univariate piecewise-constant regression: picor().
Integration of two data sources referred to the same target population which share a number of variables. Some functions can also be used to impute missing values in data sets through hot deck imputation methods. Methods to perform statistical matching when dealing  with data from complex sample surveys are available too.
Offers useful functions to perform day-to-day data manipulation 
    operations, data quality checks and post modelling statistical checks.
    One can effortlessly change class of a number of variables to factor, 
    remove duplicate observations from the data, create deciles of a 
    variable, perform data quality checks for continuous (integer or numeric), 
    categorical (factor) and date variables, and compute goodness of fit 
    measures such as auc for statistical models. The functions are consistent 
    for objects of class 'data.frame' and 'data.table', which is an enhanced 
    'data.frame' implemented in the package 'data.table'.
A collection of algorithms and functions to aid statistical modeling. Includes limiting dilution analysis (aka ELDA), growth curve comparisons, mixed linear models, heteroscedastic regression, inverse-Gaussian probability calculations, Gauss quadrature and a secure convergence algorithm for nonlinear models.  Also includes a number of advanced generalized linear model functions including new Tweedie and Digamma glm families and a secure convergence algorithm.
Statnet is a collection of packages for statistical network analysis that are 
  designed to work together because they share common data representations and 'API' 
  design.  They provide an integrated set of tools for the representation, 
  visualization, analysis, and simulation of many different forms of network data.  
  This package is designed to make it easy to install and load the 
  key 'statnet' packages in a single step.  Learn more about 'statnet' 
  at <http://www.statnet.org>.  Tutorials for many packages can be found 
  at <https://github.com/statnet/Workshops/wiki>.  For an introduction to functions in this package, 
  type help(package='statnet').
Non-statistical utilities used by the software developed by the Statnet Project. They may also be of use to others.
A graphical user interface for network modeling with the 'statnet'
    software <https://github.com/statnet>.
A collection of statistical tools for social network analysis, with strong emphasis on the analysis of discrete powerlaw distributions and statistical hypothesis tests.
Statistical performance measures used in the econometric literature to evaluate conditional covariance/correlation matrix estimates (MSE, MAE, Euclidean distance, Frobenius distance, Stein distance, asymmetric loss function, eigenvalue loss function and the loss function defined in Eq. (4.6) of Engle et al. (2016) <doi:10.2139/ssrn.2814555>). Additionally, compute Eq. (3.1) and (4.2) of Li et al. (2016) <doi:10.1080/07350015.2015.1092975> to compare the factor loading matrix. The statistical performance measures implemented have been previously used in, for instance, Laurent et al. (2012) <doi:10.1002/jae.1248>,  Amendola et al. (2015) <doi:10.1002/for.2322> and  Becker et al. (2015) <doi:10.1016/j.ijforecast.2013.11.007>.
A small collection of data on graduate statistics programs from the United States.
Generates a random quotation from a data base of quotes on topics
    in statistics, data visualization and science.
A set of methods to implement Generalized Method of Moments and Maximal
    Likelihood methods for Random Utility Models. These methods are meant to
    provide inference on rank comparison data. These methods accept full,
    partial, and pairwise rankings, and provides methods to break down full or
    partial rankings into their pairwise components. Please see Generalized
    Method-of-Moments for Rank Aggregation from NIPS 2013 for a description of
    some of our methods.
Tools to help download, process and analyse the UK road collision data collected using the
  'STATS19' form. The data are provided as 'CSV' files with detailed road safety data about the
  circumstances of car crashes and other incidents on the roads resulting in 
  casualties in Great Britain from 1979, the types
  (including make and model) of vehicles involved and the consequential casualties.  The
  statistics relate only to personal casualties on public roads that are reported
  to the police, and subsequently recorded, using the 'STATS19' accident reporting form. See
  the Department for Transportation website 
  <https://data.gov.uk/dataset/cb7ae6f0-4be6-4935-9277-47e5ce24a11f/road-safety-data> for more
  information on these data.
Makes it possible through simple functions and commands to get and
    organize official danish statistics within a wide range of subjects. Visit
    the <http://statbank.dk/> and <http://api.statbank.dk/console> for further
    exploration of Statistics Denmark data.
Use this package to scrap Test Matches, One Day Internationals, Twenty-20 Internationals data of a player from ESPNCricinfo's Statsguru (<http://stats.espncricinfo.com/ci/engine/stats/index.html>) and then visualize their batting, bowling and fielding performances in the form of charts and graphs.
Provides functions and datasets to support inference with the open access book
            "An Introduction to Bayesian Thinking", available online
             <https://statswithr.github.io/book> and  online videos for the
             "Statistics with R Specialization" <https://www.coursera.org/specializations/statistics>.  
             which includes an introduction to Bayesian inference and decision
             making for one and two sample credible intervals and 
             hypothesis testing for Gaussian and Binomial data, in addition to
             frequentist inference using model-based and randomization-based methods.
             To help with understanding concepts, 'shiny' applications are used to aide 
             visualization of sampling distributions, credible intervals, hypothesis testing, 
             Lindley's and Bartlett's paradoxes. For development versions or to report issues, please visit
             <https://github.com/StatsWithR/statsr>.
Visualization functions in the applications of translational medicine (TM) and biomarker (BM) development to compare groups by statistically visualizing data and/or results of analyses, such as visualizing data by displaying in one figure different groups' histograms, boxplots, densities, scatter plots, error-bar plots, or trajectory plots, by displaying scatter plots of top principal components or dendrograms with data points colored based on group information, or visualizing volcano plots to check the results of whole genome analyses for gene differential expression.
Provides an implementation of simultaneous tolerance bounds (STB), useful for checking whether a numeric vector fits to a hypothetical null-distribution or not.
             Furthermore, there are functions for computing STB (bands, intervals) for random variates of linear mixed models fitted with package 'VCA'. All kinds of, possibly transformed 
             (studentized, standardized, Pearson-type transformed) random variates (residuals, random effects), can be assessed employing STB-methodology.
Spatio-temporal change of support (STCOS) methods are designed for statistical inference
	on geographic and/or time domains that differ from those on which the data were observed. 'stcos'
	implements a parsimonious class of Bayesian hierarchical spatio-temporal models for STCOS with
	Gaussian outcomes introduced by Bradley, Wikle, and Holan <doi:10.1002/sta4.94>.
Estimates a covariance matrix using Stein's isotonized covariance
    estimator, or a related estimator suggested by Haff.
Contains three main functions including
  stddiff.numeric(), stddiff.binary() and stddiff.category().
  These are used to calculate the standardized difference between two groups.
  It is especially used to evaluate the balance between two groups
  before and after propensity score matching.
Contains functionality for regression standardization. 
   Four general classes of models are allowed; generalized linear models,
   Conditional generalized estimating equation models, 
   Cox proportional hazards models and shared frailty gamma-Weibull models. Sjolander, A. (2016) <doi:10.1007/s10654-016-0157-3>.
Allows the creation and manipulation of C++ std::vector's in R.
Functions related to multivariate measures of independence and ICA:
 -estimate independent components by minimizing distance covariance;
 -conduct a test of mutual independence based on distance covariance; 
 -estimate independent components via infomax (a popular method but generally performs poorer than mdcovica, ProDenICA, and/or fastICA, but is useful for comparisons);
 -order indepedent components by skewness;
 -match independent components from multiple estimates;
 -other functions useful in ICA.
Steem is a blockchain-based social media platform (see <https://en.wikipedia.org/wiki/Steemit>). The Steem social activity data are saved in the Steem blockchain, the SteemDB database, the SteemSQL database, and so on. 'steemr' is an R package that downloads the Steem data from the SteemDB and SteemSQL servers, re-organizes the data in a user-friendly way, and visualizes the data for further analysis.
steepness is a package that computes steepness as a
        property of dominance hierarchies. Steepness is defined as the
        absolute slope of the straight line fitted to the normalized
        David's scores. The normalized David's scores can be obtained
        on the basis of dyadic dominance indices corrected for chance
        or by means of proportions of wins. Given an observed
        sociomatrix, it computes hierarchy's steepness and estimates
        statistical significance by means of a randomization test.
A set of functions for finding and analysing Steiner trees. It has applications in
    biological pathway network analysis. Sadeghi (2013) <doi:10.1186/1471-2105-14-144>.
Routines for computing different types of linear estimators, based on instrumental variables (IVs), including the semi-parametric Stein-like (SPS) estimator, originally introduced by Judge and Mittelhammer (2004)  <DOI:10.1198/016214504000000430>.
A package to manage and display stellar tracks and
        isochrones from Pisa low-mass database. Includes tools for
        isochrones construction and tracks interpolation.
Estimation of the parameters of a spatio-temporal model
        using the EM algorithm, estimation of the parameter standard
        errors using a spatio-temporal parametric bootstrap, spatial
        mapping.
Explore and analyse the genealogy of textual or musical traditions, from their variants, with various stemmatological methods, mainly the disagreement-based algorithms suggested by Camps and Cafiero (2015) <doi:10.1484/M.LECTIO-EB.5.102565>.
It estimates the parameters of a censored or missing data in spatio-temporal models using the SAEM algorithm (Delyon et al., 1999 <doi:10.1214/aos/1018031103>). This algorithm is a stochastic approximation of the widely used EM algorithm and an important tool for models in which the E-step does not have an analytic form. Besides the expressions obtained to estimate the parameters to the proposed model, we include the calculations for the observed information matrix using the method developed by Louis (1982) <https://www.jstor.org/stable/2345828>. To examine the performance of the fitted model, case-deletion measure are provided.
Collection of model estimation, and model plotting functions 
             related to the STEPCAM family of community assembly models. 
             STEPCAM is a STEPwise Community Assembly Model that infers 
             the relative contribution of Dispersal Assembly, Habitat Filtering 
             and Limiting Similarity from a dataset consisting of the 
             combination of trait and abundance data. See also <http://onlinelibrary.wiley.com/wol1/doi/10.1890/14-0454.1/abstract> for more information.
A method to explore the treatment-covariate interactions in survival or generalized 
	linear model (GLM) for continuous, binomial and count data arising from two or more treatment 
	arms of a clinical trial. A permutation distribution approach to inference is implemented, 
	based on permuting the covariate values within each treatment group. 
	Bonetti M, Gelber RD (2004) <DOI:10.1093/biostatistics/5.3.465>.
	Marco Bonetti, David Zahrieh, Bernard F. Cole, and Richard D. Gelber (2009) <doi:10.1002/sim.3524>.
	Ann A. Lazar, Bernard F. Cole, Marco Bonetti, and Richard D. Gelber (2010) <doi:10.1200/JCO.2009.27.9182>.
	Lazar AA,Bonetti M,Cole BF,Yip WK,Gelber RD (2016) <doi:10.1177/1740774515609106>.
 	Wai-Ki Yip,Marco Bonetti,Bernard F Cole,William Barcella,Xin Victoria Wang,Ann Lazar,and Richard D Gelber (2016) <doi:10.1177/1740774516643297>.
	Wang XV, Cole B, Bonetti M, Gelber RD (2016) < doi:10.1002/sim.6958>.
	Wai-Ki Yip (2017, ISBN:978-3-319-48846-2).
Model Selection Based on Combined Penalties. This package implements a stepwise forward variable selection algorithm based on a penalized likelihood criterion that combines the L0 with L2 or L1 norms.
L2 penalized logistic regression for both continuous and discrete predictors, with forward stagewise/forward stepwise variable selection procedure.
Allows fitting of step-functions to univariate serial data where neither the number of jumps nor their positions is known by implementing the multiscale regression estimators SMUCE (K. Frick, A. Munk and H. Sieling, 2014) <doi:10.1111/rssb.12047> and HSMUCE (F. Pein, H. Sieling and A. Munk, 2017) <doi:10.1111/rssb.12202>. In addition, confidence intervals for the change-point locations and bands for the unknown signal can be obtained.
Stepwise regression analysis for variable selection can be used to get the best candidate final regression model in univariate or multivariate regression analysis with the 'forward' and 'stepwise' steps. Procedure can use Akaike information criterion, corrected Akaike information criterion, Bayesian information criterion, Hannan and Quinn information criterion, corrected Hannan and Quinn information criterion, Schwarz criterion and significance levels as selection criteria. Multicollinearity detection in regression model are performed by checking tolerance value. Continuous variables nested within class effect and weighted stepwise regression are also considered in this package.
Software to simulate population dynamics across space and time.
Provides function to estimate multiple change points
    using marginal likelihood method. See the Manual file in data folder for
    a detailed description of all functions, and a walk through tutorial.
    For more information of the method, please see Du, Kao and Kou (2016) 
    <doi:10.1080/01621459.2015.1006365>.
A stepwise approach to identifying recombination
        breakpoints in a sequence alignment.
Collection of stepwise procedures to conduct multiple hypotheses testing. The details of the stepwise algorithm can be found in Romano and Wolf (2007) <DOI:10.1214/009053606000001622> and Hsu, Kuan, and Yen (2014) <DOI:10.1093/jjfinec/nbu014>.
Functions for the collection of 3D points and curves using a stereo camera setup.
Work with containers over the Docker API.  Rather than
    using system calls to interact with a docker client, using the
    API directly means that we can receive richer information from
    docker.  The interface in the package is automatically generated
    using the 'OpenAPI' (a.k.a., 'swagger') specification, and all
    return values are checked in order to make them type stable.
Functions implementing the procedure of entropy comparison between two data samples after the renormalization of respective probability distributions with the algorithm designed by Klimontovich (Zeitschrift fur Physik B Condensed Matter. 1987, Volume 66, Issue 1, pp 125-127) and extended by Anishchenko (Proc. SPIE 2098, Computer Simulation in Nonlinear Optics. 1994, pp.130-136).  The package also includes data preparation tools which can also be used separately for various applications.
A set of functions for computing the Standardized Temperature Index (STI).
In base R, object attributes are lost when objects are modified by
    common data operations such as subset, filter, slice, append, extract
    etc. This packages allows objects to be marked as 'sticky' and have
    attributes persisted during these operations or when inserted
    into or extracted from recursive (i.e. list- or table-like) objects.
Functions for simplified emulation of time series computer model output in model parameter space using Gaussian processes. Stilt can be used more generally for Kriging of spatio-temporal fields. There are functions to predict at new parameter settings, to test the emulator using cross-validation (which includes information on 95% confidence interval empirical coverage), and to produce contour plots over 2D slices in model parameter space.
Regression trunk model estimation proposed by Dusseldorp and Meulman (2004) <doi:10.1007/bf02295641>
             and Dusseldorp, Conversano, Van Os (2010) <doi:10.1198/jcgs.2010.06089>, integrating a regression tree
             and a multiple regression model.
A consistently well behaved method of interpolation based on piecewise rational functions using Stineman's algorithm.
The Citizens' Service Bureau of the City of St. Louis is a clearing house for 
    non-emergency service requests. This package provides functions for downloading, 
    categorizing problem requests, cleaning and subsetting CSB data, and projecting the data
    using the x and y coordinates included with CSB data releases.
Decompose a time series into seasonal, trend, and remainder
    components using an implementation of Seasonal Decomposition of Time
    Series by Loess (STL) that provides several enhancements over the STL
    method in the stats package.  These enhancements include handling missing
    values, providing higher order (quadratic) loess smoothing with automated
    parameter choices, frequency component smoothing beyond the seasonal and
    trend components, and some basic plot methods for diagnostics.
The Structural Topic Model (STM) allows researchers 
  to estimate topic models with document-level covariates. 
  The package also includes tools for model selection, visualization,
  and estimation of topic-covariate regressions. Methods developed in
  Roberts et al (2014) <doi:10.1111/ajps.12103> and 
  Roberts et al (2016) <doi:10.1080/01621459.2016.1141684>.
Generates an interactive visualization of topic correlations/
    hierarchy in a Structural Topic Model (STM) of Roberts, Stewart, and Tingley.
    The package performs a hierarchical clustering of topics which are then exported
    to a JSON object and visualized using D3.
Analyses spatio-temporal data, decomposing data in n-dimensional arrays and using the median polish technique.
Rapidly build accurate genetic prediction models for genome-wide association or whole-genome sequencing study data by smooth-threshold multivariate genetic prediction (STMGP) method. Variable selection is performed using marginal association test p-values with an optimal p-value cutoff selected by Cp-type criterion. Quantitative and binary traits are modeled respectively via linear and logistic regression models. A function that works through PLINK software (Purcell et al. 2007 <DOI:10.1086/519795>, Chang et al. 2015 <DOI:10.1186/s13742-015-0047-8>) <https://www.cog-genomics.org/plink2> is provided. Covariates can be included in regression model.
Provides an application that acts as a GUI for the 'stm' text analysis package.
This app enables interactive validation, interpretation and visualization of structural topic models from the 'stm' package by Roberts and others (2014) <doi:10.1111/ajps.12103>. It also includes helper functions for model diagnostics and extracting data from effect estimates.
Implementation of the family of generalised age-period-cohort
    stochastic mortality models. This family of models encompasses many models
    proposed in the actuarial and demographic literature including the 
    Lee-Carter (1992) <doi:10.2307/2290201> and
    the Cairns-Blake-Dowd (2006) <doi:10.1111/j.1539-6975.2006.00195.x> models. 
    It includes functions for fitting mortality models, analysing their 
    goodness-of-fit and performing mortality projections and simulations.
Plots a QQ-Norm Plot with several Gaussian simulations.
Allow to identify motifs in spatial-time series. A motif is a previously unknown subsequence of a (spatial) time series with relevant number of occurrences. For this purpose, the Combined Series Approach (CSA) is used.
Fit a spatial-temporal occupancy models using
    a probit formulation instead of a traditional logit
    model.
This is an R package accompanying the paper "Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles" by Sameer S Bajikar, Christiane Fuchs, Andreas Roller, Fabian J Theis and Kevin A Janes (PNAS 2014, 111(5), E626-635). In this paper, we measure expression profiles from small heterogeneous populations of cells, where each cell is assumed to be from a mixture of lognormal distributions. We perform maximum likelihood estimation in order to infer the mixture ratio and the parameters of these lognormal distributions from the cumulated expression measurements.
Efficient algorithms for fully Bayesian estimation of stochastic volatility (SV) models via Markov chain Monte Carlo (MCMC) methods. Methodological details are given in Kastner and Frühwirth-Schnatter (2014) <doi:10.1016/j.csda.2013.01.002>; the most common use cases are described in Kastner (2016) <doi:10.18637/jss.v069.i05>. Also incorporates SV with leverage.
With this package, users can obtain the real-time price and volume information of stocks in China market, as well as the information of the stock index. This package adopted the API from Sina Finance (http://finance.sina.com.cn/).
Provides a mixture model for clustering individuals (or sampling groups) into stocks based on their genetic profile. Here, sampling groups are individuals that are sure to come from the same stock (e.g. breeding adults or larvae). The mixture (log-)likelihood is maximised using the EM-algorithm after find good starting values via a K-means clustering of the genetic data. Details can be found in Foster, Feutry, Grewe, Berry, Hui, Davies (in press) Reliably Discriminating Stock Structure with Genetic Markers: Mixture Models with Robust and Fast Computation. Molecular Ecology Resources.
Functions for analyzing stocks or other investments. Main features are loading and aligning historical data for ticker symbols, calculating performance metrics for individual funds or portfolios (e.g. annualized growth, maximum drawdown, Sharpe/Sortino ratio), and creating graphs. C++ code is used to improve processing speed where possible.
Given a list of substance compositions, a list of
        substances involved in a process, and a list of constraints in
        addition to mass conservation of elementary constituents, the
        package contains functions to build the substance composition
        matrix, to analyze the uniqueness of process stoichiometry, and
        to calculate stoichiometric coefficients if process
        stoichiometry is unique.  (See Reichert, P. and Schuwirth, N.,
        A generic framework for deriving process stoichiometry in
        enviromental models, Environmental Modelling and Software 25,
        1241-1251, 2010 for more details.)
A variable selection procedure for low to moderate size linear regressions models. This method repeatedly splits the data into two sets, one for estimation and one for validation, to obtain an empirically optimized threshold which is then used to screen for variables to include in the final model.
Provides multiple sources of stopwords, for use in text analysis and natural language processing.
Storm is a distributed real-time computation system. Similar to how
 Hadoop provides a set of general primitives for doing batch processing, Storm
 provides a set of general primitives for doing real-time computation.

    Storm includes a "Multi-Language" (or "Multilang") Protocol to allow
 implementation of Bolts and Spouts in languages other than Java.  This R
 extension provides implementations of utility functions to allow an application
 developer to focus on application-specific functionality rather than Storm/R
 communications plumbing.
Allows users to input tracking data for a hurricane
    or other tropical storm, along with a data frame of grid points at which
    to model wind speeds. Functions in this package will then calculate wind
    speeds at each point based on wind model equations. This modeling framework
    is currently set up to model winds for North American locations with 
    Atlantic basin storms. This work was supported 
    in part by grants from the National Institute of Environmental Health 
    Sciences (R00ES022631), the National Science Foundation (1331399), and the 
    Department of Energy (DE-FG02-08ER64644).
Creates and manages simple key-value stores.  These can
    use a variety of approaches for storing the data.  This package
    implements the base methods and support for file system, in-memory
    and DBI-based database stores.
An implementation of 1) the hypergeometric test for over-representation of literary themes in a storyset (a list of stories) relative to a background list of stories, and 2) a recommendation system that takes a user-selected story as input and returns a ranked list of similar stories on the basis of shared themes. The package is currently implemented for the episodes of the Star Trek television franchise series The Original Series (TOS), The Animated Series (TAS), The Next Generation (TNG), and Voyager (VOY).
A toolkit for Reliability Availability and Maintainability (RAM) modeling of industrial process systems.
Combining Predictive Analytics and Experimental Design to Optimize Results. To be utilized to select a test data calibrated training population in high dimensional prediction problems and assumes that the explanatory variables are observed for all of the individuals. Once a "good" training set is identified, the response variable can be obtained only for this set to build a model for predicting the response in the test set. The algorithms in the package can be tweaked to solve some other subset selection problems.
Tools for transport planning with an emphasis on spatial transport
    data and non-motorized modes. Enables common transport planning tasks including:
    downloading and cleaning transport datasets; creating geographic "desire lines"
    from origin-destination (OD) data; route assignment, locally and via
    interfaces to routing services such as <http://cyclestreets.net/>;
    calculation of route segment attributes such as bearing and aggregate flow;
    and 'travel watershed' analysis.
    See Lovelace and Ellison (2018) <doi:10.32614/RJ-2018-053>.
Utilities to estimate parameters of the models with survival functions 
             induced by stochastic covariates. Miscellaneous functions for data preparation 
             and simulation are also provided. For more information, see: 
             (i)"Stochastic model for analysis of longitudinal data on aging and mortality" 
             by Yashin A. et al. (2007), 
             Mathematical Biosciences, 208(2), 538-551, <DOI:10.1016/j.mbs.2006.11.006>;
             (ii) "Health decline, aging and mortality: how are they related?" 
             by Yashin A. et al. (2007), 
             Biogerontology 8(3), 291(302), <DOI:10.1007/s10522-006-9073-3>.
Many of the models encountered in applications of point process methods to the study of spatio-temporal phenomena are covered in 'stpp'. This package provides statistical tools for analyzing the global and local second-order properties of spatio-temporal point processes, including estimators of the space-time inhomogeneous K-function and pair correlation function. It also includes tools to get static and dynamic display of spatio-temporal point patterns. See Gabriel et al (2013) <doi:10.18637/jss.v053.i02>.
Methods for decomposing seasonal data: STR (a Seasonal-Trend decomposition procedure based on Regression) and Robust STR. In some ways, STR is similar to Ridge Regression and Robust STR can be related to LASSO. They allow for multiple seasonal components, multiple linear covariates with constant, flexible and seasonal influence. Seasonal patterns (for both seasonal components and seasonal covariates) can be fractional and flexible over time; moreover they can be either strictly periodic or have a more complex topology. The methods provide confidence intervals for the estimated components. The methods can be used for forecasting.
Searches for short tandem repeats (STR) in a specified region of any genome. This analysis can be expanded such that several regions (chromosomes) are studied. These STRs can be grouped into hotspot as well as flanking regions of user specified width. Hotspots are defined by the double strand break maps from Pratto et al. (2014) <doi:10.1126/science.1256442>. Moreover, the user can also search for a specified motif in a DNAStringSet-object, or a fasta-file, or a specified region of any genome. For an application of STR detections please see Heissl et al. (2018) <doi:10.1101/431841>.
Regression-based ranking of pathogen strains with respect to their contributions to natural epidemics, using demographic and genetic data sampled in the curse of the epidemics. This package also includes the GMCPIC test.
Analysis of biological data from stranded marine mammals: mortality-at-age (Heligman, L. and Pollard, J.H. 1980 <doi:10.1017/S0020268100040257>), life tables, Leslie matrices, etc.
Functions for the stratigraphic analysis of phylogenetic trees.
Create data frames with virtual groups that can be used with 
    'dplyr' to efficiently compute resampled statistics, generate the data for
    hypothetical outcome plots, and fit multiple models on resampled variations
    of the original data.
An implementation of the stratification index proposed by Zhou (2012) <DOI:10.1177/0081175012452207>.
  The package provides two functions, srank, which returns stratum-specific
  information, including population share and average percentile rank; and strat,
  which returns the stratification index and its approximate standard error.
  When a grouping factor is specified, strat also provides a detailed decomposition
  of the overall stratification into between-group and within-group components.
A toolkit for analyzing stratified population genetic data. 
  Functions are provided for summarizing and checking loci 
  (haploid, diploid, and polyploid), single stranded DNA sequences,
  calculating most population subdivision metrics, and running external programs 
  such as structure and fastsimcoal. The package is further described in 
  Archer et al (2016) <doi:10.1111/1755-0998.12559>.
An Optimization Algorithm Applied to
    Stratification Problem.This function aims
    at constructing optimal strata with an optimization algorithm
    based on a global optimisation technique called Biased
    Random Key Genetic Algorithms.
Identifies individuals in a social network who should be the intervention
    subjects for a network intervention in which you have a group of targets, a
    group of avoiders, and a group that is neither.
Users can build and test customized quantitative trading strategies. Some quantitative trading strategies are already implemented, e.g. various moving-average filters with trend following approaches.
    The implemented class called "Strategy" allows users to access several methods to analyze performance figures, plots and backtest the strategies.
    Furthermore, custom strategies can be added, a generic template is available. The custom strategies require a certain input and output so they can be called from the Strategy-constructor.
Variants of the strategy frequency estimation method by Dal Bo & Frechette (2011) <doi:10.1257/aer.101.1.411>, including the adaptation to estimate choice parameters of behavior strategies by Breitmoser (2015) <doi:10.1257/aer.20130675>, and the extension in the spirit of latent-class regression by Dvorak & Fehrler (2018) <doi:10.2139/ssrn.2986445>.
Univariate stratification of survey populations with a generalization of the 
  Lavallee-Hidiroglou method of stratum construction. The generalized method takes into account 
  a discrepancy between the stratification variable and the survey variable. The determination 
  of the optimal boundaries also incorporate, if desired, an anticipated non-response, a take-all 
  stratum for large units, a take-none stratum for small units, and a certainty stratum to ensure 
  that some specific units are in the sample. The well known cumulative root frequency rule of 
  Dalenius and Hodges and the geometric rule of Gunning and Horgan are also implemented.
Stratified covariate balancing through naturally occurring strata to adjust for confounding and interaction effects. Contains 4 primary functions which perform stratification, sensitivity analysis and return adjusted odds along with naturally occurring strata.
Random Forest-like tree ensemble that works with groups of predictor variables. When building a tree, a number of variables is taken randomly from each group separately, thus ensuring that it considers variables from each group for the splits. Useful when rows contain information about different things (e.g. user information and product information) and it's not sensible to make a prediction with information from only one group of variables, or when there are far more variables from one group than the other and it's desired to have groups appear evenly on trees.
    Trees are grown using the C5.0 algorithm rather than the usual CART algorithm. Supports parallelization (multithreaded), missing values in predictors, and categorical variables (without doing One-Hot encoding in the processing). Can also be used to create a regular (non-stratified) Random Forest-like model, but made up of C5.0 trees and with some additional control options.
    As it's built with C5.0 trees, it works only for classification (not for regression).
This implements the stratification of univariate populations under stratified sampling designs using the method of Khan et al. (2002) <doi:10.1177/0008068320020518>, Khan et al. (2008) (<http://www.statcan.gc.ca/pub/12-001-x/2008002/article/10761-eng.pdf>) and Khan et al. (2015) <doi:10.1080/02664763.2015.1018674>. It determines the Optimum Strata Boundaries (OSB) and Optimum Sample Sizes (OSS) for the study variable, y, using the best-fit frequency distribution of a survey variable (if data is available) or a hypothetical distribution (if data is not available). The method formulates the problem of determining the OSB as mathematical programming problem which is solved by using a dynamic programming technique. If a dataset of the population is available to the surveyor, the method estimates its best-fit distribution and determines the OSB and OSS under Neyman allocation directly. When the dataset is not available, stratification is made based on the assumption that the values of the study variable, y, are available as hypothetical realizations of proxy values of y from recent surveys. Thus, it requires certain distributional assumptions about the study variable. At present, it handles stratification for the populations where the study variable follows a continuous distribution, namely, Pareto, Triangular, Right-triangular, Weibull, Gamma, Exponential, Uniform, Normal, Log-normal and Cauchy distributions.
Includes bases for litholog generation: graphical functions
    based on R base graphics, interval management functions and svg importation 
    functions among others. Also include stereographic projection functions, 
    and other functions made to deal with large datasets while keeping options
    to get into the details of the data.
    When using for publication please cite Wouters, S., Da Silva, A.C. Crucifix, 
    M., Sinnesael, M., Zivanovic, M., Boulvain, F., Devleeschouwer, X., 2019,
    Litholog generation with the StratigrapheR package and signal decomposition 
    for cyclostratigraphic purposes. Geophysical Research Abstracts Vol. 21, 
    EGU2019-5520, 2019, EGU General Assembly 2019. 
    <http://hdl.handle.net/2268/234402>
    The palaeomagnetism functions are based on:
    Tauxe, L., 2010. Essentials of Paleomagnetism. University of California 
    Press. <https://earthref.org/MagIC/books/Tauxe/Essentials/>;
    Allmendinger, R. W., Cardozo, N. C., and Fisher, D., 2013, Structural 
    Geology Algorithms: Vectors & Tensors: Cambridge, England, Cambridge
    University Press, 289 pp.;
    Cardozo, N., and Allmendinger, R. W., 2013, Spherical projections
    with OSXStereonet: Computers & Geosciences, v. 51, no. 0, p. 193 - 205,
    <doi: 10.1016/j.cageo.2012.07.021>.
Provides functions to estimate a strategic selection estimator. A strategic selection estimator is an agent error model in which the two random components are not assumed to be orthogonal. In addition this package provides generic functions to print and plot objects of its class as well as the necessary functions to create tables for LaTeX. There is also a function to create dyadic data sets.
For a single, known pathogen phylogeny, provides functions for enumeration of the set of compatible epidemic transmission trees, and for uniform sampling from that set. Optional arguments allow for incomplete sampling with a known number of missing individuals, multiple sampling, and known infection time limits. Always assumed are a complete transmission bottleneck and no superinfection or reinfection. See Hall and Colijn (2019) <doi:10.1093/molbev/msz058> for methodology.
An Optimization Algorithm Applied
  to stratification Problem.
  It is aims to delimit the population strata
  and defining the allocation of sample,considering
  the following objective: minimize the sample size given
  a fixed precision level. Exhaustive enumeration method
  is applied in small problems, while in problems with greater
  complexity the algorithm is based on metaheuristic Variable
  Neighborhood Decomposition Search with Path Relink.
A framework for data stream modeling and associated data mining tasks such as clustering and classification. The development of this package was supported in part by NSF IIS-0948893 and NIH R21HG005912.
Numerically solve and plot solutions of a parametric ordinary
        differential equations model of growth, death, and respiration of
        macroinvertebrate and algae taxa dependent on pre-defined environmental
        factors. The model (version 1.0) is introduced in Schuwirth, N. and
        Reichert, P., (2013) <DOI:10.1890/12-0591.1>. This package includes
        model extensions and the core functions introduced and used in
        Kattwinkel, M. et al. (2016) <DOI:10.1021/acs.est.5b04068>,
        Mondy, C. P., and Schuwirth, N. (2017) <DOI:10.1002/eap.1530>,
        and Paillex, A. et al. (2017) <DOI:10.1111/fwb.12927>.
Implementation of analytical models for estimating streamflow 
    depletion due to groundwater pumping, and other related tools. Functions
    are broadly split into two groups: (1) analytical streamflow depletion
    models, which estimate streamflow depletion for a single stream reach
    resulting from groundwater pumping; and (2) depletion apportionment 
    equations, which distribute estimated streamflow depletion among multiple
    stream reaches within a stream network. See Zipper et al. (2018) <doi:10.1029/2018WR022707>
    for more information on depletion apportionment equations and Zipper et
    al. (2019) <doi:10.31223/osf.io/uqbd7> for more information on analytical
    depletion functions, which combine analytical models and depletion apportionment
    equations.
I provide functions to calculate Gross Primary Productivity, Net Ecosystem Production, and Ecosystem Respiration from single station diurnal Oxygen curves.
Interface for data stream clustering algorithms implemented in the MOA (Massive Online Analysis) framework (Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer (2010). MOA: Massive Online Analysis, Journal of Machine Learning Research 11: 1601-1604).
Functions to access Twitter's filter, sample, and user streams, and to
    parse the output into data frames.
Forms queries to submit to the Cleveland Federal Reserve Bank web
    site's financial stress index data site.  Provides query functions for both
    the composite stress index and the components data. By default the download
    includes daily time series data starting September 25, 1991.  The functions
    return a class of either type easing or cfsi which contain a list of items
    related to the query and its graphical presentation.  The list includes the
    time series data as an xts object.  The package provides four lattice time
    series plots to render the time series data in a manner similar to the
    bank's own presentation.
Reliability of (normal) stress-strength models and for building two-sided or one-sided confidence intervals according to different approximate procedures.
There are some things that I wish were easier with the 'stringr' 
    or 'stringi' packages. The foremost of these is the extraction of numbers 
    from strings. 'stringr' and 'stringi' make you figure out the regular 
    expression for yourself; 'strex' takes care of this for you. There are many 
    other handy functionalities in 'strex'. Contributions to this package are 
    encouraged: it is intended as a miscellany of string manipulation functions 
    that cannot be found in 'stringi' or 'stringr'.
The strided iterator adapts multidimensional buffers to work with 
  the C++ standard library and range-based for-loops. Given a pointer or iterator
  into a multidimensional data buffer, one can generate an iterator range using
  make_strided to construct strided versions of the standard library's begin and
  end. For constructing range-based for-loops, a strided_range class is provided.
  These help authors to avoid integer-based indexing, which in some cases can impede
  algorithm performance and introduce indexing errors. This library exists
  primarily to expose the header file to other R projects.
Takes a list of character strings and forms an adjacency matrix for
    the times the specified characters appear together in the strings provided. For
    use in social network analysis and data wrangling. Simple package, comprised of
    three functions.
Base R already ships with string handling capabilities 'out-
    of-the-box' but lacks streamlined function names and workflow. The
    'stringi' ('stringr') package on the other hand has well named functions,
    extensive Unicode support and allows for a streamlined workflow. On the other
    hand it adds dependencies and regular expression interpretation between base R
    functions and 'stringi' functions might differ. This packages aims at providing
    a solution to the use case of unwanted dependencies on the one hand but the need
    for streamlined text processing on the other. The packages' functions are solely
    based on wrapping base R functions into 'stringr'/'stringi' like function names.
    Along the way it adds one or two extra functions and last but not least provides
    all functions as generics, therefore allowing for adding methods for other text
    structures besides plain character vectors.
Implements an approximate string matching version of R's native
    'match' function. Can calculate various string distances based on edits
    (Damerau-Levenshtein, Hamming, Levenshtein, optimal sting alignment), qgrams (q-
    gram, cosine, jaccard distance) or heuristic metrics (Jaro, Jaro-Winkler). An
    implementation of soundex is provided as well. Distances can be computed between
    character vectors while taking proper care of encoding or between integer
    vectors representing generic sequences. This package is built for speed and
    runs in parallel by using 'openMP'. An API for C or C++ is exposed as well.
Pass named and unnamed character vectors into specified positions
    in strings. This represents an attempt to replicate some of python's string
    formatting.
Fast, correct, consistent, portable,
    as well as convenient character string/text processing in every locale
    and any native encoding. Owing to the use of the 'ICU'
    (International Components for Unicode) library,
    the package provides 'R' users with platform-independent functions
    known to 'Java', 'Perl', 'Python', 'PHP', and 'Ruby' programmers. Available
    features include: pattern searching (e.g., with 'Java'-like regular
    expressions or the 'Unicode' collation algorithm), random string generation,
    case mapping, string transliteration, concatenation,
    Unicode normalization, date-time formatting and parsing, and many more.
A consistent, simple and easy to use set of
    wrappers around the fantastic 'stringi' package. All function and
    argument names (and positions) are consistent, all functions deal with
    "NA"'s and zero length vectors in the same way, and the output from
    one function is easy to feed into the input of another.
The strip function deletes components of R model outputs that are useless for specific purposes, such as predict[ing], print[ing], summary[izing], etc.
For making Trellis-type conditioning plots without strip labels.
    This is useful for displaying the structure of results from factorial designs
    and other studies when many conditioning variables would clutter the display
    with layers of redundant strip labels. Settings of the variables are encoded by
    layout and spacing in the trellis array and decoded by a separate legend. The
    functionality is implemented by a single S3 generic strucplot() function that
    is a wrapper for the Lattice package's xyplot() function. This allows access to
    all Lattice graphics capabilities in the usual way.
Extracts plain text from RTF (Rich Text Format) file.
Loading, identifying, aggregating, manipulating, and analysing short tandem repeat regions of massively parallel sequencing data in forensic genetics. 'STRMPS' can work with the package 'STRaitRazoR' (an R interface to the 'STRaitRazor' commandline tool) for added speed. 'STRaitRazoR' only works on linux and can found at <https://github.com/svilsen/STRaitRazoR>. The analyses and framework implemented in this package relies on the papers of Vilsen et al. (2017) <doi:10.1016/j.fsigen.2017.01.017> and Vilsen et al. (2018) <doi:10.1016/j.fsigen.2018.04.003>. Lastly, note that the parallelisation in the package relies on mclapply() and, thus, speed-ups will only be seen on UNIX based systems.
R Codes and Datasets for Stroup, W. W. (2012). Generalized Linear Mixed Models: Modern Concepts, Methods and Applications, CRC Press.
Testing, monitoring and dating structural changes in (linear)
             regression models. strucchange features tests/methods from
	     the generalized fluctuation test framework as well as from
	     the F test (Chow test) framework. This includes methods to
	     fit, plot and test fluctuation processes (e.g., CUSUM, MOSUM,
	     recursive/moving estimates) and F statistics, respectively.
             It is possible to monitor incoming data online using
             fluctuation processes.
             Finally, the breakpoints in regression models with structural
             changes can be estimated together with confidence intervals.
             Emphasis is always given to methods for visualizing the data.
Perform more powerful false discovery control (FDR) for microbiome data, taking into account the prior phylogenetic relationship among bacteria species.  As a general methodology, it is applicable to any type of (genomic) data with prior structure information.
Tree-structured modelling of categorical predictors or measurement
    units.
Performs multiple testing corrections that take specific structure of hypotheses into account.
Implements a broad class of latent variable and structural equation models for general pedigree data.
An open source platform for validation and process control.
    Tools to analyze data from internal validation of forensic short tandem
    repeat (STR) kits are provided. The tools are developed to provide
    the necessary data to conform with guidelines for internal validation
    issued by the European Network of Forensic Science Institutes (ENFSI)
    DNA Working Group, and the Scientific Working Group on DNA Analysis Methods
    (SWGDAM). A front-end graphical user interface is provided.
    More information about each function can be found in the
    respective help documentation.
Fit the basic structural time series model by maximum likelihood.
This package defines an S4 class for structural time series models 
  and provides some basic methods to work with it.
Construct subtests from a pool of items by using ant-colony-optimization, genetic algorithms, brute force, or random sampling.
  Schultze (2017) <doi:10.17169/refubium-622>.
Create stubs of functions for use while testing.
Download, navigate and analyse the Student-Life dataset. 
    The Student-Life dataset contains passive and automatic sensing data 
    from the phones of a class of 48 Dartmouth college students. 
    It was collected over a 10 week term. Additionally, the dataset contains ecological 
    momentary assessment results along with pre-study and post-study mental  
    health surveys. The intended use is to assess 
    mental health, academic performance and behavioral trends. 
    The raw dataset and additional information is 
    available at <https://studentlife.cs.dartmouth.edu/>.
Integrated environmental modelling requires coupling sub-models at 
    different spatial and temporal scales, thus accounting for change of support 
    procedures (aggregation and disaggregation). We contribute to state-of-the-art 
    open source tools that support uncertainty propagation analysis in temporal and 
    spatio-temporal domains. We implement the tool for uncertainty propagation in 
    environmental modelling, with examples in the urban water domain. 
    The functionalities of the class setup and the methods and functions MC.setup, 
    MC.sim, MC.analysis, MC.analysis_generic and Agg.t are contained, which are used for setting up, 
    running and analysing Monte Carlo uncertainty propagation simulations, and for 
    spatio-temporal aggregation. We also implement functionalities to model and predict 
    variables that vary in space and time. stUPscales takes uncertainty characterisation 
    and propagation a step further by including temporal and spatio-temporal auto- and
    cross-correlation, resulting in more realistic (spatio-)temporal series of environmental 
    variables. Due to its modularity, the package allows the implementation of additional 
    methods and functions for spatio-temporal disaggregation of model inputs and outputs, 
    when linking models across multiple space-time scales.
Implementations of the Single Transferable Vote counting 
    system. By default, it uses the Cambridge method for surplus allocation
    and Droop method for quota calculation.  Fractional surplus allocation
    and the Hare quota are available as options.
Estimation of
 multivariate Student's t dynamic regression models for a given degrees of freedom and lag length. Users can also specify the trends and dummies of any kind in matrix form.
Pretty-prints R code without changing the user's
    formatting intent.
Estimates distinctiveness in speakers' (authors') style. Fits models that can be used for predicting speakers of new texts. Methods developed in Spirling et al (2018) <doi:10.2139/ssrn.3235506> (working paper).
Supervised and unsupervised multivariate methods, supplemented by GUI and some visualizations, to perform various analyses in the field of computational stylistics, authorship attribution, etc. For further reference, see Eder et al. (2016), <https://journal.r-project.org/archive/2016/RJ-2016-007/index.html> You are also encouraged to visit the Computational Stylistics Group's website <https://computationalstylistics.github.io/>, where a reasonable amount of information about the package and related projects are provided.
Calculate empirical subcopula and dependence measures from a given bivariate sample, and Bernstein copula approximations.
The three functions in the package compute the maximum likelihood estimates of the informants' competence scores, tests for two answer keys with known groups, and finds "best" split of the informants into sub-culture groups.
A test for the existence of a subgroup with enhanced treatment effect. And, a sample size calculation procedure for the subgroup detection test.
Produces various measures of expected treatment effect heterogeneity under an assumption of homogeneity across subgroups. Graphical presentations are created to compare these expected differences with the observed differences.
Developed to assist in discovering interesting subgroups in high-dimensional data. The PRIM implementation is based on the 1998 paper "Bump hunting in high-dimensional data" by Jerome H. Friedman and Nicholas I. Fisher. <doi:10.1023/A:1008894516817> PRIM involves finding a set of "rules" which combined imply unusually large (or small) values of some other target variable. Specifically one tries to find a set of sub regions in which the target variable is substantially larger than overall mean. The objective of bump hunting in general is to find regions in the input (attribute/feature) space with relatively high (low) values for the target variable. The regions are described by simple rules of the type if: condition-1 and ... and condition-n then: estimated target value. Given the data (or a subset of the data), the goal is to produce a box B within which the target mean is as large as possible. There are many problems where finding such regions is of considerable practical interest.  Often these are problems where a decision maker can in a sense choose or select the values of the input variables so as to optimize the value of the target variable. In bump hunting it is customary to follow a so-called covering strategy. This means that the same box construction (rule induction) algorithm is applied sequentially to subsets of the data.
Function Wrapper contains four algorithms for developing threshold-based multivariate (prognostic/predictive) biomarker signatures via bootstrapping and aggregating of thresholds from trees, Monte-Carlo variations of the Adaptive Indexing method and Patient Rule Induction Method. Variable selection is automatically built-in to these algorithms. Final signatures are returned with interaction plots for predictive signatures. Cross-validation performance evaluation and testing dataset results are also output.
Provides functions for obtaining a variety of 
    graphical displays that may be useful in the subgroup analysis setting. 
    An example with a prostate cancer dataset is provided.
    The graphical techniques considered include level plots, mosaic plots,
    contour plots, bar charts, Venn diagrams, tree plots, forest plots, 
    Galbraith plots, L'Abbé plots, the subpopulation treatment effect pattern 
    plot, alluvial plots, circle plots and UpSet plots.
Classical methods for combining summary data from genome-wide
    association studies (GWAS) only use marginal genetic effects and power can
    be compromised in the presence of heterogeneity. 'subgxe' is a R package
    that implements p-value assisted subset testing for association (pASTA),
    a method developed by Yu et al. (2019) <doi:10.1159/000496867>. pASTA
    generalizes association analysis based on subsets by incorporating
    gene-environment interactions into the testing procedure.
Creates probability maps of incident and enlarging lesion voxels
    from a baseline and followup magnetic resonance imaging study in 
    patients with multiple sclerosis.
Effect modification occurs if a treatment effect is larger or more stable in certain subgroups defined by observed covariates.  The submax or subgroup-maximum method of Lee et al. (2017) <arXiv:1702.00525> does an overall test and separate tests in subgroups, correcting for multiple testing using the joint distribution.
Complementary indexes calculation to the Outlying Mean Index analysis to explore niche shift of a community and biological constraint within an Euclidean space, with graphical displays.
Routines for identifying metabolic subpathways mediated by microRNAs (miRNAs) through topologically locating miRNAs and genes within reconstructed Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathway graphs embedded by miRNAs. (1) This package can obtain the reconstructed KEGG metabolic pathway graphs with genes and miRNAs as nodes, through converting KEGG metabolic pathways to graphs with genes as nodes and compounds as edges, and then integrating miRNA-target interactions verified by low-throughput experiments from four databases (TarBase, miRecords, mirTarBase and miR2Disease) into converted pathway graphs. (2) This package can locate metabolic subpathways mediated by miRNAs by topologically analyzing the "lenient distance" of miRNAs and genes within reconstructed KEGG metabolic pathway graphs.(3) This package can identify significantly enriched miRNA-mediated metabolic subpathways based on located subpathways by hypergenomic test. (4) This package can support six species for metabolic subpathway identification, such as caenorhabditis elegans, drosophila melanogaster, danio rerio, homo sapiens, mus musculus and rattus norvegicus, and user only need to update interested organism-specific environment variables.
Identify dysfunctional subpathways competitively regulated by lncRNAs through integrating lncRNA-mRNA expression profile and pathway topologies.
The subplex algorithm for unconstrained optimization, developed by Tom Rowan <http://www.netlib.org/opt/subplex.tgz>.
Create and handle multiple sub-processes in R, exchange
  data over standard input and output streams, control their life cycle.
Estimation of copula using ranks and subsampling. The main feature of this method is that simulation studies show a low sensitivity to dimension, on realistic cases.
This subsample winner algorithm (SWA) for regression with a large-p data (X, Y) selects the important variables (or features) among the p features X in explaining the response Y.  The SWA first uses a base procedure,  here a linear regression, on each of subsamples randomly drawn from the p variables, and then computes the scores of all features, i.e., the p variables,  according to the performance of these features collected in each of the subsample analyses. It then obtains the 'semifinalist' of the features based on the resulting scores and determines the 'finalists', i.e., the important features, from the 'semifinalist'.  Fan, Sun and Qiao (2017) <http://sr2c.case.edu/swa-reg/>.
Functions for computing subscores for a test using different
    methods in both classical test theory (CTT) and item response theory (IRT). This
    package enables three sets of subscoring methods within the framework of CTT
    and IRT: Wainer's augmentation method, Haberman's three subscoring methods, and
    Yen's objective performance index (OPI). The package also includes the function
    to compute Proportional Reduction of Mean Squared Errors (PRMSEs) in Haberman's
    methods which are used to examine whether test subscores are of added value.
Identifying outcome relevant subgroups has now become as simple as possible! The formerly lengthy and tedious search for the needle in a haystack will be replaced by a single, comprehensive and coherent presentation.
 The central result of a subgroup screening is a diagram in which each single dot stands for a subgroup.
 The diagram may show thousands of them. The position of the dot in the diagram is determined by the
 sample size of the subgroup and the statistical measure of the treatment effect in that subgroup. The
 sample size is shown on the horizontal axis while the treatment effect is displayed on the vertical axis. Furthermore,
 the diagram shows the line of no effect and the overall study results. For small subgroups, which
 are found on the left side of the plot, larger random deviations from the mean study effect are expected,
 while for larger subgroups only small deviations from the study mean can be expected to be chance findings.
 So for a study with no conspicuous subgroup effects, the dots in the figure are expected to form a
 kind of funnel. Any deviations from this funnel shape hint to conspicuous subgroups.
A collection of functions which (i) assess the quality of variable subsets as surrogates for a full data set, in either an exploratory data analysis or in the context of a multivariate linear model, and (ii) search for subsets which are optimal under various criteria.
An interface to 'OpenSubspace', an open source framework for
  evaluation and exploration of subspace clustering algorithms in WEKA 
  (see <http://dme.rwth-aachen.de/de/opensubspace> for more
  information).  Also performs visualization.
Naive and adjusted treatment effect estimation for subgroups. Model 
  averaging (Bornkamp et.al, 2016 <doi:10.1002/pst.1796>) and bagging (Rosenkranz, 2016 <doi:10.1002/bimj.201500147>) are proposed to address the problem of selection bias in
  treatment effect estimates for subgroups. The package can be used for all 
  commonly encountered type of outcomes in clinical trials (continuous, binary, 
  survival, count). Additional functions are provided to build the subgroup 
  variables to be used and to plot the results using forest plots.
Contains functions for choosing subgroup specific optimal doses in a phase I dose finding clinical trial allowing for subgroup combination and simulating a clinical trial under the subgroup specific time to event continual reassessment method.
subtype performs a biclustering procedure on a input
        dataset and assess whether resulting clusters are promising
        subtypes. Note that the R-package rsmooth should be installed
        before implementing subtype. rsmooth can be downloaded from
        http://www.meb.ki.se/~yudpaw.
Substitution matrices are important parameters in protein alignment algorithms. These matrices represent the likelihood that an amino acid will be substituted for another during mutation. This tool allows users to apply predefined and custom matrices and then explore the resulting alignments with interactive visualizations.  'SubVis' requires the availability of a web browser.
Identify sudden gains based on the three criteria outlined by Tang and DeRubeis (1999) <doi:10.1037/0022-006X.67.6.894> to a selection of repeated measures. Sudden losses, defined as the opposite of sudden gains can also be identified. Two different datasets can be created, one including all sudden gains/losses and one including one selected sudden gain/loss for each case. It can extract scores around sudden gains/losses. It can plot the average change around sudden gains/losses and trajectories of individual cases.
Generates, plays, and solves Sudoku puzzles.  The GUI
        playSudoku() needs package "tkrplot" if you are not on Windows.
Tools for making, retrieving, displaying and solving sudoku games.
    This package is an alternative to the earlier sudoku-solver package,
    'sudoku'.  The present package uses a slightly different algorithm, has a
    simpler coding and presents a few more sugar tools, such as plot and print
    methods.  Solved sudoku games are of some interest in Experimental Design
    as examples of Latin Square designs with additional balance constraints.
This is a package for the subsampling method of robust
        estimation of linear regression models
Create a hexagon tilegram from spatial polygons. Each polygon is
    represented by a hexagon tile, placed as close to it's original centroid
    as possible, with a focus on maintaining spatial relationship to a focal
    point. Developed to aid visualisation and analysis of spatial distributions
    across Australia, which can be challenging due to the concentration of the
    population on the coast and wide open interior.
Provides 'ggplot2' graphics for analysing time
    series data. It aims to fit into the 'tidyverse' and grammar of
    graphics framework for handling temporal data.
An adaptation of classical region/gene-based association analysis
    techniques to the use of summary statistics (P values and effect sizes)
    and correlations between genetic variants as input. It is a tool to perform
    the most popular and efficient gene-based tests using the results of genome-wide
    association (meta-)analyses without having the original genotypes and
    phenotypes at hand.
Functions to speed up the exploratory analysis of simple
    datasets using 'dplyr' and 'ggplot2'. Functions are provided to do the 
    common tasks of calculating confidence intervals and visualising the 
    results.
Data frame summaries, cross-tabulations,
  weight-enabled frequency tables and common univariate
  statistics in concise tables available in a variety of
  formats (plain ASCII, Markdown and HTML). A good 
  point-of-entry for exploring data, both for experienced
  and new R users.
Provides methods for estimating, projecting, and plotting spatio-temporal 
  under-five mortality rates, described in Mercer et al. (2015) <doi:10.1214/15-AOAS872>
  and Li et al. (2019) <doi:10.1371/journal.pone.0210645>.
Make interactive 'd3.js' sequence sunburst diagrams in R with the
    convenience and infrastructure of an 'htmlwidget'.
Get sun position, sunlight phases (times for sunrise, sunset, dusk, etc.),
        moon position and lunar phase for the given location and time. Most calculations are based on the 
        formulas given in Astronomy Answers articles about position of the sun and the planets : 
        <https://www.aa.quae.nl/en/reken/zonpositie.html>.
Survival analysis for unbalanced clusters using Archimedean copulas (Prenen et al. (2016) <DOI:10.1111/rssb.12174>).
Provides a way to call the functions in 'SUNDIALS' C ODE solving library (<https://computation.llnl.gov/projects/sundials>). Currently the serial version of ODE solver, 'CVODE' and sensitivity calculator 'CVODES' from the 'SUNDIALS' library are implemented. The package requires ODE to be written as an 'R' or 'Rcpp' function and does not require the 'SUNDIALS' library to be installed on the local machine.
Functions for drawing samples according to Sunter's
        sampling design, and for computing first and second order
        inclusion probabilities
Implements the self-updating process clustering algorithms proposed
    in Shiu and Chen (2016) <doi:10.1080/00949655.2015.1049605>.
Methodology for supervised grouping aka "clustering" of
   potentially many predictor variables, such as genes etc.
Clusters features under the assumption that each cluster has a
 random effect and there is an outcome variable that is related to the random 
 effects by a linear regression. In this way the cluster analysis is 
 ``supervised'' by the outcome variable. An alternate specification is that 
 features in each cluster have the same compound symmetric normal distribution, 
 and the conditional distribution of the outcome given the features
 has the same coefficient for each feature in a cluster.
Biclusters are submatrices in the data matrix which
        satisfy certain conditions of homogeneity. Package contains
        functions for generating robust biclusters with respect to the
        initialization parameters for a given bicluster solution
        contained in a bicluster set in data, the procedure is also
        known as ensemble biclustering. The set of biclusters is
        evaluated based on the similarity of its elements (the
        overlap), and afterwards the hierarchical tree is constructed
        to obtain cut-off points for the classes of robust biclusters.
        The result is a number of robust (or super) biclusters with
        none or low overlap.
A Comprehensive Test Suite for Markov Chain
        Nonconvergence.
Identification of sets of objects with shared features is a common operation in all disciplines. Analysis of intersections among multiple sets is fundamental for in-depth understanding of their complex relationships. This package implements a theoretical framework for efficient computation of statistical distributions of multi-set intersections based upon combinatorial theory, and provides multiple scalable techniques for visualizing the intersection statistics. The statistical algorithm behind this package was published in Wang et al. (2015) <doi:10.1038/srep16923>.
Likelihood evaluations for stationary Gaussian time series are typically obtained via the Durbin-Levinson algorithm, which scales as O(n^2) in the number of time series observations.  This package provides a "superfast" O(n log^2 n) algorithm written in C++, crossing over with Durbin-Levinson around n = 300.  Efficient implementations of the score and Hessian functions are also provided, leading to superfast versions of inference algorithms such as Newton-Raphson and Hamiltonian Monte Carlo.  The C++ code provides a Toeplitz matrix class packaged as a header-only library, to simplify low-level usage in other packages and outside of R.
A system for generating extendable and customizable heatmaps for exploring complex datasets, including big data and data with multiple data types.
Implements the super learner prediction method and contains a
    library of prediction algorithms to be used in the super learner.
Witten and Tibshirani (2011) Supervised multidimensional scaling for visualization, classification, and bipartite ranking. Computational Statistics and Data Analysis 55(1): 789-801.
The idea is to provide a standard interface 
             to users who use both R and Python for building machine learning models. 
             This package provides a scikit-learn's fit, predict interface to 
             train machine learning models in R.
Produces ANOVA tables in the format used by Judd, McClelland, and Ryan (2017, ISBN:978-1138819832) in their introductory textbook, Data Analysis. This includes proportional reduction in error and formatting to improve ease the transition between the book and R.
Supervised principal components for regression and
        survival analsysis. Especially useful for high-dimnesional
        data, including microarray data.
Dimension reduction of complex data with supervision from auxiliary information. The package contains a series of methods for different data types (e.g., multi-view or multi-way data) including the supervised singular value decomposition (SupSVD), supervised sparse and functional principal component (SupSFPC), supervised integrated factor analysis (SIFA) and supervised PARAFAC/CANDECOMP factorization (SupCP). When auxiliary data are available and potentially affect the intrinsic structure of the data of interest, the methods will accurately recover the underlying low-rank structure by taking into account the supervision from the auxiliary data. For more details, see the paper by Gen Li, <DOI:10.1111/biom.12698>.
Image Segmentation using Superpixels, Affinity Propagation and Kmeans Clustering. The R code is based primarily on the article "Image Segmentation using SLIC Superpixels and Affinity Propagation Clustering, Bao Zhou, International Journal of Science and Research (IJSR), 2013" <https://pdfs.semanticscholar.org/6533/654973054b742e725fd433265700c07b48a2.pdf>.
Tools for analysing the agreement of two or more rankings of the same items. Examples are importance rankings of predictor variables and risk predictions of subjects. Benchmarks for agreement are computed based on random permutation and bootstrap.
The Patient Rule Induction Method (PRIM) is typically
  used for "bump hunting" data mining to identify regions with abnormally
  high concentrations of data with large or small values. This package
  extends this methodology so that it can be applied to binary classification
  problems and used for prediction.
Calculates the sup MZ value to detect the unknown structural break points under Heteroskedasticity as given in Ahmed et al. (2017) (<DOI: 10.1080/03610926.2016.1235200>).
Downloads data supplementary materials from manuscripts,
    using papers' DOIs as references. Facilitates open, reproducible
    research workflows: scientists re-analyzing published datasets can
    work with them as easily as if they were stored on their own
    computer, and others can track their analysis workflow
    painlessly. The main function suppdata() returns a (temporary)
    location on the user's computer where the file is stored, making
    it simple to use suppdata() with standard functions like
    read.csv().
Ten distributions supplementing those built into R.
        Inverse Gauss, Kruskal-Wallis, Kendall's Tau, Friedman's chi
        squared, Spearman's rho, maximum F ratio, the Pearson product
        moment correlation coefficient, Johnson distributions, normal
        scores and generalized hypergeometric distributions. In
        addition two random number generators of George Marsaglia are
        included.
The 'support' package provides the functions sp() and sp_seq() for computing the support points in Mak and Joseph (2018) <DOI:10.1214/17-AOS1629>. Support points can be used as a representative sample of a desired distribution, or a representative reduction of a big dataset (e.g., an "optimal" thinning of Markov-chain Monte Carlo sample chains). This work was supported by USARO grant W911NF-14-1-0024 and NSF DMS grant 1712642.
Provides three basic functions that support an implementation of object case (Case 1) best-worst scaling: one for converting a two-level orthogonal main-effect design/balanced incomplete block design into questions; one for creating a data set suitable for analysis; and one for calculating count-based scores.
Provides three basic functions that support an implementation of Case 2 (profile case) best-worst scaling. The first is to convert an orthogonal main-effect design into questions, the second is to create a dataset suitable for analysis, and the third is to calculate count-based scores.
Provides seven basic functions that support an implementation of choice experiments.
Calculates likelihood based support intervals for
    several common data types including binomial, Poisson, normal, lm(), and
    glm(). For the binomial, Poisson, and normal data likelihood intervals are
    calculated via root finding algorithm. Additional parameters allow the
    user to specify whether they would like to receive a parametric bootstrap
    estimate of the confidence level of said support interval. For lm() and glm(),
    the function returns profile likelihoods for each coefficient in the model.
An implementation of the surrogate approach to residuals and 
  diagnostics for ordinal and general regression models; for details, see Liu 
  and Zhang (2017) <doi:10.1080/01621459.2017.1292915>. These residuals can be 
  used to construct standard residual plots for model diagnostics (e.g., 
  residual-vs-fitted value plots, residual-vs-covariate plots, Q-Q plots, etc.). 
  The package also provides an 'autoplot' function for producing standard 
  diagnostic plots using 'ggplot2' graphics. The package currently supports 
  cumulative link models from packages 'MASS', 'ordinal', 'rms', and 'VGAM'. 
  Support for binary regression models using the standard 'glm' function is also 
  available.
SURFACE is a data-driven phylogenetic comparative method for fitting stabilizing selection models to continuous trait data, building on the ouch package. The main functions fit a series of Hansen models using stepwise AIC, then identify cases of convergent evolution where multiple lineages have shifted to the same adaptive peak.
Create sampling designs using the surface reconstruction algorithm.
  Original method by: Olsson, D. 2002. A method to optimize soil sampling from 
  ancillary data. Poster presenterad at: NJF seminar no. 336, 
  Implementation of Precision Farming in Practical Agriculture, 10-12 
  June 2002, Skara, Sweden.
Multi-Objective optimization based on surrogate models.
  Important functions: build_surmodel, train_hego, train_mego, train_sme.
In a clinical trial, it frequently occurs that the most credible outcome to evaluate the effectiveness of a new therapy (the true endpoint) is difficult to measure. In such a situation, it can be an effective strategy to replace the true endpoint by a (bio)marker that is easier to measure and that allows for a prediction of the treatment effect on the true endpoint (a surrogate endpoint). The package 'Surrogate' allows for an evaluation of the appropriateness of a candidate surrogate endpoint based on the meta-analytic, information-theoretic, and causal-inference frameworks. Part of this software has been developed using funding provided from the European Union's Seventh Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.
Provides functions to estimate the proportion of treatment effect on a censored primary outcome that is explained by the treatment effect on a censored surrogate outcome/event. All methods are described in detail in "Assessing the Value of a Censored Surrogate Outcome" by Parast L, Tian L, and Cai T which is currently in press at Lifetime Data Analysis. The main functions are (1) R.q.event() which calculates the proportion of the treatment effect (the difference in restricted mean survival time at time t) explained by surrogate outcome information observed up to a selected landmark time, (2) R.t.estimate() which calculates the proportion of the treatment effect explained by primary outcome information only observed up to a selected landmark time, and (3) IV.event() which calculates the incremental value of the surrogate outcome information.
Provides functions to test for a treatment effect in terms of the difference in survival between a treatment group and a control group using surrogate marker information obtained at some early time point in a time-to-event outcome setting. Nonparametric kernel estimation is used to estimate the test statistic and perturbation resampling is used for variance estimation. More details will be available in the future in: Parast L, Cai T, Tian L (2019). Using a Surrogate Marker for Early Testing of a Treatment Effect. Biometrics, In press.
Provides functions for the evaluation of
    surrogate endpoints when both the surrogate and the true endpoint are failure
    time variables. The approaches implemented are:
    (1) the two-step approach (Burzykowski et al, 2001) <DOI:10.1111/1467-9876.00244> with a copula model (Clayton, Plackett, Hougaard) at
    the first step and either a linear regression of log-hazard ratios at the second
    step (either adjusted or not for measurement error);
    (2) mixed proportional hazard models estimated via mixed Poisson GLM
    (Rotolo et al, 2019 <DOI:10.1177/0962280217718582>).
Nonparametric and semiparametric estimations of the time-dependent ROC curve for an incomplete failure time data with surrogate failure time endpoints.
suRtex was designed for easy descriptive statistic reporting of categorical survey data (e.g., Likert scales) in LaTeX. suRtex takes a matrix or data frame and produces the LaTeX code necessary for a sideways table creation. Mean, median, standard deviation, and sample size are optional.
Performs inference of several model-free group contrast measures, which include difference/ratio of cumulative incidence rates at given time points, quantiles, and restricted mean survival times (RMST). Two kinds of covariate adjustment procedures (i.e., regression and augmentation) for inference of the metrics based on RMST are also included.
The package provides a variety of functions to estimate
        time-dependent true/false positive rates and AUC curves from a
        set of censored survival data.
Tests for equality of two survival functions based on integrated weighted differences of two Kaplan-Meier curves.
Gradient boosting for optimizing loss functions of different types of survival data, 
  including stratified data. 
  Emily Morris, Kevin He, Yanming Li, Yi Li, Jian Kang (2018) <arXiv:1803.07715> see for 
  description of methods.
Three new methods to perform outlier detection in a survival context. In total there are six methods provided, the first three methods are traditional residual-based outlier detection methods, the second three are the concordance-based. Package developed during the work on the two following publications: Pinto J., Carvalho A. and Vinga S. (2015) <doi:10.5220/0005225300750082>; Pinto J.D., Carvalho A.M., Vinga S. (2015) <doi:10.1007/978-3-319-27926-8_22>.
Performs inference for C of risk prediction models with
        censored survival data, using the method proposed by Uno et al.
        (2011). Inference for the difference in C between two competing
        prediction models is also implemented.
Estimates correlation coefficients with associated
   confidence limits 
   for bivariate, partially censored survival times. Uses
   the iterative multiple imputation approach proposed
   by Schemper, Kaider, Wakounig and Heinze, Statistics
   in Medicine 2013. Provides a scatterplot function to visualize the bivariate 
   distribution, either on the original time scale or as copula.
Various functions for discrete time survival analysis and longitudinal analysis. SIMEX method for correcting for bias for errors-in-variables
  in a mixed effects model. Asymptotic mean and variance of different proportional hazards test statistics using different ties methods given two
  survival curves and censoring distributions. Score test and Wald test for regression analysis of grouped survival data. Calculation of survival
  curves for events defined by the response variable in a mixed effects model crossing a threshold with or without confirmation.
Contains routines for computing the one-sided/two-sided integrated/maximally selected EL statistics for simultaneous testing, the one-sided/two-sided EL tests for pointwise testing, and an initial test that precedes one-sided testing to exclude the possibility of crossings or alternative orderings.
Relative survival, AER and SMR based on French death rates
Summary statistics, two-sample tests, rank tests, generalised linear models, cumulative link models, Cox models, loglinear models, and general maximum pseudolikelihood estimation for multistage stratified, cluster-sampled, unequally weighted survey samples. Variances by Taylor series linearisation or replicate weights. Post-stratification, calibration, and raking. Two-phase subsampling designs. Graphics. PPS sampling without replacement. Principal components, factor analysis.
Tools for using different kinds of bootstrap
    for estimating sampling variation using complex survey
    data.
Data obtained from surveys contains information not only about the
    survey responses, but also the survey metadata, e.g. the original survey
    questions and the answer options. The 'surveydata' package makes it easy to
    keep track of this metadata, and to easily extract columns with
    specific questions.
Help generate slides for surveys or experiments.
 The resulted slides allow the subject to respond with the use of the mouse (usual keyboard input is 
 replaced with clicking on a virtual keyboard on the slide). Subjects' responses are saved to the user-
 specified location in the form of R-readable text file. To allow flexibility, each function in 
 this package generates a particular type of slides thus general R function writing skills are 
 required to compile these edited slides.
At present, the only functionality is the calculation of optimal one-sided winsorizing cutoffs. The main function is optimal.onesided.cutoff.bygroup. It calculates the optimal tuning parameter for one-sided winsorisation, and so calculates winsorised values for a variable of interest. See the help file for this function for more details and an example.
Tools for sample survey planning, including sample size calculation, estimation of expected precision for the estimates of totals, and calculation of optimal sample size allocation.
Calculate point estimates and their standard errors in complex household surveys using bootstrap replicates. Bootstrapping considers survey design with a rotating panel. A comprehensive description of the methodology can be found under <https://statistikat.github.io/surveysd/articles/methodology.html>.
The Gini concentration test for survival data is a nonparametric test based on the Gini index for testing the equality of two survival distributions from the point of view of concentration. The package compares different nonparametric tests (asymptotic Gini test, permutation Gini test, log-rank test, Gray-Tsiatis test and Wilcoxon test) and computes their p-values.
Sample size calculation utilizing the information fraction and the alpha spending function 
             in a group sequential clinical trial with censored survival data from underlying generalized 
             gamma survival distributions or log-logistic survival distributions.
             Hsu, C.-H., Chen, C.-H, Hsu, K.-N. and Lu, Y.-H. (2018) A useful design utilizing the 
             information fraction in a group sequential clinical trial with censored survival data. 
             To appear in Biometrics.
Contains a suite of functions for survival analysis in health economics. These can be used to run survival models under a frequentist (based on maximum likelihood) or a Bayesian approach (both based on Integrated Nested Laplace Approximation or Hamiltonian Monte Carlo). The user can specify a set of parametric models using a common notation and select the preferred mode of inference. The results can also be post-processed to produce probabilistic sensitivity analysis and can be used to export the output to an Excel file (e.g. for a Markov model, as often done by modellers and practitioners).
Performs inference for a class of measures to compare
        competing risk prediction models with censored survival data.
        The class includes the integrated discrimination improvement
        index (IDI) and category-less net reclassification index (NRI).
Newly developed methods for the estimation of several probabilities
      in an illness-death model. The package can be used to obtain nonparametric and 
      semiparametric estimates for: transition probabilities, occupation probabilities, 
      cumulative incidence function and the sojourn time distributions. 
      Additionally, it is possible to fit proportional hazards regression models
      in each transition of the Illness-Death Model. Several auxiliary 
      functions are also provided which can be used for marginal 
      estimation of the survival functions.
Contains the core survival analysis routines, including
	     definition of Surv objects, 
	     Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models,
	     and parametric accelerated failure time models.
A high-level interface to perform survival analysis, 
    including Kaplan-Meier analysis and log-rank tests and Cox regression.
    Aims at providing a clear and elegant syntax, support for use in a pipeline, structured output and plotting.
    Builds upon the 'survminer' package for Kaplan-Meier plots and provides
    a customizable implementation for forest plots.
    Kaplan & Meier (1958) <doi:10.1080/01621459.1958.10501452>
    Cox (1972) <JSTOR:2985181>
    Peto & Peto (1972) <doi:10.2307/2344317>.
In routine practice, biomarker performance is calculated by 
    splitting a patient cohort at some arbitrary level, often by median gene 
    expression. The logic behind this is to divide patients into “high” or “low”
    expression groups that in turn correlate with either good or poor prognosis.
    However, this median-split approach assumes that the data set composition 
    adheres to a strict 1:1 proportion of high vs. low expression, that for 
    every one “low” there is an equivalent “high”. In reality, data sets are 
    often heterogeneous in their composition (Perou, CM et al., 2000 
    <doi:10.1038/35021093>)- i.e. this 1:1 relationship is unlikely to exist and
    the true relationship unknown. Given this limitation, it remains difficult 
    to determine where the most significant separation should be made. For 
    example, estrogen receptor (ER) status determined by immunohistochemistry is
    standard practice in predicting hormone therapy response, where ER is found 
    in an ~1:3 ratio (-:+) in the population (Selli, C et al., 2016 
    <doi:10.1186/s13058-016-0779-0>). We would expect therefore, upon dividing 
    patients by ER expression, 25% to be classified “low” and 75% “high”, and 
    an otherwise 50-50 split to incorrectly classify 25% of our patient cohort,
    rendering our survival estimate under powered. 'survivALL' is a data-driven 
    approach to calculate the relative survival estimates for all possible 
    points of separation - i.e. at all possible ratios of “high” vs. “low” - 
    allowing a measure’s relationship with survival to be more reliably 
    determined and quantified. We see this as a solution to a flaw in common 
    research practice, namely the failure of a true biomarker as part of a 
    meta-analysis.
Estimate the regression coefficients and the baseline hazard 
      of proportional hazard Cox models using maximum penalised likelihood.  
      A 'non-parametric' smooth estimate of the baseline hazard function 
      is provided.
Compute time-dependent ROC curve from censored survival
        data using Kaplan-Meier (KM) or Nearest Neighbor Estimation
        (NNE) method of Heagerty, Lumley & Pepe (Biometrics, Vol 56 No
        2, 2000, PP 337-344)
Performs support vectors analysis for data sets with survival
    outcome. Three approaches are available in the package: The regression approach
    takes censoring into account when formulating the inequality constraints of
    the support vector problem. In the ranking approach, the inequality constraints
    set the objective to maximize the concordance index for comparable pairs
    of observations. The hybrid approach combines the regression and ranking
    constraints in the same model.
Microarray gene expression data can be analyzed individually or jointly using merging methods or meta-analysis to predict patients' survival and risk assessment.
Three breast cancer gene expression data sets that can be used for package 'survJamda'. This package contains the gene expression and phenotype data of GSE1992, GSE3143 and GSE4335.
Kernel weighting methods for estimation of proportional hazards models with intermittently observed longitudinal covariates.
Contains the function 'ggsurvplot()' for drawing easily beautiful
    and 'ready-to-publish' survival curves with the 'number at risk' table
    and 'censoring count plot'. Other functions are also available to plot 
    adjusted curves for `Cox` model and to visually examine 'Cox' model assumptions.
A collection of functions to help in the analysis of
    right-censored survival data. These extend the methods available in
    package:survival.
Fits hazard and excess hazard models with multidimensional penalized splines allowing for time-dependent effects, non-linear effects and interactions between several continuous covariates.
Presmoothed estimators of survival, density, cumulative and non-cumulative hazard functions with right-censored survival data.
The main function of this package allows estimation of a Weibull Regression for a right-censored endpoint, one interval-censored covariate, and an arbitrary number of non-censored covariates. Additional functions allow to switch between different parametrizations of Weibull regression used by different R functions, inference for the mean difference of two arbitrarily censored Normal samples, and estimation of canonical parameters from censored samples for several distributional assumptions.
Performs two-sample comparisons using the restricted mean survival time (RMST) as a summary measure of the survival time distribution. Three kinds of between-group contrast metrics (i.e., the difference in RMST, the ratio of RMST and the ratio of the restricted mean time lost (RMTL)) are computed. It performs an ANCOVA-type covariate adjustment as well as unadjusted analyses for those measures.
Performs the procedure proposed by Horiguchi et al. (2018) <doi:10.1002/sim.7661>. The method specifies a set of truncation time points tau's for calculating restricted mean survival times (RMST), performs testing for equality, and estimates the difference in RMST between two groups at the specified tau's. Multiplicity by specifying several tau's is taken into account in this procedure.
Simulation of simple and complex survival data including recurrent and multiple events and competing risks.
Conduct asymptotic and empirical power and sample size calculations for Single-Nucleotide Polymorphism (SNP) association studies with right censored time to event outcomes.
Implements functions to plot survival curves. Provides the capability to add numbers at risk tables, and allows for using the pipe operator to create more complex plots.
Targeted estimates of marginal cumulative incidence in survival
    settings with and without competing risks, including estimators that respect
    bounds (Benkeser, Carone, and Gilbert. Statistics in Medicine, 2017.
    <doi:10.1002/sim.7337>).
Package performs Cox regression and survival distribution function estimation when the survival times are subject to double truncation. The estimation procedure for each method involves inverse probability weighting, where the weights correspond to the inverse of the selection probabilities and are estimated using the survival times and truncation times only. Both methods require that the survival and truncation times are quasi-independent. A test for checking this independence assumption is also included in this package. The functions available in this package for Cox regression, survival distribution function estimation, and testing independence under double truncation are based on the following methods, respectively: Rennert and Xie (2017) <doi:10.1111/biom.12809>, Shen (2010) <doi:10.1007/s10463-008-0192-2>, Martin and Betensky (2005) <doi:10.1198/016214504000001538>.
Functional programming principles to iteratively run Cox 
    regression and plot its results. The results are reported in tidy data 
    frames. Additional utility functions are available for working with 
    other aspects of survival analysis such as survival curves, C-statistics, 
    etc.
Survival models may have very different structures. This package contains functions 
  for creating a unified representation of a survival models, which can be further processed by various 
  survival explainers. Tools implemented in 'survxai' help to understand how input variables are used in 
  the model and what impact do they have on the final model prediction. Currently, four explanation methods are implemented. 
  We can divide them into two groups: local and global.
The alpha-Sutte indicator (alpha-Sutte) was originally from developed of Sutte indicator. Sutte indicator can using to predict the movement of stocks. As the development of science, then Sutte indicator developed to predict not only the movement of stocks but also can forecast data on financial, insurance, and others time series data. Ahmar, A.S. (2017) <doi:10.17605/osf.io/rknsv>.
Implements the s-values proposed by Ed. Leamer.
    It provides a context-minimal approach for sensitivity analysis using extreme
    bounds to assess the sturdiness of regression coefficients.
Accurate identification of genes that are truly differentially expressed over two sample varieties, after adjusting for hidden subject-specific effects of residual heterogeneity.
Implements data-driven identification methods for structural vector autoregressive (SVAR) models. 
             Based on an existing VAR model object (provided by e.g. VAR() from the 'vars' package), the structural 
             impact matrix is obtained via data-driven identification techniques (i.e. changes in volatility (Rigobon, R. (2003) <doi:10.1162/003465303772815727>), 
             least dependent innovations (Herwartz, H., Ploedt, M., (2016) <doi:10.1016/j.jimonfin.2015.11.001>) or non-Gaussian maximum likelihood (Lanne, M., Meitz, M., Saikkonen, P. (2017) <doi:10.1016/j.jeconom.2016.06.002>).
2d and 3d space-varying coefficient models are fitted to
        regular grid data using either a full B-spline tensor product
        approach or a sequential approximation. The latter one is
        computationally more efficient. Resolution increment is
        enabled.
R bindings to SVD and eigensolvers (PROPACK, nuTRLan).
Rapidly construct standard dialog boxes for your GUI, including 
  message boxes, input boxes, list, file or directory selection, ... In case R
  cannot display GUI dialog boxes, a simpler command line version of these
  interactive elements is also provided as fallback solution.
Reimplementation of the svDialogs dialog boxes in Tcl/Tk
Some visualization tools based on Singular Value Decomposition
Obtain parameters of Svensson's Method, including percentage agreement, 
    systematic change and individual change. Also, the contingency table can be generated. 
    Svensson's Method is a rank-invariant nonparametric method for the analysis of ordered scales 
    which measures the level of change both from systematic and individual aspects. For the details, 
    please refer to Svensson E. Analysis of systematic and random differences between paired ordinal 
    categorical data [dissertation]. Stockholm: Almqvist & Wiksell International; 1993.
A graphics device for R that produces 'Scalable Vector Graphics'.
  'svglite' is a fork of the older 'RSvgDevice' package.
This 'htmlwidget' provides pan and zoom interactivity to R
    graphics, including 'base', 'lattice', and 'ggplot2'. The interactivity is
    provided through the 'svg-pan-zoom.js' library. Various options to the widget
    can tailor the pan and zoom experience to nearly any user desire.
The SciViews svGUI package eases the management of Graphical User
  Interfaces (GUI) in R. It is independent from any particular GUI widgets (Tk,
  Gtk2, native, ...). It centralizes info about GUI elements currently used,
  and it dispatches GUI calls to the particular toolkits in use in function of
  the context (is R run at the terminal, within a Tk application, a HTML page?).
Creates 3D animated, interactive visualizations that can be viewed in a web browser.
Implements a simple HTTP server allowing to connect GUI clients to R
Function for the GUI API to interact with external IDE/code editors.
Functions to manage the GUI client, like Komodo with the
  SciViews-K extension
Solve large-scale regularised linear/kernel classification by using ADMM and IADMM algorithms. This package provides linear L2-regularised primal classification (both ADMM and IADMM are available), kernel L2-regularised dual classification (IADMM) as well as L1-regularised primal classification (both ADMM and IADMM are available).
Miscellaneous functions for SciViews or general use: manage a
  temporary environment attached to the search path for temporary variables you
  do not want to save() or load(), test if Aqua, Mac, Win, ... Show progress
  bar, etc.
Implements the SVM-Maj algorithm to train data with support vector machine
  as described in Groenen et al. (2008) <doi:10.1007/s11634-008-0020-9>.
  This algorithm uses two efficient updates, one for linear kernel and one 
  for the nonlinear kernel.
Causal effect estimation in observational data often requires identifying a set of untreated observations that are comparable to some treated group of interest.  This package provides a suite of functions for identifying such a set of observations and for implementing standard and new diagnostics tools.  The primary function, svmmatch(), uses support vector machines to identify a region of common support between treatment and control groups.  A sensitivity analysis, balance checking, and assessment of the region of overlap between treated and control groups is included.  The Bayesian implementation allows for recovery of uncertainty estimates for the treatment effect and all other parameters.
Computes the entire regularization path for the two-class svm classifier
		with essentially the same cost as a single SVM fit.
Implementation of Support Vector Machines Plus (SVM+) for classification problems. See (Vladimir et. al, 2009, <doi:10.1016/j.neunet.2009.06.042>) for theoretical details and see (Li et. al, 2016, <https://github.com/okbalefthanded/svmplus_matlab>) for implementation details in 'MATLAB'.
Determines networks of significant synchronization between the discrete states of nodes; see Tumminello et al <doi:10.1371/journal.pone.0017994>.
Computes the entire solution paths for Support Vector Regression(SVR) with respect to the regularization parameter, lambda and epsilon in epsilon-intensive loss function, efficiently. We call each path algorithm svrpath and epspath. See Wang, G. et al (2008) <doi:10.1109/TNN.2008.2002077> for details regarding the method.
Various tools for semantic vector spaces, such as
    correspondence analysis (simple, multiple and discriminant), latent
    semantic analysis, probabilistic latent semantic analysis, non-negative
    matrix factorization, latent class analysis and EM clustering. Furthermore,
    there are specialized distance measures, plotting functions and some helper
    functions.
Implements a simple socket server allowing to connect GUI clients to R
Supporting functions for the GUI API (Sweave functions)
Set of tools aimed at wrapping some of the functionalities of the
  packages tools, utils and codetools into a nicer format so that an IDE can use
  them.
A complete unit test system and functions to implement its GUI part
High level management of widgets, windows and other graphical resources.
Contains functions for computing diagnostics for fixed effects linear regression models fitted with survey data. Extensions of standard diagnostics to complex survey data are included: standardized residuals, leverages, Cook's D, dfbetas, dffits, condition indexes, and variance inflation factors.
Builds, evaluates and validates a nomogram with survey data and right-censored outcomes.
This package deals with data which stem from survey designs including plausible values. This package has been created to handle data from Large Scale Assessments like PISA, PIAAC etc. which use  complex survey designs to draw the sample and plausible values to report person related estimates. Various functions/statistics (mean, quantile, GLM etc.) are provided to handle this kind of data.
This algorithm conducts variable selection in the classification setting. It repeatedly subsamples variables and runs linear discriminant analysis (LDA) on the subsampled variables. Variables are scored based on the AUC and the t-statistics. Variables then enter a competition and the semi-finalist variables will be evaluated in a final round of LDA classification. The algorithm then outputs a list of variable selected. Qiao, Sun and Fan (2017) <http://people.math.binghamton.edu/qiao/swa.html>.
A collection of 'HTML', 'JavaScript', and 'CSS' assets that
  dynamically generate beautiful documentation from a 'Swagger' compliant API:
  <https://swagger.io/specification/>.
Collection of functions to connect the structure of the data with the information on the samples. Three types of associations are covered: 1. linear model of principal components. 2. hierarchical clustering analysis. 3. distribution of features-sample annotation associations. Additionally, the inter-relation between sample annotations can be analyzed. Simple methods are provided for the correction of batch effects and removal of principal components.
A null model randomizing semi-quantitative multi-classes (or ordinal) data by swapping sub-matrices while both the row and the column marginal sums are held constant.
Three ensemble learning algorithms based on support vector machines. 
    They all train support vector machines on subset of data and combine the result.
There are numerous places to create and download color
    palettes. These are usually shared in 'Adobe' swatch file formats of
    some kind. There is also often the need to use standard palettes
    developed within an organization to ensure that aesthetics are carried over
    into all projects and output. Now there is a way to read these swatch
    files in R and avoid transcribing or converting color values by hand or
    or with other programs. This package provides functions to read and
    inspect 'Adobe Color' ('ACO'), 'Adobe Swatch Exchange' ('ASE'), 'GIMP Palette' ('GPL'),
    'OpenOffice' palette ('SOC') files and 'KDE Palette' ('colors') files. Detailed
    descriptions of 'Adobe Color' and 'Swatch Exchange' file formats as well as other
    swatch file formats can be found at
    <http://www.selapa.net/swatches/colors/fileformats.php>.
The Soil and Water Assessment Tool is a river basin or
        watershed scale model developed by Dr. Jeff Arnold for the
        USDA-ARS.
A set of tools for examining the design and analysis aspects of stepped wedge cluster randomized trials (SW CRT) based on a repeated cross-sectional sampling scheme (Hussey MA and Hughes JP (2007) Contemporary Clinical Trials 28:182-191. <doi:10.1016/j.cct.2006.05.007>).
Implements the Sliding Window Discrete Fourier Transform (SWDFT). Also provides 
             statistical methods based on the SWDFT, and graphical tools to display the outputs.
Tidies up the forecasting modeling and prediction work flow, 
    extends the 'broom' package 
    with 'sw_tidy', 'sw_glance', 'sw_augment', and 'sw_tidy_decomp' functions 
    for various forecasting models,
    and enables converting 'forecast' objects to 
    "tidy" data frames with 'sw_sweep'.
Structural handling of identity numbers used in the Swedish
    administration such as personal identity numbers ('personnummer') and
    organizational identity numbers ('organisationsnummer').
The Swiss Ephemeris is a high precision ephemeris based upon the
    DE431 ephemerides from NASA's JPL. It covers the time range 13201 BC to
    AD 17191. This package uses the semi-analytic theory by Steve Moshier.
    For faster and more accurate calculations, the compressed Swiss Ephemeris
    data is available in the 'swephRdata' package. To access this data package,
    run 'install.packages("swephRdata", repos = "https://rstub.github.io/drat/",
    type = "source")'. The size of the 'swephRdata' package is approximately
    115 MB. The user can also use the original JPL DE431 data.
Collection of conversion, analytical, geodesic, mapping, and
    plotting functions. Used to support packages and code written by
    researchers at the Southwest Fisheries Science Center of the National
    Oceanic and Atmospheric Administration.
Simulation extrapolation and inverse probability weighted generalized estimating equations method for longitudinal data with missing observations and measurement error in covariates. References: Yi, G. Y. (2008) <doi:10.1093/biostatistics/kxm054>; Cook, J. R. and Stefanski, L. A. (1994) <doi:10.1080/01621459.1994.10476871>; Little, R. J. A. and Rubin, D. B. (2002, ISBN:978-0-471-18386-0).
Use the R console as an interactive learning
    environment. Users receive immediate feedback as they are guided through
    self-paced lessons in data science and R programming.
A set of tools for writing and sharing interactive courses
    to be used with swirl.
Ozone, NOx (= Sum of nitrogen monoxide and nitrogen dioxide), nitrogen monoxide, ambient temperature, dew point, wind speed and wind direction at 3 sites around lake of Lucerne in Central Switzerland in 30 min time resolution for year 2004.
Builds upon the real time data service as well as the archive for national votes <https://opendata.swiss/api/3/action/package_show?id=echtzeitdaten-am-abstimmungstag-zu-eidgenoessischen-abstimmungsvorlagen> and cantonal votes <https://opendata.swiss/api/3/action/package_show?id=echtzeitdaten-am-abstimmungstag-zu-kantonalen-abstimmungsvorlagen>. It brings the results of Swiss popular votes, aggregated at the geographical level of choice, into R.
Provides a number of useful functions to employ MrP for small area prediction in Switzerland. Based on a hierarchical model and survey data one can derive cantonal preference measures. The package allows to automatize the prediction and post-stratification steps. It further provides adequate print, summary, map, and plot functions for objects of its class.
Functions for estimating the parameters from the latent
  state process and the functions corresponding to the J states as
  proposed by De Souza and Heckman (2013).
Provides an abstraction for managing, installing,
    and switching between sets of installed R packages. This allows users to
    maintain multiple package libraries simultaneously, e.g. to maintain
    strict, package-version-specific reproducibility of many analyses, or
    work within a development/production release paradigm. Introduces a
    generalized package installation process which supports multiple repository
    and non-repository sources and tracks package provenance.
Provides a simple plugin to the switchr
	     framework which allows users to publish package and
	     session manifests as gists.
Functions to connect the widely used Storm Water Management Model (SWMM)
  of the United States Environmental Protection Agency (US EPA) 
  <https://www.epa.gov/water-research/storm-water-management-model-swmm> to R with
  currently two main goals: (1) Run a SWMM simulation from R and (2) provide fast 
  access to simulation results, i.e. SWMM's binary '.out'-files. High performance is achieved
  with help of Rcpp. Additionally, reading SWMM's '.inp' and '.rpt' files is supported to 
  glance model structures and to get direct access to simulation summaries.
Tools for retrieving, organizing, and analyzing environmental
    data from the System Wide Monitoring Program of the National Estuarine
    Research Reserve System <http://cdmo.baruch.sc.edu/>. These tools
    address common challenges associated with continuous time series data
    for environmental decision making.
