This package is a collection of R functions implemented
        from published and available analytic solutions for the
        One-Dimensional Boussinesq Equation (ground-water). In
        particular, the function "beq.lin" is the analytic solution of
        the linearized form of Boussinesq Equation between two
        different head-based boundary (Dirichlet) conditions;
        "beq.song" is the non-linear power-series analytic solution of
        the motion of a wetting front over a dry bedrock (Song at al,
        2007, see complete reference on function documentation).
        Bugs/comments/questions/collaboration of any kind are warmly
        welcomed.
Response transformations for overdispersed generalized linear
    models and variance component models using nonparametric profile maximum
    likelihood estimation. The main function is optim.boxcox().
Download daily box office information (how much each movie earned
    in theaters) using data from either Box Office Mojo (<http://www.boxofficemojo.com/>) or
   The Numbers (<http://www.the-numbers.com/>).
Correlation chart of two set (x and y) of data.  
  Using Quartiles with boxplot style. 
  Visualize the effect of factor.
An R interface for the remote file hosting service 'Box' 
    (<https://www.box.com/>). In addition to uploading and downloading files,
    this package includes functions which mirror base R operations for local 
    files, (e.g. box_load(), box_save(), box_read(), box_setwd(), etc.), as well
    as 'git' style functions for entire directories (e.g. box_fetch(), 
    box_push()).
Run basic pattern analyses on character sets, digits, or combined
    input containing both characters and numeric digits. Useful for data
    cleaning and for identifying columns containing multiple or nonstandard
    formats.
Implements biplot (2d and 3d) of multivariate data based
             on principal components analysis and diagnostic tools of the quality of the reduction.
Calculates nonparametric pointwise confidence intervals for the survival distribution for right censored data. Has two-sample tests for dissimilarity (e.g., difference, ratio or odds ratio) in survival at a fixed time. Especially important for small sample sizes or heavily censored data. Includes mid-p options.
bPeaks is a simple approach to identify transcription factor binding sites from ChIP-seq data. Our general philosophy is to provide an easy-to-use tool, well-adapted for small eukaryotic genomes (< 20 Mb). bPeaks uses a combination of 4 cutoffs (T1, T2, T3 and T4) to mimic "good peak" properties as described by biologists who visually inspect the ChIP-seq data on a genome browser. For yeast genomes, bPeaks calculates the proportion of peaks that fall in promoter sequences. These peaks are good candidates as transcription factor binding sites.
Model-based clustering for phylogeographic data comprising mtDNA sequences and geographical locations along with optional environmental characteristics, aiming to identify migration events that led to homogeneous population clusters.
Nonparametric multivariate kernel density \
             estimation using a back-projected kernel.
Implements functions to update Bayesian Predictive Power Computations after not stopping a clinical trial at an interim analysis. Such an interim analysis can either be blinded or unblinded. Code is provided for Normally distributed endpoints with known variance, with a prominent example being the hazard ratio.
QTL mapping toolkit for inbred crosses and recombinant
        inbred lines. Includes maximum likelihood and Bayesian tools.
Specify and fit the Bradley-Terry model, including structured versions in which the parameters are related to explanatory variables through a linear predictor and versions with contest-specific effects, such as a home advantage.
Facilities are provided for fitting the simple, unstructured Bradley-Terry model to networks of binary comparisons. The implemented methods are designed to scale well to large, potentially sparse, networks. A fairly high degree of scalability is achieved through the use of EM and MM algorithms, which are relatively undemanding in terms of memory usage (relative to some other commonly used methods such as iterative weighted least squares, for example). Both maximum likelihood and Bayesian MAP estimation methods are implemented. The package provides various standard methods for a newly defined 'btfit' model class, such as the extraction and summarisation of model parameters and the simulation of new datasets from a fitted model. Tools are also provided for reshaping data into the newly defined "btdata" class, and for analysing the comparison network, prior to fitting the Bradley-Terry model. This package complements, rather than replaces, the existing 'BradleyTerry2' package. (BradleyTerry2 has rather different aims, which are mainly the specification and fitting of "structured" Bradley-Terry models in which the strength parameters depend on covariates.)
Provides functions to generate, format, and style surface plots for visualizing combined action data.  Also provides functions for reporting on a BRAID analysis, including plotting curve-shifts, calculating IAE values, and producing full BRAID analysis reports.
Contains functions for evaluating, analyzing, and fitting combined action dose response surfaces with the Bivariate Response to Additive Interacting Dose (BRAID) model of combined action.
Blind users do not have access to the graphical output from R
    without printing the content of graphics windows to an embosser of some kind. This
    is not as immediate as is required for efficient access to statistical output.
    The functions here are created so that blind people can make even better use
    of R. This includes the text descriptions of graphs, convenience functions
    to replace the functionality offered in many GUI front ends, and experimental
    functionality for optimising graphical content to prepare it for embossing as
    tactile images.
A set of tools for performing graph theory analysis of brain MRI
    data. It is best suited to data from a Freesurfer analysis (cortical
    thickness, volumes, local gyrification index, surface area), but also works
    with e.g., tractography data from FSL and fMRI data from DPABI. It contains
    a graphical user interface for graph visualization and data exploration and
    several functions for generating useful figures.
This includes functions for creating 3D and 4D images using WebGL, RGL, and JavaScript Commands.  This package relies on the X ToolKit (XTK, https://github.com/xtk/X#readme).
This package computes the correlation matrix for each
        scale of a wavelet decomposition, namely the one performed by
        the R package waveslim (Whitcher, 2000). An hypothesis test is
        applied to each entry of one matrix in order to construct an
        adjacency matrix of a graph. The graph obtained is finally
        analysed using the small-world theory (Watts and Strogatz,
        1998) and using the computation of efficiency (Latora, 2001),
        tested using simulated attacks. The brainwaver project is
        complementary to the camba project for brain-data
        preprocessing. A collection of scripts (with a makefile) is
        avalaible to download along with the brainwaver package, see
        information on the webpage mentioned below.
Simulation and parameter estimation of multitype Bienayme - Galton - Watson processes.
Tests the parallel regression assumption for ordinal logit models generated with the function polr() from the package 'MASS'.
Test the robustness of a user's Qualitative Comparative Analysis
    solutions to randomness, using the bootstrapped assessment: baQCA(). This
    package also includes a function that provides recommendations for improving
    solutions to reach typical significance levels: brQCA(). After applying recommendations 
    from brQCA(), QCAdiff() shows which cases are excluded from the final result.
A function to produce MCMC samples for posterior inference in semiparametric Bayesian discrete time competing risks recurrent events models.
Estimates geometry of SICM pipette tips by fitting a physical model to recorded breakage-current data.
Species richness estimation is an important problem in biodiversity analysis. This package provides methods for total species richness estimation (observed plus unobserved) and a method for modelling total diversity with covariates. breakaway() estimates total (observed plus unobserved) species richness. Microbial diversity datasets are characterized by a large number of rare species and a small number of highly abundant species. The class of models implemented by breakaway() is flexible enough to model both these features. breakaway_nof1() implements a similar procedure however does not require a singleton count. betta() provides a method for modelling total diversity with covariates in a way that accounts for its estimated nature and thus accounts for unobserved taxa, and betta_random() permits random effects modelling.
Performs multiple change-point detection in data sequences, or data sequence
    segmentation, using computationally efficient multiscale methods. This version only
    implements the "Tail-Greedy Unbalanced Haar" change-point detection methodology; more
    methods will be added in future versions. To start with, see the function
    segment.mean.
Implements the Cross-Entropy (CE) method, which is a model based stochastic optimization technique to estimate both the number and their corresponding locations of break-points in continuous and discrete measurements (Priyadarshana and Sofronov (2015), Priyadarshana and Sofronov (2012a), Priyadarshana and Sofronov (2012b)).
Reads several formats of 13C data (IRIS/Wagner, BreathID) and CSV.
        Creates artificial sample data for testing. 
        Fits Maes/Ghoos, Bluck-Coward self-correcting formula using 'nls', 'nlme'.
        See Bluck L J C and Coward W A 2006 <doi:10.1088/0967-3334/27/3/006>.
        This package contains a refactored subset of github package 
        'dmenne/d13cbreath' without database and display functions. Methods to 
        fit breath test curves with Bayesian Stan methods are refactored to 
        github package 'dmenne/breathteststan'. For a Shiny GUI, see 
        package 'dmenne/breathtestshiny'.
Stan-based curve-fitting function
  for use with package 'breathtestcore' by the same author.
  Stan functions are refactored here for easier testing.
A collection of functions to analyse, visualize and interpret wind data
         and to calculate the potential energy production of wind turbines.
brew implements a templating framework for mixing text and
        R code for report generation. brew template syntax is similar
        to PHP, Ruby's erb module, Java Server Pages, and Python's psp
        module.
Retrieves and parses graduate admissions survey data from the Grad Cafe website (http://thegradcafe.com).
Fit generalized linear models with binomial responses using either an adjusted-score approach to bias reduction or maximum penalized likelihood where penalization is by Jeffreys invariant prior. These procedures return estimates with improved frequentist properties (bias, mean squared error) that are always finite even in cases where the maximum likelihood estimates are infinite (data separation). Fitting takes place by fitting generalized linear models on iteratively updated pseudo-data. The interface is essentially the same as 'glm'.  More flexibility is provided by the fact that custom pseudo-data representations can be specified and used for model fitting. Functions are provided for the construction of confidence intervals for the reduced-bias estimates.
Estimation and inference from generalized linear models based on various methods for bias reduction. The 'brglmFit' fitting method can achieve reduction of estimation bias by solving either the mean bias-reducing adjusted score equations in Firth (1993) <doi:10.1093/biomet/80.1.27> and Kosmidis and Firth (2009) <doi:10.1093/biomet/asp055>, or the median bias-reduction adjusted score equations in Kenne et al. (2016) <arXiv:1604.04768>, or through the direct subtraction of an estimate of the bias of the maximum likelihood estimator from the maximum likelihood estimates as in Cordeiro and McCullagh (1991) <http://www.jstor.org/stable/2345592>. Estimation in all cases takes place via a quasi Fisher scoring algorithm, and S3 methods for the construction of of confidence intervals for the reduced-bias estimates are provided. In the special case of generalized linear models for binomial and multinomial responses, the adjusted score approaches return estimates with improved frequentist properties, that are also always finite, even in cases where the maximum likelihood estimates are infinite (e.g. complete and quasi-complete separation). 'brglm2' also provides pre-fit and post-fit methods for detecting separation and infinite maximum likelihood estimates in binomial response generalized linear models.
Decomposes the empirical Brier score into reliability, resolution and uncertainty. Two different estimators for the components are provided: The original estimators proposed by Murphy (1974), and the bias-corrected estimators proposed by Ferro and Fricker (2012). Sampling variances of all the components are estimated. This package applies only to probabilistic predictions of binary events.
An implementation of the bridge distribution with logit-link in
    R. In Wang and Louis (2003) <DOI:10.1093/biomet/90.4.765>, such a univariate
    bridge distribution was derived as the distribution of the random intercept that
    'bridged' a marginal logistic regression and a conditional logistic regression.
    The conditional and marginal regression coefficients are a scalar multiple
    of each other. Such is not the case if the random intercept distribution was
    Gaussian.
BRIC-seq is a genome-wide approach for determining RNA stability in mammalian cells.
    This package provides a series of functions for performing quality check of your BRIC-seq data,
    calculation of RNA half-life for each transcript and comparison of RNA half-lives between two conditions.
Provides functions for estimating marginal likelihoods, Bayes factors,
             posterior model probabilities, and normalizing constants in general,
             via different versions of bridge sampling (Meng & Wong, 1996,
             <http://www3.stat.sinica.edu.tw/statistica/j6n4/j6n43/j6n43.htm>).
A spatio-temporal exposure-hazard model for assessing biological
    risk and impact. The model is based on stochastic geometry for describing
    the landscape and the exposed individuals, a dispersal kernel for the
    dissemination of contaminants and an ecotoxicological equation.
Provides two main functions, il() and fil(). The il() function implements the EM algorithm developed by Ibrahim and Lipsitz (1996) <DOI:10.2307/2533068> to estimate the parameters of a logistic regression model with the missing response when the missing data mechanism is nonignorable. The fil() function implements the algorithm proposed by Maity et. al. (2017+) <https://github.com/arnabkrmaity/brlrmr> to reduce the bias produced by the method of Ibrahim and Lipsitz (1996) <DOI:10.2307/2533068>.
Fits novel models for the conditional relative risk, risk difference and odds ratio.
Fit Bayesian generalized (non-)linear multilevel models 
    using Stan for full Bayesian inference. A wide range of distributions 
    and link functions are supported, allowing users to fit -- among others -- 
    linear, robust linear, count data, survival, response times, ordinal, 
    zero-inflated, hurdle, and even self-defined mixture models all in a 
    multilevel context. Further modeling options include non-linear and 
    smooth terms, auto-correlation structures, censored data, meta-analytic 
    standard errors, and quite a few more. In addition, all parameters of the 
    response distribution can be predicted in order to perform distributional 
    regression. Prior specifications are flexible and explicitly encourage 
    users to apply prior distributions that actually reflect their beliefs.
    Model fit can easily be assessed and compared with posterior predictive 
    checks and leave-one-out cross-validation.
Bayesian regularization for feed-forward neural networks.
Handles very large numbers in R.  Real numbers are held
        using their natural logarithms, plus a logical flag indicating
        sign.  The package includes a vignette that gives a
        step-by-step introduction to using S4 methods.
Miscellaneous R functions, including functions related to
    graphics (mostly for base graphics), permutation tests, running
    mean/median, and general utilities.
Convert statistical analysis objects from R into tidy data frames,
    so that they can more easily be combined, reshaped and otherwise processed
    with tools like 'dplyr', 'tidyr' and 'ggplot2'. The package provides three
    S3 generics: tidy, which summarizes a model's statistical findings such as
    coefficients of a regression; augment, which adds columns to the original
    data such as predictions, residuals and cluster assignments; and glance, which
    provides a one-row summary of model-level statistics.
A lossless compressed data format that uses a combination of the
    LZ77 algorithm and Huffman coding. Brotli is similar in speed to deflate (gzip)
    but offers more dense compression.
An R interface for the Brown Dog which allows researchers to leverage Brown Dog Services 
    that provides modules to identify the conversion options for a file, to convert file to appropriate
    format, or to extract data from a file. See <http://browndog.ncsa.illinois.edu/> for more information.
Bayesian estimation and variable selection for quantile
 regression models.
Implementation of the Bayesian inference for the two independent Poisson samples model, using the semi-conjugate family of prior distributions.
Includes methods for fetching 'phylogenies' from a variety
    of sources, currently includes 'Phylomatic'
    (<http://phylodiversity.net/phylomatic>), with more in the future.
Analyses of large-scale -omics datasets commonly use p-values as the indicators of statistical significance. However, considering p-value alone neglects the importance of effect size (i.e., the mean difference between groups) in determining the biological relevance of a significant difference. Here, we present a novel algorithm for computing a new statistic, the biological relevance testing (BRT) index, in the frequentist hypothesis testing framework to address this problem.
Fully-interactive R interface to the 'OpenBUGS' software for Bayesian analysis using MCMC sampling.  Runs natively and stably in 32-bit R under Windows.  Versions running on Linux and on 64-bit R under Windows are in "beta" status and less efficient.
Collection of functions, data sets and code examples 
 for evaluations of field trials with the objective of equivalence assessment.
Tools to fit Bayesian state-space models to animal tracking data. Models are provided for location 
    filtering, location filtering and behavioural state estimation, and their hierarchical versions. 
    The models are primarily intended for fitting to ARGOS satellite tracking data but options exist to fit 
    to other tracking data types. For Global Positioning System data, consider the 'moveHMM' package. 
    Simplified Markov Chain Monte Carlo convergence diagnostic plotting is provided but users are encouraged 
    to explore tools available in packages such as 'coda' and 'boa'.
Contains functions to perform Bayesian inference
    using a spectral analysis of Gaussian process priors.
    Gaussian processes are represented with a Fourier series 
    based on cosine basis functions. Currently the package
    includes parametric linear models, partial linear additive
    models with/without shape restrictions, generalized linear
    additive models with/without shape restrictions, and  
    density estimation model. To maximize computational 
    efficiency, the actual Markov chain Monte Carlo sampling 
    for each model is done using codes written in FORTRAN 90.
Data sets for book "Basic Statistics and Data Analysis" by
    Larry J. Kitchens.
Exposes the binary search functions of the C++ standard library (std::lower_bound, std::upper_bound) plus other convenience functions, allowing faster lookups on sorted vectors.
The integration of Bayesian variable and sparse group variable selection approaches for regression models.
Bayesian survival model using Weibull regression on both scale and shape parameters. Dependence of shape parameter on covariates permits deviation from proportional-hazard assumption, leading to dynamic - i.e. non-constant with time - hazard ratios between subjects. Bayesian Lasso shrinkage in the form of two Laplace priors - one for scale and one for shape coefficients - allows for many covariates to be included. Cross-validation helper functions can be used to tune the shrinkage parameters. Monte Carlo Markov Chain (MCMC) sampling using a Gibbs wrapper around Radford Neal's univariate slice sampler (R package MfUSampler) is used for coefficient estimation.
The function estimates the hazard function non parametrically from a survival object (possibly adjusted for covariates). The smoothed estimate is based on B-splines from the perspective of generalized linear mixed models. Left truncated and right censoring data are allowed.
Bayes screening and model discrimination follow-up designs.
Bayesian inference on the (discrete) power spectrum of time series.
Implementation of a Metropolis-within-Gibbs MCMC algorithm to flexibly estimate the spectral density of a stationary time series.  The algorithm updates a nonparametric B-spline prior using the Whittle likelihood to produce pseudo-posterior samples and is based on the work presented by Edwards, Meyer, and Christensen (2017) <arXiv:1707.04878>.
The Bootstrap framework lets you add some JavaScript functionality to your web site by
  adding attributes to your HTML tags - Bootstrap takes care of the JavaScript
  <https://getbootstrap.com/javascript>. If you are using R Markdown or Shiny, you can
  use these functions to create collapsible sections, accordion panels, modals, tooltips,
  popovers, and an accordion sidebar framework (not described at Bootstrap site).
Some functions for nonparametric and semiparametric
        Bayesian models for random effects meta-analysis
This package models the quantile process as a function of
        predictors.
Functions to compute the asymptotic covariance matrices of mixing and unmixing matrix estimates of the following blind source separation (BSS) methods: symmetric and squared symmetric FastICA, regular and adaptive deflation-based FastICA, FOBI, JADE, AMUSE and deflation-based and symmetric SOBI. Also functions to estimate these covariances based on data are available.
Efficient methods for Bayesian inference of state space models 
    via particle Markov chain Monte Carlo and importance sampling type corrected 
    Markov chain Monte Carlo. Gaussian, Poisson, binomial, or negative binomial 
    observation densities and Gaussian state dynamics, as well as general 
    non-linear Gaussian models are supported.
It provides the density, distribution function, quantile function, random number generator, reliability function, failure rate, likelihood function,
             moments and EM algorithm for Maximum Likelihood estimators, also empirical quantile and generated envelope for a given sample, all this for the three parameter
             Birnbaum-Saunders model based on Skew-Normal Distribution.
             Additionally, it provides the random number generator for the mixture of Birnbaum-Saunders model based on Skew-Normal distribution.
Functional gradient descent algorithm for a variety of convex and non-convex loss functions, for both classical and robust regression and classification problems.
Time series regression using dynamic linear models fit using
  MCMC. See Scott and Varian (2014) <DOI:10.1504/IJMMNO.2014.059942>, among many
  other sources.
Kernel density estimation dedicated to urban geography.
Temporal Exponential Random Graph Models (TERGM) estimated by maximum pseudolikelihood with bootstrapped confidence intervals or Markov Chain Monte Carlo maximum likelihood. Goodness of fit assessment for ERGMs, TERGMs, and SAOMs. Micro-level interpretation of ERGMs and TERGMs.
Trend filtering uses the generalized
    lasso framework to fit an adaptive polynomial of degree k to
    estimate the function f_0 at each input x_i in the model: y_i =
    f_0(x_i) + epsilon_i, for i = 1, ..., n, and epsilon_i
    is sub-Gaussian with E(epsilon_i) = 0.  Bayesian trend filtering adapts
    the genlasso framework to a fully Bayesian hierarchical model, estimating
    the penalty parameter lambda within a tractable Gibbs sampler.
Performs 'BTLLasso' (Schauberger and Tutz, 2017: Subject-Specific Modelling of Paired Comparison Data - a Lasso-Type Penalty Approach), a method to include different types of variables in paired
    comparison models and, therefore, to allow for heterogeneity between subjects. Variables can be subject-specific, object-specific and subject-object-specific and
    can have an influence on the attractiveness/strength of the objects. Suitable L1 penalty terms are used 
    to cluster certain effects and to reduce the complexity of the models.
Tools for inferring asynchronous Boolean
    models from single-cell expression data.
BTSPAS provides advanced Bayesian methods to estimate
	     abundance and run-timing from temporally-stratified
	     Petersen mark-recapture experiments. Methods include
	     hierarchical modelling of the capture probabilities
  	     and spline smoothing of the daily run size. This version 
	     uses JAGS to sample from the posterior distribution.
This package contains functions for data preparation, parameter estimation, scoring, and plotting for the BG/BB, BG/NBD and Pareto/NBD models.
Provides advanced statistical methods to describe and predict customers'
  purchase behavior in a non-contractual setting. It uses historic transaction records to fit a
  probabilistic model, which then allows to compute quantities of managerial interest on a cohort-
  as well as on a customer level (Customer Lifetime Value, Customer Equity, P(alive), etc.). This
  package complements the BTYD package by providing several additional buy-till-you-die models, that
  have been published in the marketing literature, but whose implementation are complex and non-trivial.
  These models are: NBD, MBG/NBD, BG/CNBD-k, MBG/CNBD-k, Pareto/NBD (HB), Pareto/NBD (Abe) and Pareto/GGG.
Implements a method of correcting for publication bias and
    uncertainty when planning sample sizes in a future study from an original study. See Anderson, Kelley, & Maxwell (submitted, revised and resubmitted).
Buckley-James regression for right-censoring survival data with high-dimensional covariates. Implementations for survival data include boosting with componentwise linear least squares, componentwise smoothing splines, regression trees and MARS. Other high-dimensional tools include penalized regression for survival data.
