Fitting and testing probabilistic knowledge structures,
  especially the basic local independence model (BLIM, Doignon & Flamagne,
  1999), using the minimum discrepancy maximum likelihood (MDML) method
  (Heller & Wickelmaier, 2013 <doi:10.1016/j.endm.2013.05.145>).
A tool for inferring kinase activity changes from 
    phosphoproteomics data. 'pKSEA' uses kinase-substrate 
    prediction scores to weight observed changes in 
    phosphopeptide abundance to calculate a phosphopeptide-level 
    contribution score, then sums up these contribution scores by 
    kinase to obtain a phosphoproteome-level kinase activity 
    change score (KAC score). 'pKSEA' then assesses the 
    significance of changes in predicted substrate abundances for 
    each kinase using permutation testing. This results in a 
    permutation score (pKSEA significance score) reflecting the 
    likelihood of a similarly high or low KAC from random chance, 
    which can then be interpreted in an analogous manner to an 
    empirically calculated p-value. 'pKSEA' contains default 
    databases of kinase-substrate predictions from 'NetworKIN' 
    (NetworKINPred_db) <http://networkin.info> 
    Horn, et. al (2014) <doi:10.1038/nmeth.2968>
    and of known kinase-substrate links from 'PhosphoSitePlus' 
    (KSEAdb) <https://www.phosphosite.org/>
    Hornbeck PV, et. al (2015) <doi:10.1093/nar/gku1267>.
Applying the global sensitivity analysis workflow to investigate 
    the parameter uncertainty and sensitivity in physiologically based 
    kinetic (PK) models, especially the physiologically based 
    pharmacokinetic/toxicokinetic model with multivariate outputs. 
    The package also provides some functions to check the convergence 
    and sensitivity of model parameters. The workflow was first mentioned 
    in Hsieh et al., (2018) <doi:10.3389/fphar.2018.00588>, then further 
    refined (Hsieh et al., 2020 <doi:10.1016/j.softx.2020.100609>).
Tools to process legacy format summary redistricting data files
    produced by the United States Census Bureau pursuant to P.L. 94-171. These
    files are generally available earlier but are difficult to work with as-is.
A semi-parametric estimation method for the Cox model
    with left-truncated data using augmented information
    from the marginal of truncation times.
Assessment of the prevalence of plastic
    debris in bird nests based on bootstrap replicates. The package allows for 
    calculating bootstrapped 95% confidence intervals for the estimated prevalence 
    of debris. Combined with a Bayesian approach, the resampling simulations can 
    be also used to define appropriate sample sizes to detect prevalence of plastics. 
    The method has wide application, and can also be applied to estimate confidence 
    intervals and define sample sizes for the prevalence of plastics ingested by any 
    other organisms. The method is described in Tavares et al. (Submitted).
Clusters GPS coordinates into places (i.e., meaningful stops). Additionally, categorizes places into types (e.g., home, cafe, gym). Places are identified as home using a rules-based algorithm defining home as the stop occurring most frequently during the night. Other places (e.g., cafe, gym) are identified using the Google Maps API.
Functions to prepare rankings data and fit the Plackett-Luce model
    jointly attributed to Plackett (1975) <doi:10.2307/2346567> and Luce
    (1959, ISBN:0486441369). The standard Plackett-Luce model is generalized
    to accommodate ties of any order in the ranking. Partial rankings, in which
    only a subset of items are ranked in each ranking, are also accommodated in
    the implementation. Disconnected/weakly connected networks implied by the
    rankings may be handled by adding pseudo-rankings with a hypothetical item.
    Optionally, a multivariate normal prior may be set on the log-worth
    parameters and ranker reliabilities may be incorporated as proposed by
    Raman and Joachims (2014) <doi:10.1145/2623330.2623654>. Maximum a
    posteriori estimation is used when priors are set. Methods are provided to
    estimate standard errors or quasi-standard errors for inference as well as
    to fit Plackett-Luce trees. See the package website or vignette for further
    details.
Provides methods for plotting potentially large (raster) images
    interactively on a plain HTML canvas. In contrast to package 'mapview'
    data are plotted without background map, but data can be projected to
    any spatial coordinate reference system.
    Supports plotting of classes 'RasterLayer', 'RasterStack', 'RasterBrick'
    (from package 'raster') as well as 'png' files located on disk. 
    Interactivity includes zooming, panning, and mouse location information. 
    In case of multi-layer 'RasterStacks' or 'RasterBricks', RGB image plots 
    are created (similar to 'raster::plotRGB' - but interactive).
Supports the creation of 'burndown' charts and 'gantt' diagrams.
Solves the electromagnetic problem of reflection and transmission at a planar multilayer interface. Also computed are the decay rates and emission profile for a dipolar emitter.
An extensive set of plane geometry routines. Provides R6 classes representing triangles, circles, circular arcs, ellipses, elliptical arcs and lines, and their plot methods. Also provides R6 classes representing transformations: rotations, reflections, homotheties, scalings, general affine transformations, inversions, Möbius transformations.
Calculates an acceptance sampling plan, (sample size and acceptance number) based in MIL STD 105E, Dodge  Romig and MIL STD 414 tables and procedures. The arguments for each function are related to lot size, inspection level and quality level. The specific plan operating curve (OC), is calculated by the binomial distribution.
It includes functions to download and process the 'Planet NICFI' (Norway's International Climate and Forest Initiative) Satellite Imagery utilizing the Planet Mosaics API <https://developers.planet.com/docs/basemaps/reference/#tag/Basemaps-and-Mosaics>. 'GDAL' (library for raster and vector geospatial data formats) and 'aria2c' (paralleled download utility) must be installed and configured in the user's Operating System.
The goal of 'planets' is to provide of very simple and accessible data containing basic information from all known planets.
Coupled leaf gas exchange model, A-Ci curve simulation and
    fitting, Ball-Berry stomatal conductance models, 
    leaf energy balance using Penman-Monteith, Cowan-Farquhar
    optimization, humidity unit conversions. 
    See Duursma (2015) <doi:10.1371/journal.pone.0143346>.
Provides wrapping functions to add to capabilities to 'plantecophys' 
    (Duursma, 2015, <doi:10.1371/journal.pone.0143346>). Key added capabilities 
    include temperature responses of mesophyll conductance (gm, gmeso), apparent 
    Michaelis-Menten constant for rubisco carboxylation in air (Km, Kcair),and
    photorespiratory CO2 compensation point (GammaStar) for fitting A-Ci or A-Cc
    curves for C3 plants (for temperature responses of gm, Km, & GammaStar,  see 
    Bernacchi et al., 2002, <doi:10.1104/pp.008250>; for theory on fitting A-Ci 
    or A-Cc curves, see Farquhar et al., 1980; <doi:10.1007/BF00386231>, von 
    Caemmerer, 2000, ISBN:064306379X; Ethier & Livingston, 2004 
    <doi:10.1111/j.1365-3040.2004.01140.x>; and Gu et al., 2010, 
    <doi:10.1111/j.1365-3040.2010.02192.x>). Includes the ability to fit the 
    Arrhenius and modified Arrhenius temperature response functions (see Medlyn 
    et al., 2002, <doi:10.1046/j.1365-3040.2002.00891.x>) for maximum rubisco 
    carboxylation rates (Vcmax) and maximum electron transport rates (Jmax) (see
    Farquhar et al., 1980; <doi:10.1007/BF00386231>).
Estimation, prediction, thresholding, transformation, and plotting for partially linear additive quantile regression.  Intuitive functions for fitting and plotting partially linear additive quantile regression models.  Uses and works with functions from the 'quantreg' package.
Aims at detecting single nucleotide variation
    (SNV) and insertion/deletion (INDEL) in circulating tumor DNA (ctDNA), used
    as a surrogate marker for tumor, at each base position of an Next Generation
    Sequencing (NGS) analysis. Mutations are assessed by comparing the minor-allele
    frequency at each position to the measured PER in control samples.
Contains functions developed to combine the results of querying a plasmid database using
    short-read sequence typing with the results of a blast analysis against the query results.
Tools for interacting with data from experiments done in microtiter
    plates. Easily read in plate-shaped data and convert it to tidy format, 
    combine plate-shaped data with tidy data, and view tidy data in plate shape.
Collection of functions for working with multi-well microtitre
    plates, mainly 96, 384 and 1536 well plates.
Data and analysis from an experiment with improving touch 
    typing speed, using the tDCS PlatoWork headset produced by PlatoScience.
We present 'Platypus', an open-source software platform providing a user-friendly interface to investigate B-cell receptor and T-cell receptor repertoires from scSeq experiments. 'Platypus' provides a framework to automate and ease the analysis of single-cell immune repertoires while also incorporating transcriptional information involving unsupervised clustering, gene expression and gene ontology (Yermanos A, et al (2021) <doi:10.1093/nargab/lqab023>).
Implements schemes for estimating player or 
  team skill based on dynamic updating. Implemented methods include 
  Elo, Glicko, Glicko-2 and Stephenson. Contains pdf documentation 
  of a reproducible analysis using approximately two million chess 
  matches. Also contains an Elo based method for multi-player games
  where the result is a placing or a score. This includes zero-sum
  games such as poker and mahjong.
We present a penalized log-density estimation method using Legendre polynomials with lasso penalty to adjust estimate's smoothness. Re-expressing the logarithm of the density estimator via a linear combination of Legendre polynomials, we can estimate parameters by maximizing the penalized log-likelihood function. Besides, we proposed an implementation strategy that builds on the coordinate decent algorithm, together with the Bayesian information criterion (BIC).
Perform tests for pleiotropy of multiple traits of various variable types on genotypes for a genetic marker.
It performs a fast multi-trait genome-wide association analysis based on seemingly unrelated regressions. It tests for pleiotropic effects based on a series of Intersection-Union Wald tests. The package can handle large and unbalanced data and plot results.
Log-multiplicative association models (LMA) are
    models for cross-classifications of categorical variables
    where interactions are represented by products of category
    scale values and an association parameter. Maximum
    likelihood estimation (MLE) fails for moderate to large
    numbers of categorical variables. The 'pleLMA' package
    overcomes this limitation of MLE by using pseudo-likelihood
    estimation to fit the models to small or large
    cross-classifications dichotomous or multi-category variables.
    Originally proposed by Besag (1974,
    <doi:10.1111/j.2517-6161.1974.tb00999.x>), pseudo-likelihood
    estimation takes large complex models and breaks it down
    into smaller ones. Rather than maximizing the likelihood
    of the joint distribution of all the variables, a
    pseudo-likelihood function, which is the product likelihoods
    from conditional distributions, is maximized. LMA models can
    be derived from a number of different frameworks including
    (but not limited to) graphical models and uni-dimensional
    and multi-dimensional item response theory models. More
    details about the models and estimation can be found in
    the vignette.
The portmanteau local feature discriminant approach first identifies 
    the local discriminant features and their differential structures, 
    then constructs the discriminant rule by pooling the identified local
    features together. This method is applicable to high-dimensional 
    matrix-variate data.
Functions for estimating probabilistic latent feature models with a disjunctive, conjunctive or additive mapping rule on (aggregated) binary three-way data.
Provides a graphical user interface for viewing and designing various types of graphs of the data. The graphs can be saved in different formats of an image.
Sequential Monte Carlo (SMC) inference for fully Bayesian
  Gaussian process (GP) regression and classification models by
  particle learning (PL) following Gramacy & Polson (2011) <arXiv:0909.5262>.
  The sequential nature of inference
  and the active learning (AL) hooks provided facilitate thrifty 
  sequential design (by entropy) and optimization
  (by improvement) for classification and
  regression models, respectively.
  This package essentially provides a generic
  PL interface, and functions (arguments to the interface) which
  implement the GP models and AL heuristics.  Functions for 
  a special, linked, regression/classification GP model and 
  an integrated expected conditional improvement (IECI) statistic 
  provide for optimization in the presence of unknown constraints.
  Separable and isotropic Gaussian, and single-index correlation
  functions are supported.
  See the examples section of ?plgp and demo(package="plgp") 
  for an index of demos.
Provides tools for image manipulation that will help you to
    quantify plant leaf area, disease severity, number of disease lesions,
    and obtain statistics of image objects such as grains, pods, pollen,
    leaves, and more. Tools to segment images and create binary images
    using the method of automatic threshold selection proposed by
    Otsu (1979) <doi:10.1109/tsmc.1979.4310076> are also provided.
Item response theory based methods are used to compute
        linking constants and conduct chain linking of unidimensional
        or multidimensional tests for multiple groups under a common
        item design.  The unidimensional methods include the Mean/Mean,
        Mean/Sigma, Haebara, and Stocking-Lord methods for dichotomous
        (1PL, 2PL and 3PL) and/or polytomous (graded response, partial
        credit/generalized partial credit, nominal, and multiple-choice
        model) items.  The multidimensional methods include the least
        squares method and extensions of the Haebara and Stocking-Lord
        method using single or multiple dilation parameters for
        multidimensional extensions of all the unidimensional
        dichotomous and polytomous item response models.  The package
        also includes functions for importing item and/or ability
        parameters from common IRT software, conducting IRT true score
        and observed score equating, and plotting item response
        curves/surfaces, vector plots, information plots, and comparison 
        plots for examining parameter drift.
Provide function that reads binary genotype produced by 'PLINK' <https://www.cog-genomics.org/plink/1.9/input#bed> into a R matrix, or scan the genotype one variant at a time like apply(), it also provides functions that reads and writes genotype relatedness/kinship matrices created by 'PLINK' <https://www.cog-genomics.org/plink/1.9/distance#make_rel> or 'GCTA' <https://cnsgenomics.com/software/gcta/#MakingaGRM>. Currently it does not support writing back into 'PLINK' binary, it is best used for bringing data produced by 'PLINK' and 'GCTA' into R environment.
Genotyping arrays enable the direct measurement of an individuals
    genotype at thousands of markers. 'plinkQC' facilitates genotype quality
    control for genetic association studies as described by Anderson and
    colleagues (2010) <doi:10.1038/nprot.2010.116>. It makes 'PLINK' basic
    statistics (e.g. missing genotyping rates per individual, allele frequencies
    per genetic marker) and relationship functions accessible from 'R' and
    generates a per-individual and per-marker quality control report.
    Individuals and markers that fail the quality control can subsequently be
    removed to generate a new, clean dataset. Removal of individuals based on
    relationship status is optimised to retain as many individuals as possible
    in the study.
PLIS is a multiple testing procedure for testing several
        groups of hypotheses. Linear dependency is expected from the
        hypotheses within the same group and is modeled by hidden
        Markov Models. It is noted that, for PLIS, a smaller p value
        does not necessarily imply more significance because of
        dependency among the hypotheses. A typical application of PLIS
        is to analyze genome wide association studies datasets, where
        SNPs from the same chromosome are treated as a group and
        exhibit strong linear genomic dependency.
A set of estimators for models and (robust) covariance matrices, and tests for panel data
             econometrics, including within/fixed effects, random effects, between, first-difference, 
             nested random effects as well as instrumental-variable (IV) and Hausman-Taylor-style models,
             panel generalized method of moments (GMM) and general FGLS models,
             mean groups (MG), demeaned MG, and common correlated effects (CCEMG) and pooled (CCEP) estimators
             with common factors, variable coefficients and limited dependent variables models.
             Test functions include model specification, serial correlation, cross-sectional dependence,
             panel unit root and panel Granger (non-)causality. Typical references are general econometrics 
             text books such as Baltagi (2021), Econometric Analysis of Panel Data, ISBN-13:978-3-030-53952-8, 
             Hsiao (2014), Analysis of Panel Data <doi:10.1017/CBO9781139839327>, and Croissant and Millo (2018), 
             Panel Data Econometrics with R, ISBN-13:978-1-118-94918-4.
Fit finite mixtures of Plackett-Luce models for partial top rankings/orderings within the Bayesian framework. It provides MAP point estimates via EM algorithm and posterior MCMC simulations via Gibbs Sampling. It also fits MLE as a special case of the noninformative Bayesian analysis with vague priors. In addition to inferential techniques, the package assists other fundamental phases of a model-based analysis for partial rankings/orderings, by including functions for data manipulation, simulation, descriptive summary, model selection and goodness-of-fit evaluation. Main references on the methods are Mollica and Tardella (2017) <doi.org/10.1007/s11336-016-9530-0> and Mollica and Tardella (2014) <doi/10.1002/sim.6224>.
Utilizes the 'lme4' and 'optimx' packages (previously the optim()
    function from 'stats') to estimate (generalized) linear mixed models (GLMM)
    with factor structures using a profile likelihood approach, as outlined in
    Jeon and Rabe-Hesketh (2012) <doi:10.3102/1076998611417628> and
    Rockwood and Jeon (2019) <doi:10.1080/00273171.2018.1516541>.
    Factor analysis and item response models can be extended to allow
    for an arbitrary number of nested and crossed random effects,
    making it useful for multilevel and cross-classified models.
Performs bivariate composite likelihood and full
        information maximum likelihood estimation for polytomous
        logit-normit (graded logistic) item response theory (IRT)
        models.
The Poisson-lognormal model and variants (Chiquet, Mariadassou and Robin, 
    2020 <doi:10.1101/2020.10.07.329383>) can be used for 
    a variety of multivariate problems when count data are at play, including 
    principal component analysis for count data, discriminant analysis, model-based clustering and 
    network inference. Implements variational algorithms to fit such models accompanied with a set of 
    functions for visualization and diagnostic.
A simple header-only logging library for C++.
  Add 'LinkingTo: plogr' to 'DESCRIPTION', and '#include <plogr.h>' in your C++ modules to use it.
Multivariate ordered probit model, i.e. the extension of the scalar ordered probit model where the observed variables have dimension greater than one. Estimation of the parameters is done via maximization of the pairwise likelihood, a special case of the composite likelihood obtained as product of bivariate marginal distributions. The package uses the Fortran 77 subroutine SADMVN by Alan Genz, with minor adaptations made by Adelchi Azzalini in his "mvnormt" package for evaluating the two-dimensional Gaussian integrals involved in the pairwise log-likelihood. Optimization of the latter objective function  is performed via quasi-Newton box-constrained optimization algorithm, as implemented in nlminb.
A method for the quantitative prediction with much predictors. 
    This package provides functions to construct the quantitative prediction 
    model with less overfitting and robust to noise.
Visualizes a matrix object plainly as heatmap. It provides S3 functions to plot simple matrices and loading matrices.
Functions for viewing 2-D and 3-D data, including perspective plots, slice plots, surface plots, scatter plots, etc. Includes data sets from oceanography.
The 'rgl' implementation of plot3D functions.
An implementation of the ternary plot for interpreting regression
    coefficients of trinomial regression models, as proposed in Santi, Dickson
    and Espa (2019) <doi:10.1080/00031305.2018.1442368>. Ternary plots can be
    drawn using either 'ggtern' package (based on 'ggplot2') or 'Ternary'
    package (based on standard graphics).
Proof of concept for implementing grammar of graphics using base plot. The bbplot() function initializes a 'bbplot' object to store input data, aesthetic mapping, a list of layers and theme elements. The object will be rendered as a graphic using base plot command if it is printed.
This function plots a contour line with a user-defined probability and tightness of fit.
Easily visualize and animate 'tabledap' and 'griddap' objects obtained via the 'rerddap' package in a simple one-line command,  using either base graphics or 'ggplot2' graphics. 'plotdap' handles extracting and reshaping the data,  map projections and continental outlines.  Optionally the data can be animated through time using the 'gganmiate' package.
Provides a ggplot2 front end to plot summary statistics on danish provinces,
    regions, municipalities, and zipcodes. The needed geoms of each of the four levels are 
    inherent in the package, thus making these types of plots easy for the user. This is essentially 
    an updated port of the previously available 'mapDK' package by Sebastian Barfort.
When analyzing data, plots are a helpful tool for visualizing data and interpreting statistical models. This package provides a set of simple tools for building plots incrementally, starting with an empty plot region, and adding bars, data points, regression lines, error bars, gradient legends, density distributions in the margins, and even pictures. The package builds further on R graphics by simply combining functions and settings in order to reduce the amount of code to produce for the user. As a result, the package does not use formula input or special syntax, but can be used in combination with default R plot functions. Note: Most of the functions were part of the package 'itsadug', which is now split in two packages: 1. the package 'itsadug', which contains the core functions for visualizing and evaluating nonlinear regression models, and 2. the package 'plotfunctions', which contains more general plot functions.
The main function, plot_GMM, is used for plotting output from Gaussian mixture models (GMMs), 
    including both densities and overlaying mixture weight component curves from the fit GMM. The package
    also include the function, plot_cut_point, which plots the cutpoint (mu) from the GMM over a histogram
    of the distribution with several color options. Finally, the package includes the function, plot_mix_comps, 
    which is used in the plot_GMM function, and can be used to create a custom plot for overlaying mixture 
    component curves from GMMs. For the plot_mix_comps function, usage most often will be specifying 
    the "fun" argument within "stat_function" in a ggplot2 object.
An extension to 'ggplot2' and 'magick'. It contains 
    three groups of functions: Functions in the first group draw 'ggplot2' - based plots: geom_shading_bar() draws barplot 
	with shading colors in each bar. geom_rect_cm(), geom_circle_cm() and geom_ellipse_cm() draw rectangles, circles 
	and ellipses with centimeter as their unit. Thus their sizes do not change when the coordinate system or the aspect ratio 
	changes. annotation_transparent_text() draws labels with transparent texts. annotation_shading_polygon() draws irregular 
	polygons with shading colors. Functions in the second group generate coordinates for regular shapes and make linear transformations. Functions in the third group are 'magick' - based functions facilitating image processing.
Hidden Markov Models are useful for modeling
 sequential data. This package provides several functions
 implemented in C++ for explaining the algorithms used for
 Hidden Markov Models (forward, backward, decoding,
 learning).
Examines the characteristics of a data frame and a formula to
    automatically choose the most suitable type of plot out of the following supported
    options: scatter, violin, box, bar, density, hexagon bin, spine plot, and
    heat map. The aim of the package is to let the user focus on what to plot,
    rather than on the "how" during exploratory data analysis. It also automates
    handling of observation weights, logarithmic axis scaling, reordering of
    factor levels, and overlaying smoothing curves and median lines. Plots are
    drawn using 'ggplot2'.
Create interactive web graphics from 'ggplot2' graphs and/or a custom interface to the (MIT-licensed) JavaScript library 'plotly.js' inspired by the grammar of graphics.
Includes 'JavaScript' files that allow 'plotly' maps to render without an internet connection.
Markov chain Monte Carlo diagnostic plots. The purpose of the
  package is to combine existing tools from the 'coda' and 'lattice' packages,
  and make it easy to adjust graphical details.
Plot marginal effects for interactions estimated
    from linear models.
The main function, plot_mm(), is used for plotting output from mixture models, 
    including both densities and overlaying mixture weight component curves from the fit models. In line with the
    tidyverse, the package also includes the plot_cut_point() function to visualize the cutpoint (mu) from the model
    over a histogram of the data density with several color options. Finally, the package includes the plot_mix_comps() 
    helper function, which is used for both added customization as well as in the plot_mm() function. 
    Supported model objects include: 'mixtools', 'EMCluster', and 'flexmix', with more from each forthcoming. 
    Supported mixture model specifications include mixtures of univariate Gaussians, multivariate Gaussians, Gammas, 
    logistic regressions, linear regressions, and Poisson regressions.
Plot model surfaces for a wide variety of models
        using partial dependence plots and other techniques.
        Also plot model residuals and other information on the model.
Plot principal component histograms around a bivariate
        scatter plot.
Useful set of tools for plotting network diagrams in any kind of project.
Creates an envelope around a set of plotted points. The
        envelope is compact with a boundary that is continuous, smooth
        and convex. Each point is represented as a circle and the
        circles and connecting lines are the solution to the multiple
        pulley problem. This method can be used to highlight regions in
        a two-dimensional space.
Lots of plots, various labeling, axis and color scaling functions.
Most ROC curve plots obscure the cutoff values and inhibit
    interpretation and comparison of multiple curves. This attempts to address
    those shortcomings by providing plotting and interactive tools. Functions
    are provided to generate an interactive ROC curve plot for web use, and
    print versions. A Shiny application implementing the functions is also
    included.
Functions for making visual exploratory data analysis with nested data easier.
Figures rendered on graphics devices are usually 
 rescaled to fit pre-determined device dimensions. 'plotscale' 
 implements the reverse: desired plot dimensions are specified 
 and device dimensions are calculated to accommodate marginal
 material, giving consistent proportions for plot elements.
 Default methods support grid graphics such as lattice and ggplot.
 See "example('devsize')" and "vignette('plotscale')".
Contains a graphical user interface to generate the diagnostic
    plots proposed by Bauer (2005; <doi:10.1207/s15328007sem1204_1>), 
    Pek & Chalmers (2015; <doi:10.1080/10705511.2014.937790>), and
    Pek, Chalmers, R. Kok, & Losardo (2015; <doi:10.3102/1076998615589129>) to investigate
    nonlinear bivariate relationships in latent regression models using structural
    equation mixture models (SEMMs).
Small self-contained plots for use in larger plots or to
    delegate plotting in other functions. Also contains a number of
    alternative color palettes and HSL color space based tools to modify
    colors or palettes.
Prediction limits for the Poisson distribution
  are produced from both frequentist and Bayesian viewpoints. Limiting results
  are provided in a Bayesian setting with uniform, Jeffreys and gamma as prior
  distributions. More details on the methodology are discussed in Bejleri and
  Nandram (2018) <doi:10.1080/03610926.2017.1373814>.
Fit Log Linear by Linear Association models and Rasch family models by pseudolikelihood estimation
Fitting power logit regression models for bounded
  continuous data, in which the density generator may be normal, Student-t, 
  power exponential, slash, hyperbolic, sinh-normal, or type II logistic. 
  Diagnostic tools associated with the fitted model, such as the residuals, 
  local influence measures, leverage measures, and goodness-of-fit statistics,
  are implemented. The estimation process follows the maximum likelihood approach
  and, currently, the package supports two types of estimators: the usual maximum 
  likelihood estimator and the penalized maximum likelihood estimator. More details
  about power logit regression models are described in 
  Queiroz and Ferrari (2022) <arXiv:2202.01697>.
This package provides statistical inference tools applied to Partial Linear Regression (PLR)
 models. Specifically, point estimation, confidence intervals estimation, bandwidth selection, goodness-of-fit tests and analysis of
 covariance are considered. 
 Kernel-based methods, combined with ordinary least squares estimation, are used and time series 
 errors are allowed. In addition, these techniques are also implemented for both parametric (linear) 
 and nonparametric regression models.
Multivariate regression methods
	Partial Least Squares Regression (PLSR), Principal Component
	Regression (PCR) and Canonical Powered Partial Least Squares (CPPLS).
plsdepot contains different methods for PLS analysis of
        one or two data tables such as Tucker's Inter-Battery, NIPALS,
        SIMPLS, SIMPLS-CA, PLS Regression, and PLS Canonical Analysis.
        The main reference for this software is the awesome book (in
        French) 'La Regression PLS: Theorie et Pratique' by Michel
        Tenenhaus.
The plsdof package provides Degrees of Freedom estimates
        for Partial Least Squares (PLS) Regression. Model selection for
        PLS is based on various information criteria (aic, bic, gmdl)
        or on cross-validation. Estimates for the mean and covariance
        of the PLS regression coefficients are available. They allow
        the construction of approximate confidence intervals and the
        application of test procedures (Kramer and Sugiyama 
        2012 <doi:10.1198/jasa.2011.tm10107>).
        Further, cross-validation procedures for Ridge Regression and 
        Principal Components Regression are available.
Routines for PLS-based genomic analyses,
        implementing PLS methods for classification with
        microarray data and prediction of transcription factor
        activities from combined ChIP-chip analysis. The >=1.2-1
        versions include two new classification methods for microarray
        data: GSIM and Ridge PLS. The >=1.3 versions includes a
        new classification method combining variable selection and
        compression in logistic regression context: logit-SPLS; and
        an adaptive version of the sparse PLS.
Estimation, hypothesis tests, and variable selection in partially linear single-index models. Please see H. (2010) at <doi:10.1214/10-AOS835> for more details.
Bindings for additional regression models for use with the
    'parsnip' package, including ordinary and spare partial least squares
    models for regression and classification (Rohart et al (2017)
    <doi:10.1371/journal.pcbi.1005752>).
Fit a model with potentially many linear and smooth predictors. Interaction
    effects can also be quantified. Variable selection is done using penalisation. For l1-type penalties
    we use iterative steps alternating between using linear predictors (lasso) and smooth predictors
    (generalised additive model).
Provides partial least squares analysis for the analysis of the relation
    between two high-dimensional data sets. Includes permutation testing and
    bootstrapping for resulting latent variables (following McIntosh & Lobaugh (2004) <doi:10.1016/j.neuroimage.2004.07.020>)
    and several visualization functions.
Provides Partial least squares Regression for (weighted) beta regression models (Bertrand 2013,  <http://journal-sfds.fr/article/view/215>) and k-fold cross-validation of such models using various criteria. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.
Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models in high dimensional settings <doi:10.1093/bioinformatics/btu660>, Bastien, P., Bertrand, F., Meyer N., Maumy-Bertrand, M. (2015), Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data, Bioinformatics, 31(3):397-404. Cross validation criteria were studied in <arXiv:1810.02962>, Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data.
Provides (weighted) Partial least squares Regression for generalized linear models and repeated k-fold cross-validation of such models using various criteria <arXiv:1810.01005>. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.
Interfaces and methods for variable selection in Partial Least
    Squares. The methods include filter methods, wrapper methods and embedded
    methods. Both regression and classification is supported.
Calculates and depicts probabilistic long-term effects
    in binary models with temporal dependence variables. The package performs
    two tasks. First, it calculates the change in the probability of the event
    occurring given a change in a theoretical variable. Second, it calculates
    the rolling difference in the future probability of the event for two
    scenarios: one where the event occurred at a given time and one where the
    event does not occur. The package is consistent with the recent movement to
    depict meaningful and easy-to-interpret quantities of interest with the
    requisite measures of uncertainty. It is the first to make it easy for
    researchers to interpret short- and long-term effects of explanatory
    variables in binary autoregressive models, which can have important
    implications for the correct interpretation of these models.
Converts English phrases to singular or plural form based on
    the length of an associated vector.  Contains helper functions to
    create natural language lists from vectors and to include the length
    of a vector in natural language.
Kernel density estimation with global bandwidth selection
	     via "plug-in".
Gives the ability to automatically generate and serve an HTTP API
    from R functions using the annotations in the R documentation around your
    functions.
Gives the ability to automatically deploy a plumber API
    from R functions on 'DigitalOcean' and other cloud-based servers.
Build 'Plumber' APIs that can be used in 'Tableau' workbooks.
    Annotations in R comments allow APIs to conform to the 'Tableau Analytics
    Extension' specification, so that R code can be used to power 'Tableau'
    workbooks.
The base R data.frame, like any vector, is
    copied upon modification. This behavior is at odds with
    that of GUIs and interactive graphics. To rectify this,
    plumbr provides a mutable, dynamic tabular data model.
    Models may be chained together to form the complex
    plumbing necessary for sophisticated graphical
    interfaces. Also included is a
    general framework for linking datasets; an typical
    use case would be a linked brush.
Tools are provided to create plural, singular and regular forms of 
    English words along with tools to augment the built-in rules to fit 
    specialized needs. Core functionality is based on a JavaScript library, 
    <https://github.com/blakeembrey/pluralize>.
Efficient procedures for fitting an entire regression
        sequences with different model types.
Retrieves a 'pluscode' by inputting latitude and longitude. Includes additional functions to retrieve neighbouring 'pluscodes'.
plusser provides an API interface to Google+ so that posts,
    profiles and pages can be automatically retrieved.
A set of tools that solves a common set of problems: you need
    to break a big problem down into manageable pieces, operate on each
    piece and then put all the pieces back together.  For example, you
    might want to fit a model to each spatial location or time point in
    your study, summarise data by panels or collapse high-dimensional
    arrays to simpler summary statistics. The development of 'plyr' has
    been generously supported by 'Becton Dickinson'.
Performs Penalized Multivariate Analysis: a penalized
        matrix decomposition, sparse principal components analysis,
        and sparse canonical correlation analysis, described in
        Witten, Tibshirani and Hastie (2009)
        <doi:10.1093/biostatistics/kxp008> and Witten and Tibshirani
        (2009) Extensions of sparse canonical correlation analysis,
        with applications to genomic data
        <doi:10.2202/1544-6115.1470>.
We innovatively defined a pathway mutation accumulate perturbation score (PMAPscore) to reflect the position and the cumulative effect of the genetic mutations at the pathway level. Based on the PMAPscore of pathways, identified prognosis-related pathways altered by somatic mutation and predict immunotherapy efficacy by constructing a multiple-pathway-based risk model (Tarca, Adi Laurentiu et al (2008) <doi:10.1093/bioinformatics/btn577>).
Monte Carlo based model choice for applied phylogenetics of
    continuous traits. Method described in  Carl Boettiger, Graham Coop,
    Peter Ralph (2012) Is your phylogeny informative? Measuring
    the power of comparative methods, Evolution 66 (7)
    2240-51. <doi:10.1111/j.1558-5646.2011.01574.x>.
Parsimonious Mixtures of Contaminated Gaussian Distributions
Aims to utilize model-based clustering (unsupervised)
        for high dimensional and ultra large data, especially in a distributed
        manner. The code employs 'pbdMPI' to perform a
        expectation-gathering-maximization algorithm
        for finite mixture Gaussian
        models. The unstructured dispersion matrices are assumed in the
        Gaussian models. The implementation is default in the single program
        multiple data programming model. The code can be executed
        through 'pbdMPI' and MPI' implementations such as 'OpenMPI'
        and 'MPICH'.
        See the High Performance Statistical Computing website
	<https://snoweye.github.io/hpsc/>
	for more information, documents and examples.
Note, that the 'PMCMR' package is superset
	     by the novel 'PMCMRplus' package. The 'PMCMRplus'
	     package contains all functions from 
	     'PMCMR' and many more parametric and non-parametric
	     multiple comparison procedures, one-factorial trend
	     tests, as well as improved method functions, 
	     such as print, summary and plot. 
	     The 'PMCMR' package is no longer maintained,
	     but kept for compatibility of reverse depending
	     packages for some time.
For one-way layout experiments the one-way ANOVA can
	     be performed as an omnibus test. All-pairs multiple comparisons 
	     tests (Tukey-Kramer test, Scheffe test, LSD-test) 
	     and many-to-one tests (Dunnett test) for normally distributed 
	     residuals and equal within variance are available. Furthermore,
	     all-pairs tests (Games-Howell test, Tamhane's T2 test, 
	     Dunnett T3 test, Ury-Wiggins-Hochberg test) and many-to-one
	     (Tamhane-Dunnett Test) for normally distributed residuals 
	     and heterogeneous variances are provided. Van der Waerden's normal
	     scores test for omnibus, all-pairs and many-to-one tests is
	     provided for non-normally distributed residuals and homogeneous
	     variances. The Kruskal-Wallis, BWS and Anderson-Darling
	     omnibus test and all-pairs tests
	     (Nemenyi test, Dunn test, Conover test, Dwass-Steele-Critchlow-
	     Fligner test) as well as many-to-one (Nemenyi test, Dunn test,
	     U-test) are given for the analysis of variance by ranks. 
             Non-parametric trend tests (Jonckheere test, Cuzick test,
	     Johnson-Mehrotra test, Spearman test) are included. 
	     In addition, a Friedman-test for one-way ANOVA with repeated 
	     measures on ranks (CRBD) and Skillings-Mack test for unbalanced 
	     CRBD is provided with consequent all-pairs tests (Nemenyi test, 
	     Siegel test, Miller test, Conover test, Exact test)
	     and many-to-one tests (Nemenyi test, Demsar test, Exact test). 
	     A trend can be tested with Pages's test. Durbin's test 
	     for a two-way balanced incomplete block design (BIBD) is given 
	     in this package as well as Gore's test for CRBD with multiple
	     observations per cell is given.  Outlier tests, Mandel's k- and
	     h statistic as well as functions for Type I error and Power 
	     analysis as well as generic summary, print and plot methods 
             are provided.
Paired mass distance (PMD) analysis proposed in Yu, Olkowicz and Pawliszyn (2018) <doi:10.1016/j.aca.2018.10.062> for gas/liquid chromatography–mass spectrometry (GC/LC-MS) based non-targeted analysis. PMD analysis including GlobalStd algorithm and structure/reaction directed analysis. GlobalStd algorithm could found independent peaks in m/z-retention time profiles based on retention time hierarchical cluster analysis and frequency analysis of paired mass distances within retention time groups. Structure directed analysis could be used to find potential relationship among those independent peaks in different retention time groups based on frequency of paired mass distances. Reactomics analysis could also be performed to build PMD network, assign sources and make biomarker reaction discovery. GUIs for PMD analysis is also included as 'shiny' applications.
Allows to download current and historical METAR weather reports
  extract and parse basic parameters and present main weather information. 
  Current reports are downloaded from Aviation Weather Center 
  <https://www.aviationweather.gov/metar> and historical reports from
  Iowa Environmental Mesonet web page of Iowa State University
  ASOS-AWOS-METAR <http://mesonet.agron.iastate.edu/AWOS/>.
Routines for state estimate in a linear
    Gaussian state space model and a simple stochastic volatility model using
    particle filtering. Parameter inference is also carried out in these models
    using the particle Metropolis-Hastings algorithm that includes the particle
    filter to provided an unbiased estimator of the likelihood. This package is
    a collection of minimal working examples of these algorithms and is only
    meant for educational use and as a start for learning to them on your own.
Check available classification and regression data sets from the PMLB repository and download them.
    The PMLB repository (<https://github.com/EpistasisLab/pmlbr>) contains a curated collection of data sets for evaluating and comparing machine learning algorithms.
    These data sets cover a range of applications, and include binary/multi-class classification problems and 
    regression problems, as well as combinations of categorical, ordinal, and continuous features.
    There are currently over 150 datasets included in the PMLB repository.
Given a sample with additive measurement error, the package estimates the deconvolution density - that is, the density of the underlying distribution of the sample without measurement error. The method maximises the log-likelihood of the estimated density, plus a quadratic smoothness penalty. The distribution of the measurement error can be either a known family, or can be estimated from a "pure error" sample. For known error distributions, the package supports Normal, Laplace or Beta distributed error. For unknown error distribution, a pure error sample independent from the data is used.
The Predictive Model Markup Language (PMML) is an XML-based language which provides a way for applications to define machine learning, statistical and data mining models and to share models between PMML compliant applications. More information about the PMML industry standard and the Data Mining Group can be found at <http://dmg.org/>. The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products. The package isofor (used for anomaly detection) can be installed with devtools::install_github("gravesee/isofor").
Allows for data to be transformed before using it to construct models. Builds structures to allow functions in the PMML package to
    output transformation details in addition to the model in the resulting PMML file. The Predictive Model Markup Language (PMML) is an XML-based language which provides a way for applications to define machine learning, statistical and data mining models and to share models between PMML compliant applications. More information about the PMML industry standard and the Data Mining Group can be found at <http://www.dmg.org>. The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products, which integrate with web services, relational database systems and deploy natively on Hadoop in conjunction with Hive, Spark or Storm, as well as allow predictive analytics to be executed for IBM z Systems mainframe applications and real-time, streaming analytics platforms.
Provides a simple interface for extracting various elements from
  the publicly available PubMed XML files, incorporating PubMed's regular
  updates, and combining the data with the NIH Open Citation Collection. See
  Schoenbachler and Hughey (2021) <doi:10.7717/peerj.11071>.
Descriptive statistics (mean rank, pairwise frequencies, and marginal matrix), Analytic Hierarchy Process models (with Saaty's and Koczkodaj's inconsistencies), probability models (Luce models, distance-based models, and rank-ordered logit models) and visualization with multidimensional preference analysis for ranking data are provided. Current, only complete rankings are supported by this package.
Computes the minimum sample size required for the development of a new multivariable prediction model using the criteria proposed by Riley et al. (2018) <doi: 10.1002/sim.7992>. pmsampsize can be used to calculate the minimum sample size for the development of models with continuous, binary or survival (time-to-event) outcomes. Riley et al. (2018) <doi: 10.1002/sim.7992> lay out a series of criteria the sample size should meet. These aim to minimise the overfitting and to ensure precise estimation of key parameters in the prediction model.
Implements multinomial CDF (P(N1<=n1, ..., Nk<=nk)) and tail probabilities (P(N1>n1, ..., Nk>nk)), as well as probabilities with both constraints (P(l1<N1<=u1, ..., lk<Nk<=uk)). Uses a method suggested by Bruce Levin (1981) <doi:10.1214/aos/1176345593>.
Provides an R implementation of the Particle Metropolis within
    Gibbs sampler for model parameter, covariance matrix and random effect
    estimation. A more general implementation of the sampler based on the paper
    by Gunawan, D., Hawkins, G. E., Tran, M. N., Kohn, R., & Brown, S. D.
    (2020) <doi:10.1016/j.jmp.2020.102368>. An HTML tutorial document describing
    the package is available at <https://newcastlecl.github.io/samplerDoc/> and
    includes several detailed examples, some background and troubleshooting
    steps.
Functions and examples for 'Portfolio
  Management with R': backtesting investment and
  trading strategies, computing profit/loss and
  returns, analysing trades, handling lists of
  transactions, reporting, and more.
Generate nicely formatted HTML tables to display estimation results for
  pharmacometric models.
Pharmacometric tools for common data analytical tasks; closed-form solutions for calculating concentrations at given 
    times after dosing based on compartmental PK models (1-compartment, 2-compartment and 3-compartment, covering infusions, zero- 
    and first-order absorption, and lag times, after single doses and at steady state, per Bertrand & Mentre (2008) 
    <http://lixoft.com/wp-content/uploads/2016/03/PKPDlibrary.pdf>); parametric simulation from NONMEM-generated parameter estimates 
    and other output; and parsing, tabulating and plotting results generated by Perl-speaks-NONMEM (PsN).
Provides tools for downloading, reading and analyzing the PNADC,
	a household survey from Brazilian Institute of Geography and Statistics - IBGE.
	The data must be downloaded from the official website <https://www.ibge.gov.br/>. 
	Further analysis must be made using package 'survey'.
This package provides an easy and simple way to read, write and display bitmap images stored in the PNG format. It can read and write both files and in-memory raw vectors.
An R package for Probit-Normal Marginalized Transition
        Random Effects Models
Provides tools for downloading, reading and analyzing the PNS,
	a household survey from Brazilian Institute of Geography and Statistics - IBGE.
	The data must be downloaded from the official website <https://www.ibge.gov.br/>. 
	Further analysis must be made using package 'survey'.
PNW-Inspired Palettes for 'R' data visualizations. Palettes are variable 
	in length and checked for colorblind accessibility from hue, saturation, 
	and lightness value scaling using the 'Chroma.js  Color Palette Helper' 
	<https://gka.github.io/palettes/>.
Presence-only model with Elastic Net penalty is a regularized generalized linear model training on the presence-absence response. This package provides functions for tuning and fitting the presence-only model. The presence-only model can be used to predict regulatory effects of genetic variants at sequence-level resolution by integrating a large number of epigenetic features and massively parallel reporter assays (MPRAs).
Computes the optimal flow, Nash flow and the Price of Anarchy for any routing game defined within the game theoretical framework. The input is a routing game in the form of it’s cost and flow functions. Then transforms this into an optimisation problem, allowing both Nash and Optimal flows to be solved by nonlinear optimisation. See <https://en.wikipedia.org/wiki/Congestion_game> and Knight and Harper (2013) <doi:10.1016/j.ejor.2013.04.003> for more information.
Functions that interface with the 'Pocket' API (<https://getpocket.com/developer/>). Allows the user to get, add, and modify items in their own 'Pocket' account.
Penalized orthogonal-components regression (POCRE) is a supervised dimension reduction method for high-dimensional data. It sequentially constructs orthogonal components (with selected features) which are maximally correlated to the response residuals. POCRE can also construct common components for multiple responses and thus build up latent-variable models.
Provides functions to implement and simulate the partial order continual reassessment method (PO-CRM) of Wages, Conaway and O'Quigley (2011) <doi:10.1177/1740774511408748> for use in Phase I trials of combinations of agents. Provides a function for generating a set of initial guesses (skeleton) for the toxicity probabilities at each combination that correspond to the set of possible orderings of the toxicity probabilities specified by the user.
This tool computes the probability of detection (POD) curve and the limit of detection (LOD), i.e. the number of copies of the target DNA sequence required to ensure a 95 % probability of detection (LOD95). Other quantiles of the LOD can be specified.
    This is a reimplementation of the mathematical-statistical modelling of the validation of qualitative polymerase chain reaction (PCR) methods within a single laboratory as provided by the commercial tool 'PROLab' <http://quodata.de/>. The modelling itself has been described by Uhlig et al. (2015) <doi:10.1007/s00769-015-1112-9>.
Set of functions that implement the PoDBAY method, described in the publication 'A method to estimate probability of disease and vaccine efficacy from clinical trial immunogenicity data' by Julie Dudasova, Regina Laube, Chandni Valiathan, Matthew C. Wiener, Ferdous Gheyas, Pavel Fiser, Justina Ivanauskaite, Frank Liu and Jeffrey R. Sachs (NPJ Vaccines, 2021), <doi:10.1038/s41541-021-00377-6>.
Attempts to clean optical character recognition (OCR) errors in 
  legacy Scottish Post Office Directories. Further attempts to match records from 
  trades and general directories.
The poems package provides a framework of interoperable R6 classes 
    (Chang, 2020, <https://CRAN.R-project.org/package=R6>) for building ensembles 
    of viable models via the pattern-oriented modeling (POM) approach (Grimm et al.,
    2005, <doi:10.1126/science.1116681>). The package includes classes for 
    encapsulating and generating model parameters, and managing the POM workflow. 
    The workflow includes: model setup; generating model parameters via Latin 
    hyper-cube sampling (Iman & Conover, 1980, <doi:10.1080/03610928008827996>); 
    running multiple sampled model simulations; collating summary results; and 
    validating and selecting an ensemble of models that best match known patterns. 
    By default, model validation and selection utilizes an approximate Bayesian 
    computation (ABC) approach (Beaumont et al., 2002, 
    <doi:10.1093/genetics/162.4.2025>), although alternative user-defined 
    functionality could be employed. The package includes a spatially explicit 
    demographic population model simulation engine, which incorporates default 
    functionality for density dependence, correlated environmental stochasticity, 
    stage-based transitions, and distance-based dispersal. The user may customize
    the simulator by defining functionality for translocations, harvesting, 
    mortality, and other processes, as well as defining the sequence order for the 
    simulator processes. The framework could also be adapted for use with other 
    model simulators by utilizing its extendable (inheritable) base classes.
Estimate large covariance matrices in approximate factor
        models by thresholding principal orthogonal complements.
Provides tools for downloading, reading and analyzing the POF,
	a household survey from Brazilian Institute of Geography and Statistics - IBGE.
	The data must be downloaded from the official website <https://www.ibge.gov.br/>. 
	Further analysis must be made using package 'survey'.
Bayesian variable selection for regression models of under-reported
    count data as well as for (overdispersed) Poisson, negative binomal and
    binomial logit regression models using spike and slab priors.
The data sets used in the online course ,,PogromcyDanych''. You can process data in many ways. The course Data Crunchers will introduce you to this variety. For this reason we will work on datasets of different size (from several to several hundred thousand rows), with various level of complexity (from two to two thousand columns) and prepared in different formats (text data, quantitative data and qualitative data). All of these data sets were gathered in a single big package called PogromcyDanych to facilitate access to them. It contains all sorts of data sets such as data about offer prices of cars, results of opinion polls, information about changes in stock market indices, data about names given to newborn babies, ski jumping results or information about outcomes of breast cancer patients treatment.
Implementation of both the exact and approximation methods for computing the cdf of the Poisson binomial distribution as described in Hong (2013) <doi: 10.1016/j.csda.2012.10.006>. It also provides the pmf, quantile function, and random number generation for the Poisson binomial distribution. The C code for fast Fourier transformation (FFT) is written by R Core Team (2019)<https://www.R-project.org/>, which implements the FFT algorithm in Singleton (1969) <doi: 10.1109/TAU.1969.1162042>.
Implements the methods described in the paper, Witten (2011) Classification and Clustering of Sequencing Data using a Poisson Model, Annals of Applied Statistics 5(4) 2493-2518.
Functions for obtaining the density, random deviates 
             and maximum likelihood estimates of the Poisson lognormal 
             distribution and the bivariate Poisson lognormal distribution.
Provides an implementation of a rare variant association test that utilizes protein tertiary structure to increase signal and to identify likely causal variants. Performs structure-guided collapsing, which leads to local tests that borrow information from neighboring variants on a protein and that provide association information on a variant-specific level. For details of the implemented method see West, R. M.,  Lu, W., Rotroff, D. M., Kuenemann, M., Chang, S-M., Wagner M. J., Buse, J. B., Motsinger-Reif, A., Fourches, D., and Tzeng, J-Y. (2019) <doi:10.1371/journal.pcbi.1006722>.
Validate data in data frames, 'tibble' objects, 'Spark'
    'DataFrames', and database tables. Validation pipelines can be made using
    easily-readable, consecutive validation steps. Upon execution of the
    validation plan, several reporting options are available. User-defined
    thresholds for failure rates allow for the determination of appropriate
    reporting actions. Many other workflows are available including an
    information management workflow, where the aim is to record, collect, and
    generate useful information on data tables.
The function pointdensity returns a density count and the temporal average for
    every point in the original list. The dataframe returned includes four
    columns: lat, lon, count, and date_avg. The "lat" column is the original
    latitude data; the "lon" column is the original longitude data; the "count"
    is the density count of the number of points within a radius of
    radius*grid_size (the neighborhood); and the date_avg column includes the
    average date of each point in the neighborhood.
Estimate specification models for the state-dependent level of an optimal quantile/expectile forecast.
  Wald Tests and the test of overidentifying restrictions are implemented. Plotting of the estimated specification model is possible.
  The package contains two data sets with forecasts and realizations: the daily accumulated precipitation at London, UK from the high-resolution model of the
  European Centre for Medium-Range Weather Forecasts (ECMWF, <https://www.ecmwf.int/>) and GDP growth Greenbook data by the US Federal Reserve.
  See Schmidt, Katzfuss and Gneiting (2015) <arXiv:1506.01917> for more details on the identification and estimation of a directive behind a point forecast.
R has no built-in pointer functionality. The 'pointr' package fills this gap and lets you create pointers to R objects, including subsets of dataframes. This makes your R code more readable and maintainable.
Functions to calculate and plot event and pointer years as well as resilience indices. Designed for dendroecological applications, but also suitable to analyze patterns in other ecological time series.
Read and write PO and POT files, for package translations.
Generation of multiple count, binary and continuous variables simultaneously 
             given the marginal characteristics and association structure. Throughout the package,
             the word 'Poisson' is used to imply count data under the assumption of Poisson distribution. 
             The details of the method are explained in Amatya et al. (2015) <DOI:10.1080/00949655.2014.953534>.
Provides the probability, distribution, and quantile functions and random number generator for the Poisson-Binomial distribution.  This package relies on FFTW to implement the discrete Fourier transform, so that it is much faster than the existing implementation of the same algorithm in R.
Generation of multiple count, binary and ordinal variables simultaneously 
             given the marginal characteristics and association structure. Throughout the package,
             the word 'Poisson' is used to imply count data under the assumption of Poisson distribution. The details of the method are explained in Amatya, A. and Demirtas, H. (2015) <DOI:10.1080/00949655.2014.953534>.
Generation of a chosen number of count, binary, ordinal, and continuous random variables, with specified correlations and marginal properties. The details of the method are explained in Demirtas (2012) <DOI:10.1002/sim.5362>.
Generation of multiple count, binary, ordinal and normal variables simultaneously given the marginal characteristics and association structure.
             The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>.
Functions to create confidence intervals for ratios of Poisson
    rates under misclassification using double sampling.
Computation of robust standard errors of Poisson fixed effects 
    models, following Wooldridge (1999).
Creates a non-negative low-rank approximate factorization of a sparse counts matrix by maximizing Poisson
    likelihood with L1/L2 regularization (e.g. for implicit-feedback recommender systems or bag-of-words-based topic modeling)
    (Cortes, (2018) <arXiv:1811.01908>), which usually leads to very sparse user and item factors (over 90% zero-valued).
    Similar to hierarchical Poisson factorization (HPF), but follows an optimization-based approach with regularization
    instead of a hierarchical prior, and is fit through gradient-based methods instead of variational inference.
Generation of count (assuming Poisson distribution) and continuous data (using Fleishman polynomials) simultaneously. The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>.
Generates multivariate data with count and continuous variables with a pre-specified correlation matrix. The count and continuous variables are assumed to have Poisson and normal marginals, respectively. The data generation mechanism is a combination of the normal to anything principle and a connection between Poisson and normal correlations in the mixture.
             The details of the method are explained in Yahav et al. (2012) <DOI:10.1002/asmb.901>.
Contains functions and classes for simulating, plotting and analysing homogenous and non-homogenous Poisson processes.
High dimensional mixtures of Poisson Generalized Linear models with three different parameterizations of Poisson means are considered. Moreover, partitioning the response variables into a set of blocks is possible. The package estimates parameters via EM algorithm. For an efficient initialization, a random splitting small-EM is introduced.
Efficient implementations of multiple exact and approximate methods as described in Hong (2013) <doi:10.1016/j.csda.2012.10.006>, Biscarri, Zhao & Brunner (2018) <doi:10.1016/j.csda.2018.01.007> and Zhang, Hong & Balakrishnan (2018) <doi:10.1080/00949655.2018.1440294> for computing the probability mass, cumulative distribution and quantile functions, as well as generating random numbers for both the ordinary and generalized Poisson binomial distribution.
R functions for the computation of Least Square based on transformation (L2T) and robust M-estimators based on transformations (MT-estimators) for Poisson regression models.
Implementation of the exact, normal approximation, and simulation-based methods for computing the probability mass function (pmf) and cumulative distribution function (cdf) of the Poisson-Multinomial distribution, together with a random number generator for the distribution. The exact method is based on multi-dimensional fast Fourier transformation (FFT) of the characteristic function of the Poisson-Multinomial distribution. The normal approximation method uses a multivariate normal distribution to approximate the pmf of the distribution based on central limit theorem. The simulation method is based on the law of large numbers. Details about the methods are available in Lin, Wang, and Hong (2022) <arXiv:2201.04237>.
For a multivariate dataset with independent Poisson measurement error, calculates principal components of transformed latent Poisson means. T. Kenney, T. Huang, H. Gu (2019) <arXiv:1904.11745>.
Bindings for Poisson regression models for use with the
    'parsnip' package. Models include simple generalized linear models,
    Bayesian models, and zero-inflated Poisson models (Zeileis, Kleiber,
    and Jackman (2008) <doi:10.18637/jss.v027.i08>).
Type testRoundOfPoker() to demonstrate the game of Texas Hold ‘Em poker.  Rotate the dealer button, deal cards, rank each hand, compare ranks, break ties (if necessary), determine the winner, output a textual summary, and output a graphical user interface.
Create hexagonal shape sticker image.
  'polaroid' can be used in user's web browser.
  'polaroid' can be used in 'shinyapps.io'.
  In both way, user can download created 'hexSticker' as 'PNG' image.
  'polaroid' is built based on 'argonDash', 'colourpicker' and 'hexSticker' R package.
Latent class analysis and latent class regression models 
    for polytomous outcome variables.  Also known as latent structure analysis.
Tools for simplifying the creation and management of data structures
    suitable for dealing with policy portfolios, that is, two-dimensional spaces 
    of policy instruments and policy targets. The package also allows to generate measures of
    portfolio characteristics and facilitates their visualization.
Learn optimal policies via doubly robust empirical
 welfare maximization over trees. Given doubly robust reward estimates, this package
 finds a rule-based treatment prescription policy, where the policy takes the form of
 a shallow decision tree that is globally (or close to) optimal.
This is a collection of data and functions for common metrics in political science research. Data measuring ideology, and functions calculating geographical diffusion and ideological diffusion - geog.diffuse() and ideo.dist(), respectively. Functions derived from methods developed in: Soule and King (2006) <doi:10.1086/499908>, Berry et al. (1998) <doi:10.2307/2991759>, Cruz-Aceves and Mallinson (2019) <doi:10.1177/0160323X20902818>, and Grossback et al. (2004) <doi:10.1177/1532673X04263801>.
Bundles the datasets and functions used in the textbook by Philip
    Pollock and Barry Edwards, an R Companion to Essentials of Political Analysis,
    Second Edition.
Easily add modern authentication and user administration to your 'shiny' apps. 
  Customize user sign in and registration pages to match your brand.  Control who can
  access one or more of your 'shiny' apps. Also, deploy & host your apps with Polished Hosting.
Be responsible when scraping data from websites by following polite principles: introduce yourself, ask for permission, take slowly and never ask twice.
Detecting markers of politeness in English natural language. This package allows researchers to easily visualize and quantify politeness between groups of documents. This package combines prior research on the linguistic markers of politeness. We thank the Spencer Foundation, the Hewlett Foundation, and Harvard's Institute for Quantitative Social Science for support.
Supports analysis of aerobiological data. 
    Available features include determination of pollen season limits, 
    replacement of outliers (Kasprzyk and Walanus (2014) <doi:10.1007/s10453-014-9332-8>),
    calculation of growing degree days (Baskerville and Emin (1969) <doi:10.2307/1933912>), 
    and determination of the base temperature for growing degree days
    (Yang et al. (1995) <doi:10.1016/0168-1923(94)02185-M).
Tools to estimate pollinator body size and co-varying traits. This package contains novel Bayesian predictive models of pollinator body size (for bees and hoverflies) as well as preexisting predictive models for pollinator body size (currently implemented for ants, bees, butterflies, flies, moths and wasps) as well as bee tongue length and foraging distance, total field nectar loads and wing loading. An additional GitHub repository <https://github.com/liamkendall/pollimetrydata> provides model objects to use the bodysize function internally. All models are described in Kendall et al (2018) <doi:10.1101/397604>.
Calculate common types of tables for weighted survey data.
    Options include topline and (2-way and 3-way) crosstab tables of
    categorical or ordinal data as well as summary tables of weighted
    numeric variables. Optionally, include the margin of error at
    selected confidence intervals including the design effect. The
    design effect is calculated as described by 
    Kish (1965) <doi:10.1002/bimj.19680100122> beginning
    on page 257. Output takes the form of tibbles (simple data frames).
    This package conveniently handles labelled data, such as that
    commonly used by 'Stata' and 'SPSS.' Complex survey design is 
    not supported at this time.
Package for corpus analysis using the Corpus Workbench 
    ('CWB', <http://cwb.sourceforge.net/>) as an efficient back end for indexing
    and querying large corpora. The package offers functionality to flexibly create
    subcorpora and to carry out basic statistical operations (count, co-occurrences
    etc.). The original full text of documents can be reconstructed and inspected at
    any time. Beyond that, the package is intended to serve as an interface to 
    packages implementing advanced statistical procedures. Respective data structures
    (document-term matrices, term-co-occurrence matrices etc.) can be created based 
    on the indexed corpora.
Routines for the polynomial spline fitting routines
  hazard regression, hazard estimation with flexible tails, logspline,
  lspec, polyclass, and polymars, by C. Kooperberg and co-authors.
Functions for evaluating the mass density, cumulative distribution function, quantile function and random variate generation for the Polya-Aeppli distribution, also known as the geometric compound Poisson distribution.  More information on the implementation can be found at Conrad J. Burden (2014) <arXiv:1406.2780>.
Simulate via Markov chain Monte Carlo (hit-and-run algorithm)
    a Dirichlet distribution conditioned to satisfy a finite set of linear
    equality and inequality constraints (hence to lie in a convex polytope
    that is a subset of the unit simplex).
Tools for creating, viewing, and assessing qualitative
 palettes with many (20-30 or more) colors. See Coombes and colleagues
 (2019) <doi:10.18637/jss.v090.c01>.
R port of Angus Johnson's open source library Clipper. Performs polygon clipping operations (intersection, union, set minus, set difference) for polygonal regions of arbitrary complexity, including holes. Computes offset polygons (spatial buffer zones, morphological dilations, Minkowski dilations) for polygonal regions and polygonal lines. Computes Minkowski Sum of general polygons. There is a function for removing self-intersections from polygon data.
Computes polychoric and polyserial correlations by quick "two-step" methods or ML, 
  optionally with standard errors; tetrachoric and biserial correlations are special cases.
Numerical integration of continuously differentiable
    functions f(x,y) over simple closed polygonal domains.
    The following cubature methods are implemented:
    product Gauss cubature (Sommariva and Vianello, 2007,
    <doi:10.1007/s10543-007-0131-2>),
    the simple two-dimensional midpoint rule
    (wrapping 'spatstat.geom' functions),
    adaptive cubature for radially symmetric functions via line
    integrate() along the polygon boundary (Meyer and Held, 2014,
    <doi:10.1214/14-AOAS743>, Supplement B),
    and integration of the bivariate Gaussian density based on
    polygon triangulation.
    For simple integration along the axes, the 'cubature' package
    is more appropriate.
Implements a Gibbs sampling algorithm to perform Bayesian inference
    on biallelic SNP frequencies, genotypes and heterozygosity (observed and
    expected) in a population of autopolyploids. See the published paper in
    Molecular Ecology Resources: Blischak et al. (2016) <doi:10.1111/1755-0998.12493>.
Infer the genetic composition of individuals
    in terms of haplotype dosages for a haploblock, based
    on bi-allelic marker dosages, for any ploidy level.
    Reference: Voorrips and Tumino: PolyHaplotyper: haplotyping in polyploids based on bi-allelic marker dosage data.
    Submitted to BMC Bioinformatics (2021).
A wrapper around the C++ library 'polylabel' from 'Mapbox', 
    providing an efficient routine for finding the approximate pole of 
    inaccessibility of a polygon, which usually serves as an excellent candidate
    for labeling of a polygon.
Creation of linkage maps in polyploid species from marker dosage
    scores of an F1 cross from two heterozygous parents. Currently works for outcrossing diploid, autotriploid, autotetraploid and autohexaploid species, 
    as well as segmental allotetraploids. Methods are described in a manuscript of Bourke et al. (2018) <doi:10.1093/bioinformatics/bty371>. Since version 1.1.0,
    both discrete and probabilistic genotypes are acceptable input; for more details on the latter see Liao et al. (submitted, 2020).
Implementation of class "polyMatrix" for storing a matrix of polynomials and implements 
  basic matrix operations; including a determinant and characteristic polynomial.
  It is based on the package 'polynom' and uses a lot of its methods to implement matrix operations.
  This package includes 3 methods of triangularization of polynomial matrices:
  Extended Euclidean algorithm which is most classical but numerically unstable;
  Sylvester algorithm based on LQ decomposition;
  Interpolation algorithm is based on LQ decomposition and Newton interpolation.
  Both methods are described in 
  D. Henrion & M. Sebek, Reliable numerical methods for polynomial matrix triangularization,
  IEEE Transactions on Automatic Control (Volume 44, Issue 3, Mar 1999, Pages 497-508) <doi:10.1109/9.751344>
  and in 
  Salah Labhalla, Henri Lombardi & Roger Marlin, 
  Algorithmes de calcule de la reduction de Hermite d'une matrice a coefficients polynomeaux,
  Theoretical Computer Science (Volume 161, Issue 1-2, July 1996, Pages 69-92) <doi:10.1016/0304-3975(95)00090-9>.
A collection of functions to implement a class for univariate
  polynomial manipulations.
Implements univariate polynomial operations in R, including
  polynomial arithmetic, finding zeros, plotting, and some operations on
  lists of polynomials.
Functions to perform paternity exclusion via allele
    matching, in autopolyploid species having ploidy 4, 6, or 8. The
    marker data used can be genotype data (copy numbers known) or
    'allelic phenotype data' (copy numbers not known).
Analyse prescription drug deliveries to calculate several indicators of polypharmacy corresponding to the various definitions found in the literature.
  Bjerrum, L., Rosholm, J. U., Hallas, J., & Kragstrup, J. (1997) <doi:10.1007/s002280050329>.
  Chan, D.-C., Hao, Y.-T., & Wu, S.-C. (2009a) <doi:10.1002/pds.1712>.
  Fincke, B. G., Snyder, K., Cantillon, C., Gaehde, S., Standring, P., Fiore, L., ... Gagnon, D.R. (2005) <doi:10.1002/pds.966>.
  Hovstadius, B., Astrand, B., & Petersson, G. (2009) <doi:10.1186/1472-6904-9-11>.
  Hovstadius, B., Astrand, B., & Petersson, G. (2010) <doi:10.1002/pds.1921>.
  Kennerfalk, A., Ruigómez, A., Wallander, M.-A., Wilhelmsen, L., & Johansson, S. (2002) <doi:10.1345/aph.1A226>.
  Masnoon, N., Shakib, S., Kalisch-Ellett, L., & Caughey, G. E. (2017) <doi:10.1186/s12877-017-0621-2>.
  Narayan, S. W., & Nishtala, P. S. (2015) <doi:10.1007/s40801-015-0020-y>.
  Nishtala, P. S., & Salahudeen, M. S. (2015) <doi:10.1159/000368191>.
  Park, H. Y., Ryu, H. N., Shim, M. K., Sohn, H. S., & Kwon, J. W. (2016) <doi:10.5414/cp202484>.
  Veehof, L., Stewart, R., Haaijer-Ruskamp, F., & Jong, B. M. (2000) <doi:10.1093/fampra/17.3.261>.
Tools for reshaping, plotting, and manipulating matrices of orthogonal polynomials.
Quantitative trait loci (QTL) analysis and exploration of meiotic patterns in 
    autopolyploid bi-parental F1 populations. 
    For all ploidy levels, identity-by-descent (IBD) probabilities can be estimated.
    Significance thresholds, exploring QTL allele effects and visualising results are provided. 
    For more background and to reference the package see <doi:10.1093/bioinformatics/btab574>.
Read depth data from genotyping-by-sequencing (GBS) or restriction 
  site-associated DNA sequencing (RAD-seq) are imported and used to make Bayesian
  probability estimates of genotypes in polyploids or diploids.  The genotype 
  probabilities, posterior mean genotypes, or most probable genotypes can then
  be exported for downstream analysis.  'polyRAD' is described by Clark et al.
  (2019) <doi:10.1534/g3.118.200913>.  A variant calling pipeline for highly
  duplicated genomes is also included and is described by Clark et al. (2020)
  <doi:10.1101/2020.01.11.902890>.
Automate formation and evaluation of polynomial regression models. The motivation for this package is described in 'Polynomial Regression As an Alternative to Neural Nets' by Xi Cheng, Bohdan Khomtchouk, Norman Matloff, and Pete Mohanty (<arXiv:1806.06850>).
A collection of tools to handle microsatellite data of
 any ploidy (and samples of mixed ploidy) where allele copy number is not
 known in partially heterozygous genotypes.  It can import and export data in
 ABI 'GeneMapper', 'Structure', 'ATetra', 'Tetrasat'/'Tetra', 'GenoDive', 'SPAGeDi',
 'POPDIST', 'STRand', and binary presence/absence formats.  It can calculate
 pairwise distances between individuals using a stepwise mutation model or
 infinite alleles model, with or without taking ploidies and allele frequencies
 into account.  These distances can be used for the calculation of clonal
 diversity statistics or used for further analysis in R.  Allelic diversity
 statistics and Polymorphic Information Content are also available.  polysat can 
 assist the user in estimating the ploidy of samples, and it can estimate allele 
 frequencies in populations, calculate pairwise or global differentiation statistics 
 based on those frequencies, and export allele frequencies to 'SPAGeDi' and 'adegenet'.  
 Functions are also included for assigning alleles to isoloci in cases where one pair 
 of microsatellite primers amplifies alleles from two or more independently
 segregating isoloci.  polysat is described by Clark and Jasieniuk (2011)
 <doi:10.1111/j.1755-0998.2011.02985.x> and Clark and Schreier (2017)
 <doi:10.1111/1755-0998.12639>.
Perform classic chi-squared tests and Ripol et al(1999)
             binomial confidence interval approach for autopolyploid
             dominant markers. Also, dominant markers may be generated
             for families of offspring where either one or both of the
             parents possess the marker. Missing values and
             misclassified markers may be generated at random.
Fits Bayesian mixture models to estimate marker dosage for dominant markers in autopolyploids using JAGS (1.0 or greater) as outlined in Baker et al "Bayesian estimation of marker dosage in sugarcane and other autopolyploids" (2010, <doi:10.1007/s00122-010-1283-z>). May be used in conjunction with polySegratio for simulation studies and comparison with standard methods.
This algorithm classifies the trends into linear, quadratic, cubic, concealed and no-trend types. The "concealed trends" are those trends that possess quadratic or cubic forms, but the net change from the start of the time period to the end of the time period hasn't been significant. The "no-trend" category includes simple linear trends with statistically in-significant slope coefficient.
Routines for flexible functional form estimation via basis
    regression, with model selection via the adaptive LASSO or SCAD to prevent
    overfitting.
This package fits a patch occupancy model
POM-aSPU test evaluates an association between an ordinal response and multiple phenotypes, for details see Kim and Pan (2017) <DOI:10.1002/gepi.22033>.
Implements the method described at the UCLA Statistical
    Consulting site <https://stats.idre.ucla.edu/r/dae/ordinal-logistic-regression/>
    for checking if the proportional odds assumption holds for a 
    cumulative logit model.
Provides the infrastructure to define and analyze the solutions of Partially Observable Markov Decision Process (POMDP) models. Interfaces for various exact and approximate solution algorithms are available including value iteration, point-based value iteration and SARSOP. Smallwood and Sondik (1973) <doi:10.1287/opre.21.5.1071>.
Installs 'pomdp-solve', a program to solve Partially Observable Markov Decision Processes (POMDPs) using a variety of exact and approximate value iteration algorithms. Smallwood and Sondik (1973) <doi:10.1287/opre.21.5.1071>.
Calculations of an information criterion are proposed to check the quality of simulations results of Agent-based models (ABM/IBM) or other non-linear rule-based models. The POMDEV measure (Pattern Oriented Modelling DEViance) is based on the Kullback-Leibler divergence and likelihood theory. It basically indicates the deviance of simulation results from field observations. Once POMDEV scores and metropolis-hasting sampling on different model versions are effectuated, POMIC scores (Pattern Oriented Modelling Information Criterion) can be calculated. This method could be further developed to incorporate multiple patterns assessment. Piou C, U Berger and V Grimm (2009) <doi:10.1016/j.ecolmodel.2009.05.003>.
Runs generalized and multinominal logistic (GLM and MLM) models, as well as random forest (RF), Bagging (BAG), and Boosting (BOOST). This package prints out to predictive outcomes easy for the selected data and data splits.
Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models).  The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods.  It is also a versatile platform for implementation of inference methods for general POMP models.
An implementation of a hybrid method of person-oriented method and perturbation on the model. Pompom is the initials of the two methods. The hybrid method will provide a multivariate intraindividual variability metric (iRAM). The person-oriented method used in this package refers to uSEM (unified structural equation modeling, see Kim et al., 2007, Gates et al., 2010 and Gates et al., 2012 for details). Perturbation on the model was conducted according to impulse response analysis introduced in Lutkepohl (2007). 
    Kim, J., Zhu, W., Chang, L., Bentler, P. M., & Ernst, T. (2007) <doi:10.1002/hbm.20259>. 
    Gates, K. M., Molenaar, P. C. M., Hillary, F. G., Ram, N., & Rovine, M. J. (2010) <doi:10.1016/j.neuroimage.2009.12.117>. 
    Gates, K. M., & Molenaar, P. C. M. (2012) <doi:10.1016/j.neuroimage.2012.06.026>. 
    Lutkepohl, H. (2007, ISBN:3540262393).
Finds equivalence classes corresponding to a symmetric relation
    or undirected graph.  Finds total order consistent with partial order
    or directed graph (so-called topological sort).
Enables the creation of object pools, which make it
    less computationally expensive to fetch a new object. Currently the
    only supported pooled objects are 'DBI' connections.
The 2017 American College of Cardiology and American Heart
  Association blood pressure guideline recommends using 10-year predicted 
  atherosclerotic cardiovascular disease risk to guide the decision to 
  initiate or intensify antihypertensive medication. The guideline recommends 
  using the Pooled Cohort risk prediction equations to predict 10-year 
  atherosclerotic cardiovascular disease risk. This package implements the 
  original Pooled Cohort risk prediction equations and also incorporates 
  updated versions based on more contemporary data and statistical methods.
  References:
  Goff DC, Lloyd-Jones DM, Bennett G, Coady S, D’Agostino RB, Gibbons R, 
  Greenland P, Lackland DT, Levy D, O’Donnell CJ, and Robinson JG (2014) 
  <doi:10.1016/j.jacc.2014.03.006>
  Yadlowsky S, Hayward RA, Sussman JB, McClelland RL, Min YI, and Basu S (2018)
  <doi:10.7326/m17-3011>.
Calculates the pooled mean group (PMG) estimator for dynamic panel data models, as described by Pesaran, Shin and Smith (1999) <doi:10.1080/01621459.1999.10474156>.
Functions for the computation of f- and D-statistics (estimation of 'Fst', Patterson's 'F2', 'F3', 'F3*', 'F4' and D parameters) in population genomics studies from allele count or Pool-Seq read count data and for the fitting, building and visualization of admixture graphs. The package also includes several utilities to manipulate Pool-Seq data stored in standard format (e.g., such as 'vcf' files or 'rsync' files generated by the the 'PoPoolation' software) and perform conversion to alternative format (as used in the 'BayPass' and 'SelEstim' software). As of version 2.0, the package also includes utilities to manipulate standard allele count data (e.g., stored in 'TreeMix', 'BayPass' or 'SelEstim' format).
Functions for calculating power and fitting regression models in studies where a biomarker is measured in "pooled" samples rather than for each individual. Approaches for handling measurement error follow the framework of Schisterman et al. (2010) <doi:10.1002/sim.3823>.
Functions for pooling/combining the results (i.e., p-values) from (dependent) hypothesis tests. Included are Fisher's method, Stouffer's method, the inverse chi-square method, the Bonferroni method, Tippett's method, and the binomial test. Each method can be adjusted based on an estimate of the effective number of tests or using empirically derived null distribution using pseudo replicates. For Fisher's, Stouffer's, and the inverse chi-square method, direct generalizations based on multivariate theory are also available (leading to Brown's method, Strube's method, and the generalized inverse chi-square method). An introduction can be found in Cinar and Viechtbauer (2022) <doi:10.18637/jss.v101.i01>.
An easy-to-use tool for working with presence/absence tests on 'pooled'
    or 'grouped' samples. The primary application is for estimating prevalence of 
    a marker in a population based on the results of tests on pooled specimens.
    This sampling method is often employed in surveillance of rare conditions in
    humans or animals (e.g. molecular xenomonitoring). The package was initially
    conceived as an R-based alternative to the molecular xenomonitoring software,
    'PoolScreen' <https://sites.uab.edu/statgenetics/software/>. However, it goes
    further, allowing for estimates of prevalence to be adjusted for hierarchical
    sampling frames, and perform flexible mixed-effect regression analyses
    (McLure et al. Environmental Modelling and Software.
    <DOI:10.1016/j.envsoft.2021.105158>). The package is currently in early stages,
    however more features are planned or in the works: e.g. adjustments for
    imperfect test specificity/sensitivity, functions for helping with optimal
    experimental design, and functions for spatial modelling.
Gene-based association tests using the actual impurity reduction (AIR) variable importance. The function aggregates AIR importance measures from a group of SNPs or probes and outputs a p-value for each gene. The procedures builds upon the method described in <doi:10.1093/Bioinformatics/Bty373> and will be published soon.
A replication of key functionality from 'dplyr' and the wider 'tidyverse' using only 'base'.
Population dynamic models underpin a range of analyses and applications in ecology and epidemiology. The various approaches for analysing population dynamics models (MPMs, IPMs, ODEs, POMPs, PVA) each require the model to be defined in a different way. This makes it difficult to combine different modelling approaches and data types to solve a given problem. 'pop' aims to provide a flexible and easy to use common interface for constructing population dynamic models and enabling to them to be fitted and analysed in lots of different ways.
Simulate the dynamic of lion populations using a specific Individual-Based Model (IBM) compiled in C.
Simulate the dynamic of wolf populations using a specific Individual-Based Model (IBM) compiled in C, see Chapron et al. (2016) <doi:10.1016/j.ecolmodel.2016.08.012>.
Infers the trends of one or several animal populations over time 
    from series of counts. It does so by accounting for count precision 
    (provided or inferred based on expert knowledge, e.g. guesstimates), 
    smoothing the population rate of increase over time, and accounting for the
    maximum demographic potential of species. Inference is carried out in a 
    Bayesian framework. This work is part of the FRB-CESAB working group
    AfroBioDrivers 
    <https://www.fondationbiodiversite.fr/en/the-frb-in-action/programs-and-projects/le-cesab/afrobiodrivers/>.
Construct and analyze projection matrix models from a demography study of marked individuals classified by age or stage. The package covers methods described in Matrix Population Models by Caswell (2001) and Quantitative Conservation Biology by Morris and Doak (2002).
Tools for modelling populations and demography using matrix projection models,
 with deterministic and stochastic model implementations. Includes population projection,
 indices of short- and long-term population size and growth, perturbation analysis,
 convergence to stability or stationarity, and diagnostic and manipulation tools.
The primary goal of phase I clinical trials is to find the maximum tolerated dose (MTD). To reach this objective, we introduce a new design for phase I clinical trials, the posterior predictive (PoP) design. The PoP design is an innovative model-assisted design that is as simply as the conventional algorithmic designs as its decision rules can be pre-tabulated prior to the onset of trial, but is of more flexibility of selecting diverse target toxicity rates and cohort sizes. The PoP design has desirable properties, such as coherence and consistency. Moreover, the PoP design provides better empirical performance than the BOIN and Keyboard design with respect to high average probabilities of choosing the MTD and slightly lower risk of treating patients at subtherapeutic or overly toxic doses.
Optimal experimental designs for both population and individual
    studies based on nonlinear mixed-effect models. Often this is based on a
    computation of the Fisher Information Matrix. This package was developed
    for pharmacometric problems, and examples and predefined models are available
    for these types of systems. The methods are described in Nyberg et al. 
    (2012) <doi:10.1016/j.cmpb.2012.05.005>, and Foracchia et al. (2004) 
    <doi:10.1016/S0169-2607(03)00073-7>.
Enables computation of epidemiological statistics, including those 
    where counts or mortality rates of the reference population are used. 
    Currently supported: excess hazard models (Dickman, Sloggett, Hills, and 
    Hakulinen (2012) <doi:10.1002/sim.1597>), rates, mean survival times, 
    relative/net survival (in particular the Ederer II (Ederer and Heise (1959))
    and Pohar Perme (Pohar Perme, Stare, and Esteve 
    (2012) <doi:10.1111/j.1541-0420.2011.01640.x>) estimators), 
    and standardized incidence and mortality ratios, 
    all of which can be easily adjusted for by covariates such as 
    age. Fast splitting and aggregation of 'Lexis' objects (from package 'Epi') 
    and other computations achieved using 'data.table'.
Provides efficient tools for population genomics data analysis,
	able to process individual loci, large sets of loci, or whole genomes. PopGenome <DOI:10.1093/molbev/msu136> not only 
	implements a wide range of population genetics statistics, but also facilitates the easy 
	implementation of new algorithms by other researchers. PopGenome is optimized for speed via 
	the seamless integration of C code.
Provides several data sets and functions to accompany the book "Population Genetics with R: An Introduction for Life Scientists" (2021, ISBN:9780198829546).
Provides beginner friendly framework to analyse population genetic
    data. Based on 'adegenet' objects it uses 'knitr' to create comprehensive reports on spatial genetic data. 
    For detailed information how to use the package refer to the comprehensive
    tutorials or visit <http://www.popgenreport.org/>.
Provides functions to estimate the kinship matrix of individuals from a large set of biallelic SNPs, and extract inbreeding coefficients and the generalized FST (Wright's fixation index).  Method described in Ochoa and Storey (2021) <doi:10.1371/journal.pgen.1009241>.
Provides a suite of tools for various methods of estimating
    confidence intervals for the mean of selected populations.
Estimates DNA target concentration by classifying digital PCR (polymerase chain reaction) droplets as positive, negative, or rain, using Expectation-Maximization Clustering. The fitting is accomplished using the 'EMMIXskew' R package (v. 1.0.3) by Kui Wang, Angus Ng, and Geoff McLachlan (2018) as based on their paper "Multivariate Skew t Mixture Models: Applications to Fluorescence-Activated Cell Sorting Data" <doi:10.1109/DICTA.2009.88>.
Population genetic analyses for hierarchical analysis of partially
    clonal populations built upon the architecture of the 'adegenet' package. 
    Originally described in Kamvar, Tabima, and Grünwald (2014) 
    <doi:10.7717/peerj.281> with version 2.0 described in Kamvar, Brooks, and 
    Grünwald (2015) <doi:10.3389/fgene.2015.00208>.
Functions that facilitate the elaboration of population pyramids.
Implements the Bayesian hierarchical model described by Wheldon, Raftery, Clark and Gerland (see: <doi:10.1080/01621459.2012.737729>) for simultaneously estimating age-specific population counts, fertility rates, mortality rates and net international migration flows, at the national level.
Kohonen's self-organizing maps with a number of distinguishing features:
    (1) An efficient, single threaded, stochastic training algorithm inspired by ideas from tensor algebra.  Provides significant speedups over traditional single-threaded training algorithms. No special accelerator hardware required (see <doi:10.1007/978-3-030-01057-7_60>).
    (2) Automatic centroid detection and visualization using starbursts.
    (3) Two models of the data: (a) a self organizing map model, (b) a centroid based clustering model.
    (4) A number of easily accessible quality metrics for the self organizing map and the centroid based cluster model (see <doi:10.1007/978-3-319-28518-4_4>).
Functions to estimate and plot smooth or linear population trends, or population indices, 
    from animal or plant count survey data.
Run population simulations using an Individual-Based Model (IBM) compiled in C.
Run simulations to assess the impact of various designs features
    and the underlying biological behaviour on the outcome of a Patient Derived
    Xenograft (PDX) population study. This project can either be deployed to a
    server as a 'shiny' app or installed locally as a package and run the app
    using the command 'populationPDXdesignApp()'.
Areal interpolation of population data obtained
    by census surveys. Given a set of source zone polygons such as
    census tracts or city blocks alongside with population counts and a 
    target zone of incogruent yet superimposed polygon features (such as
    individual buildings) populR transforms population counts from the 
    former to the latter using Areal Weighted and Volume Weighted 
    Interpolation methods.
The main attribute of 'PopVar' is the prediction of genetic variance in bi-parental populations, 
    from which the package derives its name. 'PopVar' contains a set of functions that use phenotypic and genotypic
    data from a set of candidate parents to 1) predict the mean, genetic variance, and superior progeny value of all, 
    or a defined set of pairwise bi-parental crosses, and 2) perform cross-validation to estimate genome-wide prediction 
    accuracy of multiple statistical models. More details are available in Mohammadi, Tiede, and Smith (2015, <doi:10.2135/cropsci2015.01.0030>). 
    A dataset 'think_barley.rda' is included for reference and examples.
The name of the package is derived from the French, 'pour' ridge, and provides functionality for ridge-type estimation of a potpourri of models. Currently, this estimation concerns that of various Gaussian graphical models from different study designs. Among others it considers the regular Gaussian graphical model and a mixture of such models. The porridge-package implements the estimation of the former either from i) data with replicated observations by penalized loglikelihood maximization using the regular ridge penalty on the parameters (van Wieringen, Chen, 2021, <doi:10.1002/sim.9028>) or ii) from non-replicated data by means of either a ridge estimator with multiple shrinkage targets (as presented in van Wieringen et al. 2020, <doi:10.1016/j.jmva.2020.104621>) or the generalized ridge estimator that allows for both the inclusion of quantitative and qualitative prior information on the precision matrix via element-wise penalization and shrinkage (van Wieringen, 2019, <doi:10.1080/10618600.2019.1604374>). Additionally, the porridge-package facilitates the ridge penalized estimation of a mixture of Gaussian graphical models (Aflakparast et al., 2018, <doi:10.1002/bimj.201700102>).
Obtener listado de datos, acceder y extender series del Portal de 
    Datos de Hacienda.Las proyecciones se realizan con 'forecast', 
    Hyndman RJ, Khandakar Y (2008) <doi:10.18637/jss.v027.i03>. 
    Search, download and forecast time-series from the Ministry of Economy 
    of Argentina. Forecasts are built with the 'forecast' package, 
    Hyndman RJ, Khandakar Y (2008) <doi:10.18637/jss.v027.i03>.
Download and generate summaries for the rodent,
    plant, ant, and weather data from the Portal Project. Portal is a
    long-term (and ongoing) experimental monitoring site in the Chihuahua
    desert. The raw data files can be found at
    <https://github.com/weecology/portaldata>.
Contains common univariate and multivariate portmanteau test statistics in time series based on the asymptotic distributions and the Monte Carlo significance tests. Simulate univariate and multivariate data from seasonal and nonseasonal time series models. See Mahdi and McLeod (2012) <doi:10.1111/j.1467-9892.2011.00752.x> and
  Mahdi and McLeod (2020) <arXiv:2005.00931>.
Classes for analysing and implementing equity portfolios,
    including routines for generating tradelists and calculating
    exposures to user-specified risk factors.
Simplify your portfolio optimization process by applying a contemporary modeling way to model and solve your portfolio problems. While most approaches and packages are rather complicated this one tries to simplify things and is agnostic regarding risk measures as well as optimization solvers. Some of the methods implemented are described by Konno and Yamazaki (1991) <doi:10.1287/mnsc.37.5.519>, Rockafellar and Uryasev (2001) <doi:10.21314/JOR.2000.038> and Markowitz (1952) <doi:10.1111/j.1540-6261.1952.tb01525.x>.
Portfolio optimization and analysis routines and graphics.
Automated backtesting of multiple portfolios over multiple 
    datasets of stock prices in a rolling-window fashion. Intended for 
    researchers and practitioners to backtest a set of different portfolios, 
    as well as by a course instructor to assess the students in their portfolio 
    design in a fully automated and convenient manner, with results conveniently 
    formatted in tables and plots. Each portfolio design is easily defined as a
    function that takes as input a window of the stock prices and outputs the 
    portfolio weights. Multiple portfolios can be easily specified as a list 
    of functions or as files in a folder. Multiple datasets can be conveniently 
    extracted randomly from different markets, different time periods, and 
    different subsets of the stock universe. The results can be later assessed 
    and ranked with tables based on a number of performance criteria (e.g., 
    expected return, volatility, Sharpe ratio, drawdown, turnover rate, return 
    on investment, computational time, etc.), as well as plotted in a number of 
    ways with nice barplots and boxplots.
R interface to PortfolioEffect cloud service for estimating
    high frequency price variance, quarticity, microstructure noise variance, 
    and other metrics in both aggregate and rolling window flavors. 
    Constructed estimators could use client-side market data or access 
    HF intraday price history for all major US Equities.
    See <https://www.portfolioeffect.com/> for more information on the 
    PortfolioEffect high frequency portfolio analytics platform.
R interface to PortfolioEffect cloud service for backtesting
    high frequency trading (HFT) strategies, intraday portfolio analysis
    and optimization. Includes auto-calibrating model pipeline for market
    microstructure noise, risk factors, price jumps/outliers, tail risk
    (high-order moments) and price fractality (long memory). Constructed
    portfolios could use client-side market data or access HF intraday price
    history for all major US Equities. See <https://www.portfolioeffect.com/>
    for more information on the PortfolioEffect high frequency portfolio
    analytics platform.
Two functions for financial portfolio optimization by linear programming are provided. One function implements Benders decomposition algorithm and can be used for very large data sets. The other, applicable for moderate sample sizes, finds optimal portfolio which has the smallest distance to a given benchmark portfolio.
Risk Attribution of a portfolio with Volatility Risk Analysis.
Designed to aid both academic researchers and asset managers in conducting factor based portfolio sorts.  
    Provides functionality to sort assets into portfolios for up to three factors via a conditional or unconditional sorting procedure.
Provides a set of basic tools for generating, analyzing, summarizing and visualizing finite partially ordered sets. In particular, it implements flexible and very efficient algorithms for the extraction of linear extensions and for the computation of mutual ranking probabilities and other user-defined functionals, over them. The package is meant as a computationally efficient "engine", for the implementation of data analysis procedures, on systems of multidimensional ordinal indicators and partially ordered data, in the spirit of Fattore, M. (2016) "Partially ordered sets and the measurement of multidimensional ordinal deprivation", Social Indicators Research <DOI:10.1007/s11205-015-1059-6>, and Fattore M. and Arcagni, A. (2018) "A reduced posetic approach to the measurement of multidimensional ordinal deprivation", Social Indicators Research <DOI:10.1007/s11205-016-1501-4>.
In linear LS regression, calculate for a given design matrix
  the multiplier K of coefficient standard errors such that the
  confidence intervals [b - K*SE(b), b + K*SE(b)] have a
  guaranteed coverage probability for all coefficient estimates
  b in any submodels after performing arbitrary model selection.
A collection of R Markdown templates for creating simple and easy 
  to personalize single page websites.
Free UK geocoding using data from Office for National Statistics.
    It is using several functions to get information about post codes, outward
    codes, reverse geocoding, nearest post codes/outward codes, validation, or
    randomly generate a post code. API wrapper around <https://postcodes.io>.
Use 'rmarkdown' and 'pagedown' to generate
    HTML and PDF conference posters.
Provides useful tools for both users and developers of packages 
  for fitting Bayesian models or working with output from Bayesian models. 
  The primary goals of the package are to: 
  (a) Efficiently convert between many different useful formats of
  draws (samples) from posterior or prior distributions.
  (b) Provide consistent methods for operations commonly performed on draws, 
  for example, subsetting, binding, or mutating draws.
  (c) Provide various summaries of draws in convenient formats.
  (d) Provide lightweight implementations of state of the art posterior 
  inference diagnostics. References: Vehtari et al. (2021) 
  <doi:10.1214/20-BA1221>.
An implementation of a non-parametric statistical model using a
    parallelised Monte Carlo sampling scheme. The method implemented in this
    package allows non-parametric inference to be regularized for small sample
    sizes, while also being more accurate than approximations such as
    variational Bayes. The concentration parameter is an effective sample size
    parameter, determining the faith we have in the model versus the data. When
    the concentration is low, the samples are close to the exact Bayesian
    logistic regression method; when the concentration is high, the samples are
    close to the simplified variational Bayes logistic regression. The method is
    described in full in the paper Lyddon, Walker, and Holmes (2018),
    "Nonparametric learning from Bayesian models with randomized objective
    functions" <arXiv:1806.11544>.
Generate all necessary R/Rmd/shell files for data processing after running 'GGIR' (v2.4.0) for accelerometer data. In part 1, all csv files in the GGIR output directory were read, transformed and then merged. In part 2, the GGIR output files were checked and summarized in one excel sheet. In part 3, the merged data was cleaned according to the number of valid hours on each night and the number of valid days for each subject. In part 4, the cleaned activity data was imputed by the average Euclidean norm minus one (ENMO) over all the valid days for each subject. Finally, a comprehensive report of data processing was created using Rmarkdown, and the report includes few exploratory plots and multiple commonly used features extracted from minute level actigraphy data.
Functions to convert geometry and 'hstore' data types from
    'PostgreSQL' into standard R objects, as well as to simplify
    the import of R data frames (including spatial data frames) into 'PostgreSQL'.
    Note: This package is deprecated. For new projects, we recommend 
    using the 'sf' package to interface with geodatabases.
Implements a range of facilities for post-hoc analysis and
             summarizing linear models, generalized linear models and 
             generalized linear mixed models, including grouping and clustering 
             via pairwise comparisons using graph representations and efficient
             algorithms for finding maximal cliques of a graph. 
             Includes also non-parametric toos for post-hoc analysis.
             It has S3 methods for printing summarizing, and producing plots, 
             line and barplots suitable for post-hoc analyses.
Functions to estimate the incubation period distribution of post-infectious syndrome which is defined as the time between the symptom onset of the antecedent infection and that of the post-infectious syndrome.
This is a wrapper for the Mercury Parser API. The Mercury Parser is 
    a single API endpoint that takes a URL and gives you back the content reliably 
    and easily. 
    With just one API request, Mercury takes any web article and returns 
    only the relevant content — headline, author, body text, relevant images and 
    more — free from any clutter. It’s reliable, easy-to-use and free.
    See the webpage here: <https://mercury.postlight.com/>.
Provides adds postfix and infix logic operators for
    if, then, unless, and otherwise.
Implements the Phylogeny-Guided Microbiome OTU-Specific Association 
    Test method, which boosts the testing power by adaptively borrowing 
    information from phylogenetically close OTUs (operational taxonomic units)
    of the target OTU. This method
    is built on a kernel machine regression framework and allows for flexible 
    modeling of complex microbiome effects, adjustments for covariates, and 
    can accommodate both continuous and binary outcomes.
The aim of 'postpack' is to provide the infrastructure for a standardized workflow for 'mcmc.list' objects.
    These objects can be used to store output from models fitted with Bayesian inference using
    'JAGS', 'WinBUGS', 'OpenBUGS', 'NIMBLE', 'Stan', or even custom MCMC algorithms. Although the 'coda' R package provides
    some methods for these objects, it is somewhat limited in easily performing post-processing tasks for
    specific nodes. Models are ever increasing in their complexity and the number of tracked nodes, and oftentimes
    a user may wish to summarize/diagnose sampling behavior for only a small subset of nodes at a time
    for a particular question or figure. Thus, many 'postpack' functions support performing tasks on a
    subset of nodes, where the subset is specified with regular expressions. The functions in 'postpack'
    streamline the extraction, summarization, and diagnostics of specific monitored nodes after model fitting.
    Further, because there is rarely only ever one model under consideration, 'postpack' scales efficiently 
    to perform the same tasks on output from multiple models simultaneously, facilitating rapid assessment 
    of model sensitivity to changes in assumptions.
Some functions useful to perform a Peak Over Threshold
        analysis in univariate and bivariate cases, see Beirlant et al. (2004)
        <doi:10.1002/0470012382>. A user guide is available in the vignette.
Provides functions to compute the potential model as defined by
    Stewart (1941) <doi:10.1126/science.93.2404.89>. Several options are available
    to customize the model, such as the possibility to fine-tune the distance
    friction functions or to use custom distance matrices. Some computations are
    parallelized to improve their efficiency.
Translating messages in R packages is managed using the po top-level directory and the 'gettext' program. This package provides some helper functions for building this support in R packages, e.g. common validation & I/O tasks.
Do Markov chain Monte Carlo (MCMC) simulation of Potts models
   (Potts, 1952, <doi:10.1017/S0305004100027419>),
   which are the multi-color generalization of Ising models
   (so, as as special case, also simulates Ising models).
   Use the Swendsen-Wang algorithm (Swendsen and Wang, 1987,
   <doi:10.1103/PhysRevLett.58.86>) so MCMC is fast.
   Do maximum composite likelihood estimation of parameters
   (Besag, 1975, <doi:10.2307/2987782>,
   Lindsay, 1988, <doi:10.1090/conm/080>).
There are three sets of functions. The first produces
             basic properties of a graph and generates samples from
             multinomial distributions to facilitate the simulation
	     functions (they maybe used for other purposes as well).
	     The second provides various simulation functions for a
	     Potts model in Potts, R. B. (1952)
	     <doi:10.1017/S0305004100027419>.
	     The third currently includes only one function which
	     computes the normalizing constant of a Potts model
	     based on simulation results.
Computes the D', Wn, and conditional asymmetric linkage disequilibrium (ALD) measures for pairs of genetic loci. Performs these linkage disequilibrium (LD) calculations on phased genotype data recorded using Genotype List (GL) String or columnar formats. Alternatively, generates expectation-maximization (EM) estimated haplotypes from phased data, or performs LD calculations on EM estimated haplotypes. Performs sign tests comparing LD values for phased and unphased datasets, and generates heat-maps for each LD measure. Described by Osoegawa et al. (2019a) <doi:10.1016/j.humimm.2019.01.010>, and Osoegawa et. al. (2019b) <doi:10.1016/j.humimm.2019.05.018>.
The Phylogenetic Ornstein-Uhlenbeck Mixed Model (POUMM) allows to 
    estimate the phylogenetic heritability of continuous traits, to test 
    hypotheses of neutral evolution versus stabilizing selection, to quantify 
    the strength of stabilizing selection, to estimate measurement error and to
    make predictions about the evolution of a phenotype and phenotypic variation 
    in a population. The package implements combined maximum likelihood and 
    Bayesian inference of the univariate Phylogenetic Ornstein-Uhlenbeck Mixed 
    Model, fast parallel likelihood calculation, maximum likelihood 
    inference of the genotypic values at the tips, functions for summarizing and
    plotting traces and posterior samples, functions for simulation of a univariate 
    continuous trait evolution model along a phylogenetic tree. So far, the 
    package has been used for estimating the heritability of quantitative traits
    in macroevolutionary and epidemiological studies, see e.g. 
    Bertels et al. (2017) <doi:10.1093/molbev/msx246> and 
    Mitov and Stadler (2018) <doi:10.1093/molbev/msx328>. The algorithm for 
    parallel POUMM likelihood calculation has been published in 
    Mitov and Stadler (2019) <doi:10.1111/2041-210X.13136>.
An implementation of the Partition Of variation (POV) method as
    developed by Dr. Thomas A Little <https://thomasalittleconsulting.com> in
    1993 for the analysis of semiconductor data for hard drive manufacturing.
    POV is based on sequential sum of squares and is an exact method that
    explains all observed variation. It quantitates both the between and within
    factor variation effects and can quantitate the influence of both continuous
    and categorical factors.
Provides an interface to compute poverty and inequality indicators for more than 160 countries and regions from the World Bank's database of household surveys. It has the same functionality as the 'Povcalnet' website (<http://iresearch.worldbank.org/PovcalNet/>). 'Povcalnet' is a computational tool that allows users to estimate poverty rates for regions, sets of countries or individual countries, over time and at any poverty line. 'Povcalnet' is managed jointly by the data and research group in the World Bank's development economics division. It draws heavily upon a strong collaboration with the poverty and equity global practice, which is responsible for the gathering and harmonization of the underlying survey data.
Density, distribution function, quantile function and random generation for the family of power and reversal power distributions.
Full pattern summation of X-ray powder diffraction data as
  described in Chipera and Bish (2002) <doi:10.1107/S0021889802017405> and
  Butler and Hillier (2021) <doi:10.1016/j.cageo.2020.104662>.
  Derives quantitative estimates of crystalline and amorphous phase
  concentrations in complex mixtures.
Contains functions to obtain the operational characteristics of 
    bioequivalence studies in Two-Stage Designs (TSD) via simulations.
Basic functions for power analysis and effect size calculation.
Functions for bootstrapping the power of ANOVA designs
    based on estimated means and standard deviations of the conditions.
    Please refer to the documentation of the boot.power.anova() function
    for further details.
A power analysis tool for jointly testing the cause-1 cause-specific hazard and the any-cause hazard with competing risks data.
Power and sample size calculation for bulk tissue and single-cell eQTL analysis
             based on ANOVA, simple linear regression, or linear mixed effects model. It can also calculate power/sample size 
             for testing the association of a SNP to a continuous type phenotype.
             Please see the reference: Dong X, Li X, Chang T-W, Scherzer CR, Weiss ST, Qiu W. (2021) <doi:10.1093/bioinformatics/btab385>.
Analytical power calculations for GxE and GxG interactions for case-control studies of candidate genes and genome-wide association studies (GWAS). This includes power calculation for four two-step screening and testing procedures.  It can also calculate power for GxE and GxG without any screening.
Facilitates simulating and analyzing data coming from HDX-MS 
             experiments along with the possibility of comparing the power of 
             the tests verifying differences in the levels of deuterium uptake. 
             The simulation of mass spectra is a fast version of 
             <https://github.com/kanzy/HX-MS-Simulations>.
Extensions of 'dplyr' and 'fuzzyjoin' join functions. Packed with
    features to preprocess the data, apply various data checks, and deal with
    conflicting columns.
An implementation of the generalized power analysis for the local average treatment effect (LATE), proposed by Bansak (2020) <doi:10.1214/19-STS732>. Power analysis is in the context of estimating the LATE (also known as the complier average causal effect, or CACE), with calculations based on a test of the null hypothesis that the LATE equals 0 with a two-sided alternative. The method uses standardized effect sizes to place a conservative bound on the power under minimal assumptions. Package allows users to recover power, sample size requirements, or minimum detectable effect sizes. Package also allows users to work with absolute effects rather than effect sizes, to specify an additional assumption to narrow the bounds, and to incorporate covariate adjustment.
An implementation of maximum likelihood estimators
    for a variety of heavy tailed distributions, including both the
    discrete and continuous power law distributions. Additionally, a
    goodness-of-fit based approach is used to estimate the lower cut-off
    for the scaling region.
An implementation of the sample size computation method for network
    models proposed by Constantin et al. (2021) <doi:10.31234/osf.io/j5v7u>.
    The implementation takes the form of a three-step recursive algorithm
    designed to find an optimal sample size given a model specification and a
    performance measure of interest. It starts with a Monte Carlo simulation
    step for computing the performance measure and a statistic at various sample
    sizes selected from an initial sample size range. It continues with a
    monotone curve-fitting step for interpolating the statistic across the entire
    sample size range. The final step employs stratified bootstrapping to quantify
    the uncertainty around the fitted curve.
Functions to 
        calculate power and sample size for testing
        (1) mediation effects; 
        (2) the slope in a simple linear regression; 
        (3) odds ratio in a simple logistic regression;
        (4) mean change for longitudinal study with 2 time points;
        (5) interaction effect in 2-way ANOVA; and
        (6) the slope in a simple Poisson regression.
Miscellaneous functions for a descriptive analysis and initial Bayesian and classical 
             inference for the power parameter of the the Power Normal (PN) distribution. This 
             miscellaneous will be extend for more distributions into the power family and the 
             three-parameter model.
(1) To estimate the power of testing for linkage using an
        affected sib pair design, as a function of the recurrence risk
        ratios. We will use analytical power formulae as implemented in
        R. These are based on a Mathematica notebook created by Martin
        Farrall. (2) To examine how the power of the transmission
        disequilibrium test (TDT) depends on the disease allele
        frequency, the marker allele frequency, the strength of the
        linkage disequilibrium, and the magnitude of the genetic
        effect. We will use an R program that implements the power
        formulae of Abel and Muller-Myhsok (1998). These formulae allow
        one to quickly compute power of the TDT approach under a
        variety of different conditions. This R program was modeled on
        Martin Farrall's Mathematica notebook.
Computation of matrix and scalar exponentiation.
Functions to calculate power and
                sample size for testing main effect or interaction effect in
                the survival analysis of epidemiological studies
                (non-randomized studies), taking into account the 
                correlation between the covariate of the
                interest and other covariates. Some calculations also take 
                into account the competing risks and stratified analysis. 
                This package also includes
                a set of functions to calculate power and sample size
                for testing main effect in the survival analysis of 
                randomized clinical trials and conditional logistic regression for nested case-control study.
Contains functions to calculate power and sample size for
    various study designs used in bioequivalence studies. Use known.designs() to
    see the designs supported. Power and sample size can be obtained based on
    different methods, amongst them prominently the TOST procedure (two one-sided
    t-tests). See README and NEWS for further information.
Includes tools to calculate statistical power, minimum detectable effect size (MDES), MDES difference (MDESD), and minimum required sample size for various multilevel randomized experiments (MRE) with continuous outcomes.
 Accomodates 14 types of MRE designs to detect main treatment effect, seven types of MRE designs to detect moderated treatment effect (2-1-1, 2-1-2, 2-2-1, 2-2-2, 3-3-1, 3-3-2, and 3-3-3 designs; <total.lev> - <trt.lev> - <mod.lev>),
 five types of MRE designs to detect mediated treatment effects (2-1-1, 2-2-1, 3-1-1, 3-2-1, and 3-3-1 designs; <trt.lev> - <med.lev> - <out.lev>), four types of partially nested (PN) design to detect main treatment effect,
 and three types of PN designs to detect mediated treatment effects (2/1, 3/1, 3/2; <trt.arm.lev> / <ctrl.arm.lev>). 
 See 'PowerUp!' Excel series at <https://www.causalevaluation.org/>.
The PP package includes estimation of (MLE, WLE, MAP, EAP, ROBUST)
    person parameters for the 1,2,3,4-PL model and the GPCM (generalized
    partial credit model). The parameters are estimated under the assumption
    that the item parameters are known and fixed. The package is useful e.g. in
    the case that items from an item pool / item bank with known item parameters
    are administered to a new population of test-takers and an ability
    estimation for every test-taker is needed.
Exploratory projection pursuit is a method to discovers
	structure in multivariate data. At heart this package uses
	a projection index to evaluate how interesting a specific
	three-dimensional projection of multivariate data (with more
	than three dimensions) is. Typically, the main structure
	finding algorithm starts at a random projection and then
	iteratively changes the projection direction to move to
	a more interesting one. In other words, the projection index
	is maximised over the projection direction to find the most
	interesting projection. This maximum is, though, a local
	maximum. So, this code has the ability to restart the
	algorithm from many different starting positions automatically.
	Routines exist to plot a density estimate of projection indices 
	over the runs, this enables the user to obtain an idea of
	the distribution of the projection indices,
	and, hence,  which ones might be interesting. Individual
	projection solutions, including those identified as interesting,
	can be extracted and plotted individually. The package
	can make use of the mclapply() function to execute multiple runs in
	parallel to speed up index discovery. Projection pursuit is
	similar to independent component analysis. This package
	uses a projection index that maximises an entropy measure to
	look for projections that exhibit non-normality, and operates
	on sphered data. Hence, information from this package is
	different from that obtained from principal components analysis,
	but the rationale behind both methods is similar. 
        Nason, G. P. (1995) <doi:10.2307/2986135>.
Calculates the Probability Plot Correlation Coefficient (PPCC) 
             between a continuous variable X and a specified distribution. The corresponding 
	     composite hypothesis test that was first introduced by 
	     Filliben (1975) <doi: 10.1080/00401706.1975.10489279> 
	     can be performed to test whether the sample 
	     X is element of either the Normal, log-Normal, Exponential,
	     Uniform, Cauchy, Logistic, Generalized Logistic, Gumbel (GEVI), Weibull,
	     Generalized Extreme Value, Pearson III (Gamma 2), Mielke's Kappa, Rayleigh
	     or Generalized Logistic Distribution. The PPCC test is performed with
	     a fast Monte-Carlo simulation.
Implements recently developed projection 
    pursuit algorithms for finding optimal linear cluster
    separators. The clustering algorithms use optimal
    hyperplane separators based on minimum density, Pavlidis et. al (2016) <http://jmlr.org/papers/volume17/15-307/15-307.pdf>;
    minimum normalised cut, Hofmeyr (2017) <doi:10.1109/TPAMI.2016.2609929>;
    and maximum variance ratio clusterability, Hofmeyr and Pavlidis (2015) <doi:10.1109/SSCI.2015.116>.
Partitioning clustering divides the objects in a data set into non-overlapping subsets or clusters by using the prototype-based probabilistic and possibilistic clustering algorithms. This package covers a set of the functions for Fuzzy C-Means (Bezdek, 1974) <doi:10.1080/01969727308546047>, Possibilistic C-Means (Krishnapuram & Keller, 1993) <doi:10.1109/91.227387>, Possibilistic Fuzzy C-Means (Pal et al, 2005) <doi:10.1109/TFUZZ.2004.840099>, Possibilistic Clustering Algorithm (Yang et al, 2006) <doi:10.1016/j.patcog.2005.07.005>, Possibilistic C-Means with Repulsion (Wachs et al, 2006) <doi:10.1007/3-540-31662-0_6> and the other variants of hard and soft clustering algorithms. The cluster prototypes and membership matrices required by these partitioning algorithms are initialized with different initialization techniques that are available in the package 'inaparc'. As the distance metrics, not only the Euclidean distance but also a set of the commonly used distance metrics are available to use with some of the algorithms in the package.
A simple interface for interacting with ProPublica's 'Congress' API,
    which provides data about current and former members of both chambers of the
    U.S. Congress and can be found at the following URL: <https://projects.propublica.org/api-docs/congress-api/>.
Calculates partial and semi-partial
        (part) correlations along with p-value.
Spatial Analysis for exploration of Pakistan Population Census 2017 (<http://www.pbscensus.gov.pk/>). It uses data from R package 'PakPC2017'.
A suite of diagnostic tools for univariate point processes.
    This includes tools for simulating and fitting both common and more
    complex temporal point processes. We also include functions to 
    visualise these point processes and collect existing diagnostic
    tools of Brown et al. (2002) <doi:10.1162/08997660252741149> and
    Wu et al. (2021) <doi:10.1002/9781119821588.ch7>,
    which can be used to assess the fit of a chosen point process
    model.
Implements projection pursuit forest algorithm for supervised classification.
Methods for fitting point processes with parameters of generalised additive 
      model (GAM) form are provided. For an introduction to point processes see Cox, D.R & 
      Isham, V. (Point Processes, 1980, CRC Press), GAMs see Wood, S.N. (2017) 
      <doi:10.1201/9781315370279>, and the fitting approach see Wood, S.N., Pya, N. & 
      Safken, B. (2016) <doi:10.1080/01621459.2016.1180986>.
Projection Pursuit (PP) algorithm for dimension reduction based on Gaussian Mixture Models (GMMs) for density estimation using Genetic Algorithms (GAs) to maximise an approximated negentropy index. For more details see Scrucca and Serafini (2019) <doi:10.1080/10618600.2019.1598871>.
The Poverty Probability Index (PPI) is a poverty measurement tool 
    for organizations and businesses with a mission to serve the poor. The PPI 
    is statistically-sound, yet simple to use: the answers to 10 questions about 
    a household’s characteristics and asset ownership are scored to compute the 
    likelihood that the household is living below the poverty line – or above by 
    only a narrow margin. This package contains country-specific lookup data tables
    used as reference to determine the poverty likelihood of a household based
    on their score from the country-specific PPI questionnaire. These lookup 
    tables have been extracted from documentation of the PPI found at 
    <https://www.povertyindex.org> and managed by Innovations for Poverty Action 
    <https://www.poverty-action.org>.