This Systems Biology Package (Gelius-Dietrich et. al. (2012) <doi:10.1186/1752-0509-7-125>) implements algorithms for constraint based analyses of metabolic networks, e.g. flux-balance analysis (FBA), minimization of metabolic adjustment (MOMA), regulatory on/off minimization (ROOM), robustness analysis and flux variability analysis. The package is easily extendable for additional algorithms. Most of the current LP/MILP solvers are supported via additional packages.
Different techniques of cost constrained flux balance analysis.
        1- MetabOlic Modeling with ENzyme kineTics 'MOMENT' which uses enzyme kinetic data and enzyme molecular 
         weights to constrain flux balance analysis(FBA) and it is described in Adadi, R., Volkmer, B., Milo, R., 
         Heinemann, M., & Shlomi, T. (2012). Prediction of Microbial Growth Rate versus Biomass Yield by a Metabolic Network 
           with Kinetic Parameters, 8(7). <doi:10.1371/journal.pcbi.1002575>. 
        2- an improvement of 'MOMENT' that considers multi-functional enzymes 'ccFBA'. 
         Described in Desouki, Abdelmoneim. Algorithms for improving the predictive power of flux balance analysis. PhD dissertation, 2016.
         FBA is a mathematical technique to find fluxes in metabolic models at steady state. 
         It is described in Orth, J.D., Thiele, I. and Palsson, B.O.  What is flux balance analysis? Nat. Biotech. 28, 245-248(2010).
Implement cycle-Free flux balance analysis, flux variability, and Random Sampling of solution space. Flux balance analysis is a technique to find fluxes in metabolic models at steady state. It is described in Orth, J.D., Thiele, I. and Palsson, B.O. What is flux balance analysis? Nat. Biotech. 28, 245-248 (2010).
Implements dynamic FBA technique proposed by Varma et al 1994.
An English language syllable counter, plus readability score
    measure-er. For readability, we support 'Flesch' Reading Ease and
    'Flesch-Kincaid' Grade Level ('Kincaid' 'et al'. 1975)
    <https://stars.library.ucf.edu/cgi/viewcontent.cgi?article=1055&context=istlibrary>,
    Automated Readability Index ('Senter' and Smith 1967)
    <http://www.dtic.mil/cgi-bin/GetTRDoc?AD=AD0667273>,
    Simple Measure of Gobbledygook (McLaughlin 1969)
    <https://www.semanticscholar.org/paper/SMOG-Grading-A-New-Readability-Formula.-Laughlin/5fccb74c14769762b3de010c5e8a1a7ce700d17a>,
    and 'Coleman-Liau' (Coleman and 'Liau' 1975) <doi:10.1037/h0076540>. The
    package has been carefully optimized and should be very efficient, both in
    terms of run time performance and memory consumption. The main methods are
    'vectorized' by document, and scores for multiple documents are computed in
    parallel via 'OpenMP'.
Implements tidy syllabification of transcription. 
  Based on @kylebgorman's 'python' implementation <https://github.com/kylebgorman/syllabify>.
Provides the hyphenation algorithm used for 'TeX'/'LaTeX'
        and similar software, as proposed by Liang (1983,
        <https://tug.org/docs/liang/>). Mainly contains the function
        hyphen() to be used for hyphenation/syllable counting of text
        objects. It was originally developed for and part of the
        'koRpus' package, but later released as a separate package so
        it's lighter to have this particular functionality available
        for other packages. Support for various languages needs be
        added on-the-fly or by plugin packages
        (<https://undocumeantit.github.io/repos>); this package does
        not include any language specific data. 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 'sylly'
        package. Due to some restrictions on CRAN, the full package
        sources are only available from the project homepage. 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>).
Functions for fitting the Autoregressive and Moving Average Symmetric Model for univariate time series introduced by Maior and Cysneiros (2018), <doi:10.1007/s00362-016-0753-z>. Fitting method: conditional maximum likelihood estimation. For details see: Wei (2006), Time Series Analysis: Univariate and Multivariate Methods, Section 7.2.
Symbolic data analysis methods: importing/exporting data from ASSO XML Files, distance calculation for symbolic data (Ichino-Yaguchi, de Carvalho measure), zoom star plot, 3d interval plot, multidimensional scaling for symbolic interval data, dynamic clustering based on distance matrix, HINoV method for symbolic data, Ichino's feature selection method, principal component analysis for symbolic interval data, decision trees for symbolic data based on optimal split with bagging, boosting and random forest approach (+visualization), kernel discriminant analysis for symbolic data, Kohonen's self-organizing maps for symbolic data, replication and profiling, artificial symbolic data generation.
 (Milligan, G.W., Cooper, M.C. (1985) <doi:10.1007/BF02294245>,
 Breiman, L. (1996), <doi:10.1007/BF00058655>,
 Hubert, L., Arabie, P. (1985), <doi:10.1007%2FBF01908075>,
 Ichino, M., & Yaguchi, H. (1994), <doi:10.1109/21.286391>,
 Rand, W.M. (1971) <doi:10.1080/01621459.1971.10482356>,
 Calinski, T., Harabasz, J. (1974) <doi:10.1080/03610927408827101>,
 Breckenridge, J.N. (2000) <doi:10.1207/S15327906MBR3502_5>,
 Groenen, P.J.F, Winsberg, S., Rodriguez, O., Diday, E. (2006) <doi:10.1016/j.csda.2006.04.003>,
 Walesiak, M., Dudek, A. (2008) <doi:10.1007/978-3-540-78246-9_11>,
 Dudek, A. (2007), <doi:10.1007/978-3-540-70981-7_4>).
Package that implements various symbol plots (bars,
        profiles, stars, Chernoff faces, color icons, stick figures).
A matrix-like class to represent a symmetric matrix partitioned
    into file-backed blocks.
Symbolic central and non-central moments of the multivariate normal distribution. Computes a standard representation, LateX code, and values at specified mean and covariance matrices.
Analyze data from the 'Healthcare Social Graph' via access to the 'Symplur' API.
    The 'Healthcare Social Graph' contains billions of healthcare social media data points. Hundreds of published journal articles have leveraged data from the 'Healthcare Social Graph'.
    About the 'Symplur' API: <https://api.symplur.com/v1/docs/>.
    About 'Symplur' research: <https://www.symplur.com/healthcare-social-media-research/>.
    Credit to Professor Larry Chu, MD at Stanford University School of Medicine for the idea of the 'SympluR' package.
Contains methods for simulation and for evaluating the pdf, cdf, and quantile functions for symmetric stable, symmetric classical tempered stable, and symmetric power tempered stable distributions.
A collection of functions required for genomic prediction which were developed within the Synbreed project for synergistic plant and animal breeding (<http://www.synbreed.tum.de>). This covers data processing, data visualization, and analysis. All functions are embedded within the framework of a single, unified data object. The implementation is flexible with respect to a wide range of data formats in plant and animal breeding. This research was funded by the German Federal Ministry of Education and Research (BMBF) within the AgroClustEr Synbreed - Synergistic plant and animal breeding (FKZ 0315528A).
Data sets for the 'synbreed' package with three data sets from cattle, maize and mice to illustrate the functions in the 'synbreed' R package. All data sets are stored in the gpData format introduced in the 'synbreed'
 package. This research was funded by the German Federal Ministry of Education and Research (BMBF) within the AgroClustEr Synbreed - Synergistic plant and animal breeding (FKZ 0315528A).
Methods for computing spatial, temporal, and spatiotemporal
    statistics including: empirical univariate, bivariate and multivariate
    variograms; fitting variogram models; phase locking and synchrony analysis;
    generating autocorrelated and cross-correlated matrices.
This package carries out synchrosqueezed wavelet transform. The package is a translation of MATLAB Synchrosqueezing Toolbox, version 1.1 originally developed by Eugene Brevdo (2012). The C code for curve_ext was authored by Jianfeng Lu, and translated to Fortran by Dongik Jang. Synchrosqueezing is based on the papers: [1] Daubechies, I., Lu, J. and Wu, H. T. (2011) Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis, 30. 243-261. [2] Thakur, G., Brevdo, E., Fukar, N. S. and Wu, H-T. (2013) The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications. Signal Processing, 93, 1079-1094.
The function 'syncSubsample' subsamples temporal data of different entities so that the result only contains synchronal events. The function 'mci' calculates the Movement Coordination Index (MCI, see reference on help page for function 'mci') of a data set created with the function 'syncSubsample'.
Random number generation designed for cross-language usage.
Analysis of metacommunities based on functional traits and
    phylogeny of the community components. The functions that are offered here
    implement for the R environment methods that have been available in the
    SYNCSA application written in C++ (by Valerio Pillar, available at <http://
    ecoqua.ecologia.ufrgs.br/ecoqua/SYNCSA.html>).
Infers sympatry matrices from distributional data and analyzes them in order to identify groups of species cohesively connected.
Framework to perform synthetic likelihood inference
    for models where the likelihood function is unavailable or
    intractable.
Computes a synoptic climate classification through daily reanalysis data and
    creates a climate or environmental regionalization based on the previous synoptic climate classification.
It implements various estimators of mutual information, such as
	the maximum likelihood and the Millow-Madow estimator, various Bayesian
	estimators, the shrinkage estimator, and the Chao-Shen estimator. It also
	offers wrappers to the kNN and kernel density estimators. Furthermore, it
	provides various index of performance evaluation such as precision, recall,
	FPR, F-Score, ROC-PR Curves and so on. Lastly, it provides a brand new way
	of generating synthetic RNA-Seq Network with known dependence structure.
A set of functions for generating 'SPSS' syntax files from the R environment.
Implements the synthetic control group method for comparative case studies as described in Abadie and Gardeazabal (2003) and Abadie, Diamond, and Hainmueller (2010, 2011, 2014). The synthetic control method allows for effect estimation in settings where a single unit (a state, country, firm, etc.) is exposed to an event or intervention. It provides a data-driven procedure to construct synthetic control units based on a weighted combination of comparison units that approximates the characteristics of the unit that is exposed to the intervention. A combination of comparison units often provides a better comparison for the unit exposed to the intervention than any comparison unit alone.
Provides access to curated American Community Survey (ACS) base tables via a wrapper
  to library(acs). Builds synthetic micro-datasets at any user-specified geographic level with
  ten default attributes; and, conducts spatial microsimulation modeling (SMSM) via simulated
  annealing.  SMSM is conducted in parallel by default. Lastly, we provide functionality for
  data-extensibility of micro-datasets.
A tool for producing synthetic versions of microdata containing confidential information so that they are safe to be released to users for exploratory analysis. The key objective of generating synthetic data is to replace sensitive original values with synthetic ones causing minimal distortion of the statistical information contained in the data set. Variables, which can be categorical or continuous, are synthesised one-by-one using sequential modelling. Replacements are generated by drawing from conditional distributions fitted to the original data using parametric or classification and regression trees models. Data are synthesised via the function syn() which can be largely automated, if default settings are used, or with methods defined by the user. Optional parameters can be used to influence the disclosure risk and the analytical quality of the synthesised data. For a description of the implemented method see Nowok, Raab and Dibben (2016) <doi:10.18637/jss.v074.i11>.
Drop-in replacements for the base system2() function with fine control
    and consistent behavior across platforms. Supports clean interruption, timeout, 
    background tasks, and streaming STDIN / STDOUT / STDERR over binary or text 
    connections. Arguments on Windows automatically get encoded and quoted to work 
    on different locales.
Loading system fonts and Google Fonts
    <https://fonts.google.com/> into R, in order to
    support other packages such as 'R2SWF' and 'showtext'.
Provides functions for constructing mathematical models of dynamical systems from measured input-output data.
Econometric estimation of simultaneous
 systems of linear and nonlinear equations using Ordinary Least
 Squares (OLS), Weighted Least Squares (WLS), Seemingly Unrelated
 Regressions (SUR), Two-Stage Least Squares (2SLS), Weighted
 Two-Stage Least Squares (W2SLS), and Three-Stage Least Squares (3SLS).
A toolbox for systemic risk based on liabilities matrices. Contains
    a Gibbs sampler for liabilities matrices where only row and column sums of the
    liabilities matrix as well as some other fixed entries are observed. Includes models 
    for power law distribution on the degree distribution.
Deals with standard Young tableaux (field of combinatorics). Performs enumeration, counting, random generation, the Robinson-Schensted correspondence, and conversion to and from paths on the Young lattice.
Extracts sentiment and sentiment-derived plot arcs
    from text using a variety of sentiment dictionaries conveniently
    packaged for consumption by R users.  Implemented dictionaries include
    "syuzhet" (default) developed in the Nebraska Literary Lab
    "afinn" developed by Finn {\AA}rup Nielsen, "bing" developed by Minqing Hu
    and Bing Liu, and "nrc" developed by Mohammad, Saif M. and Turney, Peter D.
    Applicable references are available in README.md and in the documentation
    for the "get_sentiment" function.  The package also provides a hack for
    implementing Stanford's coreNLP sentiment parser. The package provides
    several methods for plot arc normalization.
Contains functions for applying the T^2-test for equivalence.
    The T^2-test for equivalence is a multivariate two-sample equivalence test. 
    Distance measure of the test is the Mahalanobis distance.
    For multivariate normally distributed data the T^2-test for equivalence 
    is exact and UMPI.
    The function T2EQ() implements the T^2-test for equivalence 
    according to Wellek (2010) <DOI:10.1201/ebk1439808184>.
    The function T2EQ.dissolution.profiles.hoffelder() implements a variant 
    of the T^2-test for equivalence according to Hoffelder (2016) 
    <http://www.ecv.de/suse_item.php?suseId=Z|pi|8430> for the 
    equivalence comparison of highly variable dissolution profiles.
Contains functions for creating various types of summary tables, e.g. comparing characteristics across levels of a categorical variable and summarizing fitted generalized linear models, generalized estimating equations, and Cox proportional hazards models. Functions are available to handle data from simple random samples as well as complex surveys.
Sometimes you need to split your data and work on the two chunks independently before bringing them back together. 'Taber' allows you to do that with its two functions.
Some functions are included in this package for writing tables in Excel format suitable for distribution.
A specialization of 'dplyr' data manipulation verbs that parse and build expressions
    which are ultimately evaluated by 'data.table', letting it handle all optimizations. A set of
    additional verbs is also provided to facilitate some common operations on a subset of the data.
Create HTML tables of descriptive statistics, as one would expect
    to see as the first table (i.e. "Table 1") in a medical/epidemiological journal
    article.
Table 1 is the classical way to describe the patients in a
    clinical study. The amount of splits in the data in such a table is
    limited. Table1Heatmap draws a heatmap of all crosstables that can be
    generated with the data. Users can choose between showing the actual
    crosstables or direction of effect of associations, and highlight
    associations by number of patients or p-values.
A collection of time-saving wrappers for reproducible export of
    summary tables commonly used in scientific articles, to .xls/.xlsx multi-tab spreadsheets, while controlling spreadsheet layout. Powered by 'XLConnect'/'rJava' utilities.
A tool to create and style HTML tables with CSS. These can
    be exported and used in any application that accepts HTML (e.g. 'shiny',
    'rmarkdown', 'PowerPoint'). It also provides functions to create CSS files
    (which also work with shiny).
Provides two classes extending 'data.table' class. Simple
    'tableList' class wraps 'data.table' and any additional structures together.
    More complex 'tableMatrix' class combines 'data.table' and
    'matrix'. See <http://github.com/InferenceTechnologies/tableMatrix> for more
    information and examples.
Provides a user friendly interface to 
  generation of booktab style tables using 'xtable'.
Creates 'Table 1', i.e., description of baseline patient
    characteristics, which is essential in every medical research.
    Supports both continuous and categorical variables, as well as
    p-values and standardized mean differences. Weighted data are
    supported via the 'survey' package. See 'github' for a screen cast.
    'tableone' was inspired by descriptive statistics functions in
    'Deducer' , a Java-based GUI package by Ian Fellows. This package
    does not require GUI or Java, and intended for command-line users.
'R' interface to the 'Tabler' HTML template. See more here <https://tabler.io>. 
    'tablerDash' is a light 'Bootstrap 4' dashboard template. There are different 
     layouts available such as a one page dashboard or a multi page template,
     where the navigation menu is contained in the navigation bar. A fancy example
     is available at <https://dgranjon.shinyapps.io/shinyMons/>.
Computes and displays complex tables of summary statistics.
  Output may be in LaTeX, HTML, plain text, or an R
  matrix for further processing.
Allows to work with 'Table Schema' (<http://specs.frictionlessdata.io/table-schema/>). 'Table Schema' is well suited for use cases around handling and validating tabular data in text formats such as 'csv', but its utility extends well beyond this core usage, towards a range of applications where data benefits from a portable schema format. The 'tableschema.r' package can load and validate any table schema descriptor, allow the creation and modification of descriptors, expose methods for reading and streaming data that conforms to a 'Table Schema' via the 'Tabular Data Resource' abstraction.
A wrapper to a set of algorithms designed to recognise positional cues present in hierarchical for-human Tables (which would normally be interpreted visually by the human brain) to decompose, then reconstruct the data into machine-readable LongForm Dataframes.
A tableplot is a visualisation of a (large) dataset with a dozen of variables, both numeric and categorical. Each column represents a variable and each row bin is an aggregate of a certain number of records. Numeric variables are visualized as bar charts, and categorical variables as stacked bar charts. Missing values are taken into account. Also supports large 'ffdf' datasets from the 'ff' package.
Creates guitar tablature from R code by providing functions for describing and organizing musical structures and wrapping around the 'LilyPond' backend (<http://lilypond.org>).
    'LilyPond' is open source music engraving software for generating high quality sheet music based on markup language. 'tabr' provides a wrapper around this
    software and generates files following the 'LilyPond' markup syntax to be subsequently processed by 'LilyPond' into sheet music pdf files.
    A standalone 'LilyPond' file can be created or the package can make a system call to 'LilyPond' directly to render the guitar tablature output.
    While 'LilyPond' caters to sheet music in general, 'tabr' is focused on leveraging it specifically for creating quality guitar tablature.
    'tabr' offers nominal MIDI file support in addition to its focus on tablature transcription. See the 'tuneR' package for more general use of MIDI files in R.
An easy way to examine archaeological count data (artifacts, faunal 
    remains, etc.). This package includes several measures of diversity: e.g. 
    richness and rarefaction (Chao1, Chao2, ACE, ICE, etc.), diversity/dominance 
    and evenness (Brillouin, Shannon, Simpson, etc.), turnover and similarity 
    (Brainerd-Robinson, ...). Most of these methods are described and discussed 
    in Maguran (1988) <doi:10.1007/978-94-015-7358-0>. It also provides matrix 
    seriation methods (reciprocal ranking, CA-based seriation) for 
    chronological modeling and dating. The package make it easy to visualize 
    count data and statistical thresholds: rank/abundance plots, Ford (1972) 
    <isbn:0913134082> and Bertin (1977) <isbn:2082111121> diagrams, etc.
Facilities to work with vector and raster data in efficient 
 repeatable and systematic work flow. Missing functionality in existing packages 
 is included here to allow extraction from raster data with 'simple features' and 
 'Spatial' types and to make extraction consistent and straightforward. Extract cell 
 numbers from raster data and  return the cells, values and weights as a data frame 
 rather than as lists of matrices or vectors. The functions here allow spatial data 
 to be used without special handling for the format currently in use.
Bindings for the 'Tabula' <http://tabula.technology/> 'Java'
    library, which can extract tables from PDF documents. The 'tabulizerjars'
    package <https://github.com/ropensci/tabulizerjars> provides versioned
    'Java' .jar files, including all dependencies, aligned to releases of
    'Tabula'.
'Java' .jar files for the 'Tabula' <http://tabula.technology/>
    'Java' library, which are required by the 'tabulizer' R package. The version
    numbering of this package corresponds to versions of 'tabula-java' library
    releases <https://github.com/tabulapdf/tabula-java/releases/>.
Tabu search algorithm for binary configurations. A basic version of the algorithm as described by Fouskakis and Draper (2007) <doi:10.1111/j.1751-5823.2002.tb00174.x>.
To facilitate the analysis of positron emission tomography (PET) 
    time activity curve (TAC) data, and to encourage open science and 
    replicability, this package supports data loading and analysis of multiple 
    TAC file formats. Functions are available to analyze loaded TAC data for 
    individual participants or in batches. Major functionality includes weighted
    TAC merging by region of interest (ROI), calculating models including 
    standardized uptake value ratio (SUVR) and distribution volume ratio (DVR, 
    Logan et al. 1996 <doi:10.1097/00004647-199609000-00008>), basic plotting 
    functions and calculation of cut-off values (Aizenstein et al. 2008
    <doi:10.1001/archneur.65.11.1509>). Please see the walkthrough vignette for
    a detailed overview of 'tacmagic' functions.
Extensions to 'lattice', providing new high-level
  functions, methods for existing functions, panel functions, and a theme.
Contains functions for the easy display of statistical tests as well as
    some convenience functions for data cleanup. It is meant to ease existing workflows
    with packages like 'sjPlot', 'dplyr', and 'ggplot2'. The primary components are the functions
    prefixed with 'tadaa_', which are built to work in an interactive environment, but also print
    tidy markdown tables powered by 'pixiedust' for the creation of 'RMarkdown' reports.
Provides functions implementing minimal distance estimation methods for parametric tail dependence models.
Set of tools to estimate the probability in the upper tail of the aggregate loss distribution using different methods: Panjer recursion, Monte Carlo simulations, Markov bound, Cantelli bound, Moment bound, and Chernoff bound.
Implements meta-programming functions for automatically
    translating recursive functions into looping functions or trampolines.
Implements the tail-rank statistic for selecting biomarkers
  from a microarray data set, an efficient nonparametric test focused
  on the distributional tails. See
  <http://bioinformatics.mdanderson.org/TailRank/tolstoy-new.pdf>.
A tool to help create shiny apps for selecting and annotating 
    elements of images. Users must supply images, questions, and answer
    choices. The user interface is a dynamic shiny app, that displays the images
    and questions and answer choices. The data generated can be saved to a
    file that can be used for subsequent analysis. The original purpose was to
    annotate still images from tennis video for face recognition and emotion 
    detection purposes.
It includes functions for applying methodologies utilized for single-process kinetic analysis of solid-state processes were recently summarized and described in the Recommendation of ICTAC Kinetic Committee. These methods work with the basic kinetic equation. The Methodologies included refers to  Avrami, Friedman, Kissinger, Ozawa, OFM, Mo, Starink, isoconversional methodology (Vyazovkin) according to ICATAC Kinetics Committee recommendations as reported in Vyazovkin S, Chrissafis K, Di Lorenzo ML, et al. ICTAC Kinetics Committee recommendations for collecting experimental thermal analysis data for kinetic computations. Thermochim Acta. 2014;590:1-23. <doi:10.1016/J.TCA.2014.05.036> .
Includes marginal maximum likelihood estimation and joint maximum
    likelihood estimation for unidimensional and multidimensional 
    item response models. The package functionality covers the 
    Rasch model, 2PL model, 3PL model, generalized partial credit model, 
    multi-faceted Rasch model, nominal item response model, 
    structured latent class model, mixture distribution IRT models, 
    and located latent class models. Latent regression models and 
    plausible value imputation are also supported. For details see
    Adams, Wilson and Wang, 1997 <doi:10.1177/0146621697211001>,
    Adams, Wilson and Wu, 1997 <doi:10.3102/10769986022001047>,
    Formann, 1982 <doi:10.1002/bimj.4710240209>,
    Formann, 1992 <doi:10.1080/01621459.1992.10475229>.
The Tanaka method enhances the representation of topography on a map using shaded contour lines. In this simplified implementation of the method, north-west white contours represent illuminated topography and south-east black contours represent shaded topography.
Density, distribution function, quantile function, random
    generation and survival function for the Tangent Burr Type XII Distribution as
    defined by SOUZA, L. New Trigonometric Class of Probabilistic Distributions.
    219 p. Thesis (Doctorate in Biometry and Applied Statistics) - Department of
    Statistics and Information, Federal Rural University of Pernambuco, Recife,
    Pernambuco, 2015 (available at <http://www.openthesis.org/documents/New-trigonometric-classes-probabilistic-distributions-602633.html>) and BRITO, C. C. R. Method Distributions generator and
    Probability Distributions Classes. 241 p. Thesis (Doctorate in Biometry and
    Applied Statistics) - Department of Statistics 
    University of Pernambuco, Recife, Pernambuco, 2014 (available upon request).
A two-stage regression method that can be used when various input data types are correlated, for example gene expression and methylation in drug response prediction. In the first stage it uses the upstream features (such as methylation) to predict the response variable (such as drug response), and in the second stage it uses the downstream features (such as gene expression) to predict the residuals of the first stage. In our manuscript (Aben et al., 2016, <doi:10.1093/bioinformatics/btw449>), we show that using TANDEM prevents the model from being dominated by gene expression and that the features selected by TANDEM are more interpretable.
Provides an extensible formula system to quickly and easily create
    production quality tables. The steps of the process are formula parser,
    statistical content generation from data, to rendering. Each step of the process
    is separate and user definable thus creating a set of building blocks for
    highly extensible table generation. A user is not limited by any of the 
    choices of the package creator other than the formula grammar. For example,
    one could chose to add a different S3 rendering function and output a format
    not provided in the default package. Or possibly one would rather have Gini
    coefficients for their statistical content. Routines to achieve New England
    Journal of Medicine style, Lancet style and Hmisc::summaryM() statistics are
    provided. The package contains rendering for HTML5, Rmarkdown and an indexing
    format for use in tracing and tracking are provided.
Implementation of functions for fitting taper curves (a semiparametric linear mixed effects taper model) to diameter measurements along stems. Further functions are provided to estimate the uncertainty around the predicted curves, to calculate timber volume (also by sections) and marginal (e.g., upper) diameters. For cases where tree
  heights are not measured, methods for estimating
  additional variance in volume predictions resulting from uncertainties in
  tree height models (tariffs) are provided.  The example data include the taper curve parameters for Norway spruce used in the 3rd German NFI fitted to 380 trees and a subset of section-wise diameter measurements of these trees. The functions implemented here are detailed in the following publication: Kublin, E., Breidenbach, J., Kaendler, G. (2013) A flexible stem taper and volume prediction method based on mixed-effects B-spline regression, Eur J For Res, 132:983-997.
Wrapper for using 'tapkee' command line utility, it allows to run it from inside R and catch the results for further analysis and plotting. 'Tapkee' is a program for fast dimension reduction (see <http://tapkee.lisitsyn.me/> for more details).
Manager of tick-by-tick transaction data that performs 'cleaning', 'aggregation' and 'import' in an efficient and fast way. The package engine, written in C++, exploits the 'zlib' and 'gzstream' libraries to handle gzipped data without need to uncompress them. 'Cleaning' and 'aggregation' are performed according to Brownlees and Gallo (2006) <DOI:10.1016/j.csda.2006.09.030>. Currently, TAQMNGR processes raw data from WRDS (Wharton Research Data Service, <https://wrds-web.wharton.upenn.edu/wrds/>).
Identification and estimation of the autoregressive threshold models with Gaussian noise, as well as positive-valued time series. The package provides the identification of the number of regimes, the thresholds and the autoregressive orders, as well as the estimation of remain parameters. The package implements the methodology from the 2005 paper: Modeling Bivariate Threshold Autoregressive Processes in the Presence of Missing Data <DOI:10.1081/STA-200054435>.
Implement the Tariff algorithm for coding cause-of-death from verbal autopsies. The Tariff method was originally proposed in James et al (2011) <DOI:10.1186/1478-7954-9-31> and later refined as Tariff 2.0 in Serina, et al. (2015) <DOI:10.1186/s12916-015-0527-9>. Note that this package was not developed by authors affiliated with the Institute for Health Metrics and Evaluation and thus unintentional discrepancies may exist between the this implementation and the implementation available from IHME.
A collection of various utility and convenience functions.
Interactive shiny application for working with textmining and text analytics. Various visualizations  are provided.
Schedule R scripts/processes with the Windows task scheduler. This
    allows R users to automate R processes on specific time points from R itself.
The ta-test is a modified two-sample or two-group t-test of Gosset (1908). In small samples with less than 15 replicates,the ta-test significantly reduces type I error rate but has almost the same power with the t-test and hence can greatly enhance reliability or reproducibility of discoveries in biology and medicine. The ta-test can test single null hypothesis or multiple null hypotheses without needing to correct p-values.
Functions to combine data.frames in ways that require additional effort in 
  base R, and to add metadata (id, title, ...) that can be used for printing and 
  xlsx export. The 'Tatoo_report' class is provided as a 
  convenient helper to write several such tables to a workbook, one table per 
  worksheet. Tatoo is built on top of 'openxlsx', but intimate knowledge of 
  that package is not required to use tatoo.
Evaluates traveltimes and ray paths using predefined Earth
        (or other planet) models.  Includes phase plotting routines.
        The IASP91 (Kennett and Engdahl, 1991 <doi:10.1111/j.1365-246X.1991.tb06724.x>)
	and AK135 (Kennett et al., 1995 <doi:10.1111/j.1365-246X.1995.tb03540.x>)
	Earth models are included, and most important arrival phases can be
	evaluated.
Computes the t* statistic corresponding to the tau* population
          coefficient introduced by Bergsma and Dassios (2014) <DOI:10.3150/13-BEJ514>
          and does so in O(n^2) time following the algorithm of Heller and
          Heller (2016) <arXiv:1605.08732> building off of the work of Weihs,
          Drton, and Leung (2016) <DOI:10.1007/s00180-015-0639-x>. Also allows for
          independence testing using the asymptotic distribution of t* as described by
          Nandy, Weihs, and Drton (2016) <arXiv:1602.04387>.
'Tautulli' (<http://tautulli.com>) is a monitoring application for 'Plex' Media Servers 
  (<https://www.plex.tv>) which collects a lot of data about media items and server 
  usage such as play counts. This package interacts with the 'Tautulli' API of any 
  specified server to get said data into R. The 'Tautulli' API documentation is available
  at <https://github.com/Tautulli/Tautulli/blob/master/API.md>.
Portfolio optimization typically requires an estimate of a covariance matrix of asset returns. There are many approaches for constructing such a covariance matrix, some using the sample covariance matrix as a starting point. This package provides implementations for two such methods: random matrix theory and shrinkage estimation. Each method attempts to clean or remove noise related to the sampling process from the sample covariance matrix.
Base library of types for tawny and related packages. This is meant to be a common library and has no functionality aside from defining types.
Provides taxonomic classes for
    groupings of taxonomic names without data, and those
    with data. Methods provided are "taxonomically aware", in
    that they know about ordering of ranks, and methods that
    filter based on taxonomy also filter associated data.
Computation and visualization of Taxicab Correspondence Analysis, Choulakian (2006) <doi:10.1007/s11336-004-1231-4>.  Classical correspondence analysis (CA) is a statistical method to analyse 2-dimensional tables of positive numbers and is typically applied to contingency tables (Benzecri, J.-P. (1973). L'Analyse des Donnees. Volume II. L'Analyse des Correspondances. Paris, France: Dunod).  Classical CA is based on the Euclidean distance.  Taxicab CA is like classical CA but is based on the Taxicab or Manhattan distance.  For some tables, Taxicab CA gives more informative results than classical CA.
Interacts with a suite of web 'APIs' for taxonomic tasks,
    such as getting database specific taxonomic identifiers, verifying
    species names, getting taxonomic hierarchies, fetching downstream and
    upstream taxonomic names, getting taxonomic synonyms, converting
    scientific to common names and vice versa, and more.
Tools for working with 'taxonomic' databases, including
    utilities for downloading databases, loading them into various
    'SQL' databases, cleaning up files, and providing a 'SQL' connection
    that can be used to do 'SQL' queries directly or used in 'dplyr'.
Handling taxonomic lists through objects of class 'taxlist'.
    This package provides functions to import species lists from 'Turboveg'
    (<https://www.synbiosys.alterra.nl/turboveg>) and the possibility to create
    backups from resulting R-objects.
    Also quick displays are implemented as summary-methods.
Functions for assigning taxonomy to NCBI accession numbers and taxon IDs based on NCBI's accession2taxid and taxdump files. This package allows the user to downloads NCBI data dumps and create a local database for fast and local taxonomic assignment.
Automated standardization of taxonomic names and removal of orthographic errors in plant species names using 'The Plant List' website (www.theplantlist.org).
Some tools to work with taxonomic name lists.
Solves Teitz and Bart's p-median problem - given a set of
    points attempts to find subset of size p such that summed distances of any
    point in the set to the nearest point in p is minimised.  Although
    generally effective, this algorithm does not guarantee that a globally
    optimal subset is found.
Estimation of the survivor average causal effect under outcomes truncated by death, which requires the existence of a substitution variable. It can be applied to both experimental and observational data.
This package provides functions to assist researchers
        working in the field of tuberculosis diagnostics.  Functions
        for the interpretation of two popular interferon-gamma release
        assays are provided, and additional functionality is planned.
Our method introduces mathematically well-defined measures for tightness of branches in a hierarchical tree. Statistical significance of the findings is determined, for all branches of the tree, by performing permutation tests, optionally with generalized Pareto p-value estimation.
Extends the test-based Bayes factor (TBF) methodology to multinomial regression models and discrete time-to-event models with competing risks. The TBF methodology has been well developed and implemented for the generalised linear model [Held et al. (2015) <doi:10.1214/14-STS510>] and for the Cox model [Held et al. (2016) <doi:10.1002/sim.7089>].
Facilitate the movement between data frames to 'xts'. Particularly
    useful when moving from 'tidyverse' to the widely used 'xts' package, which is
    the input format of choice to various other packages. It also allows the user 
    to use a 'spread_by' argument for a character column 'xts' conversion.
Boosting the likelihood of conditional and shift transformation models.
Provides rolling statistical functions based
    on date and time windows instead of n-lagged observations.
Functions to perform the reliability/survival
  analysis using a parametric Transform-both-sides (TBS) model.
Tensor Composition Analysis (TCA) allows the deconvolution of two-dimensional data (features by observations) coming from a mixture of sources into a three-dimensional matrix of signals (features by observations by sources). TCA further allows to test the features in the data for different statistical relations with an outcome of interest while modeling source-specific effects (TCA regression); particularly, it allows to look for statistical relations between source-specific signals and an outcome. For example, TCA can deconvolve bulk tissue-level DNA methylation data (methylation sites by individuals) into a tensor of cell-type-specific methylation levels for each individual (methylation sites by individuals by cell types) and it allows to detect cell-type-specific relations (associations) with an outcome of interest. For more details see Rahmani et al. (2018) <DOI:10.1101/437368>.
Automatically downloads and processes TCGA genomics and clinical data into a format convenient for statistical analyses in the R environment.
The Cancer Genome Atlas (TCGA) is a program aimed at improving our understanding of Cancer Biology. Several TCGA Datasets are available online. 'TCGAretriever' helps accessing and downloading TCGA data hosted on 'cBioPortal' via its Web Interface (see <http://www.cbioportal.org/web_api.jsp> for more information). Features of 'TCGAretriever' include: 1) it is very simple to use (get all the TCGA data you need with a few lines of code); 2) performance (smooth and reliable data download via 'httr'); 3) it is tailored for downloading large volumes of data.
Implementation of Time-course Gene Set Analysis (TcGSA), a method for analyzing longitudinal gene-expression data at the gene set level.
Analyze RNA-seq data with variance component score test accounting 
    for data heteroscedasticity through precision weights. Perform both gene-wise and gene set
    analyses, and can deal with longitudinal data. Method is detailed in: Agniel D & Hejblum BP (2017) 
    Variance component score test for time-course gene set analysis of longitudinal RNA-seq data, 
    Biostatistics, 18(4):589-604.
A wrapper for The Cancer Imaging Archive's REST API. The Cancer
    Imaging Archive (TCIA) hosts de-identified medical images of cancer
    available for public download, as well as rich metadata for each image
    series. TCIA provides a REST API for programmatic access to the data.
    This package provides simple functions to access each API endpoint.
    For more information, see <https://github.com/pamelarussell/TCIApathfinder>
    and TCIA's website.
A series of additional Tcl commands and Tk widgets with style
  and various functions (under Windows: DDE exchange, access to the
  registry and icon manipulation) to supplement the tcltk package
Provides functions for robust trimmed clustering. The methods are described in Garcia-Escudero (2008) <doi:10.1214/07-AOS515>, Fritz et al. (2012) <doi:10.18637/jss.v047.i12> and others.
The 1311 time series from the tourism forecasting competition conducted in 2010 and described in Athanasopoulos et al. (2011) <DOI:10.1016/j.ijforecast.2010.04.009>.
A set of tools for processing and modeling high-throughput and
    high-content chemical screening data. The package was developed for the
    the chemical screening data generated by the US EPA ToxCast program, but
    can be used for diverse chemical screening efforts.
Platform for the advanced analysis of T cell receptor and
    Immunoglobulin repertoires data and visualisation of the analysis results.
Tools for the statistical analysis of persistent homology and for
    density clustering. For that, this package provides an R interface for the
    efficient algorithms of the C++ libraries 'GUDHI' <http://gudhi.gforge.inria.fr/>, 'Dionysus' <http://www.mrzv.org/software/dionysus/>, and 'PHAT' <https://bitbucket.org/phat-code/phat/>. This package also implements the methods in Fasy et al. (2014) <doi:10.1214/14-AOS1252> and Chazal et al. (2014) <doi:10.1145/2582112.2582128>  for analyzing the statistical significance of persistent homology features.
Topological Data Analysis using Mapper (discrete Morse theory).
    Generate a 1-dimensional simplicial complex from a filter 
    function defined on the data:  1. Define a filter function (lens) on the 
    data.  2. Perform clustering within within each level set and generate 
    one node (vertex) for each cluster.  3. For each pair of clusters in 
    adjacent level sets with a nonempty intersection, generate one edge 
    between vertices.  The function mapper1D uses a filter function with
    codomain R, while the the function mapper2D uses a filter function with
    codomain R^2.
A comprehensive toolset for any
    useR conducting topological data analysis, specifically via the
    calculation of persistent homology in a Vietoris-Rips complex.
    The tools this package currently provides can be conveniently split
    into three main sections: (1) calculating persistent homology; (2)
    conducting statistical inference on persistent homology calculations;
    (3) visualizing persistent homology and statistical inference.
    The published form of TDAstats can be found in Wadhwa et al. (2018)
    <doi:10.21105/joss.00860>.   
    For a general background on computing persistent homology for
    topological data analysis, see Otter et al. (2017)
    <doi:10.1140/epjds/s13688-017-0109-5>.
    To learn more about how the permutation test is used for
    nonparametric statistical inference in topological data analysis,
    read Robinson & Turner (2017) <doi:10.1007/s41468-017-0008-7>.
    To learn more about how TDAstats calculates persistent homology,
    you can visit the GitHub repository for Ripser, the software that
    works behind the scenes at <https://github.com/Ripser/ripser>.
    This package has been published as Wadhwa et al. (2018)
    <doi:10.21105/joss.00860>.
A boosted Tweedie compound Poisson model using the gradient boosting. It is capable of fitting a flexible nonlinear Tweedie compound Poisson model (or a gamma model) and capturing interactions among predictors.
The Time-Delay Correlation algorithm (TDCor) reconstructs the topology of a gene regulatory network (GRN) from time-series transcriptomic data.  The algorithm is described in details in Lavenus et al., Plant Cell, 2015.  It was initially developed to infer the topology of the GRN controlling lateral root formation in Arabidopsis thaliana.  The time-series transcriptomic dataset which was used in this study is included in the package to illustrate how to use it.
Deconvolution of instrument responses from seismic traces and seismogram lists from RSEIS.  Includes pre-calculated instrument responses for several common instruments.
Tuned Data Mining in R ('TDMR') performs the complete tuning of a 
    data mining task (predictive analytics, that is classification and regression). 
    Preprocessing parameters and modeling parameters can be tuned simultaneously. 
    It incorporates a variety of tuners (among them 'SPOT' and 'CMA' with package 'rCMA') and 
    allows integration of additional tuners. Noise handling in the data mining optimization  
    process is supported, see Koch et al. (2015) <doi:10.1016/j.asoc.2015.01.005>.
Set of functions designed to help in the
    analysis of TDP sensors. Features includes dates and time conversion, weather
    data interpolation, daily maximum of tension analysis and calculations required
    to convert sap flow density data to sap flow rates at the tree and plot scale (For more information see : Granier (1985) <DOI:10.1051/forest:19850204> & Granier (1987) <DOI:10.1093/treephys/3.4.309>).
Implementation of target diagrams using 'lattice' and 'ggplot2' graphics. Target diagrams provide a graphical overview of the respective contributions of the unbiased RMSE and MBE to the total RMSE (Jolliff, J. et al., 2009. "Summary Diagrams for Coupled Hydrodynamic-Ecosystem Model Skill Assessment." Journal of Marine Systems 76: 64–82.)
Compute time-dependent ROC curve from censored survival data using
    nonparametric weight adjustments.
Functions for performing time domain signal coding as used in Chesmore (2001) <doi:10.1016/S0003-682X(01)00009-3>, and related tasks. This package creates the standard S-matrix and A-matrix (with variable lag), has tools to convert coding matrices into distributed matrices, provides published codebooks and allows for extraction of code sequences.
Functions and examples are provided for Transmission/disequilibrium tests
             for extended marker haplotypes, as in
             Clayton, D. and Jones, H. (1999) "Transmission/disequilibrium tests
             for extended marker haplotypes". Amer. J. Hum. Genet., 65:1161-1169,
             <doi:10.1086/302566>.
Provides functions to estimate the insertion and deletion rates of transposable element (TE) families. The estimation of insertion rate consists of an improved estimate of the age distribution that takes into account random mutations, and an adjustment by the deletion rate. A hypothesis test for a uniform insertion rate is also implemented. This package implements the methods proposed in Dai et al (2018).
Different approaches for selecting the threshold in generalized Pareto distributions. Most of them are based on minimizing the AMSE-criterion or at least by reducing the bias of the assumed GPD-model. Others are heuristically motivated by searching for stable sample paths, i.e. a nearly constant region of the tail index estimator with respect to k, which is the number of data in the tail. The third class is motivated by graphical inspection. In addition to the very helpful eva package which includes many goodness of fit tests for the generalized Pareto distribution, the sequential testing procedure provided in Thompson et al. (2009) <doi:10.1016/j.coastaleng.2009.06.003> is also implemented here.
Several functions for communicating Bayesian thinking including Bayes rule for deciding among spinners, visualizations for Bayesian inference for one proportion and for one mean, and comparison of two proportions using a discrete prior.
Contains apps and gadgets for teaching data analysis and 
    statistics concepts along with how to implement them in R.  Includes 
    tools to make app development easier and faster by nesting apps together.
Demonstration functions that can be used in a classroom to demonstrate statistical concepts, or on your own to better understand the concepts or the programming.
Allows the user to draw probabilistic samples and make inferences from a finite population based on several sampling designs.
Can fit neural networks with up to two hidden layer and two different error functions. Also able to handle a weight decay. But just able to compute one output neuron and very slow.
Implements models of leaf temperature using energy balance. It uses units to ensure that parameters are properly specified and transformed before calculations. It allows separate lower and upper surface conductances to heat and water vapour, so sensible and latent heat loss are calculated for each surface separately  as in Foster and Smith (1986) <doi:10.1111/j.1365-3040.1986.tb02108.x>. It's straightforward to model leaf temperature over environmental gradients such as light, air temperature, humidity, and wind. It can also model leaf temperature over trait gradients such as leaf size or stomatal conductance. Other references are Monteith and Unsworth (2013, ISBN:9780123869104), Nobel (2009, ISBN:9780123741431), and Okajima et al. (2012) <doi:10.1007/s11284-011-0905-5>.
Provides color palettes corresponding to professional sports teams. These
    can be useful in creating data graphics that are themed for particular teams.
TED performs Turbulence time series Event Detection and classification.
The typicality and eccentricity data analysis (TEDA) framework was
    put forward by Angelov (2013) <DOI:10.14313/JAMRIS_2-2014/16>. It has been further developed into multiple
    different techniques since, and provides a non-parametric way of determining how
    similar an observation, from a process that is not purely random, is to other
    observations generated by the process. This package provides code to use the
    batch and recursive TEDA methods that have been published.
For fitting multiple linear regressions, the ordinary least squares approach is sensitive to outliers and/or violations of model assumptions. The trimmed elemental estimators are more robust to such situations. This package contains functions for computing the trimmed elemental estimates, as well as for creating the bias-corrected and accelerated bootstrap confidence intervals based on elemental regressions.
Fits mixtures of multivariate t-distributions (with eigen-decomposed covariance structure) via the expectation conditional-maximization algorithm under a clustering or classification paradigm.
Functions for interacting directly with the Taiwan Economic Journal API to offer data in R. For more information go to <https://api.tej.com.tw>.
Implementation of the remote effects spatial process (RESP) model for teleconnection.  The RESP model is a geostatistical model that allows a spatially-referenced variable (like average precipitation) to be influenced by covariates defined on a remote domain (like sea surface temperatures).  The RESP model is introduced in Hewitt et al. (2018) <doi:10.1002/env.2523>.  Sample code for working with the RESP model is available at <https://jmhewitt.github.io/research/resp_example>. This material is based upon work supported by the National Science Foundation under grant number AGS 1419558. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
R wrapper around the Telegram Bot API (http://core.telegram.org/bots/api) to access Telegram's messaging facilities with ease (e.g. you send messages, images, files from R to your smartphone).
Provides a pure interface for the 'Telegram Bot API'
    <http://core.telegram.org/bots/api>. In addition to the pure API
    implementation, it features a number of tools to make the development of
    'Telegram' bots with R easy and straightforward, providing an easy-to-use
    interface that takes some work off the programmer.
Using The Free Evocation of Words Technique method with some functions, this package will make a
    social representation and other analysis. The Free Evocation of Words Technique consists of collecting a number of words evoked by a subject facing exposure to an inducer term. The purpose of this technique is to understand the relationships created between words evoked by the individual and the inducer term. This technique is included in the theory of social representations, therefore, on the information transmitted by an individual, seeks to create a profile that define a social group.
Method to estimate the effect of the trend in predictor variables on the observed trend
 of the response variable using mixed models with temporal autocorrelation. See Fernández-Martínez et
 al. (2017 and 2019) <doi:10.1038/s41598-017-08755-8> <doi:10.1038/s41558-018-0367-7>.
This is the data companion package to the package tempcycles.
    This package includes the metadata, linear, and cycling parameters from
    "Recent geographic convergence in diurnal and annual temperature cycling
    flattens global thermal profiles", Wang & Dillon, Nature Climate Change,
    4, 988-992 (2014). doi:10.1038/nclimate2378.
Temporal disaggregation methods are used to disaggregate and
    interpolate a low frequency time series to a higher frequency series, where
    either the sum, the average, the first or the last value of the resulting
    high frequency series is consistent with the low frequency series. Temporal
    disaggregation can be performed with or without one or more high frequency
    indicator series. Contains the methods of Chow-Lin, Santos-Silva-Cardoso, 
    Fernandez, Litterman, Denton and Denton-Cholette.
Fits temperature response models to rate measurements taken at different temperatures. Etienne Low-Decarie,Tobias G. Boatman, Noah Bennett,Will Passfield,Antonio Gavalas-Olea,Philipp Siegel, Richard J. Geider (2017) <doi:10.1002/ece3.3576> .
Provides tools to work with template code and text in R. It aims to
    provide a simple substitution mechanism for R-expressions inside these
    templates. Templates can be written in other languages like 'SQL', can
    simply be represented by characters in R, or can themselves be R-expressions
    or functions.
Estimates conditional distributions and conditional quantiles.  The versions of the methods in this package are primarily for use in multiple step procedures where the first step is to estimate a conditional distribution.  In particular, there are functions for implementing distribution regression, quantile regression, and versions of local linear distribution regression; all in a unified framework.  Distribution regression provides a way to flexibly model the distribution of some outcome Y conditional on covariates X without imposing parametric assumptions on the conditional distribution but providing more structure than fully nonparametric estimation (See Foresi and Peracchi (1995) <doi:10.2307/2291056> and Chernozhukov, Fernandez-Val, and Melly (2013) <doi:10.3982/ECTA10582>).
TEMPO (TEmporal Modeling of Pathway Outliers) is a pathway-based outlier detection approach for finding pathways showing 
    significant changes in temporal expression patterns across conditions.  Given a gene expression data set where each sample is characterized by 
    an age or time point as well as a phenotype (e.g. control or disease), and a collection of gene sets or pathways, TEMPO ranks each pathway
    by a score that characterizes how well a partial least squares regression (PLSR) model can predict age as a function of gene expression in the controls
    and how poorly that same model performs in the disease. TEMPO v1.0.3 is described in Pietras (2018) <doi:10.1145/3233547.3233559>.
Performs likelihood-based estimation and inference on time to event data, possibly subject to non-informative right censoring. fitParaSurv() provides maximum likelihood estimates of model parameters and distributional characteristics. compParaSurv() compares the mean and median survival experiences of two treatment arms. Candidate distributions currently include the exponential, gamma, generalized gamma, log-logistic, log-normal, and Weibull.
Analysis and visualization of data from temporal sensory methods, including for temporal check-all-that-apply (TCATA) and temporal dominance of sensations (TDS).
Implementation of ZENIT-POLAR substitution cipher method of encryption using by default the TENIS-POLAR cipher. This last cipher of encryption became famous through the collection of Brazilian books "Os Karas" by the author Pedro Bandeira. For more details, see "A Cryptographic Dictionary" (GC&CS, 1944).
The tensor product of two arrays is notionally an outer
        product of the arrays collapsed in specific extents by summing
        along the appropriate diagonals.
Provides convenience functions for advanced
        linear algebra with tensors and computation with datasets of
        tensors on a higher level abstraction. It includes Einstein and
        Riemann summing conventions, dragging, co- and contravariate
        indices, parallel computations on sequences of tensors.
Bayesian Tensor Factorization for decomposition of tensor data sets using the trilinear CANDECOMP/PARAFAC (CP) factorization, with automatic component selection. The complete 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 method performs factorization for three-way tensor datasets and the inference is implemented with Gibbs sampling.
Contains several utility functions for manipulating tensor-valued data (centering, multiplication from a single mode etc.) and the implementations of the following blind source separation methods for tensor-valued data: 'tPCA', 'tFOBI', 'tJADE', k-tJADE', 'tgFOBI', 'tgJADE', 'tSOBI', 'tNSS.SD', 'tNSS.JD', 'tNSS.TD.JD', 'tPP' and 'tTUCKER'.
Interface to 'TensorFlow' <https://www.tensorflow.org/>, 
  an open source software library for numerical computation using data
  flow graphs. Nodes in the graph represent mathematical operations, 
  while the graph edges represent the multidimensional data arrays 
  (tensors) communicated between them. The flexible architecture allows
  you to deploy computation to one or more 'CPUs' or 'GPUs' in a desktop, 
  server, or mobile device with a single 'API'. 'TensorFlow' was originally
  developed by researchers and engineers working on the Google Brain Team 
  within Google's Machine Intelligence research organization for the 
  purposes of conducting machine learning and deep neural networks research,
  but the system is general enough to be applicable in a wide variety
  of other domains as well.
Provides methods to manipulate and store sparse tensors. Tensors 
  are multidimensional generalizations of matrices (two dimensional) and 
  vectors (one dimensional).
A collection of functions for Kronecker structured covariance
    estimation and testing under the array normal model. For estimation,
    maximum likelihood and Bayesian equivariant estimation procedures are
    implemented. For testing, a likelihood ratio testing procedure is
    available. This package also contains additional functions for manipulating
    and decomposing tensor data sets. This work was partially supported by NSF
    grant DMS-1505136. Details of the methods are described in
    Gerard and Hoff (2015) <doi:10.1016/j.jmva.2015.01.020> and
    Gerard and Hoff (2016) <doi:10.1016/j.laa.2016.04.033>.
The TEQR package contains software to calculate the operating characteristics for the TEQR and the ACT designs.The TEQR (toxicity equivalence range) design is a toxicity based cumulative cohort design with added safety rules. The ACT (Activity constrained for toxicity) design  is also a cumulative cohort design with additional safety rules. The unique feature of this design is that dose is escalated based on lack of activity rather than on lack of toxicity and is de-escalated only if an unacceptable level of toxicity is experienced.
This package is for the comparison of various types of A+B
        escalation rules for dose finding trials.
An integrated set of extensions to the 'ergm' package to analyze and simulate network evolution based on exponential-family random graph models (ERGM). 'tergm' is a part of the 'statnet' suite of packages for network analysis.
Plots ternary diagrams using the standard graphics functions.  
  An alternative to 'ggtern', which uses the 'ggplot2' family of plotting functions.
Simulation of reconstructed phylogenetic trees under tree-wide time-heterogeneous birth-death processes and estimation of diversification parameters under the same model. Speciation and extinction rates can be any function of time and mass-extinction events at specific times can be provided. Trees can be simulated either conditioned on the number of species, the time of the process, or both. Additionally, the likelihood equations are implemented for convenience and can be used for Maximum Likelihood (ML) estimation and Bayesian inference.
Bindings to 'Tesseract' <https://opensource.google.com/projects/tesseract>: 
     a powerful optical character recognition (OCR) engine that supports over 100 languages.
     The engine is highly configurable in order to tune the detection algorithms and
     obtain the best possible results.
A common way of validating a biological assay for is through a
    procedure, where m levels of an analyte are measured with n replicates at each
    level, and if all m estimates of the coefficient of variation (CV) are less
    than some prespecified level, then the assay is declared validated for precision
    within the range of the m analyte levels. Two limitations of this procedure are:
    there is no clear statistical statement of precision upon passing, and it is
    unclear how to modify the procedure for assays with constant standard deviation.
    We provide tools to convert such a procedure into a set of m hypothesis tests.
    This reframing motivates the m:n:q procedure, which upon completion delivers
    a 100q% upper confidence limit on the CV. Additionally, for a post-validation
    assay output of y, the method gives an ``effective standard deviation interval''
    of log(y) plus or minus r, which is a 68% confidence interval on log(mu), where
    mu is the expected value of the assay output for that sample. Further, the m:n:q
    procedure can be straightforwardly applied to constant standard deviation assays.
    We illustrate these tools by applying them to a growth inhibition assay.
Different multiple testing procedures for correlation tests are implemented. These procedures were shown to theoretically control asymptotically the Family Wise Error Rate (Roux (2018) <https://tel.archives-ouvertes.fr/tel-01971574v1>) or the False Discovery Rate (Cai & Liu (2016) <doi:10.1080/01621459.2014.999157>). The package gather four test statistics used in correlation testing, four FWER procedures with either single step or stepdown versions, and four FDR procedures.
Functions for imputing missing item responses for dichotomous and
    polytomous test and assessment data. This package enables missing imputation
    methods that are suitable for test and assessment data, including: listwise (LW)
    deletion, treating as incorrect (IN), person mean imputation (PM), item mean
    imputation (IM), two-way imputation (TW), logistic regression imputation (LR),
    and EM imputation.
Provides two approaches of comparison; the univariate and the multivariate analysis in two or more populations. Since the main obstacle of performing systematic comparisons in anthropological studies is the absence of raw data, the current package offer a solution for this problem by allowing the use of published summary statistics of metric data (mean, standard deviation and sex specific sample size) as illustrated by the works of  Greene, D. L. (1989) <doi:10.1002/ajpa.1330790113> and Konigsberg, L. W. (1991) <doi:10.1002/ajpa.1330840110>.
Provides data sets for teaching statistics and data science courses. 
    It includes a sample of data from John Edmund Kerrich's famous 
    coinflip experiment. These are data that I use for teaching SOC 4015 / SOC 
    5050 at Saint Louis University (SLU). The package also contains an R Markdown
    template with the required formatting for assignments in my courses 
    SOC 4015, SOC 4650, SOC 5050, and SOC 5650 at SLU.
tester allows you to test characteristics of common R objects.
A collection of testing enhancements and utilities.
    Including utilities for extracting inline test blocks from 
    package source files.
Provides test statistics, p-value, and confidence intervals based on 9 hypothesis tests for dependence.
Test functions are often used to test computer code.
  They are used in optimization to test algorithms and in
  metamodeling to evaluate model predictions. This package provides
  test functions that can be used for any purpose.
  Some functions are taken from <https://www.sfu.ca/~ssurjano>, but
  their R code is not used.
Provides a bootstrap test which decides whether two dose response curves can be assumed as equal concerning their maximum absolute deviation. A plenty of choices for the model types are available, which can be found in the 'DoseFinding' package, which is used for the fitting of the models.
Provides two convenience functions assert() and test_pkg() to
    facilitate testing R packages.
GUI for entering test items and obtaining raw
   and transformed scores. The results are shown on the
   console and can be saved to a tabular text file for further
   statistical analysis. The user can define his own tests and
   scoring procedures through a GUI.
Software testing is important, but, in part because it is 
    frustrating and boring, many of us avoid it. 'testthat' is a testing framework 
    for R that is easy learn and use, and integrates with your existing 'workflow'.
Utility functions and 'RStudio' addins for writing, running and organizing 
  automated tests. Integrates tightly with the packages 'testthat', 'devtools' 
  and 'usethis'.
  Hotkeys can be assigned to the 'RStudio' addins for running tests in a single
  file or to switch between a source file and the associated test file. In
  addition, testthis provides function to manage and run tests in 
  subdirectories of the test/testthat directory.
The satisfaction Analysis using the tetraclasse model from Sylvie Llosa. Llosa (1997) <http://www.jstor.org/stable/40592578>.
Checks LaTeX documents and .bib files for typing errors, such as spelling errors, incorrect quotation marks. Also provides useful functions for parsing and linting bibliography files.
Randomizing exams with 'LaTeX'.
    If you can compile your main document with 'LaTeX', the program should be able to compile the randomized
    versions without much extra effort when creating the document.
Statistical extreme value modelling of threshold excesses, maxima
    and multivariate extremes. Univariate models for threshold excesses and maxima
    are the Generalised Pareto, and Generalised Extreme Value model respectively.
    These models may be fitted by using maximum (optionally penalised-)likelihood,
    or Bayesian estimation, and both classes of models may be fitted with covariates
    in any/all model parameters. Model diagnostics support the fitting process.
    Graphical output for visualising fitted models and return level estimates is
    provided. For serially dependent sequences, the intervals declustering algorithm
    of Ferro and Segers (2003) <doi:10.1111/1467-9868.00401> is provided, with
    diagnostic support to aid selection of threshold and declustering horizon.
    Multivariate modelling is performed via the conditional approach of Heffernan
    and Tawn (2004) <doi:10.1111/j.1467-9868.2004.02050.x>, with graphical tools for
    threshold selection and to diagnose estimation convergence.
An extension of ExPosition for two table analyses, specifically, discriminant analyses.
Compile and preview snippets of 'LaTeX'. Can be used directly from the R console, from 'RStudio', 
    in Shiny apps and R Markdown documents. Must have 'pdflatex' or 'xelatex' or 'lualatex' in 'PATH'.
Converts coefficients, standard errors, significance stars, and goodness-of-fit statistics of statistical models into LaTeX tables or HTML tables/MS Word documents or to nicely formatted screen output for the R console for easy model comparison. A list of several models can be combined in a single table. The output is highly customizable. New model types can be easily implemented.
Unifies different text to speech engines, such as
    Google, Microsoft, and Amazon.  Text synthesis can be done
    in any engine with a simple switch of an argument denoting
    the service requested.
Fast and memory-friendly tools for text vectorization, topic
    modeling (LDA, LSA), word embeddings (GloVe), similarities. This package
    provides a source-agnostic streaming API, which allows researchers to perform
    analysis of collections of documents which are larger than available RAM. All
    core functions are parallelized to benefit from multicore machines.
Tools to clean and process text.  Tools are geared at checking for substrings that
          are not optimal for analysis and replacing or removing them (normalizing) with more
          analysis friendly substrings (see Sproat, Black, Chen, Kumar, Ostendorf, & Richards
          (2001) <doi:10.1006/csla.2001.0169>) or extracting them into new variables. For
          example, emoticons are often used in text but not always easily handled by analysis
          algorithms.  The replace_emoticon() function replaces emoticons with word
          equivalents.
Provides a framework to download, parse, and store text datasets
    on the disk and load them when needed. Includes various sentiment lexicons
    and labeled text data sets for classification and analysis.
Implements the approach described in Fong and Grimmer (2016) <https://aclweb.org/anthology/P/P16/P16-1151.pdf> for 
	automatically discovering latent treatments from a corpus and estimating the average marginal component effect (AMCE) of 
	each treatment.  The data is divided into a training and test set.  The supervised Indian Buffet Process (sibp) is used 
	to discover latent treatments in the training set.  The fitted model is then applied to the test set to infer the values 
	of the latent treatments in the test set.  Finally, Y is regressed on the latent treatments in the test set to estimate 
	the causal effect of each treatment.
A tool for extracting some generic features (e.g., number of
    words, line breaks, characters per word, URLs, lower case, upper case,
    commas, periods, exclamation points, etc.) from strings of text.
Provides functionalities based on the paper "Time Varying Dictionary and the Predictive Power of FED Minutes" (Lima, 2018) <doi:10.2139/ssrn.3312483>. It selects the most predictive terms, that we call time-varying dictionary using supervised machine learning techniques as lasso and elastic net.
The software application Praat can be used to annotate
    waveform data (e.g., to mark intervals of interest or to label events).
    (See <http://www.fon.hum.uva.nl/praat/> for more information about Praat.)
    These annotations are stored in a Praat TextGrid object, which consists of
    a number of interval tiers and point tiers. An interval tier consists of
    sequential (i.e., not overlapping) labeled intervals. A point tier consists
    of labeled events that have no duration. The 'textgRid' package provides
    S4 classes, generics, and methods for accessing information that is stored
    in Praat TextGrid objects.
Contains real images of the same textile material 
    with/without local defects, which were used in 
    Bui and Apley (2017) <doi:10.1080/00401706.2017.1302362>.
Multinomial (inverse) regression inference for text documents and associated attributes.  For details see: Taddy (2013 JASA) Multinomial Inverse Regression for Text Analysis <arXiv:1012.2098> and Taddy (2015, AoAS), Distributed Multinomial Regression, <arXiv:1311.6139>. A minimalist partial least squares routine is also included.  Note that the topic modeling capability of earlier 'textir' is now a separate package, 'maptpx'.
An aid for text mining in R, with a syntax that
    should be familiar to experienced R users. Provides a wrapper for several 
    topic models that take similarly-formatted input and give similarly-formatted
    output. Has additional functionality for analyzing and diagnostics for
    topic models.
A framework for text mining and topic modelling. It provides an easy interface for using different topic modeling methods within R, by integrating the already existing packages. Full functionality of the package requires a local installation of 'TreeTagger'.
Statistical exploration of textual corpora using several methods
    from French 'Textometrie' (new name of 'Lexicometrie') and French 'Data Analysis' schools.
    It includes methods for exploring irregularity of distribution of lexicon features across
    text sets or parts of texts (Specificity analysis); multi-dimensional exploration (Factorial analysis), etc. 
    Those methods are used in the TXM software.
The 'textrank' algorithm is an extension of the 'Pagerank' algorithm for text. The algorithm allows to summarize text by calculating how sentences are related to one another. This is done by looking at overlapping terminology used in sentences in order to set up links between sentences. The resulting sentence network is next plugged into the 'Pagerank' algorithm which identifies the most important sentences in your text and ranks them. 
    In a similar way 'textrank' can also be used to extract keywords. A word network is constructed by looking if words are following one another. On top of that network the 'Pagerank' algorithm is applied to extract relevant words after which relevant words which are following one another are combined to get keywords.  
    More information can be found in the paper from Mihalcea, Rada & Tarau, Paul (2004) <http://www.aclweb.org/anthology/W04-3252>.
A small collection of convenience tools for reading text documents into R.
Converting text to numerical features requires specifically
    created procedures, which are implemented as steps according to the 
    'recipes' package. These steps allows for tokenization, filtering,
    counting (tf and tfidf) and feature hashing.
Function for sparse regression on raw text, regressing a labeling
    vector onto a feature space consisting of all possible phrases.
Tools for measuring similarity among documents and detecting
    passages which have been reused. Implements shingled n-gram, skip n-gram,
    and other tokenizers; similarity/dissimilarity functions; pairwise
    comparisons; minhash and locality sensitive hashing algorithms; and a
    version of the Smith-Waterman local alignment algorithm suitable for
    natural language.
Tools that can be used to reshape and restructure text data.
Tools that stem and lemmatize text.  Stemming is a process that removes
         endings such as affixes.  Lemmatization is the process of grouping inflected
         forms together as a single base form.
It offers functions for splitting, parsing, tokenizing and creating a vocabulary for big text data files. Moreover, it includes functions for building a document-term matrix and extracting information from those (term-associations, most frequent terms). It also embodies functions for calculating token statistics (collocations, look-up tables, string dissimilarities) and functions to work with sparse matrices. Lastly, it includes functions for Word Vector Representations (i.e. 'GloVe', 'fasttext') and incorporates functions for the calculation of (pairwise) text document dissimilarities. The source code is based on 'C++11' and exported in R through the 'Rcpp', 'RcppArmadillo' and 'BH' packages.
Utilities for handling character vectors
  that store human-readable text (either plain or with
  markup, such as HTML or LaTeX). The package provides,
  in particular, functions that help with the
  preparation of plain-text reports (e.g. for expanding
  and aligning strings that form the lines of such
  reports); the package also provides generic functions for
  transforming R objects to HTML and to plain text.
Interface to 'TensorFlow' Datasets, a high-level library for 
    building complex input pipelines from simple, re-usable pieces. 
    See <https://www.tensorflow.org/programmers_guide/datasets> for additional
    details.
Tools to deploy 'TensorFlow' <https://www.tensorflow.org/> models across 
  multiple services. Currently, it provides a local server for testing 'cloudml' 
  compatible services.
Statistical interpretation of forensic glass transfer
        (Simulation of the probability distribution of recovered glass
        fragments).
Interface to 'TensorFlow' Estimators 
    <https://www.tensorflow.org/programmers_guide/estimators>, a high-level 
    API that provides implementations of many different model types 
    including linear models and deep neural networks.
Interface to 'TensorFlow IO', Datasets and filesystem extensions maintained by `TensorFlow SIG-IO` <https://github.com/tensorflow/community/blob/master/sigs/io/CHARTER.md>.
We provide the cumulative distribution function (CDF),
    quantile, and statistical power calculator for a collection of 
    thresholding Fisher's p-value combination methods, including Fisher's 
    p-value combination method, truncated product method and, in particular, soft-thresholding
    Fisher's p-value combination method which is proven to be optimal in some
    context of signal detection. The p-value calculator for the omnibus version
    of these tests are also included. For reference, please see Hong Zhang and Zheyang Wu. "TFisher Tests: Optimal and Adaptive Thresholding for Combining p-Values", submitted.
In putative Transcription Factor Binding Sites (TFBSs) 
             identification from sequence/alignments,
             we are interested in the significance of certain match score.
             TFMPvalue provides the accurate calculation of P-value with 
             score threshold for Position Weight Matrices, 
             or the score with given P-value. 
             It is an interface to code originally made available by 
             Helene Touzet and Jean-Stephane Varre, 2007, 
             Algorithms Mol Biol:2, 15.
             Touzet and Varre (2007) <DOI:10.1186/1748-7188-2-15>.
Utilities for simple manipulation and quick  	
	plotting of time series data. These utilities use the tframe package
	which provides a programming kernel for time series. Extensions to
	tframe provided in tframePlus can also be used. See the Guide vignette
	for examples.
A kernel of functions for programming 
	time series methods in a way that is relatively independently of the 
	representation of time. Also provides plotting, time windowing, 
	and some
	other utility functions which are specifically intended for time series.
	See the Guide distributed as a vignette, or ?tframe.Intro for more
	details. (User utilities are in package tfplot.)
Extensions and additional 'tframe' utilities.
Create and manage unique directories for each 'TensorFlow' 
  training run. Provides a unique, time stamped directory for each run
  along with functions to retrieve the directory of the latest run or 
  latest several runs.
A collection of useful tools for programming and writing-scripts. 
    Several functions are simple wrappers around base R functions that extend 
    their functionality while also providing some convenient properties–regular
    expression functions that automatically detect look-ahead and look-behind 
    statements, a read-line function that suppresses incomplete-final-line 
    warnings and automatically opens and closes connections, a version of 
    substrings that starts from the end of strings, etc. Other functions are 
    useful for checking whether packages are installed, omitting 
    missing data, and showing in-use connections.
Connects R to TrueFX(tm) for free streaming real-time and
        historical tick-by-tick market data for dealable interbank
        foreign exchange rates with millisecond detail.
Deconvolving thermoluminescence glow curves according to various 
        kinetic models (first-order, second-order, general-order, and mixed-order) using 
        a modified Levenberg-Marquardt algorithm. It provides the possibility of setting 
        constraints or fixing any of parameters. It offers an interactive way to initialize 
        parameters by clicking with a mouse on a plot at positions where peak maxima should 
        be located. The optimal estimate is obtained by "trial-and-error". It also provides 
        routines for simulating first-order, second-order, and general-order glow peaks.
Density, distribution function, quantile function and random generation for the Truncated Generalised Gamma Distribution (also in log10(x) and ln(x) space).
Bayesian nonstationary, semiparametric nonlinear regression 
 and design by treed Gaussian processes (GPs) with jumps to the limiting 
 linear model (LLM).  Special cases also implemented include Bayesian 
 linear models, CART, treed linear models, stationary separable and 
 isotropic GPs, and GP single-index models.  Provides 1-d and 2-d plotting functions 
 (with projection and slice capabilities) and tree drawing, designed for 
 visualization of tgp-class output.  Sensitivity analysis and 
 multi-resolution models are supported. Sequential experimental 
 design and adaptive sampling functions are also provided, including ALM, 
 ALC, and expected improvement.  The latter supports derivative-free
 optimization of noisy black-box functions.
Functions to compute and plot tracheidograms, as in De Soto et al. (2011) <doi:10.1139/x11-045>.
Rapid advancement in high-throughput gene expression measurement technologies has resulted in genome- scale time series datasets. Uncovering the underlying temporal sequence of gene regulatory events in the form of time-varying Gene Regulatory Networks (GRNs) demands computationally fast, accurate and highly scalable algorithms. To provide a flexible framework in a significantly time-efficient manner, a novel algorithm, namely TGS (Ashish Anand et al., 2018 <doi:10.1109/TCBB.2018.2861698>), is proposed here. TGS is shown to consume only 29 minutes for a microarray dataset with 4028 genes. Moreover, it provides the flexibility and time-efficiency, without losing the accuracy. Nevertheless, TGS’s main memory requirement grows exponentially with the number of genes, which it tackles by restricting the maximum number of regulators for each gene. Relaxing this restriction remains an important challenge as the true number of regulators is not known a prior.
Contains data sets used in other packages Torsten Hothorn
  maintains.
Find out who maintains the packages you use in
             your current session or in your package library and
             maybe say 'thank you'.
A tool for starring GitHub repositories.
Provides a simple interface to search available data provided by
    Theia (<https://theia.cnes.fr>), download it, and manage it. Data can be downloaded
    based on a search result or from a cart file downloaded from Theia website.
A means to explore the structure of 16S rRNA surveys using a Structural 
  Topic Model coupled with functional prediction. The user provides an abundance 
  table, sample metadata, and taxonomy information, and themetagenomics infers 
  associations between topics and sample features, as well as topics and predicted 
  functional content. Functional prediction can be accomplished via Tax4Fun (for 
  Silva references) and PICRUSt (for GreenGeenes references).
A collection of functions and routines for inputting thermal
    image video files, plotting and converting binary raw data into estimates of
    temperature.  First published 2015-03-26.  Written primarily for research purposes
    in biological applications of thermal images.  v1 included the base calculations 
    for converting thermal image binary values to temperatures. v2 included additional
    equations for providing heat transfer calculations and an import function for thermal
    image files (v2.2.3 fixed error importing thermal image to windows OS). v3. Added numerous
    functions for converting thermal image, videos, rewriting and exporting.  
    v3.1. Added new functions to convert files. v3.2.  Fixed the various functions related to finding frame times.
Calculates several thermal comfort indexes using temperature, wind speed and relative humidity values, calculating indexes such as Humidex, windchill, Discomfort Index and others.
Temperature measurement data, equations and methods for thermocouples,
    wire RTD, thermistors, IC thermometers, bimetallic strips and the ITS-90.
An approach to the visualization, analysis, and interpretation of 
  (microbial) community composition data, especially those originating from 
  amplicon sequencing. Analysis techniques include constrained and unconstrained
  ordination and visualizing taxonomic abundances and spatial patterns, among 
  others. Methods intended to assist bioinformaticians and ecologists in 
  selecting read trimming by quality scores and preprocessing/denoising of 
  datasets are also provided.
A step-up test for genetic rare variants in a gene or in a pathway. The method determines an optimal grouping of rare variants analytically. The method has been described in Hoffmann TJ, Marini NJ, and Witte JS (2010) <doi:10.1371/journal.pone.0013584>.
Methods and tools for generating forecasts at different temporal
    frequencies using a hierarchical time series approach.
This package interacts directly with the Thinknum API to offer data
    in a number of formats usable in R
Some tools for cleaning up messy 'Excel' files to be suitable for R. People who have been working with 'Excel' for years built more or less complicated sheets with names, characters, formats that are not homogeneous. To be able to use them in R nowadays, we built a set of functions that will avoid the majority of importation problems and keep all the data at best.
Key-value store, implemented as a wrapper around 'LMDB';
    the "lightning memory-mapped database" <https://symas.com/lmdb/>.
    'LMDB' is a transactional key value store that uses a memory map
    for efficient access.  This package wraps the entire 'LMDB'
    interface (except duplicated keys), and provides objects for
    transactions and cursors.
Design and analyze three-arm non-inferiority or superiority trials
    which follow a gold-standard design, i.e. trials with an experimental treatment,
    an active, and a placebo control. Method for the following distributions are implemented:
    Poisson (Mielke and Munk (2009) <arXiv:0912.4169>), negative binomial (Muetze et al. (2016) <doi:10.1002/sim.6738>),
    normal (Pigeot et al. (2003) <doi:10.1002/sim.1450>; Hasler et al. (2009) <doi:10.1002/sim.3052>), 
    binary (Friede and Kieser (2007) <doi:10.1002/sim.2543>), nonparametric (Muetze et al. (2017) <doi:10.1002/sim.7176>),
    exponential (Mielke and Munk (2009) <arXiv:0912.4169>).
This package implements a thresholded version of the EEBoost
    algorithm described in [Wolfson (2011, JASA)]. EEBoost is a general-purpose
    method for variable selection which can be applied whenever inference would
    be based on an estimating equation. The package currently implements
    variable selection based on the Generalized Estimating Equations, but can
    also accommodate user-provided estimating functions. Thresholded EEBoost is
    a generalization which allows multiple variables to enter the model at each
    boosting step.
Implements the Maximum Likelihood estimator for baseline, placebo, and treatment groups (three-group) experiments with non-compliance proposed by Gerber, Green, Kaplan, and Kern (2010).
Create interactive 3D scatter plots, network plots, and
    globes using the 'three.js' visualization library (<https://threejs.org>).
Component analysis for three-way data arrays by means of Candecomp/Parafac, Tucker3, Tucker2 and Tucker1 models.
A connector to the 'What3Words' (http://what3words.com/) service, which represents each 3m by 3m square on earth
             with a unique trio of English-language words.
Fit a threshold regression model based on the first-hitting-time of a boundary by the sample path of a Wiener diffusion process. The threshold regression methodology is well suited to applications involving survival and time-to-event data.
Fit a threshold regression model for Interval Censored Data based on the first-hitting-time of a boundary by the sample path of a Wiener diffusion process. The threshold regression methodology is well suited to applications involving survival and time-to-event data.
Defines the classes used to identify
  outliers (threshing) and compute the number of significant principal
  components and number of clusters (reaping) in a joint application
  of PCA and hierarchical clustering. See Wang et al., 2018,
  <doi:10.1186/s12859-017-1998-9>.
Functions that provide point and interval estimations of optimum thresholds for continuous diagnostic tests. The methodology used is based on minimizing an overall cost function in the two- and three-state settings. The package also provides functions for sample size determination and estimation of diagnostic accuracy measures. It also includes graphical tools.
Provides functions for the selection of thresholds for use in 
    extreme value models, based mainly on the methodology in 
    Northrop, Attalides and Jonathan (2017) <doi:10.1111/rssc.12159>.
    It also performs predictive inferences about future extreme values, 
    based either on a single threshold or on a weighted average of inferences 
    from multiple thresholds, using the 'revdbayes' package 
    <https://cran.r-project.org/package=revdbayes>.   
    At the moment only the case where the data can be treated as 
    independent identically distributed observations is considered.
Pure R implementation of Apache Thrift.
    This library doesn't require any code generation.
    To learn more about Thrift go to <https://thrift.apache.org>.
Fit the Simultaneous Systems of Linear Equations using Three-stage Least Squares.
Provides a 'tbl_df' class (the 'tibble') that provides
    stricter checking and better formatting than the traditional data frame.
Built on top of the 'tibble' package, 'tibbletime' is an extension
  that allows for the creation of time aware tibbles. Some immediate
  advantages of this include: the ability to perform time-based subsetting
  on tibbles, quickly summarising and aggregating results by time periods,
  and creating columns that can be used as 'dplyr' time-based groups.
Functions to execute orders in backtesting using tick data. A testing platform was established by the four major execution functions, namely 'LimitBuy', 'LimitSell', 'MarketBuy' and 'MarketSell', which enclosed all tedious aspects (such as queueing for order executions and calculate actual executed volumes) for order execution using tick data. Such that one can focus on the logic of strategies, rather than its execution.
Implements tic-tac-toe game to play on console, either with human or AI players.
  Various levels of AI players are trained through the Q-learning algorithm.
This package provides the timing functions 'tic' and 'toc' that
    can be nested. One can record all timings while a complex script is
    running, and examine the values later. It is also possible to instrument
    the timing calls with custom callbacks. In addition, this package provides
    class 'Stack', implemented as a vector, and class 'List', implemented as a
    list, both of which support operations 'push', 'pop', 'first', 'last' and
    'clear'.
Utilities to generate wiki reports in TiddlyWiki format.
Tidal analysis of evenly spaced observed time series (time step 1 to 60 min) with or
    without shorter gaps.
    The analysis should preferably cover an observation period of at least 19 years.
    For shorter periods low frequency constituents are not taken into account, in accordance with the Rayleigh-Criterion.
    The main objective of this package is to synthesize or predict a tidal time series.
Implements harmonic analysis of tidal and sea-level data.
  Over 400 harmonic tidal constituents can be estimated, all with 
  daily nodal corrections. Time-varying mean sea-levels can also
  be used.
Calculate Characteristics of Quasi-Periodic Time Series, e.g. Estuarine Water Levels.
Tide analysis and prediction of predominantly semi-diurnal tides
    with two high waters and two low waters during one lunar day (~24.842 hours,
    ~1.035 days). The analysis should preferably cover an observation period of at
    least 19 years. For shorter periods, for example, the nodal cycle can not be
    taken into account, which particularly affects the height calculation. The main
    objective of this package is to produce tide tables.
Compose data for and extract, manipulate, and visualize posterior draws from Bayesian models
    ('JAGS', 'Stan', 'rstanarm', 'brms', 'MCMCglmm', 'coda', ...) in a tidy data format. Functions are provided
    to help extract tidy data frames of draws from Bayesian models and that generate point
    summaries and intervals in a tidy format. In addition, 'ggplot2' 'geoms' and 'stats' are provided for
    common visualization primitives like points with multiple uncertainty intervals, eye plots (intervals plus
    densities), and fit curves with multiple, arbitrary uncertainty bands.
Compute arbitrary non-parametric bootstrap statistics on data in
    tidy data frames.
An integrated R interface to the decennial US Census and American Community Survey APIs and
    the US Census Bureau's geographic boundary files.  Allows R users to return Census and ACS data as
    tidyverse-ready data frames, and optionally returns a list-column with feature geometry for many 
    geographies.
Analyze lines of R code using tidy principles. This allows you to 
    input lines of R code and output a data frame with one row per function 
    included. Additionally, it facilitates code classification via included lexicons.
Handle genomic data within data frames just as you would with 'GRanges'.
    This packages provides method to deal with genomic intervals the "tidy-way" which makes
    it simpler to integrate in the the general data munging process. The API is inspired by the
    popular 'bedtools' and the genome_join() method from the 'fuzzyjoin' package.
A graph, while not "tidy" in itself, can be thought of as two tidy
    data frames describing node and edge data respectively. 'tidygraph'
    provides an approach to manipulate these two virtual data frames using the
    API defined in the 'dplyr' package, as well as provides tidy interfaces to 
    a lot of common graph algorithms.
Provides functions to access historical and real-time national 'hydrometric'
    data from Water Survey of Canada data sources (<http://dd.weather.gc.ca/hydrometric/csv/> and
    <http://collaboration.cmc.ec.gc.ca/cmc/hydrometrics/www/>) and then applies tidy data principles.
Functions and methods for imputing missing values (NA) in tables and list 
    patterned after the tidyverse approach of 'dplyr' and 'rlang'; works with 
    data.tables as well.
Provides feedback about basic 'dplyr' operations.
An interface to the 'mclust' package to easily
    carry out latent profile analysis ("LPA"). Provides functionality to
    estimate commonly-specified models.  Follows a tidy approach, in that
    output is in the form of a data frame that can subsequently be
    computed on. Also has functions to interface to the commercial 'MPlus'
    software via the 'MplusAutomation' package.
The tidy modeling "verse" is a collection of packages for 
    modeling and statistical analysis that share the underlying design
    philosophy, grammar, and data structures of the tidyverse.
Provides functions for visualising generalised
    additive models and getting predicted values using tidy tools from the 'tidyverse' packages.
Tidy tools for 'NetCDF' data sources. Explore the contents of a 
 'NetCDF' source (file or URL) presented as variables organized by grid with a 
 database-like interface. The hyper_filter() interactive function translates the 
 filter value or index expressions to array-slicing form. No data is read until 
 explicitly requested, as a data frame or list of arrays via hyper_tibble() or 
 hyper_array().
Bayesian analysis used here to answer the question: "when looking at resampling results, are the differences between models 'real'?" To answer this, a model can be created were the performance statistic is the resampling statistics (e.g. accuracy or RMSE). These values are explained by the model types. In doing this, we can get parameter estimates for each model's affect on performance and make statistical (and practical) comparisons between models. The methods included here are similar to Benavoli et al (2017) <http://jmlr.org/papers/v18/16-305.html>.
It parses a fitted 'R' model object, and returns a formula
    in 'Tidy Eval' code that calculates the predictions.
    It works with several databases back-ends because it leverages 'dplyr'
    and 'dbplyr' for the final 'SQL' translation of the algorithm. It currently
    supports lm(), glm(), randomForest(), ranger() and earth() models.
Bringing financial analysis to the 'tidyverse'. The 'tidyquant' 
    package provides a convenient wrapper to various 'xts', 'zoo', 'quantmod', 'TTR' 
    and 'PerformanceAnalytics' package 
    functions and returns the objects in the tidy 'tibble' format. The main 
    advantage is being able to use quantitative functions with the 'tidyverse'
    functions including 'purrr', 'dplyr', 'tidyr', 'ggplot2', 'lubridate', etc. See 
    the 'tidyquant' website for more information, documentation and examples.
The purpose of this package is to access the 
    United States Census Bureau's Quarterly Workforce Indicator data. Additionally, 
    the data will be retrieved in a tidy format for further manipulation with full variable
    descriptions added if desired. Information about the United States Census Bureau's 
    Quarterly Workforce Indicator is available at 
    <https://www.census.gov/data/developers/data-sets/qwi.html>.
An evolution of 'reshape2'. It's designed
    specifically for data tidying (not general reshaping or aggregating)
    and works well with 'dplyr' data pipelines.
With the objective of including data from RSS feeds into your analysis, 'tidyRSS' parses RSS, Atom XML, JSON and geoRSS feeds and returns a tidy data frame.
Utility to convert text based summary of rule based models to a tidy dataframe (where each row represents a rule) with related metrics such as support, confidence and lift. Rule based models from these packages are supported: 'C5.0', 'rpart' and 'Cubist'.
A backend for the selecting functions of the 'tidyverse'.
    It makes it easy to implement select-like functions in your own
    packages in a way that is consistent with other 'tidyverse'
    interfaces for selection.
Produce a data file containing the output of statistical models
  and assist with a workflow aimed at writing scientific papers using
  'R Markdown'. Supported statistical functions include: t.test(), cor.test(),
  lm(), glm(), aov(), anova(), and several others. The package is based on tidy
  data principles and the 'tidyverse' (Wickham, 2017).
Functions to generate stopword lists in 53 languages, in a way consistent across all the languages supported. The generated lists are based on the morphological tagset from the Universal Dependencies.
Calculation of string distance following the tidy
    data principles. Built on top of the 'stringdist' package.
Text mining for word processing and sentiment analysis using
    'dplyr', 'ggplot2', and other tidy tools.
A selection of various tools to extend a data analysis workflow based on the 'tidyverse' packages.
  This includes high-level data frame editing methods (in the style of 'mutate'/'mutate_at'), some methods in the style of
  'purrr' and 'forcats', 'lookup' methods for dict-like lists, a generic method for lumping a data frame by a given count,
  various low-level methods for special treatment of 'NA' values, 'python'-style tuple-assignment and 'truthy'/'falsy' checks,
  saving to PDF and PNG from a pipe and various small utilities.
Read General Transit Feed Specification (GTFS) zipfiles into a list of R dataframes. Perform validation of the data structure against the specification. Analyze the headways and frequencies at routes and stops. Create maps and perform spatial analysis on the routes and stops. Please see the GTFS documentation here for more detail: <http://gtfs.org/>.
Phylogenetic tree generally contains multiple components including node, edge, branch and associated data. 'tidytree' provides an approach to convert tree object to tidy data frame as well as provides tidy interfaces to manipulate tree data.
The 'tidyverse' is a set of packages that work in harmony
    because they share common data representations and 'API' design. This
    package is designed to make it easy to install and load multiple
    'tidyverse' packages in a single step. Learn more about the 'tidyverse'
    at <https://tidyverse.org>.
Imports non-tabular from Excel files into R.  Exposes cell content,
    position and formatting in a tidy structure for further manipulation.
    Tokenizes Excel formulas.  Supports '.xlsx' and '.xlsm' via the embedded
    'RapidXML' C++ library <http://rapidxml.sourceforge.net>.  Does not support
    '.xlsb' or '.xls'.
This package provides an easy and simple way to read, write and display bitmap images stored in the TIFF format. It can read and write both files and in-memory raw vectors.
Temporally resolved groups of typical differences (errors) between two time series are determined and visualized
Pre-process for discrete time series data set which is not continuous at the column
    of 'date'. Refilling records of missing 'date' and other columns to the hollow data set so that
    final data set is able to be dealt with time series analysis.
A collection of data sets and functions that are useful in the
    teaching of statistics at an elementary level to students who may have
    little or no previous experience with the command line.  The functions for
    elementary inferential procedures follow a uniform interface for user
    input.  Some of the functions are instructional applets that can only be
    run on the R Studio integrated development environment with package
    'manipulate' installed.  Other instructional applets are Shiny apps
    that may be run locally. In teaching the package is used alongside of
    package 'mosaic', 'mosaicData' and 'abd', which are therefore listed as
    dependencies.
Infers the V genotype of an individual from immunoglobulin (Ig)
    repertoire sequencing data (AIRR-Seq, Rep-Seq). Includes detection of 
    any novel alleles. This information is then used to correct existing V 
    allele calls from among the sample sequences.
    Citations:
    Gadala-Maria, et al (2015) <doi:10.1073/pnas.1417683112>.
The functions needed to perform tight clustering Algorithm.
Write modelling results into a database for
    'tigreBrowser', a web-based tool for browsing figures and summary
    data of independent model fits, such as Gaussian process models
    fitted for each gene or other genomic element. The browser is
    available at <https://github.com/PROBIC/tigreBrowser>.
Download TIGER/Line shapefiles from the United States Census Bureau 
    (<https://www.census.gov/geo/maps-data/data/tiger-line.html>) and load into R as 'SpatialDataFrame' or 'sf' objects.
Provides a graphics output device for R that records plots
        in a LaTeX-friendly format. The device transforms plotting
        commands issued by R functions into LaTeX code blocks. When
        included in a LaTeX document, these blocks are interpreted with
        the help of 'TikZ'---a graphics package for TeX and friends
        written by Till Tantau. Using the 'tikzDevice', the text of R
        plots can contain LaTeX commands such as mathematical formula.
        The device also allows arbitrary LaTeX code to be inserted into
        the output stream.
R spatial objects for Tilegrams.
  Tilegrams are tiled maps where the region size is proportional to
  the certain characteristics of the dataset.
Tools for creating and detecting tiling schemes for raster data sets.
Creates geographic map tiles from geospatial map files or non-geographic map tiles from simple image files. 
    This package provides a tile generator function for creating map tile sets for use with packages such as 'leaflet'. 
    In addition to generating map tiles based on a common raster layer source, it also handles the non-geographic edge case, producing map tiles from arbitrary images.
    These map tiles, which have a non-geographic, simple coordinate reference system (CRS), can also be used with 'leaflet' when applying the simple CRS option.
    Map tiles can be created from an input file with any of the following extensions: tif, grd and nc for spatial maps and png, jpg and bmp for basic images.
    This package requires 'Python' and the 'gdal' library for 'Python'. 
    'Windows' users are recommended to install 'OSGeo4W' (<https://trac.osgeo.org/osgeo4w/>) as an easy way to obtain the required 'gdal' support for 'Python'.