Analyze bullet lands using nonparametric methods. We provide a
    reading routine for x3p files (see <http://www.openfmc.org> for more
    information) and a host of analysis functions designed to assess the
    probability that two bullets were fired from the same gun barrel.
View and analyze data where bunching is expected. Estimate counter-
    factual distributions. For earnings data, estimate the compensated
    elasticity of earnings w.r.t. the net-of-tax rate.
All final tables of Germany's highest football (soccer!) league, the Bundesliga. Contains data from 1964 to 2016.
Functionalities for process analysis in R. This packages implements an S3-class for event log objects, and related handler functions. Imports related packages for subsetting event data, computation of descriptive statistics, handling of Petri Net objects and visualization of process maps. See also packages 'edeaR','processmapR', 'eventdataR' and 'processmonitR'.
Basic tools to analyze forest fire history data (e.g. FHX2) in R.
A suite of functions for finance, including the estimation
	of variance matrices via a statistical factor model or
	Ledoit-Wolf shrinkage.
Script search, corner, genetic optimization, permutation tests, write expect test.
An implementation of Jon Kleinberg's burst detection algorithm.  Uses an infinite Markov model to detect periods of increased activity in a series of discrete events with known times, and provides a simple visualization of the results.
Implementation of the Generalized Pairwise Comparisons. This test
    enables to compare two groups of observations in randomized trials(e.g treated
    vs. control patients) on several prioritized outcomes. Pairwise comparisons
    require consideration of all possible pairs of individuals, one taken from the
    treatment group and the other taken from the control group. The outcomes of the
    two individuals forming a pair are compared. Thresholds of minimal clinically
    significant differences can be defined. It is possible to analyse simultaneously
    several outcomes by prioritizing the variables that capture them. The highest
    priority is assigned to the variable considered the most clinically relevant.
    A natural way of handling uninformative or neutral pairs is to consider the
    outcomes in descending order of priority: whenever a pair is uninformative or
    neutral for an outcome of higher priority, the outcomes of lower priority are
    examined In the case of time-to-event endpoint, four methods to handle censored
    observations are available in this package (Gehan, Peto, Efron, and Peron).
R/C++ implementation of the model proposed by Primiceri ("Time Varying Structural Vector Autoregressions and Monetary Policy", Review of Economic Studies, 2005), with functionality for computing posterior predictive distributions and impulse responses.
This package implements a simple alternative to the
        traditional Venn diagram. It depicts each overlap as a separate
        bubble with area proportional to the overlap size. Relation of
        the bubbles to input sets is shown by their their arrangement.
An R interface to the Stark-Parker implementation of an
        algorithm for bounded-variable least squares
Functions that solve boundary value problems ('BVP') of systems of ordinary
             differential equations ('ODE') and differential algebraic equations ('DAE').
             The functions provide an interface to the FORTRAN functions
             'twpbvpC', 'colnew/colsys', and an R-implementation of the shooting method.
The functions in this package focus on analyzing
        case-control association studies involving a group of genetic
        variants.  In particular, we are interested in modeling the
        outcome variable as a function of a multivariate genetic
        profile using Bayesian model uncertainty and variable selection
        techniques.  The package incorporates functions to analyze data
        sets involving common variants as well as extensions to model
        rare variants via the Bayesian Risk Index (BRI) as well as
        haplotypes.  Finally, the package also allows the incorporation
        of external biological information to inform the marginal
        inclusion probabilities via the iBMU.
Whole-genome regression methods on Bayesian framework fitted via EM
    or Gibbs sampling, with optional sampling techniques and kernel term.
Performs the 'Baumgartner-Weiss-Schindler' two-sample test of equal
   probability distributions.
Shows statistics about bytes contained in a file 
  as a circle graph of deviations from mean in sigma increments. 
  The function can be useful for statistically analyze the content of files 
  in a glimpse: text files are shown as a green centered crown, compressed 
  and encrypted files should be shown as equally distributed variations with 
  a very low CV (sigma/mean), and other types of files can be classified between 
  these two categories depending on their text vs binary content, which can be 
  useful to quickly determine how information is stored inside them (databases, 
  multimedia files, etc).
c060 provides additional functions to perform stability selection, model validation and parameter tuning for glmnet models
Methods for detecting safety signals in clinical trials using groupings of adverse events by body-system or system organ class.The package title c212 is in reference to the original Engineering and Physical Sciences Research Council (UK) funded project which was named CASE 2/12.
Compare two classifications or clustering solutions that may or may
    not have the same number of classes, and that might have hard or soft
    (fuzzy, probabilistic) membership. Calculate various metrics to assess how
    the clusters compare to each other. The calculations are simple, but provide
    a handy tool for users unfamiliar with matrix multiplication. This package
    is not geared towards traditional accuracy assessment for classification/
    mapping applications - the motivating use case is for comparing a
    probabilistic clustering solution to a set of reference or existing class
    labels that could have any number of classes (that is, without having to
    degrade the probabilistic clustering to hard classes).
This package allows inferring gene regulatory networks
        with direct physical interactions from microarray expression
        data using C3NET.
C5.0 decision trees and rule-based models for pattern recognition.
Computation and visualization of simple, multiple and joint correspondence analysis.
A wrapper for the 'CanIStream.It' API for searching across the
    most popular streaming, rental, and purchase services to find where a
    movie is available. See <http://www.canistream.it/> for more information.
Performs correspondence analysis on a two-way contingency
        table and produces bootstrap-based elliptical confidence
        regions around the projected coordinates for the category
        points. Includes routines to plot the results in a variety of
        styles. Also reports the standard numerical output for
        correspondence analysis.
Computes classification accuracy and consistency indices under Item Response Theory. Implements the total score IRT-based methods in Lee, Hanson & Brennen (2002) and Lee (2010), the IRT-based methods in Rudner (2001, 2005), and the total score nonparametric methods in Lathrop & Cheng (2014). For dichotomous and polytomous tests.
Parameters of a user-specified probability distribution are modelled by a multi-layer perceptron artificial neural network. This framework can be used to implement probabilistic nonlinear models including mixture density networks, heteroscedastic regression models, zero-inflated models, and the like.
Hansen's (1995) Covariate-Augmented
        Dickey-Fuller (CADF) test. The only required argument is y, the
        Tx1 time series to be tested. If no stationary covariate X is
        passed to the procedure, then an ordinary ADF test is
        performed. The p-values of the test are computed using the
        procedure illustrated in Lupi (2009).
Using Java GUI for R (JGR), CADStat provides a user
    interface for several statistical methods -
    scatterplot, boxplot, linear regression, generalized linear
    regression, quantile regression, conditional probability
    calculations, and regression trees.
Encrypts and decrypts strings using either the Caesar cipher or a
    pseudorandom number generation (using set.seed()) method.
Simulate plasma caffeine concentrations using population pharmacokinetic model described in Lee, Kim, Perera, McLachlan and Bae (2015) <doi:10.1007/s00431-015-2581-x>.
Provides functions for the estimation of the conditional Akaike 
			 information in generalized mixed-effects models fitted with (g)lmer 
			 form lme4.
Cairo graphics device that can be use to create high-quality vector (PDF, PostScript and SVG) and bitmap output (PNG,JPEG,TIFF), and high-quality rendering in displays (X11 and Win32). Since it uses the same back-end for all output, copying across formats is WYSIWYG. Files are created without the dependence on X11 or other external programs. This device supports alpha channel (semi-transparent drawing) and resulting images can contain transparent and semi-transparent regions. It is ideal for use in server environments (file output) and as a replacement for other devices that don't have Cairo's capabilities such as alpha support or anti-aliasing. Backends are modular such that any subset of backends is supported.
This device uses Cairo and GTK to draw to the screen,
        file (png, svg, pdf, and ps) or memory (arbitrary GdkDrawable
        or Cairo context). The screen device may be embedded into RGtk2
        interfaces and supports all interactive features of other graphics
        devices, including getGraphicsEvent().
Implements several string comparison algorithms, including calACS (count all common subsequences), lenACS (calculate the lengths of all common subsequences), and lenLCS (calculate the length of the longest common subsequence). Some algorithms differentiate between the more strict definition of subsequence, where a common subsequence cannot be separated by any other items, from its looser counterpart, where a common subsequence can be interrupted by other items. This difference is shown in the suffix of the algorithm (-Strict vs -Loose). For example, q-w is a common subsequence of q-w-e-r and q-e-w-r on the looser definition, but not on the more strict definition. calACSLoose Algorithm from Wang, H. All common subsequences (2007) IJCAI International Joint Conference on Artificial Intelligence, pp. 635-640.
Arithmetic operations scalar multiplication, addition, subtraction, multiplication and division of LR fuzzy numbers (which are on the basis of extension principle) have a complicate form for using in fuzzy Statistics, fuzzy Mathematics, machine learning, fuzzy data analysis and etc. Calculator for LR Fuzzy Numbers package relieve and aid applied users to achieve a simple and closed form for some complicated operator based on LR fuzzy numbers and also the user can easily draw the membership function of the obtained result by this package.
Contains greedy algorithms for coarse approximation linear
    functions.
Functions to impute using Random Forest under Full Conditional Specifications (Multivariate Imputation by Chained Equations). The CALIBER programme is funded by the Wellcome Trust (086091/Z/08/Z) and the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research programme (RP-PG-0407-10314). The author is supported by a Wellcome Trust Clinical Research Training Fellowship (0938/30/Z/10/Z).
Automated parameter estimation for complex (ecological) models in R. 
  This package allows the parameter estimation or calibration of complex models, 
  including stochastic ones. It is a generic tool that can be used for fitting 
  any type of models, especially those with non-differentiable objective functions. 
  It supports multiple phases and constrained optimization. 
  It implements maximum likelihood estimation methods and automated construction 
  of the objective function from simulated model outputs. 
  See <http://roliveros-ramos.github.io/calibrar> for more details.
Package for drawing calibrated scales with tick marks on (non-orthogonal) 
             variable vectors in scatterplots and biplots.
Function to calculate weights and estimates for panel data with non-response.
Performs Bayesian calibration of computer models as per
 Kennedy and O'Hagan 2001.  The package includes routines to find the
 hyperparameters and parameters; see the help page for stage1() for a
 worked example using the toy dataset.  A tutorial is provided in the
 calex.Rnw vignette; and a suite of especially simple one dimensional
 examples appears in inst/doc/one.dim/.
It is sometimes useful to perform a computation in a
    separate R process, without affecting the current R process at all.
    This packages does exactly that.
A multi-array post-processing method of allele-specific copy-number estimates (ASCNs).
The code takes an n x p data matrix and fits a Causal Additive Model (CAM) for estimating the causal structure of the underlying process. The output is a p x p adjacency matrix (a one in entry (i,j) indicates an edge from i to j). Details of the algorithm can be found in: P. Bühlmann, J. Peters, J. Ernest: "CAM: Causal Additive Models, high-dimensional order search and penalized regression", Annals of Statistics 42:2526-2556, 2014.
Tools for the analysis of finite semiparametric mixtures.
        These are useful when data is heterogeneous, e.g. in
        pharmacokinetics or meta-analysis. The NPMLE and VEM algorithms
        (flexible support size) and EM algorithms (fixed support size)
        are provided for univariate and bivariate data.
The package "camel" provides the implementation of a family of high-dimensional calibrated machine learning tools, including (1) LAD, SQRT Lasso and Calibrated Dantzig Selector for estimating sparse linear models; (2) Calibrated Multivariate Regression for estimating sparse multivariate linear models; (3) Tiger, Calibrated Clime for estimating sparse Gaussian graphical models. We adopt the combination of the dual smoothing and monotone fast iterative soft-thresholding algorithm (MFISTA). The computation is memory-optimized using the sparse matrix output, and accelerated by the path following and active set tricks.
Copernicus Atmosphere Monitoring Service (CAMS) radiations service 
    provides time series of global, direct, and diffuse irradiations on horizontal
    surface, and direct irradiation on normal plane for the actual weather 
    conditions as well as for clear-sky conditions.
    The geographical coverage is the field-of-view of the Meteosat satellite,
    roughly speaking Europe, Africa, Atlantic Ocean, Middle East. The time coverage
    of data is from 2004-02-01 up to 2 days ago. Data are available with a time step
    ranging from 15 min to 1 month. For license terms and to create an account,
    please see <http://www.soda-pro.com/web-services/radiation/cams-radiation-service>.
Management of and data extraction from camera trap photographs in wildlife studies. The package provides a workflow for storing and sorting camera trap photographs, computes record databases and detection/non-detection matrices for occupancy and spatial capture-recapture analyses with great flexibility. In addition, it provides simple mapping functions (number of species, number of independent species detections by station) and can visualise activity data.
Functions to perform the following analyses: i) inferring epistasis from RNAi double knockdown data; ii) identifying gene pairs of multiple mutation patterns; iii) assessing association between gene pairs and survival; and iv) calculating the smallworldness of a graph (e.g., a gene interaction network).  Data and analyses are described in Wang, X., Fu, A. Q., McNerney, M. and White, K. P. (2014). Widespread genetic epistasis among breast cancer genes. Nature Communications. 5 4828. <doi:10.1038/ncomms5828>.
Timing copy number changes using estimates of mutational allele frequency from resequencing of tumor samples.
Functions for computing and visualizing 
	generalized canonical discriminant analyses and canonical correlation analysis
	for a multivariate linear model.
	Traditional canonical discriminant analysis is restricted to a one-way 'MANOVA'
	design and is equivalent to canonical correlation analysis between a set of quantitative
	response variables and a set of dummy variables coded from the factor variable.
	The 'candisc' package generalizes this to higher-way 'MANOVA' designs
	for all factors in a multivariate linear model,
	computing canonical scores and vectors for each term. The graphic functions provide low-rank (1D, 2D, 3D) 
	visualizations of terms in an 'mlm' via the 'plot.candisc' and 'heplot.candisc' methods. Related plots are
	now provided for canonical correlation analysis when all predictors are quantitative.
A statistical framework and computational procedure for identifying
  the sub-populations within a tumor, determining the mutation profiles of each 
  subpopulation, and inferring the tumor's phylogenetic history. The input are 
  variant allele frequencies (VAFs) of somatic single nucleotide alterations 
  (SNAs) along with allele-specific coverage ratios between the tumor and matched
  normal sample for somatic copy number alterations (CNAs). These quantities can
  be directly taken from the output of existing software. Canopy provides a 
  general mathematical framework for pooling data across samples and sites to 
  infer the underlying parameters. For SNAs that fall within CNA regions, Canopy
  infers their temporal ordering and resolves their phase.  When there are 
  multiple evolutionary configurations consistent with the data, Canopy outputs 
  all configurations along with their confidence assessment.
Datasets are collected here for differentially (up- and down-)
        expressed proteins identified in proteomic studies of cancer and in cell
        culture experiments. Tables of amino acid compositions of proteins are
        used for calculations of chemical composition, projected into selected
        basis species. Plotting functions are used to visualize the compositional
        differences and thermodynamic potentials for proteomic transformations.
Extract CANSIM (Statistics Canada) tables and transform them into readily usable data in panel (wide) format. It can also extract more than one table at a time and produce the resulting merge by time period and geographical region.
Enables creation of visualizations using the CanvasXpress framework
    in R. CanvasXpress is a standalone JavaScript library for reproducible research
    with complete tracking of data and end-user modifications stored in a single
    PNG image that can be played back. See <http://canvasxpress.org> for more
    information.
Combines complementary information across multiple related
    phenotypes to infer directed epistatic interactions between genetic markers.
    This analysis can be applied to a variety of engineered and natural populations.
Functions for performing phylogenetic comparative analyses.
Quantitative analysis to support companion animal population
    management. Some functions assist survey sampling tasks (calculate sample 
    size for simple and complex designs, select sampling units and estimate 
    population parameters) while others assist the modelling of population 
    dynamics. For sampling methods see: Levy PS & Lemeshow S. (2013), 
    ISBN-10: 0470040076; Lumley (2010), ISBN: 978-0-470-28430-8. For 
    modelling of population dynamics see: Baquero et al (2016) 
    <https://doi.org/10.1016/j.prevetmed.2015.11.009>; Baquero et al (2016), 
    ISSN 1679-9216; Amaku et al (2010) 
    <http://dx.doi.org/10.1590/S1020-49892009000400003>.
Implements approximation methods for natural capital asset prices suggested by Fenichel and Abbott (2014) <doi:10.1086/676034> in Journal of the Associations of Environmental and Resource Economists (JAERE), Fenichel et al. (2016) <doi:10.1073/pnas.1513779113> in Proceedings of the National Academy of Sciences (PNAS), and Yun et al. (2017) in PNAS (accepted), and their extensions: creating Chebyshev polynomial nodes and grids, calculating basis of Chebyshev polynomials, approximation and their simulations for: V-approximation (single and multiple stocks, PNAS), P-approximation (single stock, PNAS), and Pdot-approximation (single stock, JAERE). Development of this package was generously supported by the Knobloch Family Foundation.
Provides a method for automatically numbering figures,
    tables, or other objects.  Captions can be displayed in full, or as citations.
    This is especially useful for adding figures and tables to R markdown
    documents without having to numbering them manually.
Get text from images of text using Captricity Optical Character
    Recognition (OCR) API. Captricity allows you to get text from handwritten
    forms --- think surveys --- and other structured paper documents. And it can
    output data in form a delimited file keeping field information intact. For more
    information, read <https://shreddr.captricity.com/developer/overview/>.
Calibration of penalized criteria for model selection. The calibration methods available are based on the slope heuristics.
Fits models from Miller et al. 2005 to estimate population
        sizes from natural populations. Several models are implemented.
        Package also includes functions to perform a likelihood ratio
        test to choose between models, perform parametric bootstrapping
        to obtain confidence intervals and multiple functions to
        simulate data.
Functions and Datasets to Accompany J. Fox and S. Weisberg, 
  An R Companion to Applied Regression, Second Edition, Sage, 2011.
Implements a class of univariate and multivariate spatial generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation. The response variable can be binomial, Gaussian or Poisson, and spatial autocorrelation is modelled by  a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution. A number of different models are available for univariate spatial data, including models with no random effects as well as random effects modelled by different types of CAR prior. Additionally,  a multivariate CAR (MCAR) model for multivariate spatial data is available, as is a two-level hierarchical model for individuals within areas. Full details are given in the vignette accompanying this package. The initial creation of this package was supported by the Economic and Social Research Council (ESRC) grant RES-000-22-4256, and on-going development has / is supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1, ESRC grant ES/K006460/1, and Innovate UK / Natural Environment Research Council (NERC) grant NE/N007352/1.
Spatio-temporal data from Scotland used in the vignettes accompanying the CARBayes (spatial modelling) and CARBayesST (spatio-temporal modelling) packages. For the CARBayes vignette the data include the Scottish lip cancer data and property price and respiratory hospitalisation data from the Greater Glasgow and Clyde health board. For the CARBayesST vignette the data include spatio-temporal data on property sales and respiratory hospitalisation and air pollution from the Greater Glasgow and Clyde health board.
Implements a class of spatio-temporal generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation. The response variable can be binomial, Gaussian or Poisson, but for some models only the binomial and Poisson data likelihoods are available. The spatio-temporal autocorrelation is modelled by  random effects,  which are assigned conditional autoregressive (CAR) style prior distributions. A number of different random effects structures are available, including Bernardinelli et al. (1995) <doi:10.1002/sim.4780142112>, Rushworth et al. (2014) <doi:10.1016/j.sste.2014.05.001> and Lee et al. (2016) <doi:10.1214/16-AOAS941>. Full details are given in the vignette accompanying this package. The creation of this package was supported by the Engineering and Physical Sciences Research Council (EPSRC) grant EP/J017442/1 and the Medical Research Council (MRC) grant MR/L022184/1.
The number of bird or bat fatalities from collisions with buildings, towers or wind energy turbines can be estimated based on carcass searches and experimentally assessed carcass persistence times and searcher efficiency. Functions for estimating the probability that a bird or bat that died is found by a searcher are provided. Further functions calculate the posterior distribution of the number of fatalities based on the number of carcasses found and the estimated detection probability.
Datasets to Accompany J. Fox and S. Weisberg, 
  An R Companion to Applied Regression, Third Edition, Sage (forthcoming).
Identification of cardinal dates
             (begin, time of maximum, end of mass developments)
             in ecological time series using fitted Weibull functions.
Includes over 100 mixed-effects model structures describing the relationship between drug concentration and QT interval, heart rate/pulse rate or blood pressure. Given an exposure-response dataset, the tool fits each model structure to the observed data.
Implements the regression approach 
  of Zuber and Strimmer (2011) "High-dimensional regression and variable 
  selection using CAR scores" SAGMB 10: 34, <DOI:10.2202/1544-6115.1730>.
  CAR scores measure the correlation between the response and the 
  Mahalanobis-decorrelated  predictors.  The squared CAR score is a 
  natural measure of variable importance and provides a canonical 
  ordering of variables. This package provides functions for estimating 
  CAR scores, for variable selection using CAR scores, and for estimating 
  corresponding regression coefficients. Both shrinkage as well as 
  empirical estimators are available.
The R package CARE1, the first part of the program CARE
        (Capture-Recapture) in
        http://chao.stat.nthu.edu.tw/softwareCE.html, can be used to
        analyze epidemiological data via sample coverage approach (Chao
        et al. 2001a). Based on the input of records from several
        incomplete lists (or samples) of individuals, the R package
        CARE1 provides output of population size estimate and related
        statistics.
Misc functions for training and plotting classification and
    regression models.
Functions for creating ensembles of caret models: caretList
    and caretStack.  caretList is a convenience function for fitting multiple
    caret::train models to the same dataset. caretStack will make linear or
    non-linear combinations of these models, using a caret::train model as a
    meta-model, and caretEnsemble will make a robust linear combination of
    models using a glm.
This is a package for estimating the population size of
        migratory caribou herds based on large scale aggregations
        monitored by radio telemetry. It implements the methodology
        found in the article by Rivest et al. (1998) about caribou
        abundance estimation. It also includes a function based on the
        Lincoln-Petersen Index as applied to radio telemetry data by
        White and Garrott (1990).
Includes commands for bootstrapping and permutation tests, a command for created grouped bar plots, and a demo of the quantile-normal plot for data drawn from different distributions.
Functions to calculate and plot ecological quality ratios (EQR) as specified by Ballesteros et al. 2007.