We provide new tools for the identification of prognostic and predictive biomarkers. For further details we refer the reader to the paper <arXiv:2202.01970> (Zhu et al., 2022).
Implements data processing described in <doi:10.1126/sciadv.abk3283> to align modern differentially private data with formatting of older US Census data releases. The primary goal is to read in Census Privacy Protected Microdata Files data in a reproducible way. This includes tools for aggregating to relevant levels of geography by creating geographic identifiers which match the US Census Bureau's numbering. Additionally, there are tools for grouping race numeric identifiers into categories, consistent with OMB (Office of Management and Budget) classifications. Functions exist for downloading and linking to existing sources of privacy protected microdata.
An implementation of the one-step privacy-protecting method for estimating the overall and site-specific hazard ratios using inverse probability weighted Cox models in distributed data network studies, as proposed by Shu, Yoshida, Fireman, and Toh (2019) <doi: 10.1177/0962280219869742>. This method only requires sharing of summary-level riskset tables instead of individual-level data. Both the conventional inverse probability weights and the stabilized weights are implemented.
Toolkit for fitting point process models with sequences of LASSO penalties ("regularisation paths"), as described in Renner, I.W. and Warton, D.I. (2013) <doi:10.1111/j.1541-0420.2012.01824.x>. Regularisation paths of Poisson point process models or area-interaction models can be fitted with LASSO, adaptive LASSO or elastic net penalties. A number of criteria are available to judge the bias-variance tradeoff.
Efficient statistical inference of two-sample MR (Mendelian Randomization) analysis. 
    It can account for the correlated instruments and the horizontal pleiotropy, 
    and can provide the accurate estimates of both causal effect and horizontal 
    pleiotropy effect as well as the two corresponding p-values. There are two main 
    functions in the 'PPMR' package. One is PMR_individual() for individual level data, 
    the other is PMR_summary() for summary data.
Provides functions that fit hierarchical Gaussian and probit ordinal models.  
                A (covariate dependent) product partition model is used as a prior.
                If a covariate dependent product partition model  is selected,  
                then all the options detailed in Page, G.L.; Quintana, F.A.; (2018)
                <doi:10.1007/s11222-017-9777-z> are available.  If covariate values
                are missing, then the approach detailed in Page, G.L.; Quintana, F.A.; 
                Mueller, P (2020) <arXiv:1912.13119> is employed.  Also included in the
                package is a function that fits a Gaussian likelihood spatial product 
                partition model that is detailed in Page, G.L.; Quintana, F.A.; (2016) 
                <DOI:10.1214/15-BA971>.
Assessment for statistically-based PPQ sampling plan, including calculating the passing probability, optimizing the baseline and high performance cutoff points, visualizing the PPQ plan and power dynamically. The analytical idea is based on the simulation methods from the textbook Burdick, R. K., LeBlond, D. J., Pfahler, L. B., Quiroz, J., Sidor, L., Vukovinsky, K., & Zhang, L. (2017). Statistical Methods for CMC Applications. In Statistical Applications for Chemistry, Manufacturing and Controls (CMC) in the Pharmaceutical Industry (pp. 227-250). Springer, Cham.
A toolbox for deterministic, probabilistic and privacy-preserving record linkage techniques. Combines the functionality of the 'Merge ToolBox' (<http://record-linkage.de>) with current privacy-preserving techniques.
Functions to select samples using PPS (probability proportional to size) sampling. The package also includes a function for stratified simple random sampling, a function to compute joint inclusion probabilities for Sampford's method of PPS sampling, and a few utility functions. The user's guide pps-ug.pdf is included in the .../pps/doc directory. The methods are described in standard survey sampling theory books such as Cochran's "Sampling Techniques"; see the user's guide for references.
Stochastic block model used for dynamic graphs represented by Poisson processes.
    To model recurrent interaction events in continuous time, an extension of the stochastic block model is proposed where every individual belongs to a latent group and interactions between two individuals follow a conditional inhomogeneous Poisson process with intensity driven by the individuals’ latent groups. The model is shown to be identifiable and its estimation is based on a semiparametric variational expectation-maximization algorithm. Two versions of the method are developed, using either a nonparametric histogram approach (with an adaptive choice of the partition size) or kernel intensity estimators. The number of latent groups can be selected by an integrated classification likelihood criterion.
    Y. Baraud and L. Birgé (2009). <doi:10.1007/s00440-007-0126-6>.
    C. Biernacki, G. Celeux and G. Govaert (2000). <doi:10.1109/34.865189>.
    M. Corneli, P. Latouche and F. Rossi (2016). <doi:10.1016/j.neucom.2016.02.031>.
    J.-J. Daudin, F. Picard and S. Robin (2008). <doi:10.1007/s11222-007-9046-7>.
    A. P. Dempster, N. M. Laird and D. B. Rubin (1977). <http://www.jstor.org/stable/2984875>.
    G. Grégoire (1993). <http://www.jstor.org/stable/4616289>.
    L. Hubert and P. Arabie (1985). <doi:10.1007/BF01908075>.
    M. Jordan, Z. Ghahramani, T. Jaakkola and L. Saul (1999). <doi:10.1023/A:1007665907178>.
    C. Matias, T. Rebafka and F. Villers (2018). <doi:10.1093/biomet/asy016>.
    C. Matias and S. Robin (2014). <doi:10.1051/proc/201447004>.
    H. Ramlau-Hansen (1983). <doi:10.1214/aos/1176346152>.
    P. Reynaud-Bouret (2006). <doi:10.3150/bj/1155735930>.
Functions are available to calibrate designs over a range of posterior and predictive thresholds, to plot the various design options, and to obtain the operating characteristics of optimal accuracy and optimal efficiency designs.
This is an implementation of the partial profile score feature 
    selection (PPSFS) approach to generalized linear (interaction) models. 
    The PPSFS is highly scalable even for ultra-high-dimensional feature space. 
    See the paper by Xu, Luo and Chen (2021, <doi:10.4310/21-SII706>).
The PPS is an asymmetric, data-type-agnostic score that can detect 
    linear or non-linear relationships between two columns. The score ranges 
    from 0 (no predictive power) to 1 (perfect predictive power). It can be useful
    for data exploration purposes, in the same way correlation analysis is.
    For more information on PPS, see Wetschoreck (2020) <https://towardsdatascience.com/rip-correlation-introducing-the-predictive-power-score-3d90808b9598> 
    or github <https://github.com/paulvanderlaken/ppsr>.
Tools for exploring projection pursuit classification tree using
    various projection pursuit indexes.
This package allows users to calculate the number of
    under-five child deaths caused by consumption of poor quality
    antimalarials across 39 sub-Saharan nations. The package supports one
    function, that starts an interactive web tool created using
    the shiny R package. The web tool runs locally on the user's machine.
    The web tool allows users to set input parameters (prevalence of poor
    quality antimalarials, case fatality rate of children who take poor
    quality antimalarials, and sample size) which are then used to perform
    an uncertainty analysis following the Latin hypercube
    sampling scheme. Users can download the output figures as PDFs, and the
    output data as CSVs. Users can also download their input parameters
    for reference. This package was designed to accompany the analysis
    presented in:
    J. Patrick Renschler, Kelsey Walters, Paul Newton, Ramanan Laxminarayan
    "Estimated under-five deaths associated with poor-quality
    antimalarials in sub-Saharan Africa", 2014. Paper submitted.
An efficient tool designed for differential analysis of large-scale RNA sequencing (RNAseq) data and Bisulfite sequencing (BSseq) data in the presence of individual relatedness and population structure. 'PQLseq' first fits a Generalized Linear Mixed Model (GLMM) with adjusted covariates, predictor of interest and random effects to account for population structure and individual relatedness, and then performs Wald tests for each gene in RNAseq or site in BSseq.
Distance-based parametric bootstrap tests for clustering with 
  spatial neighborhood information. Some distance measures, 
  Clustering of presence-absence, abundance and multilocus genetic data 
  for species delimitation, nearest neighbor 
  based noise detection. Genetic distances between communities.
  Tests whether various distance-based regressions
  are equal. Try package?prabclus for on overview.
Provides a large number of functions from numerical analysis and
    linear algebra, numerical optimization, differential equations,
    time series, plus some well-known special mathematical functions.
    Uses 'MATLAB' function names where appropriate to simplify porting.
Sample size calculations for practical equivalence trial design with a time to event endpoint.
Functions and datasets to support Valliant, Dever, and Kreuter, Practical Tools for Designing and Weighting Survey Samples (2nd edition, 2018). Contains functions for sample size calculation for survey samples using stratified or clustered one-, two-, and three-stage sample designs. Other functions compute variance components for multistage designs and sample sizes in two-phase designs. A number of example data sets are included.
The Prais-Winsten estimator (Prais & Winsten, 1954) takes into account AR(1) serial correlation of the errors in a linear regression model. The procedure recursively estimates the coefficients and the error autocorrelation of the specified model until sufficient convergence of the AR(1) coefficient is attained.
Build friendly R packages that
    praise their users if they have done something
    good, or they just need it to feel better.
A toolbox of fast, native and parallel implementations of various information-based importance criteria estimators and feature selection filters based on them, inspired by the overview by Brown, Pocock, Zhao and Lujan (2012) <https://www.jmlr.org/papers/v13/brown12a.html>.
 Contains, among other, minimum redundancy maximal relevancy ('mRMR') method by Peng, Long and Ding (2005) <doi:10.1109/TPAMI.2005.159>; joint mutual information ('JMI') method by Yang and Moody (1999) <https://papers.nips.cc/paper/1779-data-visualization-and-feature-selection-new-algorithms-for-nongaussian-data>; double input symmetrical relevance ('DISR') method by Meyer and Bontempi  (2006) <doi:10.1007/11732242_9> as well as joint mutual information maximisation ('JMIM') method by Bennasar, Hicks and Setchi (2015) <doi:10.1016/j.eswa.2015.07.007>.
A testing workbench to evaluate tools that calculate precision-recall curves. 
    Saito and Rehmsmeier (2015) <doi:10.1371/journal.pone.0118432>.
Clustering is unsupervised and exploratory in nature. Yet, it can be performed through penalized regression with grouping pursuit. In this package, we provide two algorithms for fitting the penalized regression-based clustering (PRclust) with non-convex grouping penalties, such as group truncated lasso, MCP and SCAD. One algorithm is based on quadratic penalty and difference convex method. Another algorithm is based on difference convex and ADMM, called DC-ADD, which is more efficient. Generalized cross validation and stability based method were provided to select the tuning parameters. Rand index, adjusted Rand index and Jaccard index were provided to estimate the agreement between estimated cluster memberships and the truth.
Provides an easy-to-use yet adaptable set of tools to conduct person-center analysis using a two-step clustering procedure. As described in Bergman and El-Khouri (1999) <DOI:10.1002/(SICI)1521-4036(199910)41:6%3C753::AID-BIMJ753%3E3.0.CO;2-K>, hierarchical clustering is performed to determine the initial partition for the subsequent k-means clustering procedure.
An implementation of the "Design Analysis" proposed by 
    Gelman and Carlin (2014) <doi:10.1177/1745691614551642>. It combines 
    the evaluation of Power-Analysis with other inferential-risks as 
    Type-M error (i.e. Magnitude) and Type-S error (i.e. Sign). See also
    Altoè et al. (2020) <doi:10.3389/fpsyg.2019.02893> and 
    Bertoldo et al. (2020) <doi:10.31234/osf.io/q9f86>.
Derives prediction rule ensembles (PREs). Largely follows the
    procedure for deriving PREs as described in Friedman & Popescu (2008; 
    <DOI:10.1214/07-AOAS148>), with adjustments and improvements. The 
    main function pre() derives prediction rule ensembles consisting of 
    rules and/or linear terms for continuous, binary, count, multinomial, 
    and multivariate continuous responses. Function gpe() derives 
    generalized prediction ensembles, consisting of rules, hinge and linear 
    functions of the predictor variables.
It contains functions to analyze the precipitation
    intensity, concentration and anomaly.
An open-access tool/framework to download, validate, visualize, and analyze multi-source precipitation data across various spatio-temporal scales. Ultimately providing the hydrology science community with the tools for consistent and reproducible analysis regarding precipitation.
Estimate sample size based on precision rather than power.
    'precisely' is a study planning tool to calculate sample size based on
    precision. Power calculations are focused on whether or not an
    estimate will be statistically significant; calculations of precision
    are based on the same principles as power calculation but turn the
    focus to the width of the confidence interval. 'precisely' is based on
    the work of 'Rothman and Greenland' (2018).
Provides a selection of tools that make it easier to place elements onto a (base R) plot exactly where you want them. It allows users to identify points and distances on a plot in terms of inches, pixels, margin lines, data units, and proportions of the plotting space, all in a manner more simple than manipulating par().
Most of the time floating point arithmetic does
  approximately the right thing.  When adding sums or having products
  of numbers that greatly differ in magnitude, the floating point
  arithmetic may be incorrect.  This package implements the Kahan
  (1965) sum <doi:10.1145/363707.363723>, Neumaier (1974) sum
  <doi:10.1002/zamm.19740540106>, pairwise-sum (adapted from 'NumPy',
  See Castaldo (2008) <doi:10.1137/070679946> for a discussion of
  accuracy), and arbitrary precision sum (adapted from the fsum in
  'Python' ; Shewchuk (1997)
  <https://people.eecs.berkeley.edu/~jrs/papers/robustr.pdf>).  In addition,
  products are changed to long double precision for accuracy, or
  changed into a log-sum for accuracy.
Useful git hooks for R building on top of the multi-language
    framework 'pre-commit' for hook management. This package provides git
    hooks for common tasks like formatting files with 'styler' or spell
    checking as well as wrapper functions to access the 'pre-commit'
    executable.
Accurate calculations and visualization of precision-recall and ROC (Receiver Operator Characteristics)
    curves. Saito and Rehmsmeier (2015) <doi:10.1371/journal.pone.0118432>.
A computational model for predicting proteins encoded by circadian genes. The support vector machine has been employed with Laplace kernel for prediction of circadian proteins, where compositional, transitional and physico-chemical features were utilized as numeric features. User can predict for the test dataset using the proposed computational model. Besides, the user can also build their own training model using their training dataset, followed by prediction for the test set.
Based on different statistical definitions of discrimination, several methods have been proposed to detect and mitigate social inequality in machine learning models. 
  This package aims to provide an alternative to fairness treatment in predictive models. The ROC method implemented in this package
  is described by Kamiran, Karim and Zhang (2012) <https://ieeexplore.ieee.org/document/6413831/>.
Performs genomic prediction of hybrid performance using eight GS methods including GBLUP, BayesB, RKHS, PLS, LASSO, Elastic net, Random forest and XGBoost. It also provides fast cross-validation and mating design scheme for training population (Xu S et al (2016) <doi:10.1111/tpj.13242>; Xu S (2017) <doi:10.1534/g3.116.038059>).
Draw 2 dimensional and three dimensional plot for multiple regression models using package 'ggplot2' and 'rgl'.
   Supports linear models (lm), generalized linear models (glm) and local polynomial regression fittings (loess).
We included functions to assess the performance of
 risk models. The package contains functions for the various measures that are
 used in empirical studies, including univariate and multivariate odds ratios
 (OR) of the predictors, the c-statistic (or area under the receiver operating
 characteristic (ROC) curve (AUC)), Hosmer-Lemeshow goodness of fit test,
 reclassification table, net reclassification improvement (NRI) and
 integrated discrimination improvement (IDI). Also included are functions
 to create plots, such as risk distributions, ROC curves, calibration plot,
 discrimination box plot and predictiveness curves. In addition to functions
 to assess the performance of risk models, the package includes functions to
 obtain weighted and unweighted risk scores as well as predicted risks using
 logistic regression analysis. These logistic regression functions are
 specifically written for models that include genetic variables, but they
 can also be applied to models that are based on non-genetic risk factors only.
 Finally, the package includes function to construct a simulated dataset with 
 genotypes, genetic risks, and disease status for a hypothetical population, which 
 is used for the evaluation of genetic risk models.
A one-function package containing 'prediction()', a type-safe alternative to 'predict()' that always returns a data frame. The 'summary()' method provides a data frame with average predictions, possibly over counterfactual versions of the data (a la the 'margins' command in 'Stata'). Marginal effect estimation is provided by the related package, 'margins' <https://cran.r-project.org/package=margins>. The package currently supports common model types (e.g., "lm", "glm") from the 'stats' package, as well as numerous other model classes from other add-on packages. See the README or main package documentation page for a complete listing.
A common problem faced by journal reviewers and authors is the question of
     whether the results of a replication study are consistent with the original
     published study. One solution to this problem is to examine the effect size
     from the original study and generate the range of effect sizes that could
     reasonably be obtained (due to random sampling) in a replication attempt
     (i.e., calculate a prediction interval). This package has functions that calculate
     the prediction interval for the correlation (i.e., r),
     standardized mean difference (i.e., d-value), and mean.
Functions to get prediction intervals and prediction points of future observations from any continuous distribution.
Three prediction algorithms described in the paper
        "On-line predictive linear regression" Annals of Statistics 37,
        1566 - 1590 (2009)
Providing functions to diagnose 
  and make inferences from various linear models, such as those obtained from 'aov', 
  'lm', 'glm', 'gls', 'lme', and 'lmer'. Inferences include predicted 
  means and standard errors, contrasts, multiple comparisons, permutation tests and graphs.
Predicts the most common race of a surname and based on U.S. Census
    data, and the most common first named based on U.S. Social Security Administration data.
Methods for assessing the performance of a prediction model with respect to identifying patient-level treatment benefit. All methods are applicable for continuous and binary outcomes, and for any type of statistical or machine-learning prediction model as long as it uses baseline covariates to predict outcomes under treatment and control.
An implementation of prediction intervals for overdispersed count data,
            for overdispersed binomial data and for linear random effects models.
Implements the predictive k-means method for clustering observations, using a mixture of experts model to allow covariates to influence cluster centers. Motivated by air pollution epidemiology settings, where cluster membership needs to be predicted across space. Includes functions for predicting cluster membership using spatial splines and principal component analysis (PCA) scores using either multinomial logistic regression or support vector machines (SVMs). For method details see Keller et al. (2017) <doi:10.1214/16-AOAS992>.
Recent years have seen an increased interest in novel methods
    for analyzing quantitative data from experimental psychology. Currently, however, they lack an
    established and accessible software framework. Many existing implementations provide no guidelines,
    consisting of small code snippets, or sets of packages. In addition, the use of existing packages
    often requires advanced programming experience. 'PredPsych' is a user-friendly toolbox based on
    machine learning predictive algorithms. It comprises of multiple functionalities for multivariate
    analyses of quantitative behavioral data based on machine learning models.
An implementation of reliability estimation methods described in the paper (Bosnic, Z., & Kononenko, I. (2008) <doi:10.1007/s10489-007-0084-9>), which allows you to test the reliability of a single predicted instance made
             by your model and prediction function. It also allows you to make a correlation test to estimate which reliability estimate is the most 
             accurate for your model.
Provides additional functions for evaluating predictive models, including plotting calibration curves and model-based Receiver Operating Characteristic (mROC) based on Sadatsafavi et al (2021) <arXiv:2003.00316>.
Makes the time series prediction easier by automatizing this process
  using four main functions: prep(), modl(), pred() and postp(). Features different
  preprocessing methods to homogenize variance and to remove trend and seasonality.
  Also has the potential to bring together different predictive models to make comparatives.
  Features ARIMA and Data Mining Regression models (using caret).
Allows users to derive multi-objective weights from pairwise comparisons, which
    research shows is more repeatable, transparent, and intuitive other techniques. These weights
    can be rank existing alternatives or to define a multi-objective utility function for optimization.
This is an accessible template for 'pkgdown'. It uses two
    bootstrap themes, Flatly and Darkly and utilizes the
    'prefers-color-scheme' CSS variable to automatically serve either of
    the two based on user’s operating system setting, or allowing them to
    manually toggle between them.
Design and analyze two-stage randomized trials with a continuous
    outcome measure. The package contains functions to compute the required 
    sample size needed to detect a given preference, treatment, and selection 
    effect; alternatively, the package contains functions that can report the 
    study power given a fixed sample size. Finally, analysis functions are 
    provided to test each effect using either summary data (i.e. means, 
    variances) or raw study data <doi:10.18637/jss.v094.c02>.
Generates design matrix for analysing real paired comparisons and derived paired comparison data (Likert type items/ratings or rankings) using a loglinear approach. Fits loglinear Bradley-Terry model (LLBT) exploiting an eliminate feature. Computes pattern models for paired comparisons, rankings, and ratings. Some treatment of missing values (MCAR and MNAR). Fits latent class (mixture) models for paired comparison, rating and ranking patterns using a non-parametric ML approach.
Dynamize headers or R code within 'Rmd' files to prevent proliferation of 'Rmd' files for similar reports. Add in external HTML document within 'rmarkdown' rendered HTML doc.
Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically linking a response vector to covariate data through cluster membership. The package allows Bernoulli, Binomial, Poisson, Normal, survival and categorical response, as well as Normal and discrete covariates. It also allows for fixed effects in the response model, where a spatial CAR (conditional autoregressive) term can be also included. Additionally, predictions may be made for the response, and missing values for the covariates are handled. Several samplers and label switching moves are implemented along with diagnostic tools to assess convergence. A number of R functions for post-processing of the output are also provided. In addition to fitting mixtures, it may additionally be of interest to determine which covariates actively drive the mixture components. This is implemented in the package as variable selection. The main reference for the package is Liverani, Hastie, Azizi, Papathomas and Richardson (2015) <doi:10.18637/jss.v064.i07>.
A database containing the names 
  of the babies born in Quebec between 1980 and 2020.
Prepares data for statistical analysis (e.g., analysis of variance
  ;ANOVA) by enabling the user to easily and quickly merge (using the
  file_merge() function) raw data files into one merged table and then
  aggregate the merged table (using the prep() function) into a finalized
  table while keeping track and summarizing every step of the preparation.
  The finalized table contains several possibilities for dependent measures of
  the dependent variable. Most suitable when measuring variables in an
  interval or ratio scale (e.g., reaction-times) and/or discrete values such
  as accuracy. Main functions included are file_merge() and prep(). The
  file_merge() function vertically merges individual data files (in a long
  format) in which each line is a single observation to one single dataset.
  The prep() function aggregates the single dataset according to any
  combination of grouping variables (i.e., between-subjects and
  within-subjects independent variables, respectively), and returns a data
  frame with a number of dependent measures for further analysis for each cell
  according to the combination of provided grouping variables. Dependent
  measures for each cell include among others means before and after rejecting
  all values according to a flexible standard deviation criteria, number of
  rejected values according to the flexible standard deviation criteria,
  proportions of rejected values according to the flexible standard deviation
  criteria, number of values before rejection, means after rejecting values
  according to procedures described in Van Selst & Jolicoeur (1994; suitable
  when measuring reaction-times), standard deviations, medians, means according
  to any percentile (e.g., 0.05, 0.25, 0.75, 0.95) and harmonic means. The data
  frame prep() returns can also be exported as a txt file to be used for
  statistical analysis in other statistical programs.
A figure region is prepared, creating a plot region with suitable background color, grid lines or shadings, and providing axes and labeling if not suppressed. Subsequently, information carrying graphics elements can be added (points, lines, barplot with add=TRUE and so forth).
Provides classes to pre-process microarray gene
  expression data as part of the OOMPA collection of packages
  described at <http://oompa.r-forge.r-project.org/>.
Preprocess numeric data matrices into desired transformed representations.
    Standardization, Unitization, Cubitization and adaptive intervals are offered.
An interactive document for preprocessing the dataset using  'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://analyticmodels.shinyapps.io/PREPShiny/>.
Miscellaneous small utilities are provided to mitigate issues with messy, inconsistent or high dimensional data and help for preprocessing and preparing analyses.
Provides a collection of templates to author preregistration documents for scientific studies in PDF format.
Preregistrations, or more generally, registrations, enable
    explicit timestamped and (often but not necessarily publicly) frozen
    documentation of plans and expectations as well as decisions and
    justifications. In research, preregistrations are commonly used to
    clearly document plans and facilitate justifications of deviations from
    those plans, as well as decreasing the effects of publication bias by
    enabling identification of research that was conducted but not published.
    Like reporting guidelines, (pre)registration forms often have specific
    structures that facilitate systematic reporting of important items. The
    'preregr' package facilitates specifying (pre)registrations in R and
    exporting them to a human-readable format (using R Markdown partials or
    exporting to an 'HTML' file) as well as human-readable embedded data
    (using 'JSON'), as well as importing such exported (pre)registration
    specifications from such embedded 'JSON'.
Provides a set of functions useful when evaluating the results of presence-absence models. Package includes functions for calculating threshold dependent measures such as confusion matrices, pcc, sensitivity, specificity, and Kappa, and produces plots of each measure as the threshold is varied. It will calculate optimal threshold choice according to a choice of optimization criteria. It also includes functions to plot the threshold independent ROC curves along with the associated AUC (area under the curve).
Makes output files from select PreSens Fiber Optic Oxygen
    Transmitters easier to work with in R. See <http://www.presens.de> for more
    information about PreSens (Precision Sensing GmbH). Note: this package is
    neither created nor maintained by PreSens.
Consists of custom wrapper functions using packages
    'openxlsx', 'flextable', and 'officer' to create highly formatted MS office friendly output of your data frames.
    These viewer friendly outputs are intended to match expectations of professional looking presentations
    in business and consulting scenarios. The functions are opinionated in the sense that they expect the input data
    frame to have certain properties in order to take advantage of the automated formatting.
Compilation and digitalization of the official registry of victims of state terrorism in Argentina during the last
             military coup. The original data comes from RUVTE-ILID (2019) <https://www.argentina.gob.ar/sitiosdememoria/ruvte/informe> and <http://basededatos.parquedelamemoria.org.ar/registros/>. The title, presentes, comes from present in spanish.
Originally as an R version of Preseq <doi:10.1038/nmeth.2375>, the package has extended its functionality to predict the r-species accumulation curve (r-SAC), which is the number of species represented at least r times as a function of the sampling effort. When r = 1, the curve is known as the species accumulation curve, or the library complexity curve in high-throughput genomic sequencing. The package includes both parametric and nonparametric methods, as described by Deng C, et al. (2018) <arXiv:1607.02804v3>.
Computes probability-scale residuals and residual correlations
    for continuous, ordinal, binary, count, and time-to-event data <doi:10.18637/jss.v094.i12>.
Bland (2009) <doi:10.1136/bmj.b3985> recommended to
    base study sizes on the width of the confidence interval rather the power of 
    a statistical test. The goal of 'presize' is to provide functions for such 
    precision based sample size calculations. For a given sample size, the 
    functions will return the precision (width of the confidence interval), and 
    vice versa.
Provides a function for estimating the transition probabilities in an illness-death model. 
            The transition probabilities can be estimated from the unsmoothed landmark estimators developed 
            by de Una-Alvarez and Meira-Machado (2015) <doi:10.1111/biom.12288>.
            Presmoothed estimates can also be obtained through the use of a parametric family of binary
            regression curves, such as logit, probit or cauchit. The additive logistic regression model 
            and nonparametric regression are also alternatives which have been implemented.
            The idea behind the presmoothed landmark estimators is to use the presmoothing techniques 
            developed by  Cao et al. (2005) <doi:10.1007/s00180-007-0076-6> in the 
            landmark estimation of the transition probabilities.
Create a web app that makes it easier to test web clients
  without using the internet. It includes a web app framework with path
  matching and parameters and templates. Can parse various 'HTTP' request
  bodies. Can send 'JSON' data or files from the disk. Includes a web app
  that implements the <https://httpbin.org> web service.
This is a computational package designed to identify the most sensitive interactions within a network which must be estimated most accurately in order to produce qualitatively robust predictions to a press perturbation. This is accomplished by enumerating the number of sign switches (and their magnitude) in the net effects matrix when an edge experiences uncertainty. The package produces data and visualizations when uncertainty is associated to one or more edges in the network and according to a variety of distributions. The software requires the network to be described by a system of differential equations but only requires as input a numerical Jacobian matrix evaluated at an equilibrium point. This package is based on Koslicki, D., & Novak, M. (2017) <doi:10.1007/s00285-017-1163-0>.
Provides 'RStudio' addins to prettify 'HTML', 'CSS', 'SCSS', 'JavaScript', 'JSX', 'Markdown', 'C(++)', 'LaTeX', 'Python', 'Julia', 'XML', 'Java', 'JSON', 'Ruby', and to reindent 'C(++)', 'Fortran', 'Java', 'Julia', 'Python', 'SAS', 'Scala', 'Shell', 'SQL' and "TypeScript". Two kinds of addins are provided: 'Prettify' and 'Indent'. The 'Indent' addins only reindent the code, while the 'Prettify' addins also modify the code, e.g. trailing semi-colons are added to 'JavaScript' code when they are missing.
Drop-in replacements for standard base graphics
    functions. The replacements are prettier versions of the originals.
Replace the standard print method for functions with one that
    performs syntax highlighting, using ANSI colors, if the terminal
    supports them.
Creating tiny yet beautiful documents and vignettes from R
    Markdown. The package provides the 'html_pretty' output format as an
    alternative to the 'html_document' and 'html_vignette' engines that
    convert R Markdown into HTML pages. Various themes and syntax highlight
    styles are supported.
One of the main advantages of using Generalised
    Linear Models is their interpretability.  The goal of 'prettyglm' is to
    provide a set of functions which easily create beautiful coefficient
    summaries which can readily be shared and explained.
Simple and crisp publication-quality graphics for the ExPosition family of packages.
  See An ExPosition of the Singular Value Decomposition in R (Beaton et al 2014) <doi:10.1016/j.csda.2013.11.006>.
Automates the process of creating a scale bar and north arrow in
    any package that uses base graphics to plot in R. Bounding box tools help find
    and manipulate extents. Finally, there is a function to automate the process
    of setting margins, plotting the map, scale bar, and north arrow, and resetting
    graphic parameters upon completion.
Functions for conventionally formatting descriptive stats,
 reshaping data frames and formatting R output as HTML.
Pretty, human readable formatting of quantities.
    Time intervals: '1337000' -> '15d 11h 23m 20s'.
    Vague time intervals: '2674000' -> 'about a month ago'.
    Bytes: '1337' -> '1.34 kB'.
The prevalence package provides Frequentist and Bayesian methods for prevalence assessment studies. IMPORTANT: the truePrev functions in the prevalence package call on JAGS (Just Another Gibbs Sampler), which therefore has to be available on the user's system. JAGS can be downloaded from http://mcmc-jags.sourceforge.net/.
Easy and efficient access to the API provided by 'Prevedere', 
  an industry insights and predictive analytics company. Query and 
  download indicators, models and workbenches built with 'Prevedere' for further 
  analysis and reporting <https://www.prevedere.com/>.
For working with the 'Prevision.io' AI model management platform's API <https://prevision.io/>.
Provides functions for both likelihood-based
    and Bayesian analysis of spatially referenced prevalence data. For a tutorial on the use of the R package, see Giorgi and Diggle (2017) <doi:10.18637/jss.v078.i08>.
Spatial estimation of a prevalence surface
    or a relative risks surface, using data from a Demographic and Health
    Survey (DHS) or an analog survey, see Larmarange et al. (2011)
    <doi:10.4000/cybergeo.24606>.
Functions to simulate point prevalence studies (PPSs) of healthcare-associated infections (HAIs) and to convert prevalence to incidence in steady state setups. 
    Companion package to the preprint Willrich et al., From prevalence to incidence - a new approach in the hospital setting;
    <doi:10.1101/554725> , where methods are explained in detail.
Standardize the pre-processing of genomic variants before 
    performing a bacterial genome-wide association study (bGWAS). 'prewas'
    creates a variant matrix (where each row is a variant, each column is a 
    sample, and the entries are presence - 1 - or absence - 0 - of the variant) 
    that can be used as input for bGWAS tools. When creating the binary variant
    matrix, 'prewas' can perform 3 pre-processing steps including: dealing with 
    multiallelic SNPs, (optional) dealing with SNPs in overlapping genes, and 
    choosing a reference allele. 'prewas' can output matrices for use with both 
    SNP-based bGWAS and gene-based bGWAS. This method is described in Saund et 
    al. (2020) <doi:10.1099/mgen.0.000368>. 'prewas' can also provide 
    gene matrices for variants with specific annotations from the 'SnpEff' 
    software (Cingolani et al. 2012).
Estimate False Discovery Rates (FDRs) for importance metrics from
    random forest runs.
Preparing a scanner data set for price dynamics calculations (data selecting, data classification, data matching, data filtering). Computing bilateral and multilateral indexes. For details on these methods see: Diewert and Fox (2020) 
    <doi:10.1080/07350015.2020.1816176>, Białek (2019) <doi:10.2478/jos-2019-0014> or Białek (2020) <doi:10.2478/jos-2020-0037>.
Functions to aid in micro and macro economic analysis and handling of price and
    currency data. Includes extraction of relevant inflation and exchange rate data from World Bank
    API, data cleaning/parsing, and standardisation. Inflation adjustment
    calculations as found in Principles of Macroeconomics by Gregory Mankiw et al (2014). Current
    and historical end of day exchange rates for 171 currencies from the European Central Bank
    Statistical Data Warehouse (2020) <https://sdw.ecb.europa.eu/curConverter.do>.
An implementation of the van Westendorp Price
    Sensitivity Meter in R, which is a survey-based approach
	to analyze consumer price preferences and sensitivity
    (van Westendorp 1976, isbn:9789283100386).
Implementation of an oligonucleotide primer and probe design algorithm using a linearly scaling approximation of set coverage. A detailed description available at Smolander and Tamminen, 2021; <doi:10.1101/2021.09.06.459073>.
Patient Rule Induction Method (PRIM) for bump hunting in high-dimensional data.
Implements a unified framework of parametric simplex method for a variety of sparse learning problems (e.g., Dantzig selector (for linear regression), sparse quantile regression, sparse support vector machines, and compressive sensing) combined with efficient hyper-parameter selection strategies. The core algorithm is implemented in C++ with Eigen3 support for portable high performance linear algebra. For more details about parametric simplex method, see Haotian Pang (2017) <https://papers.nips.cc/paper/6623-parametric-simplex-method-for-sparse-learning.pdf>.
Use Prime Factorization for simplifying computations,
    for instance for ratios of large factorials.
Implements the primePCA algorithm, developed and analysed in Zhu, Z., Wang, T. and Samworth, R. J. (2019) High-dimensional principal component analysis with heterogeneous missingness. <arXiv:1906.12125>.
Functions are primarily functions for systems of ordinary differential equations, difference equations, and eigenanalysis and projection of demographic matrices; data are for examples.
Identifies potential target sequences for a given
    set of primers and generates phylogenetic trees annotated with the
    taxonomies of the predicted amplification products.
Fast functions for dealing with prime numbers, such as testing
    whether a number is prime and generating a sequence prime numbers.
    Additional functions include finding prime factors and Ruth-Aaron pairs,
    finding next and previous prime numbers in the series, finding or estimating
    the nth prime, estimating the number of primes less than or equal to an
    arbitrary number, computing primorials, prime k-tuples (e.g., twin primes),
    finding the greatest common divisor and smallest (least) common multiple,
    testing whether two numbers are coprime, and computing Euler's totient
    function. Most functions are vectorized for speed and convenience.
R interface to 'PRIMME' <http://www.cs.wm.edu/~andreas/software/>, a C library for computing a few
    eigenvalues and their corresponding eigenvectors of a real symmetric or complex
    Hermitian matrix, or generalized Hermitian eigenproblem.  It can also compute
    singular values and vectors of a square or rectangular matrix. 'PRIMME' finds
    largest, smallest, or interior singular/eigenvalues and can use preconditioning
    to accelerate convergence. General description of the methods are provided in the papers
    Stathopoulos (2010, <doi:10.1145/1731022.1731031>) and Wu (2017, <doi:10.1137/16M1082214>).
    See 'citation("PRIMME")' for details.
Fitting a principal curve to a data matrix in arbitrary dimensions. 
  Hastie and Stuetzle (1989) <doi:10.2307/2289936>.
Provides capabilities beyond principal components
    analysis to focus on finding structure in low variability
    subspaces. Constructs and plots simple basis vectors for
    pre-defined and user-defined measures of simplicity.
Extends the S3 generic function knit_print() in 'knitr'
    to automatically print some objects using an appropriate format such as
    Markdown or LaTeX. For example, data frames are automatically printed as
    tables, and the help() pages can also be rendered in 'knitr' documents.
Provides methods for reducing the number of features within a data set. See Bauer JO (2021) <doi:10.1145/3475827.3475832> and Bauer JO, Drabant B (2021) <doi:10.1016/j.jmva.2021.104754> for more information on principal loading analysis.
In gene sequencing methods, the topological features of protein-protein interaction (PPI) networks are 
    often used, such as ToppNet <https://toppgene.cchmc.org>. In this study, a candidate gene prioritization method
    was proposed for non-communicable diseases considering disease risks transferred between genes in weighted disease 
    PPI networks with weights for nodes and edges based on functional information.
Prioritize candidate cancer drugs for drug repositioning based on the random walk with restart algorithm in a drug-drug functional similarity network. 1) We firstly constructed a drug-drug functional similarity network by integrating pathway activity and drug activity derived from the NCI-60 cancer cell lines. 2) Secondly, we calculated drug repurposing score according to a set of approved therapeutic drugs of interested cancer based on the random walk with restart algorithm in the drug-drug functional similarity network. 3) Finally, the permutation test was used to calculate the statistical significance level for the drug repurposing score.
Translates beliefs into prior information in the form of Beta and Gamma distributions. It can be mainly used for the generation of priors on the prevalence of disease and the sensitivity/specificity of diagnostic tests.
This uses a mixed integer mathematical programming (MIP)
        approach for building and solving multi-action planning problems, 
        where the goal is to find an optimal combination of management actions that
        abate threats, in an efficient way while accounting for spatial aspects. 
        Thus, optimizing the connectivity and conservation effectiveness of 
        the prioritized units and of the deployed actions. The package is capable of 
        handling different commercial (gurobi) and non-commercial (symphony) MIP solvers. 
        Gurobi optimization solver can be installed using comprehensive instructions in 
        the gurobi installation vignette of the prioritizr package (available in 
        <https://prioritizr.net/articles/gurobi_installation_guide.html>). Methods used in the 
        package refers to Salgado-Rojas et al. (2020) <doi:10.1016/j.ecolmodel.2019.108901>,
        Beyer et al. (2016) <doi:10.1016/j.ecolmodel.2016.02.005>, Cattarino et al. (2015)
        <doi:10.1371/journal.pone.0128027> and Watts et al. (2009) <doi:10.1016/j.envsoft.2009.06.005>. 
        See the prioriactions website for more information, documentations and examples.
Systematic conservation prioritization using mixed integer linear
    programming (MILP). It provides a flexible interface for building and
    solving conservation planning problems. Once built, conservation planning
    problems can be solved using a variety of commercial and open-source exact
    algorithm solvers. By using exact algorithm solvers, solutions can be
    generated that are guaranteed to be optimal (or within a pre-specified
    optimality gap). Furthermore, conservation problems can be constructed to
    optimize the spatial allocation of different management actions or zones,
    meaning that conservation practitioners can identify solutions that benefit
    multiple stakeholders. To solve large-scale or complex conservation
    planning problems, users should install the Gurobi optimization software
    (available from <https://www.gurobi.com/>) and the 'gurobi' R package (see
    Gurobi Installation Guide vignette for details). Additionally, the 'rcbc' R
    package (available at <https://github.com/dirkschumacher/rcbc>) can be used
    to generate solutions using the CBC optimization software
    (<https://projects.coin-or.org/Cbc>).
Conservation planning datasets for learning how to use the
    'prioritizr' package <https://CRAN.R-project.org/package=prioritizr>.
Fits successive Lasso models for several blocks of (omics) data with different priorities and takes the predicted values as an offset for the next block.
Allows users to access the Oregon State Prism climate data
    (<https://prism.nacse.org/>). Using the web service API data
    can easily downloaded in bulk and loaded into R for spatial analysis.
    Some user friendly visualizations are also provided.
Implements Penalized Regression with Inferred Seasonality Module (PRISM) to generate forecast estimation of weekly unemployment initial claims using 'Google Trends' data. It includes required data and tools for backtesting the performance in 2007-2020.
Loads and processes huge text
    corpora processed with the sally toolbox (<http://www.mlsec.org/sally/>).
    sally acts as a very fast preprocessor which splits the text files into
    tokens or n-grams. These output files can then be read with the PRISMA
    package which applies testing-based token selection and has some
    replicate-aware, highly tuned non-negative matrix factorization and
    principal component analysis implementation which allows the processing of
    very big data sets even on desktop machines.
Systematic reviews should be described in a high degree of 
    methodological detail. The 'PRISMA' Statement calls for a high level of 
    reporting detail in systematic reviews and meta-analyses. An integral part 
    of the methodological description of a review is a flow diagram. 
    This package produces an interactive flow diagram that conforms to the 
    'PRISMA2020' preprint. When made interactive, the reader/user can click 
    on each box and be directed to another website or file online (e.g. a 
    detailed description of the screening methods, or a list of excluded full 
    texts), with a mouse-over tool tip that describes the information linked 
    to in more detail. Interactive versions can be saved as HTML files, 
    whilst static versions for inclusion in manuscripts can be saved as 
    HTML, PDF, PNG, SVG, PS or WEBP files.
Creates 'PRISMA' <http://prisma-statement.org/> diagram from a minimal dataset of included and excluded studies and allows for more custom diagrams. 'PRISMA' diagrams are used to track the identification, screening, eligibility, and inclusion of studies in a systematic review.
Plot a PRISMA <http://prisma-statement.org/> flow
    chart describing the identification, screening, eligibility and
    inclusion or studies in systematic reviews. The PRISMA statement
    defines an evidence-based, minimal set of items for reporting in
    systematic reviews and meta-analyses. PRISMA should be used for the
    reporting of studies evaluating randomized clinical trials (RCT), and
    is also for reporting on systematic reviews of other types of
    research. There is also a function to generate flow charts describing
    exclusions and inclusions for any kind of study.
Manipulate and visualize colors in a intuitive,
    low-dependency and functional way.
Download, parses and tidies information from the World Prison Brief project <http://www.prisonstudies.org/>.
Enables user to perform the following:
    1. Roll 'n' number of die/dice (roll()).
    2. Toss 'n' number of coin(s) (toss()).
    3. Play the game of Rock, Paper, Scissors.
    4. Choose 'n' number of card(s) from a pack of 52 playing cards (Joker optional).
Implements two differentially private algorithms for 
  estimating L2-regularized logistic regression coefficients. A randomized
  algorithm F is epsilon-differentially private (C. Dwork, Differential
  Privacy, ICALP 2006 <DOI:10.1007/11681878_14>), if 
     |log(P(F(D) in S)) - log(P(F(D') in S))| <= epsilon
  for any pair D, D' of datasets that differ in exactly one record, any
  measurable set S, and the randomness is taken over the choices F makes.
Estimation of prevalence ratios using logistic models and confidence intervals with delta and bootstrap methods.
Survey sampling using permanent random numbers (PRN's). A solution to the
    problem of unknown overlap between survey samples, which leads to a low
    precision in estimates when the survey is repeated or combined with other
    surveys. The PRN solution is to supply the U(0, 1) random numbers to the 
    sampling procedure, instead of having the sampling procedure generate them.
    In Lindblom (2014) <doi:10.2478/jos-2014-0047>, and therein cited articles,
    it is shown how this is carried out and how it improves the estimates. This
    package supports two common fixed-size sampling procedures (simple random 
    sampling and probability-proportional-to-size sampling) and includes a 
    function for transforming the PRN's in order to control the sample overlap.
Optogenetics is a new tool to study neuronal circuits that have been genetically modified to allow stimulation by flashes of light.  This package implements the methodological framework, Point-process Response model for Optogenetics (PRO), for analyzing data from these experiments.  This method provides explicit nonlinear transformations to link the flash point-process with the spiking point-process.  Such response functions can be used to provide important and interpretable scientific insights into the properties of the biophysical process that governs neural spiking in response to optogenetic stimulation.
A collection of tools to facilitate standardized analysis 
    and graphical procedures when using the National Cancer Institute’s 
    Patient-Reported Outcomes version of the Common Terminology Criteria for 
    Adverse Events (PRO-CTCAE). NOTE: Some tools here require additional 
    packages be installed which are not available on CRAN at the time of this 
    version update ('ProAE' v0.2.8). These packages and their respective
    repositories are listed here: 'ggpattern' 
    <https://github.com/coolbutuseless/ggpattern>.
A framework for performing elementary probability
 calculations on finite sample spaces, which may be represented by data frames
 or lists.  Functionality includes setting up sample spaces, counting tools,
 defining probability spaces, performing set algebra, calculating probability
 and conditional probability, tools for simulation and checking the law of
 large numbers, adding random variables, and finding marginal distributions.
 Characteristic functions for all base R distributions are included.
Models can be improved by post-processing class probabilities, by: recalibration, conversion to hard probabilities, assessment of equivocal zones, and other activities. 'probably' contains tools for conducting these operations.
Functions and datasets  to accompany J. Albert and J. Hu, "Probability and Bayesian Modeling", CRC Press, (2019, ISBN: 1138492566).
Hybridization probes for target sequences can be made based on melting temperature value calculated by R package 'TmCalculator' <https://CRAN.R-project.org/package=TmCalculator> and methods extended from Beliveau, B. J.,(2018) <doi:10.1073/pnas.1714530115>, and those hybridization probes can be used to capture specific target regions in fluorescence in situ hybridization and next generation sequence experiments.
Contains functions that are useful for probing moderation effects (or interactions) including techniques such as pick-a-point (also known as spotlight analysis) and Johnson-Neyman (also known as floodlight analysis). Plot function is also provided to facilitate visualization of results from each of these techniques.
Probabilistic Fisher discriminant analysis (pFDA) is a probabilistic version of the popular and powerful Fisher linear discriminant analysis for dimensionality reduction and classification.
Fast estimation of binomial spatial probit regression models with spatial autocorrelation for big datasets.
Computes predicted probabilities and marginal effects for 
  binary & ordinal logit and probit, (partial) generalized ordinal & 
  multinomial logit models estimated with the glm(), clm() (in the 
  'ordinal' package), and vglm() (in the 'VGAM' package) functions.
Estimates unsupervised outlier probabilities for multivariate numeric data with many observations from a nonparametric outlier statistic.
Training of reconciliation weights for probabilistic forecasts to optimise total energy (or variogram) score using Stochastic Gradient Descent with automatically differentiated gradients. See Panagiotelis, Gamakumara, Athanasopoulos and Hyndman, (2020) <https://www.monash.edu/business/ebs/research/publications/ebs/wp26-2020.pdf> for a description of the methods.
It allows the user to determine sample sizes, select probabilistic samples,
 make estimates of different parameters for the total finite population and in studio domains,
 using the main design drawings.
An interactive document on  the topic of basic probability using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://analyticmodels.shinyapps.io/BayesShiny/>.
Confidence intervals and point estimation for R under various parametric model assumptions; likelihood inference based on classical first-order approximations and higher-order asymptotic procedures.
Tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). (Partial) area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.
Package for processing downloaded MODIS Calibrated radiances
    Product HDF files. Specifically, MOD02 calibrated radiance product files, and
    the associated MOD03 geolocation files (for MODIS-TERRA). The package will be
    most effective if the user installs MRTSwath (MODIS Reprojection Tool for swath
    products; <https://lpdaac.usgs.gov/tools/modis_reprojection_tool_swath>, and
    adds the directory with the MRTSwath executable to the default R PATH by editing
    ~/.Rprofile.
Provides tools for exploratory process data analysis. Process data refers to the data describing
    participants' problem-solving processes in computer-based assessments. It is often recorded in computer
    log files. This package provides functions to read, process, and write process data. It also implements
    two feature extraction methods to compress the information stored in process data into standard 
    numerical vectors. This package also provides recurrent neural network based models that relate response processes 
    with other binary or scale variables of interest. The functions that involve training and evaluating neural networks 
    are wrappers of functions in 'keras'.
A small, dependency-free way to generate random names. Methods 
    provided include the adjective-surname approach of Docker containers 
    ('<https://github.com/moby/moby/blob/master/pkg/namesgenerator/names-generator.go'),
    and combinations of common English or Spanish words.
Provides animated process maps based on the 'procesmapR' package.
  Cases stored in event logs created with with 'bupaR' S3 class eventlog() are
  rendered as tokens (SVG shapes) and animated according to their occurrence 
  times on top of the process map. For rendering SVG animations ('SMIL') and the
  'htmlwidget' package are used.
Check compliance of event-data from (business) processes with respect to specified rules. Rules supported are of three types: frequency (activities that should (not) happen x number of times), order (succession between activities) and exclusiveness (and and exclusive choice between activities).
Visualize event logs using directed graphs, i.e. process maps. Part of the 'bupaR' framework.
Functions for constructing dashboards for business process monitoring. Building on the event log objects class from package 'bupaR'. Allows the use to assemble custom shiny dashboards based on process data.
Perform moderation, mediation, moderated mediation and moderated moderation. 
   Inspired from famous 'PROCESS' macro for 'SPSS' and 'SAS' created by Andrew Hayes.
Tools to run system processes in the background.  It can
    check if a background process is running; wait on a background process
    to finish; get the exit status of finished processes; kill background
    processes. It can read the standard output and error of the processes,
    using non-blocking connections. 'processx' can poll a process for
    standard output or error, with a timeout. It can also poll several
    processes at once.
Portable '/proc/self/maps' as a data frame.
    Determine which library or other region is mapped to a specific
    address of a process. --
    R packages can contain native code, compiled to shared libraries at build or
    installation time.
    When loaded, each shared library occupies a portion of the address space of
    the main process.
    When only a machine instruction pointer is available (e.g. from a backtrace
    during error inspection or profiling), the address space map determines
    which library this instruction pointer corresponds to.
Estimates corrected Procrustean correlation between matrices for removing overfitting effect. Coissac Eric and Gonindard-Melodelima Christelle (2019) <doi:10.1101/842070>.
A direct and flexible method for estimating an ICA model. This approach estimates the densities for each component directly via a tilted Gaussian. The tilt functions are estimated via a GAM Poisson model. Details can be found in "Elements of Statistical Learning (2nd Edition)" in Section 14.7.4.
Implements the methods proposed by Olley, G.S. and Pakes, A. (1996) <doi:10.2307/2171831>, Levinsohn, J. and Petrin, A. (2003) <doi:10.1111/1467-937X.00246>, Ackerberg, D.A. and Caves, K. and Frazer, G. (2015) <doi:10.3982/ECTA13408> and Wooldridge, J.M. (2009) <doi:10.1016/j.econlet.2009.04.026> for structural productivity estimation .
Create a project directory structure, along with typical files
    for that project.  This allows projects to be quickly and easily created,
    as well as for them to be standardized. Designed specifically with scientists
    in mind (mainly bio-medical researchers, but likely applies to other fields).
Fast and user friendly implementation of nonparametric estimators
    for censored event history (survival) analysis. Kaplan-Meier and
    Aalen-Johansen method.
Levels and changes of productivity and profitability are measured with various indices.
  The package contains the multiplicatively complete Färe-Primont, Fisher, Hicks-Moorsteen, 
  Laspeyres, Lowe, and Paasche indices, as well as the classic Malmquist productivity index.
  Färe-Primont and Lowe indices verify the transitivity property and can therefore be used for 
  multilateral or multitemporal comparison.
  Fisher, Hicks-Moorsteen, Laspeyres, Malmquist, and Paasche indices are not transitive and are 
  only to be used for binary comparison.
  All indices can also be decomposed into different components, providing insightful information 
  on the sources of productivity and profitability changes.
  In the use of Malmquist productivity index, the technological change index can be further 
  decomposed into bias technological change components.
  The package also allows to prohibit technological regression (negative technological change). In 
  the case of the Fisher, Hicks-Moorsteen, Laspeyres, Paasche and the transitive Färe-Primont 
  and Lowe indices, it is furthermore possible to rule out technological change. 
  Deflated shadow prices can also be obtained. Besides, the package allows parallel computing as 
  an option, depending on the user's computer configuration. 
  All computations are carried out with the nonparametric Data Envelopment Analysis (DEA), and 
  several assumptions regarding returns to scale are available.
  All DEA linear programs are implemented using 'lp_solve'.
Framework for visualising tables of counts, proportions
  and probabilities. The framework is called product plots, alluding to
  the computation of area as a product of height and width, and the
  statistical concept of generating a joint distribution from the
  product of conditional and marginal distributions. The framework,
  with extensions, is sufficient to encompass over 20 visualisations
  previously described in fields of statistical graphics and 'infovis',
  including bar charts, mosaic plots, 'treemaps', equal area plots and
  fluctuation diagrams.
An alternative data structure for the profiling information
    generated by Rprof().
Programs to determine student grades and create
  examinations from Question banks.  Programs will create numerous
  multiple choice exams, randomly shuffled, for different versions of same question list.
Computes profile extrema functions for arbitrary functions. If the function is expensive-to-evaluate it computes profile extrema by emulating the function with a Gaussian process (using package 'DiceKriging'). In this case uncertainty quantification on the profile extrema can also be computed. The different plotting functions for profile extrema give the user a tool to better locate excursion sets.
Like similar profiling tools,
  the 'proffer' package automatically detects
  sources of slowness in R code.
  The distinguishing feature of 'proffer' is its utilization of
  'pprof', which supplies interactive visualizations
  that are efficient and easy to interpret.
  Behind the scenes, the 'profile' package converts
  native Rprof() data to a protocol buffer
  that 'pprof' understands.
  For the documentation of 'proffer',
  visit <https://r-prof.github.io/proffer/>.
  To learn about the implementations and methodologies of
  'pprof', 'profile', and protocol buffers,
  visit <https://github.com/google/pprof>.
  <https://developers.google.com/protocol-buffers>,
  and <https://github.com/r-prof/profile>, respectively.
Defines a data structure for profiler data, and methods to read and
    write from the 'Rprof' and 'pprof' file formats.
Provides profile likelihoods for a parameter of interest in commonly used statistical models. The models include linear models, generalized linear models, proportional odds models, linear mixed-effects models, and linear models for longitudinal responses fitted by generalized least squares. The package also provides plots for normalized profile likelihoods as well as the maximum profile likelihood estimates and the kth likelihood support intervals.
Provides tools that can be used to calculate, evaluate, plot and use for inference the profiles of *arbitrary* inference functions for *arbitrary* 'glm'-like fitted models with linear predictors. More information on the methods that are implemented can be found in Kosmidis (2008) <https://www.r-project.org/doc/Rnews/Rnews_2008-2.pdf>.
A suite of multivariate methods and data visualization 
      tools to implement profile analysis and cross-validation techniques described in 
      Davison & Davenport (2002) <DOI: 10.1037/1082-989X.7.4.468>, Bulut (2013), and other published and unpublished resources.
      The package includes routines to perform criterion-related profile analysis, profile analysis 
      via multidimensional scaling, moderated profile analysis, profile analysis by group, and a 
      within-person factor model to derive score profiles.
Get data / Define model / ??? / Profit! 'ProFit' is a Bayesian galaxy fitting tool that uses a fast 'C++' image generation library and a flexible interface to a large number of likelihood samplers.
A simple and light-weight API for memory profiling of R expressions.  The profiling is built on top of R's built-in memory profiler ('utils::Rprofmem()'), which records every memory allocation done by R (also native code).
Combine probabilistic forecasts using CRPS learning algorithms proposed in Berrisch, Ziel (2021) <arXiv:2102.00968> <doi:10.1016/j.jeconom.2021.11.008>. The package implements multiple online learning algorithms like Bernstein online aggregation; see Wintenberger (2014) <arXiv:1404.1356>. Quantile regression is also implemented for comparison purposes. Model parameters can be tuned automatically with respect to the loss of the forecast combination. Methods like predict(), update(), plot() and print() are available for convenience. This package utilizes the optim C++ library for numeric optimization <https://github.com/kthohr/optim>.
Core package containing all the tools for simple and advanced source extraction. This is used to create inputs for 'ProFit', or for source detection, extraction and photometry in its own right.
Provides an R interface for the PROFOUND database <doi:10.5880/PIK.2019.008>. The 
    PROFOUND database contains a wide range of data  to evaluate vegetation models and simulate climate 
    impacts at the forest stand scale. It includes 9 forest sites across Europe, and provides for them a site 
    description as well as soil, climate, CO2, Nitrogen deposition, tree-level, forest stand-level 
    and remote sensing data. Moreover, for a subset of 5 sites, also time series of carbon fluxes, 
    energy balances and soil water are available.
An alternative data structure and visual rendering
    for the profiling information generated by Rprof.
Tools for examining Rprof profile output.
Interactive visualizations for profiling R code.
Implementing the Progeny Clustering algorithm, the 'progenyClust' package assesses the clustering stability and identifies the optimal clustering number for a given data matrix. It uses k-means clustering as a default, provides a tailored hierarchical clustering function, and can be customized to work with other clustering algorithms and different parameter settings. The package includes a main function progenyClust(), plot and summary methods for 'progenyClust' object, a function hclust.progenyClust() for hierarchical clustering, and two example datasets (test and cell) for testing.
Prognostic ROC curve is an alternative graphical approach to represent the discriminative capacity of the marker: a receiver operating characteristic (ROC) curve by plotting 1 minus the survival in the high-risk group against 1 minus the survival in the low-risk group. This package contains functions to assess prognostic ROC curve. The user can enter the survival according to a model previously estimated or the user can also enter individual survival data for estimating the prognostic ROC curve by using Kaplan-Meier estimator. The area under the curve (AUC) corresponds to the probability that a patient in the low-risk group has a longer lifetime than a patient in the high-risk group. The prognostic ROC curve provides complementary information compared to survival curves. The AUC is assessed by using the trapezoidal rules. When survival curves do not reach 0, the prognostic ROC curve is incomplete and the extrapolations of the AUC are performed by assuming pessimist, optimist and non-informative situations.
Configurable Progress bars, they may include percentage,
    elapsed time, and/or the estimated completion time. They work in
    terminals, in 'Emacs' 'ESS', 'RStudio', 'Windows' 'Rgui' and the
    'macOS' 'R.app'. The package also provides a 'C++' 'API', that works
    with or without 'Rcpp'.
A minimal, unifying API for scripts and packages to report progress updates from anywhere including when using parallel processing.  The package is designed such that the developer can to focus on what progress should be reported on without having to worry about how to present it.  The end user has full control of how, where, and when to render these progress updates, e.g. in the terminal using utils::txtProgressBar() or progress::progress_bar(), in a graphical user interface using utils::winProgressBar(), tcltk::tkProgressBar() or shiny::withProgress(), via the speakers using beep::beepr(), or on a file system via the size of a file. Anyone can add additional, customized, progression handlers. The 'progressr' package uses R's condition framework for signaling progress updated. Because of this, progress can be reported from almost anywhere in R, e.g. from classical for and while loops, from map-reduce API:s like the lapply() family of functions, 'purrr', 'plyr', and 'foreach'. It will also work with parallel processing via the 'future' framework, e.g. future.apply::future_lapply(), furrr::future_map(), and 'foreach' with 'doFuture'. The package is compatible with Shiny applications.
Currently non-operational, a harmless wrapper to allow package 'reproj' to install and 
  function while relying on the 'proj4' package.
A simple interface to lat/long projection and datum
	     transformation of the PROJ.4 cartographic projections library. It
	     allows transformation of geographic coordinates from one projection
	     and/or datum to another.
A clustering approach applicable to every projection method is proposed here. The two-dimensional scatter plot of any projection method can construct a topographic map which displays unapparent data structures by using distance and density information of the data. The generalized U*-matrix renders this visualization in the form of a topographic map, which can be used to automatically define the clusters of high-dimensional data. The whole system is based on Thrun and Ultsch, "Using Projection based Clustering to Find Distance and Density based Clusters in High-Dimensional Data" <DOI:10.1007/s00357-020-09373-2>. Selecting the correct projection method will result in a visualization in which mountains surround each cluster. The number of clusters can be determined by counting valleys on the topographic map. Most projection methods are wrappers for already available methods in R. By contrast, the neighbor retrieval visualizer (NeRV) is based on C++ source code of the 'dredviz' software package, and the Curvilinear Component Analysis (CCA) is translated from 'MATLAB' ('SOM Toolbox' 2.0) to R.
Provides functions and graphics for projecting daily incidence based on past incidence, and estimates of the serial interval and reproduction number. Projections are based on a branching process using a Poisson-distributed number of new cases per day, similar to the model used for estimating R in 'EpiEstim' or in 'earlyR', and described by Nouvellet et al. (2017) <doi:10.1016/j.epidem.2017.02.012>. The package provides the S3 class 'projections' which extends 'matrix', with accessors and additional helpers for handling, subsetting, merging, or adding these objects, as well as dedicated printing and plotting methods.
Management problems of deterministic and stochastic projects. It obtains the duration of a project and the appropriate slack for each activity in a deterministic context. In addition it obtains a schedule of activities' time (Castro, Gómez & Tejada (2007) <doi:10.1016/j.orl.2007.01.003>). It also allows the management of resources. When the project is done, and the actual duration for each activity is known, then it can know how long the project is delayed and make a fair delivery of the delay between each activity (Bergantiños, Valencia-Toledo & Vidal-Puga (2018) <doi:10.1016/j.dam.2017.08.012>). In a stochastic context it can estimate the average duration of the project and plot the density of this duration, as well as, the density of the early and last times of the chosen activities. As in the deterministic case, it can make a distribution of the delay generated by observing the project already carried out.
Provides a project infrastructure with a focus on
    manuscript creation. Creates a project folder with a single command,
    containing subdirectories for specific components, templates for
    manuscripts, and so on.
Provides functions to
    automatically build a directory structure for a new R
    project. Using this structure, 'ProjectTemplate'
    automates data loading, preprocessing, library
    importing and unit testing.
Provides programmatic access to 'GitHub' API with a
    focus on project management.  Key functionality includes
    setting up issues and milestones from R objects or 'YAML' configurations,
    querying outstanding or completed tasks, and generating progress updates
    in tables, charts, and RMarkdown reports. Useful for those using 'GitHub' in personal,
    professional, or academic settings with an emphasis on streamlining
    the workflow of data analysis projects.
Performs projection predictive feature selection for generalized linear
    models and generalized linear and additive multilevel models (see Piironen,
    Paasiniemi and Vehtari, 2020, <doi:10.1214/20-EJS1711>; Catalina, Bürkner
    and Vehtari, 2020, <arXiv:2010.06994>). The package is compatible with the
    'rstanarm' and 'brms' packages, but other reference models can also be used.
    See the documentation as well as the package vignette for more information
    and examples.
Provides functions for calculating and analyzing the proliferative 
    index (PI) from an RNA-seq dataset. As described in Ramaker & Lasseigne, 
    et al. bioRxiv, 2016 <doi:10.1101/063057>.
Algorithms and subroutines for patient-reported outcome data analysis.
Functions which can be used to support the Multicriteria Decision Analysis (MCDA) process 
    involving multiple criteria, by PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations).
The PROMETHEE method is a multi-criteria decision-making method addressing with outranking problems.
            The method establishes a preference structure between the alternatives, having a preference function for
            each criterion. IN this context, three variants of the method is carried out: PROMETHEE I (Partial Outranking),
            PROMETHEE II (Total Outranking), and PROMETHEE III (Outranking by Intervals).
Provides fundamental abstractions for doing asynchronous programming
    in R using promises. Asynchronous programming is useful for allowing a single
    R process to orchestrate multiple tasks in the background while also attending
    to something else. Semantics are similar to 'JavaScript' promises, but with a
    syntax that is idiomatic R.
Deploy, maintain, and invoke predictive models using the 'Alteryx
    Promote' REST API.  'Alteryx Promote' is available at the URL:
    <https://www.alteryx.com/products/alteryx-promote>.
Analysis and measurement of promotion effectiveness on a given target variable (e.g. daily sales). After converting promotion schedule into dummy or smoothed predictor variables, the package estimates the effects of these variables controlled for trend/periodicity/structural change using prophet by Taylor and Letham (2017) <doi:10.7287/peerj.preprints.3190v2> and some prespecified variables (e.g. start of a month).
Set the 'R' prompt dynamically, from a function. The package
    contains some examples to include various useful dynamic information
    in the prompt: the status of the last command (success or failure);
    the amount of memory allocated by the current 'R' process; the name of
    the R package(s) loaded by 'pkgload' and/or 'devtools'; various 'git'
    information: the name of the active branch, whether it is dirty,
    if it needs pushes pulls. You can also create your own prompt if you
    don't like the predefined examples.
In 'Shiny' apps, it is sometimes useful to store information
    on a particular item in a tooltip. 'Prompter' allows you to easily 
    create such tooltips, using 'Hint.css'.
Carrying out inferences about any linear combination of proportions and the ratio of two proportions.
Propagation of uncertainty using higher-order Taylor expansion and Monte Carlo simulation.
Computes two-sample confidence intervals for single, paired and independent proportions.
Implementation of propensity clustering and
        decomposition as described in Ranola et al. (2013) <doi:10.1186/1752-0509-7-21>. 
        Propensity decomposition can be viewed on the
        one hand as a generalization of the eigenvector-based
        approximation of correlation networks, and on the other hand as
        a generalization of random multigraph models and
        conformity-based decompositions.
Allows to parse 'Java' properties files in the context of
  'R Service Bus' applications.
Implements a procedure for forecasting time series data based on
    an additive model where non-linear trends are fit with yearly, weekly, and
    daily seasonality, plus holiday effects. It works best with time series
    that have strong seasonal effects and several seasons of historical data.
    Prophet is robust to missing data and shifts in the trend, and typically
    handles outliers well.
Abundant statistical literature has revealed the importance of constructing and evaluating various methods for constructing confidence intervals (CI) for single binomial proportion (p). We comprehensively provide procedures in frequentist (approximate with or without adding pseudo counts or continuity correction or exact) and in Bayesian cultures. Evaluation procedures for CI warrant active computational attention and required summaries pertaining to  four criterion (coverage probability, expected length, p-confidence, p-bias, and error) are implemented.
A package for selecting the most relevant features (genes) in the high-dimensional binary classification problems. The discriminative features are identified using analyzing the overlap between the expression values across both classes. The package includes functions for measuring the proportional overlapping score for each gene avoiding the outliers effect. The used measure for the overlap is the one defined in the "Proportional Overlapping Score (POS)" technique for feature selection. A gene mask which represents a gene's classification power can also be produced for each gene (feature). The set size of the selected genes might be set by the user. The minimum set of genes that correctly classify the maximum number of the given tissue samples (observations) can be also produced.
The bioinformatic evaluation of gene co-expression often begins with
    correlation-based analyses. However, correlation lacks validity when
    applied to relative data, including count data generated by next-generation
    sequencing. This package implements several metrics for proportionality, including
    phi [Lovell et al (2015) <DOI:10.1371/journal.pcbi.1004075>] and
    rho [Erb and Notredame (2016) <DOI:10.1007/s12064-015-0220-8>]. This package also
    implements several metrics for differential proportionality. Unlike correlation,
    these measures give the same result for both relative and absolute data.
Contains functions to run propensity-biased allocation to balance covariate distributions in sequential trials and propensity-constrained randomization to balance covariate distributions in trials with known baseline covariates at time of randomization.  Currently only supports trials comparing two groups.
An API wrapper around the 'ProPublica' API <https://projects.propublica.org/api-docs/congress-api/> for U.S. Congressional Bills. Users can include their API key, U.S. Congress, branch, and offset ranges, to return a dataframe of all results within those parameters. This package is different from the 'RPublica' package because it is for the 'ProPublica' U.S. Congress data API, and the 'RPublica' package is for the Nonprofit Explorer, Forensics, and Free the Files data APIs.
Provides wrapper functions to access the ProPublica's Congress and Campaign Finance APIs.
    The Congress API provides near real-time access to legislative data from the House of 
    Representatives, the Senate and the Library of Congress.
    The Campaign Finance API provides data from United States Federal Election Commission 
    filings and other sources. The API covers summary information for candidates and 
    committees, as well as certain types of itemized data.
    For more information about these APIs go to: <https://www.propublica.org/datastore/apis>.
Offers a variety of tools, such as specific plots and regression model approaches, for analyzing different patient reported questionnaires. Specially, mixed-effects models based on the beta-binomial distribution are implemented to deal with binomial data with over-dispersion (see Najera-Zuloaga J., Lee D.-J. and Arostegui I. (2017) <doi:10.1177/0962280217690413>).
An extensible repository of accurate, up-to-date functions to
    score commonly used patient-reported outcome (PRO), quality of life
    (QOL), and other psychometric and psychological measures.
    'PROscorer', together with the 'PROscorerTools' package, is a system
    to facilitate the incorporation of PRO measures into research studies
    and clinical settings in a scientifically rigorous and reproducible
    manner.  These packages and their vignettes are intended to help
    establish and promote "best practices" to improve the planning,
    scoring, and reporting of PRO-like measures in research.  The
    'PROscorer' "Instrument Descriptions" vignette contains descriptions
    of each instrument scored by 'PROscorer', complete with references.
    These instrument descriptions are suitable for inclusion in formal
    study protocol documents, grant proposals, and manuscript Method
    sections.  Each 'PROscorer' function is composed of helper functions
    from the 'PROscorerTools' package, and users are encouraged to
    contribute new functions to 'PROscorer'.  More scoring functions are
    currently in development and will be added in future updates.
Provides a reliable and flexible toolbox to score 
    patient-reported outcome (PRO), Quality of Life (QOL), and other 
    psychometric measures. The guiding philosophy is that scoring errors can 
    be eliminated by using a limited number of well-tested, well-behaved 
    functions to score PRO-like measures. The workhorse of the package is 
    the 'scoreScale' function, which can be used to score most single-scale 
    measures. It can reverse code items that need to be reversed before 
    scoring and pro-rate scores for missing item data. Currently, three 
    different types of scores can be output: summed item scores, mean item 
    scores, and scores scaled to range from 0 to 100. The 'PROscorerTools' 
    functions can be used to write new functions that score more complex 
    measures. In fact, 'PROscorerTools' functions are the building blocks of 
    the scoring functions in the 'PROscorer' package (which is a repository 
    of functions that score specific commonly-used instruments). Users are 
    encouraged to use 'PROscorerTools' to write scoring functions for their 
    favorite PRO-like instruments, and to submit these functions for 
    inclusion in 'PROscorer' (a tutorial vignette will be added soon). The 
    long-term vision for the 'PROscorerTools' and 'PROscorer' packages is to 
    provide an easy-to-use system to facilitate the incorporation of PRO 
    measures into research studies in a scientifically rigorous and 
    reproducible manner. These packages and their vignettes are intended to 
    help establish and promote "best practices" for scoring and describing 
    PRO-like measures in research.
Perform scale linking to establish relationships between instruments
    that measure similar constructs according to the PROsetta Stone methodology, as in Choi, Schalet, Cook, & Cella (2014) <doi:10.1037/a0035768>.
Implementation of penalized regression with second-generation p-values for variable
    selection. The algorithm can handle linear regression, GLM, and Cox regression. S3 methods print(), summary(), coef(), predict(), and plot() are available for the algorithm. Technical details
    can be found at Zuo et al. (2021) <doi:10.1080/00031305.2021.1946150>.
Functions to preprocess spectroscopic data 
    and conduct (representative) sample selection/calibration sampling.
An environment to simulate the development of annual plant populations with regard to population dynamics and genetics, especially herbicide resistance. It combines genetics on the individual level (Renton et al. 2011) with a stochastic development on the population level (Daedlow, 2015).
	Renton, M, Diggle, A, Manalil, S and Powles, S (2011) <doi:10.1016/j.jtbi.2011.05.010> 
	Daedlow, Daniel (2015, doctoral dissertation: University of Rostock, Faculty of Agriculture and Environmental Sciences.)
In a typical protein labelling procedure, proteins are chemically tagged with a functional group, usually at specific sites, then digested into peptides, which are then analyzed using matrix-assisted laser desorption ionization - time of flight mass spectrometry (MALDI-TOF MS) to generate peptide fingerprint. Relative to the control, peptides that are heavier by the mass of the labelling group are informative for sequence determination. Searching for peptides with such mass shifts, however, can be difficult. This package, designed to tackle this inconvenience, takes as input the mass list of two or multiple MALDI-TOF MS mass lists, and makes pairwise comparisons between the labeled groups vs. control, and restores centroid mass spectra with highlighted peaks of interest for easier visual examination. Particularly, peaks differentiated by the mass of the labelling group are defined as a “pair”, those with equal masses as a “match”, and all the other peaks as a “mismatch”.For more bioanalytical background information, refer to following publications: Jingjing Deng (2015) <doi:10.1007/978-1-4939-2550-6_19>; Elizabeth Chang (2016) <doi:10.7171/jbt.16-2702-002>.
Read Protein Data Bank (PDB) files, performs its analysis, and 
    presents the result using different visualization types including 3D. The 
    package also has additional capability for handling Virus Report data from 
    the National Center for Biotechnology Information (NCBI) database.
    Nature Structural Biology 10, 980 (2003) <doi:10.1038/nsb1203-980>.
    US National Library of Medicine (2021) <https://www.ncbi.nlm.nih.gov/datasets/docs/reference-docs/data-reports/virus/>.
This package provides functions to identify the optimal
        solution that maximizes numbers of detectable differentiated
        proteins from a multi-stage clinical proteomic study.
Provides methods for making inference in isobaric labelled
    LC-MS/MS experiments, i.e. iTRAQ experiments. It provides a function that
    reasonably parses a CSV-export from Proteome Discoverer(TM) into a data
    frame that can be easily handled in R. Functions and methods are provided
    for quality control, filtering, norming, and the calculation of response
    variables for further analysis. The merging of multiple iTRAQ experiments
    with respect to a reference is also covered.
Seq2seq time-feature analysis based on variational model, with a wide range of distributions available for the latent variable.
An object oriented system using object-based, also
	called prototype-based, rather than class-based object oriented ideas.
Greedy algorithm described in Bien and Tibshirani (2011)
        Prototype Selection for Interpretable Classification. Annals of
        Applied Statistics. 5(4). 2403-2424
Performs minimax linkage hierarchical clustering.  Every cluster
    has an associated prototype element that represents that cluster as
    described in Bien, J., and Tibshirani, R. (2011), "Hierarchical Clustering 
    with Prototypes via Minimax Linkage," The Journal of the American 
    Statistical Association, 106(495), 1075-1084.
Pure C++ implementations for reading and writing several common data 
    formats based on Google protocol-buffers. Currently supports 'rexp.proto' for 
    serialized R objects, 'geobuf.proto' for binary geojson, and 'mvt.proto' for 
    vector tiles. This package uses the auto-generated C++ code by protobuf-compiler, 
    hence the entire serialization is optimized at compile time. The 'RProtoBuf' 
    package on the other hand uses the protobuf runtime library to provide a general-
    purpose toolkit for reading and writing arbitrary protocol-buffer data in R.
'The Proton Game' is a console-based data-crunching game for younger and older data scientists.
  Act as a data-hacker and find Slawomir Pietraszko's credentials to the Proton server.
  You have to solve four data-based puzzles to find the login and password.
  There are many ways to solve these puzzles. You may use loops, data filtering, ordering, aggregation or other tools.
  Only basics knowledge of R is required to play the game, yet the more functions you know, the more approaches you can try.
  The knowledge of dplyr is not required but may be very helpful.
  This game is linked with the ,,Pietraszko's Cave'' story available at http://biecek.pl/BetaBit/Warsaw.
  It's a part of Beta and Bit series.
  You will find more about the Beta and Bit series at http://biecek.pl/BetaBit.
Procedures for testing for group-wide signal in clusters of variables. Tests can be performed for single groups in isolation (univariate) or multiple groups together (multivariate). Specific tests include the exact and approximate (un)selective likelihood ratio tests described in Reid et al (2015), the selective F test and marginal screening prototype test of Reid and Tibshirani (2015). User may pre-specify columns to be included in prototype formation, or allow the function to select them itself. A mixture of these two is also possible. Any variable selection is accounted for using the selective inference framework. Options for non-sampling and hit-and-run null reference distributions.
Comprehensive toolkit for generating various numerical
    features of protein sequences described in Xiao et al. (2015)
    <DOI:10.1093/bioinformatics/btv042>. For full functionality,
    the software 'ncbi-blast+' is needed, see
    <https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastDocs&DOC_TYPE=Download>
    for more information.
'ProTracker' is a popular music tracker to sequence
    music on a Commodore Amiga machine. This package offers the
    opportunity to import, export, manipulate and play 'ProTracker'
    module files. Even though the file format could be considered
    archaic, it still remains popular to this date. This package
    intends to contribute to this popularity and therewith
    keeping the legacy of 'ProTracker' and the Commodore Amiga
    alive.
Useful functions and workflows for proteomics quality control and data analysis of both limited proteolysis-coupled mass spectrometry (LiP-MS) (Feng et. al. (2014) <doi:10.1038/nbt.2999>) and regular bottom-up proteomics experiments. Data generated with search tools such as 'Spectronaut', 'MaxQuant' and 'Proteome Discover' can be easily used due to flexibility of functions.
Helps with quality checks, visualizations 
    and analysis of mass spectrometry data, coming from proteomics 
    experiments. The package is developed, tested and used at the Functional 
    Genomics Center Zurich <https://fgcz.ch>. We use this package
    mainly for prototyping, teaching, and having fun with proteomics data.
    But it can also be used to do data analysis for small scale data sets.
Tools for Natural Language Processing in French and texts from Marcel Proust's collection 
  "A La Recherche Du Temps Perdu". The novels contained in this collection are 
  "Du cote de chez Swann ", "A l'ombre des jeunes filles en fleurs","Le Cote de Guermantes", 
  "Sodome et Gomorrhe I et II", "La Prisonniere", "Albertine disparue", and "Le Temps retrouve".
Uses provenance post-execution to help the user understand
    and debug their script by providing functions to look at intermediate steps and
    data values, their forwards and backwards lineage, and to understand the
    steps leading up to warning and error messages. 'provDebugR' uses
    provenance produced by 'rdtLite' (available on CRAN), stored in PROV-JSON format.
Bundles a number of established statistical methods to facilitate the visual interpretation of large datasets in sedimentary geology. Includes functionality for adaptive kernel density estimation, principal component analysis, correspondence analysis, multidimensional scaling, generalised procrustes analysis and individual differences scaling using a variety of dissimilarity measures. Univariate provenance proxies, such as single-grain ages or (isotopic) compositions are compared with the Kolmogorov-Smirnov, Kuiper or Sircombe-Hazelton L2 distances. Categorical provenance proxies such as chemical compositions are compared with the Aitchison and Bray-Curtis distances, and point-counting data with the chi-square distance. Also included are tools to plot compositional and point-counting data on ternary diagrams and point-counting data on radial plots, to calculate the sample size required for specified levels of statistical precision, and to assess the effects of hydraulic sorting on detrital compositions. Includes an intuitive query-based user interface for users who are not proficient in R.
A simple package to grab a Bible proverb corresponding to the
    day of the month.
Inspects provenance collected by the 'rdt' or 'rdtLite' packages, 
    or other tools providing compatible PROV JSON output created by 
    the execution of a script, and find differences between two provenance 
    collections. Factors under examination included the hardware and 
    software used to execute the script, versions of attached libraries, 
    use of global variables, modified inputs and outputs, and changes 
    in main and sourced scripts. Based on detected changes, 'provExplainR' 
    can be used to study how these factors affect the behavior of 
    the script and generate a promising diagnosis of the causes of different 
    script results. More information about 'rdtLite' and associated tools is available 
    at <https://github.com/End-to-end-provenance/> and Barbara Lerner, 
    Emery Boose, and Luis Perez (2018), Using Introspection to Collect 
    Provenance in R, Informatics, <doi:10.3390/informatics5010012>.
Creates and manages a provenance graph corresponding to the 
    provenance created by the 'rdtLite' package, which
    collects provenance from R scripts.  'rdtLite' is available on CRAN.
    The provenance format is an extension of the 
    W3C PROV JSON format (<https://www.w3.org/Submission/2013/SUBM-prov-json-20130424/>).
    The extended JSON provenance format is described 
    in <https://github.com/End-to-end-provenance/ExtendedProvJson>.
R functions to access provenance information collected by 'rdt' or 
    'rdtLite'.  The information is stored inside a 'ProvInfo' object and can be 
    accessed through a collection of functions that will return the requested 
    data. The exact format of the JSON created by 'rdt' and 'rdtLite' is described 
    in <https://github.com/End-to-end-provenance/ExtendedProvJson>.
Uses provenance collected by 'rdtLite' package or comparable tool
    to display information about input files, output files, and exchanged files
    for a single R script or a series of R scripts.
Displays provenance graphically for provenance collected by the 'rdt' or
  'rdtLite' packages, or other tools providing compatible PROV JSON output. The exact 
  format of the JSON created by 'rdt' and 'rdtLite' is described in 
  <https://github.com/End-to-end-provenance/ExtendedProvJson>.  More information about
  rdtLite and associated tools is available at <https://github.com/End-to-end-provenance/> 
  and Barbara Lerner, Emery Boose, and Luis Perez (2018), Using Introspection to Collect 
  Provenance in R, Informatics, <doi: 10.3390/informatics5010012>.
Functions to measure Alpha and Beta Proximity to Irreplaceability.
  The methods for Alpha and Beta irreplaceability were first described in:
    Baisero D., Schuster R. & Plumptre A.J.
    Redefining and Mapping Global Irreplaceability.
    Conservation Biology 2021;1-11.
    <doi:10.1111/cobi.13806>.
Provides an extensible framework for the efficient calculation of auto- and cross-proximities, along with implementations of the most popular ones.
Computes proximity between rows or columns of large matrices efficiently in C++.
    Functions are optimised for large sparse matrices using the Armadillo and Intel TBB libraries.
    Among several built-in similarity/distance measures, computation of correlation,
    cosine similarity and Euclidean distance is particularly fast.
Determine minimal protein set explaining
    peptide spectrum matches. Utility functions for creating fasta amino acid databases with decoys and contaminants.
    Peptide false discovery rate estimation for target decoy search results on psm, precursor, peptide and protein
    level. Computing dynamic swath window sizes based on MS1 or MS2 signal distributions.
Utilize the Bayesian prior and posterior predictive checking
   approach to provide a statistical assessment of replication success 
   and failure. The package is based on the methods proposed in 
   Zhao,Y., Wen X.(2021) <arXiv:2105.03993>.
Reverse depends for a given package are queued such that multiple
 workers can run the tests in parallel.
Computes the areas under the precision-recall (PR) and ROC curve for weighted (e.g., soft-labeled) and unweighted data. In contrast to other implementations, the interpolation between points of the PR curve is done by a non-linear piecewise function. In addition to the areas under the curves, the curves themselves can also be computed and plotted by a specific S3-method. References: Davis and Goadrich (2006) <doi:10.1145/1143844.1143874>; Keilwagen et al. (2014) <doi:10.1371/journal.pone.0092209>; Grau et al. (2015) <doi:10.1093/bioinformatics/btv153>.
Construct pharmacogenomics (PGx) polygenic risk score (PRS) with PRS-PGx-Unadj (unadjusted), PRS-PGx-CT (clumping and thresholding), PRS-PGx-L, -GL, -SGL (penalized regression), PRS-PGx-Bayes (Bayesian regression). Package is based on ''Pharmacogenomics Polyenic Risk Score for Drug Response Prediction Using PRS-PGx Methods'' by Zhai, S., Zhang, H., Mehrotra, D.V., and Shen, J., 2021 (submitted).
Tests periodicity in short time series using response surface regression.
Intended for larger-than-memory tabular data, 'prt' objects provide an interface to read row and/or column subsets into memory as data.table objects. Data queries, constructed as 'R' expressions, are evaluated using the non-standard evaluation framework provided by 'rlang' and file-backing is powered by the fast and efficient 'fst' package.
Compute bending energies, principal warps, partial warp scores, and the non-affine component of shape variation for 2D landmark configurations, as well as Mardia-Dryden distributions and self-similar distributions of landmarks, as described in Mitteroecker et al. (2020) <doi:10.1093/sysbio/syaa007>. Working examples to decompose shape variation into small-scale and large-scale components, and to decompose the total shape variation into outline and residual shape components are provided. Two landmark datasets are provided, that quantify skull morphology in humans and papionin primates, respectively from Mitteroecker et al. (2020) <doi:10.5061/dryad.j6q573n8s> and Grunstra et al. (2020) <doi:10.5061/dryad.zkh189373>.
Useful tools to pry back the covers of R and understand the
    language at a deeper level.
Data sets and functions used in the polish book 
    "Przewodnik po pakiecie R" (The Hitchhiker's Guide to the R). 
    See more at <http://biecek.pl/R>. Among others you will find here 
    data about housing prices, cancer patients, running times and many others.
List, query and manipulate all system processes, on
    'Windows', 'Linux' and 'macOS'.
A collection of functions that primarily produce graphics
        to aid in a Propensity Score Analysis (PSA).  Functions
        include: cat.psa and box.psa to test balance within strata of
        categorical and quantitative covariates, circ.psa for a
        representation of the estimated effect size by stratum,
        loess.psa that provides a graphic and loess based effect size
        estimate, and various balance functions that provide measures
        of the balance achieved via a PSA in a categorical covariate.
Algorithms to implement various Bayesian penalized survival regression models including: semiparametric proportional hazards models with lasso priors (Lee et al., Int J Biostat, 2011 <doi:10.2202/1557-4679.1301>) and three  other shrinkage and group priors (Lee et al., Stat Anal Data Min, 2015 <doi:10.1002/sam.11266>); parametric accelerated failure time models with group/ordinary lasso prior (Lee et al. Comput Stat Data Anal, 2017 <doi:10.1016/j.csda.2017.02.014>).
A tool which aims to help evaluate the effect of external borrowing using an integrated approach described in Lewis et al., (2019) <doi:10.1080/19466315.2018.1497533> that combines propensity score and Bayesian dynamic borrowing methods.
Segmentation of allele-specific DNA copy number data and detection of regions with abnormal copy number within each parental chromosome.  Both tumor-normal paired and tumor-only analyses are supported.
Bayesian analysis of item-response theory (IRT) models,
	     roll call analysis; computing highest density regions; maximum
	     likelihood estimation of zero-inflated and hurdle models for count
	     data; goodness-of-fit measures for GLMs; data sets used
	     in writing	and teaching at the Political Science
	     Computational Laboratory; seats-votes curves.
Provides a number of functions to
  simplify and automate the scoring, comparison, and evaluation of
  different ways of creating composites of data.  It is particularly
  aimed at facilitating the creation of physiological composites of
  metabolic syndrome symptom score (MSSS) and allostatic load (AL).
Produces power spectral density estimates through iterative
    refinement of the optimal number of sine-tapers at each frequency. This
    optimization procedure is based on the method of Riedel and Sidorenko
    (1995), which minimizes the Mean Square Error (sum of variance and bias)
    at each frequency, but modified for computational stability. The same
    procedure can now be used to calculate the cross spectrum (multivariate
    analyses).
A toolbox in symbolic data framework as a statistical learning and data mining solution for symbolic polygonal data analysis. This study is a new approach in data analysis and it was proposed by 
            Silva et al. (2019) <doi:10.1016/j.knosys.2018.08.009>. The package presents the estimation of main descriptive statistical measures, e.g, mean, covariance, variance, correlation and coefficient of variation. 
            In addition, a method to obtain polygonal data from classical data is presented. Empirical probability distribution function based on symbolic polygonal histogram and a regression model with its main measures are presented.
This R package includes functions for gathering commonly used and
    regularly maintained data set in political science. It also includes
    functions for combining components from these data sets into variables that
    have been suggested in the literature, but are not regularly maintained.
Functions that allow you to generate and compare power spectral density (PSD) 
	plots given time series data. Fast Fourier Transform (FFT) is used to take a time series 
	data, analyze the oscillations, and then output the frequencies of these oscillations 
	in the time series in the form of a PSD plot.Thus given a time series, the dominant 
	frequencies in the time series can be identified. Additional functions in this package 
	allow the dominant frequencies of multiple groups of time series to be compared with each other. 
	To see example usage with the main functions of this package, please visit
	this site: <https://yhhc2.github.io/psdr/articles/Introduction.html>. 
	The mathematical operations used to generate the PSDs are described in these sites:
	<https://www.mathworks.com/help/matlab/ref/fft.html>.
	<https://www.mathworks.com/help/signal/ug/power-spectral-density-estimates-using-fft.html>.
Functions for creating Latin Hypercubes with
    prescribed correlations and performing parameter space exploration.
    Also implements the PLUE method.
    Based on the package sensitivity, by Gilles Pujol,
    Bertrand Iooss & Alexandre Janon.
Various functions for computing pseudo-observations for censored data regression. Computes pseudo-observations for modeling: competing risks based on the cumulative incidence function, survival function based on the restricted mean,  survival function based on the Kaplan-Meier estimator see Klein et al. (2008) <doi:10.1016/j.cmpb.2007.11.017>.
Efficient calculation of pseudo-ranks and (pseudo)-rank based test statistics. In case of equal sample sizes, pseudo-ranks and mid-ranks are equal. When used for inference mid-ranks may lead to paradoxical results. Pseudo-ranks are in general not affected by such a problem <doi:10.18637/jss.v095.c01>.
Contains the core methods for the evaluation of principal
    surrogates in a single clinical trial. Provides a flexible interface for
    defining models for the risk given treatment and the surrogate, the models
    for integration over the missing counterfactual surrogate responses, and the
    estimation methods. Estimated maximum likelihood and pseudo-score can be used
    for estimation, and the bootstrap for inference. A variety of post-estimation
    summary methods are provided, including print, summary, plot, and testing.
Pattern Sequence Based Forecasting (PSF) takes univariate
    time series data as input and assist to forecast its future values.
    This algorithm forecasts the behavior of time series
    based on similarity of pattern sequences. Initially, clustering is done with the
    labeling of samples from database. The labels associated with samples are then
    used for forecasting the future behaviour of time series data. The further
    technical details and references regarding PSF are discussed in Vignette.
Pooling, backward and forward selection of linear, logistic and Cox regression models in 
	multiply imputed datasets. Backward and forward selection can be done 
	from the pooled model using Rubin's Rules (RR), the D1, D2, D3, D4 and 
	the median p-values method. This is also possible for Mixed models. 
	The models can contain continuous, dichotomous, categorical and restricted 
	cubic spline predictors and interaction terms between	all these type of predictors. 
	The stability of the models	can be evaluated using bootstrapping and cluster 
	bootstrapping. The package further contains functions to pool the model performance 
	as ROC/AUC, R-squares, scaled Brier score, H&L test and calibration	plots for logistic 
	regression models. Internal validation can be	done with cross-validation or bootstrapping. 
	The adjusted intercept after shrinkage of pooled regression coefficients can be obtained. 
	Backward and forward selection as part of internal validation is possible. 
	A function to externally validate logistic prediction models in multiple imputed 
	datasets is available and a function to compare models. 
	Eekhout (2017) <doi:10.1186/s12874-017-0404-7>.
	Wiel (2009) <doi:10.1093/biostatistics/kxp011>.
	Marshall (2009) <doi:10.1186/1471-2288-9-57>.
Functions to generate ensembles of generalized linear models using
             a greedy projected subset gradient descent algorithm. The sparsity 
             and diversity tuning parameters are selected by cross-validation.
Implements projected sparse Gaussian process Kriging (Ingram 'et. al.', 2008, <doi:10.1007/s00477-007-0163-9>) as an additional method for the 'intamap' package. More details on implementation (Barillec 'et. al.', 2010, <doi:10.1016/j.cageo.2010.05.008>).
In the situation when multiple alternative treatments or
    interventions available, different population groups may respond differently
    to different treatments. This package implements a method that discovers
    the population subgroups in which a certain treatment has a better effect
    than the other alternative treatments. This is done by first estimating the
    treatment effect for a given treatment and its uncertainty by computing random
    forests, and the resulting model is summarized by a decision tree in which the
    probabilities that the given treatment is best for a given subgroup is shown in
    the corresponding terminal node of the tree.
Makes it easy to build panel data in wide format from Panel Survey
    of Income Dynamics ('PSID') delivered raw data. Downloads data directly from
    the PSID server using the 'SAScii' package. 'psidR' takes care of merging
    data from each wave onto a cross-period index file, so that individuals can be
    followed over time. The user must specify which years they are interested in,
    and the 'PSID' variable names (e.g. ER21003) for each year (they differ in each
    year). The package offers helper functions to retrieve variable names from different
    waves. There are different panel data designs and sample subsetting criteria
    implemented ("SRC", "SEO", "immigrant" and "latino" samples).
Generalization of the SIMEX algorithm from Cook & Stefanski (1994) <doi:10.2307/2290994> for the calculation of inbreeding depression or heritability on pedigree structures affected by missing or misassigned paternities. It simulates errors and tracks the behavior of the estimate as a function of the error proportion. It extrapolates back a true value corresponding to the null error rate.
Data and statistics of Pakistan Social and Living Standards Measurement (PSLM) survey 2014-15 from Pakistan Bureau of Statistics (<http://www.pbs.gov.pk/>).
Provides an implementation of particle swarm optimisation consistent with the standard PSO 2007/2011 by Maurice Clerc. Additionally a number of ancillary routines are provided for easy testing and graphics.
Particle swarm optimization - a basic variant.
Implements an n-dimensional parameter space partitioning algorithm for evaluating the global behaviour of formal computational models as described by Pitt, Kim, Navarro and Myung (2006) <doi:10.1037/0033-295X.113.1.57>.
Estimation and inference of spatial and spatio-temporal 
    semiparametric models including spatial or spatio-temporal non-parametric 
    trends, parametric and non-parametric covariates and, possibly, a spatial 
    lag for the dependent variable and temporal correlation in the noise.
    The spatio-temporal trend can be decomposed in ANOVA way including main and 
    interaction functional terms. Use of SAP algorithm to estimate the spatial 
    or spatio-temporal trend and non-parametric covariates. The methodology of 
    these models can be found in next references
    Basile, R. et al. (2014), <doi:10.1016/j.jedc.2014.06.011>;
    Rodriguez-Alvarez, M.X. et al. (2015) <doi:10.1007/s11222-014-9464-2> and,
    particularly referred to the focus of the package, Minguez, R., 
    Basile, R. and Durban, M. (2020) <doi:10.1007/s10260-019-00492-8>.
Spearman's rank correlation test with precomputed exact
        null distribution for n <= 22.
Smoothing splines with penalties on order m derivatives.
Inference of infectious disease outcomes using generalized additive
    (mixed) models with penalized basis splines (P-Splines). See 
    <https://medrxiv.org/cgi/content/short/2020.07.14.20138180v1>.
Performs demographic, bifurcation and evolutionary analysis of physiologically structured population models, which is a class of models that consistently translates continuous-time models of individual life history to the population level.
    A model of individual life history has to be implemented specifying the individual-level functions that determine the life history, such as development and mortality rates and fecundity. 
    M.A. Kirkilionis, O. Diekmann, B. Lisser, M. Nool, B. Sommeijer & A.M. de Roos (2001) <doi:10.1142/S0218202501001264>.
    O.Diekmann, M.Gyllenberg & J.A.J.Metz (2003) <doi:10.1016/S0040-5809(02)00058-8>.
    A.M. de Roos (2008) <doi:10.1111/j.1461-0248.2007.01121.x>.
Provides quasi-Newton methods to minimize partially separable
    functions. The methods are largely described by  
    Nocedal and Wright (2006) <doi:10.1007/978-0-387-40065-5>.
Contains functions to compute statistics relevant to performance scientists, coaches, and athletes. The functions within the package that report group-wide statistics have to do with measurement instrument reliability and individual change of athletes. Many of them come from the work of Hopkins (2000) <doi:10.2165/00007256-200030010-00001>. There are also functions that apply a particular statistical concept to each measurement in the data.
Includes functions and data used in the book "Presenting Statistical Results Effectively", Andersen and Armstrong (2022, 978-1446269800).
High-quality real-world data can be transformed into scientific
    real-world evidence (RWE) for regulatory and healthcare decision-making
    using proven analytical methods and techniques. For example, propensity
    score (PS) methodology can be applied to pre-select a subset of real-world
    data containing patients that are similar to those in the current clinical
    study in terms of covariates, and to stratify the selected patients together
    with those in the current study into more homogeneous strata. Then, methods
    such as the power prior approach or composite likelihood approach can be
    applied in each stratum to draw inference for the parameters of interest.
    This package provides functions that implement the PS-integrated RWE
    analysis methods proposed in Wang et al. (2019)
    <doi:10.1080/10543406.2019.1657133>, Wang et al. (2020)
    <doi:10.1080/10543406.2019.1684309> and Chen et al. (2020)
    <doi:10.1080/10543406.2020.1730877>.
Power and Sample Size for Health Researchers is a Shiny application that brings together a 
    series of functions  related to sample size and power calculations for common analysis in the healthcare 
    field. There are functionalities to calculate the power, sample size to estimate or test hypotheses for means and 
    proportions (including test for correlated groups, equivalence, non-inferiority and superiority), association, correlations coefficients, 
    regression coefficients (linear, logistic and Cox), linear mixed model, 
    Cronbach's alpha, interobserver agreement, intraclass correlation coefficients,
    limit of agreement on Bland-Altman plots,
    area under the curve, sensitivity and specificity incorporating the prevalence of disease. 
    You can also use the online version at <https://hcpa-unidade-bioestatistica.shinyapps.io/PSS_Health/>.
Test-based Image structural similarity measure and test of independence. 
    This package implements the key functions of two tasks: (1) computing
    image structural similarity measure PSSIM of Wang, Maldonado and
    Silwal (2011) <DOI:10.1016/j.csda.2011.04.021>;
    and (2) test of independence between a response and 
    a covariate in presence of heteroscedastic treatment effects 
    proposed by Wang, Tolos, and Wang (2010) <DOI:10.1002/cjs.10068>.
Estimates parameters of a piecewise exponential model for time to progression and time from progression to death with interval censoring of the time to progression and covariates for each distribution using proportional hazards.
Returns almost all features that has been extracted from Position Specific 
             Scoring Matrix (PSSM) so far, which is a matrix of L rows (L is protein length) 
             and 20 columns produced by 'PSI-BLAST' which is a program to produce
             PSSM Matrix from multiple sequence alignment of proteins
             see <https://www.ncbi.nlm.nih.gov/books/NBK2590/> for mor details. some 
             of these features are described in Zahiri, J., et al.(2013)
             <DOI:10.1016/j.ygeno.2013.05.006>,
             Saini, H., et al.(2016)
             <DOI:10.17706/jsw.11.8.756-767>,
             Ding, S., et al.(2014)
             <DOI:10.1016/j.biochi.2013.09.013>,
             Cheng, C.W., et al.(2008)
             <DOI:10.1186/1471-2105-9-S12-S6>,
             Juan, E.Y., et al.(2009)
             <DOI:10.1109/CISIS.2009.194>.
Implements estimation and testing procedures for evaluating an intermediate biomarker response as a principal surrogate of a clinical response to treatment (i.e., principal stratification effect modification analysis), as described in Juraska M, Huang Y, and Gilbert PB (2020), Inference on treatment effect modification by biomarker response in a three-phase sampling design, Biostatistics, 21(3): 545-560 <doi:10.1093/biostatistics/kxy074>. The methods avoid the restrictive 'placebo structural risk' modeling assumption common to past methods and further improve robustness by the use of nonparametric kernel smoothing for biomarker density estimation. A randomized controlled two-group clinical efficacy trial is assumed with an ordered categorical or continuous univariate biomarker response measured at a fixed timepoint post-randomization and with a univariate baseline surrogate measure allowed to be observed in only a subset of trial participants with an observed biomarker response (see the flexible three-phase sampling design in the paper for details). Bootstrap-based procedures are available for pointwise and simultaneous confidence intervals and testing of four relevant hypotheses. Summary and plotting functions are provided for estimation results.
A network-based systems biology tool for flexible identification of phenotype-specific subpathways in the cancer gene expression data 
  with multiple categories (such as multiple subtype or developmental stages of cancer). Subtype Set Enrichment Analysis (SubSEA) and Dynamic Changed 
  Subpathway Analysis (DCSA) are developed to flexible identify subtype specific and dynamic changed subpathways respectively. The operation modes 
  include extraction of subpathways from biological pathways, inference of subpathway activities in the context of gene expression data, identification 
  of subtype specific subpathways with SubSEA, identification of dynamic changed subpathways associated with the cancer developmental stage with DCSA, 
  and visualization of the activities of resulting subpathways by using box plots and heat maps. Its capabilities render the tool could find the specific
  abnormal subpathways in the cancer dataset with multi-phenotype samples.
Provides a framework for analysing state sequences with probabilistic suffix trees (PST), the construction that stores variable length Markov chains (VLMC). Besides functions for learning and optimizing VLMC models, the PST library includes many additional tools to analyse sequence data with these models: visualization tools, functions for sequence prediction and artificial sequences generation, as well as for context and pattern mining. The package is specifically adapted to the field of social sciences by allowing to learn VLMC models from sets of individual sequences possibly containing missing values, and by accounting for case weights. The  library also allows to compute probabilistic divergence between two models, and to fit segmented VLMC, where sub-models fitted to distinct strata of the learning sample are stored in a single PST. This software results from research work executed within the framework of the Swiss National Centre of Competence in Research LIVES, which is financed by the Swiss National Science Foundation. The authors are grateful to the Swiss National Science Foundation for its financial support.
Calculating Pst values to assess differentiation among populations from a set of quantitative traits is the primary purpose of such a package. The bootstrap method provides confidence intervals and distribution histograms of Pst. Variations of Pst in function of the parameter c/h^2 are studied as well. Finally, the package proposes different transformations especially to eliminate any variation resulting from allometric growth (calculation of residuals from linear regressions, Reist standardizations or Aitchison transformation).
The propensity score is one of the most widely used tools in
    studying the causal effect of a treatment, intervention, or policy. Given that
    the propensity score is usually unknown, it has to be estimated, implying that
    the reliability of many treatment effect estimators depends on the correct
    specification of the (parametric) propensity score. This package implements the
    data-driven nonparametric diagnostic tools for detecting propensity score
    misspecification proposed by Sant'Anna and Song (2019) <doi:10.1016/j.jeconom.2019.02.002>.
Functions to simulate Poisson or Normally distributed responses relative to 
  a baseline and compute achieved significance level and powers for tests on the
  simulated responses.
Provides propensity score weighting methods to control for confounding in causal inference with dichotomous treatments and continuous/binary outcomes. It includes the following functional modules: (1) visualization of the propensity score distribution in both treatment groups with mirror histogram, (2) covariate balance diagnosis, (3) propensity score model specification test, (4) weighted estimation of treatment effect, and (5) augmented estimation of treatment effect with outcome regression. The weighting methods include the inverse probability weight (IPW) for estimating the average treatment effect (ATE), the IPW for average treatment effect of the treated (ATT), the IPW for the average treatment effect of the controls (ATC), the matching weight (MW), the overlap weight (OVERLAP), and the trapezoidal weight (TRAPEZOIDAL). Sandwich variance estimation is provided to adjust for the sampling variability of the estimated propensity score. These methods are discussed by Hirano et al (2003) <DOI:10.1111/1468-0262.00442>, Lunceford and Davidian (2004) <DOI:10.1002/sim.1903>, Li and Greene (2013) <DOI:10.1515/ijb-2012-0030>, and Li et al (2016) <DOI:10.1080/01621459.2016.1260466>.
Supports propensity score weighting analysis of observational studies and randomized trials. Enables the estimation and inference of average causal effects with binary and multiple treatments using overlap weights (ATO), inverse probability of treatment weights (ATE), average treatment effect among the treated weights (ATT), matching weights (ATM) and entropy weights (ATEN), with and without propensity score trimming. These weights are members of the family of balancing weights introduced in Li, Morgan and Zaslavsky (2018) <doi:10.1080/01621459.2016.1260466> and Li and Li (2019) <doi:10.1214/19-AOAS1282>.
Kappa, ICC, reliability coefficient, parallel analysis, 
     multi-traits multi-methods, spherical representation of a correlation matrix.
A general purpose toolbox for personality, psychometric theory and experimental psychology.   Functions are primarily for multivariate analysis and scale construction using factor analysis, principal component analysis, cluster analysis and reliability analysis, although others provide basic descriptive statistics. Item Response Theory is done using  factor analysis of tetrachoric and polychoric correlations. Functions for analyzing data at multiple levels include within and between group statistics, including correlations and factor analysis.   Functions for simulating and testing particular item and test structures are included. Several functions  serve as a useful front end for structural equation modeling.  Graphical displays of path diagrams, factor analysis and structural equation models are created using basic graphics. Some of the functions are written to support a book on psychometric theory as well as publications in personality research. For more information, see the <https://personality-project.org/r/> web page.
Tools for computing bare-bones and psychometric meta-analyses and for generating psychometric data for use in meta-analysis simulations. Supports bare-bones, individual-correction, and artifact-distribution methods for meta-analyzing correlations and d values. Includes tools for converting effect sizes, computing sporadic artifact corrections, reshaping meta-analytic databases, computing multivariate corrections for range variation, and more. Bugs can be reported to <https://github.com/psychmeta/psychmeta/issues> or <issues@psychmeta.com>.
The main goal of the psycho package is to provide tools for psychologists, neuropsychologists and neuroscientists, 
   to facilitate and speed up the time spent on data analysis. It aims at supporting best practices and tools to format the output 
   of statistical methods to directly paste them into a manuscript, ensuring statistical reporting standardization and conformity.
Psychometric mixture models based on 'flexmix' infrastructure. At the moment Rasch mixture models
  with different parameterizations of the score distribution (saturated vs. mean/variance specification),
  Bradley-Terry mixture models, and MPT mixture models are implemented. These mixture models can be estimated
  with or without concomitant variables. See vignette('raschmix', package = 'psychomix') for details on the
  Rasch mixture models.
Multi-group (dynamical) structural equation models in combination with confirmatory network models from cross-sectional, time-series and panel data <doi:10.31234/osf.io/8ha93>. Allows for confirmatory testing and fit as well as exploratory model search.
Infrastructure for psychometric modeling such as data classes (for
  item response data and paired comparisons), basic model fitting functions (for
  Bradley-Terry, Rasch, parametric logistic IRT, generalized partial credit,
  rating scale, multinomial processing tree models), extractor functions for
  different types of parameters (item, person, threshold, discrimination,
  guessing, upper asymptotes), unified inference and visualizations, and various
  datasets for illustration.  Intended as a common lightweight and efficient
  toolbox for psychometric modeling and a common building block for fitting
  psychometric mixture models in package "psychomix" and trees based on
  psychometric models in package "psychotree".
Recursive partitioning based on psychometric models,
  employing the general MOB algorithm (from package partykit) to obtain
  Bradley-Terry trees, Rasch trees, rating scale and partial credit trees, and
  MPT trees.
Helper functions for producing reports in Psychology (Reproducible Research). Provides required formatted strings (APA style) for use in 'Knitr'/'Latex' integration within *.Rnw files.
Implementation of 'PsychroLib'
    <https://github.com/psychrometrics/psychrolib> library which contains
    functions to enable the calculation properties of moist and dry air in both
    metric (SI) and imperial (IP) systems of units. References: Meyer, D. and
    Thevenard, D (2019) <doi:10.21105/joss.01137>.
Provides text mining methods for social science research. The
    package implements estimation, inference, summarization, and goodness-of-fit
    methods for topic models including Latent Dirichlet Allocation (LDA),
    supervised LDA, and supervised LDA with covariates using Bayesian Markov Chain
    Monte Carlo. A description of the key models and estimation methods is available
    in Wilcox, Jacobucci, Zhang, & Ammerman (2021). <doi:10.31234/osf.io/62tc3>.
Support functions,  data sets, and vignettes for the 'psych' package. Contains several of the biggest data sets for the 'psych' package as well as one vignette. A few helper functions for file manipulation are included as well. For more information, see the <https://personality-project.org/r/> web page.
A beginner-friendly R package for modeling in psychology or
    related field. It allows fitting models, plotting, checking goodness
    of fit, and model assumption violations all in one place. It also
    produces beautiful and easy-to-read output.
Person fit statistics based on Quality Control measures are provided for questionnaires and tests given a specified IRT model. Statistics based on Cumulative Sum (CUSUM) charts are provided. Options are given for banks with polytomous and dichotomous data.
Data and examples from meta-analyses in psychology research.
Apply the popular real-time monitoring strategy
    proposed by Phillips, Shi and Yu (2015a,b;PSY) <doi:10.1111/iere.12132>, <doi:10.1111/iere.12131>, along with a new
    bootstrap procedure designed to mitigate the potential impact of
    heteroskedasticity and to effect family-wise size control in recursive
    testing algorithms (Phillips and Shi, forthcoming).
Provides functions and data-sets that are helpful 
  for teaching statistics and data analysis. It was originally designed for use
  when teaching students in the Psychology Department at Nottingham Trent University.
An assortment of functions that could be useful in analyzing data from psychophysical experiments. It includes functions for calculating d' from several different experimental designs, links for m-alternative forced-choice (mafc) data to be used with the binomial family in glm (and possibly other contexts) and self-Start functions for estimating gamma values for CRT screen calibrations.
The constructs used to study the human psychology have
    many definitions and corresponding instructions for eliciting
    and coding qualitative data pertaining to constructs' content and
    for measuring the constructs. This plethora of definitions and
    instructions necessitates unequivocal reference to specific
    definitions and instructions in empirical and secondary research.
    This package implements a human- and machine-readable standard for
    specifying construct definitions and instructions for measurement
    and qualitative research based on 'YAML'. This standard facilitates
    systematic unequivocal reference to specific construct definitions
    and corresponding instructions in a decentralized manner (i.e.
    without requiring central curation; Peters (2020)
    <doi:10.31234/osf.io/xebhn>).
A multiway method to decompose a tensor (array) of any order, as a generalisation of SVD also supporting non-identity metrics and penalisations. 2-way SVD with these extensions is also available. The package includes also some other multiway methods: PCAn (Tucker-n) and PARAFAC/CANDECOMP with these extensions.
We provide inference for personalized medicine models. Namely, we answer the questions: (1) how much better does a purported personalized recommendation engine for treatments do over a business-as-usual approach and (2) is that difference statistically significant?