Implements an algorithm for variable selection in high-dimensional linear regression using the "tilted correlation", a new way of measuring the contribution of each variable to the response which takes into account high correlations among the variables in a data-driven way.
Cox proportional hazard and competing risk regression analyses can be performed with time-to-event data as covariates.
Efficient routines for manipulation of date-time objects while
   accounting for time-zones and daylight saving times. The package includes
   utilities for updating of date-time components (year, month, day etc.),
   modification of time-zones, rounding of date-times, period addition and
   subtraction etc. Parts of the 'CCTZ' source code, released under the Apache
   2.0 License, are included in this package. See
   <https://github.com/google/cctz> for more details.
The 'timeDate' class fulfils the conventions of the ISO 8601 
	standard as well as of the ANSI C and POSIX standards. Beyond
	these standards it provides the "Financial Center" concept
	which allows to handle data records collected in different time 
	zones and mix them up to have always the proper time stamps with 
	respect to your personal financial center, or alternatively to the GMT
	reference time. It can thus also handle time stamps from historical 
	data records from the same time zone, even if the financial 
	centers changed day light saving times at different calendar
	dates.
We provide a toolbox to estimate the time delay between the brightness time series of gravitationally lensed quasar images via Bayesian and profile likelihood approaches. The model is based on a state-space representation for  irregularly observed time series data generated from a latent continuous-time Ornstein-Uhlenbeck process. Our Bayesian method adopts scientifically motivated hyper-prior distributions and a Metropolis-Hastings within Gibbs sampler, producing posterior samples of the model parameters that include the time delay. A profile likelihood of the time delay is a simple approximation to the marginal posterior distribution of the time delay. Both Bayesian and profile likelihood approaches complement each other, producing almost identical results; the Bayesian way is more principled but the profile likelihood is easier to implement.
Helps to visualize multi-variate time-series having numeric and factor variables.
    You can use the package for visual analysis of data by plotting the data for each variable in the desired order and study
    interaction between a factor and a numeric variable by creating overlapping plots.
An easy tool for plotting annotated timelines, grouped timelines, and exploratory graphics (boxplot/histogram/density plot/scatter plot/line plot). Filter, summarize date data by duration and convert to calendar units.
Approaches for incorporating time into network analysis. Methods include: construction of time-ordered networks (temporal graphs); shortest-time and shortest-path-length analyses; resource spread calculations; data resampling and rarefaction for null model construction; reduction to time-aggregated networks with variable window sizes; application of common descriptive statistics to these networks; vector clock latencies; and plotting functionalities. The package supports <doi:10.1371/journal.pone.0020298>.
Extract useful time components of a date object, such as
        day of week, weekend, holiday, day of month, etc, and put it in
        a data frame.  This can be used to create many predictor
        variables out of a single time variable, which can then be used
        in a regression or decision tree.  Also includes function
        plotCalendarHeatmap which draws a calendar and overlays a
        heatmap based on values.
Provides a 'timeR' class that makes timing codes easier. One can create 'timeR' objects and use them to record all timings, and extract recordings as data frame for later use.
Programs for Martinussen and Scheike (2006), `Dynamic Regression
    Models for Survival Data', Springer Verlag.  Plus more recent developments.
    Additive survival model, semiparametric proportional odds model, fast
    cumulative residuals, excess risk models and more. Flexible competing risks
    regression including GOF-tests. Two-stage frailty modelling. PLS for the
    additive risk model. Lasso in the 'ahaz' package.
Estimation of time-dependent ROC curve and area under time dependent ROC curve (AUC) in the presence of censored data, with or without competing risks. Confidence intervals of AUCs and tests for comparing AUCs of two rival markers measured on the same subjects can be computed, using the iid-representation of the AUC estimator. Plot functions for time-dependent ROC curves and AUC curves are provided. Time-dependent Positive Predictive Values (PPV) and Negative Predictive Values (NPV) can also be computed.
Computes bootstrap CI for the sample ACF and periodogram
A negative binomial mixed-effects (NBME) model to detect
    nonparallel differential expression(NPDE) genes and parallel differential
    expression(PDE) genes in the time course RNA-seq data.
Provides a class and various tools for financial time series.
	This includes basic functions such as scaling and sorting, subsetting,
	mathematical operations and statistical functions.
Estimate and return the needed parameters for visualizations designed for 'OpenBudgets.eu' <http://openbudgets.eu/> time series data. Calculate time series model and forecast parameters in budget time series data of municipalities across Europe, according to the 'OpenBudgets.eu' data model. There are functions for measuring deterministic and stochastic trend of the input time series data with 'ACF', 'PACF', 'Phillips Perron' test, 'Augmented Dickey Fuller (ADF)' test, 'Kwiatkowski-Phillips-Schmidt-Shin (KPSS)' test, 'Mann Kendall' test for monotonic trend and 'Cox and Stuart' trend test, decomposing with local regression models or 'stl' decomposition, fitting the appropriate 'arima' model and provide forecasts for the input 'OpenBudgets.eu' time series fiscal data. Also, can be used generally to extract visualization parameters convert them to 'JSON' format and use them as input in a different graphical interface.
Archive and manage times series data from official statistics. The 'timeseriesdb' package was designed to manage a large catalog of time series from official statistics which are typically published on a monthly, quarterly or yearly basis. Thus timeseriesdb is optimized to handle updates caused by data revision as well as elaborate, multi-lingual meta information.
Get the time series index, signature, and summary from time series objects and
    time-based tibbles. Create future time series based on properties of 
    existing time series index.  
    Coerce between time-based tibbles ('tbl') and 'xts', 'zoo', and 'ts'.
Objects to manipulate sequential and seasonal time series. Sequential time series based on time instants and time durations are handled. Both can be regularly or unevenly spaced (overlapping durations are allowed). Only POSIX* format are used for dates and times. The following classes are provided : 'POSIXcti', 'POSIXctp', 'TimeIntervalDataFrame', 'TimeInstantDataFrame', 'SubtimeDataFrame' ; methods to switch from a class to another and to modify the time support of series (hourly time series to daily time series for instance) are also defined. Tools provided can be used for instance to handle environmental monitoring data (not always produced on a regular time base).
A interface to the TimeTree of Life Webpage (www.timetree.org). TimeTree is a public database for information on the evolutionary timescale of life. This package includes functions for searching divergence time for  taxa or all nodes of a phylogeny.
Create rich and fully interactive timeline visualizations.
    Timelines can be included in Shiny apps and R markdown documents, or viewed
    from the R console and 'RStudio' Viewer. 'timevis' includes an extensive API
    to manipulate a timeline after creation, and supports getting data out of
    the visualization into R. Based on the 'vis.js' Timeline module and the
    'htmlwidgets' R package.
Estimates time varying regression effects under Cox type models in
    survival data using classification and regression tree. The codes in this package were 
    originally written in S-Plus for the paper "Survival Analysis with Time-Varying Regression
    Effects Using a Tree-Based Approach," by Xu, R. and Adak, S. (2002) <doi:10.1111/j.0006-341X.2002.00305.x>, Biometrics, 58: 305-315.
    Development of this package was supported by NIH grants AG053983 and AG057707,
    and by the UCSD Altman Translational Research Institute, NIH grant UL1TR001442.
    The content is solely the responsibility of the authors and does not necessarily
    represent the official views of the NIH.
    The example data are from the Honolulu Heart Program/Honolulu Asia Aging Study (HHP/HAAS).
Date sequence, relative date calculations, and date manipulation with business days
  and holidays.  Works with Date and POSIXt classes.
Prediction and ranking of drug combinations based on their drug-target interaction profiles and single-drug sensitivities in a given cancer cell line or patient-derived sample.
A problem-solving environment (PSE) for fitting
         separable nonlinear models to measurements arising in physics
         and chemistry experiments; has been extensively applied to
         time-resolved spectroscopy and FLIM-FRET data.
Functions for statistical analysis, prediction and control of time
 series based mainly on Akaike and Nakagawa (1988) <ISBN 978-90-277-2786-2>.
A universal non-uniform random number generator
  for quite arbitrary distributions with piecewise twice
  differentiable densities.
Non-parametric resampling-based inference tests for TExPosition.
Instead of nesting function calls, annotate and transform 
    functions using "#." comments.
A 'tufte'-alike style for 'rmarkdown'.
 A modern take on the 'Tufte' design for pdf and html vignettes,
 building on the 'tufte' package with additional contributions
 from the 'knitr' and 'ggtufte' package, and also acknowledging
 the key influence of 'envisioned css'.
Generate a palette of tints, shades or both from a single colour.
Creates useful files and folders for data
  analysis  projects and provides functions to manage data,
  scripts and output files. Also provides a project
  template for 'Rstudio'.
Provides a lightweight (zero-dependency) and easy to use 
    unit testing framework. Main features: install tests with 
    the package. Test results are treated as data that can be stored and manipulated. 
    Test files are R scripts interspersed with test commands, that can be programmed
    over. Fully automated build-install-test sequence for packages. Skip tests when
    not run locally (e.g. on CRAN). Flexible and configurable output printing. Compare
    computed output with output stored with the package.
Helper functions to install and maintain the 'LaTeX' distribution
  named 'TinyTeX' (<https://yihui.name/tinytex/>), a lightweight, cross-platform,
  portable, and easy-to-maintain version of 'TeX Live'. This package also
  contains helper functions to compile 'LaTeX' documents, and install missing
  'LaTeX' packages automatically.
Get latest 'TIOBE' Programming Index Tables. 
            The 'TIOBE' Programming Community index is an indicator 
            of the popularity of programming languages from 
            <https://www.tiobe.com/tiobe-index/>.
Assist performing tip-dating of phylogenetic trees with BEAST.
TIPOM is based on a methodology that was developed in the
  1960s by Bernardino Bagolini. The basic idea is to use
  the three simple dimensions of length, width and thickness
  of each lithic artefact to classify them in discrete
  groups and infer their function.
Using the idea of "tipping point" (proposed in
   Gregory Campbell, Gene Pennello and Lilly Yue(2011)
   <DOI:10.1080/10543406.2011.550094>) to visualize  the results
   of sensitivity analysis for missing data, the package provides
   a set of functions to list out all the possible combinations
   of the values of missing data in two treatment arms, calculate
   corresponding estimated treatment effects and p values and draw
   a colored heat-map to visualize them. It could deal with randomized
   experiments with a binary outcome or a continuous outcome. In addition,
   the package provides a visualized method to compare various imputation
   methods by adding the rectangles or convex hulls on the basic plot.
'Htmlwidget' of 'Tippyjs' to add tooltips to 'Shiny' apps and 'R markdown' documents.
The strength of evidence provided by epidemiological and observational 
            studies is inherently limited by the potential for unmeasured confounding. 
            We focus on three key quantities: the observed bound of the confidence interval
            closest to the null, a plausible residual effect size for an unmeasured continuous 
            or binary confounder, and a realistic mean difference or prevalence difference for 
            this hypothetical confounder. Building on the methods put forth by 
            Lin, Psaty, & Kronmal (1998) <doi:10.2307/2533848>, we can use these quantities to 
            assess how an unmeasured confounder may tip our result to insignificance, rendering the
            study inconclusive.
Functions and S3 classes for time indexes and time indexed
        series, which are compatible with FAME frequencies.
GUI to analyze mass spectrometric data on the relative
        abundance of two substances from a titration series.
Uses indicator species scores across binary partitions of
  a sample set to detect congruence in taxon-specific changes of abundance
  and occurrence frequency along an environmental gradient as evidence of
  an ecological community threshold.  Relevant references include: Baker,
  ME and RS King. 2010. A new method for detecting and interpreting
  biodiversity and ecological community thresholds.  Methods in Ecology and
  Evolution 1(1): 25:37. King, RS and ME Baker. 2010. Considerations for
  identifying and interpreting ecological community thresholds.  Journal
  of the North American Benthological Association 29(3):998-1008. Baker ME
  and RS King. 2013. Of TITAN and straw men: an appeal for greater
  understanding of community data. Freshwater Science 32(2):489-506.
This data set provides information on the fate of passengers on
    the fatal maiden voyage of the ocean liner "Titanic", summarized according
    to economic status (class), sex, age and survival. Whereas the base R
    Titanic data found by calling data("Titanic") is an array resulting from
    cross-tabulating 2201 observations, these data sets are the individual
    non-aggregated observations and formatted in a machine learning context
    with a training sample, a testing sample, and two additional data sets
    that can be used for deeper machine learning analysis. These data sets
    are also the data sets downloaded from the Kaggle competition and thus
    lowers the barrier to entry for users new to R or machine learing.
Functions to design phase 1 trials using an isotonic regression based design incorporating time-to-event information. Simulation and design functions are available, which incorporate information about followup and DLTs, and apply isotonic regression to devise estimates of DLT probability.
A collection of functions to plot acid/base titration 
    curves (pH vs. volume of titrant), complexation titration curves 
    (pMetal vs. volume of EDTA), redox titration curves (potential 
    vs.volume of titrant), and precipitation titration curves (either 
    pAnalyte or pTitrant vs. volume of titrant). Options include the 
    titration of mixtures, the ability to overlay two or more 
    titration curves, and the ability to show equivalence points.
Pairwise evolutionary distance estimation between protein sequences with the TKF91 and TKF92 model, which consider all the possible paths of transforming from one sequence to another.
Provides 'TK' widget tools for the 'rgl' package.
Simple mechanism for placing R graphics in a Tk widget.
An optimal alternating optimization algorithm for estimation of precision matrices of sparse tensor graphical models, and an efficient inference procedure for support recovery of the precision matrices.
Contains functions for training and applying two-level random forest and hidden Markov models for human behavior classification from raw tri-axial accelerometer and/or GPS data. Includes functions for training a two-level model, applying the model to data, and computing performance.
A series of function to make thermoluminescence dating using the MAAD or the SAR protocol.
    This package completes the R package "Luminescence."
TLE implements a general framework for robust fitting of
        finite mixture models. Parameter estimation is performed using
        the EM algorithm.
Computation of effects under linear, logistic and Poisson regression models with transformed variables. Logarithm and power transformations are allowed. Effects can be displayed both numerically and graphically in both the original and the transformed space of the variables.
Fit a linear mixed effects model for censored data with
        Student-t or normal distributions. The errors are assumed
        independent and identically distributed.
Calculates empirical TL-moments (trimmed L-moments) of arbitrary 
    order and trimming, and converts them to distribution parameters.
Functions for point and interval estimation in
	     error-in-variables models via total least squares or
	     generalized total least squares method. See Golub and Van
	     Loan (1980) <doi:10.1137/0717073>, Gleser (1981)
	     <https://www.jstor.org/stable/2240867>, Ivan Markovsky
	     and Huffel (2007) <doi:10.1016/j.sigpro.2007.04.004> for
	     more information.
A framework for text mining applications within R.
This package provides a tm Source to create corpora from
  a corpus prepared in the format used by the Alceste application (i.e.
  a single text file with inline meta-data). It is able to import both
  text contents and meta-data (starred) variables.
A plug-in for the text mining framework tm to support text mining 
             in a distributed way. The package provides a convenient interface for
             handling distributed corpus objects based on distributed list objects.
Provides a 'tm' Source to create corpora from
  articles exported from the 'Europresse' content provider as
  HTML files. It is able to read both text content and meta-data
  information (including source, date, title, author and pages).
Provides a 'tm' Source to create corpora from
  articles exported from the Dow Jones 'Factiva' content provider as
  XML or HTML files. It is able to read both text content and meta-data
  information (including source, date, title, author, subject,
  geographical coverage, company, industry, and various
  provider-specific fields).
Provides a 'tm' Source to create corpora from
  articles exported from the 'LexisNexis' content provider as
  HTML files. It is able to read both text content and meta-data
  information (including source, date, title, author and pages).
  Note that the file format is highly unstable: there is no warranty
  that this package will work for your corpus, and you may have
  to adjust the code to adapt it to your particular format.
A plug-in for the tm text mining framework providing mail handling
  functionality.
Facilitate text retrieval from feed
    formats like XML (RSS, ATOM) and JSON. Also direct retrieval from
    HTML is supported. As most (news) feeds only incorporate small
    fractions of the original text tm.plugin.webmining even retrieves
    and extracts the text of the original text source.
Useful functions to connect to 'TM1' <https://www.ibm.com/uk-en/marketplace/planning-and-analytics> instance from R via REST API. With the functions in the package, data can be imported from 'TM1' via mdx view or native view, data can be sent to 'TM1', processes and chores can be executed, and cube and dimension metadata information can be taken.
Thematic maps are geographical maps in which spatial data distributions are visualized. This package offers a flexible, layer-based, and easy to use approach to create thematic maps, such as choropleths and bubble maps.
Set of tools for reading and processing spatial data. The aim is to supply the workflow to create thematic maps. This package also facilitates 'tmap', the package for visualizing thematic maps.
With this tool, a user should be able to quickly implement
    complex random effect models through simple C++ templates. The package combines
    'CppAD' (C++ automatic differentiation), 'Eigen' (templated matrix-vector
    library) and 'CHOLMOD' (sparse matrix routines available from R) to obtain
    an efficient implementation of the applied Laplace approximation with exact
    derivatives. Key features are: Automatic sparseness detection, parallelism
    through 'BLAS' and parallel user templates.
Enables all 'rstan' functionality for a 'TMB' model object, in particular MCMC sampling and chain visualization. Sampling can be performed with or without Laplace approximation for the random effects.
A Text mining toolkit for Chinese, which includes facilities for 
    Chinese string processing, Chinese NLP supporting, encoding detecting and 
    converting. Moreover, it provides some functions to support 'tm' package 
    in Chinese.
Provides an R-interface to the TMDb API (see TMDb API on <http://docs.themoviedb.apiary.io/#>). The Movie Database (TMDb) is a popular user editable database for movies and TV shows (see <https://www.themoviedb.org>).
Random number generation of truncated multivariate Gaussian distributions using Hamiltonian Monte Carlo. The truncation is defined using linear and/or quadratic polynomials.
Miscellaneous utility functions for data manipulation,
    data tidying, and working with gene expression data.
Targeted maximum likelihood estimation of point treatment effects (Targeted Maximum Likelihood Learning, The International Journal of biostatistics, 2(1), 2006.  This version automatically estimates the additive treatment effect among the treated (ATT) and among the controls (ATC).  The tmle() function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes.  Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values.  The population mean is calculated when there is missingness, and no variation in the treatment assignment. The tmleMSM() function estimates the parameters of a marginal structural model for a binary point treatment effect. Effect estimation stratified by a binary mediating variable is also available. An ID argument can be used to identify repeated measures. Default settings call 'SuperLearner' to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.
Targeted minimum loss estimation (TMLE) of a non-parametric variable importance measure of a continuous exposure 'X' on an outcome 'Y', taking baseline covariates 'W' into account.
Methods and feature set definitions for feature or gene set
  enrichment analysis in transcriptional and metabolic profiling data.
  Package includes tests for enrichment based on ranked lists of features,
  functions for visualisation and multivariate functional analysis.
Trauma Mortality prediction for ICD-9, ICD-10, and AIS lexicons in
    long or wide format based on Dr. Alan Cook's tmpm mortality model.
Provides conditional maximum likelihood (CML) estimation of item parameters in multistage designs (Zwitser & Maris, 2013, <doi:10.1007/s11336-013-9369-6>) and CML estimation for conventional designs. Additional features are the likelihood ratio test (Andersen, 1973, <doi:10.1007/BF02291180>) and simulation of multistage designs.
Efficient sampling of truncated multivariate (scale) mixtures of normals under linear inequality constraints is nontrivial due to the analytically intractable normalizing constant. Meanwhile, traditional methods may subject to numerical issues, especially when the dimension is high and dependence is strong.    Algorithms proposed by Li and Ghosh (2015) <doi: 10.1080/15598608.2014.996690> are adopted for overcoming difficulties in simulating truncated distributions. Efficient rejection sampling for simulating truncated univariate normal distribution is included in the package, which shows superiority in terms of acceptance rate and numerical stability compared to existing methods and R packages. An efficient function for sampling from truncated multivariate normal distribution subject to convex polytope restriction regions based on Gibbs sampler for conditional truncated univariate distribution is provided. By extending the sampling method, a function for sampling truncated multivariate Student's t distribution is also developed.     Moreover, the proposed method and computation remain valid for high dimensional and strong dependence scenarios. Empirical results in Li and Ghosh (2015) <doi: 10.1080/15598608.2014.996690> illustrated the superior performance in terms of various criteria (e.g. mixing and integrated auto-correlation time).
Importance sampling from the truncated multivariate normal using the GHK (Geweke-Hajivassiliou-Keane) simulator.
  Unlike Gibbs sampling which can get stuck in one truncation sub-region depending on initial values, this package allows 
  truncation based on disjoint regions that are created by truncation of absolute values. The GHK algorithm uses simple Cholesky
  transformation followed by recursive simulation of univariate truncated normals hence there are also no convergence issues. 
  Importance sample is returned along with sampling weights, based on which, one can calculate integrals over truncated regions
  for multivariate normals.
Random number generation for the truncated multivariate normal and Student t distribution. 
  Computes probabilities, quantiles and densities, 
  including one-dimensional and bivariate marginal densities. Computes first and second moments (i.e. mean and covariance matrix) for the double-truncated multinormal case.
Temporal and cross-sectional network autocorrelation models. These are models for variation in attributes of nodes nested in a network (e.g., drinking behavior of adolescents nested in a school class, or democracy versus autocracy of countries nested in the network of international relations). These models can be estimated for cross-sectional data or panel data, with complex network dependencies as predictors, multiple networks and covariates, arbitrary outcome distributions, and random effects or time trends. Basic references: Doreian, Teuter and Wang (1984) <doi:10.1177/0049124184013002001>; Hays, Kachi and Franzese (2010) <doi:10.1016/j.stamet.2009.11.005>; Leenders, Roger Th. A. J. (2002) <doi:10.1016/S0378-8733(01)00049-1>.
Node centrality measures for temporal networks. Available measures are temporal degree centrality, temporal closeness centrality and temporal betweenness centrality defined by Kim and Anderson (2012) <doi:10.1103/PhysRevE.85.026107>. Applying the REN algorithm by Hanke and Foraita (2017) <doi:10.1186/s12859-017-1677-x> when calculating the centrality measures keeps the computational running time linear in the number of graph snapshots. Further, all methods can run in parallel up to the number of nodes in the network.
R package for analyzing weighted, two-mode, and longitudinal networks.
Identification of conditionally essential genes using high-throughput sequencing data from transposon mutant libraries.
Construction of the Total Operating Characteristic (TOC) Curve and the Receiver (aka Relative) Operating Characteristic (ROC) Curve for spatial and non-spatial data. The TOC method is a modification of the ROC method which measures the ability of an index variable to diagnose either presence or absence of a characteristic. The diagnosis depends on whether the value of an index variable is above a threshold. Each threshold generates a two-by-two contingency table, which contains four entries: hits (H), misses (M), false alarms (FA), and correct rejections (CR). While ROC shows for each threshold only two ratios, H/(H + M) and FA/(FA + CR), TOC reveals the size of every entry in the contingency table for each threshold (Pontius Jr., R.G., Si, K. 2014. The total operating characteristic to measure diagnostic ability for multiple thresholds. Int. J. Geogr. Inf. Sci. 28 (3), 570-583).
This is a simple addin to 'RStudio' that finds all 'TODO', 'FIX ME', 'CHANGED' etc. comments in your project and shows them as a markers list.
Use the <http://toggl.com> time tracker api through R.
Create browsers for reading full texts from a token list format.
    Information obtained from text analyses (e.g., topic modeling, word scaling)
    can be used to annotate the texts.
Convert natural language text into tokens. Includes tokenizers for
    shingled n-grams, skip n-grams, words, word stems, sentences, paragraphs,
    characters, shingled characters, lines, tweets, Penn Treebank, regular
    expressions, as well as functions for counting characters, words, and sentences,
    and a function for splitting longer texts into separate documents, each with
    the same number of words.  The tokenizers have a consistent interface, and
    the package is built on the 'stringi' and 'Rcpp' packages for  fast
    yet correct tokenization in 'UTF-8'.
Imports the fundamental definitions and utilities of the Time Oriented Language (TOL), focused on time series analysis and stochastic processes, and provides the basis for the integration of TOL in R. See <https://www.tol-project.org> for more information about the TOL project.
Statistical tolerance limits provide the limits between which we can expect to find a specified proportion of a sampled population with a given level of confidence.  This package provides functions for estimating tolerance limits (intervals) for various univariate distributions (binomial, Cauchy, discrete Pareto, exponential, two-parameter exponential, extreme value, hypergeometric, Laplace, logistic, negative binomial, negative hypergeometric, normal, Pareto, Poisson-Lindley, Poisson, uniform, and Zipf-Mandelbrot), Bayesian normal tolerance limits, multivariate normal tolerance regions, nonparametric tolerance intervals, tolerance bands for regression settings (linear regression, nonlinear regression, nonparametric regression, and multivariate regression), and analysis of variance tolerance intervals.  Visualizations are also available for most of these settings.
Implements two tests for same-source of toolmarks. The chumbley_non_random() test follows the paper "An Improved Version of a Tool Mark Comparison Algorithm" by Hadler and Morris (2017) <doi:10.1111/1556-4029.13640>. This is an extension of the Chumbley score as previously described in "Validation of Tool Mark Comparisons Obtained Using a Quantitative, Comparative, Statistical Algorithm" by Chumbley et al (2010) <doi:10.1111/j.1556-4029.2010.01424.x>. fixed_width_no_modeling() is based on correlation measures in a diamond shaped area of the toolmark as described in Hadler (2017).
Uplift modeling aims at predicting the causal effect of an action such as a medical
    treatment or a marketing campaign on a particular individual, by taking into consideration
    the response to a treatment. In order to simplify the task for practitioners in uplift modeling,
    we propose a combination of tools that can be separated into the following ingredients: 
    i) quantization, ii) visualization, iii) feature engineering, iv) feature selection and, 
    v) model validation. For more details, please read Belbahri et Al. (2019) 
    <https://dms.umontreal.ca/~murua/research/UpliftRegression.pdf>.
Provides functions for multivariate analysis with compositional data. Includes a function for doing compositional canonical correlation analysis. This analysis requires two data matrices of compositions, which can be adequately transformed and used as entries in a specialized program for canonical correlation analysis, that is able to deal with singular covariance matrices. The methodology is described in Graffelman et al. (2017) <doi:10.1101/144584>.
An R wrapper for using 'TooManyCells', a command line program for clustering, visualizing, and quantifying cell clade relationships. See <https://gregoryschwartz.github.io/too-many-cells/> for more details.
Language specific cardinal to ordinal number conversion.
Provides an interface to the C code for Latent Dirichlet
	     Allocation (LDA) models and Correlated Topics Models
	     (CTM) by David M. Blei and co-authors and the C++ code
	     for fitting LDA models using Gibbs sampling by Xuan-Hieu
	     Phan and co-authors.
Provides implementation of the "Topic SCORE" algorithm that is
	     proposed by Tracy Ke and Minzhe Wang. The singular value decomposition
	     step is optimized through the usage of svds() function in 'RSpectra'
	     package, on a 'dgRMatrix' sparse matrix. Also provides a column-wise
	     error measure in the word-topic matrix A, and an algorithm for
	     recovering the topic-document matrix W given A and D based on
	     quadratic programming.
		The details about the techniques are explained in the paper "A new SVD approach to optimal topic estimation" by Tracy Ke and Minzhe Wang (2017) <arXiv:1704.07016>.
For multiple ranked input lists (full or partial) representing the same set of N objects, the package TopKLists offers (1) statistical inference on the lengths of informative top-k lists, (2) stochastic aggregation of full or partial lists, and (3) graphical tools for the statistical exploration of input lists, and for the visualization of aggregation results.
Set of hydrological functions including an R
        implementation of the hydrological model TOPMODEL, which is
        based on the 1995 FORTRAN version by Keith Beven. From version
        0.7.0, the package is put into maintenance mode.
Using Gaussian graphical models we propose a novel approach to
             perform pathway analysis using gene expression. Given the
             structure of a graph (a pathway) we introduce two statistical
             tests to compare the mean and the concentration matrices between
             two groups. Specifically, these tests can be performed on the
             graph and on its connected components (cliques).
Evaluation of alternatives based on multiple criteria using TOPSIS method.
The goal of tor (to-R) is to help you to import
    multiple files from a single directory at once, and to do so as
    quickly, flexibly, and simply as possible.
A framework for statistical analysis in content analysis. In addition to a pipeline for preprocessing text corpora and linking to the latent Dirichlet allocation from the 'lda' package, plots are offered for the descriptive analysis of text corpora and topic models. In addition, an implementation of Chang's intruder words and intruder topics is provided.
Fit an Instrumental Variables Two Stage Least Squares model
Two one-sided tests (TOST) procedure to test equivalence for t-tests, correlations, differences between proportions, and meta-analyses, including power analysis for t-tests and correlations. Allows you to specify equivalence bounds in raw scale units or in terms of effect sizes. See: Lakens (2017) <doi:10.1177/1948550617697177>.
Download summary files from Census Bureau <https://www2.census.gov/> 
    and extract data, in particular high resolution data at 
    block, block group, and tract level, from decennial census and 
    American Community Survey 1-year and 5-year estimates.
For a given phylogenetic tree, calculates the Total Cophenetic Index.
  Reference: A. Mir, F. Rossello, L. A. Rotger (2013).
  A new balance index for phylogenetic trees.
  Math. Biosci. 241, 125-136 <doi:10.1016/j.mbs.2012.10.005>.
R implementation of the software tools developed in the H-CUP
    (Healthcare Cost and Utilization Project) <https://www.hcup-us.ahrq.gov>
    and AHRQ (Agency for Healthcare Research and Quality)
    <https://www.ahrq.gov>.  It currently contains functions for mapping ICD-9
    codes to the AHRQ comorbidity measures and translating ICD-9
    (resp. ICD-10) codes to ICD-10 (resp. ICD-9) codes based on GEM (General
    Equivalence Mappings) from CMS (Centers for Medicare and Medicaid
    Services).
Tools for analyzing count data with the Touchard model (Matsushita et al., 2018, Comm Stat Th Meth <doi:10.1080/03610926.2018.1444177>). It includes univariate estimation (ML and MM) and regression tools developed by Andrade et al. (submitted).
Implements geodesic interpolation and basis
    generation functions that allow you to create new tour
    methods from R.
The GUI allows user to control the tour by checkboxes for
        the variable selection, slider for the speed, and toggle boxes
        for pause.
Data analysis package for estimating potential biological effects from chemical concentrations in environmental samples. Included are a set of functions to analyze, visualize, and organize measured concentration data as it relates to user-selected chemical-biological interaction benchmark data such as water quality criteria. The intent of these analyses is to develop a better understanding of the potential biological relevance of environmental chemistry data. Results can be used to prioritize which chemicals at which sites may be of greatest concern. These methods are meant to be used as a screening technique to predict potential for biological influence from chemicals that ultimately need to be validated with direct biological assays. A description of the analysis can be found in Blackwell et al. (2017) <doi:10.1021/acs.est.7b01613>.
Calculates sample size and dose allocation for binary toxicity 
 tests, using the Fish Embryo Toxicity Test as example. 
 An optimal test design is obtained by running 
 (i) spoD (calculate the number of individuals to test under control 
 conditions), (ii) setD (estimate the minimal sample size per treatment
 given the users precision requirements) and (iii) doseD (construct 
 an individual dose scheme).
Estimation of transition probabilities for the illness-death model. Both the Aalen-Johansen estimator for a Markov model and a novel non-Markovian estimator by de Una-Alvarez and Meira-Machado (2015) <doi:10.1111/biom.12288>, see also Balboa and de Una-Alvarez (2018) <doi:10.18637/jss.v083.i10>, are included.
Tools for estimating and inferring two-way partial area under receiver operating characteristic curves (two-way pAUC), partial area under receiver operating characteristic curves (pAUC), and partial area under ordinal dominance curves (pODC). Methods includes Mann-Whitney statistic and Jackknife, etc.
Tools to calculate trait probability density functions (TPD) at
    any scale (e.g. populations, species, communities). TPD functions are used to compute
    several indices of functional diversity, as well as its partition across scales.
    These indices constitute a unified framework that incorporates the underlying
    probabilistic nature of trait distributions into uni- or multidimensional
    functional trait-based studies. See Carmona et al. (2016) <doi:10.1016/j.tree.2016.02.003> for
    further information.
This package implements the greedy approximation for tree
        preserving embedding.
We described a novel Topology-based pathway enrichment analysis, which integrated the global position of the nodes and the topological property of the pathways in  Kyoto Encyclopedia of Genes and Genomes Database.
             We also provide some functions to obtain the latest information about pathways to finish pathway enrichment analysis using this method.
A bioinformatics tool for the estimation of the tumor purity from sequencing data. 
    It uses the set of putative clonal somatic single nucleotide variants within copy number neutral
    segments to call tumor cellularity.
Provides a simple approach for constructing dynamic materials
  modeling suggested by Prasad and Gegel <doi:10.1007/BF02664902>. It
  can easily generate various processing-maps based on this model as well. The 
  calculation result in this package contains full materials constants, information
  about power dissipation efficiency factor, and rheological properties, can
  be exported completely also, through which further analysis and customized
  plots will be applicable as well.
Estimation of transition probabilities for the
        illness-death model and or the three-state progressive model.
Regression models for temporal process responses with
        time-varying coefficient.
Plant ecologists often need to collect "traits" data
    about plant species which are often scattered among various
    databases: TR8 contains a set of tools which take care of
    automatically retrieving some of those functional traits data
    for plant species from publicly available databases (Biolflor,
    The Ecological Flora of the British Isles, LEDA traitbase, Ellenberg
    values for Italian Flora, Mycorrhizal intensity databases, Catminat, BROT,
    PLANTS, Jepson Flora Project).
    The TR8 name, inspired by "car plates" jokes, was chosen since
    it both reminds of the main object of the package and is
    extremely short to type.
Better looking call stacks after an error.
'BEAST2' (<http://www.beast2.org>) is a widely used
  Bayesian phylogenetic tool, that uses DNA/RNA/protein data
  and many model priors to create a posterior of jointly estimated 
  phylogenies and parameters.
  'Tracer' (<http://tree.bio.ed.ac.uk/software/tracer/>) is a GUI tool 
  to parse and analyze the files generated by 'BEAST2'.
  This package provides a way to parse and analyze 'BEAST2' input
  files without active user input, but using
  R function calls instead.
Contains functions to standardize tracheid profiles
    using the traditional method (Vaganov) and a new method to standardize
    tracheidograms based on the relative position of tracheids within tree rings.
Automatically stores objects in files on disk
  so that files are rewritten when objects are changed, and
  so that objects are accessible but do not occupy memory
  until they are accessed. Keeps track of times when objects
  are created and modified, and caches some basic
  characteristics of objects to allow for fast summaries of
  objects.  Also provides a command history mechanism that
  saves the last command to a history file after each
  command completes.
Obtain population density and body size structure, using video material or image sequences as input. Functions assist in the creation of image sequences from videos, background detection and subtraction, particle identification and tracking. An artificial neural network can be trained for noise filtering. The goal is to supply accurate estimates of population size, structure and/or individual behavior, for use in  evolutionary and ecological studies.
Provides infrastructure for handling running, cycling and swimming data from GPS-enabled tracking devices within R. The package provides methods to extract, clean and organise workout and competition data into session-based and unit-aware data objects of class 'trackeRdata' (S3 class). The information can then be visualised, summarised, and analysed through flexible and extensible methods. Frick and Kosmidis (2017) <doi: 10.18637/jss.v082.i07>, which is updated and maintained as one of the vignettes, provides detailed descriptions of the package and its methods, and real-data demonstrations of the package functionality.
Provides an integrated user interface and workflow for
             the analysis of running, cycling and swimming data from GPS-enabled
             tracking devices through the 'trackeR' <https://CRAN.R-project.org/package=trackeR> R package.
Automatically annotates R-based artifacts
	     with relevant descriptive and provenance-related
    	     and provides a backend-agnostic storage and
	     discoverability system for organizing, retrieving, and
	     interrogating such artifacts.
Reconstructs animal tracks from magnetometer, accelerometer, depth and optional speed data.  Designed primarily using data from Wildlife Computers Daily Diary tags deployed on northern fur seals.
Functions for working with magnetic resonance images. Reading and
    writing of popular file formats (DICOM, Analyze, NIfTI-1, NIfTI-2, MGH);
    interactive and non-interactive visualisation; flexible image manipulation;
    metadata and sparse image handling.
A collection of tools for trade practitioners, including the ability to calibrate different consumer demand systems and simulate the effects of tariffs and quotas under different competitive regimes.
Tree Ring Analysis of Disturbance Events in R (TRADER) package provides only one way for disturbance reconstruction from tree-ring data.
Access 'Open Trade Statistics' API from R to download international trade data.
Contains trades from the five major assets classes and also
    functionality to use pricing curves, rating tables, CSAs and add-on tables. The
    implementation follows an object oriented logic whereby each trade inherits from
    more abstract classes while also the curves/tables are objects. There is a lot
    of functionality focusing on the counterparty credit risk calculations however
    the package can be used for trading applications in general.
Estimate and return either the traffic speed or the car entries in the city of Thessaloniki using historical traffic data. It's used in transport pilot <http://trafficstatusprediction.imet.gr/> of the 'BigDataEurope' project <https://www.big-data-europe.eu/>. There are functions for processing these data, training a neural network, select the most appropriate model and predict the traffic speed or the car entries for a selected time date.
Estimation, selection and comparison of several families of
 transformations. The families of transformations included in the package are 
 the following: Bickel-Doksum (Bickel and Doksum 1981 <doi:10.2307/2287831>), 
 Box-Cox, Dual (Yang 2006 <doi:10.1016/j.econlet.2006.01.011>), 
 Glog (Durbin et al. 2002 <doi:10.1093/bioinformatics/18.suppl_1.S105>), 
 gpower (Kelmansky et al. 2013 <doi:10.1515/sagmb-2012-0030>), 
 Log, Log-shift opt (Feng et al. 2016 <doi:10.1002/sta4.104>),
 Manly, modulus (John and Draper 1980 <doi:10.2307/2986305>), 
 Neglog (Whittaker et al. 2005 <doi:10.1111/j.1467-9876.2005.00520.x>), 
 Reciprocal and Yeo-Johnson. The package simplifies to 
 compare linear models with untransformed and transformed dependent variable 
 as well as linear models where the dependent variable is transformed with 
 different transformations. Furthermore, the package employs maximum likelihood 
 approaches, moments optimization and divergence minimization to estimate the optimal 
 transformation parameter.
Assistance for handling ecological trait data and applying the 
    Ecological Trait-Data Standard terminology (Schneider et al. 2018
    <doi:10.1101/328302>). There are two major use cases: (1) preparation of
    own trait datasets for upload into public data bases, and (2) harmonizing
    trait datasets from different sources by re-formatting them into a unified
    format. See 'traitdataform' website for full documentation.
An interface for creating GUIs modeled in part after the
    traits UI module for python. The basic design takes advantage of
    the model-view-controller design pattern. The interface makes basic
    dialogs quite easy to produce.
Species trait data from many different sources, including
    sequence data from 'NCBI', plant trait data from 'BETYdb', plant data
    from the USDA plants database, data from 'EOL' 'Traitbank', 
    Coral traits data (<https://coraltraits.org>), 'Birdlife' International, 
    and more.
Implements a predictive model of community assembly called 'Traitspace' (Laughlin et al. 2012, Ecology Letters). Traitspace is a hierarchical Bayesian model that translates the theory of trait-based environmental filtering into a statistical model that incorporates intraspecific trait variation to predict the relative abundances and the distributions of species. 
	The package includes functions to plot the predicted and the observed values. It also includes functions to compare the predicted values against the observed values using a variety of different distance measures and to implement permutation tests to test their statistical significance.
Implements the three-step procedure proposed by Leffondree et al. (2004) to identify clusters of individual longitudinal trajectories. The procedure involves (1) calculating 24 measures describing the features of the trajectories; (2) using factor analysis to select a subset of the 24 measures and (3) using cluster analysis to identify clusters of trajectories, and classify each individual trajectory in one of the clusters.
Contains a set of methods for trajectory data preparation, such as filtering, compressing and clustering, and for trajectory pattern discovery.
Classes and methods for trajectory data, with support for nesting individual Track objects in track sets (Tracks) and track sets for different entities in collections of Tracks. Methods include selection, generalization, aggregation, intersection, simulation, and plotting.
A toolbox to assist with statistical analysis of 2-dimensional animal trajectories.
    It provides simple access to algorithms for calculating and assessing a variety of 
    characteristics such as speed and acceleration, as well as multiple measures of 
    straightness or tortuosity. McLean & Skowron Volponi (2018) <doi:10.1111/eth.12739>.
Formula-based user-interfaces to specific transformation models
  implemented in package 'mlt'. Available models include Cox models, some parametric 
  survival models (Weibull, etc.), models for ordered categorical variables,
  normal and non-normal (Box-Cox type) linear models, and continuous outcome logistic regression
  (Lohse et al., 2017, <DOI:10.12688/f1000research.12934.1>). The underlying theory
  is described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291>.
Toolbox for the manipulation, description and rendering of sequences, and more generally the mining of sequence data in the field of social sciences. Although the toolbox is primarily intended for analyzing state or event sequences that describe life courses such as family formation histories or professional careers, its features also apply to many other kinds of categorical sequence data. It accepts many different sequence representations as input and provides tools for converting sequences from one format to another. It offers several functions for describing and rendering sequences, for computing distances between sequences with different metrics (among which optimal matching), original dissimilarity-based analysis tools, and simple functions for extracting the most frequent subsequences and identifying the most discriminating ones among them. A user's guide can be found on the TraMineR web page.
Collection of ancillary functions and utilities to be used in conjunction with the 'TraMineR' package for sequence data exploration. Most of the functions are in test phase, lack systematic consistency check of the arguments and are subject to changes. Once fully checked, some of the functions of this collection could be included in a next release of 'TraMineR'.
Matching terminal restriction fragment length
        polymorphism ('TRFLP') profiles between unknown samples and a
        database of known samples.  TRAMPR facilitates analysis of
        many unknown profiles at once, and provides tools for working
        directly with electrophoresis output through to generating
        summaries suitable for community analyses with R's rich set of
        statistical functions.  TRAMPR also resolves the issues of
        multiple 'TRFLP' profiles within a species, and shared 'TRFLP'
        profiles across species.
Transcribes audio to text with the HP IDOL API. Includes functions to upload files, 
	     retrieve transcriptions, and monitor jobs.
In order to smoothly animate the transformation of polygons and 
    paths, many aspects needs to be taken into account, such as differing number 
    of control points, changing center of rotation, etc. The 'transformr' 
    package provides an extensive framework for manipulating the shapes of 
    polygons and paths and can be seen as the spatial brother to the 'tweenr' 
    package.
Bindings for the Google Translate API v2
translateR provides easy access to the Google and Microsoft APIs. The package is easy to use with the related R package "stm" for the estimation of multilingual topic models.
R  version 2.1.0 and later support Korean translations of program messages.  The continuous efforts have been made by <http://developer.r-project.org/TranslationTeams.html>   The R Documentation files are licensed under the General Public License, version 2 or 3.   This means that the pilot project to translate them into Korean has permission to reproduce them and translate them.  This work is done with GNU 'gettext' utilities.  The portable object template is updated a weekly basis or whenever changes are necessary.  Comments and corrections via email to the maintainer is of course most welcome.   In order to voluntarily participate in or offer your help with this translation, please contact the maintainer.   To check the change and progress of Korean translation, please visit <http://www.openstatistics.net>.
A unified estimation procedure for the analysis of right censored data using linear transformation models.
Implementation of two transportation problem algorithms. 
             1. North West Corner Method 
             2. Minimum Cost Method or Least cost method.
             For more technical details about the algorithms please refer below URLs.
             <http://www.universalteacherpublications.com/univ/ebooks/or/Ch5/nw.htm>.
             <http://personal.maths.surrey.ac.uk/st/J.F/chapter7.pdf>.
Solve optimal transport problems. Compute Wasserstein distances (a.k.a. Kantorovitch, Fortet--Mourier, Mallows, Earth Mover's, or minimal L_p distances), return the corresponding transference plans, and display them graphically. Objects that can be compared include grey-scale images, (weighted) point patterns, and mass vectors.
A structural transformation model for a latent, quasi-independent truncation time as a function of the observed dependent truncation time and the event time, and an unknown dependence parameter. The dependence parameter is chosen to minimize the conditional Kendall's tau (Martin and Betensky, 2005) <doi:10.1198/016214504000001538>. The marginal distribution for the truncation time and the event time are completely left unspecified.
The trapezoid package provides dtrapezoid, ptrapezoid,
        qtrapezoid, and rtrapezoid functions for the trapezoidal
        distribution.
Contains R functions for simulating and estimating integer-valued trawl processes as 
    described in the article "Modelling, simulation and inference for
    multivariate time series of counts using trawl processes" by A. E. D. Veraart 
    (Journal of Multivariate Analysis, 2018, to appear, preprint available at:
    <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3100076> ) and for 
    simulating random vectors from the bivariate negative binomial and the bi- and trivariate 
    logarithmic series distributions.
Transmission Ratio Distortion (TRD) is a genetic phenomenon where the two alleles from either parent are not transmitted to the offspring at the expected 1:1 ratio under Mendelian inheritance, leading to spurious signals in genetic association studies. Functions in this package are developed to account for this phenomenon using loglinear model and Transmission Disequilibrium Test (TDT). Some population information can also be calculated.
A suite of descriptive and inferential methods designed to evaluate one or more biomarkers for their ability to guide patient treatment recommendations.  Package includes functions to assess the calibration of risk models; and plot, evaluate, and compare markers. Please see the reference Janes H, Brown MD, Huang Y, et al. (2014) <doi:10.1515/ijb-2012-0052> for further details.
Provides users the ability to categorize categorical variables dependent on a response variable. 
    It creates a decision tree by using one of the categorical variables (class factor) and the selected response variable. 
    The decision tree is created from the rpart() function from the 'rpart' package. The rules from the leaves of
    the decision tree are extracted, and used to recategorize the appropriate categorical variable (predictor). This
    step is performed for each of the categorical variables that is fed into the data component of
    the function. Only variables containing more than 2 factor levels will be considered in the function. 
    The final output generates a data set containing the recategorized variables or a list containing a mapping table
    for each of the candidate variables. For more details see T. Hastie et al (2009, ISBN: 978-0-387-84857-0).
Interface to the API for 'TreeBASE' <http://treebase.org>
    from 'R.' 'TreeBASE' is a repository of user-submitted phylogenetic
    trees (of species, population, or genes) and the data used to create
    them.
User-friendly analysis of hierarchical multinomial processing tree (MPT) 
    models that are often used in cognitive psychology. Implements the latent-trait 
    MPT approach (Klauer, 2010) <DOI:10.1007/s11336-009-9141-0> and the beta-MPT 
    approach (Smith & Batchelder, 2010) <DOI:10.1016/j.jmp.2009.06.007> to model 
    heterogeneity of participants. MPT models are conveniently specified by an
    .eqn-file as used by other MPT software and data are provided by a .csv-file 
    or directly in R. Models are either fitted by calling JAGS or by an MPT-tailored 
    Gibbs sampler in C++ (only for nonhierarchical and beta MPT models). Provides 
    tests of heterogeneity and MPT-tailored summaries and plotting functions.
    A detailed documentation is available in Heck, Arnold, & Arnold (2018) 
    <DOI:10.3758/s13428-017-0869-7>.
Bootstrapped response and correlation functions,
    seasonal correlations and evaluation of reconstruction
    skills for use in dendroclimatology and dendroecology.
Create a measure of inter-point dissimilarity useful 
 for clustering mixed data, and, optionally, perform the clustering.
The centre of mass is a crucial data for arborists in order to
    consolidate a tree using steel or dynamic cables. Given field-recorded data
    on branchiness of a tree, the package: (i) computes and plots the centre of
    mass of the tree itself, (ii) computes branches slenderness coefficient in
    order to aid the arborist identify potentially dangerous branches, and
    (iii) computes the force acting on a ground plinth and its best position
    relating to the tree centre of mass, should the tree need to be stabilized
    by a steel cable.
Performs sparse discriminant analysis on a combination of
    node and leaf predictors when the predictor variables are
    structured according to a tree, as described in Fukuyama et
    al. (2017) <doi:10.1371/journal.pcbi.1005706>.
Functions for estimating times of common ancestry and molecular clock rates of evolution using a variety of evolutionary models, parametric and nonparametric bootstrap confidence intervals, methods for detecting outlier lineages, root-to-tip regression, and a statistical test for selecting molecular clock models. The methods are described in Volz, E.M. and S.D.W. Frost (2017) <doi:10.1093/ve/vex025>.
The model estimates air pollution removal by dry deposition on trees. It also estimates or uses hourly values for aerodynamic resistance, boundary layer resistance, canopy resistance, stomatal resistance, cuticular resistance, mesophyll resistance, soil resistance, friction velocity and deposition velocity. It also allows plotting graphical results for a specific time period. The pollutants are nitrogen dioxide, ozone, sulphur dioxide, carbon monoxide and particulate matter. Baldocchi D (1994) <doi:10.1093/treephys/14.7-8-9.1069>. Farquhar GD, von Caemmerer S, Berry JA (1980) Planta 149: 78-90. Hirabayashi S, Kroll CN, Nowak DJ (2015) i-Tree Eco Dry Deposition Model. Nowak DJ, Crane DE, Stevens JC (2006) <doi:10.1016/j.ufug.2006.01.007>. US EPA (1999) PCRAMMET User's Guide. EPA-454/B-96-001. Weiss A, Norman JM (1985) Agricultural and Forest Meteorology 34: 205—213.
Treelets provides a novel construction of multi-scale bases that extends
        wavelets to non-smooth signals. It returns a multi-scale orthonormal 
	basis, where the final computed basis functions are supported 
	on nested clusters in a hierarchical tree.  Both the tree and
	the basis, which are constructed simultaneously, reflect the 
	internal structure of the data.
Algorithms for tree detection, noise removal, stem modelling, 3D visualization and manipulation of terrestrial 'LiDAR' (but not only) point clouds, currently focusing on high performance applications for forest inventory - being fully compatible with the 'LAS' infrastructure provided by 'lidR'. For in depth descriptions of the stem classification and segmentation algorithms check out Conto et al. (2017) <doi:10.1016/j.compag.2017.10.019>.
S4 class and methods for intuitive and efficient phylogenetic tree manipulation.
A treemap is a space-filling visualization of hierarchical
    structures. This package offers great flexibility to draw treemaps.
Provides 'ggplot2' geoms for drawing treemaps.
(i) For a given species phylogeny on present day data which is calibrated to calendar-time, a method for estimating maximum likelihood speciation and extinction processes is provided. The method allows for non-constant rates. Rates may change (1) as a function of time, i.e. rate shifts at specified times or mass extinction events (likelihood implemented as LikShifts, optimization as bd.shifts.optim and visualized as bd.shifts.plot) or (2) as a function of the number of species, i.e. density-dependence (likelihood implemented as LikDD and optimization as bd.densdep.optim) or (3) extinction rate may be a function of species age (likelihood implemented as LikAge and optimization as bd.age.optim.matlab). Note that the methods take into account the whole phylogeny, in particular it accounts for the "pull of the present" effect. (1-3) can take into account incomplete species sampling, as long as each species has the same probability of being sampled. For a given phylogeny on higher taxa (i.e. all but one species per taxa are missing), where the number of species is known within each higher taxa, speciation and extinction rates can be estimated under model (1) (implemented within LikShifts and bd.shifts.optim with groups !=0). (ii) For a given phylogeny with sequentially sampled tips, e.g. a virus phylogeny, rates can be estimated under a model where rates vary across time using bdsky.stt.optim based on likelihood LikShiftsSTT (extending LikShifts and bd.shifts.optim). Furthermore, rates may vary as a function of host types using LikTypesSTT (multitype branching process extending functions in R package diversitree). This function can furthermore calculate the likelihood under an epidemiological model where infected individuals are first exposed and then infectious.
An implementation of permutation tests in R, supporting both exact and asymptotic K sample test of data locations. The p value of exact tests is found using tree algorithms. Tree algorithms treat permutations of input data as tree nodes and perform constraint depth-first searches for permutations that fall into the critical region of a test systematically. Pruning of tree search and optimisations at C level enable exact tests for certain large data sets.
Matches phylogenetic trees and trait data, and
    allows simultaneous manipulation of the tree and data using 'dplyr'.
Searches for phylogenetic trees that are optimal using a user-defined criterion. 
  Implements Profile Parsimony (Faith and Trueman, 2001) <doi:10.1080/10635150118627>, and
  Successive Approximations (Farris, 1969) <doi:10.2307/2412182>.
  Handles inapplicable data using the algorithm of Brazeau, Guillerme and Smith
  (2019) <doi:10.1093/sysbio/syy083>.
Simulation methods for phylogenetic trees where (i) all tips are sampled at one time point or (ii) tips are sampled sequentially through time. (i) For sampling at one time point, simulations are performed under a constant rate birth-death process, conditioned on having a fixed number of final tips (sim.bd.taxa()), or a fixed age (sim.bd.age()), or a fixed age and number of tips (sim.bd.taxa.age()). When conditioning on the number of final tips, the method allows for shifts in rates and mass extinction events during the birth-death process (sim.rateshift.taxa()). The function sim.bd.age() (and sim.rateshift.taxa() without extinction) allow the speciation rate to change in a density-dependent way. The LTT plots of the simulations can be displayed using LTT.plot(), LTT.plot.gen() and LTT.average.root(). TreeSim further samples trees with n final tips from a set of trees generated by the common sampling algorithm stopping when a fixed number m>>n of tips is first reached (sim.gsa.taxa()). This latter method is appropriate for m-tip trees generated under a big class of models (details in the sim.gsa.taxa() man page). For incomplete phylogeny, the missing speciation events can be added through simulations (corsim()). (ii) sim.rateshifts.taxa() is generalized to sim.bdsky.stt() for serially sampled trees, where the trees are conditioned on either the number of sampled tips or the age. Furthermore, for a multitype-branching process with sequential sampling, trees on a fixed number of tips can be simulated using sim.bdtypes.stt.taxa(). This function further allows to simulate under epidemiological models with an exposed class. The function sim.genespeciestree() simulates coalescent gene trees within birth-death species trees, and sim.genetree() simulates coalescent gene trees.
Provides a flexible simulation tool for phylogenetic trees under a general model for speciation and extinction. Trees with a user-specified number of extant tips, or a user-specified stem age are simulated. It is possible to assume any probability distribution for the waiting time until speciation and extinction. Furthermore, the waiting times to speciation / extinction may be scaled in different parts of the tree, meaning we can simulate trees with clade-dependent diversification processes. At a speciation event, one species splits into two. We allow for two different modes at these splits: (i) symmetric, where for every speciation event new waiting times until speciation and extinction are drawn for both daughter lineages; and (ii) asymmetric, where a speciation event results in one species with new waiting times, and another that carries the extinction time and age of its ancestor. The symmetric mode can be seen as an vicariant or allopatric process where divided populations suffer equal evolutionary forces while the asymmetric mode could be seen as a peripatric speciation where a mother lineage continues to exist.
Tools for the exploration of distributions of phylogenetic trees.
    This package includes a 'shiny' interface which can be started from R using
    treespaceServer(). 
    For further details see Jombart et al. (2017) <DOI:10.1111/1755-0998.12676>.
Combine a list of taxa with a phylogeny to generate a starting tree for use in
    total evidence dating analyses.
An implementation of TreeThresh, a locally adaptive version of EbayesThresh.
Provides a collection of true type and open type Star Trek-themed fonts.
Trelliscope is a scalable, flexible, interactive approach to visualizing data (Hafen, 2013 <doi:10.1109/LDAV.2013.6675164>). This package provides methods that make it easy to create a Trelliscope display specification for TrelliscopeJS. High-level functions are provided for creating displays from within 'dplyr' or 'ggplot2' workflows. Low-level functions are also provided for creating new interfaces.
Provides access to Trello API (<https://developers.trello.com/>).
    A family of GET functions make it easy to retrieve cards, labels, members,
    teams and other data from both public and private boards. Server responses
    are formatted upon retrieval. Automated paging allows for large requests
    that exceed server limit. See <https://github.com/jchrom/trelloR> for more
    information.
The analysis of environmental data often requires
	     the detection of trends and change-points. 
	     This package includes tests for trend detection
	     (Cox-Stuart Trend Test, Mann-Kendall Trend Test, 
	     (correlated) Hirsch-Slack Test,
             partial Mann-Kendall Trend Test, multivariate (multisite)
	     Mann-Kendall Trend Test, (Seasonal) Sen's slope, 
	     partial Pearson and Spearman correlation trend test),
             change-point detection (Lanzante's test procedures, 
	     Pettitt's test, Buishand Range Test,
	     Buishand U Test, Standard Normal Homogeinity Test),
	     detection of non-randomness (Wallis-Moore Phase Frequency Test,
	     Bartels rank von Neumann's ratio test, Wald-Wolfowitz Test)
	     and the two sample Robust Rank-Order Distributional Test.
Innovative Trend Analysis is a graphical method to examine the trends in time series data. Sequential Mann-Kendall test uses the intersection of prograde and retrograde series to indicate the possible change point in time series data. Distribution free cumulative sum charts indicate location and significance of the change point in time series.
  Zekai, S. (2011). <doi:10.1061/(ASCE)HE.1943-5584.0000556>.
  Grayson, R. B. et al. (1996). Hydrological Recipes: Estimation Techniques in Australian Hydrology. Cooperative Research Centre for Catchment Hydrology, Australia, p. 125. 
  Sneyers, S. (1990). On the statistical analysis of series of observations. Technical note no 5 143, WMO No 725 415. Secretariat of the World Meteorological Organization, Geneva, 192 pp.
Estimation of causal odds ratio and power calculation given trends in exposure prevalence    and outcome frequencies of stratified data.
Performs the detection of point anomalies and linear trend changes 
    for univariate time series by implementing the bottom-up unbalanced wavelet transformation 
    proposed by H. Maeng and P. Fryzlewicz (2019) <http://personal.lse.ac.uk/maengh/>. 
    The estimated number and locations of the change-points are returned with the 
    piecewise-linear estimator for signal.
Access Google Trends information. This package provides a tidy wrapper to the 'gtrendsR' package.
    Use four spaces when indenting paragraphs within the Description.
Provides three estimators for tensor response regression (TRR) and tensor predictor regression (TPR) models with tensor envelope structure. The three types of estimation approaches are generic and can be applied to any envelope estimation problems. The full Grassmannian (FG) optimization is often associated with likelihood-based estimation but requires heavy computation and good initialization; the one-directional optimization approaches (1D and ECD algorithms) are faster, stable and does not require carefully chosen initial values; the SIMPLS-type is motivated by the partial least squares regression and is computationally the least expensive.
Simulate genotypes for case-parent triads, case-control, and quantitative trait samples with realistic linkage diequilibrium structure and allele frequency distribution. For studies of epistasis one can simulate models that involve specific SNPs at specific sets of loci, which we will refer to as "pathways". TriadSim generates genotype data by resampling triad genotypes from existing data. The details of the method is described in the manuscript under preparation "Simulating Autosomal Genotypes with Realistic Linkage Disequilibrium and a Spiked in Genetic Effect" Shi, M., Umbach, D.M., Wise A.S., Weinberg, C.R.
A showcase of Bayesian clinical trial designs, implemented in 
    'RStan' and R, including the Continual Reassessment Method (CRM) and EffTox.
    Given the emphasis on flexibility in Bayesian analysis,
    the implementations in a common language and style may serve as a cookbook
    to encourage the use of Bayesian methods in trials. Compiled 'RStan' models
    are installed, alongside helper classes and functions to work with models.
    There is a preponderance of early phase trial designs because this is where 
    Bayesian methods are used most. If there is a published Bayesian design you
    want implemented in 'Stan', get in touch.
functions and examples in Sample Size Calculation in
        Clinical Research.
Provides the "r, q, p, and d" distribution functions for the triangle distribution.
Measuring angles between points in a landscape is much easier
    than measuring distances. When the location of three points is known the
    position of the observer can be determined based solely on the angles between
    these points as seen by the observer. This task (known as triangulation)
    however requires onerous calculations - these calculations are automated by this
    package.
Functions to make manipulation of object attributes easier. 
    It also contains a few functions that extend the 'dplyr' package for data 
    manipulation, and it provides new pipe operators, including the pipe '%@>%' 
    similar to the 'magrittr' '%>%', but with the additional functionality to 
    enable attributes propagation.
A flexible color scale for ternary compositions with options for
  discretization, centering and scaling.
'Radix trees', or 'tries', are key-value data structures optimised for efficient lookups, similar in purpose
             to hash tables. 'triebeard' provides an implementation of 'radix trees' for use in R programming and in
             developing packages with 'Rcpp'.
A complete data set of historic GB trig points in British National Grid (OSGB36) coordinate reference system. Trig points (aka triangulation stations) are fixed survey points used to improve the accuracy of map making in Great Britain during the 20th Century. Trig points are typically located on hilltops so still serve as a useful navigational aid for walkers and hikers today.
Propensity score matching for non-binary treatments.
Provides various commonly-used response time trimming
    methods, including the recursive / moving-criterion methods reported by
    Van Selst and Jolicoeur (1994). By passing trimming functions raw data files,
    the package will return trimmed data ready for inferential testing.
Creates point and probability forecasts from the trees in a random forest using a trimmed opinion pool.
Several statistical test functions as well as a function for exploratory data analysis to investigate classifiers allocating individuals to one of three disjoint and ordered classes. In a single classifier assessment the discriminatory power is compared to classification by chance. In a comparison of two classifiers the null hypothesis corresponds to equal discriminatory power of the two classifiers.
The package contains functions that 1) estimates
        gene-environment interaction between a SNP and a continuous
        non-genetic attribute by fitting a generalized additive model
        to case-parent trio data, 2) produces graphical displays of
        estimated interaction, 3) performs permutation test of
        gene-environment interaction; 4) simulates informative
        case-parent trios.
Fit a trio model via penalized maximum likelihood. The model is fit for a path of values of the penalty parameter. This package is based on Noah Simon, et al. (2011) <doi:10.1080/10618600.2012.681250>.
Functions for accessing and manipulating spatial data for animal
    tracking, with straightforward coercion from and to other formats. Filter
    for speed and create time spent maps from animal track data. There are
    coercion methods to convert between 'trip' and 'ltraj' from 'adehabitatLT', 
    and between 'trip' and 'psp' and 'ppp' from 'spatstat'. Trip objects
    can be created from raw or grouped data frames, and from types in the 'sp', 
    'sf', 'amt', 'trackeR', 'mousetrap', and other packages.
A constrained two-dimensional Delaunay triangulation package
  providing both triangulation and generation of voronoi mosaics of 
  irregular spaced data.
Data handling and estimation functions for animal movement
    estimation from archival or satellite tags. Helper functions are included
    for making image summaries binned by time interval from Markov Chain Monte Carlo
    simulations.
Social Relation Model (SRM) analyses for single or multiple
    round-robin groups are performed. These analyses are either based on one
    manifest variable, one latent construct measured by two manifest variables,
    two manifest variables and their bivariate relations, or two latent
    constructs each measured by two manifest variables. Within-group t-tests
    for variance components and covariances are provided for single groups.
    For multiple groups two types of significance tests are provided:
    between-groups t-tests (as in SOREMO) and enhanced standard errors based on
    Lashley and Bond (1997) <DOI:10.1037/1082-989X.2.3.278>. Handling for missing values is provided.
Computing diversity measures on tripartite graphs. This package first implements a parametrized family of such diversity measures which apply on probability distributions. Sometimes called "True Diversity", this family contains famous measures such as the richness, the Shannon entropy, the Herfindahl-Hirschman index, and the Berger-Parker index. Second, the package allows to apply these measures on probability distributions resulting from random walks between the levels of tripartite graphs. By defining an initial distribution at a given level of the graph and a path to follow between the three levels, the probability of the walker's position within the final level is then computed, thus providing a particular instance of diversity to measure.
A new bioinformatic tool for comparing transcriptomes of two biological samples from the same or different species. The mapping is conducted based on the overlap of the associated genes of different samples. More examples and detailed explanations are available in the vignette. For detailed description of the method, please refer to Li, Wei Vivian, Yiling Chen, and Jingyi Jessica Li. "TROM: A testing-based method for finding transcriptomic similarity of biological samples." Statistics in biosciences 9.1 (2017): 105-136 <doi:10.1007/s12561-016-9163-y>.
It includes functions like tropical addition, tropical multiplication for vectors and matrices. In tropical algebra, the tropical sum of two numbers is their minimum and the tropical product of two numbers is their ordinary sum. For more information see also I. Simon (1988) Recognizable sets with multiplicities in the tropical semi ring: Volume 324 Lecture Notes I Computer Science, pages 107-120 <doi: 10.1007/BFb0017135>.
A compilation of fish stock assessment methods for the
    analysis of length-frequency data in the context of data-poor
    fisheries. Includes methods and examples included in the FAO
    Manual by P. Sparre and S.C. Venema (1998), "Introduction to tropical fish
    stock assessment"
    (<http://www.fao.org/docrep/W5449E/W5449E00.htm>), as well as
    other more recent methods.
Estimates the trophic position of a consumer relative 
    to a baseline species. It implements a Bayesian approach which combines an 
    interface to the 'JAGS' MCMC library of 'rjags' and stable isotopes. Users are
    encouraged to test the package and send bugs and/or errors to
    trophicposition-support@googlegroups.com.
Some of the basic tropical algebra functionality is provided for sparse matrices by applying sparse matrix storage techniques. Some of these are addition and multiplication of vectors and matrices, dot product of the vectors in tropical form and some general equations are also solved using tropical algebra.
Class definitions and constructors for pseudo-vectors containing
    all permutations, combinations and subsets of objects taken from a vector.
    Simplifies working with structures commonly encountered in combinatorics.
Read General Transit Feed Specification (GTFS) zipfiles into a list of R dataframes. Perform validation of the data structure against the specification. Analyze the headways and frequencies at routes and stops. Please see the GTFS documentation here for more detail: <http://gtfs.org/>.
Functions and datasets for the reader of the book "The R Software: Fundamentals of Programming and Statistical Analysis".
Recursive partytioning of transformation models with
  corresponding random forest for conditional transformation models 
  as described in 'Transformation Forests' (Hothorn and Zeileis, 2017, <arXiv:1701.02110>) 
  and 'Top-Down Transformation Choice' (Hothorn, 2018, <DOI:10.1177/1471082X17748081>).
An implementation of the TrueSkill algorithm (Herbrich,
        R., Minka, T. and Grapel, T) in R; a Bayesian skill rating
        system with inference by approximate message passing on a
        factor graph. Used by Xbox to rank gamers and identify
        appropriate matches.
        http://research.microsoft.com/en-us/projects/trueskill/default.aspx
        Current version allows for one player per team. Will update as
        time permits.  Requires R version 3.0 as it is written with
        Reference Classes.  URL:
        https://github.com/bhoung/trueskill-in-r Acknowledgements to
        Doug Zongker and Heungsub Lee for their python implementations
        of the algorithm and for the liberal reuse of Doug's code
        comments (@dougz and @sublee on github).
A collection of functions to deal with the truncated univariate and multivariate normal and Student distributions, described in Botev (2017) <doi:10.1111/rssb.12162> and Botev and L'Ecuyer (2015) <doi:10.1109/WSC.2015.7408180>.
A collection of tools to evaluate probability density
        functions, cumulative distribution functions, quantile functions
        and random numbers for truncated random variables.  These functions are
        provided to also compute the expected value and variance. Nadarajah
        and Kotz (2006) developed most of the functions. QQ plots can be produced.
        All the probability functions in the stats, stats4 and evd
        packages are automatically available for truncation..
Goodness-of-fit tests and some adjusted exploratory tools
        allowing for left truncated data
Density, probability, quantile and random number
        generation functions for the truncated normal distribution.
Estimation of models for truncated Gaussian variables by maximum likelihood.
Semi-parametric estimation of truncated linear regression models
Does local optimization using two derivatives and trust regions.
    Guaranteed to converge to local minimum of objective function.
Trust region algorithm for nonlinear optimization. Efficient when
    the Hessian of the objective function is sparse (i.e., relatively few nonzero
    cross-partial derivatives). See Braun, M. (2014) <doi:10.18637/jss.v060.i04>.
Advanced tryCatch() and try() functions for better error handling
             (logging, stack trace with source code references and support for post-mortem analysis via dump files).
Contains R functions and datasets detailed in the book
        "Time Series Analysis with Applications in R (second edition)" by Jonathan Cryer and Kung-Sik Chan.
Tsallis distribution also known as the q-exponential family distribution. Provide distribution d, p, q, r functions, fitting and testing functions. Project initiated by Paul Higbie and based on Cosma Shalizi's code.
Time series toolkit with identical behavior for all
  time series classes: 'ts','xts', 'data.frame', 'data.table', 'tibble', 'zoo',
  'timeSeries', 'tsibble'. Also converts reliably between these classes.
Different estimates are provided to solve the blind source separation problem for multivariate time series with stochastic volatility (Matilainen, Nordhausen and Oja (2015) <doi:10.1016/j.spl.2015.04.033>; Matilainen, Miettinen, Nordhausen, Oja and Taskinen (2017) <doi:10.17713/ajs.v46i3-4.671>) and supervised dimension reduction problem for multivariate time series (Matilainen, Croux, Nordhausen and Oja (2017) <doi:10.1016/j.ecosta.2017.04.002>). Different functions based on AMUSE and SOBI are also provided for estimating the dimension of the white noise subspace.
Performs the two-sample comparisons using the following exact test procedures: the exact likelihood-ratio test (LRT) for equality of two normal populations proposed in Zhang et al. (2012); the combined test based on the LRT and Shapiro-Wilk test for normality via the Bonferroni correction technique; the newly proposed density-based empirical likelihood (DBEL) ratio test. To calculate p-values of the DBEL procedures, three procedures are used: (a) the traditional Monte Carlo (MC) method implemented in C++, (b) a new interpolation method based on regression techniques to operate with tabulated critical values of the test statistic; (c) a Bayesian type method that uses the tabulated critical values as the prior information and  MC generated DBEL-test-statistic's values as data.
A set of measures of dissimilarity between time series to perform time series clustering. Metrics based on raw data, on generating models and on the forecast behavior are implemented. Some additional utilities related to time series clustering are also provided, such as clustering algorithms and cluster evaluation metrics.
Utilities for comparing the equality of series on two databases.
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
Likelihood-based methods for model fitting and assessment, prediction and intervention analysis of count time series following generalized linear models are provided. Models with the identity and with the logarithmic link function are allowed. The conditional distribution can be Poisson or Negative Binomial.
A set of functions to implement Time Series Cointegrated System (TSCS)
    spatial interpolation and relevant data visualization.
Illustrates the various 'TSdbi' packages with a vignette using time series 
	data from several sources. The vignette also illustrates some simple time series
	manipulation and plotting using packages 'tframe' and 'tfplot'.
A terribly-simple data base for numeric
  time series, written purely in R, so no external
  database-software is needed. Series are stored in
  plain-text files (the most-portable and enduring file
  type) in CSV format. Timestamps are encoded using R's
  native numeric representation for 'Date'/'POSIXct', which
  makes them fast to parse, but keeps them accessible
  with other software. The package provides tools for
  saving and updating series in this standardised
  format, for retrieving and joining data, for
  summarising files and directories, and for coercing
  series from and to other data types (such as 'zoo'
  series).
Provides a common interface to time series databases. The
	objective is to define a standard interface so users can retrieve time 
	series data from various sources with a simple, common, set of 
	commands, and so programs can be written to be portable with respect 
	to the data source. The SQL implementations also provide a database 
	table design, so users needing to set up a time series database 
	have a reasonably complete way to do this easily. The interface 
	provides for a variety of options with respect to the representation 
	of time series in R. The interface, and the SQL implementations, also
	handle vintages of time series data (sometime called editions or 
	real-time data). There is also a (not yet well tested) mechanism to
	handle multilingual data documentation.
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
ARIMA-model-based decomposition of quarterly and 
 monthly time series data.
 The methodology is developed and described, among others, in 
 Burman (1980) <DOI:10.2307/2982132> and 
 Hillmer and Tiao (1982) <DOI:10.2307/2287770>.
Calculate optimal Zhong's two-/three-stage Phase II designs (see Zhong (2012) <doi:10.1016/j.cct.2012.07.006>). Generate Target Toxicity decision table for Phase I dose-finding (two-/three-stage). This package also allows users to run dose-finding simulations based on customized decision table.
Determining training set for genomic selection using a genetic algorithm (Holland J.H. (1975) <DOI:10.1145/1216504.1216510>) or simple exchange algorithm (change an individual every iteration). Three different criteria are used in both algorithms, which are r-score (Ou J.H., Liao C.T. (2018) <DOI:10.6342/NTU201802290>), PEV-score (Akdemir D. et al. (2015) <DOI:10.1186/s12711-015-0116-6>) and CD-score (Laloe D. (1993) <DOI:10.1186/1297-9686-25-6-557>). Phenotypic data for candidate set is not necessary for all these methods. By using it, one may readily determine a training set that can be expected to provide a better training set comparing to random sampling.
Disaggregates low frequency time series data to higher frequency series. Implements the following methods for temporal disaggregation: Boot, Feibes and Lisman (1967) <DOI:10.2307/2985238>, Chow and Lin (1971) <DOI:10.2307/1928739>, Fernandez (1981) <DOI:10.2307/1924371> and Litterman (1983) <DOI:10.2307/1391858>.
A set of commonly used distance measures and some additional functions which, although initially not designed for this purpose, can be used to measure the dissimilarity between time series. These measures can be used to perform clustering, classification or other data mining tasks which require the definition of a distance measure between time series.
Implements a method for identifying subgroups with superior response relative to the overall sample.
Implements nonlinear autoregressive (AR) time series models. For univariate series, a non-parametric approach is available through additive nonlinear AR. Parametric modeling and testing for regime switching dynamics is available when the transition is either direct (TAR: threshold AR) or smooth (STAR: smooth transition AR, LSTAR). For multivariate series, one can estimate a range of TVAR or threshold cointegration TVECM models with two or three regimes. Tests can be conducted for TVAR as well as for TVECM (Hansen and Seo 2002 and Seo 2006).
Computes various entropies of given time series. This is the initial version that includes ApEn() and SampEn() functions for calculating approximate entropy and sample entropy. Approximate entropy was proposed by S.M. Pincus in "Approximate entropy as a measure of system complexity", Proceedings of the National Academy of Sciences of the United States of America, 88, 2297-2301 (March 1991). Sample entropy was proposed by J. S. Richman and J. R. Moorman in "Physiological time-series analysis using approximate entropy and sample entropy", American Journal of Physiology, Heart and Circulatory Physiology, 278, 2039-2049 (June 2000). This package also contains FastApEn() and FastSampEn() functions for calculating fast approximate entropy and fast sample entropy. These are newly designed very fast algorithms, resulting from the modification of the original algorithms. The calculated values of these entropies are not the same as the original ones, but the entropy trend of the analyzed time series determines equally reliably. Their main advantage is their speed, which is up to a thousand times higher. A scientific article describing their properties has been submitted to The Journal of Supercomputing and in present time it is waiting for the acceptance.
Time series analysis and computational finance.
Routines for the analysis of nonlinear time series. This
        work is largely inspired by the TISEAN project, by Rainer
        Hegger, Holger Kantz and Thomas Schreiber:
        <http://www.mpipks-dresden.mpg.de/~tisean/>.
Implements an Entropy measure of dependence based on the Bhattacharya-Hellinger-Matusita distance. Can be used as a (nonlinear) autocorrelation/crosscorrelation function for continuous and categorical time series. The package includes tests for serial dependence and nonlinearity based on it. Some routines have a parallel version that can be used in a multicore/cluster environment. The package makes use of S4 classes.
Multiscale multifractal analysis (MMA) (Gierałtowski et al.,
    2012)<DOI:10.1103/PhysRevE.85.021915> is a time series analysis method,
    designed to describe scaling properties of fluctuations within the signal
    analyzed. The main result of this procedure is the so called Hurst surface
    h(q,s) , which is a dependence of the local Hurst exponent h (fluctuation
    scaling exponent) on the multifractal parameter q and the scale of observation s
    (data window width).
Tools for downloading and organizing data from Tehran Stock Exchange (TSE) <http://new.tse.ir/en/>. It also performs some descriptive data analysis for assets.
Forecasting of long memory time series in presence of structural break by using TSF algorithm by Papailias and Dias (2015) <doi:10.1016/j.ijforecast.2015.01.006>.
Extraction of Factors from Multivariate Time Series. See ?00tsfa-Intro for more details.
A 'fame' interface for 'TSdbi'.
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
Methods for extracting various features from time series data. The features provided are those from Hyndman, Wang and Laptev (2013) <doi:10.1109/ICDMW.2015.104>, Kang, Hyndman and Smith-Miles (2017) <doi:10.1016/j.ijforecast.2016.09.004> and from Fulcher, Little and Jones (2013) <doi:10.1098/rsif.2013.0048>. Features include spectral entropy, autocorrelations, measures of the strength of seasonality and trend, and so on. Users can also define their own feature functions.
Allows to forecast time series using nearest neighbors regression
    Francisco Martinez, Maria P. Frias, Maria D. Perez-Godoy and Antonio J.
    Rivera (2017) <doi:10.1007/s10462-017-9593-z>. When the forecasting horizon
    is higher than 1, two multi-step ahead forecasting strategies can be used.
    The model built is autoregressive, that is, it is only based on the 
    observations of the time series. The nearest neighbors used in a prediction
    can be consulted and plotted.
To provide a high dimensional grouped variable selection approach for detection of whole-genome SNP effects and SNP-SNP interactions, as described in Fang et al. (2017, under review).
This gui shows realisations of times series, currently ARMA and GARCH processes. It might be helpful for teaching and studying.
Two-stage procedure compares hazard rate functions,
    which may or may not cross each other.
Provides a 'tbl_ts' class (the 'tsibble') to store
    and manage temporal data in a data-centric format, which is built on
    top of the 'tibble'. The 'tsibble' aims at easily manipulating and
    analysing temporal data, including counting and filling in time gaps,
    aggregate over calendar periods, performing rolling window
    calculations, and etc.
Provides diverse datasets in the 'tsibble' data structure. These datasets are useful for learning and demonstrating how tidy temporal data can tidied, visualised, and forecasted.
Functions for analysing and forecasting intermittent demand/slow moving items time series.
An implementation of the time-series Susceptible-Infected-Recovered (TSIR) model using a number of different fitting options for infectious disease time series data. The manuscript based on this package can be found here <https://doi:10.1371/0185528>. The method implemented here is described by Finkenstadt and Grenfell (2000) <DOI: 10.1111/1467-9876.00187>.
A novel and fast two stage method for simultaneous multiple change point detection and variable selection for piecewise stationary autoregressive (PSAR) processes and linear regression model. It also simultaneously performs variable selection for each autoregressive model and hence the order selection.
Implementations of a number of functions used to mine numeric time-series data. It covers the implementation of SAX transformation, univariate motif discovery (based on the random projection method), multivariate motif discovery (based on graph clustering), and several functions used for the ease of visualizing the motifs discovered. The details of SAX transformation can be found in J. Lin. E. Keogh, L. Wei, S. Lonardi, Experiencing SAX: A novel symbolic representation of time series, Data Mining and Knowledge Discovery 15 (2) (2007) 107-144. Details on univariate motif discovery method implemented can be found in B. Chiu, E. Keogh, S. Lonardi, Probabilistic discovery of time series motifs, ACM SIGKDD, Washington, DC, USA, 2003, pp. 493-498. Details on the multivariate motif discovery method implemented can be found in A. Vahdatpour, N. Amini, M. Sarrafzadeh, Towards unsupervised activity discovery using multi-dimensional motif detection in time series, IJCAI 2009 21st International Joint Conference on Artificial Intelligence.
Methods to 
	retrieve data from several different sources. This include historical
	quote data from 'Yahoo' and 'Oanda', economic data from 'FRED', and
	'xls' and 'csv' data from different sources.
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
Return the first four moments of the SMN distributions (Normal, Student-t, Pearson VII, Slash or Contaminated Normal).
Tools for specifying time series regression models
A toolkit implementing the Matrix Profile concept that was created by CS-UCR <http://www.cs.ucr.edu/~eamonn/MatrixProfile.html>.
A 'MySQL' interface for 'TSdbi'.
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
Temporal SNA tools for continuous- and discrete-time longitudinal networks having vertex, edge, and attribute dynamics stored in the 'networkDynamic' format. This work was supported by grant R01HD68395 from the National Institute of Health.
A "pure R" implementation of the t-SNE algorithm.
An ODBC interface for 'TSdbi'.
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
Detection of outliers in time series following the 
    Chen and Liu (1993) <DOI:10.2307/2290724> procedure. 
    Innovational outliers, additive outliers, level shifts, 
    temporary changes and seasonal level shifts are considered.
Basic infrastructure and some algorithms for the traveling
    salesperson problem (also traveling salesman problem; TSP).
    The package provides some simple algorithms and
    an interface to the Concorde TSP solver and its implementation of the
    Chained-Lin-Kernighan heuristic. The code for Concorde
    itself is not included in the package and has to be obtained separately.
Adjusts for correlations among the rows and columns via
        the Transposable Sphering Algorithm when conducting large-scale
        inference on the rows of a data matrix.
Prediction intervals for ARIMA and structural time series
    models using importance sampling approach with uninformative priors for model
    parameters, leading to more accurate coverage probabilities in frequentist
    sense. Instead of sampling the future observations and hidden states of the
    state space representation of the model, only model parameters are sampled,
    and the method is based solving the equations corresponding to the conditional
    coverage probability of the prediction intervals. This makes method relatively
    fast compared to for example MCMC methods, and standard errors of prediction
    limits can also be computed straightforwardly.
Instance feature calculation and evolutionary instance generation
    for the traveling salesman problem. Also contains code to "morph" two TSP
    instances into each other. And the possibility to conveniently run a couple
    of solvers on TSP instances.
A 'PostgreSQL' interface for 'TSdbi'.
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
Functions for time series preprocessing, decomposition, prediction and accuracy assessment using automatic linear modelling. The generated linear models and its yielded prediction errors can be used for benchmarking other time series prediction methods and for creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used.
Time Series Qn is a package with applications of the Qn estimator of Rousseeuw and Croux (1993) <doi:10.1080/01621459.1993.10476408> to univariate and multivariate Time Series in time and frequency domains. More specifically, the robust estimation of autocorrelation or autocovariance matrix functions from Ma and Genton (2000, 2001) <doi:10.1111/1467-9892.00203>, <doi:10.1006/jmva.2000.1942> and Cotta (2017) <doi:10.13140/RG.2.2.14092.10883> are provided. The robust pseudo-periodogram of Molinares et. al. (2009) <doi:10.1016/j.jspi.2008.12.014> is also given. This packages also provides the M-estimator of the long-memory parameter d based on the robustification of the GPH estimator proposed by Reisen et al. (2017) <doi:10.1016/j.jspi.2017.02.008>.
Methods for representations (i.e. dimensionality reduction, preprocessing, feature extraction) of time series to help more accurate and effective time series data mining.
    Non-data adaptive, data adaptive, model-based and data dictated (clipped) representation methods are implemented. Also min-max and z-score normalisations, and forecasting accuracy measures are implemented.
Time Series Segmented Residual Trends is a method for the automated detection
    of land degradation from remotely sensed vegetation and climate datasets.
    TSS-RESTREND incorporates aspects of two existing degradation detection methods:
    RESTREND which is used to control for climate variability, and BFAST which
    is used to look for structural changes in the ecosystem. The full details
    of the testing and justification of the TSS-RESTREND method (version 0.1.02)
    are published in Burrell et al., (2017). <doi:10.1016/j.rse.2017.05.018>.
    The changes to the method introduced in version 0.2.03 focus on the inclusion 
    of temperature as an additional climate variable. This allows for land 
    degradation assessment in temperature limited drylands. A paper that details
    this work is currently under review. There are also a number of bug fixes
    and speed improvements. The version under active development and additional  
    example scripts can be can be found at <https://
    github.com/ArdenB/TSSRESTREND>.
Methods to 
	retrieve data in the Statistical Data and Metadata Exchange ('SDMX') format
	from several database. (For example,
	'EuroStat', the European Central Bank, the Organisation for Economic 
	Co-operation and Development, the 'Unesco' Institute for Statistics,
	and the International Labor Organization.)
	This is a wrapper for package 'RJSDMX'.
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
Execution of various time series models and choosing the best one
    either by a specific error metric or by picking the best one by majority vote.
    The models are based on the "forecast" package, written by Prof. Rob Hyndman.
Standard SQL query functions used by 
	SQL plugins packages for the 'TSdbi' interface to time series databases. 
	It will mainly be used by other packages rather than directly by end
	users. The one exception is the function 'TSquery' which can be used to
	construct a time series from a database containing observations over
	time (e.g. balance statements for multiple years), but where the database
	is not specifically designed to store time series (as with other 
	'TSdbi' SQL plugin packages).
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
An 'SQLite' interface for 'TSdbi'.
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
Functions for statistical analysis, modeling and simulation of time
 series with state space model, based on the methodology in Kitagawa
 (1993, ISBN: 4-00-007703-1 and 2005, ISBN: 4-00-005455-4).
Provides a set of tools for descriptive and predictive analysis of time series data. That includes functions for interactive visualization of time series objects and as well utility functions for automation time series forecasting.
Plot official statistics' time series conveniently: automatic legends, highlight windows, stacked bar chars with positive and negative contributions, sum-as-line option, two y-axes with automatic horizontal grids that fit both axes and other popular chart types. 'tstools' comes with a plethora of defaults to let you plot without setting an abundance of parameters first, but gives you the flexibility to tweak the defaults. In addition to charts, 'tstools' provides a super fast, 'data.table' backed time series I/O that allows the user to export / import long format, wide format and transposed wide format data to various file types.
A ternary search tree is a type of prefix tree with up to three children and the ability for incremental string search. The package uses this ability for word auto-completion and spell checking. Includes a dataset with the 10001 most frequent English words.
Interactive laboratory of Time Series based in Box-Jenkins methodology.
Includes: (i) tests and visualisations that can help the modeller explore time series components and perform decomposition; (ii) modelling shortcuts, such as functions to construct lagmatrices and seasonal dummy variables of various forms; (iii) an implementation of the Theta method; (iv) tools to facilitate the design of the forecasting process, such as ABC-XYZ analyses; and (v) "quality of life" functions, such as treating time series for trailing and leading values.
Fitting tree-structured varying coefficient models. Simultaneous detection of covariates with varying coefficients and effect modifiers that induce varying coefficients if they are present.
Tools for timescale decomposition of the classic variance ratio of community ecology. Tools are as described in Zhao et al (in prep), extending commonly used methods introduced by Peterson et al (1975) <doi: 10.2307/1936306>.
Accompanies the text Applied Time Series Analysis with R, 2nd edition by Woodward, Gray, and Elliott. It is helpful for data analysis and for time series instruction.
Characterisation of the extremal dependence structure of time series, avoiding pre-processing and filtering as done typically with peaks-over-threshold methods. It uses the conditional approach of Heffernan and Tawn (2004) <DOI:10.1111/j.1467-9868.2004.02050.x> which is very flexible in terms of extremal and asymptotic dependence structures, and Bayesian methods improve efficiency and allow for deriving measures of uncertainty. For example, the extremal index, related to the size of clusters in time, can be estimated and samples from its posterior distribution obtained.
This interface was created to develop a standard procedure 
 to analyse temporal trend in the framework of the OSPAR convention.
 The analysis process run through 4 successive steps : 1) manipulate your data, 2)
 select the parameters you want to analyse, 3) build your regulated 
 time series, 4) perform diagnosis and analysis and 5) read the results. 
 Statistical analysis call other package function such as Kendall tests
 or cusum() function.
U.S. Department of the Treasury, Alcohol and Tobacco Tax and
    Trade Bureau (TTB) collects data and reports on monthly beer
    industry production and operations. This data package includes
    a collection of 10 years (2006 - 2015) worth of data on materials
    used at U.S. breweries in pounds reported by the Brewer's Report
    of Operations and the Quarterly Brewer's Report of Operations
    forms, ready for data analysis. This package also includes historical
    tax rates on distilled spirits, wine, beer, champagne, and tobacco
    products as individual data sets.
The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes respond with quick, transient dynamics, while other genes change their expression slowly over time. The existing methods for detecting significant expression dynamics often fail when the expression dynamics show a large heterogeneity. Moreover, these methods often cannot cope with irregular and sparse measurements. The method proposed here is specifically designed for the analysis of perturbation responses. It combines different scores to capture fast and transient dynamics as well as slow expression changes, and performs well in the presence of low replicate numbers and irregular sampling times. The results are given in the form of tables including links to figures showing the expression dynamics of the respective transcript. These allow to quickly recognise the relevance of detection, to identify possible false positives and to discriminate early and late changes in gene expression. An extension of the method allows the analysis of the expression dynamics of functional groups of genes, providing a quick overview of the cellular response. The performance of this package was tested on microarray data derived from lung cancer cells stimulated with epidermal growth factor (EGF). Paper: Albrecht, Marco, et al. (2017)<DOI:10.1186/s12859-016-1440-8>.
Interactive shiny application for working with ttest on data. Results for analysis are provided.
C source code and R wrappers for the tth/ttm TeX to 
  HTML/MathML translators.
Computing the first two moments of the truncated multivariate t (TMVT) distribution under the double truncation. Appling the slice sampling algorithm to generate random variates from the TMVT distribution.
Functions and data to construct technical trading rules with R.