The caroline R library contains dozens of functions useful
        for: database migration (dbWriteTable2), database style joins &
        aggregation (nerge, groupBy & bestBy), data structure
        conversion (nv, tab2df), legend table making (sstable &
        leghead), plot annotation (labsegs & mvlabs), data
        visualization (violins, pies & raPlot), character string
        manipulation (m & pad), file I/O (write.delim), batch scripting
        and more.  The package's greatest
        contributions lie in the database style merge, aggregation and
        interface functions as well as in it's extensive use and
        propagation of row, column and vector names in most functions.
Mainly used to build tables that are commonly presented for
    bio-medical/health research, such as basic characteristic tables or
    descriptive statistics.
CRISPR-Analyzer for pooled CRISPR screens (caRpools) provides an end-to-end analysis of CRISPR screens including quality control, hit candidate analysis, visualization and automated report generation using R markdown. Needs MAGeCK (http://sourceforge.net/p/mageck/wiki/Home/), bowtie2 for all functions. CRISPR (clustered regularly interspaced short palindromic repeats) is a method to perform genome editing. See <https://en.wikipedia.org/wiki/CRISPR> for more information on
  CRISPR.
This package fits Bayesian conditional autoregressive models for spatial and spatiotemporal data on a lattice.  It uses OpenCL kernels running on GPUs to perform rejection sampling to obtain independent samples from the joint posterior distribution of model parameters.
It implements the CARS procedure, which is a two-sample multiple testing procedure that utilizes an additional auxiliary variable to capture the sparsity information, hence improving power. The CARS procedure is shown to be asymptotically valid and optimal for FDR control. For more information, please see the website <http://www-bcf.usc.edu/~wenguans/Papers/CARS.html> and the accompanying paper.
Construct continuous and non-contiguous area cartograms.
Create and integrate maps in your R workflow. This package allows
    various cartographic representations such as proportional symbols, chroropleth,
    typology, flows or discontinuities. In addition, it also proposes some useful
    features like cartographic palettes, layout (scale, north arrow, title...), labels,
    legends or access to cartographic API to ease the graphic presentation of maps.
A censored time series class is designed. An estimation procedure
    is implemented to estimate the Censored AutoRegressive time series with
    eXogenous covariates (CARX), assuming normality of the innovations. Some other
    functions that might be useful are also included.
Functions, data sets and exercises solutions for the book 'Séries Temporelles Avec R' (Yves Aragon, edp sciences, 2016). For all chapters, a vignette is available with some additional material and exercises solutions.
Implements the case-base sampling approach of Hanley and Miettinen (2009) <DOI:10.2202/1557-4679.1125>, 
    Saarela and Arjas (2015) <DOI:10.1111/sjos.12125>, and Saarela (2015) <DOI:10.1007/s10985-015-9352-x>, for fitting flexible hazard 
    regression models to survival data with single event type or multiple competing causes via logistic and multinomial regression. 
    From the fitted hazard function, cumulative incidence, risk functions of time, treatment and profile 
    can be derived. This approach accommodates any log-linear hazard function of prognostic time, treatment, 
    and covariates, and readily allows for non-proportionality. We also provide a plot method for visualizing 
    incidence density via population time plots.
Allows users to identify similar cases for qualitative case studies using statistical matching methods.
Efficient tree manipulation functions including pruning, rerooting, calculation of most-recent common ancestors, calculating distances from the tree root and calculating pairwise distance matrices. Calculation of phylogenetic signal and mean trait depth (trait conservatism). Ancestral state reconstruction and hidden character prediction of discrete characters, using Maximum Likelihood and Maximum Parsimony methods. Simulating and fitting models of trait evolution, and generating random trees using birth-death models.
Analysis of categorical-variable with missing values
Categorical data analysis program package.
This R-package contains examples from the book "Regression for Categorical Data", Tutz 2011, Cambridge University Press. The names of the examples refer to the chapter and the data set that is used.
Based on fishery Catch Dynamics instead of fish Population Dynamics (hence CatDyn) and using high-frequency or medium-frequency catch in biomass or numbers, fishing nominal effort, and mean fish body weight by time step, from one or two fishing fleets, estimate stock abundance, natural mortality rate, and fishing operational parameters. It includes methods for data organization, plotting standard exploratory and analytical plots, predictions, for 77 types of models of increasing complexity, and 56 likelihood models for the data.
Provides several methods for factor analysis in high dimension (both n,p >> 1) and methods to adjust for possible confounders in multiple hypothesis testing.
Gives methods to create a catenary object and then plot it and get
    properties of it. Can construct from parameters or endpoints. Also can get
    catenary fitted to data.
Contains some commonly used categorical variable encoders, such as 'LabelEncoder' and 'OneHotEncoder'. Inspired by the encoders implemented in Python 'sklearn.preprocessing' package (see <http://scikit-learn.org/stable/modules/preprocessing.html>).
A multi-factor dimensionality reduction based forward selection method for genetic association mapping.
Detect and quantify community assembly processes using trait values of individuals or populations, the T-statistics and other metrics, and dedicated null models.
Functions designed to simulate data that conform to basic
          unidimensional IRT models (for now 3-parameter binary response models
          and graded response models) along with Post-Hoc CAT simulations of
          those models with various item selection methods, ability estimation
          methods, and termination criteria.
Performs analysis of sinusoidal rhythms in time series data:  actogram, smoothing, autocorrelation, crosscorrelation, several flavors of cosinor.
Formal psychological models, independently-replicated data sets against which to test them, and simulation archives.
Although many software tools can perform meta-analyses on genetic case-control data,
    none of these apply to combined case-control and family-based (TDT) studies. This package conducts
    fixed-effects (with inverse variance weighting) and random-effects [DerSimonian and Laird (1986)
    <DOI:10.1016/0197-2456(86)90046-2>] meta-analyses on combined genetic data. Specifically, this package
    implements a fixed-effects model [Kazeem and Farrall (2005) <DOI:10.1046/j.1529-8817.2005.00156.x>]
    and a random-effects model [Nicodemus (2008) <DOI:10.1186/1471-2105-9-130>] for combined studies.
Structure learning and parameter estimation of discrete Bayesian networks using likelihood-based criteria.  Exhaustive search for fixed node orders and stochastic search of optimal orders via simulated annealing algorithm are implemented.
Contains several basic utility functions including: moving
        (rolling, running) window statistic functions, read/write for
        GIF and ENVI binary files, fast calculation of AUC, LogitBoost
        classifier, base64 encoder/decoder, round-off-error-free sum
        and cumsum, etc.
Provides routines for the generation of response patterns under unidimensional dichotomous and polytomous computerized adaptive testing (CAT) framework. It holds many standard functions to estimate ability, select the first item(s) to administer and optimally select the next item, as well as several stopping rules. Options to control for item exposure and content balancing are also available (Magis and Raiche (2012) <doi:10.18637/jss.v048.i08>).
`ctab' creates (multiway) percentage tables. `sqtab'
        contains a set of functions for estimating models for square
        tables such as quasi-independence, symmetry, uniform
        association. Examples show how to use these models in a
        loglinear model using glm or in a multinomial logistic model
        using mlogit or clogit
Provides methods of computerized adaptive testing for survey researchers. Includes functionality for data fit with the classic item response methods including the latent trait model, Birnbaum`s three parameter model, the graded response, and the generalized partial credit model.  Additionally, includes several ability parameter estimation and item selection routines.  During item selection, all calculations are done in compiled C++ code.
This function conducts the Cochran-Armitage trend test to a 2 by k contingency table. It will report the test statistic (Z) and p-value.A linear trend in the frequencies will be calculated, because the weights (0,1,2) will be used by default.
Functions and data to estimate causal dose response functions given continuous, ordinal, or binary treatments.
Functions for identification and transportation of causal effects. Provides a conditional causal effect identification algorithm (IDC) by Shpitser, I. and Pearl, J. (2006) <http://ftp.cs.ucla.edu/pub/stat_ser/r329-uai.pdf>, an algorithm for transportability from multiple domains with limited experiments by Bareinboim, E. and Pearl, J. (2014) <http://ftp.cs.ucla.edu/pub/stat_ser/r443.pdf> and a selection bias recovery algorithm by Bareinboim, E. and Tian, J. (2015) <http://ftp.cs.ucla.edu/pub/stat_ser/r445.pdf>. All of the previously mentioned algorithms are based on a causal effect identification algorithm by Tian , J. (2002) <http://ftp.cs.ucla.edu/pub/stat_ser/r309.pdf>.
Estimate causal effects of one variable on another, currently for
  binary data only. Methods include instrumental variable bounds, adjustment by a 
  given covariate set, adjustment by an induced covariate set using a variation of 
  the PC algorithm, and an effect bounding method (the Witness Protection Program) 
  based on covariate adjustment with observable independence constraints.
This package implements various estimators for average
        treatment effects---an inverse probability weighted (IPW)
        estimator, an augmented inverse probability weighted (AIPW)
        estimator, and a standard regression estimator---that make use
        of generalized additive models for the treatment assignment
        model and/or outcome model.
Implements a Bayesian approach to causal impact estimation in time
  series, as described in Brodersen et al. (2015) <DOI:10.1214/14-AOAS788>.
  See the package documentation on GitHub
  <https://google.github.io/CausalImpact/> to get started.
The causalsens package provides functions to perform sensitivity analyses and to study how various assumptions about selection bias affects estimates of causal effects.
The Causata package provides utilities for 
    extracting data from the Causata application, training binary classification 
    models, and exporting models as PMML for scoring.
Provides six variants of two-way correspondence analysis (ca):
   simple ca, singly ordered ca, doubly ordered ca, non symmetrical ca,
   singly ordered non symmetrical ca, and doubly ordered non symmetrical
   ca.
Implements clustering techniques such as Proximus and Rock, utility functions for efficient computation of cross distances and data manipulation.
Contains function which subsets the input data frame based on the variable types and returns list of data frames.
Detect contextual anomalies in time-series data with Bayesian data
  analysis. It focuses on determining a normal range of target value, and
  provides simple-to-use functions to abstract the outcome.
Compare baseline characteristics between two or more groups. The variables being compared can be factor and numeric variables. The function will automatically judge the type and distribution of the variables, and make statistical description and bivariate analysis.
The clustering of binary data with reducing the dimensionality (CLUSBIRD) proposed by Yamamoto and Hayashi (2015) <doi:10.1016/j.patcog.2015.05.026>.
Implements the covariate balancing propensity score (CBPS) proposed
    by Imai and Ratkovic (2014) <DOI:10.1111/rssb.12027>. The propensity score is
    estimated such that it maximizes the resulting covariate balance as well as the
    prediction of treatment assignment. The method, therefore, avoids an iteration
    between model fitting and balance checking. The package also implements several
    extensions of the CBPS beyond the cross-sectional, binary treatment setting.
    The current version implements the CBPS for longitudinal settings so that it can
    be used in conjunction with marginal structural models from Imai and Ratkovic
    (2015) <DOI:10.1080/01621459.2014.956872>, treatments with three- and four-
    valued treatment variables, continuous-valued treatments from Fong, Hazlett,
    and Imai (2015) <http://imai.princeton.edu/research/files/CBGPS.pdf>, and the
    situation with multiple distinct binary treatments administered simultaneously.
    In the future it will be extended to other settings including the generalization
    of experimental and instrumental variable estimates. Recently add the optimal
    CBPS which chooses the optimal balancing function and results in doubly robust
    and efficient estimator for the treatment effect.
The data and meta data from Statistics
    Netherlands (www.cbs.nl) can be browsed and downloaded. The client uses
    the open data API of Statistics Netherlands.
The package provide a set of functions that extend the
        cancor function with new numerical and graphical outputs. It
        also include a regularized extension of the cannonical
        correlation analysis to deal with datasets with more variables
        than observations.
Client for Climate Change, Agriculture, and Food Security ('CCAFS')
    General Circulation Models ('GCM') data. Data is stored in Amazon 'S3', from
    which we provide functions to fetch data.
Variational Bayesian algorithms for learning canonical correlation analysis (CCA), inter-battery factor analysis (IBFA), and group factor analysis (GFA). Inference with several random initializations can be run with the functions CCAexperiment() and GFAexperiment().
Canonical correlation analysis and maximum correlation via
    projection pursuit, as well as fast implementations of correlation
    estimators, with a focus on robust and non-parametric methods.
Class Cover Catch Digraphs, neighborhood graphs, and
        relatives.
ccChooser can be used to developing and evaluation of core
        collections for germplasm collections (entire collection). This
        package used to develop a core collection for biological
        resources like genbanks. A core collection is defined as a
        sample of accessions that represent, with the lowest possible
        level of redundancy, the genetic diversity (the richness of
        gene or genotype categories) of the entire collection. The
        establishing a core collection that represents genetic
        diversity of the entire collection with minimum loss of its
        original diversity and minimum redundancies is an important
        problem for gene-banks curators and crop breeders. ccChooser
        establish core collection base on phenotypic data (agronomic,
        morphological, phenological).
Routines for solving convex optimization problems with cone constraints by means of interior-point methods. The implemented algorithms are partially ported from CVXOPT, a Python module for convex optimization (see <http://cvxopt.org> for more information).
Estimates the Concordance Correlation Coefficient to assess agreement. The scenarios considered are non-repeated measures, non-longitudinal repeated measures (replicates) and longitudinal repeated measures. The estimation approaches implemented are variance components and U-statistics approaches.
This package implements the combined cluster and discriminant analysis method for finding homogeneous groups of data with known origin as described in Kovacs et. al (2014): Classification into homogeneous groups using combined cluster and discriminant analysis (CCDA). Environmental Modelling & Software. DOI: http://dx.doi.org/10.1016/j.envsoft.2014.01.010
Implementation of the CCDr (Concave penalized Coordinate Descent with reparametrization) structure learning algorithm as described in Aragam and Zhou (2015) <http://www.jmlr.org/papers/v16/aragam15a.html>. This is a fast, score-based method for learning Bayesian networks that uses sparse regularization and block-cyclic coordinate descent.
Functions for estimating and simulating the family of the
        CC-GARCH models.
Contains a function, also called 'cchs', that calculates Estimator III of Borgan et al (2000), <DOI:10.1023/A:1009661900674>. This estimator is for fitting a Cox proportional hazards model to data from a case-cohort study where the subcohort was selected by stratified simple random sampling.
Convex Clustering methods, including K-means algorithm,
  On-line Update algorithm (Hard Competitive Learning) and Neural Gas
  algorithm (Soft Competitive Learning), and calculation of several
  indexes for finding the number of clusters in a data set.
Classification method that classifies a sample according
        to the class with the maximum mean (or any other function of)
        correlation between the test and training samples with known
        classes.
Estimate the direct and indirect (mediation) effects of treatment on the outcome when intermediate variables (mediators) are compositional and high-dimensional. Sohn, M.B. and Li, H. (2017). Compositional Mediation Analysis for Microbiome Studies. (AOAS: In revision).
Tools to simulate networks based on Congruence Class models.
Significance tests for canonical correlation analysis,
        including asymptotic tests and a Monte Carlo method
Tests of association between SNPs or pairs of SNPs and binary phenotypes, in case-control / case-population / case-control-population studies.
Implements a method for identifying and removing
				the cell-cycle effect from scRNA-Seq data. The description of the 
				method is in Barron M. and Li J. (2016) <doi:10.1038/srep33892>. Identifying and removing 
				the cell-cycle effect from single-cell RNA-Sequencing data. Submitted. 
				Different from previous methods, ccRemover implements a mechanism that
				formally tests whether a component is cell-cycle related or not, and thus
				while it often thoroughly removes the cell-cycle effect, it preserves
				other features/signals of interest in the data.
Implements the uniform scaled beta distribution and
  the continuous convolution kernel density estimator.
Consensus analysis, model-based clustering, and cultural consensus theory applications to response data (e.g. questionnaires). The models are applied using hierarchical Bayesian inference. The current package version supports binary, ordinal, and continuous data formats.
Coupled-dipole simulations for electromagnetic scattering of light by sub-wavelength particles in arbitrary 3-dimensional configurations. Scattering and absorption spectra are simulated by inversion of the interaction matrix, or by an order-of-scattering approximation scheme. High-level functions are provided to simulate spectra with varying angles of incidence, as well as with full angular averaging.
Supplies deliberately verbose wrappers for 'tidyr::gather()' and 'tidyr::spread()', and an explanatory vignette.  Useful for training and for enforcing preconditions.
A constant database is a data structure created by Daniel
        J. Bernstein in his cdb package. Its format consists on a
        sequence of (key,value)-pairs. This R package replicates the
        basic utilities for reading (cdbget) and writing (cdbdump)
        constant databases.
The U.S. Centers for Disease Control (CDC) maintains a portal
    <http://gis.cdc.gov/grasp/fluview/fluportaldashboard.html> for
    accessing state, regional and national influenza statistics as well as
    Mortality Surveillance Data. The Flash interface makes it difficult and 
    time-consuming to select and retrieve influenza data. This package 
    provides functions to access the data provided by the portal's underlying API.
Gives conditional distance correlation and performs the conditional distance correlation sure independence screening procedure for ultrahigh dimensional data. The conditional distance correlation is a novel conditional dependence measurement of two random variables given a third variable. The conditional distance correlation sure independence screening is used for screening variables in ultrahigh dimensional setting.
Designed by and for the community of differential privacy algorithm developers. It can be used to empirically evaluate and visualize Cumulative Distribution Functions incorporating noise that satisfies differential privacy, with numerous options made to streamline collection of utility measurements across variations of key parameters, such as epsilon, domain size, sample size, data shape, etc. Developed by researchers at Harvard PSI.
Employs a two-parameter family of
    distributions for modelling random variables on the (0, 1) interval by
    applying the cumulative distribution function (cdf) of one parent
    distribution to the quantile function of another.
This package proposes a statistical downscaling method for
        cumulative distribution functions (CDF), as well as the
        computation of the Cram\`er-von Mises statistics U, and the
        Kolmogorov-Smirnov statistics KS.
Coordinate Descent Algorithms for Lasso Penalized L1, L2,
        and Logistic Regression
Downloads USDA National Agricultural Statistics Service (NASS) 
    cropscape data for a specified state. Utilities for fips, abbreviation, 
    and name conversion are also provided. Full functionality requires an 
    internet connection, but data sets can be cached for later off-line use.
Functions for cognitive diagnosis modeling
    and multidimensional item response modeling for
    dichotomous and polytomous data. This package
    enables the estimation of the DINA and DINO model,
    the multiple group (polytomous) GDINA model,
    the multiple choice DINA model, the general diagnostic
    model (GDM), the multidimensional linear compensatory
    item response model and the structured latent class
    model (SLCA).
Components of Canadian Credit Aggregates and Monetary Aggregates with continuity adjustments.
Wrapper functions to model and extract various quantitative information from absorption spectra of chromophoric dissolved organic matter (CDOM).
Classification is based on the recently developed phylogenetic
    approach by Assis and Bachtrog (2013). The method classifies the
    evolutionary mechanisms retaining pairs of duplicate genes (conservation,
    neofunctionalization, subfunctionalization, or specialization) by comparing gene
    expression profiles of duplicate genes in one species to those of their single-
    copy ancestral genes in a sister species.
This is an implementation of constrained dual scaling for
    detecting response styles in categorical data, including utility functions. The
    procedure involves adding additional columns to the data matrix representing the
    boundaries between the rating categories. The resulting matrix is then doubled
    and analyzed by dual scaling. One-dimensional solutions are sought which provide
    optimal scores for the rating categories. These optimal scores are constrained
    to follow monotone quadratic splines. Clusters are introduced within which the
    response styles can vary. The type of response style present in a cluster can
    be diagnosed from the optimal scores for said cluster, and this can be used to
    construct an imputed version of the data set which adjusts for response styles.
Functions for statistical inference of canonical vine (C-vine)
        and D-vine copulas. Tools for bivariate exploratory data analysis and for bivariate
        as well as vine copula selection are provided. Models can be estimated
        either sequentially or by joint maximum likelihood estimation.
        Sampling algorithms and plotting methods are also included.
        Data is assumed to lie in the unit hypercube (so-called copula
        data).
Provides tools for sampling from a conditional copula density decomposed via 
     Pair-Copula Constructions as C- or D- vine. Here, the vines which can be used for such a 
     sampling are those which sample as first the conditioning variables (when following the 
     sampling algorithms shown in Aas et al. (2009) <DOI:10.1016/j.insmatheco.2007.02.001>). 
     The used sampling algorithm is presented and discussed in Bevacqua et al. (2017) 
     <DOI:10.5194/hess-2016-652>, and it is a modified version of that from Aas et al. (2009) 
     <DOI:10.1016/j.insmatheco.2007.02.001>. A function is available to select the best vine 
     (based on information criteria) among those which allow for such a conditional sampling. 
     The package includes a function to compare scatterplot matrices and pair-dependencies of 
     two multivariate datasets.
Cross-Entropy Clustering (CEC) divides the data into Gaussian type clusters. It performs the automatic reduction of unnecessary clusters, while at the same time allows the simultaneous use of various type Gaussian mixture models.
This package is a wrapper for the C implementation of the 25 benchmark functions for the CEC 2005 Special Session on Real-Parameter Optimization. The original C code by Santosh Tiwari and related documentation are available at http://www.ntu.edu.sg/home/EPNSugan/index_files/CEC-05/CEC05.htm.
This package provides R wrappers for the C implementation of 28 benchmark functions defined for the  Special Session and Competition on Real-Parameter Single Objective Optimization at CEC-2013. The focus of this package is to provide an open-source and multi-platform implementation of the CEC2013 benchmark functions, in order to make easier for researchers to test the performance of new optimization algorithms in a reproducible way. The original C code (Windows only) was provided by Jane Jing Liang, while GNU/Linux comments were made by Janez Brest. This package was gently authorised for publication on CRAN by Ponnuthurai Nagaratnam Suganthan. The official documentation is available at http://www.ntu.edu.sg/home/EPNSugan/index_files/CEC2013/CEC2013.htm. Bugs reports/comments/questions are very welcomed (in English, Spanish or Italian).
Model building, surrogate model
    based optimization and Efficient Global Optimization in combinatorial
    or mixed search spaces.
Contains a number of common astronomy conversion routines, particularly the HMS and degrees schemes, which can be fiddly to convert between on mass due to the textural nature of the former. It allows users to coordinate match datasets quickly. It also contains functions for various cosmological calculations.
Helper functions to work with spreadsheets and the "A1:D10" style
    of cell range specification.