Implements p-value computations using an approximation to the cumulative distribution function for a variety of tests for periodicity. These tests include harmonic regression tests with normal and double exponential errors as well as modifications of Fisher's g test. An accompanying vignette illustrates the application of these tests.
Efficient algorithms to implement Probit Tensor Factorization (PTF) model for statistical relational learning, which not only inherits the computation efficiency from the classic tensor factorization model but also accounts for the binary nature of relational data. The methodology is based on Ye Liu (2021) <https://repository.lib.ncsu.edu/bitstream/handle/1840.20/37507/etd.pdf?sequence=1> "Computational Methods for Complex Models with Latent Structure".
Function pip3d() tests whether a point in 3D space is
  within, exactly on, or outside an enclosed surface defined by a triangular mesh.
  Function pip2d() tests whether a point in 2D space is within, exactly on, or outside a polygon.
  For a reference, see: Liu et al., A new point containment test algorithm based on preprocessing
  and determining triangles, Computer-Aided Design 42(12):1143-1150.
Permute treatment labels for taxa and environmental gradients to
    generate an empirical distribution of change points.  This is an extension
    for the 'TITAN2' package <https://cran.r-project.org/package=TITAN2>.
Contains utilities for the analysis of post-translational modifications (PTMs) in proteins, 
    with particular emphasis on the sulfoxidation of methionine residues. Features include the ability to download, 
    filter and analyze data from the sulfoxidation database 'MetOSite', and integrate data from other main PTMs 
    (other databases). Utilities to search and characterize S-aromatic motifs in proteins are also provided. 
    In addition, functions to analyze sequence environments around modifiable residues in proteins can be found. 
    For instance, 'ptm' allows to search for amino acids either overrepresented or avoided around the modifiable 
    residues from the proteins of interest. Functions tailored to test statistical hypothesis related to
    these differential sequence environments are also implemented. A number of utilities to assess the effect
    of the modification/mutation of a given residue on the protein stability, have also been included in this package.
    Further and detailed information regarding the methods in this package can be found in (Aledo (2020) <https://metositeptm.com>).
Fits the Poisson-Tweedie generalized linear mixed model
    described in Signorelli et al. (2021, <doi:10.1177/1471082X20936017>).
    Likelihood approximation based on adaptive Gauss Hermite quadrature
    rule.
Fits and analyses time dependent marked point process models with an emphasis on earthquake modelling. For a more detailed introduction to the package, see the topic "PtProcess". A list of recent changes can be found in the topic "Change Log".
Various applications in invasive species biology,
    conservation biology, epidemiology and elsewhere involve 
    sampling of sets of 2D points from a posterior distribution.
    The number of such point sets may be large, say 1000 or 10000. 
    This package facilitates visualisation of such output by 
    constructing seven nested polygons representing the location and 
    variability of the point sets. This can be used, for example, to 
    visualise the range boundary of a species, and uncertainty in 
    the location of that boundary.
Utility functions produced specifically for (but not limited to) 
    working with 'ProjectTemplate' data pipelines. This package helps to quickly
    create and manage sequentially numbered scripts, quickly set up logging with
    'log4r' and functions to help debug and monitor procedures.
A collection of functions to simulate, estimate and forecast a wide range of regression based dynamic models for positive time series. 
	This package implements the results presented in Prass, T.S.; Carlos, J.H.; Taufemback, C.G. and Pumi, G. (2022). "Positive Time Series Regression" <arXiv:2201.03667>.
Various estimation methods for the shape parameter of Pareto
    distributed data. This package contains functions for various estimation 
    methods such as maximum likelihood 
    (Newman, 2005)<doi:10.1016/j.cities.2012.03.001>, 
    Hill's estimator (Hill, 1975)<doi:10.1214/aos/1176343247>, 
    least squares (Zaher et al., 2014)<doi:10.9734/BJMCS/2014/10890>, 
    method of moments (Rytgaard, 1990)<doi:10.2143/AST.20.2.2005443>, 
    percentiles (Bhatti et al., 2018)<doi:10.1371/journal.pone.0196456>,
    and weighted least squares (Nair et al., 2019) to estimate the shape 
    parameter of Pareto distributed data. It also provides both a heuristic 
    method (Hubert et al., 2013)<doi:10.1016/j.csda.2012.07.011> and a 
    goodness of fit test 
    (Gulati and Shapiro, 2008)<doi:10.1007/978-0-8176-4619-6> for testing for 
    Pareto data as well as a method for generating Pareto distributed data.
Access the 'Public Transport Victoria' Timetable API 
    <https://www.ptv.vic.gov.au/footer/data-and-reporting/datasets/ptv-timetable-api/>,
    with results returned as familiar R data structures. Retrieve information on
    stops, routes, disruptions, departures, and more.
Parametric Time Warping aligns patterns, i.e. it aims to
        put corresponding features at the same locations. The algorithm
        searches for an optimal polynomial describing the warping. It
        is possible to align one sample to a reference, several samples
        to the same reference, or several samples to several
        references. One can choose between calculating individual
        warpings, or one global warping for a set of samples and one
        reference. Two optimization criteria are implemented: RMS (Root
        Mean Square error) and WCC (Weighted Cross Correlation). Both
	warping of peak profiles and of peak lists are supported. A
	vignette for the latter is contained in the inst/doc directory
	of the source package - the vignette source can be found on
	the package github site.
Contains a dataset of words used in 15.000 randomly extracted pages
    from the Portuguese Wikipedia (<https://pt.wikipedia.org/>).
Package to Percentile estimation of fetal weight for twins by chorionicity (dichorionic-diamniotic or monochorionic-diamniotic).
Generates Proteomics (PTX) quality control (QC) reports for shotgun LC-MS data analyzed with the 
             MaxQuant software suite (from .txt files) or mzTab files (ideally from OpenMS 'QualityControl' tool).
             Reports are customizable (target thresholds, subsetting) and available in HTML or PDF format.
             Published in J. Proteome Res., Proteomics Quality Control: Quality Control Software for MaxQuant Results (2015)
             <doi:10.1021/acs.jproteome.5b00780>.
Bayesian variable selection for linear regression models using hierarchical
  priors. There is a prior that combines information across responses and one
  that combines information across covariates, as well as a standard spike and
  slab prior for comparison. An MCMC samples from the marginal posterior
  distribution for the 0-1 variables indicating if each covariate belongs to the
  model for each response.
Get chunks of XML scholarly articles without
    having to know how to work with XML. Custom mappers
    for each publisher and for each article section pull
    out the information you want. Works with outputs from
    package 'fulltext', 'xml2' package documents, and file
    paths to XML documents.
A toolbox for making R functions and capabilities more
    accessible to students and professionals from Epidemiology and
    Public Health related disciplines. Includes a function to report 
    coefficients and confidence intervals from models using robust
    standard errors (when available), functions that expand 'ggplot2'
    plots and functions relevant for introductory papers in Epidemiology 
    or Public Health. Please note that use of the 
    provided data sets is for educational purposes only.
Performs sensitivity analysis for publication bias in meta-analyses (per Mathur & VanderWeele, 2020 [<https://osf.io/s9dp6>]).
    These analyses enable statements such as: "For publication bias to shift the observed point estimate to the null,
    'significant' results would need to be at least 30-fold more likely to be published than negative or 'nonsignificant'
    results." Comparable statements can be made regarding shifting to a chosen non-null value or shifting the confidence
    interval. Provides a worst-case meta-analytic point estimate under maximal publication bias obtained simply by conducting
    a standard meta-analysis of only the negative and "nonsignificant" studies.
Tools for Bayesian estimation of meta-analysis models that account 
    for publications bias or p-hacking. For publication bias, this package 
    implements a variant of the p-value based selection model of Hedges (1992)
    <doi:10.1214/ss/1177011364> with discrete selection probabilities. It also 
    implements the mixture of truncated normals model for p-hacking described 
    in Moss and De Bin (2019) <arXiv:1911.12445>.
A bunch of convenience functions that transform the results of some basic statistical analyses
       into table format nearly ready for publication. This includes descriptive tables, tables of
       logistic regression and Cox regression results as well as forest plots.
Text mining of PubMed Abstracts (text and XML) from <https://pubmed.ncbi.nlm.nih.gov/>.
Easy function for text-mining the 'PubMed' repository based on defined sets of terms.
    The relationship between fix-terms (related to your research topic) and pub-terms (terms which pivot around your research focus)
    is calculated using the pointwise mutual information algorithm ('PMI'). Church, Kenneth Ward and Hanks, Patrick (1990) <https://www.aclweb.org/anthology/J90-1003/>
    A text file is generated with the 'PMI'-scores for each fix-term. Then for each collocation pairs (a fix-term + a pub-term),
    a text file is generated with related article titles and publishing years. Additional Author section will follow in the next version updates.
A set of tools to extract bibliographic content from 'PubMed' database using 'NCBI' REST API <https://www.ncbi.nlm.nih.gov/home/develop/api/>.
Create a word cloud using the abstract of publications from 'Pubmed'.
'PubTator' <https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator/> is a National Center for Biotechnology Information (NCBI) tool that enhances the annotation of articles on PubMed <https://www.ncbi.nlm.nih.gov/pubmed/>. It makes it possible to rapidly identify potential relationships between genes or proteins using text mining techniques. In contrast, manually searching for and reading the annotated articles would be very time consuming. 'PubTator' offers both an online interface and a RESTful API, however, neither of these approaches are well suited for frequent, high-throughput analyses. The package 'pubtatordb' provides a set of functions that make it easy for the average R user to download 'PubTator' annotations, create, and then query a local version of the database.
Efficient algorithm for solving PU (Positive and Unlabeled) problem in low or high dimensional setting with lasso or group lasso penalty. The algorithm uses Maximization-Minorization and (block) coordinate descent. Sparse calculation and parallel computing are supported for the computational speed-up. See Hyebin Song, Garvesh Raskutti (2018) <arXiv:1711.08129>.
R Interface to Pullword Service for natural language processing
    in Chinese. It enables users to extract valuable words from text by deep learning models. 
    For more details please visit the official site (in Chinese) <http://www.pullword.com/>.
A method of clustering functional data using
    subregion information of the curves. It is intended to supplement the
    'fda' and 'fda.usc' packages in functional data object clustering. It
    also facilitates the printing and plotting of the results in a tree
    format and limits the partitioning candidates into a specific set of
    subregions.
Model selection for penalized graphical models using the Stability Approach to Regularization Selection ('StARS'), with options for speed-ups including Bounded StARS (B-StARS), batch computing, and other stability metrics (e.g., graphlet stability G-StARS). Christian L. Müller, Richard Bonneau, Zachary Kurtz (2016) <arXiv:1605.07072>.
A tool for analyzing the transcription, processing and degradation rates of genes by 4sU-seq (the Metabolic Label 4-thiouridine) data and RNA-seq (RNA sequencing) data. It can not only recognize the transcriptional dynamic rates at the measurement time points, but also obtain continuous changes in transcriptional dynamics. More importantly, it is able to predict the trend of mRNA (Mature RNA) transcription and expression changes in the future.
R package to query and get data out of a Pumilio sound archive system (http://ljvillanueva.github.io/pumilio/).
Estimates power, minimum detectable effect size (MDES) and sample size requirements. The context is multilevel randomized experiments with multiple outcomes. The estimation takes into account the use of multiple testing procedures. Development of this package was supported by a grant from the Institute of Education Sciences (R305D170030). For a full package description, including a detailed technical appendix, see <arXiv:2112.15273>.
Adsorption isotherm equations are linearized plots of different solid-liquid phase equilibria used in calculating different parameters related to the adsorption process. Isotherm equations deals with physical adsorption of gases and  vapor and gives the most important characteristics  of industrial adsorbents that include pore volume, pore size or energy distribution. PUPAIM has 28 documented adsorption isotherm models listed by Dabrowski (2001) <doi:10.1016/S0001-8686(00)00082-8> and Ayawei et al.(2017) <doi:10.1155/2017/3039817>. These models could be easily  fitted in R using adsorption data (Ce and Qe) obtained from experiments.
Provides a unified pipeline to clean, prepare, plot,
    and run basic analyses on pupillometry experiments.
Pupillometric data collected using SR Research Eyelink eye trackers 
    requires significant preprocessing. This package contains functions for 
    preparing pupil dilation data for visualization and statistical analysis. 
    Specifically, it provides a pipeline of functions which aid in data validation,
    the removal of blinks/artifacts, downsampling, and baselining, among others. 
    Additionally, plotting functions for creating grand average and conditional 
    average plots are provided. See the vignette for samples of the functionality.
    The package is designed for handling data collected with SR Research 
    Eyelink eye trackers using Sample Reports created in SR Research Data 
    Viewer.
Image-based color matching using the "Mycological Colour Chart" by Rayner (1970, ISBN:9780851980263) and its associated fungal pigments. This package will assist mycologists in identifying color during morphological analysis.
PurBayes is an MCMC-based algorithm that uses
        next-generation sequencing data to estimate tumor purity and
        clonality for paired tumor-normal data.
Proteins reside in either the cell plasma or in the
    cell membrane. A membrane protein goes through the 
    membrane at least once. Given the amino acid sequence of a
    membrane protein, the tool
    'PureseqTM' (<https://github.com/PureseqTM/pureseqTM_package>,
    as described in "Efficient And Accurate Prediction Of Transmembrane 
    Topology From Amino acid sequence only.", Wang, Qing, et al (2019), 
    <doi:10.1101/627307>),
    can predict the topology of a membrane protein. This package
    allows one to use 'PureseqTM' from R.
Enables the removal of training data from fitted R models while
    retaining predict functionality. The purged models are more portable as their
    memory footprints do not scale with the training sample size.
Inbreeding-purging analysis of pedigreed populations, including the computation of the inbreeding coefficient, partial, ancestral and purged inbreeding coefficients, and measures of the opportunity of purging related to the individual reduction of inbreeding load.
    In addition, functions to calculate the effective population size and other parameters relevant to population genetics are included.
    See López-Cortegano E. (2021) <doi:10.1093/bioinformatics/btab599>.
Simple method of purging independent variables of mediating effects. First, regress the direct variable on the indirect variable. Then, used the stored residuals as the new purged (direct) variable in the updated specification. This purging process allows for use of a new direct variable uncorrelated with the indirect variable. Please cite the method and/or package using Waggoner, Philip D. (2018) <doi:10.1177/1532673X18759644>.
A complete and consistent functional programming
    toolkit for R.
Some functions at the intersection of 'dplyr' and
    'purrr' that formerly lived in 'purrr'.
Package for projection pursuit (PP) with 17 methods and grand tour with 3 methods. Being that projection pursuit searches for low-dimensional linear projections in high-dimensional data structures, while grand tour is a technique used to explore multivariate statistical data through animation.
Create sliders from left, right, top and bottom which may include any html or 'Shiny' input or output.
Send push notifications to mobile devices or the desktop
    using 'Pushover' <https://pushover.net>. These notifications can
    display things such as results, job status, plots, or any other text
    or numeric data.
To Simplify the time consuming and error prone task of assembling complex data sets for non-linear mixed effects modeling. Users are able to select from different absorption processes such as zero and first order, or a combination of both. Furthermore, data sets containing data from several entities, responses, and covariates can be simultaneously assembled.
Likelihood based population viability analysis in the
  presence of observation error and missing data.
  The package can be used to fit, compare, predict,
  and forecast various growth model types using data cloning.
Contains functions to compute and plot confidence distributions, confidence densities, p-value functions and s-value (surprisal) functions for several commonly used estimates. Instead of just calculating one p-value and one confidence interval, p-value functions display p-values and confidence intervals for many levels thereby allowing to gauge the compatibility of several parameter values with the data. These methods are discussed by Infanger D, Schmidt-Trucksäss A. (2019) <doi:10.1002/sim.8293>; Poole C. (1987) <doi:10.2105/AJPH.77.2.195>; Schweder T, Hjort NL. (2002) <doi:10.1111/1467-9469.00285>; Bender R, Berg G, Zeeb H. (2005) <doi:10.1002/bimj.200410104> ; Singh K, Xie M, Strawderman WE. (2007) <doi:10.1214/074921707000000102>; Rothman KJ, Greenland S, Lash TL. (2008, ISBN:9781451190052); Amrhein V, Trafimow D, Greenland S. (2019) <doi:10.1080/00031305.2018.1543137>; Greenland S. (2019) <doi:10.1080/00031305.2018.1529625> and Rafi Z, Greenland S. (2020) <doi:10.1186/s12874-020-01105-9>.
The calculation of p-variation of the finite sample data. 
	This package is a realisation of the procedure described in Butkus, V. & Norvaisa, R. Lith Math J (2018). <doi: 10.1007/s10986-018-9414-3>
	The formal definitions and reference into literature are given in vignette.
Computes nonparametric p-values for the potential class
        memberships of new observations as well as cross-validated
        p-values for the training data. The p-values are based on
        permutation tests applied to an estimated Bayesian likelihood
        ratio, using a plug-in statistic for the Gaussian model, 'k
        nearest neighbors', 'weighted nearest neighbors' or
        'penalized logistic regression'.
        Additionally, it provides graphical displays and quantitative
        analyses of the p-values.
An implementation of multiscale bootstrap resampling for
             assessing the uncertainty in hierarchical cluster analysis.
             It provides SI (selective inference) p-value, AU (approximately unbiased)
             p-value and BP (bootstrap probability) value for each cluster in a dendrogram.
Enables the manufacturing, analysis and display of pressure volume curves. From the progression of the curves, turgor loss point, osmotic potential and apoplastic fraction can be derived. Methods adapted from Bartlett, Scoffoni and Sack (2012) <doi:10.1111/j.1461-0248.2012.01751.x>.
Simplifies the manufacturing, analysis and display of pressure volume and leaf drying curves. From the progression of the curves turgor loss point, osmotic potential, apoplastic fraction as well as minimum conductance and stomatal closure can be derived. Methods adapted from Bartlett, Scoffoni, Sack (2012) <doi:10.1111/j.1461-0248.2012.01751.x> and Sack, Scoffoni, PrometheusWikiContributors (2011) <http://prometheuswiki.org/tiki-index.php?page=Minimum+epidermal+conductance+%28gmin%2C+a.k.a.+cuticular+conductance%29>.
A suite of likelihood ratio test based methods to use in pharmacovigilance. Contains various testing and post-processing functions.
Estimates (and controls for) phylogenetic signal through phylogenetic eigenvectors regression (PVR) and phylogenetic signal-representation (PSR) curve, along with some plot utilities.
The pvsR package facilitates data retrieval from Project
    Vote Smart's rich online data base on US politics via the Project Vote
    Smart application programming interface (PVS API). The functions in this
    package cover most PVS API classes and methods and return the
    requested data in a data frame.
Contains functions which allow the user to perform time series regression quickly using the Power Weighted Densities (PWD) approach. alphahat_LR_one_Rcpp() is the main workhorse function within this package.
Calculates various functions needed for design and monitoring survival trials
    accounting for complex situations such as delayed treatment effect, treatment crossover, non-uniform accrual,
    and different censoring distributions between groups. The event time distribution is assumed to be
    piecewise exponential (PWE) distribution and the entry time is assumed to be piecewise uniform distribution.
    As compared with Version 1.2.1, two more types of hybrid crossover are added. 
    A bug is corrected in the function "pwecx" that calculates the crossover-adjusted survival, distribution, 
    density, hazard and cumulative hazard functions. 
    Also, to generate the crossover-adjusted event time random variable,  a more efficient 
    algorithm is used and the output includes crossover indicators.
Utilities for working with air quality monitoring data
    with a focus on small particulates (PM2.5) generated by wildfire
    smoke. Functions are provided for downloading available data from
    the United States 'EPA' <https://www.epa.gov/outdoor-air-quality-data> and
    it's 'AirNow' air quality site <https://www.airnow.gov>.
    Additional sources of PM2.5 data made accessible by the package include:
    'AIRSIS' (aka "Oceaneering", not public)
    and 'WRCC' <https://wrcc.dri.edu/cgi-bin/smoke.pl>.
    Data compilations are hosted by the USFS 'AirFire' research team
    <https://www.airfire.org>.
Power analysis functions along the lines of Cohen (1988).
User friendly functions for power and sample size analysis at one-way and two-way ANOVA settings take either effect size or delta and sigma as arguments. They are designed for both one-way and two-way ANOVA settings. In addition, a function for plotting power curves is available for power comparison, which can be easily visualized by statisticians and clinical researchers.
Statistical power analysis for designs including t-tests, correlations, 
    multiple regression, ANOVA, mediation, and logistic regression. Functions accompany 
    Aberson (2019) <doi:10.4324/9781315171500>.
Power analysis for AB testing. The calculations are based on the Welch's unequal variances t-test,
  which is generally preferred over the Student's t-test when sample sizes and variances of the two groups are
  unequal, which is frequently the case in AB testing. In such situations, the Student's t-test will give 
  biased results due to using the pooled standard deviation, unlike the Welch's t-test.
This is a package for calculating two differing notions of power, or deriving sample
 sizes for specified requisite power in multiple testing experiments under a variety of methods for
 control of the distribution of the False Discovery Proportion (FDP). More specifically, one can
 choose to control the FDP distribution according to control of its (i) mean, e.g. the usual BH-FDR
 procedure, or via the probability that it exceeds a given value, delta, via (ii) the Romano
 procedure, or via (iii) my procedure based upon asymptotic approximation. Likewise, we can think of
 the power in multiple testing experiments in terms of a summary of the distribution of the
 True Positive Proportion (TPP). The package will compute power, sample size or any other missing
 parameter required for power based upon (i) the mean of the TPP which is the average power
 (ii) the probability that the TPP exceeds a given value, lambda, via my asymptotic approximation
 procedure. The theoretical results are described in Izmirlian, G. (2020) Stat. and Prob. letters,
 <doi:10.1016/j.spl.2020.108713>, and an applied  paper describing the methodology with a
 simulation study is in preparation.
Tools for the evaluation of interim analysis plans for sequentially
 monitored trials on a survival endpoint; tools to construct efficacy and 
 futility boundaries, for deriving power of a sequential design at a specified
 alternative, template for evaluating the performance of candidate plans at a 
 set of time varying alternatives. See Izmirlian, G. (2014) <doi:10.4310/SII.2014.v7.n1.a4>.
Statistical power simulation for testing the Rasch Model based on a three-way analysis of variance design with mixed classification.
The Penn World Table provides purchasing power parity and
	national income accounts converted to international prices for
	189 countries for some or all of the years 1950-2010.
The Penn World Table 10.x (<http://www.ggdc.net/pwt/>) provides information
	on relative levels of income, output, input, and productivity
	for 183 countries between 1950 and 2019.
The Penn World Table 8.x provides information on relative levels of
	income, output, inputs, and productivity for 167 countries
	between 1950 and 2011.
The Penn World Table 9.x (<http://www.ggdc.net/pwt/>) provides information
	on relative levels of income, output, inputs, and productivity
	for 182 countries between 1950 and 2017.
Provides a set of functions for reading and writing PC-Axis files, used by different statistical organizations around the globe for data dissemination.
Generic interface for the PX-Web/PC-Axis API. The
    PX-Web/PC-Axis API is used by organizations such as Statistics Sweden
    and Statistics Finland to disseminate data. The R package can interact
    with all PX-Web/PC-Axis APIs to fetch information about the data
    hierarchy, extract metadata and extract and parse statistics to R
    data.frame format. PX-Web is a solution to disseminate PC-Axis data
    files in dynamic tables on the web.  Since 2013 PX-Web contains an API
    to disseminate PC-Axis files.
Function to read PX-Web data into R via API. The example code reads data from the three national statistical institutes, Statistics Norway, Statistics Sweden and Statistics Finland.
Given a SpatialPolygonsDataFrame and a set of populations for each polygon,
 compute a population density estimate based on Tobler's pycnophylactic interpolation
 algorithm. The result is a SpatialGridDataFrame.
Deterministic Pena-Yohai initial estimator for robust S estimators
    of regression. The procedure is described in detail in
    Pena, D., & Yohai, V. (1999) <doi:10.2307/2670164>.
Allow to run 'pylint' on Python files with a R command or a 'RStudio' addin. The report appears in the RStudio viewer pane as a formatted HTML file.
Provides access to the latest 'Amazon Mechanical Turk' ('MTurk') <https://www.mturk.com> Requester API (version '2017–01–17'), replacing the now deprecated 'MTurkR' package.
Drawing population pyramid using (1) data.frame or (2) vectors.
	 The former is named as pyramid() and the latter pyramids(), as wrapper
	 function of pyramid().  pyramidf() is the function to draw population
	 pyramid within the specified frame.
Using the R package 'reticulate', this package creates an interface to the 'pysd' toolset.
    The package provides an R interface to a number of 'pysd' functions, and can read files in
    'Vensim' 'mdl' format, and 'xmile' format. The resulting simulations are returned as a 'tibble', and from 
    that the results can be processed using 'dplyr' and 'ggplot2'. The package has been tested  using 'python3'.
Read and write 'GraphPad Prism' '.pzfx' files in R.
Mortality Rates are usually published following an abridged description, 
              i.e., by age groups 0, [1, 5[, [5, 10[, [10, 15[ and so on. For some applications, 
              a detailed (single) ages description is required. Despite the huge number of the 
              proposed methods in the literature, there is a limited number of methods ensuring a
              high performance at lower and higher ages in the same time. For example, the 6-terms 
              'Lagrange' interpolation function is well adapted to mortality  interpolation at lower
              ages (with unequal intervals) but is not adapted to higher ages. On the other hand, 
              the 'Karup-King' method allows a good performance at higher ages but not adapted to lower ages.
              Interested readers can refer to the book of Shryock, Siegel and Associates (1993) for a detailed
              overview of the two cited methods.The package Q2q allows combining both the two methods to allow
              interpolating mortality rates at all ages. First, it starts by implementing each method separately,
              then the resulted curves are joined based on a 5-age averaged error between the two curves.
Construct message-passing style objects with types and features. Q7 types uses composition instead of inheritance in creating derived types, allowing defining any code segment as feature and associating any feature to any object. Compared to R6, Q7 is simpler and more flexible, and is more friendly in syntax.
Functions for descriptive statistics,
  data management, and data visualization.
A copula-based measure for quantifying asymmetry in dependence and associations. Documentation and theory about 'qad' is provided
    by the paper by Junker, Griessenberger & Trutschnig (2021, <doi:10.1016/j.csda.2020.107058>), and the paper by Trutschnig (2011, <doi:10.1016/j.jmaa.2011.06.013>).
A tool for automatic generation of sibling items from a parent item model defined by
           the user. It is an implementation of the process automatic item generation (AIG) focused
           on generating quantitative multiple-choice type of items (see Embretson, Kingston
           (2018) <doi:10.1111/jedm.12166>).
Implements heuristics for the Quadratic Assignment Problem (QAP). Currently only a simulated annealing heuristic is available.
Estimates QAPE using bootstrap procedures. The residual, parametric and double bootstrap is used. The test of normality using Cholesky decomposition is added.
Calculates the right-tail probability of quadratic forms of Gaussian variables using the skewness-kurtosis ratio matching method, modified Liu-Tang-Zhang method and Satterthwaite-Welch method. The technical details can be found in Hong Zhang, Judong Shen and Zheyang Wu (2020) <arXiv:2005.00905>.
Functions for a scientific quality assurance of meteorological data.
Implements the Bayesian quantile regression model for binary longitudinal data 
             (QBLD) developed in Rahman and Vossmeyer (2019) <DOI:10.1108/S0731-90532019000040B009>.
             The model handles both fixed and random effects and implements both a blocked
             and an unblocked Gibbs sampler for posterior inference.
Query the Breeding Management System 'BMS'
    <https://bmspro.io> database (using 'BrAPI' <https://brapi.org> calls)
    to help breeders as targeted end-users retrieving data directly into
    their analyzing pipelines.
An extensive set of functions to perform Qualitative Comparative Analysis:
             crisp sets ('csQCA'), temporal ('tQCA'), multi-value ('mvQCA')
             and fuzzy sets ('fsQCA'), using a GUI - graphical user interface.
             'QCA' is a methodology that bridges the qualitative and quantitative divide
             in social science research. It uses a Boolean algorithm, resulting in a
             minimal causal configuration that explains a given phenomenon.
Clustered set-relational data in Qualitative Comparative Analysis
    (QCA) can have a hierarchical structure, a panel structure or repeated cross
    sections. 'QCAcluster' allows QCA researchers to supplement the analysis
    of pooled the data with a disaggregated perspective focusing on selected 
    partitions of the data. The pooled data can be partitioned along the 
    dimensions of the clustered data (individual cross sections or time series) 
    to perform partition-specific truth table minimizations. Empirical 
    researchers can further calculate the weight that each partition has on the 
    parameters of the pooled solution and the diversity of the cases under 
    analysis within and across partitions 
    (see <https://ingorohlfing.github.io/QCAcluster/>).
Implements tests for Type I error in Qualitative Comparative Analysis (QCA) that take into account the multiple hypothesis tests inherent in the procedure. Tests can be carried out on three variants of QCA: crisp-set QCA (csQCA), multi-value QCA (mvQCA) and fuzzy-set QCA (fsQCA). For fsQCA, the fsQCApermTest() command implements a permutation test that provides 95% confidence intervals for the number of counterexamples and degree of consistency, respectively. The distributions of permuted values can be plotted against the observed values. For csQCA and mvQCA, simple binomial tests are implemented in csQCAbinTest() and mvQCAbinTest(), respectively.
Researchers working with Qualitative Comparative Analysis (QCA)
    can use the package to estimate power of a sufficient term
    using permutation tests. A term can be anything: A condition, conjunction or 
    disjunction of any combination of these. The package further allows users to plot 
    the estimation results and to estimate the number of cases required to achieve a
    certain level of power, given a prespecified null and alternative hypothesis.
    Reference for the article introducing power estimation for QCA is: 
    Rohlfing, Ingo (2018) <doi:10.1017/pan.2017.30> 
    (ungated version: <doi:10.17605/OSF.IO/PC4DF>).
Provides advanced functionality for performing configurational comparative research with Qualitative Comparative Analysis (QCA), including crisp-set, multi-value, and fuzzy-set QCA. It also offers advanced tools for sensitivity diagnostics and methodological evaluations of QCA.
Helper functions for Qualitative Comparative Analysis: evaluate and
    plot Boolean formulae on fuzzy set score data, apply Boolean operations, compute
    consistency and coverage measures.
CBA postprocessing algorithm that creates smaller models for datasets containing quantitative (numerical) attributes. Article describing QCBA is published in Tomas Kliegr (2017) <arXiv:1711.10166>. The package can also postprocess results of the SBRL package, which is no longer in CRAN, but can be obtained from <https://github.com/cran/sbrl>.
Shewhart quality control charts for continuous, attribute and count data. Cusum and EWMA charts. Operating characteristic curves. Process capability analysis. Pareto chart and cause-and-effect chart. Multivariate control charts.
Functions to calculate Average Sample Numbers (ASN), Average Run Length (ARL1) and value of k, k1 and k2 for quality control charts under repetitive sampling as given in Aslam et al. (2014) (<DOI:10.7232/iems.2014.13.1.101>).
Tools for (automated and manual) quality control of
  the results of Epigenome-Wide Association Studies.
Tools for (automated and manual) quality control of
  the results of Genome Wide Association Studies
Provides functions for the joint analysis of K sets
    of p-values obtained for a same list of items. This joint analysis is
    performed by querying a composed hypothesis, i.e. an arbitrary complex
    combination of simple hypotheses, as described in Mary-Huard et al.
    (2021) <arXiv:2104.14601>. The null distribution corresponding to the
    composed hypothesis of interest is obtained by fitting non-parametric
    mixtures models (one for each of the simple hypothesis of the complex
    combination). Type I error rate control is achieved through Bayesian
    False Discovery Rate control.  The 3 main functions of the package
    GetHinfo(), qch.fit() and qch.test() correspond to the 3 steps for
    querying a composed hypothesis (composed H0/H1 formulation, inferring
    the null distribution and testing the null hypothesis).
Implements the Quantile Composite-based Path Modeling approach 
	(Davino and Vinzi, 2016 <doi:10.1007/s11634-015-0231-9>; 
	 Dolce et al., 2021 <doi:10.1007/s11634-021-00469-0>). The method complements the traditional PLS Path Modeling approach, analyzing the entire distribution of outcome variables and, therefore, overcoming the classical exploration of only average effects. It exploits quantile regression to investigate changes in the relationships among constructs and between constructs and observed variables.
Quality control of chromatin immunoprecipitation libraries (ChIP-seq) by quantitative polymerase chain reaction (qPCR). This function calculates Enrichment value with respect to reference for each histone modification (specific to 'Vii7' software <http://www.thermofisher.com/ca/en/home/life-science/pcr/real-time-pcr/real-time-pcr-instruments/viia-7-real-time-pcr-system/viia-7-software.html>). This function is applicable to full panel of histone modifications described by International Human Epigenomic Consortium (IHEC).
Univariate and multivariate SQC tools that completes and increases
    the SQC techniques available in R. Apart from integrating different R packages 
    devoted to SQC ('qcc','MSQC'), provides nonparametric tools that are highly 
    useful when Gaussian assumption is not met. 
    This package computes standard univariate control charts for individual measurements, 
    'X-bar', 'S', 'R', 'p', 'np', 'c', 'u', 'EWMA' and 'CUSUM'. In addition, it 
    includes functions to perform multivariate control charts such as 'Hotelling T2', 
    'MEWMA' and 'MCUSUM'. As representative feature, multivariate nonparametric 
    alternatives based on data depth are implemented in this package: 'r', 'Q' and 
    'S' control charts. In addition, Phase I and II control charts for functional 
    data are included. This package also allows the estimation of the most complete 
    set of capability indices from first to fourth generation, covering the nonparametric 
    alternatives, and performing the corresponding capability analysis graphical outputs,
    including the process capability plots. See Flores et al. (2021) <doi:10.32614/RJ-2021-034>.
Simulates a 5 qubit Quantum Computer and evaluates quantum circuits with 1,2
             qubit quantum gates.
Quantile correlation-sure independence screening (QC-SIS) and composite quantile correlation-sure independence screening (CQC-SIS) for ultrahigh-dimensional data.
Primarily, the 'qcv' package computes key indices related to the Quantifying Construct
    Validity procedure (QCV; Westen & Rosenthal, 2003 <doi:10.1037/0022-3514.84.3.608>; 
    see also Furr & Heuckeroth, in press). The qcv() function is the heart of the 'qcv' package, 
    but additional functions in the package provide useful ancillary information related to the QCV procedure.
Automates many of the tasks associated with quantitative discourse analysis of transcripts containing discourse
              including frequency counts of sentence types, words, sentences, turns of talk, syllables and other assorted
              analysis tasks. The package provides parsing tools for preparing transcript data. Many functions enable the user
              to aggregate data by any number of grouping variables, providing analysis and seamless integration with other R
              packages that undertake higher level analysis and visualization of text. This affords the user a more efficient
              and targeted analysis. 'qdap' is designed for transcript analysis, however, many functions are applicable to other
              areas of Text Mining/ Natural Language Processing.
A collection of text analysis dictionaries and word lists for use with
        the 'qdap' package.
A collection of regular expression tools associated with
        the 'qdap' package that may be useful outside of the context of
        discourse analysis. Tools include
        removal/extraction/replacement of abbreviations, dates, dollar
        amounts, email addresses, hash tags, numbers, percentages,
        citations, person tags, phone numbers, times, and zip codes.
A collection of tools associated with the 'qdap' package that may be useful outside of the
            context of text analysis.
Allows practitioners to determine (i) if two univariate distributions (which can be continuous, discrete, or even mixed) are equal, (ii) how two distributions differ (shape differences, e.g., location, scale, etc.), and (iii) where two distributions differ (at which quantiles), all using nonparametric LP statistics. The primary reference is Jungreis, D. (2019, Technical Report).
Calculate the risk of developing type 2 diabetes using risk prediction algorithms derived by 'ClinRisk'.
This package provides different specifications of a Quadrilateral
    Dissimilarity Model which can be used to fit same-different judgments
    in order to get a predicted matrix that satisfies regular minimality
    [Colonius & Dzhafarov, 2006, Measurement and representations of
    sensations, Erlbaum]. From such a matrix, Fechnerian distances can be
    computed.
Quantile-based estimators (Q-estimators) can be used to fit any parametric distribution, using its quantile function. Q-estimators are usually more robust than standard maximum likelihood estimators. The method is described in: Sottile G. and Frumento P. (2022). Robust estimation and regression with parametric quantile functions. <doi:10.1016/j.csda.2022.107471>.
The computation of quadratic form (QF) distributions is often not trivial and it requires numerical routines. The package contains functions aimed at evaluating the exact distribution of quadratic forms (QFs) and ratios of QFs. In particular, we propose to evaluate density, quantile and distribution functions of positive definite QFs and ratio of independent positive QFs by means of an algorithm based on the numerical inversion of Mellin transforms.
Accurate estimates of the diets of predators are required
    in many areas of ecology, but for many species current methods are
    imprecise, limited to the last meal, and often biased. The diversity
    of fatty acids and their patterns in organisms, coupled with the
    narrow limitations on their biosynthesis, properties of digestion in
    monogastric animals, and the prevalence of large storage reservoirs of
    lipid in many predators, led to the development of quantitative
    fatty acid signature analysis (QFASA) to study predator diets.
An implementation of Quantitative Fatty Acid Signature
    Analysis (QFASA) in R.  QFASA is a method of estimating the diet
    composition of predators.  The fundamental unit of information in
    QFASA is a fatty acid signature (signature), which is a vector of
    proportions describing the composition of fatty acids within lipids.
    Signature data from at least one predator and from samples of all
    potential prey types are required.  Calibration coefficients, which
    adjust for the differential metabolism of individual fatty acids by
    predators, are also required. Given those data inputs, a predator
    signature is modeled as a mixture of prey signatures and its diet
    estimate is obtained as the mixture that minimizes a measure of
    distance between the observed and modeled signatures.  A variety of
    estimation options and simulation capabilities are implemented.
    Please refer to the vignette for additional details and references.
Option pricing (financial derivatives) techniques mainly following textbook 'Options, Futures and Other Derivatives', 9ed by John C.Hull, 2014. Prentice Hall. Implementations are via binomial tree option model (BOPM), Black-Scholes model, Monte Carlo simulations, etc. 
  This package is a result of Quantitative Financial Risk Management course (STAT 449 and STAT 649) at Rice University, Houston, TX, USA, taught by Oleg Melnikov, statistics PhD student, as of Spring 2015.
Smooth additive quantile regression models, fitted using
    the methods of Fasiolo et al. (2020) <doi:10.1080/01621459.2020.1725521>.
    See Fasiolo at al. (2021) <doi:10.18637/jss.v100.i09> for an introduction to the package. Differently from
    'quantreg', the smoothing parameters are estimated automatically by marginal
    loss minimization, while the regression coefficients are estimated using either
    PIRLS or Newton algorithm. The learning rate is determined so that the Bayesian
    credible intervals of the estimated effects have approximately the correct
    coverage. The main function is qgam() which is similar to gam() in 'mgcv', but
    fits non-parametric quantile regression models.
General purpose toolbox for simulating quantum versions of game theoretic models (Flitney and Abbott 2002) <arXiv:quant-ph/0208069>. Quantum (Nielsen and Chuang 2010, ISBN:978-1-107-00217-3) versions of models that have been handled are: Penny Flip Game (David A. Meyer 1998) <arXiv:quant-ph/9804010>, Prisoner's Dilemma (J. Orlin Grabbe 2005) <arXiv:quant-ph/0506219>, Two Person Duel (Flitney and Abbott 2004) <arXiv:quant-ph/0305058>, Battle of the Sexes (Nawaz and Toor 2004) <arXiv:quant-ph/0110096>, Hawk and Dove Game (Nawaz and Toor 2010) <arXiv:quant-ph/0108075>, Newcomb's Paradox (Piotrowski and Sladkowski 2002) <arXiv:quant-ph/0202074> and Monty Hall Problem (Flitney and Abbott 2002) <arXiv:quant-ph/0109035>.
Density, distribution function, quantile function and 
  random generation for the q-gaussian distribution with parameters mu and sig.
G-computation for a set of time-fixed exposures with
    quantile-based basis functions, possibly under linearity and
    homogeneity assumptions. This approach estimates a regression line
    corresponding to the expected change in the outcome (on the link
    basis) given a simultaneous increase in the quantile-based category
    for all exposures. Works with continuous, binary, and right-censored
    time-to-event outcomes.  Reference: Alexander P. Keil, Jessie P.
    Buckley, Katie M. OBrien, Kelly K. Ferguson, Shanshan Zhao, and
    Alexandra J. White (2019) A quantile-based g-computation approach to
    addressing the effects of exposure mixtures; <doi:10.1289/EHP5838>.
G-computation for a set of time-fixed exposures
    with quantile-based basis functions, possibly under linearity and
    homogeneity assumptions. Effect measure modification in this method is a way
    to assess how the effect of the mixture varies by a binary, categorical or continuous variable.  
    Reference: Alexander P. Keil, Jessie P.
    Buckley, Katie M. OBrien, Kelly K. Ferguson, Shanshan Zhao, and
    Alexandra J. White (2019) A quantile-based g-computation approach to
    addressing the effects of exposure mixtures; <doi:10.1289/EHP5838>.
Provides an infrastructure for efficient processing of large-scale genetic and phenotypic data including core functions for: 1) fitting linear mixed models, 2) constructing marker-based genomic relationship matrices, 3) estimating genetic parameters (heritability and correlation), 4) performing genomic prediction and genetic risk profiling, and 5) single or multi-marker association analyses.
    Rohde et al. (2019) <doi:10.1101/503631>.
Compute various quantitative genetics parameters from a Generalised Linear Mixed Model (GLMM) estimates. Especially, it yields the observed phenotypic mean, phenotypic variance and additive genetic variance.
Weighted network visualization and analysis, as well as Gaussian graphical model computation. See Epskamp et al. (2012) <doi:10.18637/jss.v048.i04>.
A 'shiny' application for teaching introductory quantitative genetics
    and plant breeding through interactive simulations. The application relies on 
    established plant breeding and quantitative genetic theory found in 
    Falconer and Mackay (1996, ISBN:0582243025) and Bernardo (2010, ISBN:978-0972072427).
Two linear mixed model approaches: REML(restricted maximum likelihood) and MINQUE (minimum norm quadratic unbiased estimation) approaches and several resampling techniques are integrated for various quantitative genetics analyses. With these two types of approaches, various unbalanced data structures, missing data, and any irregular genetic  mating designs can be analyzed and statistically tested. This package also offers fast computations for many large data sets.
Multivariate description of the state changes of a qualitative variable by  
             Correspondence Analysis and Clustering. See:
             Deville, J.C., & Saporta, G. (1983). 
                 Correspondence analysis, with an extension towards nominal time series. 
                 Journal of econometrics, 22(1-2), 169-189.
             Corrales, M.L., & Pardo, C.E. (2015) <doi:10.15332/s2027-3355.2015.0001.01>. 
                 Analisis de datos longitudinales cualitativos con analisis de correspondencias y clasificacion. 
                 Comunicaciones en Estadistica, 8(1), 11-32.
This function produces both the numerical and graphical summaries of the QTL hotspot detection in the genomes that are available on the worldwide web including the flanking markers of QTLs.
Nomogram is constructed to predict cumulative incidence
    rate, which is calculated by adjusting for competing causes other
    than that of interest. K-fold cross-validation function is implemented
    to validate the performance. Competing risk version of concordance index
    is calculated. Methods are as described in: Kattan MW, Heller G, 
    Brennan MF (2003) <doi:10.1002/sim.1574>.
The quantity-intensity (Q/I) relationships, first introduced by Beckett
  (1964), can be employed to assess the K supplying capacity of different soils 
  based on solid-solution exchange equilibria. Such relationships describe the 
  changes in K+ concentration in the soil solution (or the intensity factor) in 
  relation to the corresponding changes in K+ at exchange sites of the soil 
  (or the capacity or quantity factor). Activity ratio of K to Ca or Ca+Mg 
  is generally used as the variable denoting the intensity, whereas, change 
  in exchangeable K is used to denote the quantity factor.
Functions for making run charts 
    [Anhoej, Olesen (2014) <doi:10.1371/journal.pone.0113825>] and basic
    Shewhart control charts [Mohammed, Worthington, Woodall (2008)
    <doi:10.1136/qshc.2004.012047>] for measure and count data.
    The main function, qic(), creates run and control charts and has a
    simple interface with a rich set of options to control data analysis
    and plotting, including options for automatic data aggregation by
    subgroups, easy analysis of before-and-after data, exclusion of one
    or more data points from analysis, and splitting charts into
    sequential time periods.
    Missing values and empty subgroups are handled gracefully.
Functions for making run charts, Shewhart control charts and
    Pareto charts for continuous quality improvement. Included control charts
    are: I, MR, Xbar, S, T, C, U, U', P, P', and G charts. Non-random variation
    in the form of minor to moderate persistent shifts in data over time is
    identified by the Anhoej rules for unusually long runs and unusually few
    crossing  [Anhoej, Olesen (2014) <doi:10.1371/journal.pone.0113825>].
    Non-random variation in the form of larger, possibly transient, shifts is
    identified by Shewhart's 3-sigma rule [Mohammed, Worthington, Woodall (2008)
    <doi:10.1136/qshc.2004.012047>].
Developed to perform the estimation and inference for regression 
    coefficient parameters in longitudinal marginal models using the method of 
    quadratic inference functions. Like generalized estimating equations, this 
    method is also a quasi-likelihood inference method. It has been showed that 
    the method gives consistent estimators of the regression coefficients even if 
    the correlation structure is misspecified, and it is more efficient than GEE 
    when the correlation structure is misspecified. Based on Qu, A., Lindsay, 
    B.G. and Li, B. (2000) <doi:10.1093/biomet/87.4.823>.
Qiita is a technical knowledge sharing and collaboration platform for programmers.
  See <https://qiita.com/api/v2/docs> for more information.
Nonlinear machine learning tool for classification, clustering
        and dimensionality reduction. It integrates 12 q-kernel functions and
        15 conditional negative definite kernel functions and includes the 
        q-kernel and conditional negative definite kernel version of
        density-based spatial clustering of applications with noise,
        spectral clustering, generalized discriminant analysis, principal
        component analysis, multidimensional scaling, locally linear embedding,
        sammon's mapping and t-Distributed stochastic neighbor embedding.
'QuantLib' bindings are provided for R using 'Rcpp' via an evolved version
 of the initial header-only 'Quantuccia' project offering an subset of 'QuantLib' (now
 maintained separately just for the calendaring subset). See the included file 'AUTHORS'
 for a full list of contributors to 'QuantLib' (and hence also 'Quantuccia').
Extension of the functionality of the Matrix package for using sparse matrices. Some of the functions are very general, while other are highly specific for special data format as used for quantitative language comparison (QLC).
Functions to implement Q-learning for estimating optimal
        dynamic treatment regimes from two stage sequentially
        randomized trials, and to perform inference via m-out-of-n
        bootstrap for parameters indexing the optimal regime.
Implements Q-Learning, a model-free form of reinforcement
        learning, described in work by Strehl, Li, Wiewiora, Langford &
        Littman (2006) <doi:10.1145/1143844.1143955>.
Empirical adjustment of the distribution of variables originating from (regional) climate model simulations using quantile mapping.
A multivariate copula-based dependence measure. For more information, see Griessenberger, Junker, Trutschnig (2021), On a multivariate copula-based dependence measure and its estimation, <arXiv:2109.12883>.
Analysis of Q methodology, used to identify distinct perspectives existing within a group.
  This methodology is used across social, health and environmental sciences to understand diversity of attitudes, discourses, or decision-making styles (for more information, see <https://qmethod.org/>).
  A single function runs the full analysis. Each step can be run separately using the corresponding functions: for automatic flagging of Q-sorts (manual flagging is optional), for statement scores, for distinguishing and consensus statements, and for general characteristics of the factors.
  The package allows to choose either principal components or centroid factor extraction, manual or automatic flagging, a number of mathematical methods for rotation (or none), and a number of correlation coefficients for the initial correlation matrix, among many other options.
  Additional functions are available to import and export data (from raw *.CSV, 'HTMLQ' and 'FlashQ' *.CSV, 'PQMethod' *.DAT and 'easy-htmlq' *.JSON files), to print and plot, to import raw data from individual *.CSV files, and to make printable cards.
  The package also offers functions to print Q cards and to generate Q distributions for study administration.
  See further details in the package documentation, and in the web pages below, which include a cookbook, guidelines for more advanced analysis (how to perform manual flagging or change the sign of factors), data management, and a beta graphical user interface for online and offline use.
Implementation of methods for estimation of quantitative maps
    from Multi-Parameter Mapping (MPM) acquisitions (Weiskopf et al. (2013)
    <doi:10.3389/fnins.2013.00095>) including adaptive
    smoothing methods in the framework of the ESTATICS model
    (Estimating the apparent transverse relaxation time (R2*) from images with
    different contrasts, Weiskopf et al. (2014) <doi:10.3389/fnins.2014.00278>).
    The smoothing method is described in Mohammadi et al. (2017).
    <doi:10.20347/WIAS.PREPRINT.2432>. Usage of the package is also described in
    Polzehl and Tabelow (2019),
    Magnetic Resonance Brain Imaging, Chapter 6, Springer, Use R! Series.
    <doi:10.1007/978-3-030-29184-6_6>.
Basic functions for building parsers, with an application to PC-AXIS format files.
The new QOI file format offers a very simple but efficient image compression algorithm. This package provides an easy and simple way to read, write and display bitmap images stored in the QOI (Quite Ok Image) format. It can read and write both files and in-memory raw vectors.
There are three functions: qol, miss_qol and miss_patient takes input of the data set containing the answers of QOL questionnaire. It will compute the three types of domain based scale scores: Global, Functional, and Symptoms. In case of missing data, the miss_qol and miss_patient functions will make the required changes and then calculate the domain-wise scale scores. Finally, provide an output replacing the question columns with the domain-based scale scores in the original data set.
Model fitting, optimal model selection and calculation of various features that are essential in the analysis of quantitative real-time polymerase chain reaction (qPCR).
Content-preserving transformations transformations of PDF files such 
    as split, combine, and compress. This package interfaces directly to the 'qpdf' 
    C++ API and does not require any command line utilities. Note that 'qpdf' does
    not read actual content from PDF files: to extract text and data you need the
    'pdftools' package.
Efficiently solve quadratic problems with linear inequality, equality and box constraints. The method used is outlined in D. Goldfarb, and A. Idnani (1983) <doi:10.1007/BF02591962>.
Active set method solver for the solution of indefinite quadratic programs, subject to lower bounds on linear functions of the variables and simple bounds on the variables themselves. The function QPmin() implements an algorithm similar to the one described in Gould (1991) <doi:10.1093/imanum/11.3.299>  with the exception that an efficient sparse internal representation of the basis matrix is maintained thus allowing the solution of somewhat large problems.
Computes noncompartmental pharmacokinetic parameters
 for drug concentration profiles.  For each profile, data
 imputations and adjustments are made as necessary and
 basic parameters are estimated. Supports single dose, multi-dose,
 and multi-subject data.  Supports steady-state calculations
 and various routes of drug administration. See ?qpNCA and vignettes.
 Methodology follows Rowland and Tozer (2011, ISBN:978-0-683-07404-8), 
 Gabrielsson and Weiner (1997, ISBN:978-91-9765-100-4), and
 Gibaldi and Perrier (1982, ISBN:978-0824710422).
A system to implement the Q-Q boxplot.  It is implemented as an
    extension to 'ggplot2'.  The Q-Q boxplot is an amalgam of the boxplot and the
    Q-Q plot and allows the user to rapidly examine summary statistics and tail
    behavior for multiple distributions in the same pane.  As an extension of
    the 'ggplot2' implementation of the boxplot, possible modifications to the 
    boxplot extend to the Q-Q boxplot.
Provides functionality for creating Quantile-Quantile (QQ) and Probability-Probability (PP) plots with simultaneous 
    testing bands to asses significance of sample deviation from a reference distribution.
Create Q-Q and manhattan plots for GWAS data from PLINK results.
Provides users the necessary utility functions to generate permutation-based QQ plots and also estimate inflation factor based on the empirical NULL distribution. While it has general utility, it is particularly helpful when the skewness of the Fisher's Exact test in sparse data situations with imbalanced case-control sample sizes renders the reliance on the uniform chi-square expected distribution inappropriate.
Extensions of 'ggplot2' Q-Q plot functionalities.
Get data about the Brazilian soccer championship since 2014. Official data can 
  be found at <https://www.cbf.com.br/futebol-brasileiro/competicoes/campeonato-brasileiro-serie-a/>.
Provides the function qqtest which incorporates uncertainty in its
    qqplot display(s) so that the user might have a better sense of the
    evidence against the specified distributional hypothesis.  qqtest draws a
    quantile quantile plot for visually assessing whether the data come from a
    test distribution that has been defined in one of many ways.  The vertical
    axis plots the data quantiles, the horizontal those of a test distribution.
    The default behaviour generates 1000 samples from the test distribution and
    overlays the plot with shaded pointwise interval estimates for the ordered
    quantiles from the test distribution.  A small number of independently
    generated exemplar quantile plots can also be overlaid.  Both the interval
    estimates and the exemplars provide different comparative information to
    assess the evidence provided by the qqplot for or against the hypothesis
    that the data come from the test distribution (default is normal or
    gaussian).  Finally, a visual test of significance (a lineup plot) can also
    be displayed to test the null hypothesis that the data come from the test
    distribution.
Presents an explanatory animation of normal quantile-quantile plots based on a water-filling analogy.  The animation presents a normal QQ plot as the parametric plot of the water levels in vases defined by two distributions.  The distributions decorate the axes in the normal QQ plot and are optionally shown as vases adjacent to the plot.  The package draws QQ plots for several distributions, either as samples or continuous functions.
Functions are provided that implement the 
    use of the Fieller's formula methodology, for 
    calculating a confidence interval for a ratio of
    (commonly, correlated) means.  See Fieller (1954)
    <doi:10.1111/j.2517-6161.1954.tb00159.x>.  Here,
    the application of primary interest is to studies
    of insect mortality response to increasing doses
    of a fumigant, or, e.g., to time in coolstorage.
    The formula is used to calculate a confidence
    interval for the dose or time required to achieve
    a specified mortality proportion, commonly 0.5 
    or 0.99.  Vignettes demonstrate link functions
    that may be considered, checks on fitted models,
    and alternative choices of error family.  Note
    in particular the betabinomial error family.
    See also Maindonald, Waddell, and Petry (2001) 
    <doi:10.1016/S0925-5214(01)00082-5>.
Upload raster data and easily create interactive quantitative risk analysis 'QRA' visualizations. Select
    from numerous color palettes, base-maps, and different configurations.
Tools that create D3 JavaScript force directed graph from R. D3 JavaScript was created by Michael Bostock. See http://d3js.org/ and, more specifically for Force Directed Graph https://github.com/mbostock/d3/wiki/Force-Layout.
The ANU Quantum Random Number Generator provided by the Australian National University
 generates true random numbers in real-time by measuring the quantum fluctuations of the vacuum. This package offers an interface using their API.
 The electromagnetic field of the vacuum exhibits random fluctuations in phase and amplitude at all frequencies.
 By carefully measuring these fluctuations, one is able to generate ultra-high bandwidth random numbers.
 The quantum Random Number Generator is based on the papers by Symul et al., (2011) <doi:10.1063/1.3597793>
 and Haw, et al. (2015) <doi:10.1103/PhysRevApplied.3.054004>.
 The package offers functions to retrieve a sequence of random integers or hexadecimals and true random samples from a normal or uniform distribution.
A Quantile Rank-score based test for the identification of expression quantitative trait loci.
Parametric modeling of quantile regression coefficient functions.
Nonlinear and Penalized parametric modeling of quantile regression coefficient functions. Sottile G, Frumento P, Chiodi M and Bottai M (2020) <doi:10.1177/1471082X19825523>.
Quantile regression in varying-coefficient models (VCM) using one particular nonparametric technique called P-splines. The functions can be applied on three types of VCM; (1) Homoscedastic VCM, (2) Simple heteroscedastic VCM, and (3) General heteroscedastic VCM.
Quick Response codes (QR codes) are a type of matrix bar code and can be
    used to authenticate transactions, provide access to multi-factor authentication
    services and enable general data transfer in an image. QR codes use four standardized 
    encoding modes (numeric, alphanumeric, byte/binary, and kanji) to efficiently store 
    data. Matrix barcode generation is performed efficiently in C via the included
    'libqrencoder' library created by Kentaro Fukuchi.
The QRI_func() function performs quantile regression analysis using age and sex as predictors to calculate the Quantile Regression Index (QRI) score for each individual’s regional brain imaging metrics and then averages across the regional scores to generate an average tissue specific score for each subject. The QRI_plot() is used to plot QRI and generate the normative curves for individual measurements.
This function aims to calculate risk of developing cardiovascular disease of individual patients in next 10 years. The package was based on published risk prediction algorithm <https://qrisk.org/three/src.php>.
Joint estimation of quantile specific intercept and slope parameters in a linear regression setting.
Quantile regression (QR) for Linear 
             Mixed-Effects Models via the asymmetric Laplace distribution (ALD). 
             It uses the Stochastic Approximation of the EM (SAEM) algorithm for 
             deriving exact maximum likelihood estimates and full inference results 
             for the fixed-effects and variance components. 
             It also provides graphical summaries for assessing the algorithm 
             convergence and fitting results.
Provides functions/methods to accompany the book
 Quantitative Risk Management: Concepts, Techniques and Tools by
 Alexander J. McNeil, Ruediger Frey, and Paul Embrechts.
Various data sets (stocks, stock indices, constituent data, FX,
 zero-coupon bond yield curves, volatility, commodities) for Quantitative
 Risk Management practice.
Implements the robust algorithm for fitting finite mixture models based on quantile regression proposed by Emir et al., 2017 (unpublished).
Functions and data sets for reproducing selected results from
  the book "Quantitative Risk Management: Concepts, Techniques and Tools".
  Furthermore, new developments and auxiliary functions for Quantitative
  Risk Management practice.
Functionality for generating (randomized) quasi-random numbers in
  high dimensions.
Quantile regression (QR) for Nonlinear
             Mixed-Effects Models via the asymmetric Laplace distribution (ALD). 
             It uses the Stochastic Approximation of the EM (SAEM) algorithm for 
             deriving exact maximum likelihood estimates and full inference results 
             for the fixed-effects and variance components.
             It also provides prediction and graphical summaries for assessing the algorithm
             convergence and fitting results.
Fit quantile regression neural network models with optional
    left censoring, partial monotonicity constraints, generalized additive
    model constraints, and the ability to fit multiple non-crossing quantile
    functions following Cannon (2011) <doi:10.1016/j.cageo.2010.07.005>
    and Cannon (2018) <doi:10.1007/s00477-018-1573-6>.
Provides functions for quickly writing and reading any R object to and from disk.
Molecular descriptors and outcomes for several public domain data sets
A quantum computer simulator framework with up to 24 qubits. It allows to
    define general single qubit gates and general controlled single
    qubit gates. For convenience, it currently provides the
    most common gates (X, Y, Z, H, Z, S, T, Rx, Ry, Rz, CNOT, SWAP, Toffoli or
    CCNOT, Fredkin or CSWAP). 'qsimulatR' supports plotting of circuits and is able to
    export circuits to 'Qiskit' <https://qiskit.org/>, a python package
    which can be used to run on IBM's hardware <https://quantum-computing.ibm.com/>.
Computes scores from Q-sort data, using criteria sorts and
  derived scales from subsets of items.
  The 'qsort' package includes descriptions and scoring procedures
  for four different Q-sets commonly used in developmental psychology research:
  Attachment Q-set (version 3.0) (Waters, 1995, <doi:10.1111/j.1540-5834.1995.tb00214.x>);
  California Child Q-set (Block and Block, 1969, <doi:10.1037/0012-1649.21.3.508>);
  Maternal Behaviour Q-set (version 3.1)
  (Pederson et al., 1999, <https://ir.lib.uwo.ca/cgi/viewcontent.cgi?article=1000&context=psychologypub>);
  Preschool Q-set (Baumrind, 1968 revised by Wanda Bronson, <doi:10.1111/j.1540-5834.1995.tb00214.x>).
Provides functions for quickly writing (and 
  reading back) a data.frame to file in 'SQLite' format. The name 
  stands for *Store Tables using 'SQLite'*, or alternatively for *Quick 
  Store Tables* (either way, it could be pronounced as *Quest*). 
  For data.frames containing the supported data 
  types it is intended to work as a drop-in replacement for the 
  'write_*()' and 'read_*()' functions provided by similar packages.
Run lapply() calls in parallel by submitting them to 
    'gridengine' clusters using the 'qsub' command.
Provides several methods for computing the Quantile Treatment Effect (QTE) and Quantile Treatment Effect on the Treated (QTT). The main cases covered are (i) Treatment is randomly assigned, (ii) Treatment is as good as randomly assigned after conditioning on some covariates (also called conditional independence or selection on observables) using the methods developed in Firpo (2007) <doi:10.1111/j.1468-0262.2007.00738.x>, (iii) Identification is based on a Difference in Differences assumption (several varieties are available in the package e.g. Athey and Imbens (2006) <doi:10.1111/j.1468-0262.2006.00668.x> Callaway and Li (2019) <https://ssrn.com/abstract=3013341>, Callaway, Li, and Oka (2018) <doi:10.1016/j.jeconom.2018.06.008>).
Analysis of experimental crosses to identify genes
  (called quantitative trait loci, QTLs) contributing to variation in
  quantitative traits.
  Broman et al. (2003) <doi:10.1093/bioinformatics/btg112>.
Conduct multiple quantitative trait loci (QTL) and QTL-by-environment interaction (QEI) mapping via ordinary or compressed variance component mixed models with random- or fixed QTL/QEI effects. First, each position on the genome is detected in order to obtain a negative logarithm P-value curve against genome position. Then, all the peaks on each effect (additive or dominant) curve or on each locus curve are viewed as potential main-effect QTLs and QEIs, all their effects are included in a multi-locus model, their effects are estimated by both least angle regression and empirical Bayes (or adaptive lasso) in backcross and F2 populations, and true QTLs and QEIs are identified by likelihood radio test. See Zhou et al. (2022) <doi:10.1093/bib/bbab596> and Wen et al. (2018) <doi:10.1093/bib/bby058>.
Conduct multiple quantitative trait loci (QTL) mapping under the framework of random-QTL-effect linear mixed model. First, each position on the genome is detected in order to obtain a negative logarithm P-value curve against genome position. Then, all the peaks on each effect (additive or dominant) curve are viewed as potential QTL, all the effects of the potential QTL are included in a multi-QTL model, their effects are estimated by empirical Bayes in doubled haploid population or by adaptive lasso in F2 population, and true QTL are identified by likelihood radio test. See Wen et al. (2018) <doi:10.1093/bib/bby058>.
Provides a set of tools to perform quantitative
    trait locus (QTL) analysis in experimental crosses. It is a
    reimplementation of the 'R/qtl' package to better handle
    high-dimensional data and complex cross designs.
    Broman et al. (2018) <doi:10.1534/genetics.118.301595>.
Functions to convert data structures among the 'qtl2', 'qtl', and 'DOQTL' packages for mapping quantitative trait loci (QTL).
Uses the 'fst' package to store genotype probabilities on disk for the 'qtl2' package. These genotype probabilities are a central data object for mapping quantitative trait loci (QTL), but they can be quite large. The facilities in this package enable the genotype probabilities to be stored on disk, leading to reduced memory usage with only a modest increase in computation time.
Functions to plot QTL (quantitative trait loci) analysis
    results and related diagnostics.
    Part of 'qtl2', an upgrade of the 'qtl'
    package to better handle high-dimensional data and complex cross
    designs.
Routines in 'qtl2' to study allele patterns in quantitative trait loci (QTL)
    mapping over a chromosome.
    Useful in crosses with more than two alleles to identify how sets of alleles,
    genetically different strands at the same locus, have different response levels.
    Plots show profiles over a chromosome.
    Can handle multiple traits together.
    See <https://github.com/byandell/qtl2pattern>.
We implement an
    adaptation of Jiang & Zeng's (1995) <https://www.genetics.org/content/140/3/1111> likelihood ratio test for testing
    the null hypothesis of pleiotropy against the alternative hypothesis,
    two separate quantitative trait loci. The test differs from that in Jiang & Zeng (1995) <https://www.genetics.org/content/140/3/1111> 
    and that in Tian et al. (2016) <doi:10.1534/genetics.115.183624> in
    that our test accommodates multiparental populations.
Datasets for the book, A Guide to QTL Mapping with R/qtl.
    Broman and Sen (2009) <doi:10.1007/978-0-387-92125-9>.
Densitometric evaluation of the photo-archived quantitative thin-layer chromatography (TLC) plates.
Web-based interactive charts (using D3.js) for the analysis of
    experimental crosses to identify genetic loci (quantitative trait
    loci, QTL) contributing to variation in quantitative traits.
    Broman (2015) <doi:10.1534/genetics.114.172742>.
Functions to infer co-mapping trait hotspots and causal models.
  Chaibub Neto E, Keller MP, Broman AF, Attie AD, Jansen RC, Broman KW, Yandell BS (2012) 
  Quantile-based permutation thresholds for QTL hotspots. Genetics 191 : 1355-1365. 
  <doi:10.1534/genetics.112.139451>.
  Chaibub Neto E, Broman AT, Keller MP, Attie AD, Zhang B, Zhu J, Yandell BS (2013)
  Modeling causality for pairs of phenotypes in system genetics. Genetics 193 : 1003-1013.
  <doi:10.1534/genetics.112.147124>.
Provides tools for joint analysis of multiple traits in a backcross (BC) or recombinant inbred lines (RIL) population. It can be used to select an optimal subset of traits for multiple-trait mapping, analyze multiple traits via the SURE model, which can associate different QTL with different traits, and perform multiple-trait composite multiple-interval mapping.
Functions to Simultaneously Infer Causal Graphs and Genetic Architecture.
  Includes acyclic and cyclic graphs for data from an experimental cross with a modest number (<10) of phenotypes driven by
  a few genetic loci (QTL).
  Chaibub Neto E, Keller MP, Attie AD, Yandell BS (2010)
  Causal Graphical Models in Systems Genetics: a unified framework for joint inference of causal network and genetic architecture for correlated phenotypes.
  Annals of Applied Statistics 4: 320-339.
  <doi:10.1214/09-AOAS288>.
Performs random-effect multiple interval mapping (REMIM) in full-sib families of autopolyploid species based on restricted maximum likelihood (REML) estimation and score statistics, as described in Pereira et al. (2020) <doi:10.1534/genetics.120.303080>.
This software provides tools for quantitative trait mapping in populations such as advanced intercross lines where relatedness among individuals should not be ignored. It can estimate background genetic variance components, impute missing genotypes, simulate genotypes, perform a genome scan for putative quantitative trait loci (QTL), and plot mapping results. It also has functions to calculate identity coefficients from pedigrees, especially suitable for pedigrees that consist of a large number of generations, or estimate identity coefficients from genotypic data in certain circumstances.
Provides methods for estimation of mean- and quantile-optimal treatment regimes from censored data. 
    Specifically, we have developed distinct functions for three types of right censoring for static treatment using quantile criterion: (1) independent/random censoring, (2) treatment-dependent random censoring, and (3) covariates-dependent random censoring. It also includes a function to estimate quantile-optimal dynamic treatment regimes for independent censored data. Finally, this package also includes a simulation data generative model of a dynamic treatment experiment proposed in literature.
Functions for unconditional and conditional quantiles. These
    include methods for transformation-based quantile regression,
    quantile-based measures of location, scale and shape, methods for quantiles
    of discrete variables, quantile-based multiple imputation, restricted
    quantile regression, and directional quantile classification. A vignette
	is given in Geraci (2016, The R Journal) <doi:10.32614/RJ-2016-037> and
	included in the package.
Quantitative Analysis of Complex Networks. This package offers a set of topological network measures to analyze complex Networks structurally.
This package gives you the exact first four permutation moments for the most commonly used quadratic form statistics, which need not be positive definite. The extension of this work to quadratic forms greatly expands the utility of density approximations for these problems, including for high-dimensional applications, where the statistics must be extreme in order to exceed stringent testing thresholds. Approximate p-values are obtained by matching the exact moments to the Pearson family of distributions using the PearsonDS package.
Given inputs A,B and C, this package solves the matrix equation A*X^2 - B*X - C = 0.
Create surface forms from matrix or 'raster' data for flexible plotting and
 conversion to other mesh types. The functions 'quadmesh' or 'triangmesh'
 produce a continuous surface as a 'mesh3d' object as used by the 'rgl'
 package. This is used for plotting raster data in 3D (optionally with
 texture), and allows the application of a map projection without data loss and 
 many processing applications that are restricted by inflexible regular grid rasters.
 There are discrete forms of these continuous surfaces available with
 'dquadmesh' and 'dtriangmesh' functions.
This package contains routines and documentation for
        solving quadratic programming problems.
Extends the quadprog package to solve quadratic programs with
    absolute value constraints and absolute values in the objective function.
It will assist the user to find simple quadratic roots from any quadratic equation.
Fits classical sparse regression models with
    efficient active set algorithms by solving quadratic problems as described by 
    Grandvalet, Chiquet and Ambroise (2017) <arXiv:1210.2077>. Also provides a few 
    methods for model selection purpose (cross-validation, stability selection).
Provides functionality for working with raster-like quadtrees
    (also called “region quadtrees”), which allow for variable-sized
    cells. The package allows for flexibility in the quadtree creation
    process.  Several functions defining how to split and aggregate cells
    are provided, and custom functions can be written for both of these
    processes. In addition, quadtrees can be created using other quadtrees
    as “templates”, so that the new quadtree's structure is identical to
    the template quadtree. The package also includes functionality for
    modifying quadtrees, querying values, saving quadtrees to a file, and
    calculating least-cost paths using the quadtree as a resistance
    surface.
Exact one-sided p-values and confidence intervals for an outcome variable defined on an interval measurement scale with only qualitative and ordinal information available.
Used for testing for qualitative interactions between
    treatment effects and patient subgroups. Here the term treatment effect
    means a comparison result of two treatments within each patient subgroup.
    Models included in this package are Gaussian, binomial and Cox models.
    Methods included here are interval based graphical approach and Gail Simon
    LRT.
Provides a set of functions for taking qualitative GIS data, hand drawn on a map, and 
   converting it to a simple features object. These tools are focused on data that are drawn on a map
   that contains some type of polygon features. For each area identified on the map, the id numbers
   of these polygons can be entered as vectors and transformed using qualmap.
Automatic generation of distinct qualitative color palettes,
    optionally adapted to color deficiency. It takes a subspace of the HSL color
    space as input and projects it to the DIN99d color space where it selects
    and return colors that are maximally distinct.
Provides functions to access survey results directly into R
    using the 'Qualtrics' API. 'Qualtrics'
    <https://www.qualtrics.com/about/> is an online survey and data
    collection software platform. See <https://api.qualtrics.com/> for
    more information about the 'Qualtrics' API.  This package is
    community-maintained and is not officially supported by 'Qualtrics'.
Qualitative methods for the validation of dynamic models.
    It contains (i) an orthogonal set of deviance measures for absolute,
    relative and ordinal scale and (ii) approaches accounting for time
    shifts. The first approach transforms time to take time delays and speed
    differences into account. The second divides the time series into
    interval units according to their main features and finds the longest
    common subsequence (LCS) using a dynamic programming algorithm.
Implements indices of qualitative variation proposed by Wilcox
    (1973).
These functions use data augmentation and Bayesian techniques for the assessment of single-member and incomplete ensembles of climate projections. It provides unbiased estimates of climate change responses of all simulation chains and of all uncertainty variables. It additionally propagates uncertainty due to missing information in the estimates.
  - Evin, G., B. Hingray, J. Blanchet, N. Eckert, S. Morin, and D. Verfaillie. (2019) <doi:10.1175/JCLI-D-18-0606.1>.
These functions use smoothing-splines, data augmentation and Bayesian techniques for the assessment of single-member and incomplete ensembles of climate projections.
  - Cheng, C.-I. and P. L. Speckman (2012) <doi:10.1016/j.csda.2012.05.020>.
  - Evin, G., B. Hingray, J. Blanchet, N. Eckert, S. Morin, and D. Verfaillie. (2019) <doi:10.1175/JCLI-D-18-0606.1>.
Functions for interacting directly with the Quandl API to offer data in a number of formats usable in R, downloading a zip with all data from a Quandl database, and the ability to search. This R package uses the Quandl API. For more information go to <https://docs.quandl.com>. For more help on the package itself go to <https://www.quandl.com/tools/r>.
Methods which streamline the descriptive analysis of quantitative
    matrices. Matrix columns are samples while rows are features i.e. proteins, genes.
    Includes methods for visualization (e.g. Heatmaps, Volcanos, pairwise QQ, Bland-Altman plot),
    summary statistics (e.g. CV), data normalization methods (e.g. robustscale). Read function for Progenesis.
Non-parametric methods as local normal regression, polynomial local regression and penalized cubic B-splines regression are used to estimate quantiles curves. See Fan and Gijbels (1996) <doi:10.1201/9780203748725> and Perperoglou et al.(2019) <doi:10.1186/s12874-019-0666-3>.
Functions to manipulate dates and count days for quantitative finance analysis. The 'quantdates' package considers leap, holidays and business days for relevant calendars in a financial context to simplify quantitative finance calculations, consistent with International Swaps and Derivatives Association (ISDA) (2006) <https://www.isda.org/book/2006-isda-definitions/> regulations.
A fast, flexible, and comprehensive framework for 
    quantitative text analysis in R.  Provides functionality for corpus management,
    creating and manipulating tokens and ngrams, exploring keywords in context, 
    forming and manipulating sparse matrices
    of documents by features and feature co-occurrences, analyzing keywords, computing feature similarities and
    distances, applying content dictionaries, applying supervised and unsupervised machine learning, 
    visually representing text and text analyses, and more.
Scaling models and classifiers for sparse matrix objects representing 
    textual data in the form of a document-feature matrix.  Includes original 
    implementations of 'Laver', 'Benoit', and Garry's (2003) <doi:10.1017/S0003055403000698>,
    'Wordscores' model, Perry and 'Benoit's' (2017) <arXiv:1710.08963> class affinity scaling model, 
    and 'Slapin' and 'Proksch's' (2008) <doi:10.1111/j.1540-5907.2008.00338.x> 'wordfish'
    model, as well as methods for correspondence analysis, latent semantic analysis,
    and fast Naive Bayes and linear 'SVMs' specially designed for sparse textual data.
Plotting functions for visualising textual data.  Extends 'quanteda' and 
   related packages with plot methods designed specifically for text data, textual statistics, 
   and models fit to textual data. Plot types include word clouds, lexical dispersion plots, 
   scaling plots, network visualisations, and word 'keyness' plots.
Textual statistics functions formerly in the 'quanteda' package.
    Textual statistics for characterizing and comparing textual data. Includes 
    functions for measuring term and document frequency, the co-occurrence of 
    words, similarity and distance between features and documents, feature entropy, 
    keyword occurrence, readability, and lexical diversity.  These functions 
    extend the 'quanteda' package and are specially designed for sparse textual data.
Provides different functions for quantifying qualitative survey data. It supports the Carlson-Parkin method, the regression approach, the balance approach and the conditional expectations method.
Code for centroid, median and quantile classifiers.
Functions for simulation, estimation, and model selection of finite mixtures of Tukey's g-and-h
            distributions.
Based on Alan D. Hutson (1999) <doi:10.1080/02664769922458>, "Calculating nonparametric confidence intervals for quantiles using fractional order statistics", Journal of Applied Statistics, 26:3, 343-353.
Estimation and inference methods for the cross-quantilogram.
    The cross-quantilogram is a measure of nonlinear dependence between
    two variables, based on either unconditional or conditional quantile
    functions.  The cross-quantilogram can be considered as an extension
    of the correlogram, which is a correlation function over multiple lag
    periods and mainly focuses on linear dependency.  One can use the
    cross-quantilogram to detect the presence of directional
    predictability from one time series to another.  This package provides
    a statistical inference method based on the stationary bootstrap.  See
    Linton and Whang (2007) <doi:10.1016/j.jeconom.2007.01.004> for
    univariate time series analysis and Han, Linton, Oka and Whang (2016)
    <doi:10.1016/j.jeconom.2016.03.001> for multivariate time series
    analysis.
Integration of the 'units' and 'errors' packages for a complete
    quantity calculus system for R vectors, matrices and arrays, with automatic
    propagation, conversion, derivation and simplification of magnitudes and
    uncertainties. Documentation about 'units' and 'errors' is provided in the
    papers by Pebesma, Mailund & Hiebert (2016, <doi:10.32614/RJ-2016-061>) and
    by Ucar, Pebesma & Azcorra (2018, <doi:10.32614/RJ-2018-075>), included in
    those packages as vignettes; see 'citation("quantities")' for details.
A re-implementation of quantile kriging. Quantile kriging was described by Plumlee and Tuo (2014) <doi:10.1080/00401706.2013.860919>.  With computational savings when dealing with replication from the recent paper by Binois, Gramacy, and Ludovski (2018) <doi:10.1080/10618600.2018.1458625> it is now possible to apply quantile kriging to a wider class of problems.  In addition to fitting the model, other useful tools are provided such as the ability to automatically perform leave-one-out cross validation.
Specify, build, trade, and analyse quantitative financial trading strategies.
Modifies the distance matrix obtained from data with batch effects, so as to improve the performance of sample pattern detection, such as clustering, dimension reduction, and construction of networks between subjects. The method has been published in Bioinformatics (Fei et al, 2018, <doi:10.1093/bioinformatics/bty117>). Also available on 'GitHub' <https://github.com/tengfei-emory/QuantNorm>.
Estimation methods for optimal treatment regimes under three different criteria, namely marginal quantile, marginal mean, and mean absolute difference. For the first two criteria, both one-stage and two-stage estimation method are implemented. A doubly robust estimator for estimating the quantile-optimal treatment regime is also included.
Estimation and inference methods for models of conditional quantiles: 
  Linear and nonlinear parametric and non-parametric (total variation penalized) models 
  for conditional quantiles of a univariate response and several methods for handling
  censored survival data.  Portfolio selection methods based on expected shortfall
  risk are also now included. See Koenker (2006) <doi:10.1017/CBO9780511754098> and
  Koenker et al. (2017) <doi:10.1201/9781315120256>.
Implements the nonparametric quantile regression method developed by Belloni, Chernozhukov, and Fernandez-Val (2011) to partially linear quantile models. Provides point estimates of the conditional quantile function and its derivatives based on series approximations to the nonparametric part of the model. Provides pointwise and uniform confidence intervals using analytic and resampling methods.
Quantile Regression Forests is a tree-based ensemble
        method for estimation of conditional quantiles. It is
        particularly well suited for high-dimensional data. Predictor
        variables of mixed classes can be handled. The package is
        dependent on the package 'randomForest', written by Andy Liaw.
Fits non-crossing regression quantiles as a function of linear covariates and multiple smooth terms, including varying coefficients, via B-splines with L1-norm difference penalties. 
    The smoothing parameters are estimated as part of the model fitting, see Muggeo and others (2021) <doi:10.1177/1471082X20929802>. Monotonicity and concavity 
    constraints on the fitted curves are allowed, see Muggeo and others (2013) <doi:10.1007/s10651-012-0232-1> and also <doi:10.13140/RG.2.2.12924.85122> for some code examples.
This is the implementation of quantile regression forests for the fast random forest package 'ranger'.
Methods to determine, smooth and plot quantile periodograms for
    univariate and multivariate time series. See Kley (2016) <doi:10.18637/jss.v070.i03>
    for a description and tutorial.
Contains basic structures and operations used frequently in quantum computing. Intended to be a convenient tool to help learn quantum mechanics and algorithms. Can create arbitrarily sized kets and bras and implements quantum gates, inner products, and tensor products. Creates arbitrarily controlled versions of all gates and can simulate complete or partial measurements of kets. Has functionality to convert functions into equivalent quantum gates and model quantum noise. Includes larger applications, such as Steane error correction <DOI:10.1103/physrevlett.77.793>, Quantum Fourier Transform and Shor's algorithm (Shor 1999), Grover's algorithm (1996), Quantum Approximation Optimization Algorithm (QAOA) (Farhi, Goldstone, and Gutmann 2014) <arXiv:1411.4028>, and a variational quantum classifier (Schuld 2018) <arXiv:1804.00633>. Can be used with the gridsynth algorithm <arXiv:1212.6253> to perform decomposition into the Clifford+T set.
Enables the user to calculate Value at Risk (VaR)
    and Expected Shortfall (ES) by means of various types of historical
    simulation. Currently plain-, age-, volatility-weighted- and filtered
    historical simulation are implemented in this package. Volatility weighting
    can be carried out via an exponentially weighted moving average model
    (EWMA) or other GARCH-type models. The performance can be assessed via
    Traffic Light Test, Coverage Tests and Loss Functions. The methods of the
    package are described in Gurrola-Perez, P. and Murphy, D. (2015)
    <https://EconPapers.repec.org/RePEc:boe:boeewp:0525> as well as McNeil, J.,
    Frey, R., and Embrechts, P. (2015) <https://ideas.repec.org/b/pup/pbooks/10496.html>.
An implementation of two interaction indices between extractive
    activity and groundwater resources based on hazard and vulnerability
    parameters used in the assessment of natural hazards. One index is based
    on a discrete choice model and the other is relying on an artificial
    neural network.
Calculates the number of four-taxon subtrees consistent with a pair
  of cladograms, calculating the symmetric quartet distance of Bandelt & Dress (1986),
  Reconstructing the shape of a tree from observed dissimilarity data,
  Advances in Applied Mathematics, 7, 309-343 <doi:10.1016/0196-8858(86)90038-2>, 
  and using the tqDist algorithm of Sand et al. (2014), tqDist: a library for
  computing the quartet and triplet distances between binary or general trees,
  Bioinformatics, 30, 2079–2080 <doi:10.1093/bioinformatics/btu157>
  for pairs of binary trees.
Convert R Markdown documents and 'Jupyter' notebooks to a variety of
  output formats using 'Quarto'.
Identify differentially expressed genes in RNA-seq count data using quasi-Poisson or quasi-negative binomial models with 'QL', 'QLShrink' and 'QLSpline' methods described by Lund, Nettleton, McCarthy, and Smyth (2012) <DOI:10.1515/1544-6115.1826>. Report bias-reduced estimates of log fold changes.
Various quantile-based clustering algorithms: algorithm CU (Common theta and Unscaled variables), algorithm CS (Common theta and Scaled variables through lambda_j), algorithm VU (Variable-wise theta_j and Unscaled variables) and algorithm VW (Variable-wise theta_j and Scaled variables through lambda_j). Hennig, Viroli, Anderlucci (2018) <arXiv:1806.10403v1>.
Translate 'SQL' 'SELECT' statements into lists of 'R' expressions.
Offers a suite of functions to prepare questionnaire data for analysis (perhaps other types of data as well). By data preparation, I mean data analytic tasks to get your raw data ready for statistical modeling (e.g., regression). There are functions to investigate missing data, reshape data, validate responses, recode variables, score questionnaires, center variables, aggregate by groups, shift scores (i.e., leads or lags), etc. It provides functions for both single level and multilevel (i.e., grouped) data. With a few exceptions (e.g., ncases()), functions without an "s" at the end of their primary word (e.g., center_by()) act on atomic vectors, while functions with an "s" at the end of their primary word (e.g., centers_by()) act on multiple columns of a data.frame.
Set of functions to make the processing and analysis of
    surveys easier : interactive shiny apps and addins for data recoding,
    contingency tables, dataset metadata handling, and several convenience
    functions.
A method for prediction of environmental conditions based on 
    transcriptome data linked with the environmental gradients. This package 
    provides functions to overview gene-environment relationships, to construct 
    the prediction model, and to predict environmental conditions where the 
    transcriptomes were generated. This package can quest for candidate genes 
    for the model construction even in non-model organisms' transcriptomes 
    without any genetic information.
Implementation of a computationally efficient method for
    simulating queues with arbitrary arrival and service times. 
    Please see Ebert, Wu, Mengersen & Ruggeri (2020, <doi:10.18637/jss.v095.i05>) 
    for further details.
It provides versatile tools for analysis of birth and death based Markovian Queueing Models
    and Single and Multiclass Product-Form Queueing Networks.
    It implements M/M/1, M/M/c, M/M/Infinite, M/M/1/K, M/M/c/K, M/M/c/c, M/M/1/K/K, M/M/c/K/K, M/M/c/K/m, M/M/Infinite/K/K,
    Multiple Channel Open Jackson Networks, Multiple Channel Closed Jackson Networks,
    Single Channel Multiple Class Open Networks, Single Channel Multiple Class Closed Networks
    and Single Channel Multiple Class Mixed Networks.
    Also it provides a B-Erlang, C-Erlang and Engset calculators.
    This work is dedicated to the memory of D. Sixto Rios Insua.
Calculates the Quandle, Rack and Degenerate Homology groups of
    Racks and Biracks (as well as Quandles and Biquandles). In addition, a test is
    provided to ascertain if a given set with one or two given functions is indeed a
    biquandle or not.
Provides functions for assigning treatments in randomized experiments using
    near-optimal threshold blocking. The package is made with large data sets in
    mind and derives blocks more than an order of magnitude quicker than other
    methods.
Property based testing, inspired by 
    the original 'QuickCheck'. This package builds on 
    the property based testing framework provided by 
    'hedgehog' and is designed to seamlessly integrate with 
    'testthat'.
Provides several convenience functions for searching and pulling data from
    the 'USDA NASS Quick Stats API' <https://quickstats.nass.usda.gov/api>. 
    Users can easily search for specific data items, and then download county-level or 
    state-level Census of Agricultural data from a specified year.
While analyzing geospatial data, easy visualization is often
    needed that allows for quick plotting, and simple, but easy interactivity.
    Additionally, visualizing geospatial data in projected coordinates is also
    desirable. The 'quickmapr' package provides a simple method to visualize 
    'sp', 'sf' (via coercion to 'sp'), and 'raster' objects, allows for basic 
    zooming, panning, identifying,labeling, selecting, and measuring spatial 
    objects.  Importantly, it does not require that the data be in geographic 
    coordinates.
Provides functions for constructing near-optimal generalized full matching.
    Generalized full matching is an extension of the original full matching method
    to situations with more intricate study designs. The package is made with
    large data sets in mind and derives matches more than an order of magnitude
    quicker than other methods.
For fitting N-mixture models using either FFT or asymptotic approaches. FFT N-mixture models extend the work of Cowen et al. (2017) <doi:10.1111/biom.12701>. Asymptotic N-mixture models extend the work of Dail and Madsen (2011) <doi:10.1111/j.1541-0420.2010.01465.x>, to consider asymptotic solutions to the open population N-mixture models. The FFT models are derived and described in "Parker, M.R.P., Elliott, L., Cowen, L.L.E. (2022). Computational efficiency and precision for replicated-count and batch-marked hidden population models [Manuscript in preparation]. Department of Statistics and Actuarial Sciences, Simon Fraser University.". The asymptotic models are derived and described in: "Parker, M.R.P., Elliott, L., Cowen, L.L.E., Cao, J. (2022). Fast asymptotic solutions for N-mixtures on large populations [Manuscript in preparation]. Department of Statistics and Actuarial Sciences, Simon Fraser University.".
A high-level plotting system, built using 'grid' graphics, that is
    optimized for speed and modularity. This has great utility for quick
    visualizations when testing code, with the key benefit that visualizations
    are updated independently of one another.
Quickly fits and plots psychometric functions (normal, logistic,
    Weibull or any or any function defined by the user) for multiple groups.
A set of functions to extract results from regression models and
 plot the effect size using 'ggplot2' seamlessly. While 'broom' is useful to
 convert statistical analysis objects into tidy data frames, 'coefplot' is adept at showing
 multivariate regression results. With specific outcome, this package could build regression models
 automatically, extract results into a data frame and provide a quicker way to summarize
 models' statistical findings using 'ggplot2'.
Helps to perform linear regression analysis by reducing manual effort. Reduces the independent variables based on specified p-value and Variance Inflation Factor (VIF) level.
Test whether equality and order constraints hold for all 
    individuals simultaneously by comparing Bayesian mixed models through Bayes 
    factors. A tutorial style vignette and a quickstart guide are available, via
    vignette("manual", "quid"), and vignette("quickstart", "quid") respectively.
    See Haaf and Rouder (2017) <doi:10.1037/met0000156>; Haaf, Klaassen and Rouder
    (2019) <doi:10.31234/osf.io/a4xu9>; and Rouder & Haaf (2021) <doi:10.5334/joc.131>.
Provides tools for an automated identification of diagnostic molecular characters, i.e. such columns in a given nucleotide or amino acid alignment that allow to distinguish taxa from each other. These characters can then be used to complement the formal descriptions of the taxa, which are often based on morphological and anatomical features. Especially for morphologically cryptic species, this will be helpful. QUIDDICH distinguishes between four different types of diagnostic characters. For more information, see "Kuehn, A.L., Haase, M. 2019. QUIDDICH: QUick IDentification of DIagnostic CHaracters."
Simplifies output suppression logic in R packages, as it's common
    to develop some form of it in R. 'quietR' intends to simplify that problem 
    and allow a set of simple toggle functions to be used to suppress console 
    output.
Functions to convert text data for labelling into format appropriate for importing into Qualtrics. Supports multiple language, including right-to-left scripts as well as different response types. Outputs an Advance Format .txt file that read into Qualtrics.
Programmatic access to the 'PGS' Catalog.
    This package provides easy access to 'PGS' Catalog data by
    accessing the REST API <https://www.pgscatalog.org/rest/>.
Grows a qualitative interaction tree. Quint is a tool for subgroup analysis, suitable for data from a two-arm randomized controlled trial.
Diagnostics methods for quantile regression models for detecting influential observations:
  robust distance methods for general quantile regression models; generalized Cook's distance and 
  Q-function distance method for quantile regression models using aymmetric Laplace distribution. Reference
  of this method can be found in Luis E. Benites, Víctor H. Lachos, Filidor E. Vilca (2015) <arXiv:1509.05099v1>; 
  mean posterior probability and Kullback–Leibler divergence methods for Bayes quantile regression model.
  Reference of this method is Bruno Santos, Heleno Bolfarine (2016) <arXiv:1601.07344v1>.
A facility for writing functions that quote their arguments,
 may sometimes evaluate them in the environment where they were quoted,
 and may pass them as quoted to other functions.
Full text, in data frames containing one row per verse, of the 
    Qur'an in Arabic (with and without vowels) and in English (the Yusuf Ali 
    and Saheeh International translations), formatted to be convenient for 
    text analysis.
Thresholding based tests for null hypothesis of the form A beta =c, and the Quantile Universal Threshold (QUT) for lasso regularization of Generalized Linear Models (GLM) and square-root lasso to obtain a sparse model with a good compromise between high true positive rate and low false discovery rate. Giacobino et al. (2017) <doi:10.1214/17-EJS1366>. Sardy et al. (2017) <arXiv:1708.02908>.
Functions to compute quasi variances and associated measures of approximation error.
The modeling and prediction of graph-associated time series(GATS) based on continuous time quantum walk. This software is mainly used for feature extraction, modeling, prediction and result evaluation of GATS, including continuous time quantum walk simulation, feature selection, regression analysis, time series prediction, and series fit calculation.
A collection of (wrapper) functions the creator found useful
    for quickly placing data summaries and formatted regression results into
    '.Rnw' or '.Rmd' files. Functions for generating commonly used graphics,
    such as receiver operating curves or Bland-Altman plots, are also provided
    by 'qwraps2'.  'qwraps2' is a updated version of a package 'qwraps'. The
    original version 'qwraps' was never submitted to CRAN but can be found at
    <https://github.com/dewittpe/qwraps/>. The implementation and limited scope
    of the functions within 'qwraps2' <https://github.com/dewittpe/qwraps2/> is
    fundamentally different from 'qwraps'.
Generalized eigenvalues and eigenvectors
        use QZ decomposition (generalized Schur decomposition).
        The decomposition needs an N-by-N non-symmetric
        matrix A or paired matrices (A,B) with eigenvalues reordering
        mechanism. The decomposition functions are mainly based Fortran
        subroutines in complex*16 and double precision of LAPACK
        library (version 3.10.0 or later).
Allows the user to learn Bayesian networks from datasets containing thousands of variables. It focuses on score-based learning, mainly the 'BIC' and the 'BDeu' score functions. It provides state-of-the-art algorithms for the following tasks: (1) parent set identification - Mauro Scanagatta (2015) <http://papers.nips.cc/paper/5803-learning-bayesian-networks-with-thousands-of-variables>; (2) general structure optimization - Mauro Scanagatta (2018) <doi:10.1007/s10994-018-5701-9>, Mauro Scanagatta (2018) <http://proceedings.mlr.press/v73/scanagatta17a.html>; (3) bounded treewidth structure optimization - Mauro Scanagatta (2016) <http://papers.nips.cc/paper/6232-learning-treewidth-bounded-bayesian-networks-with-thousands-of-variables>; (4) structure learning on incomplete data sets - Mauro Scanagatta (2018) <doi:10.1016/j.ijar.2018.02.004>. Distributed under the LGPL-3 by IDSIA.
Memoization can be used to speed up repetitive and computational expensive function calls.  The first time a function that implements memoization is called the results are stored in a cache memory.  The next time the function is called with the same set of parameters, the results are momentarily retrieved from the cache avoiding repeating the calculations.  With this package, any R object can be cached in a key-value storage where the key can be an arbitrary set of R objects.  The cache memory is persistent (on the file system).
Functions for creating plots and image files in a unified way
    regardless of output format (EPS, PDF, PNG, SVG, TIFF, WMF, etc.). Default
    device options as well as scales and aspect ratios are controlled in a uniform
    way across all device types. Switching output format requires minimal changes
    in code. This package is ideal for large-scale batch processing, because it
    will never leave open graphics devices or incomplete image files behind, even on
    errors or user interrupts.
A file set refers to a set of files located in one or more directories on the file system.  This package provides classes and methods to locate, setup, subset, navigate and iterate such sets.  The API is designed such that these classes can be extended via inheritance to provide a richer API for special file formats.  Moreover, a specific name format is defined such that filenames and directories can be considered to have full names which consists of a name followed by comma-separated tags.  This adds additional flexibility to identify file sets and individual files.  NOTE: This package's API should be considered to be in an beta stage.  Its main purpose is currently to support the aroma.* packages, where it is one of the main core components; if you decide to build on top of this package, please contact the author first.
DEPRECATED. Do not start building new projects based on this package. Cross-platform alternatives are the following packages: bigmemory (CRAN), ff (CRAN), BufferedMatrix (Bioconductor).  The main usage of it was inside the aroma.affymetrix package. (The package currently provides a class representing a matrix where the actual data is stored in a binary format on the local file system.  This way the size limit of the data is set by the file system and not the memory.)
Performs the Joint and Individual Variation Explained (JIVE) decomposition on a list of data sets when the data share a dimension, returning low-rank matrices that capture the joint and individual structure of the data [O'Connell, MJ and Lock, EF (2016) <doi:10.1093/bioinformatics/btw324>]. It provides two methods of rank selection when the rank is unknown, a permutation test and a Bayesian Information Criterion (BIC) selection algorithm. Also included in the package are three plotting functions for visualizing the variance attributed to each data source: a bar plot that shows the percentages of the variability attributable to joint and individual structure, a heatmap that shows the structure of the variability, and principal component plots.
Methods readMat() and writeMat() for reading and writing MAT files.  For user with MATLAB v6 or newer installed (either locally or on a remote host), the package also provides methods for controlling MATLAB (trademark) via R and sending and retrieving data between R and MATLAB.
Methods that simplify the setup of S3 generic functions and S3 methods.  Major effort has been made in making definition of methods as simple as possible with a minimum of maintenance for package developers.  For example, generic functions are created automatically, if missing, and naming conflict are automatically solved, if possible.  The method setMethodS3() is a good start for those who in the future may want to migrate to S4.  This is a cross-platform package implemented in pure R that generates standard S3 methods.
Methods and classes for object-oriented programming in R with or without references.  Large effort has been made on making definition of methods as simple as possible with a minimum of maintenance for package developers.  The package has been developed since 2001 and is now considered very stable.  This is a cross-platform package implemented in pure R that defines standard S3 classes without any tricks.
The use of proxies is required in certain network environments. 
    Despite the power of system level software, it is still inconvenient to 
    switch proxy networks at random in R's console. This package is designed 
    to provide one-click switching between proxy and non-proxy states.
The RSP markup language makes any text-based document come alive.  RSP provides a powerful markup for controlling the content and output of LaTeX, HTML, Markdown, AsciiDoc, Sweave and knitr documents (and more), e.g. 'Today's date is <%=Sys.Date()%>'.  Contrary to many other literate programming languages, with RSP it is straightforward to loop over mixtures of code and text sections, e.g. in month-by-month summaries.  RSP has also several preprocessing directives for incorporating static and dynamic contents of external files (local or online) among other things.  Functions rstring() and rcat() make it easy to process RSP strings, rsource() sources an RSP file as it was an R script, while rfile() compiles it (even online) into its final output format, e.g. rfile('report.tex.rsp') generates 'report.pdf' and rfile('report.md.rsp') generates 'report.html'.  RSP is ideal for self-contained scientific reports and R package vignettes.  It's easy to use - if you know how to write an R script, you'll be up and running within minutes.
Processing pipeline for 'SamBada' from pre- to post-processing. 
	'SamBada' is a landscape genomic software designed to run univariate or multivariate logistic regression between the presence of a genotype and one or several environmental variables. See Stucki (2017) <doi:10.1111/1755-0998.12629> and <https://github.com/Sylvie/sambada>. 
	The package provides functions that can be classified into four categories: 
	1) Install 'SamBada' 
	2) Preprocessing (prepare genomic file into standards compatible with 'SamBada' and apply quality-control; retrieve environmental conditions at sampling location; prepare environmental file including removal of correlated variables and computation of population structure) 
	3) Processing (run 'SamBada' on multiple cores using 'Supervision')
	4) Post-processing (calculate p-values and q-values, produce interactive Manhattan plots and query 'Ensembl' database, produce maps).
An integrated solution to perform
    a series of text mining tasks such as importing and cleaning a corpus, and
    analyses like terms and documents counts, lexical summary, terms
    co-occurrences and documents similarity measures, graphs of terms,
    correspondence analysis and hierarchical clustering. Corpora can be imported
    from spreadsheet-like files, directories of raw text files,
    as well as from 'Dow Jones Factiva', 'LexisNexis', 'Europresse' and 'Alceste' files.
Utility functions useful when programming and developing R packages.
Estimation of reproduction numbers for disease outbreak, based on
    incidence data. The R0 package implements several documented methods. It is
    therefore possible to compare estimations according to the methods used.
    Depending on the methods requested by user, basic reproduction number
    (commonly denoted as R0) or real-time reproduction number (referred to as
    R(t)) is computed, along with a 95% Confidence Interval. Plotting outputs
    will give different graphs depending on the methods requested : basic
    reproductive number estimations will only show the epidemic curve
    (collected data) and an adjusted model, whereas real-time methods will also
    show the R(t) variations throughout the outbreak time period. Sensitivity
    analysis tools are also provided, and allow for investigating effects of
    varying Generation Time distribution or time window on estimates.
This is a companion package of the book "R Programming: Zero to Pro"  <https://r02pro.github.io/>. It contains the datasets used in the book and provides interactive exercises corresponding to the book. It covers a wide range of topics including visualization, data transformation, tidying data, data input and output.
Utilities for sparse signal recovery suitable for compressed sensing. L1, L2 and TV penalties, DFT basis matrix, simple sparse signal generator, mutual cumulative coherence between two matrices and examples, Lp complex norm, scaling back regression coefficients.
Since December 2019, Wuhan City, Hubei Province has continued to carry out surveillance of influenza and related diseases, and found multiple cases of viral pneumonia, all of which were diagnosed with viral pneumonia / pulmonary infection. On January 12, 2020, the World Health Organization officially named the new coronavirus causing the pneumonia epidemic in Wuhan as "2019 New Coronavirus (2019-nCoV)". The current epidemic situation is very serious, here we developed an R package for 2019-nCoV analysis(Real-time monitoring and Visualization) by querying real-time statistics of 2019-nCoV virus cases from <https://view.inews.qq.com/g2/getOnsInfo?name=disease_h5&callback=1580373566110> and performing follow-up analysis.
R^2 measure of explained variation under the semiparametric additive hazards model is estimated. The measure can be used as a measure of predictive capability and therefore it can be adopted in model selection process. Rava, D. and Xu, R. (2020) <arXiv:2003.09460>.
A series of functions to call 'AD Model Builder' (i.e.,
    compile and run models) from within R, read the results back
    into R as 'admb' objects, and provide standard accessors (i.e.
    coef(), vcov(), etc.)
An R interface to estimate structured additive regression (STAR) models with 'BayesX'.
Multivariate optimal allocation for different domains in one and two stages stratified sample design. R2BEAT extends the Neyman (1934) – Tschuprow (1923) allocation method to the case of several variables, adopting a generalization of the Bethel’s proposal (1989).R2BEAT develops this methodology but, moreover, it allows to determine the sample allocation in the multivariate and multi-domains case of estimates for two-stage stratified samples. It also allows to perform both Primary Stage Units and Secondary Stage Units selection. This package requires the availability of ReGenesees, that can be installed from <https://github.com/DiegoZardetto/ReGenesees>.
This package provides generic functions to analyze the distribution
  of two continuous variables: 'conf2d' to calculate a smooth empirical
  confidence region, and 'freq2d' to calculate a frequency distribution.
Suite of tools for using 'D3', a library for producing dynamic, interactive data
  visualizations. Supports translating objects into 'D3' friendly data structures, rendering
  'D3' scripts, publishing 'D3' visualizations, incorporating 'D3' in R Markdown, creating
  interactive 'D3' applications with Shiny, and distributing 'D3' based 'htmlwidgets' in R
  packages.
Provides functions for aligning 2D gas chromatography mass spectrometry derived metabolite peaks obtained from primary processing and generates an alignment table that
        allows for a comparison of common peaks across samples and metabolite identification. Publication describing the package in detail is available at the following citation: Ryne C. Ramaker, Emily Gordon, Sara J. Cooper (2017) <doi:10.1101/179168>.
Despite the predominant use of 'R' for data manipulation and various robust statistical calculations, in recent years, more people from various disciplines are beginning to use 'R' for other purposes. A critical milestone that has enabled large influx of users to the 'R' community is the development of the 'Tidyverse' family of packages and 'Rmarkdown'. With the latter, one can write all kinds of documents and produce output in formats such 'HTML' and 'PDF' very easily. In doing this seemlessly, further tools are needed for such users to easily and freely write in 'R' for all kinds of purposes. The 'r2dictionary' introduces a means for users to directly search for definitions of terms from within the 'R' environment. The source dictionary is an original work of 'The Online Plain Text English Dictionary (OPTED)'.
These tools help you to assess if a corporate lending
    portfolio aligns with climate goals. They summarize key climate
    indicators attributed to the portfolio (e.g. production, emission
    factors), and calculate alignment targets based on climate scenarios.
    They implement in R the last step of the free software 'PACTA' (Paris
    Agreement Capital Transition Assessment;
    <https://2degrees-investing.org/>). Financial institutions use 'PACTA'
    to study how their capital allocation decisions align with climate
    change mitigation goals.
These datasets support the implementation in R of the
    software 'PACTA' (Paris Agreement Capital Transition Assessment),
    which is a free tool that calculates the alignment between corporate
    lending portfolios and climate scenarios
    (<https://2degrees-investing.org/>). Financial institutions use
    'PACTA' to study how their capital allocation decisions align with
    climate change mitigation goals. Because both financial institutions
    and market data providers keep their data private, this package
    provides fake, public data to enable the development and use of
    'PACTA' in R.
These tools implement in R a fundamental part of the software
    'PACTA' (Paris Agreement Capital Transition Assessment), which is a
    free tool that calculates the alignment between financial portfolios
    and climate scenarios (<https://2degrees-investing.org/>). Financial
    institutions use 'PACTA' to study how their capital allocation
    decisions align with climate change mitigation goals. This package
    matches data from corporate lending portfolios to asset level data
    from market-intelligence databases (e.g. power plant capacities,
    emission factors, etc.). This is the first step to assess if a
    financial portfolio aligns with climate goals.
Create plots to visualize the alignment of a corporate
    lending financial portfolio to climate change scenarios based on
    climate indicators (production and emission intensities) across key
    climate relevant sectors of the 'PACTA' methodology (Paris Agreement
    Capital Transition Assessment; <https://2degrees-investing.org/>).
    Financial institutions use 'PACTA' to study how their capital
    allocation decisions align with climate change mitigation goals.
2dRUE is a methodology to make a diagnostic of land
        condition in a large territory during a given time period. The
        following projects have funded this package: DeSurvey IP (EC
        FP6 Integrated Project contract No. 003950), DesertWatch (ESA
        DUE contract No. 18487/04/I-LG) and MesoTopos (Junta de
        Andalucia PE ref. RNM-4023).
Some heavily used base R functions are reconstructed to also be compliant to data.table objects. Also, some general helper functions that could be of interest for working with data.table objects are included.
The model R squared and semi-partial R squared for the linear and
    generalized linear mixed model (LMM and GLMM) are computed with confidence
    limits. The R squared measure from Edwards et.al (2008) <DOI:10.1002/sim.3429>
    is extended to the GLMM using penalized quasi-likelihood (PQL) estimation
    (see Jaeger et al. 2016 <DOI:10.1080/02664763.2016.1193725>). Three methods
    of computation are provided and described as follows. First, The
    Kenward-Roger approach. Due to some inconsistency between the 'pbkrtest'
    package and the 'glmmPQL' function, the Kenward-Roger approach in the
    'r2glmm' package is limited to the LMM. Second, The method introduced
    by Nakagawa and Schielzeth (2013) <DOI:10.1111/j.2041-210x.2012.00261.x>
    and later extended by Johnson (2014) <DOI:10.1111/2041-210X.12225>.
    The 'r2glmm' package only computes marginal R squared for the LMM and does
    not generalize the statistic to the GLMM; however, confidence limits and
    semi-partial R squared for fixed effects are useful additions. Lastly, an
    approach using standardized generalized variance (SGV) can be used for
    covariance model selection. Package installation instructions can be found
    in the readme file.
Includes HTML function and methods to write in an HTML
        file. Thus, making HTML reports is easy. Includes a function
        that allows redirection on the fly, which appears to be very
        useful for teaching purpose, as the student can keep a copy of
        the produced output to keep all that he did during the course.
        Package comes with a vignette describing how to write HTML
        reports for statistical analysis. Finally, a driver for 'Sweave'
        allows to parse HTML flat files containing R code and to
        automatically write the corresponding outputs (tables and
        graphs).
Providing wrapper functions to implement Bayesian analysis in JAGS.  Some major features include monitoring convergence of a MCMC model using Rubin and Gelman Rhat statistics, automatically running a MCMC model till it converges, and implementing parallel processing of a MCMC model for multiple chains.
Generates both total- and level-specific R-squared measures from
    Rights and Sterba’s (2019) <doi:10.1037/met0000184> framework of R-squared measures for multilevel
    models with random intercepts and/or slopes, which is based on a complete
    decomposition of variance. Additionally generates graphical 
    representations of these R-squared measures to allow visualizing and 
    interpreting all measures in the framework together as an integrated set.
    This framework subsumes 10 previously-developed R-squared measures for 
    multilevel models as special cases of 5 measures from the framework, and it
    also includes several newly-developed measures. Measures in the framework 
    can be used to compute R-squared differences when comparing multilevel 
    models (following procedures in Rights & Sterba (2020) <doi:10.1080/00273171.2019.1660605>).
An R command interface to the 'MLwiN' multilevel
    modelling software package.
Using this package,
 it is possible to call a BUGS model, summarize inferences and
 convergence in a table and graph, and save the simulations in arrays for easy access
 in R.
R wrapper for the JPMML-R library <https://github.com/jpmml/jpmml-r>,
    which converts R models to Predictive Model Markup Language (PMML).
Provides a simple set of wrappers to easily use 
         RDCOMClient for generating Microsoft PowerPoint presentations. Warning:this package is soon to be archived from CRAN.
Provides a friendly, object oriented API for creating PowerPoint
    slide decks in R.
Implementation of hash tables (hash sets and hash maps) in R, 
	featuring arbitrary R objects as keys, 
	arbitrary hash and key-comparison functions, 
	and customizable behaviour upon queries of missing keys.
Create production-ready Rich Text Format (RTF) table and figure with flexible format.
When creating a package, authors may sometimes struggle with coming up with easy and straightforward function names, and at the same time hoping that other packages do not already have the same function names. In trying to meet this goal, sometimes, function names are not descriptive enough and may confuse the potential users. The purpose of this package is to serve as a package function short form generator and also provide shorthand names for other functions. Having this package will entice authors to create long function names without the fear of users not wanting to use their packages because of the long names. In a way, everyone wins - the authors can use long descriptive function names, and the users can use this package to make short functions names while still using the package in question.
Create plots and LaTeX tables that look like SPSS output for use in teaching materials.  Rather than copying-and-pasting SPSS output into documents, R code that mocks up SPSS output can be integrated directly into dynamic LaTeX documents with tools such as knitr.  Functionality includes statistical techniques that are typically covered in introductory statistics classes: descriptive statistics, common hypothesis tests, ANOVA, and linear regression, as well as box plots, histograms, scatter plots, and line plots (including profile plots).
Wrapper for widely used 'SUNDIALS' software (SUite of Nonlinear and DIfferential/ALgebraic Equation Solvers) and more precisely to its 'CVODES' solver. It is aiming to solve ordinary differential equations (ODE) and optionally pending forward sensitivity problem. The wrapper is made 'R' friendly by allowing to pass custom parameters to user's callback functions. Such functions can be both written in 'R' and in 'C++' ('RcppArmadillo' flavor). In case of 'C++', performance is greatly improved so this option is highly advisable when performance matters. If provided, Jacobian matrix can be calculated either in dense or sparse format. In the latter case 'rmumps' package is used to solve corresponding linear systems. Root finding and pending event management are optional and can be specified as 'R' or 'C++' functions too. This makes them a very flexible tool for controlling the ODE system during the time course simulation. 'SUNDIALS' library was published in Hindmarsh et al. (2005) <doi:10.1145/1089014.1089020>.