Time-Temperature Superposition analysis is often applied to frequency modulated data obtained by Dynamic
		Mechanic Analysis (DMA) and Rheometry in the analytical chemistry and physics
		areas. These techniques provide estimates of material mechanical properties
		(such as moduli) at different temperatures in a wider range of time. This
		package provides the Time-Temperature superposition Master Curve at a referred
		temperature by the three methods: the two wider used methods, Arrhenius based
		methods and WLF, and the newer methodology based on derivatives procedure.
		The Master Curve is smoothed by B-splines basis. The package output is composed
		of plots of experimental data, horizontal and vertical shifts, TTS data, and TTS
		data fitted using B-splines with bootstrap confidence intervals.
A screening process utilizing training and testing samples to filter out uninformative DNA methylation sites. Surrogate variables (SVs) of DNA methylation are included in the filtering process to explain unknown factor effects.
Tensor-train is a compact representation for higher-order tensors. Some algorithms for performing tensor-train decomposition are available such as TT-SVD, TT-WOPT, and TT-Cross. For the details of the algorithms, see I. V. Oseledets (2011) <doi:10.1137/090752286>, Yuan Longao, et al (2017) <arXiv:1709.02641>, I. V. Oseledets (2010) <doi:10.1016/j.laa.2009.07.024>.
Implementation of Time to Target plot based on the work 
             of Ribeiro and Rosseti (2015) <DOI:10.1007/s11590-014-0760-8>, 
             that describe a numerical method that gives the probability of 
             an algorithm A finds a solution at least as good as a given 
             target value in smaller computation time than algorithm B.
This package makes Travel To Work Area from a commuting flow data frame.
Get comments posted on YouTube videos, information on how many 
    times a video has been liked, search for videos with particular content, and 
    much more. You can also scrape captions from a few videos. To learn more about
    the YouTube API, see <https://developers.google.com/youtube/v3/>.
Get statistics and reports from YouTube. To learn more about
    the YouTube Analytics and Reporting API, see <https://developers.google.com/youtube/reporting/>.
Performs Three-Mode Principal Components Analysis,
    which carries out Tucker Models.
Provides R Markdown output formats to use Tufte styles for PDF and HTML output.
Custom template and output formats for use with rmarkdown. Produce
    Edward Tufte-style handouts in html formats with full support for rmarkdown
    features
Perform the conventional Tukey test from formula, lm, aov, aovlist and lmer objects.
Tukey regions are polytopes in the Euclidean space, viz. upper-level sets of the Tukey depth function on given data. The bordering hyperplanes of a Tukey region are computed as well as its vertices, facets, centroid, and volume. In addition, the Tukey median set, which is the non-empty Tukey region having highest depth level, and its barycenter (= Tukey median) are calculated. Tukey regions are visualized in dimension two and three. For details see Liu, Mosler, and Mozharovskyi (2017) <arXiv:1412.5122>. See file LICENSE.note for additional license information.
Provides wrapper functions to the multiple marginal model function mmm() of package 'multcomp' to implement the trend test of Tukey, Ciminera and Heyse (1985) <DOI:10.2307/2530666> for general parametric models.
Integrates several popular high-dimensional methods based on Linear Discriminant Analysis (LDA) and provides a comprehensive and user-friendly toolbox for linear, semi-parametric and tensor-variate classification as mentioned in Yuqing Pan, Qing Mai and Xin Zhang (2019) <arXiv:1904.03469>. Functions are included for covariate adjustment, model fitting, cross validation and prediction.
Provides an R-interface to the Tumblr web API (see Tumblr v2 API on https://www.tumblr.com/docs/en/api/v2). Tumblr is a microblogging platform and social networking website (https://www.tumblr.com).
A tool to obtain tumor growth rates from clinical trial patient data.  Output includes individual and summary data for tumor growth rate estimates as well as optional plots of the observed and predicted tumor quantity over time.
Generic methods for parameter tuning of classification algorithms using multiple scoring functions.
Analyze music and speech, extract features like MFCCs, handle wave files and their representation in various ways, read mp3, read midi, perform steps of a transcription, ...
        Also contains functions ported from the 'rastamat' 'Matlab' package.
Tuning random forest with one line. The package is mainly based on the packages 'ranger' and 'mlrMBO'.
Functions to find all matches or non-matches, orphans, and
    duplicate or other replicated elements.
Algorithms for accelerating the convergence of slow,
        monotone sequences from smooth, contraction mapping such as the
        EM and MM algorithms. It can be used to accelerate any smooth,
        linearly convergent acceleration scheme.  A tutorial style
        introduction to this package is available in a vignette on the
        CRAN download page or, when the package is loaded in an R
        session, with vignette("turboEM").
Package for analyzing TURF (Total Unduplicated Reach and Frequency) data in R. 
    No looping in TURF algorithm results in fast processing times. Allows for individual-level 
	weights, depth specification, and user-truncated combination set(s). Allows user to substitute 
	Monte Carlo simulated combination set(s) after set(s) exceed a user-specified limit.
Package designed for working with vectors and lists of vectors,
    mainly for turning them into other indexed data structures.
An implementation of turtle graphics
    <http://en.wikipedia.org/wiki/Turtle_graphics>.
    Turtle graphics comes from Papert's language Logo and has
    been used to teach concepts of computer programming.
Helps the R users to get data from 'Tushare Pro'<https://tushare.pro>.
    'Tushare Pro' is a platform as well as a community with a lot of staffs working in financial area.
    We support financial data such as stock price, financial report statements and digital coins data.
DataCamp Light (<https://github.com/datacamp/datacamp-light>) is a light-weight implementation of the DataCamp UI,
  that allows you to embed interactive exercises inside HTML documents. The tutorial package makes it easy to create these
  HTML files from R Markdown files. An extension to knitr, tutorial detects appropriately formatted code chunks and replaces them
  with DataCamp Light readable chunks in the resulting HTML file.
Models of time-uncertain time series addressing frequency and non-frequency behavior of continuous and discrete (counting) data.
The model, developed at the Vienna University of Technology, is a lumped conceptual rainfall-runoff model, following the structure of the HBV model. 
             The model can also be run in a semi-distributed fashion.
             The model runs on a daily or shorter time step and consists of a snow routine, a soil moisture routine and a flow routing routine. 
             See Parajka, J., R. Merz, G. Bloeschl (2007) <DOI:10.1002/hyp.6253> Uncertainty and multiple objective calibration in regional water balance modelling: case study in 320 Austrian catchments, Hydrological Processes, 21, 435-446.
Total Variation Denoising is a regularized denoising method which
    effectively removes noise from piecewise constant signals whilst preserving
    edges.  This package contains a C++ implementation of Condat's very fast 1D
    squared error loss TVD algorithm.  Additional methods and loss functions may
    be added in future versions.
Functions for managing cashflows and interest rate curves.
Provides tools for denoising noisy signal and images via
    Total Variation Regularization. Reducing the total variation of
    the given signal is known to remove spurious detail while preserving
    essential structural details. For the seminal work on the topic,
    see Rudin et al (1992) <doi:10.1016/0167-2789(92)90242-F>.
Fitting time-varying coefficient models both for single and multi-equation regressions, using kernel smoothing techniques.
Use a regularization likelihood method to achieve variable selection purpose. Likelihood can be worked with penalty lasso, smoothly clipped absolute deviations (SCAD), and minimax concave penalty (MCP). 
    Tuning parameter selection techniques include cross validation (CV), Bayesian information criterion (BIC) (low and high), stability of variable selection (sVS), stability of BIC (sBIC), and stability of estimation (sEST).
    More details see Jiwei Zhao, Yang Yang, and Yang Ning (2018) <arXiv:1703.06379> "Penalized pairwise pseudo likelihood for variable selection with nonignorable missing data." Statistica Sinica.
Provides functions for propensity score
        estimating and weighting, nonresponse weighting, and diagnosis
        of the weights.
Maximum likelihood computations for Tweedie families, including the series expansion (Dunn and Smyth, 2005; <doi10.1007/s11222-005-4070-y>) and the Fourier inversion  (Dunn and Smyth, 2008; <doi:10.1007/s11222-007-9039-6>), and related methods.
In order to create smooth animation between states of data,
    tweening is necessary. This package provides a range of functions for
    creating tweened data that can be used as basis for animation. Furthermore 
    it adds a number of vectorized interpolaters for common R data 
    types such as numeric, date and colour.
This is an improved implementation of the package 'StreamR' to
    capture tweets and store it into R, SQLite, 'postGIS' data base or GIS format. The package
    performs a description of harvested data and performs space time exploratory analysis.
An R wrapper around the API of TheyWorkForYou, a parliamentary 
    monitoring site that scrapes and repackages Hansard (the UK's parliamentary 
    record) and augments it with information from the Register of Members' 
    Interests, election results, and voting records to provide a unified 
    source of information about UK legislators and their activities. See 
    <http://www.theyworkforyou.com> for details.
Twiddler is an interactive tool that automatically creates
        a Tcl/Tk GUI for manipulating variables in any R expression.
        See the documentation of the function twiddle to get started.
The Twilio web service provides an API for computer programs
    to interact with telephony. The included functions wrap the SMS and MMS 
    portions of Twilio's API, allowing users to send and receive text messages
    from R. See <https://www.twilio.com/docs/> for more information.
Provides an interface to the Twitter web API.
Implementation of a Bayesian two-way latent structure model for integrative genomic clustering.  The model clusters samples in relation to distinct data sources, with each subject-dataset receiving a latent cluster label, though cluster labels have across-dataset meaning because of the model formulation.  A common scaling across data sources is unneeded, and inference is obtained by a Gibbs Sampler.  The model can fit multivariate Gaussian distributed clusters or a heavier-tailed modification of a Gaussian density.  Uniquely among integrative clustering models, the formulation makes no nestedness assumptions of samples across data sources -- the user can still fit the model if a study subject only has information from one data source. The package provides a variety of post-processing functions for model examination including ones for quantifying observed alignment of clusterings across genomic data sources.  Run time is optimized so that analyses of datasets on the order of thousands of features on fewer than 5 datasets and hundreds of subjects can converge in 1 or 2 days on a single CPU.  See "Swanson DM, Lien T, Bergholtz H, Sorlie T, Frigessi A, Investigating Coordinated Architectures Across Clusters in Integrative Studies: a Bayesian Two-Way Latent Structure Model, 2018, <doi:10.1101/387076>, Cold Spring Harbor Laboratory" at <https://www.biorxiv.org/content/early/2018/08/07/387076.full.pdf> for model details.
This package implements the nonparametric test of equality
        between two copulas proposed by Remillard and Scaillet in their
        2009 JMVA paper.
Estimation of gene-treatment interactions in randomized clinical trials exploiting gene-treatment independence.
Application of two-regression algorithms for wearable
    research devices. It provides an easy way for users to read in
    device data files and apply an appropriate two-regression
    algorithm. More information is available from
    Hibbing PR, LaMunion SR, Kaplan AS, & Crouter SE (2017)
    <doi:10.1249/MSS.0000000000001532>.
Fast randomization based two sample tests.
  Testing the hypothesis that two samples come from the same distribution 
  using randomization to create p-values. Included tests are: Kolmogorov-Smirnov,
  Kuiper, Cramer-von Mises, and Anderson-Darling. There is also a very efficient test
  based on the Wasserstein Distance. The default test 'two_sample' builds on the 
  Wasserstein distance by using a weighting scheme like that of Anderson-Darling.
  We also include the permutation scheme to make test building simple for others.
For high-dimensional data whose main feature is a large number, p, of variables but a small sample size, the null hypothesis that the marginal distributions of p variables are the same for two groups is tested. We propose a test statistic motivated by the simple idea of comparing, for each of the p variables, the empirical characteristic functions computed from the two samples. If one rejects this global null hypothesis of no differences in distributions between the two groups, a set of permutation p-values is reported to identify which variables are not equally distributed in both groups.
Implements a variety of non-parametric methods for computing one-stage and two-stage confidence intervals, as well as point estimates of threshold values.
Conditional logistic regression with longitudinal follow up and
    individual-level random coefficients: A stable and efficient
    two-step estimation method.
Carries out analyses of two-way tables with one observation per cell, together with graphical displays 
    for an additive fit and
    a diagnostic plot for removable 'non-additivity' via a power transformation of the response.
    It implements Tukey's Exploratory Data Analysis methods, including a 
    1-degree-of-freedom test for row*column 'non-additivity', linear in the row and column effects.
Provides functions to produce rudimentary ascii graphics
        directly in the terminal window. Provides a basic plotting
        function (and equivalents of curve, density, acf and barplot)
        as well as a boxplot function.
This queue is a data structure that lets
  parallel processes send and receive messages,
  and it can help coordinate the work
  of complicated parallel tasks.
  Processes can push new messages to the queue,
  pop old messages, and obtain a
  log of all the messages ever pushed. File locking
  preserves the integrity of the data even when
  multiple processes access the queue simultaneously.
Provides a simple type annotation for R that is usable in scripts,
    in the R console and in packages. It is intended as a convention to allow other
    packages to use the type information to provide error checking,
    automatic documentation or optimizations.
Despite there being a section in RFC 7231
    <https://tools.ietf.org/html/rfc7231#section-5.5.3> defining a suggested
    structure for 'User-Agent' headers this data is notoriously difficult
    to parse consistently. A function is provided that will take in user agent
    strings and return structured R objects. This is a 'V8'-backed package
    based on the 'ua-parser' project <https://github.com/ua-parser>.
The Unmanned Aerial Vehicle Mission Planner provides an easy to use work flow for planning autonomous obstacle avoiding surveys of (almost) ready to fly unmanned aerial vehicles to retrieve aerial or spot related data. It creates either intermediate flight control files for the DJI phantom series or ready to upload control files for the pixhawk based flight controller as used in the 3DR Solo. Additionally it contains some useful tools for digitizing and data manipulation.
Get 'UBCI' & Price from 'Upbit'.
     'ubci' is the abbreviation of 'UpBit Crypto Index' and is the name of the index provided by 'upbit'.
     The 'upbit' is one of the cryptocurrency exchange in Korea. The 'ubci' package is a wrapper around the ticker
     information and 'ubci' API supplied by 'upbit'.
Two Phase I designs are implemented in the package: the classical 3+3 and the Continual Reassessment Method. Simulations tools are also available to estimate the operating characteristics of the methods with several user-dependent options.
Provides a set of functions that can be used to obtain better predictive performance on cost-sensitive and cost/benefits tasks (for both regression and classification). This includes re-sampling approaches that modify the original data set biasing it towards the user preferences.
This suite of packages is developed to predicts resistome for unknown chemical compounds. It utilizes two algorithms viz. deterministic model and stochastic model (manuscript under preparation) for the prediction of drug class. Once the drug class is predicted, the resistome profile of Escherichia coli and Pseudomonas aeruginosa for the drug class is fetched from the database.
This package contains latex, knitr and R Markdown templates that
    adhere to the UC Berkeley Graduate Division's thesis guidelines. The
    templates are located in the inst/ directory.
Clustering and classification inference for high dimension low sample size (HDLSS)
    data with U-statistics. The package contains implementations of nonparametric statistical
    tests for sample homogeneity, group separation, clustering, and classification of 
    multivariate data. The methods have high statistical power and are tailored for data
    in which the dimension L is much larger than sample size n. See Gabriela B. Cybis,
    Marcio Valk and Sílvia RC Lopes (2018) <doi:10.1080/00949655.2017.1374387> and Marcio 
    Valk and Gabriela B. Cybis (2018) <arXiv:1805.12179>.
An algorithm for general-purpose unconstrained non-linear optimization.
             The algorithm is of quasi-Newton type with BFGS updating of the inverse
             Hessian and soft line search with a trust region type monitoring of the
             input to the line search algorithm. The interface of 'ucminf' is
             designed for easy interchange with 'optim'.
Changes the column names of the inputted dataset to the correct
    names from the Uniform Crime Report codebook for the "Offenses Known and
    Clearance by Arrest" datasets from 1998-2014.
Provides a web app for downloading, analyzing and visualizing datasets
  from UCSC Xena, which is a collection of 
  UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others.
Download and explore datasets from UCSC Xena data hubs, which are
    a collection of UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others.
    Databases are normalized so they can be combined, linked, filtered, explored and downloaded.
A client for the Urban Dictionary <http://www.urbandictionary.com/> API.
The udder quarter infection data set contains infection times of individual cow udder quarters with Corynebacterium bovis (Laevens et al. 1997 <DOI:10.3168/jds.S0022-0302(97)76295-7>). Obviously, the four udder quarters are clustered within a cow, and udder quarters are sampled only approximately monthly, generating interval-censored data. The data set contains both covariates that change within a cow (e.g., front and rear udder quarters) and covariates that change between cows (e.g., parity [the number of previous calvings]). The correlation between udder infection times within a cow also is of interest, because this is a measure of the infectivity of the agent causing the disease. Various models have been applied to address the problem of interdependence for right-censored event times. These models, as applied to this data set, can be found back in the publications found in the reference list.
This natural language processing toolkit provides language-agnostic
    'tokenization', 'parts of speech tagging', 'lemmatization' and 'dependency
    parsing' of raw text. Next to text parsing, the package also allows you to train
    annotation models based on data of 'treebanks' in 'CoNLL-U' format as provided
    at <http://universaldependencies.org/format.html>. The techniques are explained
    in detail in the paper: 'Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0
    with UDPipe', available at <doi:10.18653/v1/K17-3009>.
Provides simple bindings to Unidata's udunits library.
This is a new version of the 'userfriendlyscience' package,
    which has grown a bit unwieldy. Therefore, distinct functionalities
    are being 'consciously uncoupled' into different packages. This package
    contains the general-purpose tools and utilities (see the
    'behaviorchange' and the 'rosetta' packages for other functionality), and
    is the most direct 'successor' of the original 'userfriendlyscience' package.
    For example, this package contains a number of basic functions to create
    higher level plots, such as diamond plots, to easily plot sampling
    distributions, to generate confidence intervals, to plan study sample sizes
    for confidence intervals, and to do some basic operations such as
    (dis)attenuate effect size estimates.
Maximum likelihood estimation of univariate Gaussian Mixture Autoregressive (GMAR),
    Student's t Mixture Autoregressive (StMAR) and Gaussian and Student's t Mixture Autoregressive (G-StMAR) models, 
    quantile residual tests, graphical diagnostics, forecast and simulate from GMAR, StMAR and G-StMAR processes. 
    Also general linear constraints and restricting autoregressive parameters to be the same for all regimes are supported. 
    Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2015) <doi:10.1111/jtsa.12108>, 
    Mika Meitz, Daniel Preve, Pentti Saikkonen (2018) <arXiv:1805.04010>.
Construct a Hidden Markov Model with states learnt by unsupervised classification.
A set of custom 'R' 'Markdown' templates for documents and
   presentations with the University of Illinois at Urbana-Champaign (UIUC)
   color scheme and identity standards.
Full listing of UK baby names occurring more than three times per year between 1996 and 2015, and rankings of baby name popularity by decade from 1904 to 1994.
A set of tools to create a UK Biobank <http://www.ukbiobank.ac.uk/> dataset from a UKB fileset (.tab, .r, .html),
    visualize primary demographic data for a sample subset, query ICD diagnoses,
    retrieve genetic metadata, read and write standard file formats for genetic analyses.
Allows users to access live UK gas market information via National Grid's API.
A time series of the national grid demand (high-voltage electric power transmission network) in the UK since 2011.
Downloads data from the 'UK Police' public data API, 
    the full docs of which are available at <https://data.police.uk/docs/>. 
    Includes data on police forces and police force areas, crime reports, 
    and the use of stop-and-search powers.
A generic reference Bayesian analysis of unidimensional mixture distributions obtained by a location-scale parameterisation of the model is implemented. The including functions simulate and summarize posterior samples for location-scale mixture models using a weakly informative prior. There is no need to define priors for scale-location parameters except two hyperparameters in which are associated with a Dirichlet prior for weights and a simplex.
Uniform manifold approximation and projection is a technique
    for dimension reduction. The algorithm was described by McInnes and
    Healy (2018) in <arXiv:1802.03426>. This package provides an interface
    for two implementations. One is written from scratch, including components
    for nearest-neighbor search and for embedding. The second implementation
    is a wrapper for 'python' package 'umap-learn' (requires separate
    installation, see vignette for more details).
By gaining the property of emergence through self-organization, the enhancement of SOMs(self organizing maps) is called Emergent SOM (ESOM). The result of the projection by ESOM is a grid of neurons which can be visualised as a three dimensional landscape in form of the Umatrix. Further details can be found in the referenced publications (see url). This package offers tools for calculating and visualising the ESOM as well as Umatrix, Pmatrix and UStarMatrix. All the functionality is also available through graphical user interfaces implemented in 'shiny'.
Calculates one-sample unbiased central moment estimates and
    two-sample pooled estimates up to 6th order, including unbiased estimates
    of powers and products of central moments. Provides the machinery for
    obtaining unbiased central moment estimators beyond 6th order.
Does uniformly most powerful (UMP) and uniformly most
    powerful unbiased (UMPU) tests.  At present only distribution implemented
    is binomial distribution.  Also does fuzzy tests and confidence intervals
    (following Geyer and Meeden, 2005, <doi:10.1214/088342305000000340>)
    for the binomial
    distribution (one-tailed procedures based on UMP test and two-tailed
    procedures based on UMPU test).
The Ultimate Microrray Prediction, Reality and Inference
  Engine (UMPIRE) is a package to facilitate the simulation of realistic
  microarray data sets with link to associate outcomes. See Zhang and
  Coombes (2012) <doi:10.1186/1471-2105-13-S13-S1>.
Quickly create, run, and report structural equation and twin models.
    See '?umx' for help, and umx_open_CRAN_page("umx") for NEWS.
A dataset is said to be unbalanced when the class of interest (minority class) is much rarer than normal behaviour (majority class). The cost of missing a minority class is typically much higher that missing a majority class. Most learning systems are not prepared to cope with unbalanced data and several techniques have been proposed. This package implements some of most well-known techniques and propose a racing algorithm to select adaptively the most appropriate strategy for a given unbalanced task.
The package implements top-down and bottom-up algorithms
        for nonparametric function estimation in Gaussian noise using
        Unbalanced Haar wavelets.
If a procedure consists of several stages and there are several scenarios that can be selected for each stage,
	uncertainty of the procedure can be decomposed by stages or scenarios.
	cum_uncertainty() is used to decompose uncertainty based on the cumulative uncertainty.
	stage_uncertainty() and scenario_uncertainty() is used to decompose uncertainty based on the second order interaction ANOVA model.
	In stage_uncertainty() and scenario_uncertainty(), the uncertainty from interaction effect from two stages is distributed equally to each stage.
Provides a basic (random) data, grids, 6 models of uncertainty, 3 automatic interpolations (idw, spline, kriging), variogram and basic data visualization.
Functions for the determination of an uncertain interval, i.e., a
    range of test scores that are inconclusive and do not allow a diagnosis, other
    than 'Uncertain' (Reference: J.A. Landsheer (2016) <doi:10.1371/journal.pone.0166007>).
Implements the Gaussian method of first and second order, the Kragten numerical method and the Monte Carlo simulation method for uncertainty estimation and analysis.
Consensus clustering by the unification of clustering results from multiple datasets using external specifications.
Dataset contains select attributes for each match result since 1949-1950 season for UNC men's basketball team.
Calculate several understandability metrics of BPMN models. BPMN stands for business process modelling notation and is a language for expressing business processes into business process diagrams. Examples of these understandability metrics are: average connector degree, maximum connector degree, sequentiality, cyclicity, diameter, depth, token split, control flow complexity, connector mismatch, connector heterogeneity, separability, structuredness and cross connectivity. See R documentation and paper on metric implementation included in this package for more information concerning the metrics.
Computes a universal numeric fingerprint (UNF) for an R data
    object. UNF is a cryptographic hash or signature that can be used to uniquely
    identify (a version of) a rectangular dataset, or a subset thereof. UNF can
    be used, in tandem with a DOI, to form a persistent citation to a versioned
    dataset.
Stereological unfolding of the joint size-shape-orientation distribution of spheroidal shaped particles.
Versatile method for ungrouping histograms (binned count data) 
 assuming that counts are Poisson distributed and that the underlying sequence 
 on a fine grid to be estimated is smooth. The method is based on the composite 
 link model and estimation is achieved by maximizing a penalized likelihood. 
 Smooth detailed sequences of counts and rates are so estimated from the binned 
 counts. Ungrouping binned data can be desirable for many reasons: Bins can be 
 too coarse to allow for accurate analysis; comparisons can be hindered when 
 different grouping approaches are used in different histograms; and the last 
 interval is often wide and open-ended and, thus, covers a lot of information 
 in the tail area. Age-at-death distributions grouped in age classes and 
 abridged life tables are examples of binned data. Because of modest assumptions, 
 the approach is suitable for many demographic and epidemiological applications. 
 For a detailed description of the method and applications see 
 Rizzi et al. (2015) <doi:10.1093/aje/kwv020>.
Nonparametric estimation of a unimodal or U-shape covariate effect under additive hazards model.
Data from Unicode 12.0.0 and related utilities.
Efficient procedures for constructing uniform design of experiments under various space-filling criteria. It is based on a stochastic and adaptive threshold accepting algorithm with flexible initialization, adaptive threshold, and stochastic evolution. The package may also construct the augmented uniform designs in a sequential manner.
Uniform sampling of Directed Acyclic Graphs (DAG) using exact
 enumeration by relating each DAG to a sequence of outpoints (nodes with no
 incoming edges) and then to a composition of integers as suggested by
 Kuipers, J. and Moffa, G. (2015) <doi:10.1007/s11222-013-9428-y>.
Probability functions and family for glm() of the unifed distribution (Quijano Xacur, 2018; <arXiv:1812.00251>).
Uniform sampling on various geometric shapes, such as spheres, ellipsoids, simplices.
Goodness-of-fit tests for the uniform distribution.
Perform L1 or L2 isotonic and unimodal regression on 1D weighted or unweighted input vector and isotonic regression on 2D weighted or unweighted input vector. It also performs L infinity isotonic and unimodal regression on 1D unweighted input vector. Reference: Quentin F. Stout (2008) <doi:10.1016/j.csda.2008.08.005>. Spouge, J., Wan, H. & Wilbur, W.(2003) <doi:10.1023/A:1023901806339>. Q.F. Stout (2013) <doi:10.1007/s00453-012-9628-4>.
Define and use graphical elements of corporate design manuals in R. The 'unikn' package provides color functions (by defining dedicated colors and color palettes, and commands for changing, viewing, and using them) and styled text elements (e.g., for marking, underlining, or plotting colored titles). The pre-defined range of colors and text functions is based on the corporate design of the University of Konstanz <https://www.uni-konstanz.de/>, but can be adapted and extended for other institutions and purposes.
Implementation of "light" stemmers for French, German, Italian, Spanish, Portuguese, Finnish, Swedish. 
  They are based on the same work as the "light" stemmers found in 'SolR' <https://lucene.apache.org/solr/> or 'ElasticSearch' <https://www.elastic.co/fr/products/elasticsearch>. 
  A "light" stemmer consists in removing inflections only for noun and adjectives. 
  Indexing verbs for these languages is not of primary importance compared to nouns and adjectives. 
  The stemming procedure for French is described in (Savoy, 1999) <doi:10.1002/(SICI)1097-4571(1999)50:10%3C944::AID-ASI9%3E3.3.CO;2-H>.
For each string in a set of strings, determine a unique tag that is a substring of fixed size k unique to that string, if it has one. If no such unique substring exists, the least frequent substring is used. If multiple unique substrings exist, the lexicographically smallest substring is used. This lexicographically smallest substring of size k is called the "UniqTag" of that string.
Univariate spline regression. It is possible to add the shape constraint of unimodality and predefined or
	self-defined penalties on the B-spline coefficients.
The uc.check() function checks whether the roots of a given polynomial lie outside the Unit circle. You can also easily draw an unit circle.
United is a software tool which can be downloaded at the following
    website <http://www.schroepl.net/pbm/software/united/>. In general, it is
    a virtual manager game for football teams. This package contains helpful
    functions for determining an optimal formation for a virtual match in
    United. E.g. knowing that the opponent has a strong defensive it is
    advisable to beat him in the midfield. Furthermore, this package contains
    functions for computing the optimal usage of hardness in a game.
Simplifies regression tests by comparing objects produced by test
    code with earlier versions of those same objects.  If objects are unchanged
    the tests pass, otherwise execution stops with error details.  If in
    interactive mode, tests can be reviewed through the provided interactive
    environment.
Support for measurement units in R vectors, matrices
	and arrays: automatic propagation, conversion, derivation
	and simplification of units; raising errors in case of unit
	incompatibility. Compatible with the POSIXct, Date and difftime 
	classes. Uses the UNIDATA udunits library and unit database for 
	unit compatibility checking and conversion.
	Documentation about 'units' is provided in the paper by Pebesma, Mailund &
  Hiebert (2016, <doi:10.32614/RJ-2016-061>), included in this package as a
  vignette; see 'citation("units")' for details.
Concise TAP <http://testanything.org/> compliant unit testing package. Authored tests can be run using CMD check with minimal implementation overhead.
Assess essential unidimensionality using external validity
    information  using the procedure proposed by Ferrando & Lorenzo-Seva
    (2019) <doi:10.1177/0013164418824755>. Provides two indices for assessing
    differential  and incremental validity, both based on a second-order 
    modelling schema for the general factor.
Well known outlier detection techniques in the univariate case. Methods to deal with skewed distribution are included too. The Hidiroglou-Berthelot (1986) method to search for outliers in ratios of historical data is implemented as well. When available, survey weights can be used in outliers detection.
Pseudo-random number generation of 17 univariate distributions.
Bindings to system utilities found in most Unix systems such as
    POSIX functions which are not part of the Standard C Library.
Separate a data frame in two based on key columns. The function unjoin() provides
   an inside-out version of a nested data frame. This is used to identify duplication and normalize it
   (in the database sense) by linking two tables with the redundancy removed. This is a basic 
   requirement for detecting topology within spatial structures that has motivated the need
   for this package as a building block for workflows within more applied projects.
Fits hierarchical models of animal abundance and occurrence to data collected using survey methods such as point counts, site occupancy sampling, distance sampling, removal sampling, and double observer sampling. Parameters governing the state and observation processes can be modeled as functions of covariates.
Tools for converting data from complex or irregular layouts to a
    columnar structure.  For example, tables with multilevel column or row
    headers, or spreadsheets.  Header and data cells are selected by their
    contents and position, as well as formatting and comments where available,
    and are associated with one other by their proximity in given directions.
    Functions for data frames and HTML tables are provided.
Provides half-normal plots, reference plots, and Pareto plots
    of effects from an unreplicated experiment, along with various 
    pseudo-standard-error measures, simulated reference distributions, 
    and other tools. Many of these methods are described in 
    Daniel C. (1959) <doi:10.1080/00401706.1959.10489866> and/or
    Lenth R.V. (1989) <doi:10.1080/00401706.1989.10488595>, but some new
    approaches are added and integrated in one package.
Wraps the 'unrtf' utility to extract text from RTF files. Supports
    document conversion to HTML, LaTeX or plain text. Output in HTML is recommended
    because 'unrtf' has limited support for converting between character encodings.
Performs a test for second-order stationarity of time series based
    on unsystematic sub-samples.
Hubbell's Unified Neutral Theory of Biodiversity.
Historical voting data of the United Nations General Assembly. This
    includes votes for each country in each roll call, as well as descriptions and
    topic classifications for each vote.
Implements empirical Bayes approaches to genotype
       polyploids from next generation sequencing data while
       accounting for allelic bias, overdispersion, and sequencing
       error. The main function is flexdog(), which allows the specification
       of many different genotype distributions. An experimental
       function that takes into account varying levels of relatedness
       is implemented in mupdog(). Also provided are functions to
       simulate genotypes, rgeno(), and read-counts, rflexdog(), as well as
       functions to calculate oracle genotyping error rates, oracle_mis(), and
       correlation with the true genotypes, oracle_cor(). These latter two
       functions are useful for read depth calculations. Run
       browseVignettes(package = "updog") in R for example usage. See also
       Gerard et al. (2018) <doi:10.1101/281550> for details on the
       implemented methods.
An integrated package for building and testing uplift models
Provides a variety of plots and metrics to evaluate
    uplift models including the 'R uplift' package's Qini metric and Qini plot,
    a port of the 'python pylift' module's plotting function, and
    an alternative plot (in beta) useful for continuous outcomes.
    Background: Radcliffe (2007) <https://pdfs.semanticscholar.org/147b/32f3d56566c8654a9999c5477dded233328e.pdf>.
An implementation of the UPMASK method for performing membership
    assignment in stellar clusters in R. It is prepared to use photometry and
    spatial positions, but it can take into account other types of data. The
    method is able to take into account arbitrary error models, and it is
    unsupervised, data-driven, physical-model-free and relies on as few
    assumptions as possible. The approach followed for membership assessment is
    based on an iterative process, dimensionality reduction, a clustering
    algorithm and a kernel density estimation.
Fitting a Unified Probability Model for household-community tuberculosis transmission dynamics.
Creates visualizations of intersecting sets using a novel matrix
    design, along with visualizations of several common set, element and attribute
    related tasks (Conway 2017) <doi:10.1093/bioinformatics/btx364>.
Variance approximations for the 
    Horvitz-Thompson total estimator in Unequal Probability Sampling
    using only first-order inclusion probabilities. 
    See Matei and Tillé (2005) and Haziza, Mecatti and Rao (2008) for details.
'Elasticsearch' is an open-source, distributed, document-based datastore
    (<https://www.elastic.co/products/elasticsearch>).
    It provides an 'HTTP' 'API' for querying the database and extracting datasets, but that
    'API' was not designed for common data science workflows like pulling large batches of
    records and normalizing those documents into a data frame that can be used as a training
    dataset for statistical models. 'uptasticsearch' provides an interface for 'Elasticsearch'
    that is explicitly designed to make these data science workflows easy and fun.
Provide a set of wrappers to call all the endpoints of UptimeRobot API
  which includes various kind of ping, keep-alive and speed tests.
  See <https://uptimerobot.com/> for more information.
'Upwave' <https://www.upwave.io/> is a task management app
        organising tasks on boards and cards. 'upwaver' is a wrapper
        around the 'Upwave' API <https://upwavehq.github.io/api/> that
        allows listing and updating those boards and cards.
Estimation and Inference for Unconditional Quantile Regression for cross-sectional and panel data (see Firpo et al. (2009) <DOI:10.3982/ECTA6822>).
Calculate unified measures
  that quantify the effect of a covariate on a binary dependent variable
  (e.g., for meta-analyses).
  This can be particularly important
  if the estimation results are obtained
  with different models/estimators
  (e.g., linear probability model, logit, probit, ...)
  and/or with different transformations of the explanatory variable of interest
  (e.g., linear, quadratic, interval-coded, ...).
  The calculated unified measures are:
  (a) semi-elasticities of linear, quadratic, or interval-coded covariates and
  (b) effects of linear, quadratic, interval-coded, or categorical covariates
  when a linear or quadratic covariate changes between distinct intervals,
  the reference category of a categorical variable
  or the reference interval of an interval-coded variable needs to be changed,
  or some categories of a categorical covariate
  or some intervals of an interval-coded covariate need to be grouped together.
  Approximate standard errors of the unified measures are also calculated.
  All methods that are implemented in this package 
  are described in the 'vignette'  
  "Extracting and Unifying Semi-Elasticities and Effect Sizes
  from Studies with Binary Dependent Variables"
  that is included in this package.
Unit root and cointegration tests encountered in applied 
 econometric analysis are implemented.
Allows using two URL shortening services, which also provide
    expanding and analytic functions. Specifically developed for 'Bit.ly' (which requires OAuth2) and 'is.gd' (no API key).
A toolkit for all URL-handling needs, including encoding and decoding,
    parsing, parameter extraction and modification. All functions are
    designed to be both fast and entirely vectorised. It is intended to be
    useful for people dealing with web-related datasets, such as server-side
    logs, although may be useful for other situations involving large sets of
    URLs.
Seasonal unit roots and seasonal stability tests.
    P-values based on response surface regressions are available for both tests.
    P-values based on bootstrap are available for seasonal unit root tests.
    A parallel implementation of the bootstrap method requires a CUDA capable GPU 
    with compute capability >= 3.0, otherwise a debugging version fully coded in R is used.
The boundaries for geographical units in the United States of
    America contained in this package include state, county, congressional
    district, and zip code tabulation area. Contemporary boundaries are provided
    by the U.S. Census Bureau (public domain). Historical boundaries for the
    years from 1629 to 2000 are provided form the Newberry Library's 'Atlas of
    Historical County Boundaries' (licensed CC BY-NC-SA). Additional  data is
    provided in the 'USAboundariesData' package; this package provides an
    interface to access that data.
This package contains functions to read cancer data from SEER (http://seer.cancer.gov/) and IARC (http://www.iarc.fr) to create datasets at the county level based on US census information.
US Census 2000 Designated Places shapefiles and additional
        demographic data from the SF1 100 percent files. This data set
        contains polygon files in lat/lon coordinates and the
        corresponding demographic data for a number of different
        variables.
US 2000 Census Tract shapefiles and additional demographic
        data from the SF1 100 percent files. This data set contains
        polygon files in lat/lon coordinates and the corresponding
        demographic data for a number of different variables.
US Census 2010 shape files and additional demographic data
        from the SF1 100 percent files. This package contains a number
        of helper functions for the UScensus2010blk,
        UScensus2010blkgrp, UScensus2010tract, UScensus2010cdp
        packages.
This is a framework that aims to provide methods and tools for assessing the impact of different sources of uncertainties (e.g.positional uncertainty) on performance of species distribution models (SDMs).)
Functions to re-arrange, extract, and work with distances.
A set of little functions that have been found useful to do little
    odds and ends such as plotting the results of K-means clustering, substituting
    special text characters, viewing parts of a data.frame, constructing formulas
    from text and building design and response matrices.
Contains a number of functions that serve
    two goals. First, to make R more accessible to people migrating
    from SPSS by adding a number of functions that behave roughly like
    their SPSS equivalents (also see <https://rosettastats.com>). Second,
    to make a number of slightly more advanced functions more user
    friendly to relatively novice users. The package also conveniently
    houses a number of additional functions that are intended to
    increase the quality of methodology and statistics in psychology,
    not by offering technical solutions, but by shifting perspectives,
    for example towards reasoning based on sampling distributions as
    opposed to on point estimates.
Automate package and project setup tasks that are otherwise
    performed manually. This includes setting up unit testing, test 
    coverage, continuous integration, Git, 'GitHub', licenses, 'Rcpp', 'RStudio' 
    projects, and more.
Compiled and cleaned the county-level estimates of fertilizer, 
    nitrogen and phosphorus, from 1945 to 2012 in United States of America (USA).  The commercial fertilizer data were originally 
    generated by USGS based on the sales data of commercial fertilizer. The manure data were estimated
    based on county-level population data of livestock, poultry, and other animals.
    See the user manual for detailed data sources and cleaning methods.  
    'usfertilizer' utilized the tidyverse to clean the original data and provide 
    user-friendly dataframe. Please note that USGS does not endorse this package. Also data from 1986 is not available for now.
A map of the USA from the United States Geological Survey (USGS).
    Irucka worked with this data set while a Cherokee Nation Technology
    Solutions (CNTS) USGS Contractor and/or USGS employee. It is replaced by
    'states2k'.
A collection of data sets to accompany the
    textbook "Using R for Introductory Statistics," second
    edition.
Implements mixtures of unrestricted skew-t factor analyzer models via the EM algorithm.
The Universal Scalability Law is a model to predict hardware and
    software scalability. It uses system capacity as a function of load to
    forecast the scalability for the system.
Obtain United States map data frames of varying region types (e.g. county, 
    state). The map data frames include Alaska and Hawaii conveniently placed to the
    bottom left, as they appear in most maps of the US. Convenience functions for plotting
    choropleths and working with FIPS codes are also provided.
A set of functions leading to multivariate response L1 regression. 
    This includes functions on computing Euclidean inner products and norms, 
    weighted least squares estimates on multivariate responses, function to compute 
    fitted values and residuals. This package is a companion to the book "U-Statistics,
    M-estimation and Resampling", by Arup Bose and Snigdhansu Chatterjee, to appear 
    in 2017 as part of the "Texts and Readings in Mathematics" (TRIM) series of 
    Hindustan Book Agency and Springer-Verlag.
Forms a query to submit for US Treasury yield curve data, posting
    this query to the US Treasury web site's data feed service.  By default the
    download includes data yield data for 12 products from January 1, 1990,
    some of which are NA during this span.  The caller can pass parameters to
    limit the query to a certain year or year and month, but the full download
    is not especially large.  The download data from the service is in XML
    format.  The package's main function transforms that XML data into a numeric 
    data frame with treasury product items (constant maturity yields for 12 kinds 
    of bills, notes, and bonds) as columns and dates as row names. The function 
    returns a list which includes an item for this data frame as well as query-related
    values for reference and the update date from the service.
Three functions are provided: first function changes time from local to UTC, other changes from UTC to local and third returns difference between local and UTC. %h+% operator is also provided it adds hours to a time.
Process and print 'UTF-8' encoded international text (Unicode). Input, validate, normalize, encode, format, and display.
A collection of functions to make building customized ready-to-export tables for publication purposes easier or expedite summarization of a large dataset for review. Includes methods for automatically building a table from data or building the table row-by-row. Key functions include build_row() & build_table() for summarizing columns of data overall or by stratum with appropriate testing and build_event_row() & build_event_table() for creating tables that summarize time-to-event model (Cox PH) parameters, supporting both univariate and multivariate methods.
A variety of tools for preparing and summarizing data for publication purposes. Function verbs include "tabulate" for creating usable tabulated data from models, "paste" for generating human-readable statistics from a variety of summarizable data types, "calc" for reliably calculating differences between data points, and "test" for conducting simple statistical tests which return human-readable results.
A small set of functions for making visuals for publication in 'ggplot2'. Key functions include geom_stepconfint() for drawing a step confidence interval on a Kaplan-Meir curve and theme_white()/theme_black() which are minimalist 'ggplot2' themes with transparent backgrounds.
Construct and plot objective hierarchies and associated value and utility functions. 
  Evaluate the values and utilities and visualize the results as colored objective hierarchies or tables. 
  Visualize uncertainty by plotting median and quantile intervals within the nodes of objective hierarchies.
  Get numerical results of the evaluations in standard R data types for further processing.
Contains functions for simulating phase 12 trial designs described by Chapple and Thall (2019) including simulation and the EFF-TOX trial and simulation and implementation of the U12 trial. Functions for implementing the EFF-TOX trial are found in the package 'Phase123'.
The most used functions on IPEA (Instituto de Pesquisa Economica Aplicada). 
             Most of functions deal with brazilian names. 
             It can guess the women single's name, extract prepositions or extract the first name.
Multi-label learning strategies and others procedures to support multi-
  label classification in R. The package provides a set of multi-label procedures such as
  sampling methods, transformation strategies, threshold functions, pre-processing 
  techniques and evaluation metrics. A complete overview of the matter can be seen in
  Zhang, M. and Zhou, Z. (2014) <doi:10.1109/TKDE.2013.39> and Gibaja, E. and 
  Ventura, S. (2015) A Tutorial on Multi-label Learning.
Tools for generating and handling of UUIDs (Universally Unique Identifiers).
A method for estimating log-normalizing constants (or free
        energies) and expectations from multiple distributions (such as
        multiple generalized ensembles).
A set of tools designed to facilitate easy adoption of R for students in introductory classes with little programming experience. Compiles output from existing routines together in an intuitive format, and adds functionality to existing functions. For instance, the regression function can perform linear models, generalized linear models, Cox models, or generalized estimating equations. The user can also specify multiple-partial F-tests to print out with the model coefficients. We also give many routines for descriptive statistics and plotting.
An implementation of the Uniform Manifold Approximation and 
    Projection dimensionality reduction by McInnes et al. (2018) 
    <arXiv:1802.03426>. It also provides means to transform new data and to 
    carry out supervised dimensionality reduction. An implementation of the 
    related LargeVis method of Tang et al. (2016) <arXiv:1602.00370> is also 
    provided. This is a complete re-implementation in R (and C++, via the 'Rcpp'
    package): no Python installation is required. See the uwot website 
    (<https://github.com/jlmelville/uwot>) for more documentation and examples.
An R interface to Google's open source JavaScript engine. This 
    package can now be compiled either with V8 version 6 or 7 (LTS) from 
    nodejs or with the legacy 3.14/3.15 branch of V8.
Non-Domestic VAERS vaccine data for 01/01/2016 - 06/14/2016. If
    you want to explore the full VAERS data for 1990 - Present (data, symptoms,
    and vaccines), then check out the 'vaersND' package from the URL below. The
    URL and BugReports below correspond to the 'vaersND' package, of which
    'vaersNDvax' is a small subset (2016 only). 'vaersND' is not hosted on CRAN
    due to the large size of the data set. To install the Suggested 'vaers' and
    'vaersND' packages, use the following R code:
    'devtools::install_git("https://gitlab.com/iembry/vaers.git",
    build_vignettes = TRUE)' and
    'devtools::install_git("https://gitlab.com/iembry/vaersND.git",
    build_vignettes = TRUE)'. "VAERS is a national vaccine safety
    surveillance program co-sponsored by the US Centers for Disease Control and
    Prevention (CDC) and the US Food and Drug Administration (FDA). VAERS is a
    post-marketing safety surveillance program, collecting information about
    adverse events (possible side effects) that occur after the administration
    of vaccines licensed for use in the United States." For more information
    about the data, visit <https://vaers.hhs.gov/index>. For information about
    vaccination/immunization hazards, visit
    <http://www.questionuniverse.com/rethink.html/#vaccine>.
US VAERS vaccine data for 01/01/2018 - 06/14/2018. If you want to
    explore the full VAERS data for 1990 - Present (data, symptoms, and
    vaccines), then check out the 'vaers' package from the URL below. The URL
    and BugReports below correspond to the 'vaers' package, of which 'vaersvax'
    is a small subset (2018 only). 'vaers' is not hosted on CRAN due to the
    large size of the data set. To install the Suggested 'vaers' and 'vaersND'
    packages, use the following R code:
    'devtools::install_git("<https://gitlab.com/iembry/vaers.git>",
    build_vignettes = TRUE)' and
    'devtools::install_git("<https://gitlab.com/iembry/vaersND.git>",
    build_vignettes = TRUE)'. "The Vaccine Adverse Event Reporting System (VAERS)
    is a national early warning system to detect possible safety problems in
    U.S.-licensed vaccines. VAERS is co-managed by the Centers for Disease Control
    and Prevention (CDC) and the U.S. Food and Drug Administration (FDA)." For
    more information about the data, visit <https://vaers.hhs.gov/>. For
    information about
    vaccination/immunization hazards, visit
    <http://www.questionuniverse.com/rethink.html#vaccine>.
Provides access to the 'Vagalume' API <https://api.vagalume.com.br>. 
    The data extracted is basically lyrics of songs and information about
    artists/bands.
Fits generalized additive models (GAMs) using a variational approximations (VA) framework. In brief, the VA framework provides a fully or at least closed to fully tractable lower bound approximation to the marginal likelihood of a GAM when it is parameterized as a mixed model (using penalized splines, say). In doing so, the VA framework aims offers both the stability and natural inference tools available in the mixed model approach to GAMs, while achieving computation times comparable to that of using the penalized likelihood approach to GAMs. See Hui et al. (2018) <doi:10.1080/01621459.2018.1518235>.
A set of basic tools to transform functions into functions with
    input validation checks, in a manner suitable for both programmatic and
    interactive use.
A binding for the 'valection' program which offers various ways to 
  sample the outputs of competing algorithms or parameterizations, and fairly 
  assess their performance against each other. The 'valection' C library is 
  required to use this package and can be downloaded from:
  <http://labs.oicr.on.ca/boutros-lab/software/valection>. Cooper CI, et al; Valection: Design Optimization for Validation and Verification Studies; Biorxiv 2018; <doi:10.1101/254839>.
Methods and tools for analysing and validating the outputs
    and modelled functions of artificial neural networks (ANNs) in terms
    of predictive, replicative and structural validity. Also provides a
    method for fitting feed-forward ANNs with a single hidden layer.
Declare data validation rules and data quality indicators; confront
    data with them and analyze or visualize the results. The package supports
    rules that are per-field, in-record, cross-record or cross-dataset. Rules
    can be automatically analyzed for rule type and connectivity.
The current implementation uses the C++ library 'RapidJSON' to supply the schema functionality, it supports JSON Schema Draft v4. As of 2016-09-09, 'RapidJSON' passed 262 out of 263 tests in JSON Schema Test Suite (JSON Schema draft 4).
An implementation of statistical tests for the validation of rating systems as described in the ECB Working paper ''Advances in multivariate back-testing for credit risk underestimation'', by F. Coppens, M. Mayer, L. Millischer, F.  Resch, S. Sauer, K. Schulze (ECB WP series, forthcoming).
Rule sets with validation rules may contain redundancies or contradictions. 
  Functions for finding redundancies and problematic rules are provided, 
  given a set a rules formulated with 'validate'.
The algorithm implemented in this package was
    designed to quickly estimates the distribution of the 
    log-rank especially for heavy unbalanced groups. VALORATE 
    estimates the null distribution and the p-value of the 
    log-rank test based on a recent formulation. For a given 
    number of alterations that define the size of survival 
    groups, the estimation involves a weighted sum of 
    distributions that are conditional on a co-occurrence term 
    where mutations and events are both present. The estimation 
    of conditional distributions is quite fast allowing the 
    analysis of large datasets in few minutes 
    <http://bioinformatica.mty.itesm.mx/valorate>.
Historical results for the state of Virginia lottery draw games. Data were downloaded from https://www.valottery.com/.
Read and manipulate genome intervals and signals. Provides
             functionality similar to command-line tool suites within R,
             enabling interactive analysis and visualization of genome-scale data.
Pricing of variable annuity life insurance
    contracts by means of Monte Carlo methods. Monte Carlo is used to price
    the contract in case the policyholder cannot surrender while
    Least Squares Monte Carlo is used if the insured can surrender.
    This package implements the pricing framework and algorithm described in
    Bacinello et al. (2011) <doi:10.1016/j.insmatheco.2011.05.003>.
    It also implements the state-dependent fee structure
    discussed in Bernard et al. (2014) <doi:10.1017/asb.2014.13> as well as 
    a function which prices the contract by resolving the partial differential equation 
    described in MacKay et al. (2017) <doi:10.1111/jori.12094>.
Implementation of a Monte Carlo simulation engine for valuing synthetic portfolios of 
    variable annuities, which reflect realistic features of common annuity contracts in practice. 
    It aims to facilitate the development and dissemination of research related to the efficient 
    valuation of a portfolio of large variable annuities. The main valuation methodology was 
    proposed by Gan (2017) <doi:10.1515/demo-2017-0021>.
Identify and analyze conserved waters within crystallographic 
  protein structures and molecular dynamics simulation trajectories. Statistical 
  parameters for each water cluster, informative graphs, and a PyMOL session 
  file to visually explore the conserved waters and protein are returned. 
  Hydrophilicity is the propensity of waters to congregate near specific protein 
  atoms and is related to conserved waters. An informatics derived set of 
  hydrophilicity values are provided based on a large, high-quality X-ray 
  protein structure dataset.
Provides low-level access to 'GDAL' functionality for R packages. The aim is to minimize
  the level of interpretation put on the 'GDAL' facilities, to enable direct use of it for a variety of purposes. 
  'GDAL' is the 'Geospatial Data Abstraction Library' a translator for raster and vector geospatial data formats 
  that presents a single raster abstract data model and single vector abstract data model to the calling application 
  for all supported formats <http://gdal.org/>. Other available packages 'rgdal' and 'sf' also provide access to 
  the 'GDAL' library, but neither can be used for these lower level tasks, and both do many other tasks.
Estimation, Hypothesis Testing, Prediction for Stationary Vector Autoregressive Models
Implementation of the variable banding procedure for modeling local dependence and estimating precision matrices that is introduced in Yu & Bien (2016) and is available at <https://arxiv.org/abs/1604.07451>.
Tool for easy and efficient discretization of continuous and categorical data. 
    The package calculates the most optimal binning of a given explanatory variable with respect to a 
    user-specified target variable. The purpose is to assign a unique Weight-of-Evidence value 
    to each of the calculated binpoints in order to recode the original variable. 
    The package allows users to impose certain restrictions on the functional form on the resulting 
    binning while maximizing the overall information value in the original data. 
    The package is well suited for logistic scoring models where input variables may be subject to 
    restrictions such as linearity by e.g. regulatory authorities. An excellent source describing in 
    detail the development of scorecards, and the role of Weight-of-Evidence coding in credit scoring 
    is (Siddiqi 2006, ISBN: 978–0-471–75451–0). The package utilizes the discrete nature of decision trees and 
    Isotonic Regression to accommodate the trade-off between flexible functional forms and maximum 
    information value.
Easily create list-like structures with constant, read-only 
    variables. Combines the flexibility of lists with read-only fields.
    Supports defensive programming by throwing errors on field assignment,
    helping mitigate the introduction of logical runtime bugs.
Fast algorithms for fitting Bayesian variable selection
    models and computing Bayes factors, in which the outcome (or
    response variable) is modeled using a linear regression or a
    logistic regression. The algorithms are based on the variational
    approximations described in "Scalable variational inference for
    Bayesian variable selection in regression, and its accuracy in
    genetic association studies" (P. Carbonetto & M. Stephens, 2012,
    <DOI:10.1214/12-BA703>). This software has been applied to large
    data sets with over a million variables and thousands of samples.
Generation of domain variables, linearization of several nonlinear population statistics (the ratio of two totals, weighted income percentile, relative median income ratio, at-risk-of-poverty rate, at-risk-of-poverty threshold, Gini coefficient, gender pay gap, the aggregate replacement ratio, the relative median income ratio, median income below at-risk-of-poverty gap, income quintile share ratio, relative median at-risk-of-poverty gap), computation of regression residuals in case of weight calibration, variance estimation of sample surveys by the ultimate cluster method (Hansen, Hurwitz and Madow,Theory, vol. I: Methods and Applications; vol. II: Theory. 1953, New York: John Wiley and Sons), variance estimation for longitudinal, cross-sectional measures and measures of change for single and multistage stage cluster sampling designs (Berger, Y. G., 2015, <doi:10.1111/rssa.12116>). Several other precision measures are derived - standard error, the coefficient of variation, the margin of error, confidence interval, design effect.
Generating functions for both optimal and ordinary difference sequences, and the difference-based estimation functions.
Computes Value at risk and expected shortfall, two most popular measures of financial risk, for over one hundred parametric distributions,  including all commonly known distributions.  Also computed are the corresponding probability density function and cumulative distribution function.
Captures and compiles the genes and variants related to a disease, a phenotype or a clinical feature from the public databases including HPO (Human Phenotype Ontology, <http://human-phenotype-ontology.github.io/about.html>), Orphanet <http://www.orpha.net/consor/cgi-bin/index.php>, OMIM (Online Mendelian Inheritance in Man, <http://www.omim.org>), ClinVar <http://www.ncbi.nlm.nih.gov/clinvar>, and UniProt (Universal Protein Resource, <http://www.uniprot.org>) and PubMed abstracts. HPO provides a standardized vocabulary of phenotypic abnormalities encountered in human disease. HPO currently contains approximately 11,000 terms and over 115,000 annotations to hereditary diseases. Orphanet is the reference portal for information on rare diseases and orphan drugs, whose aim is to help improve the diagnosis, care and treatment of patients with rare diseases. OMIM is a continuously updated catalog of human genes and genetic disorders and traits, with particular focus on the molecular relationship between genetic variation and phenotypic expression. ClinVar is a freely accessible, public archive of reports of the relationships among human variations and phenotypes, with supporting evidence. UniProt focuses on amino acid altering variants imported from Ensembl Variation databases. For Homo sapiens, the variants including human polymorphisms and disease mutations in the UniProt are manually curated  from UniProtKB/Swiss-Prot. Additionally, PubMed provides the primary and latest source of the information. Text mining was employed to capture the information from PubMed abstracts.
Variables are the fundamental parts of each programming language but handling them might be frustrating for programmers from time to time. This package contains some functions to help user (especially data explorers) to make more sense of their variables and take the most out of variables and hardware resources. These functions are written, collected and crafted over 5 years of experience in statistical data analysis on high-dimensional data and for each of them there was a need. Functions in this package are suppose to be efficient and easy to use, hence they will be frequently updated to make them more convenient.
Abstract descriptions of (yet) unobserved variables.
Implements variable screening techniques for ultra-high
    dimensional regression settings.  Techniques for independent (iid) data,
    varying-coefficient models, and longitudinal data are implemented. The package
     currently contains three screen functions: screenIID(), screenLD() and screenVCM(),
     and six methods for simulating dataset: simulateDCSIS(), simulateLD, simulateMVSIS(),
     simulateMVSISNY(), simulateSIRS() and simulateVCM(). The package is based on the work of
    Li-Ping ZHU, Lexin LI, Runze LI, and Li-Xing ZHU (2011) <DOI:10.1198/jasa.2011.tm10563>,
    Runze LI, Wei ZHONG, & Liping ZHU (2012) <DOI:10.1080/01621459.2012.695654>,
    Jingyuan LIU, Runze LI, & Rongling WU (2014) <DOI:10.1080/01621459.2013.850086>
    Hengjian CUI, Runze LI, & Wei ZHONG (2015) <DOI:10.1080/01621459.2014.920256>, and
    Wanghuan CHU, Runze LI and Matthew REIMHERR (2016) <DOI:10.1214/16-AOAS912>.
    Special thanks are due to Ling Zhang for providing detailed testing and proposing
    a method for speed improvement on the simulation of data with AR-1 structure.
Uses a Bayesian model to
    estimate the variability in a repeated
    measure outcome and use that as an outcome or a predictor
    in a second stage model.
Provides functions for the variance gamma
        distribution. Density, distribution and quantile functions.
        Functions for random number generation and fitting of the
        variance gamma to data. Also, functions for computing moments
        of the variance gamma distribution of any order about any
        location. In addition, there are functions for checking the
        validity of parameters and to interchange different sets of
        parameterizations for the variance gamma distribution.
This is a 'sparklyr' extension integrating 'VariantSpark' and R. 'VariantSpark' is 
  a framework based on 'scala' and 'spark' to analyze genome datasets, 
  see <https://bioinformatics.csiro.au/>. It was tested on datasets with 3000 samples 
  each one containing 80 million features in either unsupervised clustering approaches 
  and supervised applications, like classification and regression. The genome datasets
  are usually writing in VCF, a specific text file format used 
  in bioinformatics for storing gene sequence variations. So, 'VariantSpark' is a great 
  tool for genome research, because it is able to read VCF files, run analyses and return 
  the output in a 'spark' data frame.
Computes the random forest variable importance (VIMP) for the conditional inference random forest (cforest) of the 'party' package. Includes a function (varImp) that computes the VIMP for arbitrary measures from the 'measures' package. For calculating the VIMP regarding the measures accuracy and AUC two extra functions exist (varImpACC and varImpAUC).
Applying Monte Carlo permutation to generate pointwise variogram envelope and checking for spatial dependence at different scales using permutation test. Empirical Brown's method and Fisher's method are used to compute overall p-value for hypothesis test.
The goal of the package is to equip the 'jmcm' package (current version 0.1.8.0) 
    with estimations of the covariance of estimated parameters. Two methods are provided. 
    The first method is to use the inverse of estimated Fisher's information matrix, see 
    M. Pourahmadi (2000) <doi:10.1093/biomet/87.2.425>, M. Maadooliat, M. Pourahmadi and 
    J. Z. Huang (2013) <doi:10.1007/s11222-011-9284-6>, and W. Zhang, C. Leng, C. Tang (2015) 
    <doi:10.1111/rssb.12065>. The second method is bootstrap based, see Liu, R.Y. (1988) 
    <doi:10.1214/aos/1176351062> for reference.
A computational toolbox of heuristics approaches for performing variable ranking and feature selection based on mutual information well adapted for multivariate system epidemiology datasets. The core function is a general implementation of the minimum redundancy maximum relevance model. R. Battiti (1994) <doi:10.1109/72.298224>. Continuous variables are discretized using a large choice of rule. Variables ranking can be learned with a sequential forward/backward search algorithm. The two main problems that can be addressed by this package is the selection of the most representative variable within a group of variables of interest (i.e. dimension reduction) and variable ranking with respect to a set of features of interest.
Methods for fitting semi-parametric mean and variance models, with normal or censored data. Also extended to allow a regression in the location, scale and shape parameters.
Estimation, lag selection, diagnostic testing, forecasting, causality analysis, forecast error variance decomposition and impulse response functions of VAR models and estimation of SVAR and SVEC models.
An algorithm which identifies the morphometric features that significantly discriminate two taxa and validates the morphological distinctness between them via a Monte-Carlo test, polar coordinates and overlap of the area under the density curve.
Feature selection using Sequential Forward Floating feature Selection and Jeffries-Matusita distance. It returns a suboptimal set of features to use for image classification. Reference: Dalponte, M., Oerka, H.O., Gobakken, T., Gianelle, D. & Naesset, E. (2013). Tree Species Classification in Boreal Forests With Hyperspectral Data. IEEE Transactions on Geoscience and Remote Sensing, 51, 2632-2645, <DOI:10.1109/TGRS.2012.2216272>.
Full model selection (detection of the relevant features and estimation of the number of clusters) for model-based clustering (see reference here <doi:10.1007/s11222-016-9670-1>). Data to analyze can be continuous, categorical, integer or mixed. Moreover, missing values can occur and do not necessitate any pre-processing. Shiny application permits an easy interpretation of the results.
Variable selection from random forests using both
        backwards variable elimination (for the selection of small sets
        of non-redundant variables) and selection based on the
        importance spectrum (somewhat similar to scree plots; for the
        selection of large, potentially highly-correlated variables).
        Main applications in high-dimensional data (e.g., microarray
        data, and other genomics and proteomics applications).
Provides routines for identifying structural shocks in vector autoregressions (VARs) using sign restrictions.
Computes a portfolio of European options that replicates
        the cost of capturing the realised variance of an equity index.
Implements the Wild bootstrap tests for autocorrelation in vector autoregressive models of Ahlgren, N. & Catani, P. 
	(2016, <doi:10.1007/s00362-016-0744-0>), the Combined LM test for ARCH in VAR models of Catani, P. & Ahlgren, N. 
	(2016, <doi:10.1016/j.ecosta.2016.10.006>), and Bootstrap determination of the co-integration rank (Cavaliere, G., Rahbek, A.,
	& Taylor, A. M. R., 2012, 2014).
Provides an interface to a 'HashiCorp' vault server over
  its http API (typically these are self-hosted; see
  <https://www.vaultproject.io>).  This allows for secure storage and
  retrieval of secrets over a network, such as tokens, passwords and
  certificates.  Authentication with vault is supported through
  several backends including user name/password and authentication via
  'GitHub'.
Efficient algorithm for solving discrete mixture
		regression model for rare variant association analysis.
		Uses variational Bayes algorithm to efficiently search over
		model space.  Outputs an approximate likelihood ratio test
		as well as variant level posterior probabilities of
		association.
Fit and simulate latent position and cluster
        models for network data, using a fast Variational Bayes approximation.
Efficient algorithm for solving ultra-sparse
		regularized regression models using a variational
		Bayes algorithm with a spike (l0) prior.  Algorithm
		is solved on a path, with coordinate updates, and is
		capable of generating very sparse models.  There are
		very general model diagnostics for controling type-1
		error included in this package.
Provides a new data structure, vector binary tree, to make
  your data visiting and management more efficient. if your data has
  very structurized column names with specific connecting pattern, it can 
  read, split, and factorize these names, then build the mapping from all
  string objects to an array or tensor, through vector binary tree, by
  which the batched data processing can be implemented easily. The methods
  of array and tensor are also applicable.
ANOVA and REML estimation of linear mixed models is implemented, once following
 Searle et al. (1991, ANOVA for unbalanced data), once making use of the 'lme4' package.
 The primary objective of this package is to perform a variance component analysis (VCA)
 according to CLSI EP05-A3 guideline "Evaluation of Precision of Quantitative Measurement
 Procedures" (2014). There are plotting methods for visualization of an experimental design,
 plotting random effects and residuals. For ANOVA type estimation two methods for computing
 ANOVA mean squares are implemented (SWEEP and quadratic forms). The covariance matrix of 
 variance components can be derived, which is used in estimating confidence intervals. Linear
 hypotheses of fixed effects and LS means can be computed. LS means can be computed at specific
 values of covariables and with custom weighting schemes for factor variables. See ?VCA for a
 more comprehensive description of the features.
Visualization techniques, data sets, summary and inference
        procedures aimed particularly at categorical data. Special
        emphasis is given to highly extensible grid graphics. The
        package was package was originally inspired by the book 
	"Visualizing Categorical Data" by Michael Friendly and is 
	now the main support package for a new book, 
	"Discrete Data Analysis with R" by Michael Friendly and 
	David Meyer (2015).
Provides additional data sets, methods and documentation to complement the 'vcd' package for Visualizing Categorical Data
    and the 'gnm' package for Generalized Nonlinear Models.
	In particular, 'vcdExtra' extends mosaic, assoc and sieve plots from 'vcd' to handle 'glm()' and 'gnm()' models and
	adds a 3D version in 'mosaic3d'.  Additionally, methods are provided for comparing and visualizing lists of
	'glm' and 'loglm' objects. This package is now a support package for the book, "Discrete Data Analysis with R" by
  Michael Friendly and David Meyer.
Facilitates easy manipulation of variant call format (VCF) data.
    Functions are provided to rapidly read from and write to VCF files. Once
    VCF data is read into R a parser function extracts matrices of data. This
    information can then be used for quality control or other purposes. Additional
    functions provide visualization of genomic data. Once processing is complete
    data may be written to a VCF file (*.vcf.gz). It also may be converted into
    other popular R objects (e.g., genlight, DNAbin). VcfR provides a link between
    VCF data and familiar R software.
Methods for faster extraction (about 5x faster in a few test cases) of variance-covariance matrices and standard errors from models. Methods in the 'stats' package tend to rely on the summary method, which may waste time computing other summary statistics which are summarily ignored.
Record test suite 'HTTP' requests and replays them during
    future runs. A port of the Ruby gem of the same name
    (<https://github.com/vcr/vcr/>). Works by hooking into the 'webmockr'
    R package for matching 'HTTP' requests by various rules ('HTTP' method,
    'URL', query parameters, headers, body, etc.), and then caching
    real 'HTTP' responses on disk in 'cassettes'. Subsequent 'HTTP' requests
    matching any previous requests in the same 'cassette' use a cached
    'HTTP' response.
Recursive partitioning for varying coefficient generalized linear models and ordinal linear mixed models. Special features are coefficient-wise partitioning, non-varying coefficients and partitioning of time-varying variables in longitudinal regression.
Defines new notions of prototype and size that are used 
    to provide tools for consistent and well-founded type-coercion and 
    size-recycling, and are in turn connected to ideas of type- and 
    size-stability useful for analyzing function interfaces.
Analyze Peptide Array Data and characterize peptide
    sequence space. Allows for high level visualization of global signal, Quality control based
    on replicate correlation and/or relative Kd, calculation of peptide Length/Charge/Kd parameters,
    Hits selection based on RFU Signal, and amino acid composition/basic motif recognition with RFU
    signal weighting. Basic signal trends can be used to generate peptides that follow the observed
    compositional trends.
Facilities for constructing variance dispersion graphs, fraction-
    of-design-space plots and similar graphics for exploring the properties of
    experimental designs. The design region is explored via random sampling, which
    allows for more flexibility than traditional variance dispersion graphs. A
    formula interface is leveraged to provide access to complex model formulae.
    Graphics can be constructed simultaneously for multiple experimental designs
    and/or multiple model formulae. Instead of using pointwise optimization to
    find the minimum and maximum scaled prediction variance curves, which can be
    inaccurate and time consuming, this package uses quantile regression as an
    alternative.
Uses a modification of the published FORTRAN code in "A Computer Program for Generating Variance Dispersion Graphs" by G. Vining, Journal of Quality Technology, Vol. 25 No. 1 January 1993, to produce variance dispersion graphs. Also produces fraction of design space plots, and contains data frames for several minimal run response surface designs.
Functions for creating variance dispersion graphs, fraction of design space plots, and contour plots of scaled prediction variances for second-order response surface designs in spherical and cuboidal regions. Also, some standard response surface designs can be generated.
An extension to the 'testthat' package that makes it easy
    to add graphical unit tests. It provides a Shiny application to
    manage the test cases.
Contains variable, diversity, and joining sequences and accompanying functions that enable both the extraction of and comparison between immune V-D-J genomic segments from a variety of species. Sources include IMGT from MP Lefranc (2009) <doi:10.1093/nar/gkn838> and Vgenerepertoire from publication DN Olivieri (2014) <doi:10.1007/s00251-014-0784-3>.
This provides web-based visual data-mining tools by adding
    interactive functions to 'ggplot2' graphics. Brushing and linking between the
    multiple plots is one of the main feature of this package. Currently scatter
    plots, histograms, parallel coordinate plots and choropleth maps are supported.
Provides statistical methods for the design and analysis of a calibration study, which aims for calibrating measurements using two different methods. The package includes sample size calculation, sample selection,  regression analysis with error-in measurements and change-point regression. The method is described in Tian, Durazo-Arvizu, Myers, et al. (2014) <DOI:10.1002/sim.6235>.
A package for applying affine and similarity transformations on vector spatial data (sp objects). Transformations can be defined from control points or directly from parameters. If redundant control points are provided Least Squares is applied allowing to obtain residuals and RMSE.
Automates set operations (i.e., comparisons of overlap) between multiple vectors.
    It also contains a function for automating reporting in 'RMarkdown', by generating markdown output for easy analysis, as well as an 'RMarkdown' template for use with 'RStudio'.
The base 'sets' tools follow the algebraic definition
 that each element of a set must be unique. 
 Since it's often helpful to compare all elements of two vectors,
 this toolset treats every element as unique for counting purposes.
 For ease of use, all functions in vecsets have an argument
 'multiple' which, when set to FALSE,
 reverts them to the base::set tools functionality.
A 2D statistical analysis is performed, both numerical and graphical, of a set of vectors. Since a vector has two components (module and azimuth) vector analysis is performed in three stages: modules are analyzed by means of linear statistics, azimuths are analyzed by circular statistics, and the joint analysis of modules and azimuths is done using density maps that allow detecting another distribution properties (i.e. anisotropy) and outliers. Tests and circular statistic parameters have associated a full range of graphing: histograms, maps of distributions, point maps, vector maps, density maps, distribution modules and azimuths.
This package performs a 3D statistical analysis, both numerical and graphical, of a set of vectors. Since a vector has three components (a module and two angles) vectorial analysis is performed in two stages: modules are analyzed by means of linear statistics and orientations are analyzed by spherical statistics. Tests and spherical statistic parameters are accompanied by a graphs as: density maps, distribution modules and angles. The tests, spherical statistic parameters and graphs allow us detecting another distribution properties (i.e. anisotropy) and outliers.
The 'Vega-Lite' 'JavaScript' framework provides a higher-level grammar
    for visual analysis, akin to 'ggplot' or 'Tableau', that generates complete 'Vega'
    specifications. Functions exist which enable building a valid 'spec' from scratch
    or importing a previously created 'spec' file. Functions also exist to export 'spec'
    files and to generate code which will enable plots to be embedded in properly
    configured web pages. The default behavior is to generate an 'htmlwidget'.
Ordination methods, diversity analysis and other
  functions for community and vegetation ecologists.
Static and dynamic 3D plots to be used with ordination
    results and in diversity analysis, especially with the vegan package.
Vega and Vega-Lite parse text in JSON notation to render 
  chart-specifications into HTML. This package is used to facilitate the 
  rendering. It also provides a means to interact with signals, events,
  and datasets in a Vega chart using JavaScript or Shiny.
A set of functions to: (1) perform fuzzy clustering of vegetation data [De Caceres et al. (2010) <doi:10.1111/j.1654-1103.2010.01211.x>]; (2) to assess ecological community ressemblance on the basis of structure and composition [De Caceres et al. (2013): <doi:10.1111/2041-210X.12116>]; and (3) to perform community trajectory analysis [De Caceres et al. (2019): <doi:10.1002/ecm.1350>].
Handling of vegetation data from different sources (
	Turboveg <http://www.synbiosys.alterra.nl/turboveg/>; 
	the German national repository <http://www.vegetweb.de> and others. 
    Taxonomic harmonization (given appropriate taxonomic lists,
    e.g. the German taxonomic standard list "GermanSL", <http://germansl.infinitenature.org>).
This package computes diversity for community data sets
        using the methods outlined by Jost (2006, 2007). While there
        are differing opinions on the ideal way to calculate diversity
        (e.g. Magurran 2004), this method offers the advantage of
        providing diversity numbers equivalents, independent alpha and
        beta diversities, and the ability to incorporate 'order' (q) as
        a continuous measure of the importance of rare species in the
        metrics. The functions provided in this package largely
        correspond with the equations offered by Jost in the cited
        papers. The package computes alpha diversities, beta
        diversities, gamma diversities, and similarity indices.
        Confidence intervals for diversity measures are calculated
        using a bootstrap method described by Chao et al. (2008).  For
        datasets with many samples (sites, plots), sim.table creates
        tables of all pairwise comparisons possible, and for grouped
        samples sim.groups calculates pairwise combinations of within-
        and between-group comparisons.
Collection of common methods to determine growing season length in
  a simple manner. Start and end dates of the vegetation periods are calculated
  solely based on daily mean temperatures and the day of the year.
Import and handling data from vegetation-plot databases, especially
    data stored in 'Turboveg' (<https://www.synbiosys.alterra.nl/turboveg>).
    Also import/export routines for exchange of data with 'Juice'
    (<http://www.sci.muni.cz/botany/juice>) are implemented.
Elaboration of vehicular emissions inventories,
    consisting in four stages, pre-processing activity data, preparing 
    emissions factors, estimating the emissions and post-processing of emissions 
    in maps and databases. More details in Ibarra-Espinosa et al (2018) <doi:10.5194/gmd-11-2209-2018>.
    Before using VEIN you need to know the vehicular composition of your study area, in other words,
    the combination of of type of vehicles, size and fuel of the fleet. Then, it is recommended to
    start with the function inventory to create a structure of directories and template scripts.
Functions for downloading, reshaping, culling, cleaning, and analyzing fossil data from the Paleobiology Database <https://paleobiodb.org>.
C++ accelerated raster manipulation and extraction.
A set of functions for generating HTML to
    embed hosted video in your R Markdown documents or Shiny applications.
Draws and displays Venn diagrams up to 7 sets, and any Boolean union of set intersections.
A set of functions to generate high-resolution Venn and Euler plots. Includes handling for several special cases, including two-case scaling, and extensive customization of plot shape and structure.
Calculates and displays Venn and Euler Diagrams
Provides variable selection and estimation routines for models
    with main effects stratified on multiple binary factors. The 'vennLasso' package
    is an implementation of the method introduced in Huling, et al. (2017) <doi:10.1111/biom.12769>.
Calculate and plot Venn diagrams in 2D and 3D.
Utilities for verifying discrete, continuous and probabilistic forecasts, and forecasts expressed as parametric distributions are included.
Installs specified versions of R packages hosted on CRAN and
    provides functions to list available versions and the versions of currently
    installed packages. These tools can be used to help make R projects and
    packages more reproducible. 'versions' fits in the narrow gap between
    the 'devtools' install_version() function and the 'checkpoint' package.
    devtools::install_version() installs a stated package version from source files
    stored on the CRAN archives. However CRAN does not store binary versions of
    packages so Windows users need to have RTools installed and Windows and OSX
    users get longer installation times. 'checkpoint' uses the Revolution Analytics
    MRAN server to install packages (from source or binary) as they were available
    on a given date. It also provides a helpful interface to detect the packages
    in use in a directory and install all of those packages for a given date.
    'checkpoint' doesn't provide install.packages-like functionality however, and
    that's what 'versions' aims to do, by querying MRAN. As MRAN only goes back to
    2014-09-17, 'versions' can't install packages archived before this date.
When given a description of a polyhedral set by a system of linear inequalities Ax <= b, produce the list of the vertices of the set.
Creates Vertex Similarity matrix of an undirected graph based
  on the method stated by E. A. Leicht, Petter Holme, AND M. E. J. Newman in
  their paper <DOI:10.1103/PhysRevE.73.026120>.
Permits to apply the 'Vertex Sort' algorithm (Jothi et al. (2009) <10.1038/msb.2009.52>) to a graph in order to elucidate its hierarchical structure. It also allows graphic visualization of the sorted graph by exporting the results to a cytoscape friendly format. Moreover, it offers five different algorithms of graph randomization: 1) Randomize a graph with preserving node degrees, 2) with preserving similar node degrees, 3) without preserving node degrees, 4) with preserving node in-degrees and 5) with preserving node out-degrees.
Multi-precission library that allows to store and operate with arbitrarily big integers without
    loss of precision. It includes a large list of tools to work with them, like:
      - Arithmetic and logic operators
      - Modular-arithmetic operators
      - Computer Number Theory utilities
      - Probabilistic primality tests
      - Factorization algorithms
      - Random generators of diferent types of integers.
Simple functions for calculating the image gradient, image hessian, volume ratio filter, and Frangi vesselness filter of 3-dimensional volumes.
Integrated data management library that offers a variety of tools concerning the loading and manipulation of environmental data available from different Venezuelan governmental sources. Facilities are provided to plot temporal and spatial data as well as understand the health of a collection of meteorological data.
Declarative template-based framework for verifying that objects
  meet structural requirements, and auto-composing error messages when they do
  not.
R Codes and Datasets for Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998). Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
Computation the value of one of two uniformly 
    distributed marginals if the copula probability value is known
    and the value of the second marginal is also known.
    Computation and plotting corresponding cumulative
    distribution function or survival function.
    The numerical definition of a common area limited by lines
    of the cumulative distribution function and survival function.
    Approximate quantification of the probability of this area.
    In addition to 'amh', the copula dimension may be larger than 2.
Variance function estimation for models proposed by W. Sadler in his variance function program 
			('VFP', <http://www.aacb.asn.au/resources/useful-tools/variance-function-program-v14>). Here, the idea is
			to fit multiple variance functions to a data set and consequently assess which function reflects
			the relationship 'Var ~ Mean' best. For 'in-vitro diagnostic' ('IVD') assays modeling this relationship
			is of great importance when individual test-results are used for defining follow-up treatment of patients.
Realization of published methods to analyze visual field (VF) progression. Introduction to the plotting methods (designed by author TE) for VF output visualization. A sample dataset for two eyes, each with 10 follow-ups is included. The VF analysis methods could be found in -- Musch et al. (1999) <doi:10.1016/S0161-6420(99)90147-1>, Nouri-Mahdavi et at. (2012) <doi:10.1167/iovs.11-9021>, Schell et at. (2014) <doi:10.1016/j.ophtha.2014.02.021>, Aptel et al. (2015) <doi:10.1111/aos.12788>.
Empirical models for runoff, erosion, and phosphorus loss 
    across a vegetated filter strip, given slope, soils, climate, and 
    vegetation (Gall et al., 2018) <doi:10.1007/s00477-017-1505-x>. 
    It also includes functions for deriving climate parameters from 
    measured daily weather data, and for simulating rainfall. Models 
    implemented include MUSLE (Williams, 1975) and APLE (Vadas et al., 
    2009 <doi:10.2134/jeq2008.0337>).
An implementation of about 6 major classes of
    statistical regression models. The central algorithm is
    Fisher scoring and iterative reweighted least squares.
    At the heart of this package are the vector generalized linear
    and additive model (VGLM/VGAM) classes. VGLMs can be loosely
    thought of as multivariate GLMs. VGAMs are data-driven
    VGLMs that use smoothing. The book "Vector Generalized
    Linear and Additive Models: With an Implementation in R"
    (Yee, 2015) <DOI:10.1007/978-1-4939-2818-7> gives details of
    the statistical framework and the package. Currently only
    fixed-effects models are implemented. Many (150+) models and
    distributions are estimated by maximum likelihood estimation
    (MLE) or penalized MLE. The other classes are RR-VGLMs
    (reduced-rank VGLMs), quadratic RR-VGLMs, reduced-rank VGAMs,
    RCIMs (row-column interaction models)---these classes perform
    constrained and unconstrained quadratic ordination (CQO/UQO)
    models in ecology, as well as constrained additive ordination
    (CAO). Note that these functions are subject to change;
    see the NEWS and ChangeLog files for latest changes.
Data sets to accompany the VGAM package and
	the book "Vector Generalized Linear and
	Additive Models: With an Implementation in R" (Yee, 2015)
	<DOI:10.1007/978-1-4939-2818-7>.
	These are used to illustrate vector generalized
	linear and additive models (VGLMs/VGAMs), and associated models
	(Reduced-Rank VGLMs, Quadratic RR-VGLMs, Row-Column
	Interaction Models, and constrained and unconstrained ordination
	models in ecology).
Extending the functionalities of the 'VGAM' package with
         additional functions and datasets. At present, 'VGAMextra'
         comprises new family functions (ffs) to estimate several time
         series models by maximum likelihood using Fisher scoring, 
         unlike popular packages in CRAN relying on optim(), including
         ARMA-GARCH-like models, the Order-(p, d, q) ARIMAX model (non-
         seasonal), the Order-(p) VAR model, error correction models
         for cointegrated time series, and ARMA-structures with Student-t 
         errors. For independent data, new ffs to estimate the inverse-
         Weibull, the inverse-gamma, the generalized beta of the second
         kind and the general multivariate normal distributions are
         available. In addition, 'VGAMextra' incorporates new VGLM-links
         for the mean-function, and the quantile-function (as an alternative
         to ordinary quantile modelling) of several 1-parameter distributions,
         that are compatible with the class of VGLM/VGAM family functions.
         Currently, only fixed-effects models are implemented. All functions
         are subject to change; see the NEWS for further details on the
         latest changes.
This package provides an implementation of Linear discriminant analysis and quadratic discriminant analysis that works fine in very high dimension (when there are many more variables than observations).
The "Vertical and Horizontal Inheritance Consistence Analysis" method is described in the following publication: "VHICA: a new method to discriminate between vertical and horizontal transposon transfer: application to the mariner family within Drosophila" by G. Wallau. et al. (2016) <DOI:10.1093/molbev/msv341>. The purpose of the method is to detect horizontal transfers of transposable elements, by contrasting the divergence of transposable element sequences with that of regular genes.
The Variable Infiltration Capacity (VIC) model is a macroscale
    hydrologic model that solves full water and energy balances, originally
    developed by Xu Liang at the University of Washington (UW).
    The version of VIC source code used is of 5.0.1 on <https://github.com/UW-Hydro/VIC/>,
    see Hamman et al. (2018).
    Development and maintenance of the current official version
    of the VIC model at present is led by the UW Hydro (Computational Hydrology
    group) in the Department of Civil and Environmental Engineering at UW. VIC is
    a research model and in its various forms it has been applied to most of the
    major river basins around the world, as well as globally. If you make use of
    this model, please acknowledge the appropriate references listed in the help
    page of this package or on the references page
    <http://vic.readthedocs.io/en/master/Documentation/References/>
    of the VIC official documentation website.
    These should include Liang et al. (1994) plus any references relevant to the
    features you are using Reference: Liang, X., D. P. Lettenmaier, E. F. Wood, and
    S. J. Burges (1994), A simple hydrologically based model of land surface water
    and energy fluxes for general circulation models, J. Geophys. Res., 99(D7),
    14415-14428, <doi:10.1029/94JD00483>. Hamman et al. (2018) about VIC 5.0.1 also can
    be considered:
    Hamman, J. J., Nijssen, B., Bohn, T. J., Gergel, D. R., and Mao, Y. (2018), The
    Variable Infiltration Capacity model version 5 (VIC-5): infrastructure improvements
    for new applications and reproducibility, Geosci. Model Dev., 11, 3481-3496, 
    <doi:10.5194/gmd-11-3481-2018>.
Converts Vietnam's provinces' names and ID
    across different formats. Handles diacritics and different spellings.
A set of tools to compute viewshed in 3D from Terrestrial Laser Scanner data and prepare the data prior to visibility calculation.
This package implements a fast regression algorithm for
        building linear model for large data as defined in the paper
        "VIF-Regression: A Fast Regression Algorithm for Large Data
        (2011), Journal of the American Statistical Association, Vol.
        106, No. 493: 232-247" by Dongyu Lin, Dean P. Foster, and Lyle
        H. Ungar.
Contains a function to support the following paper:
    Xiaoping Shi, Xiang-Sheng Wang, Dongwei Wei, Yuehua Wu (2016), <DOI:10.1007/s00180-015-0587-5>,
    A sequential multiple change-point detection procedure via VIF regression,
    Computational Statistics, 31(2): 671-691.
Conducts linear regression using variational Bayesian inference, particularly optimized for genome-wide association mapping and whole-genome prediction which use a number of DNA markers as the explanatory variables. Provides seven regression models which select the important variables (i.e., the variables related to response variables) among the given explanatory variables in different ways (i.e., model structures).
New tools for the visualization of missing and/or imputed values
    are introduced, which can be used for exploring the data and the structure of
    the missing and/or imputed values. Depending on this structure of the missing
    values, the corresponding methods may help to identify the mechanism generating
    the missing values and allows to explore the data including missing values.
    In addition, the quality of imputation can be visually explored using various
    univariate, bivariate, multiple and multivariate plot methods. A graphical user
    interface available in the separate package VIMGUI allows an easy handling of
    the implemented plot methods.
A graphical user interface for the methods implemented in the
    package VIM. It allows an easy handling of the
    implemented plot and imputation methods.
Calculate point estimates of and valid confidence intervals for 
    nonparametric variable importance measures in high and low dimensions, 
    using flexible estimators of the underlying regression functions. For more information
    about the methods, please see Williamson et al. (2017) <https://biostats.bepress.com/uwbiostat/paper422/>.
Provides tools for the statistical analysis of vine copula models.
    The package includes tools for parameter estimation, model selection,
    simulation, goodness-of-fit tests, and visualization. Tools for estimation,
    selection and exploratory data analysis of bivariate copula models are also
    provided.
Implements D-vine quantile regression models with
  parametric or nonparametric pair-copulas. See 
  Kraus and Czado (2017) <doi:10.1016/j.csda.2016.12.009> and
  Schallhorn et al. (2017) <arXiv:1705.08310>.
Implementation of the vine graphical model for building
    high-dimensional probability distributions as a factorization of
    bivariate copulas and marginal density functions. This package
    provides S4 classes for vines (C-vines and D-vines) and methods
    for inference, goodness-of-fit tests, density/distribution
    function evaluation, and simulation.
A lattice violin-plot is overlayed with the arithmetic
        mean and standard deviation.
A violin plot is a combination of a box plot and a kernel density plot. This package allows extensive customisation of violin plots.
viopoints draws one dimensional scatter plots with jitter
        using kernel density estimates in a similar way to violin
        plots.
A general framework for constructing variable importance plots from 
  various types machine learning models in R. Aside from some standard model-
  based variable importance measures, this package also provides a novel 
  approach based on partial dependence plots (PDPs) and individual conditional 
  expectation (ICE) curves as described in Greenwell et al. (2018) 
  <arXiv:1805.04755>.
Generate a violin point plot, a combination of a violin/histogram
    plot and a scatter plot by offsetting points within a category based on their
    density using quasirandom noise.
Computation of volatility impulse response function for multivariate time series model using algorithm by Jin, Lin and Tamvakis (2012) <doi.org/10.1016/j.eneco.2012.03.003>.
Implementation of the 'viridis' - the default -, 'magma', 'plasma', 
    'inferno', and 'cividis' color maps for 'R'. 'viridis', 'magma', 'plasma', 
    and 'inferno' are ported from 'matplotlib' <http://matplotlib.org/>, a 
    popular plotting library for 'python'. 'cividis', was developed by Jamie R. 
    Nuñez and Sean M. Colby. These color maps are designed in such a way that 
    they will analytically be perfectly perceptually-uniform, both in regular 
    form and also when converted to black-and-white. They are also designed to 
    be perceived by readers with the most common form of color blindness (all 
    color maps in this package) and color vision deficiency ('cividis' only).
Implementation of the 'viridis' - the default -, 'magma', 'plasma', 
    'inferno', and 'cividis' color maps for 'R'. 'viridis', 'magma', 'plasma', 
    and 'inferno' are ported from 'matplotlib' <http://matplotlib.org/>, a 
    popular plotting library for 'python'. 'cividis', was developed by Jamie R. 
    Nuñez and Sean M. Colby. These color maps are designed in such a way that 
    they will analytically be perfectly perceptually-uniform, both in regular 
    form and also when converted to black-and-white. They are also designed to 
    be perceived by readers with the most common form of color blindness (all 
    color maps in this package) and color vision deficiency ('cividis' only). 
    This is the 'lite' version of the more complete 'viridis' package that can 
    be found at <https://cran.r-project.org/package=viridis>.
Tools to generate virtual environmental drivers with a given temporal autocorrelation, and to simulate pollen curves at annual resolution over millennial time-scales based on these drivers and virtual taxa with different life traits and niche features. It also provides the means to simulate quasi-realistic pollen-data conditions by applying simulated accumulation rates and given depth intervals between consecutive samples.
Provides a framework for generating virtual species distributions,
    a procedure increasingly used in ecology to improve species distribution
    models. This package integrates the existing methodological approaches with the
    objective of generating virtual species distributions with increased ecological
    realism.
Provides users with a simple and convenient
             mechanism to manage and query a 'Virtuoso' database using the 'DBI' (DataBase Interface)
             compatible 'ODBC' (Open Database Connectivity) interface.
             'Virtuoso' is a high-performance "universal server," which can act
             as both a relational database, supporting standard Structured Query
             Language ('SQL') queries, while also supporting data following the
             Resource Description Framework ('RDF') model for Linked Data.