This package is an object-oriented implementation of one-dimensional cellular automata. It supports many of the features offered by Mathematica, including elementary rules, user-defined rules, radii, user-defined seeding, and plotting.
This package implements a methodology for using cell
        volume distributions to estimate cell growth rates and division
        times that is described in the paper entitled "Cell Volume
        Distributions Reveal Cell Growth Rates and Division Times", by
        Michael Halter, John T. Elliott, Joseph B. Hubbard, Alessandro
        Tona and Anne L. Plant, which is in press in the Journal of
        Theoretical Biology.  In order to reproduce the analysis used
        to obtain Table 1 in the paper, execute the command
        "example(fitVolDist)".
Tools for detecting cellwise outliers and robust methods to analyze data which may contain them.
Implementation of the Coarsened Exact Matching algorithm.
Conditional expectation manifolds are an approach to compute principal curves and surfaces.
Implementations of threshold regression approaches for linear
	     regression models with a covariate subject to random censoring,
	     including deletion threshold regression and completion threshold regression.
	     Reverse survival regression, which flip the role of response variable and the
	     covariate, is also considered.
Fit censored linear regression models where the random errors follow a finite mixture of Scale Mixture Normal distributions.
             Fit censored linear models of finite mixture multivariate Student-t and Normal distributions.
             Fit censored mixture regression models based on scale mixture of normal distributions.
Implement an interval censor method 
 to break ties when using data with ties to fitting a 
 bivariate copula.
Maximum Likelihood estimation of censored regression (Tobit) models
   with cross-sectional and panel data.
Fits univariate censored linear regression model under Normal or Student-t distribution
Fits linear regression models for censored spatial data. Provides different estimation methods as the SAEM (Stochastic Approximation of Expectation Maximization) algorithm and seminaive that uses Kriging prediction to estimate the response at censored locations and predict new values at unknown locations. Also offers graphical tools for assessing the fitted model.
Contains selected variables from the time series profiles for statistical areas level 2 from the 2006, 2011, and 2016 censuses of population and housing, Australia. Also provides methods for viewing the questions asked for convenience during analysis.
A wrapper for the U.S. Census Bureau APIs that returns data frames of 
	Census data and metadata. Available datasets include the 
	Decennial Census, American Community Survey, Small Area Health Insurance Estimates,
	Small Area Income and Poverty Estimates, and Population Estimates and Projections.
	See <https://www.census.gov/data/developers/data-sets.html> for more information.
Converts the United States Census geographic code for city, state (FIP and ICP),
    region, and birthplace, into the name of the region. e.g. takes an input of
    Census city code 5330 to it's actual city, Philadelphia. Will return NA for code
    that doesn't correspond to real location.
Use the US Census API to collect summary data tables
    for SF1 and ACS datasets at arbitrary geographies.
The 'Censys' public search engine enables researchers to quickly ask 
    questions about the hosts and networks that compose the Internet. Details on how 
    'Censys' was designed and how it is operated are available at <https://www.censys.io/about>. 
    Both basic and extended research access queries are made available. More information 
    on the SQL dialect used by the 'Censys' engine can be found at 
    <https://cloud.google.com/bigquery/docs/reference/legacy-sql>.
Calculates centrality indices additional to the 'igraph' package centrality functions.
Optimization solver based on the Cross-Entropy method.
Use pathway topology information to assign weight to
        pathway nodes.
Performs cepstral based discriminant analysis of groups of time series 
    when there exists Variability in power spectra from time series within the same group 
    as described in R.T. Krafty (2016) "Discriminant Analysis of Time Series in the 
    Presence of Within-Group Spectral Variability" Journal of Time Series Analysis.
Functions and Data to support Context Driven Exploratory Projection Pursuit.
Retorna detalhes de dados de CEPs brasileiros, bairros, logradouros e tal. (Returns info of Brazilian postal codes, city names, addresses and so on.)
Implements the iterated RMCD method of Cerioli (2010)
	for multivariate outlier detection via robust Mahalanobis distances. Also
	provides the finite-sample RMCD method discussed in the paper, as well as 
	the methods provided in Hardin and Rocke (2005) <doi:10.1198/106186005X77685> 
	and Green and Martin (2017).
An implementation of the covariance estimation method
   proposed in Chi and Lange (2014), "Stable estimation of a covariance matrix guided by nuclear norm penalties,"
   Computational Statistics and Data Analysis 80:117-128.
Collection of perceptually uniform colour maps made by Peter Kovesi (2015) "Good Colour Maps: How to Design Them" <arXiv:1509.03700> 
    at the Centre for Exploration Targeting (CET).
Analysis of configuration frequencies for simple and repeated measures, multiple-samples CFA, hierarchical CFA, bootstrap CFA, functional CFA, Kieser-Victor CFA, and Lindner's test using a conventional and an accelerated algorithm.
Numerical integration of cause-specific survival curves to arrive at cause-specific cumulative incidence functions,
  with three usage modes: 1) Convenient API for parametric survival regression followed by competing-risk analysis, 2) API for
  CFC, accepting user-specified survival functions in R, and 3) Same as 2, but accepting survival functions in C++.
Estimate quantiles using formula (18) from
        http://www.jaschke-net.de/papers/CoFi.pdf (Yaschke; 2001)
This project provides a group of new functions to calculate the
    outputs of the two main components of the Canadian Forest Fire Danger Rating
    System (CFFDRS) at various time scales: the Fire Weather Index (FWI) System and
    the Fire Behaviour Prediction (FBP) System. Some functions have two versions,
    table and raster based.
Comprehensive data analysis software, and the name "cg" stands for "compare groups." Its genesis and evolution are driven by common needs to compare administrations, conditions, etc. in medicine research and development. The current version provides comparisons of unpaired samples, i.e. a linear model with one factor of at least two levels. It also provides comparisons of two paired samples. Good data graphs, modern statistical methods, and useful displays of results are emphasized.
A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors, each of which may have a shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The ShapeSelect routine chooses a subset of predictor variables and describes the component relationships with the response. For each predictor, the user needs only specify a set of possible shape or order restrictions. A model selection method chooses the shapes and orderings of the relationships as well as the variables. The cone information criterion (CIC) is used to select the best combination of variables and shapes. A genetic algorithm may be used when the set of possible models is large. In addition, the cgam routine implements a two-dimensional isotonic regression using warped-plane splines without additivity assumptions.  It can also fit a convex or concave regression surface with triangle splines without additivity assumptions.
The cgAUC can calculate the AUC-type measure of Obuchowski(2006) when gold standard is continuous, and find the optimal linear combination of variables with respect to this measure.
Provides a basic set of R functions for querying the Cancer 
  Genomics Data Server (CGDS), hosted by the Computational Biology Center at 
  Memorial-Sloan-Kettering Cancer Center (MSKCC).
Efficient procedures for fitting an entire regression
        sequences with different model types.
Functions to analyze microarray comparative genome
        hybridization data using the Smith-Waterman algorithm
Spatial smoothing and hot spot detection using the fused
        lasso regression
Provides functions to import data from Agilent CGH arrays and process them according to the cghRA workflow. Implements several algorithms such as WACA, STEPS and cnvScore and an interactive graphical interface.
cghseg is an R package dedicated to the analysis of CGH
        profiles using segmentation models.
Fit composite Gaussian process (CGP) models as described in Ba and Joseph (2012) "Composite Gaussian Process Models for Emulating Expensive Functions", Annals of Applied Statistics. The CGP model is capable of approximating complex surfaces that are not second-order stationary.  Important functions in this package are CGP, print.CGP, summary.CGP, predict.CGP and plotCGP.
A set of tools the author has found useful for performing quick observations or evaluations of data, including a variety of ways to list objects by size, class, etc. Several other tools mimic Unix shell commands, including 'head', 'tail' ,'pushd' ,and 'popd'. The functions 'seqle' and 'reverse.seqle' mimic the base 'rle' but can search for linear sequences. The function 'splatnd' allows the user to generate zero-argument commands without the need for 'makeActiveBinding' .
Various statistical methods and models which are
    typically used for the estimation of outstanding claims reserves
    in general insurance, including those to estimate the claims
    development result as required under Solvency II.
Implements various mainstream and specialised changepoint methods for finding single and multiple changepoints within data.  Many popular non-parametric and frequentist methods are included.  The cpt.mean(), cpt.var(), cpt.meanvar() functions should be your first point of call.
Implements the multiple changepoint algorithm PELT with a
    nonparametric cost function based on the empirical distribution of the data. The cpt.np() function should be your first point of call.
    This package is an extension to the \code{changepoint} package which uses parametric changepoint methods.  For further information on the methods see the
    documentation for \code{changepoint}.
A multiple testing procedure for clustered alternative hypotheses. It is assumed that the p-values under the null hypotheses follow U(0,1) and that the distributions of p-values from the alternative hypotheses are stochastically smaller than U(0,1). By aggregating information, this method is more sensitive to detecting signals of low magnitude than standard methods. Additionally, sporadic small p-values appearing within a null hypotheses sequence are avoided by averaging on the neighboring p-values.
Advertisers use a variety of online marketing channels to reach consumers and they want to know the degree each channel contributes to their marketing success. It's called the online multi-channel attribution problem. This package contains a probabilistic algorithm for the attribution problem. The model uses a k-order Markov representation to identifying structural correlations in the customer journey data. The package also contains three heuristic algorithms (first-touch, last-touch and linear-touch approach) for the same problem. The algorithms are implemented in C++.
Shiny Web Application for the Multichannel Attribution Problem. It is basically a user-friendly graphical interface for running and comparing all the attribution models in package 'ChannelAttribution'. For customizations or interest in other statistical methodologies for web data analysis please contact <davide.altomare@gmail.com>.
Computes and plot the results of the 0-1 test for chaos proposed
    by Gottwald and Melbourne (2004) <DOI:10.1137/080718851>. The algorithm is
    available in parallel for the independent values of parameter c.
The main objective of the package is to enter a word of at least two letters based on which an Iterated Function System with Probabilities is constructed, and a two-dimensional fractal containing the chosen word infinitely often is generated via the Chaos Game. Additionally, the package allows to project the two-dimensional fractal on several three-dimensional surfaces and to transform the fractal into another fractal with uniform marginals.
The Characteristic Functions Toolbox (CharFun) consists of a set of algorithms for evaluating selected characteristic functions and algorithms for numerical inversion of the (combined and/or compound) characteristic functions, used to evaluate the probability density function (PDF) and the cumulative distribution function (CDF).
This package provides functions to compute Marcus, Marcus-Levich-Jortner or Landau-Zener charge transfer rates. These rates can then be used to perform kinetic Monte Carlo simulations to estimate charge carrier mobilities in molecular materials. The preparation of this package was supported by the the Fondazione Cariplo (PLENOS project, ref. 2011-0349).
Make fake data, supporting addresses, person names, dates,
    times, colors, coordinates, currencies, digital object identifiers
    ('DOIs'), jobs, phone numbers, 'DNA' sequences, doubles and integers
    from distributions and within a range.
CHAT is a collection of tools developed for tumor subclonality analysis using high density DNA SNP array data and sequencing data. The pipeline consists of four major compartments: 1) tumor aneuploid genome proportion (AGP) calculation and ploidy estimation. 2) segment-specific AGP calculation and absolute copy number estimation for somatic CNAs. 3) cancer cell fraction correction for somatic SNVs in clonal or subclonal sCNA regions. 4) number of subclones estimation using Dirichlet process prior followed by MCMC approach.
A compilation of historical through contemporary climate
        measurements scraped from the Environment Canada Website
        Including tools for scraping data, creating metadata and
        formating temperature files.
Contains methods for creating multivariate Chebyshev
  approximation of functions on a hypercube. Some methods for
  non-Chebyshev grids are also provided.
Utility functions that allow checking the basic validity of a function argument or any other value, 
             including generating an error and assigning a default in a single line of code. The main purpose of
             the package is to provide simple and easily readable argument checking to improve code robustness.
A set of functions to calculate check digits according to
        various algorithms and to verify whether a string ends in a
        valid check digit
Tests and assertions to perform frequent argument checks. A
    substantial part of the package was written in C to minimize any worries
    about execution time overhead.
The goal of checkpoint is to solve the problem of package
    reproducibility in R. Specifically, checkpoint allows you to install packages
    as they existed on CRAN on a specific snapshot date as if you had a CRAN time
    machine. To achieve reproducibility, the checkpoint() function installs the
    packages required or called by your project and scripts to a local library
    exactly as they existed at the specified point in time. Only those packages
    are available to your project, thereby avoiding any package updates that came
    later and may have altered your results. In this way, anyone using checkpoint's
    checkpoint() can ensure the reproducibility of your scripts or projects at any
    time. To create the snapshot archives, once a day (at midnight UTC) Microsoft
    refreshes the Austria CRAN mirror on the "Microsoft R Archived Network"
    server (<https://mran.microsoft.com/>). Immediately after completion
    of the rsync mirror process, the process takes a snapshot, thus creating the
    archive. Snapshot archives exist starting from 2014-09-17.
Provides a flexible, extendable representation of an ecological community and a range of functions for analysis and visualisation, focusing on food web, body mass and numerical abundance data. Allows inter-web comparisons such as examining changes in community structure over environmental, temporal or spatial gradients.
Simple functions for plotting linear
	calibration functions and estimating standard errors for measurements
	according to the Handbook of Chemometrics and Qualimetrics: Part A
	by Massart et al. There are also functions estimating the limit
	of detection (LOD) and limit of quantification (LOQ).
	The functions work on model objects from - optionally weighted - linear
	regression (lm) or robust linear regression ('rlm' from the 'MASS' package).
Contains a set of methods for fitting models and methods for
    validating the resulting models. The statistical methodologies comprise
    a comprehensive collection of approaches whose validity and utility have
    been accepted by experts in the Cheminformatics field. As promising new
    methodologies emerge from the statistical and data-mining communities, they
    will be incorporated into the laboratory. These methods are aimed at discovering
    quantitative structure-activity relationships (QSARs). However, the user can
    directly input their own choices of descriptors and responses, so the capability
    for comparing models is effectively unlimited.
R companion to the book "Introduction to Multivariate Statistical Analysis in Chemometrics" written by K. Varmuza and P. Filzmoser (2009).
Functions and scripts used in the book "Chemometrics with R - Multivariate Data Analysis in the Natural Sciences and Life Sciences" by Ron Wehrens, Springer (2011).
The package provides data sets used in the book
        "Chemometrics with R - Multivariate Data Analysis in the
        Natural Sciences and Life Sciences" by Ron Wehrens, Springer
        (2011).
A collection of functions for top-down exploratory data analysis
    of spectral data obtained via nuclear magnetic resonance (NMR), infrared (IR) or
    Raman spectroscopy. Includes functions for plotting and inspecting spectra, peak
    alignment, hierarchical cluster analysis (HCA), principal components analysis
    (PCA) and model-based clustering. Robust methods appropriate for this type of
    high-dimensional data are available. ChemoSpec is designed with metabolomics
    data sets in mind, where the samples fall into groups such as treatment and
    control. Graphical output is formatted consistently for publication quality
    plots. ChemoSpec is intended to be very user friendly and help you get usable
    results quickly. A vignette covering typical operations is available.
Provides an alternative approach to multiple testing
        by calculating a simultaneous upper confidence bounds for the
        number of true null hypotheses among any subset of the hypotheses of interest. 
	Some of the functions in this package are optionally enhanced by the 'gurobi'
	software and its accompanying R package. For their installation, please follow the 
	instructions at www.gurobi.com and http://www.gurobi.com/documentation, respectively.
The software matches the current history to the closest history in a time series to build a forecast.
Light weight implementation of the standard distribution 
  functions for the chi distribution, wrapping those for the chi-squared 
  distribution in the stats package.
Provides two index partitions for three-way contingency tables:
   partition of the association measure chi-squared and of the predictability index tau 
   under several representative hypotheses about the expected frequencies (hypothesized probabilities).
Calculation of standard deviation scores adduced from different
    growth standards (WHO, UK, Germany, Italy, China, etc). Therefore, the calculation of SDS-values
    for different measures like BMI, weight, height, head circumference, different
    ratios, etc. are easy to carry out. Also, references for laboratory values in
    children are available: serum lipids, iron-related blood parameters. In the
    new version, there are also functions combining the lms() function from package 'gamlss' with
    resampling methods for using with repeated measurements and family dependencies. A searchable list
    of items can be found here: <https://github.com/mvogel78/childsds/wiki>.
The phenology of plants (i.e. the timing of their annual life
    phases) depends on climatic cues. For temperate trees and many other plants,
    spring phases, such as leaf emergence and flowering, have been found to result
    from the effects of both cool (chilling) conditions and heat. Fruit tree
    scientists (pomologists) have developed some metrics to quantify chilling
    and heat. 'chillR' contains functions for processing temperature records into
    chilling (Chilling Hours, Utah Chill Units and Chill Portions) and heat units
    (Growing Degree Hours). Regarding chilling metrics, Chill Portions are often
    considered the most promising, but they are difficult to calculate. This package
    makes it easy. 'chillR' also contains procedures for conducting a PLS analysis
    relating phenological dates (e.g. bloom dates) to either mean temperatures or
    mean chill and heat accumulation rates, based on long-term weather and phenology
    records. As of version 0.65, it also includes functions for generating weather
    scenarios with a weather generator, for conducting climate change analyses
    for temperature-based climatic metrics and for plotting results from such
    analyses.
Efforts are made to make Chinese text mining easier, faster, and robust to errors. 
    Document term matrix can be generated by only one line of code; detecting encoding, 
    segmenting and removing stop words are done automatically. 
	Some convenient tools are also supplied.
A collection of functions to pre-process amplification curve data from polymerase chain reaction (PCR) or isothermal amplification reactions. Contains functions to normalize and baseline amplification curves, to detect both the start and end of an amplification reaction, several smoothers (e.g., LOWESS, moving average, cubic splines, Savitzky-Golay), a function to detect false positive amplification reactions and a function to determine the amplification efficiency. Quantification point (Cq) methods include the first (FDM) and second approximate derivative maximum (SDM) methods (calculated by a 5-point-stencil) and the cycle threshold method. Data sets of experimental nucleic acid amplification systems (VideoScan HCU, capillary convective PCR (ccPCR)) and commercial systems are included. Amplification curves were generated by helicase dependent amplification (HDA), ccPCR or PCR. As detection system intercalating dyes (EvaGreen, SYBR Green) and hydrolysis probes (TaqMan) were used.
Nonparametric Tests to identify the differential enrichment region for two conditions or time-course ChIP-seq data. It includes: data preprocessing function, estimation of a small constant used in hypothesis testing, a kernel-based two sample nonparametric test, two assumption-free  two sample nonparametric test.
An exact and a variational inference for
    coupled Hidden Markov Models applied to the joint detection of copy number variations.
Change point regression models are also called two-phase regression, break-point regression, split-point regression, structural change models and threshold regression models. Hypothesis testing in change point logistic regression with or without interaction terms (Fong, Y., Di, C., Perma, S. (2014) <doi:10.1002/sim.6419>). Several options are provided for testing in models with interaction, including a maximum of likelihood ratios test that determines p-value through Monte Carlo. Estimation under change point model is also included with model-robust confidence intervals for continuous threshold regression models (Fong, Y., Di, C., Huang, Y., Gilbert, P. (2017) <doi:10.1111/biom.12623>).
An integrated set of tools for thermodynamic calculations in compositional
  biology and geochemistry. Thermodynamic properties are taken from a database for minerals
  and inorganic and organic aqueous species including biomolecules, or from amino acid
  group additivity for proteins. High-temperature properties are calculated using the
  revised Helgeson-Kirkham-Flowers equations of state for aqueous species. Functions are
  provided to define a system using basis species, automatically balance reactions,
  calculate the chemical affinities of reactions for selected species, and plot the results
  on potential diagrams or equilibrium activity diagrams. Experimental features are
  available to calculate activity coefficients for aqueous species or for multidimensional
  optimization of thermodynamic variables using an objective function.
Processes data from Lattes CV 
    (<http://lattes.cnpq.br/>) XML files. Extract, condition, and plot 
    lists of journal and conference papers, book chapters, books, 
    and more.
This package consists of functions to design DCMs and other types of choice 
  studies (including MaxDiff and other tradeoffs)
Implements an MCMC algorithm to estimate a hierarchical
        multinomial logit model with a normal heterogeneity
        distribution. The algorithm uses a hybrid Gibbs Sampler with a
        random walk metropolis step for the MNL coefficients for each
        unit. Dependent variable may be discrete or continuous.
        Independent variables may be discrete or continuous with
        optional order constraints. Means of the distribution of
        heterogeneity can optionally be modeled as a linear function of
        unit characteristics variables.
Amends errors, augments data and aids analysis of John Snow's map
    of the 1854 London cholera outbreak. The original data come from
    Rusty Dodson and Waldo Tobler's 1992 digitization of Snow's map. Those
    data, <http://www.ncgia.ucsb.edu/pubs/snow/snow.html>, are no longer
    available. However, they are preserved in the 'HistData' package,
    <https://CRAN.R-project.org/package=HistData>.
Choplump Tests are Permutation Tests for Comparing Two Groups with Some Positive but Many Zero Responses
Resampling is a standard step in particle filtering and in
    sequential Monte Carlo. This package implements the chopthin resampler, which
    keeps a bound on the ratio between the largest and the smallest weights after
    resampling.
Learning the structure of graphical models from datasets with thousands of variables.
    More information about the research papers detailing the theory behind Chordalysis is available at
    <http://www.francois-petitjean.com/Research> (KDD 2016, SDM 2015, ICDM 2014, ICDM 2013).
    The R package development site is <https://github.com/HerrmannM/Monash-ChoR>.
Maximum likelihood estimation in respondent driven samples.
Choropleths are thematic maps where geographic regions, such as
    states, are colored according to some metric, such as the number of people
    who live in that state. This package simplifies this process by 1.
    Providing ready-made functions for creating choropleths of common maps. 2.
    Providing data and API connections to interesting data sources for making
    choropleths. 3. Providing a framework for creating choropleths from
    arbitrary shapefiles. 4. Overlaying those maps over reference maps from
    Google Maps.
Contains an administrative-level-1 map of the world.
    Administrative-level-1 is the generic term for the largest sub-national
    subdivision of a country. This package was created for use with the
    choroplethr package.
Contains 3 maps. 1) US States 2) US Counties 3) Countries of the
    world.