Using the 'Ming' library
    <https://github.com/libming/libming> to create Flash animations.
    Users can either use the 'SWF' device swf() to generate 'SWF' file
    directly through plotting functions like plot() and lines(),
    or convert images of other formats ('SVG', 'PNG', 'JPEG') into 'SWF'.
Direct insertion of symbols (e.g. currencies, letters, arrows, mathematical symbols and so on) into 'Rmarkdown' documents and 'Shiny' applications by incorporating 'HTML' hex codes.
Performs goodness-of-fit tests for capture-recapture models. Also contains several functions to process capture-recapture data.
This package uses either the statconnDCOM server (via the
        rcom package) or the RDCOMClient to communicate with MS-Word
        via the COM interface.
Invoke a 'BUGS' model in 'OpenBUGS' or 'WinBUGS', a class "bugs" for 'BUGS' 
  results and functions to work with that class.
  Function write.model() allows a 'BUGS' model file to be written.  
  The class and auxiliary functions could be used with other MCMC programs, including 'JAGS'.
This is a collection of useful functions and data for
        Stats 330
Create rich and fully interactive 3D visualizations of molecular data.
    Visualizations can be included in Shiny apps and R markdown documents, or viewed
    from the R console and 'RStudio' Viewer. 'r3dmol' includes an extensive API
    to manipulate the visualization after creation, and supports getting data out of
    the visualization into R. Based on the '3dmol.js' and the 'htmlwidgets' R package.
Provides a flexible and easy-to-use interface for the Physiological Processes Predicting Growth (3-PG) model written in Fortran. The r3PG serves as a flexible and easy-to-use interface for the 3-PGpjs (monospecific, evenaged and evergreen forests) described in Landsberg & Waring (1997) <doi:10.1016/S0378-1127(97)00026-1> and the 3-PGmix (deciduous, uneven-aged or mixed-species forests) described in Forrester & Tang (2016) <doi:10.1016/j.ecolmodel.2015.07.010>.
Create and combine HTML and PDF reports from within R.
    Possibility to design tables and listings for reporting and also include R plots.
Provides a collection of functions for basic
    database and document management operations such as add, get, list access
    or delete. Every cdbFunction() gets and returns a list() containing the
    connection setup. Such a list can be generated by cdbIni().
Interface for the 'Google Ads API'. 'Google Ads' is an online
    advertising service that enables advertisers to display advertising to web
    users (see <https://developers.google.com/google-ads/> for more information).
Since the early 1970s eyewitness testimony researchers have recognised the 
    importance of estimating properties such as lineup bias (is the lineup biased against 
    the suspect, leading to a rate of choosing higher than one would expect by chance?), 
    and lineup size (how many reasonable choices are in fact available to the witness? 
    A lineup is supposed to consist of a suspect and a number of additional members, 
    or foils, whom a poor-quality witness might mistake for the perpetrator). Lineup 
    measures are descriptive, in the first instance, but since the earliest articles in 
    the literature researchers have recognised the importance of reasoning inferentially 
    about them. This package contains functions to compute various properties of 
    laboratory or police lineups, and is intended for use by researchers in forensic 
    psychology and/or eyewitness testimony research. Among others, the r4lineups package 
    includes functions for calculating lineup proportion, functional size, various 
    estimates of effective size, diagnosticity ratio, homogeneity of the diagnosticity 
    ratio, ROC curves for confidence x accuracy data and the degree of similarity of 
    faces in a lineup.
A collection of R functions for use with Stock Synthesis, a
    fisheries stock assessment modeling platform written in ADMB by Dr. Richard
    D.  Methot at the NOAA Northwest Fisheries Science Center. The functions
    include tools for summarizing and plotting results, manipulating files,
    visualizing model parameterizations, and various other common stock
    assessment tasks.
For the 9 counties in the San Francisco Bay area 
    the Metropolitan Transportation Commission
    maintains an Application Programming Interface
    with information on more than 20 transit operators. 
    This package allows users to retrieve
    information about transit schedules in the General Transit Feed Specification
    as published by the Metropolitan Transportation Commission at the following url: 
    <https://511.org/developers/list/apis/>.
Rapid realistic routing on multimodal transport networks (walk, 
             bike, public transport and car) using 'R5', the Rapid Realistic 
             Routing on Real-world and Reimagined networks engine 
             <https://github.com/conveyal/r5>. The package allows users to 
             generate detailed routing analysis or calculate travel time matrices
             using seamless parallel computing on top of the R5 Java machine.
             While R5 is developed by Conveyal, the package r5r is independently 
             developed by a team at the Institute for Applied Economic Research
             (Ipea) with contributions from collaborators. Apart from the 
             documentation in this package, users will find additional information
             on R5 documentation at <https://docs.conveyal.com/>. Although we try
             to keep new releases of r5r in synchrony with R5, the development of R5
             follows Conveyal's independent update process. Hence, users should
             confirm the R5 version implied by the Conveyal user manual (see
             <https://docs.conveyal.com/changelog>) corresponds with the R5 version
             that r5r depends on.
Creates classes with reference semantics, similar to R's built-in
    reference classes. Compared to reference classes, R6 classes are simpler
    and lighter-weight, and they are not built on S4 classes so they do not
    require the methods package. These classes allow public and private
    members, and they support inheritance, even when the classes are defined in
    different packages.
After defining an R6 class, R62S3 is used to automatically generate optional S3/S4 generics and methods for dispatch. Also allows piping for R6 objects.
The implemented R6 class 'SCM' aims to simplify working with structural causal models. The missing data mechanism can be defined as a part of the structural model. The class contains methods for 1) defining a structural causal model via functions, text or conditional probability tables, 2) printing basic information on the model, 3) plotting the graph for the model using packages 'igraph' or 'qgraph', 4) simulating data from the model, 5) applying an intervention, 6) checking the identifiability of a query using the R packages 'causaleffect' and 'dosearch', 7) defining the missing data mechanism, 8) simulating incomplete data from the model according to the specified missing data mechanism and 9) checking the identifiability in a missing data problem using the R package 'dosearch'. In addition, there are functions for running experiments and doing counterfactual inference using simulation.
Provides reference classes implementing some useful data structures.
    The package implements these data structures by using the reference class R6.
    Therefore, the classes of the data structures are also reference classes which means that their instances are passed by reference.
    The implemented data structures include stack, queue, double-ended queue, doubly linked list, set, dictionary and binary search tree.
    See for example <https://en.wikipedia.org/wiki/Data_structure> for more information about the data structures.
Useful methods and data fields to extend the
             bare bones 'R6' class provided by the 'R6' package - ls-method, 
             hashes, warning- and message-method, general get-method and a 
             debug-method that assigns self and private to the global environment.
Generate boilerplate code for R6 classes. Given R6 class create getters 
    and/or setters for selected class fields or use RStudio addins to insert methods 
    straight into class definition.
Build robust and maintainable software with object-oriented design 
    patterns in R. Design patterns abstract and present in neat, well-defined 
    components and interfaces the experience of many software designers and 
    architects over many years of solving similar problems. These are solutions 
    that have withstood the test of time with respect to re-usability,
    flexibility, and maintainability. 'R6P' provides abstract base classes with 
    examples for a few known design patterns. The patterns were selected by 
    their applicability to analytic projects in R. Using these patterns in R 
    projects have proven effective in dealing with the complexity that 
    data-driven applications possess.
Functionality for performing a principled reference analysis in the Bayesian normal-normal hierarchical model used for Bayesian meta-analysis, as described in Ott, Plummer and Roos (2021, "How vague is vague? How informative is informative? Reference analysis for Bayesian meta-analysis", under minor revision for Statistics in Medicine). Computes a reference posterior, induced by a minimally informative improper reference prior for the between-study (heterogeneity) standard deviation. Determines additional proper anti-conservative (and conservative) prior benchmarks. Includes functions for reference analyses at both the posterior and the prior level, which, given the data, quantify the informativeness of a heterogeneity prior of interest relative to the minimally informative reference prior and the proper prior benchmarks. The functions operate on data sets which are compatible with the 'bayesmeta' package.
Infers V-D-J haplotypes and gene deletions from AIRR-seq data for Ig and TR chains, 
    based on J, D, or V genes as anchor, by adapting a Bayesian framework.
    It also calculates a Bayes factor, a number that indicates the certainty level of the inference, for each haplotyped gene.
    Citation:
    Gidoni, et al (2019) <doi:10.1038/s41467-019-08489-3>.
    Peres and Gidoni, et al (2019) <doi:10.1093/bioinformatics/btz481>.
Facilitates the design and generation of optimal color (or symbol) codes that can be used to mark and identify individual animals. These codes are made such that the IDs are robust to partial erasure: even if sections of the code are lost, the entire identity of the animal can be reconstructed. Thus, animal subjects are not confused and no ambiguity is introduced.
Conduct simulations of the Response Adaptive Block Randomization (RABR) design to evaluate its type I error rate, power and operating characteristics for binary and continuous endpoints. For more details of the proposed method, please refer to Zhan et al. (2020) <arXiv:2004.07356>.
Solves the individual bioenergetic balance for different aquaculture sea fish (Sea Bream and Sea Bass; Brigolin et al., 2014 <doi:10.3354/aei00093>) and shellfish (Mussel and Clam; Brigolin et al., 2009 <doi:10.1016/j.ecss.2009.01.029>; Solidoro et al., 2000 <doi:10.3354/meps199137>). Allows for spatialized model runs and population simulations.
Provide function for work with 'AcademyOcean API'
    <https://academyocean.com/api>.
Application of 'RaceID' allows inference of cell types and prediction of lineage trees by he StemID2 algorithm. Herman, J.S., Sagar, Grün D. (2018) <DOI:10.1038/nmeth.4662>.
Implements a computational framework for a pattern-based, 
    zoneless analysis, and visualization of (ethno)racial topography 
    (Dmowska, Stepinski, and Nowosad (2020) <doi:10.1016/j.apgeog.2020.102239>).
    It is a reimagined
    approach for analyzing residential segregation and racial diversity based on 
    the concept of 'landscape’ used in the domain of landscape ecology.
Contains functions useful for reading in Licor 6800 files,
    correcting and analyzing rapid A/Ci response (RACiR) data. Requires some
    user interaction to adjust the calibration (empty chamber) data file to 
    a useable range. Calibration uses a 1st to 5th order polynomial as 
    suggested in Stinziano et al. (2017) <doi:10.1111/pce.12911>.
    Data can be processed individually or batch processed for all files paired
    with a given calibration file. RACiR is a trademark of LI-COR Biosciences,
    and used with permission.
Interface for loading data from 'ActiveCampaign API v3' 
    <https://developers.activecampaign.com/reference>. Provide functions
    for getting data by deals, contacts, accounts, campaigns and messages.
Fit a variety of models to Rank Abundance Data
Systematically transform immunoassay data, evaluate if the data is normally distributed, and pick the right method for cut point determination based on that evaluation. This package can also produce plots that are needed for reports, so data analysis and visualization can be done easily.
It has tools for normalization of rank abundance
     distributions (RAD) to a desired number of ranks using MaxRank
     Normalization method.
     RADs are commonly used in biology/ecology and mathematically equivalent
     to complementary cumulative distributions (CCDFs) which are used in
     physics, linguistics and sociology and more generally in data science.
Fundamental formulas for Radar, for attenuation, range, velocity,
    effectiveness, power, scatter, doppler, geometry, radar equations, etc.
    Based on Nick Guy's Python package PyRadarMet
Creates the radar-boxplot, a plot that was created by the 
    author during his Ph.D. in forest resources. 
    The radar-boxplot is a visualization feature suited for  
    multivariate classification/clustering. It provides an intuitive 
    deep understanding of the data.
Create interactive radar charts using the 'Chart.js' 'JavaScript' library
    and the 'htmlwidgets' package. 'Chart.js' <http://www.chartjs.org/> is a 
    lightweight library that supports several types of simple chart using the 'HTML5' 
    canvas element. This package provides an R interface specifically to the 
    radar chart, sometimes called a spider chart, for visualising multivariate data.
Nuclear Decay Data for Dosimetric Calculations from the 
    International Commission on Radiological Protection from ICRP 
    Publication 107. Ann. ICRP 38 (3). Eckerman, Keith and Endo, Akira 2008 
    <doi:10.1016/j.icrp.2008.10.004> 
    <https://www.icrp.org/publication.asp?id=ICRP%20Publication%20107>. 
    This is a database of the physical data needed in calculations of 
    radionuclide-specific protection and operational quantities. The 
    data is prescribed by the ICRP, the international authority on 
    radiation dose standards, for estimating dose from the intake of or 
    exposure to radionuclides in the workplace and the environment. 
    The database contains information on the half-lives, decay chains, 
    and yields and energies of radiations emitted in nuclear transformations 
    of 1252 radionuclides of 97 elements.
Shiny-based interactive gadgets of radial visualization methods and extensions thereof.
A platform-independent browser-based interface for business
    analytics in R, based on the shiny package. The application combines the
    functionality of 'radiant.data', 'radiant.design', 'radiant.basics',
    'radiant.model', and 'radiant.multivariate'.
The Radiant Basics menu includes interfaces for probability 
    calculation, central limit theorem simulation, comparing means and proportions, 
    goodness-of-fit testing, cross-tabs, and correlation. The application extends 
    the functionality in 'radiant.data'.
The Radiant Data menu includes interfaces for loading, saving,
    viewing, visualizing, summarizing, transforming, and combining data. It also
    contains functionality to generate reproducible reports of the analyses
    conducted in the application.
The Radiant Design menu includes interfaces for design of
    experiments, sampling, and sample size calculation. The application extends
    the functionality in 'radiant.data'.
The Radiant Model menu includes interfaces for linear and logistic
    regression, naive Bayes, neural networks, classification and regression trees,
    model evaluation, collaborative filtering, decision analysis, and simulation. 
    The application extends the functionality in 'radiant.data'.
The Radiant Multivariate menu includes interfaces for perceptual
    mapping, factor analysis, cluster analysis, and conjoint analysis. The
    application extends the functionality in 'radiant.data'.
RadioSonde is a collection of programs for reading and
        plotting SKEW-T,log p diagrams and wind profiles for data
        collected by radiosondes (the typical weather balloon-borne
        instrument), which we will call "flights", "sondes", or
        "profiles" throughout the associated documentation.  The raw
        data files are in a common format that has a header followed by
        specific variables.  Use "help(ExampleSonde)" for the full
        explanation of the data files.
Radioactive doses estimation using individual chromosomal aberrations information. See Higueras M, Puig P, Ainsbury E, Rothkamm K. (2015) <doi:10.1088/0952-4746/35/3/557>.
Make your phrase or sentence into something funny! Pass a string with the keywords in, 
    and get out a bit of humor.
Implementation of ADMIXTURE for individual ancestry inference in R. Specifically, ADMIXTURE is a software tool for maximum likelihood estimation of individual ancestries from multilocus SNP genotype datasets, see <https://www.genetics.ucla.edu/software/admixture/>. Users can use 'radmixture' to calculate ancestry components with different public datasets. It is very convenient and fast for personal genotype data. For more details, see <https://github.com/wegene-llc/radmixture/blob/master/README.md>.
Designed for the import, analysis, and visualization of dosimetric and volumetric data in Radiation Oncology, the tools herein enable import of dose-volume histogram information from multiple treatment planning system platforms and 3D structural representations and dosimetric information from 'DICOM-RT' files.  These tools also enable subsequent visualization and statistical analysis of these data.
Generate random user data from the Random User Generator API.
    For more information, see <https://randomuser.me/>.
Provides functions for radiation safety, also known as
    "radiation protection" and "radiological control". The science of 
    radiation protection is called "health physics" and its engineering 
    functions are called "radiological engineering". Functions in this 
    package cover many of the computations needed by radiation safety 
    professionals. Examples include: obtaining updated calibration and
    source check values for radiation monitors to account for radioactive 
    decay in a reference source, simulating instrument readings to better
    understand measurement uncertainty, correcting instrument readings 
    for geometry and ambient atmospheric conditions. Many of these 
    functions are described in Johnson and Kirby (2011, ISBN-13:  
    978-1609134198). Utilities are also included for developing inputs 
    and processing outputs with radiation transport codes, such as MCNP, 
    a general-purpose Monte Carlo N-Particle code that can be used for 
    neutron, photon, electron, or coupled neutron/photon/electron transport
    (Werner et. al. (2018) <doi:10.2172/1419730>).
Offers a handful of useful wrapper functions which streamline
    the reading, analyzing, and visualizing of variant call format (vcf) files in R.
    This package was designed to facilitate an explicit pipeline for optimizing
    Stacks (Rochette et al., 2019) (<doi:10.1111/mec.15253>)
    parameters during de novo (without a reference genome) assembly and variant
    calling of restriction-enzyme associated DNA sequence (RADseq) data. The
    pipeline implemented here is based on the 2017 paper "Lost in Parameter Space"
    (Paris et al., 2017) (<doi:10.1111/2041-210X.12775>) which
    establishes clear recommendations for optimizing the parameters
    'm', 'M', and 'n', during the process of assembling loci.
Contains 4 different functions for radon and soil gas transport in a porous medium.
An implementation of the radviz projection in R. It enables the visualization of
    multidimensional data while maintaining the relation to the original dimensions.
    This package provides functions to create and plot radviz projections, and a number of summary
    plots that enable comparison and analysis. For reference see Ankerst *et al.* (1996) 
    (<https://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.68.1811>) for original implementation, 
    see Di Caro *et al* (2012) (<https://link.springer.com/chapter/10.1007/978-3-642-13672-6_13>) 
    for the original method for dimensional anchor arrangements, see Demsar *et al.* (2007) 
    (<doi:10.1016/j.jbi.2007.03.010>) for the original Freeviz implementation.
Aims at loading Google Adwords data into R. Adwords is an online
    advertising service that enables advertisers to display advertising copy to web
    users (see <https://developers.google.com/adwords/> for more information). 
    Therefore the package implements three main features. First, the package
    provides an authentication process for R with the Google Adwords API (see 
    <https://developers.google.com/adwords/api/> for more information) via OAUTH2.
    Second, the package offers an interface to apply the Adwords query language in
    R and query the Adwords API with ad-hoc reports. Third, the received data are
    transformed into suitable data formats for further data processing and data
    analysis.
The Proportional Subdistribution Hazard (PSH) model has been popular for estimating the effects of the covariates on the cause of interest in Competing Risks analysis. The fast accumulation of large scale datasets has posed a challenge to classical statistical methods. Current penalized variable selection methods show unsatisfactory performance in ultra-high dimensional data. We propose a novel method, the Random Approximate Elastic Net (RAEN), with a robust and generalized solution to the variable selection problem for the PSH model. Our method shows improved sensitivity for variable selection compared with current methods.
A series of shortcuts for routine tasks originally developed by Rafael A. Irizarry to facilitate data exploration.
Functions for calculating life history metrics using matrix population models ('MPMs'). Described in Jones et al. (2021) <doi:10.1101/2021.04.26.441330>.
Anti-Grain Geometry (AGG) is a high-quality and high-performance
    2D drawing library. The 'ragg' package provides a set of graphic devices 
    based on AGG to use as alternative to the raster devices provided through
    the 'grDevices' package.
Data objects in 'R' can be rendered as 'HTML' tables using the
    'JavaScript' library 'ag-grid' (typically via 'R Markdown' or 'Shiny'). The
    'ag-grid' library has been included in this 'R' package. The package name
    'RagGrid' is an abbreviation of 'R agGrid'.
Proper L2-penalized maximum likelihood estimators for precision 
  matrices and supporting functions to employ these estimators in a graphical 
  modeling setting. For details see van Wieringen & Peeters (2016) 
  <doi:10.1016/j.csda.2016.05.012> and associated publications.
The ragt2ridges-package provides ridge maximum likelihood estimation of vector auto-regressive processes: the VAR(1), VAR(2) and VARX(1) model (more to be added). Prior knowledge may be incorporated in the estimation through a) specification of the edges believed to be absent in the time series chain graph, and b) a shrinkage target towards which the parameter estimate is shrunken for large penalty parameter values. Estimation functionality is accompanied by methodology for penalty parameter selection. In addition, the package offers supporting functionality for the exploitation of estimated models. Among others, i) a procedure to infer the support of the non-sparse ridge estimate (and thereby of the time series chain graph) is implemented, ii) a table of node-wise network summary statistics, iii) mutual information analysis, and iv) impulse response analysis. Cf. Miok et al. (2017) <DOI:10.1002/bimj.201500269> and Miok et al. (2019) <DOI:10.1002/bimj.201700195> for details on the implemented methods.
Algorithms to price American and European
    equity options, convertible bonds and a
    variety of other financial derivatives. It uses an
    extension of the usual Black-Scholes model in which
    jump to default may occur at a probability specified
    by a power-law link between stock price and hazard
    rate as found in the paper by Takahashi, Kobayashi,
    and Nakagawa (2001) <doi:10.3905/jfi.2001.319302>.  We
    use ideas and techniques from Andersen and
    Buffum (2002) <doi:10.2139/ssrn.355308> and
    Linetsky (2006) <doi:10.1111/j.1467-9965.2006.00271.x>.
A modified implementation of stepwise regression that greedily searches 
    the space of interactions among features in order to build polynomial regression models.
    Furthermore, the hypothesis tests conducted are valid-post model selection
    due to the use of a revisiting procedure that implements an alpha-investing
    rule. As a result, the set of rejected sequential hypotheses is proven to 
    control the marginal false discover rate. When not searching for polynomials,
    the package provides a statistically valid algorithm
    to run and terminate stepwise regression. For more information, see 
    Johnson, Stine, and Foster (2019) <arXiv:1510.06322>.
Visualizing functional data and identifying functional outliers.
By using 'RAINBOWR' (Reliable Association INference By Optimizing Weights with R), users can test multiple SNPs (Single Nucleotide Polymorphisms) simultaneously by kernel-based (SNP-set) methods. This package can also be applied to haplotype-based GWAS (Genome-Wide Association Study). Users can test not only additive effects but also dominance and epistatic effects. In detail, please check our paper on PLOS Computational Biology: Kosuke Hamazaki and Hiroyoshi Iwata (2020) <doi:10.1371/journal.pcbi.1007663>.
Various statistical and mathematical ranking and rating methods with incomplete information are included. This package is initially designed for the scoring system in a high school project showcase to rank student research projects, where each judge can only evaluate a set of projects in a limited time period. See Langville, A. N. and Meyer, C. D. (2012), Who is Number 1: The Science of Rating and Ranking, Princeton University Press <doi:10.1515/9781400841677>, and Gou, J. and Wu, S. (2020), A Judging System for Project Showcase: Rating and Ranking with Incomplete Information, Technical Report.
An R implementation of the Reinert text clustering method. For more 
    details about the algorithm see the included vignettes or Reinert (1990) 
    <doi:10.1177/075910639002600103>.
Determination of rainfall-runoff erosivity factor.
An implementation of the RainFARM (Rainfall Filtered Autoregressive Model) stochastic precipitation downscaling method (Rebora et al. (2006) <doi:10.1175/JHM517.1>). Adapted for climate downscaling according to D'Onofrio et al. (2018) <doi:10.1175/JHM-D-13-096.1> and for complex topography as in Terzago et al. (2018) <doi:10.5194/nhess-18-2825-2018>. The RainFARM method is based on the extrapolation to small scales of the Fourier spectrum of a large-scale precipitation field, using a fixed logarithmic slope and random phases at small scales, followed by a nonlinear transformation of the resulting linearly correlated stochastic field. RainFARM allows to generate ensembles of spatially downscaled precipitation fields which conserve precipitation at large scales and whose statistical properties are consistent with the small-scale statistics of observed precipitation, based only on knowledge of the large-scale precipitation field.
A robust alternative to the aJIVE (angle based Joint and Individual Variation Explained) method (Feng et al 2018: <doi:10.1016/j.jmva.2018.03.008>) for the estimation of joint and individual components in the presence of outliers in multi-source data. It decomposes the multi-source data into joint, individual and residual (noise) contributions. The decomposition is robust to outliers and noise in the data. The method is illustrated in Ponzi et al (2021) <arXiv:2101.09110>.
Functions for performing spatial microsimulation ('raking')
    in R.
The goal of 'ralger' is to facilitate web scraping in R.
Prepare and analyze data from large-scale assessments and surveys with
    complex sampling and assessment design (see 'Rutkowski', 2010
    <doi:10.3102/0013189X10363170>). Such studies are, for example,
    international assessments like 'TIMSS', 'PIRLS' and 'PISA'. A graphical
    interface is available for the non-technical user.The package includes
    functions to covert the original data from 'SPSS' into 'R' data sets
    keeping the user-defined missing values, merge data from different
    respondents and/or countries, generate variable dictionaries, modify
    data, produce descriptive statistics (percentages, means, percentiles,
    benchmarks) and multivariate statistics (correlations, linear
    regression, binary logistic regression). The number of supported
    studies and analysis types will increase in future. For a general
    presentation of the package, see 'Mirazchiyski', 2021a
    (<doi:10.1186/s40536-021-00114-4>). For detailed technical aspects of the
    package, see 'Mirazchiyski', 2021b (<doi:10.3390/psych3020018>).
Provides a programmatic interface to the citation information and alternate metrics provided by 'Altmetric'. Data from Altmetric allows researchers to immediately track the impact of their published work, without having to wait for citations. This allows for faster engagement with the audience interested in your work. For more information, visit <https://www.altmetric.com/>.
Characterizing environmental microbiota diversity using amplicon-based next generation sequencing (NGS) data. Functions are developed to manipulate operational taxonomic unit (OTU) table, perform descriptive and inferential statistics, and generate publication-quality plots.
Pre-processing and polymer identification of Raman spectra of plastics. Pre-processing includes normalisation functions, peak identification based on local maxima, smoothing process and removal of spectral region of no interest. Polymer identification can be performed using Pearson correlation coefficient or Euclidean distance (Renner et al. (2019), <doi:10.1016/j.trac.2018.12.004>), and the comparison can be done with a user-defined database or with the database already implemented in the package, which currently includes 356 spectra, with several spectra of plastic colorants.
Parser generator for R using combinatory parsers. It
    is inspired by combinatory parsers developed in Haskell.
Estimate the parameters, the number of classes and cluster vertices of a random network into groups with homogeneous connection profiles. The clustering is performed for directed graphs with typed edges (edges are assumed to be drawn from multinomial distributions) for which a partition of the vertices is available.
Provides an R interface for using 'AmCharts' Library. Based on
    'htmlwidgets', it provides a global architecture to generate 'JavaScript' source
    code for charts. Most of classes in the library have their equivalent in R
    with S4 classes; for those classes, not all properties have been referenced but
    can easily be added in the constructors. Complex properties (e.g. 'JavaScript'
    object) can be passed as named list. See examples at 
    <http://datastorm-open.github.io/introduction_ramcharts/> 
    and <http://www.amcharts.com/> for
    more information about the library. The package includes the free version
    of 'AmCharts' Library. Its only limitation is a small link to the web site
    displayed on your charts. If you enjoy this library, do not hesitate to refer
    to this page <http://www.amcharts.com/online-store/> to purchase a licence,
    and thus support its creators and get a period of Priority Support. See also
    <http://www.amcharts.com/about/> for more information about 'AmCharts' company.
Creates JavaScript charts. The charts can be included in 'Shiny' apps and R markdown documents, or viewed from the R console and 'RStudio' viewer. Based on the JavaScript library 'amCharts 4' and the R packages 'htmlwidgets' and 'reactR'. Currently available types of chart are: vertical and horizontal bar chart, radial bar chart, stacked bar chart, vertical and horizontal Dumbbell chart, line chart, scatter chart, range area chart, gauge chart, boxplot chart, pie chart, and 100% stacked bar chart.
It is widely documented in psychology, economics and other disciplines that socio-economic agent may not pay full attention to all available alternatives, rendering standard revealed preference theory invalid. This package implements the estimation and inference procedures of Cattaneo, Ma, Masatlioglu and Suleymanov (2020) <arXiv:1712.03448> and Cattaneo, Cheung, Ma, and Masatlioglu (2021) <arXiv:2110.10650>, which utilizes standard choice data to partially identify and estimate a decision maker's preference and attention. For inference, several simulation-based critical values are provided.
A feature clustering algorithm for non-targeted mass spectrometric metabolomics data. This method is compatible with gas and liquid chromatography coupled mass spectrometry, including indiscriminant tandem mass spectrometry <DOI: 10.1021/ac501530d> data.
Function for adapting the shape of the random walk Metropolis proposal
    as specified by robust adaptive Metropolis algorithm by Vihola (2012) <doi:10.1007/s11222-011-9269-5>. 
    The package also includes fast functions for rank-one Cholesky update and downdate.
    These functions can be used directly from R or the corresponding C++ header files 
    can be easily linked to other R packages.
Use R to interface with the 'TD Ameritrade' API <https://developer.tdameritrade.com/>.
    Functions include authentication, trading, price requests, account information, and option 
    chains. A user will need a TD brokerage account and TD Ameritrade developer app. See README 
    for authentication process and examples.
Additional matrix functionality for R including: (1) wrappers for 
    the base matrix function that allow matrices to be created from character
    strings and lists (the former is especially useful for creating block
    matrices), (2) better printing of large matrices via the generic "pretty" 
    print function, and (3) a number of convenience functions for users more
    familiar with other scientific languages like 'Julia', 'Matlab'/'Octave', or
    'Python'+'NumPy'.
Contains functions to download, cache and read in 'Excel' version of the
    RAM Legacy Stock Assessment Data Base, an online compilation of stock assessment
    results for commercially exploited marine populations from around the world. 
    The database is named after Dr. Ransom A. Myers whose original stock-recruitment database,
    is no longer being updated. More information about the database can be found at
    <https://ramlegacy.org/>. Ricard, D., Minto, C., Jensen, O.P. and Baum, J.K. (2012) <doi:10.1111/j.1467-2979.2011.00435.x>.
Provides an efficient procedure for fitting the entire solution
    path for high-dimensional regularized quadratic generalized linear models with
    interactions effects under the strong or weak heredity constraint.
We rewrite of RAMpath software developed by John McArdle and Steven Boker as an R package. In addition to performing regular SEM analysis through the R package lavaan, RAMpath has unique features.  First, it can generate path diagrams according to a given model. Second, it can display path tracing rules through path diagrams and decompose total effects into their respective direct and indirect effects as well as decompose variance and covariance into individual bridges. Furthermore, RAMpath can fit dynamic system models automatically based on latent change scores and generate vector field plots based upon results obtained from a bivariate dynamic system. Starting version 0.4, RAMpath can conduct power analysis for both univariate and bivariate latent change score models.
Bayesian geostatistical modeling of Gaussian processes using a
    reparameterized and marginalized posterior sampling (RAMPS) algorithm
    designed to lower autocorrelation in MCMC samples.  Package performance is
    tuned for large spatial datasets.
R-based access to mass-spectrometry (MS) data. While many packages 
  exist to process MS data, many of these make it difficult to 
  access the underlying mass-to-charge ratio (m/z), intensity, and 
  retention time of the files 
  themselves. This package is designed to format MS data in a tidy fashion and 
  allows the user perform the plotting and analysis.
Provides a solution path for Reinforced Angle-based Multicategory Support Vector Machines, with linear learning, polynomial learning, and Gaussian kernel learning. C. Zhang, Y. Liu, J. Wang and H. Zhu. (2016) <doi:10.1080/10618600.2015.1043010>.
Provides randomization tests and graphical diagnostics for assessing randomized assignment and covariate balance for a binary treatment variable. See Branson (2021) <arXiv:1804.08760> for details.
Implements the algorithm by Pourahmadi and Wang (2015) <doi:10.1016/j.spl.2015.06.015> for generating a random p x p correlation matrix. Briefly, the idea is to represent the correlation matrix using Cholesky factorization and p(p-1)/2 hyperspherical coordinates (i.e., angles), sample the angles from a particular distribution and then convert to the standard correlation matrix form. The angles are sampled from a distribution with pdf proportional to sin^k(theta) (0 < theta < pi, k >= 1) using the efficient sampling algorithm described in Enes Makalic and Daniel F. Schmidt (2018) <arXiv:1809.05212>.
Generate random positions (latitude/longitude), 
    Well-known text ('WKT') points or polygons, or 'GeoJSON' points or 
    polygons.
A novel numerical algorithm that provides functionality for estimating the exact 95% confidence interval of the location parameter in the random effects model, and is much faster than the naive method. Works best when the number of studies is between 6-20.
Generates random names with additional information including fake
    SSNs, gender, location, zip, age, address, and nationality.
Provides random number generating functions that
    are much more context aware than the built-in functions. The
    functions are also much safer, as they check for incompatible 
    values, and more reproducible.
The true random number service provided by the RANDOM.ORG
 website created by Mads Haahr samples atmospheric noise via radio tuned to
 an unused broadcasting frequency together with a skew correction algorithm
 due to John von Neumann.  More background is available in the included
 vignette based on an essay by Mads Haahr.  In its current form, the package
 offers functions to retrieve random integers, randomized sequences and
 random strings.
The Function performs a parallel analysis using simulated polychoric correlation matrices. The nth-percentile of the eigenvalues distribution obtained from both the randomly generated and the real data polychoric correlation matrices is returned. A plot comparing the two types of eigenvalues (real and simulated) will help determine the number of real eigenvalues that outperform random data. The function is based on the idea that if real data are non-normal and the polychoric correlation matrix is needed to perform a Factor Analysis, then the Parallel Analysis method used to choose a non-random number of factors should also be based on randomly generated polychoric correlation matrices and not on Pearson correlation matrices. Random data sets are simulated assuming or a uniform or a multinomial distribution or via the bootstrap method of resampling (i.e., random permutations of cases). Also Multigroup Parallel analysis is made available for random (uniform and multinomial distribution and with or without difficulty factor) and bootstrap methods. An option to choose between default or full output is also available as well as a parameter to print Fit Statistics (Chi-squared, TLI, RMSEA, RMR and BIC) for the factor solutions indicated by the Parallel Analysis. Also weighted correlation matrices may be considered for PA.
We implement adaptive estimation of the joint density linear model where the coefficients - intercept and slopes - are random and independent from regressors which support is a proper subset. The estimator proposed in Gaillac and Gautier (2019) <arXiv:1905.06584> is based on Prolate Spheroidal Wave Functions which are computed  efficiently in 'RandomCoefficients'. This package also provides a parallel implementation of the estimator.
Simple methods to generate attractive random colors. The random
  colors are from a wrapper of 'randomColor.js'
  <https://github.com/davidmerfield/randomColor>. In addition, it also generates
  optimally distinct colors based on k-means (inspired by 'IWantHue'
  <https://github.com/medialab/iwanthue>).
Methods for the inference on and the simulation of Gaussian fields are provided. Furthermore, methods for the simulation of extreme value random fields are provided. Main geostatistical parts are based among others on the books by Christian Lantuejoul <doi:10.1007/978-3-662-04808-5>.
Various utilities are provided that might be used in spatial statistics and elsewhere. It delivers a method for solving linear equations that checks the sparsity of the matrix before any algorithm is used.
Classification and regression based on a forest of trees using random inputs, based on Breiman (2001) <DOI:10.1023/A:1010933404324>.
A set of tools to help explain which variables are most important in a random forests. Various variable importance measures are calculated and visualized in different settings in order to get an idea on how their importance changes depending on our criteria (Hemant Ishwaran and Udaya B. Kogalur and Eiran Z. Gorodeski and Andy J. Minn and Michael S. Lauer (2010) <doi:10.1198/jasa.2009.tm08622>, Leo Breiman (2001) <doi:10.1023/A:1010933404324>).
Fits non-linear regression models on dependant data with Generalised Least Square (GLS) based Random Forest (RF-GLS) detailed in Saha, Basu and Datta (2020) <arXiv:2007.15421>.
Fast OpenMP parallel computing of Breiman's random forests for univariate, multivariate, unsupervised, survival, competing risks, class imbalanced classification and quantile regression. New Mahalanobis splitting for correlated outcomes.  Extreme random forests and randomized splitting.  Suite of imputation methods for missing data.  Fast random forests using subsampling. Confidence regions and standard errors for variable importance. New improved holdout importance. Case-specific importance.  Minimal depth variable importance. Visualize trees on your Safari or Google Chrome browser. Anonymous random forests for data privacy.
A bagging predictor based on generalized linear models (GLMs) is implemented. The method is published in 
   Song, Langfelder and Horvath (2013) <doi:10.1186/1471-2105-14-5>.
Allows the user to conduct randomization-based inference for a wide variety of experimental scenarios. The package leverages a potential outcomes framework to output randomization-based p-values and null intervals for test statistics geared toward any estimands of interest, according to the specified null and alternative hypotheses. Users can define custom randomization schemes so that the randomization distributions are accurate for their experimental settings. The package also creates visualizations of randomization distributions and can test multiple test statistics simultaneously.
Contains a function to randomize subjects, patients in groups of 
   sequences (treatment sequences).
   If a blocksize is given, the randomization will be done within blocks.
   The randomization may be controlled by a Wald-Wolfowitz runs test.
   Functions to obtain the p-value of that test are included.
   The package is mainly intended for randomization of bioequivalence studies
   but may be used also for other clinical crossover studies.
   Contains two helper functions sequences() and williams() to get the sequences 
   of commonly used designs in BE studies.
This tool enables the user to choose a randomization procedure
    based on sound scientific criteria. It comprises the generation of
    randomization sequences as well the assessment of randomization procedures
    based on carefully selected criteria. Furthermore, 'randomizeR' provides a
    function for the comparison of randomization procedures.
Generates random assignments for common experimental designs and 
	    random samples for common sampling designs.
Fits standard and random effects latent class models. The single level random effects model is described in Qu et al <doi:10.2307/2533043> and the two level random effects model in Beath and Heller <doi:10.1177/1471082X0800900302>. Examples are given for their use in diagnostic testing.
Function for generating random gender and ethnicity correct first and/or last names. Names are chosen proportionally based upon their probability of appearing in a large scale data base of real names.
Ensemble model, for classification, regression
	and unsupervised learning, based on a forest of unpruned 
	and randomized binary decision trees. Each tree is grown 
	by sampling, with replacement, a set of variables at each node. 
	Each cut-point is generated randomly, according to the continuous 
	Uniform distribution. For each tree, data are either bootstrapped 
	or subsampled. The unsupervised mode introduces clustering, dimension reduction
	and variable importance, using a three-layer engine. Random Uniform Forests are mainly 
	aimed to lower correlation between trees (or trees residuals), to provide a deep analysis 
	of variable importance and to allow native distributed and incremental learning.
Based on data of real user-agent strings, we can set filtering conditions
    and randomly sample user-agent strings from the user-agent string pool.
Performs random projection using Johnson-Lindenstrauss (JL) Lemma (see William B.Johnson and Joram Lindenstrauss (1984) <doi:10.1090/conm/026/737400>). Random Projection is a dimension reduction technique, where the data in the high dimensional space is projected into the low dimensional space using JL transform. The original high dimensional data matrix is multiplied with the low dimensional projection matrix which results in reduced matrix. The projection matrix can be generated using the projection function that is independent to the original data. Then finally apply the classification task on the projected data.
Connects to the site <http://quotesondesign.com/> 
            that uses the 'WordPress' built-in REST API 
            to provide a way for you to grab quotes.
Several non parametric randomness tests for numeric sequences
Provides (1) pseudo random generators - general linear congruential generators, 
    multiple recursive generators and generalized feedback shift register (SF-Mersenne Twister
    algorithm and WELL generators); (2) quasi random generators - the Torus algorithm, the
    Sobol sequence, the Halton sequence (including the Van der Corput sequence) and (3) some
    generator tests - the gap test, the serial test, the poker test.
    See e.g. Gentle (2003) <doi:10.1007/b97336>. The package can be provided
    without the rngWELL dependency on demand.
    Take a look at the Distribution task view of types and tests of random number generators.
    Version in Memoriam of Diethelm and Barbara Wuertz.
Implements random variables by means of S4 classes and methods.
Provides tools for filtering occurrence records, generating alpha-hull-derived range polygons and mapping species distributions.
A collection of tools to create species range maps based on 
    occurrence data, statistics, and spatial objects. Other tools in this 
    collection can be used to analyze the environmental characteristics of 
    the species ranges. Plotting options to represent results in various 
    manners are also available. Results obtained using these tools can be 
    used to explore the distribution of species and define areas of occupancy 
    and extent of occurrence of species. Other packages help to explore species 
    distributions using distinct methods, but options presented in this set of 
    tools (e.g., using trend surface analysis and concave hull polygons) are 
    exclusive. Description of methods, approaches, and comments for some of the 
    tools implemented here can be found in: 
    IUCN (2001) <https://portals.iucn.org/library/node/10315>,
    Peterson et al. (2011) <https://www.degruyter.com/princetonup/view/title/506966>, 
    and Graham and Hijmans (2006) <doi:10.1111/j.1466-8238.2006.00257.x>.
Tools for generation of (life-history) traits and diversity maps on hexagonal or square grids. Valcu et al.(2012) <doi:10.1111/j.1466-8238.2011.00739.x>.
Range Modeling Metadata Standards (RMMS) address three challenges: 
  they (i) are designed for convenience to encourage use, (ii) accommodate a wide 
  variety of applications, and (iii) are extensible to allow the community of range 
  modelers to steer it as needed. RMMS are based on a data dictionary that specifies 
  a hierarchical structure to catalog different aspects of the range modeling process. 
  The dictionary balances a constrained, minimalist vocabulary to improve 
  standardization with flexibility for users to provide their own values. 
  Merow et al. (2019) <DOI:10.1111/geb.12993> describe the standards in 
  more detail. Note that users who prefer to use the R package 'ecospat' can obtain it from
  <https://github.com/ecospat/ecospat>.
A fast implementation of Random Forests, particularly suited for high
          dimensional data. Ensembles of classification, regression, survival and
          probability prediction trees are supported. Data from genome-wide association
          studies can be analyzed efficiently. In addition to data frames, datasets of
          class 'gwaa.data' (R package 'GenABEL') and 'dgCMatrix' (R package 'Matrix') 
          can be directly analyzed.
Displays palette of 5 colors based on photos depicting the unique 
             and vibrant culture of Punjab in Northern India. Since Punjab translates 
             to ``Land of 5 Rivers'' there are 5 colors per palette. If users need 
             more than 5 colors, they can merge 2 to 3 palettes to create their own 
             color-combination, or they can cherry-pick their own custom colors. 
             Users can view up to 3 palettes together. Users can also list all the 
             palette choices. And last but not least, users can see the photo that 
             inspired a particular palette.
Performs aggregation of ordered lists based
        on the ranks using several different algorithms:
        Cross-Entropy Monte Carlo algorithm, Genetic algorithm, and a
        brute force algorithm (for small problems).
Easily compute an aggregate ranking (also called a median ranking or a
             consensus ranking) according to the axiomatic approach presented
             by Cook et al. (2007). This approach minimises the number of violations
             between all candidate consensus rankings and all input (partial) rankings,
             and draws on a branch and bound algorithm and a heuristic algorithm to
             drastically improve speed. The package also provides an option to bootstrap
             a consensus ranking based on resampling input rankings (with 
             replacement). Input rankings can be either incomplete (partial) or complete.
             Reference: Cook, W.D., Golany, B., Penn, M. and Raviv, T. (2007) <doi:10.1016/j.cor.2005.05.030>.
Implementation of a model-based clustering algorithm for
    ranking data (C. Biernacki, J. Jacques (2013) <doi:10.1016/j.csda.2012.08.008>). 
    Multivariate rankings as well as partial rankings are taken
    into account. This algorithm is based on an extension of the Insertion
    Sorting Rank (ISR) model for ranking data, which is a meaningful and
    effective model parametrized by a position parameter (the modal ranking,
    quoted by mu) and a dispersion parameter (quoted by pi). The heterogeneity
    of the rank population is modelled by a mixture of ISR, whereas conditional
    independence assumption is considered for multivariate rankings.
Implements Kornbrot's rank difference test as described in 
    <doi:10.1111/j.2044-8317.1990.tb00939.x>. This method is a modified 
    Wilcoxon signed-rank test which produces consistent and meaningful 
    results for ordinal or monotonically-transformed data.
Implements distance based probability models for ranking data. 
    The supported distance metrics include Kendall distance, Spearman distance, Footrule distance, Hamming distance,
    Weighted-tau distance and Weighted Kendall distance.
    Phi-component model and mixture models are also supported.
The rankFD() function calculates the Wald-type statistic (WTS) and the ANOVA-type
	     statistic (ATS) for nonparametric factorial designs, e.g., for count, ordinal or score data
	     in a crossed design with an arbitrary number of factors.
	     Brunner, E., Bathke, A. and Konietschke, F. (2018) <doi:10.1007/978-3-030-02914-2>.
Rank-hazard plots Karvanen and Harrell (2009) <DOI:10.1002/sim.3591> visualize the relative importance of covariates in a proportional hazards model. The key idea is to rank the covariate values and plot the relative hazard as a function of ranks scaled to interval [0,1]. The relative hazard is plotted in respect to the reference hazard, which can bee.g. the hazard related to the median of the covariate.
Functions to generate plots and tables for comparing independently-
    sampled populations. Companion package to "A Primer on Visualizations
    for Comparing Populations, Including the Issue of Overlapping Confidence
    Intervals" by Wright, Klein, and Wieczorek (2019)
    <DOI:10.1080/00031305.2017.1392359> and "A Joint Confidence Region for an
    Overall Ranking of Populations" by Klein, Wright, and Wieczorek (2020)
    <DOI:10.1111/rssc.12402>.
Methods for ranking responses of a single response question or a multiple response question
Implementation of Kernelized score functions and other semi-supervised learning algorithms for node label ranking in biomolecular networks. RANKS can be easily applied to a large set of different relevant problems in computational biology, ranging from automatic protein function prediction, to gene disease prioritization and drug repositioning, and more in general to any bioinformatics problem that can be formalized as a node label ranking problem in a graph. The modular nature of the implementation allows to experiment with different score functions and kernels and to easily compare the results with baseline network-based methods such as label propagation and random walk algorithms, as well as to enlarge the algorithmic scheme by adding novel user-defined score functions and kernels.
Provides methods for measuring and describing uncertainty in
             rankings.  See Rising (2021) <arXiv:2107.03459> for background.
Random vectors with arbitrary Lipschitz density are generated using acceptance/ rejection. The method is based on G. Beliakov (2005) <doi:10.1016/j.cpc.2005.03.105>.
Finds the k nearest neighbours for every point in a given dataset
    in O(N log N) time using Arya and Mount's ANN library (v1.1.3). There is
    support for approximate as well as exact searches, fixed radius searches
    and 'bd' as well as 'kd' trees. The distance is computed using the L2
    (Euclidean) metric. Please see package 'RANN.L1' for the same
    functionality using the L1 (Manhattan, taxicab) metric.
To find the reversal association between variables.
Provide common functions and resources for registry specific
    R-packages at Rapporteket
    <https://rapporteket.github.io/rapporteket/articles/short_introduction.html>.
    This package is relevant for developers of packages/registries at
    Rapporteket.
Access services specified in OpenAPI (formerly Swagger) format.
  It is not a code generator. Client is generated dynamically as a list of R 
  functions.
Access to the C-level R date and datetime code is provided for
 C-level API use by other packages via registration of native functions.
 Client packages simply include a single header 'RApiDatetime.h' provided
 by this package, and also 'import' it.  The R Core group is the original
 author of the code made available with slight modifications by this package.
Provides JSON parsing capability through the 'Rapidjson' 'C++' header-only library.
A collection of 'HTML', 'JavaScript', 'CSS' and fonts
  assets that generate 'RapiDoc' documentation from an 'OpenAPI' Specification:
   <https://mrin9.github.io/RapiDoc/>.
Quickly computes polygenic scores from GWAS summary statistics of either case-control or quantitative traits, without LD matrix computation or parameter tuning. Reales,G., Vigorito, E., Kelemen,M., Wallace,C. (2021) <doi:10.1101/2020.07.24.220392> "RápidoPGS: A rapid polygenic score calculator for summary GWAS data without a test dataset".
Facilitates efficient polygon search using kd trees.
    Coordinate level spatial data can be aggregated to higher geographical
    identities like census blocks, ZIP codes or police district boundaries.
    This process requires mapping each point in the given data set to a
    particular identity of the desired geographical hierarchy. Unless efficient
    data structures are used, this can be a daunting task. The operation
    point.in.polygon() from the package sp is computationally expensive.
    Here, we exploit kd-trees as efficient nearest neighbor search algorithm
    to dramatically reduce the effective number of polygons being searched.
Package to perform non-invasive fetal testing for aneuploidies
    using sequencing count data from cell-free DNA
A 'Java' 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.
Provides XML parsing capability through the 'Rapidxml' 'C++' header-only library.
This package provides other packages with access to the internal 
 R serialization code. Access to this code is provided at the C function
 level by using the registration of native function mechanism. Client
 packages simply include a single header file RApiSerializeAPI.h provided by
 this package.

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

    The R Core group is the original author of the serialization code made
 available by this package.
Bindings to kernel methods for enforcing security restrictions. 
    AppArmor can apply mandatory access control (MAC) policies on a given task 
    (process) via security profiles with detailed ACL definitions. In addition
    this package implements bindings for setting process resource limits (rlimit),
    uid, gid, affinity and priority. The high level R function 'eval.secure' 
    builds on these methods to perform dynamic sandboxing: it evaluates a single
    R expression within a temporary fork which acts as a sandbox by enforcing 
    fine grained restrictions without affecting the main R process. A portable 
    version of this function is now available in the 'unix' package.
An easy way to determine which directories on the
    users computer you should use to save data, caches and logs. A port of
    Python's 'Appdirs' (<https://github.com/ActiveState/appdirs>) to
    R.
Facilitating the creation of reproducible statistical
    report templates. Once created, rapport templates can be exported to
    various external formats (HTML, LaTeX, PDF, ODT etc.) with pandoc as the
    converter backend.
Helper functions that act as wrappers to more advanced statistical
    methods with the advantage of having sane defaults for quick reporting.
Loading data from AppsFlyer Pull API
    <https://support.appsflyer.com/hc/en-us/articles/207034346-Using-Pull-API-aggregate-data>.
Calculate sketches of a data set reducing the number of observations using random projections. These can be used for Bayesian or frequentist linear regression on large data sets as described in Geppert et. al (2017) <doi:10.1007/s11222-015-9608-z>.
The Agricultural Production Systems sIMulator ('APSIM') is a widely
    used to simulate the agricultural systems for multiple crops. This package 
    is designed to create, modify and run 'apsimx' files in the 'APSIM' Next 
    Generation <https://www.apsim.info/>.
Performs wood cell anatomical data analyses on spatially explicit xylem (tracheids) datasets 
                  derived from thin sections of woody tissue. The package includes functions for visualisation, 
                  detection and alignment of continuous tracheid radial file (defined as rows) and individual tracheid position 
                  within an annual ring of coniferous species. This package is designed to be used with elaborate cell output, 
                  e.g. as provided with ROXAS (von Arx & Carrer, 2014 <doi:10.1016/j.dendro.2013.12.001>). The package has been validated for Picea abies, 
                  Larix Siberica, Pinus cembra and Pinus sylvestris.
Biodiversity is in crisis. The overarching aim of conservation
    is to preserve biodiversity patterns and processes. To this end, protected
    areas are established to buffer species and preserve biodiversity processes.
    But resources are limited and so protected areas must be cost-effective.
    This package contains tools to generate plans for protected areas
    (prioritizations), using spatially explicit targets for biodiversity
    patterns and processes. To obtain solutions in a feasible amount  of time,
    this package uses the commercial 'Gurobi' software package (obtained from
    <http://www.gurobi.com/>). For more information on using
    this package, see Hanson et al. (2018) <doi:10.1111/2041-210X.12862>.
Retrieve air monitoring data and associated metadata from the US
  Environmental Protection Agency's Air Quality System service using functions.
  See <https://aqs.epa.gov/aqsweb/documents/data_api.html> for details about
  the US EPA Data Mart API.
Generate a table of cumulative water influx into hydrocarbon reservoirs over time using un-steady and pseudo-steady state models. Van Everdingen, A. F. and Hurst, W. (1949) <doi:10.2118/949305-G>. Fetkovich, M. J. (1971) <doi:10.2118/2603-PA>. Yildiz, T. and Khosravi, A. (2007) <doi:10.2118/103283-PA>.
Implementation of an alternating direction method of multipliers
    algorithm for fitting a linear model with tree-based lasso regularization, 
    which is proposed in Algorithm 1 of Yan and Bien (2018) <arXiv:1803.06675>. 
    The package allows efficient model fitting on the entire 2-dimensional 
    regularization path for large datasets. The complete set of functions 
    also makes the entire process of tuning regularization parameters and 
    visualizing results hassle-free.
A custom implementation of the apriori algorithm and binomial tests to identify combinations of features (genes, variants etc) significantly enriched for simultaneous mutations/events from sparse Boolean input, see Vijay Kumar Pounraja, Santhosh Girirajan (2021). Version 1.1 includes a minor adjustment to the number of combinations to be considered for multiple testing correction. This updated version is more conservative in its approach and hence more selective. <doi:10.1101/2021.10.01.462832>.
Includes functions for the calculation of spatially and non-spatially explicit rarefaction curves using different indices of taxonomic, functional and phylogenetic diversity. The user can also rarefy any biodiversity metric as provided by a self-written function (or an already existent one) that gives as output a vector with the values of a certain index of biodiversity calculated per plot (Ricotta, C., Acosta, A.,  Bacaro, G., Carboni, M., Chiarucci, A.,  Rocchini, D., Pavoine, S. (2019) <doi:10.1016/j.ecolind.2019.105606>; Bacaro, G., Altobelli, A., Cameletti, M., Ciccarelli, D., Martellos, S., Palmer, M. W., … Chiarucci, A. (2016) <doi:10.1016/j.ecolind.2016.04.026>; Bacaro, G., Rocchini, D., Ghisla, A., Marcantonio, M., Neteler, M., & Chiarucci, A. (2012) <doi:10.1016/j.ecocom.2012.05.007>).
Tests gene-environment interaction for rare genetic variants using Sequence Kernel Association Test (SKAT) type gene-based tests. Includes two tests for the interaction term only, and one joint test for genetic main effects and gene-environment interaction.
Randomization tests for the statistical comparison of \emph{i} = two or more individual-based, sample-based or coverage-based rarefaction curves. The ecological null hypothesis is that the \emph{i} samples were all drawn randomly from a single assemblage, with (necessarily) a single underlying species abundance distribution. The biogeographic null hypothesis is that the \emph{i} samples were all drawn from different assemblages that, nonetheless, share similar species richness and species abundance distributions
Allows calculation of rarity weights for species and indices of rarity for assemblages of species according to different methods (Leroy et al. 2012, Insect. Conserv. Divers. 5:159-168 <doi:10.1111/j.1752-4598.2011.00148.x>; Leroy et al. 2013, Divers. Distrib. 19:794-803 <doi:10.1111/ddi.12040>).
Interface to easily access data via the United States Department of Agriculture (USDA)'s Agricultural Resource Management Survey (ARMS) 
  Data API <https://www.ers.usda.gov/developer/data-apis/arms-data-api/>. The downloaded data can be saved for later off-line use. 
  Also provide relevant information and metadata for each of the input variables needed for sending the data inquery.
Previously an R wrapper of the 'ARPACK' library
    <http://www.caam.rice.edu/software/ARPACK/>, and now a shell of the
    R package 'RSpectra', an R interface to the 'Spectra' library
    <http://yixuan.cos.name/spectra/> for solving large scale
    eigenvalue/vector problems. The current version of 'rARPACK'
    simply imports and exports the functions provided by 'RSpectra'.
    New users of 'rARPACK' are advised to switch to the 'RSpectra' package.
MCMC based sampling of binary matrices with fixed margins as used in exact Rasch model tests.
Inspired by Karl Broman`s reader on using 'knitr'
    with 'asciidoc'
    (<https://kbroman.org/knitr_knutshell/pages/asciidoc.html>), this is
    merely a wrapper to 'knitr' and 'asciidoc'.
Software to perform supervised and pixel based raster image classification. It has been designed to facilitate land-cover analysis. Five classification algorithms can be used: Maximum Likelihood Classification, Multinomial Logistic Regression, Neural Networks, Random Forests and Support Vector Machines. The output includes the classified raster and standard classification accuracy assessment such as the accuracy matrix, the overall accuracy and the kappa coefficient. An option for in-sample verification is available.
We propose a general ensemble classification framework, RaSE algorithm, for the sparse classification problem. In RaSE algorithm, for each weak learner, some random subspaces are generated and the optimal one is chosen to train the model on the basis of some criterion. To be adapted to the problem, a novel criterion, ratio information criterion (RIC) is put up with based on Kullback-Leibler divergence. Besides minimizing RIC, multiple criteria can be applied, for instance, minimizing extended Bayesian information criterion (eBIC), minimizing training error, minimizing the validation error, minimizing the cross-validation error, minimizing leave-one-out error. There are various choices of base classifier, for instance, linear discriminant analysis, quadratic discriminant analysis, k-nearest neighbour, logistic regression, decision trees, random forest, support vector machines. RaSE algorithm can also be applied to do feature ranking, providing us the importance of each feature based on the selected percentage in multiple subspaces. RaSE framework can be extended to the general prediction framework, including both classification and regression. We can use the selected percentages of variables for variable screening. The latest version added the variable screening function for both regression and classification problems.
Algorithms for the spatial stratification of landscapes, sampling and modeling of 
    spatially-varying phenomena. These algorithms offer a simple framework for the stratification 
    of geographic space based on raster layers representing landscape factors and/or factor scales. 
    The stratification process follows a hierarchical approach, which is based on first level units 
    (i.e., classification units) and second-level units (i.e., stratification units). Nonparametric 
    techniques allow to measure the correspondence between the geographic space and the landscape 
    configuration represented by the units. These correspondence metrics are useful to define 
    sampling schemes and to model the spatial variability of environmental phenomena. The 
    theoretical background of the algorithms and code examples are presented in Fuentes, Dorantes,
    and Tipton (2021). <doi:10.31223/X50S57>.
Reading, writing, manipulating, analyzing and modeling of spatial data. The package implements basic and high-level functions for raster data and for vector data operations such as intersections. See the manual and tutorials on <https://rspatial.org/> to get started.
R-based access to a large set of data variables relevant to forest ecology in British Columbia (BC), Canada. Layers
    are in raster format at 100m resolution in the BC Albers projection, hosted at the Federated Research Data Repository (FRDR)
    with <doi:10.20383/101.0283>. The collection includes: elevation; biogeoclimatic zone; wildfire; cutblocks; forest attributes from
    Hansen et al. (2013) <doi:10.1139/cjfr-2013-0401> and Beaudoin et al. (2017) <doi:10.1139/cjfr-2017-0184>; and rasterized
    Forest Insect and Disease Survey (FIDS) maps for a number of insect pest species, all covering the period 2001-2018.
    Users supply a polygon or point location in the province of BC, and 'rasterbc' will download the overlapping raster tiles
    hosted at FRDR, merging them as needed and returning the result in R as a 'SpatRaster' object. Metadata associated with these
    layers, and code for downloading them from their original sources can be found in the 'github' repository
    <https://github.com/deankoch/rasterbc_src>.
Providing functions to calculate indices of diversity on numerical matrices based on information theory. The rationale behind the package is described in Rocchini, Marcantonio and Ricotta (2017) <doi:10.1016/j.ecolind.2016.07.039> and Rocchini, Marcantonio,..., Ricotta (2021) <doi:10.1101/2021.01.23.427872>.
Fast alternatives to several relatively slow 'raster' package
  functions. For large rasters, the functions run from 5 to
  approximately 100 times faster than the 'raster' package functions
  they replace. The 'fasterize' package, on which one function in this
  package depends, includes an implementation of the scan line
  algorithm attributed to Wylie et al. (1967)
  <doi:10.1145/1465611.1465619>.
This is a wrapper function for image(), which makes reasonable
    raster plots with nice axis and other useful features.
Provides R functions to selectively rasterize components
             of 'grid' output.
Performs kernel based estimates on in-memory raster images 
  from the raster package.  These kernel estimates include local means
  variances, modes, and quantiles.  All results are in the form of 
  raster images, preserving original resolution and projection attributes.
A S4 class has been created such that complex operations can be
    executed on each cells of a raster map. The raster of objects contains the
    traditional raster map with the addition of a list of generic objects: one
    object for each raster cells. It allows to write few lines of R code for complex
    map algebra. Two environmental applications about frequency analysis of raster
    map of precipitation and creation of a raster map of soil water retention curves
    have been presented.
It aims to easily and rapidly generate raster data in R, even for very large datasets, with an aesthetics-based mapping syntax that should be familiar to users of the 'ggplot2' package. While 'rasterly' does not attempt to reproduce the full functionality of the 'Datashader' graphics pipeline system for Python, the 'rasterly' API has several core elements in common with that software package.
The ability to plot raster graphics in PDF files can be useful
    when one needs multi-page documents, but the plots contain so many
    individual elements that (the usual) use of vector graphics results in
    inconveniently large file sizes. Internally, the package plots each
    individual page as a PNG, and then combines them in one PDF file.
Create a spatial raster, as the ones provided by 'terra',
    from regular pictures.
Methods for enhanced visualization and interaction with raster data. It implements visualization methods for quantitative data and categorical data, both for univariate and multivariate rasters. It also provides methods to display spatiotemporal rasters, and vector fields. See the website for examples.
Automated test assembly of linear and adaptive tests using the 
    mixed-integer programming. The full documentation and tutorials are at 
    <https://github.com/xluo11/Rata>.
A simplified version of the Portal Project Database designed for
    teaching. It provides a real world example of life-history, population, and
    ecological data, with sufficient complexity to teach many aspects of data
    analysis and management, but with many complexities removed to allow
    students to focus on the core ideas and skills being taught. The full
    database (which should be used for research) is available at
    <https://github.com/weecology/PortalData>.
Allows to limit the rate at which one or more functions can be called.
The Evolutionary Rate Matrix is a variance-covariance matrix which describes both the rates of trait evolution and the evolutionary correlation among multiple traits. This package has functions to estimate these parameters using Bayesian MCMC. It is possible to test if the pattern of evolutionary correlations among traits has changed between predictive regimes painted along the branches of the phylogenetic tree. Regimes can be created a priori or estimated as part of the MCMC under a joint estimation approach. The package has functions to run MCMC chains, plot results, evaluate convergence, and summarize posterior distributions.
Fit statistical models based on the Dawid-Skene model - Dawid
    and Skene (1979) <doi:10.2307/2346806> - to repeated categorical
    rating data.  Full Bayesian inference for these models is supported
    through the Stan modelling language. 'rater' also allows the user to
    extract and plot key parameters of these models.
A function which performs exact rate ratio tests and returns an object of class htest.
The kappa statistic implemented by Fleiss is a very popular index for assessing the reliability of agreement among multiple observers. It is used both in the psychological and in the psychiatric field. Other fields of application are typically medicine, biology and engineering. Unfortunately,the kappa statistic may behave inconsistently in case of strong agreement between raters, since this index assumes lower values than it would have been expected. We propose a modification kappa implemented by Fleiss in case of nominal and ordinal variables. Monte Carlo simulations are used both to testing statistical hypotheses and to calculating percentile bootstrap confidence intervals based on proposed statistic in case of nominal and ordinal data.
Computes confidence intervals for the rate (or risk)
    difference ('RD') or rate ratio (or relative risk, 'RR') for 
    binomial proportions or Poisson rates, or for odds ratio 
    ('OR', binomial only). Also confidence intervals for a single 
    binomial or Poisson rate, and intervals for matched pairs. 
    Includes skewness-corrected asymptotic score ('SCAS') methods, 
    which have been developed in Laud (2017) <doi:10.1002/pst.1813>
    from Miettinen & Nurminen (1985) <doi:10.1002/sim.4780040211> and 
    Gart & Nam (1988) <doi:10.2307/2531848>. The same score produces 
    hypothesis tests analogous to the test for binomial RD and RR by 
    Farrington & Manning (1990) <doi:10.1002/sim.4780091208>. 
    The package also includes MOVER methods
    (Method Of Variance Estimates Recovery) for all contrasts, derived 
    from the Newcombe method but using equal-tailed Jeffreys intervals,
    and generalised for Bayesian applications incorporating prior 
    information. So-called 'exact' methods for strictly conservative 
    coverage are approximated using continuity corrections.
    Also includes methods for stratified calculations (e.g. meta-analysis),
    either assuming fixed effects (matching the CMH test) or incorporating 
    stratum heterogeneity.
A collection of randomization tests, data sets and examples. The current version focuses on five testing problems and their implementation in empirical work. First, it facilitates the empirical researcher to test for particular hypotheses, such as comparisons of means, medians, and variances from k populations using robust permutation tests, which asymptotic validity holds under very weak assumptions, while retaining the exact rejection probability in finite samples when the underlying distributions are identical. Second, the description and implementation of a permutation test for testing the continuity assumption of the baseline covariates in the sharp regression discontinuity design (RDD) as in Canay and Kamat (2018) <https://goo.gl/UZFqt7>. More specifically, it allows the user to select a set of covariates and test the aforementioned hypothesis using a permutation test based on the Cramer-von Misses test statistic. Graphical inspection of the empirical CDF and histograms for the variables of interest is also supported in the package. Third, it provides the practitioner with an effortless implementation of a permutation test based on the martingale decomposition of the empirical process for testing for heterogeneous treatment effects in the presence of an estimated nuisance parameter as in Chung and Olivares (2021) <doi:10.1016/j.jeconom.2020.09.015>. Fourth, this version considers the two-sample goodness-of-fit testing problem under covariate adaptive randomization and implements a permutation test based on a prepivoted Kolmogorov-Smirnov test statistic. Lastly, it implements an asymptotically valid permutation test based on the quantile process for the hypothesis of constant quantile treatment effects in the presence of an estimated nuisance parameter.
Designed to be compatible with the R package 'DBI' (Database Interface)
    when connecting to Amazon Web Service ('AWS') Athena <https://aws.amazon.com/athena/>.
    To do this 'Python' 'Boto3' Software Development Kit ('SDK')
    <https://boto3.amazonaws.com/v1/documentation/api/latest/index.html> is used as a driver.
Describes a new procedure of reducing items in a rating scale called Rating Scale Reduction (RSR). The new stop criterion in RSR procedure is added (stop global max). The function order is replaced by sort.list.
We implement a test of the rational expectations hypothesis based on the marginal distributions of realizations and subjective beliefs from D'Haultfoeuille, Gaillac, and Maurel (2018) <doi:10.3386/w25274>. This test can be used in cases where realizations and subjective beliefs are observed in two different datasets that cannot be matched, or when they are observed in the same dataset. The package also computes the estimator of the minimal deviations from rational expectations than can be rationalized by the data.
Functions to manipulate rational functions, including basic
    arithmetic operators, derivatives, and integrals with EXPLICIT forms.
Calculation of ratios between two data sets containing environmental data like
    element concentrations by different methods. Additionally plant element 
    concentrations can be corrected for adhering particles (soil, airborne dust).
An R interface to United States Environmental Protection Agency (EPA)  
    Assessment, Total Maximum Daily Load (TMDL) Tracking and Implementation System 
    ('ATTAINS') data. 'ATTAINS' is the EPA database used to track information 
    provided by states about water quality assessments conducted under federal 
    Clean Water Act requirements. ATTAINS information and API information is available at <https://www.epa.gov/waterdata/attains>.
The R Analytic Tool To Learn Easily (Rattle) provides a 
  collection of utilities functions for the data scientist. A
  Gnome (RGtk2) based graphical interface is included with 
  the aim to provide a simple and intuitive introduction to R 
  for data science, allowing a user to quickly load data from a CSV file 
  (or via ODBC), transform and explore the data, 
  build and evaluate models, and export models as PMML (predictive
  modelling markup language) or as scores. A key aspect of the GUI
  is that all R commands are logged and commented through the log tab.
  This can be saved as a standalone R script file and as
  an aid for the user to 
  learn R or to copy-and-paste directly into R itself.
  Note that RGtk2 and cairoDevice have been archived on CRAN.
  See <https://rattle.togaware.com> for installation instructions.
Rare variant association tests: burden tests (Bocher et al. 2019 <doi:10.1002/gepi.22210>) and the Sequence Kernel Association Test (Bocher et al. 2021 <doi:10.1038/s41431-020-00792-8>) in the whole genome; and genetic simulations.
Includes multiple cross-platform read/write interfaces for
    'RAVE' project. 'RAVE' stands for "R analysis and visualization of human 
    intracranial electroencephalography data". The whole project aims at 
    providing powerful free-source package that analyze brain recordings from 
    patients with electrodes placed on the cortical surface or inserted into 
    the brain. 'raveio' as part of this project provides tools to read/write 
    neurophysiology data from/to 'RAVE' file structure, as well as several 
    popular formats including  'EDF(+)', 'Matlab', 'BIDS-iEEG', and 'HDF5', 
    etc. Documentation and examples about 'RAVE' project are provided at 
    <https://openwetware.org/wiki/RAVE>, and the paper by John F. Magnotti, 
    Zhengjia Wang, Michael S. Beauchamp (2020) 
    <doi:10.1016/j.neuroimage.2020.117341>; see 'citation("raveio")' for 
    details.
Provides access to the 'Ravelry' API <https://www.ravelry.com/groups/ravelry-api>. An R wrapper for pulling data from 'Ravelry.com', an organizational tool for crocheters, knitters, spinners, and weavers. You can retrieve pattern, yarn, author, and shop information by search or by a given id.
Provides an I/O interface between R data.frames and
    Raven DataFrames. Defines functions to both read and write DataFrame
    files, as well as serialize/deserialize data.frames/DataFrames.
Utilities for processing input and output files associated with the Raven Hydrological Modelling Framework. Includes various plotting functions, model diagnostics, reading output files into extensible time series format, and support for writing Raven input files. 
    The 'RavenR' package is also archived at Chlumsky et al. (2020) <doi:10.5281/zenodo.4248183>.
    The Raven Hydrologic Modelling Framework method can be referenced with Craig et al. (2020) <doi:10.1016/j.envsoft.2020.104728>.
Implementation of the R-Average method for parameter estimation of averaging models of the Anderson's Information Integration Theory by Vidotto, G., Massidda, D., & Noventa, S. (2010) <https://www.uv.es/psicologica/articulos3FM.10/3Vidotto.pdf>.
Implemented fast and memory-efficient 'Notch'-filter, 
    'Welch-periodogram', and discrete wavelet transform algorithm for hours of 
    high-resolution signals; providing fundamental toolbox
    for 'iEEG' preprocess pipelines. 
    Documentation and examples about 'RAVE' project are provided at 
    <https://openwetware.org/wiki/RAVE>, and the paper by John F. Magnotti, 
    Zhengjia Wang, Michael S. Beauchamp (2020) 
    <doi:10.1016/j.neuroimage.2020.117341>; see 'citation("ravetools")' for 
    details.
In order to facilitate R instruction for actuaries, we have organized several 
  sets of publicly available data of interest to non-life actuaries. In addition, we suggest 
  a set of packages, which most practicing actuaries will use routinely. Finally, there is 
  an R markdown skeleton for basic reserve analysis.
Assess LC–MS system performance by visualizing instrument log files and monitoring raw quality control samples within a project.
The Kolmogorov-Smirnov (K-S) statistic is a standard method to measure the model strength for 
    credit risk scoring models. This package calculates the K–S statistic and 
    plots the true-positive rate and false-positive rate to measure the model strength. 
    This package was written with the credit marketer, who uses risk models in 
    conjunction with his campaigns. The users could read more details from 
    Thrasher (1992) <doi:10.1002/dir.4000060408> and 'pyks' <https://pypi.org/project/pyks/>.
This is an R wrapper from the AWS Command Line Interface that 
  provides methods to manage the user configuration on Amazon Web Service. You 
  can create as many profiles as you want, manage them, and delete them. The 
  profiles created with this tool work with all AWS products such as S3, 
  Glacier, and EC2. It also provides a function to automatically install 
  AWS CLI, but you can download it and install it manually if you prefer.
Uses convolution-based techniques to generate simulated camera bokeh, depth of field, and other camera effects, using an image and an optional depth map. Accepts both filename inputs and in-memory array representations of images and matrices. Includes functions to perform 2D convolutions, reorient and resize images/matrices, add image overlays, generate camera vignette effects, and add titles to images.
Downloads and parses 'SDF' (Structural Description Format) and 'PDB' (Protein Database) files for 3D rendering.
Render scenes using pathtracing. Build 3D scenes out of spheres, cubes, planes, disks, triangles, cones, curves, line segments, cylinders, ellipsoids, and 3D models in the 'Wavefront' OBJ file format or the PLY Polygon File Format. Supports several material types, textures, multicore rendering, and tone-mapping. Based on the "Ray Tracing in One Weekend" book series. Peter Shirley (2018) <https://raytracing.github.io>.
Uses a combination of raytracing and multiple hill shading methods to produce 2D and 3D data visualizations and maps. Includes water detection and layering functions, programmable color palette generation, several built-in textures for hill shading, 2D and 3D plotting options, a built-in path tracer, 'Wavefront' OBJ file export, and the ability to save 3D visualizations to a 3D printable format.
Rossby wave ray paths are traced from 
             a determined source, specified wavenumber, and direction
             of propagation. "raytracing" also works with a set of 
             experiments changing these parameters, making possible the
             identification of Rossby wave sources automatically. 
             The theory used here is based on classical studies, 
             such as Hoskins and Karoly (1981) <doi:10.1175/1520-0469(1981)038%3C1179:TSLROA%3E2.0.CO;2>,
             Karoly (1983) <doi:10.1016/0377-0265(83)90013-1>, 
             Hoskins and Ambrizzi (1993) <doi:10.1175/1520-0469(1993)050%3C1661:RWPOAR%3E2.0.CO;2>,
             and Yang and Hoskins (1996) <doi:10.1175/1520-0469(1996)053%3C2365:PORWON%3E2.0.CO;2>.
Rasterize images using a 3D software renderer. 3D scenes are created either by importing external files, building scenes out of the included objects, or by constructing meshes manually. Supports point and directional lights, anti-aliased lines, shadow mapping, transparent objects, translucent objects, multiple materials types, reflection, refraction, environment maps, multicore rendering, bloom, tone-mapping, and screen-space ambient occlusion.
Interface to the API for the 'Bielefeld' Academic Search
    Engine ('BASE') (<https://www.base-search.net/>). 'BASE' is a
    search engine for more than 150 million scholarly documents from more
    than 7000 sources. Methods are provided for searching for documents,
    as well as getting information on higher level groupings of documents:
    collections and repositories within collections. Search includes
    faceting, so you can get a high level overview of number of documents
    across a given variable (e.g., year). 'BASE' asks users to respect a
    rate limit, but does not enforce it themselves; we enforce that
    rate limit.
An approach to age-depth modelling that uses Bayesian statistics to reconstruct accumulation histories for deposits, through combining radiocarbon and other dates with prior information. See Blaauw & Christen (2011).
A model of the form Y = f(x) + s(x) Z is fit where functions f and s are modeled with ensembles of trees and Z is standard normal.  
    This model is developed in the paper 'Heteroscedastic BART Via Multiplicative Regression Trees' 
    (Pratola, Chipman, George, and McCulloch, 2019, <arXiv:1709.07542v2>).
    BART refers to Bayesian Additive Regression Trees. See the R-package 'BART'.
    The predictor vector x may be high dimensional.
    A Markov Chain Monte Carlo (MCMC) algorithm provides Bayesian posterior uncertainty for both f and s.
    The MCMC uses the recent innovations in 
    Efficient Metropolis--Hastings proposal mechanisms for Bayesian regression tree models 
    (Pratola, 2015, Bayesian Analysis, <doi:10.1214/16-BA999>).
'BaseX' <https://basex.org> is a XML database engine and a compliant 'XQuery 3.1' processor with full support of 'W3C Update Facility'. This package is a full client-implementation of the client/server protocol for 'BaseX' and provides functionalities to create, manipulate and query on XML-data.
A Pure R implementation of Bayesian Global Optimization with Gaussian Processes.
The Brazilian Central Bank API delivers many datasets which regard economic
  activity, regional economy, international economy, public finances, credit
  indicators and many more. For more information please see <http://dadosabertos.bcb.gov.br/>.
  These datasets can be accessed through 'rbcb' functions and can be obtained in
  different data structures common to R ('tibble', 'data.frame', 'xts', ...).
Issues RPC-JSON calls to 'bitcoind', the daemon of Bitcoin Cash (BCH), to
    extract transaction data from the blockchain. BCH is a fork of Bitcoin that permits
    a greater number of transactions per second. A BCH daemon is available under an MIT
    license from the Bitcoin Unlimited website <https://www.bitcoinunlimited.info>.
Implementing the BDAT tree taper Fortran routines, which were
  developed for the German National Forest Inventory (NFI), to calculate 
  diameters, volume, assortments, double bark thickness and biomass for
  different tree species based on tree characteristics and sorting information.
  See Kublin (2003) <doi:10.1046/j.1439-0337.2003.00183.x> for details.
Interpretation of time series data is affected by model choices. Different models can give different or even contradicting estimates of patterns, trends, and mechanisms for the same data--a limitation alleviated by the Bayesian estimator of abrupt change,seasonality, and trend (BEAST) of this package. BEAST seeks to improve time series decomposition by forgoing the "single-best-model" concept and embracing all competing models into the inference via a Bayesian model averaging scheme. It is a flexible tool to uncover abrupt changes (i.e., change-points), cyclic variations (e.g., seasonality), and nonlinear trends in time-series observations. BEAST not just tells when changes occur but also quantifies how likely the detected changes are true. It detects not just piecewise linear trends but also arbitrary nonlinear trends. BEAST is applicable to real-valued time series data of all kinds, be it for remote sensing, economics, climate sciences, ecology, and hydrology. Example applications include its use to identify regime shifts in ecological data, map forest disturbance and land degradation from satellite imagery, detect market trends in economic data, pinpoint anomaly and extreme events in climate data, and unravel system dynamics in biological data. Details on BEAST are reported in Zhao et al. (2019) <doi:10.1016/j.rse.2019.04.034>.
Implements an interface to Minecraft (Bedrock Edition) worlds. Supports the analysis and management of these worlds and game saves.
Basic R package to access data structures offered by any
    BEFdata portal instance.
rbenchmark is inspired by the Perl module Benchmark, and
        is intended to facilitate benchmarking of arbitrary R code. The
        library consists of just one function, benchmark, which is a
        simple wrapper around system.time.  Given a specification of
        the benchmarking process (counts of replications, evaluation
        environment) and an arbitrary number of expressions, benchmark
        evaluates each of the expressions in the specified environment,
        replicating the evaluation as many times as specified, and
        returning the results conveniently wrapped into a data frame.
An implementation of robust bent line regression. It can fit the bent line regression and test the existence of change point,
    for the paper, "Feipeng Zhang and Qunhua Li (2016). Robust bent line regression, submitted."
Provides S3 class objects and methods for built environment data to ease the use of working with these data and facilitate other packages that make use of this data structure.
Tool-set to support Bayesian evidence synthesis.  This
    includes meta-analysis, (robust) prior derivation from historical
    data, operating characteristics and analysis (1 and 2 sample
    cases). Please refer to Weber et al. (2021) <doi:10.18637/jss.v100.i19>
    for details on applying this package while Neuenschwander et al. (2010)
    <doi:10.1177/1740774509356002> and Schmidli et al. (2014)
    <doi:10.1111/biom.12242> explain details on the methodology.
The package contains functions to generate random numbers
        from the beta distribution and random vectors from the
        Dirichlet distribution.
A robust backfitting algorithm for additive models based on (robust) local polynomial 
             kernel smoothers. It includes both bounded and re-descending (kernel) M-estimators, and
             it computes predictions for points outside the training set if desired. See
             Boente, Martinez and Salibian-Barrera (2017) <doi:10.1080/10485252.2017.1369077> and 
             Martinez and Salibian-Barrera (2021) <doi:10.21105/joss.02992> for details.
Facilities for working with Atlantis box-geometry model (BGM) 
 files. Atlantis is a deterministic, biogeochemical, whole-of-ecosystem model. 
 Functions are provided to read from BGM files directly, preserving their 
 internal topology, as well as helper functions to generate spatial data from these
 mesh forms. This functionality aims to simplify the creation and modification of box 
 and geometry as well as the ability to integrate with other data sources.
Methods that allow video reading and loading in R. Also provides nine different methods for  background subtraction.
Interface to 'Biodiversity' 'Heritage' Library ('BHL')
    (<https://www.biodiversitylibrary.org/>) API
    (<https://www.biodiversitylibrary.org/docs/api3.html>). 'BHL' is a
    repository of 'digitized' literature on 'biodiversity'
    studies, including 'floras', research papers, and more.
Provides a complete interface to 'LibBi', a library for Bayesian inference (see <http://libbi.org> and <doi:10.18637/jss.v067.i10> for more information). This includes functions for manipulating 'LibBi' models, for reading and writing 'LibBi' input/output files, for converting 'LibBi' output to provide traces for use with the coda package, and for running 'LibBi' to conduct inference.
Contains a collection of helper functions to use with 'RBi', the R interface to 'LibBi', described in Murray et al. (2015) <doi:10.18637/jss.v067.i10>. It contains functions to adapt the proposal distribution and number of particles in particle Markov-Chain Monte Carlo, as well as calculating the Deviance Information Criterion (DIC) and converting between times in 'LibBi' results and R time/dates.
Implementation of the algorithms (with minor modifications)
    to correct bias in quantitative DNA methylation analyses as described
    by Moskalev et al. (2011) <doi:10.1093/nar/gkr213>. Publication:
    Kapsner et al. (2021) <doi:10.1002/ijc.33681>.
Read and write 'Bibtex' files. Convert between bibliography
    formats, including 'Bibtex', 'Biblatex', 'PubMed', 'Endnote', and
    'Bibentry'.  Includes a port of the 'bibutils' utilities by Chris
    Putnam <https://sourceforge.net/projects/bibutils/>. Supports all
    bibliography formats and character encodings implemented in
    'bibutils'.
Manually bin data using weight of evidence and information value. Includes other binning 
    methods such as equal length, quantile and winsorized. Options for combining levels of categorical
    data are also available. Dummy variables can be generated based on the bins created using any of 
    the available binning methods. References: Siddiqi, N. (2006) <doi:10.1002/9781119201731.biblio>.
The MOSAICbioacc application is a turnkey package providing bioaccumulation
    factors (BCF/BMF/BSAF) from a toxicokinetic (TK) model fitted to
    accumulation-depuration data. It is designed to fulfil the requirements
    of regulators when examining applications for market authorization of active
    substances. See Ratier et al. (2021) <doi:10.1101/2021.09.08.459421>.
Currently fully supports Enrichr, JASPAR, miEAA, PANTHER,
    Reactome, STRING, and UniProt! The goal of rbioapi is to provide a
    user-friendly and consistent interface to biological databases and
    services: In a way that insulates the user from technicalities of
    using web services API and creates a unified and easy-to-use interface
    to biological and medical web services. This an ongoing project; New
    databases and services will be added periodically. Feel free to
    suggest any databases or services you often use.
A toolkit for working with Biological Observation Matrix ('BIOM') files.
    Features include reading/writing all 'BIOM' formats, rarefaction, alpha
    diversity, beta diversity (including 'UniFrac'), summarizing counts by 
    taxonomic level, and sample subsetting. Standalone functions for 
    reading, writing, and subsetting phylogenetic trees are also provided. 
    All CPU intensive operations are encoded in C with multi-thread support.
Functions for connecting to BioUML server, querying BioUML repository and launching BioUML analyses.
Interface to the 'USGS' 'BISON' (<https://bison.usgs.gov/>)
    API, a 'database' for species occurrence data. Data comes from
    species in the United States from participating data providers. You can get
    data via 'taxonomic' and location based queries. A simple function
    is provided to help visualize data.
A simple implementation of Binary Indexed Tree by R. The BinaryIndexedTree class supports construction of Binary Indexed Tree from a vector, update of a value in the vector and query for the sum of a interval of the vector.
An R package for the BitCoinCharts.com API.
An R wrapper to the overly complicated 'Bitmoji' API allowing users 
  to access and plot various comic strips using their personalized 'Bitstrips'
  avatar (<https://www.bitmoji.com/>).
R interface to access prices and market data with the 
    'Bloomberg Data License' service from 
    <https://www.bloomberg.com/professional/product/data-license/>. 
    As a prerequisite, a valid Data License from 'Bloomberg' is needed 
    together with the corresponding SFTP credentials and whitelisting 
    of the IP from which accessing the service. 
    This software and its author are in no way affiliated, 
    endorsed, or approved by 'Bloomberg' or any of its affiliates.
    'Bloomberg' is a registered trademark.
An R Interface to 'Bloomberg' is provided via the 'Blp API'.
An R-shiny application to plot datalogger time series at a microsecond precision as Acceleration, Temperature, 
  Pressure, Light intensity from CATS, AXY-TREK LUL and WACU bio-loggers. It is possible to link behavioral labels extracted
  from 'BORIS' software <http://www.boris.unito.it> or manually written in a csv file.
  CATS bio-logger are manufactured by <http://www.cats.is>, AXY-TREK are manufactured by <http://www.technosmart.eu> and 
  LUL and WACU are manufactured by <http://www.iphc.cnrs.fr/-MIBE-.html>.
Provides four boolean matrix factorization (BMF) methods.
             BMF has many applications like data mining and categorical data 
             analysis. BMF is also known as boolean matrix decomposition (BMD) 
             and was found to be an NP-hard (non-deterministic polynomial-time)
             problem. Currently implemented methods are 
             'Asso' Miettinen, Pauli and others (2008) 
                    <doi:10.1109/TKDE.2008.53>,
             'GreConD' R. Belohlavek, V. Vychodil (2010) 
                      <doi:10.1016/j.jcss.2009.05.002> ,
             'GreConDPlus' R. Belohlavek, V. Vychodil (2010) 
                      <doi:10.1016/j.jcss.2009.05.002> ,
             'topFiberM' A. Desouki, M. Roeder, A. Ngonga (2019) 
                       <arXiv:1903.10326>.
Implements reference based multiple imputation allowing for the imputation of longitudinal datasets using predefined strategies.
Creation, manipulation, simulation of linear Gaussian Bayesian
             networks from text files and more...
The Biological Magnetic Resonance Data Bank (BMRB,<http://
    www.bmrb.io/>) collects, annotates, archives, and disseminates (worldwide
    in the public domain) the important spectral and quantitative data derived
    from NMR(Nuclear Magnetic Resonance) spectroscopic investigations of biological
    macromolecules and metabolites. This package provides an interface to BMRB
    database for easy data access and includes a minimal set of data visualization
    functions. Users are encouraged to make their own data visualizations using BMRB
    data.
Provides a convenient way of accessing data published by the Reserve Bank of New Zealand (RBNZ) on their website, <https://www.rbnz.govt.nz/statistics>. A range of financial and economic data is provided in spreadsheet format including exchange and interest rates, commercial lending statistics, Reserve Bank market operations, financial institution statistics, household financial data, New Zealand debt security information, and economic indicators. This package provides a method to download those spreadsheets and read them directly into R.
A native R plotting library that provides a flexible declarative interface for creating interactive web-based graphics, backed by the Bokeh visualization library <https://bokeh.pydata.org/>.
This is a simple package which provides a function
      that boosts pre-ready or custom-made classifiers. Package
      uses Discrete AdaBoost (<doi:10.1006/jcss.1997.1504>) and Real AdaBoost
      (<doi:10.1214/aos/1016218223>) for two class,
      SAMME (<doi:10.4310/SII.2009.v2.n3.a8>) and
      SAMME.R (<doi:10.4310/SII.2009.v2.n3.a8>)
      for multiclass classification.
Scalable implementation of classification and regression forests, as described by Breiman (2001), <DOI:10.1023/A:1010933404324>.
The RBP curve is a visual tool to assess the
    performance of prediction models.
Bridge Regression estimation with linear restrictions defined in Yuzbasi et al. (2019) <arXiv:1910.03660>. Special cases of this approach fit the restricted LASSO, restricted RIDGE and restricted Elastic Net estimators.
Provides a fast calculation of the Blyth-Still-Casella confidence interval. The implementation follows the 'StatXact' 9 manual (Cytel 2010) and "Refining Binomial Confidence Intervals" by George Casella (1986) <doi:10.2307/3314658>.
Implementation of the RPC-JSON API for Bitcoin and utility functions for address creation and content analysis of the blockchain.
The regression-based (RB) approach is a method to test the missing data mechanism.
			This package contains two functions that test the type of missing data (Missing Completely 
			At Random vs Missing At Random) on the basis of the RB approach. The first function applies 
			the RB approach independently on each variable with missing data, using the completely 
			observed variables only. The second function tests the missing data mechanism globally 
			(on all variables with missing data) with the use of all available information. The 
			algorithm is adapted both to continuous and categorical data.
Tu & Zhou (1999) <doi:10.1002/(SICI)1097-0258(19991030)18:20%3C2749::AID-SIM195%3E3.0.CO;2-C> showed that comparing the means of populations whose data-generating distributions are non-negative with excess zero observations is a problem of great importance in the analysis of medical cost data. In the same study, Tu & Zhou discuss that it can be difficult to control type-I error rates of general-purpose statistical tests for comparing the means of these particular data sets. This package allows users to perform a modified bootstrap-based t-test that aims to better control type-I error rates in these situations.
Rbundler manages a project-specific library for dependency
    package installation. By specifying dependencies in a DESCRIPTION file
    in a project's root directory, one may install and use dependencies
    in a repeatable fashion without requiring manual maintenance.
    rbundler creates a project-specific R library in
    `PROJECT_ROOT/.Rbundle` (by default) and a project-specific
    `R_LIBS_USER` value, set in `PROJECT_ROOT/.Renviron`. It supports
    dependency management for R standard "Depends", "Imports",
    "Suggests", and "LinkingTo" package dependencies. rbundler also
    attempts to validate and install versioned dependencies, such
    as ">=", "==", "<=". Note that, due to the way R manages package
    installation, differing nested versioned dependencies are not
    allowed. For example, if your project depends on packages A (== 1),
    and B (== 2), but package A depends on B (== 1), then a nested
    dependency violation will cause rbundler to error out.
Residual balancing is a robust method of constructing weights for
  marginal structural models, which can be used to estimate (a) the average treatment effect in 
  a cross-sectional observational study, (b) controlled direct/mediator effects in causal mediation
  analysis, and (c) the effects of time-varying treatments in panel data (Zhou and Wodtke 2020
  <doi:10.1017/pan.2020.2>). This package provides three functions, rbwPoint(), rbwMed(), and rbwPanel(),
  that produce residual balancing weights for estimating (a), (b), (c), respectively.
Relational Class Analysis (RCA) is a method for detecting
        heterogeneity in attitudinal data (as described in Goldberg
        A., 2011, Am. J. Soc, 116(5)).
Regionalization of multiscale spatial processes based on a criterion for spatial aggregation error. The multiscale representation is a truncated Karhunen-Loaeve expansion using Obled-Creutin eigenfunctions. The method is incorporated within a Bayesian framework using an MCMC implementation of a latent spatial model. Bradley, J. R., Wikle, C. K., and Holan, S. H. (2017) <doi:10.1111/rssb.12179>.
Its main strength is to classify hemispherical 
    photographs of the plant canopy with algorithms specially developed for 
    such a task and well documented in 
    Díaz and Lencinas (2015) <doi:10.1109/lgrs.2015.2425931> and 
    Díaz and Lencinas (2018) <doi:10.1139/cjfr-2018-0006>. It supports 
    non-circular hemispherical photography.
Regularized calibrated estimation for causal inference and missing-data problems with high-dimensional data, based on Tan (2020a) <doi:10.1093/biomet/asz059>, Tan (2020b) <doi:10.1214/19-AOS1824> and Sun and Tan (2020) <arXiv:2009.09286>.
Calculate the flow of particles between polygons by two integration methods:
  integration by a cubature method and integration on a grid of points.
  Annie Bouvier, Kien Kieu, Kasia Adamczyk and Herve Monod (2009)
  <doi:10.1016/j.envsoft.2008.11.006>.
Tools for basic and advance cancer statistics and graphics.
	Groups individual data, merges registry data and population data, calculates age-specific rate, age-standardized rate, cumulative risk, estimated annual percentage rate with standards error. Creates graphics across variable and
    time, such as age-specific trends, bar chart and period-cohort trends.
There are different numeric optimizations which are used in order to estimate coefficients in models such as linear regression and neural networks. This package covers parameter estimation in linear regression using different methods such as batch gradient descent, stochastic gradient descent, minibatch gradient descent and coordinate descent. Kiwiel, Krzysztof C (2001) <doi:10.1007/PL00011414> Yu Nesterov (2004) <ISBN:1-4020-7553-7> Ferguson, Thomas S (1982) <doi:10.1080/01621459.1982.10477894> Zeiler, Matthew D (2012) <arXiv:1212.5701> Wright, Stephen J (2015) <arXiv:1502.04759>.
Provides an interface to the National Topographic System (NTS),
    which is the way in which a number of freely available Canadian datasets are
    organized. CanVec and CanVec+ datasets, which include all data used to create
    Canadian topographic maps, are two such datasets that are useful in creating
    vector-based maps for locations across Canada. This packages searches CanVec
    data by location, plots it using pretty defaults, and exports it to human-
    readable shapefiles for use in another GIS.
Estimation of abundance and other demographic parameters for closed 
             populations, open populations and the robust design in capture-recapture  
             experiments using loglinear models.
Translation of the 'MATLAB' program 'Carb' (Nathan and Mauz 2008 <DOI:10.1016/j.radmeas.2007.12.012>; Mauz and Hoffmann 2014) for dose rate modelling for carbonate-rich samples in the context of trapped charged dating (e.g., luminescence dating) applications.
Enables the calibration and analysis of radiocarbon dates, often but not exclusively for the purposes of archaeological research. It includes functions not only for basic calibration, uncalibration, and plotting of one or more dates, but also a statistical framework for building demographic and related longitudinal inferences from aggregate radiocarbon date lists, including: Monte-Carlo simulation test (Timpson et al 2014 <doi:10.1016/j.jas.2014.08.011>), random mark permutation test (Crema et al 2016 <doi:10.1371/journal.pone.0154809>) and spatial permutation tests (Crema, Bevan, and Shennan 2017 <doi:10.1016/j.jas.2017.09.007>).
Provides color schemes for maps and other graphics
    designed by 'CARTO' as described at <https://carto.com/carto-colors/>.
    It includes four types of palettes: aggregation, diverging, qualitative, 
    and quantitative.
This packages provides a direct interface (without the
	     use of Java) to the most basic functionality of Apache
	     Cassanda such as login, updates and queries.
A set of tools and functions to help with species conservation assessments (Red List threat assessments). Includes Extent of occurrence, Area of Occupancy, Minimum Enclosing Rectangle, a geographic Projection Wizard and Species batch processing.
Calculate 22 summary statistics coded in C on time-series vectors to enable 
    pattern detection, classification, and regression applications in the 
    feature space as proposed by Lubba et al. (2019) <doi:10.1007/s10618-019-00647-x>.
Provides implementations of a classifier based on the
    "Classification Based on Associations" (CBA). It can be used for building
    classification models from association rules. Rules are pruned in the order of
    precedence given by the sort criteria and a default rule is added. The final
    classifier labels provided instances. CBA was originally proposed by Liu,
    B. Hsu, W. and Ma, Y. Integrating Classification and Association Rule
    Mining. Proceedings KDD-98, New York, 27-31 August. AAAI. pp80-86 (1998, ISBN:1-57735-070-7).
Tools for large, sparse optimal matching of treated units
	and control units in observational studies.  Provisions are
	made for refined covariate balance constraints, which include
	fine and near-fine balance as special cases.  Matches are 
	optimal in the sense that they are computed as solutions to
	network optimization problems rather than greedy algorithms.
	See Pimentel, et al.(2015) <doi:10.1080/01621459.2014.997879> 
	and Pimentel (2016), Obs. Studies 2(1):4-23. The rrelaxiv 
	package, which provides an alternative solver for
	the underlying network flow problems, carries an
	academic license and is not available on CRAN, but
	may be downloaded from Github at 
	<https://github.com/josherrickson/rrelaxiv/>.
A collection of functions to estimate Rogers-Castro migration age schedules using 'Stan'. This model which describes the fundamental relationship between migration and age in the form of a flexible multi-exponential migration model was most notably proposed in Rogers and Castro (1978) <doi:10.1068/a100475>.
Nonparametric maximum likelihood estimation methods
    for random coefficient binary response  models and some related
    functionality for sequential processing of hyperplane arrangements.
    See J. Gu and R. Koenker (2020) <DOI:10.1080/01621459.2020.1802284>.
Tools for optimal subset matching of treated units
	and control units in observational studies, with support
	for refined covariate balance constraints, (including
	fine and near-fine balance as special cases). A close 
	relative is the 'rcbalance' package.  See Pimentel, 
	et al.(2015) <doi:10.1080/01621459.2014.997879>
	and Pimentel and Kelz (2020) 
	<doi:10.1080/01621459.2020.1720693>. The rrelaxiv 
	package, which provides an alternative solver for
	the underlying network flow problems, carries an
	academic license and is not available on CRAN, but
	may be downloaded from Github at 
	<https://github.com/josherrickson/rrelaxiv/>.
Functions to implement the parametric and non-parametric bootstrap 
    confidence interval methods described in Morrison and Simon (2017) <arXiv:1702.06986>.
Identify, convert and handle dates as used within the Swedish cancer register and associated cancer quality registers in Sweden. Especially the cancer register sometimes use nonstandard date variables where day and/or month can be "00" or were the date format is a mixture of"%Y-%m-%d", "%Y%m%d" and "%y%V" (two digit year and week number according to ISO 8601,which is not completely supported by R).These dates must be approximated to valid dates before being used in for example survival analysis. The package also includes some convenient functions for calculating "lead times" (relying on 'difftime') and introduce a "year" class with relevant S3-methods to handle yearly cohort.
Functions either required by other Swedish Regional Cancer Center packages or standalone functions outside the scope of other packages.
The handling of an API key (misnomer for password) for protected
    data can be difficult. This package provides secure convenience functions for 
    entering / handling API keys and pulling data directly into memory. By default
    it will load from REDCap instances, but other sources are injectable via
    inversion of control.
The RCC_PCA criterion is a tool to determine the optimal number of components to retain in PCA;See Alshammri (2021).
R interface to (some of) cddlib
    (<https://github.com/cddlib/cddlib>).
    Converts back and forth between two representations of a convex polytope:
    as solution of a set of linear equalities and inequalities and as
    convex hull of set of points and rays.
    Also does linear programming and redundant generator elimination
    (for example, convex hull in n dimensions).  All functions can use exact
    infinite-precision rational arithmetic.
With this package we provide an easy method to compute robust and conditional Data Envelopment Analysis (DEA), 
             Free Disposal Hull (FDH) and Benefit of the Doubt (BOD) scores. 
             The robust approach is based on the work of Cazals, Florens and Simar (2002) <doi:10.1016/S0304-4076(01)00080-X>. 
             The conditional approach is based on Daraio and Simar (2007) <doi:10.1007/s11123-007-0049-3>.
             Besides we provide graphs to help with the choice of m. 
             We relay on the 'Benchmarking' package to compute the efficiency scores and on the 'np' package to compute non parametric estimation of similarity among units.
Allows the user to access functionality in the
    'CDK', a Java framework for chemoinformatics. This allows the user to load
    molecules, evaluate fingerprints, calculate molecular descriptors and so on.
    In addition, the 'CDK' API allows the user to view structures in 2D.
An R interface to the Chemistry Development Kit, a Java library
    for chemoinformatics. Given the size of the library itself, this package is
    not expected to change very frequently. To make use of the CDK within R, it is
    suggested that you use the 'rcdk' package. Note that it is possible to directly
    interact with the CDK using 'rJava'. However 'rcdk' exposes functionality in a more
    idiomatic way. The CDK library itself is released as LGPL and the sources can be
    obtained from <https://github.com/cdk/cdk>.
Performs 2D Delaunay triangulation, constrained or unconstrained, with the help of the C++ library 'CDT'. A function to plot the triangulation is provided. The constrained Delaunay triangulation has applications in geographic information systems.
An implementation of a stochastic heuristic method for performing multidimensional function optimization. The method is inspired in the Cross-Entropy Method. It does not relies on derivatives, neither imposes particularly strong requirements into the function to be optimized. Additionally, it takes profit from multi-core processing to enable optimization of time-consuming functions.
Example dataset for 'Rcell' package. Contains images and cell data object.
To facilitate using 'cereal' with Rcpp.
    'cereal' is a header-only C++11 serialization library.
    'cereal' takes arbitrary data types and reversibly turns them into
    different representations, such as compact binary encodings, XML,
    or JSON. 'cereal' was designed to be fast, light-weight, and easy
    to extend - it has no external dependencies and can be easily
    bundled with other code or used standalone. Please see
    <http://uscilab.github.io/cereal> for more information.
Set of analytical procedures based on advanced data analysis in support of Brazil's public sector external control activity.
Download and open manifest files provided by the Copernicus Global Land Service data <https://land.copernicus.eu/global/>. The manifest files are available at: <https://land.copernicus.vgt.vito.be/manifest/>. Also see: <https://land.copernicus.eu/global/access/>. Before you can download the data, you will first need to register to create a username and password.
Conjugate gradient minimization of nonlinear functions
        with box constraints incorporating the Dai/Yuan update. This
        implementation should be used in place of the "CG" algorithm
        of the optim() function.
A simple data science challenge system using R Markdown and 'Dropbox' <https://www.dropbox.com/>.
    It requires no network configuration, does not depend on external platforms
    like e.g. 'Kaggle' <https://www.kaggle.com/> and can be easily installed on a personal computer.
Provides a dataset of functions in all base packages of R versions 1.0.1 onwards.
R package for chess validations, pieces movements and check
    detection. Also integrates functions to plot chess boards given a
    Forsyth Edwards and Portable Game notations.
Provides algorithms to locate multiple
    distributional change-points in piecewise stationary time series. The
    algorithms are provably consistent, even in the presence of long-range
    dependencies. Knowledge of the number of change-points is not
    required. The code is written in Go and interfaced with R.
Parses the 'ArchieML' format from the New York Times <http://archieml.org>.
  Also provides utilities for retrieving Google Drive documents for parsing.
A set of functions to generate, access and analyze standard data products from archival tagging data.
An implementation of simulated maximum likelihood method for the estimation of Binary (Probit and Logit), Ordered (Probit and Logit) and Poisson models with random parameters for cross-sectional and longitudinal data as presented in Sarrias (2016) <doi:10.18637/jss.v074.i10>.
Collection of portable choice dialog widgets
Provides a list of functions for the statistical analysis and the post-processing of the Markov Chains simulated by ChronoModel (see <http://www.chronomodel.fr> for more information).  ChronoModel is a friendly software to construct a chronological model in a Bayesian framework.  Its output is  a sampled Markov chain from the posterior distribution of dates component  the chronology. The functions can also be applied  to the  analyse of mcmc output generated by Oxcal software.
A simple and flexible way to generate Circos 2D track plot images for genomic data visualization is implemented in this package. The types of plots include: heatmap, histogram, lines, scatterplot, tiles and plot items for further decorations include connector, link (lines and ribbons), and text (gene) label. All functions require only R graphics package that comes with R base installation.
A programmatic interface to the Species+ <https://speciesplus.net/> database via the Species+/CITES Checklist API <https://api.speciesplus.net/>.
Client for 'Citoid' (<https://www.mediawiki.org/wiki/Citoid>),
    an API for getting citations for various scholarly work identifiers
    found on 'Wikipedia'.
Functions to assist manipulation of matrix 
             row and column labels for all types of matrix mathematics
             where row and column labels are to be respected.
'Yandex Clickhouse' (<https://clickhouse.com/>) is a high-performance relational column-store database to enable
    big data exploration and 'analytics' scaling to petabytes of data. Methods are
    provided that enable working with 'Yandex Clickhouse' databases via
    'DBI' methods and using 'dplyr'/'dbplyr' idioms.
'Climacell' is a weather platform that provides hyper-local forecasts and weather 
    data. This package enables the user to query the core layers of the 
    time line interface of the 'Climacell' v4 API <https://www.climacell.co/weather-api/>. 
    This package requires a valid API key. See vignettes for instructions on use.
R tools for integrating with the www.clinicalcodes.org web
    repository
Leverages the functionality of 'clipboard.js', a JavaScript library
    for HMTL5-based copy to clipboard from web pages (see <https://clipboardjs.com>
    for more information), and provides a reactive copy-to-clipboard UI button 
    component, called 'rclipButton', and a a reactive copy-to-clipboard UI link 
    component, called 'rclipLink', for 'shiny' R applications.
Graphical toolbox for clustering and classification of data frames.
    It proposes a graphical interface to process clustering and classification methods on features
    data-frames, and to view initial data as well as resulted cluster or classes. According to the
    level of available labels, different approaches are proposed: unsupervised clustering,
    semi-supervised clustering and supervised classification. 
    To assess the processed clusters or classes, the toolbox can import and show some supplementary
    data formats: either profile/time series, or images. 
    These added information can help the expert to label clusters (clustering), or to constrain data
    frame rows (semi-supervised clustering), using Constrained spectral embedding algorithm by 
    Wacquet et al. (2013) <doi:10.1016/j.patrec.2013.02.003> and the methodology provided by 
    Wacquet et al. (2013) <doi:10.1007/978-3-642-35638-4_21>.
Tool for providing access to the Java version 'CMAEvolutionStrategy' of
    Nikolaus Hansen. 'CMA-ES' is the Covariance Matrix Adaptation Evolution Strategy,
    see https://www.lri.fr/~hansen/cmaes_inmatlab.html#java.
Run 'R CMD check' from 'R' and capture the results
    of the individual checks. Supports running checks in the background,
    timeouts, pretty printing and comparing check results.
A platform-independent basic-statistics GUI (graphical user interface) for R, based on the tcltk package.
Various statistical, graphics, and data-management functions used by the Rcmdr package in the R Commander GUI for R.
R Commander plug-in for repeated-measures and mixed-design
    ('split-plot') ANOVA.
    It adds a new menu entry for repeated measures that allows
    to deal with up to three within-subject factors and optionally with
    one or several between-subject factors.
    It also provides supplementary options to oneWayAnova() and
    multiWayAnova() functions, such as choice of ANOVA type, display of
    effect sizes and post hoc analysis for multiWayAnova().
A plug-in for R Commander ('Rcmdr'). The package is a Graphical
    User Interface (GUI) in which several biclustering methods can be executed,
    followed by diagnostics and plots of the results. Further, the GUI also has
    the possibility to connect the methods to more general diagnostic packages for
    biclustering. Biclustering methods from 'biclust', 'fabia', 's4vd', 'iBBiG',
    'isa2', 'BiBitR', 'rqubic' and 'BicARE' are implemented. Additionally, 'superbiclust' and
    'BcDiag' are also implemented to be able to further investigate results. The
    GUI also provides a couple of extra utilities to export, save, search through
    and plot the results. 'RcmdrPlugin.BiclustGUI' also provides a very specific
    framework for biclustering in which new methods, diagnostics and plots can be
    added. Scripts were prepared so that R-package developers can freely design
    their own dialogs in the GUI which can then be added by the maintainer of
    'RcmdrPlugin.BiclustGUI'. These scripts do not required any knowledge of 'tcltk'
    and 'Rcmdr' and are easy to fill in. (Note: rqubic currently requires manual installation through BiocManager::install('rqubic').)
Adds menu items to the R Commander for implementing case 1 (object case) best-worst scaling (BWS1) from designing choice sets to measuring preferences for items. BWS1 is a question-based survey method that constructs various combinations of items (choice sets) using the experimental designs, asks respondents to select the best and worst items in each choice set, and then measures preferences for the items by analyzing the responses. For details on BWS1, refer to Louviere et al. (2015) <doi:10.1017/CBO9781107337855>.
This package provides a Rcmdr "plug-in" based on coin (Conditional Inference Procedures in a Permutation Test Framework).
Adds menu items to the R Commander for parametric analysis of dichotomous choice contingent valuation (DCCV) data. CV is a question-based survey method to elicit individuals' preferences for goods and services. This package depends on functions regarding parametric DCCV analysis in the package DCchoice. See Carson and Hanemann (2005) <doi:10.1016/S1574-0099(05)02017-6> for DCCV.
Adds menu items for discrete choice experiments (DCEs) to the R Commander. DCE is a question-based survey method that designs various combinations (profiles) of attribute levels using the experimental designs, asks respondents to select the most preferred profile in each choice set, and then measures preferences for the attribute levels by analyzing the responses. For details on DCEs, refer to Louviere et al. (2000) <doi:10.1017/CBO9780511753831>.
We provide an Rcmdr plug-in based on the depthTools 
        package, which implements different robust statistical tools 
        for the description and analysis of gene expression data based 
        on the Modified Band Depth, namely, the scale curves for 
        visualizing the dispersion of one or various groups of samples
        (e.g. types of tumors), a rank test to decide whether two
        groups of samples come from a single distribution and two 
        methods of supervised classification techniques, the DS and 
        TAD methods.
Provides a platform-independent GUI for design of experiments.
 The package is implemented as a plugin to the R-Commander, which is a more general 
 graphical user interface for statistics in R based on tcl/tk. 
 DoE functionality can be accessed through the menu Design that is added to the 
 R-Commander menus.
Este paquete proporciona una interfaz grafica de usuario (GUI) para algunos de los procedimientos estadisticos detallados en un curso de 'Estadistica aplicada a las Ciencias Sociales mediante el programa informatico R' (EACSPIR). LA GUI se ha desarrollado como un Plugin del programa R-Commander.
Rcmdr plug-in GUI extension for Evidence Based Medicine medical indicators calculations (Sensitivity, specificity, absolute risk reduction, relative risk, ...).
A Rcmdr "plug-in" for the EcoVirtual package, designed primarily for teaching ecological models using simulations.