             'RDF' data can be queried using 'SPARQL' ('SPARQL' Protocol and 'RDF' Query Language)
             queries, a graph-based query that supports semantic reasoning.
             This allows users to leverage the performance of local or remote 'Virtuoso' servers using
             popular 'R' packages such as 'DBI' and 'dplyr', while also providing a 
             high-performance solution for working with large 'RDF' triplestores from 'R.'
             The package also provides helper routines to install, launch, and manage
             a 'Virtuoso' server locally on 'Mac', 'Windows' and 'Linux' platforms using
             the standard interactive installers from the 'R' command-line.  By 
             automatically handling these setup steps, the package can make using 'Virtuoso'
             considerably faster and easier for a most users to deploy in a local
             environment. Managing the bulk import of triples
             from common serializations with a single intuitive command is another key
             feature of this package.  Bulk import performance can be tens to
             hundreds of times faster than the comparable imports using existing 'R' tools,
             including 'rdflib' and 'redland' packages.
Use VirusTotal, a Google service that analyzes files and URLs 
    for viruses, worms, trojans etc., provides category of the content hosted by a 
    domain from a variety of prominent services, provides passive DNS information,
    among other things. See <http://www.virustotal.com> for more information.
Create preliminary exploratory data visualisations of an entire 
    dataset to identify problems or unexpected features using 'ggplot2'.
A graphical tool designed to visualize and to give an overview of behavioral observations realized on individuals or groups. Visualization of raw data during experimental observations of the realization of a procedure. It graphically presents an overview of individuals and group actions usually acquired from timestamps during video recorded sessions. Options of the package allow adding graphical information as statistical indicators (mean, standard deviation, quantile or statistical test) but also for each action green or black zones providing visual information about the accuracy of the realized actions.
Provides an R interface to the 'vis.js' JavaScript charting
    library. It allows an interactive visualization of networks.
Provides a convenient interface for constructing plots to visualize the fit of regression models arising from a wide variety of models in R ('lm', 'glm', 'coxph', 'rlm', 'gam', 'locfit', 'lmer', 'randomForest', etc.)
Create interactive timelines or Gantt charts that
  are usable in the 'RStudio' viewer pane, in 'R Markdown' documents and in 'Shiny' 
  apps. Hover the mouse pointer over a point or task to show details or drag a rectangle to zoom in. 
  Timelines and their components can afterwards be manipulated using 'plotly_build()', which 
  transforms the plot into a mutable list.
Provides a visualization for characterizing subgroups defined by a decision tree structure. The visualization simplifies the ability to interpret individual pathways to subgroups; each sub-plot describes the distribution of observations within individual terminal nodes and percentile ranges for the associated inner nodes.
Visualize and compute percentiles/probabilities of normal, t, f, chi square 
    and binomial distributions.
A collection of tools for analyzing the field of vision. It provides a framework for development and use of innovative methods for visualization, statistical analysis, and clinical interpretation of visual-field loss and its change over time. It is intended to be a tool for collaborative research.
Graphs the pdf or pmf and highlights what area or probability is
    present in user defined locations. Visualize is able to provide lower tail,
    bounded, upper tail, and two tail calculations. Supports strict and equal
    to inequalities.  Also provided on the graph is the mean and variance of
    the distribution.
Generates a 3D graph which plots the volatility skew of a stock or a selected stock option parameter over time.
    The default setting plots the net parameter position  of a double vertical spread over time.
Displays multivariate data, based on Sammon's nonlinear mapping.
Visualizes vowel variation in f0, F1, F2, F3 and duration.
Implements the novel testing approach by Janitza et al.(2015)
    <http://nbn-resolving.de/urn/resolver.pl?urn=nbn:de:bvb:19-epub-25587-4>
    for the permutation variable importance measure in a random forest and the
    PIMP-algorithm by Altmann et al.(2010) <doi:10.1093/bioinformatics/btq134>.
    Janitza et al.(2015) <http://nbn-resolving.de/urn/resolver.pl?urn=nbn:de:bvb:19-epub-25587-4>
    do not use the "standard" permutation variable
    importance but the cross-validated permutation variable
    importance for the novel test approach. The cross-validated
    permutation variable importance is not based on the out-of-bag
    observations but uses a similar strategy which is inspired by
    the cross-validation procedure. The novel test approach can be
    applied for classification trees as well as for regression
    trees. However, the use of the novel testing approach has not
    been tested for regression trees so far, so this routine is
    meant for the expert user only and its current state is rather
    experimental.
Provides templates and functions to simplify the production and maintenance of curriculum vitae.
Provides fitting routines for four versions of the
        Vitality family of mortality models.
Provides an easy to calculate variable importance measure based on 
  Ceteris Paribus plot and is calculated in eight variants. We obtain eight 
  variants measure through the possible combinations of three parameters such as 
  absolute_deviation, point and density.
Provides an interface to the VK API <https://vk.com/dev/methods>.
      VK <https://vk.com/> is the largest European online social networking
      service, based in Russia.
Contains functions to set up risk-adjusted quality control charts
    in health care. For the variable life adjusted display (VLAD) proposed by 
    Lovegrove et al. (1997) <doi:10.1016/S0140-6736(97)06507-0> signaling rules
    derived in Wittenberg et al. (2018) <doi: 10.1002/sim.7647> are implemented. 
    Additionally, for the risk-adjusted cumulative sum chart based on log-likelihood
    ratio statistic introduced by Steiner et al. (2000) <doi:10.1093/biostatistics/1.4.441>
    average run length and control limits can be computed.
Using frequency matrices, very low frequency variants (VLFs) are assessed for amino acid and nucleotide sequences.  The VLFs are then compared to see if they occur in only one member of a species, singleton VLFs, or if they occur in multiple members of a species, shared VLFs.  The amino acid and nucleotide VLFs are then compared to see if they are concordant with one another.  Amino acid VLFs are also assessed to determine if they lead to a change in amino acid residue type, and potential changes to protein structures.
Functions, Classes & Methods for estimation, prediction, and
  simulation (bootstrap) of Variable Length Markov Chain ('VLMC') Models.
The cumulative sum (CUSUM) control chart is considered to be an alternative or complementary to Shewhart control charts in statistical process control (SPC) applications, owing to its higher sensitivity to small shifts in the process mean. It utilizes all the available data rather than the last few ones used in Shewhart control charts for quick decision making. V-mask is a traditional technique for separating meaningful data from unusual circumstances in a Cumulative Sum (CUSUM) control chart; for see details about v-mask see Montgomery (1985, ISBN:978-0471656319). The mask is a V-shaped overlay placed on the CUSUM chart so that one arm of the V lines up with the slope of data points, making it easy to see data points that lie outside the slope and to determine whether these points should be discarded as random events, or treated as a performance trend that should be addressed. But, complex computations is one disadvantage V-mask method for detect small changes in mean using CUSUM control chart. Package 'vMask' can help to the applied users to overcome this challenge by considering six different methods which each of them are based on different information.
A port and extension to the original 'Matlab' code made public by Dragomiretskiy and Zosso, for conducting Variational Mode Decomposition (VMD) as described  within their 2013 publication (publication: <doi:10.1109/TSP.2013.2288675>, source: <https://goo.gl/fJH1d5>).
The tools you need to process, analyze, combine, integrate and
    plot your fishery data: the georeferenced dataset from the Vessel Monitoring
    System (VMS), from the Automatic Information System (AIS) or other tracking
    devices, as well as the catches or landings dataset from the Logbook or Vessel
    Register. Package 'vmsbase' is equipped by Viewer Tools to visually inspect data
    at different steps of the analyses and to produce effective outputs for reports
    and scientific publications. Viewers are conceived to show the VMS pings, to
    visualize single or multiple tracks for fishing vessels, or to represent the VMS
    data on Google Viewer, so that the user can produce easy to interpret and more
    realistic visualization of both fishing effort and effort behaviour. Package
    'vmsbase' represents the implementation of several R routines which have been
    developed by the "Tor Vergata" University of Rome Team involved in the Italian
    National Program for the Data Collection Framework for Fisheries Data between
    2009-2012.
Provide tools for finding multiple-objective optimal designs for
         estimating the shape of dose-response, the ED50 (the dose producing an effect
         midway between the expected responses at the extreme doses) and the MED (the
         minimum effective dose level) for the 2,3,4-parameter logistic models and for
         evaluating its efficiencies for the three objectives. The acronym VNM stands
         for V-algorithm using Newton Raphson method to search multiple-objective
         optimal design.
Create adjacency matrices of vocalisation graphs from
  dataframes containing sequences of speech and silence intervals,
  transforming these matrices into Markov diagrams, and generating
  datasets for classification of these diagrams by 'flattening' them
  and adding global properties (functionals) etc.  Vocalisation
  diagrams date back to early work in psychiatry (Jaffe and Feldstein,
  1970) and social psychology (Dabbs and Ruback, 1987) but have only
  recently been employed as a data representation method for machine
  learning tasks including meeting segmentation (Luz, 2012)
  <doi:10.1145/2328967.2328970> and classification (Luz,
  2013) <doi:10.1145/2522848.2533788>.
Provides an R interface for 'volesti' C++ package. 'volesti' computes
             estimations of volume of polytopes given by a set of points or linear inequalities
             or Minkowski sum of segments (zonotopes). There are two algorithms for volume
             estimation (I.Z. Emiris and V. Fisikopoulos (2014) <arXiv:1312.2873> and 
             B. Cousins, S. Vempala (2016) <arXiv:1409.6011>) as well as algorithms for sampling, 
             rounding and rotating polytopes. Moreover, 'volesti' provides algorithms for 
             estimating copulas (L. Cales, A. Chalkis, I.Z. Emiris, V. Fisikopoulos (2018) 
             <arXiv:1803.05861>).
Volleyball match statistics of the German volleyball first division league (seasons 2013/2014 to 
    2018/2019). The data has been collected from the official volleyball first division homepage 
    (<www.volleyball-bundesliga.de>) and contains information on teams, staff, sets, matches, and player-in-match 
    statistics (extracted automatically from the official match reports).
The d3.js framework with the plugins d3-voronoi-map, d3-voronoi-treemap and d3-weighted-voronoi
        are used to generate Voronoi treemaps in R and in a shiny application.
        The computation of the Voronoi treemaps are based on Nocaj and Brandes (2012)
        <doi:10.1111/j.1467-8659.2012.03078.x>.
Facilitate Post Vortex Simulation Analysis by offering
    tools to collate multiple Vortex (v10) output files into one R object, and
    analyse the collated output statistically. Vortex is a software for
    the development of individual-based model for population dynamic simulation
    (see <http://www.vortex10.org/Vortex10.aspx>).
Contains selected data from two publications,
    Campbell 'et' 'al'. (2016) <DOI:10.1080/14486563.2015.1028486>
    and 'Pacioni' 'et' 'al'. (2017) <DOI:10.1071/PC17002>.
    The data is provided both as raw outputs from the population viability
    analysis software 'Vortex' and packaged as R objects.
    The R package 'vortexR' uses the raw data provided here to illustrate its
    functionality of parsing raw 'Vortex' output into R objects.
A suite of tools for collecting and constructing networks from social media data.
    Provides easy-to-use functions for collecting data across popular platforms (Twitter, YouTube 
    and Reddit) and generating different types of networks for analysis.
Voss package provides functionality for generating
        realizations of a fractal Brownian function on uniform 1D & 2D
        grid with classic and generic versions of the Voss algorithm
        (random sequential additions)
Counting election votes and determining election results by different methods, including the single transferable vote, approval, score and plurality methods.
'ProPublica' <https://projects.propublica.org/represent/> makes United States
    Congress member votes available and has developed their own unique cartogram to visually
    represent this data. Tools are provided to retrieve voting data, prepare voting data 
    for plotting with 'ggplot2', create vote cartograms and theme them.
Various methods to count ballots in voting systems are provided.
	Functions to check validity of ballots are also provided to ensure flexibility.
Calculates voter transitions comparing two elections, using the function solve.QP() in package 'quadprog'.
Procedures for the manipulation, normalization, and plotting of phonetic and sociophonetic vowel formant data.  vowels is the backend for the NORM website.
Parametric and semiparametric inference for massively parallel
    models, i.e., a large number of models with common design matrix, as often
    occurs with brain imaging data.
Functions for the mass-univariate voxelwise analysis of medical imaging data that follows the NIfTI <http://nifti.nimh.nih.gov> format.
Tools for tree crown structure description based on T-LiDAR data voxelisation
Visual predictive checks are a commonly used diagnostic plot in pharmacometrics, showing how certain statistics (percentiles) for observed data compare to those same statistics for data simulated from a model. The package can generate VPCs for continuous, categorical, censored, and (repeated) time-to-event data.
In recognition that there are many factors (genetic loci, macro-
    genetic factors such as sex, and environmental factors) that influence the
    extent of environmental variation, the 'vqtl' package conducts genome scans
    that accommodate and target these factors. The main functions of this package,
    scanonevar() and scanonevar.perm() take as input a cross object from the popular
    'qtl' package, as described in Corty and Valdar (2019) <doi:10.1534/g3.118.200642>.
Estimation of varying regression segments and a change point in
    2-segment regression models with heteroscedastic variances, and with or
    without a smoothness constraint at the change point.
vrmlgen creates 3D scatter and bar plots, visualizations of 3D meshes, parametric functions and height maps in web-formats like the Virtual Reality Markup Language (VRML, filetype .wrl) and the LiveGraphics3D format.
The goal of 'vroom' is to read and write data (like
    'csv', 'tsv' and 'fwf') quickly. When reading it uses a quick initial
    indexing step, then reads the values lazily , so only the data you
    actually use needs to be read.  The writer formats the data in
    parallel and writes to disk asynchronously from formatting.
This is a package to visualize risk prediction models. For each
    predictor, a color bar represents the contribution to the linear predictor or
    latent variable. A conversion from the linear predictor to the estimated risk
    or survival is also given. (Cumulative) contribution charts enable to visualize
    how the estimated risk for one particular observation is obtained by the model.
    Several options allow to choose different color maps, and to select the zero
    level of the contributions. The package is able to deal with 'glm', 'coxph',
    'mfp', 'multinom' and 'ksvm' objects. For 'ksvm' objects, the visualization
    is not always exact. Functions providing tools to indicate the accuracy of the
    approximation are provided in addition to the visualization.
A collection of statistical tests for martingale difference hypothesis
Performs variable selection/feature reduction under a clustering or classification framework. In particular, it can be used in an automated fashion using mixture model-based methods (tEIGEN and MCLUST are currently supported).
Calculates the enrichment of associated variant set (AVS) for an array of genomic regions. The AVS is the collection of disjoint LD blocks computed from a list of disease associated SNPs and their linked (LD) SNPs. VSE generates a null distribution of matched random variant sets (MRVSs) from 1000 Genome Project Phase III data that are identical to AVS, LD block by block. It then computes the enrichment of AVS intersecting with user provided genomic features (e.g., histone marks or transcription factor binding sites) compared with the null distribution.
An implementation of Vasicek and Song goodness-of-fit tests. Several functions are provided to estimate differential Shannon entropy, i.e., estimate Shannon entropy of real random variables with density, and test the goodness-of-fit of some family of distributions, including uniform, Gaussian, log-normal, exponential, gamma, Weibull, Pareto, Fisher, Laplace and beta distributions; see Lequesne and Regnault (2018) <arXiv:1806.07244>.
Implementation of Visual Studio Team Services <https://www.visualstudio.com/team-services/> API calls. 
    It enables the extraction of information about repositories, build and release definitions and individual releases. 
    It also helps create repositories and work items within a project without logging into Visual Studio. 
    There is the ability to use any API service with a shell for any non-predefined call.
Three steps variable selection procedure based on random forests.
    Initially developed to handle high dimensional data (for which number of
    variables largely exceeds number of observations), the package is very
    versatile and can treat most dimensions of data, for regression and
    supervised classification problems. First step is dedicated to eliminate
    irrelevant variables from the dataset. Second step aims to select all
    variables related to the response for interpretation purpose. Third step
    refines the selection by eliminating redundancy in the set of variables
    selected by the second step, for prediction purpose.
    Genuer, R. and Poggi, J.M. and Tuleau-Malot, C. (2015)
    <https://journal.r-project.org/archive/2015-2/genuer-poggi-tuleaumalot.pdf>.
Automatically generates HTML variable documentation including variable names, labels, classes, value labels (if applicable), value ranges, and summary statistics. See the vignette "vtable" for a package overview.
Designed to facilitate the assimilation, analysis and synthesis of animal location and movement data collected by the VEMCO suite of acoustic transmitters and receivers. As well as database and geographic information capabilities the principal feature of VTrack is the qualification and identification of ecologically relevant events from the acoustic detection and sensor data. This procedure condenses the acoustic detection database by orders of magnitude, greatly enhancing the synthesis of acoustic detection data.
A 'data.frame' processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner.
    'vtreat' prepares variables so that data has fewer exceptional cases, making
    it easier to safely use models in production. Common problems 'vtreat' defends
    against: 'Inf', 'NA', too many categorical levels, rare categorical levels, and new
    categorical levels (levels seen during application, but not during training). Reference: 
    "'vtreat': a data.frame Processor for Predictive Modeling", 'Zumel', 'Mount', 2016, DOI:10.5281/zenodo.1173314.
A tool for calculating and drawing "variable trees". Variable trees display information about hierarchical subsets of a data frame defined by values of categorical variables.
Contains functions for visualization univariate data: ccdplot and qddplot.
Gaze data from the Visual World Paradigm requires significant
    preprocessing prior to plotting and analyzing the data. This package 
    provides functions for preparing visual world eye-tracking data for 
    statistical analysis and plotting. It can prepare data for linear 
    analyses (e.g., ANOVA, Gaussian-family LMER, Gaussian-family GAMM) as
    well as logistic analyses (e.g., binomial-family LMER and binomial-family GAMM).
    Additionally, it contains various plotting functions for creating grand average and
    conditional average plots. See the vignette for samples of the functionality.
    Currently, the functions in this package are designed for handling data
    collected with SR Research Eyelink eye trackers using Sample Reports created
    in SR Research Data Viewer.  While we would like to add functionality 
    for data collected with other systems in the future, the current package is 
    considered to be feature-complete; further updates will mainly entail maintenance
	and the addition of minor functionality.
Functions and data for use in visual word recognition research: 
	 Computation of neighbors (Hamming and Levenshtein
	 distances), average distances to neighbors (e.g., OLD20),
	 and Coltheart's N. Also includes the LD1NN algorithm to
	 detect bias in the composition of a lexical decision task. Most of
	 the functions support parallel execution. Supplies wordlists for 
	 several languages. Uses the string distance functions from the stringdist package by Mark van der Loo.
Set of functions that improves the graphical presentations of the functions 'wave.correlation' and 'spin.correlation' (waveslim package, Whitcher 2012) and the 'wave.multiple.correlation' and 'wave.multiple.cross.correlation' (wavemulcor package, Fernandez-Macho 2012b). The plot outputs (heatmaps) can be displayed in the screen or can be saved as PNG or JPG images or as PDF or EPS formats. The W2CWM2C package also helps to handle the (input data) multivariate time series easily as a list of N elements (times series) and provides a multivariate data set (dataexample) to exemplify its use. A description of the package was published in Computing in Science & Engineering (Volume:16, Issue: 6) on Sep. 09, 2014, doi:10.1109/MCSE.2014.96.
R interface to a W3C Markup Validation service.
  See <http://validator.w3.org/> for more information.
Downloads and tidies Aswath Damodaran's Cost of Capital Data.
Multivariate weather generator for daily climate variables based 
    on weather-states using a Markov chain for modeling the succession of weather states. 
    Conditionally to the weather states, the multivariate variables are modeled using the family 
    of Complete Skew-Normal distributions. Parameters are estimated on measured series. Data must 
    include the variable 'Rain' and can accept as many other variables as desired.
waffect (pronounced 'double-u affect' for 'weighted
        affectation') is a package to simulate phenotypic (case or
        control) datasets under a disease model H1 such that the total
        number of cases is constant across all the simulations (the
        constrain in the title). The package also makes it possible to
        generate phenotypes in the case of more than two classes, so
        that the number of phenotypes belonging to each class is
        constant across all the simulations. waffect is used to assess
        empirically the statistical power of Genome Wide Association
        studies.
Square pie charts (a.k.a. waffle charts) can be used
    to communicate parts of a whole for categorical quantities. To emulate the
    percentage view of a pie chart, a 10x10 grid should be used with each square
    representing 1% of the total. Modern uses of waffle charts do not
    necessarily adhere to this rule and can be created with a grid of any
    rectangular shape. Best practices suggest keeping the number of categories
    small, just as should be done when creating pie charts. Tools are provided
    to create waffle charts as well as stitch them together, and to use glyphs
    for making isotype pictograms.
Fit the fixed effect panel data model with heteroskedasticity and
    autocorrelation correction.
Full screen splash loading screens for 'Shiny'.
Generates random data sets including: data.frames, lists,
        and vectors.
A wrapper for the 'walkalytics' API (see <https://dev.walkalytics.com/>), which is a web service
    that calculates walking isochrones for source locations.  Basic, limited API access is free.
Bayesian dynamic regression models where the regression 
    coefficients can vary over time as random walks. 
    Gaussian, Poisson, and binomial observations are supported. 
    The Markov chain Monte Carlo computations are done using 
    Hamiltonian Monte Carlo provided by Stan, using a state space representation 
    of the model in order to marginalise over the coefficients for efficient sampling. 
    For non-Gaussian models, walker uses the importance sampling type estimators based on 
    approximate marginal MCMC as in Vihola, Helske, Franks (2017, <arXiv:1609.02541>).
A collection of functions to perform the Application
        Programming Interface (API) calls associated with the Walk
        Score website (www.walkscore.com) within the R environment.
        These functions can be used to query the Walk Score and Transit
        Score database for a wide variety of information using R
        scripts. This package includes the simple Walk Score and
        Transit Score API calls, which return the scores associated
        with an input location, as well as calls which return some data
        used to calculate the scores. These functions are especially
        useful for mass data collection and gathering Walk Score and
        Transit Score values for large lists of locations.
The 'shiny' application 'wallace' is a modular platform for reproducible modeling of species niches and distributions. 'wallace' guides users through a complete analysis, from the acquisition of species occurrence and environmental data to visualizing model predictions on an interactive map, thus bundling complex workflows into a single, streamlined interface.
A prediction model is calibrated if, roughly, for any percentage x we can expect that x subjects out of 100 experience the event among all subjects that have a predicted risk of x%. A calibration plot provides a simple, yet useful, way of assessing the calibration assumption. The Wally plot consists of a sequence of usual calibration plots. Among the plots contained within the sequence, one is the actual calibration plot which has been obtained from the data and the others are obtained from similar simulated data under the calibration assumption. It provides the investigator with a direct visual understanding of the shape and sampling variability that are common under the calibration assumption. The original calibration plot from the data is included randomly among the simulated calibration plots, similarly to a police lineup. If the original calibration plot is not easily identified then the calibration assumption is not contradicted by the data. The method handles the common situations in which the data contain censored observations and occurrences of competing events.
Provides API access to the Walmart Open API <https://developer.walmartlabs.com/>, 
    that contains data about stores, Value of the day and products which 
    includes names, sale prices, shipping rates and taxonomies.
A toolbox of common robust statistical tests, including robust
  descriptives, robust t-tests, and robust ANOVA. It is also available as a
  module for 'jamovi' (see <https://www.jamovi.org> for more information).
  Walrus is based on the WRS2 package by Patrick Mair, which is in turn based on
  the scripts and work of Rand Wilcox. These analyses are described in depth in
  the book 'Introduction to Robust Estimation & Hypothesis Testing'.
The outcome of various rehabilitation strategies for water distribution systems can be modeled with the Water Management Simulator (WaMaSim). Pipe breaks and the corresponding damage and rehabilitation costs are simulated. It is mainly intended to be used as educational tool for the Water Infrastructure Experimental and Computer Laboratory at ETH Zurich, Switzerland.
The 'libmagic' library provides functions to determine
    'MIME' type and other metadata from files through their "magic"
    attributes. This is useful when you do not wish to rely solely on
    the honesty of a user or the extension on a file name. It also
    incorporates other metadata from the mime-db database
    <https://github.com/jshttp/mime-db>.
A tool to streamline the analysis of animal acoustic signal structure. The package offers functions for downloading avian vocalizations from the open-access online repository Xeno-Canto <https://www.xeno-canto.org>, manipulating sound files, detecting acoustic signals, assessing performance of methods that measure acoustic similarity, conducting cross-correlations, dynamic time warping, measuring acoustic parameters and analysing interactive vocal signals, among others. Most functions working iteratively allow parallelization to improve computational efficiency.
This estimates precise weaning ages
	for a given skeletal population
	by analyzing the stable nitrogen isotope ratios of them.
	Bone collagen turnover rates estimated anew and
	the approximate Bayesian computation (ABC)
	were adopted in this package.
Mixed effects modeling with warping for functional data using B-
    spline. Warping coefficients are considered as random effects, and warping
    functions are general functions, parameters representing the projection onto B-
    spline basis of a part of the warping functions. Warped data are modelled by a
    linear mixed effect functional model, the noise is Gaussian and independent from
    the warping functions.
Urban water and sanitation survey dataset collected by Water and
    Sanitation for the Urban Poor (WSUP) with technical support from 
    Valid International. These citywide surveys have been collecting data 
    allowing water and sanitation service levels across the entire city to be 
    characterised, while also allowing more detailed data to be collected in 
    areas of the city of particular interest. These surveys are intended to 
    generate useful information for others working in the water and sanitation
    sector. Current release version includes datasets collected from a survey 
    conducted in Dhaka, Bangladesh in March 2017. This survey in Dhaka is one of 
    a series of surveys to be conducted by WSUP in various
    cities in which they operate including Accra, Ghana; Nakuru, Kenya; 
    Antananarivo, Madagascar; Maputo, Mozambique; and, Lusaka, Zambia. This 
    package will be updated once the surveys in other cities are completed and 
    datasets have been made available.
Time series outlier detection by mean of non parametric test. Outlier detection regarding two methodologies: single time series variability (a vector) and grouped similar time series (a data frame). Andrea Venturini(2011) Statistica-Universita' Bologna, Vol.71, pp.329-344.
Helpful tools for data processing and visualisation of results of the hydrological model WASIM-ETH.
Tools and functions to calculate actual Evapotranspiration
    using surface energy balance models.
Imports U.S. Geological Survey (USGS) daily hydrologic data from USGS web services (see <https://waterservices.usgs.gov/> for more information), plots the data, addresses some common data problems, and calculates and plots anomalies.
Provides support for creating waterfall charts in R
    using both traditional base and lattice graphics.
A not uncommon task for quants is to create 'waterfall charts'. There seems to be no simple way to do this in 'ggplot2' currently. This package contains a single function (waterfall) that simply draws a waterfall chart in a 'ggplot2' object. Some flexibility is provided, though often the object created will need to be modified through a theme.
Lets you connect to any of the Consortium of Universities for the Advancement
    of Hydrologic Sciences, Inc. ('CUAHSI') Water Data Center 'WaterOneFlow' web services
    and read any 'WaterML' hydrological time series data file. To see list of available
    web services, see <http://hiscentral.cuahsi.org>. All versions of 'WaterML'
    (1.0, 1.1 and 2.0) and both types of the web service protocol ('SOAP' and 'REST') are supported.
    The package has six data download functions: GetServices(): show all public web
    services from the HIS Central Catalog. HISCentral_GetSites() and HISCentral_GetSeriesCatalog():
    search for sites or time series from the HIS Central catalog based on geographic bounding box,
    server, or keyword. GetVariables(): Show a data.frame with all variables on the server.
    GetSites(): Show a data.frame with all sites on the server. GetSiteInfo(): Show what variables,
    methods and quality control levels are available at the specific site. GetValues(): Given a site
    code, variable code, start time and end time, fetch a data.frame of all the observation time
    series data values. The GetValues() function can also parse 'WaterML' data from a custom URL or
    from a local file. The package also has five data upload functions:
    AddSites(), AddVariables(), AddMethods(), AddSources(), and AddValues().
    These functions can be used for uploading data to a 'HydroServer Lite' Observations
    Data Model ('ODM') database via the 'JSON' data upload web service interface.
The goal of 'waterquality' is to convert satellite-reflectance imagery
    to a host of pre-defined water quality algorithms designed for the detection of 
    chlorophyll-a, blue-green algae (Phycocyanin), and turbidity. 
    This package is able to process the following sensor configurations: 
    WorldView-2, Sentinel-2, Landsat-8, MODIS, and MERIS.
Methods for watersheds aggregation and spatial drainage network analysis.
Provides Water Year Hydrologic Classification Indices based on measured 
    unimpaired runoff (in million acre-feet). Data is provided by California Department of Water Resources
    and subject to revision.
Wrap-around Time Series (WATS) plots for interrupted time series
    designs with seasonal patterns.
Computes Bayesian wavelet shrinkage credible intervals for
	nonparametric regression.
	The method uses cumulants to derive Bayesian credible intervals for
	wavelet regression estimates.
	The first four cumulants of the posterior distribution of the
	estimates are expressed in terms of the observed data and integer
	powers of the mother wavelet functions.
	These powers are closely approximated by linear combinations of
	wavelet scaling functions at an appropriate finer scale.
	Hence, a suitable modification of the discrete wavelet transform allows
	the posterior cumulants to be found efficiently for any data set.
	Johnson transformations then yield the credible intervals themselves.
	Barber, S., Nason, G.P. and Silverman, B.W. (2002)
	<doi:10.1111/1467-9868.00332>.
Makes available code necessary to reproduce figures and
        tables in papers on the WaveD method for wavelet deconvolution
        of noisy signals as presented in The WaveD Transform in R,
        Journal of Statistical Software Volume 21, No. 3, 2007.
A wealth of Full Waveform (FW) Light Detection and Ranging (LiDAR) data are available to 
	the public from different sources, which is poised to boost the extensive 
	application of FW LiDAR data. However, we lack a handy and open source tool 
	that can be used by ecological and remote sensing communities for processing and analyzing FW LiDAR 
	data. To this end, we introduce 'waveformlidar', an R package dedicated to 
	FW LiDAR processing, analysis and visualization as a solution to the constraint.
	Specifically, this package provides several commonly used waveform processing methods
	such as Gaussian, adaptive Gaussian and Weibull decompositions, and 
	deconvolution approaches (Gold and Richard-Lucy (RL)) with customized settings. 
	In addition, we also develop some functions to derive commonly used waveform metrics
	for characterizing vegetation structure. Moreover, a new way to directly visualize 
	FW LiDAR data is developed through converting waveforms into points to form the Hyper
	Point cloud (HPC), which can be easily adopted and subsequently analyzed with existing 
	discrete-return LiDAR processing tools such as 'LAStools' and 'FUSION'. Basic explorations of 
	the HPC such as 3D voxelization of the HPC and conversion from original waveforms to 
	composite waveforms are also available in this package. All of these functions are 
	developed based on small-footprint FW LiDAR data, but they can be easily transplanted to
	the large footprint FW LiDAR data such as Geoscience Laser Altimeter System (GLAS) 
	and Global Ecosystem Dynamics Investigation (GEDI) data analysis. References: Zhou et al. (2017a) <doi:10.1016/j.isprsjprs.2017.04.021>;
	Zhou et al. (2017b) <doi:10.1016/j.rse.2017.08.012>; Zhou et al. (2018a) <doi:10.3390/rs10010039>;Zhou et al. (2018b) <doi:10.3390/rs10121949>.
Real probability distributions can be represented as the square of
    an orthogonal sum in the Hermite basis. This representation is formally
    similar to the representation of quantum mechanical states as wave
    functions, whose squared modulus is a probability density. This is
    described in more detail in "Wave function representation of probability
    distributions," by Madeleine B. Thompson <arXiv:1712.07764>. This package
    provides a reference implementation of the technique.
Fits hybrid Wavelet ANN model for time series forecasting using algorithm by Anjoy and Paul (2017) <DOI: 10.1007/s00521-017-3289-9>.
Fits hybrid Wavelet ARIMA model for time series forecasting using algorithm by Aminghafari and Poggi (2012) <doi:10.1142/S0219691307002002>.
Wavelet analysis and reconstruction of time series, cross-wavelets and phase-difference (with filtering options), significance with simulation algorithms.
Estimation of the long memory parameter using wavelets. Other estimation techniques like 
             GPH (Geweke and Porter-Hudak,1983, <DOI:10.1111/j.1467-9892.1983.tb00371.x>) 
             and Semiparametric methods(Robinson, P. M.,1995, <DOI:10.1214/aos/1176324317>) also have included.
Contains functions for computing and plotting
        discrete wavelet transforms (DWT) and maximal overlap discrete
        wavelet transforms (MODWT), as well as their inverses.
        Additionally, it contains functionality for computing and
        plotting wavelet transform filters that are used in the above
        decompositions as well as multiresolution analyses.
Wavelet routines that calculate single sets of 
             wavelet multiple correlations (WMC) and cross-correlations (WMCC)
             out of n variables. 
             They can later be plotted in single graphs, as an alternative to trying 
             to make sense out of several sets of wavelet correlations or 
             wavelet cross-correlations. 
             The code is based on the calculation, at each wavelet scale, of the 
             square root of the coefficient of determination in a linear combination 
             of variables for which such coefficient of determination is a maximum. 
             The code provided here is based on the wave.correlation routine in 
             Brandon Whitcher's waveslim R package Version: 1.6.4, which in turn is 
             based on wavelet methodology developed in Percival and Walden (2000) <DOI:10.1017/CBO9780511841040>; 
             Gençay, Selçuk and Whitcher (2002) <DOI:10.1016/B978-012279670-8.50013-6> and others.
             Version 2 incorporates wavelet local multiple correlations (WLMC).
             These are like the previous global WMC but consisting in one
             single set of multiscale correlations along time. That is, at each time
             t, they are calculated by letting a window of weighted wavelet 
             coefficients around t move along time. Six weight functions are provided.
             Namely, the  uniform window, Cleveland's tricube window, Epanechnikov's 
             parabolic window, Bartlett's triangular window and Wendland's truncated 
             power window and the Gaussian window.
             Version 2.2 incorporates an auxiliary function that calculates local 
             multiple correlations (LMC). They are calculated by letting move along time 
             a window of weighted time series values around t. Any of the six weight 
             functions mentioned above can be used.
Functions for calculating the
    fetch (length of open water distance along given directions)
    and estimating wave energy from wind and wave monitoring data.
For multivariate datasets, this function enables the estimation of missing data using the Weighted AVERage of all possible Regressions using the data available.
Basic wavelet routines for time series (1D), image (2D) 
  and array (3D) analysis.  The code provided here is based on
  wavelet methodology developed in Percival and Walden (2000);
  Gencay, Selcuk and Whitcher (2001); the dual-tree complex wavelet
  transform (DTCWT) from Kingsbury (1999, 2001) as implemented by
  Selesnick; and Hilbert wavelet pairs (Selesnick 2001, 2002).  All
  figures in chapters 4-7 of GSW (2001) are reproducible using this 
  package and R code available at the book website(s) below.
Performs 1, 2 and 3D real and complex-valued wavelet transforms,
	nondecimated transforms, wavelet packet transforms, nondecimated
	wavelet packet transforms, multiple wavelet transforms,
	complex-valued wavelet transforms, wavelet shrinkage for
	various kinds of data, locally stationary wavelet time series,
	nonstationary multiscale transfer function modeling, density
	estimation.
Provides scalogram based wavelet tools for time series analysis: wavelet power spectrum, scalogram, windowed scalogram, windowed scalogram difference (see Bolos et al. (2017) <doi:10.1016/j.amc.2017.05.046>), scale index and windowed scale index (Benitez et al. (2010) <doi:10.1016/j.camwa.2010.05.010>).
Supplies bootstrap alternatives to traditional hypothesis-test
  and confidence-interval procedures such as one-sample and two-sample
  inferences for means, medians, standard deviations, and proportions; simple
  linear regression; and more. Suitable for general audiences, including
  individual and group users, introductory statistics courses, and more advanced
  statistics courses that desire an introduction to bootstrap methods.
Provides efficient implementation of the Wild Binary Segmentation and Binary
    Segmentation algorithms for estimation of the number and locations of
    multiple change-points in the piecewise constant function plus Gaussian
    noise model.
Tools for searching and downloading data and statistics from
  the World Bank Data API (<http://data.worldbank.org/developers/api-overview>)
  and the World Bank Data Catalog API (<http://data.worldbank.org/developers/data-catalog-api>).
Implements detection for the number and locations of
    the change-points in a time series using the Wild Binary Segmentation and
    the Locally Stationary Wavelet model.
A flexible method for modeling cumulative effects of time-varying exposures, weighted according to their relative proximity in time, and represented by time-dependent covariates. The current implementation estimates the weight function in the Cox proportional hazards model. The function that assigns weights to doses taken in the past is estimated using cubic regression splines.
Retrieval the leaf area index (LAI) and soil moisture (SM) from  microwave backscattering data using water cloud model (WCM) model . The WCM algorithm attributed to Pervot et al.(1993) <doi:10.1016/0034-4257(93)90053-Z>. The authors are grateful to SAC, ISRO, Ahmedabad for providing financial support to Dr. Prashant K Srivastava to conduct this research work.
Calculates Pearson, Spearman, polychoric, and polyserial correlation coefficients, in weighted or unweighted form. The package implements tetrachoric correlation as a special case of the polychoric and biserial correlation as a specific case of the polyserial.
The package contains the WCQ method for detection of QTL
        effects in a small mapping population. It also contains
        implementation of the Chen-Qin two-sample and one-sample test
        of means.
Search, extract and format data from the World Bank's
    World Development Indicators.
Provides efficient implementations of weighted dependence measures
  and related asymptotic tests for independence. Implemented measures are
  the Pearson correlation, Spearman's rho, Kendall's tau, Blomqvist's beta, and
  Hoeffding's D; see, e.g., Nelsen (2006) <doi:10.1007/0-387-28678-0> and
  Hollander et al. (2015, ISBN:9780470387375).
There are a number of binary files associated with the
    'Webdriver'/'Selenium' project (see <http://www.seleniumhq.org/download/>,
    <https://sites.google.com/a/chromium.org/chromedriver/>,
    <https://github.com/mozilla/geckodriver>,
    <http://phantomjs.org/download.html> and
    <https://github.com/SeleniumHQ/selenium/wiki/InternetExplorerDriver> for
    more information). This package provides functions to download these
    binaries and to manage processes involving them.
Fetch and clean data from the World Database on Protected
    Areas (WDPA). Data is obtained from Protected Planet
    <http://protectedplanet.net>.
Provides means for downloading historical weather data from 
    the Environment and Climate Change Canada website 
    (<http://climate.weather.gc.ca/historical_data/search_historic_data_e.html>). 
    Data can be downloaded from multiple stations and over large date ranges 
    and automatically processed into a single dataset. Tools are also provided 
    to identify stations either by name or proximity to a location.
Functions to convert between weather metrics, including conversions
    for metrics of temperature, air moisture, wind speed, and precipitation.
    This package also includes functions to calculate the heat index from
    air temperature and air moisture.
Handle instant weather forecasts and geographical information. It combines multiple sources of information to obtain instant weather forecasts.
Chemical information from around the web. This package interacts
    with a suite of web APIs for chemical information.
Freely available online differential-diagnosis generating tools are
    changing clinical medicine and biomedical research. With webddx, useRs can
    generate differential diagnosis lists given a set of symptoms. The web tools
    would likely be directly used in clinical practice, but programmatic interaction
    and data manipulation can sharply increase efficiency and reproducibility
    of research in clinical informatics. Relevant visualizations can also be
    created with webddx.
A client for the 'WebDriver' 'API'. It allows driving a
    (probably headless) web browser, and can be used to test web
    applications, including 'Shiny' apps. In theory it works with any
    'WebDriver' implementation, but it was only tested with 'PhantomJS'.
Functions for easily creating interactive web pages using
    'R Markdown' that students can use in self-guided learning.
The web version WebGestalt <http://www.webgestalt.org> supports 12 organisms, 354 gene identifiers and 321,251 function categories. Users can upload the data and functional categories with their own gene identifiers. In addition to the Over-Representation Analysis, WebGestalt also supports Gene Set Enrichment Analysis and Network Topology Analysis. The user-friendly output report allows interactive and efficient exploration of enrichment results. The WebGestaltR package not only supports all above functions but also can be integrated into other pipeline or simultaneously analyze multiple gene lists.
Displays geospatial data on an interactive 3D globe in the web browser.
Stubbing and setting expectations on 'HTTP' requests.
    Includes tools for stubbing 'HTTP' requests, including expected
    request conditions and response conditions. Match on
    'HTTP' method, query parameters, request body, headers and
    more. Can be used for unit tests or outside of a testing 
    context.
Lossless webp images are 26% smaller in size compared to PNG. Lossy
    webp images are 25-34% smaller in size compared to JPEG. This package reads
    and writes webp images into a 3 (rgb) or 4 (rgba) channel bitmap array using
    conventions from the 'jpeg' and 'png' packages.
This is a collection of tools for conducting both basic and advanced statistical power analysis including correlation, proportion, t-test, one-way ANOVA, two-way ANOVA, linear regression, logistic regression, Poisson regression, mediation analysis, longitudinal data analysis, structural equation modeling and multilevel modeling. It also serves as the engine for conducting power analysis online at <https://webpower.psychstat.org>.
Several analysis-related functions for the book entitled 
    "Web-based Analysis without R in Your Computer"(written in Korean, ISBN 978-89-5566-185-9) 
    by Keon-Woong Moon. The main function plot.htest() shows the distribution of statistic for 
    the object of class 'htest'.
R is used by a vast array of people for a vast array of purposes
    - including web analytics. This package contains functions for consuming and
    munging various common forms of request log, including the Common and Combined
    Web Log formats and various Amazon access logs.
Functions that allow for accessing domains and a number of search engines.
Takes screenshots of web pages, including Shiny applications and R
    Markdown documents.
Provides a 'WebSocket' client interface for R.
    'WebSocket' is a protocol for low-overhead real-time communication:
    <https://en.wikipedia.org/wiki/WebSocket>.
Provides functions to query data from the 'WebTRIS' Traffic Flow API (from Highways England) into tidy data frames.
    The API documentation is available here: <http://webtris.highwaysengland.co.uk/api/swagger/ui/index>.
A Stata-style `webuse()` function for importing named datasets from Stata's online collection.
High performance in-memory http request parser for application/json, 
    multipart/form-data, and application/x-www-form-urlencoded. Includes live demo
    of hosting and parsing multipart forms with either 'httpuv' or 'Rhttpd'.
Provides functions to create factor variables with contrasts based on weighted effect coding, and their interactions. In weighted effect coding the estimates from a first order regression model show the deviations per group from the sample mean. This is especially useful when a researcher has no directional hypotheses and uses a sample from a population in which the number of observation per group is different.
Western Electric Company Rules (WECO) have been widely used for
    Shewhart control charts in order to increase the sensitivity of detecting
    assignable causes of process change. This package implements eight commonly
    used WECO rules and allow to apply the combination of these individual rules
    for detecting the deviation from a stable process. The package also provides
    a web-based graphical user interface to help users conduct the analysis.
Provides functionality for working with differentials,
           k-forms, wedge products, Stokes's theorem, and related concepts
	   from the exterior calculus.  The canonical reference would be:
	   M. Spivak (1965, ISBN:0-8053-9021-9). "Calculus on Manifolds",
	   Benjamin Cummings.
Secondary analysis of case-control studies using a weighted estimating equation (WEE) approach: logistic regression for binary secondary outcomes, linear regression and quantile regression for continuous secondary outcomes.
Performs a goodness-of-fit test of Weibull distribution (weibullness test). For more details, see Park (2017) <http://journals.sfu.ca/ijietap/index.php/ijie/article/view/2848>. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. NRF-2017R1A2B4004169).
Life data analysis in the graphical tradition of Waloddi Weibull. Methods derived from Robert B. Abernethy (2008, ISBN 0-965306-3-2), Wayne Nelson (1982, ISBN: 9780471094586) <DOI:10.1002/0471725234>, William Q. Meeker and  Lois A. Escobar (1998, ISBN: 1-471-14328-6), John I. McCool, (2012, ISBN: 9781118217986) <DOI:10.1002/9781118351994>.
Weighted descriptive statistics is the discipline of quantitatively describing the main features of real-valued fuzzy data which usually given from a fuzzy population. One can summarize this special kind of fuzzy data numerically or graphically using this package. To interpret some of the properties of one or several sets of real-valued fuzzy data, numerically summarize is possible by some weighted statistics which are designed in this package such as mean, variance, covariance and correlation coefficent. Also, graphically interpretation can be given by weighted histogram and weighted scatter plot using this package to describe properties of real-valued fuzzy data set.
Clusters state sequences and weighted data. It provides an optimized weighted PAM algorithm as well as functions for aggregating replicated cases, computing cluster quality measures for a range of clustering solutions and plotting clusters of state sequences.
This packages contains the Weighted Portmanteau Tests as
        described in "New Weighted Portmanteau Statistics for Time
        Series Goodness-of-Fit Testing' accepted for publication by the
        Journal of the American Statistical Association.
Fast computation of
 Receiver Operating Characteristic (ROC) curves
 and Area Under the Curve (AUC)
 for weighted binary classification problems
 (weights are example-specific cost values).
Has functions to implement the weighted scores method and CL1 information criteria as an intermediate step for variable/correlation selection for longitudinal categorical and count data in Nikoloulopoulos, Joe and Chaganty (2011, Biostatistics, 12: 653-665) and Nikoloulopoulos (2015a,2015b).
Generates weights to form equivalent groups in observational studies by easing and extending the functionality of the R packages 'twang' (Ridgeway et al., 2017) <https://CRAN.R-project.org/package=twang> for generalized boosted modeling, 'CBPS' (Fong, Ratkovic, & Imai, 2018) <https://CRAN.R-project.org/package=CBPS> for covariate balancing propensity score weighting, 'ebal' (Hainmueller, 2014) <https://CRAN.R-project.org/package=ebal> for entropy balancing, 'optweight' (Greifer, 2018) <https://CRAN.R-project.org/package=optweight> for optimization-based weights, 'ATE' (Haris & Chan, 2015) <https://CRAN.R-project.org/package=ATE> for empirical balancing calibration weighting, and SuperLearner (Polley, LeDell, Kenney, & van der Laan, 2018) <https://CRAN.R-project.org/package=SuperLearner>. Also allows for assessment of weights and checking of covariate balance by interfacing directly with 'cobalt' (Greifer, 2018) <https://CRAN.R-project.org/package=cobalt>.
Estimation of observation-specific weights for incomplete longitudinal data and bootstrap procedure for weighted quantile regressions.
Estimates the Vevea and Hedges (1995)
    weight-function model. By specifying arguments, users can
    also estimate the modified model described in Vevea and Woods (2005), 
    which may be more practical with small datasets. Users 
    can also specify moderators to estimate a linear model. 
    The package functionality allows users to easily extract the 
    results of these analyses as R objects for other uses. In addition, 
    the package includes a function to launch both models as 
    a Shiny application. Although the Shiny application is also available online, 
    this function allows users to launch it locally if they choose.
Provides a variety of functions for producing simple weighted statistics, such as weighted Pearson's correlations, partial correlations, Chi-Squared statistics, histograms, and t-tests.  Also now includes some software for quickly recoding survey data and plotting point estimates from interaction terms in regressions (and multiply imputed regressions). NOTE: Weighted partial correlation calculations pulled to address a bug.
The weightTAPSPACK subsets The American Panel Survey (TAPS) data by outcome and covariates, models the attrition rates, imputes data for attrited individuals, and finds weights for analysis.
Provides computational support for flow over weirs, such as sharp-crested, broad-crested, and embankments. Initially, the package supports broad- and sharp-crested weirs.
Implementation of Johansen's general formulation of Welch-James's statistic with Approximate Degrees of Freedom, which makes it suitable for testing 
    any linear hypothesis concerning cell means in univariate and multivariate mixed model designs when the data pose non-normality and non-homogeneous variance. Some 
    improvements, namely trimmed means and Winsorized variances, and bootstrapping for calculating an empirical critical value, have been added to the classical formulation. 
    The code departs from a previous SAS implementation by L.M. Lix and H.J. Keselman, available at <http://supp.apa.org/psycarticles/supplemental/met_13_2_110/SAS_Program.pdf> and
    published in Keselman, H.J., Wilcox, R.R., and Lix, L.M. (2003) <DOI:10.1111/1469-8986.00060>.
Convert 'WKT' to 'GeoJSON' and 'GeoJSON' to 'WKT'. Functions
    included for converting between 'GeoJSON' to 'WKT', creating both
    'GeoJSON' features, and non-features, creating 'WKT' from R objects
    (e.g., lists, data.frames, vectors), and linting 'WKT'.
Run mixed-effects models that include weights at every level. The WeMix package fits a weighted mixed model, also known as a multilevel, mixed, or hierarchical linear model (HLM). The weights could be inverse selection probabilities, such as those developed for an education survey where schools are sampled probabilistically, and then students inside of those schools are sampled probabilistically. Although mixed-effects models are already available in R, WeMix is unique in implementing methods for mixed models using weights at multiple levels. Both linear and logit models are supported. Models may have up to three levels.
Palettes generated mostly from 'Wes Anderson' movies.
The distributions of the weight of evidence (log Bayes factor) favouring case over noncase status in a test dataset (or test folds generated by cross-validation) can be used to quantify the performance of a diagnostic test (McKeigue (2018), <doi:10.1177/0962280218776989>). The package can be used with any test dataset on which you have observed case-control status and have computed prior and posterior probabilities of case status using a model learned on a training dataset. To quantify how the predictor will behave as a risk stratifier, the quantiles of the distributions of weight of evidence in cases and controls can be calculated and plotted.
Provides a computationally efficient way of fitting
	     weighted linear fixed effects estimators for causal
	     inference with various weighting schemes. Weighted linear
	     fixed effects estimators can be used to estimate the
	     average treatment effects under different identification
	     strategies. This includes stratified randomized
	     experiments, matching and stratification for
	     observational studies, first differencing, and
	     difference-in-differences. The package implements methods
	     described in Imai and Kim (2017) "When should We Use
	     Linear Fixed Effects Regression Models for Causal
	     Inference with Longitudinal Data?", available at
	     <https://imai.fas.harvard.edu/research/FEmatch.html>.
Implementation of Weighted Fast Greedy algorithm for community detection in networks with mixed types of attributes.
Provides a large English words list and tools to find words by patterns. In particular, anagram finder and scrabble word finder.
Integrates sophisticated mixed modelling methods with a whole genome approach to detecting significant QTL in linkage maps.
Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data as originally described in Horvath and Zhang (2005) <doi:10.2202/1544-6115.1128> and Langfelder and Horvath (2008) <doi:10.1186/1471-2105-9-559>. Includes functions for rudimentary data cleaning, construction of correlation networks, module identification, summarization, and relating of variables and modules to sample traits. Also includes a number of utility functions for data manipulation and visualization.
Weighted generalized estimating equations (WGEE) is an extension of generalized linear models to longitudinal clustered data by incorporating the correlation within-cluster when data is missing at random (MAR). The parameters in mean, scale correlation structures are estimated based on quasi-likelihood. Multiple model selection criterion are provided for selection of mean model and working correlation structure based on WGEE/GEE.
Functions for the analysis of whole-genome sequencing studies to simultaneously detect the existence, and estimate the locations of association signals at genome-wide scale. The functions allow genome-wide association scan, candidate region scan and single window test.
Functions for determining the effect of data weights on the variance of survey data: users will load a data set which has a weights column, and the package will calculate the design effect (DEFF), weighting loss, root design effect (DEFT), effective sample size (ESS), and/or weighted margin of error.
Inferences about counterfactuals are essential for prediction,
      answering what if questions, and estimating causal effects.
      However, when the counterfactuals posed are too far from the data at
      hand, conclusions drawn from well-specified statistical analyses
      become based largely on speculation hidden in convenient modeling
      assumptions that few would be willing to defend. Unfortunately,
      standard statistical approaches assume the veracity of the model
      rather than revealing the degree of model-dependence, which makes this
      problem hard to detect. WhatIf offers easy-to-apply methods to
      evaluate counterfactuals that do not require sensitivity testing over
      specified classes of models. If an analysis fails the tests offered
      here, then we know that substantive inferences will be sensitive to at
      least some modeling choices that are not based on empirical evidence,
      no matter what method of inference one chooses to use. WhatIf
      implements the methods for evaluating counterfactuals discussed in
      Gary King and Langche Zeng, 2006, "The Dangers of Extreme
      Counterfactuals," Political Analysis 14 (2) <DOI:10.1093/pan/mpj004>; 
      and Gary King and Langche Zeng, 2007, "When Can History Be Our Guide? The 
      Pitfalls of Counterfactual Inference," International Studies 
      Quarterly 51 (March) <DOI:10.1111/j.1468-2478.2007.00445.x>.
Builds complex plots, heatmaps in particular, using natural semantics. Bigger plots can be assembled using directives such as 'LeftOf', 'RightOf', 'TopOf', and 'Beneath' and more. Other features include clustering, dendrograms and integration with 'ggplot2' generated grid objects. This package is particularly designed for bioinformaticians to assemble complex plots for publication.
Robust and reliable functions to return informative outputs to 
        console with the run or source location of a command. This can be from
        the  'RScript'/R terminal commands or 'RStudio' console, source editor, 
        'Rmarkdown' document and a Shiny application.
Retrieve geographical information for airports using their IATA or ICAO codes.
White Box Cluster Algorithm Design allows you to create Representative based cluster algorithm by using reusable components. 
     This way one can recreate already available cluster algorithms (i.e. K-Means, K-Means++, PAM) 
     but also create new cluster algorithms not available in the literature or any other software. 
     For more information see papers <doi:10.1007/s10462-009-9133-6> and <doi:10.1016/j.datak.2012.03.005>.
logicless templating, reuse templates in many programming
        languages including R
Provides a set of functions to make tracking the hidden movements 
  of the 'Jack' player easier. By tracking every possible path Jack might have 
  traveled from the point of the initial murder including special movement such 
  as through alleyways and via carriages, the police can more accurately narrow 
  the field of their search. Additionally, by tracking all possible hideouts from 
  round to round, rounds 3 and 4 should have a vastly reduced field of search.
Implements the whitening methods (ZCA, PCA, Cholesky,
 ZCA-cor, and PCA-cor) discussed in Kessy, Lewin, and Strimmer (2018)
 "Optimal whitening and decorrelation", <doi:10.1080/00031305.2016.1277159>,
 as well as the whitening approach to canonical correlation analysis allowing
 negative canonical correlations described in Jendoubi and Strimmer (2019)
 "A whitening approach to probabilistic canonical correlation analysis for omics
 data integration", <doi:10.1186/s12859-018-2572-9>.
Shinohara (2014) <DOI:10.1016/j.nicl.2014.08.008>
    introduced 'WhiteStripe', an intensity-based normalization of T1 
    and T2 images, where normal 
    appearing white matter performs well, but requires segmentation.
    This method performs white matter mean and standard deviation
    estimates on data that has been rigidly-registered to the 'MNI'
    template and uses histogram-based methods.
Provides programmatic access to the World Health Organization API.
Look up the username and full name of the current user,
    the current user's email address and 'GitHub' username,
    using various sources of system and configuration information.
Retrieve data from the 'Whoapi' (https://whoapi.com) store of
    domain information, including a domain's geographic location, registration
    status and search prominence.
Provides very fast access to whole genome, population scale variation data
	from VCF files and sequence data from FASTA-formatted files.
	It also reads in alignments from FASTA, Phylip, MAF and other file formats.
	Provides easy-to-use interfaces to genome annotation from UCSC and Bioconductor and gene ontology data
	from AmiGO and is capable to read, modify and write PLINK .PED-format pedigree files.
Utilities to generate bounding boxes from 'WKT' (Well-Known Text) objects and R data types, validate
             'WKT' objects and convert object types from the 'sp' package into 'WKT' representations.
Fit, forecast, predict massive spacio-temporal data
Extends the glmnet and relaxnet packages with polynomial basis expansions. Basis expansion is applied to the predictors and a subset of the basis functions is chosen using relaxnet. Predictors may be screened using correlation or t-tests. Screening is done separately within cross-validation folds. Cross-validation may be used to select the order of basis expansion and alpha, the elastic net tuning parameter.
Provides two functions 'frameableWidget()', and 'frameWidget()'.
  The 'frameableWidget()' is used to add extra code to a 'htmlwidget' which
  allows is to be rendered correctly inside a responsive 'iframe'.
  The 'frameWidget()' is a 'htmlwidget' which displays content of another 'htmlwidget'
  inside a responsive 'iframe'.
  These functions allow for easier embedding of 'htmlwidgets' in content management systems
  such as 'wordpress', 'blogger' etc.
  They also allow for separation of widget content from main HTML content where
  CSS of the main HTML could interfere with the widget.
Encapsulates the pattern of untidying data into a wide matrix,
  performing some processing, then turning it back into a tidy form. This
  is useful for several operations such as co-occurrence counts,
  correlations, or clustering that are mathematically convenient on wide matrices.
The german Wikibook "GNU R" introduces R to new users.
        This package is a collection of functions and datas used in the
        german WikiBook "GNU R"
An API client for the 'Wikidata Query Service'
    <https://query.wikidata.org/>.
An API client for the Wikidata <http://wikidata.org/> store of
    semantic data.
Scrape lake metadata tables from Wikipedia <http://www.wikipedia.org>.
Provides an interface to the Wikipedia web application programming
    interface (API), using internet connexion.Three functions provide details for
    a specific Wikipedia page : all links that are present, all pages that link
    to, all the contributions (revisions for main pages, and discussions for talk
    pages). Two functions provide details for a specific user : all contributions,
    and general information (as name, gender, rights or groups). It provides
    additional information compared to others packages, as WikipediR. It does not
    need login. The multiplex network that can be constructed from the results
    of the functions of WikipediaR can be modeled as Stochastic Block Model as in
    Barbillon P., Donnet, S., Lazega E., and Bar-Hen A. : Stochastic Block Models
    for Multiplex networks: an application to networks of researchers, ArXiv
    1501.06444, http://arxiv.org/abs/1501.06444.
Public attention is an interesting field of study. The
    internet not only allows to access information in no time on
    virtually any subject but via page access statistics gathered
    by website authors the subject of attention as well can be
    studied.
A wrapper for the MediaWiki API, aimed particularly at the
    Wikimedia 'production' wikis, such as Wikipedia. It can be used to retrieve
    page text, information about users or the history of pages, and elements of
    the category tree.
Download public domain works from Wikisource <https://wikisource.org/>, a free library from the Wikimedia Foundation project.
'Taxonomic' information from 'Wikipedia', 'Wikicommons',
    'Wikispecies', and 'Wikidata'. Functions included for getting
    taxonomic information from each of the sources just listed, as
    well performing taxonomic search.
This package provides functions to perform fast variable
        selection based on the Wilcoxon rank sum test in the
        cross-validation or Monte-Carlo cross-validation settings, for
        use in microarray-based binary classification.
Dynamic interaction refers to spatial-temporal associations
    in the movements of two (or more) animals. This package provides tools for
    calculating a suite of indices used for quantifying dynamic interaction with
    wildlife telemetry data. For more information on each of the methods employed
    see the references within. The package (as of version 0.3) also has new tools for
    automating contact analysis in large tracking datasets. The package draws 
    heavily on the classes and methods developed in the 'adehabitat' packages.
Provides insight into how the best hand for a poker game changes based on the game dealt, players who stay in until the showdown and wildcards added to the base game.  At this time the package does not support player tactics, so draw poker variants are not included.
Tool-set of modules for creating web-based applications that use plot based strategies to visualize and analyze multi-omics data.
    This package utilizes the 'shiny' and 'plotly' frameworks to provide a user friendly dashboard for interactive plotting.
Provides a tool to fit and compare the wind turbine power curves 
    with successful curve fitting techniques. Facilitates to examine and compare 
    the performance of a user-defined power curve fitting techniques. 
    Also, provide features to generate power curve discrete points from a graphical 
    power curves. Data on the power curves of the wind turbine from major 
    manufacturers are provided.
Analysing convergent evolution using the Wheatsheaf index.
The genetic algorithm is designed to optimize small wind farms with
    irregular shapes. The algorithm works with a fixed amount of turbines, a fixed
    rotor radius and a mean wind speed value for every incoming wind direction.
    If required, it can include a terrain effect model which downloads an
    'SRTM' elevation model automatically and loads a Corine Land Cover raster,
    which has to be downloaded previously. Further information can be found in the
    description of the function 'windfarmGA'. To start an optimization run, either the
    function 'windfarmGA' or 'genAlgo' can be used. The function 'windfarmGA' checks
    the user inputs interactively and then runs the function 'genAlgo'. If the input
    parameters are already known, an optimization can be run directly via the function
    'genAlgo'. Their output is identical.
Helps for interfacing with the operating system
    particularly for Windows.
Fits a model to data separately for each treatment group and then calculates the win-Ratio as a function of follow-up time.
Data sets from the World Input Output Database, for the years 1995-2011.
Provides simple, fast functions for maximum likelihood and Bayesian estimates of wildlife population parameters, suitable for use with simulated data or bootstraps. Early versions were indeed quick and dirty, but optional error-checking routines and meaningful error messages have been added. Includes single and multi-season occupancy, closed capture population estimation, survival, species richness and distance measures.
In the course of a genome-wide association study, the situation often arises that some phenotypes are known with greater precision than others.
    It could be that some individuals are known to harbor more micro-environmental variance than others.
    In the case of inbred strains of model organisms, it could be the case that more organisms were observed from some strains than others, so the strains with more organisms have better-estimated means.
    Package 'wISAM' handles this situation by allowing for weighting of each observation according to residual variance.
    Specifically, the 'weight' parameter to the function conduct_scan() takes the precision of each observation (one over the variance).
Perform the Wild Scale-Enhanced (WiSE) bootstrap.  Specifically, the user may supply a single or multiple equally-spaced time series and use the WiSE bootstrap to select a wavelet-smoothed model.  Conversely, a pre-selected smooth level may also be specified for the time series.  Quantities such as the bootstrap sample of wavelet coefficients, smoothed bootstrap samples, and specific hypothesis testing and confidence region results of the wavelet coefficients may be obtained.  Additional functions are available to the user which help format the time series before analysis.  This methodology is recommended to aid in model selection and signal extraction.
    Note:  This package specifically uses wavelet bases in the WiSE bootstrap methodology, but the theoretical construct is much more versatile.
A Shiny application for learning Bayesian Decision Networks from data. This package can be used for probabilistic reasoning (in the observational setting), causal inference (in the presence of interventions) and learning policy decisions (in Decision Network setting). Functionalities include end-to-end implementations for data-preprocessing, structure-learning, exact inference, approximate inference, extending the learned structure to Decision Networks and policy optimization using statistically rigorous methods such as bootstraps, resampling, ensemble-averaging and cross-validation. In addition to Bayesian Decision Networks, it also features correlation networks, community-detection, graph visualizations, graph exports and web-deployment of the learned models as Shiny dashboards.
A set of functions to run code 'with' safely and temporarily
    modified global state. Many of these functions were originally a part of the
    'devtools' package, this provides a simple package with limited dependencies
    to provide access to these functions.
Utility functions to convert between the 'Spatial' classes
    specified by the package 'sp', and the well-known binary '(WKB)'
    representation for geometry specified by the Open Geospatial Consortium.
    Supports 'Spatial' objects of class 'SpatialPoints',
    'SpatialPointsDataFrame', 'SpatialLines', 'SpatialLinesDataFrame',
    'SpatialPolygons', and 'SpatialPolygonsDataFrame'. Supports 'WKB' geometry
    types 'Point', 'LineString', 'Polygon', 'MultiPoint', 'MultiLineString', and
    'MultiPolygon'. Includes extensions to enable creation of maps with
    'TIBCO Spotfire'.
Converts weekly data to monthly data.
     Users can use three types of week formats: ISO week, epidemiology week (epi week) and calendar date.
Use various regression models for the analysis of win loss endpoints 
             adjusting for non-binary and multivariate covariates.
A GUI R implementation of hierarchical Bayesian models of working memory, used for analyzing change detection data.
Distance discriminant analysis method is one of
        classification methods according to multiindex performance
        parameters.However,the traditional Mahalanobis distance
        discriminant method treats with the importance of all
        parameters equally,and exaggerates the role of parameters which
        changes a little.The weighted Mahalanobis distance is used in
        discriminant analysis method to distinguish the importance of
        each parameter.In the concrete application,firstly based on the
        principal component analysis scheme,a new group of parameters
        and their corresponding percent contributions of the parameters
        are calculated ,and the weighted matrix is regarded as the
        diagonal matrix of the contributions rates.Setting data to
        standardization,then the weighted Mahalanobis distance can be
        calculated.Besides the methods metioned above,bayes method is
        also given.
Analyzing the texture of an image from a multifractal wavelet leader analysis.
Software to book Wavelet Methods for Time Series Analysis,
        Donald B. Percival and Andrew T. Walden, Cambridge University
        Press, 2000.
Power calculator for the two-sample Wilcoxon-Mann-Whitney rank-sum test for a continuous outcome (Mollan, Trumble, Reifeis et. al., Jan. 2019) <arXiv:1901.04597>, (Mann and Whitney 1947) <doi:10.1214/aoms/1177730491>, (Shieh, Jan, and Randles 2006) <doi:10.1080/10485250500473099>.
Calculates the minimal sample size for the Wilcoxon-Mann-Whitney test
    that is needed for a given power and two sided type I error rate. The method works for metric data with and without
    ties, count data, ordered categorical data, and even dichotomous data.
    But data is needed for the reference group to generate synthetic data for the treatment group based on a relevant effect.
    For details, see Brunner, E., Bathke A. C. and Konietschke, F: Rank- and Pseudo-Rank Procedures in Factorial Designs - Using R and SAS, Springer Verlag, to appear.
This is a set of minimization tools (maximum likelihood estimation and least square fitting) to solve examples in the Johan Gabrielsson and Dan Weiner's book "Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications" 5th ed. (ISBN:9198299107). Examples include linear and nonlinear compartmental model, turn-over model, single or multiple dosing bolus/infusion/oral models, allometry, toxicokinetics, reversible metabolism, in-vitro/in-vivo extrapolation, enterohepatic circulation, metabolite modeling, Emax model, inhibitory model, tolerance model, oscillating response model, enantiomer interaction model, effect compartment model, drug-drug interaction model, receptor occupancy model, and rebound phenomena model.
New tools for the imputation of missing values in high-dimensional
    data are introduced using the non-parametric nearest neighbor methods. It
    includes weighted nearest neighbor imputation methods that use specific
    distances for selected variables. It includes an automatic procedure of cross
    validation and does not require prespecified values of the tuning parameters.
    It can be used to impute missing values in high-dimensional data when the sample
    size is smaller than the number of predictors. For more information see Faisal
    and Tutz (2017) <doi:10.1515/sagmb-2015-0098>.
Estimates W-NOMINATE scores using parliamentary voting data
	(Poole et al 2007) <doi:10.18637/jss.v042.i14> supplied though a
	'rollcall' object from package 'pscl'.
Shows the relationship between an independent and dependent variable through Weight of Evidence and Information Value.
Implements an automated binning of numeric variables and factors with
 respect to a dichotomous target variable.
 Two approaches are provided: An implementation of fine and coarse classing that
 merges granular classes and levels step by step. And a tree-like approach that
 iteratively segments the initial bins via binary splits. Both procedures merge,
 respectively split, bins based on similar weight of evidence (WOE) values and
 stop via an information value (IV) based criteria.
 The package can be used with single variables or an entire data frame. It provides
 flexible tools for exploring different binning solutions and for deploying them to
 (new) data.
Segment a numeric variable based on a dichotomous dependent variable by using the weight of evidence (WOE) approach (Ref: Siddiqi, N. (2006) <doi:10.1002/9781119201731.biblio>).
    The underlying algorithm adopts a recursive approach to create segments that are diverse in respect of their WOE values and meet the demands of user-defined parameters. The algorithm also aims to maintain a monotonic trend in WOE values of consecutive segments. As such, it can be particularly helpful in improving robustness of linear and logistic regression models.
Implements a spatiotemporal boundary detection model with a dissimilarity
    metric for areal data with inference in a Bayesian setting using Markov chain
    Monte Carlo (MCMC). The response variable can be modeled as Gaussian (no nugget),
    probit or Tobit link and spatial correlation is introduced at each time point
    through a conditional autoregressive (CAR) prior. Temporal correlation is introduced
    through a hierarchical structure and can be specified as exponential or first-order
    autoregressive. Full details of the package can be found in the accompanying vignette.
    Furthermore, the details of the package can be found in "Diagnosing Glaucoma 
    Progression with Visual Field Data Using a Spatiotemporal Boundary Detection Method", 
    by Berchuck et al (2018), <arXiv:1805.11636>. The paper is in press at the Journal of 
    the American Statistical Association.
Students learning both econometrics and R may find the introduction 
    to both challenging. However, if the text is "Introductory Econometrics: 
    A Modern Approach" by Jeffrey M. Wooldridge, they are in luck! 
    The wooldridge data package aims to lighten the task by efficiently loading 
    any data set found in the text with a single command. Data sets have all been 
    compressed to a fraction of their original size and are well documented. 
    Documentation files contain the page numbers of the text where each set is used, 
    the original source, time of publication, and notes suggesting ideas for further 
    exploratory data analysis and research. If one need's to brush-up on model syntax, a 
    vignette contains R solutions to examples from each chapter of the text. 
    Data sets are from the 6th edition (Wooldridge 2016, ISBN-13: 978-1-305-27010-7), 
    and are backwards compatible with all versions of the text.
For a given Sentence-Aligned Parallel Corpus, it aligns words for each sentence pair. It considers one-to-many and symmetrization alignments. Moreover, it evaluates the quality of word alignment based on this package and some other software. It also builds an automatic dictionary of two languages based on given parallel corpus.
Tools for connecting to Wordbank, an open repository for
    developmental vocabulary data.
Functionality to create pretty word clouds, visualize differences and similarity between documents, and avoid over-plotting in scatter plots with text.
A fast visualization tool for creating wordcloud
    by using 'wordcloud2.js'. 'wordcloud2.js' is a JavaScript library to create 
    wordle presentation on 2D canvas or HTML <https://timdream.org/wordcloud2.js/>.
Matches words in one file with words in another file and shows index(row number) for the matches
An interface to WordNet using the Jawbone Java API to WordNet.
  WordNet (<http://wordnet.princeton.edu/>) is a large lexical database of
  English.  Nouns, verbs, adjectives and adverbs are grouped into sets of
  cognitive synonyms (synsets), each expressing a distinct concept.  Synsets
  are interlinked by means of conceptual-semantic and lexical relations.
  Please note that WordNet(R) is a registered tradename.  Princeton
  University makes WordNet available to research and commercial users
  free of charge provided the terms of their license
  (<http://wordnet.princeton.edu/wordnet/license/>) are followed, and
  proper reference is made to the project using an appropriate
  citation (<http://wordnet.princeton.edu/wordnet/citing-wordnet/>).
Collects several classical word pools used most often
    to provide lists of words in psychological studies of learning and memory. It
    provides a simple function, 'pickList' for selecting random samples of words
    within given ranges.
Serves for rendering MS Word documents with R inline code and inserting tables and plots.
An interactive laboratory for research on distributional semantic models ('DSM',
             see <https://en.wikipedia.org/wiki/Distributional_semantics> for more information).
Provides a workflow for your analysis projects by combining
  literate programming ('knitr' and 'rmarkdown') and version control
  ('Git', via 'git2r') to generate a website containing time-stamped,
  versioned, and documented results.
Functions to import data from more than 30,000 surface
    meteorological sites around the world managed by the National Oceanic and Atmospheric Administration (NOAA) Integrated Surface
    Database (ISD, see <https://www.ncdc.noaa.gov/isd>).
Retrieves taxonomic information from  <http://www.marinespecies.org> using WoRMS' RESTful Webservice. Utility functions aim at taxonomic consistency.
Client for World Register of Marine Species
    (<http://www.marinespecies.org/>). Includes functions for each
    of the API methods, including searching for names by name, date and
    common names, searching using external identifiers, fetching
    synonyms, as well as fetching taxonomic children and
    taxonomic classification.
R clients to the 'Web of Science' and 'InCites' 
  <https://clarivate.com/products/data-integration/> APIs, which 
  allow you to programmatically download publication and citation data
  indexed in the 'Web of Science' and 'InCites' databases.
Implementing weighted predictiveness curve to visualize the marker-by-treatment relationship and measure the performance of biomarkers for guiding treatment decision.
Supplies permutation-test alternatives to traditional hypothesis-test
  procedures such as two-sample tests for means, medians, and standard deviations;
  correlation tests; tests for homogeneity and independence; and more. Suitable for
  general audiences, including individual and group users, introductory statistics
  courses, and more advanced statistics courses that desire an introduction to
  permutation tests.
Weighted Piecewise Kernel Density Estimation for large data.
Data from the United Nation's World Population Prospects 2008
Data from the United Nation's World Population Prospects
        2010
Data from the United Nation's World Population Prospects 2012
Provides data from the United Nation's World Population Prospects 2015.
Provides data from the United Nation's World Population Prospects 2017.
Explore data in the 'wpp2017' (or 2015, 2012, 2010) package using a 'shiny' interface.
Functions to assist in the processing and
    exploration of data from environmental monitoring programs.
    The package name stands for "water quality" and reflects the
    original focus on time series data for physical and chemical
    properties of water, as well as the biota. Intended for
    programs that sample approximately monthly, quarterly or
    annually at discrete stations, a feature of many legacy data
    sets. Most of the functions should be useful for analysis of
    similar-frequency time series regardless of the subject
    matter.
Fits weighted quantile sum regression models, calculates weighted quantile sum index and estimated component weights.
Supports systematic scrutiny, modification, and integration of
    data. The function status() counts rows that have missing values in 
    grouping columns (returned by na() ), have non-unique combinations of 
    grouping columns (returned by dup() ), and that are not locally sorted
    (returned by unsorted() ). Functions enumerate() and itemize() give 
    sorted unique combinations of columns, with or without occurrence counts,
    respectively. Function ignore() drops columns in x that are present in y,
    and informative() drops columns in x that are entirely NA; constant() returns
    values that are constant, given a key.  Data that have defined unique 
    combinations of grouping values behave more predictably during merge operations.
Computes the pdf, cdf, quantile, random numbers  for any wrapped G distributions.  Computes maximum likelihood estimates of the parameters, standard errors, 95 percent confidence intervals, value of Cramer-von Misses statistic, value of Anderson Darling statistic, value of Kolmogorov Smirnov test statistic and its $p$-value, value of Akaike Information Criterion, value of Consistent Akaike Information Criterion, value of Bayesian Information Criterion, value of Hannan-Quinn information criterion, minimum value of the negative log-likelihood function and convergence status when the wrapped distribution is fitted  to some data.
Tools for writing and debugging R code. Provides: 
    '%.>%' dot-pipe (an 'S3' configurable pipe),
    'let()' 
    (converts non-standard evaluation interfaces to parametric standard
    evaluation interfaces, inspired by 'gtools:strmacro()' and 'base::bquote()'), 
    'build_frame()'/'draw_frame()' ('data.frame' example tools),
    'qc()' (quoting concatenate), 
    ':=' (named map builder),
    and more.
A wrapper around Michel Scheffers's 'libassp' (<http://libassp.sourceforge.net/>). 
    The 'libassp' (Advanced Speech Signal Processor) library aims at providing
    functionality for handling speech signal files in most common audio formats
    and for performing analyses common in phonetic science/speech science. This
    includes the calculation of formants, fundamental frequency, root mean
    square, auto correlation, a variety of spectral analyses, zero crossing
    rate, filtering etc. This wrapper provides R with a large subset of
    'libassp's signal processing functions and provides them to the user in a
    (hopefully) user-friendly manner.
A powerful yet simple graphical tool available in the field of psychometrics is the Wright Map (also known as item maps or item-person maps), which presents the location of both respondents and items on the same scale. Wright Maps are commonly used to present the results of dichotomous or polytomous item response models. The 'WrightMap' package provides functions to create these plots from item parameters and person estimates stored as R objects. Although the package can be used in conjunction with any software used to estimate the IRT model (e.g. 'TAM', 'mirt', 'eRm' or 'IRToys' in 'R', or 'Stata', 'Mplus', etc.),  'WrightMap' features special integration with 'ConQuest' to facilitate reading and plotting its output directly.The 'wrightMap' function creates Wright Maps based on person estimates and item parameters produced by an item response analysis. The 'CQmodel' function reads output files created using 'ConQuest' software and creates a set of data frames for easy data manipulation, bundled in a 'CQmodel' object. The 'wrightMap' function can take a 'CQmodel' object as input or it can be used to create Wright Maps directly from data frames of person and item parameters.
Function for writing a SNNS pattern file from a data.frame
        or matrix.
Zero-dependency data frame to xlsx exporter based on 'libxlsxwriter'. 
    Fast and no Java or Excel required.
Cross-platform Perl based R function to create Excel 2003 (XLS) and Excel 2007 (XLSX)
             files from one or more data frames. Each data frame will be
             written to a separate named worksheet in the Excel spreadsheet.
             The worksheet name will be the name of the data frame it contains
             or can be specified by the user.
A collection of robust statistical methods based on Wilcox' WRS functions. It implements robust t-tests (independent and dependent samples), robust ANOVA (including between-within subject designs), quantile ANOVA, robust correlation, robust mediation, and nonparametric ANCOVA models based on robust location measures.
Water resources system simulator is a tool for simulation and analysis of large-scale water resources systems. 'WRSS' proposes functions and methods for construction, simulation and analysis of primary storage and hydropower water resources features (e.g. reservoirs, aquifers, and etc.) based on Standard Operating Policy (SOP).
A collection of implementations of classical and novel
        algorithms for weighted sampling without replacement.
Includes performance measurements and results of repeated
        experiment runs (for correctness checks) for code in the
        'wrswoR' package.
An adaptation for estuaries (tidal waters) of weighted regression
    on time, discharge, and season to evaluate trends in water quality time series.
Predicts individual race/ethnicity using surname, geolocation,
    and other attributes, such as gender and age. The method utilizes the Bayes'
    Rule to compute the posterior probability of each racial category for any given
    individual. The package implements methods described in Imai and Khanna (2015)
    "Improving Ecological Inference by Predicting Individual Ethnicity from Voter
    Registration Records" <DOI:10.1093/pan/mpw001>.
Entropy weighted k-means (ewkm) is a weighted subspace
        clustering algorithm that is well suited to very high
        dimensional data. Weights are calculated as the importance of
        a variable with regard to cluster membership.  The two-level
        variable weighting clustering algorithm tw-k-means (twkm)
        introduces two types of weights, the weights on individual
        variables and the weights on variable groups, and they are
        calculated during the clustering process.  The feature group
        weighted k-means (fgkm) extends this concept by grouping
        features and weighting the group in addition to weighting
        individual features.
A parallel implementation of Weighted Subspace Random Forest.  The
    Weighted Subspace Random Forest algorithm was proposed in the
    International Journal of Data Warehousing and Mining by Baoxun Xu,
    Joshua Zhexue Huang, Graham Williams, Qiang Wang, and Yunming Ye
    (2012) <DOI:10.4018/jdwm.2012040103>.  The algorithm can classify
    very high-dimensional data with random forests built using small
    subspaces.  A novel variable weighting method is used for variable
    subspace selection in place of the traditional random variable
    sampling.This new approach is particularly useful in building
    models from high-dimensional data.
We propose weighted SVM methods with penalization form. By
        adding weights to loss term, we can build up weighted SVM
        easily and examine classification algorithm properties under
        weighted SVM. Through comparing each of test error rates, we
        conclude that our Weighted SVM with boosting has predominant
        properties than the standard SVM have, as a whole.
Tools for a wavelet-based approach to analyzing spatial synchrony, principally in ecological data. Some tools will be useful for studying community synchrony. See, for instance, Sheppard et al (2016) <doi: 10.1038/NCLIMATE2991>, Sheppard et al (2017) <doi: 10.1051/epjnbp/2017000>, Sheppard et al (2019) <doi: 10.1371/journal.pcbi.1006744>.
Perform the calculation of W-test, diagnostic checking, calculate
    minor allele frequency (MAF) and odds ratio.
Computes the Weighted Topological Overlap with positive and negative signs (wTO) networks given a data frame containing the mRNA count/ expression/ abundance per sample, and a vector containing the interested nodes of interaction (a subset of the elements of the full data frame). It also computes the cut-off threshold or p-value based on the individuals bootstrap or the values reshuffle per individual. It also allows the construction of a consensus network, based on multiple wTO networks. The package includes a visualization tool for the networks.  More about the methodology can be found at <arXiv:1711.04702>.
An R client that provides remote access to satellite image time series.
  The client allows Earth observation users to obtain time series from data sets available in a Web Time Series Server. 
  The functions include: (a) listing the data sets available in the server; 
  (b) describing the contents of a data set; (c) retrieving a time series based on spatial location and temporal filters.
Allows form managers to download entries from their respondents
    using Wufoo JSON API (<https://www.wufoo.com>). Additionally, the Wufoo reports - when public - can be
    also acquired programmatically. Note that building new forms within this package
    is not supported.
A function for randomly sampling from the 'Wunderground' API
    <http://api.wunderground.com>.  Provides multistage sampling strategies,
    respects API usage limits, and collects and saves 'Wunderground' data.
Methods to calculate and interpret climate change signals and time series from climate multi-model ensembles. Climate model output in binary 'NetCDF' format is read in and aggregated over a specified region to a data.frame for statistical analysis. Global Circulation Models, as the 'CMIP5' simulations, can be read in the same way as Regional Climate Models, as e.g. the 'CORDEX' or 'ENSEMBLES' simulations. The package has been developed at the 'Wegener Center for Climate and Global Change' at the University of Graz, Austria.
Select data analysis plots, under a standardized calling interface implemented on top of 'ggplot2' and 'plotly'.  
   Plots of interest include: 'ROC', gain curve, scatter plot with marginal distributions, 
   conditioned scatter plot with marginal densities,
   box and stem with matching theoretical distribution, and density with matching theoretical distribution.
This tool, wood vision tool, is intended to facilitate preprocessing and analyzing 2-dimensional wood images toward automated recognition. The former includes some basics such as functions to RGB to grayscale, gray to binary, cropping, rotation(bilinear), median/mean/Gaussian filter, and Canny/Sobel edge detection. The latter includes gray level co-occurrence matrix (GLCM), Haralick parameters, local binary pattern (LBP), higher order local autocorrelation (HLAC), Fourier transform (radial and azimuthal integration), and Gabor filtering. The functions are intended to read data using  'readTIFF(x,info=T)' from 'tiff' package. The functions in this packages basically assumes the grayscale images as input data, thus the color images should be subjected to the function rgb2gray() before used for some other functions.
Book is "Linear Mixed Models: A Practical Guide Using
        Statistical Software" published in 2006 by Chapman Hall / CRC
        Press
Calculate the (weighted) win loss statistics including the win ratio, win difference and win product 
 and their variances, with which the p-values are also calculated. The variance estimation is based on 
 Luo et al. (2015) <doi:10.1111/biom.12225> and Luo et al. (2017) <doi:10.1002/sim.7284>. This package also calculates general win loss statistics with 
 user-specified win loss function with variance estimation based on 
 Bebu and Lachin (2016) <doi:10.1093/biostatistics/kxv032>.
 This version corrected an error when outputting confidence interval for win difference.
Provides a tool for extracting information (entities and relations between them) in text datasets. It also emphasizes the results exploration with graphical displays. It is a rule-based system and works with hand-made dictionaries and local grammars defined by users. 'x.ent' uses parsing with Perl functions and JavaScript to define user preferences through a browser and R to display and support analysis of the results extracted. Local grammars are defined and compiled with the tool Unitex, a tool developed by University Paris Est that supports multiple languages. See ?xconfig for an introduction.
The 'X13-ARIMA-SEATS' <https://www.census.gov/srd/www/x13as/> methodology and software is a widely used software and developed by the US Census Bureau. It can be accessed from 'R' with this package and 'X13-ARIMA-SEATS' binaries are provided by the 'R' package 'x13binary'.
A graphical user interface for the x12 package
The US Census Bureau provides a seasonal adjustment program now
 called 'X-13ARIMA-SEATS' building on both earlier programs called X-11 and
 X-12 as well as the SEATS program by the Bank of Spain. The US Census Bureau
 offers both source and binary versions -- which this package integrates for
 use by other R packages.
The x3p file format  is specified in ISO standard 5436:2000 to 
    describe 3d surface measurements. 'x3ptools' allows reading, writing and 
    basic modifications to the 3D surface measurements.
Create HTML5 slides with R Markdown and the JavaScript library
    'remark.js' (<https://remarkjs.com>).
Simulation of purebred and crossbred genomic data as well as pedigree and phenotypes are possible by this package. 'xbreed' can be used for the simulation of populations with flexible genome structures and trait genetic architectures. It can also be used to evaluate breeding schemes and generate genetic data to test statistical tools.
Functions to extract business financial information from
  an Extensible Business Reporting Language ('XBRL') instance file and the
  associated collection of files that defines its 'Discoverable' Taxonomy
  Set ('DTS').
Functions for Estimating a (c)DCC-GARCH Model in large dimensions
    based on a publication by Engle et,al (2017) <doi:10.1080/07350015.2017.1345683> and Nakagawa et,al (2018) <doi:10.3390/ijfs6020052>.
    This estimation method is consist of composite likelihood method by Pakel et al. (2014) <http://paneldataconference2015.ceu.hu/Program/Cavit-Pakel.pdf>
    and (Non-)linear shrinkage estimation of covariance matrices by Ledoit and Wolf (2004,2015,2016). (<doi:10.1016/S0047-259X(03)00096-4>,
    <doi:10.1214/12-AOS989>, <doi:10.1016/j.jmva.2015.04.006>).
Extensions of Exponential Random Graph Models (ERGM): Temporal Exponential Random Graph Models (TERGM), Generalized Exponential Random Graph Models (GERGM), Temporal Network Autocorrelation Models (TNAM), and Relational Event Models (REM). This package acts as a meta-package for several sub-packages on which it depends.
Datasets and definitions of generic functions used in dependencies of the 'xergm' package.
Read and write XES Files to create event log objects used by the 'bupaR' framework. XES (Extensible Event Stream) is the `IEEE` standard for storing and sharing event data (see <http://standards.ieee.org/findstds/standard/1849-2016.html> for more info).
Miscellaneous functions commonly used in other packages maintained by 'Yihui Xie'.
This tool enables in-database scoring of 'XGBoost' models built in R, by translating trained model objects into SQL query. 
  'XGBoost' <https://xgboost.readthedocs.io/en/latest/index.html> provides parallel tree boosting (also known as gradient boosting machine, or GBM) algorithms
  in a highly efficient, flexible and portable way. GBM algorithm is introduced by Friedman (2001) <doi:10.1214/aos/1013203451>, 
  and more details on 'XGBoost' can be found in Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>.
Extreme Gradient Boosting, which is an efficient implementation
    of the gradient boosting framework from Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>.
    This package is its R interface. The package includes efficient linear 
    model solver and tree learning algorithms. The package can automatically 
    do parallel computation on a single machine which could be more than 10 
    times faster than existing gradient boosting packages. It supports
    various objective functions, including regression, classification and ranking.
    The package is made to be extensible, so that users are also allowed to define
    their own objectives easily.
Interface to the XGobi and XGvis programs for graphical
        data analysis.
The central goal of XGR by Fang et al. (2016) <doi:10.1186/s13073-016-0384-y> is to provide a data interpretation system necessary to do "big data" science. It is designed to make a user-defined gene or SNP list (or genomic regions) more interpretable by comprehensively utilising ontology annotations and interaction networks to reveal relationships and enhance opportunities for biological discovery. XGR is unique in supporting a broad range of ontologies (including knowledge of biological and molecular functions, pathways, diseases and phenotypes - in both human and mouse) and different types of networks (including functional, physical and pathway interactions). There are two core functionalities of XGR. The first is to provide basic infrastructures for easy access to built-in ontologies and networks. The second is to support data interpretations via 1) enrichment analysis using either built-in or custom ontologies, 2) similarity analysis for calculating semantic similarity between genes (or SNPs) based on their ontology annotation profiles, 3) network analysis for identification of gene networks given a query list of (significant) genes, SNPs or genomic regions, and 4) annotation analysis for interpreting genomic regions using co-localised functional genomic annotations (such as open chromatin, epigenetic marks, TF binding sites and genomic segments) and using nearby gene annotations (by ontologies). Together with its web app, XGR aims to provide a user-friendly tool for exploring genomic relations at the gene, SNP and genomic region level.
R scripts for plotting and assessing XHMM whole-exome-sequencing-based CNV calls. XHMM (eXome Hidden Markov Model) is a C++ software package (http://atgu.mgh.harvard.edu/xhmm) written to call copy number variation (CNV) from next-generation sequencing projects, where exome capture was used (or targeted sequencing, more generally). This R package enables the user to visualize both the PCA normalization performed by XHMM and the CNVs it has called.
Conduct the likelihood ratio tests for Hardy-Weinberg equilibrium at marker loci on the X chromosome.
Provides a simple XML tree parser/generator. It includes functions to read XML files into R objects, get information out of and into nodes, and write R objects back
                    to XML code. It's not as powerful as the 'XML' package and doesn't aim to be, but for simple XML handling it could be useful. It was originally developed for
                    the R GUI and IDE 'RKWard' <https://rkward.kde.org>, to make plugin development easier.
Plotting ggplot2 graphs using the XKCD style.
The XKCD color survey asked participants to name colours. Randall Munroe published the top thousand(roughly) names and their sRGB hex values. This package lets you use them.
Download data from individual XKCD comics, written by Randall Munroe <https://xkcd.com/>.
Provides comprehensive functionality to read, write and format Excel data.