A programmatic interface to the Chromosome Counts Database
    (http://ccdb.tau.ac.il/). This package is part of the rOpenSci suite
    (http://ropensci.org)
chromoR provides users with a statistical pipeline for analysing chromosomal interactions data (Hi-C data).It combines wavelet methods and a Bayesian approach for correction (bias and noise) and comparison (detecting significant changes between Hi-C maps) of Hi-C contact maps.In addition, it also support detection of change points in 1D Hi-C contact profiles.
Provides chronological objects which can handle dates and times.
Functions for use in perturbing data prior to use of nonparametric smoothers
             and clustering.
Text data can be processed chunkwise using 'dplyr' commands. These
    are recorded and executed per data chunk, so large files can be processed with
    limited memory using the 'LaF' package.
Calculation of consensus values for atomic weights, isotope amount ratios, and isotopic abundances with the associated uncertainties using multivariate meta-regression approach for consensus building.
Generative models for complex networks with conditionally independent dyadic structure. Now supports directed arcs!
Estimate of cumulative incidence function in two samples. Provide weighted summary statistics based on various methods and weights.
Functions for the input/output and visualization of
    medical imaging data in the form of 'CIFTI' files 
    <https://www.nitrc.org/projects/cifti/>.
Many experiments in neuroscience involve randomized and fast stimulation while the continuous outcome measures respond at much slower time scale, for example event-related fMRI.  This package provide valid statistical tools with causal interpretation under these challenging settings, without imposing model assumptions.
This package provides functions to compute a method for identifying the instar of Curculionid larvae from the observed distribution of the headcapsule size of mature larvae.
Functions for computing, comparing and demonstrating top informative centrality measures within a network.
Detecting and visualizing nonlinear interaction effects of single nucleotide polymorphisms or epistatic interactions, especially high-order epistatic interactions, are important topics in bioinformatics because of their significant mathematical and computational challenges. We present CINOEDV (Co-Information based N-Order Epistasis Detector and Visualizer) for detecting, visualizing, and analyzing high-order epistatic interactions by introducing virtual vertices into the construction of a hypergraph. CINOEDV was developed as an alternative to existing software to build a global picture of epistatic interactions and unexpected high-order epistatic interactions, which might provide useful clues for understanding the underlying genetic architecture of complex diseases.
Plot confidence interval from the objects of statistical tests such as
  t.test(), var.test(), cor.test(), prop.test() and fisher.test() ('htest' class),
  Tukey test [TukeyHSD()], Dunnett test [glht() in 'multcomp' package],
  logistic regression [glm()], and Tukey or Games-Howell test [posthocTGH() in
  'userfriendlyscience' package].
  Users are able to set the styles of lines and points.
  This package contains the function to calculate odds ratios and their confidence
  intervals from the result of logistic regression.
Isotonic regression (IR), as well as a great small-sample improvement to IR called
    CIR, interval estimates for both, and additional utilities.
This package contains functions for fitting circumplex
        structural models for correlation matrices (with negative
        correlation) by the method of maximum likelihood.
Circular layout is an efficient way for the visualization of huge 
    amounts of information. Here this package provides an implementation 
    of circular layout generation in R as well as an enhancement of available 
    software. The flexibility of the package is based on the usage of low-level 
    graphics functions such that self-defined high-level graphics can be easily 
    implemented by users for specific purposes. Together with the seamless 
    connection between the powerful computational and visual environment in R, 
    it gives users more convenience and freedom to design figures for 
    better understanding complex patterns behind multiple dimensional data.
A series of wrapper functions to
    implement the 10 maximum likelihood models of animal orientation
    described by Schnute and Groot (1992) <DOI:10.1016/S0003-3472(05)80068-5>. The
    functions also include the ability to use different optimizer
    methods and calculate various model selection metrics (i.e., AIC,
    AICc, BIC).
Includes functions for the analysis of circular data using distributions based on Nonnegative Trigonometric Sums (NNTS). The package includes functions for calculation of densities and distributions, for the estimation of parameters, for plotting and more.
Detection of outliers in circular-circular regression models, modifying its and estimating of models parameters.
Circular Statistics, from "Topics in circular Statistics"
        (2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific.
Circular Statistics, from "Topics in circular Statistics" (2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific.
Circular drift-diffusion model for continuous reports.
Jointly model the accuracy of cognitive responses and item choices
    within a bayesian hierarchical framework as described by Culpepper and
    Balamuta (2015) <doi:10.1007/s11336-015-9484-7>. In addition, the package
    contains the datasets used within the analysis of the paper.
A likelihood-based hypothesis testing approach is implemented for assessing causal mediation. For example, it could be used to test for mediation of a known causal association between a DNA variant, the 'instrumental variable', and a clinical outcome or phenotype by gene expression or DNA methylation, the potential mediator. Another example would be testing mediation of the effect of a drug on a clinical outcome by the molecular target. The hypothesis test generates a p-value or permutation-based FDR value with confidence intervals to quantify uncertainty in the causal inference. The outcome can be represented by either a continuous or binary variable, the potential mediator is continuous, and the instrumental variable can be continuous or binary and is not limited to a single variable but may be a design matrix representing multiple variables.
Supports quantitative
    research in scientometrics and bibliometrics. Provides
    various tools for preprocessing bibliographic
    data retrieved, e.g., from Elsevier's SciVerse Scopus,
    computing bibliometric impact of individuals,
    or modeling many phenomena encountered in the social sciences.
This package implements the approach to assign tumor gene expression dataset to the 6 CIT Breast Cancer Molecular Subtypes described in Guedj et al 2012.
This package implements the approach to assign tumor gene expression dataset to the 6 CIT Colon Cancer Molecular Subtypes described in Marisa et al 2013.
Contain an RStudio addin to insert BibTex citation in Rmarkdown documents with a minimal user interface.
Functions to append confidence intervals, prediction intervals,
    and other quantities of interest to data frames. All appended quantities
    are for the response variable, after conditioning on the model and covariates.
    This package has a data frame first syntax that allows for easy piping.
    Currently supported models include (log-) linear, (log-) linear mixed,
    and generalized linear models.
Functions and an 'RStudio' add-in that search a 'BibTeX'-file to create and
  insert formatted Markdown citations into the current document.
Input: a csv-file for each database table and a
        controlfile describing relations between tables. Output: An
        extended ER diagram
Retrieves data and estimates unmeasured flows of water through the 
  urban network. Any city may be modeled with preassembled data, but data for 
  US cities can be gathered via web services using this package and dependencies 
  'geoknife' and 'dataRetrieval'.
An R implementation of the Average Marginal Component-specific
    Effects (AMCE) estimator presented in Hainmueller, J., Hopkins, D., and Yamamoto
    T. (2014) <DOI:10.1093/pan/mpt024> Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional
    Choices via Stated Preference Experiments. Political Analysis 22(1):1-30.
Toolkit for statistical, machine learning, and targeted learning
  analyses. Functionality includes loading & auto-installing packages,
  standardizing datasets, creating missingness indicators, imputing missing
  values, creating multicore or multinode clusters, automatic SLURM integration,
  enhancing SuperLearner and TMLE with automatic parallelization, and many other
  SuperLearner analysis & plotting enhancements.
Client for 'CKAN' 'API' (http://ckan.org/). Includes interface
    to 'CKAN' 'APIs' for search, list, show for packages, organizations, and
    resources. In addition, provides an interface to the 'datastore' 'API'.
Fast optimal univariate clustering and segementation
 by dynamic programming. Three types of problem including
 univariate k-means, k-median, and k-segments are solved with
 guaranteed optimality and reproducibility. The core algorithm
 minimizes the sum of within-cluster distances using respective
 metrics. Its advantage over heuristic clustering algorithms in
 efficiency and accuracy is increasingly pronounced as the
 number of clusters k increases. Weighted k-means and unweighted
 k-segments algorithms can also optimally segment time series
 and perform peak calling. An auxiliary function generates
 histograms that are adaptive to patterns in data. In contrast to
 heuristic methods, this package provides a powerful set of tools
 for univariate data analysis with guaranteed optimality.
This package implements in C++/Rcpp various cladogenesis-related calculations that are slow in pure R.  These include the calculation of the probability of various scenarios for the inheritance of geographic range at the divergence events on a phylogenetic tree, and other calculations necessary for models which are not continuous-time markov chains (CTMC), but where change instead occurs instantaneously at speciation events.  Typically these models must assess the probability of every possible combination of (ancestor state, left descendent state, right descendent state).  This means that there are up to (# of states)^3 combinations to investigate, and in biogeographical models, there can easily be hundreds of states, so calculation time becomes an issue.  C++ implementation plus clever tricks (many combinations can be eliminated a priori) can greatly speed the computation time over naive R implementations.  CITATION INFO: This package is the result of my Ph.D. research, please cite the package if you use it!  Type: citation(package="cladoRcpp") to get the citation information.
Allows for idealisation of patch clamp recordings by implementing the non-parametric JUmp Local
  dEconvolution Segmentation filter JULES.
Implementation of the Wilkinson and Ivany (2002) approach to paleoclimate analysis, applied to isotope data extracted from clams.
Get description of images from Clarifai API. For more information,
    see <http://clarifai.com>. Clarifai uses a large deep learning cloud to come
    up with descriptive labels of the things in an image. It also provides how
    confident it is about each of the labels.
Various functions for classification, including k-nearest
  neighbour, Learning Vector Quantization and Self-Organizing Maps.
Defines the classes used for "class comparison" problems
  in the OOMPA project (<http://oompa.r-forge.r-project.org/>). Class
  comparison includes tests for differential expression; see Simon's
  book for details on typical problem types.
Defines the classes used for "class discovery" problems
  in the OOMPA project (<http://oompa.r-forge.r-project.org/>). Class
  discovery primarily consists of unsupervised clustering methods with
  attempts to assess their statistical significance.
Construct directed graphs of S4 class hierarchies and
  visualize them.  In general, these graphs typically are DAGs (directed
  acyclic graphs), often simple trees in practice.
Generates a visualization of binary classifier performance as a grid of
  diagnostic plots with just one function call. Includes ROC curves,
  prediction density, accuracy, precision, recall and calibration plots, all using
  ggplot2 for easy modification.
  Debug your binary classifiers faster and easier!
Given $p$-dimensional training data containing
    $d$ groups (the design space), a classification
    algorithm (classifier) predicts which group new data
    belongs to.  Generally the input to these algorithms is
    high dimensional, and the boundaries between groups
    will be high dimensional and perhaps curvilinear or
    multi-faceted. This package implements methods for
    understanding the division of space between the groups.
Efficient implementation of k-nearest neighbor estimator and a kernel estimator for functional data classification.
IRT classification uses the probability that candidates of
    a given ability, will answer correctly questions of a specified
    difficulty to calculate the probability of their achieving
    every possible score in a test. Due to the IRT assumption of
    conditional independence (that is every answer given is assumed
    to depend only on the latent trait being measured) the
    probability of candidates achieving these potential scores can
    be expressed by multiplication of probabilities for item
    responses for a given ability. Once the true score and the
    probabilities of achieving all other scores have been
    determined for a candidate the probability of their score lying
    in the same category as that of their true score
    (classification accuracy), or the probability of consistent
    classification in a category over administrations
    (classification consistency), can be calculated.
Selected commonly used methods for choosing univariate class intervals for mapping or other graphics purposes.
A collection of functions for the creation and application of highly optimised, robustly evaluated ensembles of support vector machines (SVMs). The package takes care of training individual SVM classifiers using a fast parallel heuristic algorithm, and combines individual classifiers into ensembles. Robust metrics of classification performance are offered by bootstrap resampling and permutation testing.
Bindings to Google's C++ library Compact Language Detector 2
    (see <https://github.com/cld2owners/cld2#readme> for more information). Probabilistically
    detects over 80 languages in plain text or HTML. For mixed-language input it returns the
    top three detected languages and their approximate proportion of the total classified 
    text bytes (e.g. 80% English and 20% French out of 1000 bytes). There is also a 'cld3'
    package on CRAN which uses a neural network model instead.
Google's Compact Language Detector 3 is a neural network model for language 
    identification and the successor of 'cld2' (available from CRAN). The algorithm is still
    experimental and takes a novel approach to language detection with different properties
    and outcomes. It can be useful to combine this with the Bayesian classifier results 
    from 'cld2'. See <https://github.com/google/cld3#readme> for more information.
A toolset to deal with the Critical Care Health Informatics Collaborative
    dataset. It is created to address various data reliability and accessibility
    problems of electronic healthcare records (EHR). It provides a unique
    platform which enables data manipulation, transformation, reduction,
    anonymisation, cleaning and validation.
Provides a set of utility tools to inspect spatial objects, facilitate
  handling and reporting of topology errors and geometry validity issues.
  Finally, it provides a geometry cleaner that will fix all geometry problems,
  and eliminate (at least reduce) the likelihood of having issues when doing
  spatial data processing.
Provides a set of fast tools for converting a textual corpus into a set of normalized
  tables. Users may make use of a Python back end with 'spaCy' <https://spacy.io>
  or the Java back end 'CoreNLP' <http://stanfordnlp.github.io/CoreNLP/>. A minimal back
  end with no external dependencies is also provided. Exposed annotation tasks include
  tokenization, part of speech tagging, named entity recognition, entity linking, sentiment
  analysis, dependency parsing, coreference resolution, and word embeddings. Summary
  statistics regarding token unigram, part of speech tag, and dependency type frequencies
  are also included to assist with analyses.
Check your R code for some of the most common layout flaws.
    Many tried to teach us how to write code less dreadful, be it implicitly as
    B. W. Kernighan and D. M. Ritchie (1988) <ISBN:0-13-110362-8>
    in 'The C Programming Language' did, be it
    explicitly as R.C. Martin (2008) <ISBN:0-13-235088-2> in
    'Clean Code: A Handbook of Agile Software Craftsmanship' did.
    So we should check our code for files too long or wide, functions with too
    many lines, too wide lines, too many arguments or too many levels of 
    nesting.
    Note: This is not a static code analyzer like pylint or the like. Checkout
    https://github.com/jimhester/lintr instead.
Implements an empirical Bayes approach for simultaneous variable clustering and regression. This version also (re)implements in C++ an R script proposed by Howard Bondell that fits the Pairwise Absolute Clustering and Sparsity (PACS) methodology (see Sharma et al (2013) <DOI:10.1080/15533174.2012.707849>).
Conditioned Latin hypercube sampling, as published by Minasny and McBratney (2006) <DOI:10.1016/j.cageo.2005.12.009>. This method proposes to stratify sampling in presence of ancillary data. An extension of this method, which propose to associate a cost to each individual and take it into account during the optimisation process, is also proposed (Roudier et al., 2012, <DOI:10.1201/b12728-46>).
Clustering categorical sequences by means of finite mixtures with Markov model components is the main utility of ClickClust. The package also allows detecting blocks of equivalent states by forward and backward state selection procedures.
Fixes data errors in numerical, factor and date variables, checks data quality and performs report tables from models and summaries.
A set of tools to read, analyze and write lists of click sequences
    on websites (i.e., clickstream). A click can be represented by a number,
    character or string. Clickstreams can be modeled as zero- (only computes
    occurrence probabilities), first- or higher-order Markov chains.
CliFlo is a web portal to the New Zealand National Climate
    Database and provides public access (via subscription) to around 6,500
    various climate stations (see <https://cliflo.niwa.co.nz/> for more
    information). Collating and manipulating data from CliFlo
    (hence clifro) and importing into R for further analysis, exploration and
    visualisation is now straightforward and coherent. The user is required to
    have an internet connection, and a current CliFlo subscription (free) if
    data from stations, other than the public Reefton electronic weather
    station, is sought.
A profile likelihood based method of estimation and inference on the correlation coefficient of bivariate data with different types of censoring and missingness.
Functions to homogenize climatological series and to produce climatological summaries and grids from the homogenized results, plus functions to draw wind-roses and Walter&Lieth diagrams.
Calculates first, and second order, average minimal depth of a
    maximal subtree for a forest object produced by the R 'ranger'
    package. This variable importance metric is implemented as described in
    Ishwaran et. al. ("High-Dimensional Variable Selection for Survival Data",
    March 2010, <doi:10.1198/jasa.2009.tm08622>).
Classification of climate according to Koeppen - Geiger, of aridity
    indices, of continentality indices, of water balance after Thornthwaite, of
    viticultural bioclimatic indices. Drawing climographs: Thornthwaite, Peguy,
    Bagnouls-Gaussen.
PCIC's implementation of Climdex routines for computation of
    extreme climate indices.
A suite of routines for downscaling coarse scale global
    climate model (GCM) output to a fine spatial resolution. Includes
    Bias-Corrected Spatial Downscaling (BCDS), Constructed Analogues
    (CA), Climate Imprint (CI), and Bias Correction/Constructed
    Analogues with Quantile mapping reordering (BCCAQ). Developed by
    the the Pacific Climate Impacts Consortium (PCIC), Victoria,
    British Columbia, Canada.
A robust constrained L1 minimization method for estimating
        a large sparse inverse covariance matrix (aka precision
        matrix), and recovering its support for building graphical
        models.  The computation uses linear programming.
Functions for fitting GEV and POT (via point process fitting)
    models for extremes in climate data, providing return values, return
    probabilities, and return periods for stationary and nonstationary models.
    Also provides differences in return values and differences in log return
    probabilities for contrasts of covariate values. Functions for estimating risk
    ratios for event attribution analyses, including uncertainty. Under the hood,
    many of the functions use functions from extRemes, including for fitting the
    statistical models.
Absolute homogeneity tests SNHT absolute 1-breaks, 1-break, 
        SD different from 1, 2-breaks, Buishand, Pettitt, von Neumann ratio and 
        ratio-rank, Worsley, and Craddock, Relative homogeneity tests SNHT 
        absolute 1-breaks, 1-break SD different from 1, 2-breaks, Peterson 
        and Easterling, and Vincent, Differences in scale between two groups Siegel–Tukey, 
        Create reference time series mean, weights/correlation, finding outliers Grubbs, 
        ESD, MAD, Tietjen Moore, Hampel, etc.
Contains functions to detect and visualise periods of climate
    sensitivity (climate windows) for a given biological response.
Utilities to make your clinical collaborations easier if not
          fun. It contains functions for designing studies such as Simon
          2-stage and group sequential designs and for data analysis such
          as Jonckheere-Terpstra test and estimating survival quantiles.
Calculates equations commonly used in clinical pharmacokinetics and clinical pharmacology, such as equations for dose individualization, compartmental pharmacokinetics, drug exposure, anthropomorphic calculations, clinical chemistry, and conversion of common clinical parameters. Where possible and relevant, it provides multiple published and peer-reviewed equations within the respective R function.
Functions for calculating clinical significance.
This package provides the estimation of an index measuring
        the clinical utility of DNA testing in the context of
        gene-environment interactions on a disease. The corresponding
        gene-environment interaction effect on the additive scale can
        also be obtained. The estimation is based on case-control or
        cohort data. The method was developed by Nguyen et al. 2013.
Simple utility functions to read from and write to the Windows,
    OS X, and X11 clipboards.
A small subset of Unicode symbols, that are useful
    when building command line applications. They fall back to
    alternatives on terminals that do not support Unicode.
    Many symbols were taken from the 'figures' 'npm' package
    (see <https://github.com/sindresorhus/figures>).
Estimation and inference for linear models where some or all of the
    fixed-effects coefficients are subject to order restrictions. This package uses
    the robust residual bootstrap methodology for inference, and can handle some
    structure in the residual variance matrix.
A set of functions to fit a boosting conditional logit model.
Tools for the fitting and cross validation of exact conditional logistic regression models with lasso and elastic net penalties. Uses cyclic coordinate descent and warm starts to compute the entire path efficiently.
Fit a sequence of conditional logistic regression models with lasso, for small to large sized samples.
Provides means of plots for comparing utilization data of compute systems.
R Interface to C API of COIN-OR Clp, depends on COIN-OR Clp Version >= 1.12.0.
This package provides and implementation of a one step
        smoothing newton method for the solution of second order cone
        programming problems, originally described by Xiaoni Chi and
        Sanyang Liu.
Central limit theorem experiments presented by data frames or plots. Functions include generating theoretical sample space, corresponding probability, and simulated results as well.
Provides several cluster-robust variance estimators
    (i.e., sandwich estimators) for ordinary and weighted least squares linear
    regression models, including the bias-reduced linearization estimator introduced 
    by Bell and McCaffrey (2002) <http://www.statcan.gc.ca/pub/12-001-x/2002002/article/9058-eng.pdf> 
    and developed further by Pustejovsky and Tipton (2017) <DOI:10.1080/07350015.2016.1247004>.
    The package includes functions for estimating the variance-
    covariance matrix and for testing single- and multiple-contrast hypotheses
    based on Wald test statistics. Tests of single regression coefficients use
    Satterthwaite or saddle-point corrections. Tests of multiple-contrast hypotheses 
    use an approximation to Hotelling's T-squared distribution.  Methods are
    provided for a variety of fitted models, including lm(), glm(), plm() (from package 'plm'),
    gls() and lme() (from 'nlme'), robu() (from 'robumeta'), and rma.uni() and rma.mv() (from
    'metafor').
CLUster Evaluation (CLUE) is a computational method for identifying optimal number of clusters in a given time-course dataset clustered by cmeans or kmeans algorithms and subsequently identify key kinases or pathways from each cluster. Its implementation in R is called ClueR. See Readme on <https://github.com/PengyiYang/ClueR> for more details.
We developed the clues R package to provide functions 
        for automatically estimating the number of clusters and 
        getting the final cluster partition without any input 
        parameter except the stopping rule for convergence. 
        The package also provides functions to
        evaluate and compare the performances of partitions of a data
        set both numerically and graphically.
Provides utilities for clustering subjects and variables of mixed data types. Similarities between subjects are measured by Gower's general similarity coefficient with an extension of Podani for ordinal variables. Similarities between variables are assessed by combination of appropriate measures of association for different pairs of data types. Alternatively, variables can also be clustered by the 'ClustOfVar' approach. The main feature of the package is the generation of a mixed-data heatmap. For visualizing similarities between either subjects or variables, a heatmap of the corresponding distance matrix can be drawn. Associations between variables can be explored by a 'confounderPlot', which allows visual detection of possible confounding, collinear, or surrogate factors for some variables of primary interest. Distance matrices and dendrograms for subjects and variables can be derived and used for further visualizations and applications. This work was supported by BMBF grant 01ZX1609B, Germany.