Export Rcmdr output to LaTeX or HTML code. The
        plug-in was originally intended to facilitate exporting Rcmdr
        output to formats other than ASCII text and to provide R
        novices with an easy-to-use, easy-to-access reference on
        exporting R objects to formats suited for printed output. The
        package documentation contains several pointers on creating
        reports, either by using conventional word processors or
        LaTeX/LyX.
EZR (Easy R) adds a variety of statistical functions, including survival analyses, ROC analyses, metaanalyses, sample size calculation, and so on, to the R commander. EZR enables point-and-click easy access to statistical functions, especially for medical statistics. EZR is platform-independent and runs on Windows, Mac OS X, and UNIX. Its complete manual is available only in Japanese (Chugai Igakusha, ISBN: 978-4-498-10918-6, Nankodo, ISBN: 978-4-524-26158-1, Ohmsha, ISBN: 978-4-274-22632-8), but an report that introduced the investigation of EZR was published in Bone Marrow Transplantation (Nature Publishing Group) as an Open article. This report can be used as a simple manual. It can be freely downloaded from the journal website as shown below. This report has been cited in more than 3,000 scientific articles.
Rcmdr Plugin for the 'FactoMineR' package.
Provides an Rcmdr "plug-in" based on the GWRM package.
Rcmdr menu support for many of the functions in the HH package.
        The focus is on menu items for functions we use in our introductory
        courses.
Provides an R Commander plugin that accompanies IPSUR, an Introduction to Probability and Statistics Using R.
A GUI front-end for 'ggplot2' supports Kaplan-Meier plot, histogram,
    Q-Q plot, box plot, errorbar plot, scatter plot, line chart, pie chart,
    bar chart, contour plot, and distribution plot.
Provides an Rcmdr "plug-in" based on the 'lfstat' package for low flow analysis.
Easy to use interface for conducting meta-analysis in R. This
    package is an Rcmdr-plugin, which allows the user to conduct analyses in a
    menu-driven, graphical user interface environment (e.g., CMA, SPSS). It
    uses recommended procedures as described in The Handbook of Research
    Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009).
Extends R Commander with a unified menu of new and pre-existing 
        statistical functions related to public management and policy analysis 
        statistics. Functions and menus have been renamed according to the 
        usage in PMGT 630 in the Master of Public Administration program at
        Brigham Young University.
An R Commander "plug-in" extending functionality of linear models
    and providing an interface to Partial Least Squares Regression and Linear and
    Quadratic Discriminant analysis. Several statistical summaries are extended,
    predictions are offered for additional types of analyses, and extra plots, tests
    and mixed models are available.
A GUI for the orloca package is provided as a Rcmdr plug-in. The package deals with continuos planar location problems.
The R commander plug-in for robust principal component analysis. The Graphical User Interface for Principal Component Analysis (PCA) with Hubert Algorithm method.
Rcmdr menu support for some of the graphics by group in
        the lattice package
This package provides a Rcmdr "plug-in" to analyze questionnaire data.
R Commander plug-in to demonstrate various actuarial and financial risks. It includes valuation of bonds and stocks, portfolio optimization, classical ruin theory, demography and epidemic.
Mixed Treatment Comparison is a methodology to compare directly and/or indirectly health strategies (drugs, treatments, devices). This package provides an 'Rcmdr' plugin to perform Mixed Treatment Comparison for binary outcome using BUGS code from Bristol University (Lu and Ades).
Rcmdr GUI extension plug-in for Receiver Operator Characteristic tools from pROC and ROCR packages. Also it ads a Rcmdr GUI extension for Hosmer and Lemeshow GOF test from the package ResourceSelection.
This package includes tools for calculating sample sizes and 
 selecting samples using various sampling designs. This package is an extension
 of RcmdrPlugin.EHESsampling which was developed as part of the EHES pilot project.
 The EHES Pilot project has received funding from the European Commission and 
 DG Sanco. The views expressed here are those of the authors and they do not represent 
 Commission's official position.
Provides a GUI for the SCVA, SCRT and SCMA packages as described in Bulte and Onghena (2013) <doi:10.22237/jmasm/1383280020>. The package is written as an Rcmdr plugin.
This package provides a GUI for the SLC package, it is
        written as an Rcmdr plug-in.
Rcmdr interface to the 'sos' package. The plug-in renders
  the 'sos' searching functionality easily accessible via the Rcmdr
  menus. It also simplifies the task of performing multiple searches and 
  subsequently obtaining the union or the intersection of the results.
This package provides a GUI for the steepness package, it
        is written as an Rcmdr plug-in.
An R Commander plug-in for the survival
  package, with dialogs for Cox models, parametric survival regression models,
  estimation of survival curves, and testing for differences in survival
  curves, along with data-management facilities and a variety of tests, 
  diagnostics and graphs.
The 'sutteForecastR' is a package of Alpha-Sutte indicator. To make the 'sutteForecastR' user friendly, so we develop an 'Rcmdr' plug-in based on the Alpha-Sutte indicator function.
Provides an Rcmdr "plug-in" based on the TeachingDemos package, and is primarily for illustrative purposes.
R Commander plugin for teaching statistical methods. 
    It adds a new menu for making easier the teaching of the main concepts about the main statistical methods.
An 'R Commander' plug-in providing an integrated solution to perform
    a series of text mining tasks such as importing and cleaning a corpus, and
    analyses like terms and documents counts, vocabulary tables, terms
    co-occurrences and documents similarity measures, time series analysis,
    correspondence analysis and hierarchical clustering. Corpora can be imported
    from spreadsheet-like files, directories of raw text files, 'Twitter' queries,
    as well as from 'Dow Jones Factiva', 'LexisNexis', 'Europresse' and 'Alceste' files.
Some extensions to Rcmdr (R Commander), randomness test, 
 variance test for one normal sample and predictions using active model, 
 made by R-UCA project and used in teaching statistics at University of Cadiz (UCA).
An R Commander plug-in for the 'WorldFlora' package. It was mainly developed to show workflows and scripts for first-time users.
Functions and data sets for simulating and fitting minification and random coefficient minification modelling.
Functions in this package will import filtered variant call format (VCF) files of SNPs data and generate data sets to detect copy number variants, visualize them and do downstream analyses with copy number variants(e.g. Environmental association analyses).
Automatically apply different strategies to optimize R code. 
    'rco' functions take R code as input, and returns R code as output.
Get your data (forms, structures, answers) from Coletum 
    <https://coletum.com> to handle and analyse.
Provides programmatic access to Colombos, a web based
    interface for exploring and analyzing comprehensive organism-specific
    cross-platform expression compendia of bacterial organisms.
Provides color schemes for maps (and other graphics)
        designed by Cynthia Brewer as described at http://colorbrewer2.org.
'NCL' (NCAR Command Language) is one of the most popular spatial 
    data mapping tools in meteorology studies, due to its beautiful output 
    figures with plenty of color palettes designed by experts 
    <https://www.ncl.ucar.edu/index.shtml>. 
    Here we translate all 'NCL' color palettes into R hexadecimal RGB colors and 
    provide color selection function, which will help users make a beautiful figure.
Utility functions for interacting with the 'COMPADRE' and 'COMADRE' databases of matrix population models. Described in Jones et al. (2021) <doi:10.1101/2021.04.26.441330>.
Functions and datasets to support "Summary and Analysis of
             Extension Program Evaluation in R" and "An R
             Companion for the Handbook of Biological Statistics". 
             Vignettes are available at <http://rcompanion.org>.
Makes easier the creation of R package or research compendium 
    (i.e. a predefined files/folders structure) so that users can focus on the 
    code/analysis instead of wasting time organizing files. A full 
    ready-to-work structure is set up with some additional features: version 
    control, remote repository creation, CI/CD configuration (check package 
    integrity under several OS, test code with 'testthat', and build and deploy 
    website using 'pkgdown'). This package heavily relies on the R packages 
    'devtools' and 'usethis' and follows recommendations made by Wickham H. 
    (2015) <ISBN:9781491910597> and Marwick B. et al. (2018) 
    <doi:10.7287/peerj.preprints.3192v2>.
Configuration management using files (JSON, YAML, separated text),
  JSON strings, and command line arguments. Command line arguments
  can be used to override configuration. Period-separated command line
  flags are parsed as hierarchical lists.
Solve some conic related problems (intersection of conics with lines and conics, arc length of an ellipse, polar lines, etc.).
Client for the CORE API (<https://core.ac.uk/docs/>).
    CORE (<https://core.ac.uk>) aggregates open access research
    outputs from repositories and journals worldwide and make them
    available to the public.
A collection of small text corpora of interesting data.
    It contains all data sets from 'dariusk/corpora'. Some examples:
    names of animals: birds, dinosaurs, dogs; foods: beer categories,
    pizza toppings; geography: English towns, rivers, oceans;
    humans: authors, US presidents, occupations; science: elements,
    planets; words: adjectives, verbs, proverbs, US president quotes.
Handling and Analysing Spherical, 
      HEALPix and Cosmic Microwave Background data on a HEALPix grid.
Bundles the datasets and functions featured in Philip H. 
    Pollock and Barry C. Edwards (Forthcoming 2022)<https://edge.sagepub.com/pollock>, "An R Companion to Political Analysis, 3rd Edition," Thousand Oaks, CA: Sage Publications.
R interface to CPLEX solvers for linear, quadratic, and (linear and quadratic) mixed integer programs. Support for quadratically constrained programming is available. See the file "INSTALL" for details on how to install the Rcplex package in Linux/Unix-like and Windows systems. Support for sparse matrices is provided by an S3-style class "simple_triplet_matrix" from package slam and by objects from the Matrix package class hierarchy.
Identifies regions of common (species) profiles (RCPs), possibly when sampling artefacts are present.  Within a region the probability of sampling all species remains approximately constant.  This is performed using mixtures-of-experts models.  The package also contains associated methods, such as diagnostics.
The 'Rcpp' package provides R functions as well as C++ classes which
 offer a seamless integration of R and C++. Many R data types and objects can be
 mapped back and forth to C++ equivalents which facilitates both writing of new
 code as well as easier integration of third-party libraries. Documentation
 about 'Rcpp' is provided by several vignettes included in this package, via the
 'Rcpp Gallery' site at <https://gallery.rcpp.org>, the paper by Eddelbuettel and
 Francois (2011, <doi:10.18637/jss.v040.i08>), the book by Eddelbuettel (2013,
 <doi:10.1007/978-1-4614-6868-4>) and the paper by Eddelbuettel and Balamuta (2018,
 <doi:10.1080/00031305.2017.1375990>); see 'citation("Rcpp")' for details.
Rcpp11 includes a header only C++11 library that facilitates 
  integration between R and modern C++.
Provides optimized functions and flexible combinatorial iterators
    implemented in C++ for solving problems in combinatorics and
    computational mathematics. Utilizes the RMatrix class from 'RcppParallel'
    for thread safety. There are combination/permutation functions with
    constraint parameters that allow for generation of all results of a vector
    meeting specific criteria (e.g. generating integer partitions
    or finding all combinations such that the sum is between two bounds).
    Capable of generating specific combinations/permutations (e.g. retrieve
    only the nth lexicographical result) which sets up nicely for
    parallelization as well as random sampling. Gmp support permits exploration
    where the total number of results is large (e.g. comboSample(10000, 500,
    n = 4)). Additionally, there are several high performance number theoretic
    functions that are useful for problems common in computational mathematics.
    Some of these functions make use of the fast integer division library
    'libdivide'. The primeSieve function is based on the segmented sieve of
    Eratosthenes implementation by Kim Walisch. It is also efficient for large
    numbers by using the cache friendly improvements originally developed by
    Tomás Oliveira. Finally, there is a prime counting function that implements
    Legendre's formula based on the work of Kim Walisch.
'Annoy' is a small C++ library for Approximate Nearest Neighbors 
 written for efficient memory usage as well an ability to load from / save to
 disk. This package provides an R interface by relying on the 'Rcpp' package,
 exposing the same interface as the original Python wrapper to 'Annoy'. See
 <https://github.com/spotify/annoy> for more on 'Annoy'. 'Annoy' is released
 under Version 2.0 of the Apache License. Also included is a small Windows
 port of 'mmap' which is released under the MIT license.
The 'APT Package Management System' provides Debian and
 Debian-derived Linux systems with a powerful system to resolve package
 dependencies. This package offers access directly from R.  This can
 only work on a system with a suitable 'libapt-pkg-dev' installation
 so functionality is curtailed if such a library is not found.
'Armadillo' is a templated C++ linear algebra library (by Conrad
 Sanderson) that aims towards a good balance between speed and ease of
 use. Integer, floating point and complex numbers are supported, as
 well as a subset of trigonometric and statistics functions. Various
 matrix decompositions are provided through optional integration with
 LAPACK and ATLAS libraries.  The 'RcppArmadillo' package includes the
 header files from the templated 'Armadillo' library. Thus users do
 not need to install 'Armadillo' itself in order to use
 'RcppArmadillo'. From release 7.800.0 on, 'Armadillo' is licensed
 under Apache License 2; previous releases were under licensed as MPL
 2.0 from version 3.800.0 onwards and LGPL-3 prior to that;
 'RcppArmadillo' (the 'Rcpp' bindings/bridge to Armadillo) is licensed
 under the GNU GPL version 2 or later, as is the rest of 'Rcpp'.
 Armadillo requires a C++11 compiler.
Access to Boost Date_Time functionality for dates,
 durations (both for days and date time objects), time zones, and 
 posix time ('ptime') is provided by using 'Rcpp modules'. The 
 posix time implementation can support high-resolution of up to 
 nano-second  precision by using 96 bits (instead of R's 64)
 to present a 'ptime' object (but this needs recompilation with
 a #define set).
Features the multiple polynomial quadratic sieve (MPQS) algorithm
    for factoring large integers and a vectorized factoring function that
    returns the complete factorization of an integer. The MPQS is based off of
    the seminal work of Carl Pomerance (1984) <doi:10.1007/3-540-39757-4_17>
    along with the modification of multiple polynomials introduced by Peter
    Montgomery and J. Davis as outlined by Robert D. Silverman (1987)
    <doi:10.1090/S0025-5718-1987-0866119-8>. Utilizes the C library
    GMP (GNU Multiple Precision Arithmetic) and 'RcppThread' for factoring
    integers in parallel. For smaller integers, a simple Elliptic
    Curve algorithm is attempted followed by a constrained version of 
    Pollard's rho algorithm. The Pollard's rho algorithm is the same algorithm
    used by the factorize function in the 'gmp' package.
'Rcpp' Access to the 'CCTZ' timezone library is provided. 'CCTZ' is
 a C++ library for translating between absolute and civil times using the rules
 of a time zone. The 'CCTZ' source code, released under the Apache 2.0 License,
 is included in this package. See <https://github.com/google/cctz> for more
 details.
It provides functions to estimate the parameters in spatial models with censored/missing responses via the Expectation-Maximization (EM) algorithm (see Dempster, Laird, and Rubin (1977)<https://www.jstor.org/stable/2984875>), the Stochastic Approximation EM (SAEM) algorithm (see Delyon, Lavielle, and Moulines (1999)<https://www.jstor.org/stable/120120>), and the Monte Carlo EM (MCEM) algorithm (see Wei and Tanner (1990)<doi:10.1080/01621459.1990.10474930>). These algorithms are widely used to compute the maximum likelihood (ML) estimates in incomplete data problems. The EM algorithm computes the ML estimates when a closed expression for the conditional expectation of the complete-data log-likelihood function is available. In the MCEM algorithm, the conditional expectation is substituted by a Monte Carlo approximation based on many independent simulations of the missing data, while the SAEM algorithm splits the E-step into a simulation step and an integration step. The SAEM algorithm was developed as an alternative to the computationally intensive MCEM algorithm. 
  This package also approximates the standard error of the estimates using the method developed by Louis (1982)<https://www.jstor.org/stable/2345828>. It also has a function that performs spatial prediction in a set of new locations. Besides the functions to estimate parameters, this package allows computing the covariance matrix and the distance matrix.
Creates a header only package to link to the CGAL 
  (Computational Geometry Algorithms Library)
  header files in Rcpp. There are a variety of potential uses for 
  the software such as Hilbert sorting, KDtree nearest neighbors, 
  and convex hull algorithms. There is only one R function in this 
  package, which returns the current version of the CGAL library 
  included. For more information about how to use the header files, 
  see the CGAL documentation at <https://www.cgal.org>. Currently
  includes the CGAL 5.4 stable release.
The 'RcppClassic' package provides a deprecated C++ library which
 facilitates the integration of R and C++. New projects should use the new 'Rcpp'
 'API' in the 'Rcpp' package.
The 'Rcpp' package contains a C++ library that facilitates the
 integration of R and C++ in various ways via a rich API. This API was
 preceded by an earlier version which has been deprecated since 2010 (but is
 still supported to provide backwards compatibility in the package
 'RcppClassic').  This package 'RcppClassicExamples' provides usage examples for
 the older, deprecated API. There is also a corresponding package
 'RcppExamples' with examples for the newer, current API which we
 strongly recommend as the basis for all new development.
Time the execution of overlapping or unique 'Rcpp' code chunks using convenient methods, seamlessly write timing results to an 'RcppClock' object in the R global environment, and summarize and/or plot the results in R.
The 'cnpy' library written by Carl Rogers provides read and write
 facilities for files created with (or for) the 'NumPy' extension for 'Python'.
 Vectors and matrices of numeric types can be read or written to and from
 files as well as compressed files. Support for integer files is available if
 the package has been built with -std=c++11 which should be the default on
 all platforms since the release of R 3.3.0.
'Rcpp' Bindings for the C code of the 'Corpus Workbench' ('CWB'), an indexing and query 
  engine to efficiently analyze large corpora (<https://cwb.sourceforge.io>). 'RcppCWB' is licensed
  under the GNU GPL-3, in line with the GPL-3 license of the 'CWB' (<https://www.r-project.org/Licenses/GPL-3>).
  The 'CWB' relies on 'pcre' (BSD license, see <http://www.pcre.org/licence.txt>)
  and 'GLib' (LGPL license, see <https://www.gnu.org/licenses/lgpl-3.0.en.html>).
  See the file LICENSE.note for further information. The package includes modified code of the
  'rcqp' package (GPL-2, see <https://cran.r-project.org/package=rcqp>). The original work of the authors
  of the 'rcqp' package is acknowledged with great respect, and they are listed as authors of this
  package. To achieve cross-platform portability (including Windows), using 'Rcpp' for wrapper code
  is the approach used by 'RcppCWB'.
A header-only C++ library is provided with support
 for dates, time zones, ISO weeks, Julian dates, and Islamic dates.
 'date' offers extensive date and time functionality for the C++11,
 C++14 and C++17 standards and was written by Howard Hinnant and released 
 under the MIT license. A slightly modified version has been accepted
 (along with 'tz.h') as part of C++20. This package regroups all
 header files from the upstream repository by Howard Hinnant so that
 other R packages can use them in their C++ code. At present, few of
 the types have explicit 'Rcpp' wrappers though these may be added as
 needed.
An efficient C++ based implementation of the 'DEoptim'
 function which performs global optimization by differential evolution.  
 Its creation was motivated by trying to see if the old approximation "easier,
 shorter, faster: pick any two" could in fact be extended to achieving all
 three goals while moving the code from plain old C to modern C++.  The
 initial version did in fact do so, but a good part of the gain was due to 
 an implicit code review which eliminated a few inefficiencies which have
 since been eliminated in 'DEoptim'.
The 'Rcpp' package provides a C++ library to make it easier
    to use C++ with R. R and 'Rcpp' provide functions for a variety of
    statistical distributions. Several R packages make functions
    available to R for additional statistical distributions. However,
    to access these functions from C++ code, a costly call to the R
    functions must be made. 'RcppDist' provides a header-only C++ library
    with functions for additional statistical distributions that can be
    called from C++ when writing code using 'Rcpp' or 'RcppArmadillo'.
    Functions are available that return a 'NumericVector' as well as
    doubles, and for multivariate or matrix distributions, 'Armadillo'
    vectors and matrices. 'RcppDist' provides functions for the following
    distributions: the four parameter beta distribution; the location-
    scale t distribution; the truncated normal distribution; the
    truncated t distribution; a truncated location-scale t distribution;
    the triangle distribution; the multivariate normal distribution*;
    the multivariate t distribution*; the Wishart distribution*; and
    the inverse Wishart distribution*. Distributions marked with an
    asterisk rely on 'RcppArmadillo'.
This package is based on the C++ code from Yusuke Sugomori,
             which implements basic machine learning methods with 
             many layers (deep learning), including dA (Denoising Autoencoder), 
             SdA (Stacked Denoising Autoencoder), RBM (Restricted Boltzmann machine) and 
             DBN (Deep Belief Nets).
Dynamic Programming implemented in 'Rcpp'.  Includes example partition and out of sample fitting applications.  Also supplies additional custom coders for the 'vtreat' package.
R and 'Eigen' integration using 'Rcpp'.
 'Eigen' is a C++ template library for linear algebra: matrices, vectors,
 numerical solvers and related algorithms.  It supports dense and sparse
 matrices on integer, floating point and complex numbers, decompositions of
 such matrices, and solutions of linear systems. Its performance on many
 algorithms is comparable with some of the best implementations based on
 'Lapack' and level-3 'BLAS'. The 'RcppEigen' package includes the header
 files from the 'Eigen' C++ template library (currently version 3.3.4). Thus
 users do not need to install 'Eigen' itself in order to use 'RcppEigen'.
 Since version 3.1.1, 'Eigen' is licensed under the Mozilla Public License
 (version 2); earlier version were licensed under the GNU LGPL version 3 or
 later. 'RcppEigen' (the 'Rcpp' bindings/bridge to 'Eigen') is licensed under
 the GNU GPL version 2 or later, as is the rest of 'Rcpp'.
Compiles 'C++' code using 'Rcpp', 'Eigen' and 'CppAD' to produce first and second order partial derivatives. Also provides an implementation of Faa' di Bruno's formula to combine the partial derivatives of composed functions, (see Hardy, M (2006) <arXiv:math/0601149v1>).
'Ensmallen' is a templated C++ mathematical optimization library 
 (by the 'MLPACK' team) that provides a simple set of abstractions for writing an
 objective function to optimize. Provided within are various standard and
 cutting-edge optimizers that include full-batch gradient descent techniques, 
 small-batch techniques, gradient-free optimizers, and constrained optimization.
 The 'RcppEnsmallen' package includes the header files from the 'Ensmallen' library
 and pairs the appropriate header files from 'armadillo' through the 
 'RcppArmadillo' package. Therefore, users do not need to install 'Ensmallen' nor
 'Armadillo' to use 'RcppEnsmallen'. Note that 'Ensmallen' is licensed under 
 3-Clause BSD, 'Armadillo' starting from 7.800.0 is licensed under Apache License 2,
 'RcppArmadillo' (the 'Rcpp' bindings/bridge to 'Armadillo') is licensed under 
 the GNU GPL version 2 or later. Thus, 'RcppEnsmallen' is also licensed under
 similar terms. Note that 'Ensmallen' requires a compiler that supports 
 'C++11' and 'Armadillo' 9.800 or later.
Examples for Seamless R and C++ integration
 The 'Rcpp' package contains a C++ library that facilitates the integration of
 R and C++ in various ways. This package provides some usage examples.
 Note that the documentation in this package currently does not cover all the
 features in the package. The site <http://gallery.rcpp.org> regroups a large
 number of examples for 'Rcpp'.
Access to a family of Gauss error functions for arbitrary complex arguments is provided via the 'Faddeeva' package by Steven G. Johnson (see <http://ab-initio.mit.edu/wiki/index.php/Faddeeva_Package> for more information).
The Google 'FarmHash' family of hash functions is used by
 the Google 'BigQuery' data warehouse via the 'FARM_FINGERPRINT'
 function. This package permits to calculate these hash digest
 fingerprints directly from R, and uses the included 'FarmHash'
 files written by G. Pike and copyrighted by Google, Inc.
Converting ascii text into (floating-point) numeric values is a
 very common problem. The 'fast_float' header-only C++ library by Daniel Lemire
 does it very well and very fast at up to or over to 1 gigabyte per second as
 described in more detail in <arXiv:2101.11408>. 'fast_float' is licensed under
 the Apache 2.0 license and provided here for use by other R packages via a simple
 'LinkingTo:' statement.
The 'getconf' command-line tool provided by 'libc' allows
 querying of a large number of system variables. This package provides
 similar functionality.
A fast implementation of the greedy algorithm for the set cover problem using 'Rcpp'.
'Rcpp' integration for 'GNU GSL' vectors and matrices
 The 'GNU Scientific Library' (or 'GSL') is a collection of numerical routines for
 scientific computing. It is particularly useful for C and C++ programs as it
 provides a standard C interface to a wide range of mathematical routines. There
 are over 1000 functions in total with an extensive test suite. The 'RcppGSL'
 package provides an easy-to-use interface between 'GSL' data structures and
 R using concepts from 'Rcpp' which is itself a package that eases the
 interfaces between R and C++. This package also serves as a prime example of
 how to build a package that uses 'Rcpp' to connect to another third-party
 library. The 'autoconf' script, 'inline' plugin and example package can all
 be used as a stanza to  write a similar package against another library.
Collection of functions to evaluate sequences, decode hidden states and estimate parameters from a single or multiple sequences of a discrete time Hidden Markov Model. The observed values can be modeled by a multinomial distribution for categorical/labeled emissions, a mixture of Gaussians for continuous data and also a mixture of Poissons for discrete values. It includes functions for random initialization, simulation, backward or forward sequence evaluation, Viterbi or forward-backward decoding and parameter estimation using an Expectation-Maximization approach.
'Hnswlib' is a C++ library for Approximate Nearest Neighbors. This 
 package provides a minimal R interface by relying on the 'Rcpp' package. See 
 <https://github.com/nmslib/hnswlib> for more on 'hnswlib'. 'hnswlib' is 
 released under Version 2.0 of the Apache License.
Header library and R functions to solve minimum cost bipartite matching problem 
 using Huhn-Munkres algorithm (Hungarian algorithm; <https://en.wikipedia.org/wiki/Hungarian_algorithm>;
 Kuhn (1955) <doi:10.1002/nav.3800020109>). 
 This is a repackaging of code written by Cong Ma in the GitHub repo <https://github.com/mcximing/hungarian-algorithm-cpp>.
R package based on 'Rcpp' for 'MeCab': Yet Another Part-of-Speech and Morphological Analyzer. 
	The purpose of this package is providing a seamless developing and analyzing environment for CJK texts.
	This package utilizes parallel programming for providing highly efficient text preprocessing 'posParallel()' function.
	For installation, please refer to README.md file.
Fast machine learning algorithms including matrix factorization 
    and divisive clustering for large sparse and dense matrices.
'MsgPack' header files are provided for use by R packages, along 
 with the ability to access, create and alter 'MsgPack' objects directly from R.
 'MsgPack' is an efficient binary serialization format. It lets you exchange
 data among multiple languages like 'JSON' but it is faster and smaller.
 Small integers are encoded into a single byte, and typical short strings
 require only one extra byte in addition to the strings themselves. This
 package provides headers from the 'msgpack-c' implementation for C and
 C++(11) for use by R, particularly 'Rcpp'. The included 'msgpack-c' headers
 are licensed under the Boost Software License (Version 1.0); the code added
 by this package as well the R integration are licensed under the GPL (>= 2).
 See the files 'COPYRIGHTS' and 'AUTHORS' for a full list of  copyright holders
 and contributors to 'msgpack-c'.
An example package which shows use of 'NLopt' functionality from
 C++ via 'Rcpp' without requiring linking, and relying just on 'nloptr' thanks
 to the exporting API added there by Jelmer Ypma. This package is a fully
 functioning, updated, and expanded version of the initial example by
 Julien Chiquet at <https://github.com/jchiquet/RcppArmadilloNLoptExample>
 also containing a large earlier pull request of mine.
A collection of open source libraries for numerical computing
    (numerical integration, optimization, etc.) and their integration with
    'Rcpp'.
High level functions for parallel programming with 'Rcpp'.
    For example, the 'parallelFor()' function can be used to convert the work of
    a standard serial "for" loop into a parallel one and the 'parallelReduce()'
    function can be used for accumulating aggregate or other values.
Allows to display a progress bar in the R
    console for long running computations taking place in c++ code,
    and support for interrupting those computations even in multithreaded
    code, typically using OpenMP.
'QuantLib' bindings are provided for R using 'Rcpp' via an updated
 variant of the header-only 'Quantuccia' project (put together initially by Peter
 Caspers) offering an essential subset of 'QuantLib' (and now maintained separately
 for the calendaring subset). See the included file 'AUTHORS' for a full list of
 contributors to both 'QuantLib' and 'Quantuccia'.
Connection to the 'Redis' key/value store using the
 C-language client library 'hiredis' (included as a fallback) with
 'MsgPack' encoding provided via 'RcppMsgPack' headers. It now also
 includes the pub/sub functions from the 'rredis' package.
Provides fast and efficient routines for
    common rolling / windowed operations. Routines for the
    efficient computation of windowed mean, median,
    sum, product, minimum, maximum, standard deviation
    and variance are provided.
The 'JSON' format is ubiquitous for data interchange, and the
 'simdjson' library written by Daniel Lemire (and many contributors) provides 
 a high-performance parser for these files which by relying on parallel 'SIMD'
 instruction manages to parse these files as faster than disk speed. See the
 <arXiv:1902.08318> paper for more details about 'simdjson'.  This package 
 parses 'JSON' from string, file, or remote URLs under a variety of settings.
R access to the Sequential Monte Carlo Template Classes
 by Johansen <doi:10.18637/jss.v030.i06> is provided. At present, four
 additional examples have been added, and the first example from the JSS
 paper has been extended. Further integration and extensions are planned.
The mature and widely-used C++ logging library 'spdlog' by Gabi Melman provides
 many desirable features. This package bundles these header files for easy use by R packages
 via a simple 'LinkingTo:' inclusion.
The 'Streamulus' (template, header-only) library by
 Irit Katriel (at <https://github.com/iritkatriel/streamulus>)
 provides a very powerful yet convenient framework for stream
 processing. This package connects 'Streamulus' to R by providing 
 both the header files and all examples.
Provides a C++11-style thread class and thread pool that can safely
    be interrupted from R. See Nagler (2021) <doi:10.18637/jss.v097.c01>.
R-level and C++-level functionality
    to generate random deviates from and calculate moments of a
    Truncated Normal distribution using the algorithm of Robert (1995) <DOI:10.1007/BF00143942>.
    In addition to RNG, functions for calculating moments, densities,
    and entropies are provided at both levels.
The configuration format defined by 'TOML' (which expands to
 "Tom's Obvious Markup Language") specifies an excellent format
 (described at <https://toml.io/en/>) suitable for both human editing
 as well as the common uses of a machine-readable format. This package
 uses 'Rcpp' to connect the 'cpptoml' parser written by Chase Geigle
 (in C++11) to R.
Provides functions to generating a vector of Universally Unique Identifiers (UUID).
  Used implementation from the Boost C++ library. Supported random (version 4) and name (version 5)
  UUIDs.
Provides the means to compile user-supplied C++ functions with
  'Rcpp' and retrieve an 'XPtr' that can be passed to other C++ components.
This header-only library provides modern, portable C++ wrappers for SIMD
    intrinsics and parallelized, optimized math implementations (SSE, AVX, NEON, AVX512).
    By placing this library in this package, we offer an efficient distribution system for
    Xsimd <https://github.com/xtensor-stack/xsimd> for R packages using CRAN.
This package provides access to some of the C level
        functions of the xts package.

  In its current state, the package is mostly a proof-of-concept to
        support adding useful functions, and does not yet add any of
        its own.
The Ziggurat generator for normally distributed random
 numbers, originally proposed by Marsaglia and Tsang (2000, 
 <doi:10.18637/jss.v005.i08>) has been improved upon a few times
 starting with Leong et al (2005, <doi:10.18637/jss.v012.i07>).
 This package provides an aggregation in order to compare different
 implementations in order to provide an 'faster but good enough'
 alternative for use with R and C++ code.
Performs parallel web crawling and web scraping. It is designed to crawl, parse and store web pages to produce data that can be directly used for analysis application. For details see Khalil and Fakir (2017) <DOI:10.1016/j.softx.2017.04.004>.
This function corrects the bias in estimated regression coefficients due to classical additive measurement error (i.e., within-person variation) in logistic regressions under the main study/external reliability study design and the main study/internal reliability study design. The output includes the naive and corrected estimators for the regression coefficients; for the variance estimates of the corrected estimators, the extra variation due to estimating the parameters in the measurement error model is ignored or taken into account. Reference: Carroll RJ, Ruppert D, Stefanski L, Crainiceanu CM (2006) <doi:10.1201/9781420010138>.
An implementation of functions for the analysis of crime incident or records
  management system data. The package implements analysis algorithms scaled for city
  or regional crime analysis units. The package provides functions for kernel density
  estimation for crime heat maps, geocoding using the 'Google Maps' API, identification 
  of repeat crime incidents, spatio-temporal map comparison across time intervals, 
  time series analysis (forecasting and decomposition), detection of optimal parameters 
  for the identification of near repeat incidents, and near repeat analysis with crime 
  network linkage.
Aims at loading Criteo online advertising campaign data into R.
    Criteo <http://www.criteo.com/> is an online advertising service that enables
    advertisers to display commercial ads to web users. The package provides
    an authentication process for R with the Criteo API <http://kb.criteo.com/
    advertising/content/5/27/en/api.html>. Moreover, the package features an
    interface to query campaign data from the Criteo API. The data can be downloaded
    and will be transformed into a R data frame.
Goldwin-Pierre correlogram. Research of critical periods in the past. Integrates a time series in a given window.
Fit a 2-parameter continual reassessment method (CRM) model (O'Quigley and Shen (1996), <doi: 10.2307/2532905>) regularized with L2 norm (Friedman et al. (2010), <doi: 10.18637/jss.v033.i01>) adjusted by the distance with the target dose limiting toxicity (DLT) rate.
'NanoString nCounter' is a medium-throughput platform that measures gene or microRNA expression levels. Here is a publication that introduces this platform: Malkov (2009) <doi:10.1186/1756-0500-2-80>. Here is the webpage of 'NanoString nCounter' where you can find detailed information about this platform <https://www.nanostring.com/scientific-content/technology-overview/ncounter-technology>. It has great clinical application, such as diagnosis and prognosis of cancer. Implements integrated system of random-coefficient hierarchical regression model to normalize data from 'NanoString nCounter' platform so that noise from various sources can be removed.
Client for various 'CrossRef' 'APIs', including 'metadata' search
    with their old and newer search 'APIs', get 'citations' in various formats
    (including 'bibtex', 'citeproc-json', 'rdf-xml', etc.), convert 'DOIs'
    to 'PMIDs', and 'vice versa', get citations for 'DOIs', and get links to
    full text of articles when available.
Provides easy symmetric file encryption using GPG with
    cryptographically strong defaults. Only symmetric encryption is 
    supported. GPG is pre-installed with most Linux distributions. 
    Windows users will need to install 'Gpg4win' (http://www.gpg4win.org/). 
    OS X users will need to install 'GPGTools' (https://gpgtools.org/).
R interface to the CSDP semidefinite programming library. Installs version 6.1.1 of CSDP from the COIN-OR website if required. An existing installation of CSDP may be used by passing the proper configure arguments to the installation command. See the INSTALL file for further details.
Cloth Simulation Filter (CSF) is an airborne LiDAR (Light Detection and Ranging) ground 
    points filtering algorithm  which is based on cloth simulation. It tries to simulate the interactions
    between the cloth nodes and the corresponding LiDAR points, the locations of the cloth nodes can be
    determined to generate an approximation of the ground surface <https://www.mdpi.com/2072-4292/8/6/501/htm>.
Provides a means to style plots through cascading style sheets.
    This separates the aesthetics from the data crunching in plots and charts.
Assists in the whole process of designing and evaluating Randomized Control Trials.
    Robust treatment assignment by strata/blocks, that handles misfits; 
    Power calculations of the minimum detectable treatment effect or minimum populations;
    Balance tables of T-test of covariates; 
    Balance Regression: (treatment ~ all x variables) with F-test of null model; 
    Impact_evaluation: Impact evaluation regressions. This function
    gives you the option to include control_vars, fixed effect variables,
    cluster variables (for robust SE), multiple endogenous variables and
    multiple heterogeneous variables (to test treatment effect heterogeneity)
    summary_statistics: Function that creates a summary statistics table with statistics 
    rank observations in n groups: Creates a factor variable with n groups. Each group has 
    a min and max label attach to each category.
    Athey, Susan, and Guido W. Imbens (2017) <arXiv:1607.00698>.
Predict fish year-class strength by calibration
  regression analysis of multiple recruitment index series.
Provides simplified methods for managing classic Rubik's cubes and many other modifications of it (such as NxNxN size cubes, void cubes and 8-coloured cubes - so called octa cubes). Includes functions of handling special syntax for managing such cubes; and different approach to plotting 3D cubes without using external libraries (for example 'OpenGL').
A wrapper for 'libcurl' <https://curl.se/libcurl/>
	Provides functions to allow one to compose general HTTP requests
        and provides convenient functions to fetch URIs, get & post
        forms, etc. and process the results returned by the Web server.
        This provides a great deal of control over the HTTP/FTP/...
        connection and the form of the request while providing a
        higher-level interface than is available just using R socket
        connections.  Additionally, the underlying implementation is
        robust and extensive, supporting FTP/FTPS/TFTP (uploads and
        downloads), SSL/HTTPS, telnet, dict, ldap, and also supports
        cookies, redirects, authentication, etc.
Provide an R interface for processing concentration-response datasets using Curvep, a response noise filtering algorithm. The algorithm was described in the publications (Sedykh A et al. (2011) <doi:10.1289/ehp.1002476> and Sedykh A (2016) <doi:10.1007/978-1-4939-6346-1_14>). Other parametric fitting approaches (e.g., Hill equation) are also adopted for ease of comparison. Also, methods for calculating the confidence interval around the activity metrics are also provided. The methods are based on the bootstrap approach to simulate the datasets (Hsieh J-H et al. <doi:10.1093/toxsci/kfy258>). The simulated datasets can be used to derive the baseline noise threshold in an assay endpoint. This threshold is critical in the toxicological studies to derive the point-of-departure (POD).
Defines classes and methods to process text-based
  cytogenetics using the CytoGPS web site, then import the results
  into R for further analysis and graphing.
Administrative regions and other spatial objects of the Czech Republic.
The native R functionalities only allow PDF exports of reference manuals. This shall be extended by converting the package documentation files into markdown files and combining them into a markdown version of the package reference manual.
Functions to convert Rd to 'roxygen' documentation. It can parse an
    Rd file to a list, create the 'roxygen' documentation and update the original
    R script (e.g. the one containing the definition of the function)
    accordingly. This package also provides utilities that can help developers
    build packages using 'roxygen' more easily. The 'formatR' package can be used
    to reformat the R code in the examples sections so that the code will be
    more readable.
Creates interactive analytic graphs with 'R'. It joins the data analysis power of R and the visualization libraries of JavaScript in one package. The package provides interactive networks, timelines, barplots, image galleries and evolving networks. Graphs are represented as 'D3.js' graphs embedded in a web page ready for its interactive analysis and exploration.
This function conducts variation partitioning and hierarchical partitioning to calculate the unique, shared (referred as to "common") and individual contributions of each predictor (or matrix) towards explained variation (R-square and adjusted R-square) on canonical analysis (RDA,CCA and db-RDA), applying the algorithm of Lai J.,Zou Y., Zhang J.,Peres-Neto P.(2022) Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca.hp R package.Methods in Ecology and Evolution,13: 782-788 <DOI:10.1111/2041-210X.13800>.
Provides basic functionalities for writing a module
    for http://datacanvas.io. The http://datacanvas.io is a big data
    analytics platform that helps data scientists to build, manage
    and share data pipelines.
Client for the web service methods provided
    by 'DataCite' (<https://www.datacite.org/>), including functions to interface with
    their 'RESTful' search API. The API is backed by 'Elasticsearch', allowing
    expressive queries, including faceting.
R implementation of the 'FAIR Data Pipeline API'. The 'FAIR Data 
    Pipeline' is intended to enable tracking of provenance of FAIR (findable, 
    accessible and interoperable) data used in epidemiological modelling.
R access to hundreds of millions data series from DBnomics API
    (<https://db.nomics.world/>).
Arps Decline Curve Analysis (DCA) models for production rate, cumulative production, nominal decline rate, the derivative of loss-ratio, and estimated ultimate recovery (EUR) predictions for oil and gas wells. Arps, J. J. (1945) <doi:10.2118/945228-G>. Robertson, S. (1988) <https://www.onepetro.org/general/SPE-18731-MS>.
Provides the tools to undertake estimation in
    Regression Discontinuity Designs. Both sharp and fuzzy designs are
    supported. Estimation is accomplished using local linear regression.
    A provided function will utilize Imbens-Kalyanaraman optimal
    bandwidth calculation. A function is also included to test the
    assumption of no-sorting effects.
Estimation of both single- and multiple-assignment Regression Discontinuity Designs 
    (RDDs). Provides both parametric (global) and non-parametric (local) estimation choices for
    both sharp and fuzzy designs, along with power analysis and assumption checks. 
    Introductions to the underlying logic and analysis of RDDs are in 
    Thistlethwaite, D. L., Campbell, D. T. (1960) <doi:10.1037/h0044319> and
    Lee, D. S., Lemieux, T. (2010) <doi:10.1257/jel.48.2.281>.
Density discontinuity testing (a.k.a. manipulation testing) is commonly employed in regression discontinuity designs and other program evaluation settings to detect perfect self-selection (manipulation) around a cutoff where treatment/policy assignment changes. This package implements manipulation testing procedures using the local polynomial density estimators: rddensity() to construct test statistics and p-values given a prespecified cutoff, rdbwdensity() to perform data-driven bandwidth selection, and rdplotdensity() to construct density plots.
Set of functions for Regression Discontinuity Design ('RDD'), for
    data visualisation, estimation and testing.
Allows caching of raw data directly in R code. This allows R
  scripts and R Notebooks to be shared and re-run on a machine without access
  to the original data. Cached data is encoded into an ASCII string that can
  be pasted into R code. When the code is run, the data is automatically
  loaded from the cached version if the original data file is unavailable.
  Works best for small datasets (a few hundred observations).
Data Envelopment Analysis for R, estimating robust DEA scores without and with environmental variables and doing returns-to-scale tests.
Classes and functions for modelling health care interventions 
  using decision trees and semi-Markov models. Mechanisms are provided for 
  associating an uncertainty distribution with each source variable and for
  ensuring transparency of the mathematical relationships between variables.
  The package terminology follows Briggs "Decision Modelling for Health
  Economic Evaluation" (2006, ISBN:978-0-19-852662-9).
Algorithms for solving a self-calibrated l1-regularized quadratic programming problem without parameter tuning. The algorithm, called DECODE, can handle high-dimensional data without cross-validation. It is found useful in high dimensional portfolio selection (see Pun (2018) <https://ssrn.com/abstract=3179569>) and large precision matrix estimation and sparse linear discriminant analysis (see Pun and Hadimaja (2019) <https://ssrn.com/abstract=3422590>).
The package provides functions for estimating the relevant
        dimension of a data set in feature spaces, applications to
        model selection, graphical illustrations and prediction.
The Resource Description Framework, or 'RDF' is a widely used
             data representation model that forms the cornerstone of the 
             Semantic Web. 'RDF' represents data as a graph rather than 
             the familiar data table or rectangle of relational databases.
             The 'rdflib' package provides a friendly and concise user interface
             for performing common tasks on 'RDF' data, such as reading, writing
             and converting between the various serializations of 'RDF' data,
             including 'rdfxml', 'turtle', 'nquads', 'ntriples', and 'json-ld';
             creating new 'RDF' graphs, and performing graph queries using 'SPARQL'.
             This package wraps the low level 'redland' R package which
             provides direct bindings to the 'redland' C library.  Additionally,
             the package supports the newer and more developer friendly
             'JSON-LD' format through the 'jsonld' package. The package
             interface takes inspiration from the Python 'rdflib' library.
Functions to interact with the 'Google DoubleClick for Publishers (DFP)' API 
    <https://developers.google.com/ad-manager/api/start> (recently renamed to 
    'Google Ad Manager'). This package is automatically compiled from the API WSDL 
    (Web Service Description Language) files to dictate how the API is structured. 
    Theoretically, all API actions are possible using this package; however, care 
    must be taken to format the inputs correctly and parse the outputs correctly. 
    Please see the 'Google Ad Manager' API reference <https://developers.google.com/ad-manager/api/rel_notes> 
    and this package's website <https://stevenmmortimer.github.io/rdfp/> for more 
    information, documentation, and examples.
Provides a client for (1) querying the DHS API for survey indicators
  and metadata (<https://api.dhsprogram.com/#/index.html>), (2) identifying surveys
  and datasets for analysis, (3) downloading survey datasets from the DHS website,
  (4) loading datasets and associate metadata into R, and (5) extracting variables
  and combining datasets for pooled analysis.
Methods for calculation and visualization of the Repertoire
    Dissimilarity Index. Citation: Bolen and Rubelt, et al (2017) 
    <doi:10.1186/s12859-017-1556-5>.
Functions and methods for manipulating SNOMED CT concepts.
    The package contains functions for loading the SNOMED CT release into
    a convenient R environment, selecting SNOMED CT concepts using regular
    expressions, and navigating the SNOMED CT ontology. It provides the
    'SNOMEDconcept' S3 class for a vector of SNOMED CT concepts (stored
    as 64-bit integers) and the 'SNOMEDcodelist' S3 class for a table
    of concepts IDs with descriptions. For more information about SNOMED CT
    visit <https://www.snomed.org/>.
A client library for 'The Guardian' (https://www.guardian.com/)
    and their API, this package allows users to search for Guardian articles and
    retrieve both the content and metadata.
A collection of functions to simulate
  dice rolls and the like. In particular, experiments and exercises can
  be performed looking at combinations and permutations of values in dice
  rolls and coin flips, together with the corresponding frequencies of
  occurrences. When applying each function, the user has to input the
  number of times (rolls, flips) to toss the dice. Needless to say, the more
  the tosses, the more the frequencies approximate the actual probabilities.
  Moreover, the package provides functions to generate non-transitive sets
  of dice (like Efron's) and to check whether a given set of dice is non-transitive
  with given probability.
The package has many function that helps to perform
        various quality check on the data.It basically provides many
        function that helps in performing Extrapolative data analysis.
The 'RDieHarder' package provides an R interface to 
 the 'DieHarder' suite of random number generators and tests that 
 was developed by Robert G. Brown and David Bauer, extending 
 earlier work by George Marsaglia and others. The 'DieHarder'
 library code is included.
We provide linear and nonlinear dimension reduction techniques.
	Intrinsic dimension estimation methods for exploratory analysis are also provided.
	For more details on the package, see the paper by You (2020) <arXiv:2005.11107>.
A common framework for calculating distance matrices.
Distance-sampling is a popular method for estimating density and 
  abundance of organisms in ecology. Rdistance contains routines that  
  assist with analysis of
  distance-sampling data collected on point or line transects. 
  Distance models are specified using regression-like formula (similar 
  to lm, glm, etc.). Abundance routines
  perform automated bootstrapping and automated detection-function 
  selection. Overall (study area) and site-level (transect or point) 
  abundance estimates are available. A large suite of classical, 
  parametric detection functions are 
  included along with some uncommon parametric 
  functions (e.g., Gamma, negative exponential) and non-parametric
  smoothed distance functions. Custom (user-defined) detection functions
  are easily implemented (see vignette). 
  The help files and vignettes have been 
  vetted by multiple authors and tested in workshop 
  settings.
The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. Under the local randomization approach, RD designs can be interpreted as randomized experiments inside a window around the cutoff. This package provides tools to perform randomization inference for RD designs under local randomization: rdrandinf() to perform hypothesis testing using randomization inference, rdwinselect() to select a window around the cutoff in which randomization is likely to hold, rdsensitivity() to assess the sensitivity of the results to different window lengths and null hypotheses and rdrbounds() to construct Rosenbaum bounds for sensitivity to unobserved confounders. See Cattaneo, Titiunik and Vazquez-Bare (2016) <https://rdpackages.github.io/references/Cattaneo-Titiunik-VazquezBare_2016_Stata.pdf> for further methodological details.
Imports real-time thermo cycler (qPCR) data from Real-time PCR
    Data Markup Language (RDML) and transforms to the appropriate formats of
    the 'qpcR' and 'chipPCR' packages. Contains a dendrogram visualization 
    for the structure of RDML object and GUI for RDML editing.
The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. The 'rdmulti' package provides tools to analyze RD designs with multiple cutoffs or scores: rdmc() estimates pooled and cutoff specific effects for multi-cutoff designs, rdmcplot() draws RD plots for multi-cutoff designs and rdms() estimates effects in cumulative cutoffs or multi-score designs. See Cattaneo, Titiunik and Vazquez-Bare (2020) <https://rdpackages.github.io/references/Cattaneo-Titiunik-VazquezBare_2020_Stata.pdf> for further methodological details.
Comprehensive toolkit for generating various numerical                                 representation schemes of DNA sequence. The descriptors and similarity
    scores included are extensively used in bioinformatics and chemogenomics.
A wrapper for the 'Deutsche Nationalbibliothek (German National
    Library) API', available at <https://www.dnb.de>. The German National Library is
    the German central archival library, collecting, archiving, bibliographically
    classifying all German and German-language publications, foreign
    publications about Germany, translations of German works, and the works of
    German-speaking emigrants published abroad between 1933 and 1945.
Test Statistics for Independence in High-Dimensional Datasets. This package consists of two functions to perform the complete independence test based on test statistics proposed by Bulut (unpublished yet) and suggested by Najarzadeh (2021) <doi: 10.1080/03610926.2019.1702699>. The Bulut's statistic is not sensitive to outliers in high-dimensional data, unlike one of Najarzadeh (2021) <doi: 10.1080/03610926.2019.1702699>. So, the Bulut's statistic can be performed robustly by using RDnp function.
Extends tools::Rd2txt() by adding customisable text and colour formatting to R documentation 
    contents. If used from a terminal, output will be displayed via file.show() otherwise contents
    will be printed in sections. Also provides stand-in replacements for ?() and help().
Wraps around the default help functionality in R. Instead of plain documentation files, documentation will show up as it does on <https://www.rdocumentation.org>, a platform that shows R documentation from 'CRAN', 'GitHub' and 'Bioconductor', together with informative stats to assess the package quality.
Get the category of content hosted by a domain. Use Shallalist <http://shalla.de/>,
    Virustotal (which provides access to lots of services) <https://www.virustotal.com/>,
    Alexa <https://aws.amazon.com/awis/>, DMOZ <https://curlie.org/>, University Domain list
    <https://github.com/Hipo/university-domains-list> or validated machine learning
    classifiers based on Shallalist data to learn about the kind of content hosted by a domain.
An R API Client for Valve's Dota2. RDota2 can be easily used 
    to connect to the Steam API and retrieve data for Valve's popular video 
    game Dota2. You can find out more about Dota2 at 
    <http://store.steampowered.com/app/570/>.
Create doxygen documentation for source code in R packages. 
  Includes a RStudio Addin, that allows to trigger the doxygenize process.
Pretty fast implementation of the Ramer-Douglas-Peucker algorithm for reducing the number of points on a 2D curve.
    Urs Ramer (1972), "An iterative procedure for the polygonal approximation of plane curves" <doi:10.1016/S0146-664X(72)80017-0>.
    David H. Douglas and Thomas K. Peucker (1973), "Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or its Caricature" <doi:10.3138/FM57-6770-U75U-7727>.
Functions for manipulation of R documentation objects,
    including functions reprompt() and ereprompt() for updating 'Rd'
    documentation for functions, methods and classes; 'Rd' macros for
    citations and import of references from 'bibtex' files for use in
    'Rd' files and 'roxygen2' comments; 'Rd' macros for evaluating and
    inserting snippets of 'R' code and the results of its evaluation or
    creating graphics on the fly; and many functions for manipulation of
    references and Rd files.
The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. The 'rdpower' package provides tools to perform power, sample size and MDE calculations in RD designs: rdpower() calculates the power of an RD design, rdsampsi() calculates the required sample size to achieve a desired power and rdmde() calculates minimum detectable effects. See Cattaneo, Titiunik and Vazquez-Bare (2019) <https://rdpackages.github.io/references/Cattaneo-Titiunik-VazquezBare_2019_Stata.pdf> for further methodological details.
Given item set, item representation vector, and item ratings, 
    find a subset with better relevance-diversity trade-off. Also provide 
    machine learning algorithm to learn item representations maximizing 
    log likelihood under DPP assumption. 
    References: [1]Laming Chen, Guoxin Zhang, and Hanning Zhou(2017)<https://lsrs2017.files.wordpress.com/2017/08/lsrs_2017_lamingchen.pdf> 
    [2]Laming Chen, Guoxin Zhang, and Hanning Zhou(2018)<https://papers.nips.cc/paper/2018/file/dbbf603ff0e99629dda5d75b6f75f966-Paper.pdf> 
    [3]Wilhelm, Mark & Ramanathan, Ajith & Bonomo, Alexander & Jain, Sagar & Chi, Ed & Gillenwater, Jennifer(2018)<doi:10.1145/3269206.3272018>.
Regression-discontinuity (RD) designs are quasi-experimental research designs popular in social, behavioral and natural sciences. The RD design is usually employed to study the (local) causal effect of a treatment, intervention or policy. This package provides tools for data-driven graphical and analytical statistical inference in RD	designs: rdrobust() to construct local-polynomial point estimators and robust confidence intervals for average treatment effects at the 	cutoff in Sharp, Fuzzy and Kink RD settings, rdbwselect() to perform bandwidth selection for the different procedures implemented, and rdplot() to conduct exploratory data analysis (RD plots).
External jars required for package 'Rdrools'.
Provides full programmatic access to the 'Dropbox' file hosting platform <https://dropbox.com>, including support for all standard file operations.
We provide a toolbox to fit and simulate a univariate or multivariate damped random walk process that is also known as an Ornstein-Uhlenbeck process or a continuous-time autoregressive model of the first order, i.e., CAR(1) or CARMA(1, 0). This process is suitable for analyzing univariate or multivariate time series data with irregularly-spaced observation times and heteroscedastic measurement errors. When it comes to the multivariate case, the number of data points (measurements/observations) available at each observation time does not need to be the same, and the length of each time series can vary. The number of time series data sets that can be modeled simultaneously is limited to ten in this version of the package. We use Kalman-filtering to evaluate the resulting likelihood function, which leads to a scalable and efficient computation in finding maximum likelihood estimates of the model parameters or in drawing their posterior samples. Please pay attention to loading the data if this package is used for astronomical data analyses; see the details in the manual. Also see Hu and Tak (2020) <arXiv:2005.08049>.
Interface to the Dryad "Solr" API, their "OAI-PMH" service, and
    fetch datasets. Dryad (<https://datadryad.org/>) is a curated host of
    data underlying scientific publications.
Provides functionality for carrying out estimation
    with data collected using Respondent-Driven Sampling. This includes
    Heckathorn's RDS-I and RDS-II estimators as well as Gile's Sequential
    Sampling estimator. The package is part of the "RDS Analyst" suite of
    packages for the analysis of respondent-driven sampling data.
    See Gile and Handcock (2010) <doi:10.1111/j.1467-9531.2010.01223.x> and
    Gile and Handcock (2015) <doi:10.1111/rssa.12091>.
R interface to DSDP semidefinite programming library. The DSDP software is a free open source implementation of an interior-point method for semidefinite programming. It provides primal and dual solutions, exploits low-rank structure and sparsity in the data, and has relatively low memory requirements for an interior-point method.
Provides a threads-type programming environment for R.
   The package gives the R programmer the clearer, more concise
   shared memory world view, and in some cases gives superior
   performance as well.  In addition, it enables parallel processing on
   very large, out-of-core matrices.
This package provides an R interface to Pete Warden's Data
        Science Toolkit.  See www.datasciencetoolkit.org for more
        information.  The source code for this package can be found at
        github.com/rtelmore/RDSTK Happy hacking!
A tree bootstrap method for estimating uncertainty in respondent-driven samples (RDS). Quantiles are estimated by multilevel resampling in such a way that preserves the dependencies of and accounts for the high variability of the RDS process.
We provide a toolbox to fit univariate and multivariate linear mixed models via data transforming augmentation. Users can also fit these models via typical data augmentation for a comparison. It returns either maximum likelihood estimates of unknown model parameters (hyper-parameters) via an EM algorithm or posterior samples of those parameters via a Markov chain Monte Carlo method. Also see Tak, You, Ghosh, Su, and Kelly (2019+) <doi:10.1080/10618600.2019.1704295> <arXiv:1911.02748>.
Implementation of density tracking by quadrature (DTQ) algorithms for stochastic differential equations (SDEs).  DTQ algorithms numerically compute the density function of the solution of an SDE with user-specified drift and diffusion functions.  The calculation does not require generation of sample paths, but instead proceeds in a deterministic fashion by repeatedly applying quadrature to the Chapman-Kolmogorov equation associated with a discrete-time approximation of the SDE.  The DTQ algorithm is provably convergent.  For several practical problems of interest, we have found the DTQ algorithm to be fast, accurate, and easy to use.
Functions for connecting to and interfacing with an 'Arduino' or similar device. Functionality includes uploading of sketches, setting and reading digital and analog pins, and rudimentary servo control. This project is not affiliated with the 'Arduino' company, <https://www.arduino.cc/>.
Handle climate data from the 'DWD' ('Deutscher Wetterdienst', see 
             <https://www.dwd.de/EN/climate_environment/cdc/cdc_node_en.html> for more information).
             Choose observational time series from meteorological stations with 'selectDWD()'.
             Find raster data from radar and interpolation according to <https://bookdown.org/brry/rdwd/raster-data.html>.
             Download (multiple) data sets with progress bars and no re-downloads through 'dataDWD()'.
             Read both tabular observational data and binary gridded datasets with 'readDWD()'.
Compute spatially explicit land-use metrics for stream survey sites in GRASS GIS and R as an open-source implementation of IDW-PLUS (Inverse Distance Weighted Percent Land Use for Streams). The package includes functions for preprocessing digital elevation and streams data, and one function to compute all the spatially explicit land use metrics described in Peterson et al. (2011) <doi:10.1111/j.1365-2427.2010.02507.x> and previously implemented by Peterson and Pearse (2017) <doi:10.1111/1752-1688.12558> in ArcGIS-Python as IDW-PLUS.
Pattern matching, extraction, replacement and other string
  processing operations using Google's RE2 <https://github.com/google/re2>
  regular-expression engine. Consistent interface (similar to 'stringr').
  RE2 uses finite-automata based techniques, and offers a
  fast and safe alternative to backtracking regular-expression engines
  like those used in 'stringr', 'stringi' and other PCRE implementations.
Interactive data tables for R, based on the 'React Table'
    JavaScript library. Provides an HTML widget that can be used in 'R Markdown'
    documents and 'Shiny' applications, or viewed from an R console.
Provides various features to streamline and enhance the styling of interactive 
    reactable tables with easy-to-use and highly-customizable functions and themes. 
    Apply conditional formatting to cells with data bars, color scales, color tiles,
    and icon sets. Utilize custom table themes inspired by popular websites such 
    and bootstrap themes. Apply sparkline line & bar charts 
    (note this feature requires the 'dataui' package which can be downloaded from
    <https://github.com/timelyportfolio/dataui>).
    Increase the portability and reproducibility of reactable tables by embedding images 
    from the web directly into cells. Save the final table output as a static image or 
    interactive file.
Building interactive web applications with R is incredibly easy
  with 'shiny'. Behind the scenes, 'shiny' builds a reactive graph that can
  quickly become intertwined and difficult to debug. 'reactlog'
  (Schloerke 2019) <doi:10.5281/zenodo.2591517> provides a visual insight into
  that black box of 'shiny' reactivity by constructing a directed dependency
  graph of the application's reactive state at any time point in a reactive
  recording.
Make it easy to use 'React' in R with 'htmlwidget' scaffolds,
              helper dependency functions, an embedded 'Babel' 'transpiler',
              and examples.
Routines for developing models that describe reaction and advective-diffusive transport in one, two or three dimensions.
  Includes transport routines in porous media, in estuaries, and in bodies with variable shape.
Functions for reading and decompressing the DBC (compressed DBF) files. Please note that this is the file format used by the Brazilian Ministry of Health (DATASUS) to publish healthcare datasets. It is not related to the FoxPro or CANdb DBC file formats.
Opens complete record(s) with .gb extension from the NCBI/GenBank Nucleotide database and returns a list containing shaped record(s). These kind of files contains detailed records of DNA samples (locus, organism, type of sequence, source of the sequence...). An example of record can be found at <https://www.ncbi.nlm.nih.gov/nuccore/HE799070>.
Implements a high performance C++ parser 
    for 'ActiGraph' 'GT3X'/'GT3X+' data format (with extension '.gt3x') 
    for 'accelerometer' samples. Activity samples can be easily read into a
    matrix or data.frame.  This allows for storing the raw 'accelerometer' 
    samples in the original binary format to reserve space.
Loads Axon Binary Files (both 'ABF' and 'ABF2') created by Axon Instruments/Molecular Devices software such as 'pClamp'.
Downloads, imports, and tidies time series data from the 
    Australian Bureau of Statistics <https://www.abs.gov.au/>.
Identifies and reads Windows BMP, JPEG, PNG, and TIFF format bitmap
    images. Identification defaults to the use of the magic number embedded in
    the file rather than the file extension. Reading of JPEG and PNG image
    depends on libjpg and libpng libraries. See file INSTALL for details if
    necessary.
Reads data files acquired by Bruker Daltonics' matrix-assisted
    laser desorption/ionization-time-of-flight mass spectrometer of the *flex
    series.
Combine multiple data files from a common directory.
    The data files will be read into R and bound together, creating a
    single large data.frame. A general function is provided along with
    a specific function for data that was collected using the open-source
    experiment builder 'OpenSesame' <https://osdoc.cogsci.nl/>.
It helps you to read (.dim) images with CRS directly into R programming. One can import both Sentinel 1 and 2 images or any processed data with this software.
A set of functions to simplify reading data from files. The main function, reader(), should read most common R datafile types without needing any parameters except the filename. Other functions provide simple ways of handling file paths and extensions, and automatically detecting file format and structure.
Providing just one primary function, 'readit' uses a set of reasonable
    heuristics to apply the appropriate reader function to the given file path.
    As long as the data file has an extension, and the data is (or can be
    coerced to be) rectangular, readit() can probably read it.
Import data written in the JCAMP-DX format. This is an instrument-independent format used in the field of spectroscopy. Examples include IR, NMR, and Raman spectroscopy. See the vignette for background and supported formats.  The official JCAMP-DX site is <http://www.jcamp-dx.org/>.
Functions for reading data sets in different formats
  for testing machine learning tools are provided. This allows to run
  a loop over several data sets in their original form, for example
  if they are downloaded from UCI Machine Learning Repository.
  The data are not part of the package and have to be downloaded
  separately.
You can use the function Read.mnist() to read data and arrange them properly from MNIST dataset (the open handwriting digit database <http://yann.lecun.com/exdb/mnist/>). With this package, you can conveniently get all of necessary informations and then immediately start to check whether your machine learning algorithm works well. It can automatically recognize the type of dataset and returns the informations in corresponding structure.
Tools to read various file types into one list of data structures, usually, but not limited to, data frames.
  Excel files are read sheet-wise, i.e., all or a selection of sheets can be read. Field delimiters and decimal
  separators are determined automatically.
Functions for reading mass spectrometry data in mzXML format.
Wraps 'tiny_obj_loader' C++ library for reading the 'Wavefront' OBJ
    3D file format including both mesh objects and materials files. The
    resultant R objects are either structured to match the 'tiny_obj_loader'
    internal data representation or in a form directly compatible with the 'rgl'
    package.
Import ODS (OpenDocument Spreadsheet) into R as a data frame. Also support writing data frame into ODS file.
Reads in text from 'unstructured' modern Microsoft Office files (XML based files) such as Word and PowerPoint.
    This does not read in structured data (from Excel or Access) as there are many other great packages to that do so already.
The goal of 'readr' is to provide a fast and friendly way to
    read rectangular data (like 'csv', 'tsv', and 'fwf').  It is designed
    to flexibly parse many types of data found in the wild, while still
    cleanly failing when data unexpectedly changes.
Download up-to-date data from the Reserve Bank of Australia 
    in a tidy data frame. Package includes functions to download current and 
    historical statistical tables 
    (<https://www.rba.gov.au/statistics/tables/>) and forecasts 
    (<https://www.rba.gov.au/publications/smp/forecasts-archive.html>). Data
    includes a broad range of Australian macroeconomic and financial time
    series.
A convenient way to read fixed-width ASCII polling datasets from providers like the Roper Center <https://ropercenter.cornell.edu>.
Read Statistical Data and Metadata Exchange (SDMX) XML data. 
    This the main transmission format used in official statistics. Data can be imported from
    local SDMX-ML files or a SDMX web-service and will be read in 'as is' into a dataframe object.
    The 'RapidXML' C++ library <http://rapidxml.sourceforge.net> is used to parse the XML data.
The goal of 'readsdr' is to bridge the design capabilities from
    specialised System Dynamics software with the powerful numerical tools 
    offered by 'R' libraries. The package accomplishes this goal by parsing 
    'XMILE' files ('Vensim' and 'Stella') models into 'R' objects to construct 
    networks (graph theory); 'ODE' functions for 'Stan'; and inputs to simulate
    via 'deSolve' as described in Duggan (2016) <doi:10.1007/978-3-319-34043-2>.
Read and write labelled sparse matrices in text format as used by
    software such as 'SVMLight', 'LibSVM', 'ThunderSVM', 'LibFM', 'xLearn', 'XGBoost', 'LightGBM',
    and others. Supports labelled data for regression, classification (binary, multi-class, multi-label),
    and ranking (with 'qid' field), and can handle header metadata and comments in files.
Function to read and write the 'Stata' file format.
Functions for importing and handling text files and formatted text
    files with additional meta-data, such including '.csv', '.tab', '.json', '.xml',
    '.html', '.pdf', '.doc', '.docx', '.rtf', '.xls', '.xlsx', and others.
'Praat' is a widely used tool for manipulating, annotating and 
    analyzing speech and acoustic data. It stores annotation data in a 
    format called a 'TextGrid'. This package provides a way to read these 
    files into R.
Quickly read contents from files or URLs. Functions provide a simple, consistent, and
    performant interface for reading (all at once or line-by-line) the contents of files or web pages.
Reads and writes data frames to 'SQLite'
    databases while preserving time zones (for POSIXct columns),
    projections (for 'sfc' columns), units (for 'units' columns), levels
    (for factors and ordered factors) and classes for logical, Date and
    'hms' columns.  It also logs changes to tables and provides more
    informative error messages.
Import excel files into R. Supports '.xls' via the embedded
    'libxls' C library <https://github.com/libxls/libxls> and '.xlsx' via
    the embedded 'RapidXML' C++ library <http://rapidxml.sourceforge.net>.
    Works on Windows, Mac and Linux without external dependencies.
Import data from 'Excel' binary (.xlsb) workbooks into R.
A framework for unit testing for realistic minimalists,
    where we distinguish between expected, acceptable, current, fallback,
    ideal, or regressive behaviour. It can also be used for monitoring
    third-party software projects for changes.
Fits a multivariate value-added model (VAM), see Broatch, Green, and Karl (2018) <doi:10.32614/RJ-2018-033> and Broatch and Lohr (2012) <doi:10.3102/1076998610396900>, with normally distributed test scores and a binary outcome indicator. A pseudo-likelihood approach, Wolfinger (1993) <doi:10.1080/00949659308811554>, is used for the estimation of this joint generalized linear mixed model.  The inner loop of the pseudo-likelihood routine (estimation of a linear mixed model) occurs in the framework of the EM algorithm presented by Karl, Yang, and Lohr (2013) <DOI:10.1016/j.csda.2012.10.004>. This material is based upon work supported by the National Science Foundation under grants DRL-1336027 and DRL-1336265.
The rearrangement operator (Hardy,
        Littlewood, and Polya 1952) for univariate, bivariate, and
        trivariate point estimates of monotonic functions. The package
        additionally provides a function that creates simultaneous
        confidence intervals for univariate functions and applies the
        rearrangement operator to these confidence intervals.
Arrange data by a set of methods. Use rearrangers to reorder
    data points and mutators to change their values. From basic utilities,
    to centering the greatest value, to swirling in 3-dimensional space,
    'rearrr' enables creativity when plotting and experimenting with data.
Functions for cleaning and summarising water quality data for use in National Pollutant Discharge Elimination Service (NPDES) permit reasonable potential analyses and water quality-based effluent limitation calculations. Procedures are based on those contained in the "Technical Support Document for Water Quality-based Toxics Control", United States Environmental Protection Agency (1991).
Collection of models and analysis methods used in regional and urban economics and (quantitative) economic geography, e.g. measures of inequality, regional disparities and convergence, regional specialization as well as accessibility and spatial interaction models.
Kiefer-Wolfowitz maximum likelihood estimation for mixture models
    and some other density estimation and regression methods based on convex
    optimization.  See Koenker and Gu (2017) REBayes: An R Package for Empirical
    Bayes Mixture Methods, Journal of Statistical Software, 82, 1--26, 
    <DOI:10.18637/jss.v082.i08>.
A programmatic client for the eBird database 
    (<https://ebird.org/home>), including functions for searching for bird 
    observations by geographic location (latitude, longitude), eBird 
    hotspots, location identifiers, by notable sightings, by region, and by 
    taxonomic name.
Random univariate and multivariate finite mixture model generation, estimation, clustering, latent class analysis and classification. Variables can be continuous, discrete, independent or dependent and may follow normal, lognormal, Weibull, gamma, Gumbel, binomial, Poisson, Dirac, uniform or circular von Mises parametric families.
Build regular expressions piece by piece using human readable code.
    This package is designed for interactive use.  For package development, use
    the rebus.* dependencies.
Build regular expressions piece by piece using human readable code.
    This package contains core functionality, and is primarily intended to be
    used by package developers.
Build regular expressions piece by piece using human readable code.
    This package contains date and time functionality, and is primarily intended
    to be used by package developers.
Build regular expressions piece by piece using human readable code.
    This package contains number-related functionality, and is primarily intended
    to be used by package developers.
Build regular expressions piece by piece using human readable code.
    This package contains Unicode functionality, and is primarily intended to be
    used by package developers.
Relevant Component Analysis (RCA) tries to find a linear
    transformation of the feature space such that the effect of irrelevant
    variability is reduced in the transformed space.
Tools are provided for estimating, testing, and simulating
    abundance in a two-event (Petersen) mark-recapture experiment. Functions are
    given to calculate the Petersen, Chapman, and Bailey estimators and associated
    variances. However, the principal utility is a set of functions to simulate
    random draws from these estimators, and use these to conduct hypothesis
    tests and power calculations. Additionally, a set of functions are provided
    for generating confidence intervals via bootstrapping. Functions are also
    provided to test abundance estimator consistency under complete or partial
    stratification, and to calculate stratified or partially stratified estimators.
    Functions are also provided to calculate recommended sample sizes.
    Referenced methods can be found in Arnason et al. (1996) <ISSN:0706-6457>, 
    Bailey (1951) <DOI:10.2307/2332575>, 
    Bailey (1952) <DOI:10.2307/1913>, 
    Chapman (1951) NAID:20001644490, 
    Cohen (1988) ISBN:0-12-179060-6, 
    Darroch (1961) <DOI:10.2307/2332748>, and
    Robson and Regier (1964) <ISSN:1548-8659>.
Provides functions allowing the user to recursively extract frequent patterns and confident rules according to indicators of minimal support and minimal confidence. These functions are described in "Recursive Association Rule Mining" Abdelkader Mokkadem, Mariane Pelletier, Louis Raimbault (2020) <arXiv:2011.14195>.
A toolset for 3D reconstruction and analysis of excavations. It provides methods to reconstruct natural and artificial surfaces based on field measurements. This allows to spatially contextualize documented subunits and features. Intended to be part of a 3D visualization workflow.
The 'Echo nest' <http://the.echonest.com> is the industry's leading
    music intelligence company, providing developer with deepest understanding of
    music content and music fans. This package can be used to access artist's data
    including songs, blogs, news, reviews etc. Song's data including audio summary,
    style, danceability, tempo etc can also be accessed.
Pathways in a database could have many redundancies among
        them. This package allows the user to set a maximum value for
        the proportion of these redundancies.
A recipe prepares your data for modeling. We provide an
    extensible framework for pipeable sequences of feature engineering
    steps provides preprocessing tools to be applied to data. Statistical
    parameters for the steps can be estimated from an initial data set and
    then applied to other data sets. The resulting processed output can
    then be used as inputs for statistical or machine learning models.
Functions to assist in performing probabilistic record linkage and
    deduplication: generating pairs, comparing records, em-algorithm for
    estimating m- and u-probabilities, forcing one-to-one matching. Can also be
    used for pre- and post-processing for machine learning methods for record
    linkage.
Functions to assist in performing probabilistic record linkage and
    deduplication: generating pairs, comparing records, em-algorithm for
    estimating m- and u-probabilities
    (I. Fellegi & A. Sunter (1969) <doi:10.1080/01621459.1969.10501049>, 
    T.N. Herzog, F.J. Scheuren, & W.E. Winkler (2007), 
    "Data Quality and Record Linkage Techniques", ISBN:978-0-387-69502-0),
    forcing one-to-one matching. Can also be
    used for pre- and post-processing for machine learning methods for record
    linkage. Focus is on memory, CPU performance and flexibility.
The analysis of different aspects of biodiversity requires specific algorithms. 
	For example, in regionalisation analyses, the high frequency of ties and zero values in 
	dissimilarity matrices produced by Beta-diversity turnover produces hierarchical 
	cluster dendrograms whose topology and bootstrap supports are affected by the order of 
	rows in the original matrix. Moreover, visualisation of biogeographical regionalisation 
	can be facilitated by a combination of hierarchical clustering and multi-dimensional 
	scaling. The recluster package provides robust techniques to visualise and analyse 
	pattern of biodiversity and to improve occurrence data for cryptic taxa. 
	Other functions 	related to recluster (e.g. the biodecrypt family) are currently 
	available in GitHub at <https://github.com/leondap/recluster>.
Provides an interface and a C++ implementation of the RecMap MP2
  construction heuristic (Panse C. (2018) <doi:10.18637/jss.v086.c01>).
  This algorithm draws maps according to a given statistical value
  (e.g., election results, population or epidemiological data).
  The basic idea of the RecMap algorithm is that each map region
  (e.g., different countries) is represented by a rectangle.
  The area of each rectangle represents the statistical value given
  as input (maintain zero cartographic error). Documentation about the usage
  of the recmap algorithm is provided by a vignette included in this package.
The ecocrop model estimates environmental suitability for plants using a limiting factor approach for plant growth following Hackett (1991) <doi:10.1007/BF00045728>. The implementation in this package is fast and flexible: it allows for the use of any (environmental) predictor variable. Predictors can be either static (for example, soil pH) or dynamic (for example, monthly precipitation).
Contains functions to interface with variables and variable details sheets, including recoding variables and converting them to PMML.
Simple, easy to use, and flexible functionality for recoding variables. It allows for simple piecewise definition of transformations.
Annotate text with entities and the relations between them. Annotate areas of interest in images with your labels. 
    Providing 'htmlwidgets' bindings to the 'recogito' <https://github.com/recogito/recogito-js> and 'annotorious' <https://github.com/recogito/annotorious> libraries.
Automatic, semi-automatic, and manual functions for
    generating color maps from images. The idea is to simplify
    the colors of an image according to a metric that is useful for
    the user, using deterministic methods whenever possible.
    Many images will be clustered well using the out-of-the-box
    functions, but the package also includes a toolbox of functions
    for making manual adjustments (layer merging/isolation, blurring,
    fitting to provided color clusters or those from another image, etc).
    Also includes export methods for other color/pattern analysis packages
    (pavo, patternize, colordistance).
Goal of 'recom' package is to remove all the comments of the Rscript file, and to reduce Rscript file size for better performance.
Turns nested lists into data.frames in an orderly manner.
Calculates evaluation metrics for implicit-feedback recommender systems
  that are based on low-rank matrix factorization models, given the fitted model
  matrices and data, thus allowing to compare models from a variety of libraries.
  Metrics include P@K (precision-at-k, for top-K recommendations), R@K (recall at k),
  AP@K (average precision at k), NDCG@K (normalized discounted cumulative gain at k),
  Hit@K (from which the 'Hit Rate' is calculated), RR@K (reciprocal rank at k, from
  which the 'MRR' or 'mean reciprocal rank' is calculated), ROC-AUC (area under the
  receiver-operating characteristic curve), and PR-AUC (area under the
  precision-recall curve).
  These are calculated on a per-user basis according to the ranking of items induced
  by the model, using efficient multi-threaded routines. Also provides functions
  for creating train-test splits for model fitting and evaluation.
Provides a research infrastructure to test and develop
    recommender algorithms including UBCF, IBCF, FunkSVD and association
    rule-based algorithms.
Provides the Book-Crossing Dataset for the package recommenderlab.
Provides the Jester Dataset for package recommenderlab.
Implements solutions to canonical models of Economics such as Monopoly Profit Maximization, Cournot's Duopoly, Solow (1956, <doi:10.2307/1884513>) growth model and Mankiw, Romer and Weil (1992, <doi:10.2307/2118477>) growth model.
Functions for reconstructing individual-level data (time, status, arm) from Kaplan-MEIER curves published in academic journals (e.g. NEJM, JCO, JAMA). The individual-level data can be used for re-analysis, meta-analysis, methodology development, etc. This package was used to generate the data for commentary such as Sun, Rich, & Wei (2018) <doi:10.1056/NEJMc1808567>. Please see the vignette for a quickstart guide.
Functions to reconstruct sessions from web log or other user trace data
             and calculate various metrics around them, producing tabular,
             output that is compatible with 'dplyr' or 'data.table' centered processes.
A lightweight toolkit to validate new observations when computing
    their predictions with a predictive model. The validation process 
    consists of two steps: (1) record relevant statistics and meta data of the
    variables in the original training data for the predictive model and
    (2) use these data to run a set of basic validation tests on the new set of 
    observations.
Provides functions for linking and deduplicating data sets.
  Methods based on a stochastic approach are implemented as well as 
  classification algorithms from the machine learning domain. For details, 
  see our paper "The RecordLinkage Package: Detecting Errors in Data" 
  Sariyar M / Borg A (2010) <doi:10.32614/RJ-2010-017>.
Functions for generating k-record values and k-record
        times
Statistical tools based on the probabilistic properties of the 
    record occurrence in a sequence of independent and identically distributed 
    continuous random variables. In particular, tools to prepare a time series 
    as well as distribution-free trend and change-point tests and graphical 
    tools to study the record occurrence.
R wrapper of the 'libmf' library
    <https://www.csie.ntu.edu.tw/~cjlin/libmf/> for recommender
    system using matrix factorization. It is typically used to
    approximate an incomplete matrix using the product of two
    matrices in a latent space. Other common names for this task
    include "collaborative filtering", "matrix completion",
    "matrix recovery", etc. High performance multi-core parallel
    computing is supported in this package.
Computes revisitation metrics for trajectory data, such as the number of revisitations for each location as well as the time spent for that visit and the time since the previous visit. Also includes functions to plot data.
Includes algorithms to facilitate the assessment of extinction
    risk of species according to the IUCN (International Union for Conservation of
    Nature, see <http://www.iucn.org> for more information) red list criteria.
Contains implementations of recurrent event data analysis routines
    including (1) survival and recurrent event data simulation from
    stochastic process point of view by the thinning method
    proposed by Lewis and Shedler (1979) <doi:10.1002/nav.3800260304>
    and the inversion method introduced in Cinlar (1975, ISBN:978-0486497976),
    (2) the mean cumulative function (MCF) estimation by the
    Nelson-Aalen estimator of the cumulative hazard rate function,
    (3) two-sample recurrent event responses comparison with the pseudo-score
    tests proposed by Lawless and Nadeau (1995) <doi:10.2307/1269617>,
    (4) gamma frailty model with spline rate function following
    Fu, et al. (2016) <doi:10.1080/10543406.2014.992524>.
The aim of this package is to manipulate relational
   data models in R.
   It provides functions to create, modify and export data models
   in json format.
   It also allows importing models created
   with 'MySQL Workbench' (<https://www.mysql.com/products/workbench/>).
   These functions are accessible through a graphical user
   interface made with 'shiny'.
   Constraints such as types, keys, uniqueness and mandatory fields are
   automatically checked and corrected when editing a model.
   Finally, real data can be confronted to a model to check their compatibility.
Access data stored in 'REDCap' databases using the Application
    Programming Interface (API).  'REDCap' (Research Electronic Data CAPture;
    <https://projectredcap.org>) is
    a web application for building and managing online surveys and databases
    developed at Vanderbilt University.  The API allows users to access data
    and project meta data (such as the data dictionary) from the web
    programmatically.  The 'redcapAPI' package facilitates the process of
    accessing data with options to prepare an analysis-ready data set
    consistent with the definitions in a database's data dictionary.
Export all data, including metadata, from a REDCap (Research
    Electronic Data Capture) Project via the REDCap API
    <https://projectredcap.org/wp-content/resources/REDCapTechnicalOverview.pdf>.
    The exported (meta)data will be processed and formatted into a stand alone R
    data package which can be installed and shared between researchers.  Several
    default reports are generated as vignettes in the resulting package.
Encapsulates functions to streamline calls from R to the REDCap
    API.  REDCap (Research Electronic Data CAPture) is a web application for
    building and managing online surveys and databases developed at Vanderbilt
    University.  The Application Programming Interface (API) offers an avenue
    to access and modify data programmatically, improving the capacity for
    literate and reproducible programming.
A collection of tools for extracting structured data from <https://www.reddit.com/>.
Computes quality control for daily precipitation datasets, 
    reconstructs the original series by estimating precipitation in missing values, 
    creates new series in a specified pair of coordinates and creates grids.
Standard and extensible Eddy-Covariance data post-processing 
  (Wutzler et al. (2018) <doi:10.5194/bg-15-5015-2018>)
  includes  
  uStar-filtering, gap-filling, and flux-partitioning.
  The Eddy-Covariance (EC)  micrometeorological technique quantifies continuous 
  exchange fluxes of gases, energy, and momentum between an ecosystem and the atmosphere.
  It is important for understanding ecosystem dynamics and upscaling exchange fluxes.
  (Aubinet et al. (2012) <doi:10.1007/978-94-007-2351-1>).
  This package inputs pre-processed (half-)hourly data and supports further processing. 
  First, a quality-check and filtering is performed based on the relationship between 
  measured flux and friction
  velocity (uStar) to discard biased data 
  (Papale et al. (2006) <doi:10.5194/bg-3-571-2006>).
  Second, gaps in the data are filled based on information from environmental conditions
  (Reichstein et al. (2005) <doi:10.1111/j.1365-2486.2005.001002.x>).
  Third, the net flux of carbon dioxide is partitioned
  into its gross fluxes in and out of the ecosystem by night-time 
  based and day-time based approaches
  (Lasslop et al. (2010) <doi:10.1111/j.1365-2486.2009.02041.x>).
Extension to 'REddyProc' that allows reading data from netCDF files.
Biologically relevant, yet mathematically sound constraints are used
    to compute the propensity and thence infer the dominant direction of reactions
    of a generic biochemical network. The reactions must be unique and their 
    number must exceed that of the reactants,i.e., reactions >= reactants + 2.  
            'ReDirection', computes the null space of a user-defined stoichiometry
    matrix. The spanning non-zero and unique reaction vectors (RVs) are 
    combinatorially summed to generate one or more subspaces recursively.  
            Every reaction is represented as a sequence of identical components 
    across all RVs of a particular subspace. The terms are evaluated with 
    (biologically relevant bounds, linear maps, tests of convergence, descriptive
    statistics, vector norms) and the terms are classified into forward-, reverse- 
    and equivalent-subsets. Since, these are mutually exclusive the probability 
    of occurrence is binary (all, 1; none, 0).
            The combined propensity of a reaction is the p1-norm of the 
    sub-propensities, i.e., sum of the products of the probability and maximum
    numeric value of a subset (least upper bound, greatest lower bound). This, 
    if strictly positive is the probable rate constant, is used to infer dominant
    direction and annotate a reaction as "Forward (f)", "Reverse (b)" or 
    "Equivalent (e)".
            The inherent computational complexity (NP-hard) per iteration suggests
    that a suitable value for the number of reactions is around 20.
            Three functions comprise ReDirection. These are check_matrix() and 
    reaction_vector() which are internal, and calculate_reaction_vector()
            which is external.
Providing the container for the DockerParallel package.
An optimized method for identifying mutually 
    exclusive genomic events.  Its main contribution is a
    statistical analysis based on the Poisson-Binomial 
    distribution that takes into account that some samples
    are more mutated than others. See [Canisius, Sander, John WM Martens,
    and  Lodewyk FA Wessels. (2016)  "A novel independence test for
    somatic alterations in cancer shows that 
    biology drives mutual exclusivity but chance explains
    most co-occurrence." Genome biology 17.1 : 1-17. <doi:10.1186/s13059-016-1114-x>].
    The mutations matrices are sparse matrices. The method developed takes 
    advantage of the advantages of this type of matrix to save 
    time and computing resources.
Enables researchers to sample redistricting plans from a pre-specified
    target distribution using Sequential Monte Carlo and Markov Chain Monte Carlo
    algorithms.  The package allows for the implementation of various constraints in
    the redistricting process such as geographic compactness and population parity
    requirements. Tools for analysis such as computation of various summary statistics
    and plotting functionality are also included. The package implements methods
    described in Fifield, Higgins, Imai and Tarr (2020) <doi:10.1080/10618600.2020.1739532>,
    Fifield, Imai, Kawahara, and Kenny (2020) <doi:10.1080/2330443X.2020.1791773>,
    and McCartan and Imai (2020) <arXiv:2008.06131>.
Reliable and flexible tools for scoring redistricting plans using 
  common measures and metrics. These functions provide key direct access to 
  tools useful for non-simulation analyses of redistricting plans, such as for 
  measuring compactness or partisan fairness. Tools are designed to work with 
  the 'redist' package seamlessly.
Provides methods to parse, query and serialize information
    stored in the Resource Description Framework (RDF). RDF is described at <https://www.w3.org/TR/rdf-primer/>.
    This package supports RDF by implementing an R interface to the Redland RDF C library,
    described at <https://librdf.org/docs/api/index.html>. In brief, RDF provides a structured graph
    consisting of Statements composed of Subject, Predicate, and Object Nodes.
A toolbox created by members of the International Union for
    Conservation of Nature (IUCN) Red List of Ecosystems Committee for
    Scientific Standards. Primarily, it is a set of tools suitable
    for calculating the metrics required for making assessments of species and
    ecosystems against the IUCN Red List of Threatened Species and the IUCN Red
    List of Ecosystems categories and criteria. See the IUCN website for
    detailed guidelines, the criteria, publications and other information.
An implementation of 'EDM' algorithms based on research software developed for internal use at the Sugihara Lab ('UCSD/SIO').  The package is implemented with 'Rcpp' wrappers around the 'cppEDM' library.  It implements the 'simplex' projection method from Sugihara & May (1990) <doi:10.1038/344734a0>, the 'S-map' algorithm from Sugihara (1994) <doi:10.1098/rsta.1994.0106>, convergent cross mapping described in Sugihara et al. (2012) <doi:10.1126/science.1227079>, and, 'multiview embedding' described in Ye & Sugihara (2016) <doi:10.1126/science.aag0863>.
Provide color schemes for maps (and other graphics) based on the
    color palettes of several Microsoft(r) products. Forked from 'RColorBrewer' v1.1-2.
A collection of 'HTML', 'JavaScript', 'CSS' and fonts
  assets that generate 'Redoc' documentation from an 'OpenAPI' Specification:
   <https://redoc.ly/redoc/>.
Regularization by Denoising uses a denoising engine to solve
  many image reconstruction ill-posed inverse problems. This is a R
  implementation of the algorithm developed by Romano et.al. (2016) <arXiv:1611.02862>. Currently,
  only the gradient descent optimization framework is implemented. Also,
  only the median filter is implemented as a denoiser engine. However,
  (almost) any denoiser engine can be plugged in. There are currently available
  3 reconstruction tasks: denoise, deblur and super-resolution. And again,
  any other task can be easily plugged into the main function 'RED'.
A 'hiredis' wrapper that includes support for
    transactions, pipelining, blocking subscription, serialisation of
    all keys and values, 'Redis' error handling with R errors.
    Includes an automatically generated 'R6' interface to the full
    'hiredis' API.  Generated functions are faithful to the
    'hiredis' documentation while attempting to match R's argument
    semantics.  Serialisation must be explicitly done by the user, but
    both binary and text-mode serialisation is supported.
A data mining approach for longitudinal and clustered data, 
        which combines the structure of mixed effects model with tree-based 
        estimation methods. See Sela, R.J. and Simonoff, J.S. (2012) RE-EM 
        trees: a data mining approach for longitudinal and clustered data 
        <doi:10.1007/s10994-011-5258-3>.
Implements an objective Bayes intrinsic conditional autoregressive 
    prior. This model provides an objective Bayesian approach 
    for modeling spatially correlated areal data using an intrinsic conditional autoregressive prior on a vector of 
    spatial random effects.
Allows users to easily create references to R objects then
    'dereference' when needed or modify in place without using reference 
    classes, environments, or active bindings as workarounds. Users can 
    also create expression references that allow subsets of any object to 
    be referenced or expressions containing references to multiple 
    objects.
This is a collection of tools to allow the medical professional to
 calculate appropriate reference ranges (intervals) with confidence intervals around
 the limits for diagnostic purposes.
Indirect method for the estimation of reference intervals using Real-World Data ('RWD'). It takes routine measurements 
	of diagnostic tests, containing pathological and non-pathological samples as input and uses sophisticated
	statistical methods to derive a model describing the distribution of the non-pathological samples. This distribution can then be 
	used to derive reference intervals. 
	Furthermore, the package offers functions for printing and plotting the results of the algorithm. 
	The method is described in detail in Ammer T., Schuetzenmeister A., Prokosch H.-U., Rauh M., Rank C.M., Zierk J. "refineR: A Novel Algorithm for Reference Interval Estimation from Real-World Data". Scientific Reports (2021) [accepted July 21, 2021].
These functions take a character vector as input, identify and 
  cluster similar values, and then merge clusters together so their values 
  become identical. The functions are an implementation of the key collision 
  and ngram fingerprint algorithms from the open source tool Open Refine 
  <https://openrefine.org/>. More info on key collision and ngram fingerprint 
  can be found here <https://docs.openrefine.org/next/technical-reference/clustering-in-depth/>.
Fits measurement error models using Monte Carlo Expectation Maximization (MCEM). For specific details on the methodology, see: Greg C. G. Wei & Martin A. Tanner (1990) A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms, Journal of the American Statistical Association, 85:411, 699-704 <doi:10.1080/01621459.1990.10474930> For more examples on measurement error modelling using MCEM, see the 'RMarkdown' vignette: "'refitME' R-package tutorial".
Provides tools for importing and working with bibliographic
    references. It greatly enhances the 'bibentry' class by providing a class
    'BibEntry' which stores 'BibTeX' and 'BibLaTeX' references, supports 'UTF-8'
    encoding, and can be easily searched by any field, by date ranges, and by
    various formats for name lists (author by last names, translator by full names,
    etc.). Entries can be updated, combined, sorted, printed in a number of styles,
    and exported. 'BibTeX' and 'BibLaTeX' '.bib' files can be read into 'R' and
    converted to 'BibEntry' objects. Interfaces to 'NCBI Entrez', 'CrossRef', and
    'Zotero' are provided for importing references and references can be created
    from locally stored 'PDF' files using 'Poppler'. Includes functions for citing
    and generating a bibliography with hyperlinks for documents prepared with
    'RMarkdown' or 'RHTML'.
A tool for refining data frame with formulas.
An R package for estimating conditional multivariate reference regions. 
    The reference region is non parametrically estimated using a kernel density estimator.
    Covariates effects on the multivariate response means vector and variance-covariance
    matrix, thus on the region shape, are estimated by flexible additive predictors. 
    Continuous covariates non linear effects might be estimated using penalized splines smoothers.
    Confidence intervals for the covariates estimated effects might be derived from
    bootstrap resampling. Kernel density bandwidth can be estimated with different methods, including
    a method that optimize the region coverage. Numerical, and graphical, summaries
    can be obtained by the user in order to evaluate reference region performance with real data.
    Full mathematical details can be found in <doi:10.1002/sim.9163> and <doi:10.1007/s00477-020-01901-1>.
Connects dataframes/tables with a remote data source. Raw data downloaded
  from the data source can be further processed and transformed using data preparation 
  code that is also baked into the dataframe/table. Refreshable dataframes can 
  be shared easily (e.g. as R data files). Their users do not need to care about
  the inner workings of the data update mechanisms.
Provides subsets with reference semantics, i.e. subsets
    which automatically reflect changes in the original object, and which
    optionally update the original object when they are changed.
Access the 'Refuge' API, a web-application for locating trans and 
    intersex-friendly restrooms, including unisex and accessible restrooms. 
    Includes data on the location of restrooms, along with directions, 
    comments, user ratings and amenities. Coverage is global, but data is 
    most comprehensive in the United States.
    See <https://www.refugerestrooms.org/api/docs/> for full API documentation.
Methods for regression for functional
    data, including function-on-scalar, scalar-on-function, and
    function-on-function regression. Some of the functions are applicable to
    image data.
Shiny applications are produced for different types of popular functional data analyses. The functional data analyses are implemented in the refund package, then refund.shiny reads in the refund object and implements an object-specific set of plots based on the object class using S3.
Contains basic tools for visualizing, interpreting, and building regression models.  It has been designed for use with the book Introduction to Regression and Modeling with R by Adam Petrie, Cognella Publishers, ISBN: 978-1-63189-250-9 <https://titles.cognella.com/introduction-to-regression-and-modeling-with-r-9781631892509>.
Performs regularization of differential item functioning (DIF) parameters 
    in item response theory (IRT) models (Belzak & Bauer, 2020)
    <https://pubmed.ncbi.nlm.nih.gov/31916799/> using a penalized expectation-maximization
    algorithm.
Produces population distribution of disease risk and statistical risk categories, and predicts risks for individuals with genotype information.
Provides wrappers around base::grep() where the first argument
    is standardized to take the data object. This makes it less of a pain to use
    regular expressions with 'magrittr' or other pipe operators.
'shiny' extension that adds regular expression filtering capabilities to 
  the choice vector of the select list.
Traditional noise filtering methods aim at removing noisy samples from a classification dataset. This package adapts classic and recent filtering techniques to be used in regression problems. To do this, it uses the approach proposed in Martin (2021) [<doi:10.1109/ACCESS.2021.3123151>]. Thus, the goal of the implemented noise filters is to eliminate samples with noise in regression datasets.
A set of functions used to automate commonly used methods in
    regression analysis. This includes plotting interactions, and calculating
    simple slopes, standardized coefficients, regions of significance
    (Johnson & Neyman, 1936; cf. Spiller et al., 2012), etc. See the reghelper
    documentation for more information, documentation, and examples.
Calculates intra-regional and inter-regional similarities based on user-provided
    spatial vector objects (regions) and spatial raster objects (cells with values).
    Implemented metrics include inhomogeneity, isolation 
    (Haralick and Shapiro (1985) <doi:10.1016/S0734-189X(85)90153-7>, 
    Jasiewicz et al. (2018) <doi:10.1016/j.cageo.2018.06.003>), 
    and distinction (Nowosad (2021) <doi:10.1080/13658816.2021.1893324>).
A fast tool to conquer the difficulties to convert various region names and administration division codes of Chinese regions. The current version enables seamlessly converting Chinese regions' formal names, common-used names, and codes between each other at the city level from 1986 to 2019.
Validating sub-national statistical typologies, re-coding across 
    standard typologies of sub-national statistics, and making valid aggregate
    level imputation, re-aggregation, re-weighting and projection down to 
    lower hierarchical levels to create meaningful data panels and time series.
A method for registering curves (functional data) that are generated from exponential family distributions.  This 
    implements the algorithms described in 'Wrobel et al. (2019)' <doi:10.1111/biom.12963>. Curve registration is an active area of 
    research in functional data analysis, and can be used to better understand patterns in functional data by separating curves 
    into phase and amplitude variability. This software handles both binary and continuous functional data, and is
    especially applicable in accelerometry and wearable technology.
Provides a generic infrastructure for creating and using registries.
An algorithm is proposed to estimate regression kink model proposed by the paper, Lixiong Yang and Jen-Je Su (2018) <doi:10.1016/j.jimonfin.2018.06.002>.
Regularized (polychotomous) logistic regression 
  by Gibbs sampling. The package implements subtly different 
  MCMC schemes with varying efficiency depending on the data type 
  (binary v. binomial, say) and the desired estimator (regularized maximum
  likelihood, or Bayesian maximum a posteriori/posterior mean, etc.) through a 
  unified interface.
Mediation analysis for multiple mediators by penalized structural equation models with different types of penalties depending on whether there are multiple mediators and only one exposure and one outcome variable (using sparse group lasso) or multiple exposures, multiple mediators, and multiple outcome variables (using lasso, L1, penalties).
This is an extension of the regression-based causal mediation analysis first proposed by Valeri and VanderWeele (2013) <doi:10.1037/a0031034> and Valeri and VanderWeele (2015) <doi:10.1097/EDE.0000000000000253>). It supports including effect measure modification by covariates(treatment-covariate and mediator-covariate product terms in mediator and outcome regression models). It also accommodates the original 'SAS' macro and 'PROC CAUSALMED' procedure in 'SAS' when there is no effect measure modification. Linear and logistic models are supported for the mediator model. Linear, logistic, loglinear, Poisson, negative binomial, Cox, and accelerated failure time (exponential and Weibull) models are supported for the outcome model.
Network-based regularization has achieved success in variable selection for 
    high-dimensional biological data due to its ability to incorporate correlations among 
    genomic features. This package provides procedures of network-based variable selection 
    for generalized linear models (Ren et al. (2017) <doi:10.1186/s12863-017-0495-5> and 
    Ren et al. (2019) <doi:10.1002/gepi.22194>). Two recent additions are the robust network 
    regularization for the survival response and the network regularization for continuous 
    response. Functions for other regularization methods will be included in the forthcoming 
    upgraded versions.
Machine learning algorithm for predicting and imputing time series. It can automatically set all the parameters needed, thus in the minimal configuration it only requires the target variable and the dependent variables if present. It can address large problems with hundreds or thousands of dependent variables and problems in which the number of dependent variables is greater than the number of observations. Moreover it can be used not only for time series but also for any other real valued target variable. The algorithm implemented includes a Bayesian stochastic search methodology for model selection and a robust estimation based on bootstrapping. 'rego' is fast because all the code is C++.
A function to plot a regression nomogram of regression 
    objects. Covariate distributions are superimposed on nomogram scales and the plot
	can be animated to allow on-the-fly changes to distribution representation and to 
	enable outcome calculation.
Provides R6 classes, methods and utilities to construct,
    analyze, summarize, and visualize regression models.
The graphical approach is proposed as a general framework for clinical trial designs involving multiple hypotheses, where decisions are made only based on the observed marginal p-values. A reverse graphical approach starts from a set of singleton graphs, and gradually add vertices into graphs until rejection of a set of hypotheses is made. See Gou, J. (2020). Reverse graphical approaches for multiple test procedures. Technical Report.
Functions to fit Gaussian linear model by maximising the
        residual log likelihood where the covariance structure can be
        written as a linear combination of known matrices.  Can be used
        for multivariate models and random effects models.  Easy
        straight forward manner to specify random effects models,
        including random interactions. Code now optimised to use
        Sherman Morrison Woodbury identities for matrix inversion in
        random effects models. We've added the ability to fit models
        using any kernel as well as a function to return the mean and
        covariance of random effects conditional on the data (best
        linear unbiased predictors, BLUPs).
        Clifford and McCullagh (2006)
        <https://www.r-project.org/doc/Rnews/Rnews_2006-2.pdf>.
The expander functions rely on the mathematics developed for the Hessian-definiteness invariance theorem for linear projection transformations of variables, described in authors' paper, to generate the full, high-dimensional gradient and Hessian from the lower-dimensional derivative objects. This greatly relieves the computational burden of generating the regression-function derivatives, which in turn can be fed into any optimization routine that utilizes such derivatives. The theorem guarantees that Hessian definiteness is preserved, meaning that reasoning about this property can be performed in the low-dimensional space of the base distribution. This is often a much easier task than its equivalent in the full, high-dimensional space. Definiteness of Hessian can be useful in selecting optimization/sampling algorithms such as Newton-Raphson optimization or its sampling equivalent, the Stochastic Newton Sampler. Finally, in addition to being a computational tool, the regression expansion framework is of conceptual value by offering new opportunities to generate novel regression problems.
Perform a supervised data analysis on a database through a 'shiny' graphical interface. It includes methods such as linear regression, penalized regression, k-nearest neighbors, decision trees, ada boosting, extreme gradient boosting, random forest, neural networks, deep learning and support vector machines.
Compiling regression results into a publishable format, conducting post-hoc hypothesis testing, and plotting moderating effects (the effect of X on Y becomes stronger/weaker as Z increases).
Performs Random Subspace Method (RSM) for high-dimensional linear regression to obtain variable importance measures. The final model is chosen based on validation set or Generalized Information Criterion.
Implementation of the methods described in the paper with the above title: Langsrud, Ø. (2019) <doi:10.1007/s11222-018-9848-9>. The package can be used to generate synthetic or hybrid continuous microdata, and the relationship to the original data can be controlled in several ways. A function for replacing suppressed tabular cell frequencies with decimal numbers is included.
Uses both ridge and lasso penalties (and extensions) to penalize
    specific parameters in structural equation models. The package offers additional
    cost functions, cross validation, and other extensions beyond traditional structural
    equation models. Also contains a function to perform exploratory mediation (XMed).
Computes linear Bayesian spectral estimates from multirate
	data for second-order stationary time series. Provides credible
	intervals and methods for plotting various spectral estimates.
For a sequence of event occurence times, we are interested in
  finding subsequences in it that are too "regular". We define regular as being
  significantly different from a homogeneous Poisson process. The departure from
  the Poisson process is measured using a L1 distance. See Di and Perlman 2007
  for more details.
In repeated measures studies with extreme large or small values it is common 
  that the subjects measurements on average are closer to the mean of the basic population. 
  Interpreting possible changes in the mean in such situations can lead to biased results 
  since the values were not randomly selected, they come from truncated sampling.
  This method allows to estimate the range of means where treatment effects are likely to occur 
  when regression toward the mean is present. 
  Ostermann, T., Willich, Stefan N. & Luedtke, Rainer. (2008). Regression toward the mean - a detection method for unknown population mean based on Mee and Chua's algorithm. BMC Medical Research Methodology.<doi:10.1186/1471-2288-8-52>.
Tools for linear, nonlinear and nonparametric regression
             and classification.  Novel graphical methods for assessment 
             of parametric models using nonparametric methods. One 
             vs. All and All vs. All multiclass classification, optional
             class probabilities adjustment.  Nonparametric regression 
             (k-NN) for general dimension, local-linear option.  Nonlinear 
             regression with Eickert-White method for dealing with 
             heteroscedasticity.  Utilities for converting time series
             to rectangular form.  Utilities for conversion between
             factors and indicator variables.  Some code related to
             "Statistical Regression and Classification: from Linear
             Models to Machine Learning", N. Matloff, 2017, CRC,
             ISBN 9781498710916.
It performs regularized simultaneous component based data
    integration for multiblock data.
A simple wrapper for calculating regression weights as defined by Aronow and 
    Samii (2015) <doi:10.1111/ajps.12185>. Given a model object and a term of
    interest, 'regweight' will calculate implicit regression weights and provide a variety of useful
    visualizations and summary statistics.
Population genetic data such as 'Single Nucleotide
        Polymorphisms' (SNPs) is often used to identify genomic regions
        that have been under recent natural or artificial selection
        and might provide clues about the molecular mechanisms of adaptation. 
        One approach, the concept of an 'Extended Haplotype Homozygosity' (EHH), 
        introduced by (Sabeti 2002) <doi:10.1038/nature01140>, has given rise to 
        several statistics designed for whole genome scans. 
        The package provides functions to compute three of these,
        namely: 'iHS' (Voight 2006) <doi:10.1371/journal.pbio.0040072> for
        detecting positive or 'Darwinian' selection within a single population as well as
        'Rsb' (Tang 2007) <doi:10.1371/journal.pbio.0050171> and 
        'XP-EHH' (Sabeti 2007) <doi:10.1038/nature06250>, targeted
        at differential selection between two populations. 
        Various plotting functions are included to facilitate
        visualization and interpretation of these statistics.
Contains example data for the 'rehh' package.
Facilitates replication of Twitter-based research by handling
    common programming tasks needed when downloading tweets.  Specifically, it ensures a 
    user does not exceed Twitter’s rate limits, and it saves tweets in moderately sized files.  
    While a user could perform these tasks in their own code, doing so may be beyond the 
    capabilities of many users.
Performs model-free reinforcement learning in R. This implementation enables the learning
    of an optimal policy based on sample sequences consisting of states, actions and rewards. In 
    addition, it supplies multiple predefined reinforcement learning algorithms, such as experience 
    replay. Methodological details can be found in Sutton and Barto (1998) <ISBN:0262039249>.
Functions from the book "Reinsurance: Actuarial and Statistical Aspects" (2017) by Hansjoerg Albrecher, Jan Beirlant and Jef Teugels <http://www.wiley.com/WileyCDA/WileyTitle/productCd-0470772689.html>.
Search R files for not installed packages and run install.packages.
Application of reinsurance treaties to claims portfolios. 
            The package creates a class Claims whose objective is to 
            store claims and premiums, on which different treaties can be applied.
            A statistical analysis can then be applied to measure the impact of
            reinsurance, producing a table or graphical output. This package can
            be used for estimating the impact of reinsurance on several portfolios
            or for pricing treaties through statistical analysis. Documentation
            for the implemented methods can be found in "Reinsurance: Actuarial
            and Statistical Aspects" by Hansjöerg Albrecher, Jan Beirlant,
            Jozef L. Teugels (2017, ISBN: 978-0-470-77268-3) and 
            "REINSURANCE: A Basic Guide to Facultative and Treaty Reinsurance"
            by Munich Re (2010) <https://www.munichre.com/site/mram/get/documents_E96160999/mram/assetpool.mr_america/PDFs/3_Publications/reinsurance_basic_guide.pdf>.
Provides several metrics for assessing relative importance in linear models. These can be printed, plotted and bootstrapped. The recommended metric is lmg, which provides a decomposition of the model explained variance into non-negative contributions. There is a version of this package available that additionally provides a new and also recommended metric called pmvd. If you are a non-US user, you can download this extended version from Ulrike Groempings web site.
Functions to safely map from a vector of keys to a vector of values, determine properties of a given relation, or ensure a relation conforms to a given type, such as many-to-many, one-to-many, injective, surjective, or bijective. Permits default return values for use similar to a vectorised switch statement, as well as safely handling large vectors, NAs, and duplicate mappings.
Inference of relatedness coefficients from a bi-allelic genotype matrix using a Maximum Likelihood estimation, Laporte, F., Charcosset, A. and Mary-Huard, T. (2017) <doi:10.1111/biom.12634>.
Data structures and algorithms for k-ary relations with
  arbitrary domains, featuring relational algebra, predicate functions,
  and fitters for consensus relations.
Relaxed Lasso is a generalisation of the Lasso shrinkage
        technique for linear regression. Both variable selection and
        parameter estimation is achieved by regular Lasso, yet both
        steps do not necessarily use the same penalty parameter. The
        results include all standard Lasso solutions but allow often
        for sparser models while having similar or even slightly better
        predictive performance if many predictor variables are present.
        The package depends on the LARS package.
Tools for the comparison of distributions. This includes nonparametric estimation of the relative distribution PDF and CDF and numerical summaries as described in "Relative Distribution Methods in the Social Sciences" by Mark S. Handcock and Martina Morris, Springer-Verlag, 1999, Springer-Verlag, ISBN 0387987789.
Functions to obtain an important number of electoral indicators described in the package, which can be divided into two large sections: The first would be the one containing the indicators of electoral disproportionality, such as, Rae index, Loosemore–Hanby index, etc. The second group is intended to study the dimensions of the party system vote, through the indicators of electoral fragmentation, polarization, volatility, etc. Moreover, multiple seat allocation simulations can also be performed based on different allocation systems, such as the D'Hondt method, Sainte-Laguë, etc. Finally, some of these functions have been built so that, if the user wishes, the data provided by the Spanish Ministry of Home Office for different electoral processes held in Spain can be obtained automatically. All the above will allow the users to carry out deep studies on the results obtained in any type of electoral process. The methods are described in: Oñate, Pablo and Ocaña, Francisco A. (1999, ISBN:9788474762815); Ruiz Rodríguez, Leticia M. and Otero Felipe, Patricia (2011, ISBN:9788474766226).
This function computes the relative entropy (H) as an index for qualitative variation of a factor.
Calculates relevance and significance values for
  simple models and for many types of regression models.
  These are introduced in
  'Stahel, Werner A.' (2021)
  "New relevance and significance measures to replace p-values."
  <https://stat.ethz.ch/~stahel/relevance/stahel-relevance2103.pdf>.
A method for fitting the entire regularization path of the
    reluctant generalized additive model (RGAM) for linear regression, logistic, 
    Poisson and Cox regression models. See Tay, J. K., and Tibshirani, R., (2019)
    <arXiv:1912.01808> for details.
Checking the reliability of predictions via the CORP approach,
    which generates provably statistically 'C'onsistent, 'O'ptimally binned, and
    'R'eproducible reliability diagrams using the 'P'ool-adjacent-violators
    algorithm. See Dimitriadis, Gneiting, Jordan (2021) <doi:10.1073/pnas.2016191118>.
Functions to facilitate inference on the relative importance of predictors in a linear or generalized linear model, and a couple of useful Tcl/Tk widgets.