Provides external JAR dependencies for the XLConnect package.
Provides a tool for non linear mapping (non linear regression) using a mixture of regression model and an inverse regression strategy. The methods include the GLLiM model (see Deleforge et al (2015) <DOI:10.1007/s11222-014-9461-5>) based on Gaussian mixtures and a robust version of GLLiM, named SLLiM (see Perthame et al (2016) <https://hal.archives-ouvertes.fr/hal-01347455>) based on a mixture of Generalized Student distributions. The methods also include BLLiM (see Devijver et al (2017) <https://arxiv.org/abs/1701.07899>) which is an extension of GLLiM with a sparse block diagonal structure for large covariance matrices (particularly interesting for transcriptomic data).
Provides a simple wrapper for some 'XLConnect' functions. 'XLConnect' is
    a package that allows for reading, writing, and manipulating Microsoft Excel
    files. This package, 'xlsimple', adds some documentation and pre-defined formatting
    to the outputted Excel file. Individual sheets can include a description on the
    first row to remind user what is in the data set. Auto filters and freeze
    rows are turned on. A brief readme file is created that provides a summary
    listing of the created sheets and, where provided, the description.
Provide R functions to read/write/format Excel 2007 and Excel 97/2000/XP/2003 file formats.
The xlsxjars package collects all the external jars
  required for the xlxs package. This release corresponds to POI
  3.10.1.
Writes beautifully formatted cross tabulations to Excel 
    using 'openxlsx'. It has been developed to help automate the process of publishing 
    Official Statistics.  The user provides a dataframe, which is outputted to Excel with 
    various types of rich formatting which are automatically detected from the structure
    of the cross tabulation.  Documentation can be found at the following
    url <https://github.com/moj-analytical-services/xltabr>.
Extract Excel files from folder.
    Also display extracted data and compute a summary of it.
    Based on the 'readxl' package.
A toolbox for meta-analysis. This package includes a collection of functions for (1) implementing robust multivariate meta-analysis of continuous or binary outcomes; and (2) a bivariate Egger's test for detecting publication bias.
This is Xiaobei's miscellaneous classes and functions useful when
    developing R packages, particularly for OOP using R Reference Class.
Many approaches for both reading and
        creating XML (and HTML) documents (including DTDs), both local
        and accessible via HTTP or FTP.  Also offers access to an
        'XPath' "interpreter".
Work with XML files using a simple, consistent interface. Built on
    top of the 'libxml2' C library.
XML2R is a framework that reduces the effort required to transform
    XML content into number of tables while preserving parent to child
    relationships.
Convert the output of 'utils::getParseData()' to an 'XML'
    tree, that one can search via 'XPath', and easier to manipulate in
    general.
The 'XML-RPC' is a remote procedure call protocol 
    based on 'XML'. The 'xmlrpc2' package is inspired by the 'XMLRPC'
	package but uses the 'curl' and 'xml2' packages instead 'RCurl' and 'XML'.
Fit Markov Networks to a wide range of high-throughput genomics data.
XMRs combine X-Bar control charts and Moving Range control charts. These functions also will recalculate the reference lines when significant change has occurred.
Tests whether a set of counts fit a given expected ratio. For
    example, a genetic cross might be expected to produce four types in the
    relative frequencies of 9:3:3:1. To see whether a set of observed counts
    fits this expectation, one can examine all possible outcomes with xmulti() or a
    random sample of them with xmonte() and find the probability of an observation
    deviating from the expectation by at least as much as the observed. As a
    measure of deviation from the expected, one can use the log-likelihood
    ratio, the multinomial probability, or the classic chi-square statistic. A
    histogram of the test statistic can also be plotted and compared with the
    asymptotic curve.
Analysis of crossover interference in experimental crosses,
    particularly regarding the gamma model. See, for example,
    Broman and Weber (2000) <doi:10.1086/302923>.
Cross platform solution to open files, directories or 'URLs'
    with their associated programs.
Allows to provide live interpretations and explanations of statistical 
    functions in R. These interpretations and explanations are shown when the explained function 
    is called by the user. They can interact with the values of the explained function's actual 
    results to offer relevant, meaningful insights. The interpretations and explanations 
    are based on an easy-to-use XML format that allows to include R code to interact with the 
    returns of the explained function.
Tools for interactive data exploration built using 'shiny'. Includes apps for descriptive 
    statistics, visualizing probability distributions, inferential statistics, linear regression, 
    logistic regression and RFM analysis.
Provides a set of functions devoted to multivariate exploratory statistics on textual data. Classical methods such as correspondence analysis and agglomerative hierarchical clustering are available. Chronologically constrained agglomerative hierarchical clustering enriched with labelled-by-words trees is offered. Given a division of the corpus into parts, their characteristic words and documents are identified. Further, accessing to 'FactoMineR' functions is very easy. Two of them are relevant in textual domain. MFA() addresses multiple lexical table allowing applications such as dealing with multilingual corpora as well as simultaneously analyzing both open-ended and closed questions in surveys. CaGalt() helps to explore the relationships between lexical choices and contextual variables. See <http://www.Xplortext.org> for examples.
Diagnostics for non-linear mixed-effects (population) 
    models from 'NONMEM' <http://www.iconplc.com/innovation/nonmem/>. 
    'xpose' facilitates data import, creation of numerical run summary 
    and provide 'ggplot2'-based graphics for data exploration and model 
    diagnostics.
A collection of functions to be used as a model
    building aid for nonlinear mixed-effects (population) analysis
    using NONMEM. It facilitates data set checkout, exploration and
    visualization, model diagnostics, candidate covariate identification
    and model comparison.
There is limited native support for external pointers in the R interface. 
    This package provides some basic tools to verify, create and modify 'externalptr' objects.
Support for interfaces from R to other languages,
    built around a class for evaluators and a combination of functions, classes and
    methods for communication. Will be used through a specific language interface
    package. Described in the book "Extending R".
Tools to analyze datasets previous to any statistical modeling.
    Has various functions designed to find inconsistencies and understanding the distribution of the data.
An implementation of the RuleFit algorithm as described in Friedman & Popescu 
  (2008) <doi:10.1214/07-AOAS148>. eXtreme Gradient Boosting ('XGBoost') is used 
  to build rules, and 'glmnet' is used to fit a sparse linear model on the raw and rule features. The result
  is a model that learns similarly to a tree ensemble, while often offering improved interpretability
  and achieving improved scoring runtime in live applications. Several algorithms for
  reducing rule complexity are provided, most notably hyperrectangle de-overlapping. All algorithms scale to 
  several million rows and support sparse representations to handle tens of thousands of dimensions.
Contains functions to identify tree-ring borders based on X-ray micro-density profiles and a Graphical User Interface to visualize density profiles and correct tree-ring borders.
A Julia interface structured according to the general
	     form described in package 'XR' and in the book "Extending R".
Digital repeat photography and near-surface remote sensing have been used by environmental scientists to study the environmental change for nearly a decade. However, a user-friendly, reliable, and robust platform to extract color-based statistics and time-series from a large stack of images is still lacking. Here, we present an interactive open-source toolkit, called 'xROI', that facilitate the process time-series extraction and improve the quality of the final data. 'xROI' provides a responsive environment for scientists to interactively a) delineate regions of interest (ROI), b) handle field of view (FOV) shifts, and c) extract and export time series data characterizing image color (i.e. red, green and blue channel digital numbers for the defined ROI). Using 'xROI', user can detect FOV shifts without minimal difficulty. The software gives user the opportunity to readjust the mask files or redraw new ones every time an FOV shift occurs. 'xROI' helps to significantly improve data accuracy and continuity.
A 'Python' interface structured according to the general
    form described in package 'XR' and in the book "Extending R".
This is a set of statistical quality control functions,  that allows plotting control charts and its iterations, process capability for variable and attribute control, highlighting the xrs_gr() function, like a first iteration for variable chart, meanwhile the we_rules() function detects non random patterns in sample.
A hierarchical Bayesian approach to assess functional impact of mutations on gene expression in cancer. Given a patient-gene matrix encoding the presence/absence of a mutation, a patient-gene expression matrix encoding continuous value expression data, and a graph structure encoding whether two genes are known to be functionally related, xseq outputs: a) the probability that a recurrently mutated gene g influences gene expression across the population of patients; 
  and b) the probability that an individual mutation in gene g in an individual patient m influences expression within that patient.
An extension for the 'xml2' package to transform XML documents
    by applying an 'xslt' style-sheet.
The circadian period of a time series data is predicted and the statistical significance of the periodicity are calculated using the chi-square periodogram.
Builds generalized linear model with automatic data transformation. 
  The 'xspliner' helps to build simple, interpretable models that inherits informations provided by more complicated ones.
  The resulting model may be treated as explanation of provided black box, that was supplied prior to the algorithm.
Tools to download and merge data files on sub-national conflict, violence and protests from <http://www.x-sub.org>.
This is the tool set for crystallographer to design and analyze crystallization experiments, especially for ribosome from Mycobacterium tuberculosis.
The 'xtensor' C++ library for numerical analysis with
 multi-dimensional array expressions is provided as a header-only
 C++14 library. It offers an extensible expression system enabling
 lazy broadcasting; an API following the idioms of the C++ standard
 library; and tools to manipulate array expressions and build upon 'xtensor'.
Can be used for coloring output in terminals.
	It was developed for the standard Ubuntu terminal but should be compatible
	with any terminal using xterm or ANSI escape sequences. If run in windows,
	RStudio, or any other platform not supporting such escape sequences it
	gracefully passes on any output without modifying it.
Contains three functions that access
    environmental data from ERD's ERDDAP service <http://coastwatch.pfeg.noaa.gov/erddap>. The xtracto() function extracts
    data along a trajectory for a given "radius" around the point. The
    xtracto_3D() function extracts data in a box. The xtractogon() function
    extracts data in a polygon. There are also two helper functions to obtain
    information about available data, and plotting functions to plot the results.
Provide for uniform handling of R's different time-based data classes by extending zoo, maximizing native format information preservation and allowing for user level customization and extension, while simplifying cross-class interoperability.
Extends standard penalized regression (Lasso and Ridge) to allow differential shrinkage based on external information with the goal of achieving a better prediction accuracy. Examples of external information include the grouping of predictors, prior knowledge of biological importance, external p-values, function annotations, etc. The choice of multiple tuning parameters is done using an Empirical Bayes approach. A majorization-minimization algorithm is employed for implementation.
Calculates a number of valuation adjustments including CVA, DVA,
    FBA, FCA, MVA and KVA. A two-way margin agreement has been implemented. For
    the KVA calculation three regulatory frameworks are supported: CEM, SA-CCR and
    IMM. The probability of default is implied through the credit spreads curve.
    Currently, only IRSwaps are supported. For more information, you can check
    one of the books regarding xVA: <http://www.cvacentral.com/books/credit-value-adjustment>.
Extrema-weighted feature extraction for varying length functional data. Functional data analysis method that performs dimensionality reduction based on predefined features and allows for quantile weighting. Method implemented as presented in Van den Boom et al. (2018) <doi:10.1093/bioinformatics/bty120>.
A suite of psychometric analysis tools for research and operation, including:
    (1) computation of probability, information, and likelihood for the 3PL, GPCM, and GRM;
    (2) parameter estimation using joint or marginal likelihood estimation method;
    (3) simulation of computerized adaptive testing using built-in or customized algorithms;
    (4) assembly and simulation of multistage testing. 
    The full documentation and tutorials are at <https://github.com/xluo11/xxIRT>.
A method for creating vertical histograms sharing a y-axis using
    base graphics.
High dimensional interaction search by brute force requires a
    quadratic computational cost in the number of variables. The xyz algorithm provably finds strong interactions in almost linear time.
    For details of the algorithm see: G. Thanei, N. Meinshausen and R. Shah (2016). The xyz algorithm for fast interaction search in high-dimensional data <https://arxiv.org/pdf/1610.05108v1.pdf>.
Provides an alternative canonical correlation/redundancy
  analysis function, with associated print, plot, and summary methods.
  A method for generating helio plots is also included.
Performs nearest neighbor-based imputation using one or more alternative 
 approaches to processing multivariate data. These include methods based on canonical 
 correlation analysis, canonical correspondence analysis, and a multivariate adaptation 
 of the random forest classification and regression techniques of Leo Breiman and Adele 
 Cutler. Additional methods are also offered. The package includes functions for 
 comparing the results from running alternative techniques, detecting imputation targets 
 that are notably distant from reference observations, detecting and correcting 
 for bias, bootstrapping and building ensemble imputations, and mapping results.
This is a simple wrapper for the yakmo K-Means library (developed by Naoki Yoshinaga, see http://www.tkl.iis.u-tokyo.ac.jp/~ynaga/yakmo/). It performs fast and robust (orthogonal) K-Means.
This collection of data exploration tools was developed at
    Yale University for the graphical exploration of complex
    multivariate data; barcode and gpairs now have their own
    packages.  The new big.read.table() provided here may be
    useful for large files when only a subset is needed.
Implements the 'libyaml' 'YAML' 1.1 parser and emitter
  (<http://pyyaml.org/wiki/LibYAML>) for R.
Tidy tools for quantifying how well model fits to a data set such as confusion matrices, class probability curve summaries, and regression metrics (e.g., RMSE).
A parser and a writer for 'WEKA' Attribute-Relation File Format
    <https://waikato.github.io/weka-wiki/arff_stable/> in pure R, with no dependencies. 
    As opposed to other R implementations, this package can read standard
    (dense) as well as sparse files, i.e. those where each row does only contain 
    nonzero components. Unlike 'RWeka', 'yarr' does not require any 'Java' installation
    nor is dependent on external software. This implementation is generalized from 
    those in packages 'mldr' and 'mldr.datasets'.
Contains a mixture of functions and data sets referred to in the introductory e-book "YaRrr!: The Pirate's Guide to R". The latest version of the e-book is available for free at <https://www.thepiratesguidetor.com>.
A collection of string functions designed for writing 
    compact and expressive R code. 'yasp' (Yet Another String Package) is simple,
    fast, dependency-free, and written in pure R. The package provides: a
    coherent set of abbreviations for paste() from package 'base' with a
    variety of defaults, such as p() for "paste" and pcc() for "paste and
    collapse with commas"; wrap(), bracket(), and others for wrapping a
    string in flanking characters; unwrap() for removing pairs of characters
    (at any position in a string); and sentence() for cleaning whitespace
    around punctuation and capitalization appropriate for prose sentences.
Yield curve or zero-coupon prices interpolation and extrapolation using the Nelson-Siegel, Svensson, Smith-Wilson models, and Hermite cubic splines.
Asks Yes-No questions with variable or custom responses.
The purpose of this package is to provide methods to interpret multiple
  linear regression and canonical correlation results including beta weights,structure coefficients, 
  validity coefficients, product measures, relative weights, all-possible-subsets regression,
  dominance analysis, commonality analysis, and adjusted effect sizes.
Deploy, maintain, and invoke models via the Yhat
    REST API.
Modelling the yield curve with some parametric models.
        The models implemented are: Nelson-Siegel, Diebold-Li and
        Svensson.  The package also includes the data of the term
        structure of interest rate of Federal Reserve Bank and European
        Central Bank.
The clustering by k-means of using the target variable.
             To determine the number of clusters with the variance of 
             the target variable in the cluster.
Compute the standard expected years of life lost (YLL),
    as developed by the Global Burden of Disease Study
    (Murray, C.J., Lopez, A.D. and World Health Organization, 1996).
    The YLL is based on comparing the age of death to an external standard life
    expectancy curve. It also computes the average YLL, which highlights premature
    causes of death and brings attention to preventable deaths
    (Aragon et al., 2008).
Build 'shiny' applications with the latest Bootstrap components
    and design utilities. Includes refreshed reactive inputs and outputs. 
    Use responsive layouts to design and construct applications for devices
    of all sizes.
Analyzing performances of cricketers and cricket teams
             based on 'yaml' match data from Cricsheet <http://cricsheet.org>.
Although there exist some R packages tailored for YouTube API (e.g., 'tuber'), downloading YouTube video subtitle (i.e., caption) in a tidy form has never been a low-hanging fruit. Using 'youtube-transcript-api Python package' under the hood, this R package provides users with a convenient way of parsing and converting a desired YouTube caption into a handy 'tibble' data_frame object. Furthermore, users can easily save a desired YouTube caption data as a tidy Excel file without advanced programming background knowledge.
For any spending function specified by the user, this 
    package provides corresponding boundaries for interim testing using
    the adaptively weighted log-rank test developed by Yang and Prentice
    (2010 <doi:10.1111/j.1541-0420.2009.01243.x>). 
    The package uses a re-sampling method to obtain stopping boundaries 
    at the interim looks.The output consists of stopping boundaries 
    and observed values of the test statistics at the interim looks, 
    along with nominal p-values defined as the probability of the test 
    exceeding the specific observed test statistic value or critical 
    value, regardless of the test behavior at other looks. 
    The asymptotic validity of the stopping boundaries is established
    in Yang (2018 <doi:10.1002/sim.7958>).
Inference procedures accommodate a flexible range of hazard ratio patterns with a two-sample semi-parametric model. This model contains the proportional hazards model and the proportional odds model as sub-models, and accommodates non-proportional hazards situations to the extreme of having crossing hazards and crossing survivor functions. Overall, this package has four major functions: 1) the parameter estimation, namely short-term and long-term hazard ratio parameters; 2) 95 percent and 90 percent point-wise confidence intervals and simultaneous confidence bands for the hazard ratio function; 3) p-value of the adaptive weighted log-rank test; 4) p-values of two lack-of-fit tests for the model. See the included "read_me_first.pdf" for brief instructions. In this version (1.1), there is no need to sort the data before applying this package.
An implementation of equilibrium-based yield per recruit methods.
    Yield per recruit methods can used to estimate the optimal yield for a fish population.
    The yield can be based on the number of fish caught (or harvested) or 
    biomass caught for all fish or just large (trophy) individuals.
Miscellaneous functions for data analysis, portfolio management, graphics, data manipulation, statistical investigation, including descriptive statistics, creating leading and lagging variables, portfolio return analysis, time series difference and percentage change calculation, stacking data for higher efficient analysis.
Simple method for comparison of gene
        expression generated across different experiments, and on
        different platforms; that does not require global
        renormalization, and is not restricted to comparison of
        identical probes. YuGene works on a range of microarray dataset
        distributions, such as between manufacturers. The resulting
        output allows direct comparisons of gene expression between
        experiments and experimental platforms.
Simulation and Inference for SDEs and Other Stochastic Processes.
Provides a graphical user interface for the 'yuima' package.
Provides a number of functions to facilitate
  extracting information in 'YAML' fragments from one or 
  multiple files, optionally structuring the information
  in a 'data.tree'. 'YAML' (recursive acronym for "YAML ain't
  Markup Language") is a convention for specifying structured
  data in a format that is both machine- and human-readable.
  'YAML' therefore lends itself well for embedding (meta)data
  in plain text files, such as Markdown files. This principle
  is implemented in 'yum' with minimal dependencies (i.e. only
  the 'yaml' packages, and the 'data.tree' package can be
  used to enable additional functionality).
Yummly.com is one of the world's largest and most powerful recipe search sites and this package aims to provide R bindings for publicly available Yummly.com Recipe API (https://developer.yummly.com/).
Provides simple statistics from instruments and observations at sites in the NEON network, and acts as a simple interface for v0 of the National Ecological Observatory Network (NEON) API.
  Statistics are generated for meteorologic and soil-based observations, and are presented for daily, annual, and one-time observations at all available NEON sites.
  Users can also retrieve any dataset publicly hosted by NEON. Metadata for NEON sites and data products can be returned, as well as information on data product availability by site and date. 
  For more information on NEON, please visit <https://www.neonscience.org>. For detailed data product information, please see the NEON data product catalog at <https://data.neonscience.org/data-product-catalog>.
Interface to the 'ZooBank' API (<http://zoobank.org/Api>) client.
    'ZooBank' (<http://zoobank.org/>) is the official registry of zoological 
    nomenclature. Methods are provided for using each of the API endpoints, 
    including for querying by author, querying for publications, get 
    statistics on 'ZooBank' activity, and more.
Principled methods for the imputation of zeros, left-censored and missing data in
    compositional data sets.
Provides a %<-% operator to perform multiple,
    unpacking, and destructuring assignment in R. The 
    operator unpacks the right-hand side of an assignment
    into multiple values and assigns these values to 
    variables on the left-hand side of the assignment.
R package accompanying the book Working with dynamic models for agriculture and environment, by Daniel Wallach (INRA), David Makowski (INRA), James W. Jones (U.of Florida), Francois Brun (ACTA). 3rd edition 2018-09-27.
Implements the estimation of local (and global) association measures: Ducher's Z, pointwise mutual information and normalized pointwise mutual information. The significance of local (and global) association is accessed using p-values estimated by permutations. Finally, using local association subgroup analysis, it identifies if the association between variables is dependent on the value of another variable.
Use behavioural variables to compute period, rhythmicity and other circadian parameters.
  Methods include computation of chi square periodograms (Sokolove and Bushell (1978) <DOI:10.1016/0022-5193(78)90022-X>),
  Lomb-Scargle periodograms (Lomb (1976) <DOI:10.1007/BF00648343>, Scargle (1982) <DOI:10.1086/160554>, Ruf (1999) <DOI:10.1076/brhm.30.2.178.1422>),
  and autocorrelation-based periodograms.
A framework that brings together an abundance of common
    statistical models found across packages into a unified interface, and
    provides a common architecture for estimation and interpretation, as well
    as bridging functions to absorb increasingly more models into the
    package. Zelig allows each individual package, for each
    statistical model, to be accessed by a common uniformly structured call and
    set of arguments. Moreover, Zelig automates all the surrounding building
    blocks of a statistical work-flow--procedures and algorithms that may be
    essential to one user's application but which the original package
    developer did not use in their own research and might not themselves
    support. These include bootstrapping, jackknifing, and re-weighting of data.
    In particular, Zelig automatically generates predicted and simulated
    quantities of interest (such as relative risk ratios, average treatment
    effects, first differences and predicted and expected values) to interpret
    and visualize complex models.
Add-on package for Zelig 5. Enables the use of a variety of logit
    and probit regressions.
Add-on package for Zelig 5. Enables the use of a variety of
    ecological inference models.
Provides an easy way to load stable Core Zelig and ancillary Zelig
    packages.
Provides an Interface to 'Zenodo' (<https://zenodo.org>) REST API, 
  including management of depositions, attribution of DOIs by 'Zenodo' and 
  upload of files.
This package provides an R wrapper for the Zendesk API
Graphical tools for visualizing high-dimensional data with a path
 of pairs. Note that this requires 'graph' from Bioconductor.
 If you want to use dynamic graphics based on 'loon', you also need to have
 'loon' installed, see <https://github.com/waddella/loon>.
Uses bootstrap to test zero order correlation being equal to a 
    partial or semi-partial correlation (one or two tailed). Confidence 
    intervals for the parameter (zero order minus partial) can also be
    determined. Implements the bias-corrected and accelerated bootstrap
    method as described in "An Introduction to the Bootstrap" Efron (1983) 
    <0-412-04231-2>.
Functions to compute compositional turnover using zeta-diversity,
    the number of species shared by multiple assemblages. The package includes
    functions to compute zeta-diversity for a specific number of
    assemblages and to compute zeta-diversity for a range of numbers of
    assemblages. It also includes functions to explain how zeta-diversity
    varies with distance and with differences in environmental variables
    between assemblages, using generalised linear models, linear models
    with negative constraints, generalised additive models,shape
    constrained additive models, and I-splines.
Performs Zoom-Focus Algorithm (ZFA) to optimize testing regions for rare variant association tests in exome sequencing data.
Microbiome count data (Operational Taxonomic Unit, OTUs) is usually overdispersed and has excessive zero counts. The 'ZIBBSeqDiscovery' assumes a zero-inflated beta-binomial model for the distribution of the count data, and employes link functions to adjust interested covariates. To fit the model, two approaches are proposed (i) a free approach which treats the overdispersion parameters for OTUs as independent, and (ii) a constrained approach which proposes a mean-overdispersion relationship to the count data. This package can be used to test the association between the composition of the microbiome counts and the interested covariates.
Detects abundance differences across clinical conditions. Besides, it takes the sparse nature of metagenomic data into account and handles compositional data efficiently.
Provides MCMC algorithms for the analysis of
        zero-inflated count models. The case of stochastic search
        variable selection (SVS) is also considered.  All MCMC samplers
        are coded in C++ for improved efficiency. A data set
        considering the demand for health care is provided.
Zillow, an online real estate company, provides real estate and
    mortgage data for the United States through a REST API. The ZillowR package
    provides an R function for each API service, making it easy to make API
    calls and process the response into convenient, R-friendly data structures.
    See <http://www.zillow.com/howto/api/APIOverview.htm> for the Zillow API
    Documentation.
Analyze count time series with excess zeros. 
    Two types of statistical models are supported: Markov regression by Yang et al.
    (2013) <doi:10.1016/j.stamet.2013.02.001> and state-space models by Yang et al. 
    (2015) <doi:10.1177/1471082X14535530>. They are also known as observation-driven and 
    parameter-driven models respectively in the time series literature. The functions used for 
    Markov regression or observation-driven models can also be used to fit ordinary regression models 
    with independent data under the zero-inflated Poisson (ZIP) or zero-inflated negative binomial (ZINB) 
    assumption. Besides, the package contains some miscellaneous functions to compute density, distribution, 
    quantile, and generate random numbers from ZIP and ZINB distributions.
Cross-Platform 'zip' Compression Library. A replacement
    for the 'zip' function, that does not require any additional
    external tools on any platform.
This package contains a database of city, state, latitude,
        and longitude information for U.S. ZIP codes from the
        CivicSpace Database (August 2004) augmented by Daniel Coven's
        federalgovernmentzipcodes.us web site (updated January 22,
        2012). Previous versions of this package (before 1.0) were
        based solely on the CivicSpace data, so an original version of
        the CivicSpace database is also included.
Implementation of three extensions of the Zipf distribution: the Marshall-Olkin 
  Extended Zipf (MOEZipf) Pérez-Casany, M., & Casellas, A. (2013) <arXiv:1304.4540>, the Zipf-Poisson Extreme (Zipf-PE) and the 
  Zipf-Poisson Stopped Sum (Zipf-PSS) distributions. 
  In log-log scale, the two first extensions allow for top-concavity 
  and top-convexity while the third one only allows for top-concavity. 
  All the extensions maintain the linearity associated with the Zipf model in the tail.
Statistical models and utilities for the analysis of word frequency distributions.
	The utilities include functions for loading, manipulating and visualizing word frequency
	data and vocabulary growth curves.  The package also implements several statistical 
	models for the distribution of word frequencies in a population.  (The name of this package 
	derives from the most famous word frequency distribution, Zipf's law.)
Fit zero-inflated Poisson hidden (semi-)Markov models with or without covariates by directly minimizing the negative log likelihood function using the gradient descent algorithm. Multiple starting values should be used to avoid local minima.
Implements Python-style zip for R. Is a more flexible version of cbind.
Generates a data frame of US zip codes and their distance to the given zip code when given a starting 
  zip code and a radius in miles. Also includes functions for use with 'choroplethrZip' which are detailed in
  the vignette.
Calculate and plot scattering matrix coefficients for plane waves at interface.
Fits beta regression and zero-or-one inflated beta regression and obtains Bayesian Inference of the model via the Markov Chain Monte Carlo approach implemented in JAGS.
The ZOIP distribution (Zeros Ones Inflated Proportional) is a proportional data distribution inflated with zeros and/or ones, this distribution is defined on the most known proportional data distributions, the beta and simplex distribution, Jørgensen and Barndorff-Nielsen (1991) <doi:10.1016/0047-259X(91)90008-P>, also allows it to have different parameterizations of the beta distribution, Ferrari and Cribari-Neto (2004) <doi:10.1080/0266476042000214501>, Rigby and Stasinopoulos (2005) <doi:10.18637/jss.v023.i07>. The ZOIP distribution has four parameters, two of which correspond to the proportion of zeros and ones, and the other two correspond to the distribution of the proportional data of your choice. The 'ZOIP' package allows adjustments of regression models for fixed and mixed effects for proportional data inflated with zeros and/or ones.
'Zoltar' <https://www.zoltardata.com/> is a website that provides a repository of model forecast results
    in a standardized format and a central location. It supports storing, retrieving, comparing, and analyzing time
    series forecasts for prediction challenges of interest to the modeling community. This package provides functions
    for working with the 'Zoltar' API, including connecting and authenticating, getting information about projects,
    models, and forecasts, deleting and uploading forecast data, and downloading scores.
An S3 class with methods for totally ordered indexed
             observations. It is particularly aimed at irregular time series
             of numeric vectors/matrices and factors. zoo's key design goals
             are independence of a particular index/date/time class and
             consistency with ts and base R by providing methods to extend
             standard generics.
The analysis and inference of faunal remains recovered from
    archaeological sites concerns the field of zooarchaeology. The zooaRch package
    provides analytical tools to make inferences on zooarchaeological data.
    Functions in this package allow users to read, manipulate, visualize, and
    analyze zooarchaeological data.
The analysis and inference of faunal remains recovered from
    archaeological sites concerns the field of zooarchaeology. The zooaRchGUI package
    provides a graphical user interface to analytical tools found in the R statistical environment
    to make inferences on zooarchaeological data. Functions in this package allow users to interactively
    read, manipulate, visualize, and analyze zooarchaeological data.
Tools for manipulating multivariate time series data by extending
    'zoo' class.
A free (open source) solution for analyzing digital
  images of plankton. In combination with ImageJ, a free image analysis
  system, it processes digital images, measures individuals, trains for
  automatic classification of taxa, and finally, measures plankton samples
  (abundances, total and partial size spectra or biomasses, etc.).
zm(), called with any active plot allow to enter an
    interactive session to zoom/navigate any plot. The development
    version, as well as binary releases can be found at
    https://github.com/cbarbu/R-package-zoom
Provides the grid search algorithm with a zoom.
    The grid search algorithm with a zoom aims to help solving difficult optimization problem
    where there are many local optima inside the domain of the target function.
    It offers suitable initial or starting value for the following optimization procedure,
    provided that the global optimum exists in the neighbourhood of the initial or starting value.
    The grid search algorithm with a zoom saves time tremendously in cases with high-dimensional arguments.
Reproducible and remixable species distribution modelling. The
    package reads user submitted modules from an online repository, runs full
    SDM workflows and returns output that is fully reproducible.
Combines a forecast of a time series, using the function forecast(), with the dynamic plots from dygraphs.
A tool for calculating z-scores and centiles for weight-for-age, 
    length/height-for-age, weight-for-length/height, BMI-for-age, 
    head circumference-for-age, age circumference-for-age, 
    subscapular skinfold-for-age, triceps skinfold-for-age based on the 
    WHO Child Growth Standards.
Generates well-known integer sequences. 'gmp' package is adopted for computing with arbitrarily large numbers. Every function has hyperlink to its corresponding item in OEIS (The On-Line Encyclopedia of Integer Sequences) in the function help page. For interested readers, see Sloane and Plouffe (1995, ISBN:978-0125586306).
Makes zebra-striped tables (tables with alternating row colors)
    in LaTeX and HTML formats easily from a data.frame, matrix, lm, aov, anova,
    glm, coxph, nls, fitdistr, mytable and cbind.mytable objects.
Read '.xls' and '.sbj' files which are written by the
   Microsoft Windows program 'z-Tree'. The latter is a software for
   developing and carrying out economic experiments
   (see <http://www.ztree.uzh.ch/> for more information).
How fast can you type R functions on your keyboard? Find out by running a 'zty.pe' game: export R functions as instructions to type to destroy opponents vessels.
Zero-variance control variates (ZV-CV, Mira et al. (2013) <doi:10.1007/s11222-012-9344-6>) is a post-processing method to reduce the variance of Monte Carlo estimators of expectations using the derivatives of the log target. Once the derivatives are available, the only additional computational effort is in solving a linear regression problem. Recently, this method has been extended to higher dimensions using regularisation (South et al., 2018 <arXiv:1811.05073>). This package can be used to easily perform ZV-CV or regularised ZV-CV when a set of samples, derivatives and function evaluations are available. Additional functions for applying ZV-CV to two estimators for the normalising constant of the posterior distribution in Bayesian statistics are also supplied.
Contains an efficient implementation of Sen's slope method (Sen, 1968) plus implementation of Xuebin Zhang's (Zhang, 1999) and Yue-Pilon's (Yue, 2002) prewhitening approaches to determining trends in climate data.
Functions and datasets for bootstrapping from the
book "Bootstrap Methods and Their Application" by A. C. Davison and
D. V. Hinkley (1997, CUP), originally written by Angelo Canty for S.
Various functions for classification, including k-nearest
neighbour, Learning Vector Quantization and Self-Organizing Maps.
Methods for Cluster analysis.  Much extended the original from
Peter Rousseeuw, Anja Struyf and Mia Hubert,
based on Kaufman and Rousseeuw (1990) "Finding Groups in Data".
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.
Functions for kernel smoothing (and density estimation)
corresponding to the book:
Wand, M.P. and Jones, M.C. (1995) "Kernel Smoothing".
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.
Functions and datasets to support Venables and Ripley,
"Modern Applied Statistics with S" (4th edition, 2002).
A rich hierarchy of matrix classes, including triangular,
symmetric, and diagonal matrices, both dense and sparse and with
pattern, logical and numeric entries.   Numerous methods for and
operations on these matrices, using 'LAPACK' and 'SuiteSparse' libraries.
Generalized additive (mixed) models, some of their extensions and
other generalized ridge regression with multiple smoothing
parameter estimation by (Restricted) Marginal Likelihood,
Generalized Cross Validation and similar, or using iterated
nested Laplace approximation for fully Bayesian inference. See
Wood (2017) <doi:10.1201/9781315370279> for an overview.
Includes a gam() function, a wide variety of smoothers, 'JAGS'
support and distributions beyond the exponential family.
Software for feed-forward neural networks with a single
hidden layer, and for multinomial log-linear models.
Recursive partitioning for classification,
regression and survival trees.  An implementation of most of the
functionality of the 1984 book by Breiman, Friedman, Olshen and Stone.
Contains the core survival analysis routines, including
definition of Surv objects,
Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models,
and parametric accelerated failure time models.
Please send bug reports to ismrp@jasp.ism.ac.jp.
Please send questions and comments to etiennead@gmail.com.
Please send questions and comments regarding pbdR to
RBigData@gmail.com
Please send bug reports to ismrp@jasp.ism.ac.jp
Please send questions and comments regarding Rcpp to
rcpp-devel@lists.r-forge.r-project.org
Please send questions and comments about Rcpp to
rcpp-devel@lists.r-forge.r-project.org
Please send questions and comments regarding RInside to
rcpp-devel@lists.r-forge.r-project.org
Please send comments and bug reports to
ismrp@jasp.ism.ac.jp
Dane R. Van Domelen <vandomed@gmail.com>
Renata T C Yokota <renatayokota@gmail.com>
Pedro Rafael Diniz Marinho <pedro.rafael.marinho@gmail.com>
Carlos J. Gil Bellosta <cgb@datanalytics.com>
Achmad Fauzi Bagus F <fauzi.pandya@gmail.com>
Cesar de Oliveira Ferreira Silva <cesaroliveira.f.silva@gmail.com>
Jose Roberto Ayala Solares <ichbinjras@gmail.com>
Pierre Lafaye De Micheaux <lafaye@unsw.edu.au>
M. A. J. Zondervan-Zwijnenburg <m.a.j.zwijnenburg@uu.nl>
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Gabriel F. R. Vasconcelos <gabrielrvsc@yahoo.com.br>
Pedro Nascimento de Lima <pedrolima.n@gmail.com>
Pierre Lafaye de Micheaux <lafaye@unsw.edu.au>
Joao Vitor Dias Monteiro <monte092@umn.edu>
Edwin Javier Castillo Carreño <edjcastilloca@unal.edu.co>
Ricardo de Matos Simoes <ricardo_dematossimoes@dfci.harvard.edu>
Karel Van den Meersche <karel.van_den_meersche@cirad.fr>
Blondeau Da Silva Stephane <Stephane.Blondeau-Da-Silva@ac-limoges.fr>
Jose Antonio Garcia Bueno <joseagbueno@gmail.com>
Anderson Rodrigo da Silva <anderson.agro@hotmail.com>
Ana Belen Nieto Librero <ananieto@usal.es>
Ricardo Puziol de Oliveira <rpuziol.oliveira@gmail.com>
Rafael de Andrade Moral <rafael.deandrademoral@mu.ie>
David Alejandro Martell Juarez <alex91599@gmail.com>
Antonio Jose Canada Martinez <ancamar2@gmail.com>
Jianzhao Gao and Xiaodan Fan <gaojz@nankai.edu.cn>
A. Jonathan R. Godfrey <a.j.godfrey@massey.ac.nz>
Rahim Alhamzawi  (University of Al-Qadisiyah) <rahim.alhamzawi@qu.edu.iq>
Sy Han (Steven) Chiou <schiou@hsph.harvard.edu>
Ivo Van Walle Developer <ivo.van.walle@gmail.com>
Ting Fung (Ralph) Ma <tingfung.ma@wisc.edu>
L. A. van der Ark <L.A.vanderArk@uva.nl>
"James P. Howard, II" <jh@jameshoward.us>
Francesca Marta Lilja Di Lascio <marta.dilascio@unibz.it>
K. Gerald van den Boogaart <support@boogaart.de>
P. Lafaye de Micheaux <lafaye@unsw.edu.au>
Sjoerd van der Spoel <R@sjoerdvanderspoel.nl>
Jonas M. B. Haslbeck <jonas.haslbeck@gmail.com>
Ernst van der Maaten <ernst.vandermaaten@gmail.com>
Eduardo L. T. Conceicao <mail@eduardoconceicao.org>
Marcelino de la Cruz Rot <marcelino.delacruz@urjc.es>
Don van den Bergh <donvdbergh@hotmail.com>
José Carlos Soage González <jsoage@uvigo.es>
Alexandre Adalardo de Oliveira <ecovirtualpackage@gmail.com>
Ardo van den Hout <Ardo.vandenHout@ucl.ac.uk>
Vineetha Warriyar. K. V. <vineethawarriyar.kod@ucalgary.ca>
Manuel Oviedo de la Fuente <manuel.oviedo@usc.es>
Caíque de Oliveira de Souza <forestgrowthsoftware@gmail.com>
Clayton Vieira Fraga Filho <forestgrowthsoftware@gmail.com>
Eduardo San Miguel Martin
<eduardo.san.miguel.martin@gmail.com>
Agnese Maria Di Brisco <agnese.dibrisco@unimib.it>
Shaiful Anuar Abu Bakar <saab@um.edu.my>
Vincent T van Hees <vincentvanhees@gmail.com>
Geraldine Van der Auwera <vdauwera@broadinstitute.org>
Anders Gonçalves da Silva <andersgs@gmail.com>
Rafael de Andrade Moral <rafael_moral@yahoo.com.br>
Stephanie van der Pas <svdpas@math.leidenuniv.nl>
Austrian Institute of Technology <hyrim@ait.ac.at>
Sai Thein Than Tun <theinthantun.sai@gmail.com>
Marcelino de la Cruz <marcelino.delacruz@urjc.es>
P Lafaye de Micheaux <lafaye@dms.umontreal.ca>
Luiz Eduardo S. Gomes <gomes.leduardo@gmail.com>
Eufrasio de A. Lima Neto <eufrasio@de.ufpb.br>
Alejandro Quintela del Rio <aquintela@udc.es>
Kevin Allan Sales Rodrigues <kevin.asr@outlook.com>
Jan van der Laan <djvanderlaan@unrealizedtime.nl>
Thiago de Paula Oliveira <thiago.paula.oliveira@usp.br>
Danilo Roberti Alves de Almeida <daniloflorestas@gmail.com>
Jesús M. Rodríguez Rodríguez <jesusmro@ono.com>
Javier Royuela del Val <jroyval@lpi.tel.uva.es>
Rosineide Fernando da Paz <rfpaz2@gmail.com>
"Antoine Tremblay, Dalhousie University" <trea26@gmail.com>
Vito M. R. Muggeo <vito.muggeo@unipa.it>
"Robin K. S. Hankin" <hankin.robin@gmail.com>
Elizabeth Holmes - NOAA Federal <eli.holmes@noaa.gov>
Euloge C. Kenne Pagui <kenne@stat.unipd.it>
Fernando Ferraz do Nascimento <fernandofn@ufpi.edu.br>
Dr. Muhammad Imdad Ullah <mimdadasad@gmail.com>
Antonio Hermes M. da Silva-Junior <hermes@ccet.ufrn.br>
Manuel David Peris Diaz <manuel.perisdiaz@uwr.edu.pl>
Caspar J. van Lissa <c.j.vanlissa@gmail.com>
Alysha M De Livera <alyshad@unimelb.edu.au>
Stephen L R Ellison <s.ellison@lgcgroup.com>
Bruno André Rodrigues coelho <bruno@brodrigues.co>
Krishna Harsha K H <khkrishnaharsha123@gmail.com>
Carlos Alberto Cardozo Delgado <cardozorpackages@gmail.com>
L. Andries van der Ark <L.A.vanderArk@uva.nl>
Sebastian J. Teran Hidalgo <sebastianteranhidalgo@gmail.com>
Julio Cesar Hernandez Sanchez <juliocesar_avila@usal.es>
Daiane Marcolino de Mattos <daiane.mattos@fgv.br>
Rune Haubo Bojesen Christensen <rune.haubo@gmail.com>
Joao Pedro Oliveira dos Santos <joaopsantos@pm.me>
Sy Han (Steven) Chiou <schiou@utdallas.edu>
Wouter van der Bijl <wouter@zoology.ubc.ca>
Euloge Clovis Kenne Pagui <kenne@stat.unipd.it>
Joaquin Bienvenido Ordieres Mere <j.ordieres@upm.es>
ACHIEPO Odilon Yapo M. <kingodilon@gmail.com>
"Varhegyi, Nikole E. (nev4g)" <nev4g@virginia.edu>
Marieke van der Maaten-Theunissen <marieketheunissen@gmail.com>
Gualberto Segundo Agamez Montalvo <gsagamez@gmail.com>
Juan Carlos Gonçalves Dosantos <juan.carlos.goncalves@udc.es>
Blondeau Da Silva Stephane <blondeaudasilva@xlim.fr>
Robbie C.M. van Aert <R.C.M.vanAert@tilburguniversity.edu>
Peter J. van der Most <p.j.van.der.most@umcg.nl>
Wagner Santos de Lima <wagnersantos.ufs@hotmail.com>
"Shingo Yamamoto (gloops, Inc.)" <si.yamamoto@gloops.com>
Maria de los Angeles Resa <maria@stat.columbia.edu>
Eliot J B McIntire <eliot.mcintire@canada.ca>
Achmad Fauzi Bagus F <uzi.fauz@gmail.com>
Manuel A. Mosquera Rodríguez <mamrguez@uvigo.es>
Pedro Mac Dowell Innecco <dmu@pedroinnecco.com>
Christian Alexander Mongeau Ospina <christian+rpkg-repec@mongeau.net>
Peter van den Berg <peter_v_d_berg@hotmail.com>
Manuel de Francisco Vera <manueldefrancisco@gmail.com>
Raymond K. W. Wong <raymondkww.dev@gmail.com>
María del Carmen Calatrava Moreno
<mc.calatrava.moreno@gmail.com>
Md Riaz Ahmed Khan <mdriazahmed.khan@jacks.sdstate.edu>
Manuel David Peris Diaz <madape@alumni.uv.es>
Titus von der Malsburg <malsburg@posteo.de>
Mario A. Martínez Araya <mma@mariomartinezaraya.com>
Rutger M. van den Bor <r.m.vandenbor@umcutrecht.nl>
Laercio Junio da Silva <laerciojdsilva@gmail.com>
Anderson Rodrigo da Silva <anderson.silva@ifgoiano.edu.br>
Niels J. de Winter <niels.de.winter@vub.be>
Prof. Juan David Velasquez H. <jdvelasq@unal.edu.co>
Jose Manuel Vera Oteo <vera.josemanuel@gmail.com>
Willem Robert van Hage <willem.van.hage@synerscope.com>
Frederick Kin Hing Phoa <fredphoa@stat.sinica.edu.tw>
Stephen A Sefick Jr. <ssefick@gmail.com>
Vinicius Quintas Souto Maior <vinicius@de.ufpe.br>
Daniel A. Dos Santos <pseudalopex_79@yahoo.com>
Jesus Maria Rodriguez Rodriguez <jesusmro@ono.com>
Gabriel Henrique Oliveira Assuncao <gabrielhenryoliver@hotmail.com>
K. Gerald van den Boogaart <boogaart@math.tu-freiberg.de>
Almut E. D. Veraart <a.veraart@imperial.ac.uk>
Silvestre Garcia de Jalon <s.garciadejalon@gmail.com>
Hafiz Zain Ul Haq <zainalibajwa4u@yahoo.com>
Antonio Fabio Di Narzo <antonio.fabio@gmail.com>
Dr. Ranjit Kumar Paul <ranjitstat@gmail.com>
Eraldo B. dos Anjos Filho <ebdaf1@de.ufpe.br>
Oscar Alberto Quijano Xacur <oscar.quijano@use.startmail.com>
Angel Garcia de la Garza <agarciadlg@gmail.com>
Giulio Valentino Dalla Riva <me@gvdallariva.net>
Joerg Schlarmann (aka Produnis) <schlarmann@produnis.de>
Olivier M. F. Martin <oliviermfmartin@gmail.com>
Juan Camilo Diaz Zapata <jucdiazza@unal.edu.co>
Department of Mathematics and Statistics, University of
Agriculture Faisalabad, Faisalabad-Pakistan.
Package 'rjags' requires JAGS executabe file (compatiable
version) should be already installed.
Maintainers are not available to give advice on using a package
they did not author.
This version is a generalisation for (v=wnl) treatments. In the
version 1.0 we used rectangular right angular (m) association
schemes with (v=2nl) and m=4,5 and 7 associated classes. The
Association schemes used in this R-package are : Rectangular
association scheme; Generalized rectangular right angular
association scheme (4); Generalized rectangular right angular
association scheme (5); Generalized rectangular right angular
association scheme (7).
One minimum cost flow problem may have several or many solutions
that are equivalent in providing the same minimum total or mean
cost. Minor differences between computers or implementations
may have the minor consequence of altering which equivalent
solution is produced.
Supported by internal research project ING01-1516 from
'Universidad de Playa Ancha'
This publication was prepared for NCES under Contract No.
ED-IES-12-D-0002 with the American Institutes for Research.
Mention of trade names, commercial products, or organizations
does not imply endorsement by the U.S. Government.
Research funded by the Polish National Science Centre grant under
the contract number DEC-2015/19/N/HS4/00205.
The header file blaswrap.h, f2c.h and fgsg.h are from the
VisualStudio library created by Julie Langou.
Few parts are still preliminary and might be changed in the near
future. And more functions will be add as easier tools to
higher efficiency in analyzing.
While reporting issues on github start subject line with
[cran-package]
This software comes with NO warranty! It is NOT intended to be
used in clinical applications! For evaluation only!
Package stable is available at http://www.robustanalysis.com
The authors are grateful to Mikhail Kanevski, Michael
Leuenberger, Carmen D. Vega Orozco and Fabian Guignard for many
fruitful discussions about the use of intrinsic dimension in
data mining.
Please, cite this package as: Marina Saez (2018). KarsTS: An
Interface For Microclimate Time Series Analysis. R package
version 2.2.
Department of Statistics, Bahauddin Zakariya University, Multan,
Pakistan.
Department of Statistics, Bahauddin Zakariya University, Multan,
Pakistan
The originale code was in matlab, see details below.
For convenience, the package includes the necessary header files
for the Eigen distribution. (This is all that is needed to use
that functionality.) You can use an alternative installation of
Eigen on your system or the one we provide. The license for the
Eigen code is very permissive and allows us to distribute it
with this package. See <http://eigen.tuxfamily.org/index.php?
title=Main_Page> and also the License section on that page.
The current version (0.4.1) fixes an issue in the add_pcoord
function where negative eigenvalues were left uncorrected when
the user specified that a correction be applied.
In order to use this package you need your personal Project Vote
Smart API key. To get an API-key you need to sign up for access
to PVS' API (see http://votesmart.org/share/api for details).
Whenever you are using pvsR make sure that your personal PVS
API key is saved as a character string in the variable pvs.key
(pvs.key <- "yourkey"). If there is no valid PVS API key saved
in the variable pvs.key the functions in this package won't
work. This program is free software; you can redistribute it
and/or modify it under the terms of the GNU General Public
License as published by the Free Software Foundation; either
version 2 of the License, or (at your option) any later
version. This program is distributed in the hope that it will
be useful, but WITHOUT ANY WARRANTY; without even the implied
warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the GNU General Public License for more details.
More information about the system requirements can be found in
the README file.
For details and derivation, see Braumoeller, Bear F. "Guarding
Against False Positives in Qualitative Comparative Analysis."
Forthcoming, Political Analysis.
This will be used generally to communicate with HTTP servers and
specifically in the SOAP package, HTML forms and for accessing
data on Web sites that require more state information than
simply URL access (e.g. cookies).
See http://www.json.org/JSON_checker/test.zip for tests. We now
use code from the libjson project
(http://libjson.sourceforge.net).
This package allows to estimate spatial spline models to small to
middle size datasets, for larger datasets it will depend on the
memory of the system. Therefore use it carefully. Currently
under development to incorporate additive components.
The name "Scrabble" is a trademark of Hasbro, Inc. in the United
States and Canada; elsewhere, it is a trademark of Mattel.
The GPU computing version can be found soon, on following url
https://sites.google.com/site/mohamedlaibwebpage/Software.
revised for compliance with CRAN by K.Pal and C.M.Livi
The author acknowledges financial support from Sao Paulo Research
Foundation (FAPESP) grant 2016/18599-4
This is experimental software distributed free of charge and
comes with no warranty!  Exercise professional caution.
This publication was prepared for NCES under Contract No.
ED-IES-12-D-0002 with American Institutes for Research. Mention
of trade names, commercial products, or organizations does not
imply endorsement by the U.S. government.
2018-03-13 21:10:22 UTC; Steven Harris Ranney
2018-01-10 08:38:11 UTC; Benjamin Schlegel
2012-06-11 14:36:21 UTC; Sophie Baillargeon
2010-01-31 13:29:31 UTC; Jens Henrik Badsberg
2019-03-13 04:47:37 UTC; Michael Kellermann
2018-05-10 17:12:26 UTC; Virasakdi's Surface
2018-10-24 16:05:44 UTC; Ashutosh Srivastava
2018-05-03 11:01:05 UTC; Benjamin Schlegel
2016-10-16 07:32:22 UTC; Engineering Broker
2015-02-26 17:45:29 UTC; Sophie Baillargeon
2015-08-23 02:08:23 UTC; Fernando de Villar
2018-05-06 15:21:24 UTC; NvTomus InDemonic
2019-02-05 01:13:03 UTC; Eleanor Pullenayegum
2019-01-29 14:25:08 UTC; Nadezda Spiridovska
2018-04-29 13:45:20 UTC; Alberto Vico Moreno
2014-08-28 18:37:46 UTC; Sophie Baillargeon
2017-01-03 15:34:03 UTC; Guybrush Threepwood
2019-05-02 03:37:50 UTC; hernandez michael
2018-12-13 11:53:36 UTC; Pedro Albuquerque
2019-01-17 15:20:21 UTC; Pedro Albuquerque
2016-07-26 19:41:07 UTC; Timothy Schwinghamer
2018-06-05 15:09:00 UTC; Daniel Lewandowski
2017-08-15 05:32:43 UTC; Aurnov Chattopadhyay
2014-10-17 18:39:03 UTC; Christiane Dargatz
2018-08-20 12:34:05 UTC; Aleksandra Grudziaz
2016-03-19 23:54:53 UTC; Sophie Baillargeon
Image Magick (for reading non PPM format), dcraw
(for reading RAW images).
None, but greater than 12 GB of RAM highly
recommended.
ImageMagick (http://imagemagick.org) or
GraphicsMagick (http://www.graphicsmagick.org) or LyX
(http://www.lyx.org) for saveGIF(); (PDF)LaTeX for saveLatex();
SWF Tools (http://swftools.org) for saveSWF(); FFmpeg
(http://ffmpeg.org) or avconv (https://libav.org/avconv.html)
for saveVideo()
FFmpeg >= 3.2: libavfilter-dev (deb),
ffmpeg-devel(rpm)
Requires external zip utility, available in path.
On windows, it's sufficient to install RTools.
JAGS (>= 3.4.0) (see
http://mcmc-jags.sourceforge.net)
C++11, JAGS >= 4.1.0
(http://mcmc-jags.sourceforge.net)
OpenCL library; single-precision AMD or Nvidia GPU;
Rtools (>= 3.3) for Windows, Xcode (>= 9.0) for Mac
OS X
Preferred genomic operations engine: 'BEDTools',
'BEDOPS' and 'Tabix (>= 1.3)'.
JAGS >=4.3.0 <http://mcmc-jags.sourceforge.net/>,
Java JDK >=1.4 <https://www.java.com/en/download/manual.jsp>
A system with a recent-enough C++11 compiler (such
as g++-4.8 or later).
Hugo (<https://gohugo.io>) and Pandoc
(<https://pandoc.org>)
GNU make, pandoc (>= 1.12.3), pandoc-citeproc
OpenBUGS (>= 3.2.2), hence Windows or Linux
MAGeCK (=0.51, from
http://sourceforge.net/p/mageck/wiki/Home/), bowtie2
(http://bowtie-bio.sourceforge.net/bowtie2/index.shtml)
OpenCL library; double-precision AMD or Nvidia GPU;
GNU make
Python (>= 2.7.0); spaCy <https://spacy.io/> (>=
2.0); Java (>= 7.0); Stanford CoreNLP
<http://nlp.stanford.edu/software/corenlp.shtml> (>= 3.9.2)
xclip (https://github.com/astrand/xclip) or xsel
(http://www.vergenet.net/~conrad/software/xsel/) for accessing
the X11 clipboard
libcurl: libcurl-devel (rpm) or
libcurl4-openssl-dev (deb).
Julia (>= 0.6.0), Convex.jl, SCS.jl, ECOS.jl
Java (>= 7.0); Stanford CoreNLP
<http://nlp.stanford.edu/ software/corenlp.shtml> (>= 3.5.2)
couchDB instance to connect to and work with.
libxml2-devel, libcurl-devel, openssl-devel,
libsecret-devel, libsodium-devel
none, one or more of JAGS (>= 3.0.0), WinBUGS (>=
1.4), OpenBUGS (>= 3.2.2), Stan
GLPK library package (e.g., libglpk-dev on
Debian/Ubuntu)
Xft or zlib (only needed for platforms other than
OSX and Windows)
LibreOffice (<https://www.libreoffice.org/>)
required to extract data from .doc files.
Requires the OpenMP library for parallel computing.
If the OpenMP library is not available, the code is executed
sequentially and a warning is printed.
Python (>= 2.7 or >= 3.5) and elex package
EnergyPlus (>= 8.3, optional)
(<https://energyplus.net>); udunits2
Display resolution >= 1280x800; RAM >= 4GB
GNU Scientific Library (GSL). Note: users should
have GSL installed.
(license-free) MATLAB Runtime (MCR) V 9.0
Python (>= 2.4), difflib, fuzzywuzzy ( >=0.15.0 ),
python-Levenshtein ( >=0.12.0 ). Detailed installation
instructions for each operating system can be found in the
README file.
Debian or Ubuntu system with access to Goto Blas,
Intel MKL, Atlas development build as well as a Nvidia GPU with
CUDA support
cxx11, gdal, libgdal, libproj, libcurl, netcdf4
The SequenomMarkers() marker design function
requires 'vcftools' and 'Perl' on 'windows', and, in addition,
'awk' and 'bash' on '*nix'.
MongoDB (>= 3.4.0), Python (>= 2.7). Installation
instructions and links can be found in the README file.
By default, git2r uses a system installation of the
libgit2 headers and library. However, if a system installation
is not available, builds and uses a bundled version of the
libgit2 source. zlib headers and library. OpenSSL headers and
library (non-macOS). LibSSH2 (optional on non-Windows) to
enable the SSH transport.
GPGME: libgpgme-dev / libgpgme11-dev (deb),
gpgme-devel (rpm) gpgme (brew). On Linux 'haveged' is
recommended for generating entropy when using the GPG key
generator.
C++11 (supporting at least std=c++0x), OpenCL
shared library (provided by an SDK such as AMD/NVIDIA) and
OpenCL headers including the C++ header file (provided by
Khronos if not by SDK)
SRAtoolkit, Salmon, Java, FastQC, MultiQC
Python (>= 2.7.0) with header files and shared
library; TensorFlow (>= 1.10; https://www.tensorflow.org/);
Tensorflow Probability (>=0.3.0;
https://www.tensorflow.org/probability/)
Python (>= 3.6) with header files and shared
library; H2O4GPU (https://github.com/h2oai/h2o4gpu)
libhdf5 (>= 1.8.12) with C++ interface
(--enable-cxx=yes) and v18 API enabled
Apache Hadoop >= 2.6.0
(https://hadoop.apache.org/releases.html#Download); Obsolete:
Hadoop core >= 0.19.1 and <= 1.0.3 or CDH3
(http://www.cloudera.com); standard unix tools (e.g., chmod)
ImageMagick (http://imagemagick.org) or
GraphicsMagick (http://www.graphicsmagick.org)
A C library with the fesetround/fegetround
functions.
gmp (optional), libxml2 (optional), glpk (optional)
Impala driver to support a 'DBI'-compatible R
interface
jupyter, jupyter_kernel_test (Python package for
testing)
libjq: jq-devel (rpm) or libjq-dev (deb)
Optional: libsecret on Linux (libsecret-1-dev on
Debian/Ubuntu, libsecret-devel on Fedora/CentOS)
CUDA 8.0 tookit, OpenMP 4.0 capable compiler
Package vignettes based on R Markdown v2 or
reStructuredText require Pandoc (http://pandoc.org). The
function rst2pdf() require rst2pdf
(https://github.com/rst2pdf/rst2pdf).
ImageJ (>=1.48), ij.jar (see
http://imagej.nih.gov/ij/), Java (>=1.6.0)
libxml2: libxml2-dev (deb), libxml2-devel (rpm)
JAGS (>= 3.0.0) (see
http://mcmc-jags.sourceforge.net)
At least trial version of LocalSolver to be
downloaded from http://www.localsolver.com/download.html
A system with zoneinfo data (e.g.
/usr/share/zoneinfo) as well as a recent-enough C++11 compiler
(such as g++-4.8 or later). On Windows the zoneinfo included
with R is used.
ImageMagick++: ImageMagick-c++-devel (rpm) or
libmagick++-dev (deb)
ADMB version 11 <http://admb-project.org/> for
use.admb=TRUE; see readme.txt
On a Unix-alike, one of the C functions
mach_absolute_time (macOS), clock_gettime or gethrtime. If none
of these is found, the obsolescent POSIX function gettimeofday
will be tried.
Imports the `curl` and `XML` packages. These have
system requirements on `libxml2-devel`, `libcurl-devel` and
`openssl-devel`.
JAGS (>= 4.1) for the script version, both JAGS and
GTK+ for the GUI version. For install instructions, see README
file.
Apache Spark 2.0+, Apache Maven 3.5+, Java JDK 8,
MLeap Runtime 0.10.1+
MonetDB, available from http://www.monetdb.org or
MonetDBLite R package
JAGS (>= 4.0.0) (see
http://mcmc-jags.sourceforge.net)
A valid account MUST be registered with Microsoft's
Cognitive Services website
<https://www.microsoft.com/cognitive-services/> in order to
obtain a (free) API key. Without an API key, this package will
not work properly.
pandoc, ipython (>= 3.0) or jupyter,
ipython/jupyter notebook optional dependencies, pywin32 (on
windows)
A compiler with C++11 support for the thread
library, Requires Rtools >= 33 (i.e. R >= 3.3.0) to build on
Windows.
CUDA >= v7.0, Nvidia GPU (e.g. GeForce or Tesla)
with compute capability >= 3.0 (Kepler)
Python (>= 2.7), nmslib ( >= 1.7.1), scipy ( >=
1.0.0), numpy ( >= 1.14.0). Detailed installation instructions
for each operating system can be found in the README file.
C++11, GNU make, An ODBC3 driver manager and
drivers.
OpenCV: libopencv-dev (Debian, Ubuntu) or
opencv-devel (Fedora)
To use the Localhost of OSRM, you need to build
OSRM
<https://github.com/Project-OSRM/osrm-backend/wiki/Building-OSRM>
locally
Python (>= 3.0.1); bencode-python3 (1.0.2)
pandoc (http://johnmacfarlane.net/pandoc) for
exporting markdown files to other formats.
For the MERLIN wrapper, MERLIN
(http://csg.sph.umich.edu/abecasis/Merlin/) must be installed
and pointed to in the PATH environment variable.
The 'Japanese.Rnw' vignette requires uplatex and
dvipdfmx.
OpenMPI (>= 1.5.4) on Solaris, Linux, Mac, and
FreeBSD. MS-MPI (Microsoft HPC Pack 2012) or MPICH2 (>=
1.4.1p1) on Windows.
OpenMPI (>= 1.5.4) on Solaris, Linux, Mac, and
FreeBSD. MS-MPI (Microsoft HPC Pack 2012 R2 MS-MPI
Redistributable Package) on Windows.
OpenMPI (>= 1.5.4) on Solaris, Linux, Mac, and
FreeBSD. MS-MPI (Microsoft MPI v7.1 (SDK) and Microsoft HPC
Pack 2012 R2 MS-MPI Redistributable Package) on Windows.
OpenMPI (>= 1.5.4) on Solaris, Linux and Mac.
(Parallel) HDF5 and (Parallel) NetCDF4 (4.1 or later)
libraries. No MPI library required on Windows.
OpenMPI (>= 1.5.4) on Solaris, Linux, Mac, and
FreeBSD. No MPI library required on Windows yet.
ssh (OpenSSH) or plink (PuTTY) on Solaris, Linux,
and Mac.
'OpenMPI' (>= 1.5.4) on Solaris, Linux, Mac, and
FreeBSD. 'MS-MPI' (Microsoft HPC Pack 2012 R2 MS-MPI
Redistributable Package) on Windows.
Linux, Mac OSX, and Windows, or 'ZeroMQ' library >=
4.0.4. Solaris 10 needs 'ZeroMQ' library 4.0.7 and 'OpenCSW'.
Poppler C++ API: libpoppler-cpp-dev (deb) or
poppler-cpp-devel (rpm). The unit tests also require the
'poppler-data' package (rpm/deb)
Phoenix NLME with Phoenix Modeling Language (PML)
license
For Windows users, Rtools (see
https://cran.r-project.org/bin/windows/Rtools/).
Python 2.7.x or Python 3.2.x-3.4.x, NumPy (Python
scientific computing package)
pandoc (>= 1.12.3) -
http://johnmacfarlane.net/pandoc
JAGS (>= 3.2.0) (see
http://mcmc-jags.sourceforge.net)
A POSIX system. Currently Linux and OS X are known
to work. GNU make.
proj 4.4.6 or higher (http://proj.maptools.org/)
pandoc (http://pandoc.org) for building Vignettes
and output reports as HTML
'python3' needs to built for the same architecture
R is built for (32 or 64 bit).
Python (>= 2.7.0) with header files and shared
library (UNIX) / static library (Windows)
Qt4 libraries and headers (http://qt.nokia.com),
cmake (http://www.cmake.org)
OpenBugs for functions bugs() and openbugs() or
WinBUGS 1.4 for function bugs()
asciidoc (<https://asciidoc.org>), source-highlight
is recommend.
Java JRE 1.6 or higher ; Xuggle-xuggler-5.4.jar in
library paths and class paths.
A valid Bloomberg installation. The API headers and
dynamic library are downloaded from
<https://github.com/Rblp/blp> during the build step. See
<https://bloomberg.github.io/blpapi-docs/cpp/3.8> as well as
<https://www.bloomberg.com/professional/support/api-library/>
for API documentation. A compiler recent enough for (at least
partial) C++11 support is required; g++-4.6.* or later should
be sufficient and g++-4.9.* or later is preferred.
GMP (GNU MP bignum library from
<http://gmplib.org/>)
A 64-bit POSIX OS such as Linux or OS X with IANA
time zone data in /usr/share/zoneinfo as well as a
recent-enough C++11 compiler (such as g++-4.9 or later which is
preferred, g++-4.8 works too). On Windows the zoneinfo included
with R is used; and time parsing support is enabled via a
backport of std::get_time from the LLVM libc++ library.
GNU make, pcre (>= 7), GLib (>= 2.0.0). On Windows,
no prior installations are necessary, as pre-built (i.e.
cross-compiled) binaries of required libraries are downloaded
from a GitHub repository (<https://github.com/PolMine/libcl>)
during installation. On macOS, static libraries of Glib are
downloaded (<https://github.com/PolMine/libglib>) if Glib is
not present.
A POSIX system. Currently Linux and OS X are known
to work.
MeCab 0.996 (or mecab-ko 0.9.2) or higher, GNU make
A C++11 compiler. Versions 4.8.*, 4.9.* or later of
GCC will be fine.
GNU make, Windows: cmd.exe and cscript.exe,
Solaris: g++ is required
An available hiredis library (eg via package
libhiredis-dev on Debian/Ubuntu, hiredis-devel on
Fedora/RedHat, or directly from source from
<https://github.com/redis/hiredis>) is preferred but otherwise
built on demand.
A C++11 compiler; g++ (>= 4.8.*) should be fine.
Python (>= 3.0) and Python headers and libraries
GNU GSL for the GSL random-number generators
mono 4.x or higher on OSX / Linux, .NET 4.x or
higher on Windows, 'msbuild' and 'nuget' available in the path
MOSEK (http://www.mosek.com) and MOSEK license.
(Python and gym only required if gym environments
are used)
Subversion for install_svn, git for install_git
'unrar' (Linux/macOS) or '7-Zip' (Windows) to work
with '.rar' files.
for building from source: GDAL >= 1.11.4, library
from https://trac.osgeo.org/gdal/wiki/DownloadSource and PROJ.4
(proj >= 4.8.0) from https://download.osgeo.org/proj/; GDAL OSX
frameworks built by William Kyngesburye at
http://www.kyngchaos.com/ may be used for source installs on
OSX. For installation with older external dependencies,
override configure checks with
--configure-args="enable-deprecated=yes". Consider source
installations using archived versions of rgdal contemporary
with installed external dependencies, for example rgdal_0.8-7
for PROJ4 4.8.0 (March 2012).
GEOS (>= 3.2.0); for building from source: GEOS
from http://trac.osgeo.org/geos/; GEOS OSX frameworks built by
William Kyngesburye at http://www.kyngchaos.com/ may be used
for source installs on OSX.
Python (2.7 or >= 3.4), rgf_python, scikit-learn
(>= 0.18.0), scipy, numpy. Detailed installation instructions
for each operating system can be found in the README file.
OpenGL, GLU Library, XQuartz (on OSX), zlib
(optional), libpng (>=1.2.9, optional), FreeType (optional),
pandoc (>=1.14, needed for vignettes)
R built as a shared or static library, 'MPI'
library.
Java JDK 1.2 or higher (for JRI/REngine JDK 1.4 or
higher), GNU make
Oracle Java 7,Apache Kafka 2.8.0-0.8.1.1
libmariadb-client-lgpl-dev or libmysqlclient-dev
(deb), mariadb-connector-c-devel or mariadb-devel (rpm),
mariadb-connector-c or mysql-connector-c (brew)
notepad.exe, mark.exe (>= 8.0) (or mark32.exe and
mark64.exe) and rel_32.exe (see README.txt)
libmariadb-client-dev | libmariadb-client-lgpl-dev
| libmysqlclient-dev (deb), mariadb-devel (rpm), mariadb |
mysql-connector-c (brew), mysql56_dev (csw)
The tools to run 'R CMD SHLIB' on 'Fortran' code.
The used 'Fortran' compiler must support pointer initialization
which is a feature of the 2008 standard.
SYMPHONY (>= 5.6.16) libraries and headers
Oracle Instant Client or Oracle Database Client
Poppler Glib interface headers and libraries
(<http://poppler.freedesktop.org/>) [Debian/Ubuntu:
libpoppler-glib-dev, Fedora: poppler-glib-devel]
libpq >= 9.0: libpq-dev (deb) or postgresql-devel
(rpm)
C++11, GNU make, Windows: cmd.exe and cscript.exe
ProtoBuf libraries and compiler version 2.2.0 or
later; version 3.0.0 or later is supported as well. On
Debian/Ubuntu these can be installed as libprotoc-dev,
libprotobuf-dev and protobuf-compiler, while on Fedora/CentOS
protobuf-devel and protobuf-compiler are needed.
A user API key (which one can request from the
website at <http://www.pushbullet.com>), and one or more
devices to push messages to which may be any one of an (Android
or iOS) phone, a (Chrome or Firefox, or Opera or Safari)
browser or the (Windows or Mac) desktop application provided
the corresponding Pushbullet 'app' has been installed on any
one of these.
Python (>= 2.7) and Python headers and libraries
(See the INSTALL file)
QuantLib library (>= 1.14) from
http://quantlib.org, Boost library from http://www.boost.org
need the RDRAND instruction on Intel CPU. and C
compiler must be able to compile a GNU-style in-line assembler.
Tcl/Tk (>= 8.5), Tktable (>= 2.9, optional)
Python (>= 2.7); keras <https://keras.io/> (>= 2.1)
The Boost 'program_options' library
(http://boost.org) is required.
Python (>=2.6) in path and NumPy module installed,
ghostscript (>=9.0)
ZeroMQ >= 3.0.0: libzmq3-dev (deb) or zeromq-devel
(rpm)
GLPK library, including -dev or -devel part
C++11, zlib headers and libraries, GNU make,
optionally also bzip2 and POSIX-compliant regex functions.
TensorFlow (https://www.tensorflow.org/),
TensorFlow Probability
(https://www.tensorflow.org/probability/)
(PDF)LaTeX (https://www.latex-project.org/) with
'pdfpages' package for studentGrowthPlot option in visualizeSGP
to bind together student growth plots into school catalogs
Python (>= 2.7) and Python headers and libraries
(See the README.md file)
For playing sounds, a command line system tool for
playing wav-files is required.
A little-endian processor is required for some
functions (see the README file).
libssh >= 0.6.0 (the original, not libssh2)
Komodo Edit (http://www.openkomodo.com), SciViews-K
(http://www.sciviews.org/SciViews-K)
Tesseract >= 3.03 (libtesseract-dev /
tesseract-devel) and Leptonica (libleptonica-dev /
leptonica-devel). On Debian you need to install the English
training data separately (tesseract-ocr-eng)
C++11, A modern compiler (>=gcc-4.9), And latexmk
is necessary to compile all output documents with the functions
provided by this package
Python (>= 2.7), python-gdal library (For Windows,
gdal installed via OSGeo4W <https://trac.osgeo.org/osgeo4w/>
recommended) clipboard
GNU Scientific Library version >= 1.8, C++11
PARI/GP >= 2.3.0 [strongly recommended for
logkda()]
nvcc (release >= 7.1) (NVIDIA Cuda Compiler driver)
V8 version 6 or 7 is recommended, but 3.14 (legacy)
is still supported as well. On Debian / Ubuntu you need either
libv8-dev or libnode-dev, on Fedora use v8-devel.
libmagic (>= 5.14) for Unix/Linux/macOS; Rtools
3.3+ for Windows
PhantomJS (http://phantomjs.org) for taking
screenshots, ImageMagick (http://www.imagemagick.org) or
GraphicsMagick (http://www.graphicsmagick.org) and OptiPNG
(http://optipng.sourceforge.net) for manipulating images.