Non-parametric tests (Wilcoxon rank sum test and Wilcoxon signed rank test) for clustered data.
Tests, utilities, and case studies for analyzing significance in clustered binary matched-pair
  data. The central function clust.bin.pair uses one of several tests to calculate a Chi-square 
  statistic. Implemented are the tests Eliasziw, Obuchowski, Durkalski, and Yang with McNemar
  included for comparison. The utility functions nested.to.contingency and paired.to.contingency
  convert data between various useful formats. Thyroids and psychiatry are the canonical
  datasets from Obuchowski and Petryshen respectively.
Methods for Cluster analysis.  Much extended the original from
	Peter Rousseeuw, Anja Struyf and Mia Hubert,
	based on Kaufman and Rousseeuw (1990) "Finding Groups in Data".
Provides functionality for the analysis of clustered data using the cluster bootstrap.
A collection of data sets for teaching cluster analysis.
Identification and visualization of groups of closely spaced mutations in the DNA sequence of cancer genome. The extremely mutated zones are searched in the symmetric dissimilarity matrix using the anti-Robinson matrix properties. Different data sets are obtained to describe and plot the clustered mutations information.
Visualise clustering algorithms with GGobi.  Contains both
    general code for visualising clustering results and specific
    visualisations for model-based, hierarchical and SOM clustering.
We developed the clusterGeneration package to provide functions 
        for generating random clusters, generating random 
        covariance/correlation matrices,
        calculating a separation index (data and population version)
        for pairs of clusters or cluster distributions, and 1-D and 2-D
        projection plots to visualize clusters.  The package also
        contains a function to generate random clusters based on
        factorial designs with factors such as degree of separation,
        number of clusters, number of variables, number of noisy
        variables.
The Partitioning Algorithm based on Recursive Thresholding
        (PART) is used to recursively uncover clusters and subclusters
        in the data. Functionality is also available for visualization
        of the clustering.
One haplotype is a combination of SNP
  (Single Nucleotide Polymorphisms) within the QTL (Quantitative Trait Loci).
  clusterhap groups together all individuals of a population with the same haplotype.
  Each group contains individual with the same allele in each SNP,
  whether or not missing data. Thus, clusterhap groups individuals,
  that to be imputed, have a non-zero probability of having the same alleles
  in the entire sequence of SNP's. Moreover, clusterhap calculates such
  probability from relative frequencies.
A dynamic programming algorithm for optimal clustering multidimensional data with sequential constraint. The algorithm minimizes the sum of squares of within-cluster distances. The sequential constraint allows only subsequent items of the input data to form a cluster. The sequential constraint is typically required in clustering data streams or items with time stamps such as video frames, GPS signals of a vehicle, movement data of a person, e-pen data, etc. The algorithm represents an extension of Ckmeans.1d.dp to multiple dimensional spaces. Similarly to the one-dimensional case, the algorithm guarantees optimality and repeatability of clustering. Method clustering.sc.dp can find the optimal clustering if the number of clusters is known. Otherwise, methods findwithinss.sc.dp and backtracking.sc.dp can be used.
Provides the Q() function to send arbitrary function calls to
    workers on HPC schedulers without relying on network-mounted storage.
    Allows using remote schedulers via SSH.
Integrative context-dependent clustering for heterogeneous
    biomedical datasets. Identifies local clustering structures in related
    datasets, and a global clusters that exist across the datasets.
This package enables researchers to calculate power for cluster-randomized crossover trials by employing a simulation-based approach.  A particular study design is specified, with fixed sample sizes for all clusters and an assumed treatment effect, and the empirical power for that study design is calculated by simulating hypothetical datasets.
Gaussian mixture models, k-means, mini-batch-kmeans and k-medoids
    clustering with the option to plot, validate, predict (new data) and estimate the
    optimal number of clusters. The package takes advantage of 'RcppArmadillo' to
    speed up the computationally intensive parts of the functions.
Nonparametric rank based tests (rank-sum tests and signed-rank tests) for clustered data, especially useful for clusters having informative cluster size and intra-cluster group size.
A function for validating microarry clusters via
        reproducibility
Calculate p-values and confidence intervals using cluster-adjusted
    t-statistics (based on Ibragimov and Muller (2010) <DOI:10.1198/jbes.2009.08046>, pairs cluster bootstrapped t-statistics, and wild cluster bootstrapped t-statistics (the latter two techniques based on Cameron, Gelbach, and Miller (2008) <DOI:10.1162/rest.90.3.414>. Procedures are included for use with GLM, ivreg, plm (pooling or fixed effects), and mlogit models.
Distance measures (GDM1, GDM2,	Sokal-Michener, Bray-Curtis, for symbolic interval-valued data), cluster quality indices (Calinski-Harabasz, Baker-Hubert, Hubert-Levine, Silhouette, Krzanowski-Lai, Hartigan, Gap,	Davies-Bouldin),	data normalization formulas, data generation (typical and non-typical data), HINoV method,	replication analysis, linear ordering methods, spectral clustering, agreement indices between two partitions, plot functions (for categorical and symbolic interval-valued data).
Allows one to assess the stability of individual objects, clusters 
    and whole clustering solutions based on repeated runs of the K-means and K-medoids 
    partitioning algorithms.
Calculate some statistics aiming to help analyzing the clustering tendency of given data. In the first version, Hopkins' statistic is implemented.
An R package that provides a suite of tools to evaluate
        clustering algorithms, clusterings, and individual clusters.
Implements a Ward-like hierarchical clustering
    algorithm including spatial/geographical constraints.
Model-based clustering of mixed data (i.e. data which consist of
    continuous, binary, ordinal or nominal variables) using a parsimonious
    mixture of latent Gaussian variable models.
Functions to perform k-prototypes partitioning clustering for
    mixed variable-type data according to Z.Huang (1998): Extensions to the k-Means
    Algorithm for Clustering Large Data Sets with Categorical Variables, Data Mining
    and Knowledge Discovery 2, 283-304, <DOI:10.1023/A:1009769707641>.
An implementation of a variable selection procedure in clustering by mixture models for discrete data (clustMMDD). Genotype data are examples of such data with two unordered observations (alleles) at each locus for diploid individual. The two-fold problem of variable selection and clustering is seen as a model selection problem where competing models are characterized by the number of clusters K, and the subset S of clustering variables. Competing models are compared by penalized maximum likelihood criteria. We considered asymptotic criteria such as Akaike and Bayesian Information criteria, and a family of penalized criteria with penalty function to be data driven calibrated.
Cluster analysis of a set of variables. Variables can be quantitative, qualitative or a mixture of both.
Provides an interface to perform cluster analysis on a corpus of text. Interfaces to 
    Quanteda to assemble text corpuses easily. Deviationalizes text vectors prior to clustering 
    using technique described by Sherin (Sherin, B. [2013]. A computational study of commonsense science: 
    An exploration in the automated analysis of clinical interview data. Journal of the Learning Sciences, 
    22(4), 600-638. Chicago. http://dx.doi.org/10.1080/10508406.2013.836654). Uses cosine similarity as distance
    metric for two stage clustering process, involving Ward's algorithm hierarchical agglomerative clustering, 
    and k-means clustering. Selects optimal number of clusters to maximize "variance explained" by clusters, 
    adjusted by the number of clusters. Provides plotted output of clustering results as well as printed output. 
    Assesses "model fit" of clustering solution to a set of preexisting groups in dataset.
A class of methods that combine dimension reduction and clustering of continuous or categorical data. For continuous data, the package contains implementations of factorial K-means (Vichi and Kiers 2001; <DOI:10.1016/S0167-9473(00)00064-5>) and reduced K-means (De Soete and Carroll 1994; <DOI:10.1007/978-3-642-51175-2_24>); both methods that combine principal component analysis with K-means clustering. For categorical data, the package provides MCA K-means (Hwang, Dillon and Takane 2006; <DOI:10.1007/s11336-004-1173-x>), i-FCB (Iodice D'Enza and Palumbo 2013, <DOI:10.1007/s00180-012-0329-x>) and Cluster Correspondence Analysis (van de Velden, Iodice D'Enza and Palumbo 2017; <DOI:10.1007/s11336-016-9514-0>), which combine multiple correspondence analysis with K-means.
A complimentary package for use with hclust; simprof tests
        to see which (if any) clusters are statistically different. The
        null hypothesis is that there is no a priori group structure.
        See Clarke, K.R., Somerfield, P.J., and Gorley R.N. 2008.
        Testing of null hypothesis in exploratory community analyses:
        similarity profiles and biota-environment linkage. J. Exp. Mar.
        Biol. Ecol. 366, 56-69.
Functions for the clustering of variables around Latent Variables.
    Each cluster of variables, which may be defined as a local or directional
    cluster, is associated with a latent variable. External variables measured on
    the same observations or/and additional information on the variables can be
    taken into account. A "noise" cluster or sparse latent variables can also de
    defined.
Variable selection for Gaussian model-based clustering as implemented in the 'mclust' package. The methodology allows to find the (locally) optimal subset of variables in a data set that have group/cluster information. A greedy or headlong search can be used, either in a forward-backward or backward-forward direction, with or without sub-sampling at the hierarchical clustering stage for starting 'mclust' models. By default the algorithm uses a sequential search, but parallelisation is also available.
Gaussian-Based Visualization of clustering achieved by mixture model on any type of data. Visualization is based on the probabilities of classification.
Package contains most of the popular internal and external
        cluster validation methods ready to use for the most of the
        outputs produced by functions coming from package "cluster".
        Package contains also functions and examples of usage for
        cluster stability approach that might be applied to algorithms
        implemented in "cluster" package as well as user defined
        clustering algorithms.
Statistical and biological validation of clustering results.
Single objective optimization using a CMA-ES.
Pure R implementation of the Covariance Matrix Adaptation -
    Evolution Strategy (CMA-ES) with optional restarts (IPOP-CMA-ES).
Calculation and plot of the stepwise Cronbach-Mesbah Curve
Collective matrix factorization (CMF) finds joint low-rank representations for a collection of matrices with shared row or column entities. This code learns variational Bayesian approximation for CMF, supporting multiple likelihood potentials and missing data, while identifying both factors shared by multiple matrices and factors private for each matrix.
Quite extensive package for the estimation of marginal models for categorical data.
Provides the source and examples for James P. Howard, II, 
             "Computational Methods for Numerical Analysis with R," 
			 <https://jameshoward.us/cmna>, a forthcoming book on
			 numerical methods in R.
The main purpose of this package is to juxtapose the different control limits obtained by modelling a data set through the COM-Poisson distribution vs. the classical Poisson distribution. Accordingly, this package offers the ability to compute the COM-Poisson parameter estimates and plot associated Shewhart control charts for a given data set.
Manhattan plot, a type of scatter plot, was widely used to display the association results. However, it is usually time-consuming and laborious for a
 non-specialist user to write scripts and adjust parameters of an elaborate plot. Moreover, the ever-growing traits measured have necessitated the 
 integration of results from different Genome-wide association study researches. Circle Manhattan Plot is the first open R package that can lay out 
 Genome-wide association study P-value results in both traditional rectangular patterns, QQ-plot and novel circular ones. United in only one bull's eye style 
 plot, association results from multiple traits can be compared interactively, thereby to reveal both similarities and differences between signals.
A toolkit for flexible modeling of count processes where data (over- or under-) dispersion exists.
    Estimations can be obtained under two data constructs where one has:
    (1) data on number of events in an s-unit time interval, or (2) only wait-time data.
    This package is supplementary to the work set forth in Zhu et al. (2016) <doi:10.1080/00031305.2016.1234976>.
Estimation, testing and regression modeling of
 subdistribution functions in competing risks, as described in Gray
 (1988), A class of K-sample tests for comparing the cumulative
 incidence of a competing risk, Ann. Stat. 16:1141-1154, and Fine JP and
 Gray RJ (1999), A proportional hazards model for the subdistribution
 of a competing risk, JASA, 94:496-509.
Estimation, testing and regression modeling of
 subdistribution functions in competing risks using quantile regressions,
 as described in Peng and Fine (2009) <DOI:10.1198/jasa.2009.tm08228>.
A collection of useful helper routines developed by
  students of the Center for the Mathematical Research, Stankin,
  Moscow.
The Satellite Application Facility on Climate Monitoring (CM SAF) 
  is a ground segment of the European Organization for the Exploitation of 
  Meteorological Satellites (EUMETSAT) and one of EUMETSATs Satellite Application 
  Facilities. The CM SAF contributes to the sustainable observing of the climate 
  system by providing Essential Climate Variables related to the energy and water 
  cycle of the atmosphere (<http://www.cmsaf.eu>). It is a joint cooperation of seven 
  National Meteorological and Hydrological Services.
  The 'cmsaf' R-package provides a small collection of R-functions, which are 
  inspired by the Climate Data Operators ('cdo'). This gives the opportunity to  
  analyse and manipulate CM SAF data without the need of installing cdo. 
  The 'cmsaf' R-package is tested for CM SAF NetCDF data, which are structured 
  in three-dimensional arrays (longitude, latitude, time) on a rectangular grid. 
  Layered CM SAF data have to be converted with the provided 'levbox_mergetime()' 
  function. The 'cmsaf' R-package functions have only minor checks for deviations 
  from the recommended data structure, and give only few specific error messages. 
  Thus, there is no warranty of accurate results.
  Scripts for an easy application of the functions are provided at the CM SAF homepage 
  (<http://www.cmsaf.eu/EN/Products/Tools/Tools_node.html>).
Various utilities for the complex multivariate Gaussian distribution.
Provides comprehensive functionalities for causal modeling with Coincidence Analysis (CNA), which is a configurational comparative method of causal data analysis that was first introduced in Baumgartner (2009) <doi:10.1177/0049124109339369>. CNA is related to Qualitative Comparative Analysis (QCA), but contrary to the latter, it is custom-built for uncovering causal structures with multiple outcomes. While previous versions have only been capable of processing dichotomous variables, the current version generalizes CNA for multi-value and continuous variables whose values are interpreted as membership scores in fuzzy sets.
Provided R functions for working with the Conditional Negative Binomial distribution.
A GUI with which users can construct and interact
        with Canonical Correspondence Analysis and Canonical Non-Symmetrical Correspondence Analysis and provides inferential results by using Bootstrap Methods.
Implementations of recent complex-valued wavelet shrinkage procedures for smoothing irregularly sampled signals.
Implementations of recent complex-valued wavelet spectral procedures for analysis of irregularly sampled signals.
Contains functions for computing the nonparametric maximum
	     likelihood estimate of a log-concave density function from
	     univariate observations. The log-density estimate is always a
	     piecewise linear function.
Methods for assigning copy number states and breakpoints in resequencing experiments of prokaryotic organisms.
This package evaluates DNA copy number data, using both their initial form (copy number as a noisy function of genomic position) and their approximation by a piecewise-constant function (segmentation), for the purpose of identifying genomic regions where the copy number differs from the norm.
Efficient computations for null models that require shuffling columns on big matrix data.
             This package provides functions for faster computation of diversity measure statistics
             when independent random shuffling is applied to the columns of a given matrix. 
             Given a diversity measure f and a matrix M, the provided functions can generate random samples 
             (shuffled matrix rows of M), the mean and variance of f, and the p-values of this measure 
             for two different null models that involve independent random shuffling of the columns of M.
             The package supports computations of alpha and beta diversity measures.
Carries out analysis of common 
             Copy Number Variants (CNVs) and imputed Single Nucleotide 
             Polymorphisms (SNPs) in population-based studies. 
             It includes tools for estimating association under a series 
             of study designs (case-control, cohort, etc), using several 
             dependent variables (class status, censored data, counts) 
             as response, adjusting for covariates and considering 
             various inheritance models. Moreover, it is possible to 
             perform epistasis studies with pairs of CNVs or imputed SNPs.
             It has been optimized in order to make feasible the analyses 
             of Genome Wide Association studies (GWAs) with hundreds of 
             thousands of genetic variants (CNVs / imputed SNPs). Also, 
             it incorporates functions for inferring copy number (CNV 
             genotype calling). Various classes and methods for generic 
             functions (print, summary, plot, anova, ...) have been 
             created to facilitate the analysis.
This package contains example data sets with Copy Number Variants and imputed SNPs to be used by CNVassoc package.
Coalescent simulators can rapidly simulate biological sequences
    evolving according to a given model of evolution.
    You can use this package to specify such models, to conduct the simulations
    and to calculate additional statistics from the results.
    It relies on existing simulators for doing the simulation, and currently
    supports the programs 'ms', 'msms' and 'scrm'. It also supports finite-sites
    mutation models by combining the simulators with the program 'seq-gen'.
Flexible framework for coalescent analyses in R. It includes a main function running the  MCMC algorithm, auxiliary functions for tree rearrangement, and some functions to compute population genetic parameters.
Functions to analyze coarse data.
    Specifically, it contains functions to (1) fit parametric accelerated
    failure time models to interval-censored survival time data, and (2)
    estimate the case-fatality ratio in scenarios with under-reporting.
    This package's development was motivated by applications to infectious
    disease: in particular, problems with estimating the incubation period and
    the case fatality ratio of a given disease.  Sample data files are included
    in the package.
Generate balance tables and plots for covariates of groups
    preprocessed through matching, weighting or subclassification, for example,
    using propensity scores. Includes integration with 'MatchIt', 'twang', 'Matching', 'optmatch', 
    'CBPS', and 'ebal' for assessing balance on the output of their preprocessing functions. Users
    can also specify data for balance assessment not generated through the above packages. Also 
    included are methods for assessing balance in clustered or multiply imputed data sets.
This package performs prediction for regression-oriented problems, aggregating in a nonlinear scheme any basic regression machines suggested by the context and provided by the user. If the user has no valuable knowledge on the data, four defaults machines wrappers are implemented so as to cover a minimal spectrum of prediction methods. If necessary, the computations may be parallelized. The method is described in Biau, Fischer, Guedj and Malley (2013), "COBRA: A Nonlinear Aggregation Strategy".
Qualitatively Constrained (Regression) Smoothing Splines via
        Linear Programming and Sparse Matrices.
Allows for pooled analysis of microarray data by batch-correcting control samples, and then applying the derived correction parameters to non-control samples to obtain bias-free, inter-dataset corrected data.
Statistical tests for the comparison between two correlations
    based on either independent or dependent groups. Dependent correlations can
    either be overlapping or nonoverlapping. A web interface is available on the
    website http://comparingcorrelations.org. A plugin for the R GUI and IDE RKWard
    is included. Please install RKWard from https://rkward.kde.org to use this
    feature. The respective R package 'rkward' cannot be installed directly from a
    repository, as it is a part of RKWard.
The algorithm extracts shared variation from a collection of data sets using regression models.
Fits predictive and symmetric co-correspondence analysis (CoCA) models to relate one data matrix
  to another data matrix. More specifically, CoCA maximises the weighted covariance 
  between the weighted averaged species scores of one community and the weighted averaged species
  scores of another community. CoCA attempts to find patterns that are common to both communities.
Statistical tests for the comparison between two or more alpha
    coefficients based on either dependent or independent groups of individuals.
    A web interface is available at http://comparingcronbachalphas.org. A plugin
    for the R GUI and IDE RKWard is included. Please install RKWard from https://
    rkward.kde.org to use this feature. The respective R package 'rkward' cannot be
    installed directly from a repository, as it is a part of RKWard.
Provides functions for summarizing and plotting the
	output from Markov Chain Monte Carlo (MCMC) simulations, as
	well as diagnostic tests of convergence to the equilibrium
	distribution of the Markov chain.
A minimum set of functions to perform compositional data analysis
    using the log-ratio approach introduced by John Aitchison in 1982. Main functions
    have been implemented in c++ for better performance.
Markov chain Monte Carlo burn-in based on "bridge" statistics, in the way of coda::heidel.diag, but including non asymptotic tabulated statistics.
Generates all necessary C functions allowing the user to work with
    the compiled-code interface of ode() and bvptwp(). The implementation supports
    "forcings" and "events". Also provides functions to symbolically compute
    Jacobians, sensitivity equations and adjoint sensitivities being the basis for
    sensitivity analysis.
Tools for analyzing R expressions
  or blocks of code and determining the dependencies between them.
  It focuses on R scripts, but can be used on the bodies of functions.
  There are many facilities including the ability to summarize  or get a high-level
  view of code, determining dependencies between variables,  code improvement
  suggestions.
Computation of Multiscale Codependence Analysis and spatial eigenvector maps, as an additional feature. Early development version.
A collection of coding functions as alternatives to the standard
    functions in the stats package, which have names starting with 'contr.'.  Their
    main advantage is that they provide a consistent method for defining marginal
    effects in factorial models.  In a simple one-way ANOVA model the
    intercept term is always the simple average of the class means.
A toolbox of ecological community dynamics metrics that are
    explicitly temporal. Functions fall into two categories: temporal diversity
    indices and community stability metrics. The diversity indices are temporal
    analogs to traditional diversity indices such as richness and rank-abundance
    curves. Specifically, functions are provided to calculate species turnover, mean
    rank shifts, and lags in community similarity between time points. The community
    stability metrics calculate overall stability and patterns of species covariance
    and synchrony over time.
Cronbach's alpha and McDonald's omega are widely used reliability or internal consistency measures in social, behavioral and education sciences. Alpha is reported in nearly every study that involves measuring a construct through multiple test items. The package 'coefficientalpha' calculates coefficient alpha and coefficient omega with missing data and non-normal data. Robust standard errors and confidence intervals are also provided. A test is also available to test the tau-equivalent and homogeneous assumptions. Version 0.5 added the bootstrap confidence intervals.
Plots the coefficients from model objects.  This very quickly shows the user the point estimates and confidence intervals for fitted models.
Simulate species occurrence and abundances (counts) along
    gradients.
Simulates the composition of samples of vegetation
        according to gradient-based vegetation theory.  Features a
        flexible algorithm incorporating competition and complex
        multi-gradient interaction.
species coexistence modeling under asymmetric dispersal
        and fluctuating source-sink dynamics;testing the proportion of
        coexistence scenarios driven by neutral and niche processes
Generate cofeature (feature by sample) matrices. The package 
  utilizes ggplot2::geom_tile() to generate the matrix allowing for easy
  additions from the base matrix.