Performs Principal Components Analysis (also known as PCA) dimensionality reduction in the context of a linear regression. In most cases, PCA dimensionality reduction is performed independent of the response variable for a regression. This captures the majority of the variance of the model's predictors, but may not actually be the optimal dimensionality reduction solution for a regression against the response variable. An alternative method, optimized for a regression against the response variable, is to use both PCA and a relative importance measure. This package applies PCA to a given data frame of predictors, and then calculates the relative importance of each PCA factor against the response variable. It outputs ordered factors that are optimized for model fit. By performing dimensionality reduction with this method, an individual can achieve a the same r-squared value as performing just PCA, but with fewer PCA factors. References: Yuri Balasanov (2017) <https://ilykei.com>.
It offers random numbers generation from members of the truncated multivariate elliptical family of distribution such as the truncated versions of the Normal, Student-t, Pearson VII, Slash, Logistic, among others. Particular distributions can be provided by specifying the density generating function. It also computes the first two moments (covariance matrix as well) for some particular distributions.
Makes relationship inference involving DNA mixtures with unknown profiles.
A set of tools to explore the behaviour statistics used for forensic DNA interpretation when close relatives are involved. The package also offers some useful tools for exploring other forensic DNA situations.
Contains functions for analysing relative survival data, including nonparametric estimators of net (marginal relative) survival, relative survival ratio, crude mortality, methods for fitting  and checking additive and multiplicative regression models, transformation approach, methods for dealing with population mortality tables. Work has been described in Pohar Perme, Pavlic (2018) <doi:10.18637/jss.v087.i08>.
Classes and functions for analyzing the performance of portfolios relative to a benchmark.
Calculate endogenous network effects in event sequences and fit relational event models (REM): Using network event sequences (where each tie between a sender and a target in a network is time-stamped), REMs can measure how networks form and evolve over time. Endogenous patterns such as popularity effects, inertia, similarities, cycles or triads can be calculated and analyzed over time.
The rema package implements a permutation-based approach for binary 
	meta-analyses of 2x2 tables, founded on conditional logistic regression, 
	that provides more reliable statistical tests when heterogeneity is 
	observed in rare event data (Zabriskie et al. 2021 <doi:10.1002/sim.9142>). 
	To adjust for the effect of heterogeneity, this method conditions on the 
	sufficient statistic of a proxy for the heterogeneity effect as opposed to
	estimating the heterogeneity variance. While this results in the model not
	strictly falling under the random-effects framework, it is akin to a 
	random-effects approach in that it assumes differences in variability due 
	to treatment. Further, this method does not rely on large-sample 
	approximations or continuity corrections for rare event data. This method
	uses the permutational distribution of the test statistic instead of
	asymptotic approximations for inference. The number of observed events 
	drives the computation complexity for creating this permutational 
	distribution. Accordingly, for this method to be computationally feasible,
	it should only be applied to meta-analyses with a relatively low number of
	observed events. To create this permutational distribution, a network 
	algorithm, based on the work of Mehta et al. (1992) <doi:10.2307/1390598> 
	and Corcoran et al. (2001) <doi:10.1111/j.0006-341x.2001.00941.x>, is 
	employed using C++ and integrated into the package.
Automatically creates separate regression models for different spatial 
    regions. The prediction surface is smoothed using a regional border smoothing 
    method. If regional models are continuous, the resulting prediction surface is 
    continuous across the spatial dimensions, even at region borders. Methodology 
    is described in Wagstaff (2021) <https://digitalcommons.usu.edu/etd/8065/>.
A small wrapper on 'regexpr' to extract the matches and
    captured groups from the match of a regular expression to a character
    vector.
Wrappers on 'regexpr' and 'gregexpr' to return the match
    results in tidy data frames.
An 'RStudio' addin providing shortcuts for writing in 'Markdown'. This package provides a series of 
             functions that allow the user to be more efficient when using 'Markdown'. For example, you can select 
             a word, and put it in bold or in italics, or change the alignment of elements inside you Rmd. The idea
             is to map all the functionalities from 'remedy' on keyboard shortcuts, so that it provides an interface 
             close to what you can find in any other text editor.
Insert/extract text "reminders" into/from function source code 
    comments or as the "comment" attribute of any object.  
    The former can be handy in development as reminders of e.g. argument
    requirements, expected objects in the calling environment, required options
    settings, etc. The latter can be used to provide information of the object and 
    as simple manual "tooltips" for users, among other things.
Reference-based multiple imputation of ordinal and binary responses under Bayesian
          framework, as described in Wang and Liu (2022) <arXiv:2203.02771>. Methods for 
          missing-not-at-random include Jump-to-Reference (J2R), Copy Reference (CR), and Delta 
          Adjustment which can generate tipping point analysis.
Implements TRACDS (Temporal Relationships 
    between Clusters for Data Streams), a generalization of 
    Extensible Markov Model (EMM). TRACDS adds a temporal or order model
    to data stream clustering by superimposing a dynamically adapting
    Markov Chain. Also provides an implementation of EMM (TRACDS on top of tNN 
    data stream clustering). Development of this 
    package was supported in part by NSF IIS-0948893 and R21HG005912 from 
    the National Human Genome Research Institute. Hahsler and Dunham (2010) <doi:10.18637/jss.v035.i05>.
Empirical orthogonal teleconnections in R.
    'remote' is short for 'R(-based) EMpirical Orthogonal TEleconnections'.
    It implements a collection of functions to facilitate empirical
    orthogonal teleconnection analysis. Empirical Orthogonal Teleconnections
    (EOTs) denote a regression based approach to decompose spatio-temporal
    fields into a set of independent orthogonal patterns. They are quite
    similar to Empirical Orthogonal Functions (EOFs) with EOTs producing
    less abstract results. In contrast to EOFs, which are orthogonal in both
    space and time, EOT analysis produces patterns that are orthogonal in
    either space or time.
A set of utilities for client/server computing with R, controlling
    a remote R session (the server) from a local one (the client).  Simply set
    up a server (see package vignette for more details) and connect to it from
    your local R session ('RStudio', terminal, etc).  The client/server
    framework is a custom 'REPL' and runs entirely in your R session without the
    need for installing a custom environment on your system.  Network
    communication is handled by the 'ZeroMQ' library by way of the 'pbdZMQ'
    package.
Download and install R packages stored in 'GitHub', 'GitLab', 
    'Bitbucket', 'Bioconductor', or plain 'subversion' or 'git' repositories. 
    This package provides the 'install_*' functions in 'devtools'. Indeed most 
    of the code was copied over from 'devtools'.
T (extent of the primary tumor), N (absence or presence and extent of regional lymph node metastasis) and M (absence or presence of distant metastasis) are three components to describe the anatomical tumor extent. TNM stage is important in treatment decision-making and outcome predicting. The existing oropharyngeal Cancer (OPC) TNM stages have not made distinction of the two sub sites of Human papillomavirus positive (HPV+) and Human papillomavirus negative (HPV-) diseases. We developed novel criteria to assess performance of the TNM stage grouping schemes based on parametric modeling adjusting on important clinical factors. These criteria evaluate the TNM stage grouping scheme in five different measures: hazard consistency, hazard discrimination, explained variation, likelihood difference, and balance. The methods are described in Xu, W., et al. (2015) <https://www.austinpublishinggroup.com/biometrics/fulltext/biometrics-v2-id1014.php>.
ENA (Shaffer, D. W. (2017) Quantitative Ethnography. ISBN: 0578191687) is a method used to identify meaningful and quantifiable patterns in discourse or reasoning. ENA moves beyond the traditional frequency-based assessments by examining the structure of the co-occurrence, or connections in coded data. Moreover, compared to other methodological approaches, ENA has the novelty of (1) modeling whole networks of connections and (2) affording both quantitative and qualitative comparisons between different network models.   Shaffer, D.W., Collier, W., & Ruis, A.R. (2016).
Fits linear models with endogenous regressor using latent instrumental variable approaches. 
    The methods included in the package are Lewbel's (1997) <doi:10.2307/2171884> higher moments approach as well as 
    Lewbel's (2012) <doi:10.1080/07350015.2012.643126> heteroscedasticity approach, Park and Gupta's (2012) <doi:10.1287/mksc.1120.0718> joint estimation method 
    that uses Gaussian copula and Kim and Frees's (2007) <doi:10.1007/s11336-007-9008-1> multilevel generalized
    method of moment approach that deals with endogeneity in a multilevel setting.
    These are statistical techniques to address the endogeneity problem where no external instrumental variables are needed.
    Note that with version 2.0.0 sweeping changes were introduced which greatly improve functionality and usability but break backwards compatibility.
Peaks Over Threshold (POT) or 'methode du renouvellement'. The distribution for the exceedances can be chosen, and heterogeneous data (including historical data or block data) can be used in a Maximum-Likelihood framework.
Supports calculations and visualization for renewable power systems and the environment. Analysis and graphical tools for DC and AC circuits and their use in electric power systems. Analysis and graphical tools for thermodynamic cycles and heat engines, supporting efficiency calculations in coal-fired power plants, gas-fired power plants. Calculations of carbon emissions and atmospheric CO2 dynamics. Analysis of power flow and demand for the grid, as well as power models for microgrids and off-grid systems. Provides resource and power generation for hydro power, wind power, and solar power.
Provides an R interface to the NCBI's 'EUtils' API, 
    allowing users to search databases like 'GenBank' 
    <https://www.ncbi.nlm.nih.gov/genbank/> and 'PubMed' 
    <https://pubmed.ncbi.nlm.nih.gov/>, process the 
    results of those searches and pull data into their R sessions.
A dependency management toolkit for R. Using 'renv', you can create
    and manage project-local R libraries, save the state of these libraries to
    a 'lockfile', and later restore your library as required. Together, these
    tools can help make your projects more isolated, portable, and reproducible.
Provides a general routine, envMU, which allows  estimation of the M envelope of span(U) given root n consistent estimators of M and U. The routine envMU does not presume a model.  This package implements response envelopes,  partial response envelopes,  envelopes in the predictor space,  heteroscedastic envelopes,  simultaneous envelopes,  scaled response envelopes,  scaled envelopes in the predictor space,  groupwise envelopes, weighted envelopes,  envelopes in logistic regression and envelopes in Poisson regression. For each of these model-based routines the package provides inference tools including bootstrap, cross validation, estimation and prediction, hypothesis testing on coefficients are included except for weighted envelopes. Tools for selection of dimension include AIC, BIC and likelihood ratio testing.   Background is available at Cook, R. D., Forzani, L. and Su, Z. (2016) <doi:10.1016/j.jmva.2016.05.006>. Optimization is based on a clockwise coordinate descent algorithm.
Provides environment modules functionality, which enables use of the Environment Modules system (<http://modules.sourceforge.net/>) from within the R environment. By default the user's login shell environment (ie. "bash -l") will be used to initialize the current session. The module function can also; load or unload specific software, list all the loaded software within the current session, and list all the applications available for loading from the module system. Lastly, the module function can remove all loaded software from the current session.
Contains utilities for the analysis of Michaelian kinetic data. Beside the classical linearization 
	methods (Lineweaver-Burk, Eadie-Hofstee, Hanes-Woolf and Eisenthal-Cornish-Bowden), features include the 
	ability to carry out weighted regression analysis that, in most cases, substantially improves the estimation 
	of kinetic parameters (Aledo (2021) <doi:10.1002/bmb.21522>). To avoid data transformation and the potential
	biases introduced by them, the package also offers functions to directly fitting data to the Michaelis-Menten 
	equation, either using ([S], v) or (time, [S]) data. Utilities to simulate substrate progress-curves (making 
	use of the Lambert W function) are also provided. The package is accompanied of vignettes that aim to orientate
	the user in the choice of the most suitable method to estimate the kinetic parameter of an Michaelian enzyme.
Tools for performing the leaf reordering for the dendrogram that preserves the hierarchical clustering result and at the same time tries to group instances from the same class together.
The complete data set of open repair data, full compliant with the Open Repair Data Standards (ORDS).
      It combines the datasets contributed by partner organizations of the Open Repair Alliance (ORA). Last updated: 2021-02-22. 
      The package also contains via quests enriched datasets on batteries,
      printer, mobiles, and tablets.
Set of utilities to facilitate the reproduction of analysis in R.
 It allow to make_structure(), clean_structure(), and run and log programs in a
 predefined order to allow secondary files, analysis and reports be constructed in
 an ordered form.
The function RepaymentPlan() calculates repayment schedule for repayment/mortgage plans.
Various functions to fit models for non-normal repeated
    measurements, such as Binary Random Effects Models with Two Levels of Nesting,
    Bivariate Beta-binomial Regression Models, Marginal Bivariate Binomial Regression Models,
    Cormack capture-recapture models, Continuous-time Hidden Markov Chain Models, 
    Discrete-time Hidden Markov Chain Models,
    Changepoint Location Models using a Continuous-time Two-state Hidden Markov Chain,
    generalized nonlinear autoregression models, multivariate Gaussian copula models,
    generalized non-linear mixed models with one random effect,  
    generalized non-linear mixed models using h-likelihood for one random effect, 
    Repeated Measurements Models for Counts with Frailty or Serial Dependence,
    Repeated Measurements Models for Continuous Variables with Frailty or Serial Dependence,
    Ordinal Random Effects Models with Dropouts, marginal homogeneity models for square
    contingency tables, correlated negative binomial models with Kalman update.
    References include Lindsey's text books, 
    JK Lindsey (2001) <isbn-10:0198508123> and JK Lindsey (1999) <isbn-10:0198505590>.
Global tests for expression data of high-dimensional sets of
    molecular features.
Utilities for accessing RePEc (Research Papers in Economics)
    through a RESTful API. You can request a code and get detailed
    information at the following page: <https://ideas.repec.org/api.html>.
Creates reports from Trello, a collaborative, project organization 
             and list-making application. <https://trello.com/>
             Reports are created by comparing individual Trello board
             cards from two different points in time and documenting any changes made
             to the cards.
Visualization platform for T cell receptor repertoire
    analysis output results. It includes comparison of sequence frequency
    among samples, network of similar sequences and convergent
    recombination source between species. Currently repertoire analysis is
    in early stage of development and requires new approaches for
    repertoire data examination and assessment as we intend to develop.
    No publication is available yet (will be available in the near
    future), Efroni (2021) <https:>.
Estimation of Bayes and local Bayes false discovery rates for
      replicability analysis (Heller & Yekutieli, 2014 <doi:10.1214/13-AOAS697> ;
      Heller at al., 2015 <doi: 10.1093/bioinformatics/btu434>).
Updates values within csv format data files using a custom, User-built
    csv format lookup file. Based on 'data.table' package.
Create presentations and display them inside the R 'REPL'
    (Read-Eval-Print loop), aka the R console. Presentations can be written in
    'RMarkdown' or any other text format. A set of convenient navigation options
    as well as code evaluation during a presentation is provided. It is great
    for tech talks with live coding examples and tutorials. While this is not a
    replacement for standard presentation formats, it's old-school looks might
    just be what sets it apart. This project has been inspired by the
    'REPLesent' project for presentations in the 'Scala' 'REPL'.
For a multisite replication project, computes the consistency metric P_orig, which is
    the probability that the original study would observe an estimated effect size as extreme or more extreme
    than it actually did, if in fact the original study were statistically consistent with the replications. Other
    recommended metrics are: (1) the probability of a true effect of scientifically meaningful size in the same direction as the estimate
    the original study; and (2) the probability of a true effect of meaningful size in the direction opposite
    the original study's estimate. These two can be computed using the package \code{MetaUtility::prop_stronger}.
    Additionally computes older metrics used in replication projects (namely expected agreement in "statistical significance" between an original study and replication studies as well
    as prediction intervals for the replication estimates). See Mathur and VanderWeele (under review; <https://osf.io/apnjk/>)
    for details.
Performs comparative bioavailability calculations for Average Bioequivalence with
  Expanding Limits (ABEL). Implemented are 'Method A' and 'Method B' and the detection of outliers.
  If the design allows, assessment of the empiric Type I Error and iteratively adjusting alpha to
  control the consumer risk.
  Average Bioequivalence - optionally with a tighter (narrow therapeutic index drugs) or wider
  acceptance range (Gulf Cooperation Council, South Africa: Cmax) - is implemented as well.
Test for effects of  both individual factors and their interaction on  replicated spatial patterns in a two factorial design, as explained in Ramon et al. (2016) <doi:10.1111/ecog.01848>.
Allows for the computation of a prior predictive p-value to test replication of relevant features of original studies. Relevant features are captured in informative hypotheses. The package also allows for the computation of power. The statistical underpinnings are described in Zondervan-Zwijnenburg (2019) <doi:10.31234/osf.io/uvh5s>.
A common problem faced by journal reviewers and authors is the question of
     whether the results of a replication study are consistent with the original
     published study. One solution to this problem is to examine the effect size
     from the original study and generate the range of effect sizes that could
     reasonably be obtained (due to random sampling) in a replication attempt
     (i.e., calculate a replication interval). If a replication effect size falls
     outside the replication interval, then that effect likely did not occur
     due to the effects of sampling error alone. Alternatively, if a replication
     effect size falls within the replication interval, then the replication
     effect could have reasonably occurred due to the effects of sampling error
     alone. This package has functions that calculate the replication interval for 
     the correlation (i.e., r), standardized mean difference (i.e., d-value), and mean. 
     The calculations used in version 2.0.0 and onward differ from past calculations 
     due to feedback during the journal review process. The new calculations allow 
     for a more precise interpretation of the replication interval.
Provides utilities for the design and analysis of replication studies.
    Features both traditional methods based on statistical significance and
    more recent methods such as the sceptical p-value; Held L. (2020) <doi:10.1111/rssa.12493>.
    Also provides related methods including the harmonic mean chi-squared test; Held, L. (2020), <doi:10.1111/rssc.12410>,
    and intrinsic credibility; Held, L. (2019) <doi:10.1098/rsos.181534>.
    Contains datasets from four large-scale replication projects.
Create, Plot and Compare Replication Timing Profiles. The method is described in Muller et al., (2014) <doi: 10.1093/nar/gkt878>.
Tools to load 'R' packages
    and automatically generate BibTeX files citing them as well as load and
    cache plain-text and 'Excel' formatted data stored on 'GitHub', and
    from other sources.
Tools for generating descriptives and report tables for different models,
    data.frames and tables and exporting them to different formats.
A data manager meant to avoid manual storage/retrieval of
    data to/from the file system. It builds one (or more) centralized
    repository where R objects are stored with rich annotations,
    including corresponding code chunks, and easily searched and
    retrieved. See Napolitano (2017) <doi:10.1037/a0028240> for further
    information.
Generates a project and repo for easy initialization of a GitHub repo for R workshops. The repo includes a README with instructions to ensure that all users have the needed packages, an 'RStudio' project with the right directories and the proper data. The repo can then be used for hosting code taught during the workshop.
Fits linear models to repeated ordinal scores using GEE methodology.
The aim of the 'report' package is to bridge the gap between 
    R’s output and the formatted results contained in your manuscript. 
    This package converts statistical models and data frames into textual 
    reports suited for publication, ensuring standardization and quality 
    in results reporting.
Contains functions to create regulatory-style statistical reports.
    Originally designed to create tables, listings, and figures for the 
    pharmaceutical, biotechnology, and medical device industries, these
    reports are generalized enough that they could be used in any industry.
    Generates text, rich-text, PDF, HTML, and Microsoft Word file formats.  
    The package specializes 
    in printing wide and long tables with automatic page wrapping and splitting.  
    Reports can be produced with a minimum of function calls, and without 
    relying on other table packages.  The package supports titles, footnotes, 
    page header, page footers, spanning headers, page by variables, 
    and automatic page numbering.
Provides an infrastructure for handling multiple R Markdown
  reports, including automated curation and time-stamping of outputs,
  parameterisation and provision of helper functions to manage dependencies.
Provides a system for reporting messages, which provides certain useful features over the standard R system, such as the incorporation of output consolidation, message filtering, assertions, expression substitution, automatic generation of stack traces for debugging, and conditional reporting based on the current "output level".
Provides an easy way to report the results of regression analysis, including:
    1. Proportional hazards regression from function 'coxph' of package 'survival';
    2. Conditional logistic regression from function 'clogit' of package 'survival';
    3. Ordered logistic regression from function 'polr' of package 'MASS';
    4. Binary logistic regression from function 'glm' of package 'stats';
    5. Linear regression from function 'lm' of package 'stats';
    6. Risk regression model for survival analysis with competing risks from function 'FGR' of package 'riskRegression';
    7. Multilevel model from function 'lme' of package 'nlme'.
Provides an easy way to report the results of ROC analysis, including:
  1. an ROC curve. 2. the value of Cutoff, AUC (Area Under Curve), ACC (accuracy),
  SEN (sensitivity), SPE (specificity),
  PLR (positive likelihood ratio), NLR (negative likelihood ratio),
  PPV (positive predictive value), NPV (negative predictive value).
These functions are especially helpful when writing reports of data analysis using "Sweave".
An R Interface to 'EPP-lab' v1.0. 'EPP-lab' is a Java program for
    projection pursuit using genetic algorithms written by Alain Berro and S. Larabi
    Marie-Sainte and is included in the package. The 'EPP-lab' sources are available
    under https://github.com/fischuu/EPP-lab.git.
Performs exploratory projection pursuit  via 'REPPlab' (Daniel Fischer, Alain Berro, Klaus Nordhausen & Anne Ruiz-Gazen (2019) <doi:10.1080/03610918.2019.1626880>) using a Shiny app.
String and binary representations of objects for several formats /
    mime types.
An implementation of Kaplan, Betancourt, Steorts (2020) <arXiv:1810.01538> that creates representative records for use in downstream tasks after entity resolution is performed. Multiple methods for creating the representative records (data sets) are provided.
Reproducible research tools automates the creation of an analysis directory structure and work flow. There are R markdown
  skeletons which encapsulate typical analytic work flow steps. Functions will create appropriate modules which may
  pass data from one step to another.
Convenience wrapper that uses the 'rmarkdown'
    package to render small snippets of code to target formats that
    include both code and output.  The goal is to encourage the sharing of
    small, reproducible, and runnable examples on code-oriented websites,
    such as <https://stackoverflow.com> and <https://github.com>, or in
    email. The user's clipboard is the default source of input code and
    the default target for rendered output. 'reprex' also extracts clean,
    runnable R code from various common formats, such as copy/paste from
    an R session.
Includes data analysis functions (e.g., to calculate effect sizes and 95% Confidence Intervals (CI) on Standardised Effect Sizes (d) for AB/BA cross-over repeated-measures experimental designs), data presentation functions (e.g., density curve overlaid on histogram), and the data sets analyzed in different research papers in software engineering (e.g., related to software defect prediction or multi-site experiment concerning the extent to which structured abstracts were clearer and more complete than conventional abstracts) to streamline reproducible research in software engineering.
Collection of high-level, machine- and OS-independent tools
    for making deeply reproducible and reusable content in R.
    The two workhorse functions are Cache and prepInputs; 
    these allow for: nested caching, robust to environments, and objects with
    environments (like functions); and data retrieval and processing 
    in continuous workflow environments. In all cases,
    efforts are made to make the first and subsequent calls of functions have 
    the same result, but vastly faster at subsequent times by way of checksums
    and digesting. Several features are still under active development, including
    cloud storage of cached objects, allowing for sharing between users. Several
    advanced options are available, see ?reproducibleOptions.
Transform coordinates from a specified source to a specified 
 target map projection. This uses the 'PROJ' library directly, by wrapping the 
 'PROJ' package (if functional), otherwise the 'proj4' package. The 'reproj()' 
 function is generic, methods may be added to remove the need for an explicit 
 source definition. If 'proj4' is in use 'reproj()' handles the requirement for 
 conversion of angular units where necessary. This is for use primarily to 
 transform generic data formats and direct leverage of the underlying
 'PROJ' library. (There are transformations that aren't possible with 'PROJ' and
 that are provided by the 'GDAL' library, a limitation which users of this 
 package should be aware of.) The 'PROJ' library is available at 
 <https://proj.org/>.
Predicting regulatory DNA elements based on epigenomic signatures. This package is more of a set of building blocks than a direct solution. REPTILE regulatory prediction pipeline is built on this R package. See <https://github.com/yupenghe/REPTILE> for more information.
Recursive lists in the form of R objects, 'JSON',
    and 'XML', for use in teaching and examples. Examples include color
    palettes, Game of Thrones characters, 'GitHub' users and repositories,
    music collections, and entities from the Star Wars universe. Data from
    the 'gapminder' package is also included, as a simple data frame and
    in nested and split forms.
In order to facilitate parsing of http requests and creating 
    appropriate responses this package provides two classes to handle a lot of
    the housekeeping involved in working with http exchanges. The infrastructure
    builds upon the 'rook' specification and is thus well suited to be combined
    with 'httpuv' based web servers.
This package contains the function run.eqs() which calls
        an EQS script file, executes the EQS estimation, and, finally,
        imports the results as R objects. These two steps can be
        performed separately: call.eqs() calls and executes EQS,
        whereas read.eqs() imports existing EQS outputs as objects into
        R. It requires EQS 6.2 (build 98 or higher).
High level and easy 'HTTP' client for 'R'. Provides functions for
    building 'HTTP' queries, including query parameters, body requests, headers,
    authentication, and more.
A single key function, 'Require' that wraps 'install.packages',
    'remotes::install_github', 'versions::install.versions', and 'base::require'
    that allows for reproducible workflows. As with other functions in a
    reproducible workflow, this package emphasizes functions that return the 
    same result whether it is the first or subsequent times running the function.
    Maturing.
Helper function to install packages for R using an external
    'requirements.txt' or a string containing diverse packages from
    several resources like Github or CRAN.
Modularizes source code. Keeps the global environment clean,
    explicifies interdependencies. Inspired by 'RequireJS'<http://requirejs.org/>.
Computes the power resulting from completely randomized and rerandomized experiments with two groups. Furthermore, computes the sample size necessary to obtain a desired level of power for completely randomized and rerandomized experiments.
General purpose R client for 'ERDDAP' servers. Includes
    functions to search for 'datasets', get summary information on
    'datasets', and fetch 'datasets', in either 'csv' or 'netCDF' format.
    'ERDDAP' information: 
    <https://upwell.pfeg.noaa.gov/erddap/information.html>.
Contains three functions that access
    environmental data from any 'ERDDAP' data web service. The rxtracto() function extracts
    data along a trajectory for a given "radius" around the point. The
    rxtracto_3D() function extracts data in a box. The rxtractogon() function
    extracts data in a polygon. All of those three function use the 'rerddap' package
    to extract the data, and should work with any 'ERDDAP' server.
    There are also two functions, plotBBox() and plotTrack() that use the 'plotdap'
    package to simplify the creation of maps of the data.
Executes a post-rotation algorithm that REorders and/or REflects FACTors (REREFACT) for each replication of a simulation study with exploratory factor analysis.
A comprehensive collection of practical and easy-to-use tools for regression analysis of recurrent events, with or without the presence of a (possibly) informative terminal event. The modeling framework is based on a joint frailty scale-change model, that includes models described in Wang et al. (2001) <doi:10.1198/016214501753209031>, Huang and Wang (2004) <doi:10.1198/016214504000001033>, Xu et al. (2017) <doi:10.1080/01621459.2016.1173557>, and Xu et al. (2019) <doi:10.5705/SS.202018.0224> as special cases. The implemented estimating procedure does not require any parametric assumption on the frailty distribution. The package also allows the users to specify different model forms for both the recurrent event process and the terminal event.
Bootstrap, permutation tests, and other resampling functions,
	featuring easy-to-use syntax.
Package of data sets from "Mathematical Statistics
    with Resampling in R" (1st Ed. 2011, 2nd Ed. 2018) by Laura Chihara and Tim Hesterberg.
Given a log-transformed expression matrix and list of informative genes: 
    subsample informative genes, cluster samples using shared nearest neighbors clustering, 
    estimate missing expression values with the distribution mean of means extrapolated 
    from these cell clusterings, and return an imputed expression matrix. See Tracy, S., 
    Yuan, G.C. and Dries, R. (2019) <doi:10.1186/s12859-019-2977-0> for more details.
Connects to Pubmed <https://pubmed.ncbi.nlm.nih.gov/> to retrieve publications related to user-defined search query.
Functions for dissimilarity analysis and memory-based learning 
    (MBL, a.k.a local modeling) in complex spectral data sets. 
    Most of these functions are based on the methods presented in 
    Ramirez-Lopez et al. (2013) <doi:10.1016/j.geoderma.2012.12.014>.
Measure single-storage water supply system performance using resilience,
    reliability, and vulnerability metrics; assess storage-yield-reliability
    relationships; determine no-fail storage with sequent peak analysis; optimize
    release decisions for water supply, hydropower, and multi-objective reservoirs
    using deterministic and stochastic dynamic programming; generate inflow
    replicates using parametric and non-parametric models; evaluate inflow
    persistence using the Hurst coefficient.
Simulates individual-based models of agricultural pest management
    and the evolution of pesticide resistance. Management occurs on a spatially
    explicit landscape that is divided into an arbitrary number of farms that
    can grow one of up to 10 crops and apply one of up to 10 pesticides. Pest
    genomes are modelled in a way that allows for any number of pest traits with
    an arbitrary covariance structure that is constructed using an evolutionary
    algorithm in the mine_gmatrix() function. Simulations are then run using the
    run_farm_sim() function. This package thereby allows for highly mechanistic
    social-ecological models of the evolution of pesticide resistance under
    different types of crop rotation and pesticide application regimes.
Flexibly restructure and aggregate data using 
  just two functions: melt and cast.
Flexibly restructure and aggregate data using just two
    functions: melt and 'dcast' (or 'acast').
Datasets with energy consumption data of different data measurement frequencies. 
    The data stems from several publicly funded research projects of the Chair of Information  
    Systems and Energy Efficient Systems at the University of Bamberg.
Electrical properties of resistor networks using matrix methods.
A resource represents some data or a computation unit. It is 
    described by a URL and credentials. This package proposes a Resource model
    with "resolver" and "client" classes to facilitate the access and the usage of the 
    resources.
Resource Selection (Probability) Functions
  for use-availability wildlife data
  based on weighted distributions as described in
  Lele and Keim (2006) <doi:10.1890/0012-9658(2006)87%5B3021:WDAEOR%5D2.0.CO;2>,
  Lele (2009) <doi:10.2193/2007-535>,
  and Solymos & Lele (2016) <doi:10.1111/2041-210X.12432>.
Provides functions for the complete analysis of respiratory data. Consists of a set of functions that allow to preprocessing respiratory data,  calculate both regular statistics and nonlinear statistics, conduct group comparison and visualize the results. Especially, Power Spectral Density ('PSD') (A. Eke (2000) <doi:10.1007/s004249900135>), 'MultiScale Entropy(MSE)' ('Madalena Costa(2002)' <doi:10.1103/PhysRevLett.89.068102>) and 'MultiFractal Detrended Fluctuation Analysis(MFDFA)' ('Jan W.Kantelhardt' (2002) <doi:10.1016/S0378-4371(02)01383-3>) were applied for the analysis of respiratory data.
Provides tools to enable the researcher to more precisely conduct
    respirometry experiments. Strong emphasis is on aquatic respirometry. Tools
    focus on helping the researcher setup and conduct experiments. Functions for
    analysis of resulting respirometry data are also provided. This package
    provides tools for intermittent, flow-through, and closed respirometry
    techniques.
Some survey participants tend to respond carelessly which complicates data analysis. This package provides functions that make it easier to explore responses and identify those that may be problematic.
Provides a structural, reproducible workflow for the
    processing and analysis of respirometry data. It contains analytical
    functions and utilities for working with oxygen time-series to determine
    respiration or oxygen production rates, and to make it easier to report and
    share analyses.
Contains three functions that query AuriQ Systems' Essentia Database and return the results in R. 'essQuery' takes a single Essentia command and captures the output in R, where you can save the output to a dataframe or stream it directly into additional analysis. 'read.essentia' takes an Essentia script and captures the output csv data into R, where you can save the output to a dataframe or stream it directly into additional analysis. 'capture.essentia' takes a file containing any number of Essentia commands and captures the output of the specified statements into R dataframes. Essentia can be downloaded for free at http://www.auriq.com/documentation/source/install/index.html.
Contains easy scripts which can be used to quickly create GUI windows for 'Rcmdr' Plugins. No knowledge about Tcl/Tk is required to make use of these scripts (These scripts are a generalisation of the template scripts in the 'RcmdrPlugin.BiclustGUI' package).
Eurostat is the statistical office of the European Union and provides high quality statistics for Europe.
             Large set of the data is disseminated through the Eurostat database (<https://ec.europa.eu/eurostat/data/database>). 
             The tools are using the REST API with the Statistical Data and Metadata eXchange (SDMX) Web Services 
             (<https://ec.europa.eu/eurostat/web/sdmx-web-services/about-this-service>) to search and download data from 
             the Eurostat database using the SDMX standard.
Duplicated restaurant data (pre-processed and formatted) for entity resolution. This package contains formatted data from a data set that contains information about different restaurants, with the Zagats portion containing 331 records and the Fodors portion containing 533 records. The following variables are included in the data set: id, name, address, city, phone, type. The data set has a respective gold data set that provides information on which records match based on id.
Models a RESTful service as if it were a nested R list.
Provides the user with functions to develop their trading strategy,
    uncover actionable trading ideas, and monitor consensus shifts with
    crowdsourced earnings and economic estimate data directly from
    <www.estimize.com>. Further information regarding the web services this
    package invokes can be found at <www.estimize.com/api>.
Debugging with restore points instead of break points. A restore
    point stores all local variables when called inside a function. The stored
    values can later be retrieved and evaluated in a modified R console that
    replicates the function's environment. To debug step by step, one can simply
    copy & paste the function body from the R script. Particularly convenient
    in combination with "RStudio". See the "Github" page inst/vignettes for a
    tutorial.
A fast Gibbs sampler for multivariate normal with affine constraints.
Allow for easy-to-use testing or evaluating of linear equality and inequality 
 restrictions about parameters and effects in (generalized) linear statistical models.
Allows to easily create high-performance full featured HTTP APIs from R
  functions. Provides high-level classes such as 'Request', 'Response',
  'Application', 'Middleware' in order to streamline server side
  application development. Out of the box allows to serve requests using
  'Rserve' package, but flexible enough to integrate with other HTTP servers
  such as 'httpuv'.
Using a CSV, LaTeX and R to easily build attractive resumes.
Implementation of the ethnicity prediction method, described 
    in "Rethnicity: Predicting Ethnicity from Names" by Fangzhou Xie (2021) <arXiv:2109.09228>.
Interface to 'Python' modules, classes, and functions. When calling
    into 'Python', R data types are automatically converted to their equivalent 'Python'
    types. When values are returned from 'Python' to R they are converted back to R
    types. Compatible with all versions of 'Python' >= 2.7.
Reconstructs retinae by morphing a flat surface with cuts (a
    dissected flat-mount retina) onto a curvilinear surface (the standard retinal
    shape). It can estimate the position of a point on the intact adult retina
    to within 8 degrees of arc (3.6% of nasotemporal axis). The coordinates in
    reconstructed retinae can be transformed to visuotopic coordinates.
Using 'Digital Object Identifiers', check for retracted (or otherwise updated) articles using 'Open Retractions' <http://openretractions.com>.
Generates disease-specific drug-response profiles that are independent of time, concentration, and cell-line. Based on the cell lines used as surrogates, the returned profiles represent the unique transcriptional changes induced by a compound in a given disease.
Provides tools for working with Type S (Sign) and
    Type M (Magnitude) errors, as proposed in Gelman and Tuerlinckx (2000)
    <doi.org/10.1007/s001800000040> and
    Gelman & Carlin (2014) <doi.org/10.1177/1745691614551642>.
    In addition to simply calculating the probability of
    Type S/M error, the package includes functions for calculating these errors
    across a variety of effect sizes for comparison, and recommended sample size
    given "tolerances" for Type S/M errors. To improve the speed of these
    calculations, closed forms solutions for the probability of a Type S/M error
    from Lu, Qiu, and Deng (2018) <doi.org/10.1111/bmsp.12132>
    are implemented. As of 1.0.0, this includes
    support only for simple research designs. See the
    package vignette for a fuller exposition on how Type S/M errors arise in
    research, and how to analyze them using the type of design analysis proposed
    in the above papers.
Assist in reproducible retrospective (ex-post) harmonization
    of data, particularly individual level survey data, by providing tools
    for organizing metadata, standardizing the coding of variables, and
    variable names and value labels, including missing values, and
    documenting the data transformations, with the help of comprehensive
    s3 classes.
A collection of tools to import and structure the (currently) single-season
    event, game-log, roster, and schedule data available from <https://www.retrosheet.org>.
    In particular, the event (a.k.a. play-by-play) files can be especially difficult to parse.
    This package does the parsing on those files, returning the requested data in the most
    practical R structure to use for sabermetric or other analyses.
Provide simple mechanism to repeatedly evaluate an expression until either it succeeds or timeout exceeded. It is useful in situations that random failures could happen.
An interface to NCBI databases such as PubMed, GenBank, or GEO
    powered by the Entrez Programming Utilities (EUtils). The nine EUtils
    provide programmatic access to the NCBI Entrez query and database
    system for searching and retrieving biological data.
Simplified scenario testing and sensitivity analysis,
    redesigned to use packages 'future' and 'furrr'. Provides
    functions for generating function argument sets using
    one-factor-at-a-time (OFAT) and (sampled) permutations.
Provides functions for the Bayesian analysis of extreme value
    models.  The 'rust' package <https://cran.r-project.org/package=rust> is
    used to simulate a random sample from the required posterior distribution.
    The functionality of 'revdbayes' is similar to the 'evdbayes' package
    <https://cran.r-project.org/package=evdbayes>, which uses Markov Chain
    Monte Carlo ('MCMC') methods for posterior simulation.  Also provided
    are functions for making inferences about the extremal index, using 
    the K-gaps model of Suveges and Davison (2010) <doi:10.1214/09-AOAS292>.
    Also provided are d,p,q,r functions for the Generalised Extreme Value 
    ('GEV') and Generalised Pareto ('GP') distributions that deal 
    appropriately with cases where the shape parameter is very close to zero.
Computation of (direct and indirect) revealed preferences, fast non-parametric tests of rationality axioms (WARP, SARP, GARP), simulation of axiom-consistent data, and detection of axiom-consistent subpopulations. Rationality tests follow Varian (1982) <doi:10.2307/1912771>, axiom-consistent subpopulations follow Crawford and Pendakur (2012) <doi:10.1111/j.1468-0297.2012.02545.x>.
R Markdown format for 'reveal.js' presentations, a framework 
  for easily creating beautiful presentations using HTML.
An implementation of the reverse ecology framework. Reverse ecology
    refers to the use of genomics to study ecology with no a priori assumptions
    about the organism(s) under consideration, linking organisms to their
    environment. It allows researchers to reconstruct the metabolic networks and
    study the ecology of poorly characterized microbial species from their
    genomic information, and has substantial potentials for microbial community
    ecological analysis.
Facilitates making a connection to the 
  'Revenera' API and executing various queries. You can use it to
  get event data and metadata. The 'Revenera' documentation 
  is available at 
  <https://docs.revenera.com/ui560/report/>. This package is not 
  supported by 'Flexera' (owner of the software).
Decoupled (e.g. separate averages) and censored (e.g. > 100 species) variables are continually reported by many well-established organizations (e.g. World Health Organization (WHO), Centers for Disease Control and Prevention (CDC), World Bank, and various national censuses).  The challenge therefore is to infer what the original data could have been given summarized information.  We present an R package that reverse engineers decoupled and/or censored count data with two main functions.  The cnbinom.pars function estimates the average and dispersion parameter of a censored univariate frequency table.  The rec function reverse engineers summarized data into an uncensored bivariate table of probabilities.
Tests linear regressions for significance reversal through leave-one(multiple)-out and shifting/addition of response values. The paradigm of the package is loosely based on the somewhat forgotten "dfstat" criterion (Belsley, Kuh & Welsch 1980 <doi:10.1002/0471725153.ch2>), which tests influential values in linear models from their effect on statistical inference, i.e. changes in p-value.
Function revgeo() allows you to use the Photon geocoder for OpenStreetMap <http://photon.komoot.de>, Google Maps <http://maps.google.com>, and Bing <https://www.bingmapsportal.com> to reverse geocode coordinate pairs with minimal hassle.
A portable Shiny tool to explore patient-level electronic health record data 
    and perform chart review in a single integrated framework. This tool supports 
    browsing clinical data in many different formats including multiple versions 
    of the 'OMOP' common data model as well as the 'MIMIC-III' data model. In 
    addition, chart review information is captured and stored securely via the 
    Shiny interface in a 'REDCap' (Research Electronic Data Capture) project 
    using the 'REDCap' API. See the 'ReviewR' website for additional information, 
    documentation, and examples.
Tools to (i) check consistency of a finite set of consumer demand observations with a number of revealed preference axioms at a given efficiency level, (ii) compute goodness-of-fit indices when the data do not obey the axioms, and (iii) compute power against uniformly random behavior.
Implements the estimation techniques described in Rousseeuw &
    Verboven (2002) <doi:10.1016/S0167-9473(02)00078-6> for the location and
    scale of very small samples.
Interaction with "RevBayes" via R. Objects created in "RevBayes" can be passed into the R environment, and many types can be converted into similar R objects. To download "RevBayes", go to <https://revbayes.github.io/download>.
Researchers commonly need to summarize scientific information, a process known as 'evidence synthesis'. The first stage of a synthesis process (such as a systematic review or meta-analysis) is to download a list of references from academic search engines such as 'Web of Knowledge' or 'Scopus'. The traditional approach to systematic review is then to sort these data manually, first by locating and removing duplicated entries, and then screening to remove irrelevant content by viewing titles and abstracts (in that order). 'revtools' provides interfaces for each of these tasks. An alternative approach, however, is to draw on tools from machine learning to visualise patterns in the corpus. In this case, you can use 'revtools' to render ordinations of text drawn from article titles, keywords and abstracts, and interactively select or exclude individual references, words or topics.
Facilitates making a connection to the 
  'Revulytics' API and executing various queries. You can use it to
  get event data and metadata. The Revulytics documentation 
  is available at <https://docs.revenera.com/ui560/report/>. This
  package is not supported by 'Flexera' (owner of the software).
Adjusts the weights of survey respondents so that the
        marginal distributions of certain variables fit more closely to
        those from a more precise source (e.g. Census Bureau's data).
Diagnostics and data preparation for random effects within estimator, random effects 
    within-idiosyncratic estimator, between-within-idiosyncratic model, and cross-classified between model.
    Mundlak, Yair (1978) <doi:10.2307/1913646>.
    Hausman, Jeffrey (1978) <doi:10.2307/1913827>.
    Allison, Paul (2009) <doi:10.4135/9781412993869>.
    Neuhaus, J.M., and J. D. Kalbfleisch (1998) <doi:10.2307/3109770>.
A friendly interface for the construction of regular
    expressions.
Provides convenience functions to communicate with
    an Experigen server: Experigen (<http://github.com/aquincum/experigen>)
    is an online framework for creating  linguistic experiments,
    and it stores the results on a dedicated server. This package can be
    used to retrieve the results from the server, and it is especially
    helpful with registered experiments, as authentication with the server
    has to happen.
Wraps some of the matrix exponentiation 
        utilities from EXPOKIT (<http://www.maths.uq.edu.au/expokit/>), 
        a FORTRAN library that is widely recommended for matrix 
        exponentiation (Sidje RB, 1998. "Expokit: A Software Package
        for Computing Matrix Exponentials." ACM Trans. Math. Softw.
        24(1): 130-156).  EXPOKIT includes functions for 
        exponentiating both small, dense matrices, and large, sparse
        matrices (in sparse matrices, most of the cells have value 0).
        Rapid matrix exponentiation is useful in phylogenetics when we 
        have a large number of states (as we do when we are inferring 
        the history of transitions between the possible geographic 
        ranges of a species), but is probably useful in other ways as 
        well.
Provides functions to compile and load Rust code from R, similar
    to how 'Rcpp' or 'cpp11' allow easy interfacing with C++ code. Also provides
    helper functions to create R packages that use Rust code. Under the hood,
    the Rust crate 'extendr' is used to do all the heavy lifting.
Provides an interface to the Facebook API.
Load data by campaigns, ads, ad sets and insights, ad account and business manager 
    from Facebook Marketing API into R. For more details see official documents by Facebook 
    Marketing API <https://developers.facebook.com/docs/marketing-apis/>.
The 'rfacts' package is an R interface to the
  Fixed and Adaptive Clinical Trial Simulator ('FACTS')
  on Unix-like systems. It programmatically invokes 'FACTS' to run clinical
  trial simulations, and it aggregates simulation output data
  into tidy data frames. These capabilities provide end-to-end
  automation for large-scale simulation pipelines, and
  they enhance computational reproducibility.
  For more information on 'FACTS' itself,
  please visit <https://www.berryconsultants.com/software/>.
A collection of fast (utility) functions for data analysis. Column- and row- wise means, medians, variances, minimums, maximums, many t, F and G-square tests, many regressions (normal, logistic, Poisson), are some of the many fast functions. References: a) Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <doi:10.7287/peerj.preprints.26605v1>. b) Tsagris M. and Papadakis M. (2018). Forward regression in R: from the extreme slow to the extreme fast. Journal of Data Science, 16(4): 771--780. <doi:10.6339/JDS.201810_16(4).00006>.
A collection of fast statistical and utility functions for data analysis. Functions for regression, maximum likelihood, column-wise statistics and many more have been included. C++ has been utilized to speed up the functions.
Random Forest with Canonical Correlation Analysis (RFCCA) is a 
  random forest method for estimating the canonical correlations between two 
  sets of variables depending on the subject-related covariates. The trees are 
  built with a splitting rule specifically designed to partition the data to 
  maximize the canonical correlation heterogeneity between child nodes. The 
  method is described in Alakus et al. (2021) <doi:10.1093/bioinformatics/btab158>. RFCCA uses 
  'randomForestSRC' package (Ishwaran and Kogalur, 2020) by freezing at the 
  version 2.9.3. The custom splitting rule feature is utilised to apply the 
  proposed splitting rule.
Tools to perform random forest consensus clustering of different data types. The package is designed to accept a list of matrices from different assays, typically from high-throughput molecular profiling so that class discovery may be jointly performed. For references, please see Tao Shi & Steve Horvath (2006) <doi:10.1198/106186006X94072> & Monti et al (2003) <doi:10.1023/A:1023949509487> .
This package facilitates searching for and downloading seismic time series in miniSEED format (a minimalist version of the Standard for the Exchange of Earthquake Data) from International Federation of Digital Seismograph Networks repositories. This package can also be used to gather information about seismic networks (stations, channels, locations, etc) and find historical earthquake data (origins, magnitudes, etc).
An R package for methods of multiple imputation using chained
    random forests. Implemented methods can handle missing data in mixed types
    of by using prediction-based or node-based conditional distributions
    constructed using random forests. For prediction-based imputation,
    the method based on the empirical distribution of out-of-bag prediction
    errors of random forests, and the method based on normality assumption are
    provided for continuous variables. And the method based on predicted
    probabilities is provided for categorical variables. For node-based
    imputation, the method based on the conditional distribution formed by
    the predicting nodes of random forests, and the method based on proximity
    measures of random forests are provided. More details of the statistical
    methods can be found in Hong et al. (2020) <arXiv:2004.14823>.
Provides the random ferns classifier by Ozuysal, Calonder, Lepetit and Fua (2009) <doi:10.1109/TPAMI.2009.23>, modified for generic and multi-label classification and featuring OOB error approximation and importance measure as introduced in Kursa (2014) <doi:10.18637/jss.v061.i10>.
RFGLS  uses a generalized least-squares method to perform single-marker association analysis, 
  in datasets of nuclear families containing parents, twins, and/or adoptees
The goal of 'rFIA' is to increase the accessibility and use of the United States Forest Services (USFS) Forest Inventory and Analysis (FIA) Database by providing a user-friendly, open source toolkit to easily query and analyze FIA Data. Designed to accommodate a wide range of potential user objectives, 'rFIA' simplifies the estimation of forest variables from the FIA Database and allows all R users (experts and newcomers alike) to unlock the flexibility inherent to the Enhanced FIA design. Specifically, 'rFIA' improves accessibility to the spatial-temporal estimation capacity of the FIA Database by producing space-time indexed summaries of forest variables within user-defined population boundaries. Direct integration with other popular R packages (e.g., 'dplyr', 'tidyr', and 'sf') facilitates efficient space-time query and data summary, and supports common data representations and API design. The package implements design-based estimation procedures outlined by Bechtold & Patterson (2005) <doi:10.2737/SRS-GTR-80>, and has been validated against estimates and sampling errors produced by FIA 'EVALIDator'. Current development is focused on the implementation of spatially-enabled model-assisted estimators to improve population, change, and ratio estimates.
Provides functionality and parsers to interact with the 
  'FieldClimate' API <https://api.fieldclimate.com/v2/docs/>.
An interface to 'figshare' (http://figshare.com), a scientific repository to archive and assign 'DOIs' to data, software, figures, and more.
From downloading financial data to perform industry-standard analysis, this package includes a suit of tools to perform financial analysis following best practices of the industry.
An integrated package for constructing random forest prediction intervals using a fast implementation package 'ranger'. This package can apply the following three methods described in Haozhe Zhang, Joshua Zimmerman, Dan Nettleton, and Daniel J. Nordman (2019) <doi:10.1080/00031305.2019.1585288>: the out-of-bag prediction interval, the split conformal method, and the quantile regression forest.
A programmatic interface to 'FishBase', re-written
    based on an accompanying 'RESTful' API. Access tables describing over 30,000
    species of fish, their biology, ecology, morphology, and more. This package also
    supports experimental access to 'SeaLifeBase' data, which contains
    nearly 200,000 species records for all types of aquatic life not covered by
    'FishBase.'
Helps fisheries scientists collect measurements from calcified
    structures and back-calculate estimated lengths at previous ages using
    standard procedures and models. This is intended to replace much of the
    functionality provided by the now out-dated 'fishBC' software
    (<https://fisheries.org/bookstore/all-titles/software/70317/>).
Automatic generation of fish drawings based on JavaScript library <https://github.com/LingDong-/fishdraw>, including JavaScript code for dynamic generation of fish drawings.
A programmatic interface to 'openfisheries.org'. This package is
    part of the 'rOpenSci' suite (http://ropensci.org).
Provides data processing and summarization of data from FishNet2.net
  in text and graphical outputs. Allows efficient filtering of information and
  data cleaning.
Rank-based (R) estimation and inference for linear models.  Estimation
        is for general scores and a library of commonly used score
        functions is included.
Functions for the detection of spatial clusters using the flexible
    spatial scan statistic developed by Tango and Takahashi (2005) <doi:10.1186/1476-072X-4-11>.
    This package implements a wrapper for the C routine used in the FleXScan 3.1.2 
    <https://sites.google.com/site/flexscansoftware/home> developed by Takahashi,
    Yokoyama, and Tango. For details, see Otani et al. (2021) <doi:10.18637/jss.v099.i13>.
Provides functions to analyse DNA fragment samples (i.e. derived from RFLP-analysis) and standalone BLAST report files (i.e. DNA sequence analysis).
Interface to the 'flsgen' neutral landscape generator <https://github.com/dimitri-justeau/flsgen>. It allows to
  - Generate fractal terrain;
  - Generate landscape structures satisfying user targets over landscape indices;
  - Generate landscape raster from landscape structures.
Tools for RFM (recency, frequency and monetary value) analysis. 
    Generate RFM score from both transaction and customer level data. Visualize the
    relationship between recency, frequency and monetary value using heatmap, 
    histograms, bar charts and scatter plots. Includes a 'shiny' app for 
    interactive segmentation. References:
    i. Blattberg R.C., Kim BD., Neslin S.A (2008) <doi:10.1007/978-0-387-72579-6_12>.
S3 implementation of the Random Forest MErging Procedure (RF-MEP), which combines two or more satellite-based datasets (e.g., precipitation products, topography) with ground observations to produce a new dataset with improved spatio-temporal distribution of the target field. In particular, this package was developed to merge different Satellite-based Rainfall Estimates (SREs) with measurements from rain gauges, in order to obtain a new precipitation dataset where the time series in the rain gauges are used to correct different types of errors present in the SREs. However, this package might be used to merge other hydrological/environmental satellite fields with point observations. For details, see Baez-Villanueva et al. (2020) <doi:10.1016/j.rse.2019.111606>. Bugs / comments / questions / collaboration of any kind are very welcomed.
Various tools for handling fuzzy measures, calculating Shapley value and interaction index,  Choquet and Sugeno integrals, as well as fitting fuzzy measures to empirical data are provided. Construction of fuzzy measures from empirical data is done by solving a linear programming problem by using 'lpsolve' package, whose source in C adapted to the R environment 
  is included. The description of the basic theory of fuzzy measures is in the manual in the Doc folder in this package. Please refer to the following:
	[1] <https://personal-sites.deakin.edu.au/~gleb/fmtools.html>
	[2] G. Beliakov, H. Bustince, T. Calvo, 'A Practical Guide to Averaging', Springer, (2016, ISBN: 978-3-319-24753-3).
	[3] G. Beliakov, S. James, J-Z. Wu, 'Discrete Fuzzy Measures', Springer, (2020, ISBN: 978-3-030-15305-2).
R access to the 'FOAAS' (F... Off As A Service) web service is provided.
Graphics for statistics on a sphere, as applied to geological fault data, crystallography, earthquake focal mechanisms, radiation patterns, ternary plots and geographical/geological maps.  Non-double couple plotting of focal spheres and source type maps are included for statistical analysis of moment tensors.
The basic algorithm to perform the folding test of unimodality. 
  Given a dataset X (d dimensional, n samples), the test checks whether the 
  distribution of the data are rather unimodal or rather multimodal. This 
  package stems from the following research publication: 
  Siffer Alban, Pierre-Alain Fouque, Alexandre Termier, and Christine Largouët. 
  "Are your data gathered?" In Proceedings of the 24th ACM SIGKDD International 
  Conference on Knowledge Discovery Data Mining, pp. 2210-2218. ACM, 2018.
  <doi:10.1145/3219819.3219994>.
Contains all the code examples in the book "R for Dummies" (2nd
    edition) by Andrie de Vries and Joris Meys. You can view the table of 
    contents as well as the sample code for each chapter.
A modified version (with great help from Ian J. Wilson) of Ian J. Wilson's program BATWING for calculating forensic trace-suspect match probabilities.
Set of tools designed for forest inventory analysis.
Provides fast implementations of Honest Random Forests, 
    Gradient Boosting, and Linear Random Forests, with an emphasis on inference 
    and interpretability. Additionally contains methods for variable 
    importance, out-of-bag prediction, regression monotonicity, and
    several methods for missing data imputation. Soren R. Kunzel, 
    Theo F. Saarinen, Edward W. Liu, Jasjeet S. Sekhon (2019) <arXiv:1906.06463>.
The R Formatter formats R source code. It is very much based on
    formatR, but tries to improve it by heuristics. For example, spaces can be
    forced around the division operator "/".
Estimate significance of importance metrics
    for a Random Forest model by permuting the response
    variable. Produces null distribution of importance
    metrics for each predictor variable and p-value of
    observed. Provides summary and visualization functions for 'randomForest' 
    results.
Implements various prediction interval methods with random forests and boosted forests.
    The package has two main functions: pibf() produces prediction intervals with boosted forests
    (PIBF) as described in Alakus et al. (2021) <arXiv:2106.08217> and rfpi() builds 15 distinct
    variations of prediction intervals with random forests (RFPI) proposed by Roy and Larocque (2020)
    <doi:10.1177/0962280219829885>.
'FRACTRAN' is an obscure yet tantalizing programming language invented by John Conway of 'Game of Life' fame. The code consists of a sequence of fractions. The rules are simple. First, select an integer to initialize the process. Second, multiply the integer by the first fraction. If an integer results, start again with the new integer. If not, try the next fraction. Finally, if no such multiplication yields an integer, terminate the program. For more information, see <https://en.wikipedia.org/wiki/FRACTRAN> .
An R interface to a modified version of the Free Evolutionary Algorithm Kit FrEAK. FrEAK is a toolkit written in Java to design and analyze evolutionary algorithms. Both the R interface and an extended version of FrEAK are contained in the RFreak package. For more information on FrEAK see http://sourceforge.net/projects/freak427/.
Assists in statistical model building to find optimal and semi-optimal higher order interactions
    and best subsets. Uses the lm(), glm(), and other R functions to fit models generated from a feasible 
    solution algorithm. Discussed in Subset Selection in Regression, A Miller (2002). Applied and explained
    for least median of squares in Hawkins (1993) <doi:10.1016/0167-9473(93)90246-P>. The feasible solution 
    algorithm comes up with model forms of a specific type that can have fixed variables, higher order 
    interactions and their lower order terms.
Methods and tools for implementing univariate and multivariate functional singular spectrum analysis for functional time series whose variables might be observed over different dimensional domains. The univariate fssa algorithm is described in Haghbin H., Najibi, S.M., Mahmoudvand R., Trinka J., Maadooliat M. (2021) and the multivariate fssa over different dimensional domains technique may be found in Trinka J., Haghbin H., and Maadooliat M. (Accepted). In addition, one may perform forecasting of univariate and multivariate fts whose variables are observed over one-dimensional domains as described in the dissertation of Trinka J. (2021) and the manuscript of Trinka J., Haghbin H., Maadooliat M. (2020) where the manuscript is to be submitted to a journal for publication.
Set of functions that enable you to use the FUSION commands (Program available in: <http://forsys.sefs.uw.edu/fusion/fusionlatest.html>).
Utilities for Random Forest model selection, class balance
    correction, significance test, cross validation and partial dependency
    plots.
Sequential permutation testing for statistical
  significance of predictors in random forests. The main function
  of the package is rfvimptest(), which allows to test for the
  statistical significance of predictors in random forests using different 
  (sequential) permutation test strategies. The advantage of
  sequential over conventional permutation tests is that they
  are computationally considerably less intensive, as the sequential
  procedure is stopped as soon as there is sufficient evidence
  for either the null or the alternative hypothesis.
An interactive data visualization and exploration toolkit
    that implements Breiman and Cutler's original random forest Java based        
    visualization tools in R, for supervised and unsupervised classification and 
    regression within the algorithm random forest.
Provides functions for accessing and retrieving data from the
    Google Analytics APIs (https://developers.google.com/analytics/). Supports
    OAuth 2.0 authorization. Package provides access to the Management, Core
    Reporting, Multi-Channel Funnels Reporting, Real Time Reporting and
    Metadata APIs. Access to all the Google Analytics accounts which the user
    has access to. Auto-pagination to return more than 10,000 rows of the
    results by combining multiple data requests. Also package provides
    shiny app to explore the core reporting API dimensions and metrics.
An Interface to the GA4GH API that allows users to easily GET responses and POST requests to
  GA4GH Servers. See <http://ga4gh.org> for more information about the GA4GH project.
This package was created to analyze multi-level one-way
        experimental designs. It is designed to handle vectorized
        observation and factor data where there are unequal sample
        sizes and population variance homogeneity can not be assumed.
        To conduct the Gabriel test, create two vectors: one for your 
        observations and one for the factor level of each observation. 
        The function, rgabriel, conduct the test and save the output as
        a vector to input into the gabriel.plot function, which produces 
        a confidence interval plot for Multiple Comparison.
This package implements a Gamma convolution model for
        background correction.
An easy way to get started with Generative Adversarial Nets (GAN) in R. The GAN algorithm was initially 
    described by Goodfellow et al. 2014 <https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf>. A GAN can be used to learn the joint distribution of complex data by 
    comparison. A GAN consists of two neural networks a Generator and a Discriminator, where the two
    neural networks play an adversarial minimax game.
    Built-in GAN models make the training of GANs in R possible in one line and make it easy to 
    experiment with different design choices (e.g. different network architectures, value functions, optimizers).
    The built-in GAN models work with tabular data (e.g. to produce synthetic data) and image data. 
    Methods to post-process the output of GAN models to enhance the quality of samples are available.
Classes and methods to efficiently handle (slice, annotate, draw ...) genomic features (such as genes or transcripts), and an interactive interface to browse them.
A programmatic interface to the Web Service methods
    provided by the Global Biodiversity Information Facility (GBIF;
    <https://www.gbif.org/developer/summary>). GBIF is a database
    of species occurrence records from sources all over the globe.
    rgbif includes functions for searching for taxonomic names,
    retrieving information on data providers, getting species occurrence
    records, getting counts of occurrence records, and using the GBIF
    tile map service to make rasters summarizing huge amounts of data.
Provides an implementation of Regularized LS-TreeBoost & LAD-TreeBoost algorithm for Regulatory Network inference from any type of expression data (Microarray/RNA-seq etc). See Mall et al (2017) <doi:10.1101/132670>.
We utilize  approximate Bayesian machinery to fit two-level conjugate hierarchical models on overdispersed Gaussian, Poisson, and Binomial data and evaluates whether the resulting approximate Bayesian interval estimates for random effects meet the nominal confidence levels via frequency coverage evaluation. The data that Rgbp assumes comprise observed sufficient statistic for each random effect, such as an average or a proportion of each group, without population-level data.  The approximate Bayesian tool equipped with the adjustment for density maximization produces approximate point and interval estimates for model parameters including second-level variance component, regression coefficients, and random effect. For the Binomial data, the package provides an option to produce  posterior samples of all the model parameters via the acceptance-rejection method. The package provides a quick way to evaluate coverage rates of the resultant Bayesian interval estimates for random effects via a parametric bootstrapping, which we call frequency method checking.
Multiblock data analysis concerns the analysis of several sets of variables (blocks) observed on the same group of individuals. The main aims of the RGCCA package are: (i) to study the relationships between blocks and (ii) to identify subsets of variables of each block which are active in their relationships with the other blocks.
Provides bindings to the 'Geospatial' Data Abstraction Library ('GDAL') (>= 1.11.4) and access to projection/transformation operations from the 'PROJ' library. Please note that 'rgdal' will be retired by the end of 2023, plan transition to sf/stars/'terra' functions using 'GDAL' and 'PROJ' at your earliest convenience. Use is made of classes defined in the 'sp' package. Raster and vector map data can be imported into R, and raster and vector 'sp' objects exported. The 'GDAL' and 'PROJ' libraries are external to the package, and, when installing the package from source, must be correctly installed first; it is important that 'GDAL' < 3 be matched with 'PROJ' < 6. From 'rgdal' 1.5-8, installed with to 'GDAL' >=3, 'PROJ' >=6 and 'sp' >= 1.4, coordinate reference systems use 'WKT2_2019' strings, not 'PROJ' strings. 'Windows' and 'macOS' binaries (including 'GDAL', 'PROJ' and their dependencies) are provided on 'CRAN'.
Allow access to both public and private end points to Coinbase Pro (erstwhile GDAX) 
    cryptocurrency exchange. For authenticated flow, users must have valid api, secret and passphrase to be able to connect.
Compute yield-stability index based on Bayesian methodology, which is useful for analyze multi-environment trials in plant breeding programs. References: Cotes Torres JM, Gonzalez Jaimes EP, and Cotes Torres A (2016) <https://revistas.unimilitar.edu.co/index.php/rfcb/article/view/2037> Seleccion de Genotipos con Alta Respuesta y Estabilidad Fenotipica en Pruebas Regionales: Recuperando el Concepto Biologico.
Earth Engine <https://earthengine.google.com/> client library for R. All
  of the 'Earth Engine' API classes, modules, and functions are made available. Additional
  functions implemented include importing (exporting) of Earth Engine spatial objects, 
  extraction of time series, interactive map display, assets management interface, 
  and metadata display. See <https://r-spatial.github.io/rgee/> for further details.
Provides popular sampling distributions C++ routines based in 
    armadillo through a header file approach.
The GenDataSample() and GenDataPopulation() functions create, 
    respectively, a sample or population of multivariate nonnormal data 
    using methods described in Ruscio and Kaczetow (2008). Both of these 
    functions call a FactorAnalysis() function to reproduce a correlation 
    matrix. The EFACompData() function allows users to determine how many 
    factors to retain in an exploratory factor analysis of an empirical data
    set using a method described in Ruscio and Roche (2012). The latter 
    function uses populations of comparison data created by calling the 
    GenDataPopulation() function.
    <DOI: 10.1080/00273170802285693>.
    <DOI: 10.1037/a0025697>.
A method 'generate()' is implemented in this package for the random
    generation of vector time series according to models obtained by 'RMAWGEN',
    'vars' or other packages.  This package was created to generalize the
    algorithms of the 'RMAWGEN' package for the analysis and generation of any
    environmental vector time series.
The method 'generate()' is extended for spatial multi-site
    stochastic generation of daily precipitation. It generates precipitation
    occurrence in several sites using logit regression (Generalized Linear
    Models) and the approach by D.S. Wilks (1998) <doi:10.1016/S0022-1694(98)00186-3> .
Download the lyrics of your favorite songs in text and table formats. 
    Also search for related songs or song information.
    More information: <https://docs.genius.com/> .
A genetic algorithm plus derivative optimizer.
Provides spatial data analysis functionalities including Exploratory Spatial Data Analysis, 
    Spatial Cluster Detection and Clustering Analysis, Regionalization, etc. based on the C++ source code 
    of 'GeoDa', which is an open-source software tool that serves as an introduction to spatial data analysis.
    The 'GeoDa' software and its documentation are available at <https://geodacenter.github.io>.
Provides the hybrid Bayesian method Geometric Density Estimation. On the one hand, it scales the dimension of our data, on the other it performs inference. The method is fully described in the paper "Scalable Geometric Density Estimation" by Y. Wang, A. Canale, D. Dunson (2016) <http://proceedings.mlr.press/v51/wang16e.pdf>.
Connectors to online and offline sources for taking IP addresses
    and geolocating them to country, city, timezone and other geographic ranges. For
    individual connectors, see the package index.
Supports analysis of spatial data processed with the 'GeoPAT' 2
    software <http://sil.uc.edu/cms/index.php?id=geopat2>. 
    Available features include creation of a grid based on the 'GeoPAT' 2
    grid header file and reading a 'GeoPAT' 2 text outputs.
An implementation of functions for the analysis of serial crime 
    incidents. The package implements algorithms for the geographical profiling 
    of serial incidents in attempt to prioritize the area in which the anchor 
    point or home base of the perpetrator is located. The geographic profiling 
    methods in the package are implemented based upon the 'Dragnet' software 
    by Canter, Coffey, Huntley, and Missen (2000) <doi:10.1023/A:1007551316253>,
    the 'CrimeStat' software by Levine (2013) 
    <https://nij.ojp.gov/topics/articles/crimestat-spatial-statistics-program-analysis-crime-incident-locations>,
    and the criminal geographic targeting model outlined in Rossmo (2000, ISBN:978-0849381294)
    and Rossmo (1995) <http://summit.sfu.ca/item/6820>.
Interface to Geometry Engine - Open Source ('GEOS') using the C 'API' for topology operations on geometries. Please note that 'rgeos' will be retired by the end of 2023, plan transition to sf functions using 'GEOS' at your earliest convenience. The 'GEOS' library is external to the package, and, when installing the package from source, must be correctly installed first. Windows and Mac Intel OS X binaries are provided on 'CRAN'. ('rgeos' >= 0.5-1): Up to and including 'GEOS' 3.7.1, topological operations succeeded with some invalid geometries for which the same operations fail from and including 'GEOS' 3.7.2. The 'checkValidity=' argument defaults and structure have been changed, from default FALSE to integer default '0L' for 'GEOS' < 3.7.2 (no check), '1L' 'GEOS' >= 3.7.2 (check and warn). A value of '2L' is also provided that may be used, assigned globally using 'set_RGEOS_CheckValidity(2L)', or locally using the 'checkValidity=2L' argument, to attempt zero-width buffer repair if invalid geometries are found. The previous default (FALSE, now '0L') is fastest and used for 'GEOS' < 3.7.2, but will not warn users of possible problems before the failure of topological operations that previously succeeded. From 'GEOS' 3.8.0, repair of geometries may also be attempted using 'gMakeValid()', which may, however, return a collection of geometries of different types.
Create, read and write 'GEXF' (Graph Exchange 'XML' Format) graph
    files (used in 'Gephi' and others). Using the 'XML' package, it allows the user to
    easily build/read graph files including attributes, 'GEXF' visual attributes (such
    as color, size, and position), network dynamics (for both edges and nodes) and
    edge weighting. Users can build/handle graphs element-by-element or massively
    through data-frames, visualize the graph on a web browser through 'gexf-js' (a
    'javascript' library) and interact with the 'igraph' package.
Regularized Greedy Forest wrapper of the 'Regularized Greedy Forest' <https://github.com/RGF-team/rgf/tree/master/python-package> 'python' package, which also includes a Multi-core implementation (FastRGF) <https://github.com/RGF-team/rgf/tree/master/FastRGF>.
Datasets from the 2021 Ghana Population and Housing Census Results. Users can access results as 'tidyverse' and 'sf'-Ready Data Frames.    The data in this package is scraped from pdf reports released by the Ghana Statistical Service website <https://census2021.statsghana.gov.gh/> . The package currently only contains datasets from the literacy and education reports. Namely, school attendance data for respondents aged 3 years and above.
This package provides some functions to create quizzes
  in the GIFT format. This format is used by several Virtual Learning
  Environments such as Moodle.
Downloading, customizing, and processing time series of satellite images for a region of interest. 'RGISTools' functions allow a unified access to multispectral images from Landsat, MODIS and Sentinel repositories. 'RGISTools' also offers capabilities for customizing satellite images, such as tile mosaicking, image cropping and new variables computation. Finally, 'RGISTools' covers the processing, including cloud masking, compositing and gap-filling/smoothing time series of images (Militino et al., 2018 <doi:10.3390/rs10030398> and Militino et al., 2019 <doi:10.1109/TGRS.2019.2904193>).
Provides medium to high level functions for 3D interactive graphics, including
    functions modelled on base graphics (plot3d(), etc.) as well as functions for
    constructing representations of geometric objects (cube3d(), etc.).  Output
    may be on screen using OpenGL, or to various standard 3D file formats including
    WebGL, PLY, OBJ, STL as well as 2D image formats, including PNG, Postscript, SVG, PGF.
A programmatic interface to the web service methods
    provided by Global Biotic Interactions (GloBI)
    (<https://www.globalbioticinteractions.org/>). GloBI provides 
    access to spatial-temporal species interaction records from 
    sources all over the world. rglobi provides methods to search 
    species interactions by location, interaction type, and 
    taxonomic name. In addition, it supports Cypher, a graph query
    language, to allow for executing custom queries on the GloBI 
    aggregate species interaction data set.
R interface to the GNU Linear Programming Kit.
  'GLPK' is open source software for solving large-scale linear programming (LP),
  mixed integer linear programming ('MILP') and other related problems.
Provides 3D plotting routines that facilitate the use of the 'rgl' package and extend its functionality. For example, the routines allow the user to directly control the camera position & orientation, as well as to generate 3D movies with a moving observer.
The contents of this package have
  been merged into rgl, so it is no longer needed.
Parse scientific names using 'gnparser'
    (<https://github.com/gnames/gnparser>), written in Go. 'gnparser'
    parses scientific names into their component parts; it utilizes
    a Parsing Expression Grammar specifically for scientific names.
Interface for loading data from 'Google Ads API', 
    see <https://developers.google.com/google-ads/api/docs/start>. 
    Package provide function for authorization and loading reports.
Provides functions for accessing and retrieving data from the
    Google Analytics API.
It fires a query to the API to get the unsampled data in R for Google Analytics Premium Accounts. It retrieves data from the Google drive document and stores it into the local drive. The path to the excel file is returned by this package. The user can read data from the excel file into R using read.csv() function.
Provides interface to Google Fit REST API v1 (see <https://developers.google.com/fit/rest/v1/reference/>).
Serves two purposes: (i) Provide a
        comfortable R interface to query the Google server for static
        maps, and (ii) Use the map as a background image to overlay
        plots within R. This requires proper coordinate scaling.
Previously, when one is working with in the Google Ecosystem (Using Google Drive etc), there is hardly any good workflow of getting the values calculated from R and getting that into Google Slides. The normal and easy way out would be to just copy your work over but when you have a number of analysis to present with a lot of changes between each environment, it just becomes quite cumbersome.
Allows to search for existing resources, including datasets, on 
    the Canadian Open Government portal (<https://open.canada.ca/en>). It is 
    also designed to allow users to easily download a range of files directly 
    from the portal in a reproducible manner.
Query functions to the GPlates <https://www.gplates.org/> desktop application and the GPlates Web Service <https://gws.gplates.org/> allow users to reconstruct coordinates, static plates, and Spatial objects without leaving the R running environment. This R extension was supported by the FAU GeoZentrum Nordbayern and is developed under the umbrella of the DFG (Deutsche Forschungsgemeinschaft) Research Unit TERSANE2 (For 2332, TEmperature Related Stressors in ANcient Extinctions).
Geological Survey of Canada (GSC) functions for exploratory data analysis with applied geochemical data, with special application to the estimation of background ranges and identification of outliers, 'anomalies', to support mineral exploration and environmental studies.  Additional functions are provided to support analytical data QA/QC, ANOVA for investigations of field sampling and analytical variability, and utility tasks.  NOTE: function caplot() for concentration-area plots employs package 'akima', however, 'akima' is only licensed for not-for-profit use.  Therefore, not-for-profit users of 'rgr' will have to independently make package 'akima' available through library(....); and use of function caplot() by for-profit users will fail.
Encode network data as strings of printable ASCII characters. Implemented 
    functions include encoding and decoding adjacency matrices, edgelists, igraph, and
    network objects to/from formats 'graph6', 'sparse6', and 'digraph6'. The formats and
    methods are described in McKay, B.D. and Piperno, A (2014)
    <doi:10.1016/j.jsc.2013.09.003>.
Data and Functions from the book R Graphics, Third Edition. There is a function to produce each figure in the book, plus several functions, classes, and methods defined in Chapter 8.
Interpreted interface between 'GRASS' geographical 
    information system and R, based on starting R from within the 'GRASS' 'GIS'
    environment, or running free-standing R in a temporary 'GRASS' location;
    the package provides facilities for using all 'GRASS' commands from the 
    R command line. This package may not be used for 'GRASS' 6, for which
    'spgrass6' should be used.
A tool for easy viewing of the documentation of 'GRASS GIS' (see 
    <https://grass.osgeo.org/>). Pages of the 'GRASS GIS' manuals found at 
    <https://grass.osgeo.org/grass78/manuals/full_index.html> and at  
    <https://grass.osgeo.org/grass78/manuals/addons/> can be viewed within the 
    Viewer pane of 'RStudio', or be opened in the user's default browser.
Fully 'DBI'-compliant interface to 'Greenplum' <https://greenplum.org/>,
    an open-source parallel database. This is an extension of the 'RPostgres'
    package <https://github.com/r-dbi/RPostgres>.
The tools and utilities to estimate the model described in "Gremlin's in the
    Data: Identifying the Information Content of Research Subjects" (Howell et al.
    (2021) <doi:10.1177/0022243720965930>)  using conjoint analysis data such as
    that collected in Sawtooth Software's 'Lighthouse' or 'Discover' products.
    Additional utilities are included for formatting the input data.
Integrates the Groovy scripting language with the R Project for Statistical Computing.
Functions to calculate Sample Number and Average Sample Number for Repetitive Group Sampling Plan Based on Cpk as given in Aslam et al. (2013) (<DOI:10.1080/00949655.2012.663374>).
This is a library to access the current API of the web speed test service 'GTmetrix'.
    It provides a convenient wrapper to start tests, get reports, and access all kinds of meta data. 
    For more information about using the API please visit <https://gtmetrix.com/api/docs/2.0/>.
Automatic open data acquisition from resources of Polish Head Office
    of Geodesy and Cartography ('Główny Urząd Geodezji i Kartografii')
    (<https://www.gov.pl/web/gugik>).
    Available datasets include various types of numeric, raster and vector data,
    such as orthophotomaps, digital elevation models (digital terrain models,
    digital surface model, point clouds), state register of borders, spatial
    databases, geometries of cadastral parcels, 3D models of buildings, and more.
    It is also possible to geocode addresses or objects using the geocodePL_get()
    function.
Implementation of the affine-invariant method of Goodman & Weare (2010) <DOI:10.2140/camcos.2010.5.65>, a method of producing Monte-Carlo samples from a target distribution.
DBI/RJDBC interface to h2 database. h2 version 1.3.175 is included.
An R interface to the 'Handsontable' JavaScript library, which is a
    minimalist Excel-like data grid editor.  See <https://handsontable.com/> for details.
Simulate a renewal Hawkes (RHawkes) self-exciting process,
    with a given immigrant hazard rate function and offspring density function.
    Calculate the likelihood of a RHawkes process with given hazard rate function
    and offspring density function for an (increasing) sequence of event times.
    Calculate the Rosenblatt residuals of the event times. Predict
    future event times based on observed event times up to a given time.
    For details see Chen and Stindl (2017) <doi:10.1080/10618600.2017.1341324>.
Non-parametric clustering of joint pattern multi-genetic/epigenetic factors. The goal of this package is to cluster subjects based on gene features including single nucleotide polymorphisms (SNPs), DNA methylation (CPG), and gene expression (GE). The novel concept follows the general K-means (Hartigan and Wong (1979) <doi:10.2307/2346830> framework but uses weighted Euclidean distances across the gene features to cluster subjects. This approach is unique in that it attempts to capture all pairwise interactions in an effort to cluster based on their complex biological interactions.
Here we performs robust hierarchical co-clustering between row and column entities of a data matrix in 
             absence and presence of outlying observations. It can be used to explore important co-clusters consisting of  
             important samples and their regulatory significant features. Please see Hasan, Badsha and Mollah (2020) 
             <doi:10.1101/2020.05.13.094946>.
An R interface for the HealthData.gov data API.  For each data resource, you can filter results (server-side) to select subsets of data.
An implementation of the 'Heroicons' icon library for 'shiny'
    applications and other 'R' web-based projects. You can search, render,
    and customize icons without 'CSS' or 'JavaScript' dependencies.
Implements the hierarchical Bayesian analysis of populations structure (hierBAPS) 
  algorithm of Cheng et al. (2013) <doi:10.1093/molbev/mst028> for clustering DNA sequences 
  from multiple sequence alignments in FASTA format. 
  The implementation includes improved defaults and plotting capabilities 
  and unlike the original 'MATLAB' version removes singleton SNPs by default.
A framework that supports creating and extending enterprise Shiny applications using best practices.
Read health level 7 transactions and write random health level 7 files.
Complete access from 'R' to the FIS 'MarketMap 
    C-Toolkit' ('FAME C-HLI'). 'FAME' is a fully integrated software and database 
    management system from FIS that provides the following capabilities: 
    Time series and cross-sectional data management;
    Financial calculation, data analysis, econometrics, and forecasting;
    Table generation and detailed multicolor, presentation-quality report writing;
    Multicolor, presentation-quality graphics;
    "What-if" analysis;
    Application development and structured programming;
    Data transfer to and from other applications;
    Tools for building customized graphical user interfaces.
Implements simple Hamiltonian Monte Carlo routines in R for
             sampling from any desired target distribution which is continuous
             and smooth. See Neal (2017) <arXiv:1701.02434> for further details
             on Hamiltonian Monte Carlo. Automatic parameter selection is not
             supported.
Hydrologic modelling system is an object oriented tool for simulation and analysis of hydrologic events. The package proposes functions and methods for construction, simulation, visualization, and calibration of a hydrologic model.
Performs the random heteroscedastic nested error regression model described in Kubokawa, Sugasawa, Ghosh and Chaudhuri (2016) <doi:10.5705/ss.202014.0070>.
Rho is used to test the generalization of inter rater reliability
    (IRR) statistics. Calculating rho starts by generating a large number of
    simulated, fully-coded data sets: a sizable collection of hypothetical
    populations, all of which have a kappa value below a given threshold -- which
    indicates unacceptable agreement. Then kappa is calculated on a sample from
    each of those sets in the collection to see if it is equal to or higher than
    the kappa in then real sample. If less than five percent of the distribution
    of samples from the simulated data sets is greater than actual observed kappa,
    the null hypothesis is rejected and one can conclude that if the two raters had
    coded the rest of the data, we would have acceptable agreement (kappa above the
    threshold).
Higher-order spectra or polyspectra of time series, such as bispectrum and bicoherence, have been investigated in abundant literature and applied to problems of signal detection in a wide range of fields. This package aims to provide a simple API to estimate and analyze them. The current implementation is based on Brillinger and Irizarry (1998) <doi:10.1016/S0165-1684(97)00217-X> for estimating bispectrum or bicoherence, Lii and Helland (1981) <doi:10.1145/355958.355961> for cross-bispectrum, and Kim and Powers (1979) <doi:10.1109/TPS.1979.4317207> for cross-bicoherence.
Evaluating risk (that a patient arises a side effect) during hospitalization is the main purpose of this package. Several methods (Parametric, non parametric and De Vielder estimation) to estimate the risk constant (R) are implemented in this package. There are also functions to simulate the different models of this issue in order to quantify the previous estimators. It is necessary to read at least the first six pages of the report to understand the topic.
Microbenchmarks for determining the run time
  performance of aspects of the R programming environment and packages
  relevant to high-performance computation.  The benchmarks are divided into
  three categories: dense matrix linear algebra kernels, sparse matrix linear
  algebra kernels, and machine learning functionality.
Control the number of threads on 'BLAS' (Aka 'GotoBLAS', 'OpenBLAS', 'ACML', 'BLIS' and 'MKL').
  And possible to control the number of threads in 'OpenMP'.
  Get a number of logical cores and physical cores if feasible.
rHpcc is an R package providing an Interface between R and
        HPCC.Familiarity with ECL (Enterprise Control Language) is a
        must to use this package.HPCC is a massive parallel-processing
        computing platform that solves Big Data problems.ECL is the
        Enterprise Control Language designed specifically for huge data
        projects using the HPCC platform.Its extreme scalability comes
        from a design that allows you to leverage every query you
        create for re-use in subsequent queries as needed. To do this,
        ECL takes a dictionary approach to building queries wherein
        each ECL definition defines an Attribute. Each previously
        defined Attribute can then be used in succeeding ECL Attribute
        definitions as the language extends itself as you use it.
Methods to scan RR interval data for Premature Ventricular Complexes (PVCs) and parameterise and plot the resulting Heart Rate Turbulence (HRT). The methodology of HRT analysis is based on the original publication by Schmidt et al. <doi:10.1016/S0140-6736(98)08428-1> and extended with suggestions from <doi:10.1088/1361-6579/ab98b3>.
Allows users to import data files containing heartbeat	positions in the most broadly used formats, to remove outliers or points with unacceptable physiological values present in the time series, to plot HRV data, and to perform time domain, frequency domain and nonlinear HRV analysis. See Garcia et al. (2017) <DOI:10.1007/978-3-319-65355-6>.
The Ryan-Holm step-down Bonferroni or Sidak procedure is to control the family-wise (experiment-wise) type I error rate in the multiple comparisons.
    This procedure provides the adjusting p-values and adjusting CIs. The methods used in this package are referenced from John Ludbrook (2000) <doi:10.1046/j.1440-1681.2000.03223.x>.
This package offers functions to perform regularized
        Hotelling's T-square test for pathway or gene set analysis. The
        package is tailored for but not limited to proteomics data, in
        which sample sizes are often small, a large proportion of the
        data are missing and/or correlations may be present.
Run 'R CMD check' on any of the 'R-hub' (<https://builder.r-hub.io/>)
    architectures, from the command line. The current architectures include
    'Windows', 'macOS', 'Solaris' and various 'Linux' distributions.
Tool for the analysis Mass Spectrometry (MS) data in the context of 
             immunopeptidomic analysis for the identification of hybrid 
             peptides and the predictions of binding affinity of all peptides 
             using 'netMHCpan' <doi:10.1093/nar/gkaa379> while providing a summary 
             of the netMHCpan output. 'RHybridFinder' (RHF) is destined for 
             researchers who are looking to analyze their MS data for the purpose 
             of identification of potential spliced peptides. This package, 
             developed mainly in base R, is based on the workflow published by 
             Faridi et al. in 2018 <doi:10.1126/sciimmunol.aar3947>.
Wrapper for 'Datamuse' API to find rhyming and other associated words.
    This includes words of similar meaning, spelling, or other related words. Learn
    more about the 'Datamuse' API here <https://www.datamuse.com/api/>.
Create and manipulate hypergraph objects. This early version of
    rhype allows for the output of matrices associated with the hypergraphs 
    themselves. It also uses these matrices to calculate hypergraph spectra and
    perform spectral comparison. Functionality coming soon includes calculation
    of hyperpaths and hypergraph centrality measures.
This package provides a set of tools for conducting exact
        or approximate randomization-based inference for experiments of
        arbitrary design. The primary functionality of the package is
        in the generation, manipulation and use of permutation matrices
        implied by given experimental designs. Among other features,
        the package facilitates estimation of average treatment
        effects, constant effects variance estimation, randomization
        inference for significance testing against sharp null
        hypotheses and visualization of data and results.
Randomization inference procedures for simple and complex randomized designs, including multi-armed trials, as described in Gerber and Green (2012, ISBN: 978-0393979954). Users formally describe their randomization procedure and test statistic. The randomization distribution of the test statistic under some null hypothesis is efficiently simulated.
Computes attributable effects based confidence interval, permutation
    test confidence interval, or asymptotic confidence interval for the
    the average treatment effect on a binary outcome.
Radiomics image analysis toolbox for 2D and 3D radiological images. RIA supports DICOM, NIfTI,
             nrrd and npy (numpy array) file formats.
             RIA calculates first-order, gray level co-occurrence matrix, gray level run length matrix and
             geometry-based statistics. Almost all calculations are done using vectorized formulas to
             optimize run speeds. Calculation of several thousands of parameters only takes minutes
             on a single core of a conventional PC.
Random-intercept accelerated failure time (AFT) model utilizing Bayesian additive regression trees (BART) for drawing causal inferences about multiple treatments while accounting for the multilevel survival data structure. It also includes an interpretable sensitivity analysis approach to evaluate how the drawn causal conclusions might be altered in response to the potential magnitude of departure from the no unmeasured confounding assumption.
Recursive algorithms for computing various relatedness
    coefficients, including pairwise kinship, kappa and identity
    coefficients. Both autosomal and X-linked coefficients are computed.
    Founders are allowed to be inbred, enabling construction of any given
    kappa coefficients (Vigeland (2020) <doi:10.1007/s00285-020-01505-x>).
    In addition to the standard pairwise coefficients, 'ribd' also
    computes a range of lesser-known coefficients, including generalised
    kinship coefficients (Karigl (1981)
    <doi:10.1111/j.1469-1809.1981.tb00341.x>; Weeks and Lange (1988)
    <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1715269>), two-locus
    coefficients (Thompson (1988) <doi:10.1093/imammb/5.4.261>) and
    multi-person coefficients. This package is part of the 'ped suite', a
    collection of packages for pedigree analysis in R. Several methods of
    'ribd' are featured in the online app 'QuickPed' available at
    <https://magnusdv.shinyapps.io/quickped>.
Gene annotation of rice (Oryza Sativa L.spp.japonica).
    The package is based on the annotation file from the website <http://plants.ensembl.org/Oryza_sativa/Info/Index>. 
    Input gene's name then return some information, including the from position, the end position, 
    the position type and the chromosome number.
Convert one biological ID to another of rice (Oryza sativa).
    Rice(Oryza sativa) has more than one form gene ID for the genome. 
    The two main gene ID for rice genome are  the RAP (The Rice Annotation Project, <https://rapdb.dna.affrc.go.jp/>, 
    and the MSU(The Rice Genome Annotation Project, <http://rice.plantbiology.msu.edu/>.
    All RAP rice gene IDs are of the form Os##g####### as explained on the website <https://rapdb.dna.affrc.go.jp/>.
    All MSU rice gene IDs are of the form LOC_Os##g##### as explained on the website <http://rice.plantbiology.msu.edu/analyses_nomenclature.shtml>.
    All SYMBOL rice gene IDs are the unique name on the NCBI(National Center for Biotechnology Information, <https://www.ncbi.nlm.nih.gov/>.
    The TRANSCRIPTID, is the transcript id of rice, are of the form Os##t#######.
    The researchers usually need to converter between various IDs. Such as converter RAP to SYMBOLS for function searching on NCBI.
    There are a lot of websites with the function for converting RAP to MSU or MSU to RA, such as 'ID Converter' <https://rapdb.dna.affrc.go.jp/tools/converter>.
    But it is difficult to convert super multiple IDs on these websites.
    The package can convert all IDs between the three IDs (RAP, MSU and SYMBOL) regardless of the number.
The Diceware method can be used to generate strong passphrases.
  In short, you roll a 6-faced dice 5 times in a row, the number obtained is
  matched against a dictionary of easily remembered words. By combining together 7
  words thus generated, you obtain a password that is relatively easy to remember,
  but would take several millions years (on average) for a powerful computer to guess.
Computes rarefaction curves, cumulated and mean species
        richness. Compares these estimates by means of randomization
        tests.
Focused on (but not exclusive to) data sets hosted on PhysioNet
    (<https://physionet.org>), 'ricu' provides utilities for download, setup
    and access of intensive care unit (ICU) data sets. In addition to
    functions for running arbitrary queries against available data sets, a
    system for defining clinical concepts and encoding their representations
    in tabular ICU data is presented.
For whole-genome analysis, idiograms are virtually the most intuitive and effective way to map and visualize the genome-wide information. RIdeogram was developed to visualize and map whole-genome data on idiograms with no restriction of species.
Linear and logistic ridge regression functions. Additionally includes special functions for 
            genome-wide single-nucleotide polymorphism (SNP) data. More details can be found in
            <doi: 10.1002/gepi.21750> and <doi: 10.1186/1471-2105-12-372>.
This package implements ridge fusion methodology for inverse covariance matrix estimation for use in quadratic discriminant analysis.  The package also contains function for model based clustering using ridge fusion for inverse matrix estimation, as well as tuning parameter selection functions.  We have also implemented QDA using joint inverse covariance estimation.
An interface to iDigBio's search API that allows downloading
  specimen records. Searches are returned as a data.frame. Other functions
  such as the metadata end points return lists of information. iDigBio is a US
  project focused on digitizing and serving museum specimen collections on the
  web. See <https://www.idigbio.org> for information on iDigBio.
Functions to compute ridit scores of vectors, compute mean ridits
  and their standard errors for vectors compared to a reference vector,
  as described in Fleiss (1981, ISBN:0-471-06428-9), and compute
  means/SEs for multiple groups in matrices. Data can be either
  counts or proportions. Emphasis is on ridit analysis of ordered categorical
  data such as Likert items and pain-rating scales.
Allows to get weather data from Automated Surface Observing
    System (ASOS) stations (airports) in the whole world thanks to the
    Iowa Environment Mesonet website.
We provide a variety of algorithms for manifold-valued data, including Fréchet summaries, hypothesis testing, clustering, visualization, and other learning tasks. See Bhattacharya and Bhattacharya (2012) <doi:10.1017/CBO9781139094764> for general exposition to statistics on manifolds.
We provide a number of algorithms to estimate fundamental statistics including Fréchet mean and geometric median for manifold-valued data. Also, C++ header files are contained that implement elementary operations on manifolds such as Sphere, Grassmann, and others. See Bhattacharya and Bhattacharya (2012) <doi:10.1017/CBO9781139094764> if you are interested in statistics on manifolds, and Absil et al (2007, ISBN:9780691132983) on computational aspects of optimization on matrix manifolds.
Retrieves efficiently and reliably Investors Exchange ('IEX') stock and market data using 'IEX Cloud API'. The platform is offered by Investors Exchange Group (IEX Group).
    Main goal is to leverage 'R' capabilities including existing packages to effectively provide financial and statistical analysis as well as visualization in support of fact-based decisions.
    In addition, continuously improve and enhance 'Riex' by applying best practices and being in tune with users' feedback and requirements.
    Please, make sure to review and acknowledge Investors Exchange Group (IEX Group) terms and conditions before using 'Riex' (<https://iexcloud.io/terms/>).
Implements the algorithms for solving sparse generalized eigenvalue problem by Tan, et. al. (2018). Sparse Generalized Eigenvalue Problem: Optimal Statistical Rates via Truncated Rayleigh Flow.  To appear in Journal of the Royal Statistical Society: Series B. <arXiv:1604.08697>.
RIFS package provides functionality for generating &
        plotting prefractals in R^n with various protofractal sets and
        partition coefficient for iterative segments
A set of tools to streamline data analysis. Learning both R and introductory statistics at the same time can be challenging, and so we created 'rigr' to facilitate common data analysis tasks and enable learners to focus on statistical concepts. We provide easy-to-use interfaces for descriptive statistics, one- and two-sample inference, and regression analyses. 'rigr' output includes key information while omitting unnecessary details that can be confusing to beginners. Heteroscedasticity-robust ("sandwich") standard errors are returned by default, and multiple partial F-tests and tests for contrasts are easy to specify. A single regression function can fit both linear and generalized linear models, allowing students to more easily make connections between different classes of models.
Functionality to download stock prices,
    cryptocurrency data, and more from the 'Tiingo' API
    <https://api.tiingo.com/>.
Tools to download data from the ilostat database
    <https://ilostat.ilo.org> together with search and
    manipulation utilities.
Provides an interface to the powerful and fairly complete computer algebra system maxima.
    It can be used to start and control 'Maxima' from within R by entering 'Maxima' commands. 
    It facilitates outputting results from 'Maxima' in 'LaTeX' and 'MathML'. 2D and 3D plots can be displayed directly. This package also registers a 'knitr'-engine 
    enabling 'Maxima' code chunks to be written in 'RMarkdown' documents.
Provides functions to read 'ImageJ' (<http://imagej.nih.gov/ij/>)
    Region of Interest (ROI) files, to plot the ROIs and to convert them to
    'spatstat' (<http://spatstat.org/>) spatial patterns.
A pure R implementation of the median cut algorithm.
    Extracts the dominant colors from an image, and turns them into
    a scale for use in plots or for fun!
The metrics() function calculates measures of scholarly impact. 
    These include conventional measures, such as the number of publications
    and the total citations to all publications, as well as modern and 
    robust metrics based on the vector of citations associated with each 
    publication, such as the h index and many of its variants or rivals.
    These methods are described in Ruscio et al. (2012) 
    <DOI: 10.1080/15366367.2012.711147>.
A programmatic interface to the API provided by the 'iNaturalist' website <https://www.inaturalist.org/> to download species occurrence data submitted by citizen scientists.
Circular / ring buffers in R and C.  There are a couple
    of different buffers here with different implementations that
    represent different trade-offs.
Loading calls data from 'Ringostat API'. 
    See <https://help.ringostat.com/knowledge-base/article/integration-with-ringostat-via-api>.
Installs shiny apps packaged as stand-alone Electron apps using Inno Setup, an open source software that builds installers for Windows programs <http://www.jrsoftware.org/ishelp/>.
C++ classes to embed R in C++ (and C) applications
 A C++ class providing the R interpreter is offered by this package
 making it easier to have "R inside" your C++ application. As R itself
 is embedded into your application, a shared library build of R is
 required. This works on Linux, OS X and even on Windows provided you
 use the same tools used to build R itself. Numerous examples are
 provided in the nine subdirectories of the examples/ directory of
 the installed package: standard, 'mpi' (for parallel computing), 'qt'
 (showing how to embed 'RInside' inside a Qt GUI application), 'wt'
 (showing how to build a "web-application" using the Wt toolkit),
 'armadillo' (for 'RInside' use with 'RcppArmadillo'), 'eigen' (for
 'RInside' use with 'RcppEigen'), and 'c_interface' for a basic C
 interface and 'Ruby' illustration.  The examples use 'GNUmakefile(s)'
 with GNU extensions, so a GNU make is required (and will use the
 'GNUmakefile' automatically). 'Doxygen'-generated documentation of
 the C++ classes is available at the 'RInside' website as well.
Functions to calculate several ecological indices of individual 
    and population niche width (Araujo's E, clustering and pairwise similarity 
    among individuals, IS, Petraitis' W, and Roughgarden's WIC/TNW) to assess 
    individual specialization based on data of resource use. Resource use can 
    be quantified by counts of categories, measures of mass or length, or 
    proportions. Monte Carlo resampling procedures are available for hypothesis 
    testing against multinomial null models.
This tool can be used to build binary interval trees using real number inputs. 
    The tree supports queries of intervals overlapping a single number or an interval (start, end). 
    Intervals with same bounds but different names are treated as distinct intervals. 
    Insertion of intervals is also allowed. Deletion of intervals is not implemented at this point.
    See  Mark de Berg, Otfried Cheong, Marc van Kreveld, Mark Overmars (2008). Computational Geometry: Algorithms and Applications, for a reference.
Allows the user to view an image in full screen when clicking on it in 'RMarkdown' documents and 'shiny' applications. 
  The package relies on the 'JavaScript' library 'intense-images'. See <https://tholman.com/intense-images/> for more information.
A wrapper for the 'Intro.js' library (For more info: <https://introjs.com/>). 
  This package makes it easy to include step-by-step introductions, and clickable hints in a 'Shiny' 
  application. It supports both static introductions in the UI, and programmatic introductions from 
  the server-side.
Streamlined data import and export by making assumptions that
    the user is probably willing to make: 'import()' and 'export()' determine
    the data structure from the file extension, reasonable defaults are used for
    data import and export (e.g., 'stringsAsFactors=FALSE'), web-based import is
    natively supported (including from SSL/HTTPS), compressed files can be read
    directly without explicit decompression, and fast import packages are used where
    appropriate. An additional convenience function, 'convert()', provides a simple
    method for converting between file types.
Constrained clustering, transfer functions, and other methods for analysing Quaternary science data.
Takes an array of IPs and the keys for the services the user wishes to use (IP Hub, IP Intel, and Proxycheck), 
    and passes these to all respective APIs. Returns a dataframe with the IP addresses (used for merging), 
    country, ISP, labels for non-US IP Addresses, VPS use, and recommendations for blocking. The package 
    also provides optional visualization tools for checking the distributions. Additional
    functions are provided to call each discrete API endpoint. The package and methods are detailed in the recent paper 
    Waggoner, Kennedy, and Clifford (2019) <doi:10.21105/joss.01285>.
Utility functions and S3 classes for IPv4 and IPv6 addresses, including 
             conversion to and from binary representation.
Ports the Ripser <arXiv:1908.02518> and Cubical Ripser
        <arXiv:2005.12692> persistent homology calculation engines from
        C++. Can be used as a rapid calculation tool in topological
        data analysis pipelines.
Parameter estimation, computation of probability, information, and 
    (log-)likelihood, and visualization of item/test characteristic curves and
    item/test information functions for three uni-dimensional item response theory
    models: the 3-parameter-logistic model, generalized partial credit model, 
    and graded response model. The full documentation and tutorials are at 
    <https://github.com/xluo11/Rirt>.
We propose numerous functions for cohort-based analyses, either for prediction or causal inference. For causal inference, it includes Inverse Probability Weighting and G-computation for marginal estimation of an exposure effect when confounders are expected. We deal with binary outcomes, times-to-events (Le Borgne, 2016, <doi:10.1002/sim.6777>), competing events (Trebern-Launay, 2018, <doi: 10.1007/s10654-017-0322-3>), and multi-state data (Gillaizeau, 2018, <doi: 10.1002/sim.7550>). For multistate data, semi-Markov model with interval censoring (Foucher, 2008, <doi: 10.1177/0962280208093889>) may be considered and we propose the possibility to consider the excess of mortality related to the disease compared to reference lifetime tables (Gillaizeau, 2017, <doi: 10.1177/0962280215586456>). For predictive studies, we propose a set of functions to estimate time-dependent receiver operating characteristic (ROC) curves with the possible consideration of right-censoring times-to-events or the presence of confounders (Le Borgne, 2018,  <doi: 10.1177/0962280217702416>). Finally, several functions are available to assess time-dependent ROC curves (Combescure, 2017, <doi: 10.1177/0962280212464542>) or survival curves (Combescure, 2014, <doi: 10.1002/sim.6111>) from aggregated data.
Implements techniques for educational resource inspection, selection, and evaluation (RISE) described in Bodily, Nyland, and Wiley (2017) 
    <doi:10.19173/irrodl.v18i2.2952>.	Automates the process of identifying learning materials that are not effectively supporting student learning in 
    technology-mediated courses by synthesizing information about access to course content and performance on assessments.
Fits an Ising model to a binary dataset using L1 regularized
    logistic regression and extended BIC. Also includes a fast lasso logistic
    regression function for high-dimensional problems. Uses the 'libLBFGS'
    optimization library by Naoaki Okazaki.
Computes 26 financial risk measures for any continuous distribution.  The 26 financial risk measures  include value at risk, expected shortfall due to Artzner et al. (1999) <DOI:10.1007/s10957-011-9968-2>, tail conditional median due to Kou et al. (2013) <DOI:10.1287/moor.1120.0577>, expectiles due to Newey and Powell (1987) <DOI:10.2307/1911031>, beyond value at risk due to Longin (2001) <DOI:10.3905/jod.2001.319161>, expected proportional shortfall due to Belzunce et al. (2012) <DOI:10.1016/j.insmatheco.2012.05.003>, elementary risk measure due to Ahmadi-Javid (2012) <DOI:10.1007/s10957-011-9968-2>, omega due to Shadwick and Keating (2002), sortino ratio due to Rollinger and Hoffman (2013), kappa  due to Kaplan and Knowles  (2004), Wang (1998)'s <DOI:10.1080/10920277.1998.10595708> risk measures, Stone (1973)'s <DOI:10.2307/2978638> risk measures, Luce (1980)'s <DOI:10.1007/BF00135033> risk measures, Sarin (1987)'s <DOI:10.1007/BF00126387> risk measures, Bronshtein and Kurelenkova (2009)'s risk measures.
A collection of functions related to the study of etiologic heterogeneity both across disease subtypes and across individual disease markers. The included functions allow one to quantify the extent of etiologic heterogeneity in the context of a case-control study, and provide p-values to test for etiologic heterogeneity across individual risk factors. Begg CB, Zabor EC, Bernstein JL, Bernstein L, Press MF, Seshan VE (2013) <doi:10.1002/sim.5902>.
Facilities for assessing R packages against a number of metrics to 
    help quantify their robustness.
Fast design of risk parity portfolios for financial investment.
    The goal of the risk parity portfolio formulation is to equalize or distribute
    the risk contributions of the different assets, which is missing if we simply
    consider the overall volatility of the portfolio as in the mean-variance
    Markowitz portfolio. In addition to the vanilla formulation, where the risk
    contributions are perfectly equalized subject to no shortselling and budget
    constraints, many other formulations are considered that allow for box
    constraints and shortselling, as well as the inclusion of additional
    objectives like the expected return and overall variance. See vignette for
    a detailed documentation and comparison, with several illustrative examples.
    The package is based on the papers:
    Y. Feng, and D. P. Palomar (2015). SCRIP: Successive Convex Optimization Methods
    for Risk Parity Portfolio Design. IEEE Trans. on Signal Processing, vol. 63,
    no. 19, pp. 5285-5300. <doi:10.1109/TSP.2015.2452219>.
    F. Spinu (2013), An Algorithm for Computing Risk Parity Weights.
    <doi:10.2139/ssrn.2297383>.
    T. Griveau-Billion, J. Richard, and T. Roncalli (2013). A fast algorithm for computing
    High-dimensional risk parity portfolios. <arXiv:1311.4057>.
Collection of functions designed to compute risk-based portfolios as described 
    in Ardia et al. (2017) <doi:10.1007/s10479-017-2474-7> and Ardia et al. (2017) <doi:10.21105/joss.00171>.
Assessing and comparing risk prediction rules for clustered data. The method is based on the paper: Rosner B, Qiu W, and Lee MLT.(2013) <doi: 10.1007/s10985-012-9240-6>.
Computes risk measures from data, as well as performs risk management procedures.
Implementation of the following methods for event history analysis.
    Risk regression models for survival endpoints also in the presence of competing
    risks are fitted using binomial regression based on a time sequence of binary
    event status variables. A formula interface for the Fine-Gray regression model
    and an interface for the combination of cause-specific Cox regression models.
    A toolbox for assessing and comparing performance of risk predictions (risk
    markers and risk prediction models). Prediction performance is measured by the
    Brier score and the area under the ROC curve for binary possibly time-dependent
    outcome. Inverse probability of censoring weighting and pseudo values are used
    to deal with right censored data. Lists of risk markers and lists of risk models
    are assessed simultaneously. Cross-validation repeatedly splits the data, trains
    the risk prediction models on one part of each split and then summarizes and
    compares the performance across splits.
Compute time-dependent Incident/dynamic accuracy measures
        (ROC curve, AUC, integrated AUC )from censored survival data
        under proportional or non-proportional hazard assumption of
        Heagerty & Zheng (Biometrics, Vol 61 No 1, 2005, PP 92-105).
Implements efficient simulation procedures to estimate tail loss probabilities and conditional excess for a stock portfolio. The log-returns are assumed to follow a t-copula model with generalized hyperbolic or t marginals.
Risk-related information (like the prevalence of conditions, the sensitivity and specificity of diagnostic tests, or the effectiveness of interventions or treatments) can be expressed in terms of frequencies or probabilities. By providing a toolbox of corresponding metrics and representations, 'riskyr' computes, translates, and visualizes risk-related information in a variety of ways. Adopting multiple complementary perspectives provides insights into the interplay between key parameters and renders teaching and training programs on risk literacy more transparent.
A set of tools to extract bibliographic content from the National
    Center for Biotechnology Information (NCBI) databases, including PubMed. The
    name RISmed is a portmanteau of RIS (for Research Information Systems, a common
    tag format for bibliographic data) and PubMed.
Automated performance of common transformations used to fulfill parametric
    assumptions of normality and identification of the best performing method 
    for the user. Output for various normality tests (Thode, 2002) corresponding 
    to the best performing method and a descriptive statistical report of the 
    input data in its original units (5-number summary and mathematical moments) 
    are also presented. Lastly, the Rankit, an empirical normal quantile transformation 
    (ENQT) (Soloman & Sawilowsky, 2009), is provided to accommodate non-standard 
    use cases and facilitate adoption.  
    <DOI: 10.1201/9780203910894>.
    <DOI: 10.22237/jmasm/1257034080>.
Implements the simulation and regression of
        integrated Isothermal Titration Calorimetry (ITC) data using
        the most commonly used one-to-one binding reaction model.
An interface to the Integrated Taxonomic Information System ('ITIS')
    (<https://www.itis.gov>). Includes functions to work with the 'ITIS' REST
    API methods (<https://www.itis.gov/ws_description.html>), as well as the
    'Solr' web service (<https://www.itis.gov/solr_documentation.html>).
Tools for randomization-based inference. Current focus is on the d^2 omnibus test of differences of means following Hansen and Bowers (2008)  <doi:10.1214/08-STS254> . This test is useful for assessing balance in matched observational studies or for analysis of outcomes in block-randomized experiments.
Finds a robust instrumental variables estimator using a
             high breakdown point S-estimator of multivariate location
             and scatter matrix.
Generates graphs, CSV files, and coordinates related to river valleys when calling the riverbuilder() function.
Reads river network shape files and computes network distances.
    Also included are a variety of computation and graphical tools designed 
    for fisheries telemetry research, such as minimum home range, kernel density 
    estimation, and clustering analysis using empirical k-functions with 
    a bootstrap envelope.  Tools are also provided for editing the river 
    networks, meaning there is no reliance on external software.
Implements several of the most popular load estimation procedures, including averaging methods, ratio estimators and regression methods. The package provides an easy-to-use tool to rapidly calculate the load for various compounds and to compare different methods. The package also supplies additional functions to easily organize and analyze the data.
Functions for reading, analysing and plotting river networks.
  For this package, river networks consist of sections and nodes with associated attributes, 
  e.g. to characterise their morphological, chemical and biological state.
  The package provides functions to read this data from text files, to analyse the network
  structure and network paths and regions consisting of sections and nodes that fulfill
  prescribed criteria, and to plot the river network and associated properties.
Sankey plots are a type of diagram that is convenient to
    illustrate how flow of information, resources etc. separates and joins,
    much like observing how rivers split and merge. For example, they can be
    used to compare different clusterings.
This R package is a flexible and efficient tool to 
    visualise both quantitative and qualitative data from river surveys. 
    It can be used to produce diagrams with the topological structure of 
    the river network.
It is devoted to the IVIVC linear level A with numerical deconvolution method. The latter is working for inequal and incompatible timepoints between impulse and response curves. A numerical convolution method is also available. Application domains include pharamaceutical industry QA/QC and R&D together with academic research.
A tool for undergraduate and graduate courses in open-channel
    hydraulics. Provides functions for computing normal and critical depths,
    steady-state water surface profiles (e.g. backwater curves) and unsteady flow
    computations (e.g. flood wave routing) as described in
    Koohafkan MC, Younis BA (2015). "Open-channel computation with R."
    The R Journal, 7(2), 249–262. <doi: 10.32614/RJ-2015-034>.
Jade is a high performance template engine heavily influenced by
    Haml and implemented with JavaScript for node and browsers.
Low-level interface to Java VM very much like .C/.Call and friends. Allows creation of objects, calling methods and accessing fields.
This is the official 'Jazz' client. 'Jazz' is a lightweight modular data processing
    framework, including a web server. It provides data persistence and computation capabilities
    accessible from 'R' and 'Python' and also through a REST API. <https://github.com/bbvadata/Jazz>
    See ?rjazz::rjazz to get a 'Jazz' server.
Clustering algorithm for high dimensional data. Assuming that P feature measurements on N objects are arranged in an N×P matrix X, this package provides clustering based on the left Gram matrix XX^T. To simulate test data, type "help('simulate_HD_data')" and to learn how to use the clustering algorithm, type "help('RJclust')". To cite this package, type 'citation("RJcluster")'.
The RJDBC package is an implementation of R's DBI interface using JDBC as a back-end. This allows R to connect to any DBMS that has a JDBC driver.
Interface around 'JDemetra+' (<https://github.com/jdemetra/jdemetra-app>), the seasonal adjustment software officially
    recommended to the members of the European Statistical System (ESS) and the European System of Central Banks.
    It offers full access to all options and outputs of 'JDemetra+', including the two leading seasonal adjustment methods
    TRAMO/SEATS+ and X-12ARIMA/X-13ARIMA-SEATS.
Functions to have nice 'rmarkdown' outputs of the 
  seasonal and trading day adjustment models made with 'RJDemetra'.
Add-in to the 'RJDemetra' package on seasonal adjustments.