Java (>= 5.0); WordNet database files (direct
download:
<http://wordnetcode.princeton.edu/3.0/WNdb-3.0.tar.gz>; Debian
and Fedora package: wordnet)
Perl (>= 5.0), Unitex (>= 3.0
http://www-igm.univ-mlv.fr/~unitex/)
The standalone program xgobi must be installed
additionally, see file README, or INSTALL.windows under Windows
libxslt: libxslt1-dev (deb), libxslt-devel (rpm)
Accurate, Adaptable, and Accessible Error Metrics for Predictive
Models
Access to Abbyy Optical Character Recognition (OCR) API
Tools for Approximate Bayesian Computation (ABC)
Data Only: Tools for Approximate Bayesian Computation (ABC)
Array Based CpG Region Analysis Pipeline
ABCDE_FBA: A-Biologist-Can-Do-Everything of Flux Balance
Analysis with this package
Implementation of Artificial Bee Colony (ABC) Optimization
Approximate Bayesian Computational Model for Estimating P2
Approximate Bayesian Computation via Random Forests
Useful Tools for Jurimetrical Analysis Used by the Brazilian
Jurimetrics Association
Modelling Multivariate Data with Additive Bayesian Networks
Measure a Subject's Abnormality with Respect to a Reference
Population
The Abnormal Blood Profile Score to Detect Blood Doping
Improved False Positive Control of Gene-Permuting GSEA with
Absolute Filtering
Time Resolved Simulations of Antibody Repertoires
An R-Shiny Application for Creating Visual Abstracts
High-Dimensional Principal Fitted Components and Abundant
Regression
Inferring Directional Conservative Causal Core Gene Networks
Abrupt Change-Point or Aberration Detection in Point Series
Functions for Processing Accelerometer Data
Missing Value Imputation for Accelerometer Data
Creation and Evaluation of Acceptance Sampling Plans
Data Quality Visualization Tools for Partially Accruing Data
Tools for Autoregressive Conditional Duration Models
Optimal Bayesian Experimental Design using the ACE Algorithm
ACE and AVAS for Selecting Multiple Regression Transformations
Estimating Dynamic Heritability and Twin Model Comparison
Align-and-Count Method comparisons of RFLP data
Estimation of Interpretable eQTL Effect Sizes Using a Log of
Linear Model
Implements ACME Estimator of Bird and Bat Mortality by Wind
Turbines
Affymetrix SNP Probe-Summarization using Non-Negative Matrix
Factorization
Modelling Dependence with Multivariate Archimax (or any
User-Defined Continuous) Copulas
Convenience functions for analytical Customer Relationship
Management
A Cost-Minimal Regular Spanning Subgraph with TreeClust
Download, Manipulate, and Present American Community Survey and
Decennial Data from the US Census
Gene Enrichment Analysis from ACSN Maps or GMT Files
Algorithmic Complexity for Short Strings
Data Only: Algorithmic Complexity of Short Strings (Computed via
Coding Theorem Method)
A Companion Package for the Book "A Course in Statistics with R"
Asymptotic Classification Theory for Cognitive Diagnosis
Finding Cancer Driver Proteins with Enriched Mutations in
Post-Translational Modification Sites
A Driver Discovery Tool for Cancer Whole Genomes
Activity Index Calculation using Raw Accelerometry Data
Read in Activity Data and Plot Actograms
Actuarial Functions and Heavy Tailed Distributions
A Package for Displaying Visual Scenes as They May Appear to an
Animal with Lower Acuity
The R Package Ada for Stochastic Boosting
Applies Multiclass AdaBoost.M1, SAMME and Bagging
Discrete and Global Optimization Routines
Archetypoid Algorithms and Anomaly Detection
A Class of Adaptive Elastic Net Methods for Censored Data
Implementation of an Accountable Data Analysis Process
Adaptive Semiparametric Additive Regression with Simultaneous
Confidence Bands and Specification Tests
Implementation of a Generic Adaptive Monte Carlo Markov Chain
Sampler
Adaptive P-Value Thresholding for Multiple Hypothesis Testing
with Side Information
Automated Deconvolution Augmentation of Profiles for Tissue
Specific Cells
Adaptive Sampling for Positive Unlabeled and Label Noise
Learning
Binomial and Multinomial Additive Hazard Models
Fit Additive Hazards Models for Survival Analysis
Discover and Install Useful RStudio Addins
Additive Data Envelopment Analysis Models
Additivity Tests in the Two Way Anova with Single Sub-class
Numbers
Analysis of Accelerated Destructive Degradation Test Data
Analysis of Ecological Data: Exploratory and Euclidean Methods
in Environmental Sciences
Adaptive Density Estimation by Bayesian Averaging
Exploratory Analysis of Genetic and Genomic Data
An S4 Lattice-Based Package for the Representation of
Multivariate Data
Analysis of Habitat Selection by Animals
Exploratory Analyses for the Phylogenetic Comparative Method
A 'shiny' Application for the (Audio-)Visualization of Adverse
Event Profiles
Adequacy of Probabilistic Models and General Purpose
Optimization
Import from and Export to Amiga Disk Files
Calculate Ad Hoc Distance Thresholds for DNA Barcoding
Identification
Adjusted Bayesian Quantile Regression Inference
Adjacency-Constrained Clustering of a Block-Diagonal Similarity
Matrix
Remove Automated and Repeated Downloads from 'RStudio' 'CRAN'
Download Logs
Adaptive Mixture of Student-t Distributions
Admixture Graph Manipulation and Fitting
Algorithms using Alternating Direction Method of Multipliers
Regularized Model with Selecting the Number of Non-Zeros
Penalized Precision Matrix Estimation via ADMM
No-U-Turn MCMC Sampling for 'ADMB' and 'TMB' Models
Fast Clustering Using Adaptive Density Peak Detection
Use Least Squares Polynomial Regression and Statistical Testing
to Improve Savitzky-Golay
Design and Analysis of Locally or Globally Efficient Adaptive
Designs
Upper Clopper-Pearson Confidence Limits for Burn-in Studies
under Additional Available Information
Solving 1D Advection Bi-Flux Diffusion Equation
Wavelet Analysis of Genomic Data from Admixed Populations
Change Point Analysis in ARIMA Forecasting
Download and Process AEMO Price and Demand Data
A Computational Tool for Aerobiological Data
Model-Based Estimation of Confounder-Adjusted Attributable
Fractions
The Attributable Fraction (AF) Described as a Function of
Disease Heritability, Prevalence and Intervention Specific
Factors
Functions for Atomic Force Microscope Force-Distance Curves
Analysis
Tools for Modelling of Animal Flight Performance
Accelerated Failure Time Model with Generalized Estimating
Equations
Adaptive Set-Based Gene-Environment Interaction Test for Rare
Variants
Aggregate Numeric, Date and Categorical Variables by an ID
Relationship Matrices for Diploid and Autopolyploid Species
Aggregation Operators and Preordered Sets
Agreement Interval of Two Measurement Methods
Statistical Procedures for Agricultural Research
Tutorial Analysis of Some Agricultural Experiments
Evapotranspiration and Energy Fluxes Spatial Analysis
Calculate Agreement or Consensus in Ordered Rating Scales
Estimation in Adaptive Group Sequential Trials
Miscellaneous plotting and utility functions
Regularization for semiparametric additive hazards regression
Functions and Data for the Book 'Applied Hierarchical Modeling
in Ecology'
An Implementation of the Artificial Hydrocarbon Networks
Fast Searching for Multiple Keywords in Multiple Texts
Analytic Hierarchy Process for Survey Data
Estimation and Testing of Average Hazard Ratios
Model Selection and Multimodel Inference Based on (Q)AIC(c)
Download Mexico City Pollution, Wind, and Temperature Data
Suite of GR Hydrological Models for Precipitation-Runoff
Modelling
Teaching Hydrological Modelling with the GR Rainfall-Runoff
Models ('Shiny' Interface Included)
Convenience Tools for Working with Airport Data
AIRR Data Representation Reference Library
Interpolation of Irregularly and Regularly Spaced Data
Adaptive Kmeans algorithm based on threshold
Anchored Kmedoids for Longitudinal Data Clustering
Atlas of Living Australia (ALA) Data and Resources in R
Immunoglobulin Clonal Lineage and Diversity Analysis
Quantile Regression Using Asymmetric Laplace Distribution
Accumulated Local Effects (ALE) Plots and Partial Dependence
(PD) Plots
Estimating Absolute Protein Quantities from Label-Free LC-MS/MS
Proteomics Data
Downloading Time Series from ALFRED Database for Various
Vintages
Determine Phytoplankton Functional Groups Based on Functional
Traits
Haplotype Two Snips Out of a Paired Group of Patients
Allows you to Easily Interact with the Algorithmia Platform
Visualizing Multiple Sequence Alignments with 'ggplot2'
Alignment of Phonetic Sequences Using the 'ALINE' Algorithm
Allele Imputation and Haplotype Reconstruction from Pedigree
Databases
Identifying Unique Multilocus Genotypes where Genotyping Error
and Missing Data may be Present
Allele Retention, Inbreeding, and Demography
A fast, unbiased and exact allelic exact test
Computes all of a word's possible spellings
Fit GLM's with High-Dimensional k-Way Fixed Effects
Algorithms for High-Throughput Sequencing of Antigen-Specific T
Cells
Generalization of the Convex Hull of a Sample of Points in the
Plane
Obtain Alpha-Outlier Regions for Well-Known Probability
Distributions
Partition/Decomposition of Additive Genetic Values by Paths of
Information
Implementation of the 3D Alpha-Shape for the Reconstruction of
3D Sets from a Point Cloud
Lightweight R Interface to the Alpha Vantage API
Data to Accompany Applied Linear Regression 3rd Edition
Data to Accompany Applied Linear Regression 4th Edition
Multivariate Curve Resolution Alternating Least Squares
(MCR-ALS)
Accelerated line search algorithm for simultaneous orthogonal
transformation of several positive definite symmetric matrices
to nearly diagonal form
Companion to Applied Linear Statistical Models
An 'API' Client for the 'Alteryx' Gallery
Optimal Experimental Designs for Accelerated Life Testing
Compare Gene Expressions from 2-Treatment RNA-Seq Experiments
Generate LaTeX Code for Auto-Multiple-Choice (AMC)
European Commission Annual Macro-Economic (AMECO) Database
Anomaly Detection with Normal Probability Functions
Additive and Multiplicative Effects Models for Networks and
Relational Data
This package includes pricing function for selected American
call options with underlying assets that generate payouts
American Politics-Inspired Color Palette Generator
Alternating Minimization Induced Active Set Algorithms
Additive Main Effects and Multiplicative Interaction Model
Stability Parameters
Automated Fitting of Moderated Nonlinear Factor Analysis Through
the 'Mplus' Program
A Multidirectional Optimum Ecotope-Based Algorithm
Generates Samples or Complete List of Brazilian IBGE (Instituto
Brasileiro De Geografia e Estatistica) Census Households,
Geocoding it by Google Maps
An Algorithm for Automatic Peak Detection in Noisy Periodic and
Quasi-Periodic Signals
Statistical Analysis of Amplicon Data of the Same Sample to
Identify Artefacts
Analysis of Codon Data under Stationarity using a Bayesian
Framework
Simple and Canonical Correspondence Analysis
Analogue and Weighted Averaging Methods for Palaeoecology
Additional Functions for Use with the Analogue Package
Compose Interoperable Analysis Pipelines & Put Them in
Production
Regression Outlier Detection, Stationary Bootstrap, Testing Weak
Stationarity, NA Imputation, and Other Tools for Data Analysis
Model Layer for Automatic Data Analysis via CSV File
Interpretation
Functions for Analysis of fMRI Datasets Stored in the ANALYZE or
NIFTI Format
Assigning Ancestry Based on Population References
Post-Processing for the Method of Anchored Distributions
Statistical analysis of surveys with anchoring vignettes
An extended Bayesian Learning Technique developed by Dr. Geoff
Webb
Adaptive Neuro Fuzzy Inference System in R
Simulate a Bus Route Creel Survey of Anglers
Tools for 'ROMS' the Regional Ocean Modeling System
Inferring Dominance Hierarchies and Estimating Uncertainty
Animal track reconstruction for high frequency 2-dimensional
(2D) or 3-dimensional (3D) movement data
A Gallery of Animations in Statistics and Utilities to Create
Animations
Animation of Observed Trajectories Using Spline-Based
Interpolation
Compute High Dimensional Orthant Probabilities
Artificial Neural Networks for Anomaly Detection
Extract Subsequences from GenBank Annotations
AnnotLists: A tool to annotate multiple lists from a specific
annotation file
A Non-Parametric Statistical Test to Compare Clustering
Structures
One-Way Analysis of Variance Based on Triangular Fuzzy Numbers
Test ANOVA Replications by Means of the Prior Predictive p-Value
Interactive Document for Working with Analysis of Variance
Import, Manipulate and Explore the Results of an 'Antares'
Simulation
Computation of the WHO Child Growth Standards
An R-Shiny Application for Smith's Mean Measure of Divergence
(MMD)
Statistical Methods for Anthropometric Data
Extract Text from Microsoft Word Documents
Install and Load Any Package from CRAN, Bioconductor or Github
Anything to 'POSIXct' or 'Date' Converter
Analysis of Overdispersed Data using S3 Methods
A-Optimal Block Designs for Comparing Test Treatments with
Controls
aoristic analysis with spatial output (kml)
Format Outputs of Statistical Tests According to APA Guidelines
Create American Psychological Association (APA) Style Tables
Create R Markdown Text for Results in the Style of the American
Psychological Association (APA)
Analysis of Unreplicated Orthogonal Experiments using All
Possible Comparisons
An Implementation of the Additive Polynomial Design Matrix
Phylogenetic Methods for Multiple Gene Data
Multiple Testing Approach using Average Power Function (APF) and
Bayes FDR Robust Estimation
Analysis with Profile Hidden Markov Models
Datasets from Hosmer, Lemeshow and Sturdivant, "Applied Logistic
Regression" (3rd Ed., 2013)
Another Plot Package: 'Bagplots', 'Iconplots', 'Summaryplots',
Slider Functions and Others
Augmented and Penalized Minimization Method L0
Pre- and Postprocessing for AP-MS data analysis based on
spectral counts
Tools for Choice Model Estimation and Application
Accurate Parentage Analysis in the Absence of Guiding
Information
Adjusted Prediction Model Performance Estimation
Approximate Path for Penalized Likelihood Estimators
Functions and Data Sets for 'Applied Predictive Modeling'
Amyloid Propensity Prediction Neural Network
Bayesian Prediction of Complex Computer Codes
Approximately Optimal Fine Balance Matching with Multiple Groups
Datasets from Draper and Smith "Applied Regression Analysis"
(3rd Ed., 1998)
Tools for the a Priori Comparison of Regression Modelling
Strategies
Amdahl's Profiler, Directed Optimization Made Easy
General Utility Functions for the 'Agricultural Production
Systems Simulator'
Edit, Run and Evaluate APSIM Simulations Easily Using R
apsrtable model-output formatter for social science
Average Positive Predictive Values (AP) for Binary Outcomes and
Censored Event Times
Functions to help display air quality model output and
monitoring data
Interface methods to use with an ActiveQuant Master Server
Integrated Development Toolbox for Aquatic Chemical Model
Generation
Another Look at the Acceptance-Rejection Method
Simulate Auto Regressive Data from Precision Matricies
Segmentation of an autoregressive Gaussian process of order 1
Rigorous Data Reduction and Error Propagation of Ar40 / Ar39
Data
Fitting Univariate Censored Linear Regression Model with
Autoregressive Errors
Bayesian Modeling of Archaeological Chronologies
Post-Processing of the Markov Chain Simulated by 'ChronoModel',
'Oxcal' or 'BCal'
Example Datasets from Archaeological Research
Finds the Archetypal Analysis of a Data Frame
Plant Root System Architecture Analysis Using DART and RSML
Files
Tools for Storing, Restoring and Searching for R Objects
Tools for Archiving, Managing and Sharing R Objects via GitHub
Plot Stacked Areas and Confidence Bands as Filled Polygons
Plots, Summary Statistics and Tools for Arena Simulation Users
Activated Region Fitting, fMRI data analysis (3D)
Fractional ARIMA (and Other Long Memory) Time Series Modeling
Arfima-MLM Estimation For Repeated Cross-Sectional Data
Accurate Estimation of Influenza Epidemics using Google Search
Data
Command Line Optional and Positional Argument Parser
Improved Communication to Users with Respect to Problems in
Function Arguments
Adaptable Regularized Hotelling's T^2 Test for High-Dimensional
Data
Efficient Computations of Standard Clustering Comparison
Measures
Archive and Unarchive Databases Using Flat Files
Data Analysis Using Regression and Multilevel/Hierarchical
Models
A Statistical Methodology to Select Covariates in
High-Dimensional Data under Dependence
Adaptive Rejection Metropolis Sampling (ARMS) via 'Rcpp'
Covariate-Adjusted Receiver Operating Characteristic Curve
Inference
Analysis of Large Affymetrix Microarray Data Sets
A Probe-Level Data File Format Used by 'aroma.affymetrix'
[deprecated]
Copy-Number Analysis of Large Microarray Data Sets
Core Methods and Classes Used by 'aroma.*' Packages Part of the
Aroma Framework
Fitting Autoregressive Censored Linear Mixed-Effects Models
Tools for Simulating Direct Behavioral Observation Recording
Procedures Based on Alternating Renewal Processes
Fast Generators and Iterators for Permutations, Combinations and
Partitions
Apply a Function to a Margin of an Array
An Arsenal of 'R' Functions for Large-Scale Statistical
Summaries
Aligned Rank Transform for Nonparametric Factorial Analysis
Time-Varying DBN Inference with the ARTIVA (Auto Regressive TIme
VArying) Model
Gene and Pathway p-Values Computed using the Adaptive Rank
Truncated Product
Mining Association Rules and Frequent Itemsets
Classification Based on Association Rules
Mining NB-Frequent Itemsets and NB-Precise Rules
Visualizing Association Rules and Frequent Itemsets
Data Sets for "Applied Survival Analysis Using R""
A Collection of Statistical Tools for Biologists
Reads Fixed-Width ASCII Data Files (.txt or .dat) that Have
Accompanying Setup Files (.sps or .sas)
Simulations for Adaptive Seamless Designs
Interface to the Australian Statistical Geography Standard
Methods for Adaptive Shrinkage, using Empirical Bayes
Interface to the Algorithm Selection Benchmark Library
Linkage Map Construction using the MSTmap Algorithm
Animal Social Network Inference and Permutations for Ecologists
A collection of functions for estimating centrographic
statistics and computational geometries for spatial point
patterns
Bayesian Inference on Causal Genetic Variants using Affected
Sib-Pairs Data
An Adaptive Sum of Powered Correlation Test (aSPC) for Global
Association Between Two Random Vectors
A General Framework for Multivariate Analysis with Optimal
Scaling
Analysis of Symmetry of Parasitic Infections
Augments 'ASReml-R' in Fitting Mixed Models and Packages
Generally in Exploring Prediction Differences
Applied Singular Spectrum Analysis (ASSA)
Detection and Correction of Spatial Bias in HTS Screens
Verbose Assertions for Tabular Data (Data.frames and
Data.tables)
Readable Check Functions to Ensure Code Integrity
A Lightweight Core of the 'assertive' Package
Assertions to Check Properties of Strings
Assertions to Check Properties of Dates and Times
Assertions to Check Properties of Matrices
Assertions to Check Properties of Models
Assertions to Check Properties of Numbers
Assertions to Check Properties of Variables
Assertive Programming for R Analysis Pipelines
Estimating Asset Correlations from Default Data
Optimal Pricing of Assets with Fixed Expiry Date
Population Assignment using Genetic, Non-Genetic or Integrated
Data in a Machine Learning Framework
A Suite of R Functions Implementing Spline Smoothing Techniques
Adaptive Subgroup Selection in Group Sequential Trials
Implements New and Existing Association Indices for Constructing
Animal Social Networks
Interactive Document for Working with Association Rule Mining
Analysis
Calculate the Assortativity Coefficient of Weighted and Binary
Networks
Statistical Tests for Genetic Association Studies
A Computational Tool for Astrochronology
Astronomy: time and position functions, misc. utilities
Applied Statistical Time Series Analysis
Adaptive SURE Thresholding Using Side Information
Average Shifted Visual Predictive Checks
Asymmetric Linkage Disequilibrium (ALD) for Polymorphic Genetic
Data
Multidimensional Scaling of Asymmetric Data
A Simple R Package for Classical Parametric Statistical Tests
and Confidence Intervals in Large Samples
Regression Analysis of Sparse Asynchronous Longitudinal Data
Calculate Power Utilizing Asymptotic Likelihood Ratio Methods
Create Tables for Reporting Clinical Trials
Inference for Average Treatment Effects using Covariate
Balancing
A GTK GUI for teaching basic concepts in statistical inference,
and doing elementary bayesian tests
Automatically Tuned Markov Chain Monte Carlo
Acoustic Traveltime Calculations for 1-D Atmospheric Models
Support Querying Axibase Time-Series Database
Tools for Attribute VSI CUSUM Control Chart
Threshold independent performance measures for probabilistic
classifiers
Variable Selection with Random Forest and the Area Under the
Curve
Model Audit - Verification, Validation, and Error Analysis
Analysis of Augmented Randomised Complete Block Designs
Analysis of Data with Mixed Measurement Error and
Misclassification in Covariates
eBird Data Extraction and Processing in R
Generates PFA Documents from R Code and Optionally Runs Them
Creates a Neighbourhood Using Locality Sensitive Hashing for
Gaussian Projections
Approximate Unconditional and Permutation Tests
Secure Authentication in Shiny with Auth0
Automatic Variable Reduction Using Principal Component Analysis
Learning to Rank Bagging Workflows with Metalearning
An Interface to the 'AutoDesk' 'API' Platform
Sparse Autoencoder for Automatic Learning of Representative
Features from Unlabeled Data
Multiple Heat Maps for Projected Coordinates
Automagically Document and Install Packages Necessary to Run R
Code
Automated Hierarchical Multiple Regression with Assumptions
Checking
Automated Transcriptome Classifier Pipeline: Comprehensive
Transcriptome Analysis
Automatic Generation of Interactive Visualizations for
Statistical Results
Development of Visualization Tools for Protein Sequence
Partial Least Squares Regression with Backward Selection of
Predictors
Minimum Distance Estimation in Autoregressive Model
Automatic Transformation of an 'R' Function into a 'shiny' App
Builds Stepwise GLMs via Train and Test Approach
An R Port of the 'ImageJ' Plugin 'Auto Threshold'
Automated Vector Autoregression Models and Networks
Check if the Title of a Package is Available, Appropriate and
Interesting
Calculation of Average Population Attributable Fractions and
Confidence Intervals
Adaptation of Virtual Twins Method from Jared Foster
Convert Dates to Arbitrary Week Definitions
Amazon 'Kinesis' Consumer Application for Stream Processing
A Simple Client to the 'AWS' Key Management Service
Client for the Amazon Alexa Web Information Services API
'AWS Key Management Service' Client Package
Boilerplate R Access to the Amazon Web Services ('AWS') Java SDK
Class and Methods Definitions for Packages 'aws', 'adimpro',
'fmri', 'dwi'
Authentication Services for Azure Active Directory
Interface to 'Container Instances', 'Docker Registry' and
'Kubernetes' in 'Azure'
Interface to 'Kusto'/'Azure Data Explorer'
Interface with Azure Machine Learning Datasets, Experiments and
Web Services
Adaptive differential evolution, b6e6rl variant
Bayesian Bacterial Growth Curve Analysis in R
Bayesian Bootstrap Predictive Mean Matching - Multiple and
Single Imputation for Discrete Data
Bubble Chart to Compare Biological Annotations by using DAVID
Modeling Framework for Cellular Communities in their
Environments
Bayesian Analysis of Computer Code Output (BACCO)
Bayesian Augmented Control for Clinical Trials
Bayesian Classification and Information Sharing (BaCIS) Tool for
the Design of Multi-Group Phase II Clinical Trials
Reimplementations of Functions Introduced Since R-3.0.0
Learning Causal Cyclic Graphs from Unknown Shift Interventions
Exploring Portfolio-Based Conjectures About Financial
Instruments
Choice of the Hyperparameter Omega in the Bayesian Adjustment
for Confounding (BAC) Algorithm
Create Badges for 'Travis', 'Repostatus' 'Codecov.io' Etc in
Github Readme
Bayesian Average Error approach to Sample Size Determination
Ensemble bagging and boosting classifiers
Bayes Factors for Informative Hypotheses
Bayesian Analysis of Item Response Theory Models
Library Driven Compound Profiling in Gas Chromatography - Mass
Spectrometry Data
Visualize Balances of Compositional Data
Balance Check for Multiple Covariates in Matched Observational
Studies
Balanced and Spatially Balanced Sampling
Statistical Inference and Sure Independence Screening via Ball
Statistics
Expression RNA-Seq Data Analysis Based on Linear Mixed Model
Access to Current and Historical Basketball Data
Functions and Datasets for Books by Jeff Gill
Protein Secondary Structure Prediction Using the Bamboo Method
Bayesian Meta-Analysis of Diagnostic Test Data
Bayesian Additive Models for Location Scale and Shape (and
Beyond)
Analysis and Visualization of Macroevolutionary Dynamics on
Phylogenetic Trees
Bayesian Model Optimized Reference Correction Method for
Assigned and Unassigned Protein NMR Spectra
Bayesian Age-Period-Cohort Modeling and Prediction
Functions for simple A/B split test and multi-armed bandit
analysis
Working with Car Parking Data for Bath and North East Somerset
Make Banner Comments with a Consistent Format
Batch Effect Removal and Addon Normalization (in Phenotype
Prediction using Gene Data)
Bayesian Prediction of Stochastic Processes
Labelling, Tracking, and Collecting Data from Biological Samples
Species Identification using DNA Barcodes
Regularized Wasserstein Distances and Barycenters
Bayesian Variable Selection and Model Averaging using Bayesian
Adaptive Sampling
Bayesian Variable Selection with Shrinking and Diffusing Priors
Fast and URL-Safe Base64 Encoder and Decoder
Sabermetrics and Advanced Baseball Statistics
Infrastructure for Computing with Basis Functions
Trace Plots, Density Plots and Chain Comparisons for MCMC
Samples
Recovering a Basic Space from Issue Scales
Construct Rich Tables for Output to 'HTML'/'Excel'
Add Trendline and Confidence Interval of Basic Regression Models
to Plot
Classification of RNA Sequences using Complex Network Theory
Age-Specific Survival Analysis from Incomplete
Capture-Recapture/Recovery Data
Big Data Statistical Analysis for High-Dimensional Models
Batching Routines in Parallel and Passing Command-Line Arguments
to R
Statistical Experiments on Batch Computing Clusters
Downloads and Organizes Financial Data for Multiple Tickers
Software for the Creation of High Density Linkage Maps in
Outcrossing Species
Consistent Batch Means Estimation of Monte Carlo Standard Errors
Bayesian Tensor Factorization Linked to External Data
Convert Categorical Representations of Logicals to Actual
Logicals
An R Package for Data Reduction by Battery Reduction
Bayesian Estimation of the Additive Main Effects and
Multiplicative Interaction Model
Bayesian Estimation of Mixtures of Multivariate Bernoulli
Distributions
Miscellaneous Functions for Bioinformatics and Bayesian
Statistics
An Implementation of Rubin's (1981) Bayesian Bootstrap
Bayesian Inference on a GPU using OpenCL
Power Calculations and Bayesian Analysis of Count Distributions
and FECRT Data using MCMC
Bayesian Analysis of Censored Regression Models Under Scale
Mixture of Skew Normal Distributions
Simulation and Analysis of Adaptive Bayesian Clinical Trials
Two Arm Bayesian Clinical Trial Design with and Without
Historical Control Data
Functions and Datasets for the book "Bayesian Data Analysis"
The Bayesian Dynamic Conditional Correlation GARCH Model
Graphical User Interface for bayesTFR, bayesLife and bayesPop
Bayesian Dynamic Factor Analysis (DFA) with 'Stan'
Tools for the Bayesian Discount Prior Function
Computation of Bayes Factors for Common Designs
Bayesian Estimation of the GARCH(1,1) Model with Student-t
Innovations
Bayesian Analysis of Generalized Elliptical Semi-Parametric
Models and Flexible Measurement Error Models
Bayesian Modeling via Frequentist Goodness-of-Fit
Bayesian Regression Model with Mixture of Two Scaled Inverse Chi
Square as Hyperprior
Bayesian Melding of GPS and DR Path for Animal Tracking
Bayesian Estimation of the ETAS Model for Earthquake Occurrences
Sample Size and Power for Comparing Inequality Constrained
Hypotheses
General-Purpose MCMC and SMC Samplers and Tools for Bayesian
Statistics
Bayesian Methods for Image Segmentation using a Potts Model
Efficient Sampling for Gaussian Linear Regression with Arbitrary
Priors
Bayesian Analysis of Contingency Table Data
Adjust Longitudinal Regression Models Using Bayesian Methodology
Bayesian Preference Learning with the Mallows Rank Model
Designing Bayesian Multi-Arm Multi-Stage Studies
Default Bayesian Hypothesis Tests for Correlation, Partial
Correlation, and Mediation
Bayesian Mixture Survival Models using Additive
Mixture-of-Weibull Hazards, with Lasso Shrinkage and
Stratification
BayesNI: Bayesian Testing Procedure for Noninferiority with
Binary Endpoints
Variable Selection in a Hierarchical Bayesian Model for a Hazard
Function
Hierarchical Bayesian Model for a Hazard Function
Hierarchical Bayesian analysis of ecological count data
Bayesian Regression Models with Global-Local Shrinkage Priors
Bayes Factors for Hierarchical Linear Models with Continuous
Predictors
Bayesian Variable Selection Using Simplified Shotgun Stochastic
Search with Screening (S5)
Bayesian Analysis of Small Area Estimation
Different Models of Posterior Distributions of Adjusted Odds
Ratio
Computation of Bayes factors for interrupted time-series designs
MCMC Sampling of Bayesian Linear Models via Summary Statistics
Bayesian Survival Regression with Flexible Error and Random
Effects Distributions
Understand and Describe Bayesian Models and Posterior
Distributions
Bayesian Analysis of Item-Level Twin Data
Bayes Factors, Model Choice and Variable Selection in Linear
Models
Visually Learning the Graphical Structure of Bayesian Networks
and Performing MCMC with 'Stan'
R Utilities Accompanying the Software Package BayesX
R Package Distribution of the BayesX C++ Sources
Global Bayesian Foraminifera Core Top Calibration
Bayesian analysis of haplotype association using Markov Chain
Monte Carlo
R Functions for Bayesian Hazard Rate Estimation
R Package for Baylor University Educational Psychology
Quantitative Courses
Chronological Bayesian Models Integrating Optically Stimulated
Luminescence and Radiocarbon Age Dating
Bayesian Fitting of Ornstein-Uhlenbeck Models to Phylogenies
Bayesian Analysis of Significantly Mutated Genes in Cancer
Bayesian Stability Analysis of Genotype by Environment
Interaction (GEI)
On Bayesian analysis of Threshold autoregressive model (BAYSTAR)
Solving and Optimizing Large-Scale Nonlinear Systems
Bayesian bandwidth estimation and semi-metric selection for the
functional kernel regression with unknown error density
Bayesian bandwidth estimation for multivariate kernel regression
with Gaussian error
Bayesian Estimation of Incoherent Neutron Scattering Backgrounds
Benthic Biotic Indices Calculation from Composition Data
Miscellaneous Helper Functions for B. Bischl
Tools for General Maximum Likelihood Estimation
Models for Continuous Traits Evolving in Macroevolutionary
Landscapes of any Shape
Gene Regulatory Network Inference with Bc3net
Bias Corrected Bootstrap Confidence Intervals
Bias-Corrected Bayesian Classification with Selected Features
Calculation of Option Prices Based on a Universal Solution
Business Cycle Dating and Plotting Tools
Bayesian composition estimator: estimating sample (taxonomic)
composition from biomarker data
The Bayesian Causal Effect Estimation Algorithm
B Cell Receptor Somatic Hyper Mutation Analysis
Causal Inference for a Binary Treatment and Continuous Outcome
using Bayesian Causal Forests
Bayesian Constrained Generalised Linear Models
Bias-Corrected Estimates for Generalized Linear Models for
Dependent Data
Radiocarbon Dating, Age-Depth Modelling, Relative Sea Level Rate
Estimation, and Non-Parametric Phase Modelling
Bayesian Palaeoclimate Reconstruction from Pollen Data
Bayesian Hierarchical Clustering Using Spike and Slab Models
A Dirichlet Process Mixture Model for Clustering Longitudinal
Gene Expression Data
Map Layers and Spatial Utilities for British Columbia
Bayesian Analysis of Change Point Problems
Behavioral change point analysis of animal movement
Bayesian Multiple Changepoint Detection Using Metadata
Advanced Analysis of B Cell Receptor Repertoire Data
Bayesian Continual Reassessment Method for Phase I
Dose-Escalation Trials
Bias-Corrected Methods for Estimating the ROC Surface of
Continuous Diagnostic Tests
A Bayesian Semiparametric Factor Analysis Model for Subtype
Identification (Clustering)
Cross-Validation for the SVD (Bi-Cross-Validation)
A User-Friendly Biodiversity Data Cleaning App for the
Inexperienced R User
Darwinizer: Darwin Core (DwC) Field Names Standardization
Efficient Determination of Sample Size in Balanced Design of
Experiments
Bayesian Structure Learning in Graphical Models using
Birth-Death MCMC
Bayesian Adaptive Designs for Phase II Trials with Binary
Endpoint
Optimisation of Bayesian Decision Problems
Inference and Design for Predictive Values in Diagnostic Tests
Bayesian Detection of Potential Risk Using Inference on Blinded
Safety Data
Remove Weekends and Holidays from ggplot2 Axes
Routines for Block Diagonal Symmetric Matrices
Bayesian Inference for Discrete Weibull Regression
Biometric Exploratory Analysis Creation House
Analysis of Illumina BeadArray SNP data including MSV markers
Analysis of Multiplex Cytometric Bead Assays
Fast Bayesian Inference in Large Gaussian Graphical Models
Visualization via Beanplots (like Boxplot/Stripchart/Violin
Plot)
Bayesian Analysis of Heterogeneous Treatment Effect
Bayesian Estimation of Change-Points in the Slope of
Multivariate Time-Series
Quantifies effects of geo/eco distance on genetic
differentiation
Extract Genotypes from a PLINK .bed File
Genomic Region Processing using Tools Such as 'BEDTools',
'BEDOPS' and 'Tabix'
Easily Play Notification Sounds on any Platform
The Bee Swarm Plot, an Alternative to Stripchart
Tools for Behavior Change Researchers and Professionals
Canonical Data Structure for Behavioural Data
Download Historical Data from the Belgrade Stock Exchange
Boltzmann Entropy of a Landscape Gradient
28 benchmark densities from Berlinet/Devroye (1994)
Benchmark and Frontier Analysis Using DEA and SFA
High Precise Measurement of R Expressions Execution Time
Benford Analysis for Data Validation and Forensic Analytics
Statistical Tests for Evaluating Conformity to Benford's Law
Bent-Cable Regression for Independent Data or Autoregressive
Time Series
Bayesian Exponential Random Graph Models
Function Collection Related to Plotting and Hydrology
Best Subset Selection in Linear, Logistic and CoxPH Models
Computations and Approximations for Bessel Functions
Bayesian Estimation Supersedes the t-Test
Best Subset GLM and Regression Utilities
Mini Games from Adventures of Beta and Bit
Detecting rare variants for quantitative traits using nuclear
families
Partitioning Beta Diversity into Turnover and Nestedness
Components
Simulation, Estimation and Forecasting of Beta-Skew-t-EGARCH
Models
Bayesian Evaluation of Variant Involvement in Mendelian Disease
Compare the Goodness of Fit of Benford's and Blondeau Da Silva's
Digit Distributions to a Given Dataset
Bayesian Spectral Inference for Stationary Time Series
Breaks For Additive Season and Trend (BFAST)
Bayesian Framework for Computational Modeling
A Suite of Packages for Analysis of Big Genomic Data
Binary Generalized Extreme Value Additive Models
Bayesian Genomic Linear Models Applied to GE Genome Selection
Example BGM Files for the Atlantis Ecosystem Model
Gaussian Mixture Modeling Algorithms and the Belief-Based
Mixture Modeling
Markov Beta and Gamma Processes for Modeling Hazard Rates
Block Gibbs Sampler with Incomplete Multinomial Distribution
Bayesian Group Sparse Multi-Task Regression
Useful Functions for Box, Hunter and Hunter II
Bayesian Hierarchical Multi-Subject Multiscale Analysis of
Functional MRI Data
Bayesian Hierarchical Regression on Clearance Rates in the
Presence of Lag and Tail Phases
Structural Bayesian Vector Autoregression Models
Bayesian Multi-Plate High-Throughput Screening of Compounds
Bias correction of the parameter estimates of the beta
regression model
R Wrapper for Java Implementation of BiBit
An R-Tool for Comprehensive Science Mapping Analysis
Maximal Biclique Enumeration in Bipartite Graphs
Integrative Inference of De Novo Cis-Regulatory Modules
Bayesian Inference for Directed Acyclic Graphs (BiDAG)
Calculates the Bidimensional Regression Between Two 2D
Configurations
Tools for Accessing the Botanical Information and Ecology
Network Database
Tools for Survey Statistics in Educational Assessment
BLAS routines for native R matrices and big.matrix objects
Utilities for 'big.matrix' Objects from Package 'bigmemory'
Read, Manipulate, Explore Chess PGN Files and R API to UCI Chess
Engines
Distances for Machine Learning and Statistics in the Context of
Big Data
Case-Control Analysis of Multi-Allelic Loci
Biochemically Intuitive Generalized Loewe Model
Scalable Least-Angle Regression and Lasso
Extending Lasso Model Fitting to Big Data
Physical and Physiological Ecosystem Properties from Eddy
Covariance Data
bounded memory linear and generalized linear models
Bounded Memory Linear and Generalized Linear Models
Making Optimal Matching Size-Scalable Using Optimal Calipers
Manage Massive Matrices with Shared Memory and Memory-Mapped
Files
A shared resource interface for Bigmemory Project packages
Interface with Google BigQuery with Shiny Compatibility
Big Quadratic Inverse Covariance Estimation
Generalized Linear Models (GLM) for Large Data Sets
An Interface to Google's 'BigQuery' 'API'
Constructing Large Systems of Structural Equations
Statistical Tools for Filebacked Big Matrices
Table, Apply, and Split Functionality for Matrix and
'big.matrix' Objects
Nonparametric Analysis of Bivariate Gap Time with Competing
Risks
Sparse Estimation of Large Time Series Models
Top Scoring Pair based methods for classification
Dimension Reduction Methods for Multivariate Time Series
Download and Aggregate Data from Public Hire Bicycle Systems
Contains Data of Billboard Hot 100 Songs
Create Interactive Chart with the JavaScript 'Billboard' Library
Power for SNP analyses using silver standard cases
Estimates Mixture Models for Case-Control Data
Threshold Cut Point of Probability for a Binary Classifier Model
Variable Selection for Binary Data Using the EM Algorithm
Mean and Variance Modeling of Binary Data
Fit and Predict a Gaussian Process Model with (Time-Series)
Binary Response
Estimate the Correlation Between Two Irregular Time Series
Multi-Class Discriminant Analysis using Binary Predictors
Methods for Analyzing Binned Income Data
Evaluation and Experimental Design for Binomial Group Testing
Nonparametric Estimation for Interval-Grouped Data
Data Generation with Binary and Continuous Non-Normal Components
Simultaneous Generation of Multivariate Binary and Normal
Variates
Binomial Confidence Intervals For Several Parameterizations
Generates binomial random numbers via the coupling from the past
algorithm
Efficient MCMC for Binomial Logit Models
Confidence Intervals and Sample Size Determination for a
Binomial Proportion under Simple Random Sampling and Pooled
Sampling
Concurrent Generation of Binary, Ordinal and Continuous Data
Analyzing Replicated ChIP Sequencing Data Using Quasi-Likelihood
Cut Numeric Values into Evenly Distributed Groups
Exact Binary Sequential Designs and Analysis
Data Preprocessing, Binning for Classification and Regression
Predict environmental conditions from biological observations
Analyse Audio Recordings and Automatically Extract Animal
Vocalizations
Interactive Circular Visualization of Genomic Data using
'htmlwidgets' and 'BioCircos.js'
Access the Bioconductor Project Package Repository
Feature Selection and Classification with the Embedded
Validation Procedures for Biomedical Data Analysis
Package for Community Ecology and Suitability Analysis
Multilevel Modeling of Dendroclimatical Fluctuations
An Interface for GenBank/GenPept Flat Files
Biodiversity Assessment Using Functional Tools
Process Biogas Data and Predict Biogas Production
Point Data Quality Assessment and Coordinate Conversion
Simulation of Dynamic Microbial Inactivation
Create Hyperlinks to Biological Databases and Resources
Modelling and Forecasting Biological Invasions
Utilities for the BIOM (Biological Observation Matrix) Format
Find Biomarkers in Two-Class Discrimination Problems
Estimating Aboveground Biomass and Its Uncertainty in Tropical
Forests
Generating Various Molecular Representations for Chemicals,
Proteins, DNAs, RNAs and Their Interactions
Ensemble Platform for Species Distribution Modeling
Sensitivity Analysis and Optimum Experiment Design for Microbial
Inactivation
Visualization of Overlapping Results with Heatmap
Simulation of deterministic and stochastic biochemical reaction
networks using Petri Nets
Biological Analysis and Visualization of Weather Radar Data
From Biological Sequences and Simulations to Correlation
Analysis
From BIOlogical Sequences to MultiDimensional Scaling
Convert a Matrix of Raw Values into Nice and Tidy Data
Tools for Electromyogram Signals (EMG) Analysis
Biomarker Selection in Penalized Regression Models
Utility Functions, Datasets and Extended Examples for Survival
Analysis
Calculation of Freshwater Biotic Indices
Tools for Biometry and Applied Statistics in Agricultural
Science
Visualising Bipartite Networks and Calculating Some (Ecological)
Indices
Bootstrap on Classical Biplots and Clustering Disjoint Biplot
Bivariate Probit with Partial Observability
Methods to Analyse Ring Re-Encounter Data
The R Package for "The Basics of Item Response Theory Using R"
Programmatic Access to Bank for International Settlements Data
Tools and Palettes for Bivariate Thematic Mapping
Estimating Cell Type Composition from Methylation Sequencing
Data
Tools to find bad commits with git bisect
Toolkit to Identify Candidate Synthetic Lethality
Approximate Bayesian Inference via Sparse Grid Quadrature
Evaluation (BISQuE) for Hierarchical Models
A S3 Class for Vectors of 64bit Integers
Binarization and Trinarization of One-Dimensional Data
Bayesian Inference of Transmission Routes Using Genome Sequences
Quantize Floating-Point Numbers for Improved Compressibility
Estimating the Parameters of Some Bivariate Distributions
Sample size calculations for bivariate longitudinal data
Bivariate Alternating Recurrent Event Data Analysis
Bivariate Residual Plots with Simulation Polygons
Generation of Bivariate Uniform Data and Its Relation to
Bivariate Binary Data
Conduct Univariate and Bivariate Wavelet Analyses
Functions to compute the biweight mean vector and covariance &
correlation matrices
Business Days Calculations and Utilities
An API Wrapper for the Bureau of Justice Statistics (BJS)
Black Box Optimization and Exploration of Parameter Space
Read and Write FWF Files in the Blaise Format
Blaker's Binomial and Poisson Confidence Limits
Plots (Slightly Extended) Bland-Altman Plots
Calculate and Analyze Blau Status for (Covert) Organizations
Black-Litterman and Copula Opinion Pooling Frameworks
A New Link Function that Blends Two Specified Link Functions
Analyze biotic homogenization of landscapes
Joint Analysis of Experiments with Mixtures and Random Effects
Bipartite Longitudinal Influence Network (BLIN) Estimation
Record Linkage for Empirically Motivated Priors
Data Exploration with Multiple Machine Learning Algorithms
Binomial linear and linear-expit regression
Data Files and Functions Accompanying the Book "Bayesian Data
Analysis in Ecology using R, BUGS and Stan"
A Simple S3 Class for Representing Vectors of Binary Data
('BLOBS')
Co-Clustering Package for Binary, Categorical, Contingency and
Continuous Data-Sets
Estimation of Large Block Covariance Matrices
Bayesian Calculation of Region-Specific Fixation Index to Detect
Local Adaptation
Block Forests: Random Forests for Blocks of Clinical and Omics
Covariate Data
blockmatrix: Tools to solve algebraic systems with partitioned
matrices
Creates strings that show a text message in 8 by 8 block letters
Generalized and Classical Blockmodeling of Valued Networks
Latent and Stochastic Block Model Estimation by a 'V-EM'
Algorithm
Randomization for block random clinical trials
Block Design for Response-Adaptive Randomization
Nested and Crossed Block Designs for Factorial, Fractional
Factorial and Unstructured Treatment Sets
Block, Assign, and Diagnose Potential Interference in Randomized
Experiments
Create Blogs and Websites with R Markdown
Impute and Analyze Data with Observations Below the Limit of
Quantification
Tools for Developing Binary Logistic Regression Models
Stochastic Rainfall Generator Bartlett-Lewis Rectangular Pulse
Model
Interactive Document for Working with Binary Logistic Regression
Analysis
Request Data from the U.S. Bureau of Labor Statistics API
An API Wrapper for the Bureau of Labor Statistics (BLS)
Multivariate Extremes: Bayesian Estimation of the Spectral
Measure
Bayesian Multivariate Analysis of Summary Statistics
Benchmark dose analysis for dose-response data
Mediation analysis with missing data using bootstrap
Hybrid Trait Evolution under Brownian Motion
Miscellaneous Functions for Panel Data, Quantiles, and Printing
Results
Bayesian Sampling for Stick-Breaking Mixtures
Bayesian Estimation for Finite Mixture of Distributions
Bundle Methods for Regularized Risk Minimization Package
Bayesian Models for Rare Variant Association Analysis
Bayesian Model Selection under Constraints
Bayesian Multi-Trait Multi-Environment for Genomic Selection
Analysis
Learning Discrete Bayesian Network Classifiers from Data
Data Generator based on Bayesian Network Model
Bayesian Network Structure Learning, Parameter Learning and
Inference
Bayesian Neural Network for High-Dimensional Nonlinear Variable
Selection
Bagged k-Nearest Neighbors Survival Prediction
Bayesian Estimation for Normal Heteroscedastic Nonlinear
Regression Models
Ferguson-Klass Type Algorithm for Posterior Normalized Random
Measures
Algorithms for Pitman-Yor Process Mixtures
Bayesian Nonparametric Model for Clustering with Mixed Scale
Variables
Simulate Genotypes from the BN-PSD Admixture Model
A Bayesian Nonparametric Algorithm for Time Series Clustering
Bayesian Non- And Semi-Parametric Model Fitting
Spatial Implementation of Bayesian Networks and Mapping
Bayesian Network Structure Learning from Data with Missing
Values
Interactive Visualization of Bayesian Networks
Bayesian Output Analysis Program (BOA) for MCMC
Board Games and Tools for Building Board Games
Profilling of Peripheral Blood Mononuclear Cells using CyTOF
Bacterium and Virus Analysis of Orthologous Groups (BOG) is a
Package for Identifying Differentially Regulated Genes in the
Light of Gene Functions
Interface to the Boilerpipe Java Library
Bayesian Optimal INterval (BOIN) Design for Single-Agent and
Drug- Combination Phase I Clinical Trials
Functions for Elementary Bayesian Inference
Australian Government Bureau of Meteorology ('BOM') Data Client
Fixed Coupon Bond Valuation Allowing for Odd Coupon Periods and
Various Day Count Conventions
An Improved Multiple Testing Procedure for Controlling False
Discovery Rates
Authoring Books and Technical Documents with R Markdown
Generate Assorted Books and Documents with R 'bookdown' Package
Optimal Estimation of Partially Observed Boolean Dynamical
Systems
Construction, Simulation and Analysis of Boolean Networks
Boosted Multivariate Trees for Longitudinal Data
A modular framework to bag or boost any estimation procedure
Bootstrap Functions (Originally by Angelo Canty for S)
Bootstrapping Estimates of Clustering Stability
Bootstrapped Confidence Intervals for (Negative) Likelihood
Ratio Tests
Bootstrap-MRMR Technique for Informative Gene Selection
Bootstrap Methods for Various Network Estimation Routines
Bootstrap Prediction Intervals and Bias-Corrected Forecasting
Bootstrapped Response and Correlation Functions
A Bootstrap Test for the Probability of Ruin in the Classical
Risk Process
Bootstrap Subsamplings of Sparse Partial Least Squares -
Discriminant Analysis for Classification and Signature
Identification
Functions for the Book "An Introduction to the Bootstrap"
Bootstrap Algorithms for Finite Population Inference
Bootstrap cis-QTL Method that Corrects for the Winner's Curse
Fast, Exact Bootstrap Principal Component Analysis for High
Dimensional Data
Bootstrap Tolerance Levels for Credit Scoring Validation
Statistics
Adjusting for Optimism in 'glmnet' Regression using
Bootstrapping
Test Stationarity using Bootstrap Wavelet Packet Tests
Transforming Behavioral Observation Records into Data Matrices
Bayesian Ordination and Regression AnaLysis
Wrapper Algorithm for All Relevant Feature Selection
A Bunch of Structure and Sequence Analysis
Convert Binary Species Range Maps into Continuous Surfaces Based
on Distance to Range Edge
Analytic Solutions for (ground-water) Boussinesq Equation
Box-Cox-Type Transformations for Linear and Logistic Models with
Random Effects
Downloads Box Office Information for Given Dates
Double Box Plot for Two-Axes Correlation
Nonparametric Bounds for the Average Causal Effect Due to Balke
and Pearl and Extensions
Biplot of Multivariate Data Based on Principal Components
Analysis
Beta Product Confidence Procedure for Right Censored Data
bPeaks: an intuitive peak-calling strategy to detect
transcription factor binding sites from ChIP-seq data in small
eukaryotic genomes
Bayesian Phylogeographic and Ecological Clustering
Bayesian Purity Model to Estimate Tumor Purity
Bayesian Projected Normal Regression Models for Circular Data
Computations Around Bayesian Predictive Power
Fits the Bradley-Terry Model to Potentially Large and Sparse
Networks of Comparison Data
Visualize Combined Action Response Surfaces and Report BRAID
Analyses
Fitting Dose Response with the BRAID Combined Action Model
Region-Level Connectivity Network Construction via Kernel
Canonical Correlation Analysis
Helper Functions to 'misc3d' and 'rgl' Packages for Brain
Imaging
Basic wavelet analysis of multivariate time series with a
visualisation and parametrisation using graph theory
Simulation and Estimation for Branching Processes
Bootstrapped Robustness Assessment for Qualitative Comparative
Analysis
Brazilian Maps from Different Geographic Levels
Species Richness Estimation and Modeling
Model Agnostic Explainers for Individual Predictions
Multiple Change-Point Detection and Segmentation
An R Package for Multiple Break-Point Detection via the
Cross-Entropy Method
Core Functions to Read and Fit 13c Time Series from Breath Tests
Stan-Based Fit to Gastric Emptying Curves
Brain Cell Type Specific Gene Expression Analysis
Templating Framework for Report Generation
Bias Reduction in Binomial-Response Generalized Linear Models
Bias Reduction in Generalized Linear Models
An Implementation of the Bridge Distribution with Logit-Link as
in Wang and Louis (2003)
Genome-Wide RNA Degradation Analysis Using BRIC-Seq Data
Bridge Sampling for Marginal Likelihoods and Bayes Factors
Fast Inference for Large Spatial Datasets using BRISC
Bias Reduction with Missing Binary Response
Bayesian Regularization for Feed-Forward Neural Networks
Convert Statistical Analysis Objects into Tidy Tibbles
A Compression Format Optimized for the Web
Bayesian Analysis of Quantile Regression Models
Bayesian Inference on the Ratio of Two Poisson Rates
Interface to the 'OpenBUGS' MCMC Software
Normalisation Tools for Inter-Condition Variability of ChIP-Seq
Data
A 'Bootstrap 4' Version of 'shinydashboard'
Safety Assessment in Agricultural Field Trials
Bayesian State-Space Models for Animal Movement
Bayesian Spectral Analysis Models using Gaussian Process Priors
Bayesian Survival Model with Lasso Shrinkage Using Generalized
Weibull Regression
Nonparametric Smoothing of the Hazard Function
Bayes Screening and Model Discrimination
Bayesian Proposal to Fit Spatial Econometric Models
Bayesian Nonparametric Spectral Density Estimation Using
B-Spline Priors
Adds Functionality to the R Markdown + Shiny Bootstrap Framework
Bayesian Semiparametric Models for Meta-Analysis
Asymptotic Covariance Matrices of Some BSS Mixing and Unmixing
Matrix Estimates
Bayesian Inference of Non-Linear and Non-Gaussian State Space
Models
Birnbaum-Saunders Model Based on Skew-Normal Distribution
Beyond the Border - Kernel Density Estimation for Urban
Geography
Bradley-Terry Model with Exponential Time Decayed Log-Likelihood
and Adaptive Lasso
Temporal Exponential Random Graph Models by Bootstrapped
Pseudolikelihood
Modelling Heterogeneity in Paired Comparison Data
Training and Analysing Asynchronous Boolean Models
Probabilistic Models for Assessing and Predicting your Customer
Base
Bucky's Archive for Data Analysis in the Social Sciences
Bias and Uncertainty Corrected Sample Size
A Deep Learning Package for Statistical Classification Analysis
Stepwise Elimination and Term Reordering for Mixed-Effects
Regression
Buckley-James Regression for Survival Data with High-Dimensional
Covariates
Automatic Groove Identification via Bayesian Changepoint
Detection
Analyze Bunching in a Kink or Notch Setting
Markov model for bursty behavior in streams
Calculates Business Duration Between Two Dates
Bayesian Analysis of a Vector Autoregressive Model with
Stochastic Volatility and Time-Varying Parameters
Bayesian Inference of Vector Autoregressive Models
A Simple alternative to proportional Venn diagrams
The Stark-Parker algorithm for bounded-variable least squares
Solvers for Boundary Value Problems of Differential Equations
Bayesian Variant Selection: Bayesian Model Uncertainty
Techniques for Genetic Association Studies
Bayesian Variable Selection in High Dimensional Settings using
Nonlocal Priors
Backward Procedure for Change-Point Detection
Baumgartner Weiss Schindler Test of Equal Distributions
Statistics About Bytes Contained in a File as a Circle Plot
Bivariate Zero-Inflated Negative Binomial Model Estimator
Extended Inference for Lasso and Elastic-Net Regularized Cox and
Generalized Linear Models
Download and Prepare C14 Dates from Different Source Databases
Methods for Detecting Safety Signals in Clinical Trials Using
Body-Systems (System Organ Classes)
Compare Two Classifications or Clustering Solutions of Varying
Structure
Infering large-scale gene networks with C3NET
C5.0 Decision Trees and Rule-Based Models
Simple, Multiple and Joint Correspondence Analysis
Bootstrap Confidence Regions for Correspondence Analysis
Classification Accuracy and Consistency under Item Response
Theory
Conditional Density Estimation Network Construction and
Evaluation
A Package to Perform Covariate Augmented Dickey-Fuller Unit Root
Tests
Provides a GUI to Several Statistical Methods
Simulation of Plasma Caffeine Concentrations by Using Population
Pharmacokinetic Model
Conditional Akaike Information Criterion for 'lme4'
Graphical Aid in Correspondence Analysis Interpretation and
Significance Testings
R Graphics Device using Cairo Graphics Library for Creating
High-Quality Bitmap (PNG, JPEG, TIFF), Vector (PDF, SVG,
PostScript) and Display (X11 and Win32) Output
Comparison of Algorithms with Iterative Sample Size Estimation
Calculations for All Common Subsequences
Calculates the Wavelet-Based Organization Index
Create, Read, Write, and Work with 'iCalander' Files, Calendars
and Scheduling Data
Multiple Imputation Using MICE and Random Forest
Automated Parameter Estimation for Complex (Ecological) Models
Calibration of Scatterplot and Biplot Axes
Calibration for Computer Experiments with Binary Responses
Weighting and Estimation for Panel Data with Non-Response
Bayesian Calibration of Complex Computer Codes
Mapping ML Scores to Calibrated Predictions
Code Calibration in a Bayesian Framework
Improved Allele-Specific Copy Number of SNP Microarrays for
Downstream Segmentation
Finite Mixture Models and Meta-Analysis Tools - Based on C.A.MAN
Mixed Computerized Adaptive Multistage Testing
Camera Trap Data Management and Preparation of Occupancy and
Spatial Capture-Recapture Analyses
Canadian Census Data and Geography from the 'CensusMapper' API
Estimation of Temporal Ordering of Cancer Abnormalities
Visualizing Generalized Canonical Discriminant and Canonical
Correlation Analysis
Accessing Intra-Tumor Heterogeneity and Tracking Longitudinal
and Spatial Clonal Evolutionary History by Next-Generation
Sequencing
Chemical Composition of Differential Protein Expression
Accessing Statistics Canada Data Table and Vectors
Directly Extracts Complete CANSIM Data Tables
Visualization Package for CanvasXpress in R
Covariate Assisted Principal (CAP) Regression for Covariance
Matrix Outcomes
Combined Analysis of Pleiotropy and Epistasis
Comparative Analyses of Phylogenetics and Evolution in R
Capital Budgeting Analysis, Annuity Loan Calculations and
Amortization Schedules
A Virtual Patient Simulator in the Context of Calcium and
Phosphate Homeostasis
Numbers Figures and Creates Simple Captions
CAlibrating Penalities Using Slope HEuristics
Estimates population size from non-invasive sampling
Automatic Calibration by Evolutionary Multi Objective Algorithm
Spatial Generalised Linear Mixed Models for Areal Unit Data
Data Used in the Vignettes Accompanying the CARBayes and
CARBayesST Packages
Spatio-Temporal Generalised Linear Mixed Models for Areal Unit
Data
Estimation of the Number of Fatalities from Carcass Searches
Companion to Applied Regression Data Sets
Identification of Cardinal Dates in Ecological Time Series
Cardiovascular Safety Exposure-Response Modeling in Early-Phase
Clinical Studies
High-Dimensional Regression and CAR Score Variable Selection
Statistical package for population size estimation in
capture-recapture models
Procedures for Computing Indices of Careless Responding
Continuous-Time Fractionally Integrated ARMA Process for
Irregularly Spaced Long-Memory Time Series Data
Estimation of caribou abundance based on large scale
aggregations monitored by radio telemetry
Functions for Statistics Classes at Carleton College
Ecological Quality Ratios Calculation and Plot
A Collection of Database, Data Structure, Visualization, and
Utility Functions for R
Build Common Tables of Summary Statistics for Reports
CRISPR AnalyzeR for Pooled CRISPR Screens
Reparameterized and marginalized posterior sampling for
conditional autoregressive models, OpenCL implementation
Predicting Categorical and Continuous Outcomes Using One in Ten
Rule
Covariate Assisted Ranking and Screening for Large-Scale
Two-Sample Inference
Correlation-Adjusted Regression Survival (CARS) Scores
Tools for Understanding Highway Performance
Censored Autoregressive Model with Exogenous Covariates
Selection, Reverse-Engineering and Prediction in Cascade
Networks
Experimental Data of Cascade Experiments in Genomics
Casualty Actuarial Society Individual Claim Simulator
Fitting Flexible Smooth-in-Time Hazards and Risk Functions via
Logistic and Multinomial Regression
Identify Similar Cases for Qualitative Case Studies
Detection of Statistically Significant Combinations of SNPs in
Association Mapping
'caret' Applications for Spatial-Temporal Models
Analysis of categorical-variable datasets with missing values
Covariate-Adjusted Tensor Classification in High-Dimensions
Taking the Pain Out of Catching and Handling Conditions
Test, Identify, Select and Mutate Categorical or Continuous
Values
Categorical Data Analysis Program Package
Fishery Stock Assessment by Catch Dynamics Models
High Dimensional Factor Analysis and Confounder Adjusted Testing
and Estimation
An R Package for Simulating IRT-Based Computerized Adaptive
Tests
Chronomics Analysis Toolkit (CAT): Periodicity Analysis
Formal Psychological Models of Categorization and Learning
Case-Control and TDT Meta-Analysis Package
Tools: moving window statistics, GIF, Base64, ROC AUC, etc
Optimal Categorisation of Continuous Variables in Prediction
Models
Generation of IRT Response Patterns under Computerized Adaptive
Testing
Create Catseye Plots Illustrating the Normal Distribution of the
Means
Special models for categorical variables
Computerized Adaptive Testing for Survey Research
Computation of the p-Value for the Exact Conditional
Cochran-Armitage Trend Test
Tools for Estimating Causal Dose Response Functions
Deriving Expressions of Joint Interventional Distributions and
Transport Formulas in Causal Models
Methods for Estimating Causal Effects from Observational Data
Estimation of Causal Effects with Generalized Additive Models
Inferring Causal Effects using Bayesian Structural Time-Series
Models
Learning Stable Structures in Kinetic Systems
Causal Learning of Mixed Graphical Models
Selection Bias Approach to Sensitivity Analysis for Causal
Effects
Estimation Methods for Causal Inference Based on Inverse
Probability Weighting
Analysis utilities for binary classification and Causata users
CRISPR Pooled Screen Analysis using Beta-Binomial Test
Contextual Bayesian Anomaly Detection in R
Compare Baseline Characteristics Between Groups
Continuous Analog of a Binomial Distribution
Clustering of Multivariate Binary Data with Dimension Reduction
via L1-Regularized Likelihood Maximization
Simulation, Estimation and Segmentation of Composite Based
Structural Equation Models
Statistics Netherlands (CBS) Open Data API Client
Bayesian Canonical Correlation Analysis and Group Factor
Analysis
(Robust) Canonical Correlation Analysis via Projection Pursuit
Concordance Correlation Coefficient for Repeated (and
Non-Repeated) Measures
Combined Cluster and Discriminant Analysis
CCDr Algorithm for Learning Sparse Gaussian Bayesian Networks
Cox Model for Case-Cohort Data with Stratified
Subcohort-Selection
Convex Clustering Methods and Clustering Indexes
Significance Tests for Canonical Correlation Analysis (CCA)
One and two locus GWAS of binary phenotype with
case-control-population design
Removes the Cell-Cycle Effect from Single-Cell RNA-Sequencing
Data
Correct and Cluster Response Style Biased Data
Tools for the Continuous Convolution Trick in Nonparametric
Estimation
Consensus Analysis, Model-Based Clustering, and Cultural
Consensus Theory Applications
Retrieve Flu Season Data from the United States Centers for
Disease Control and Prevention ('CDC') 'FluView' Portal
Quantile Regression for Random Variables on the Unit Interval
Coordinate Descent Algorithms for Lasso Penalized L1, L2, and
Logistic Regression
Tools to Download and Work with USDA Cropscape Data
Components of Canadian Monetary and Credit Aggregates
Phylogenetically Classifies Retention Mechanisms of Duplicate
Genes from Gene Expression Data
Constrained Dual Scaling for Detecting Response Styles
Statistical Inference of C- And D-Vine Copulas
Sampling from Conditional C- and D-Vine Copulas
Cost-Effectiveness Analysis using Markov Models
Benchmark for the CEC 2005 Special Session on Real-Parameter
Optimization
Benchmark functions for the Special Session and Competition on
Real-Parameter Single Objective Optimization at CEC-2013
Collection of Common Astronomical Conversion Routines and
Functions
Translate Spreadsheet Cell Ranges to Rows and Columns
Functions to fit cell volume distributions and thereby estimate
cell growth rates and division times
Climate and Ecological Niche Factor Analysis
Linear Regression with a Randomly Censored Covariate
Censored Linear Mixture Regression Models
Fits Normal and Student-t Censored Regression Model
Changes United States Census Geographic Code into Name of
Location
Show the Strength of Relationships Between Centre and Peripheral
Items
Cross-Entropy R Package for Optimization
Discriminant Analysis of Time Series in the Presence of
Within-Group Spectral Variability
Context Driven Exploratory Projection Pursuit
Outlier Detection Using the Iterated RMCD Method of Cerioli
(2010)
Identification and Analysis of ceRNA Regulation
Covariance Estimation Regularized by Nuclear Norm Penalties
Cause-Specific Framework for Competing-Risk Analysis
Estimate quantiles using any order Cornish-Fisher expansion
Canadian Forest Fire Danger Rating System
Compare Groups, Analytically and Graphically
Calculate AUC-type measure when gold standard is continuous and
the corresponding optimal linear combination of variables with
respect to it
R-Based API for Accessing the MSKCC Cancer Genomics Data Server
(CGDS)
Microarray CGH analysis using the Smith-Waterman algorithm
Array CGH Data Analysis and Visualization
L1-Penalized Censored Gaussian Graphical Models
Fits Conditional Generalized Linear Models
Clean and Analyze Continuous Glucose Monitor Data
Powell Miscellaneous Functions for Teaching and Learning
Statistics
Statistical Methods and Models for Claims Reserving in General
Insurance
Nonparametric Change Detection in Multivariate Linear
Relationships
Changepoint Analysis for Multivariate Time Series
Methods for Nonparametric Changepoint Detection
Change-Point Estimation for Expensive and High-Dimensional
Models
Change-Points Detections for Changes in Variance
Change Point Estimation for Clustered Signals
Markov Model for the Online Multi-Channel Attribution Problem
Shiny Web Application for the Multichannel Attribution Problem
Charge Transfer Rates and Charge Carrier Mobilities
Simplified Language for Plots and Charts
Check the Basic Validity of a (Function) Argument
Checks if a Number is Valid Using the Luhn Algorithm
Install Packages from Snapshots on the Checkpoint Server for
Reproducibility
Check the Properties of Common R Objects
Analysis and Visualisation of Ecological Communities
Tools for Intuitive and Flexible Statistical Analysis Workflows
Calibration Functions for Analytical Chemistry
A Cheminformatics Modeling Laboratory for Fitting and Assessing
Machine Learning Models
Multivariate Statistical Analysis in Chemometrics
Chemometrics with R - Multivariate Data Analysis in the Natural
Sciences and Life Sciences
Exploratory Chemometrics for 2D Spectroscopy
Functions Supporting Packages ChemoSpec and ChemoSpec2D
Multiple Testing Methods for Exploratory Research
Closest History Flow Field Forecasting for Bivariate Time Series
Partitioning Chi-Squared and Tau Index for Three-Way Contingency
Tables
Data and Methods Around Reference Values in Pediatrics
Processing Chill and Heat Models for Temperate Fruit Trees
Statistical Methods for Phenology Analysis in Temperate Fruit
Trees
Miscellaneous Tools for Chinese Text Mining and More
Toolkit of Helper Functions to Pre-Process Amplification Data
Nonparametric Methods for Identifying Differential Enrichment
Regions with ChIP-Seq Data
Estimation and Hypothesis Testing for Threshold Regression
Thermodynamic Calculations and Diagrams for Geochemistry
Amend, Augment and Aid Analysis of John Snow's Cholera Map
Cholesky Decomposition of the Wishart Distribution
Simplify the Creation of Choropleth Maps in R
Contains an Administrative-Level-1 Map of the World
Contains Maps Used by the 'choroplethr' Package
Interface to Chromosome Counts Database API
Interactive Visualization and Mapping of Chromosomes
Analysis of chromosomal interactions data (correction,
segmentation and comparison)
Chronological Objects which can Handle Dates and Times
Chunkwise Text-File Processing for 'dplyr'
Estimating and Testing Direct Effects in Directed Acyclic Graphs
using Estimating Equations
Weighted summary of cumulative incidence functions
Toolbox for Connectivity Informatics Technology Initiative
('CIFTI') Files
Deciphering Central Informative Nodes in Network Analysis
Centered Isotonic Regression and Dose-Response Utilities
Maximum Likelihood Analysis of Circular Data
Statistical Analysis of Circular Data using Nonnegative
Trigonometric Sums (NNTS) Models
Detection of Outliers in Circular-Circular Regression
Spatial and Spatio-Temporal Bayesian Model for Circular Data
Circular Statistics, from "Topics in Circular Statistics" (2001)
Analysis and Visualization of Circular Data
Common and Individual Structure Explained for Multiple Graphs
CIT Breast Cancer Molecular SubTypes Prediction
CIT Colon Cancer Molecular SubTypes Prediction
An RStudio Addin to Insert BibTex Citation in Rmarkdown
Documents
Confidence or Prediction Intervals, Quantiles, and Probabilities
for Statistical Models
'RStudio' Add-in to Insert Markdown Citations
Visualization of structure and contents of a database
Track Flows of Water Through an Urban System
Confidence Intervals Utilizing Uncertain Prior Information
R Client for the 'Civis data science API'
Copula-Based Joint Analysis of Multiple Phenotypes
Client for the Comprehensive Knowledge Archive Network ('CKAN')
'API'
Composite Kernel Machine Regression Based on Likelihood Ratio
Test
Measuring Morphological Diversity and Evolutionary Tempo
C++ Implementations of Phylogenetic Cladogenesis Calculations
Classical Age-Depth Modelling of Cores from Deposits
Time Series Modeling for Climate Change Proxies
Classes and Methods for "Class Comparison" Problems on
Microarrays
Classes and Methods for "Class Discovery" with Microarrays or
Proteomics
Construct Graphs of S4 Class Hierarchies
Generates a Visualization of Classifier Performance as a Grid of
Diagnostic Plots
Explore classification models in high dimensions
R Interface to the ClassyFire RESTful API
To Inspect and Manipulate Data; and to Keep Track of This
Process
The Critical Care Clinical Data Processing Tools
Cleaning Geometries from Spatial Objects
A Tidy Data Model for Natural Language Processing
Simultaneous Variables Clustering and Regression
Composite Likelihood Estimation for Spatial Data
Helpers for Developing Command Line Interfaces
Model-Based Clustering of Categorical Sequences
Fix Data and Create Report Tables from Different Objects
Analyzes Clickstreams Based on Markov Chains
Easily Download and Visualise Climate Data from CliFlo
Complex-Valued Wavelet Lifting Estimators of the Hurst Exponent
for Irregularly Sampled Time Series
Censoring Data and Likelihood-Based Correlation Estimation
Estimating Climate Stability from Climate Model Data
Climate Tools (Series Homogenization and Derived Products)
Calculate Average Minimal Depth of a Maximal Subtree for
'ranger' Package Forests
Climate Classification According to Several Indices
Climate Downscaling Library for Daily Climate Model Output
Constrained L1-minimization for Inverse (covariance) Matrix
Estimation
Set of Tools to Compute Various Climate Indices
Simulation and Analysis Tools for Clinical Dose Response
Modeling
Clinical Trial Design and Data Analysis Functions
Summary Measures for Clinical Trials with Survival Outcomes
Statistical Reporting in Clinical Trials
Read and Write from the System Clipboard
Constrained Inference for Linear Mixed Effects Models
Fitting Exact Conditional Logistic Regression with Lasso and
Elastic Net Penalties
Sparse Conditional Logistic Regression for Matched Studies
Seeking and Finding Clones in Copy Number and Sequencing Data
Composite Likelihood Inference and Diagnostics for Replicated
Spatial Ordinal Data
Interface to the Google Cloud Machine Learning Platform
Curve Linear Regression via Dimension Reduction
Constrained Likelihood Ratio Tests for a Directed Acyclic Graph
Central Limit Theorem Experiments (Theoretical and Simulation)
Cluster-Robust (Sandwich) Variance Estimators with Small-Sample
Corrections
Wilcoxon Rank Sum Test for Clustered Data
Statistical Methods for Analyzing Clustered Matched Pair Data
Clustering Dose-Response Curves and Fitting Appropriate Models
to Them
Clusters of Effects Curves in Quantile Regression Models
"Finding Groups in Data": Cluster Analysis Extended Rousseeuw et
al.
Analyze Clustered Data with Generalized Linear Models using the
Cluster Bootstrap
Causal Effects from Observational Studies with Clustered
Interference
Location and Visualization of Clustered Somatic Mutations
Explore clustering interactively using R and GGobi
Random Cluster Generation (with Specified Degree of Separation)
Identifying clusters in genomics data by recursive partitioning
Optimal Distance-Based Clustering for Multidimensional Data with
Sequential Constraint
Evaluate Function Calls on HPC Schedulers (LSF, SGE, SLURM,
PBS/Torque)
Integrative Clustering for Heterogeneous Biomedical Datasets
A Parallel Clustering Non-Uniform Memory Access ('NUMA')
Optimized Package
Power Calculations for Cluster-Randomized and Cluster-Randomized
Crossover Trials
Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans, K-Medoids
and Affinity Propagation Clustering
Reproducibility of Gene Expression Clusters
Calculate Cluster-Robust p-Values and Confidence Intervals
Searching for Optimal Clustering Procedure for a Data Set
Assessment of Stability of Individual Objects or Clusters in
Partitioning Solutions
Hierarchical Clustering with Spatial Constraints
k-Prototypes Clustering for Mixed Variable-Type Data
Variable Selection in Clustering by Mixture Models for Discrete
Data
Easy Interface for Clustering a Set of Documents and Exploring
Group- Based Patterns
Methods for Joint Dimension Reduction and Clustering
Visualise Clusterings at Different Resolutions
Interactive Document for Working with Cluster Analysis
Clustering of Variables Around Latent Variables
Variable Selection for Gaussian Model-Based Clustering
Gaussian-Based Visualization of Gaussian and Non-Gaussian
Model-Based Clustering
Covariance Matrix Adapting Evolutionary Strategy
Covariance Matrix Adaptation Evolution Strategy
Matching Algorithms for Causal Inference with Clustered Data
Computer Model Calibration for Deterministic and Stochastic
Simulators
Bi-Level Selection of Conditional Main Effects
Computational Methods for Numerical Analysis
Control Charts for Conway-Maxwell-Poisson Distribution
Subdistribution Analysis of Competing Risks
Analysis of Competing Risks Using Quantile Regressions
Misc Functions of the Center for Mathematical Research
The Complex Multivariate Gaussian Distribution
Causal Modeling with Coincidence Analysis
Canonical Non-Symmetrical Correspondence Analysis in R
Complex-Valued Wavelet Lifting for Signal Denoising
Complex-Valued Wavelet Lifting for Univariate and Bivariate Time
Series Analysis
Maximum Likelihood Estimation of a Log-Concave Density Function
Pre-process DNA Copy Number (CN) Data for Detection of CN Events
Fast Algorithms for Frequency-Preserving Null Models in Ecology
A Versatile Toolkit for Copy Number Variation Relationship Data
Analysis and Visualization
Coalition Probabilities in Multi-Party Democracies
Biclustering via Latent Block Model Adapted to Overdispersed
Count Data
Constrained B-Splines (Sparse Matrix Based)
COmbat CO-Normalization Using conTrols (COCONUT)
Extract Shared Variation in Collections of Data Sets Using
Regression Models
Statistical Comparisons of Two or more Alpha Coefficients
Output Analysis and Diagnostics for MCMC
A Basic Set of Functions for Compositional Data Analysis
Markov chain Monte Carlo burn-in based on "bridge" statistics
Automated C Code Generation for 'deSolve', 'bvpSolve'
Automatic Codebooks from Metadata Encoded in Dataset Attributes
Analysis of R Code for Reproducible Research and Code
Comprehension
Generate 'CodeMeta' Metadata for R Packages
Alternative Factor Coding Matrices for Linear Model Formulae
Robust Coefficient Alpha and Omega with Missing and Non-Normal
Data
Species coexistence modeling and analysis
Cognitive Mapping Tools Based on Coding of Textual Sources
Conditional Inference Procedures in a Permutation Test Framework
Access 'CoinDesk' Bitcoin Price Index API
Get Cryptocurrencies Market Cap Prices from Coin Market Cap
Simultaneous Confidence Interval for Multinomial Proportion
Consistent Monitoring of Stationarity and Cointegrating
Relationships
Parameter Estimation and Inference in a Cointegrating Regression
Constrained Optimization on Linear Function
Collapses Levels, Computes Information Value and WoE
Interactive Collapsible Tree Diagrams using 'D3.js'
Quickly and Neatly Collect Arguments from One Environment to
Pass to Another
Quantified Risk Assessment Data Collection
Distribution and Percentile of Sackin, Cophenetic and
Colless-Like Balance Indices of Phylogenetic Trees
Check for Namespace Collisions with Other Packages and Functions
on CRAN
Collocation Inference for Dynamic Systems
Collapsed Latent Position Cluster Model for Social Networks
Auxiliary Package for Package 'CollapsABEL'
Dengue Cases and Climate Variables in Colombo Sri Lanka
Colocalisation Tests of Two Genetic Traits
Conduct Co-Localization Analysis of Fluorescence Microscopy
Images
Distance Metrics for Image Color Similarity
Implementation of Colored Independent Component Analysis and
Spatial Colored Independent Component Analysis
Extract Colors from Windows BMP, JPEG, PNG, TIFF, and SVG Format
Images
Plot and add custom coloring to Venn diagrams for 2-dimensional,
3-dimensional and 4-dimensional data
Color Palettes using Colormaps Node Module
Optimized Rendering of Fold Changes and Confidence Values
Color Palettes for EPL, MLB, NBA, NHL, and NFL Teams
A Toolbox for Manipulating and Assessing Colors and Palettes
Color Calculations with Emphasis on Spectral Data
Tools for colors in a Hue-Saturation-Value (HSV) color model
A Colour Picker Tool for Shiny and for Selecting Colours in
Plots
Analysis of Concept Maps and Concept Landscapes
A Combined Association Test for Genes using Summary Statistics
Synergy/Antagonism Analyses of Drug Combinations
Estimation of Optimal Portfolio Weights by Combining Simple
Portfolio Strategies
Construction Methods of some Series of PBIB Designs
Exact Test from the Combinations of Mutually Exclusive
Alterations (CoMEt) Algorithm
Computational Methods for Immune Cell-Type Subsets
Print Nicely Formatted Comments for Use in Script Files
Useful Libraries for Building a Java Based GUI under R
High Performance CommonMark and Github Markdown Rendering in R
JAR Files of the Apache Commons Mathematics Library
Extract and plot common trends from a cointegration system.
Calculate P-value for Johansen Statistics
Comparative Phylogeographic Analysis using the Community Tree
Framework
Creates empty plots with compact axis notation
Compare Two Correlated C Indices with Right-censored Survival
Outcome
Interface to Diverse Estimation Methods of Causal Networks
Do a Git Style Diff of the Rows Between Two Dataframes with
Similar Structure
comparison of medical forms in CDISC ODM format
Correct for Verification Bias in Diagnostic Accuracy & Agreement
Multivariate likelihood ratio calculation and evaluation
Conformational Manipulations of Protein Atomic Structures
C++ Implementation of Component-Wise Boosting
Event-specific incidence rates for competing risks data
Tools for Retrieval and Storage of Functional Genomics Data
Conway-Maxwell-Poisson GLM and Distribution Functions
Calculate the Proportion of Permutations in Line with an
Informative Hypothesis
Robust Linear Regression with Compositional Data as Covariates
Univariate Feature Selection and Compound Covariate for
Predicting Survival
Distribution Function of Quadratic Forms in Normal Variables
Composite-Likelihood Based Analysis of Random Fields
Interface with the United Nations Comtrade API
Calculate the AQI from Pollutant Concentration
Human-Friendly Text from Unknown Strings
Optimisation with Continuous Convex Piecewise (Linear and
Quadratic) Functions
Computes and Plots Consonance (Confidence) Intervals, P-Values,
and S-Values to Form Consonance and Surprisal Functions
Approximate Conditional Inference for Logistic and Loglinear
Models
Parameter estimation in conditional GEE for recurrent event gap
times
Computation of P Values and Bayes Factors for Conditioning Data
Control How Many Times Conditions are Thrown
Conditional Density Estimation with Neural Network Conditional
Mixtures
Condition Number Regularized Covariance Estimation
Estimation of the Conditional Survival Function for Ordered
Multivariate Failure Time Data
Query-Driven Pipeline Execution and Query Templates
Conditional Visualization for Statistical Models
Primal or Dual Cone Projections with Routines for Constrained
Regression
Visualization and Analysis of Statistical Measures of Confidence
Confidence Estimation of Environmental State Classifications
Manage Environment Specific Configuration Values
Package to Parse an INI File, Including Variable Interpolation
An Implementation of Parsing and Writing Configuration File
(JSON/INI/YAML/TOML)
Descriptive Interpretations of Confidence Intervals
Confidence Interval for the Univariate Population Variance
without Normality Assumption
An Alternative Conflict Resolution Strategy
Transductive and Inductive Conformal Predictions for
Classification Problems
Sensitivity Analyses for Unmeasured Confounding in Meta-Analyses
Configural Frequencies Analysis Using Log-Linear Modeling
Estimates and Bounds for the False Discovery Proportion, by
Permutation
Downloads, Unpacks and Tidies Legislative Data from the
Brazilian Federal Senate and Chamber of Deputies
Distance Metrics for Trees Generated by Congreve and Lamsdell
Algorithms for Fitting Circles, Ellipses and Conics Based on the
Work by Prof. Nikolai Chernov
An Implementation of Conjoint Analysis Method
A package to check the cancellation axioms of conjoint
measurement
Tools for Working with Connectivity Data
Conover-Iman Test of Multiple Comparisons Using Rank Sums
Computation of Parameters Used in Preliminary Assessment of
Conservation Status
Partial Linear Least-Squares Regression using Constrained
Splines
Compute the Median Ranking(s) According to the Kemeny's
Axiomatic Approach
Reference on Constants, Units and Uncertainty
Identify Event Sequences Using Time Series Joins
Constrained, Covariance-Matching Constrained and Universal Point
or Block Kriging
Models Spatially Continuous and Discrete Population Genetic
Structure
Deque, Set, and Dict - R6 Based Container Classes with Iterators
Model-Based Clustering and Classification with the Multivariate
Contaminated Normal Distribution
Simulation and Analysis of Contextual Multi-Armed Bandit
Policies
Contouring of Non-Regular Three-Dimensional Data
Create Contour Plots from Data or a Function
Quantile Comparison for Two-Sample Right-Censored Survival Data
Disciplined Convex Programming in R using 'Convex.jl'
Income Concentration Analysis with Complex Survey Samples
Probabilistic Species Co-Occurrence Analysis in R
Co-Operation: Fast Covariance, Correlation, and Cosine
Similarity Operations
Important Concepts of Cooperative Game Theory
Cooperative Aspects of Linear Production Programming Problems
Cooperative aspects of optimal trees in weighted graphs
Automated Cleaning of Occurrence Records from Biological
Collections
General Bivariate Copula Theory and Many Utility Functions
Fitting the copCAR Regression Model for Discrete Areal Data
Copula-Based Semiparametric Analysis for Time Series Data with
Detection Limits
Coverage Probability Excursion (CoPE) Sets
Sample Size Calculation for Two Primary Time-to-Event Endpoints
in Clinical Trials
Copula-Based Estimation and Statistical Process Control for
Serially Correlated Time Series
Association Analysis of Bivariate Survival Data Based on Copulas
Copula-Based Regression Models for Bivariate Censored Data
Copula Based Bivariate Beta-Binomial Model for Diagnostic Test
Accuracy Studies
Estimation of Distribution Algorithms Based on Copulas
Copula Mixed Models for Multivariate Meta-Analysis of Diagnostic
Test Accuracy Studies
Computing Response Similarity Indices for Multiple-Choice Tests
Collection of Rudimentary Bioinformatics Tools
Community Estimation in G-Models via CORD
Set-Based Differential Covariance Testing for Genomics
Programmatic Analysis of Sediment Cores Using Computed
Tomography Imaging
Classes and Functions to Serve as the Basis for Other 'Gx'
Packages
Classification, Regression and Feature Evaluation
Core Functionality for Simulating Quantities of Interest from
Generalised Linear Models
TDT for compound heterozygous and recessive models
Record Linkage, Incorporating Imputation for Missing Agreement
Patterns, and Modeling Correlation Patterns Between Fields
The Clustering of Regression Models Method
Collection of Scripts for Interface Between 'Cornerstone' and
'R'
Confounding Robust Independent Component Analysis for Noisy and
Grouped Data
Efficient Estimation of Covariance and (Partial) Correlation
Statistics and Data Sets for Corpus Frequency Data
Power Calculations for Assessing Correlates of Risk in Clinical
Efficacy Trials
Managing, Querying and Analyzing Tokenized Text
Implementation of 2D Correlation Analysis in R
Nonparametrics with Clustered Binary and Multinomial Data
Finding Associations in Position-Wise Aligned DNA Sequence
Dataset
Linear Regression Based on Linear Structure Between Variables
Functions and Methods for Correspondence Regression
A collection of functions for graphing correlation matrices
Estimate Correlations Between Repeatedly Measured Endpoints
(E.g., Reliability) Based on Linear Mixed-Effects Models
Modeling Correlational Magnitude Transformations in
Discretization Contexts
Arbitrary Bounding of Series and Time Series Objects
Tools for processing data after a Genome Wide Association Study
Bound Constrained Optimal Sample Allocation
Tools for estimating and predicting the cosinor model
Extended Tools for Cosinor Analysis of Rhythms
Functions for cosmological distances, times, luminosities, etc
Fit Regularized Nonparametric Regression Models Using COSSO
Penalty
Copula-Based Semiparametric Models for Spatio-Temporal Data
Connect to and Work with CouchDB Databases
Locates and Counts Pixels Within Color Range(s) in Images
Estimation and Inference Methods for Counterfactual Analysis
Comprehensive Automatized Evaluation of Distribution Models for
Count Data
Discrete Response Regression for High-Dimensional Data
Penalized Estimation of Flexible Hidden Markov Models for Time
Series of Counts
Flexible Univariate Count Models Based on Renewal Processes
Mean and Variance Modeling of Count Data
Convert Counts to Fragments per Kilobase of Transcript per
Million (FPKM)
Transform Counts in RNA-Seq Data Analysis
Quantify United States County-Level Flood Measurements
Convert from UTC to Local Time for United States Counties
Compiles Meterological Data for U.S. Counties
A Residue-Residue Contact Detecting Method
Local Polynomial Regression of State Dependent Covariates in
State-Space Models
Tests on Properties of Space-Time Covariance Functions
Brownian Motion Processes for 'nlme'-Models
Produce Forest Plots to Visualize Covariate Effects
Test for Equality of Covariance Matrices
Latent Class Models with Covariate Effects on Underlying and
Measured Variables
A simultaneous regression model for the mean and covariance
Robust Covariance Estimation via Nearest Neighbor Cleaning
Model-Free Covariate Selection in High Dimensions
Tests for Determining if the Covariance Structure of
2-Dimensional Data is Separable
Statistical Tools for Covariance Analysis
Performs Segmented Linear Regression on Two Independent
Variables
Streamlined Plot Theme and Plot Annotations for 'ggplot2'
Messages, Warnings, Strings with Ascii Animals
Cox models by likelihood based boosting for a single survival
endpoint or competing risks
Duration-Based Quantities of Interest for the Cox Proportional
Hazards Model
Cox-Type Models for Interval-Censored Data
Cox Regression with Firth's Penalized Likelihood
Sparse Estimation of Cox Proportional Hazards Models via
Approximated Information Criterion
Stochastic Gradient Descent log-Likelihood Estimation in Cox
Proportional Hazards Model
Cox Regression (Proportional Hazards Model) with Multiple Causes
and Mixed Effects
Cox Proportional Hazards Regression for Right Truncated Data
Confirmatory Path Analysis through the d-sep tests
Bayesian Meta Analysis for Studying Cross-Phenotype Genetic
Associations
Methods to perform Common Principal Component Analysis (CPCA)
Efficient and Customized Preconditioned Conjugate Gradient
Method for Solving System of Linear Equations
Concordance Probability Estimates in Survival Analysis
Association Between Methylation and a Phenotype of Interest
Parallelized Genomic Prediction and GWAS
CpG Filtering Method Based on Intra-Class Correlation
Coefficients
Sequential and Batch Change Detection Using Parametric and
Nonparametric Methods
Correction of the P-Value after Multiple Coding in Generalized
Linear Models
Composition of Probabilistic Preferences (CPP)
The Conditional Probability Function of a Competing Event
Functions for Working with Danish CPR Numbers
Nonparametric Change Point Estimation for Survival Data
Simulating Survival Data from Change-Point Hazard Distributions
Quantile, Composite Quantile Regression and Regularized Versions
Conditional Maximum Likelihood for Quadratic Exponential Models
for Binary Panel Data
Power Calculation for Weighted Log-Rank Tests in Cure Rate
Models
Testing Differences Between Competing Risks Models and Their
Visualisations
Multivariate Nonparametric Cramer-Test for the
Two-Sample-Problem
Download Logs from the 'RStudio' 'CRAN' Mirror
Package Directives and Collaboration Networks in CRAN
RStudio Addin for Searching Packages in CRAN Database Based on
Keywords
Fit Continuous-Time Correlated Random Walk Models to Animal
Movement Data
Categorical Randomized Block Data Analysis
Censored Regression with Conditional Heteroscedasticity
Clustering of Genomic Regions Analysis Method
Tools for Managing SSH and Git Credentials
Functions for calculating the CreditMetrics risk model
Build Binary Classification Models in One Integrated Offering
Evaluation of Credit Risk with Structural and Reduced Form
Models
Simple Conjoint Tidying, Analysis, and Visualization
Obtain and Visualize Regulome-Gene Expression Correlations in
Cancer
Mixture Cure Generalized Odds Ratio Frailty Models for Clustered
Current Status Data
Conditional Random Fields for Labelling Sequential Data in
Natural Language Processing
Analyze Cricketers and Cricket Teams Based on ESPN Cricinfo
Statsguru
Group-Based Modelling of Longitudinal Data
Access Crime Data from the Open Crime Database
Statistical Methods for Crime Series Linkage
Fits a Model that Partitions the Covariate Space into Blocks in
a Data- Adaptive Way
Continual Reassessment Method (CRM) for Phase I Clinical Trials
Fetch 'Scholary' Full Text from 'Crossref'
CCMN and other noRMalizatioN methods for metabolomics data
Object-Oriented Implementation of CRM Designs
Downloads and Builds datasets for Climate Reference Network
Implementation Helper for '[' and '[<-' of Custom Matrix-Like
Types
A Dashboard to Visualize Scientific Workflows in 'Cromwell'
Schedule R Scripts and Processes with the 'cron' Job Scheduler
Open Data of Agricultural Production of Crops of Peru
Analysis and Search of Crossover Designs
Confidence intervals for crossover points of two simple
regression lines
Joint Distribution of Number of Crossings and Longest Run
Cross-Screening in Observational Studies that Test Many
Hypotheses
Inter-Widget Interactivity for HTML Widgets
Verbal Autopsy Data Transformation for InSilicoVA and InterVA5
Algorithms
Classes and Methods for Cross Validation of "Class Prediction"
Algorithms
Rename and Encode Data Frames Using External Crosswalk Files
Constrained Regression Model for an Ordinal Response and Ordinal
Predictors
Bayesian Nonparametric Chinese Restaurant Process Clustering
with Entropy
Cross-Recurrence Quantification Analysis for Categorical and
Continuous Time-Series
Cross-Recurrence Quantification Analysis for Dynamic Natural
Language Processing
Penalized Variable Selection in Competing Risks Regression
Competing risks regression for Stratified and Clustered data
Stepwise Covariate Selection for the Fine & Gray Competing Risks
Regression Model
A Robust and Powerful Test of Abnormal Stock Returns in
Long-Horizon Event Studies
Nonlinear Regression Parameters Estimation by 'CRS4HC' and
'CRS4HCe'
Tidying and Analyzing 'Coursera' Research Export Data
Doubly Robust Inverse Probability Weighted Augmented GEE
Estimator
Sample Size Estimation Functions for Cluster Randomized Trials
Interface to Climatic Research Unit Time-Series Version 3.21
Data
Changing the Reference Group without Re-Running the Model
Identifying Cryptic Relatedness in Genetic Association Studies
Statistical Tests for Cryptographic Randomness
Calculate the Relative Crystallinity of Starch by XRD and FTIR
Bounds on Distributional Treatment Effect Parameters
Functions for Conditional Simulation in Regression-Scale Models
Frequent Contiguous Sequential Pattern Mining of Text
CRISPR-Based Salmonella Enterica Serotype Analyzer
Connectivity Scores with Factor Analysis
Correlates of State Policy Data Set in R
Methods to Test Hypotheses on the Distribution of Spatial Point
Processes
csSAM - cell-specific Significance Analysis of Microarrays
Selection of Number of Clusters via Normalized Clustering
Instability
Substantive significance testing for regression estimates and
marginal effects
Assessing Skill of Climate Forecasts on Seasonal-to-Decadal
Timescales
Read and Write CSV Files with Selected Conventions
Import and Export CSV Data with a YAML Metadata Header
Interactive Application for Working with Contingency Tables
Correlated Trait Locus (CTL) Mapping in R
A Text Mining Toolkit for Chinese Document
Estimating the Parameters of a Continuous-Time Markov Chain from
Discrete-Time Data
Modeling Animal Movement with Continuous-Time Discrete-Space
Markov Chains
Collaborative Targeted Maximum Likelihood Estimation
Censored and Truncated Quantile Regression
Retrieve and Analyze Information on Clinical Trials from Public
Registers
Assess the Stability of Candidate Housekeeping Genes
Continuous Time Structural Equation Modelling
Interactive Document for Working with Classification Tree
Analysis
A Class of Mixture Models for Ordinal Data
Adaptive Multivariate Integration over Hypercubes
Rule- And Instance-Based Regression Modeling
Charles's Utility Function using Formula
Tools, Measures and Statistical Tests for Cultural Evolution
Analyze Multivariate Phenotypes by Combining Univariate Results
Extends ddply to allow calculation of cumulative quantities
Change Point Detection in Genomic Sequences
Partial Function Application with %<%, %-<%, and %><%
Confidence Intervals for the Current Status Model
Multiple Curve Comparisons Using Parametric Bootstrap
Tukey Curve Depth and Distance in the Space of Curves
Cusp-Catastrophe Model Fitting Using Maximum Likelihood
Evaluation Metrics for Customer Scoring Models Depending on
Binary Classifiers
Customized Training for Lasso and Elastic-Net Regularized
Generalized Linear Models
Arrange Elements on the R's Drawing Area or Inside the
PowerPoint's Slide
Customizable Higher-Order Recipe Step Functions
CUSUM Charts for Monitoring of Hospital Performance
Compute Decision Interval and Average Run Length for CUSUM
Charts
CUTOFF: A Spatio-temporal Imputation Method
Determine and Evaluate Optimal Cutpoints in Binary
Classification Tasks
Optimal Equal-HR Method to Find Two Cutpoints for U-Shaped
Relationships in Cox Model
An R package to perform LPUE standardization and stock
assessment of the English Channel cuttlefish stock using a
two-stage biomass model
Compute Expected Shortfall and Value at Risk for Continuous
Distributions
Cross-Validated Area Under the ROC Curve Confidence Intervals
Estimation of the Calibration Equation with Error-in
Observations
Efficient Design and Analysis of Cluster Randomized Trials
Tests for the Equality of Coefficients of Variation from
Multiple Groups
Lasso Penalized Precision Matrix Estimation
Calculate the predictive squared correlation coefficient
Fast Cross-Validation via Sequential Testing
Cross-validation tools for regression models
Regularized Estimators of Covariance Matrices with CV Tuning
Miscellaneous Functions for Math, Plotting, Printing,
Statistics, Strings, and Tools
extend cxxfunction by saving the dynamic shared objects
Cycle Routing and Data for Cycling Advocacy
Cyclic Coordinate Descent for Logistic, Poisson and Survival
Analysis
Query 'Team Cymru' 'IP' Address, Autonomous System Number
('ASN'), Border Gateway Protocol ('BGP'), Bogon and 'Malware'
Hash Data Services
Cycle and Phase Identification for mastication data
Agent-Based Model for Taenia_solium Transmission and Control
Computes Signaling and Population Stats for Cytometry Data on
Cytobank using 'CytobankAPI'
Bridging and Extending the CytobankAPI Package in R to the
Cytobank Web Application
Plot Fan Plots for Cytometry Data using 'ggplot2'
Automated Cytometry Gating and Annotation
Interactive Heat Maps Using 'htmlwidgets' and 'D3.js'
Tools for creating D3 JavaScript network, tree, dendrogram, and
Sankey graphs from R
Interactive Charts of Nested and Hierarchical Data with 'D3.js'
Create Interactive Collapsible Trees with the JavaScript 'D3'
Library
Data Analysis and Graphics Data and Functions
Data Sets and Functions, for Demonstrations with Expression
Arrays and Gene Sequences
Data Sets and Functions, supplementary to DAAG
Damped Anderson Acceleration with Epsilon Monotonicity for
Accelerating EM-Like Monotone Algorithms
Data Analysis using Bootstrap-Coupled Estimation
Calculating Data Agreement Criterion Scores to Rank Experts
Based on Their Beliefs
Data Analysis with Ceiling and/or Floor Data
Three-Way / Multigroup Data Analysis Through Densities
Functions Useful in the Design and ANOVA of Experiments
Design and Analysis of Experiments with R
Graphical Analysis of Structural Causal Models
Dynamical Assembly of Islands by Speciation, Immigration and
Extinction
Automating the Fitting of Double Linear Mixed Models in 'JAGS'
The DALY Calculator - Graphical User Interface for Probabilistic
DALY Calculation in R
Dynamic Assembly Model of Colonization, Local Extinction and
Speciation
Interface to Drosophila Activity Monitor System Result Files
Dams in the United States from the National Inventory of Dams
(NID)
Dandelion Plot for R-Mode Exploratory Factor Analysis
Design and Analysis of Non-Inferiority Trials
'purrr'-Like Apply Functions Over Input Elements
Importing and Analysing SNP and Silicodart Data Generated by
Genome-Wide Restriction Fragment Analysis
Statistical Tools to Analyze Your Darts Game
Interactive Data Visualization with D3.js
Tools for Reading, Processing and Writing 'DSSAT' Files
General Purpose Hierarchical Data Structure
Functions and Add-Ins for Working with 'data.world' Data Sets
and Projects
Functional Data Analysis with Linear Differential Equations
Connecting to Various Database Platforms
JAR Dependencies for the 'DatabaseConnector' Package
Swarm Intelligence for Self-Organized Clustering
Tools for Easily Combining and Cleaning Data Sets
Compare Two Data Frames and Summarise the Difference
Divide and Recombine for Large, Complex Data
Make it Easier to Enter Questionnaire Data
Estimating Finite State Machine Models from Data
Export Data from R so DataGraph can Read it
A Suite of Checks for Identification of Potential Errors in a
Data Frame as Part of the Data Screening Process