Calculates complete functional regulation analysis and visualize
    the results in a single heatmap. The provided example data is for biological
    data but the methodology can be used for large data sets to compare quantitative
    entities that can be grouped. For example, a store might divide entities into
    cloth, food, car products etc and want to see how sales changes in the groups
    after some event. The theoretical background for the calculations are provided
    in New insights into functional regulation in MS-based drug profiling, Ana Sofia
    Carvalho, Henrik Molina & Rune Matthiesen, Scientific Reports <doi:10.1038/srep18826>.
Convolution of gamma distributions in R. The convolution of 
    gamma distributions is the sum of series of gamma 
    distributions and all gamma distributions here can have different 
    parameters. This package can calculate density, distribution function 
    and do simulation work.
Copula based imputation method. A semiparametric imputation procedure for missing multivariate data based on conditional copula specifications.
Conditional inference procedures for the general independence
  problem including two-sample, K-sample (non-parametric ANOVA), correlation,
  censored, ordered and multivariate problems.
Methods for obtaining simultaneous confidence interval for
        multinomial proportion have been proposed by many authors and
        the present study include a variety of widely applicable
        procedures. Seven classical methods (Wilson, Quesenberry and
        Hurst, Goodman, Wald with and without continuity correction,
        Fitzpatrick and Scott, Sison and Glaz) and Bayesian Dirichlet
        models are included in the package. The advantage of MCMC pack
        has been exploited to derive the Dirichlet posterior directly
        and this also helps in handling the Dirichlet prior parameters.
        This package is prepared to have equal and unequal values for
        the Dirichlet prior distribution that will provide better scope
        for data analysis and associated sensitivity analysis.
We propose a consistent monitoring procedure to detect a
    structural change from a cointegrating relationship to a spurious
    relationship. The procedure is based on residuals from modified least
    squares estimation, using either Fully Modified, Dynamic or Integrated
    Modified OLS. It is inspired by Chu et al. (1996) <DOI:10.2307/2171955> in
    that it is based on parameter estimation on a pre-break "calibration" period
    only, rather than being based on sequential estimation over the full sample.
    See the discussion paper <DOI:10.2139/ssrn.2624657> for further information.
    This package provides the monitoring procedures for both the cointegration
    and the stationarity case (while the latter is just a special case of the
    former one) as well as printing and plotting methods for a clear
    presentation of the results.
Cointegration methods are widely used in empirical macroeconomics
    and empirical finance. It is well known that in a cointegrating
    regression the ordinary least squares (OLS) estimator of the
    parameters is super-consistent, i.e. converges at rate equal to the
    sample size T. When the regressors are endogenous, the limiting
    distribution of the OLS estimator is contaminated by so-called second
    order bias terms, see e.g. Phillips and Hansen (1990) <DOI:10.2307/2297545>.
    The presence of these bias terms renders inference difficult. Consequently,
    several modifications to OLS that lead to zero mean Gaussian mixture
    limiting distributions have been proposed, which in turn make
    standard asymptotic inference feasible. These methods include
    the fully modified OLS (FM-OLS) approach of Phillips and Hansen
    (1990) <DOI:10.2307/2297545>, the dynamic OLS (D-OLS) approach of Phillips
    and Loretan (1991) <DOI:10.2307/2298004>, Saikkonen (1991)
    <DOI:10.1017/S0266466600004217> and Stock and Watson (1993)
    <DOI:10.2307/2951763> and the new estimation approach called integrated
    modified OLS (IM-OLS) of Vogelsang and Wagner (2014)
    <DOI:10.1016/j.jeconom.2013.10.015>. The latter is based on an augmented
    partial sum (integration) transformation of the regression model. IM-OLS is
    similar in spirit to the FM- and D-OLS approaches, with the key difference
    that it does not require estimation of long run variance matrices and avoids
    the need to choose tuning parameters (kernels, bandwidths, lags). However,
    inference does require that a long run variance be scaled out.
    This package provides functions for the parameter estimation and inference
    with all three modified OLS approaches. That includes the automatic
    bandwidth selection approaches of Andrews (1991) <DOI:10.2307/2938229> and
    of Newey and West (1994) <DOI:10.2307/2297912> as well as the calculation of
    the long run variance.
Performs least squares constrained optimization on a linear objective function. It contains
    a number of algorithms to choose from and offers a formula syntax similar to lm().
Implements a generalized version of the CDH test (<DOI:10.1371/journal.pone.0028145> and <DOI:10.1186/s12859-016-1006-9>)
     for detecting compound heterozygosity on a
    genome-wide level, due to usage of generalized linear models it allows flexible
    analysis of binary and continuous traits with covariates.
Interactive Reingold-Tilford tree diagrams created using 'D3.js', where every node can be expanded and collapsed by clicking on it.
    Tooltips and color gradients can be mapped to nodes using a numeric column in the source data frame.
    See 'collapsibleTree' website for more information and examples.
These functions implement collocation-inference
    for continuous-time and discrete-time stochastic processes.
    They provide model-based smoothing, gradient-matching,
    generalized profiling and forwards prediction error methods.
Markov chain Monte Carlo based inference routines for collapsed latent position cluster models or social networks, which includes searches over the model space (number of clusters in the latent position cluster model). The label switching algorithm used is that of Nobile and Fearnside (2007) <doi:10.1007/s11222-006-9014-7> which relies on the algorithm of Carpaneto and Toth (1980) <doi:10.1145/355873.355883>.
Provides some low level functions for processing PLINK input and output files.
Performs the colocalisation tests described in Plagnol et al
    (2009), Wallace et al (2013) and Giambartolomei et al (2013).
Loads and displays images, selectively masks specified background
  colors, bins pixels by color using either data-dependent or automatically
  generated color bins, quantitatively measures color similarity among images
  using one of several distance metrics for comparing pixel color clusters, and 
  clusters images by object color similarity. Originally written for use with
  organism coloration (reef fish color diversity, butterfly mimicry, etc), but
  easily applicable for any image set.
It implements colored Independent Component Analysis (Lee et al., 2011) and spatial colored Independent Component Analysis (Shen et al., 2014). They are two algorithms to perform ICA when sources are assumed to be temporal or spatial stochastic processes, respectively.
Given 2-,3- or 4-dimensional data, plots a Venn diagram, i.e. 'crossing circles'. The user can specify values, labels for each circle-group and unique colors for each plotted part. Here is what it would look like for a 3-dimensional plot: http://elliotnoma.files.wordpress.com/2011/02/venndiagram.png. To see what the 4-dimensional plot looks like, go to http://elliotnoma.files.wordpress.com/2013/03/4dplot.png.
This function takes a hierarchical cluster-class object and a factor describing the groups as arguments and generates colorful dendrograms in which leaves belonging to different groups are identified by colors.
Allows to generate colors from palettes defined in the colormap module of 'Node.js'. (see <https://github.com/bpostlethwaite/colormap> for more information). In total it provides 44 distinct palettes made from sequential and/or diverging colors. In addition to the pre defined palettes you can also specify your own set of colors. There are also scale functions that can be used with 'ggplot2'.
Different methods to generate a color palette based on a specified base color and a number of colors that should be created.
Shows color patches for encoding fold changes (e.g. log ratios) together with confidence values 
    within a single diagram. This is especially useful for rendering gene expression data as well as
    other types of differential experiments. In addition to different rendering methods (ggplot extensions)
    functionality for perceptually optimizing color palettes are provided.
    Furthermore the package provides extension methods of the colorspace color-class in order to
    simplify the work with palettes (a.o. length, as.list, and append are supported).
A 'ggplot2' extension to visualize two
    variables through one color aesthetic via mapping to a color space
    projection. With this technique for 2-D color mapping, one can create a
    bivariate choropleth in R as well as other visualizations with multivariate
    color scales. Includes two new scales and a new guide for 'ggplot2'.
Color palettes for EPL, MLB, NBA, NHL, and NFL teams.
Methods and data for color science - color conversions by observer,
        illuminant and gamma. Color matching functions and chromaticity diagrams.
        Color indices, color differences and spectral data conversion/analysis.
Carries out mapping between assorted color spaces including
             RGB, HSV, HLS, CIEXYZ, CIELUV, HCL (polar CIELUV),
	     CIELAB and polar CIELAB. Qualitative, sequential, and
	     diverging color palettes based on HCL colors are provided
	     along with an interactive palette picker (with either a Tcl/Tk
	     or a shiny GUI).
Calculate with spectral properties of light sources, materials, cameras, eyes, and scanners.
    Build complex systems from simpler parts using a spectral product algebra. For light sources,
    compute CCT and CRI.  For object colors, compute optimal colors and Logvinenko coordinates.
    Work with the standard CIE illuminants and color matching functions, and read spectra from 
    text files, including CGATS files.  Sample text files, and 4 vignettes are included.
R package with handy functions to help users select and play with
    color schemes in an HSV color model
Provides access to the COLOURlovers <http://www.colourlovers.com/>
    API, which offers color inspiration and color palettes.
A colour picker that can be used as an input in Shiny apps
    or 'Rmarkdown' documents. A Plot Colour Helper tool is available as an 
    'RStudio' addin, which helps you pick colours to use in your plots. A more 
    generic Colour Picker 'RStudio' addin is also provided to let you select 
    colours for use in your R code.
Colour vision models, colour spaces and colour thresholds. Includes Vorobyev & Osorio Receptor Noise Limited models, Chittka colour hexagon, and Endler & Mielke model. Models have been extended to accept any number of photoreceptor types.
Powerful functions to select and rename columns in dataframes, lists and numeric types 
  by 'Perl' regular expression. Regular expression ('regex') are a very powerful grammar to match 
  strings, such as column names.
Provides methods for the import/export and automated analysis of concept maps.
To compute gene-based genetic association statistics from P values at multiple SNPs and genotype data of ancestry matched reference samples. COMBined Association Test (COMBAT) incorporates strengths from multiple existing gene-based tests, including VEGAS, GATES and SimpleM, and achieves much improved performance than any individual test.
A comprehensive synergy/antagonism  analyses of drug combinations with
    quality graphics and data. The analyses can be performed by Bliss independence and Loewe
    additivity models. COMBIA provides improved statistical analysis and makes only very weak assumption of data variability 
    while calculating bootstrap intervals (BIs). Finally, package saves analyzed data, 
    2D and 3D plots ready to  use in research publications. COMBIA does not require manual
    data entry. Data can be directly input from wetlab experimental platforms 
    for example fluostar, automated robots etc. One needs to call a single function only  
    to perform all analysis (examples are provided with sample data).
Suite of R functions for combination of probabilities using a game-theoretic method.
Estimation of optimal portfolio weights as combination of simple portfolio strategies, like the tangency, global minimum variance (GMV) or naive (1/N) portfolio. It is based on a utility maximizing 8-fund rule. Popular special cases like the Kan-Zhou(2007) 2-fund and 3-fund rule or the Tu-Zhou(2011) estimator are nested.
We offer two statistical tests to combine p-values: selfcontained.test vs competitive.test. The goal is to test whether a vector of pvalues are jointly significant when we combine them together.
Series of partially balanced incomplete block designs (PBIB) based on the combinatory method (S) introduced in (Imane Rezgui et al, 2014) <doi:10.3844/jmssp.2014.45.48>; and it gives their associated U-type design.
Provides iterators for combinations, permutations, subsets, and
    Cartesian product, which allow one to go through all elements without creating a
    huge set of all possible values.
Functions for computing optimal convex combinations of
        model selection criteria based on ranks, along with utility
        functions for constructing model lists, MSCs, and priors on
        model lists.
Computes community climate statistics for volume and mismatch using species' climate niches either unscaled or scaled relative to a regional species pool. These statistics can be used to describe biogeographic patterns and infer community assembly processes. Includes a vignette outlining usage.
An algorithm for identifying combinations of mutually exclusive alterations in cancer genomes. CoMEt represents the mutations in a set M of k genes with a 2^k dimensional contingency table, and then computes the tail probability of observing T(M) exclusive alterations using an exact statistical test.
Functions to calculate various common and less common thermal comfort indices, convert physical variables, and evaluate the performance of thermal comfort indices.
Provided are Computational methods for Immune Cell-type Subsets, including:(1) DCQ (Digital Cell Quantifier) to infer global dynamic changes in immune cell quantities within a complex tissue; and (2) VoCAL (Variation of Cell-type Abundance Loci) a deconvolution-based method that utilizes transcriptome data to infer the quantities of immune-cell types, and then uses these quantitative traits to uncover the underlying DNA loci.
An S4 representation of the Command design pattern. The
  Operation class is a simple implementation using closures and supports
  forward and reverse (undo) evaluation. The more complicated Protocol
  framework represents each type of command (or analytical protocol) by
  a formal S4 class. Commands may be grouped and consecutively executed
  using the Pipeline class. Example use cases include logging, do/undo,
  analysis pipelines, GUI actions, parallel processing, etc.
Autosimilarity curves, dissimilarity indexes that overweight rare species, phylogenetic and functional (pairwise and multisample) dissimilarity indexes and nestedness for phylogenetic, functional and other diversity metrics. This should be a complement to available packages, particularly 'vegan'.
Functions to
    produce nicely formatted comments to use in R-scripts (or
    Latex/HTML/markdown etc). A comment with formatting is printed to the
    console and can then be copied to a script.
Useful libraries for building a Java based GUI under R
The CommonMark specification defines a rationalized version of markdown
    syntax. This package uses the 'cmark' reference implementation for converting
    markdown text into various formats including html, latex and groff man. In
    addition it exposes the markdown parse tree in xml format. The latest version of
    this package also adds support for Github extensions including tables, autolinks
    and strikethrough text.
Java JAR files for the Apache Commons Mathematics Library for use by users and other packages.
Directly extract and plot stochastic common trends from
        a cointegration system using different approaches, currently
        including Kasa (1992) and Gonzalo and Granger (1995). 
	The approach proposed by Gonzalo and Granger, also known as
        Permanent-Transitory Decomposition, is widely used in
        macroeconomics and market microstructure literature. 
	Kasa's approach, on the other hand, has a nice property that it only
        uses the super consistent estimator: the cointegration vector
        'beta'. 
	This package also provides functions calculate P-value
        from Johansen Statistics according to the approximation method
        proposed by Doornik (1998).
	Update:
	0.7-1: Fix bugs in calculation alpha. Add formulas and more explanations.
        0.6-1: Rewrite the description file.
        0.5-1: Add functions to calculate P-value from Johansen statistic, and vice versa.
Provides functions to measure the difference between constrained and unconstrained gene tree distributions using various tree distance metrics. Constraints are enforced prior to this analysis via the estimation of a tree under the community tree model.
The CommunityCorrelogram package is designed for the geostatistical analysis of ecological community datasets with either a spatial or temporal distance component.
Comparison of two ROC curves through the methodology proposed by Ana C. Braga.
Creates empty plots with compact axis notation to which users can
    add whatever they like (points, lines, text, etc.) The notation is more
    compact in the sense that the axis-labels and tick-labels are closer to the
    axis and the tick-marks are shorter. Also, if the plot appears as part of a
    matrix, the x-axis notation is suppressed unless the plot appears along the
    bottom row and the y-axis notation is suppress unless the plot appears
    along the left column.
Functions to compare a model object to a comparison object.
  If the objects are not identical, the functions can be instructed to
  explore various modifications of the objects (e.g., sorting rows,
  dropping names) to see if the modified versions are identical.
Proposed by Harrell, the C index or concordance C, is considered an overall measure of discrimination in survival analysis between a survival outcome that is possibly right censored and a predictive-score variable, which can represent a measured biomarker or a composite-score output from an algorithm that combines multiple biomarkers. This package aims to statistically compare two C indices with right-censored survival outcome, which commonly arise from a paired design and thus resulting two correlated C indices.
Unified interface for the estimation of causal networks, including
    the methods 'backShift' (from package 'backShift'), 'bivariateANM' (bivariate
    additive noise model), 'bivariateCAM' (bivariate causal additive model),
    'CAM' (causal additive model) (from package 'CAM'), 'hiddenICP' (invariant
    causal prediction with hidden variables), 'ICP' (invariant causal prediction)
    (from package 'InvariantCausalPrediction'), 'GES' (greedy equivalence
    search), 'GIES' (greedy interventional equivalence search), 'LINGAM', 'PC' (PC
    Algorithm), 'FCI' (fast causal inference), 
    'RFCI' (really fast causal inference) (all from package 'pcalg') and
    regression.
Compares two dataframes which have the same column
    structure to show the rows that have changed. Also gives a git style diff format
    to quickly see what has changes in addition to summary statistics.
Create data summaries for quality control, extensive reports for exploring data, as well as?publication-ready univariate or bivariate tables?in several formats (plain text, HTML,LaTeX, PDF, Word or Excel. Create figures?to quickly visualise the distribution of your data (boxplots, barplots, normality-plots, etc.). Display statistics?(mean, median, frequencies, incidences, etc.). Perform the appropriate tests?(t-test, Analysis of variance, Kruskal-Wallis, Fisher, log-rank, ...) depending on the nature of the described variable (normal, non-normal or qualitative). Summarize genetic data?(Single Nucleotide Polymorphisms) data displaying Allele Frequencies and performing Hardy-Weinberg Equilibrium tests among other typical statistics and tests for these kind of data.
Input: 2 ODM files (ODM version 1.3) Output: list of
        identical, matching, similar and differing data items
A standard test is observed on all specimens.  We treat the second test (or sampled test) as being conducted on only a stratified sample of specimens.  Verification Bias is this situation when the specimens for doing the second (sampled) test is not under investigator control.  We treat the total sample as stratified two-phase sampling and use inverse probability weighting.  We estimate diagnostic accuracy (category-specific classification probabilities; for binary tests reduces to specificity and sensitivity, and also predictive values) and agreement statistics (percent agreement, percent agreement by category, Kappa (unweighted), Kappa (quadratic weighted) and symmetry tests (reduces to McNemar's test for binary tests)).  See: Katki HA, Li Y, Edelstein DW, Castle PE.  Estimating the agreement and diagnostic accuracy of two diagnostic tests when one test is conducted on only a subsample of specimens. Stat Med. 2012 Feb 28; 31(5) <doi:10.1002/sim.4422>.
Functions for calculating and evaluating likelihood ratios from uni/multivariate continuous observations
Computes density function, cumulative distribution function, quantile function and random numbers for a multisection composite distribution specified by the user.  Also fits the user specified distribution to a given data set.  More details of the package can be found in the following paper submitted to the R journal Wiegand M and Nadarajah S (2017)  CompDist: Multisection composite distributions.
The package enables to compute event-specific incidence
        rates for competing risks data, to compute rate ratios,
        event-specific incidence proportions and cumulative incidence
        functions from these, and to plot these in a comprehensive
        multi-state type graphic.
Package for the systematic retrieval and storage of
    functional genomics data via a MySQL database.
Organizing and Analyzing Social Dominance
    Hierarchy Data.
Computing the point estimator and pointwise confidence interval of the cumulative incidence function from the cause-specific hazards model.
The package contains a function (which uses a similar interface to
    the `glm' function) for the fitting of a Conway-Maxwell-Poisson GLM. There
    are also various methods for analysis of the model fit. The package also
    contains functions for the Conway-Maxwell-Poisson distribution in a similar
    interface to functions `dpois', `ppois' and `rpois'. The functions are
    generally quick, since the workhorse functions are written in C++ (thanks
    to the Rcpp package).
Performs the complementary hierarchical clustering procedure and returns X' (the expected residual matrix) and a vector of the relative gene importances.
Contains several functions to enhance approaches to the Composite Indicators methods, focusing, in particular, on the normalisation and weighting-aggregation steps.
Allows for the easy computation of complexity: the proportion of the parameter space in line with the hypothesis by chance. The package comes with a Shiny application in which the calculations can be conducted as well.
Extension of several functions to the complex domain, including the matrix exponential and logarithm, and the determinant.
Provides functionality to perform robust regression
    on compositional data. To get information on the distribution of the
    estimates, various bootstrapping methods are implemented for the
    compositional as well as for standard robust regression models, to provide
    a direct comparison between them.
Computes the probability density function, cumulative distribution function, quantile function, random numbers of any composite model based on the lognormal distribution.
Provides routines for density and moments of the
        Conway-Maxwell-Poisson distribution as well as functions for
        fitting the COM-Poisson model for over/under-dispersed count
        data.
Fit Conway-Maxwell Poisson (COM-Poisson or CMP) regression models
    to count data (Sellers & Shmueli, 2010) <doi:10.1214/09-AOAS306>. The
    package provides functions for model estimation, dispersion testing, and
    diagnostics. Zero-inflated CMP regression (Sellers & Raim, 2016)
    <doi:10.1016/j.csda.2016.01.007> is also supported.
Regression, classification, contour plots, hypothesis testing and fitting of distributions for compositional data are some of the functions included.
The package provides functions for the consistent analysis of
  compositional data (e.g. portions of substances) and positive numbers
  (e.g. concentrations) in the way proposed by Aitchison and Pawlowsky-Glahn.
Estimation, gene selection, and survival prediction based on the compound covariate method under the Cox model with high-dimensional gene expressions.
 Available are survival data for non-small-cell lung cancer patients with gene expressions (Chen et al 2007 New Engl J Med) <DOI:10.1056/NEJMoa060096>,
 statistical methods in Emura et al (2012 PLoS ONE) <DOI:10.1371/journal.pone.0047627> and
 Emura & Chen (2016 Stat Methods Med Res) <DOI:10.1177/0962280214533378>. Algorithms for generating correlated gene expressions are also available.
Computing Continuous Distributions Obtained by Compounding
        a Continuous and a Discrete Distribution
Computes the distribution function of quadratic forms in normal variables using Imhof's method, Davies's algorithm, Farebrother's algorithm or Liu et al.'s algorithm.
Different tools for describing and analysing paired comparison data are presented. Main methods are estimation of products scores according Bradley Terry Luce model. A segmentation of the individual could be conducted on the basis of a mixture distribution approach. The number of classes can be tested by the use of Monte Carlo simulations. This package deals also with multi-criteria paired comparison data.
A set of procedures for the analysis of Random Fields using likelihood and non-standard likelihood methods is provided. Spatial analysis often involves dealing with large dataset. Therefore even simple studies may be too computationally demanding. Composite likelihood inference is emerging as a useful tool for mitigating such computational problems. This methodology shows satisfactory results when compared with other techniques such as the tapering method. Moreover, composite likelihood (and related quantities) have some useful properties similar to those of the standard likelihood.