    It allows to produce quality assessments outputs (dashboards, quality report matrix, etc.).
A series of functions in some way considered useful to the author.  These include functions for subsetting tables and generating indices for arrays, conditioning and intervening in probability distributions, generating combinations and more...
Performs reversible-jump Markov chain Monte Carlo (Green, 1995)
    <doi:10.2307/2337340>, specifically the restriction introduced by 
    Barker & Link (2013) <doi:10.1080/00031305.2013.791644>. By utilising 
    a 'universal parameter' space, RJMCMC is treated as a Gibbs sampling 
    problem. Previously-calculated posterior distributions are used to 
    quickly estimate posterior model probabilities. Jacobian matrices are 
    found using automatic differentiation. For a detailed description of
    the package, see Gelling, Schofield & Barker (2019)
    <doi:10.1111/anzs.12263>.
Provides an implementation of the reversible jump piecewise deterministic Markov processes (PDMPs) methods developed in the paper Reversible Jump PDMP Samplers for Variable Selection (Chevallier, Fearnhead, Sutton 2020, <arXiv:2010.11771>). It also contains an implementation of a Gibbs sampler for variable selection in Logistic regression based on Polya-Gamma augmentation.
R-interface to gene drop programs from Alun Thomas' Java Programs for Statistical Genetics and Computational Statistics (JPSGCS).
Implementation of the Johnson Quantile-Parameterised Distribution in R. 
  The Johnson Quantile-Parameterised Distribution (J-QPD) is a flexible distribution
  system that is parameterised by a symmetric percentile triplet of quantile values
  (typically the 10th-50th-90th) along with known support bounds for the distribution.
  The J-QPD system was developed by Hadlock and Bickel (2017) <doi:10.1287/deca.2016.0343>.
  This package implements the density, quantile, CDF and random number generator functions.
Provides functions to retrieve data and metadata from providers 
			 that disseminate data by means of SDMX web services. 
			 SDMX (Statistical Data and Metadata eXchange) is a standard that 
			 has been developed with the aim of simplifying the exchange of 
			 statistical information. 
			 More about the SDMX standard and the SDMX Web Services 
			 can be found at: <https://sdmx.org>.
Converts R object into JSON objects and vice-versa.
This is a package that allows conversion to and from 
  data in Javascript object notation (JSON) format.
  This allows R objects to be inserted into Javascript/ECMAScript/ActionScript code
  and allows R programmers to read and convert JSON content to R objects.
  This is an alternative to rjson package. Originally, that was too slow for converting large R objects to JSON
  and was not extensible.  rjson's performance is now similar to this package, and perhaps slightly faster in some cases.
  This package uses methods and is readily extensible by defining methods for different classes, 
  vectorized operations, and C code and callbacks to R functions for deserializing JSON objects to R. 
  The two packages intentionally share the same basic interface. This package (RJSONIO) has many additional
  options to allow customizing the generation and processing of JSON content.
  This package uses libjson rather than implementing yet another JSON parser. The aim is to support
  other general projects by building on their work, providing feedback and benefit from their ongoing development.
Creates interactive graphs with 'R'. It joins the data analysis power of R and the visualization libraries of JavaScript in one package.
Handle 'JSON-stat' format (<https://json-stat.org>) in R.
    Not all features are supported, especially the extensive metadata
    features of 'JSON-stat'.
'JDemetra+' (<https://github.com/jdemetra/jdemetra-app>) is the seasonal adjustment software officially recommended
  to the members of the European Statistical System and the European System of Central Banks. Seasonal adjustment models performed
  with 'JDemetra+' can be stored into workspaces. 'JWSACruncher' (<https://github.com/jdemetra/jwsacruncher/releases>) is a console tool that 
  re-estimates all the multi-processing defined in a workspace and to export the result. 'rjwsacruncher' allows to launch easily the 'JWSACruncher'.
Apache 'Kafka' is an open-source message broker project developed by the Apache Software Foundation which can be thought of as a distributed, partitioned, replicated commit log service.At a high level, producers send messages over the network to the 'Kafka' cluster which in turn serves them up to consumers.See <https://kafka.apache.org/> for more information.Functions included in this package enable:1.Creating 'Kafka' producer 2.Writing messages to a topic 3.Closing 'Kafka' producer 4.Creating 'Kafka' consumer 5.Reading messages from a topic 6.Closing 'Kafka' consumer. The jars required for this package are included in a separate package 'rkafkajars'.
The 'rkafkajars' package collects all the external jars required for the 'rkafka' package.
An R interface to KEA (Version 5.0).
  KEA (for Keyphrase Extraction Algorithm) allows for extracting
  keyphrases from text documents. It can be either used for free
  indexing or for indexing with a controlled vocabulary. For more
  information see <http://www.nzdl.org/Kea/>.
External jars required for package RKEA.
'KEEL' is a popular Java software for a large number of different knowledge data discovery tasks.
    This package takes the advantages of 'KEEL' and R, allowing to use 'KEEL' algorithms in simple R code.
    The implemented R code layer between R and 'KEEL' makes easy both using 'KEEL' algorithms in R as implementing new algorithms for 'RKEEL' in a very simple way.
    It includes more than 100 algorithms for classification, regression, preprocess, association rules and imbalance learning, which allows a more complete experimentation process.
    For more information about KEEL, see <http://www.keel.es/>.
'KEEL' is a popular Java software for a large number of different knowledge data discovery tasks. Furthermore, 'RKEEL' is a package with a R code layer between R and 'KEEL', for using 'KEEL' in R code. This package includes the datasets from 'KEEL' in .dat format for its use in 'RKEEL' package. For more information about 'KEEL', see <http://www.keel.es/>.
'KEEL' is a popular Java software for a large number of different knowledge data discovery tasks. Furthermore, 'RKEEL' is a package with a R code layer between R and 'KEEL', for using 'KEEL' in R code. This package downloads and install the .jar files necessary for 'RKEEL' algorithms execution. For more information about 'KEEL', see <http://www.keel.es/>.
Calculates the Kelly criterion (Kelly, J.L. (1956) <doi:10.1002/j.1538-7305.1956.tb03809.x>) for bets given quoted prices, model predictions and commissions.
    Additionally it contains helper functions to calculate the probabilities for wins and draws in multi-leg games.
The 'KeOps' library lets you compute generic reductions of very 
    large arrays whose entries are given by a mathematical formula with CPU and 
    GPU computing support. It combines a tiled reduction scheme with an 
    automatic differentiation engine. It is perfectly suited to the efficient 
    computation of Kernel dot products and the associated gradients, even when 
    the full kernel matrix does not fit into the GPU memory.
Estimates the Hoeffding decomposition of an unknown function by solving ridge group sparse optimization problem based on reproducing kernel Hilbert spaces, and approximates its sensitivity indices (see Kamari, H., Huet, S. and Taupin, M.-L. (2019) <arXiv:1905.13695>).
Applies methods used to estimate animal homerange, but
    instead of geospatial coordinates, we use isotopic coordinates. The estimation
    methods include: 1) 2-dimensional bivariate normal kernel utilization density
    estimator, 2) bivariate normal ellipse estimator, and 3) minimum convex polygon
    estimator, all applied to stable isotope data. Additionally, functions to
    determine niche area, polygon overlap between groups and levels (confidence
    contours) and plotting capabilities.
Hybrid Mortality Modelling (HMM) provides a framework in which mortality around "the accident hump" and at very old ages can be modelled under a single model. The graphics' codes necessary for visualization of the models' output are included here. Specifically, the graphics are based on the assumption that, the mortality rates can be expressed as a function of the area under the curve between the crude mortality rates plots and the tangential transform of the force of mortality.
Methods for downloading and processing data and metadata from 'Kolada', the official Swedish regions and municipalities database <https://kolada.se/>.
It establishes a critical framework to manipulate, explore and extract biologically relevant information from mitochondrial minicircle assemblies in tens to hundreds of samples simultaneously and efficiently. This should facilitate research that aims to develop new molecular markers for identifying species-specific minicircles, or to study the ancestry of parasites for complementary insights into their evolutionary history.
A client package that makes the 'KorAP' web service API accessible from R.
  The corpus analysis platform 'KorAP' has been developed as a scientific tool to make
  potentially large, stratified and multiply annotated corpora, such as the 'German Reference Corpus DeReKo'
  or the 'Corpus of the Contemporary Romanian Language CoRoLa', accessible for linguists to let them verify
  hypotheses and to find interesting patterns in real language use.
  The 'RKorAPClient' package provides access to 'KorAP' and the corpora behind it for user-created R code,
  as a programmatic alternative to the 'KorAP' web user-interface.
  You can learn more about 'KorAP' and use it directly on 'DeReKo' at <https://korap.ids-mannheim.de/>.
Contains function rkt which computes the Mann-Kendall test (MK) and the Seasonal and the Regional Kendall Tests for trend (SKT and RKT) and  Theil-Sen's slope estimator.
Robust  kernel center matrix, robust  kernel cross-covariance operator for kernel unsupervised methods, kernel canonical correlation analysis, 
 influence function of identifying significant outliers or atypical objects from multimodal datasets. Alam, M. A,  Fukumizu, K., Wang  Y.-P. (2018) <doi:10.1016/j.neucom.2018.04.008>.
   Alam, M. A,  Calhoun, C. D.,  Wang  Y.-P. (2018) <doi:10.1016/j.csda.2018.03.013>.
This package provides functionality to read files
    containing observations which consist of arbitrary key/value
    pairs.
Functions and Datasets Required for ST370 class
The 'LabKey' client library for R makes it easy for R users to
    load live data from a 'LabKey' Server, <https://www.labkey.com/>,
    into the R environment for analysis, provided users have permissions
    to read the data. It also enables R users to insert, update, and
    delete records stored on a 'LabKey' Server, provided they have appropriate
    permissions to do so.
Standardized methods for calculating common important derived
    physical features of lakes including water density based based on
    temperature, thermal layers, thermocline depth, lake number, Wedderburn
    number, Schmidt stability and others.
A toolbox for working with base types, core R features
  like the condition system, and core 'Tidyverse' features like tidy
  evaluation.
Read and write 'las' and 'laz' binary file formats. The LAS file format is a public file format for the interchange of 3-dimensional point cloud data between data users. The LAS specifications are approved by the American Society for Photogrammetry and Remote Sensing <https://www.asprs.org/divisions-committees/lidar-division/laser-las-file-format-exchange-activities>. The LAZ file format is an open and lossless compression scheme for binary LAS format versions 1.0 to 1.4 <https://laszip.org/>.
An easy-to-use tool to employ interactivity in every-day exploratory analysis. It contains 
	a collection of most commonly used types of charts (such as scatter plots, line plots, heatmaps, bar charts),
	which can be linked to each other or to other interactive elements with just few lines of code.
Robust likelihood cross validation bandwidth for uni- and multi-variate kernel densities. It is robust against fat-tailed distributions and/or outliers. Based on "Robust Likelihood Cross-Validation for Kernel Density Estimation," Wu (2019) <doi:10.1080/07350015.2018.1424633>.
Analyzes data from repeated low-dose challenge experiments and provide vaccine efficacy estimates. In addition, this package can provide guidance to design repeated low-dose challenge studies.
Estimates the Bayesian LDA model for mixed-membership clustering based on different types of data
    (i.e., Multinomial, Bernoulli, and Binomial entries). Albuquerque, Valle and Li (2019) <doi:10.1016/j.knosys.2018.10.024>.
Linguistic Descriptions of Complex Phenomena (LDCP) is an architecture and methodology that allows us to model complex phenomena, interpreting input data, and generating automatic text reports customized to the user needs (see <doi:10.1016/j.ins.2016.11.002> and <doi:10.1007/s00500-016-2430-5>). The proposed package contains a set of methods that facilitates the development of LDCP systems. It main goal is increasing the visibility and practical use of this research line.
Common 'base' and 'stats' methods for 'rle' objects, aiming to make it possible to treat them transparently as vectors.
Leaf angle distribution is described by a number of functions
    (e.g. ellipsoidal, Beta and rotated ellipsoidal). The parameters of leaf angle
    distributions functions are estimated through different empirical relationship.
    This package includes estimations of parameters of different leaf angle
    distribution function, plots and evaluates leaf angle distribution functions,
    calculates extinction coefficients given leaf angle distribution.
    Reference: Wang(2007)<doi:10.1016/j.agrformet.2006.12.003>.
Display spatial data with interactive maps powered by the 
  open-source JavaScript library 'Leaflet' (see <http://leafletjs.com/>). Maps can be rendered in a web browser or
  displayed in the HTML viewer pane of 'RStudio'. This package is designed to be easy to use and
  can create complex maps with vector and raster data, web served map tiles and interface elements.
Provides an interface to the C implementation of the
        random number generator with multiple independent streams
        developed by L'Ecuyer et al (2002). The main purpose of this
        package is to enable the use of this random number generator in
        parallel R applications.
Allows easy access to the LEMON Graph Library set of algorithms, written in C++.
             See the LEMON project page at <https://lemon.cs.elte.hu/trac/lemon>.
             Current LEMON version is 1.3.1.
Converts LESS to CSS. 
    It uses V8 engine, where LESS parser is run. Functions for
    LESS text, file or folder conversion are provided.
    This work was supported by a junior grant research project by 
    Czech Science Foundation 'GACR' no. 'GJ18-04150Y'.
Contains functions for simulating the linear fractional stable motion according to the algorithm developed by Mazur and Otryakhin <doi:10.32614/RJ-2020-008> based on the method from Stoev and Taqqu (2004) <doi:10.1142/S0218348X04002379>, as well as functions for estimation of parameters of these processes introduced by Mazur, Otryakhin and Podolskij (2018) <arXiv:1802.06373>, and also different related quantities.
Assists in the manipulation and processing of linear features with the help of the 'sf' package.
  Makes use of linear referencing to extract data from most shape files.
  Reference for this packages methods: Albeke, S.E. et al. (2010) <doi:10.1007/s10980-010-9528-4>.
An implementation of a number of Global Trend models for time series forecasting 
    that are Bayesian generalizations and extensions of some Exponential Smoothing models. 
    The main differences/additions include 1) nonlinear global trend, 2) Student-t error 
    distribution, and 3) a function for the error size, so heteroscedasticity. The methods 
    are particularly useful for short time series. When tested on the well-known M3 dataset,
    they are able to outperform all classical time series algorithms. The models are fitted 
    with MCMC using the 'rstan' package.
An R interface for libeemd (Luukko, Helske, Räsänen, 2016) <doi:10.1007/s00180-015-0603-9>, 
    a C library of highly efficient parallelizable functions  for performing the ensemble empirical mode decomposition (EEMD), 
    its complete variant (CEEMDAN), the regular empirical mode decomposition (EMD), and bivariate EMD (BEMD). 
    Due to the possible portability issues CRAN version no longer supports OpenMP, you can install OpenMP-supported version 
    from GitHub: <https://github.com/helske/Rlibeemd/>.
Set of tools for reading, processing and visualizing small set of
    LiDAR (Light Detection and Ranging) data for forest inventory applications.
    More details were published in Silva et al. (2016) <doi:10.1080/07038992.2016.1196582>.
Uses an extension of nonnegative matrix factorization to identify shared and dataset-specific factors. See Welch J, Kozareva V, et al (2019) <doi:10.1016/j.cell.2019.05.006>, and Liu J, Gao C, Sodicoff J, et al (2020) <doi:10.1038/s41596-020-0391-8> for more details.
A series of functions that allow users
    to access the 'LinkedIn' API to get information about connections,
    search for people and jobs, share updates with their network,
    and create group discussions.  For more information about using
    the API please visit <https://developer.linkedin.com/>.
Solving a system of linear equations is one of the most fundamental
    computational problems for many fields of mathematical studies, such as
    regression problems from statistics or numerical partial differential equations.
    We provide basic stationary iterative solvers such as Jacobi, Gauss-Seidel,
    Successive Over-Relaxation and SSOR methods. Nonstationary, also known as
	Krylov subspace methods are also provided. Sparse matrix computation is also supported
    in that solving large and sparse linear systems can be manageable using 'Matrix' package
    along with 'RcppArmadillo'. For a more detailed description, see a book by Saad (2003)
    <doi:10.1137/1.9780898718003>.
Provides a set of functions for data manipulation with
    list objects, including mapping, filtering, grouping, sorting,
    updating, searching, and other useful functions. Most functions
    are designed to be pipeline friendly so that data processing with
    lists can be chained.
Data driven approach for robust regression estimation in homoscedastic and heteroscedastic context. See Wang et al. (2007), <doi:10.1198/106186007X180156> regarding homoscedastic framework.
Estimates robust rank-based fixed effects and predicts robust
    random effects in two- and three- level random effects nested models.
    The methodology is described in Bilgic & Susmann (2013) <https://journal.r-project.org/archive/2013/RJ-2013-027/>.
R parallel implementation of Local Outlier Factor(LOF) which uses multiple CPUs to significantly speed up the LOF computation for large datasets. (Note: The overall performance depends on the computers especially the number of the cores).It also supports multiple k values to be calculated in parallel, as well as various distance measures in addition to the default Euclidean distance.
A very lightweight package that writes out log messages in an opinionated way.
  Simpler and lighter than other logging packages, 'rlog' provides a compact feature set that
  focuses on getting the job done in a Unix-like way.
Processing logical operations such as AND/OR/NOT operations
    dynamically. It also handles nesting in the operations.
Rapid, simulation-based exact (restricted) likelihood ratio tests
    for testing the presence of variance components/nonparametric terms for
    models fit with nlme::lme(),lme4::lmer(), lmeTest::lmer(), gamm4::gamm4(),
    mgcv::gamm() and SemiPar::spm().
Random forest with a variety of additional features for regression, classification and survival analysis. The features include: parallel computing with OpenMP, embedded model for selecting the splitting variable (based on Zhu, Zeng & Kosorok, 2015), subject weight, variable weight, tracking subjects used in each tree, etc.
R interface to the 'LTP'-Cloud service for Natural Language Processing
    in Chinese (http://www.ltp-cloud.com/).
A collection of functions to simulate luminescence production in 
    dosimetric materials using Monte Carlo methods. Implemented are models for 
    delocalised transitions (e.g., Chen and McKeever (1997) <doi:10.1142/2781>), 
    localised transitions (e.g., Pagonis et al. (2019) <doi:10.1016/j.jlumin.2018.11.024>) 
    and tunnelling transitions (Jain et al. (2012) <doi:10.1088/0953-8984/24/38/385402> 
    and Pagonis et al. (2019) <doi:10.1016/j.jlumin.2018.11.024>). 
    Supported stimulation methods are thermal luminescence (TL), 
    continuous-wave optically stimulated luminescence (CW-OSL), 
    linearly-modulated optically stimulated luminescence (LM-OSL), 
    linearly-modulated infrared stimulated luminescence (LM-IRSL),
    and isothermal luminescence (ITL or ISO-TL).
A collection of functions to simulate luminescence signals in quartz and Al2O3 based on published models.
A collection of 'shiny' applications for the R package
    'Luminescence'. These mainly, but not exclusively, include applications for
    plotting chronometric data from e.g. luminescence or radiocarbon dating. It
    further provides access to bootstraps tooltip and popover functionality and
    contains the 'jscolor.js' library with a custom 'shiny' output binding.
R implementation of the common parsing tools 'lex' and 'yacc'.
Rasch model and extensions for survey data, using Conditional Maximum likelihood (CML). Carlo Cafiero, Sara Viviani, Mark Nord (2018) <doi:10.1016/j.measurement.2017.10.065>.
Estimation of the conditional covariance matrix using the RiskMetrics 2006 methodology of Zumbach (2007) <doi:10.2139/ssrn.1420185>.
Compute an exact CI for the population mean under a random effects model. The routines implement the algorithm described in Michael, Thronton, Xie, and Tian (2017) <https://haben-michael.github.io/research/Exact_Inference_Meta.pdf>.
Allows for production of Czekanowski's Diagrams. See K. Bartoszek, A. Vasterlund (2020) <doi:10.2478/bile-2020-0008>.
Uses refined moving average filter based on the optimal and data-driven moving average lag q or smoothing spline to estimate trend and seasonal components, as well as irregularity (residuals) for univariate time series or data.
MAGIC (Markov affinity-based graph imputation of cells) is a method for addressing technical noise in single-cell data, including under-sampling of mRNA molecules, often termed "dropout" which can severely obscure important gene-gene relationships. MAGIC shares information across similar cells, via data diffusion, to denoise the cell count matrix and fill in missing transcripts. Read more: van Dijk et al. (2018) <DOI:10.1016/j.cell.2018.05.061>.
Creates and maintains a build process for complex analytic tasks in R.
  Package allows to easily generate Makefile for the (GNU) 'make' tool, which drives the build process
  by (in parallel) executing build commands in order to update results accordingly to given dependencies
  on changed data or updated source files.
An EM algorithm to fit Mallows' Models to full or partial rankings, with or without ties.
        Based on Adkins and Flinger (1998) <doi:10.1080/03610929808832223>.
An implementation of an algorithm family for continuous
    optimization called memetic algorithms with local search chains
    (MA-LS-Chains). Memetic algorithms are hybridizations of genetic
    algorithms with local search methods. They are especially suited
    for continuous optimization.
An interface to the 'Mangal' database - a collection of ecological networks. This package includes functions to work with the 'Mangal RESTful API' methods (<https://mangal-interactions.github.io/mangal-api/>).
Edit and simplify 'geojson', 'Spatial', and 'sf'
    objects.  This is wrapper around the 'mapshaper' 'JavaScript' library
    by Matthew Bloch <https://github.com/mbloch/mapshaper/> to perform
    topologically-aware polygon simplification, as well as other
    operations such as clipping, erasing, dissolving, and converting
    'multi-part' to 'single-part' geometries.  It relies on the
    'geojsonio' package for working with 'geojson' objects, the 'sf'
    package for working with 'sf' objects, and the 'sp' and 'rgdal'
    packages for working with 'Spatial' objects.
Provides an interface to 'Mapzen'-based APIs (including 
    geocode.earth, Nextzen, and NYC GeoSearch) for geographic search 
    and geocoding, isochrone calculation, and vector data to draw map tiles. 
    See <https://www.mapzen.com/documentation/> for more information. The original 
    Mapzen has gone out of business, but 'rmapzen' can be set up to work with 
    any provider who implements the Mapzen API.
A port of the C++ routine for applying the marching cubes algorithm written by 
    Thomas Lewiner et al. (2012) <doi:10.1080/10867651.2003.10487582> into an R package. 
    The package supplies the contour3d() function, which takes a 3-dimensional array of voxel 
    data and calculates the vertices, vertex normals, and faces for a 3d mesh representing 
    the contour(s) at a given level.
Three robust marginal integration procedures for additive models based on local 
             polynomial kernel smoothers. As a preliminary estimator of the multivariate 
             function for the marginal integration procedure, a first approach uses local 
             constant M-estimators, a second one uses local polynomials of order 1 over all the
             components of covariates, and the third one uses M-estimators based on local 
             polynomials but only in the direction of interest. For this last approach, 
             estimators of the derivatives of the additive functions can be obtained. All three
             procedures can compute predictions for points outside the training set if desired. 
             See Boente and Martinez (2017) <doi:10.1007/s11749-016-0508-0> for details.
Implements a DBI-compliant interface to MariaDB
    (<https://mariadb.org/>) and MySQL (<https://www.mysql.com/>)
    databases.
Given a time series of daily temperatures, the package provides tools
    to detect extreme thermal events, including marine heat waves, and to
    calculate the exceedances above or below specified threshold values.
    It outputs the properties of all detected events and exceedances.
An interface to the software package MARK that constructs input
    files for MARK and extracts the output. MARK was developed by Gary White
    and is freely available at <http://www.phidot.org/software/mark/downloads/>
    but is not open source.
Convert R Markdown documents into a variety of formats.
Simulate random matrices and ensembles and compute their eigenvalue spectra and dispersions.
Read and write 'Matlab' MAT files from R. The 'rmatio'
    package supports reading MAT version 4, MAT version 5 and MAT
    compressed version 5. The 'rmatio' package can write version 5 MAT
    files and version 5 files with variable compression.
S3 and S4 functions are implemented for spatial multi-site
    stochastic generation of daily time series of temperature and
    precipitation. These tools make use of Vector AutoRegressive models (VARs).
    The weather generator model is then saved as an object and is calibrated by
    daily instrumental "Gaussianized" time series through the 'vars' package
    tools. Once obtained this model, it can it can be used for weather
    generations and be adapted to work with several climatic monthly time
    series.
A Bayesian credible interval is interpreted with respect to posterior probability, 
    and this interpretation is far more intuitive than that of a frequentist confidence interval. 
    However, standard highest-density intervals can be wide due to between-subjects variability and tends 
    to hide within-subjects effects, rendering its relationship with the Bayes factor less clear 
    in within-subjects (repeated-measures) designs. 
    This urgent issue can be addressed by using within-subjects intervals in within-subjects designs.
A robust clustering algorithm (Model-Based) similar to Expectation
    Maximization for finite mixture normal distributions is implemented, 
    its main advantage is that the estimator is resistant to outliers, 
    that means that results of parameter estimation are still correct when there
    are atypical values in the sample 
    (see Gonzalez, Maronna, Yohai and Zamar (2021) 
    <arxiv:2102.06851>).
Multiscale Curvature Classification of ground returns in 3-D LiDAR 
    point clouds, designed for forested environments. 'RMCC' is a porting to R of the 
    'MCC-lidar' method by Evans and Hudak (2007) <doi:10.1109/TGRS.2006.890412>.
MCFS-ID (Monte Carlo Feature Selection and Interdependency Discovery) is a Monte Carlo method-based tool for feature selection. It also allows for the discovery of interdependencies between the relevant features. MCFS-ID is particularly suitable for the analysis of high-dimensional, 'small n large p' transactional and biological data. M. Draminski, J. Koronacki (2018) <doi:10.18637/jss.v085.i12>.
Compute the repeated measures correlation, a statistical technique
    for determining the overall within-individual relationship among paired measures
    assessed on two or more occasions, first introduced by Bland and Altman (1995).
    Includes functions for diagnostics, p-value, effect size with confidence
    interval including optional bootstrapping, as well as graphing. Also includes
    several example datasets. For more details, see Bakdash and Marusich (2017) <doi:10.3389/fpsyg.2017.00456>.
Provides tools to evaluate the value of using a risk prediction instrument to decide treatment or intervention (versus no treatment or intervention).  Given one or more risk prediction instruments (risk models) that estimate the probability of a binary outcome, rmda provides functions to estimate and display decision curves and other figures that help assess the population impact of using a risk model for clinical decision making.   Here, "population" refers to the relevant patient population. Decision curves display estimates of the (standardized) net benefit over a range of probability thresholds used to categorize observations as 'high risk'. The curves help evaluate a treatment policy that recommends treatment for patients who are estimated to be 'high risk' by comparing the population impact of a risk-based policy to "treat all" and "treat none" intervention policies.  Curves can be estimated using data from a prospective cohort.  In addition, rmda can estimate decision curves using data from a case-control study if an estimate of the population outcome prevalence is available.  Version 1.4 of the package provides an alternative framing of the decision problem for situations where treatment is the standard-of-care and a risk model might be used to recommend that low-risk patients (i.e., patients below some risk threshold) opt out of treatment. Confidence intervals calculated using the bootstrap can be computed and displayed. A wrapper function to calculate cross-validated curves using k-fold cross-validation is also provided.
Estimates and simulates Kuhn-Tucker demand models with individual heterogeneity. The package implements the multiple-discrete continuous extreme value (MDCEV) model and the Kuhn-Tucker specification common in the environmental economics literature on recreation demand. Latent class and random parameters specifications can be implemented and the models are fit using maximum likelihood estimation or Bayesian estimation. All models are implemented in Stan, which is a C++ package for performing full Bayesian inference (see Stan Development Team, 2019) <https://mc-stan.org/>. The package also implements demand forecasting (Pinjari and Bhat (2011) <https://repositories.lib.utexas.edu/handle/2152/23880>) and welfare calculation (Lloyd-Smith (2018) <doi:10.1016/j.jocm.2017.12.002>) for policy simulation.
A collection of 'Lua' filters that extend the functionality
  of R Markdown templates (e.g., count words or post-process citations).
HTML formats and templates for 'rmarkdown' documents, with some extra
    features such as automatic table of contents, lightboxed figures, dynamic
    crosstab helper.
A series of functions to aid in repeated tasks for Rmd documents. All details are to my personal preference, though I am happy to add flexibility if there are use cases I am missing. I will continue updating with new functions as I add utility functions for myself.
Use 'rmarkdown' partials, also know as child documents in
		'knitr', so you can make components for HTML, PDF, and Word documents. 
		The package provides various helper functions to make certain functions easier. 
		You may want to use this package, if you want to flexibly summarise objects 
		using a combination of figures, tables, text, and HTML widgets. 
		Unlike HTML widgets, the output is Markdown and can hence be turn into other
		output formats than HTML.
Formats for R Markdown that undo modifications by
    'pandoc' and 'rmarkdown' to original 'latex' templates, such as
    smaller margins, paragraph spacing, and compact titles. In addition, 
    enhancements such as author blocks with affiliations and
    headers and footers are introduced. All of this functionality is built 
    around plugins that modify the default 'pandoc' template without relying on 
    custom templates.
'R' 'Markdown' format for 'shower' presentations, see
   <https://github.com/shower/shower>.
If you are using R Markdown documents then you have sometimes restrictions about the size of the documents, e.g. number of words, number of characters or non-whitespace characters. rmdcount() computes these counts with and without code chunks and returns the result as data frame.
A suite of tools useful to read, visualize and export bivariate motion energy time-series. Lagged synchrony between subjects can be analyzed through windowed cross-correlation. Surrogate data generation allows an estimation of pseudosynchrony that helps to estimate the effect size of the observed synchronization. Kleinbub, J. R., & Ramseyer, F. T. (2020). rMEA: An R package to assess nonverbal synchronization in motion energy analysis time-series. Psychotherapy research, 1-14. <doi:10.1080/10503307.2020.1844334>.
An 'Rcpp' interface for Eunjeon project <http://eunjeon.blogspot.com/>.
    The 'mecab-ko' and 'mecab-ko-dic' is based on a C++ library,
    and part-of-speech tagging with them is useful when the spacing of source Korean text is not correct.
    This package provides part-of-speech tagging and tokenization function for Korean text.
We provide functions to compute confidence
    intervals (CIs) for a well-defined nonlinear function of the model
    parameters (e.g., product of k coefficients) in single--level and
    multilevel structural equation models.
Functions for simple fixed and random effects
        meta-analysis for two-sample comparisons and cumulative
        meta-analyses. Draws standard summary plots, funnel plots, and
        computes summaries and tests for association and heterogeneity.
Implementation of the metalog distribution in R.
    The metalog distribution is a modern, highly flexible, data-driven distribution. 
    Metalogs are developed by Keelin (2016) <doi:10.1287/deca.2016.0338>.
    This package provides functions to build these distributions from raw data. 
    Resulting metalog objects are then useful for exploratory and probabilistic analysis.
Feasible multivariate GARCH models including DCC, GO-GARCH and Copula-GARCH.
Provides mutual information estimators based on k-nearest neighbor estimators by A. Kraskov, et al. (2004) <doi:10.1103/PhysRevE.69.066138>, S. Gao, et al. (2015) <http://proceedings.mlr.press/v38/gao15.pdf> and local density estimators by W. Gao, et al. (2017) <doi:10.1109/ISIT.2017.8006749>.
A tool for multiply imputing missing data using 'MIDAS', a deep learning method based on denoising autoencoder neural networks. This algorithm offers significant accuracy and efficiency advantages over other multiple imputation strategies, particularly when applied to large datasets with complex features. Alongside interfacing with 'Python' to run the core algorithm, this package contains functions for processing data before and after model training, running imputation model diagnostics, generating multiple completed datasets, and estimating regression models on these datasets.
Facilitates the use of data mining algorithms in classification and regression (including time series forecasting) tasks by presenting a short and coherent set of functions. Versions: 1.4.6 / 1.4.5 / 1.4.4 new automated machine learning (AutoML) and ensembles, via improved fit(), mining() and mparheuristic() functions, and new categorical preprocessing, via improved delevels() function; 1.4.3 new metrics (e.g., macro precision, explained variance), new "lssvm" model and improved mparheuristic() function; 1.4.2 new "NMAE" metric, "xgboost" and "cv.glmnet" models (16 classification and 18 regression models); 1.4.1 new tutorial and more robust version; 1.4 - new classification and regression models, with a total of 14 classification and 15 regression methods, including: Decision Trees, Neural Networks, Support Vector Machines, Random Forests, Bagging and Boosting; 1.3 and 1.3.1 - new classification and regression metrics; 1.2 - new input importance methods via improved Importance() function; 1.0 - first version.
Constraint optimization, or constraint programming, is the name given to identifying
  feasible solutions out of a very large set of candidates, where the problem can be modeled in terms 
  of arbitrary constraints. 'MiniZinc' is a free and open-source constraint modeling language. 
  Constraint satisfaction and discrete optimization problems can be formulated in a high-level 
  modeling language. Models are compiled into an intermediate representation that is understood by a
  wide range of solvers. 'MiniZinc' itself provides several solvers, for instance 'GeCode'. R users 
  can use the package to solve constraint programming problems without using 'MiniZinc' directly, 
  modify existing 'MiniZinc' models and also create their own models.
Provides header files of 'mio', a cross-platform C++11 header-only 
    library for memory mapped file IO <https://github.com/mandreyel/mio>.
It was developed especially for gene expression and metabolomics data analysis when the datasets are corrupted by outliers and missing values. The beta-divergence method was used to impute the missing values and modify the outliers.
The Rmisc library contains many functions useful for data analysis
    and utility operations.
Interface of 'MIXMOD' software for supervised, unsupervised and
    semi-supervised classification with mixture modelling.
The Rmixmod package provides model-based clustering by fitting a mixture model (e.g. Gaussian components for quantitative continuous data) to the data and identifying each cluster with one of its components. The number of components can be determined from the data, typically using the BIC criterion. In practice, however, individual clusters can be poorly fitted by Gaussian distributions, and in that case model-based clustering tends to represent one non-Gaussian cluster by a mixture of two or more Gaussian components. If the number of mixture components is interpreted as the number of clusters, this can lead to overestimation of the number of clusters. This is because BIC selects the number of mixture components needed to provide a good approximation to the density. This package, RmixmodCombi, according to \emph{Combining Mixture Components for Clustering} by J.P. Baudry, A.E. Raftery, G. Celeux, K. Lo, R. Gottardo, combines the components of the EM/BIC solution (provided by Rmixmod) hierarchically according to an entropy criterion. This yields a clustering for each number of clusters less than or equal to K. These clusterings can be compared on substantive grounds, and we also provide a way of selecting the number of clusters via a piecewise linear regression fit to the (possibly rescaled) entropy plot.
Provides an interface to many endpoints of Mixpanel's Data Export, Engage and JQL API. The R functions allow for event and profile data export as well as for segmentation, retention, funnel and addiction analysis. Results are always parsed into convenient R objects. Furthermore it is possible to load and update profiles.
Mixture Composer <https://github.com/modal-inria/MixtComp> is a project to build mixture models with
    heterogeneous data sets and partially missing data management. 
    It includes 8 models for real, categorical, counting, functional and ranking data.
Mixture Composer <https://github.com/modal-inria/MixtComp> is a project to build mixture models with
    heterogeneous data sets and partially missing data management.
    It includes models for real, categorical, counting, functional and ranking data.
    This package contains the minimal R interface of the C++ 'MixtComp' library.
Mixture Composer <https://github.com/modal-inria/MixtComp> is a project to build mixture models with
    heterogeneous data sets and partially missing data management. This package contains graphical, getter and some utility 
    functions to facilitate the analysis of 'MixtComp' output.
Sundry discrete probability distributions and helper functions.
R implementation of Maximum Likelihood Principal Component Analysis
    The main idea of this package is to have an alternative way of PCA for 
    subspace modeling that considers measurement errors.
    More details can be found in Peter D. Wentzell (2009) 
    <doi:10.1016/B978-0-444-64165-6.03029-9>.
Connect R with MOA (Massive Online Analysis -
    <https://moa.cms.waikato.ac.nz>) to build classification models and
    regression models on streaming data or out-of-RAM data.
    Also streaming recommendation models are made available.
External jars required for package RMOA. RMOA is a framework to
    build data stream models on top of MOA (Massive Online Analysis -
    <http://moa.cms.waikato.ac.nz>). The jar files are put in this R package, the modelling logic can be found in the RMOA package.
A monadic solution to pipeline analysis. All operations -- and the errors,
    warnings and messages they emit -- are merged into a directed graph. Infix
    binary operators mediate when values are stored, how exceptions are
    handled, and where pipelines branch and merge. The resulting structure may
    be queried for debugging or report generation. 'rmonad' complements, rather
    than competes with, non-monadic pipeline packages like 'magrittr' or
    'pipeR'. This work is funded by the NSF (award number 1546858).
A multiobjective optimization package based on K. Deb's 
    algorithm and inspired in 'GA' package by Luca Scrucca (2017) <DOI:10.32614/RJ-2017-008>. 
    The 'rmoo' package is a framework for multi- and many-objective optimization, 
    allowing to work with representation of real numbers, permutations and 
    binaries, offering a high range of configurations.
This is a meta-package designed to support the
        installation of Rmosek (>= 6.0) and bring the optimization
        facilities of MOSEK (>= 6.0) to the R-language. The interface
        supports large-scale optimization of many kinds: Mixed-integer
        and continuous linear, second-order cone, exponential cone and
        power cone optimization, as well as continuous semidefinite
        optimization. Rmosek and the R-language are open-source
        projects. MOSEK is a proprietary product, but unrestricted
        trial and academic licenses are available.
Arithmetic (via S4 classes and methods) for
  arbitrary precision floating point numbers, including transcendental
  ("special") functions.  To this end, the package interfaces to
  the 'LGPL' licensed 'MPFR' (Multiple Precision Floating-Point Reliable) Library
  which itself is based on the 'GMP' (GNU Multiple Precision) Library.
An interface (wrapper) to MPI. It also 
	     provides interactive R manager and worker environment.
Optimization of any Black-Box/Non-Convex Function on Hyper-Rectangular Parameter Space. It uses a Variation of Pattern Search Technique. Described in the paper : Das (2016) <arXiv:1604.08616> .
We implement causal mediation analysis using the methods proposed by Hong (2010) and Hong, Deutsch & Hill (2015) <doi:10.3102/1076998615583902>. It allows the estimation and hypothesis testing of causal mediation effects through ratio of mediator probability weights (RMPW). This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. We also implement a sensitivity analysis by extending the RMPW method to assess potential bias in the presence of omitted pretreatment or posttreatment covariates. The sensitivity analysis strategy was proposed by Hong, Qin, and Yang (2018) <doi:10.3102/1076998617749561>.
Analysis of oxygen consumption data generated by Loligo (R) Systems respirometry equipment. The package includes a function for loading data output by Loligo's 'AutoResp' software (get.witrox.data()), functions for calculating metabolic rates over user-specified time intervals, extracting critical points from data using broken stick regressions based on Yeager and Ultsch (<DOI:10.1086/physzool.62.4.30157935>), and easy functions for converting between different units of barometric pressure.
Regression modeling, testing, estimation, validation,
	graphics, prediction, and typesetting by storing enhanced model design
	attributes in the fit.  'rms' is a collection of functions that
	assist with and streamline modeling.  It also contains functions for
	binary and ordinal logistic regression models, ordinal models for
  continuous Y with a variety of distribution families, and the Buckley-James
	multiple regression model for right-censored responses, and implements
	penalized maximum likelihood estimation for logistic and ordinary
	linear models.  'rms' works with almost any regression model, but it
	was especially written to work with binary or ordinal regression
	models, Cox regression, accelerated failure time models,
	ordinary linear models,	the Buckley-James model, generalized least
	squares for serially or spatially correlated observations, generalized
	linear models, and quantile regression.
This package can be used to test any simple null
        hypothesis using the root-mean-square goodness of fit test.
        Monte Carlo estimation is used to calculate the associated
        P-value.
A Bayesian companion to the 'rms' package, 'rmsb' provides Bayesian model fitting, post-fit estimation, and graphics.  It implements Bayesian regression models whose fit objects can be processed by 'rms' functions such as 'contrast()', 'summary()', 'Predict()', 'nomogram()', and 'latex()'.  The fitting function currently implemented in the package is 'blrm()' for Bayesian logistic binary and ordinal regression with optional clustering, censoring, and departures from the proportional odds assumption using the partial proportional odds model of Peterson and Harrell (1990) <https://www.jstor.org/stable/2347760>.
Display a randomly selected quote about Richard M. Stallman
 based on the collection in the 'GNU Octave' function 'fact()' which was
 aggregated by Jordi Gutiérrez Hermoso based on the (now defunct) site
 stallmanfacts.com (which is accessible only via <http://archive.org>).
Contains several useful navigation helper functions, including easily building
    folder paths, quick viewing dataframes in 'Excel', creating date vectors and changing the
    console prompt to reflect time.
'Rcpp' bindings to the native C++ implementation of MS Numpress, that provides two compression schemes for numeric data from mass spectrometers. The library provides implementations of 3 different algorithms, 1 designed to compress first order smooth data like retention time or M/Z arrays, and 2 for compressing non smooth data with lower requirements on precision like ion count arrays. Refer to the publication (Teleman et al., (2014) <doi:10.1074/mcp.O114.037879>) for more details.
Assemble the panels of computerized adaptive multistage testing by the 
    bottom-up and the top-down approach, and simulate the administration of the assembled 
    panels. The full documentation and tutorials are at <https://github.com/xluo11/Rmst>.
    Reference: Luo and Kim (2018) <doi:10.1111/jedm.12174>.
Contains inferential and graphical routines for comparing two treatment arms in terms of the restricted mean time in favor of treatment.
An algorithm which can be used to determine an objective threshold for signal-noise separation in large random matrices (correlation matrices, mutual information matrices, network adjacency matrices) is provided. The package makes use of the results of Random Matrix Theory (RMT). The algorithm increments a suppositional threshold monotonically, thereby recording the eigenvalue spacing distribution of the matrix. According to RMT, that distribution undergoes a characteristic change when the threshold properly separates signal from noise. By using the algorithm, the modular structure of a matrix - or of the corresponding network - can be unraveled.
Efficient solvers for 10 regularized multi-task learning algorithms applicable for regression, classification, joint feature selection, task clustering, low-rank learning, sparse learning and network incorporation. Based on the accelerated gradient descent method, the algorithms feature a state-of-art computational complexity O(1/k^2). Sparse model structure is induced by the solving the proximal operator. The detail of the package is described in the paper of Han Cao and Emanuel Schwarz (2018) <doi:10.1093/bioinformatics/bty831>.
Functions for working with the Tracy-Widom laws and other distributions 
    related to the eigenvalues of large Wishart matrices.
    The tables for computing the Tracy-Widom densities and distribution
    functions were computed by functions were computed by Momar Dieng's MATLAB package "RMLab". This package is part of a collaboration between Iain Johnstone, Zongming Ma, Patrick Perry, and Morteza Shahram.
Some basic features of 'MUMPS' (Multifrontal Massively Parallel
         sparse direct Solver) are wrapped in a class whose methods can be used
         for sequentially solving a sparse linear system (symmetric or not)
         with one or many right hand sides (dense or sparse).
         There is a possibility to do separately symbolic analysis,
         LU (or LDL^t) factorization and system solving.
         Third part ordering libraries are included and can be used: 'PORD', 'METIS', 'SCOTCH'.
         'MUMPS' method was first described in Amestoy et al. (2001) <doi:10.1137/S0895479899358194>
         and Amestoy et al. (2006) <doi:10.1016/j.parco.2005.07.004>.
A toolkit of functions for nonlinear regression and repeated
    measurements not to be used by itself but called by other Lindsey packages such
    as 'gnlm', 'stable', 'growth', 'repeated', and 'event' 
    (available at <https://www.commanster.eu/rcode.html>).
Mappable vector library provides convenient way to access large datasets. Use all of your data at once, with few limits. Memory mapped data can be shared between multiple R processes. Access speed depends on storage medium, so solid state drive is recommended, preferably with PCI Express (or M.2 nvme) interface or a fast network file system. The data is memory mapped into R and then accessed using usual R list and array subscription operators. Convenience functions are provided for merging, grouping and indexing large vectors and data.frames. The layout of underlying MVL files is optimized for large datasets. The vectors are stored to guarantee alignment for vector intrinsics after memory map. The package is built on top of libMVL, which can be used as a standalone C library. libMVL has simple C API making it easy to interchange datasets with outside programs.
A memory-efficient, visualize-enhanced, parallel-accelerated Genome-Wide Association Study (GWAS) tool. It can
    (1) effectively process large data, 
    (2) rapidly evaluate population structure, 
    (3) efficiently estimate variance components several algorithms, 
    (4) implement parallel-accelerated association tests of markers three methods, 
    (5) globally efficient design on GWAS process computing, 
    (6) enhance visualization of related information. 
    'rMVP' contains three models GLM (Alkes Price (2006) <DOI:10.1038/ng1847>), MLM (Jianming Yu (2006) <DOI:10.1038/ng1702>) 
    and FarmCPU (Xiaolei Liu (2016) <doi:10.1371/journal.pgen.1005767>); variance components estimation methods EMMAX 
    (Hyunmin Kang (2008) <DOI:10.1534/genetics.107.080101>;), FaSTLMM (method: Christoph Lippert (2011) <DOI:10.1038/nmeth.1681>, 
    R implementation from 'GAPIT2': You Tang and Xiaolei Liu (2016) <DOI:10.1371/journal.pone.0107684> and 
    'SUPER': Qishan Wang and Feng Tian (2014) <DOI:10.1371/journal.pone.0107684>), and HE regression 
    (Xiang Zhou (2017) <DOI:10.1214/17-AOAS1052>).
An integrated set of tools to allow data users to conduct 
    meteorological normalisation on air quality data. This meteorological 
    normalisation technique uses predictive random forest models to remove 
    variation of pollutant concentrations so trends and interventions can be 
    explored in a robust way. For examples, see Grange et al. (2018) 
    <doi:10.5194/acp-18-6223-2018> and Grange and Carslaw (2019) 
    <doi:10.1016/j.scitotenv.2018.10.344>.
Legacy 'DBI' interface to 'MySQL' / 'MariaDB' based on old code
    ported from S-PLUS. A modern 'MySQL' client based on 'Rcpp' is available 
    from the 'RMariaDB' package.
Allows work with 'MyTarget Statistics API v2' 
	<https://target.my.com/adv/api-marketing/doc/stat-v2> and 
	'MyTarget Statistics API v3' <https://target.my.com/adv/api-marketing/doc/stat-v2#statisticsv3>
	load data by ads, campaigns, agency clients and statistic from
	your ads account.
Infer log-linear Poisson Graphical Model with an auxiliary data
    set. Hot-deck multiple imputation method is used to improve the reliability
    of the inference with an auxiliary dataset. Standard log-linear Poisson 
    graphical model can also be used for the inference and the Stability 
    Approach for Regularization Selection (StARS) is implemented to drive the 
    selection of the regularization parameter. The method is fully described in
    <doi:10.1093/bioinformatics/btx819>.
Functions for semi-automated quality control of bulk RNA-seq data.
Provides function for RNA secondary structure plotting, comparison and module mining. Given a RNA secondary structure, you can obtain stem regions, hairpin loops, internal loops, bulge loops and multibranch loops of this RNA structure using this program. They are the basic modules of RNA secondary structure. For each module you get, you can use this program to label the RNA structure with a specific color. You can also use this program to compare two RNA secondary structures to get a score that represents similarity. Reference: Reuter JS, Mathews DH (2010) <doi:10.1186/1471-2105-11-129>.
Interface to access data via the United States Department of 
  Agriculture's National Agricultural Statistical Service (NASS) 'Quick Stats' 
  web API <https://quickstats.nass.usda.gov/api>. Convenience functions 
  facilitate building queries based on available parameters and valid parameter 
  values. This product uses the NASS API but is not endorsed or certified by NASS.
Facilitates mapping by making natural earth map data from <http://www.naturalearthdata.com/> more easily available to R users.
Vector map data from <http://www.naturalearthdata.com/>. Access functions are provided in the accompanying package 'rnaturalearth'.
Image data used as examples in the loon R package.
Provides a set of functions to access a data visualization web service. For more information and a tutorial on how to use it, see https://navicell.curie.fr/pages/nav_web_service.html and https://github.com/sysbio-curie/RNaviCell.
Use the <http://api.nbp.pl/> API through R. Retrieve
    currency exchange rates and gold prices data published by the
    National Bank of Poland in form of convenient R objects.
Provides the libraries of the EUtils operations for the
        RNCBI package.
Contains functions to retrieve, organize, and visualize weather data from the NCEP/NCAR Reanalysis (<http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html>) and NCEP/DOE Reanalysis II (<http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis2.html>) datasets.  Data are queried via the Internet and may be obtained for a specified spatial and temporal extent or interpolated to a point in space and time.  We also provide functions to visualize these weather data on a map.  There are also functions to simulate flight trajectories according to specified behavior using either NCEP wind data or data specified by the user.
An interface to the Nexus Class Library which allows parsing
    of NEXUS, Newick and other phylogenetic tree file formats. It provides
    elements of the file that can be used to build phylogenetic objects
    such as ape's 'phylo' or phylobase's 'phylo4(d)'. This functionality
    is demonstrated with 'read_newick_phylo()' and 'read_nexus_phylo()'.
Extract the implied risk neutral density from options using various methods.
To incorporate neighbor genotypic identity into genome-wide association studies, the package provides a set of functions for variation partitioning and association mapping. The theoretical background of the method is described in Sato et al. (2021) <doi:10.1038/s41437-020-00401-w>.
To enable quantitative trait loci mapping of neighbor effects, this package extends a single-marker regression to interval mapping. The theoretical background of the method is described in Sato et al. (2021) <doi:10.1093/g3journal/jkab017>.
An implementation of a method based on information theory devised 
    for the identification of genes showing a significant variation of expression
    across multiple conditions. Given expression estimates from any number of 
    RNA-Seq samples and conditions it identifies genes or transcripts with a 
    significant variation of expression across all the conditions studied, 
    together with the samples in which they are over- or under-expressed. 
    Zambelli et al. (2018) <doi:10.1093/nar/gky055>.
Within this package the XML-RPC API to NEOS <https://neos-server.org/neos/> is implemented. This enables the user to pass optimization problems to NEOS and retrieve results within R.
An interface to the 'NetCDF' file formats designed by Unidata
  for efficient storage of array-oriented scientific data and descriptions.
  Most capabilities of 'NetCDF' version 4 are supported. Optional conversions
  of time units are enabled by 'UDUNITS' version 2, also from Unidata.
Interface to use and access Wilensky's 'NetLogo' (Wilensky 1999) from R using either headless (no GUI) or interactive GUI mode. Provides functions to load models, execute commands, and get values from reporters. Mostly analogous to the 'NetLogo' 'Mathematica' Link <https://github.com/NetLogo/Mathematica-Link>.
A collection of tools for measuring the similarity of text messages and tracing the flow of messages over
    time and across media.
Provides access to phyloinformatic data in 'NeXML' format.  The
    package should add new functionality to R such as the possibility to
    manipulate 'NeXML' objects in more various and refined way and compatibility
    with 'ape' objects.
Generation of univariate and multivariate data that follow the generalized 
  Poisson distribution. The details of the univariate part are explained in 
  Demirtas (2017) <doi: 10.1080/03610918.2014.968725>, and the multivariate part is 
  an extension of the correlated Poisson data generation routine that was introduced in 
  Yahav and Shmueli (2012) <doi: 10.1002/asmb.901>.
A function setVectorSeed() is provided. Its argument
        is a numeric vector of an arbitrary nonzero length, whose
        components have integer values from [0, 2^32-1]. The input
        vector is transformed using AES (Advanced Encryption Standard)
        algorithm into an initial state of Mersenne-Twister random
        number generator. The function provides a better alternative
        to the R base function set.seed(), if the input vector is
        a single integer. Initializing a stream of random numbers
        with a vector is a convenient way to obtain several streams,
        each of which is identified by several integer indices.
Provides a set of functions for working with
    Random Number Generators (RNGs). In particular, a generic
    S4 framework is defined for getting/setting the current RNG, or RNG data
    that are embedded into objects for reproducibility.
    Notably, convenient default methods greatly facilitate the way current
    RNG settings can be changed.
It is a dedicated package to WELL pseudo random generators, which were introduced in Panneton et al. (2006), ``Improved Long-Period Generators Based on Linear Recurrences Modulo 2'', ACM Transactions on Mathematical Software. But this package is not intended to be used directly, you are strongly __encouraged__ to use the 'randtoolbox' package, which depends on this package.
Tools for downloading and analyzing CDC NHANES data, with a focus
    on analytical laboratory data.
Provides very fast read and write access to images stored in the
    NIfTI-1, NIfTI-2 and ANALYZE-7.5 formats, with seamless synchronisation
    of in-memory image objects between compiled C and interpreted R code. Also
    provides a simple image viewer, and a C/C++ API that can be used by other
    packages. Not to be confused with 'RNiftyReg', which performs image
    registration and applies spatial transformations.
Provides an 'R' interface to the 'NiftyReg' image registration tools
    <https://github.com/KCL-BMEIS/niftyreg>. Linear and nonlinear registration
    are supported, in two and three dimensions.
Allows the user to implement a dark/light toggle mode in 'shiny' using the 'Nightly' 'JavaScript' library. 
    The default mode is dark/light however the user can also specify other colours.
A comprehensive suite of functions to perform and visualise 
    pairwise and network meta-analysis with aggregate binary or continuous
    missing participant outcome data. The package covers core Bayesian one-stage
    models implemented in a systematic review with multiple interventions, 
    including fixed-effect and random-effects network meta-analysis, 
    meta-regression, evaluation of the consistency assumption via the 
    node-splitting approach and the unrelated mean effects model, and 
    sensitivity analysis. Missing participant outcome data are addressed in all 
    models of the package. The package also offers a rich, user-friendly 
    visualisation toolkit that aids in appraising and interpreting the results 
    thoroughly and preparing the manuscript for journal submission. 
    The visualisation tools comprise the network plot, forest plots, panel of 
    diagnostic plots, heatmaps on the extent of missing participant outcome data
    in the network, league heatmaps on estimation and prediction, rankograms, 
    Bland-Altman plot, leverage plot, deviance scatterplot, heatmap of 
    robustness, and barplot of Kullback-Leibler divergence. The package also 
    allows the user to export the results to an Excel file at the working 
    directory.
An implementation of robust nonnegative matrix factorization (rNMF). The rNMF algorithm decomposes a nonnegative high dimension data matrix into the product of two low rank nonnegative matrices, while detecting and trimming outliers. The main function is rnmf(). The package also includes a visualization tool, see(), that arranges and prints vectorized images.
Perform the complete processing of a set of proton nuclear magnetic resonance spectra from the free induction decay (raw data) and based on a processing sequence (macro-command file). An additional file specifies all the spectra to be considered by associating their sample code as well as the levels of experimental factors to which they belong. More detail can be found in Jacob et al. (2017) <doi:10.1007/s11306-017-1178-y>.
Implementation of a Recurrent Neural Network architectures in native R, including Long Short-Term Memory (Hochreiter and Schmidhuber, <doi:10.1162/neco.1997.9.8.1735>), Gated Recurrent Unit (Chung et al., <arXiv:1412.3555>) and vanilla RNN.
Client for many 'NOAA' data sources including the 'NCDC' climate
    'API' at <https://www.ncdc.noaa.gov/cdo-web/webservices/v2>, with functions for
    each of the 'API' 'endpoints': data, data categories, data sets, data types,
    locations, location categories, and stations. In addition, we have an interface
    for 'NOAA' sea ice data, the 'NOAA' severe weather inventory, 'NOAA' Historical
    Observing 'Metadata' Repository ('HOMR') data, 'NOAA' storm data via 'IBTrACS',
    tornado data via the 'NOAA' storm prediction center, and more.
An interface to the National Oceanic and Atmospheric Administration's Operational Model Archive and Distribution System (NOMADS, see <http://nomads.ncep.noaa.gov/> for more information) that allows R users to quickly and efficiently download global and regional weather model data for processing.  rNOMADS currently supports a variety of models ranging from global weather data to an altitude of over 40 km, to high resolution regional weather models, to wave and sea ice models. It can also retrieve some archived models.  rNOMADS can retrieve binary data in grib format as well as import ascii data directly into R by interfacing with the GrADS-DODS system.
Inverse normal transformation (INT) based genetic association testing. These tests are recommend for continuous traits with non-normally distributed residuals. INT-based tests robustly control the type I error in settings where standard linear regression does not, as when the residual distribution exhibits excess skew or kurtosis. Moreover, INT-based tests dominate standard linear regression in terms of power. These tests may be classified into two types. In direct INT (D-INT), the phenotype is itself transformed. In indirect INT (I-INT), phenotypic residuals are transformed. The omnibus test (O-INT) adaptively combines D-INT and I-INT into a single robust and statistically powerful approach. See McCaw ZR, Lane JM, Saxena R, Redline S, Lin X. "Operating characteristics of the rank-based inverse normal transformation for quantitative trait analysis in genome-wide association studies" <doi:10.1111/biom.13214>.
Programmatic interface to the
    Web Service methods provided by the National 'Phenology' Network
    (<https://usanpn.org/>), which includes data on various life history
    events that occur at specific times.
Apply sensitivity analysis for offline policy evaluation, as
    implemented in Jung et al. (2017) <arXiv:1702.04690> based
    on Rosenbaum and Rubin (1983) <http://www.jstor.org/stable/2345524>.
Utility functions to retrieve data from the UK National River Flow
  Archive (<https://nrfa.ceh.ac.uk/>, terms and conditions:
  <https://nrfa.ceh.ac.uk/costs-terms-and-conditions>).
  The package contains R wrappers to the UK NRFA data temporary-API. There are
  functions to retrieve stations falling in a bounding box, to generate a map
  and extracting time series and general information. The package is fully
  described in Vitolo et al (2016) "rnrfa: An R package to Retrieve, Filter and
  Visualize Data from the UK National River Flow Archive"
  <https://journal.r-project.org/archive/2016/RJ-2016-036/RJ-2016-036.pdf>.
Routines to interact with the Numerai Machine Learning Tournament
  API <https://numer.ai>. The functionality includes the ability to automatically download the
  current tournament data, submit predictions, and to get information for your
  user. General 'GraphQL' queries can also be executed.
Creates JavaScript charts with the 'nvd3' library. So far only the multibar chart, the horizontal multibar chart, the line chart and the line chart with focus are available.
This web client interfaces Unpaywall <https://unpaywall.org/products/api>, formerly
    oaDOI, a service finding free full-texts of academic papers by linking DOIs with 
    open access journals and repositories. It provides unified access to various data sources 
    for open access full-text links including Crossref and the Directory of Open Access 
    Journals (DOAJ). API usage is free and no registration is required.
A collection of methods for the robust analysis of univariate and
    multivariate functional data, possibly in high-dimensional cases, and hence
    with attention to computational efficiency and simplicity of use. See the R 
    Journal publication of Ieva et al. (2019) <doi:10.32614/RJ-2019-032> for an 
    in-depth presentation of the 'roahd' package. See Aleman-Gomez et al. (2021) 
    <arXiv:2103.08874> for details about the concept of depthgram.
Provides an interface to the OAuth 1.0 specification
        allowing users to authenticate via OAuth to the
        server of their choice.
Base S4-classes and functions for robust asymptotic statistics.
Includes 'sysdata.rda' file for packages of the 'RobASt' - family of packages; is
        currently used by package 'RobExtremes' only.
Implementation of a variety of methods to compute
    the robustness of ecological interaction networks with binary interactions 
    as described in <arXiv:1910.10512>. In particular, using the Stochastic 
    Block Model and its bipartite counterpart, the Latent Block Model to put a 
    parametric model on the network, allows the comparison of the robustness of 
    networks differing in species richness and number of interactions. It also
    deals with networks that are partially sampled and/or with missing values.
Conditionally unbiased bounded influence estimates as described in 
  Kuensch et al. (1989) <doi:10.1080/01621459.1989.10478791> in three special cases of 
  the generalized linear model: Bernoulli, Binomial, and Poisson distributed responses.
Methods for analysis of compositional data including robust
    methods  (<doi:10.1007/978-3-319-96422-5>), imputation of missing values (<doi:10.1016/j.csda.2009.11.023>), methods to replace 
    rounded zeros (<doi:10.1080/02664763.2017.1410524>, <doi:10.1016/j.chemolab.2016.04.011>, <doi:10.1016/j.csda.2012.02.012>), 
    count zeros (<doi:10.1177/1471082X14535524>), 
    methods to deal with essential zeros (<doi:10.1080/02664763.2016.1182135>), (robust) outlier
    detection for compositional data, (robust) principal component analysis for
    compositional data, (robust) factor analysis for compositional data, (robust)
    discriminant analysis for compositional data (Fisher rule), robust regression
    with compositional predictors, functional data analysis (<doi:10.1016/j.csda.2015.07.007>) and p-splines (<doi:10.1016/j.csda.2015.07.007>), 
    contingency (<doi:10.1080/03610926.2013.824980>) 
    and compositional tables (<doi:10.1111/sjos.12326>, <doi:10.1111/sjos.12223>, <doi:10.1080/02664763.2013.856871>) 
    and (robust) Anderson-Darling normality tests for
    compositional data as well as popular log-ratio transformations (addLR, cenLR,
    isomLR, and their inverse transformations). In addition, visualisation and
    diagnostic tools are implemented as well as high and low-level plot functions
    for the ternary diagram.
Robust pairwise correlations based on estimates of scale,
  particularly on "FastQn" one-step M-estimate.
Provides robust methods to detect change-points in uni- or multivariate time series. They can cope with corrupted data and heavy tails. Focus is on the detection of abrupt changes in location, but changes scale or dependence structure can be detected as well. This package provides tests for change detection in uni- and multivariate time series based on Huberized versions of CUSUM tests proposed in Duerre and Fried (2019) <arXiv:1905.06201>, and tests for change detection in univariate time series based on 2-sample U-statistics or 2-sample U-quantiles as proposed by Dehling et al. (2015) <DOI:10.1007/978-1-4939-3076-0_12> and Dehling, Fried and Wendler (2020) <DOI:10.1093/biomet/asaa004>. Furthermore, the packages provides tests on changes in the scale or the correlation as proposed in Gerstenberger, Vogel and Wendler (2020) <DOI:10.1080/01621459.2019.1629938>, Dehling et al. (2017) <DOI:10.1017/S026646661600044X>, and Wied et al. (2014) <DOI:10.1016/j.csda.2013.03.005>.
Gene-environment (G×E) interactions have important implications to elucidate the 
    etiology of complex diseases beyond the main genetic and environmental effects. 
    Outliers and data contamination in disease phenotypes of G×E studies have been commonly 
    encountered, leading to the development of a broad spectrum of robust penalization methods. 
    Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing 
    studies. We develop a robust Bayesian variable selection method for G×E interaction 
    studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and 
    outliers in the response variable while conducting variable selection by accounting for 
    structural sparsity. In particular, the spike-and-slab priors have been imposed on both 
    individual and group levels to identify important main and interaction effects. An efficient 
    Gibbs sampler has been developed to facilitate fast computation. The Markov chain Monte Carlo 
    algorithms of the proposed and alternative methods are efficiently implemented in C++.
Locations problems, M-estimates of coefficients and scale
        in linear regression, Weights for bounded influence regression,
        Covariance matrix of the coefficient estimates, Asymptotic
        relative efficiency of regression M-estimates, Robust testing
        in linear models, High breakdown point regression, M-estimates
        of covariance matrices, M-estimates for discrete generalized
        linear models.
We provide an outlier robust alternative of the function ets() in the 'forecast' package of Hyndman and Khandakar (2008) <DOI:10.18637/jss.v027.i03>. For each method of a class of exponential smoothing variants we made a robust alternative. The class includes methods with a damped trend and/or seasonal components. The robust method is developed by robustifying every aspect of the original exponential smoothing variant. We provide robust forecasting equations, robust initial values, robust smoothing parameter estimation and a robust information criterion. The method is described in more detail in Crevits and Croux (2016) <DOI:10.13140/RG.2.2.11791.18080>.
Optimally robust estimation for extreme value distributions using S4 classes and
            methods (based on packages 'distr', 'distrEx', 'distrMod', 'RobAStBase', and
            'ROptEst').
Implementations for several robust procedures that allow for (online)
        extraction of the signal of univariate or multivariate time series by
        applying robust regression techniques to a moving time window are provided.
        Included are univariate filtering procedures based on repeated-median 
        regression as well as hybrid and trimmed filters derived from it; 
        see Schettlinger et al. (2006) <doi:10.1515/BMT.2006.010>. The adaptive 
        online repeated median by Schettlinger et al. (2010) <doi:10.1002/acs.1105> 
        and the slope comparing adaptive repeated median by Borowski and Fried (2013) 
        <doi:10.1007/s11222-013-9391-7> choose the width of the moving time 
        window adaptively. Multivariate versions are also provided; see  
        Borowski et al. (2009) <doi:10.1080/03610910802514972> for a multivariate 
        online adaptive repeated median and Borowski (2012) <doi:10.17877/DE290R-14393>  
        for a multivariate slope comparing adaptive repeated median. Furthermore, 
        a repeated-median based filter with automatic outlier replacement and 
        shift detection is provided; see Fried (2004) <doi:10.1080/10485250410001656444>.
Contains a single function named robFitConGraph() which includes two algorithms for robust estimation of scatter matrices subject to zero-constraints in its inverse. The methodology is described in Vogel & Tyler (2014) <doi:10.1093/biomet/asu041>. See robFitConGraph() function documentation for further details.
Bootstrap forecast densities for GARCH (Generalized Autoregressive Conditional Heteroskedastic) returns and volatilities using the robust residual-based bootstrap procedure of Trucios, Hotta and Ruiz (2017) <DOI:10.1080/00949655.2017.1359601>.
Execute API calls to the RobinHood <https://robinhood.com> investing platform. Functionality includes accessing account data and current holdings, retrieving investment statistics and quotes, placing and canceling orders, getting market trading hours, searching investments by popular tag, and interacting with watch lists.
Client for the Ocean Biodiversity Information System (<https://obis.org>).
Implements a series of robust Kalman filtering approaches. It implements the additive outlier robust filters of Ruckdeschel et al. (2014) <arXiv:1204.3358> and Agamennoni et al. (2018) <doi:10.1109/ICRA.2011.5979605>, the innovative outlier robust filter of Ruckdeschel et al. (2014) <arXiv:1204.3358>, as well as the innovative and additive outlier robust filter of Fisch et al. (2020) <arXiv:2007.03238>.
Functions for the determination of optimally robust influence curves and
            estimators in case of normal location and/or scale.
Functions for the determination of optimally robust influence curves and
            estimators for preprocessing omics data, in particular gene expression data.
A framework for estimating ensembles of meta-analytic models
    (assuming either presence or absence of the effect, heterogeneity, and
    publication bias). The RoBMA framework uses Bayesian model-averaging to 
    combine the competing meta-analytic models into a model ensemble, weights 
    the posterior parameter distributions based on posterior model probabilities 
    and uses Bayes factors to test for the presence or absence of the
    individual components (e.g., effect vs. no effect; Bartoš et al., 2021, 
    <doi:10.31234/osf.io/kvsp7>; Maier, Bartoš & Wagenmakers, in press, 
    <doi:10.31234/osf.io/u4cns>). Users can define a wide range of non-informative 
    or informative prior distributions for the effect size, heterogeneity, 
    and publication bias components (including selection models and PET-PEESE). 
    The package provides convenient functions for summary, visualizations, and 
    fit diagnostics.
Perform mediation analysis via a (fast and robust) bootstrap test.
Robust generalized linear models (GLM) using a mixture method, as described in Beath (2018) <doi:10.1080/02664763.2017.1414164>. This assumes that the data are a mixture of standard observations, being a generalised linear model, and outlier observations from an overdispersed generalized linear model. The overdispersed linear model is obtained by including a normally distributed random effect in the linear predictor of the generalized linear model.
Finite mixture models are a popular technique for modelling unobserved heterogeneity or to approximate general distribution functions in a semi-parametric way. They are used in a lot of different areas such as astronomy, biology, economics, marketing or medicine.
             This package is the implementation of popular robust mixture regression methods based on different algorithms including: fleximix, finite mixture models and latent class regression; CTLERob, component-wise adaptive trimming likelihood estimation; mixbi, bi-square estimation; mixL, Laplacian distribution; mixt, t-distribution; TLE, trimmed likelihood estimation.
             The implemented algorithms includes:  CTLERob stands for Component-wise adaptive Trimming Likelihood Estimation based mixture regression; mixbi stands for mixture regression based on bi-square estimation; mixLstands for mixture regression based on Laplacian distribution; TLE stands for Trimmed Likelihood Estimation based mixture regression. For more detail of the algorithms, please refer to below references. 
             Reference: Chun Yu, Weixin Yao, Kun Chen (2017) <doi:10.1002/cjs.11310>.
             NeyKov N, Filzmoser P, Dimova R et al. (2007) <doi:10.1016/j.csda.2006.12.024>.
             Bai X, Yao W. Boyer JE (2012) <doi:10.1016/j.csda.2012.01.016>.
             Wennan Chang, Xinyu Zhou, Yong Zang, Chi Zhang, Sha Cao (2020) <arXiv:2005.11599>.
Implementations of several robust nonparametric two-sample tests
    for location or scale differences. The test statistics are based on robust
    location and scale estimators, e.g. the sample median or the Hodges-Lehmann estimators
    as described in Fried & Dehling (2011) <doi:10.1007/s10260-011-0164-1>.
    The p-values can be computed via the permutation principle, the randomization principle, or by using
    the asymptotic distributions of the test statistics under the null hypothesis, which ensures
    (approximate) distribution independence of the test decision. To test for a difference in
    scale, we apply the tests for location difference to transformed observations; see Fried (2012) <doi:10.1016/j.csda.2011.02.012>.
    Random noise on a small range can be added to the original observations in order to
    hold the significance level on data from discrete distributions.
    The location tests assume homoscedasticity and the scale tests require the
    location parameters to be zero.
Generation of dynamic coappearance matrices for elements
             within a text along with utilities to aid in the generation
	     of Gephi dynamic networks.
Robustness checks for omitted variable bias. The package includes robustness checks proposed by Oster (2019). robomit the estimate i) the bias-adjusted treatment correlation or effect and ii) the degree of selection on unobservables relative to observables (with respect to the treatment variable) that would be necessary to eliminate the result based on the framework by Oster (2019). Additionally, robomit offers a set of sensitivity analysis and visualization functions. See: Oster, E. 2019. <doi:10.1080/07350015.2016.1227711>.
Provides functions to download and parse 'robots.txt' files.
        Ultimately the package makes it easy to check if bots
        (spiders, crawler, scrapers, ...) are allowed to access specific
        resources on a domain.
Calculates periodograms based on (robustly) fitting periodic functions to light curves (irregularly observed time series, possibly with measurement accuracies, occurring in astroparticle physics). Three main functions are included: RobPer() calculates the periodogram. Outlying periodogram bars (indicating a period) can be detected with betaCvMfit(). Artificial light curves can be generated using the function tsgen(). For more details see the corresponding article: Thieler, Fried and Rathjens (2016), Journal of Statistical Software 69(9), 1-36, <doi:10.18637/jss.v069.i09>.
Three-step regression and inference for cellwise and casewise contamination.
We implement the algorithm estimating the parameters of the robust regression model with compositional covariates. The model simultaneously treats outliers and provides reliable parameter  estimates. Publication reference: Mishra, A., Mueller, C.,(2019) <arXiv:1909.04990>.
Functions for the determination of optimally robust influence curves in case of
            linear regression with unknown scale and standard normal distributed errors where
            the regressor is random.
This package provides the function to calculate SVD, regularized SVD, robust SVD and robust regularized SVD method. The robust SVD methods use alternating iteratively reweighted least squares methods. The regularized SVD uses generalized cross validation to choose the optimal smoothing parameters.
An implementation of algorithms for estimation of the graphical lasso regularization parameter described in Pedro Cisneros-Velarde, Alexander Petersen and Sang-Yun Oh (2020) <http://proceedings.mlr.press/v108/cisneros20a.html>.
Allows loading and displaying an Observable notebook (online JavaScript  
    notebooks powered by <https://observablehq.com>) as an HTML Widget in an R 
    session, 'shiny' application or 'rmarkdown' document.
Fast algorithms for the Theil-Sen estimator,
  Siegel's repeated median slope estimator, and Passing-Bablok regression.
  The implementation is based on algorithms by
  Dillencourt et. al (1992) <doi:10.1142/S0218195992000020> and Matousek et. al (1998)  <doi:10.1007/PL00009190>.
  The implementation of Passing-Bablok regression is explained in detail in 
  Raymaekers J., Dufey F. (2022). Equivariant Passing-Bablok regression in quasilinear time. <arXiv:2202.08060>.
  All algorithms run in quasilinear time.
Companion package for the book: "Robust Statistics: Theory and Methods, second edition", <http://www.wiley.com/go/maronna/robust>.  This package contains code that implements the robust estimators discussed in the recent second edition of the book above, as well as the scripts reproducing all the examples in the book.
Functions to compute robust (outlier-resistant) estimates of finite
   population characteristics. The package supports the computations of robust
   means, totals, ratios, etc. Available methods are regression M- and
   GM-estimators, trimming, and winsorization. The package robsurvey
   complements the survey.
Functions for conducting robust variance estimation (RVE) meta-regression using both large and small sample RVE estimators under various weighting schemes. These methods are distribution free and provide valid point estimates, standard errors and hypothesis tests even when the degree and structure of dependence between effect sizes is unknown. Also included are functions for conducting sensitivity analyses under correlated effects weighting and producing RVE-based forest plots.
Methods for robust statistics, a state of the art in the early
  2000s, notably for robust regression and robust multivariate analysis.
An implementation of easy tools for outlier robust inference in
    two-stage least squares (2SLS) models. The user specifies a reference 
    distribution against which observations are classified as outliers or not. 
    After removing the outliers, adjusted standard errors are automatically 
    provided. Furthermore, several statistical tests for the false outlier 
    detection rate can be calculated. The outlier removing algorithm can be 
    iterated a fixed number of times or until the procedure converges. The 
    algorithms and robust inference are described in more detail in Jiao (2019) 
    <https://drive.google.com/file/d/1qPxDJnLlzLqdk94X9wwVASptf1MPpI2w/view>.
R functions for the computation of the truncated maximum
	     likelihood and the robust accelerated failure time regression 
	     for gaussian and log-Weibull case.
Functions for fitting a linear regression model with ARIMA
  errors using a filtered tau-estimate.
"Essential" Robust Statistics.
 Tools allowing to analyze data with robust methods.  This includes
 regression methodology including model selections and multivariate
 statistics where we strive to cover the book "Robust Statistics,
 Theory and Methods" by 'Maronna, Martin and Yohai'; Wiley 2006.
Fits the robust Bayesian Copas (RBC) selection model of Bai et al. (2020) <arXiv:2005.02930> for correcting and quantifying publication bias in univariate meta-analysis. Also fits standard random effects meta-analysis and the Copas-like selection model of Ning et al. (2017) <doi:10.1093/biostatistics/kxx004>.
Robust tests (RW and RF) are provided for testing the equality of two long-tailed symmetric (LTS) means when the variances are unknown and arbitrary. RW test is a robust version of Welch's two sample t test and the RF is a robust fiducial based test. The RW and RF tests are proposed using the adaptive modified maximum likelihood (AMML) estimators derived by Tiku and Surucu (2009) <doi:10.1016/j.spl.2008.12.001> and Donmez (2010) <https://open.metu.edu.tr/bitstream/handle/11511/19440/index.pdf>.
Bayesian robust fitting of linear mixed effects models through weighted likelihood equations and approximate Bayesian computation as proposed by Ruli et al. (2017) <arXiv:1706.01752>.
Implements full Bayesian analysis for calibrating mathematical models with new methodology for modeling the discrepancy function. It allows for emulation, calibration and prediction using complex mathematical model outputs and experimental data. See the reference: Mengyang Gu and Long Wang (2018) <arXiv:1707.08215>, Mengyang Gu, Fangzheng Xie and Long Wang (2021) <arXiv:1807.03829>, Mengyang Gu, Kyle Anderson and Erika McPhillips (2021) <arXiv:1810.11664>.
Collection of methods for robust covariance and (sparse) precision matrix estimation based on Loh and Tan (2018) <doi:10.1214/18-EJS1427>.
Robust mixture discriminant analysis (RMDA), proposed in Bouveyron & Girard, 2009 <doi:10.1016/j.patcog.2009.03.027>, allows to build a robust supervised classifier from learning data with label noise. The idea of the proposed method is to confront an unsupervised modeling of the data with the supervised information carried by the labels of the learning data in order to detect inconsistencies. The method is able afterward to build a robust classifier taking into account the detected inconsistencies into the labels.
Testing homogeneity for generalized exponential tilt model. This package includes a collection of functions for (1) implementing methods for testing homogeneity for generalized exponential tilt model; and (2) implementing existing methods under comparison.
An object oriented solution for robust factor analysis. In the solution, new S4 classes "Fa", "FaClassic", "FaRobust", "FaCov", "SummaryFa" are created.
This package provides robust estimation for generalized
        additive models. It implements a fast and stable algorithm in
        Wong, Yao and Lee (2013). The implementation also contains
        three automatic selection methods for smoothing parameter. They
        are designed to be robust to outliers. For more details, see
        Wong, Yao and Lee (2013).
Robust parameter estimation and prediction of Gaussian stochastic process emulators. It allows for robust parameter estimation and prediction using Gaussian stochastic process emulator. It also implements the parallel partial Gaussian stochastic process emulator for computer model with massive outputs See the reference: Mengyang Gu and Jim Berger, 2016, Annals of Applied Statistics; Mengyang Gu, Xiaojing Wang and Jim Berger, 2018, Annals of Statistics.
Robust methods for high-dimensional data, in particular linear
    model selection techniques based on least angle regression and sparse
    regression.
Provides an easy way to compute the Theil Sehn Regression method and also the Siegel Regression Method which are both robust methods base on the median of slopes between all pairs of data. In contrast with the least squared linear regression, these methods are not sensitive to outliers. Theil, H. (1992) <doi:10.1007/978-94-011-2546-8_20>, Sen, P. K. (1968) <doi:10.1080/01621459.1968.10480934>.
Computationally efficient tool for performing variable selection and obtaining robust estimates, which implements robust variable selection procedure proposed by Wang, X., Jiang, Y., Wang, S., Zhang, H. (2013) <doi:10.1080/01621459.2013.766613>. Users can enjoy the near optimal, consistent, and oracle properties of the procedures.
A method to fit linear mixed effects models robustly.
    Robustness is achieved by modification of the scoring equations
    combined with the Design Adaptive Scale approach.
Implements two-sample tests for paired data with missing values (Fong, Huang, Lemos and McElrath 2018, Biostatics, <doi:10.1093/biostatistics/kxx039>) and modified Wilcoxon-Mann-Whitney two sample location test, also known as the Fligner-Policello test.
Methods for aggregating ranked lists, especially lists of
        genes. It implements the Robust Rank Aggregation (Kolde et. al
        in preparation) and some other simple algorithms for the task.
        RRA method uses a probabilistic model for aggregation that is
        robust to noise and also facilitates the calculation of
        significance probabilities for all the elements in the final
        ranking.
A collection of functions to compute the Rao-Stirling diversity index
	(Porter and Rafols, 2009) <DOI:10.1007/s11192-008-2197-2> and its extension to
	acknowledge missing data (i.e.,	uncategorized references) by calculating its
	interval of uncertainty using	mathematical optimization as proposed in Calatrava
	et al. (2016) <DOI:10.1007/s11192-016-1842-4>.
	The Rao-Stirling diversity index is a well-established bibliometric indicator
	to measure the interdisciplinarity of scientific publications. Apart from the
	obligatory dataset of publications with their respective references and	a
	taxonomy of disciplines that categorizes references as well as a measure of
	similarity between the disciplines, the Rao-Stirling diversity index requires
	a complete categorization of all references of a publication into disciplines.
	Thus, it fails for a incomplete categorization; in this case, the robust
	extension has to be used, which encodes the uncertainty caused by missing
	bibliographic data as an uncertainty interval.
	Classification / ACM - 2012: Information systems ~ Similarity measures,
	Theory of computation ~ Quadratic	programming, Applied computing ~ Digital
	libraries and archives.
Linear regression functions using Huber and bisquare psi functions. Optimal weights are calculated using IRLS algorithm.
Functions for Robust Bayesian Small Area Estimation.
Data sets are often corrupted by outliers. When data are multivariate outliers can be classified as case-wise or cell-wise. The latters are particularly challenge to handle. We implement a robust estimation procedure for Seemingly Unrelated Regression Models which is able to cope well with both type of outliers. Giovanni Saraceno, Fatemah Alqallaf, Claudio Agostinelli (2021) <arXiv:2107.00975>.
Robustness -- 'eXperimental', 'eXtraneous', or 'eXtraordinary'
  Functionality for Robust Statistics.  Hence methods which are not well established,
  often related to methods in package 'robustbase'.  Amazingly, 'BACON()', originally by
  Billor, Hadi, and Velleman (2000) <doi:10.1016/S0167-9473(99)00101-2>
  has become established in places.  The "barrow wheel" `rbwheel()` is from
  Stahel and Mächler (2009) <doi:10.1111/j.1467-9868.2009.00706.x>.
Helps users in quickly visualizing risk-of-bias 
    assessments performed as part of a systematic review. It allows users to 
    create weighted bar-plots of the distribution of risk-of-bias judgments 
    within each bias domain, in addition to traffic-light plots of the 
    specific domain-level judgments for each study. The resulting figures are 
    of publication quality and are formatted according the risk-of-bias 
    assessment tool use to perform the assessments. Currently, the supported 
    tools are ROB2.0 (for randomized controlled trials; Sterne et al (2019)  
    <doi:10.1136/bmj.l4898>), ROBINS-I (for non-randomised studies of 
    interventions; Sterne et al (2016) <doi:10.1136/bmj.i4919>), and QUADAS-2 
    (for diagnostic accuracy studies; Whiting et al (2011) 
    <doi:10.7326/0003-4819-155-8-201110180-00009>).
Aggregates multiple Receiver Operating Characteristic (ROC) curves 
    obtained from different sources into one global ROC. Additionally, it’s 
    also possible to calculate the aggregated precision-recall (PR) curve.
Functions for a classification method based on receiver operating characteristics (ROC). Briefly, features are selected according to their ranked AUC value in the training set. The selected features are merged by the mean value to form a meta-gene. The samples are ranked by their meta-gene value and the meta-gene threshold that has the highest accuracy in splitting the training samples is determined. A new sample is classified by its meta-gene value relative to the threshold. In the first place, the package is aimed at two class problems in gene expression data, but might also apply to other problems.
Cross validate large genetic data while specifying clinical variables that should always be in the model using the function cv(). An ROC plot from the cross validation data with AUC can be obtained using rocplot(), which also can be used to compare different models. Framework was built to handle genetic data, but works for any data.
The algorithm provided in this package generates perfect sample for unimodal or multimodal posteriors. Read Once Coupling From The Past, with Metropolis-Multishift is used to generate a perfect sample for a given posterior density based on the two extreme starting paths, minimum and maximum of the most interest range of the posterior. It uses the monotone random operation of multishift coupler which allows to sandwich all of the state space in one point. It means both Markov Chains starting from the maximum and minimum will be coalesced. The generated sample is independent from the starting points. It is useful for mixture distributions too. The output of this function is a real value as an exact draw from the posterior distribution.
Sensitivity (or recall or true positive rate), false positive rate, specificity, precision (or positive predictive value), negative predictive value, misclassification rate, accuracy, F-score- these are popular metrics for assessing performance of binary classifier for certain threshold. These metrics are calculated at certain threshold values. Receiver operating characteristic (ROC) curve is a common tool for assessing overall diagnostic ability of the binary classifier. Unlike depending on a certain threshold, area under ROC curve (also known as AUC), is a summary statistic about how well a binary classifier performs overall for the classification task. ROCit package provides flexibility to easily evaluate threshold-bound metrics. Also, ROC curve, along with AUC, can be obtained using different methods, such as empirical, binormal and non-parametric. ROCit encompasses a wide variety of methods for constructing confidence interval of ROC curve and AUC. ROCit also features the option of constructing empirical gains table, which is a handy tool for direct marketing. The package offers options for commonly used visualization, such as, ROC curve, KS plot, lift plot. Along with in-built default graphics setting, there are rooms for manual tweak by providing the necessary values as function arguments. ROCit is a powerful tool offering a range of things, yet it is very easy to use.
The Reproducible Open Coding Kit ('ROCK', and this package, 'rock')
  was developed to facilitate reproducible and open coding, specifically
  geared towards qualitative research methods. Although it is a
  general-purpose toolkit, three specific applications have been
  implemented, specifically an interface to the 'rENA' package that
  implements Epistemic Network Analysis ('ENA'), means to process notes
  from Cognitive Interviews ('CIs'), and means to work with decentralized
  construct taxonomies ('DCTs').
A collection of functions for interpretation and presentation
    of regression analysis.  These functions are used
    to produce the statistics lectures in
    <https://pj.freefaculty.org/guides/>. Includes regression
    diagnostics, regression tables, and plots of interactions and
    "moderator" variables. The emphasis is on "mean-centered" and
    "residual-centered" predictors. The vignette 'rockchalk' offers a
    fairly comprehensive overview.  The vignette 'Rstyle' has advice
    about coding in R.  The package title 'rockchalk' refers to our
    school motto, 'Rock Chalk Jayhawk, Go K.U.'.
'R6' class interface for handling relational database connections using 'DBI' package as backend.
  The class allows handling of connections to e.g. PostgreSQL, MariaDB and SQLite.
  The purpose is having an intuitive object allowing straightforward handling of SQL databases.
A set of functions for receiver operating characteristic (ROC) curve 
    estimation and area under the curve (AUC) calculation.
    All functions are designed to work with aggregated data; 
    nevertheless, they can also handle raw samples. 
    In 'ROCket', we distinguish two types of ROC curve representations:
    1) parametric curves - the true positive rate (TPR) and the false positive rate (FPR) 
    are functions of a parameter (the score),
    2) functions - TPR is a function of FPR.
    There are several ROC curve estimation methods available. An introduction to 
    the mathematical background of the implemented methods (and much more) can be found in 
    de Zea Bermudez, Gonçalves, Oliveira & Subtil (2014) <https://www.ine.pt/revstat/pdf/rs140101.pdf> 
    and Cai & Pepe (2004) <doi:10.1111/j.0006-341X.2004.00200.x>.
Provides functions to complete three-dimensional rock fabric and strain analyses following the Rf Phi, Fry, and normalized Fry methods. Also allows for plotting of results and interactive 3D visualization functionality.
Connector to the REST API of a 'Rock' R server, to perform operations 
  on a remote R server session, or administration tasks. See 'Rock' documentation at
  <https://rockdoc.obiba.org/>.
Efficient diffusing of content across function documentations. Sections, parameters or dot parameters are extracted from function documentations and turned into valid Rd character strings, which are ready to diffuse into the 'roxygen' comments of another function by inserting inline code.
Non-inferiority test and diagnostic test are very important in clinical trails.
  This package is to get a p value from the non-inferiority test for ROC curves from diagnostic test.
A set of tools for working with Romanian personal numeric codes. 
    The core is a validation function which applies several verification
    criteria to assess the validity of numeric codes. This is accompanied by 
    functionality for extracting the different components of a personal numeric
    code. A personal numeric code is issued to all Romanian residents either at 
    birth or when they obtain a residence permit.
Estimates the pooled (unadjusted) Receiver Operating Characteristic (ROC) curve, the covariate-adjusted ROC (AROC) curve, and the covariate-specific/conditional ROC (cROC) curve by different methods, both Bayesian and frequentist. Also, it provides functions to obtain ROC-based optimal cutpoints utilizing several criteria. Based on Erkanli, A. et al. (2006) <doi:10.1002/sim.2496>; Faraggi, D. (2003) <doi:10.1111/1467-9884.00350>; Gu, J. et al. (2008) <doi:10.1002/sim.3366>; Inacio de Carvalho, V. et al. (2013) <doi:10.1214/13-BA825>; Inacio de Carvalho, V., and Rodriguez-Alvarez, M.X. (2018) <arXiv:1806.00473>; Janes, H., and Pepe, M.S. (2009) <doi:10.1093/biomet/asp002>; Pepe, M.S. (1998) <http://www.jstor.org/stable/2534001?seq=1>; Rodriguez-Alvarez, M.X. et al. (2011a) <doi:10.1016/j.csda.2010.07.018>; Rodriguez-Alvarez, M.X. et al. (2011a) <doi:10.1007/s11222-010-9184-1>. Please see Rodriguez-Alvarez, M.X. and Inacio, V. (20208) <arXiv:2003.13111> for more details.
Provides the robust gamma rank correlation coefficient as introduced by
	     Bodenhofer, Krone, and Klawonn (2013) <DOI:10.1016/j.ins.2012.11.026>
	     along with a permutation-based rank correlation test.
	     The rank correlation coefficient and the test are explicitly
	     designed for dealing with noisy numerical data.
Functions for (1) computing diagnostic test statistics (sensitivity, specificity, etc.) from confusion matrices with adjustment for various base rates or known prevalence based on McCaffrey et al (2003) <doi:10.1007/978-1-4615-0079-7_1>, (2) computing optimal cut-off scores with different criteria including maximizing sensitivity, maximizing specificity, and maximizing the Youden Index from Youden (1950) <https://acsjournals.onlinelibrary.wiley.com/doi/abs/10.1002/1097-0142%281950%293%3A1%3C32%3A%3AAID-CNCR2820030106%3E3.0.CO%3B2-3>, and (3) displaying and comparing classification statistics and area under the receiver operating characteristic (ROC) curves or area under the curves (AUC) across consecutive categories for ordinal variables.
ROC graphs, sensitivity/specificity curves, lift charts,
  and precision/recall plots are popular examples of trade-off
  visualizations for specific pairs of performance measures. ROCR is a
  flexible tool for creating cutoff-parameterized 2D performance curves
  by freely combining two from over 25 performance measures (new
  performance measures can be added using a standard interface).
  Curves from different cross-validation or bootstrapping runs can be
  averaged by different methods, and standard deviations, standard
  errors or box plots can be used to visualize the variability across
  the runs. The parameterization can be visualized by printing cutoff
  values at the corresponding curve positions, or by coloring the
  curve according to cutoff. All components of a performance plot can
  be quickly adjusted using a flexible parameter dispatching
  mechanism. Despite its flexibility, ROCR is easy to use, with only
  three commands and reasonable default values for all optional
  parameters.
Plots the Receiver Operating Characteristics Surface for high-throughput class-skewed data, calculates the Volume under the Surface (VUS) and the FDR-Controlled Area Under the Curve (FCAUC), and conducts tests to compare two ROC surfaces. Computes eROC curve and the corresponding AUC for imperfect reference standard.
We develop the entire solution paths for ROC-SVM presented by Rakotomamonjy. The ROC-SVM solution path algorithm greatly facilitates the tuning procedure for regularization parameter, lambda in ROC-SVM by avoiding grid search algorithm which may be computationally too intensive. For more information on the ROC-SVM, see the report in the ROC Analysis in AI workshop(ROCAI-2004) : Hernàndez-Orallo, José, et al. (2004) <doi:10.1145/1046456.1046489>.
Receiver Operating Characteristic (ROC)-guided survival trees and ensemble algorithms are implemented, providing a unified framework for tree-structured analysis with censored survival outcomes. A time-invariant partition scheme on the survivor population was considered to incorporate time-dependent covariates. Motivated by ideas of randomized tests, generalized time-dependent ROC curves were used to evaluate the performance of survival trees and establish the optimality of the target hazard/survival function. The optimality of the target hazard function motivates us to use a weighted average of the time-dependent area under the curve (AUC) on a set of time points to evaluate the prediction performance of survival trees and to guide splitting and pruning. A detailed description of the implemented methods can be found in Sun et al. (2019) <arXiv:1809.05627>.
'ODAM' (Open Data for Access and Mining) is a framework that implements a simple way to make research data broadly accessible and fully available for reuse, including by a script language such as R. The main purpose is to make a data set accessible online with a minimal effort from the data provider, and to allow any scientists or bioinformaticians to be able to explore the data set and then extract a subpart or the totality of the data according to their needs. The Rodam package has only one class, 'odamws', that provides methods to allow you to retrieve online data using 'ODAM' Web Services. This obviously requires that data are implemented according the 'ODAM' approach , namely that the data subsets were deposited in the suitable data repository in the form of TSV files associated with  their metadata also described  in TSV files. See <https://inrae.github.io/ODAM/>.
An implementation of R's DBI interface using ODBC package as a
    back-end. This allows R to connect to any DBMS that has a ODBC driver.
A collection of functions for numerical construction of optimal discriminating designs. At the current moment T-optimal designs (which maximize the lower bound for the power of F-test for regression model discrimination), KL-optimal designs (for lognormal errors) and their robust analogues can be calculated with the package.
Show physics, math and engineering students how an ODE solver
    is made and how effective R classes can be for the construction of
    the equations that describe natural phenomena. Inspiration for this work 
    comes from the book on "Computer Simulations in Physics" 
    by Harvey Gould, Jan Tobochnik, and Wolfgang Christian. 
    Book link: <http://www.compadre.org/osp/items/detail.cfm?ID=7375>.
Provides an R6 class and several utility methods to
    facilitate the implementation of models based on ordinary
    differential equations. The heart of the package is a code generator
    that creates compiled 'Fortran' (or 'R') code which can be passed to
    a numerical solver. There is direct support for solvers contained
    in packages 'deSolve' and 'rootSolve'.
Implements the robust functional analysis of variance (RoFANOVA), described in Centofanti et al. (2021) <arXiv:2112.10643>. 
    It allows testing mean differences among groups of  functional data by being robust against the presence of outliers.
Tools for grading the coding style and documentation of R
  scripts. This is the R component of Roger the Omni Grader, an
  automated grading system for computer programming projects based on
  Unix shell scripts; see <https://gitlab.com/roger-project>. The
  package also provides an R interface to the shell scripts. Inspired by
  the lintr package.
Rogue ("wildcard") taxa are leaves with uncertain phylogenetic
  position.
  Their position may vary from tree to tree under inference methods that yield a
  tree set (e.g. bootstrapping, Bayesian tree searches, maximum parsimony).
  The presence of rogue taxa in a tree set can potentially remove all
  information from a consensus tree. The information content of a consensus
  tree - a function of its resolution and branch support values - can often be
  increased by removing rogue taxa. 
  'Rogue' provides an explicitly information-theoretic approach to rogue
  detection (Smith 2022) <doi:10.1093/sysbio/syab099>,
  and an interface to 'RogueNaRok' (Aberer et al. 2013)
  <doi:10.1093/sysbio/sys078>.
The R Optimization Infrastructure ('ROI') <doi:10.18637/jss.v094.i15>
	is a sophisticated framework for handling optimization problems in R.
	Additional information can be found on the 'ROI' homepage <http://roi.r-forge.r-project.org/>.
A collection of non-linear optimization problems with box bounds
             transformed into 'ROI' optimization problems.
             This package provides a wrapper around the 'globalOptTests'
             which provides a collection of global optimization problems.
             More information can be found in the 'README' file.
The mixed integer programming library 'MIPLIB' (see <http://miplib.zib.de/>) 
	is commonly used to compare the performance of mixed integer optimization solvers.
	This package provides functions to access 'MIPLIB' from the 
	'R' Optimization Infrastructure ('ROI'). More information about 'MIPLIB'
	can be found in the paper by Koch et al. available at
	<http://mpc.zib.de/index.php/MPC/article/viewFile/56/28>.
	The 'README.md' file illustrates how to use this package.
A collection of 'ROI' optimization problems based on the 'NETLIB-LP' collection.
  'Netlib' is a software repository, which amongst many other software for scientific computing contains a collection of linear programming problems. The purpose of this package is to make 
  this problems easily accessible from 'R' as 'ROI' optimization problems.
Enhances the R Optimization Infrastructure ('ROI') package
        with the 'alabama' solver for solving nonlinear optimization problems.
Enhances the R Optimization Infrastructure (ROI) package by registering
	     the 'CPLEX' commercial solver. It allows for solving mixed integer quadratically
	     constrained programming (MIQPQC) problems as well as all
	     variants/combinations of LP, QP, QCP, IP.
Enhances the R Optimization Infrastructure ('ROI') package
        	 with the 'DEoptim' and 'DEoptimR' package. 'DEoptim' is
        	 used for unconstrained optimization and 'DEoptimR' for
        	 constrained optimization.
Enhances the 'R' Optimization Infrastructure ('ROI') package
	     with the Embedded Conic Solver ('ECOS') for solving conic optimization problems.
Enhances the 'R' Optimization Infrastructure ('ROI') package by registering
	     the free 'GLPK' solver. It allows for solving mixed integer linear programming ('MILP')
	     problems as well as all variants/combinations of 'LP', 'IP'.
Enhances the 'R' Optimization Infrastructure ('ROI') package 
    by registering the 'ipop' solver from package 'kernlab'.
Enhances the 'R' Optimization Infrastructure ('ROI') package
             with the 'lp_solve' solver.
Enhances the 'R' Optimization Infrastructure ('ROI') package
             with the possibility to obtain multiple solutions for linear 
             problems with binary variables. The main function is copied 
             (with small modifications) from the relations package.
Enhances the 'R' Optimization Infrastructure ('ROI') package
             with a connection to the 'neos' server. 'ROI' optimization
             problems can be directly be sent to the 'neos' server
             and solution obtained in the typical 'ROI' style.
Enhances the R Optimization Infrastructure ('ROI') package
        with the 'NLopt' solver for solving nonlinear optimization problems.
Enhances the R Optimization Infrastructure ('ROI') package
        with the 'optimx' package.
Enhances the 'R' Optimization Infrastructure ('ROI') package
         with the quadratic solver 'OSQP'. More information about
         'OSQP' can be found at <https://osqp.org>.
Enhances the 'R' Optimization Infrastructure ('ROI') package
         with the quadratic solver 'qpOASES'. More information about
         'qpOASES' can be found at <https://projects.coin-or.org/qpOASES/>.
Enhances the R Optimization Infrastructure ('ROI') package
    by registering the 'quadprog' solver. It allows for solving quadratic programming (QP)
    problems.
Enhances the 'R' Optimization Infrastructure ('ROI') package
         with the 'SCS' solver for solving convex cone problems.
Enhances the R Optimization Infrastructure ('ROI') package by registering
	     the 'SYMPHONY' open-source solver from the COIN-OR suite. It allows for
	     solving mixed integer linear programming (MILP) problems as well as all
	     variants/combinations of LP, IP.
Easily applying same t-tests/basic data description across several sub-groups, with the output as a nice arranged data.frame. Multiple comparison and the significance symbols are also provided.
Fast and efficient computation of rolling and expanding statistics for time-series data.
Reports errors and messages to Rollbar, the error tracking platform <https://rollbar.com>.
R Markdown output formats based on JavaScript libraries such as
    'Scrollama' (<https://github.com/russellgoldenberg/scrollama>) for storytelling.
A rolling version of the Latent Dirichlet Allocation, see Rieger et al. (2021) <https://www.statistik.tu-dortmund.de/fileadmin/user_upload/Lehrstuehle/IWuS/Forschung/rollinglda.pdf>. By a sequential approach, it enables the construction of LDA-based time series of topics that are consistent with previous states of LDA models. After an initial modeling, updates can be computed efficiently, allowing for real-time monitoring and detection of events or structural breaks.
Functions to perform propensity score matching on rolling entry interventions for which a suitable "entry" date is not observed for nonparticipants. For more details, please reference Witman et al. (2018) <https://onlinelibrary.wiley.com/doi/abs/10.1111/1475-6773.13086>.
Methods for fast rolling and expanding linear regression models. That is, series of linear regression models estimated on either an expanding window of data or a moving window of data. The methods use rank-one updates and downdates of the upper triangular matrix from a QR decomposition (see Dongarra, Moler, Bunch, and Stewart (1979) <doi:10.1137/1.9781611971811>).
Functions to convert an R colour specification to a colour name. The user can select and create different lists of colour names and different colour metrics for the conversion.
A colour list and colour metric based on the ISCC-NBS System of Color Designation for use with the 'roloc' package for converting colour specifications to colour names.
This R package embeds 'SWI'-'Prolog', <https://www.swi-prolog.org/>, so that R can send deterministic and non-deterministic queries to 'prolog' ('consult', 'query'/'submit', 'once', 'findall').
Implements the rank-ordered logit (RO-logit) model for stratified 
    analysis of continuous outcomes introduced by 
    Tan et al. (2017) <doi:10.1177/0962280217747309>. Model diagnostics based on 
    the heuristic residuals and estimates in linear scales are available from 
    the package, and outcomes with ties are supported.
Provides fast procedures for exploring all pairs of
    cutpoints of a single covariate with respect to survival and determining
    optimal cutpoints using a hierarchical method and various ordered logrank tests.
Load multiple movies, series, actors, directors etc from 'OMDB' API. More information in: <http://www.omdbapi.com/> .
Represents high-dimensional data as tables of features, samples and measurements, and a design list for tracking the meaning of individual variables. Using this format, filtering, normalization, and other transformations of a dataset can be carried out in a flexible manner. 'romic' takes advantage of these transformations to create interactive 'shiny' apps for exploratory data analysis such as an interactive heatmap.
R on FHIR is an easy to use wrapper around the 'HL7 FHIR' REST API (STU 3 and R4). It provides tools to easily read and search resources on a FHIR server and brings the results into the R environment. R on FHIR is based on the FhirClient of the official 'HL7 FHIR .NET API', also made by Firely.
This package contains the Rook specification and
 convenience software for building and running Rook applications. To
 get started, be sure and read the 'Rook' help file first.
Statistical distribution in OOP (Object Oriented Programming) way.
             This package proposes a R6 class interface to classic statistical
             distribution, and new distributions can be easily added with the
             class AbstractDist. A useful point is the generic fit() method for
             each class, which uses a maximum likelihood estimation to find the
             parameters of a dataset, see, e.g. Hastie, T. and al (2009)
             <isbn:978-0-387-84857-0>. Furthermore, the rv_histogram class
             gives a non-parametric fit, with the same accessors that for the
             classic distribution. Finally, three random generators useful to
             build synthetic data are given: a multivariate normal generator, an
             orthogonal matrix generator, and a symmetric positive definite
             matrix generator, see Mezzadri, F. (2007)
             <arXiv:math-ph/0609050>.
A set of tools to reconstruct ordered ontogenic trajectories from
    single cell RNAseq data.
Implementation of Taylor Regression Estimator (TRE), 
   Tulip Extreme Finding Estimator (TEFE), Bell Extreme Finding Estimator (BEFE),
   Integration Extreme Finding Estimator (IEFE) and 
   Integration Root Finding Estimator (IRFE) for roots, extrema and inflections of a curve .     
   Christopoulos, DT (2019) <doi:10.13140/RG.2.2.17158.32324> .
   Christopoulos, DT (2016) <doi:10.2139/ssrn.3043076> .
   Christopoulos, DT (2016) <https://veltech.edu.in/wp-content/uploads/2016/04/Paper-04-2016.pdf> .
   Christopoulos, DT (2014) <arXiv:1206.5478v2 [math.NA]> .
Routines to find the root of nonlinear functions, and to perform steady-state and equilibrium analysis of ordinary differential equations (ODE). 
  Includes routines that: (1) generate gradient and jacobian matrices (full and banded),
  (2) find roots of non-linear equations by the 'Newton-Raphson' method, 
  (3) estimate steady-state conditions of a system of (differential) equations in full, banded or sparse form, using the 'Newton-Raphson' method, or by dynamically running,
  (4) solve the steady-state conditions for uni-and multicomponent 1-D, 2-D, and 3-D partial differential equations, that have been converted to ordinary differential equations
    by numerical differencing (using the method-of-lines approach).
  Includes fortran code.
Functions for hypothesis testing in single and double Wishart
    settings, based on Roy's largest root. This test statistic is especially
    useful in multivariate analysis. The computations are based on results by
    Chiani (2014) <DOI:10.1016/j.jmva.2014.04.002> and Chiani (2016)
    <DOI:10.1016/j.jmva.2015.10.007>. They use the fact that the CDF is related
    to the Pfaffian of a matrix that can be computed in a finite number of
    iterations. This package takes advantage of the Boost and Eigen C++ libraries
    to perform multi-precision linear algebra.
Trees Classification and Regression using multivariate nodes calculated by an exhaustive numerical approach. We propose a new concept of decision tree, including multivariate knots and non hierarchical pathway. This package's model uses a multivariate nodes tree that calculates directly a risk score for each observation for the state Y observed. Nguyen JM, Gaultier A, Antonioli D (2015) <doi:10.1016/j.respe.2018.03.088> Castillo JM, Knol AC, Nguyen JM, Khammari A, Saint Jean M, Dreno B (2016) <doi:10.1684/ejd.2016.2826> Vildy S, Nguyen JM, Gaultier A, Khammari A, Dreno B (2017) <doi:10.1684/ejd.2016.2955> Nguyen JM, Gaultier A, Antonioli D (2018) <doi:10.1016/j.respe.2018.03.088>.
Selects one model with variable selection FDR controlled at a
    specified level. A q-value for each potential variable is also returned. The
    input, variable selection counts over many bootstraps for several levels of
    penalization, is modeled as coming from a beta-binomial mixture
    distribution.
The 'ropenblas' package (<https://prdm0.github.io/ropenblas/>) is useful for users of any 'GNU/Linux' distribution. It will be possible to download, compile and link the 'OpenBLAS' library (<https://www.openblas.net/>) with the R language, always by the same procedure, regardless of the 'GNU/Linux' distribution used. With the 'ropenblas' package it is possible to download, compile and link the latest version of the 'OpenBLAS' library even the repositories of the 'GNU/Linux' distribution used do not include the latest versions of 'OpenBLAS'. If of interest, older versions of the 'OpenBLAS' library may be considered. Linking R with an optimized version of 'BLAS' (<http://www.netlib.org/blas/>) may improve the computational performance of R code. The 'OpenBLAS' library is an optimized implementation of 'BLAS' that can be easily linked to R with the 'ropenblas' package.
Installs 'OpenCV' for use by other packages. 'OpenCV' <https://opencv.org/> 
    is library of programming functions mainly aimed at real-time computer 
    vision. This 'Lite' version contains the stable base version of 'OpenCV' and 
    does not contain any of its externally contributed modules.
'Rapid7' collects 'cybersecurity' data and makes it available via
    their 'Open Data' <http://opendata.rapid7.com> portal which has an API. Tools are
    provided to assist in querying for available data sets and downloading any
    data set authorized to a free, registered account.
Provides a client for the API of OpenDota. OpenDota is a web service which is provide DOTA2 real time data. Data is collected through the Steam WebAPI. With ROpenDota you can easily grab the latest DOTA2 statistics in R programming such as latest match on official international competition, analyzing your or enemy performance to learn their strategies,etc. Please see <https://github.com/rosdyana/ROpenDota> for more information.
Provide a simple interface to Bloomberg's OpenFIGI API. Please
    see <https://openfigi.com> for API details and registration. You may be
    eligible to have an API key to accelerate your loading process.
An R package for the OpenSecrets.org web services API.
OpenWeatherMap (OWM) <http://openweathermap.org/api> is a service providing weather related data.
           This package can be used to access current weather data for one location or several locations.
           It can also be used to forecast weather for 5 days with data for every 3 hours.
Provides string arithmetic, reassignment operators, logical operators
  that handle missing values, and extra logical operators such as floating point
  equality and all or nothing. The intent is to allow R users to write code that
  is easier to read, write, and maintain while providing a friendlier experience
  to new R users from other language backgrounds (such as 'Python') who are used
  to concepts such as x += 1 and 'foo' + 'bar'.
  Includes operators for not in, easy floating point comparisons, === equivalent, and SQL-like 
  like operations (), etc. 
  We also added in some extra helper functions, such as OS checks, pasting
  in Oxford comma format, and functions to get the first, last, nth, or most common 
  element of a vector or word in a string.
Reproducible, programmatic retrieval of datasets from the
    Roper Center data archive.  The Roper Center for Public Opinion
    Research <https://ropercenter.cornell.edu> maintains the largest 
    archive of public opinion data in existence, but researchers using
    these datasets are caught in a bind.  The Center's terms and conditions
    bar redistribution of downloaded datasets, but to ensure that one's 
    work can be reproduced, assessed, and built upon by others, one must
    provide access to the raw data one employed.  The `ropercenter`
    package cuts this knot by providing registered users with programmatic,
    reproducible access to Roper Center datasets from within R.
Read the data from Origin(R) project files ('*.opj')
	<https://www.originlab.com/doc/User-Guide/Origin-File-Types>.
	No write support is planned.
A suite of classes and methods which provide low-level support for modeling provenance between in-memory R objects. This is an infrastructure package and is not intended to be used directly by end-users.
Optimally robust estimation in general smoothly parameterized models using S4
            classes and methods.
Optimally robust estimation using S4 classes and methods. Old version still
            needed for current versions of ROptRegTS and RobRex.
Perform general purpose optimization in R using C++. A unified 
    wrapper interface is provided to call C functions of the five optimization 
    algorithms ('Nelder-Mead', 'BFGS', 'CG', 'L-BFGS-B' and 'SANN') underlying 
    optim().
Collection of tools to develop options strategies, value option contracts using the Black-Scholes-Merten option pricing model and calculate the option Greeks. Hull, John C. "Options, Futures, and Other Derivatives" (1997, ISBN:0-13-601589-1). Fischer Black, Myron Scholes (1973) "The Pricing of Options and Corporate Liabilities" <doi:10.1086/260062>.
Optimally robust estimation for regression-type models using S4 classes and
            methods.
Matrix reconstruction, also known as matrix completion, is the task of inferring missing entries of a partially observed matrix. This package provides a method called OptSpace, which was proposed by Keshavan, R.H., Oh, S., and Montanari, A. (2009) <doi:10.1109/ISIT.2009.5205567> for a case under low-rank assumption.