A system for mapping foreign objects to R variables and
environments
Create Interactive Web Maps with the 'JavaScript Datamaps'
Library
Create and Append a Data Dictionary for an R Dataset
Easily Source Publicly Available Data on Derivatives
A Flexible Container to Transport and Manipulate Data and
Associated Resources
Construct Reproducible Analytic Data Sets as R Packages
Performs variable level data quality checks and generates
summary statistics
Retrieval Functions for USGS and EPA Hydrologic and Water
Quality Data
Data Bank for Statistical Analysis and Visualization
An Implementation of a SAS-Style Data Step
Data and Workspace Browser for Terminals
Data Visualisation Using an HTML Page and 'D3.js'
Deconvolution Analysis Tool for Dynamic Contrast Enhanced MRI
Traduce al Español Varios Conjuntos de Datos de Práctica
Functions for "Data Analysis in Vegetation Ecology"
Discriminant Analysis with Additional Information
Discrete Bayesian Additive Regression Trees Sampler
Provides a graphical user interface to connect with databases
that use MySQL
Goodness-of-fit and two sample comparison tests using sample
entropy
Detecting Breakpoints and Estimating Segments in Trend
A Tool to Ensure the Validity of Database Writes
A Double Bootstrap Method for Analyzing Linear Models with
Autoregressive Errors
methods of distance-based gene set functional enrichment
analysis
Sequence Clustering with Discrete-Output HMMs
'DBHYDRO' Hydrologic and Water Quality Data
Discrete Beta Kernel Graduation of Mortality Data
Compute the NPMLE of distribution from doubly censored data
Distance-Based Measures of Spatial Structures
Simplifies Plotting Data Inside Databases
Density Based Clustering of Applications with Noise (DBSCAN) and
Related Algorithms
Inferring Condition-Specific Networks via Differential Network
Inference
Dynamic Correlation Analysis for High Dimensional Data
Analyzing Dichotomous Choice Contingent Valuation Data
Differential Community Detection in Paired Biological Networks
Clustering for Multivariate and Univariate Data Using
Expectation Maximization Algorithm
A Package for Image Analysis of DCE-MRI (S4 Implementation)
Data Cloud Geometry (DCG): Using Random Walks to Find Community
Structure in Social Network Analysis
Differential Co-expression Analysis and Differential Regulation
Analysis of Gene Expression Microarray Data
Analysis of Ontologies and Protein Domain Annotations
Claims Reserving under the Double Chain Ladder Model
Data Cloning and MCMC Tools for Maximum Likelihood Methods
Functions for the Detection of Spatial Clusters of Diseases
Model-Based Detection of Disease Clusters
Information Matrix for Diagnostic Classification Models
Hierarchical Models Made Easy with Data Cloning
Modify Data Using Externally Defined Modification Rules
List Linear n-Peptide Constraints for Overlapping Protein
Regions
Distance Covariance and Correlation for Time Series Analysis
Conventional Cross-validation statistics for climate-growth
model
Depth-Based Classification and Calculation of Data Depth
Variance Stabilization by Data-Driven Haar-Fisz (for
Microarrays)
Distance & Density-Based Outlier Detection
Analysis and Visualization of Droplet Digital PCR in R and on
the Web
Disease-Related Differential Proteins and Co-Expression Network
Analysis
Data-Driven Sparse PLS Robust to Missing Samples for Mono and
Multi-Block Data Sets
Learning Bayesian Networks with Mixed Variables
Deconvolution density estimation with adaptive methods for a
variable prone to measurement error
Conventional and Fuzzy Data Envelopment Analysis
Bayesian Inference for Differential Equations
Debug Tool to Watch Objects/Expressions While Running an R
Script
DEComposition of Indirect and Direct Effects
Bindings for 'Mapbox' Ear Cutting Triangulation Library
Implementation of Multi Objective Decision Analysis
Quantitative Support of Decision Making under Uncertainty
Differential Co-Expression and Differential Expression Analysis
Decode Coded Variables to Plain Text and the Other Way Around
Time Series Prediction with PSF and Decomposition Methods (EMD
and EEMD)
Deconvolution Estimation in Measurement Error Models
Identifies Signatures Present in a Tumor Sample
Deductive Correction, Deductive Imputation, and Deterministic
Correction
Data Correction and Imputation Using Deductive Methods
Interface to the 'DeepL' Translation API
Dirichlet-multinomial Evolutionary Event Profile Randomization
(DEEPR) test
Change the Default Arguments in R Functions
Create FDA-Style Data and Program Definitions
Integration of One-Dimensional Functions with Double Exponential
Formulas
Parameter Inference for Systems of Differential Equation
Models for Skewed Count Distributions Relevant to Networks
Detection of Isotope Pattern of a Mass Spectrometric Measurement
Statistical Functions for the Delaporte Distribution
Sample Size and Power Calculations using the APPLE and SEPPLE
Methods
Delaunay Triangulation and Dirichlet (Voronoi) Tessellation
Estimates Pseudotimes for Single Cell Expression Data
Estimation of Multivariate Densities Using Adaptive Partitions
Identification of Dichotomous Differential Item Functioning
(DIF) using Angoff's Delta Plot Method
Functions to Calculate Relatedness on Diploid Genetic Data
Evaluating the genetic differentiation between populations based
on Gst and D values
Differential Expression from Multiple Indicators
Deming, Theil-Sen, Passing-Bablock and Total Least Squares
Regression
Analysis of Age-Structured Demographic Models
Providing Demographic Table with the P-Value, Standardized Mean
Difference Value
Forecasting Mortality, Fertility, Migration and Population Data
Kernel Density Estimation for Demonstration Purposes
DEvelopment (of Multi-Linear QSPR/QSAR) MOdels VAlidated using
Test Set
Optimal Design Emulators via Point Processes
Extending 'dendrogram' Functionality in R
Extra Functions to Cut, Label and Colour Dendrogram Clusters
A Set of Tools for Calculating Spatial Synchrony Between
Tree-Ring Chronologies
Linear and Nonlinear Methods for Analyzing Daily and Monthly
Dendroclimatological Data
Dendrogram seriation: ordering for visualisation
Modular Leaf Ordering Methods for Dendrogram Nodes
Parameter Estimates and Real-Time Prediction of a Single Dengue
Outbreak
Differential Expression Analysis Using a Bottom-Up Model
Statistical Analyses of De Novo Genetic Variants
Visualization of Multivariate Functions, Sets, and Data
Functional Linear Mixed Models for Densely Sampled Data
Density.T.HoldOut: Non-combinatorial T-estimation Hold-Out for
density estimation
Clustering by Fast Search and Find of Density Peaks
Dens-Based Method for Partial Correlation Estimation in Large
Scale Brain Networks
Density Strips and Other Methods for Compactly Illustrating
Distributions
Global Optimization by Differential Evolution
Differential Evolution Optimization in Pure R
Statistical Methods for the Analysis of Dependently Truncated
Data
Deconvolute Mixed Genomes with Unknown Proportions
Dependent Mixture Models - Hidden Markov Models of GLMs and
Other Distributions in S4
Nonparametric Depth Functions for Multivariate Analysis
Statistical Depth Functions for Multivariate Analysis
Functions and R Code to Accompany Derivatives Markets
Seasonal Adjustment by Frequency Analysis
An Interface to the 'DescribeDisplay' 'GGobi' Plugin
Describe Data in R Using Common Descriptive Statistics
Produce Descriptive and Comparative Tables Easily
Some Functions to be Used as Shortcuts in 'RStudio'
Optimal deseasonalization for geophysical time series using AR
fitting
Computational tool for designing genetical genomics experiments
Finding Optimal Block Designs for a Generalised Linear Mixed
Model
Matched Samples that are Balanced and Representative by Design
Desirability Functions for Ranking, Selecting, and Integrating
Data
Function Optimization and Ranking via Desirability Functions
Bagging with Distance-based Regression for Differential Gene
Expression Analyses
Solvers for Initial Value Problems of Differential Equations
('ODE', 'DAE', 'DDE')
Plotting Field Plans for Agricultural Experiments
Representation of DET Curve with Confidence Intervals
Analyzing Wildlife Data with Detection Error
Detect Data Containing Personally Identifiable Information
Detect Runs of Homozygosity and Runs of Heterozygosity in
Diploid Genomes
Implementation of the DetMCD Algorithm (Robust and Deterministic
Estimation of Location and Scatter)
Density Estimation and Random Number Generation with
Distribution Element Trees
Suite of Deterministic and Robust Algorithms for Linear
Regression
Start the detrendeR Graphical User Interface (GUI)
A Computer Program to Detect Markers Responding to Selection
Clear and Condense Argument Check for User-Defined Functions
A Differential Expression Analysis Toolkit for Visual Analytics
and Data Aggregation
Data sets from Devore's "Prob and Stat for Eng (7th ed)"
Quantify the Relationship Between Development Rate and
Temperature in Ectotherms
Tools to Make Developing R Packages Easier
Develop Treatment Rules with Observational Data
Phase I/II Adaptive Dose-Finding Design for Combination Studies
Compare Two Dataframes and Return Adds, Changes, and Deletes
Dose-Finding by the Continual Reassessment Method
Explore data.frames by plotting NA and classes of each variable
Differential Functioning of Items and Tests
Meta-Analysis of Phase I Dose-Finding Early Clinical Trials
Phase I/II Adaptive Dose-Finding Design for MTA
Extrapolation and Bridging of Adult Information in Early Phase
Dose-Finding Paediatrics Studies
Phase I Control Charts (with Emphasis on Distribution-Free
Methods)
Bayesian Dose-Finding Designs using Pharmacokinetics (PK) for
Phase I Clinical Trials
Capture-Recapture Estimation using Bayesian Model Averaging
Genetic Algorithm with Incomplete Dominance for Feature
Selection
Differential Geometric Least Angle Regression
Simulating Data for PLS Mode B Structural Models
Dynamic Estimation of Group-Level Opinion
Residual Diagnostics for Hierarchical (Multi-Level / Mixed)
Regression Models
Double Hierarchical Generalized Linear Models
Independence Testing via Hilbert Schmidt Independence Criterion
Diagnostic Plot and Multivariate Summary Statistics of Weighted
Samples from Importance Sampling
Meta-Analysis of Diagnostic Accuracy Studies with Several
Cutpoints
Block Diagonal Extraction or Replacement
Functions for Visualising Simple Graphs (Networks), Plotting
Flow Diagrams
Export DiagrammeR Graphviz Graphs as SVG
Parse, Format, and Validate International Phone Numbers
Required 'libphonenumber' jars for the 'dialr' Package
Tools for Creating Tuning Parameter Values
Calculate probabilities of various dice-rolling events
Construction and Evaluation of Metamodels
Kriging Methods for Computer Experiments
Kriging-Based Optimization for Computer Experiments
Plot Methods for Computer Experiments Design and Surrogate
Dictionaries for reading microdata surveys from IBGE
Finding Patterns of Monotonicity and Convexity in Data
Treatment Effects with Multiple Periods and Groups
A Simple Implementation and Demonstration of Gradient Boosting
Compute Recency Frequency Monetary Scores for your Customer Data
Defines some physical constants and dielectric functions
commonly used in optics, plasmonics
R Interface to the ZEIT ONLINE Content API
Detection of Differential Item Functioning (DIF) in Rasch Models
by Boosting Techniques
Analyzing and Visualizing Differential Correlation Networks in
Biological Data
Calculates Confidence Intervals for two Dependent Proportions
Fast and Scalable Learning of Sparse Changes in High-Dimensional
Gaussian Graphical Model Structure
Functions from the book Solving Differential Equations in R
Solving Differential Equations (ODEs, SDEs, DDEs, DAEs)
Metrics of Difference for Comparing Pairs of Maps or Pairs of
Variables
Diffusion IRT Models for Response and Response Time Data
Detecting Gene Probes with Different Means or Variances Between
Two Groups
Multi-Fidelity Computer Experiments Using the Tuo-Wu-Yu Model
Detection of Statistically Significant Changes in Complex
Biological Networks
Display Differences Between Two Files using Codediff Library
Projects for Text Version Comparison and Analytics in R
Inference and Analysis for Generalized Quadratic Diffusions
Inference and Analysis for Diffusion Processes via Data
Imputation and Method of Lines
Inference and Analysis for Jump Generalized Quadratic Diffusions
A Penalty Approach to Differential Item Functioning in Rasch
Models
DIF and DDF Detection by Non-Linear Regression Models
Collection of Methods to Detect Dichotomous Differential Item
Functioning (DIF)
Item Focussed Trees for the Identification of Items in
Differential Item Functioning
Create Compact Hash Digests of R Objects
An application programming interface for generating null models
of social contacts based on individuals' space use
Data-Informed Link Strength. Combine multiple-relationship
networks into a single weighted network. Impute (fill-in)
missing network links
DIME (Differential Identification using Mixture Ensemble)
A Framework for Dimensionality Reduction
DiNAMIC A Method To Analyze Recurrent DNA Copy Number
Aberrations in Tumors
A Toolkit for Year-Quarter, Year-Month and Year-Isoweek Dates
Robust Tests for Differential Dispersion and Differential
Expression in RNA-Sequencing Data
Directional Penalties for Optimal Matching in Observational
Studies
Hartigan's Dip Test Statistic for Unimodality - Corrected
Bayesian Clustering of Multivariate Data Under the
Dirichlet-Process Prior
Estimating Controlled Direct Effects for Explaining Causal
Findings
Creates an Interactive Tree Structure of a Directory
Direction Analysis for Pathways and Kinases
Adjusted Means and Proportions by Direct Standardisation
Build Dirichlet Process Objects for Bayesian Modelling
Cox Models for Time-to-Event Data with Nonparametric Discrete
Group-Specific Frailties
Discrete Laplace Mixture Inference using the EM Algorithm
Discordance and Concordance of Transcriptomic Responses
Functions for Discordant Kinship Modeling
Coordinate-Descent Algorithm for Learning Sparse Discrete
Bayesian Networks
Multiple Testing Procedures with Adaptation for Discrete Tests
Multiple testing procedures for discrete test statistics
Create and Manipulate Discrete Random Variables
Discrete Weibull Distributions (Type 1 and 3)
Data preprocessing, discretization for classification
Discrimination of Alternative Reproductive Tactics (ARTs)
Tools of the Trade for Discriminant Analysis
Modelling Spatial Variation in Disease Risk for Areal Data
Estimate the Gene Expression Levels and Component Proportions of
the Normal, Stroma (Immune) and Tumor Components of Bulk Tumor
Samples
Calculate Dissimilarity Matrix for Dataset with Mixed Attributes
Calculates Disproportionate Impact When Binary Success Data are
Disaggregated by Subgroups
A Pipeline to Analyze Resistance and Tolerance from Drug Disk
Diffusion Assays
Disparity Filter Algorithm for Weighted Networks
Create Disposable R Packages for Testing
Spatial Downscaling using the Dissever Algorithm
Fitting Sample Selection Models for Discrete Response Variables
Utilities for making pairwise comparisons of multivariate data
Distance Sampling Detection Function and Abundance Estimation
Calculates Study Size Required for Distance Sampling
DiSTATIS Three Way Metric Multidimensional Scaling
Computations over Distributed Data without Aggregation
Distributional Method for the Dichotomisation of Continuous
Outcomes
Download Occurrence Data of Vascular Plants in Germany from the
FLORKART Database
'R Markdown' Format for Scientific and Technical Writing
Method Functions for Confidence Intervals and to Distill
Information from an Object
Unified Interface to Distance, Dissimilarity, Similarity
Matrices
Object Oriented Implementation of Distributions
Documentation for 'distr' Family of R Packages
Framework for the Visualization of Distributional Regression
Models
S4 Classes for Elliptically Contoured Distributions
Four Essential Functions for Statistical Distributions Analysis:
A New Functional Approach
Object Oriented Implementation of Probability Models
Estimate and Manage Empirical Distributions
Distribution Classes for Distributions from Rmetrics
Simulation Classes Based on Package 'distr'
Extensions of Package 'distr' for Teaching
Stochastics/Statistics in Secondary School
Estimation and Testing Classes Based on Package 'distr'
Provides tools for quality checks of georeferenced plant species
accessions
Diversity Dynamics using Fossil Sampling Data
Comparative 'Phylogenetic' Analyses of Diversification
A Comprehensive, General Purpose Population Genetics Analysis
Package
Building Diversity Models from Multiple Species Occupancy Models
Tools for Analysis of Diversity and Similarity in Biological
Systems
Nearest Neighbour Contingency Table Analysis
Dixon's Ratio Test for Outlier Detection
Distance Measure Based Judgment and Learning
Time Series Regression Models with Distributed Lag Models
Implementation of Adaptive or Non-Adaptive Differentiable Lasso
and SCAD Penalties in Linear Models
Use Dirichlet Laplace Prior to Solve Linear Regression Problem
and Do Variable Selection
Bayesian and Likelihood Analysis of Dynamic Linear Models
Tools for Data Diagnosis, Exploration, Transformation
Distributed-Lag Linear Structural Equation Models
Daily Based Morgan-Morgan-Finney (DMMF) Soil Erosion Model
Dynamic Modeling and Parameter Estimation in ODE Models
DMR Detection by Non-Homogeneous Hidden Markov Model from
Methylation Array Data
Delete or Merge Regressors Algorithms for Linear and Logistic
Model Selection and High-Dimensional Data
Functions and data for "Data Mining with R"
Functions and Data for the Second Edition of "Data Mining with
R"
Tools for Analysing Forensic Genetic DNA Data
Integrative Analysis of Omics Data in Terms of Network,
Evolution and Ontology
Estimating Differential Networks under Semiparametric Gaussian
Graphical Models
Differential Network Local Consistency Analysis
Simulate Dynamic Networks using Exponential Random Graph Models
(ERGM) Family
Analysis of Discretely Observed Linear
Birth-and-Death(-and-Immigration) Markov Chains
Data from the GLM Book by Dobson and Barnett
Groupwise Statistics, LSmeans, Linear Contrasts, Utilities
Provides Docstring Capabilities to R Functions
Run 'roxygen2' on (Chunks of) Single Code Files
Extract Data Tables and Comments from 'Microsoft' 'Word'
Documents
Acceptance Sampling Ideas Originated by H.F. Dodge
Full Factorials, Orthogonal Arrays and Base Utilities for DoE
Packages
Creation of Arrays by Mixed Integer Programming
Wrapper Package for Design of Experiments Functionality
Rare Variant Association Test Based on Position Density
Estimation
A Universal Foreach Parallel Adapter using the Future API of the
'future' Package
A Package to Calculate and Visualize Dominance Hierarchies
R Console Bindings for the 'Domino Command-Line Client'
Foreach Parallel Adaptor for the Rmpi Package
Foreach Parallel Adaptor for the 'parallel' Package
Foreach parallel adapter for the rredis package
Dynamics of Return to Equilibrium During Multiple Inputs
Generic Reproducible Parallel Backend for 'foreach' Loops
Causal Effect Identification from Multiple Incomplete Data
Sources
Planning and Analyzing Dose Finding Experiments
Foreach Parallel Adaptor for the 'snow' Package
Enhanced Foreign Function Interface Supporting Long Vectors
Enhanced Assignment Operator to Overwrite or Grow Objects
Dot-and-Whisker Plots of Regression Results
Survivor Function Estimation for Doubly Interval-Censored
Failure Time Data
Test Against Parametric Regression Function
Differential Exon Usage Test for RNA-Seq Data via Empirical
Bayes Shrinkage of the Dispersion Parameter
Kernel Hazard Estimation with Best One-Sided and Double
One-Sided Cross-Validation
Functions Ported from 'MMR2' Toolbox Offered in Kevin Dowd's
Book Measuring Market Risk
A Tool to Downsize Large Workflows for Testing
RStudio Addin for Teaching and Learning Data Manipulation Using
'dplyr'
Dirichlet Process Mixture Model Simulation for Clustering and
Image Segmentation
Inference of Parameters of Normal Distributions from a Mixture
of Normals
Dirichlet Process Weibull Mixture Model for Survival Data
Enhance Shiny Apps with Customizable Modules
Methods for Dimension Reduction for Regression
Dose Response Data Analysis using the 4 Parameter Logistic (4pl)
Model
Deep Time Redox Analysis of the Geobiology Ontology Network
Statistical Tools to Identify Dragon Kings
Drag and Drop Elements in 'Shiny' using 'Dragula Javascript
Library'
A Pipeline Toolkit for Reproducible Computation at Scale
Wrapper Functions for Producing Graphics
Data Representation: Bayesian Approach That's Sparse
Regularized Estimation of Dynamic Linear Regression in the
Presence of Autocorrelated Residuals (DREGAR)
Doubly Robust Generalized Estimating Equations
Differential Risk Hotspots in a Linear Network
Calculates the Test-Statistic for the Drift Burst Hypothesis
Concept Drift and Concept Shift Detection for Predictive Models
Imputing Dropout Events in Single-Cell RNA-Sequencing Data
Drug Repurposing: Integration and Systematic Investigation of
Genomic High Throughput Data
Discontinuous Regression and Image Processing
Dual Empirical Likelihood Inference under Density Ratio Models
in the Presence of Multiple Samples
Analyze Drop Out of an Experiment or Survey
Dose-Response Modeling with Smoothing Splines
Doubly-Robust Nonparametric Estimation and Inference
Implementation of a Machine Learning Framework for Predicting
Drugs Side Effects
Drug Interaction Modeling Based on Loewe Additivity Following
Harbron's Approach
Seasonal Adjustment of Daily Time Series
Dynamical Systems Approach to Infectious Disease Epidemiology
Dynamical Systems Approach to Immune Response Modeling
Discretization-Based Direct Random Sample Generation
Bayesian subgroup analysis in clinical trials
Dynamic Systems Estimation (Time Series Package)
Density Surface Modelling of Distance Sampling Data
A Language to Facilitate Simulation and Visualization of
Two-Dimensional Dynamical Systems
Spatial Modelling for Distance Sampling Data
Compute Directly Standardized Rates, Ratios and Differences
Tests and Confidence Intervals on Directly Standardized Rates
for Several Methods
Analyze Two Choice Reaction Time Data with the D*M Method
Conditional Sensitivity Analysis for Matched Observational
Studies
Recursive Partitioning for Discrete-Time Survival Trees
Exploring Disease Similarity in Terms of Dysfunctional
Regulatory Mechanisms
A Wrapper of the JavaScript Library 'DataTables'
Simplifying Descriptive Frequencies and Statistics
Cell Type Deconvolution from Gene Expressions
Diagnostic Test Assessment in the Absence of Gold Standard
Comparison of Binary Diagnostic Tests in a Paired Study Design
Doubly Truncated Data Analysis, Non Iterative
Analysis of Diffusion Weighted Imaging (DWI) Data
Dunnett-Tukey-Kramer Pairwise Multiple Comparison Test Adjusted
for Unequal Variances and Unequal Sample Sizes
Suite of functions related to discrete-time discrete-state
Markov Chains
Dose Transition Pathways for Continual Reassessment Method
Estimation and Comparison of Dynamic Treatment Regimes
Statistical Learning Methods for Optimizing Dynamic Treatment
Regimes
DTR Estimation and Inference via G-Estimation, Dynamic WOLS, and
Q-Learning
A Class for Working with Time Series Based on 'data.table' and
'R6' with Largely Optional Reference Semantics
Imputation of Time Series Based on Dynamic Time Warping
Time Series Clustering Along with Optimizations for the Dynamic
Time Warping Distance
Time-Weighted Dynamic Time Warping for Satellite Image Time
Series Analysis
Imputation of Multivariate Time Series Based on Dynamic Time
Warping
Securely Wrangle Dataset According to Data Usage Agreement
Unpacking Assignment for Lists via Pattern Matching
Simple Client for the DuckDuckGo Instant Answer API
Create dummy/indicator variables flexibly and efficiently
Automatic Creation of Dummies with Support for Predictive
Modeling
Dunn's Test of Multiple Comparisons Using Rank Sums
Software implementation of step-down and step-up Dunnett test
procedures
Bayesian inference from count data using discrete uniform priors
Analyze Dose-Volume Histograms and Check Constraints
Convenience Functions, Moving Window Statistics, and Graphics
Fast Algorithms for Large Scale Generalized Distance Weighted
Discrimination
Parametric Regression for Discrete Response
Detection of Chaotic and Regular Intervals in the Data
Interface to 'Dygraphs' Interactive Time Series Charting Library
Quadratic Programming using Cyclic Projections
Dynamic Simulation and Testing for Single-Equation ARDL Models
Dynamically visualized probability distributions and their
moments
Modelling and Analysis of Dynamic Computer Experiments
Dynamic Hazard Models using State Space Models
Methods for Detection of Clusters in Hierarchical Clustering
Dendrograms
Fit a Stochastic Dynamical Array Model to Array Data
Implementation of the Dynamic TOPMODEL Hydrological Model
Full Interactive GUI for Dynamic Biplot in R
Denoising and clustering for dynamical image sequence (2D or
3D)+T
Complexity of Short and Coarse-Grained Time Series
Dimensionality Reduction Methods in a Common Format
Resisting Neighbor Label Attack in a Dynamic Network
Fitting Dynamic Frailty Models with the EM Algorithm
Dynamic Nomograms for Linear, Generalized Linear and
Proportional Hazard Models
Fast Fourier Transform to Identify Associations Between Time
Course Omics Data
Companion Package to "Dynamic Prediction in Clinical Survival
Analysis"
Dynamic Simulations of Autoregressive Relationships
Methods for Estimating Optimal Dynamic Treatment Regimes
Common Functionality for the 'dynverse' Packages
Estimation and Prediction of Skewed Spatial Processes
Misc Functions of the Department of Statistics, Probability
Theory Group (Formerly: E1071), TU Wien
Plots of the Empirical Attainment Function
Multiple Locus Association Mapping on a Genome-Wide Scale
Estimator Augmentation and Simulation-Based Inference
Dynamic Web-Based Analytics for the Energy Industry
Properties of Bayesian Early Gating Designs
Estimation of Transmissibility in the Early Stages of a Disease
Outbreak
Early Warning Signals Toolbox for Detecting Critical Transitions
in Timeseries
Parallel Implementation of 'ETERNA 3.40' for Prediction and
Analysis of Earth Tides
Derive a Color Palette from a Particular Location on Earth
Efficient Approximate Bayesian Computation Sampling Schemes
Generate Alluvial Plots with a Single Line of Code
Analysis of Variance and Other Important Complementary Analyses
Utility functions for working with mark-recapture data
Easy Model-Builder Functions for 'OpenMx'
Tools to Easily Read/Write NetCDF Files into/from
Multidimensional R Arrays
Sample Size Estimation for Experimental Designs
Search and Retrieve Scientific Publication Records from PubMed
Easy Interface to the Statistical Disclosure Control Package
'sdcTable'
Ensemble Forecast Verification for Large Data Sets
Entropy reweighting to create balanced samples
Sample Size Calculation Method for Cost-Effectiveness Studies
Based on Expected Value of Perfect Information
Empirical Bayes Thresholding and Related Methods
Empirical Bayes Estimation of Dynamic Bayesian Networks
Genotyping and SNP Detection using Next Generation Sequencing
Data
Empirical Bayesian Lasso and Elastic Net Methods for Generalized
Linear Models
Access and Analyze eBird Status and Trends Data
Ensemble-Based Methods for Class Imbalance Problem
Derive Polygenic Risk Score Based on Emprical Bayes Theory
Genotyping and SNP calling using single-sample next generation
sequencing data
Programmatic Access to the European Central Bank's Statistical
Data Warehouse (SDW)
Simulation from Endpoint-Conditioned Continuous Time Markov
Chains
Elliptic Lambda Distribution and Option Pricing Model
Functions for Spatial Point Pattern Analysis
Equal Covariance Functions Testing by L2-Norm and Sup-Norm
A Goodness-of-Fit Test for Elliptical Distributions with
Diagnostic Capabilities
Embedding Interactive Charts Generated with ECharts Library into
Shiny Applications
Create Interactive Graphs with 'Echarts JavaScript' Version 4
Finding Rhythms Using Extended Circadian Harmonic Oscillators
(ECHO)
Efficient calculation of fine structure isotope patterns via
Fourier transforms of simplex-based elemental models
Ensemble Correlation-Based Low-Rank Matrix Completion
Environment Based Clustering for Interpretable Predictive Models
in High Dimensional Data
Interface to 'ECMWF' and 'CDS' Data Web Services
Dissimilarity-Based Functions for Ecological Analysis
Programmatic Interface to the Web Service Methods Provided by UC
Berkeley's Natural History Data
Management and Exploratory Analysis of Spatial Data in Landscape
Genetics
A Community Modeling Foundation for Eco-Hydrology
Complexity Measures for Supervised Problems
"A practical guide to ecological modelling - using R as a
simulation platform"
Coalescent-Based Simulation of Ecological Communities
General Analysis of Various Economics Demand Systems
Estimation of Parameter-Dependent Network Centrality Measures
Simulate and Sample from Ecological Interaction Networks
Null Model Analysis for Ecological Networks
Ecological Regression using Aggregate and Individual Data
An R Interface to Brazilian Central Bank and Sidra APIs and the
IPEA Data
Simulating Community Assembly and Ecological Diversification
Using Ecospace Frameworks
Procedures for Ecological Assessment of Surface Waters
Non-Parametric Multiple Change-Point Analysis of Multivariate
Data
Quantitative Estimates of Small Ectotherm Temperature Regulation
Effectiveness
Estimate ED50 and Its Confidence Interval
Experimental Design and Analysis for Tree Improvement
Exploratory Data Analysis using Random Forests
Get Evaporative Demand Drought Index Raster Data
Exploratory and Descriptive Event-Based Data Analysis
Read Data from European Data Format (EDF and EDF+) Files
Platform for EDGAR Filing Management and Textual Analysis
More Powerful Unconditional Testing of Negative Binomial Means
for Digital Gene Expression Data
Network Reconstruction and Changepoint Detection
'RStudio' Addin for Editing a 'data.frame'
Simultaneous Edit-Imputation for Continuous Microdata
Parsing, Applying, and Manipulating Data Cleaning Rules
Dynamic Model Averaging with Grid Search
Estimation of the Effective Dimension Reduction ('EDR') Space
Provides Tools for Dimension Reduction Methods
Analysis of NCES Education Survey and Assessment Data
Compute and Decompose Inequality in Education
Exploring Election and Census Highly Informative Data Nationally
for Australia
Read and Preprocess Fluorescence Excitation-Emission Matrix
(EEM) Data
Tools for Pre-Processing Emission-Excitation-Matrix (EEM)
Fluorescence Data
Convenience Functions for Education Data
Simulate and Evaluate Time Series for Environmental Epidemiology
Dimensionality Assessment Using Minimum Rank Factor Analysis
Utility Functions for Exploratory Factor Analysis
Wavelet-Based Enhanced FDR for Detecting Signals from Complete
or Incomplete Spatially Aggregated Data
Bayesian Effect Fusion for Categorical Predictors
Effect Displays for Linear, Generalized Linear, and Other Models
Test changes of a grouped response relative to baseline
Visualization of Categorical Response Models
Efficient Initials for Computing the Maximal Eigenpair
The Causal Effects for a Causal Loglinear Model
High-Level Functions to Read and Write Ensemble Forecast Time
Series in netCDF
Exploratory Graph Analysis - A Framework for Estimating the
Number of Dimensions in Multivariate Data Using Network
Psychometrics
Extensions for 'ggplot2': Custom Geom, Plot Alignment,
Symmetrised Scale, and Fixed Panel Size
Hierarchical Modelling of Faecal Egg Counts
Import and Analyse Ego-Centered Network Data
Estimation of Ego-Temporal Exponential Random Graph Models via
Expectation Maximization (EM)
Exploration and Graphics for RivEr Trends
Exploration and Graphics for RivEr Trends Confidence Intervals
Electronic Health Record (EHR) Data Processing and Analysis Tool
Delineating Reference Changes in Electronic Health Records over
Time
R Wrapper for the Energy Information Administration (EIA) API
Generates (dense) matrices that have a given set of eigenvalues
Semiparametric Factor and Regression Models for Symmetric
Relational Data
Computes confidence intervals for principal components
Ecological Inference and Higher-Dimension Data Management
Ecological Regression with Partial Identification
An Algorithm for Reducing Errors-in-Variable Bias in Simple
Linear Regression
Ecological Inference with individual and aggregate data
MCMC Procedures for Estimating Enzyme Kinetics Constants
Empirical Likelihood (EL) for Comparing Two Survival Functions
Enhanced Least Absolute Shrinkage and Selection Operator
Regression Model
Replicar metodologia de SPE/MF para calculo de elasticidade de
receita
General Purpose Interface to 'Elasticsearch'
Ising Network Estimation using Elastic Net and k-Fold
Cross-Validation
Elastic-Net for Sparse Estimation and Sparse PCA
A Lightweight Interface for Interacting with Elasticsearch from
R
Collection of Functions for Statistical Election Audits
Functions for election audits using stratified random samples
Estimation of Life Expectancies Using Multi-State Models
R Functions to Download and Clean Brazilian Electoral Data
Allocating Seats Methods and Party System Scores
An Framework for Reducing Elemental LAICPMS Data from Solid
Structures
Data Sets, Functions and Examples from the Book: "The Elements
of Statistical Learning, Data Mining, Inference, and
Prediction" by Trevor Hastie, Robert Tibshirani and Jerome
Friedman
Load Associated Press Election Results with Elex
Sampling from a Empirical Likelihood Bayesian Posterior of
Parameters Using Hamiltonian Monte Carlo
ELISA Data Analysis with Batch Correction
Functions for Drawing Ellipses and Ellipse-Like Confidence
Regions
Weierstrass and Jacobi Elliptic Functions
Implementation of the Efficient Large-Scale Online Display
Advertising Algorithm
Optimized Elo Rating Method for Obtaining Dominance Ranks
Animal Dominance Hierarchies by Elo Rating
Empirical Likelihood Analysis for the Cox Model and
Yang-Prentice (2005) Model
EM Algorithm for Maximum Likelihood Estimation by Non-Precise
Information
Data Management Tools for Real-Time Monitoring/Ecological
Momentary Assessment Data
Extra Recipes for Encoding Categorical Predictors
Tools to Analyze the Thermal Reaction Norm of Embryo Growth
Evolutionary Monte Carlo (EMC) algorithm
Computation and Visualization of Empirical Joint Distribution
(Empirical Joint CDF)
EM Algorithm for Model-Based Clustering of Finite Mixture
Gaussian Distribution
Empirical Mode Decomposition and Hilbert Spectral Analysis
Support Functions and Data for "Ecological Models and Data"
Estimating and Mapping Disaggregated Indicators
Exponentially Modified Gaussian (EMG) Distribution
EM Algorithms for Estimating Item Response Theory Models
Emissions and Statistics in R for Wastewater and Pollutants in
Combined Sewer Systems
Read and Write Ecological Metadata Language Files
Fitting log-Linear Models in Sparse Contingency Tables
Estimated Marginal Means, aka Least-Squares Means
A Mixture Model-Based Approach to the Clustering of Microarray
Expression Data
Mixture Models with Component-Wise Factor Analyzers
The EM Algorithm and Skew Mixture Distribution
A Maximum Likelihood Approach to the Analysis of Modularity
Fitting Mixed Models with Known Covariance Structures
Tools for Environmental and Ecological Survey Design
Eye Movement Analysis Package for Fixation and Saccade Detection
Expected Maximum Profit Classification Performance Measure
Evaluates the Empirical Characteristic Function for Multivariate
Samples
Routines for Performing Empirical Calibration of Observational
Study Estimates
Simulation-Based False Discovery Rate in RNA-Seq
Empirical Likelihood Ratio for Censored/Truncated Data
Empirical Likelihood Ratio Test for Two Samples with Censored
Data
Epimed Solutions Collection for Data Editing, Analysis, and
Benchmark of Health Units
EM Algorithm for Bayesian Shrinkage Approach with Structural
Information Incorporated
Tools for Fast Computing and Plotting Euclidean Minimum Spanning
Trees
Exact Multinomial Test: Goodness-of-Fit Test for Discrete
Multivariate data
Main Package of the EMU Speech Database Management System
The Expectation-Maximization Approach to Bayesian Variable
Selection
Portable Tools for 'UTF-8' Character Data
Encoding of Nucleotide Sequences into Numeric Feature Vectors
Represent Ordered Lists and Pairs as Strings
Easily Encrypt and Decrypt Data Frame/Tibble Columns or Files
using RSA Public/Private Keys
R Package for Fitting Multinomial Probit Models with Endogenous
Selection
Bayesian Measurement Models for Analyzing Endorsement
Experiments
Transmissions and Receptions in an End to End Network
E-Statistics: Multivariate Inference via the Energy of Data
Distribution Free Multivariate Control Chart Based on Energy
Test
Data Published by the United States Federal Energy Regulatory
Commission
Robust and Sparse Methods for High Dimensional Linear and
Logistic Regression
Quantitative Exploration of Elastic Net Families for Generalized
Linear Models
Data sets from "Introductory Statistics for Engineering
Experimentation"
English and European Soccer Results 1871-2016
Automated Runs and Evaluations of Ecological Niche Models
Ensemble Partial Least Squares Regression
Enrichment assessment of virtual screening approaches
Methods to Enrich R Objects with Extra Components
Extensible Package for Parallel, Batch Training of Base Learners
for Ensemble Modeling
Probabilistic Forecasting using Ensembles and Bayesian Model
Averaging
Extensible Package for Cross-Validation-Based Integration of
Base Learners
Extensible Package for Principal-Component-Regression-Based
Heterogeneous Ensemble Meta-Learning
Extensible Classes and Methods for Penalized-Regression-Based
Integration of Base Learners
Entropy Partitioning to Measure Diversity
Estimation of Entropy, Mutual Information and Related Quantities
Estimation of Entropy and Related Quantities
Tools for Exploring Differential Shannon Entropy, Differential
Coefficient of Variation and Differential Expression
MCMC Simulation and Convergence Evaluation using Entropy and
Kullback-Leibler Divergence Estimation
Miscellaneous Functions for Environmental Analyses
Detection of Structural Changes in Climate and Environment Time
Series
Various Utilities for Microbial Genomics and Metagenomics
GC/LC-MS Data Analysis for Environmental Science
Isotope Pattern, Profile and Centroid Calculation for Mass
Spectrometry
Peak Picking for High Resolution Mass Spectrometry Data
Environmental Probabilistic Risk Assessment Tools
Statistical Analysis of Environmental Space-Time Processes
Package for Environmental Statistics, Including US EPA Guidance
Export or Import Graph and Tables to 'Microsoft' Office
Statistical Functions for the Censored and Uncensored
Epanechnikov Distribution
Call 'EPANET' Functions to Simulate Pipe Networks
Ensemble Penalized Cox Regression for Survival Prediction
A Package for Statistical Analysis in Epidemiology
Elementary Epidemiological Functions for Epidemiology and
Biostatistics
Handling, Visualisation and Analysis of Epidemiological Contacts
Epidemiological Tool for Contact Tracing
Tools to Retrieve Economic Policy Institute Data Library
Extracts
EpiEstim: a package to estimate time varying reproduction
numbers from epidemic curves
Predicting Disease Spread from Flow Data
Spatial and Network Based Individual Level Models for Epidemics
Continuous Time Distance-Based and Network-Based Individual
Level Models for Epidemics
Functions and Data for "Epidemics: Models and Data in R"
Mathematical Modeling of Infectious Disease Dynamics
Tools for the Analysis of Epidemiological Data
Basic Sensitivity Analysis of Epidemiological Results
Estimation with Penalisation in Systems of Ordinary Differential
Equations
Density Estimation with Soft Information by Exponential
Epi-splines
Detecting Epistatic Selection with Partially Observed Genotype
Data
Constructing an Epistemic Model for the Games with Two Players
Flexible Contingency Tables for Epidemiology
Small Helpers and Tricks for Epidemics Analysis
A Toolkit for Using Whole Building Simulation Program
'EnergyPlus'
Endogenous Perturbation Analysis of Cancer
Methods for Calculating 'EQ-5D' Utility Index Scores
EQS Output Conversion to lavaan Functions
Tools for analyzing eQTL experiments: A complementary to Karl
Broman's 'qtl' package for genome-wide analysis
Testing the Equality of Two Covariance Matrices
Testing the Equality of a High Dimensional Set of Densities
Examine Measurement Invariance via Equivalence Testing and
Projection Method
Provides Tests and Graphics for Assessing Tests of Equivalence
Equivalence Test for the Means of Two Normal Distributions
Testing for Equivalence and Noninferiority
Uniformly Most Powerful Invariant Tests of Equivalence
Automated Spectral Deconvolution, Alignment, and Metabolite
Identification in GC/MS-Based Untargeted Metabolomics
Nonparametric Multiple Expectile Regression via ER-Boost
Fitting Tails by the Empirical Residual Coefficient of Variation
Fit, Simulate and Diagnose Exponential-Family Models for
Networks
Fit, Simulate and Diagnose Exponential-Family Models for
Networks with Count Edges
Fit, Simulate and Diagnose Exponential-Family Random Graph
Models to Egocentrically Sampled Network Data
Fit, Simulate and Diagnose Exponential-Family Models for
Rank-Order Relational Data
User-specified Terms for the statnet Suite of Packages
Local Health Department network data set
Significance Analysis of Event-Related Potentials Data
Event-Related Potential (ERP) Data Exploration Made Easy
Event-related potentials (ERP) analysis, graphics and utility
functions
Estimate Number of Latent Factors and Factor Matrix for Factor
Analysis
Extended Empirical Saddlepoint Density Approximation
Indicators of Electoral Systems and Party Systems
Effect Size Computation for Meta Analysis
Adaptive Enrichment Designs with Sample Size Re-Estimation
ESEA: Discovering the Dysregulated Pathways based on Edge Set
Enrichment Analysis
Toolkit for the simulation of financial assets and interest
rates models
Ensemble of Subset of K-Nearest Neighbours Classifiers for
Classification and Class Membership Probability Estimation
Data Preparation During and After the Use of the Experience
Sampling Methodology (ESM)
Explore and Visualize Your Data Interactively
Joint Quantile and Expected Shortfall Regression
Download Data from the European Social Survey on the Fly
Calibrating Parameters for the Samejima's Continuous IRT Model
Efficient Sequential Testing with Evidence Ratios
Estimation of Heritability in High Dimensional Sparse Linear
Mixed Models using Variable Selection
Tools for Assessing Estimability of Linear Predictions
Perform the Joint Graphical Lasso and Selects Tuning Parameters
Maximum Likelihood Estimation for Probability Functions from
Data Sets
Fast Estimators for Design-Based Inference
Estimating the Sample Mean and Standard Deviation from Commonly
Reported Quantiles in Meta-Analysis
An Implementation of Parametric and Nonparametric Event Study
Visualization and Estimation of Effect Sizes
Modeling Earthquake Data Using 'ETAS' Model
Mixed FLP and ML Estimation of ETAS Space-Time Point Processes
Interaction with the 'Ethereum' Blockchain
Ethnicity Annotation from Whole Exome Sequencing Data
Extract-Transform-Load Framework for Medium Data
Utility Functions to Execute Standard Extract/Transform/Load
Operations (using Package 'ff') on Large Data
Computes Moments of Univariate Truncated t Distribution
Client for 'etcd', a 'Key-value' Database
eulerian: A package to find eulerian paths from graphs
Area-Proportional Euler and Venn Diagrams with Ellipses
R Interface to the Europe PubMed Central RESTful Web Service
Historical Populations of European Cities, 1500-1800
Bayesian Single Arm Sample Size Calculation Software
Extreme Value Analysis with Goodness-of-Fit Testing
Parsing and Evaluation Tools that Provide More Details than the
Default
Collection of Model Evaluation Measure Functions
Sensitivity Analyses for Unmeasured Confounding or Selection
Bias in Observational Studies and Meta-Analyses
Modelling Actual, Potential and Reference Crop
Evapotranspiration
Functions for Extreme Value Distributions
Bayesian Analysis in Extreme Value Theory
Evidential Analysis of Genetic Association Data
Analysis of Scientific Evidence Using Bayesian and Likelihood
Methods
Reporting Tools for Sensitivity Analysis of Evidence Factors in
Observational Studies
Extreme Value Mixture Modelling, Threshold Estimation and
Boundary Corrected Kernel Density Estimation
Comparative and Population Genetic Analyses
Tools for Evolutionary Quantitative Genetics
Important Concepts of Evolutionary Game Theory
Evolutionary Parameter Estimation for 'Repast Simphony' Models
Mean of order p, peaks over random threshold Hill and high
quantile estimates
Extreme Value Theory for Open Set Classification - GPD and GEV
Classifiers
Evolutionary Learning of Globally Optimal Trees
Goodness-of-Fit Tests for the Exponential and Two-Parameter
Weibull Distributions
Escalation with Overdose Control using 2 Drug Combinations
Exact Tests and Confidence Intervals for 2x2 Tables
Exact P-Values and Matching Confidence Intervals for Simple
Discrete Parametric Cases
Inductive Confidence Intervals for the difference between two
proportions
Monte Carlo Exact Tests for Log-linear models
Exact Distributions for Rank and Permutation Tests
Help for Writing Unit Tests Based on Function Examples
Confidence regions for exceedance sets and contour lines
Convenient Data Exchange with Microsoft Excel
Excerpt Structuring Comments from Your Code File and Set a Table
of Contents
Excursion Sets and Contour Credibility Regions for Random Fields
Calls Copy Number Variants from Targeted Sequence Data
Convert Gene EXPression Data to FBA FLUXes
Expanding Ploidy and Allele-Frequency on Nested Subpopulations
Modular Differential Evolution for Experimenting with Operators
R Package for Designing and Analyzing Randomized Experiments
Modeling without data using expert opinion
Exponential Integral and Incomplete Gamma Function
Explanation of Predictions for Classification and Regression
Models
Interactive Interfaces for Results Exploration
Streamlined Export of Graphs and Data Tables
Multiple Effect Estimation of Chemicals in Environmental
Epidemiology
Exploratory Analysis with the Singular Value Decomposition
Spatial Analysis of Extra-Pair Paternity
Computing Expectations and Marginal Likelihoods for Permutations
Rapid Deployment of Machine Learning Algorithms
An Exponential Stochastic Block Model for Interaction Lengths
Data Sets from "Forecasting with Exponential Smoothing"
Tables, Labels and Some Useful Functions from Spreadsheets and
'SPSS' Statistics
Calculate Exposures, Assign Records to Intervals
Fast, Reliable and Elegant Reproducible Research
Convenience Functions for Botanists to Create Specimens Indices
Data Manipulation Extensions of 'Dplyr' and 'Tidyr'
Constructs the Exterior Match from Two Matched Control Groups
Maximum penalized likelihood estimation with extended lasso
penalty
An Extended Mallows Model and Its Hierarchical Version for
Ranked Data Aggregation
Extra-binomial approach for pooled sequencing data
Additional Univariate and Multivariate Distributions
Extremely Randomized Trees (ExtraTrees) Method for
Classification and Regression
Estimation of Extreme Conditional Quantiles and Probabilities
Extreme Value Statistics and Quantile Estimation
Estimation of Extreme Value Dependence for Time Series Data
Estimate Lower Extreme Quantile with the Censored Weibull MLE
and Censored Weibull Mixture
Econometric Analysis of Explosive Time Series
Easy Analysis and Visualization of Factorial Experiments
Easy Interface to Effective Concentration Calculations
selects significant non-additive interaction between two
variables using fast GLM implementation
Avoid the Typical Working Directory Pain When Using 'knitr'
Easy Data Import Using GUI File Picker and Seamless
Communication Between an Excel and R
provide an easy to use framework to conduct simulation
Tunes AdaBoost, Support Vector Machines, and Gradient Boosting
Machines
Overfitting Bayesian Mixtures of Factor Analyzers with
Parsimonious Covariance and Unknown Number of Components
Fast Covariance Estimation for Sparse Functional Data
Access to Facebook API V2 via a Set of S4 Classes
A C++ Framework for Plant-Plant Interaction IBMs
Bayesian Sparse Factor Analysis model for the inference of
pathways responsive to drug treatment
Combination of Factorial Methods and Cluster Analysis
Extract and Visualize the Results of Multivariate Data Analyses
Automatic Description of Factorial Analysis
Multivariate Exploratory Data Analysis and Data Mining
Factorial Optimal Designs for Two-Colour cDNA Microarray
Experiments
Simultaneous Change-Point and Factor Analysis
Graphical Presentation of Simple Contrasts
Bayesian quantile regression factor models
Identification of the Factors Affecting Species Richness
Bayesian Estimation of (Sparse) Latent Factor Stochastic
Volatility Models
Perform Factorial Analysis from 'FactoMineR' with a Shiny
Application
thin wrapper for the Factual.com server API
Variable Selection for Supervised Classification in High
Dimension
Distributions that are Sometimes Used in Hydrology
Data from the Book "Multivariate Statistical Modelling Based on
Generalized Linear Models", First Edition, by Ludwig Fahrmeir
and Gerhard Tutz
Faisal Conjoint Model: A New Approach to Conjoint Analysis
Simulates Data from a Data Frame of Different Variable Types
Finding Allele-Specific Copy Number in Next-Generation
Sequencing Data
Finding Allele-Specific Copy Number in Whole-Exome Sequencing
Data
Family Age-at-Onset Data Simulation and Penetrance Estimation
Probabilities for Pedigrees Given DNA Data
A Convex Formulation for Modeling Interactions with Strong
Heredity
Maximum Likelihood and Bayesian Estimation of Univariate
Probability Distributions
A Flexible Algorithm for Model Selection
Family Sequence Kernel Association Test for Rare and Common
Variants
Factor Analysis for Multiple Testing (FAMT) : simultaneous tests
under dependence in high-dimensional data
Penalized Likelihood Factor Analysis via Nonconvex Penalty
Visualisation of Sequential Probability Distributions Using Fan
Charts
ANSI Control Sequence Aware String Functions
Download Data from the FAOSTAT Database of the Food and
Agricultural Organization (FAO) of the United Nations
Influential Case Detection Methods for Factor Analysis and
Structural Equation Models
Modelization for Functional AutoRegressive Processes
Functions and Datasets for Books by Julian Faraway
Fast and Robust Deconvolution of Tumor Infiltrating Lymphocyte
from Expression Profiles using Least Trimmed Squares
Vectorised Colour Conversion and Comparison
Joint Segmentation of Correlated Time Series
Rmetrics - EBM and Asian Option Valuation
A Fast and Scalable Joint Estimator for Learning Multiple
Related Sparse Gaussian Graphical Models
Rmetrics - Analysing and Modelling Financial Assets
Implementation of the Fourier Amplitude Sensitivity Test (FAST)
Fast Estimation of a Covariance Matrix by Banding the Cholesky
Factor
A Fast Solver for Parameterized LP Problems, Constrained L1
Minimization Approach to Sparse Precision Matrix Estimation and
Dantzig Selector
Fast Hierarchical Clustering Routines for R and 'Python'
Significant Interval Discovery with Categorical Covariates
Lasso and Elastic-Net Penalized Cox's Regression in High
Dimensions Models using the Cocktail Algorithm
Fast, Low Memory-Footprint Digests of R Objects
Fast Creation of Dummy (Binary) Columns and Rows from
Categorical Variables
Raster Image Processing and Vector Recognition
Fast and Exact Computation of Gaussian Stochastic Process
Fast 'Rcpp' Implementation of Gauss-Hermite Quadrature
Fast and Stable Fitting of Generalized Linear Models using
'RcppEigen'
Efficiently Using Gaussian Processes with Rcpp and RcppEigen
Fast Drawing and Shading of Graphs of Statistical Distributions
FastHCS Robust Algorithm for Principal Component Analysis
Hierarchical Independent Component Analysis: a Multi-Scale
Sparse Non-Orthogonal Data-Driven Basis
FastICA Algorithms to Perform ICA and Projection Pursuit
Learn from Training Data then Quickly Fill in Missing Data
Efficient Jonckheere-Terpstra Test Statistics for Robust Machine
Learning and Genome-Wide Association Studies
A Fast Multiple-Kernel Method Based on a Low-Rank Approximation
Fast Probabilistic Record Linkage with Missing Data
Fast Nonconvex Deconvolution of Calcium Imaging Data
Fast Computation of Multivariate M-Estimators
Fast Marching Method for Modelling Evolving Boundaries
Extremely Fast Implementation of a Naive Bayes Classifier
FastPCS Robust Fit of Multivariate Location and Scatter
Foundations and Applications of Statistics Using R
Foundations and Applications of Statistics Using R (2nd Edition)
Fits the FastRCS Robust Multivariable Linear Regression Model
'fastText' Wrapper for Text Classification and Word
Representation
Fast Interactive Framework for Web Scripting Using R
Fast Utility Function for Time Parsing and Conversion
Two-Locus Family-Based Association Test with Polytomous Outcome
Quantification of Fate Bias in Multipotent Progenitors
Kiener Distributions and Fat Tails in Finance
Functions and Datasets for "Forest Analytics with R"
An Extensible Approach to Flux Balance Analysis
Gene by Environment Interaction and Conditional Gene Tests for
Nuclear Families
Algorithm for Searching the Space of Gaussian Directed Acyclic
Graph Models Through Moment Fractional Bayes Factors
FISH Based Normalization and Copy Number inference of SNP
microarray data
Analyzing and Managing Facebook Ads from R
Association Football (Soccer) Ranking via Poisson Regression
Fast Algorithms to Bootstrap Receiver Operating Characteristics
Curves
Standard Evaluation-Based Multivariate Function Composition
Basics of Certificates and Structured Products Valuation
Change Point Analysis in Functional Data
Inference of Fuzzy Cognitive Maps (FCMs)
Rmetrics - Bivariate Dependence Structures with Copulae
Functional Concurrent Regression for Sparse Data
A Method to Search for Differentially Expressed Genes and to
Detect Recurrent Chromosomal Copy Number Aberrations
Measuring functional diversity (FD) from multiple traits, and
other tools for functional ecology
Functional Data Analysis and Utilities for Statistical Computing
Functional Data Analysis for Density Functions by Transformation
to a Hilbert Space
Functional Data Analysis: K-Mean Alignment
Functional Data Analysis in a Mixed Model Framework
Analysis of Variance for Univariate and Multivariate Functional
Data
Functional Data Analysis and Empirical Dynamics
Functional Data Analysis and Partial Differential Equations;
Statistical Analysis of Functional and Spatial Data, Based on
Regression with Partial Differential Regularizations
Interval Testing Procedure for Functional Data
Multivariate Dependence with FDG Copulas
Dynamic Model Averaging and Dynamic Model Selection for
Continuous Outcomes
Permutation-Based FDR Point and Confidence Interval Estimation
False Discovery Rate Procedures Under Discrete and Heterogeneous
Null Distributions
Compute Sample Size that Meets Requirements for Average Power
and FDR
FDR-Control in Multiscale Change-Point Segmentation
Estimation of (Local) False Discovery Rates and Higher Criticism
Frequency Distribution Tables, Histograms and Polygons
Local Inferential Feature Significance for Multivariate Kernel
Density Estimation
Creates a Model Matrix via Feature Hashing with a Formula
Interface
Feature Extraction for Discretely-Sampled Functional Data
Some Helper Functions that Help Create Features from Data
Free Brazilian Repository for Open Soil Data
Fechnerian Scaling of Discrete Object Sets
Functions to Automate Downloading Geospatial Data Available from
Several Federated Data Sources
Client Package for the U.S. Federal Register API
Text Analysis of the US Code of Federal Regulations
Estimating Fixed Effects Individual Slope Models
Fixed Effects Nonlinear Maximum Likelihood Models
Fixed Effects Logistic Model with High-Dimensional Parameters
Estimation and Fit Assessment of Frailty Exponential Random
Graph Models
Rmetrics - Pricing and Evaluating Exotic Option
Structured Products Valuation for
ExpressCertificates/Autocallables
Rmetrics - Modelling Extreme Events in Finance
Memory-Efficient Storage of Large Data on Disk and Fast Access
Functions
Basic Statistical Functions for Package 'ff'
Force field simulation for a set of points
Forgetting Factor Methods for Change Detection in Streaming Data
Generate, Visualise, and Evaluate Fast-and-Frugal Decision Trees
Fast FFT and DCT Based on the FFTW Library
Wrapper for 'FFTW3' Includes: One-Dimensional Univariate,
One-Dimensional Multivariate, and Two-Dimensional Transform
Rmetrics - Autoregressive Conditional Heteroskedastic Modelling
Access Small ForestGEO Datasets For Examples
Feature Grouping and Selection Over an Undirected Graph
Fractional Hot Deck and Fully Efficient Fractional Imputation
Tests for Right and Interval-Censored Survival Data Based on the
Fleming-Harrington Class
Provide functions for forest inventory calculations
Focused Information Criteria for Model Comparison
Classical, Reloaded and Adaptive FastICA Algorithms
A Lightweight and Flexible Web Framework
File-Backed Matrix Class with Convenient Read and Write Access
Chunk Processing or Split-Apply-Combine on Delimited Files and
Distributed Dataframes
Effective File Navigation from the R Console
Matrix Completion, Imputation, and Inpainting Methods
Rmetrics - Importing Economic and Financial Data
Quickly Create Elegant Regression Results Tables and Plots when
Modelling
Financial Analysis and Regression Diagnostic Analysis
Financial Instrument Model Infrastructure and Meta-Data
Classifies implicit trading activity from market quotes and
computes the probability of informed trading
Time Value of Money, Time Series Analysis and Computational
Finance
Covariance Matrix Estimation and Regularization for Finance
Find all root(s) of the equation and Find root(s) of the
equation by dichotomy
Functions to Find an Acceptable Python Binary
Find Code Snippets, R Scripts, R Markdown, PDF and Text Files
with Pattern Matching
A View Generator for Multidimensional Data
Functions to Operate on Binary Fingerprint Data
Produce Charts that you See on the Fingertips Website
Computation of the Probability of Ruin Within a Finite Time
Horizon
Financial Data from U.S. Securities and Exchange Commission
Companion to Tsay (2005) Analysis of Financial Time Series
Prediction of Wildland Fire Behavior and Hazard
A Small Collection of Fish Population Datasets
The FisherEM Algorithm to Simultaneously Cluster and Visualize
High-Dimensional Data
Fisheye and Hyperbolic-space-alike Interactive Visualization
Tools in R
Iterative FISH-based Calibration of Hi-C Data
Dataset of Measurements of Fish Species at Kirkkojarvi Lake,
Finland
Fits Poisson-Sum-of-Gammas GLMs, Tweedie GLMs, and Delta
Log-Normal Models
Analytical Tool for Aquatic Respirometry
Fuzzy Inference System Design and Optimization
Transcriptomic Dynamics Models in Field Conditions
Fit ARMA or ARIMA Using Fast MLE Algorithm
Help to Fit of a Parametric Distribution to Non-Censored or
Censored Data
Modeling Over Dispersed Binomial Outcome Data Using BMD and ABD
Genotype Calling for Bi-Allelic Marker Assays
FITS (Flexible Image Transport System) Utilities
Fit Hundreds of Theoretical Distributions to Empirical Data
fitTetra is an R package for assigning tetraploid genotype
scores
Fit Univariate Mixed and Usual Distributions
Data and Code Behind the Stories and Interactives at
'FiveThirtyEight'
Algorithms for Finding Fixed Point Vectors of Functions
The Distribution of Distances Between Discrete Events in Fixed
Time
An R Client for the "Fixer.io" Currency API
Fixed Sequence Multiple Testing Procedures
Feature-Based Landscape Analysis of Continuous and Constrained
Optimization Problems
Fits Piecewise Constant Models with Data-Adaptive Knots
A Fast Large-Scale Almost Matching Exactly Approach to Causal
Inference
Implementation of optimal hierarchical clustering
Transforms Contingency Tables to Data Frames, and Analyses Them
Tools for Working with XML Files as R Dataframes
R Markdown Format for Flexible Dashboards
Tools to Work with the Flexible Dirichlet Distribution
Generalized Additive Models with Flexible Response Functions
Flexible Latent Trait Metrics using the Filtered Monotonic
Polynomial Item Response Model
Flexible Parametric Models for Censored and Truncated Data
Flexible Parametric Survival and Multi-State Models
Reconstruct Animal Paths from Solar Geolocation Loggers Data
Convert Files to and from Binary Objects (BLOBs)
Process Synchronization Using File Locks
Statistical Methods for the (Regional) Analysis of Flood
Frequency
Cross-Scale Flooding Prediction under Heavy Precipitation
Tools for Interacting with the Brazilian Flora 2020
Cytometric Diversity Indices from Gated Data
Tools for characterizing flowering traits
Forecasts future values of a univariate time series
Streamlining Design and Deployment of Complex Workflows
The Environmental Costs of Flow Regulation
Daily Streamflow Trend and Change Point Screening
Path Algorithm for the General Fused Lasso Signal Approximator
Mining Rigs for Specialized Subset Sum, Multi-Subset Sum,
Multidimensional Subset Sum, Multidimensional Knapsack,
Generalized Assignment Problems
Estimate Insect Survival from Fluorescence Data
Flux rate calculation from dynamic closed chamber measurements
Data Sets from "Forecasting: Methods and Applications" by
Makridakis, Wheelwright & Hyndman (1998)
Factorial Experiments with Minimum Level Changes
A Flexible Modelling Environment for Inverse Modelling,
Sensitivity, Identifiability and Monte Carlo Analysis
Fast Multivariate Log-Concave Density Estimation
Filtered Monotonic Polynomial IRT Models
Functional MRI Quality Assurance Routines
Variable Selection in Finite Mixture of AFT Regression and FMR
Functions for Medical Statistics Book with some Demographic Data
Regression Models with Finite Mixtures of Skew Heavy-Tailed
Errors
Rmetrics - Analysing and Modeling Multivariate Financial Return
Distributions
Fast Nearest Neighbor Search Algorithms and Applications
Rmetrics - Nonlinear and Chaotic Time Series Modelling
Summarize CRAN Check Results in the Terminal
Fit Continuous-Time State-Space Models for Filtering Argos
Satellite (and Other) Telemetry Data
Creates and Manages Folder Functions for Portable Large-Scale R
Analysis
Fonts with 'Bitstream Vera Fonts' License
Additional 'ggplot2' Themes Using 'Hind' Fonts
Additional 'ggplot2' Themes Using 'M+' Fonts
visualisation and analysis of food web networks
Rmetrics - Pricing and Evaluating Basic Options
Foraminifera and Community Ecology Analyses
Tools for Working with Categorical Variables (Factors)
Provides Foreach Looping Construct for R
Forecasting Functions for Time Series and Linear Models
Convenient Functions for Ensemble Time Series Forecasts
Forecasting for Stationary and Non-Stationary Time Series
Read Data Stored by 'Minitab', 'S', 'SAS', 'SPSS', 'Stata',
'Systat', 'Weka', 'dBase', ...
Statistical Methods in Forensic Genetics
Statistical tools for the interpretation of forensic DNA
mixtures
Systems Insights from Generation of Hydroclimatic Timeseries
Computing Essential Biodiversity Variables from Global Forest
Change Data
Approximate False Positive Rate Control in Selection Frequency
for Random Forest
Visualizes Random Forests with Feature Contributions
Forest Health Evaluation System at the Forest Stand Level in
China
Design-Based Global and Small-Area Estimations for Multiphase
Forest Inventories
Functions for Forest Mensuration and Management
Advanced Forest Plot Using 'grid' Graphics
Ecosystem and Canopy Structural Complexity Metrics from LiDAR
Forest Spatial Structure Analysis Systems
Projecting Customer Retention Based on Fader and Hardie
Probability Models
Imputation of Missing Values Through a Forward Imputation
Algorithm
Functions from the 2nd Italian Forest Inventory (INFC)
Elementary arithemtic in formal series rings
Programmatic Utilities for Manipulating Formulas, Expressions,
Calls, Assignments and Other R Objects
Add Formula Interfaces to Modelling Functions
Palaeoecological and Palaeogeographical Analysis Tools
Bayesian 4 Parameter Item Response Model
A Game for Human vs. Human or Human vs. AI
Restricted MLE for Functional Principal Components Analysis
Two Dimensional Functional Principal Component Analysis
Three Dimensional Functional Component Analysis
Reliable Comparison of Floating Point Numbers
Rmetrics - Portfolio Selection and Optimization
Computing the noncentrality parameter of the noncentral F
distribution
Data for "Forecasting: principles and practice"
Data for "Forecasting: Principles and Practice" (2nd Edition)
Approximation of First-Passage-Time Densities for Diffusion
Processes
Testing Hypotheses via Fuzzy P-Value in Fuzzy Environment
Fractionally differenced ARIMA aka ARFIMA(p,d,q) models
Fraction Proliferation via a Quiescent Growth Model
A Fractal Time Series Modeling and Analysis Package
Simulation and Parameter Estimation of Randomized Sierpinski
Carpets using the p-p-p-q-Model
Generate fractal time series with non-normal returns
distribution
Fitting Frailty Models with the EM Algorithm
Frailty Models via Hierarchical Likelihood
General Frailty Models: Shared, Joint and Nested Frailty Models
with Prediction; Evaluation of Failure-Time Surrogate Endpoints
General Semiparametric Shared Frailty Model
Simulation of the Growth of Framboidal and Sunflower Pyrite
Financial Risk Modelling and Portfolio Optimisation with R
Automatic Data Processing and Visualization for FRAP
Fuzzy Rule-Based Systems for Classification and Regression Tasks
Fast Regularized Canonical Correlation Analysis
Algorithms for Implementing Free-Knot Splines
Statistical algorithms used in common data mining course
Deterministic Computation of Text Box Metrics
Rmetrics - Regression Based Decision and Prediction
Functional Regression using Signal Compression Approach
FREQ: Estimate population size from capture frequencies
Frequency Domain Based Analysis: Dynamic PCA
Functional Time Series: Dynamic Functional Principal Components
Frequency Profiles Computing and Plotting
Spectral Decomposition of Connectedness Measures
Implementation of Frequent-Directions Algorithm for Efficient
Matrix Sketching
Feature Selection Algorithms for Computer Aided Diagnosis
Fractional Factorial Designs with 2-Level Factors
Catalogues of resolution IV 128 run 2-level fractional
factorials up to 33 factors that do have 5-letter words
Regression Analysis of Fractional Responses
Regression Analysis of Fractional Responses Under Unobserved
Heterogeneity
Regression Analysis of Panel Fractional Responses
The Generalized Hyperbolic Distribution, Related Distributions
and Their Applications in Finance
Prediction of Minimum Temperature for Frost Forecasting in
Agriculture
Cross-Platform File System Operations Based on 'libuv'
Simple Fisheries Stock Assessment Methods
Data to Support Fish Stock Assessment ('FSA') Package
Automated Feature Selection from 'caret'
Robust Data Analysis Through Monitoring and Dynamic
Visualization
'Rcpp' Implementation of 'FSelector' Entropy-Based Feature
Selection Algorithms with a Sparse Matrix Support
Import and Analysis of OMR Data from FormScanner
Wrapper Functions for 'FSL' ('FMRIB' Software Library) from
Functional MRI of the Brain ('FMRIB')
Imputation of Time Series Based on Fuzzy Logic
Social Relations Analyses with Roles ("Family SRM")
Fused Sparse Structural Equation Models to Jointly Infer Gene
Regulatory Network
Lightning Fast Serialization of Data Frames for R
Unified Sequence-Based Association Tests Allowing for Multiple
Functional Annotation Scores
A Wrapper for the API of the Danish Parliament
Programs for Analyzing Fourier Transform-Ion Cyclotron Resonance
Mass Spectrometry Data
Features and Strings for Nonparametric Regression
Rmetrics - Trading and Rebalancing Financial Instruments
R Interface to 'tslib' (a Time Series Library in C++)
Spectral Density Estimation and Comparison for Functional Time
Series
FUzzy GEnetic, a machine learning algorithm to construct
prediction model based on fuzzy logic
Sensitivity Analysis Optimized for Matched Sets of Varied Sizes
Full Text of 'Scholarly' Articles Across Many Data Sources
Fungal Species Identification using DNA Barcode with Random
Forest
Convenience Functions by Michael Chirico
Chi-Square and Exact Tests for Model-Free Functional Dependency
Functional Profiling of Microarray Expression Data
An algorithm for gene and gene set network inference
Clustering in the Discriminative Functional Subspace
Psychometric Functions from the Waller Lab
Univariate and Multivariate Model-Based Clustering in
Group-Specific Functional Subspaces
Rmetrics - Modelling Trends and Unit Roots
Model-Based Co-Clustering of Functional Data
Exploratory Data Analysis and Data Preparation Tool-Box Book
Simple Utility Providing Terminal Access to all R Functions
Functional Regression for Irregularly Timed Data
Apply Mapping Functions in Parallel using Futures
Fused Lasso for High-Dimensional Regression over Groups
Clustering and Feature Screening using L1 Fusion Penalty
A Tiny Utility Providing Polymorphic Operations
Random Matrix Generation and Manipulation
A framework for working in a functional programming paradigm in
R
Interim Analysis of Operational Futility in Randomized Trials
with Time-to-Event Endpoints and Fixed Follow-Up
Unified Parallel and Distributed Processing in R for Everyone
Apply Function to Elements in Parallel using Futures
A Future API for Parallel and Distributed Processing using
BatchJobs
A Future API for Parallel and Distributed Processing using
'batchtools'
A Future API for Parallel Processing using 'callr'
Find, Characterize, and Explore Extreme Events in Climate
Projections
Exact calculation of fuzzy decision rules for multiple testing
Join Tables Together on Inexact Matching
Multi-Criteria Decision Making Methods for Fuzzy Data
Apply Two Fuzzy Numbers on a Monotone Function
Fuzzy Rank Tests and Confidence Intervals
Fuzzy Similarity in Species Distributions
Fuzzy Stationary Probabilities from a Sequence of Observations
of an Unknown Markov Chain
Statistical Methods for Trapezoidal Fuzzy Numbers
Forward search for Mokken scale analysis
Selecting Variables in Regression Models
Fire Weather Index System and Fire Behaviour Prediction System
Calculations
A package performing Dynamic Bayesian Network inference
Find and Fill Gaps in Metabolic Networks
Grain-Size Statistics and Description of Sediment
Interactively Visualize Genetic Mutation Data using a
Lollipop-Diagram
Abbreviating Items Measures using Genetic Algorithms
Framework for Generalized Subspace Pattern Mining
GAD: Analysis of variance from general principles
A Genetic Algorithm for Learning Directed Acyclic Graphs
First Passage Time Simulation for Gaussian Diffusion Processes
Genetic and Isotopic Assignment Accounting for Habitat
Suitability
Graphical Extension with Accuracy in Parameter Estimation
(GAIPE)
Genetic Algorithms for Multivariate Statistical Models from
Large-Scale Functional Genomics Data
Genetic Algorithms and C-Steps Based LTS (Least Trimmed Squares)
Estimation
Genetic Approach to Maximize Clustering Criterion
Fit the Gambin Model to Species Abundance Distributions
Generalized linear and additive models by likelihood based
boosting
Estimating multistate models using gamboost()
Functions and Data for a Course on Modern Regression and
Classification
Generalized Additive Models for Bivariate Conditional Dependence
Structures and Vine Copulas
Applies GAMbag, GAMrsm and GAMens Ensemble Classifiers for
Binary Classification
Palettes Inspired in the TV Show "Game of Thrones"
Statistical Estimation of Game-Theoretic Models
Genetic Algorithm for Sequential Symmetric Games
Tools for Calculating Allocations in Game Theory
Generalised Additive Models for Location Scale and Shape
Fitting an Interval Response Variable Using `gamlss.family'
Distributions
Generating and Fitting K-Inflated 'discrete gamlss.family'
Distributions
Distributions for Generalized Additive Models for Location Scale
and Shape
Fitting Mixed (Inflated and Adjusted) Distributions
Fitting Mixture Distributions with GAMLSS
Fitting non linear parametric GAMLSS models
Spatial Terms in Generalized Additive Models for Location Scale
and Shape Models
Generating and Fitting Truncated `gamlss.family' Distributions
Bimodal Skew Symmetric Normal Distribution
Generalized Additive Mixed Models using 'mgcv' and 'lme4'
Comparing Nonlinear Curves and Surface Estimations by
Semiparametric Methods
Generalized Additive Mixed Model Analysis via Slice Sampling
Robust and Sparse Regression via Gamma-Divergence
Fit Regularization Path for Generalized Additive Models
Gene Association Network-based Pathway Analysis
Searching Parsimony Models with Genetic Algorithms
Functions for Drawing Gapped Cluster Heatmap with ggplot2
Authorize and Request Google Analytics Data
Generalized Array Arithmetic for Ragged Arrays with Named
Margins
Genetic Algorithm (GA) for Variable Selection from
High-Dimensional Data
Greenhouse Gas Flux Calculation from Chamber Measurements
Analyzing Gastric Emptying from MRI or Scintigraphy
Genetic Data Handling (QC, GRM, LD, PCA) & Linear Mixed Models
Easily Gate or Select Points on a Scatter Plot
The Gaussian Covariate Method for Variable Selection
Difference measures for multivariate Gaussian probability
density functions
The Greatest Mathematician Since Antiquity
Inference, Goodness-of-Fit and Forecast for Univariate Gaussian
Hidden Markov Models
Collection of functions for Gaussian quadrature
Generalize Lambda Distribution and Generalized Bootstrapping
Generalized Beta Distribution of the Second Kind: Properties,
Likelihood, Estimation
Estimation of the Generalised Beta Distribution of the Second
Kind from Grouped Data
Interface with General Bikeshare Feed Specification Files
Generalized Berk-Jones Test for Set-Based Inference in Genetic
Association Studies
Utilities for processing Rd objects and files
Inference of Ploidy from (Genotyping-by-Sequencing) GBS Data
Hyperparameter Search for Gradient Boosted Trees
Simulation of Real and Complex Numbers and Small Programming
Utilities
Guided Correction Approach for Inherited bias (GCAI.bias)
A Data Manipulation and Analysis Package for Calcium Indicator
Data
Simple Peak Alignment for Gas-Chromatography Data
Graph-based two-sample tests for categorical data
'GPU'/CPU Benchmarking in Debian-Based Systems
LASSO and Elastic Net (Adaptive) Penalized Least Squares,
Logistic Regression, HHSVM, Squared Hinge SVM and Expectile
Regression using a Fast GCD Algorithm
Analysis of Geostatistical Count Data using Gaussian Copulas
Concord method for Graphical Model Selection
Datasets for the Book Graphical Data Analysis with R
Earth Observation Data Cubes from Satellite Image Collections
Wrappers for the Geospatial Data Abstraction Library (GDAL)
Utilities
Various R Programming Tools for Data Manipulation
A Toolbox for the Analysis of Categorical Data in Social
Sciences, and Especially Geometric Data Analysis
Download, slice, and normalize GDELT data
Generalized Diffusion Magnetic Resonance Imaging
Distances and Routes on Geographical Grids
Tools to Work with Google's 'DNS-over-HTTPS' ('DoH') 'API'
Generalised Estimating Equations (GEE/WGEE) using 'Armadillo'
and S4
Adaptive Association Tests for Multiple Phenotypes using
Generalized Estimating Equations (GEE)
Marginal Proportional Hazards Mixture Cure Models with
Generalized Estimating Equations
Mediation Analysis for Generalized Linear Models Using the
Difference Method
Modified Variance Estimators for Generalized Estimating
Equations
Calculate Generalized Eigenvalues, the Generalized Schur
Decomposition and the Generalized Singular Value Decomposition
of a Matrix Pair with Lapack
Misspecified Models for Gene-Environment Interaction
File Conversion for 'Gem Infrasound Logger'
Network Meta-Analysis Using Bayesian Methods
Generalized Two-Stage Designs for Phase II Single-Arm Studies
Classes and Methods to Use Genetic Algorithms for Feature
Selection
Global ENvironmental ASsessment Information System (GENASIS)
computational tools
Analysis and Visualization Tools for Genetic Barcode Data
Clopper-Pearson Confidence Interval and Generalized Binomial
Distribution
Predict Gender from Names Using Historical Data
Predict Gender from Brazilian First Names
Segmentation and Classification of Accelerometer Data
Gene Expression Clustering and Gene Network
Identification of Periodically Expressed Genes
A Pipeline to Define Gene Families in Legumes and Beyond
Profiling a gene list into GOslim or KEGG function pie
Belief Propagation in Genotype-Phenotype Networks
Genetic Epidemiology Design and Inference
Population Genetic Data Analysis Using Genepop
Generalized Correlations and Initial Causal Path
Goodness of Fit Tests for Logistic Regression Models
Credible Visualization for Two-Dimensional Projections of Data
Blinder-Oaxaca Decomposition for Generalized Linear Model
Generate Data Containing Fake Personally Identifiable
Information
Common S3 Generics not Provided by Base R Methods Related to
Model Fitting
Identify Favorable Subsets of Germplasm Collections
Spatial Graph-Theoretic Genetic Gravity Modelling
The Forward Imputation: A Sequential Distance-Based Approach for
Imputing Missing Data
A New, Fast, and Outlier Resistant Hierarchical Clustering
Algorithm
Easily Access Song Lyrics from Genius.com
Functions for generating and manipulating binned kernel density
estimates
Implements Generalized Meta-Analysis Using Iterated Reweighted
Least Squares Algorithm
Functions for the Bayesian Analysis of GWAS Data
Application of MOSS algorithm to genome-wide association study
(GWAS)
Finding the DNA and Protein Sequences of Any Genomic or
Proteomic Loci
Methods for Analysing Forensic Ancestry Informative Markers
'Plot genome wide values for all chromosomes'
Models Dealing with Spatial Dependency in Genomic Data
Circular Genomic Permutation using Gwas p-Values of Association
Collection of Tools for Genomic Data Analysis
Plot Publication-Grade Gene and Genome Maps
Simulation of Discrete Random Variables with Given Correlation
Matrix and Marginal Distributions
A Genome-Wide Scan Statistic Framework for Whole-Genome Sequence
Data Analysis
Generalized Pathmox Approach Segmentation Tree Analysis
Power Calculations Under Genetic Model Misspecification
Generalized Ridge Trace Plots for Ridge Regression
A Generalized Multiclass Support Vector Machine
Spline Based Window Boundaries for Genomic Analyses
Analysis of Geostatistical Data using Bayes and Empirical Bayes
Methods
Analysis and Modeling for Geostatistical Count Data
A geometrical Approach to Differential expression and gene-set
enrichment
Stratified Heterogeneity Measure, Dominant Driving Force
Detection, Interaction Relationship Investigation
Fast, Dependency-Free Geodesic Distance Calculations
3D-Visualization of Geoelectric Resistivity Measurement Profiles
'ggplot2' Faceting Utilities for Geographical Data
Spatial Prediction for Function Value Data
Select Sparse Geoadditive Models for Spatial Prediction
Quantification of the effect of geographic versus environmental
isolation on genetic differentiation
Turn Geospatial Polygons into Regular or Hexagonal Grids
Convert Data from and to 'GeoJSON' or 'TopoJSON'
Web-Processing of Large Gridded Datasets
Data for topographic and Geologic Mapping
Tools for Reading and Writing ISO/OGC Geographic Metadata
Mesh Generation and Surface Tessellation
Network Visualization in the 'ggplot2' Framework
Geometric Morphometric Analyses of 2D/3D Landmark Data
Interface to the "Geonames" Spatial Query Web Service
'GeoJSON' Topology Calculations and Operations
Interface to the Geoparser.io API for Identifying and
Disambiguating Places Mentioned in Text
Geophysics, Continuum Mechanics, Gravity Modeling
Calculating Geographic Range from Occurrence Data
A Package for Generalised Linear Spatial Models
Robust Geostatistical Analysis of Spatial Data
Construction of Geostatistical Sampling Designs
Facilitate Generating of Distance Matrices Used in Package
'spacom' and Plotting Data on Maps
Classes and Methods for Working with Spectral Data with
Space-Time Attributes
Geostatistical Analysis and Design of Optimal Spatial Sampling
Networks
Spatio-Temporal; Inverse Distance Weighting and Radial Basis
Functions with Distance-Based Regression
Geostatistical Modelling with Likelihood and Bayes
Tools to Improve the Use of Geostatistic
An R Plug-in for the Distributed Hydrological Model GEOtop
Google Encoded Polyline Algorithm Format
Estimation and Fit Diagnostics for Generalized Exponential
Random Graph Models
Germination Indices and Graphics for Seed Germination in
Ecophysiological Studies
Seed Germination Indices and Curve Fitting
Generalized Structured Component Analysis (GSCA)
Tools for Making and Combining Functions
Generalised Survival Trial Assessment Tool Environment
Imports Datasets from BCB (Central Bank of Brazil) using Its
Official API
Use and Explore 'CRU' 'CL' v. 2.0 Climatology Elements
Reading Annual Financial Reports from Bovespa's DFP, FRE and FCA
System
Download and Aggregate High Frequency Trading Data from Bovespa
Access to Ethereum-Based Blockchains Through Geth Nodes
Get Landsat 8 Data from Amazon Public Data Sets
Reading Bibliometric Data from Lattes Platform
Get Meteorological Data for Hydrologic Models
Quantifying Systematic Heterogeneity in Meta-Analysis
Parsing Command-Line Arguments and Variable Interpolation
GetR: Calculate Guttman error trees in R
General-to-Specific (GETS) Modelling and Indicator Saturation
Methods
Access and Summarise World Health Organisation Tuberculosis data
Get Data for Brazilian Bonds (Tesouro Direto)
GEV Conditional Density Estimation Network
ARMA-GARCH/APARCH Models with GEV and Stable Distributions
A visual, intuitive, easy to use software giving access to a new
type of information buried into your microarray data
Tools for Working with Hansen et al. Global Forest Change
Dataset
Gross Flows Estimation under Complex Surveys
Implements Group Fused Multinomial Regression
Clustering and Inference Procedures for High-Dimensional Latent
Variable Models
Extra Coordinate Systems, 'Geoms', Statistical Transformations,
Scales and Fonts for 'ggplot2'
Categorical Scatter (Violin Point) Plots
Simpler Appearance Modification of 'ggplot2'
Visualization of a Correlation Matrix using 'ggplot2'
Analyze and Create Elegant Directed Acyclic Graphs
'ggplot2' Extension for Seasonal and Trading Day Adjustment with
'RJDemetra'
Create Dendrograms and Tree Diagrams Using 'ggplot2'
A 'ggplot2' Extension for Plotting Unimodal Distributions
Interactive 'ggplot2' Layer and Theme Aesthetic Editor
Create Tidy Data Frames of Marginal Effects for 'ggplot' from
Model Outputs
Visualization Tools for Genealogical Data
Visualisation of High-Throughput Behavioural (i.e. Ethomics)
Data
Add Marginal Histograms to 'ggplot2', and More 'ggplot2'
Enhancements
Summarise a Distribution Through Coloured Intervals
Focus on Specific Factor Levels in your ggplot()
Formula Interface to the Grammar of Graphics
Data Visualization Tools for Statistical Analysis Results
Capture the Spirit of Your 'ggplot2' Calls
Create a Half Normal Plot Using 'ggplot2'
Visualise the Results of Inferential Statistics using 'ggplot2'
Explore the Innards of 'ggplot2' Objects
Make Interactive 'ggplot2'. Extension to 'ggplot2' and 'ggiraph'
Group Lasso Penalized Learning Using a Unified BMD Algorithm
Plotting Lorenz Curve with the Blessing of 'ggplot2'
Tools for Analyzing MCMC Simulations from Bayesian Inference
Gaussian Graphical Models Using Ridge Penalty Followed by
Thresholding and Reestimation
Create Muller Plots of Evolutionary Dynamics
Geometries to Plot Networks with 'ggplot2'
Multiple Fill and Color Scales in 'ggplot2'
A 'ggplot2' Extension to Make Normal Violin Plots
Variations of Parallel Coordinate Plots for Categorical Data
Easy Plotting of Periodic Data with 'ggplot2'
Create Elegant Data Visualisations Using the Grammar of Graphics
'RStudio' Addin for Teaching and Learning 'ggplot2'
Create Ggplots via a Graphical User Interface
Convert Plot to 'grob' or 'ggplot' Object
'ggplot2' Based Tool to Facilitate Diagnostic Plots for NLME
Models
Visualizing Social Science Data with 'ggplot2'
Polygons with Holes for the Grammar of Graphics
Annotate Statistical Tests for 'ggplot2'
Compare Big Datasets to the Uniform Distribution
Quickly Explore Your Data Using 'ggplot2' and 'table1' Summary
Tables
An Implementation of Grammar of Graphics for Graphs and Networks
Allows Interactive Visualization of Data Through a Web Browser
GUI
Gaussian-Based Genome Representative Selector with
Prioritization
Automatically Position Non-Overlapping Text Labels with
'ggplot2'
Panels and Interactive Versions of Diagnostic Plots using
'ggplot2'
Scientific Journal and Sci-Fi Themed Color Palettes for
'ggplot2'
'stats' for Seasonal Adjustment on the Fly with 'ggplot2'
A 'ggplot2' Extension for Drawing Publication-Ready Sequence
Logos
North Symbols and Scale Bars for Maps Created with 'ggplot2' or
'ggmap'
A 'ggplot2'-Plot of Composition of Solvency II SCR: SF and IM
New Data Visualisations for SOMs Cluster
Extensions to 'ggplot2' for Radiation Spectra
'ggplot2' Based Plots with Statistical Details
Offers Various Swiss Maps as Data Frames and 'ggplot2' Objects
An Extension to 'ggplot2', for the Creation of Ternary Diagrams
Extra Themes, Scales and Geoms for 'ggplot2'
Time Series Visualisations Using the Grammar of Graphics
Combination Matrix Axis for 'ggplot2' to Create 'UpSet' Plots
Voronoi Diagrams and Heatmaps with 'ggplot2'
Graphical Horseshoe MCMC Sampler Using Data Augmented Block
Gibbs Sampler
A Package on Generalized Hyperbolic Distribution and Its Special
Cases
Naive Gibbs Sampling with Metropolis Steps
Gibbs Sampler for One-Way Mixed-Effects ANOVA and ANCOVA Models
Multivariate Analysis with Optimal Scaling
Reads in 'Neuroimaging' 'GIFTI' Files with Geometry Information
Random Variate Generator for the GIG Distribution
Gillespie's Stochastic Simulation Algorithm (SSA)
Group Iterative Multiple Model Estimation
Supplemental Tools for the 'gimme' R Package
Computing the Gini-Based Coefficients for Weighted and Negative
Attributes
Generalized Iterative Proportional Fitting for Relational Models
Store and Retrieve Data.frames in a Git Repository
Rstudio Addin for Version Control and Assignment Management
using Git
A Helpful Way to Install R Packages Hosted on GitHub
Quantification of Pinned Microbial Cultures
g-and-k and g-and-h Distribution Functions
Gaines and Kuklinski (2011) Estimators for Hybrid Experiments
Penalization in Large Scale Generalized Linear Array Models
Generate tables and plots to get summaries of data
Generalized Linear Autoregressive Moving Average Models
Graphical Lasso: Estimation of Gaussian Graphical Models
General Linear Ballistic Accumulator Models
Calculate Textures from Grey-Level Co-Occurrence Matrices
(GLCMs)
Estimation and Use of the Generalised (Tukey) Lambda
Distribution
Fitting Single and Mixture of Generalised Lambda Distributions
(RS and FMKL) using Various Methods
Fit GLD Regression Model and GLD Quantile Regression Model to
Empirical Data
Global and Individual Tests for Direct Effects
Learning Interactions via Hierarchical Group-Lasso
Regularization
Generalized Linear Latent Variable Models
'C' and 'Java' Source Code Generator for Fitted Glm Objects
Predicted Values and Discrete Changes for GLM
Adaptive LASSO and Network Regularized Generalized Linear Models
An Adaptive Test on High Dimensional Parameters in Generalized
Linear Models
All Hierarchical or Graphical Models for Generalized Linear
Model
Bayesian Fractional Polynomials for GLMs
Fitting Generalized Linear Models Subject to Constraints
Discretization and Grouping for Logistic Regression
Graph-Constrained Regularization for Sparse Generalized Linear
Models
Fit GLM with LEP-Based Penalized Maximum Likelihood
Generalized Linear Mixed Models via Monte Carlo Likelihood
Approximation
Generalized Linear Mixed Models using Adaptive Gaussian
Quadrature
Bootstrap Resampling for Mixed Effects and Plain Models
Generalized Linear Mixed Model Analysis via Expectation
Propagation
Generalized Linear Mixed Models with Robust Random Fields for
Spatiotemporal Modeling
Variable Selection for Generalized Linear Mixed Models by
L1-Penalized Estimation
Generalized Linear Models with Clustering
Generalized Linear Mixed Model (GLMM) for Binary Randomized
Response Data
Generalized Linear Mixed Models using Template Model Builder
Lasso and Elastic-Net Regularized Generalized Linear Models
Fit a Penalized Constrained Continuation Ratio Model for
Predicting an Ordinal Response
L1 Regularization Path for Generalized Linear Models and Cox
Proportional Hazards Model
Fit a Penalized Continuation Ratio Model for Predicting an
Ordinal Response
Truncated Lasso Regularized Generalized Linear Models
Model Selection and Multimodel Inference Made Easy
Variable Selection Deviation Measures and Instability Tests for
High-Dimensional Generalized Linear Models
Testing the additional predictive value of high-dimensional data
Bi-Level Optimization of Metabolic Network Models
Global Gene-Set Analysis for Association Studies
Generate Functions to Get or Set Global Options
Objective functions for benchmarking the performance of global
optimization algorithms
Identify Global Objects in R Expressions
Plot 2D and 3D Views of the Earth, Including Major Coastline
Fitting and Testing Generalized Logistic Distributions
Generalized Logrank Tests for Interval-censored Failure Time
Data
Genome-Wide Association and Linkage Analysis under Heterogeneity
Generalized Least Squares with Measurement Error
Glucose Variability Measures from Continuous Glucose Monitoring
Data
Genomic Mediation Analysis with Adaptive Confounding Adjustment
Distance and Travel Time Between Two Points from Google Maps
Simulation of Graphically Constrained Matrices
Fast Estimation of Gaussian Mixture Copula Models
Graph Based Multiple Comparison Procedures
Accessing a Geometallurgical Database with R
Short Term Forecasting via GMDH-Type Neural Network Algorithms
Binary Classification via GMDH-Type Neural Network Algorithms
Geometric Median, k-Median Clustering and Robust Median PCA
Mediation Analysis for Multiple and Multi-Stage Mediators
Meta-Analysis via a Unified Framework of Confidence Distribution
Inference and Clustering of Functional Data
Descriptive Statistics, Transition Plots, and More
Generalized Method of Moments and Generalized Empirical
Likelihood
Generalized Linear Mixed Model Association Tests
Multinomial Logit Models with Random Parameters
Various R Programming Tools for Model Fitting
'2D plots of linear or integer programming models'
Generalised Management Strategy Evaluation Simulator
Generalized Mass Spectrum Missing Peaks Abundance Imputation
Interface Between GMT Map-Making Software and R
Estimate Gaussian Mixture Vector Autoregressive Model
Methods for Fitting Network Time Series Models
Computation of Generalized Nash Equilibria
Goodness of Fit Test for Continuous Distribution Functions
Generalized Non-negative Matrix Factorization Based on Renyi
Divergence
Read Data from Files Readable by 'gnumeric'
Functions for Community Data and Ordinations
Model-diagnostics based on cumulative residuals
Fast DocumentTermMatrix and TermDocumentMatrix Creation
Testing Goodness-of-Fit with the Kernel Density Estimator
Khmaladze Martingale Transformation Goodness-of-Fit Test
Goodness of Fit Noise Analysis Using Monte Carlo Techniques
Tests of Fit for some Probability Distributions
Classical Goodness-of-Fit Tests for Univariate Distributions
Generalized Orthogonal GARCH (GO-GARCH) models
Gene Ontology Label Discernment and Identification
Association Analysis for Categorical Variables
Collect Data Programmatically by POST Methods to Google Forms
Retrieve Information from 'Google Knowledge Graph' API
Call Google's 'Natural Language' API, 'Cloud Translation' API,
'Cloud Speech' API and 'Cloud Text-to-Speech' API
An Interface to Google's Cloud Natural Language API
Encoding Coordinates into 'Google' Polylines
Working with Google's 'Public Data Explorer' DSPL Metadata Files
Accesses Google Maps APIs to Retrieve Data and Plot Maps
Visualization of Functional Analysis Data
Fit Generalized Odds Rate Mixture Cure Model with Interval
Censored Data
functions to get public survey data in Japan
Gaussian Processes for Pareto Front Estimation and Optimization
Batch Effect Detection via Guided Principal Components Analysis
Differential Item Functioning in Generalized Partial Credit
Models
GPCSIV, Generalized Principal Component of Symbolic Interval
variables
General Profiling Method for Delay Differential Equation
Bootstrap goodness-of-fit test for the generalized Pareto
distribution
Apply Gaussian Process in Functional data analysis
Solving Complex Game Problems using Gaussian Processes
Fast Gaussian Process Computation Using Vecchia's Approximation
Generalized Partial Linear Models (GPLM)
Various R Programming Tools for Plotting Data
Generalized Partially Linear Tree-Based Regression Model
Gaussian Process Modeling of Multi-Response and Possibly Noisy
Datasets
Analysing and plotting genotype-phenotype maps
A Minimalistic package to apply Gaussian Process in R
Gaussian Process Ranking of Multiple Time Series
Gaussian Process Regression for Mortality Rates
Generalized Propensity Score Cumulative Distribution Function
Maximum Likelihood Estimation of Multiple Membership Mixed
Models Used in Value-Added Modeling
Prediction of G Quadruplexes and Other Non-B DNA Motifs
Graph-Constrained Estimation and Hypothesis Tests
Helps Grade Assignment Submissions that are R Scripts
Interactive Color Gradient Picker Using 'htmlwidgets' and the
Modified JS Script 'jquery-gradient-picker'
Moving-Window and Direct Data Aggregation
Graphical Representation and Modeling of Metagenomic Reads
Creating Continuously Integrated Package Repositories from
Manifests
Inference on Granger-Causality in the Frequency Domain
Graphical Analysis of Variance Using ggplot2
Gene-Ranking Analysis of Pathway Expression
A Multi-Platform GUI for Drawing Customizable Graphs in R
Network Topology of Intravariable Clusters with Intervariable
Links
Graphical VAR for Experience Sampling Data
Additional Layout Algorithms for Network Visualizations
A Collection of Graphon Estimation Methods
Cluster Detection with Hypothesis Free Scan Statistic
Graceful 'ggplot'-Based Graphics and Other Functions for GAMs
Fitted Using 'mgcv'
Inference in Graphical Gaussian Models with Edge and Vertex
Symmetries
Parameter Inference and Optimal Designs for Grouped and/or
Right-Censored Count Data
GRadient-Based RECognition of Spatial Patterns in Environmental
Data
Greedy Stochastic Block Transition Models
Mixed-Effects REML Incorporating Generalized Inverses
Adaptive Group-Regularized Logistic Elastic Net Regression
GEO RNA-Seq Experiments Processing Pipeline
Simple and Scalable Statistical Modelling in R
Gene ID Mapping for Genotype-Tissue Expression (GTEx) Data
Toolbox for Model Building and Forecasting
Fit a Grey-Zone Model with Survival Data
Miscellaneous Functions for "Grid" Graphics
Redraw Base Graphics Using 'grid' Graphics
Tools for Grid-Based Survey Sampling Design
A Simulation Tool to Determine the Required Sample Size for
Repertory Grid Studies
Uses Grieg-Smith method on 2 dimentional spatial data
Calculate Optical Parameters from Spindle Stage Measurements
Generalized Regression on Orthogonal Components
Gromov-Hausdorff Type Distances for Labeled Metric Spaces
Regression Analysis of Grouped and Coarse Data
Grouped Statistical Analyses in a Tidy Way
Efficient Estimation of Grouped Survival Models Using the Exact
Likelihood Function
Independent Component Analysis for Grouped Data
Regularized Multivariate Regression for Identifying Master
Predictors Using the GroupRemMap Penalty
A GUI-Based Program to Compute Probabilities Regarding Group
Sequential Designs
Multiple Testing Procedure for Grouped Hypotheses
Wavelet Functional ANOVA Through Markov Groves
Bayesian Semi and Nonparametric Growth Curve Models that
Additionally Include Multiple Membership Random Effects
Bayesian Non-Parametric Dependent Models for Time-Indexed
Functional Data
Multivariate Normal and Elliptically-Contoured Repeated
Measurements Models
Simple Metrics to Summarize Growth Curves
Plotting, Smoothing and Growth Trait Extraction for Longitudinal
Data
Bayesian reconstruction of growth velocity
Estimate Growth Rates from Experimental Data
Fitting User-Specified Models with Group Lasso Penalty
Standardization for Group Lasso Models with Categorical
Predictors
Regularization Paths for Regression Models with Grouped
Covariates
Penalized Regression Models with Overlapping Grouped Covariates
Patterns and Statistical Differences Between Two Groups of
Strings
Alternative Implementations of Base R Functions
GRS Test for Portfolio Efficiency, Its Statistical Power
Analysis, and Optimal Significance Level Calculation
Tools for the Analysis of Gutenberg-Richter Distributions of
Earthquake Magnitudes
Gene Set Analysis with Fisher Combined Method
Gene Set Analysis using the Gamma Method
Two functions for Generalized SARIMA time series simulation
Group Sequential Designs with Negative Binomial Outcomes
Robust Estimation in the Presence of Cellwise and Casewise
Contamination and Missing Data
Semi-Supervised Generalized Structural Equation Modeling
Group Sequential Design in Equivalence Studies
Download Google Sheets Using Just the URL
Global Sea Level and Oxygen Isotope Data
Global Surface Summary of the Day ('GSOD') Weather Data Client
Group Sequential Refined Secondary Boundary
Copula Density and 2-D Hazard Estimation using Smoothing Splines
Spatial and Spatio-Temporal Geostatistical Modelling, Prediction
and Simulation
Graph-Based Sequential Change-Point Detection for Streaming Data
Utilities for Strings and Function Arguments
Estimation of the Generalized Symmetry Point, an Optimal
Cutpoint in Continuous Diagnostic Tests
Generalizability Theory for Information Retrieval Evaluation
Calculate efficiencies of group testing algorithms with
correlated responses
Routing with GTFS (General Transit Feed Specification) Data
Game-Theoretically OPtimal (GTOP) Reconciliation Method
Perform and Display Google Trends Queries
Genome and Transcriptome Wide Association Study
Access 'The Guardian' Newspaper Open Data API
Maps, data and methods related to Guerry (1833) "Moral
Statistics of France"
Adjust Estimates of Learning for Guessing
Graphical User Interface for Generalized Multistate Simulation
Model
Implementation of Sampling Formulas for the Unified Neutral
Model of Biodiversity and Biogeography, with or without Guild
Structure