Oracle Database interface (DBI) driver for R.
   This is a DBI-compliant Oracle driver based on the OCI.
Client for the 'Orcid.org' API (<https://orcid.org/>).
    Functions included for searching for people, searching by 'DOI',
    and searching by 'Orcid' 'ID'.
Predicts morphological parameters of rorquals (e.g. body mass, 
    flipper length, maximum engulfment capacity) from body length using 
    allometric equations from Kahane-Rapport and Goldbogen (2018) 
    <doi:10.1002/jmor.20846>.
Implementation of Robust Ordinal Regression for multiple criteria value-based sorting with preference information provided in form of possibly imprecise assignment examples, assignment-based pairwise comparisons, and desired class cardinalities [Kadzinski et al. 2015, <doi:10.1016/j.ejor.2014.09.050>].
Functions to deal with binary classification
  problems in the presence of imbalanced classes. Synthetic balanced samples are  
  generated according to ROSE (Menardi and Torelli, 2013).  
  Functions that implement more traditional remedies to the class imbalance
  are also provided, as well as different metrics to evaluate a learner accuracy.
  These are estimated by holdout, bootstrap or cross-validation methods.
New Markov chain Monte Carlo (MCMC) samplers new to be thoroughly tested
    and their performance accurately assessed. This requires densities
    that offer challenging properties to the novel sampling algorithms.
    One such popular problem is the Rosenbrock function. However, while its
    shape lends itself well to a benchmark problem, no codified multivariate expansion
    of the density exists. We have developed an extension to this class of distributions
    and supplied densities and direct sampler functions to assess the performance
    of novel MCMC algorithms. The functions are introduced in "An n-dimensional Rosenbrock 
    Distribution for MCMC Testing" by Pagani, Wiegand and Nadarajah (2019) <arXiv:1903.09556>.
When teaching statistics, it can often be desirable to
  uncouple the content from specific software packages. To ease such
  efforts, the Rosetta Stats website (<https://rosettastats.com>) allows
  comparing analyses in different packages. This package is the companion
  to the Rosetta Stats website, aiming to provide functions that produce
  output that is similar to output from other statistical packages, thereby
  facilitating 'software-agnostic' teaching of statistics.
'Rosette' is an API for multilingual text analysis and information
    extraction. More information can be found at <https://developer.rosette.com>.
Download and plot Open Street Map <http://www.openstreetmap.org/>,
    Bing Maps <http://www.bing.com/maps> and other tiled map sources. Use to create 
    basemaps quickly and add hillshade to vector-based maps.
Implementation of robust sparse PCA using the ROSPCA algorithm 
             of Hubert et al. (2016) <DOI:10.1080/00401706.2015.1093962>.
Implementation of the tests for rotational symmetry on the
    hypersphere proposed in García-Portugués, Paindaveine and Verdebout (2020)
    <doi:10.1080/01621459.2019.1665527>. The package also implements the
    proposed distributions on the hypersphere, based on the tangent-normal
    decomposition, and allows for the replication of the data application
    considered in the paper.
Fit and deploy rotation forest models ("Rodriguez, J.J., Kuncheva,
    L.I., 2006. Rotation forest: A new classifier ensemble method. IEEE Trans.
    Pattern Anal. Mach. Intell. 28, 1619-1630 <doi:10.1109/TPAMI.2006.211>") for binary classification.
    Rotation forest is an ensemble method where each base classifier (tree) is
    fit on the principal components of the variables of random partitions of
    the feature set.
Tools for working with rotational data, including
    simulation from the most commonly used distributions on SO(3),
    methods for different Bayes, mean and median type estimators for
    the central orientation of a sample, confidence/credible
    regions for the central orientation based on those estimators and
    a novel visualization technique for rotation data.  Most recently,
    functions to identify potentially discordant (outlying) values
    have been added.  References: Bingham, Melissa A. and Nordman, Dan J. and Vardeman, Steve B. (2009) <doi:10.1198/jasa.2009.ap08741>,
    Bingham, Melissa A and Vardeman, Stephen B and Nordman, Daniel J (2009),
    Bingham, Melissa A and Nordman, Daniel J and Vardeman, Stephen B (2010) <doi:10.1016/j.csda.2009.11.020>,
    Leon, C.A. and Masse, J.C. and Rivest, L.P. (2006) <doi:10.1016/j.jmva.2005.03.009>,
    Hartley, R and Aftab, K and Trumpf, J. (2011) <doi:10.1109/CVPR.2011.5995745>,
    Stanfill, Bryan and Genschel, Ulrike and Hofmann, Heike (2013) <doi:10.1080/00401706.2013.826145>,
    Maonton, Jonathan (2004) <doi:10.1109/ICARCV.2004.1469774>, 
    Mardia, KV and Jupp, PE (2000, ISBN:9780471953333), 
    Rancourt, D. and Rivest, L.P. and Asselin, J. (2000) <doi:10.1111/1467-9876.00180>,
    Chang, Ted and Rivest, Louis-Paul (2001), 
    Fisher, Nicholas I. (1996, ISBN:0521568900).
R build of Rothermel's (1972) model for surface fire rate of spread with some additional utilities (uncertainty propagation, standard fuel model selection, fuel model optimization by genetic algorithm) and sample datasets.
An interface to the 'Open Tree of Life' API to retrieve
    phylogenetic trees, information about studies used to assemble the
    synthetic tree, and utilities to match taxonomic names to 'Open Tree
    identifiers'. The 'Open Tree of Life' aims at assembling a
    comprehensive phylogenetic tree for all named species.
Conditionally rotate or back-up files based on
    their size or the date of the last backup; inspired by the 'Linux'
    utility 'logrotate'.
Rough Sets were introduced by Zdzislaw Pawlak on his book "Rough Sets: Theoretical Aspects of Reasoning About Data". Rough Sets provide a formal method to approximate crisp sets when the set-element belonging relationship is either known or undetermined. This enables the use of Rough Sets for reasoning about incomplete or contradictory knowledge. A decision table is a prescription of the decisions to make given some conditions. Such decision tables can be reduced without losing prescription ability. This package provides the classes and methods for knowledge reduction from decision tables as presented in the chapter 7 of the aforementioned book. This package provides functions for calculating the both the discernibility matrix and the essential parts of decision tables.
Implementations of algorithms for data analysis based on the
    rough set theory (RST) and the fuzzy rough set theory (FRST). We not only
    provide implementations for the basic concepts of RST and FRST but also
    popular algorithms that derive from those theories. The methods included in the
    package can be divided into several categories based on their functionality:
    discretization, feature selection, instance selection, rule induction and
    classification based on nearest neighbors. RST was introduced by Zdzisław
    Pawlak in 1982 as a sophisticated mathematical tool to model and process
    imprecise or incomplete information. By using the indiscernibility relation for
    objects/instances, RST does not require additional parameters to analyze the
    data. FRST is an extension of RST. The FRST combines concepts of vagueness and
    indiscernibility that are expressed with fuzzy sets (as proposed by Zadeh, in
    1965) and RST.
Decimal rounding is non-trivial in binary arithmetic.  ISO
  standard round to even is more rare than typically assumed as most decimal fractions
  are not exactly representable in binary.  Our roundX() versions explore differences
  between current and potential future versions of round() in R.
  Further, provides (some partly related) C99 math lib functions not in base R.
Visualize the objects in orbits in 2D and 3D. The packages is under developing to plot the orbits of objects in polar coordinate system. See the examples in demo.
R functions for generating and/or displaying random Chuck Norris 
  facts. Based on data from the 'Internet Chuck Norris database' ('ICNDb').
Detecting outliers using robust methods, 
 	i.e. the Median Absolute Deviation (MAD) for univariate outliers; Leys, Ley, Klein, Bernard, & Licata (2013) <doi:10.1016/j.jesp.2013.03.013>
        and the Mahalanobis-Minimum Covariance Determinant (MMCD) for multivariate outliers; Leys, C., Klein, O., Dominicy, Y. & Ley, C. (2018) <doi:10.1016/j.jesp.2017.09.011>.
        There is also the more known but less robust Mahalanobis distance method, only for comparison purposes.
In order to make sure that web request ends up in the correct 
    handler function a router is often used. 'routr' is a package implementing a
    simple but powerful routing functionality for R based servers. It is a fully
    functional 'fiery' plugin, but can also be used with other 'httpuv' based
    servers.
Much as 'roxygen2' allows one to document functions in the same file as the function itself, 'roxut'  allows one to write the unit tests in the same file as the function.  Once processed, the unit tests are moved to the appropriate directory.  Currently supports 'testthat' and 'tinytest' frameworks. The 'roxygen2' package provides much of the infrastructure.
Generate your Rd documentation, 'NAMESPACE' file,
    and collation field using specially formatted comments. Writing
    documentation in-line with code makes it easier to keep your
    documentation up-to-date as your requirements change. 'Roxygen2' is
    inspired by the 'Doxygen' system for C++.
Converts elements of 'roxygen' documentation to
    'markdown'.
Various tests as 'roxygen2' roclets: e.g. 'testthat' and 'tinytest' tests. 
  Also other static analysis tools as checking parameter documentation consistency and others.
Performs a multivariate normality test based on Royston's H test
Analysis of corneal data obtained from a Placido disk corneal topographer with calculation of irregularity indices. This package performs analyses of corneal data obtained from a Placido disk corneal topographer, with the calculation of the Placido irregularity indices and the posterior analysis. The package is intended to be easy to use by a practitioner, providing a simple interface and yielding easily interpretable results. A corneal topographer is an ophthalmic clinical device that obtains measurements in the cornea (the anterior part of the eye). A Placido disk corneal topographer makes use of the Placido disk [Rowsey et al. (1981)]<doi:10.1001/archopht.1981.03930011093022>, which produce a circular pattern of measurement nodes. The raw information measured by such a topographer is used by practitioners to analyze curvatures, to study optical aberrations, or to diagnose specific conditions of the eye (e.g. keratoconus, an important corneal disease). The rPACI package allows the calculation of the corneal irregularity indices described in [Castro-Luna et al. (2020)]<doi:10.1016%2Fj.clae.2019.12.006>, [Ramos-Lopez et al. (2013)]<doi:10.1097%2FOPX.0b013e3182843f2a>, and [Ramos-Lopez et al. (2011)]<doi:10.1097/opx.0b013e3182279ff8>. It provides a simple interface to read corneal topography data files as exported by a typical Placido disk topographer, to compute the irregularity indices mentioned before, and to display summary plots that are easy to interpret for a clinician.
Packed bar charts are a variation of treemaps for visualizing skewed data.  The concept was introduced by Xan Gregg at 'JMP'.
Design and analysis of confirmatory adaptive clinical trials with continuous, binary, and survival endpoints according to the methods described in the monograph by Wassmer and Brannath (2016) <doi:10.1007/978-3-319-32562-0>. This includes classical group sequential as well as multi-stage adaptive hypotheses tests that are based on the combination testing principle.
'PADRINO' houses textual representations of
    Integral Projection Models which can be converted from their 
    table format into full kernels to reproduce or extend an 
    already published analysis. 'Rpadrino' is an R interface to this database. For
    more information on Integral Projection Models, see Easterling et al. (2000) 
  <doi:10.1890/0012-9658(2000)081[0694:SSSAAN]2.0.CO;2>, Merow et al. (2013) 
  <doi:10.1111/2041-210X.12146>, Rees et al. (2014) <doi:10.1111/1365-2656.12178>,
  and Metcalf et al. (2015) <doi:10.1111/2041-210X.12405>. See Levin et al. (2021)
  for more information on 'ipmr', the engine that powers model reconstruction
  <doi:10.1111/2041-210X.13683>.
A part of precision agriculture is linked to the spectral image obtained from the cameras. With the image information of the agricultural experiment, the included functions facilitate the collection of spectral data associated with the experimental units. Some designs generated in R are linked to the images, which allows the use of the information of each pixel of the image in the experimental unit and the treatment. Tables and images are generated for the analysis of the precision agriculture experiment during the entire vegetative period of the crop.
A set of functions to build simple 
        GUI controls for R functions.  These are built on the 'tcltk' package. 
        Uses could include changing a parameter on a graph by animating it 
        with a slider or a "doublebutton", up to more sophisticated control 
        panels.
        Some functions for specific graphical tasks, referred to as 'cartoons',
        are provided.
Download and merge labeled single-cell RNA-seq data from the PanglaoDB <https://panglaodb.se/> into a Seurat object.
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.
Recursive partitioning for least absolute deviation regression trees. Another algorithm from the 1984 book by 
             Breiman, Friedman, Olshen and Stone in addition to the 'rpart' package (Breiman, Friedman, Olshen, Stone (1984, ISBN:9780412048418).
Plot 'rpart' models. Extends plot.rpart() and text.rpart()
             in the 'rpart' package.
This package contains additional tools for working with rpart
    objects.  Most importantly, it includes methods for converting rpart rules
    into a series of structured tables sufficient for executing the decision
    tree completely in SQL.
This package contains functions that allow to build
        classification trees for ordinal responses within the CART
        framework. The trees are grown using the Generalized Gini
        impurity function, where the misclassification costs are given
        by the absolute or squared differences in scores assigned to
        the categories of the response. Pruning is based on the total
        misclassification rate or on the total misclassification cost.
Generating visual charting patterns and noise,
    smoothing to find a signal in noisy time series and enabling
    users to apply their findings to real life data.
Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Candes, E. J., Li, X., Ma, Y., & Wright, J. (2011). Robust principal component analysis?. Journal of the ACM (JACM), 58(3), 11. prove that we can recover each component individually under some suitable assumptions. It is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the L1 norm. This package implements this decomposition algorithm resulting with Robust PCA approach.
Allows users to identify relevant clinical codes and
    automate the construction of clinical code lists for primary care database
    studies. This package is analogous to the Stata command pcdsearch.
Provides tools to read, write, visualize PDB files and perform some
    structural manipulations.
Monthly Pacific Decadal Oscillation (PDO) index
    values from January 1900 to September 2018. 
    Superseded by 'rsoi' package which includes the historical and 
    most recent monthly PDO index values together with related climate indices.
The quantitative measurement and detection of molecules in HPLC should be carried out by an accurate description of chromatographic peaks. In this package non-linear fitting using a modified Gaussian model with a parabolic variance (PVMG) has been implemented to obtain the retention time and height at the peak maximum. This package also includes the traditional Van Deemter approach and two alternatives approaches to characterize chromatographic column.
Implements the fast iterative shrinkage-thresholding algorithm
             (FISTA) algorithm to fit a Gamma distribution with an elastic net 
             penalty as described in Chen, Arakvin and Martin (2018) 
             <arxiv:1804.07780>. An implementation for the case of the 
             exponential distribution is also available, with details available 
             in Chen and Martin (2018) <https://papers.ssrn.com/abstract_id=3085672>.
Computes the influence functions time series of the returns for the risk and 
             performance measures as mentioned in Chen and Martin (2018) 
             <https://www.ssrn.com/abstract=3085672>, as well as in Zhang et al. (2019)
             <https://www.ssrn.com/abstract=3415903>. Also evaluates estimators influence
             functions at a set of parameter values and plots them to display the shapes of 
             the influence functions.
Implements the methodology of "Cannings, T. I. and Samworth, R. J. (2017) Random-projection ensemble classification, J. Roy. Statist. Soc., Ser. B. (with discussion), 79, 959--1035". The random projection ensemble classifier is a general method for classification of high-dimensional data, based on careful combination of the results of applying an arbitrary base classifier to random projections of the feature vectors into a lower-dimensional space. The random projections are divided into non-overlapping blocks, and within each block the projection yielding the smallest estimate of the test error is selected. The random projection ensemble classifier then aggregates the results of applying the base classifier on the selected projections, with a data-driven voting threshold to determine the final assignment.
Estimates of standard errors of popular risk and performance 
             measures for asset or portfolio returns using methods as described in 
             Chen and Martin (2021) <doi:10.21314/JOR.2020.446>.
This reduced piecewise exponential survival software implements the likelihood ratio test and backward elimination procedure in Han, Schell, and Kim (2012 <doi:10.1080/19466315.2012.698945>, 2014 <doi:10.1002/sim.5915>), and Han et al. (2016 <doi:10.1111/biom.12590>). Inputs to the program can be either times when events/censoring occur or the vectors of total time on test and the number of events. Outputs of the programs are times and the corresponding p-values in the backward elimination. Details about the model and implementation are given in Han et al. 2014. This program can run in R version 3.2.2 and above.
The purpose of this package is to factor out logic
    and math common to Item Factor Analysis fitting, diagnostics, and
    analysis. It is envisioned as core support code suitable for more
    specialized IRT packages to build upon. Complete access to optimized C
    functions are made available with R_RegisterCCallable().
    This software is described in Pritikin & Falk (2020) <doi:10.1177/0146621620929431>.
Rphylip provides an R interface for the PHYLIP package. All users
    of Rphylip will thus first have to install the PHYLIP phylogeny methods
    program package (Felsenstein 2013). See http://www.phylip.com for more 
	information about installing PHYLIP.
Tools for performing phylogenetic comparative methods for datasets with with multiple observations per species (intraspecific variation or measurement error) and/or missing data (Goolsby et al. 2017). Performs ancestral state reconstruction and missing data imputation on the estimated evolutionary model, which can be specified as Brownian Motion, Ornstein-Uhlenbeck, Early-Burst, Pagel's lambda, kappa, or delta, or a star phylogeny.
Work with 'Phylopic' web service (<http://phylopic.org/api/>) 
    to get 'silhouette' images of 'organisms', search names, and more.
    Includes functions for adding 'silhouettes' to both base plots and
    ggplot2 plots.
Bindings for the 'PicoSAT' solver to solve Boolean satisfiability problems (SAT).
             The boolean satisfiability problem asks the question if a given boolean formula can be TRUE; 
             i.e. does there exist an assignment of TRUE/FALSE for each variable such that the whole formula is TRUE?
             The package bundles 'PicoSAT' solver release 965 <http://www.fmv.jku.at/picosat/>.
Get information (boards, pins and
    users) from the Pinterest <http://www.pinterest.com>
    API.
Build powerful pivot tables (aka Pivot Grid, Pivot Chart, Cross-Tab)
    and dynamically slice & dice / drag 'n' drop your data. 'rpivotTable' is a
    wrapper of 'pivottable', a powerful open-source Pivot Table library implemented
    in 'JavaScript' by Nicolas Kruchten. Aligned to 'pivottable' v2.19.0.
A programmatic interface to the 'SOLR' based
    search API (<http://api.plos.org/>) provided by the Public
    Library of Science journals to search their articles.
    Functions are included for searching for articles, retrieving
    articles, making plots, doing 'faceted' searches,
    'highlight' searches, and viewing results of 'highlighted'
    searches in a browser.
Generate basic charts either by custom applications, or from a small script launched from the system console, or within the R console. Two ASCII text files are necessary:
  (1) The graph parameters file, which name is passed to the function 'rplotengine()'.
      The user can specify the titles, choose the type of the graph, graph output formats
      (e.g. png, eps), proportion of the X-axis and Y-axis, position of the legend,
      whether to show or not a grid at the background, etc.
  (2) The data to be plotted, which name is specified as a parameter ('data_filename')
      in the previous file. This data file has a tabulated format, with a single character
      (e.g. tab) between each column. Optionally, the file could include data columns for
      showing confidence intervals.
A robust Partial Least-Squares (PLS) method is implemented that is robust to outliers in the residuals as well as to leverage points. A specific weighting scheme is applied which avoids iterations, and leads to a highly efficient robust PLS estimator.
An approach to age-depth modelling that uses Bayesian statistics to reconstruct accumulation histories for 210Pb-dated deposits using prior information. It can combine 210Pb, radiocarbon, and other dates in the chronologies. See Aquino et al. (2018) <doi:10.1007/s13253-018-0328-7>. Note that parts of the code underlying 'rplum' are derived from the 'rbacon' package by the same authors, and there remains a degree of overlap between the two packages.
Really Poor Man's Graphical User Interface, used to create interactive R analysis sessions with simple R commands.
Recursively Partitioned Mixture Model for Beta and Gaussian Mixtures.  
    This is a model-based clustering algorithm that returns a hierarchy
    of classes, similar to hierarchical clustering, but also similar to
    finite mixture models.
Implements the P-model 
  (Stocker et al., 2020 <doi:10.5194/gmd-13-1545-2020>),
  predicting acclimated parameters of the enzyme kinetics of C3 photosynthesis,
  assimilation, and dark respiration rates as a function of the environment
  (temperature, CO2, vapour pressure deficit, light, atmospheric pressure).
Functions to allow users to build and analyze design consistent 
    tree and random forest models using survey data from a complex sample 
    design.  The tree model algorithm can fit a linear model to survey data 
    in each node obtained by recursively partitioning the data.  The splitting 
    variables and selected splits are obtained using a randomized permutation 
    test procedure which adjusted for complex sample design features used to 
    obtain the data. Likewise the model fitting algorithm produces 
    design-consistent coefficients to any specified least squares linear model 
    between the dependent and independent variables used in the end nodes.
    The main functions return the resulting binary tree or random forest as 
    an object of "rpms" or "rpms_forest" type. The package also provides methods
    modeling a "boosted" tree or forest model and a tree model for zero-inflated
    data as well as a number of functions and methods available for use with 
    these object types.
A set of functions to analyze and print the development of a
    commodity using the Point and Figure (P&F) approach. A P&F processor can be used
    to calculate daily statistics for the time series. These statistics can be used
    for deeper investigations as well as to create plots. Plots can be generated as
    well known X/O Plots in plain text format, and additionally in a more graphical
    format.
Wrapper for the 'PoetryDB' API <http://poetrydb.org> that allows for interaction and data extraction from the 
  database in an R interface. The 'PoetryDB' API is a database of poetry and poets implemented with 'MongoDB' to 
  enable developers and poets to easily access one of the most comprehensive poetry databases currently available.
A polyhedra database scraped from various sources as R6 objects and 'rgl' visualizing capabilities.
PDF tools based on the Poppler PDF rendering library.
  See <http://poppler.freedesktop.org/> for more information on Poppler.
Collection of tools to calculate portfolio performance metrics. Portfolio performance is a key measure for investors. These metrics are important to analyse how effectively their money has been invested. This package uses portfolio theories to give investor tools to evaluate their portfolio performance. For more information see,  Markowitz, H.M. (1952), <doi:10.2307/2975974>. Analysis of Investments & Management of Portfolios [2012, ISBN:978-8131518748].
A collection of tools used to generate
        various types of random portfolios.  The weights of these
        portfolios are random variables derived from truncated
        continuous random variables.
Provides an interface between R and 'PostGIS'-enabled
    'PostgreSQL' databases to transparently transfer spatial
    data. Both vector (points, lines, polygons) and raster data are
    supported in read and write modes. Also provides convenience
    functions to execute common procedures in 'PostgreSQL/PostGIS'.
Fully DBI-compliant Rcpp-backed interface to
    PostgreSQL <https://www.postgresql.org/>, an open-source relational
    database.
Database interface and 'PostgreSQL' driver for 'R'.
 This package provides a Database Interface 'DBI' compliant 
 driver for 'R' to access 'PostgreSQL' database systems.  
 In order to build and install this package from source, 'PostgreSQL' 
 itself must be present your system to provide 'PostgreSQL' functionality 
 via its libraries and header files. These files are provided as
 'postgresql-devel' package under some Linux distributions.
 On 'macOS' and 'Microsoft Windows' system the attached 'libpq' library source will be used.
The significance of mean difference tests in clinical trials is established if at least r null hypotheses are rejected among m that are simultaneously tested. This package enables one to compute necessary sample sizes for single-step (Bonferroni) and step-wise procedures (Holm and Hochberg). These three procedures control the q-generalized family-wise error rate (probability of making at least q false rejections). Sample size is computed (for these single-step and step-wise procedures) in a such a way that the r-power (probability of rejecting at least r false null hypotheses, i.e. at least r significant endpoints among m) is above some given threshold, in the context of tests of difference of means for two groups of continuous endpoints (variables). Various types of structure of correlation are considered. It is also possible to analyse data (i.e., actually test difference in means) when these are available. The case r equals 1 is treated in separate functions that were used in Lafaye de Micheaux et al. (2014) <doi:10.1080/10543406.2013.860156>.
Using some association schemes to obtain a new series of resolvable partially pairwise balanced designs (RPPBD) and space-filling designs.
Reads in sample description and slide description files and
        annotates the expression values taken from GenePix results files
	(text file format used by many microarray scanner and software providers). 
	After normalization data can be visualized as boxplot, heatmap or dotplot.
Provides tools for the analysis of reverse-phase protein arrays (RPPAs), which are also known as "tissue lysate arrays" or simply "lysate arrays". The package's primary purpose is to input a set of quantification files representing dilution series of samples and control points taken from scanned RPPA slides and determine a relative log concentration value for each valid dilution series present in each slide and provide graphical visualization of the input and output data and their relationships. Other optional features include generation of quality control scores for judging the quality of the input data, spatial adjustment of sample points based on controls added to the slides, and various types of normalization of calculated values across a set of slides. The package was derived from a previous package named SuperCurve. For a detailed description of data inputs and outputs, usage  information, and a list of related papers describing methods used in the package please review the vignette "Guide_to_RPPASPACE". Hu (2007) <doi:10.1093/bioinformatics/btm283>.
Performs joint selection in Generalized Linear Mixed Models (GLMMs) using penalized likelihood methods. Specifically, the Penalized Quasi-Likelihood (PQL) is used as a loss function, and penalties are then augmented to perform simultaneous fixed and random effects selection. Regularized PQL avoids the need for integration (or approximations such as the Laplace's method) during the estimation process, and so the full solution path for model selection can be constructed relatively quickly.
Read, write and manipulate 'Praat' TextGrid, PitchTier, Pitch, IntensityTier, Formant, Sound, and Collection files <https://www.fon.hum.uva.nl/praat/>.
Wrapper to retrieve market data, explore available markets, and plot historical price data from the 'PredictIt' public API (<https://www.predictit.org/api/marketdata/all/>).
    The package comes with a demo 'shiny' application for illustrating example use cases.
    License to use data made available via the API is for non-commercial use and 'PredictIt' is the sole source of such data.
Routines to select and visualize the maxima for a given strict
    partial order. This especially includes the computation of the Pareto
    frontier, also known as (Top-k) Skyline operator (see Börzsönyi, et al. 
    (2001) <doi:10.1109/ICDE.2001.914855>), and some generalizations 
    known as database preferences (see Kießling (2002) 
    <doi:10.1016/B978-155860869-6/50035-4>).
Implements a 'DBI' compliant interface to Presto. Presto is
    an open source distributed SQL query engine for running interactive
    analytic queries against data sources of all sizes ranging from
    gigabytes to petabytes: <https://prestodb.io/>.
Estimates disease prevalence for a given index date using existing
    registry data extended with Monte Carlo simulations following the method of Crouch et al (2014) <doi: 10.1016/j.canep.2014.02.005>.
'Eprime' is a set of programs for administering
    psychological experiments by computer. This package provides functions
    for loading, parsing, filtering and exporting data in the text files
    produced by 'Eprime' experiments.
Provides a set of functions to facilitate building formatted strings
    under various replacement rules: C-style formatting, variable-based formatting,
    and number-based formatting. C-style formatting is basically identical to built-in
    function 'sprintf'. Variable-based formatting allows users to put variable names
    in a formatted string which will be replaced by variable values. Number-based
    formatting allows users to use index numbers to represent the corresponding
    argument value to appear in the string.
Fitting latent class mixed multinomial probit (LCMMNP) models to simulated
    or empirical choice data via Bayesian estimation. The number of latent 
    classes can be updated within the algorithm on a weight-based strategy. 
    For a reference on the method see Oelschlaeger and Bauer (2021) 
    <https://trid.trb.org/view/1759753>.
The A() function calculates the A statistic, a nonparametric
    measure of effect size for two independent groups that’s also known as
    the probability of superiority (Ruscio, 2008), along with its standard
    error and a confidence interval constructed using bootstrap methods 
    (Ruscio & Mullen, 2012). Optional arguments can be specified to 
    calculate variants of the A statistic developed for other research 
    designs (e.g., related samples, more than two independent groups or 
    related samples; Ruscio & Gera, 2013). 
    <DOI: 10.1037/1082-989X.13.1.19>.
    <DOI: 10.1080/00273171.2012.658329>.
    <DOI: 10.1080/00273171.2012.738184>.
Performs all steps in the credit scoring process. This package allows the user to follow all the necessary steps for building an effective scorecard. It provides the user functions for coarse binning of variables, Weights of Evidence (WOE) transformation, variable clustering, custom binning, visualization, and scaling of logistic regression coefficients. The results will generate a scorecard that can be used as an effective credit scoring tool to evaluate risk. For complete details on the credit scoring process, see Siddiqi (2005, ISBN:047175451X).
Robust, reliable and flexible paths to files below
    a project root. The 'root' of a project is defined as a directory that
    matches a certain criterion, e.g., it contains a certain regular file.
Use JSON templates to create folders and files structure for data science projects. Includes customized templates and accepts your own as JSON files.
Protocol Buffers are a way of encoding structured data in an
 efficient yet extensible format. Google uses Protocol Buffers for almost all
 of its internal 'RPC' protocols and file formats.  Additional documentation
 is available in two included vignettes one of which corresponds to our 'JSS'
 paper (2016, <doi:10.18637/jss.v071.i02>. Either version 2 or 3 of the
 'Protocol Buffers' 'API' is supported.
Based on RPS tools, a rather complete resistant shape
    analysis of 2D and 3D datasets based on landmarks can be performed. In addition, 
    landmark-based resistant shape analysis of individual asymmetry in 2D for
    matching or object symmetric structures is also possible.
Implementation of the relative placement algorithm widely used in the scoring of Lindy Hop and West Coast Swing dance contests.
Implements methods described by the paper Robins and Tsiatis (1991) <DOI:10.1080/03610929108830654>. These use g-estimation to estimate the causal effect of a treatment in a two-armed randomised control trial where non-compliance exists and is measured, under an assumption of an accelerated failure time model and no unmeasured confounders.
Performs goodness of fits tests for both high and low-dimensional linear models.
    It can test for a variety of model misspecifications including nonlinearity and heteroscedasticity.
    In addition one can test the significance of potentially large groups of variables, and also
    produce p-values for the significance of individual variables in high-dimensional linear
    regression.
Estimating repeatability (intra-class
    correlation) from Gaussian, binary, proportion and Poisson data.
Client for accessing data journalism APIs from ProPublica <http://www.propublica.org/>.
Extract code only or with the output from an Rpubs <https://rpubs.com/> article and write the code-block to R script file.
An R interface to the Pushbullet messaging service which
 provides fast and efficient notifications (and file transfer) between
 computers, phones and tablets.  An account has to be registered at the 
 site <https://www.pushbullet.com> site to obtain a (free) API key.
Generate the PVT (Pressure-Volume-Temperature) properties of dry gas, wet gas, black oil, and water samples in a tabular format at a constant temperature from the atmospheric pressure up to the pressure of interest using correlations. Spivey, J. P., McCain Jr., W. D. and North, R. (2004) <doi:10.2118/04-07-05>. Sutton, R. P. (2007) <doi:10.2118/97099-PA>. Vasquez, M., and Beggs, H. D. (1980) <doi:10.2118/6719-PA>.
Provides access to ArcGIS geoprocessing tools by building an 
             interface between R and the ArcPy Python side-package via the 
             reticulate package.
Aims to create a single isolated 'Miniconda' 
    and 'Python' environment for reproducible pipeline scripts. 
    The package provides utilities to run system command within the 'conda'
    environment, making it easy to install, launch, manage, and stop 
    'Jupyter-lab'.
Constructs various robust quality control charts based on the median or Hodges-Lehmann estimator (location) and the median absolute deviation (MAD) or Shamos estimator (scale). The estimators used for the robust control charts are all unbiased with a sample of finite size. For more details, see Park, Kim and Wang (2022) <doi:10.1080/03610918.2019.1699114>. In addition, using this R package, the conventional quality control charts such as X-bar, S, R, p, np, u, c, g and h charts are also easily constructed. This work was partially supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (NRF-2017R1A2B4004169).
Implements the 'rquery' piped Codd-style query algebra using 'data.table'.  This allows
   for a high-speed in memory implementation of Codd-style data manipulation tools.
It computes the Schmidt decomposition of bipartite quantum systems, discrete or continuous,
    and their respective entanglement metrics. See Artur Ekert, Peter L. Knight (1995) <doi:10.1119/1.17904> 
    for more details.
Performs penalized quantile regression for LASSO, SCAD and MCP functions including group penalties. Provides a function that automatically generates lambdas and evaluates different models with cross validation or BIC, including a large p version of BIC.
Hypocenter estimation and analysis of seismic data collected continuously, or in trigger mode. The functions organize other functions from RSEIS and GEOmap to help researchers pick, locate, and store hypocenters for detailed seismic investigation.
The 'RQuantLib' package makes parts of 'QuantLib' accessible from R
 The 'QuantLib' project aims to provide a comprehensive software framework
 for quantitative finance. The goal is to provide a standard open source library
 for quantitative analysis, modeling, trading, and risk management of financial
 assets.
An implementation of the QUEFTS (Quantitative Evaluation of the Native Fertility of Tropical Soils) model. The model (1) estimates native nutrient (N, P, K) supply of soils from a few soil chemical properties; and (2) computes crop yield given that supply, crop parameters, fertilizer application, and crop attainable yield. See Janssen et al. (1990) <doi:10.1016/0016-7061(90)90021-Z> for the technical details and Sattari et al. (2014) <doi:10.1016/j.fcr.2013.12.005> for a recent evaluation and improvements.
A piped query generator based on Edgar F. Codd's relational
    algebra, and on production experience using 'SQL' and 'dplyr' at big data
    scale.  The design represents an attempt to make 'SQL' more teachable by
    denoting composition by a sequential pipeline notation instead of nested
    queries or functions.   The implementation delivers reliable high 
    performance data processing on large data systems such as 'Spark',
    databases, and 'data.table'. Package features include: data processing trees
    or pipelines as observable objects (able to report both columns
    produced and columns used), optimized 'SQL' generation as an explicit
    user visible table modeling step, plus explicit query reasoning and checking.
Enables researchers to conduct multivariate statistical analyses
    of survey data with randomized response technique items from several designs,
    including mirrored question, forced question, and unrelated question. This
    includes regression with the randomized response as the outcome and logistic
    regression with the randomized response item as a predictor. In addition,
    tools for conducting power analysis for designing randomized response items
    are included. The package implements methods described in Blair, Imai, and Zhou
    (2015) ''Design and Analysis of the Randomized Response Technique,'' Journal
    of the American Statistical Association 
    <http://graemeblair.com/papers/randresp.pdf>.
Three methods to calculate R2 for models with correlated errors, 
    including Phylogenetic GLS, Phylogenetic Logistic Regression, Linear Mixed 
    Models (LMMs), and Generalized Linear Mixed Models (GLMMs). See details in 
    Ives 2018 <doi:10.1093/sysbio/syy060>.
Analyzes and predicts from matrix population models (Caswell 2006) <doi:10.1002/9781118445112.stat07481>.
An implementation of the Real-time Adaptive Penalization (RAP) algorithm through which to iteratively update a regularization parameter in a streaming context.
The minimal 'rrapply'-package contains a single function rrapply(), providing an extended implementation of 'R'-base rapply() by allowing to recursively apply a function to elements of a nested list based on a general condition function and including the possibility to prune or aggregate nested list elements from the result. In addition, special arguments can be supplied to access the name, location, parents and siblings in the nested list of the element under evaluation. The rrapply() function builds upon rapply()'s native 'C' implementation and requires no other package dependencies.
Implementation of Robust Regression tailored to deal with Asymmetric noise Distribution, which was originally proposed by Takeuchi & Bengio & Kanamori (2002) <doi:10.1162/08997660260293300>. In addition, this implementation is extended as introducing potential feature regularization by LASSO etc.
Replication Rate (RR) is the probability of replicating a statistically significant association in genome-wide association studies. This R-package provide the estimation method for replication rate which makes use of the summary statistics from the primary study. We can use the estimated RR to determine the sample size of the replication study, and to check the consistency between the results of the primary study and those of the replication study.
A tool to exchange data between R and 'Raven' sound analysis software (Cornell Lab of Ornithology). Functions work on data formats compatible with the R package 'warbleR'.
Software for genomic prediction with the RR-BLUP mixed model (Endelman 2011, <doi:10.3835/plantgenome2011.08.0024>). One application is to estimate marker effects by ridge regression; alternatively, BLUPs can be calculated based on an additive relationship matrix or a Gaussian kernel.
An implementation of robust boosting algorithms for regression in R. This includes the RRBoost method proposed in the paper "Robust Boosting for Regression Problems" (Ju X and Salibian-Barrera M. 2020) <doi:10.1016/j.csda.2020.107065> (to appear in Computational Statistics and Data Science). It also implements previously proposed boosting algorithms in the simulation section of the paper: L2Boost, LADBoost, MBoost (Friedman, J. H. (2001) <doi:10.1214/aos/1013203451>) and Robloss (Lutz et al. (2008) <doi:10.1016/j.csda.2007.11.006>).
Robust Location and Scatter Estimation and Robust
        Multivariate Analysis with High Breakdown Point:
        principal component analysis (Filzmoser and Todorov (2013), <doi:10.1016/j.ins.2012.10.017>),
        linear and quadratic discriminant analysis (Todorov and Pires (2007)),
        multivariate tests (Todorov and Filzmoser (2010) <doi:10.1016/j.csda.2009.08.015>),
        outlier detection (Todorov et al. (2010) <doi:10.1007/s11634-010-0075-2>).
        See also Todorov and Filzmoser (2009) <ISBN-13:978-3838108148>,
        Todorov and Filzmoser (2010) <doi:10.18637/jss.v032.i03> and
        Boudt et al. (2019) <doi:10.1007/s11222-019-09869-x>.
Provides methods for multiway data analysis by means of Parafac
    and Tucker 3 models. Robust versions (Engelen and Hubert (2011) <doi:10.1016/j.aca.2011.04.043>) and versions
    for compositional data are also provided (Gallo (2015) <doi:10.1080/03610926.2013.798664>, Di Palma et al. (2018) <doi:10.1080/02664763.2017.1381669>.
Robust multivariate methods for high dimensional data including
        outlier detection (Filzmoser and Todorov (2013) <doi:10.1016/j.ins.2012.10.017>), 
        robust sparse PCA (Croux et al. (2013) <doi:10.1080/00401706.2012.727746>, Todorov and Filzmoser (2013) <doi:10.1007/978-3-642-33042-1_31>), 
        robust PLS (Todorov and Filzmoser (2014) <doi:10.17713/ajs.v43i4.44>), 
        and robust sparse classification (Ortner et al. (2020) <doi:10.1007/s10618-019-00666-8>).
Robust Location and Scatter Estimation and Robust
        Multivariate Analysis with High Breakdown Point for Incomplete
        Data (missing values) (Todorov et al. (2010) <doi:10.1007/s11634-010-0075-2>).
Makes it easy to import the data from a 'RRD' database 
   (<https://oss.oetiker.ch/rrdtool/>) directly into R data structures. The 
   resulting objects are 'tibble' objects or a list of 'tibble' objects, making
   it easy to manipulate the data.  The package uses 'librrd' to import the 
   numerical data in a 'RRD' database directly into R data structures without 
   using intermediate formats.
Make use of the hardware random number accessed by the 'RdRand'
 instruction in recent Intel CPUs (Ivy Bridge and later).
 'DRNG' is "Digital Random Number Generator".
Processes standard recommendation datasets (e.g., a user-item rating matrix) as input and generates rating predictions and lists of recommended items. Standard algorithm implementations which are included in this package are the following: Global/Item/User-Average baselines, Weighted Slope One, Item-Based KNN, User-Based KNN, FunkSVD, BPR and weighted ALS. They can be assessed according to the standard offline evaluation methodology (Shani, et al. (2011) <doi:10.1007/978-0-387-85820-3_8>) for recommender systems using measures such as MAE, RMSE, Precision, Recall, F1, AUC, NDCG, RankScore and coverage measures. The package (Coba, et al.(2017) <doi: 10.1007/978-3-319-60042-0_36>) is intended for rapid prototyping of recommendation algorithms and education purposes.
'IUCN' Red List (<http://apiv3.iucnredlist.org/api/v3/docs>) client.
    The 'IUCN' Red List is a global list of threatened and endangered species.
    Functions cover all of the Red List 'API' routes. An 'API' key is required.
Superclasses 'PostgreSQL' connection to help enable full 'dplyr' functionality on 'Redshift'.
'OpenRefine' (formerly 'Google Refine') is a popular, open source data cleaning software. This package enables users to programmatically trigger data transfer between R and 'OpenRefine'. Available functionality includes project import, export and deletion.
Assists for presentation and visualization of data from the Norwegian Health Quality Registries following the standardization based on the requirement specified by the National Service for Health Quality Registries. This requirement can be accessed from (<https://www.kvalitetsregistre.no/resultater-til-publisering-pa-nett>). Unfortunately the website is only available in Norwegian.
An R and Repast integration tool for running individual-based
    (IbM) simulation models developed using 'Repast Simphony' Agent-Based framework
    directly from R code supporting multicore execution. This package 
    integrates 'Repast Simphony' models within R environment, making easier 
    the tasks of running and analyzing model output data for 
    automated parameter calibration and for carrying out uncertainty and
    sensitivity analysis using the power of R environment.
Functions for studying realized genetic relatedness between people. Users will be able to simulate inheritance patterns given pedigree structures, generate SNP marker data given inheritance patterns, and estimate realized relatedness between pairs of individuals using SNP marker data. See Wang (2017) <doi:10.1534/genetics.116.197004>. This work was supported by National Institutes of Health grants R37 GM-046255.
Feature Selection with Regularized Random Forest. This
    package is based on the 'randomForest' package by Andy Liaw.
    The key difference is the RRF() function that builds a
    regularized random forest. Fortran original by Leo Breiman 
    and Adele Cutler, R port by Andy Liaw and Matthew Wiener, 
    Regularized random forest for classification by Houtao Deng, 
    Regularized random forest for regression by Xin Guan.
    Reference: Houtao Deng (2013) <arXiv:1306.0237>.
Testing and inference for regression models using residual randomization methods. The basis of inference is an invariance assumption on the regression errors, e.g., clustered errors, or doubly-clustered errors.
Collection of functions for fitting distributions to given data or
    by known quantiles. Two main functions fit.perc() and fit.cont() provide
    users a GUI that allows to choose a most appropriate distribution without
    any knowledge of the R syntax. Note, this package is a part of the 'rrisk'
    project.
We implement full-ranked, rank-penalized, and adaptive nuclear norm penalized estimation methods using multivariate mixture models proposed by Kang, Chen, and Yao (2022+).
Fit the reduced-rank multinomial logistic regression model for Markov
    chains developed by Wang, Abner, Fardo, Schmitt, Jicha, Eldik and Kryscio
    (2021)<doi:10.1002/sim.8923> in R. It combines the ideas of multinomial
    logistic regression in Markov chains and reduced-rank. It is very useful in 
    a study where multi-states model is assumed and each transition among the 
    states is controlled by a series of covariates. The key advantage is to 
    reduce the number of parameters to be estimated. The final coefficients for 
    all the covariates and the p-values for the interested covariates will be 
    reported. The p-values for the whole coefficient matrix can be calculated by 
    two bootstrap methods.
Functions for creating and manipulating RNA secondary structure plots.
Computation of the International Roughness Index (IRI) given a
    longitudinal road profile. The IRI can be calculated for a single road segment
    or for a sequence of segments with a fixed length (e. g. 100m). For the latter,
    an overlap of the segments can be selected. The IRI and likewise the algorithms
    for its determination are defined in Sayers, Michael W; Gillespie, Thomas D;
    Queiroz, Cesar A.V. 1986. The International Road Roughness Experiment (IRRE) :
    establishing correlation and a calibration standard for measurements. World
    Bank technical paper; no. WTP 45. Washington, DC : The World Bank. (ISBN
    0-8213-0589-1) available from <http://documents.worldbank.org/curated/en/326081468740204115>.
Execute API calls to the 'Matba Rofex' <https://apihub.primary.com.ar> trading platform. Functionality includes accessing account data and current holdings, retrieving investment quotes, placing and canceling orders, and getting reference data for instruments.
Fetches information from the 'SHERPA/RoMEO' API
  <http://www.sherpa.ac.uk/romeo/apimanual.php> which indexes policies of
  journal regarding the archival of scientific manuscripts before and/or after
  peer-review as well as formatted manuscripts.
Multivariate regression methodologies including
    classical reduced-rank regression (RRR)
    studied by Anderson (1951) <doi:10.1214/aoms/1177729580> and
    Reinsel and Velu (1998) <doi:10.1007/978-1-4757-2853-8>,
    reduced-rank regression via adaptive nuclear norm penalization
    proposed by Chen et al. (2013) <doi:10.1093/biomet/ast036> and
    Mukherjee et al. (2015) <doi:10.1093/biomet/asx080>,
    robust reduced-rank regression (R4) proposed by
    She and Chen (2017) <doi:10.1093/biomet/asx032>,
    generalized/mixed-response reduced-rank regression (mRRR) proposed by
    Luo et al. (2018) <doi:10.1016/j.jmva.2018.04.011>,
    row-sparse reduced-rank regression (SRRR) proposed by
    Chen and Huang (2012) <doi:10.1080/01621459.2012.734178>,
    reduced-rank regression with a sparse singular value decomposition (RSSVD)
    proposed by Chen et al. (2012) <doi:10.1111/j.1467-9868.2011.01002.x>
    and sparse and orthogonal factor regression (SOFAR) proposed by
    Uematsu et al. (2019) <doi:10.1109/TIT.2019.2909889>.
Functions for phylogenetic analysis (Castiglione et al, 2018 <doi:10.1111/2041-210X.12954>). The functions perform the estimation of phenotypic evolutionary rates, identification of phenotypic evolutionary rate shifts, quantification of direction and size of evolutionary change in multivariate traits, the computation of ontogenetic shape vectors and test for morphological convergence.
Linear model calculations are made for many random versions of data.  
    Using residual randomization in a permutation procedure, sums of squares are 
    calculated over many permutations to generate empirical probability distributions 
    for evaluating model effects.  This packaged is described by 
    Collyer & Adams (2018).  Additionally, coefficients, statistics, fitted values, and residuals generated over many 
    permutations can be used for various procedures including pairwise tests, prediction, classification, and
    model comparison.  This package should provide most tools one could need for the analysis of
    high-dimensional data, especially in ecology and evolutionary biology, but certainly other fields, as well.
Reduced-rank regression, diagnostics and graphics.
Univariate and multivariate methods to analyze randomized response 
    (RR) survey designs (e.g., Warner, S. L. (1965). Randomized response: A 
    survey technique for eliminating evasive answer bias. Journal of the 
    American Statistical Association, 60, 63–69, <doi:10.2307/2283137>). 
    Besides univariate estimates of true proportions, RR variables can be used 
    for correlations, as dependent variable in a logistic regression (with or 
    without random effects), or as predictors in a linear regression
    (Heck, D. W., & Moshagen, M. (2018). RRreg: An R package for correlation and 
    regression analyses of randomized response data. Journal of Statistical 
    Software, 85(2), 1–29, <doi:10.18637/jss.v085.i02>). For simulations and 
    the estimation of statistical power, RR data can be generated according to 
    several models. The implemented methods also allow to test the link between 
    continuous covariates and dishonesty in cheating paradigms such as the 
    coin-toss or dice-roll task (Moshagen, M., & Hilbig, B. E. (2017). 
    The statistical analysis of cheating paradigms. Behavior Research Methods, 
    49, 724–732, <doi:10.3758/s13428-016-0729-x>).
Methods for estimating online robust reduced-rank regression. 
    The Gaussian maximum likelihood estimation method is described in Johansen, S. (1991) <doi:10.2307/2938278>.
    The majorisation-minimisation estimation method is partly described in Zhao, Z., & Palomar, D. P. (2017) <doi:10.1109/GlobalSIP.2017.8309093>.
    The description of the generic stochastic successive upper-bound minimisation method
    and the sample average approximation can be found in Razaviyayn, M., Sanjabi, M., & Luo, Z. Q. (2016) <doi:10.1007/s10107-016-1021-7>.
Non-linear transformations of data to better discover latent effects. Applies a sequence of three transformations (1) a Gaussianizing transformation, (2) a Z-score transformation, and (3) an outlier removal transformation. A publication describing the method has the following citation: Gregory J. Hunt, Mark A. Dane, James E. Korkola, Laura M. Heiser & Johann A. Gagnon-Bartsch (2020) "Automatic Transformation and Integration to Improve Visualization and Discovery of Latent Effects in Imaging Data", Journal of Computational and Graphical Statistics, <doi:10.1080/10618600.2020.1741379>.
Makes documents containing plots and tables from a table of R codes. 
    Can make "HTML", "pdf('LaTex')", "docx('MS Word')" and "pptx('MS Powerpoint')" documents with or without R code.
    In the package, modularized 'shiny' app codes are provided. These modules are intended for reuse across applications.
Point and interval estimation of linear parameters with data
    obtained from complex surveys (including stratified and clustered samples)
    when randomization techniques are used. The randomized response technique
    was developed to obtain estimates that are more valid when studying
    sensitive topics. Estimators and variances for 14 randomized response
    methods for qualitative variables and 7 randomized response methods for
    quantitative variables are also implemented. In addition, some data sets
    from surveys with these randomization methods are included in the package.
Implementation of Gibbs sampling algorithm for Bayesian Estimation
    of the Reduced Reparameterized Unified Model ('rrum'), described by 
    Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>.
Advanced response surface analysis. The main function RSA computes
    and compares several nested polynomial regression models (full second- or 
    third-order polynomial, shifted and rotated squared difference model, 
    rising ridge surfaces, basic squared difference model, asymmetric or 
    level-dependent congruence effect models). The package provides plotting 
    functions for 3d wireframe surfaces, interactive 3d plots, and contour plots. 
    Calculates many surface parameters (a1 to a5, principal axes, stationary point,
    eigenvalues) and provides standard, robust, or bootstrapped standard errors
    and confidence intervals for them.
The package contains all the data sets related to the book
        written by the maintainer of the package.
Robust Small Area Estimation. Robust Basic Unit- and Area-Level Models
Provides a model agnostic tool for white-box model trained on features extracted from a black-box model. For more information see: Gosiewska et al. (2020) <doi:10.1016/j.dss.2021.113556>.
Provides access to geocomputing and terrain analysis functions
    of the geographical information system (GIS) 'SAGA' (System for Automated
    Geoscientific Analyses) from within R by running the command line version of
    SAGA. This package furthermore provides several R functions for handling ASCII
    grids, including a flexible framework for applying local functions (including
    predict methods of fitted models) and focal functions to multiple grids. SAGA
    GIS is available under GPLv2 / LGPLv2 licence from 
    <http://sourceforge.net/projects/saga-gis/>.
Provides an R scripting interface to the open-source 'SAGA-GIS' 
    (System for Automated Geoscientific Analyses Geographical Information
    System) software. 'Rsagacmd' dynamically generates R functions for every
    'SAGA-GIS' geoprocessing tool based on the user's currently installed
    'SAGA-GIS' version. These functions are contained within an S3 object
    and are accessed as a named list of libraries and tools. This structure
    facilitates an easier scripting experience by organizing the large number
    of 'SAGA-GIS' geoprocessing tools (>700) by their respective library.
    Interactive scripting can fully take advantage of code autocompletion tools
    (e.g. in 'Rstudio'), allowing for each tools syntax to be quickly
    recognized. Furthermore, the most common types of spatial data (via the
    'raster', 'terra', 'sp', and 'sf' packages) along with non-spatial data are
    automatically passed from R to the 'SAGA-GIS' command line tool for
    geoprocessing operations, and the results are loaded as the appropriate R
    object. Outputs from individual 'SAGA-GIS' tools can also be chained using
    pipes from the 'magrittr' and 'dplyr' packages to combine complex
    geoprocessing operations together in a single statement. 'SAGA-GIS' is
    available under a GPLv2 / LGPLv2 licence from
    <https://sourceforge.net/projects/saga-gis/> including Windows x86/x64
    binaries. SAGA-GIS is also included in Debian/Ubuntu default software
    repositories and is available for macOS using homebrew (<https://brew.sh/>)
    from the osgeo/osgeo4mac (<https://github.com/OSGeo/homebrew-osgeo4mac>)
    formula tap, as well as being bundled within the 'QGIS' application bundle
    for macOS. Rsagacmd has currently been tested on 'SAGA-GIS' versions
    from 2.3.1 to 8.0.1 on Windows, Linux and macOS.
Assists in processing reflectance data, developing empirical models using stepwise regression and a generalized linear modeling approach, cross-
    validation, and analysis of trends in water quality conditions (specifically chl-a) and climate conditions using the Theil-Sen estimator.
Classes and functions to create and summarize different types
    of resampling objects (e.g. bootstrap, cross-validation).
Resampling Stats (http://www.resample.com) is an add-in for
    running randomization tests in Excel worksheets. The workflow is (1) to define
    a statistic of interest that can be calculated from a data table, (2) to
    randomize rows ad/or columns of a data table to simulate a null hypothesis
    and (3) and to score the value of the statistic from many randomizations. The
    relative frequency distribution of the statistic in the simulations is then
    used to infer the probability of the observed value be generated by the null
    process (probability of Type I error). This package intends to translate this
    logic for R for teaching purposes. Keeping the original workflow is favored over
    performance.
Implements the Gibbs sampling algorithm to randomly sample association rules with one pre-chosen item as the consequent from a transaction dataset. The Gibbs sampling algorithm was proposed in G. Qian, C.R. Rao, X. Sun and Y. Wu (2016) <DOI:10.1073/pnas.1604553113>.
Downloading, customizing, and processing time series of satellite images for a region of interest. 'rsat' functions allow a unified access to multispectral images from Landsat, MODIS and Sentinel repositories. 'rsat' also offers capabilities for customizing satellite images, such as tile mosaicking, image cropping and new variables computation. Finally, 'rsat' covers the processing, including cloud masking, compositing and gap-filling/smoothing time series of images (Militino et al., 2018 <doi:10.3390/rs10030398> and Militino et al., 2019 <doi:10.1109/TGRS.2019.2904193>).
SaTScan(TM) (http://www.satscan.org) is software for finding regions in 
    Time, Space, or Time-Space that have excess risk, based on scan statistics, and 
	uses Monte Carlo hypothesis testing to generate P-values for these regions.  The 
	rsatscan package provides functions for writing R data frames in 
	SaTScan-readable formats, for setting SaTScan parameters, for running SaTScan in 
	the OS, and for reading the files that SaTScan creates.
Retrieve, update, delete job information from <https://saucelabs.com/>. Poll the 'SauceLabs' services
    current status and access supported platforms. Send and retrieve files from 'SauceLabs' and manage tunnels associated
    with 'SauceConnect'.
Package to Handle R Requests from R Service Bus Applications with JSON Payloads in a generic way.
    The incoming request is encoded as a string (character vector of length one) containing 
    the JSON file passed through by the client.
Provides an R interface to the Registry of Standard Biological Parts API maintained by the iGEM Foundation: <https://igem.org/Main_Page>. Facilitates retrieval of
    the part number, authorship, date of entry, url, short description, type, and sequence following the guidelines set forth at <http://parts.igem.org/Registry_API/Guidelines>. 
    All Registry content falls under Creative Commons Attribution-ShareAlike: <https://creativecommons.org/licenses/by-sa/4.0/>.
Performs robust and sparse correlation matrix estimation. Robustness is achieved based on a simple robust pairwise correlation estimator, while sparsity is obtained based on thresholding. The optimal thresholding is tuned via cross-validation. See Serra, Coretto, Fratello and Tagliaferri (2018) <doi:10.1093/bioinformatics/btx642>.
A statistical tool for multivariate modeling and clustering using stepwise cluster analysis. The modeling output of rSCA is constructed as a cluster tree to represent the complicated relationships between multiple dependent and independent variables. A free tool (named rSCA Tree Generator) for visualizing the cluster tree from rSCA is also released and it can be downloaded at <https://rscatree.weebly.com/>.
'Scala' <http://www.scala-lang.org/> is embedded in 'R' and callbacks from 'Scala' to 'R' are available. Support is provided to write 'R' packages that access 'Scala'. After installation, please run 'rscala::scalaConfig()'.  The vignette provides an update of the original paper <doi:10.18637/jss.v092.i04>.
As an advanced approach to computerized adaptive testing (CAT), 
  shadow testing (van der Linden(2005) <doi:10.1007/0-387-29054-0>) dynamically 
  assembles entire shadow tests as a part of 
  selecting items throughout the testing process.
  Selecting items from shadow tests guarantees the compliance of all content 
  constraints defined by the blueprint. 'RSCAT' is an R package for the 
  shadow-test approach to CAT. The objective of 
  'RSCAT' is twofold: 1) Enhancing the effectiveness of shadow-test CAT simulation;
  2) Contributing to the academic and scientific community for CAT research.
  RSCAT is currently designed for dichotomous items based on the three-parameter logistic (3PL) model.
Evaluates R source codes by variable and/or functions names. Similar source codes should deliver similarity coefficients near one. Since neither the frequency nor the order of the used names is considered, a manual inspection of the R source code is required to check for similarity. Possible use cases include detection of code clones for improving
    software quality and of plagiarism amongst students' assignments.
Scelestial infers a lineage tree from single-cell DNA mutation matrix. 
	It generates a tree with approximately maximum parsimony through 
	a Steiner tree approximation algorithm.
The SciMark 2.0 benchmark was originally developed in Java as a benchmark for numerical and scientific computational performance. It measures the performance of several computational kernels which are frequently occurring in scientific applications. This package is a simple wrapper around the ANSI C implementation of the benchmark.
Client for Rserve, allowing to connect to Rserve instances and issue commands.
Provides a tool for mass deployment of shiny apps to 'RStudio Connect' or 'Shiny Server'. Multiple user accounts and servers can be configured for deployment.
Programmatic deployment interface for 'RPubs', 'shinyapps.io', and
    'RStudio Connect'. Supported content types include R Markdown documents,
    Shiny applications, Plumber APIs, plots, and static web content.
Provides a generic implementation of the 'RStudio' connection contract to 
    make it easier for database connections, and other type of connections, opened 
    via R packages integrate with the connections pane inside the 'RStudio' interactive
    development environment (IDE).
Uses Elsevier 'Scopus' API
    <https://dev.elsevier.com/sc_apis.html> to download 
    information about authors and their citations.
A method to download Department of Education College
     Scorecard data using the public API
     <https://collegescorecard.ed.gov/data/documentation/>. It is based on
     the 'dplyr' model of piped commands to select and filter data in a
     single chained function call.  An API key from the U.S. Department of
     Education is required.
Symbolic Data Analysis (SDA) was proposed by professor Edwin Diday in 1987, the main purpose of SDA is to substitute the set of rows (cases) in the data table for  a concept (second order statistical unit). This package implements, to the symbolic case, certain techniques of automatic classification, as well as some linear models.
Ray Shooting Depth functions are provided for bivariate analysis. This mainly includes functions for computing the bivariate depth as well as RS median. Drawing functions for depth bags are also provided.
This is a sudoku game package with a shiny application for playing .
Set of classes and methods to read data and metadata documents
  exchanged through the Statistical Data and Metadata Exchange (SDMX) framework,
  currently focusing on the SDMX XML standard format (SDMX-ML).
Response surface designs with neighbour effects are suitable for experimental situations 
            where it is expected that the treatment combination administered to one experimental unit 
            may affect the response on neighboring units as well as the response on the unit to which it is applied. 
            Integrating these effects in the response surface model improves the experiment's precision 
            (Jaggi, S., Sarika and Sharma, V.K. (2010)<http://krishi.icar.gov.in/jspui/handle/123456789/4364>; 
            Verma A., Jaggi S., Varghese, E.,Varghese, C.,Bhowmik, A., Datta, A. and Hemavathi M. (2021)<DOI: 10.1080/03610918.2021.1890123>). 
            This package includes sym(), asym1(), asym2() functions that generates response surface designs which are rotatable under a 
            polynomial model of a given order without interaction term incorporating neighbour effects.
A Bayesian-weighted estimator and two unweighted estimators are
 developed to estimate the number of newly found rare species in additional
 ecological samples. Among these methods, the Bayesian-weighted estimator
 and an unweighted (Chao-derived) estimator are of high accuracy and
 recommended for practical applications.
 Technical details of the proposed estimators have been well described
 in the following paper: Shen TJ, Chen YH (2018) A Bayesian
 weighted approach to predicting the number of newly discovered
 rare species. Conservation Biology, In press.
SEA performs simultaneous feature-set testing for (gen)omics data. It tests the unified null hypothesis controls the family-wise error rate for all possible pathways. The unified null hypothesis is defined as: "The proportion of true features in the set is less than or equal to the threshold c", where c is selected by the user. Family-wise error rate control is provided through use of closed testing with Simes test. For more information on closed testing with Simes see Goeman et al. (2019) <doi:10.1093/biomet/asz041> and for more information about the properties and performance of SEA procedure see Ebrahimpoor et al. (2019) <doi:10.1093/bib/bbz074>.
An implementation of the analysis about seed components from Borenstein et.al. 2008.
Estimate the percentage of seeds in a seedlot that contain stacks
    of genetically modified traits.  Estimates are calculated using a
    multinomial group testing model with maximum likelihood estimation of the
    parameters.
Multiple interactive codes to view and analyze seismic data, via spectrum analysis, wavelet transforms, particle motion, hodograms.  Includes general time-series tools, plotting, filtering, interactive display.
Provides a set of R bindings for the 'Selenium 2.0 WebDriver'
    (see <https://selenium.dev/documentation/en/>
    for more information) using the 'JsonWireProtocol' (see
    <https://github.com/SeleniumHQ/selenium/wiki/JsonWireProtocol> for more
    information). 'Selenium 2.0 WebDriver' allows driving a web browser
    natively as a user would either locally or on a remote machine using
    the Selenium server it marks a leap forward in terms of web browser
    automation. Selenium automates web browsers (commonly referred to as
    browsers). Using RSelenium you can automate browsers locally or
    remotely.
A robust procedure is implemented to estimate means and covariance matrix of multiple variables with missing data using Huber weight and then to estimate a structural equation model.
Analyses sentiment of a sentence in English and assigns score to it. It can classify sentences to the following categories of sentiments:- Positive, Negative, very Positive, very negative, 
              Neutral. For a vector of sentences, it counts the number of sentences in each
              category of sentiment.In calculating the score, negation and various degrees
              of adjectives are taken into consideration. It deals only with English sentences.
Rserve acts as a socket server (TCP/IP or local sockets) 
	     which allows binary requests to be sent to R. Every
	     connection has a separate workspace and working
	     directory. Client-side implementations are available
	     for popular languages such as C/C++ and Java, allowing
	     any application to use facilities of R without the need of
	     linking to R code. Rserve supports remote connection,
	     user authentication and file transfer. A simple R client
	     is included in this package as well.
An R implementation for the Strain Elevation and
    Tension embedding algorithm from Bourne (2020)
    <doi:10.1007/s41109-020-00329-4>. The package embeds graphs and
    networks using the Strain Elevation and Tension embedding (SETSe)
    algorithm. SETSe represents the network as a physical system, where
    edges are elastic, and nodes exert a force either up or down based on
    node features. SETSe positions the nodes vertically such that the
    tension in the edges of a node is equal and opposite to the force it
    exerts for all nodes in the network. The resultant structure can then
    be analysed by looking at the node elevation and the edge strain and
    tension. This algorithm works on weighted and unweighted networks as
    well as networks with or without explicit node features.  Edge
    elasticity can be created from existing edge weights or kept as a
    constant.
A report of statistical findings (RSF) project
    template is generated using a 'bookdown' format. 'YAML' fields can be
    further customized. Additional helper functions provide extra features
    to the RSF.
Slow Feature Analysis (SFA), ported to R based on
    'matlab' implementations of SFA: 'SFA toolkit' 1.0 by Pietro Berkes and 'SFA toolkit'
    2.8 by Wolfgang Konen.
This is a collection of functions designed for simulating, estimating and forecasting seasonal functional autoregressive time series of order one. These methods are addressed in the manuscript: <https://www.monash.edu/business/ebs/research/publications/ebs/wp16-2019.pdf>.
Functions for estimating models using a Hierarchical Bayesian (HB) framework. The flexibility comes in allowing the user to specify the likelihood function directly instead of assuming predetermined model structures. Types of models that can be estimated with this code include the family of discrete choice models (Multinomial Logit, Mixed Logit, Nested Logit, Error Components Logit and Latent Class) as well ordered response models like ordered probit and ordered logit. In addition, the package allows for flexibility in specifying parameters as either fixed (non-varying across individuals) or random with continuous distributions. Parameter distributions supported include normal, positive/negative log-normal, positive/negative censored normal, and the Johnson SB distribution. Kenneth Train's Matlab and Gauss code for doing Hierarchical Bayesian estimation has served as the basis for a few of the functions included in this package. These Matlab/Gauss functions have been rewritten to be optimized within R. Considerable code has been added to increase the flexibility and usability of the code base. Train's original Gauss and Matlab code can be found here: <http://elsa.berkeley.edu/Software/abstracts/train1006mxlhb.html> See Train's chapter on HB in Discrete Choice with Simulation here: <http://elsa.berkeley.edu/books/choice2.html>; and his paper on using HB with non-normal distributions here: <http://eml.berkeley.edu//~train/trainsonnier.pdf>. The authors would also like to thank the invaluable contributions of Stephane Hess and the Choice Modelling Centre: <https://cmc.leeds.ac.uk/>.
Contains a variety of functions, based around
    regime shift analysis of paleoecological data.
    Citations:
    Rodionov() from Rodionov (2004) <doi:10.1029/2004GL019448>
    Lanzante() from Lanzante (1996) <doi:10.1002/(SICI)1097-0088(199611)16:11%3C1197::AID-JOC89%3E3.0.CO;2-L>  
    Hellinger_trans from Numerical Ecology, Legendre & Legendre (ISBN 9780444538680)
    rolling_autoc from Liu, Gao & Wang (2018) <doi:10.1016/j.scitotenv.2018.06.276>
    Sample data sets lake_data & lake_RSI processed from Bush, Silman & Urrego (2004) <doi:10.1126/science.1090795>.
The main purpose of this package is to perform simulation-based
   estimation of stochastic actor-oriented models for longitudinal network
   data collected as panel data. Dependent variables can be single or
   multivariate networks, which can be directed, non-directed, or two-mode;
   and associated actor variables.
   There are also functions for testing parameters and checking goodness of fit.
   An overview of these models is given in Tom A.B. Snijders (2017), Stochastic
   Actor-Oriented Models for Network Dynamics, Annual Review of Statistics and
   Its Application, 4, 343-363 <doi: 10.1146/annurev-statistics-060116-054035>.
   An extensive manual, scripts, and much further information is at the Siena
   website <http://www.stats.ox.ac.uk/~snijders/siena/>.
Provides a way to transform an image into a mosaic composed from a set of smaller images (tiles). It also contains a simple function for creating the tiles from a folder of images directly through R, without the need of any external code. At this moment only the JPEG format is supported, either as input (image and tiles) or output (mosaic transformed image).
Summarise results from simulation studies and compute Monte Carlo
  standard errors of commonly used summary statistics. This package is modelled 
  on the 'simsum' user-written command in 'Stata' (White I.R., 2010 
  <https://www.stata-journal.com/article.html?article=st0200>), further extending
  it with additional functionality.
Easy-to-use functions for downloading air quality data from the 
    Mexican National Air Quality Information System (SINAICA).  Allows you to 
    query pollution and meteorological parameters from more than a hundred
    monitoring stations located throughout Mexico. See <https://sinaica.inecc.gob.mx> 
    for more information.
Makes  operations with raster images, such as map
    viewing in time series, export values in time series for specific, total or limited
    within a polygon locations. Makes data processing of remote sensing of climatic variables distributed in the space (maps 2D) and the time (time series).
Functions for interacting with the Adobe Analytics API V1.4
    (<https://api.omniture.com/admin/1.4/rest/>).
Provides functions and examples for testing hypothesis about the population mean and variance on samples drawn by r-size biased sampling schemes.
This RSKC package contains a function RSKC which runs the robust sparse K-means clustering algorithm.
Create custom keyboard shortcuts to examine code selected in the 'Rstudio' editor.
             F3 can for example yield 'str(selection)' and F7 open the source
             code of CRAN and base package functions on 'github'.
Provides users functions for sleep data management and analysis such as European Data Format (EDF) to Morpheo Data Format (MDF) conversion: P.Bouchequet, D.Jin, G.Solelhac, M.Chennaoui, D.Leger (2018) <doi:10.1016/j.msom.2018.01.130> "Morpheo Data Format (MDF), un nouveau format de donnees simple, robuste et performant pour stocker et analyser les enregistrements de sommeil". Provides hypnogram statistics computing and visualisation functions from the American Academy of Sleep Medicine (AASM) manual "The AASM Manual for the Scoring of Sleep and Associated Events" <https://aasm.org/clinical-resources/scoring-manual/>.
Implements the "Stemming Algorithm for the Portuguese Language" <DOI:10.1109/SPIRE.2001.10024>.
Functions that simplify submitting R scripts to a 'Slurm' 
    workload manager, in part by automating the division of embarrassingly
    parallel calculations across cluster nodes.
Provides functions to generate response-surface designs, 
    fit first- and second-order response-surface models, 
    make surface plots, obtain the path of steepest ascent, 
    and do canonical analysis. A good reference on these methods 
    is Chapter 10 of Wu, C-F J and Hamada, M (2009) 
    "Experiments: Planning, Analysis, and Parameter Design Optimization"
    ISBN 978-0-471-69946-0. An early version of the package is
    documented in Journal of Statistical Software <doi:10.18637/jss.v032.i07>.
We provide functions to perform an empirical small telescopes
    analysis. This package contains 2 functions, SmallTelescopes() and 
    EstimatePower(). Users only need to call SmallTelescopes() to conduct 
    the analysis. For more information on small telescopes analysis see
    Uri Simonsohn (2015) <doi:10.1177/0956797614567341>.
Aims at loading Facebook and Instagram advertising data from
    'Smartly.io' into R. 'Smartly.io' is an online advertising service that enables
    advertisers to display commercial ads on social media networks (see <http://www.smartly.io/> for more information).
    The package offers an interface to query the 'Smartly.io' API and loads data directly into R for further data processing and data analysis.
A small package for calculating the matrices in Shiller (1991, <doi:10.1016/S1051-1377(05)80028-2>) that serve as the foundation for many repeat-sales price indexes.
Provides methods for model building and model evaluation of mixed effects models using 
    'Monolix' <https://monolix.lixoft.com>. 'Monolix' is a software tool for nonlinear mixed effects 
    modeling that must have been installed in order to use 'Rsmlx'. 
    Among other tasks, 'Rsmlx' provides a powerful tool for automatic PK model building, performs 
    statistical tests for model assessment, bootstrap simulation and likelihood profiling for 
    computing confidence intervals. 'Rsmlx' also proposes several automatic covariate search methods for 
    mixed effects models.
The Stuttgart Neural Network Simulator (SNNS) is a library
    containing many standard implementations of neural networks. This
    package wraps the SNNS functionality to make it available from
    within R. Using the 'RSNNS' low-level interface, all of the
    algorithmic functionality and flexibility of SNNS can be accessed.
    Furthermore, the package contains a convenient high-level
    interface, so that the most common neural network topologies and
    learning algorithms integrate seamlessly into R.
A programmatic interface to various 'SNP' 'datasets'
    on the web: 'OpenSNP' (<https://opensnp.org>), and 'NBCIs' 'dbSNP' database
    (<https://www.ncbi.nlm.nih.gov/projects/SNP/>). Functions
    are included for searching for 'NCBI'. For 'OpenSNP', functions are included 
    for getting 'SNPs', and data for 'genotypes', 'phenotypes', annotations, 
    and bulk downloads of data by user.
Provides easier interaction with
    'Socrata' open data portals <https://dev.socrata.com>.
    Users can provide a 'Socrata' data set resource URL,
    or a 'Socrata' Open Data API (SoDA) web query,
    or a 'Socrata' "human-friendly" URL,
    returns an R data frame. Converts dates to 'POSIX'
    format and manages throttling by 'Socrata'.
    Users can upload data to 'Socrata' portals directly
    from R.
Downloads Southern Oscillation Index, Oceanic Nino
    Index, North Pacific Gyre Oscillation data, North Atlantic Oscillation
    and Arctic Oscillation. Data sources are described in the help files for each function.
General Non-linear Optimization Using Augmented Lagrange Multiplier Method.
Minimally adjust the values of numerical records in a data.frame, such
    that each record satisfies a predefined set of equality and/or inequality
    constraints. The constraints can be defined using the 'validate' package. 
    The core algorithms have recently been moved to the 'lintools' package,
    refer to 'lintools' for a more basic interface and access to a version
    of the algorithm that works with sparse matrices.
Data cleaning including 1) generating datasets for time-series and case-crossover analyses based on raw hospital records, 2)linking individuals to a areal map, 3)picking out cases living within a certain distance from a site, etc. For more information, please refer to Zhang W,etc. (2018) <doi: 10.1016/j.envpol.2018.08.030>.
An extension package for 'sparklyr' that provides an R interface to
    H2O Sparkling Water machine learning library (see <https://github.com/h2oai/sparkling-water> for more information).
Implements many algorithms for statistical learning on 
  sparse matrices - matrix factorizations, matrix completion, 
  elastic net regressions, factorization machines. 
  Also 'rsparse' enhances 'Matrix' package by providing methods for 
  multithreaded <sparse, dense> matrix products and native slicing of 
  the sparse matrices in Compressed Sparse Row (CSR) format.
  List of the algorithms for regression problems:
  1) Elastic Net regression via Follow The Proximally-Regularized Leader (FTRL) 
  Stochastic Gradient Descent (SGD), as per McMahan et al(, <doi:10.1145/2487575.2488200>)
  2) Factorization Machines via SGD, as per Rendle (2010, <doi:10.1109/ICDM.2010.127>)
  List of algorithms for matrix factorization and matrix completion:
  1) Weighted Regularized Matrix Factorization (WRMF) via Alternating Least 
  Squares (ALS) - paper by Hu, Koren, Volinsky (2008, <doi:10.1109/ICDM.2008.22>)
  2) Maximum-Margin Matrix Factorization via ALS, paper by Rennie, Srebro 
  (2005, <doi:10.1145/1102351.1102441>)
  3) Fast Truncated Singular Value Decomposition (SVD), Soft-Thresholded SVD, 
  Soft-Impute matrix completion via ALS - paper by Hastie, Mazumder 
  et al. (2014, <arXiv:1410.2596>)
  4) Linear-Flow matrix factorization, from 'Practical linear models for 
  large-scale one-class collaborative filtering' by Sedhain, Bui, Kawale et al 
  (2016, ISBN:978-1-57735-770-4)
  5) GlobalVectors (GloVe) matrix factorization via SGD, paper by Pennington, 
  Socher, Manning (2014, <https://aclanthology.org/D14-1162/>)
  Package is reasonably fast and memory efficient - it allows to work with large
  datasets - millions of rows and millions of columns. This is particularly useful 
  for practitioners working on recommender systems.
Implementation of Nelson rules for control charts in 'R'. The 'Rspc' implements some Statistical Process Control methods, namely Levey-Jennings type of I (individuals) chart, Shewhart C (count) chart and Nelson rules (as described in Montgomery, D. C. (2013) Introduction to statistical quality control. Hoboken, NJ: Wiley.). Typical workflow is taking the time series, specify the control limits, and list of Nelson rules you want to evaluate. There are several options how to modify the rules (one sided limits, numerical parameters of rules, etc.). Package is also capable of calculating the control limits from the data (so far only for i-chart and c-chart are implemented).
Functions that compute rational approximations of fractional elliptic stochastic partial differential equations. The package also contains functions for common statistical usage of these approximations. The main reference for the methods is Bolin and Kirchner (2020) <doi:10.1080/10618600.2019.1665537>, which can be generated by the citation function in R.
R interface to the 'Spectra' library
    <https://spectralib.org/> for large-scale eigenvalue and SVD
    problems. It is typically used to compute a few
    eigenvalues/vectors of an n by n matrix, e.g., the k largest eigenvalues,
    which is usually more efficient than eigen() if k << n. This package
    provides the 'eigs()' function that does the similar job as in 'Matlab',
    'Octave', 'Python SciPy' and 'Julia'. It also provides the 'svds()' function
    to calculate the largest k singular values and corresponding
    singular vectors of a real matrix. The matrix to be computed on can be
    dense, sparse, or in the form of an operator defined by the user.
Conversion between attitude representations: DCM, Euler angles, Quaternions, and Euler vectors.
    Plus conversion between 2 Euler angle set types (xyx, yzy, zxz, xzx, yxy, zyz, xyz, yzx, zxy, xzy, yxz, zyx).
    Fully vectorized code, with warnings/errors for Euler angles (singularity, out of range, invalid angle order), 
    DCM (orthogonality, not proper, exceeded tolerance to unity determinant) and Euler vectors(not unity).
    Also quaternion and other useful functions.
    Based on SpinCalc by John Fuller and SpinConv by Paolo de Leva.
Implementation of various spirometry equations 
    in R, currently the GLI-2012 (Global Lung Initiative; 
    Quanjer et al. 2012 <doi:10.1183/09031936.00080312>) and the 
    NHANES3 (National Health and Nutrition Examination Survey; 
    Hankinson et al. 1999 <doi:10.1164/ajrccm.159.1.9712108>) 
    equations. 
    Contains user-friendly functions to calculate predicted 
    and LLN (Lower Limit of Normal) values for different 
    spirometric parameters such as FEV1 (Forced Expiratory 
    Volume in 1 second), FVC (Forced Vital Capacity), etc, 
    and to convert absolute spirometry measurements 
    to percent (%) predicted and z-scores.
Provides an interface to the Spotify API <https://developer.spotify.com/documentation/web-api/>.
An implementation of functionalities to transform directed graphs that are bound to a set of
  known forbidden paths. There are several transformations, following the rules provided by Villeneuve 
  and Desaulniers (2005) <doi: 10.1016/j.ejor.2004.01.032>, and Hsu et al. (2009) <doi: 10.1007/978-3-642-03095-6_60>. 
  The resulting graph is generated in a data-frame format. See rsppfp website for more information, 
  documentation an examples.
The SPRITE algorithm creates possible distributions of discrete responses
    based on reported sample parameters, such as mean, standard deviation and range 
    (Heathers et al., 2018, <doi:10.7287/peerj.preprints.26968v1>). This package implements it, 
    drawing heavily on the code for Nick Brown's 'rSPRITE' Shiny app <http://shiny.ieis.tue.nl/sprite/>. 
    In addition, it supports the modeling of distributions based on multi-item (Likert-type) 
    scales and the use of restrictions on the frequency of particular responses.
Calculate generalized R-squared, partial R-squared, and partial correlation coefficients for generalized linear (mixed) models (including quasi models with well defined variance functions).
Allows the user to generate and execute select, insert, update and delete 'SQL' queries the underlying database without having to explicitly write 'SQL' code.
Embeds the SQLite database engine in R and
    provides an interface compliant with the DBI package. The source for
    the SQLite engine and for various extensions in a recent version is
    included. System libraries will never be consulted because
    this package relies on static linking for the plugins it includes;
    this also ensures a consistent experience across all installations.
A comprehensive tool written in R Shiny to explore, manage and update SQLite Databases.
Parser for 'SQL' statements. Currently, it supports parsing of only 'SELECT' statements.
Conducts statistical downscaling of daily CMIP5 (Coupled Model Intercomparison Project 5) climate change scenario data at a station level using empirical quantile mapping method by Jaepil Cho et al. (2016) <doi:10.1002/ird.2035>.
An implementation of calculating the R-squared measure as a total mediation effect size measure and its confidence interval for moderate- or high-dimensional mediator model. It gives an option to filter out non-mediators using variable selection method. This R package is directly related to the paper "Estimation of mediation effect for high-dimensional omics mediators with application to the Framingham Heart Study" with <doi:10.1101/774877>.
Methods and tools for Singular Spectrum Analysis including decomposition,
             forecasting and gap-filling for univariate and multivariate time series.
             General description of the methods with many examples can be found in the book
             Golyandina (2018, <doi:10.1007/978-3-662-57380-8>).
             See 'citation("Rssa")' for details.
Ranked set sampling (RSS) is introduced as an advanced method for data collection which is substantial for the statistical and methodological analysis in scientific studies by McIntyre (1952) (reprinted in 2005) <doi:10.1198/000313005X54180>. This package introduces the first package  that implements the RSS and its modified versions for sampling. With 'RSSampling', the researchers can sample with basic RSS and the modified versions, namely, Median RSS, Extreme RSS, Percentile RSS, Balanced groups RSS, Double RSS, L-RSS, Truncation-based RSS, Robust extreme RSS. The 'RSSampling' also allows imperfect ranking using an auxiliary variable (concomitant) which is widely used in the real life applications. Applicants can also use this package for parametric and nonparametric inference such as mean, median and variance estimation, regression analysis and some distribution-free tests where the the samples are obtained via basic RSS.
Provide useful tools which supplement the use of Simulx software and R connectors (Monolix Suite). 'Simulx' is an easy, efficient and flexible application for clinical trial simulations. You need 'Simulx' software to be installed in order to use 'RsSimulx' package. Among others tasks, 'RsSimulx' provides the same functions as package 'mlxR' does with a compatibility with 'Simulx' software.
A collection of implementations of semi-supervised classifiers
    and methods to evaluate their performance. The package includes implementations
    of, among others, Implicitly Constrained Learning, Moment Constrained Learning,
    the Transductive SVM, Manifold regularization, Maximum Contrastive Pessimistic
    Likelihood estimation, S4VM and WellSVM.
Reservoir Systems Standard Operation Policy. A system for simulation of supply reservoirs. It proposes functionalities for plotting and evaluation of supply reservoirs systems.
Provides functions to access, search and download spacetime earth
    observation data via SpatioTemporal Asset Catalog (STAC). This package 
    supports the version 1.0.0 of the STAC specification
    (<https://github.com/radiantearth/stac-spec>). 
    For further details see Simoes et al. (2021) <doi:10.1109/IGARSS47720.2021.9553518>.
An extremely simple stack data type, implemented with 'R6'
    classes. The size of the stack increases as needed, and the amortized
    time complexity is O(1). The stack may contain arbitrary objects.
Provides fast, persistent (side-effect-free) stack, queue and
    deque (double-ended-queue) data structures. While deques include a superset
    of functionality provided by queues, in these implementations queues are
    more efficient in some specialized situations. See the documentation for
    rstack, rdeque, and rpqueue for details.
User-facing R functions are provided to parse, compile, test,
    estimate, and analyze Stan models by accessing the header-only Stan library
    provided by the 'StanHeaders' package. The Stan project develops a probabilistic
    programming language that implements full Bayesian statistical inference
    via Markov Chain Monte Carlo, rough Bayesian inference via 'variational'
    approximation, and (optionally penalized) maximum likelihood estimation via
    optimization. In all three cases, automatic differentiation is used to quickly
    and accurately evaluate gradients without burdening the user with the need to
    derive the partial derivatives.
Estimates previously compiled regression models using the 'rstan'
    package, which provides the R interface to the Stan C++ library for Bayesian
    estimation. Users specify models via the customary R syntax with a formula and
    data.frame plus some additional arguments for priors.
Perform sigmoidal Emax model fit using 'Stan' in a formula notation, without writing 'Stan' model code.
Provides various tools for developers of R packages interfacing
    with 'Stan' <https://mc-stan.org>, including functions to set up the required 
    package structure, S3 generics and default methods to unify function naming 
    across 'Stan'-based R packages, and vignettes with recommendations for 
    developers.
A simple R -> Stata interface allowing the user to
    execute Stata commands (both inline and from a .do file)
    from R.
Provides a simple and intuitive pipe-friendly framework, coherent with the 'tidyverse' design philosophy, 
    for performing basic statistical tests, including t-test, Wilcoxon test, ANOVA, Kruskal-Wallis and correlation analyses. 
    The output of each test is automatically transformed into a tidy data frame to facilitate visualization. 
    Additional functions are available for reshaping, reordering, manipulating and visualizing correlation matrix.  
    Functions are also included to facilitate the analysis of factorial experiments, including purely 'within-Ss' designs 
    (repeated measures), purely 'between-Ss' designs, and mixed 'within-and-between-Ss' designs. 
    It's also possible to compute several effect size metrics, including "eta squared" for ANOVA, "Cohen's d" for t-test and 
    'Cramer V' for the association between categorical variables. 
    The package contains helper functions for identifying univariate and multivariate outliers, assessing normality and homogeneity of variances.
R interface for china national data <http://data.stats.gov.cn/>, 
    some convenient functions for accessing the national data are provided.
'STG' is a method for feature selection in neural network. The procedure is based on probabilistic relaxation of the l0 norm of features, or the count of the number of selected features. The framework simultaneously learns either a nonlinear regression or classification function while selecting a small subset of features. Read more: Yamada et al. (2020) <https://proceedings.mlr.press/v119/yamada20a.html>.
Simulation of random orthonormal matrices from linear and quadratic exponential family distributions on the Stiefel manifold. The most general type of distribution covered is the matrix-variate  Bingham-von Mises-Fisher distribution. Most of the simulation methods are presented in Hoff(2009) "Simulation of the Matrix Bingham-von Mises-Fisher Distribution, With Applications to Multivariate and Relational Data" <doi:10.1198/jcgs.2009.07177>. The package also includes functions for optimization on the Stiefel manifold based on algorithms described in Wen and Yin (2013) "A feasible method for optimization with orthogonality constraints" <doi:10.1007/s10107-012-0584-1>.
A wrapper for the 'Spatiotemporal Observation Annotation Tool' ('STOAT', <https://www.mol.org/stoat>) which allows users to run annotation jobs and retrieve results in the R environment.
Toolbox for remote sensing image processing and analysis such as
    calculating spectral indices, principal component transformation, unsupervised
    and supervised classification or fractional cover analyses.
While streaming processing provides opportunities to deal with extremely large and ever growing data sets in (near) real time, the development of streaming algorithms for complex models is often cumbersome: the software packages that facilitate streaming processing in production environments do not provide statisticians with the simulation, estimation, and plotting tools they are used to. Developers of streaming algorithms would thus benefit from the flexibility of [R] to create, plot and compute data while developing streaming algorithms. Package RStorm implements a streaming architecture modeled on Storm for easy development and testing of streaming algorithms in [R]. RStorm is not intended as a production package, but rather a development tool for streaming algorithms.
Set of tools to read and manipulate various data formats for fisheries. Mainly
  catered towards scientific trawl survey sampling ('biotic') data, acoustic
  trawl data, and commercial fishing catch ('landings') data. Among the
  supported data formats are the data products from the Norwegian Institute
  Marine Research ('IMR') and the International Council for the Exploration of
  the Sea (ICES).
R implementation of generalized survival models (GSMs), smooth accelerated failure time (AFT) models and Markov multi-state models. For the GSMs, g(S(t|x))=eta(t,x) for a link function g, survival S at time t with covariates x and a linear predictor eta(t,x). The main assumption is that the time effect(s) are smooth <doi:10.1177/0962280216664760>. For fully parametric models with natural splines, this re-implements Stata's 'stpm2' function, which are flexible parametric survival models developed by Royston and colleagues. We have extended the parametric models to include any smooth parametric smoothers for time. We have also extended the model to include any smooth penalized smoothers from the 'mgcv' package, using penalized likelihood. These models include left truncation, right censoring, interval censoring, gamma frailties and normal random effects <doi:10.1002/sim.7451>, and copulas. For the smooth AFTs, S(t|x) = S_0(t*eta(t,x)), where the baseline survival function S_0(t)=exp(-exp(eta_0(t))) is modelled for natural splines for eta_0, and the time-dependent cumulative acceleration factor eta(t,x)=\int_0^t exp(eta_1(u,x)) du for log acceleration factor eta_1(u,x). The Markov multi-state models allow for a range of models with smooth transitions to predict transition probabilities, length of stay, utilities and costs, with differences, ratios and standardisation.
Functions to access data from the 'Strava v3 API' <https://developers.strava.com/>.
Unified object oriented interface for multiple independent streams of random numbers from different sources.
A convenience interface for communicating with the Stripe payment processor to accept payments online. See <https://stripe.com> for more information.
As of 'RStudio' v1.3, the preferences in the Global Options
    dialog (and a number of other preferences that aren’t) are now saved
    in simple, plain-text JSON files. This package provides an interface
    for working with these 'RStudio' JSON preference files to easily make
    modifications without using the point-and-click option menus. This is
    particularly helpful when working on teams to ensure a unified
    experience across machines and utilizing settings for best practices.
Access the RStudio API (if available) and provide informative error
    messages when it's not.
A collection of efficient and effective tools and
	algorithms for subgroup discovery and analytics. The package
	integrates an R interface to the org.vikamine.kernel library
	of the VIKAMINE system <http://www.vikamine.org> implementing
	subgroup discovery, pattern mining and analytics in Java.
Produces tables with the level of replication (number of replicates) and the
    experimental uncoded values of the quantitative factors to be used for rotatable Central
    Composite Design (CCD) experimentation and a 2-D contour plot of the corresponding
    variance of the predicted response according to
    Mead et al. (2012) <doi:10.1017/CBO9781139020879> design_ccd(), and analyzes
    CCD data with response surface methodology ccd_analysis(). A rotatable CCD
    provides values of the variance of the predicted response that are concentrically
    distributed around the average treatment combination used in the experimentation, which
    with uniform precision (implied by the use of several replicates at the average
    treatment combination) improves greatly the search and finding of an optimum response.
    These properties of a rotatable CCD represent undeniable advantages over the classical
    factorial design, as discussed by Panneton et al. (1999) <doi:10.13031/2013.13267> and
    Mead et al. (2012) <doi:10.1017/CBO9781139020879.018> among others.
The software suite, 'Freesurfer', is a open-source software suite involving the segmentation of brain MRIs (see <http://freesurfer.net/> for more information). This package provides functionality to import the data generated by 'Freesurfer'; functions to easily manipulate the data; and provides brain specific normalisation commonly used when studying structural brain MRIs. This package has been designed using an installation of and data generated from 'Freesurfer' version 5.3.
Provides functions to estimate the proportion of treatment effect on the primary outcome that is explained by the treatment effect on the surrogate marker.
A range of functions for the design and
    analysis of disease surveillance activities. These functions were
    originally developed for animal health surveillance activities but can be
    equally applied to aquatic animal, wildlife, plant and human health
    surveillance activities. Utilities are included for sample size calculation
    and analysis of representative surveys for disease freedom, risk-based
    studies for disease freedom and for prevalence estimation.
    This package is based on Cameron A., Conraths F., Frohlich A., Schauer B.,
    Schulz K., Sergeant E., Sonnenburg J., Staubach C. (2015). R package of 
    functions for risk-based surveillance. Deliverable 6.24, WP 6 - Decision 
    making tools for implementing risk-based surveillance, Grant Number 
    no. 310806, RISKSUR (<https://www.fp7-risksur.eu/sites/default/files/documents/Deliverables/RISKSUR_%28310806%29_D6.24.pdf>). 
    Many of the 'RSurveillance' functions are incorporated into the 'epitools'
    website: Sergeant, ESG, 2019. Epitools epidemiological calculators. 
    Ausvet Pty Ltd. Available at: <http://epitools.ausvet.com.au>.
A geographic information system (GIS) graphical user interface (GUI) that
  provides data viewing, management, and analysis tools.
Low-rank matrix decompositions are fundamental tools and widely used for data
  analysis, dimension reduction, and data compression. Classically, highly accurate 
  deterministic matrix algorithms are used for this task. However, the emergence of 
  large-scale data has severely challenged our computational ability to analyze big data. 
  The concept of randomness has been demonstrated as an effective strategy to quickly produce
  approximate answers to familiar problems such as the singular value decomposition (SVD). 
  The rsvd package provides several randomized matrix algorithms such as the randomized 
  singular value decomposition (rsvd), randomized principal component analysis (rpca), 
  randomized robust principal component analysis (rrpca), randomized interpolative 
  decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot 
  functions are provided.
Computing singular value decomposition with robustness is a challenging task. 
    This package provides an implementation of computing robust SVD using density power 
    divergence (<arXiv:2109.10680>). It combines the idea of robustness and efficiency in estimation
    based on a tuning parameter. It also provides utility functions to simulate various
    scenarios to compare performances of different algorithms.
Renders vector-based svg images into high-quality custom-size
    bitmap arrays using 'librsvg2'. The resulting bitmap can be written to
    e.g. png, jpeg or webp format. In addition, the package can convert
    images directly to various formats such as pdf or postscript.
Allows to link data to Swiss administrative divisions (municipalities,
    districts, cantons) and to plot and save customised maps thereof.  Furthermore, the
    package allows to generate tailored templates for data collection.  The used geodata
    is publicly available on the Swiss Federal Statistical Office website
    <https://www.bfs.admin.ch/bfs/de/home/dienstleistungen/geostat/geodaten-bundesstatistik.html>.
An R interface to the SYMPHONY solver for mixed-integer linear programs.
'SyncroSim' is a generalized framework for managing scenario-based 
    datasets (<https://syncrosim.com/>). 'rsyncrosim' provides an interface to 
    'SyncroSim'. Simulation models can be added to 'SyncroSim' in order to 
    transform these datasets, taking advantage of general features such as 
    defining scenarios of model inputs, running Monte Carlo simulations, and 
    summarizing model outputs. 'rsyncrosim' requires 'SyncroSim' 2.3.5 or higher 
    (API documentation: <https://docs.syncrosim.com/>).
Various functions for querying and reshaping dependency trees, 
    as for instance created with the 'spacyr' or 'udpipe' packages.
    This enables the automatic extraction of useful semantic relations from texts,
    such as quotes (who said what) and clauses (who did what). Method proposed in 
    Van Atteveldt et al. (2017) <doi:10.1017/pan.2016.12>.
Functions to write messages to the 'syslog' system logger API,
  available on all 'POSIX'-compatible operating systems. Features include
  tagging messages with a priority level and application type, as well as
  masking (hiding) messages below a given priority level.
Provides a programmatic interface to the 'Request Tracker' (RT)
  HTTP API <https://rt-wiki.bestpractical.com/wiki/REST>. 'RT' is a popular
  ticket tracking system.
Performs one-sample t-test based on robustified statistics using median/MAD (TA) and Hodges-Lehmann/Shamos (TB). For more details, see Park and Wang (2018)<arXiv:1807.02215>. This work was partially supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. NRF-2017R1A2B4004169).
Play the classic game of tic-tac-toe (naughts and crosses).
Contains functions for random generation of R x C and 2 x 2 x K contingency tables. In addition to the generation of contingency tables over predetermined intraclass-correlated clusters, it is possible to generate contingency tables without intraclass correlations under product multinomial, multinomial, and Poisson sampling plans. It also consists of a function for generation of random data from a given discrete probability distribution function. See Demirhan (2016) <https://journal.r-project.org/archive/2016-1/demirhan.pdf> for more information.
Reporting tables often have structure that goes beyond simple rectangular
  data. The 'rtables' package provides a framework for declaring complex multi-level tabulations
  and then applying them to data. This framework models both tabulation and the resulting
  tables as hierarchical, tree-like objects which support sibling sub-tables, arbitrary splitting
  or grouping of data in row and column dimensions, cells containing multiple values,
  and the concept of contextual summary computations. A convenient pipe-able interface is provided
  for declaring table layouts and the corresponding computations, and then applying them to data.
Storing huge data in RData format causes problems because
        of the necessity to load the whole file to the memory in order
        to access and manipulate objects inside such file; rtape is a
        simple solution to this problem. The package contains several
        wrappers of R built-in serialize/unserialize mechanism allowing
        user to quickly append objects to a tape-like file and later
        iterate over them requiring only one copy of each stored object
        to reside in memory a time.
Discretize AR(1) process following Tauchen (1986) <http://www.sciencedirect.com/science/article/pii/0165176586901680>. A discrete Markov chain that approximates in the sense of weak convergence a continuous-valued univariate Autoregressive process of first order is generated. It is a popular method used in economics and in finance.
We provide functions to perform taxometric analyses. This package contains 46 functions, but only 5 should be called directly by users. CheckData() should be run prior to any taxometric analysis to ensure that the data are appropriate for taxometric analysis. RunTaxometrics() performs taxometric analyses for a sample of data. RunCCFIProfile() performs a series of taxometric analyses to generate a CCFI profile. CreateData() generates a sample of categorical or dimensional data. ClassifyCases() assigns cases to groups using the base-rate classification method.
The Randomized Trait Community Clustering method (Triado-Margarit et al., 2019,
    <doi:10.1038/s41396-019-0454-4>) is a statistical approach which allows to determine whether
    if an observed trait clustering pattern is related to an increasing environmental constrain.
    The method 1) determines whether exists or not a trait clustering on the sampled communities
    and 2) assess if the observed clustering signal is related or not to an increasing environmental
    constrain along an environmental gradient. Also, when the effect of the environmental gradient
    is not linear, allows to determine consistent thresholds on the community assembly based on trait-values.
Tools for analyzing sales report files of iTunes Connect.
Upload R data.frame to Arm Treasure Data, see <https://www.treasuredata.com/>. 
  You can execute database or table handling for resources on Arm Treasure Data.
Create interactive visualisations of Rt estimates using 'D3.js' 
  (Gibbs et al. (2020) <doi:10.5281/zenodo.4011842>). Developed primarily targeting Rt estimates 
  generated by the 'EpiNow2' package, 'RtD3' aims to make simple, beautiful 
  visualisations that help researchers explore their results and share them with others.
Robust tail dependence estimation for bivariate models. This package is based on two papers by the authors:'Robust and bias-corrected estimation of the coefficient of tail dependence' and 'Robust and bias-corrected estimation of probabilities of extreme failure sets'. This work was supported by a research grant (VKR023480) from VILLUM FONDEN and an international project for scientific cooperation (PICS-6416).
Provides response time distributions (density/PDF,
       distribution function/CDF, quantile function, and random
       generation): (a) Ratcliff diffusion model (Ratcliff &
       McKoon, 2008, <doi:10.1162/neco.2008.12-06-420>) based on C
       code by Andreas and Jochen Voss and (b) linear ballistic
       accumulator (LBA; Brown & Heathcote, 2008,
       <doi:10.1016/j.cogpsych.2007.12.002>) with different
       distributions underlying the drift rate.
Exploit controlled vocabularies organized on tematres servers.
A collection of R Markdown templates for nicely structured, reproducible 
    data analyses in R. The templates have embedded examples on how to write 
    citations, footnotes, equations and use colored message/info boxes, how to 
    cross-reference different parts/sections in the report, provide a nice 
    table of contents (toc) with a References section and proper R session 
    information as well as examples using DT tables and ggplot2 graphs. 
    The bookdown Lite template theme supports code folding.
A set of tools for creation, manipulation, and modeling
    of tensors with arbitrary number of modes. A tensor in the context of data
    analysis is a multidimensional array. rTensor does this by providing a S4
    class 'Tensor' that wraps around the base 'array' class. rTensor
    provides common tensor operations as methods, including matrix unfolding,
    summing/averaging across modes, calculating the Frobenius norm, and taking
    the inner product between two tensors. Familiar array operations are
    overloaded, such as index subsetting via '[' and element-wise operations.
    rTensor also implements various tensor decomposition, including CP, GLRAM,
    MPCA, PVD, and Tucker. For tensors with 3 modes, rTensor also implements
    transpose, t-product, and t-SVD, as defined in Kilmer et al. (2013). Some
    auxiliary functions include the Khatri-Rao product, Kronecker product, and
    the Hadamard product for a list of matrices.
Models and displays tephra transport through custom (windy, turbulent, heterogeneous) atmosphere over custom topography. Includes a Lagrangian (particle-tracking) tephra transport model and a function to save snapshots of model as png files.
A small language extension for succinct conditional assignment using `?` and `:`, emulating the conditional ternary operator syntax using in C, Java, JavaScript and other languages.
For natural language processing and analysis of qualitative text
    coding structures which provide a way to bind together text and text data
    are fundamental. The package provides such a structure and accompanying
    methods in form of R6 objects. The 'rtext' class allows for text handling
    and text coding (character or regex based) including data updates on
    text transformations as well as aggregation on various levels.
    Furthermore, the usage of R6 enables inheritance and passing by reference
    which should enable 'rtext' instances to be used as back-end for R based
    graphical text editors or text coding GUIs.
A machine learning package for automatic text classification 
	that makes it simple for novice users to get started with machine 
	learning, while allowing experienced users to easily experiment 
	with different settings and algorithm combinations. The package 
	includes eight algorithms for ensemble classification (svm, slda, 
	boosting, bagging, random forests, glmnet, decision trees, neural 
	networks), comprehensive analytics, and thorough documentation.
This package contains several functions for calculation of texture metrics for Grey Level Co-occurrence matrices
A set of R functions to output Rich Text Format (RTF) files with high resolution tables and graphics that may be edited with a standard word processor such as Microsoft Word.
Methods for comparing different regression algorithms for 
    describing the temporal dynamics of secondary tree growth (xylem and 
    phloem). Users can compare the accuracy of the most common fitting methods 
    usually used to analyse xylem and phloem data, i.e., Gompertz function and 
    General Additive Models (GAMs); and an algorithm newly introduced to the 
    field, i.e., Bayesian Regularised Neural Networks (brnn). The core function
    of the package is XPSgrowth(), while the results can be interpreted using 
    implemented generic S3 methods, such as plot() and summary().
Allow function for using 'TGStat Stat API' and
    'TGStat Search API', for more details see <https://api.tgstat.ru/docs/ru/start/intro.html>.
    'TGStat' provide telegram channel analytics data.
The goal of 'Rthingsboard' is to provide interaction with the API of 'ThingsBoard' (<https://thingsboard.io/>), an open-source IoT platform for device management, data collection, processing and visualization.
A suite of custom R Markdown formats and templates for
    authoring journal articles and conference submissions.
It provides functions for processing Articulate
    Assistant Advanced™ (AAA) export files and plot tongue contour data from any
    system.
Calculates tide heights based on tide station harmonics.  It
    includes the harmonics data for 637 US stations.  The harmonics data
    was converted from
    <https://github.com/poissonconsulting/rtide/blob/master/data-raw/harmonics-dwf-20151227-free.tar.bz2>,
    NOAA web site data processed by David Flater for 'XTide'.  The code to
    calculate tide heights from the harmonics is based on 'XTide'.
The name of this package grew out of our research on temporal interpersonal 
    emotion systems (TIES), hence 'rties'. It provides tools for using a set of models to 
    investigate temporal processes in bivariate (e.g., dyadic) systems. The general approach
    is to model, one dyad at a time, the dynamics of a variable that is assessed repeatedly 
    from both partners, extract the parameter estimates for each dyad, and then use those 
    parameter estimates as input to a latent profile analysis to extract groups of dyads with
    qualitatively distinct dynamics. Finally, the profile memberships can be used to either 
    predict, or be predicted by, another variable of interest. Currently, 2 models are supported:
    1) inertia-coordination, and 2) a coupled-oscillator. Extended documentation is provided 
    in vignettes. Theoretical background can be found in Butler (2011) <doi:10.1177/1088868311411164> 
    and Butler & Barnard (2019) <doi:10.1097/PSY.0000000000000703>.
Our method integrates information from all sequenced samples, thus avoiding loss of alleles due to low coverage. Moreover, it increases the statistical power to uncover sequencing or alignment errors.
R tools to measure and compare inequality, welfare and poverty using the EU statistics on income and living conditions surveys.
Color palettes from famous artists and paintings.
Rarefy data, calculate diversity and plot the results.
'STK++' <http://www.stkpp.org> is a collection of C++ classes
    for statistics, clustering, linear algebra, arrays (with an 'Eigen'-like API),
    regression, dimension reduction, etc. The integration of the library to 'R' is
    using 'Rcpp'. The 'rtkore' package includes the header files from the 'STK++'
    core library. All files contain only template classes and/or inline functions.
    'STK++' is licensed under the GNU LGPL version 2 or later. 'rtkore' (the 'stkpp'
    integration into 'R') is licensed under the GNU GPL version 2 or later. See file
    LICENSE.note for details.
A toolkit for Commodities 'analytics', risk management and
    trading professionals. Includes functions for API calls to
    'Morningstar Commodities' and 'Genscape'.
A set of tools to process and calculate 
             metrics on point clouds derived from terrestrial LiDAR 
             (Light Detection and Ranging; TLS). Its creation is based on key 
             aspects of the TLS application in forestry and ecology. 
             Currently, the main routines are based on filtering, neighboring 
             features of points, voxelization, canopy structure, 
             and the creation of artificial stands. It is written using 
             data.table and C++ language and in most of the functions it is 
             possible to use parallel processing to speed-up the routines.
Fit response-time extended multinomial processing tree (RT-MPT) models by Klauer and
			Kellen (2018) <doi:10.1016/j.jmp.2017.12.003>. The RT-MPT class not only incorporate
			frequencies like traditional multinomial processing tree (MPT) models, but also latencies. This enables it
			to estimate process completion times and encoding plus motor execution times next to the process probabilities
			of traditional MPTs. 'rtmpt' is a Bayesian framework and posterior samples are sampled using a Metropolis-Gibbs
			sampler like the one described in the Klauer and Kellen (2018), but with some modifications. Other than in
			the original C++ program we use the free and open source GNU Scientific Library (GSL). There is also the
			possibility to suppress single process completion times.
Truncated Newton function minimization with bounds constraints
	based on the 'Matlab'/'Octave' codes of Stephen Nash.
Allows you to interact with the API of the "Todoist" platform.
    'Todoist' <https://todoist.com/> provides an online task manager service for teams.
Aimed at seismic tomography, the package
  plots tomographic images, and allows one to interact and query
  three-dimensional tomographic models.
  Vertical cross-sectional cuts can be extracted by mouse click.
  Geographic information can be added easily.
Geostatistical interpolation of data with irregular spatial support such as runoff related data or data from administrative units.
'R' implementation and interface of the Machine Learning platform 
    'PyTorch' <https://pytorch.org/> developed in 'Python'. It requires a 'conda'
    environment with 'torch' and 'torchvision' Python packages to provide 
    'PyTorch' functions, methods and classes. The key object in 'PyTorch' is the 
    tensor which is in essence a multidimensional array. These tensors are fairly 
    flexible in performing calculations in CPUs as well as 'GPUs' to accelerate 
    tensor operations.
A toolkit for the analysis of paths from spatial tracking experiments (such as the Morris water maze) and calculation of goal-finding strategies. 
    This package is centered on an approach using machine learning for path classification.
Measuring information flow between time series with Shannon and Rényi transfer entropy. See also Dimpfl and Peter (2013) <doi:10.1515/snde-2012-0044> and Dimpfl and Peter (2014) <doi:10.1016/j.intfin.2014.03.004> for theory and applications to financial time series. Additional references can be found in the theory part of the vignette.
Provides datasets related to the Star Trek fictional universe and functions for working with the data.
    The package also provides access to real world datasets based on the televised series and other related licensed media productions.
    It interfaces with the Star Trek API (STAPI) (<http://stapi.co/>), 
    Memory Alpha (<https://memory-alpha.fandom.com/wiki/Portal:Main>), and Memory Beta (<https://memory-beta.fandom.com/wiki/Main_Page>) 
    to retrieve data, metadata and other information relating to Star Trek.
    It also contains several local datasets covering a variety of topics. 
    The package also provides functions for working with data from other Star Trek-related 
    R data packages containing larger datasets not stored in 'rtrek'.
The traditional linear regression trend, Modified Mann-Kendall (MK)
    non-parameter trend and bootstrap trend are included in this package. Linear 
    regression trend is rewritten by '.lm.fit'. MK trend is rewritten by 'Rcpp'.
    Finally, those functions are about 10 times faster than previous version 
    in R.
    Reference:
    Hamed, K. H., & Rao, A. R. (1998). A modified Mann-Kendall trend test for 
    autocorrelated data. Journal of hydrology, 204(1-4), 182-196. 
    <doi:10.1016/S0022-1694(97)00125-X>.
Analyze download logs from the CRAN RStudio mirror 
            (<http://cran.rstudio.com/>). 
            This CRAN mirror is the default one used in RStudio.
            The available data is the result of parsed and anonymised raw log data from
            that CRAN mirror.
This is a port of Jonathan Shewchuk's Triangle library to
    R. From his description: "Triangle generates exact Delaunay
    triangulations, constrained Delaunay triangulations, conforming
    Delaunay triangulations, Voronoi diagrams, and high-quality
    triangular meshes. The latter can be generated with no small or
    large angles, and are thus suitable for finite element analysis."
The TRIM model is widely used for estimating growth and decline of
    animal populations based on (possibly sparsely available) count data. The
    current package is a reimplementation of the original TRIM software developed
    at Statistics Netherlands by Jeroen Pannekoek. See
    <https://www.cbs.nl/en-gb/society/nature-and-environment/indices-and-trends%2d%2dtrim%2d%2d>
    for more information about TRIM.
Embeds sources and headers from Tina's Random
    Number Generator ('TRNG') C++ library. Exposes some functionality for
    easier access, testing and benchmarking into R. Provides examples of
    how to use parallel RNG with 'RcppParallel'. The methods and
    techniques behind 'TRNG' are illustrated in the package vignettes and
    examples. Full documentation is available in Bauke (2021)
    <https://github.com/rabauke/trng4/blob/v4.23.1/doc/trng.pdf>.
Process phylogenetic trees with tropical support vector machine and principal component analysis defined with tropical geometry. Details about tropical support vector machine are available in : Tang, X., Wang, H. & Yoshida, R. (2020) <arXiv:2003.00677>. Details about tropical principle component analysis are available in : Page, R., Yoshida, R. & Zhang L. (2020) <doi:10.1093/bioinformatics/btaa564> and Yoshida, R., Zhang, L. & Zhang, X. (2019) <doi:10.1007/s11538-018-0493-4>.
Designed to support the application of plant trait data providing easy applicable functions
    for the basic steps of data preprocessing, e.g. data import, data exploration, selection of columns
    and rows, excluding trait data according to different attributes, geocoding, long- to wide-table
    transformation, and data export. 'rtry' was initially developed as part of the TRY R project to
    preprocess trait data received via the TRY database.