This package contains several functions for calculating the most
    widely used effect sizes (ES), along with their variances, confidence
    intervals and p-values.  The output includes ES's of d (mean difference), g
    (unbiased estimate of d), r (correlation coefficient), z' (Fisher's z), and
    OR (odds ratio and log odds ratio). In addition, NNT (number needed to
    treat), U3, CLES (Common Language Effect Size) and Cliff's Delta are
    computed. This package uses recommended formulas as described in The
    Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, &
    Valentine, 2009).
Interface with and extract data from the United Nations Comtrade API <https://comtrade.un.org/data/>. Comtrade provides country level shipping data for a variety of commodities, these functions allow for easy API query and data returned as a tidy data frame.
Continuous smoothing of probability density function on a compact or semi-infinite support is performed using four continuous associated kernels: extended beta, gamma, lognormal and reciprocal inverse Gaussian. The cross-validation technique is also implemented for bandwidth selection.
Simple functions for joining strings. Construct human-friendly messages whose elements aren't known in advance, like in stop, warning, or message, from clean code.
The concaveman function ports the 'concaveman' (<https://github.com/mapbox/concaveman>) library from 'mapbox'. It computes the concave polygon(s) for one or several set of points.
There are 4 main functions in this package: ckmeans(), lcvqe(), mpckm() and ccls(). They take an unlabeled dataset and two lists of must-link and cannot-link constraints as input and produce a clustering as output.
Continuous convex piecewise linear (ccpl) resp. quadratic (ccpq) functions can be implemented with sorted breakpoints and slopes. This includes functions that are ccpl (resp. ccpq) on a convex set (i.e. an interval or a point) and infinite out of the domain. These functions can be very useful for a large class of optimisation problems. Efficient manipulation (such as log(N) insertion) of such data structure is obtained with map standard template library of C++ (that hides balanced trees). This package is a wrapper on such a class based on Rcpp modules.
The four functions svdcp (cp for column partitioned),
        svdbip or svdbip2 (bip for bi-partitioned), and svdbips (s for
        a simultaneous optimization of one set of r solutions),
        correspond to a "SVD by blocks" notion, by supposing each block
        depending on relative subspaces, rather than on two whole
        spaces as usual SVD does. The other functions, based on this
        notion, are relative to two column partitioned data matrices x
        and y defining two sets of subsets xi and yj of variables and
        amount to estimate a link between xi and yj for the pair (xi,
        yj) relatively to the links associated to all the other pairs.
A set of utilities for matching products in different classification codes used in international trade research. It supports concordance between HS (Combined), ISIC Rev. 2,3, and SITC1,2,3,4 product classification codes, as well as BEC, NAICS, and SIC classifications. It also provides code nomenclature / descriptions look-up, Rauch classification look-up (via concordance to SITC2) and trade elasticity look-up (via concordance to SITC2/3 or HS3.ss).
Implements concordance regression which can be used to estimate generalized odds of concordance.
	Can be used for non- and semi-parametric survival analysis with non-proportional hazards, for binary and 
    for continuous outcome data.
Higher order likelihood-based inference for logistic and 
  loglinear models
Apply and visualize conditional formatting to data frames in R.
    It renders a data frame with cells formatted according to
    criteria defined by rules, using a syntax similar to 'ggplot2'. The table is
    printed either opening a web browser or within the 'RStudio' viewer if
    available. The conditional formatting rules allow to highlight cells
    matching a condition or add a gradient background to a given column. This
    package supports both 'HTML' and 'LaTeX' outputs in 'knitr' reports, and
    exporting to an 'xlsx' file.
Solves for the mean parameters, the variance parameter, and their asymptotic variance in a conditional GEE for recurrent event gap times, as described by Clement and Strawderman (2009) in the journal Biostatistics. Makes a parametric assumption for the length of the censored gap time.
Code for a variety of nonlinear conditional independence tests: 
  Kernel conditional independence test (Zhang et al., UAI 2011, <arXiv:1202.3775>),
  Residual Prediction test (based on Shah and Buehlmann, <arXiv:1511.03334>),
  Invariant environment prediction,
  Invariant target prediction,
  Invariant residual distribution test,
  Invariant conditional quantile prediction (all from Heinze-Deml et al., <arXiv:1706.08576>).
Set of functions for the easy analyses of conditioning data.
Implements specialized conditions, i.e., typed errors,
    warnings and messages. Offers a set of standardized conditions (value error,
    deprecated warning, io message, ...) in the fashion of Python's built-in
    exceptions.
Conditional density estimation with mixtures for
        heavy-tailed distributions
Computes conditional multivariate normal probabilities, random deviates and densities.
An implementation of the computational strategy for the
    comprehensive analysis of condition-dependent operon maps in prokaryotes
    proposed by Fortino et al. (2014) <DOI:10.1186/1471-2105-15-145>. 
    It uses RNA-seq transcriptome profiles to improve prokaryotic operon map inference.
Method to implement some newly developed methods for the
    estimation of the conditional survival function.
Exploring fitted models by interactively taking 2-D and 3-D
  sections in data space.
Routines doing cone projection and quadratic programming, as well as doing estimation and inference for constrained parametric regression and shape-restricted regression problems.
This small library contains a series of simple tools for
        constructing and manipulating confounded and fractional
        factorial designs.
Functions for estimating and reporting multi-year averages and
    corresponding confidence intervals and distributions. A potential use case
    is reporting the chemical and ecological status of surface waters according
    to the European Water Framework Directive.
Manage configuration values across multiple environments (e.g.
  development, test, production). Read values using a function that determines
  the current environment and returns the appropriate value.
Enhances the 'ini' package by adding the ability to interpolate variables.
	     The INI configuration file is read into an R6 ConfigParser object (loosely inspired by Pythons ConfigParser module)
	     and the keys can be read, where '%(....)s' instances are interpolated by other included options or outside variables.
Implements the JSON, INI, YAML and TOML parser for R setting and writing of configuration file. The functionality of this package is similar to that of package 'config'.
Produces descriptive interpretations of confidence intervals.
    Includes (extensible) support for various test types, specified as sets
    of interpretations dependent on where the lower and upper confidence limits
    sit.
Implementation of conformal prediction using caret models for classification and regression.
Conducts sensitivity analyses for unmeasured confounding in
    random-effects meta-analysis per Mathur & VanderWeele (in preparation).
    Given output from a random-effects meta-analysis with a relative risk
    outcome, computes point estimates and inference for: (1) the proportion
    of studies with true causal effect sizes more extreme than a specified threshold
    of scientific significance; and (2) the minimum bias factor and confounding
    strength required to reduce to less than a specified threshold the proportion
    of studies with true effect sizes of scientifically significant size.
    Creates plots and tables for visualizing these metrics across a range of bias values.
    Provides tools to easily scrape study-level data from a published forest plot or 
    summary table to obtain the needed estimates when these are not reported.
Offers several functions for Configural Frequencies
    Analysis (CFA), which is a useful statistical tool for the analysis of
    multiway contingency tables. CFA was introduced by G. A. Lienert as
    'Konfigurations Frequenz Analyse - KFA'. Lienert, G. A. (1971). 
    Die Konfigurationsfrequenzanalyse: I. Ein neuer Weg zu Typen und Syndromen. 
    Zeitschrift für Klinische Psychologie und Psychotherapie, 19(2), 99–115.
For multiple testing.
    Computes estimates and confidence bounds for the
    False Discovery Proportion (FDP), the fraction of false positives among
    all rejected hypotheses.
    The methods in the package use permutations of the data. Doing so, they
    take into account the dependence structure in the data.
Downloads and tidies data from the Brazilian Federal Senate and Chamber of Deputies Application Programming Interfaces available at <http://legis.senado.gov.br/dadosabertos/> and <http://www.camara.leg.br/SitCamaraWS/> respectively.
Geometric circle fitting with Levenberg-Marquardt (a, b, R), Levenberg-Marquardt reduced (a, b), Landau, Spath and Chernov-Lesort. Algebraic circle fitting with Taubin, Kasa, Pratt and Fitzgibbon-Pilu-Fisher. Geometric ellipse fitting with ellipse LMG (geometric parameters) and conic LMA (algebraic parameters). Algebraic ellipse fitting with Fitzgibbon-Pilu-Fisher and Taubin.
plot conics (ellipses, hyperbolas, parabolas)
Provides a set of tools that can be used across 'data.frame' and
    'imputationList' objects.
Conjoint is a simple package that implements a conjoint
        analysis method to measure the preferences.
Implementation of a procedure (Domingue, 2012; see also
        Karabatsos, 2001 and Kyngdon, 2011) to test the single and
        double cancellation axioms of conjoint measure in data that is
        dichotomously coded and measured with error.
Converts 'LaTeX' files (with extension '.tex') generated by R Sweave using package 'knitr' to Rich Text Format files (with extension '.rtf'). Rich Text Format files can be read and written by most word processors.
Collects several different methods for analyzing and
    working with connectivity data in R.  Though primarily oriented towards
    marine larval dispersal, many of the methods are general and useful for
    terrestrial systems as well.
Computes the Conover-Iman test (1979) for stochastic dominance and reports the results among multiple pairwise comparisons after a Kruskal-Wallis test for stochastic dominance among k groups (Kruskal and Wallis, 1952). The interpretation of stochastic dominance requires an assumption that the CDF of one group does not cross the CDF of the other. conover.test makes k(k-1)/2 multiple pairwise comparisons based on Conover-Iman t-test-statistic of the rank differences. The null hypothesis for each pairwise comparison is that the probability of observing a randomly selected value from the first group that is larger than a randomly selected value from the second group equals one half; this null hypothesis corresponds to that of the Wilcoxon-Mann-Whitney rank-sum test. Like the rank-sum test, if the data can be assumed to be continuous, and the distributions are assumed identical except for a difference in location, Conover-Iman test may be understood as a test for median difference. conover.test accounts for tied ranks. The Conover-Iman test is strictly valid if and only if the corresponding Kruskal-Wallis null hypothesis is rejected.
Multi-species estimation of geographical range parameters
	for preliminary assessment of conservation status following Criterion B of the 
	International Union for Conservation of Nature (IUCN, 
	see <http://www.iucnredlist.org>).
Consonance Analysis is a useful numerical and graphical approach
    for evaluating the consistency of the measurements and the panel of people
    involved in sensory evaluation. It makes use of several uni and multivariate
    techniques either graphical or analytical. It shows the implementation of this
    procedure in a graphical interface.
Given response y, continuous predictor x, and covariate matrix, the relationship between E(y) and x is estimated with a shape constrained regression spline.  Function outputs fits and various types of inference.
Compute the median ranking according to the Kemeny's axiomatic approach. 
    Rankings can or cannot contain ties, rankings can be both complete or incomplete. 
    The package contains both branch-and-bound algorithms and heuristic solutions recently proposed.
    The package also provide some useful utilities for deal with preference rankings.
    Essential references:
    Emond, E.J., and Mason, D.W. (2002) <doi:10.1002/mcda.313>; 
    D'Ambrosio, A., Amodio, S., and Iorio, C. (2015) <doi:10.1285/i20705948v8n2p198>; 
    Amodio, S., D'Ambrosio, A., and Siciliano R. (2016) <doi:10.1016/j.ejor.2015.08.048>; 
    D'Ambrosio, A., Mazzeo, G., Iorio, C., and Siciliano, R. (2017) <doi:10.1016/j.cor.2017.01.017>.
CODATA internationally recommended values of the fundamental physical 
    constants, provided as symbols for direct use within the R language. Optionally, 
    the values with errors and/or the values with units are also provided if the 
    'errors' and/or the 'units' packages are installed. The Committee on Data
    for Science and Technology (CODATA) is an interdisciplinary committee of the
    International Council for Science which periodically provides the internationally 
    accepted set of values of the fundamental physical constants. This package 
    contains the "2014 CODATA" version, published on 25 June 2015:
    Mohr, P. J., Newell, D. B. and Taylor, B. N. (2016)
    <DOI:10.1103/RevModPhys.88.035009>, <DOI:10.1063/1.4954402>.
Provides functions for
        efficient computations of nonlinear spatial predictions with
        local change of support. This package supplies functions for
        tow-dimensional spatial interpolation by constrained,
        covariance-matching constrained and universal (external drift)
        kriging for points or block of any shape for data with a
        nonstationary mean function and an isotropic weakly stationary
        variogram. The linear spatial interpolation methods,
        constrained and covariance-matching constrained kriging,
        provide approximately unbiased prediction for nonlinear target
        values under change of support. This package
        extends the range of geostatistical tools available in R and
        provides a veritable alternative to conditional simulation for
        nonlinear spatial prediction problems with local change of
        support.
Fits mixtures of multivariate contaminated normal distributions
        (with eigen-decomposed scale matrices) via the expectation conditional-
	maximization algorithm under a clustering or classification paradigm.
Various utilities for evaluating continued fractions.
Bayesian analysis of complete and incomplete contingency tables.
Create contour lines for a non regular series of points, potentially from a non-regular canvas.
Provides functions for making contour plots.
  The contour plot can be created from grid data, a function,
  or a data set. If non-grid data is given, then a Gaussian
  process is fit to the data and used to create the contour plot.
One degree of freedom contrasts for lm, glm, gls, and geese objects.
Nonparametric two-sample procedure for comparing the median survival time.
Converts between the Date class and d/m/y for several
        calendars, including Persian, Islamic, and Hebrew
This is a pedagogical package, designed to help students understanding convergence of
             random variables. It provides a way to investigate interactively various modes of
	     convergence (in probability, almost surely, in law and in mean) of a sequence of i.i.d.
	     random variables. Visualisation of simulated sample paths is possible through interactive
	     plots. The approach is illustrated by examples and exercises through the function
	     'investigate', as described in
	     Lafaye de Micheaux and Liquet (2009) <http://dx.doi.org/10.1198/tas.2009.0032>.
	     The user can study his/her own sequences of random variables.
Converts graphical file formats (SVG,
    PNG, JPEG, BMP, GIF, PDF, etc) to one another. The exceptions are the
    SVG file format that can only be converted to other formats and in contrast,
    PDF format, which can only be created from others graphical formats.
    The main purpose of the package was to provide a solution for converting SVG
    file format to PNG which is often needed for exporting graphical files
    produced by R widgets.
Provides conversion functionality between a broad range of
    scientific, historical, and industrial unit types.
Quantifies and assesses the significance of convergent evolution.
Package convexjlr provides a simple high-level wrapper for
    Julia package 'Convex.jl' (see <https://github.com/JuliaOpt/Convex.jl> for
    more information),
    which makes it easy to describe and solve convex optimization problems in R.
    The problems can be dealt with include:
    linear programs,
    second-order cone programs,
    semidefinite programs,
    exponential cone programs.
Variance estimation on indicators of income concentration and
    poverty using complex sample survey designs. Wrapper around the
    survey package.
Fits convolution-based nonstationary
    Gaussian process models to point-referenced spatial data. The nonstationary
    covariance function allows the user to specify the underlying correlation
    structure and which spatial dependence parameters should be allowed to
    vary over space: the anisotropy, nugget variance, and process variance.
    The parameters are estimated via maximum likelihood, using a local
    likelihood approach. Also provided are functions to fit stationary spatial
    models for comparison, calculate the Kriging predictor and standard errors,
    and create various plots to visualize nonstationarity.
This R package applies the probabilistic model of species co-occurrence (Veech 2013) to a set of species distributed among a set of survey or sampling sites. The algorithm calculates the observed and expected frequencies of co-occurrence between each pair of species. The expected frequency is based on the distribution of each species being random and independent of the other species. The analysis returns the probabilities that a more extreme (either low or high) value of co-occurrence could have been obtained by chance. The package also includes functions for visualizing species co-occurrence results and preparing data for downstream analyses.
Read and preprocess fasta format data, and construct the co-occurrence network for downstream analyses. This R package is to construct the co-occurrence network with the algorithm developed by Du (2008) <DOI:10.1101/gr.6969007>. It could be used to transform the data with high-dimension, such as DNA or protein sequence, into co-occurrence networks. Co-occurrence network could not only capture the co-variation pattern between variables, such as the positions in DNA or protein sequences, but also reflect the relationship between samples. Although it is originally used in DNA and protein sequences, it could be also used to other kinds of data, such as RNA, SNP, etc.
Fast implementations of the co-operations: covariance,
    correlation, and cosine similarity.  The implementations are
    fast and memory-efficient and their use is resolved
    automatically based on the input data, handled by R's S3
    methods.  Full descriptions of the algorithms and benchmarks
    are available in the package vignettes.
Computes several cooperative games and allocation rules associated with minimum cost spanning tree problems and minimum cost arborescence problems.
Extensive functions for bivariate copula (bicopula) computations and related
 operations concerning oft cited bicopula theory described by Nelsen (2006), Joe (2014), and
 other selected works. The lower, upper, product, and select other bicopula are implemented.
 Arbitrary bicopula expressions include the diagonal, survival copula, the dual of a copula,
 co-copula, numerical bicopula density, and maximum likelihood estimation. Level
 curves (sets), horizontal and vertical sections also are supported. Numerical derivatives and
 inverses of a bicopula are provided; simulation by the conditional distribution method thus is
 supported. Bicopula composition, convex combination, and products are provided. Support
 extends to Kendall Function as well as the Lmoments thereof, Kendall Tau, Spearman Rho and
 Footrule, Gini Gamma, Blomqvist Beta, Hoeffding Phi, Schweizer-Wolff Sigma, tail dependency
 (including pseudo-polar representation) and tail order, skewness, and bivariate Lmoments.
 Evaluators of positively/negatively quadrant dependency, left increasing and right
 decreasing are available. Kullback-Leibler divergence, Vuong's procedure, Spectral Measure,
 and Lcomoments for copula inference are available. Quantile and median regressions for
 V with respect to U and U with respect to V are available. Empirical copulas (EC) are supported.
Provides tools for fitting the copCAR (Hughes, 2015)
    <DOI:10.1080/10618600.2014.948178> regression model for discrete
	areal data. Three types of estimation are supported (continuous
	extension, composite marginal likelihood, and distributional transform),
	for three types of outcomes (Bernoulli, negative binomial, and Poisson).
Provides functions to  compute and plot Coverage
    Probability Excursion (CoPE) sets
    for real valued functions on a 2-dimensional domain. CoPE sets are obtained
    from repeated noisy observations of the function on the entire domain.
    They are designed to bound the excursion
    set of the target function at a given level from above and below with
    a predefined probability. The target
    function can be a parameter in spatially-indexed linear regression.
    Support by NIH grant R01 CA157528 is gratefully acknowledged.
The program implements the COPPE-Cosenza Fuzzy Hierarchy Model. 
    The model was based on the evaluation of local alternatives, representing 
    regional potentialities, so as to fulfill demands of economic projects. 
    After defining demand profiles in terms of their technological coefficients, 
    the degree of importance of factors is defined so as to represent  
    the productive activity. The method can detect a surplus of supply without 
    the restriction of the distance of classical algebra, defining a hierarchy 
    of location alternatives. In COPPE-Cosenza Model, the distance between 
    factors is measured in terms of the difference between grades of memberships
    of the same factors belonging to two or more  sets under comparison. The 
    required factors are classified under the following linguistic variables: 
    Critical (CR); Conditioning (C); Little Conditioning (LC); and Irrelevant 
    (I). And the alternatives can assume the following linguistic variables: 
    Excellent (Ex), Good (G), Regular (R), Weak (W), Empty (Em), Zero (Z) and 
    Inexistent (In). The model also provides flexibility, allowing different 
    aggregation rules to be performed and defined by the Decision Maker. Such 
    feature is considered in this package, allowing the user to define other 
    aggregation matrices, since it considers the same linguistic variables 
    mentioned.
Computes the required number of patients for two time-to-event end-points as primary endpoint in phase III clinical trial.
Classes (S4) of commonly used elliptical, Archimedean,
 extreme-value and other copula families, as well as their rotations,
 mixtures and asymmetrizations. Nested Archimedean copulas, related
 tools and special functions. Methods for density, distribution, random
 number generation, bivariate dependence measures, Rosenblatt transform,
 Kendall distribution function, perspective and contour plots. Fitting of
 copula models with potentially partly fixed parameters, including
 standard errors. Serial independence tests, copula specification tests
 (independence, exchangeability, radial symmetry, extreme-value
 dependence, goodness-of-fit) and model selection based on
 cross-validation. Empirical copula, smoothed versions, and
 non-parametric estimators of the Pickands dependence function.
Modelling of sensitivity and specificity on their natural scale
    using copula based bivariate beta-binomial distribution to yield marginal
    mean sensitivity and specificity. The intrinsic negative correlation between
    sensitivity and specificity is modelled using a copula function. A forest plot
    can be obtained for categorical covariates or for the model with intercept only.
Provides a platform where EDAs (estimation of
    distribution algorithms) based on copulas can be implemented and
    studied. The package offers complete implementations of various
    EDAs based on copulas and vines, a group of well-known
    optimization problems, and utility functions to study the
    performance of the algorithms. Newly developed EDAs can be easily
    integrated into the package by extending an S4 class with generic
    functions for their main components.
Estimation and statistical process control are performed under copula-based time-series models.
This R-packages presents a bivariate, copula-based model
        for the joint distribution of a pair of continuous and discrete
        random variables. The two marginal random variables are modeled
        via generalized linear models, and their joint distribution
        (represented by a parametric copula family) is estimated using
        maximum-likelihood techniques.
It has functions to implement the copula mixed models for bivariate and trivariate meta-analysis of diagnostic test accuracy studies.
Contains several IRT and non-IRT based statistical indices proposed in the literature for detecting answer copying on multiple-choice examinations. Includes the indices that have been shown as effective and reliable based on the simulation studies. Provides results for the Omega index, Generalized Binomial Test, K index, K1 and K2 indices, and S1 and S2 indices.
Calculates the co-ranking matrix to assess the
    quality of a dimensionality reduction.
Provides a bundle of basic and fundamental bioinformatics tools,
    such as network querying and alignment, subnetwork extraction and search,
    network biomarker identification.
Perform a correlational class analysis of the data, resulting in a partition of the data into separate modules.
Generate high-dimensional correlated count random
        variables with a prespecified Pearson correlation.
Partition data points (variables) into communities/clusters, similar to clustering algorithms, such as k-means and hierarchical clustering.  This package implements a clustering algorithm based on a new metric CORD, defined for high dimensional parametric or semi-parametric distributions.  Read http://arxiv.org/abs/1508.01939 for more details.
Functions for calculating the OPTICS Cordillera. The OPTICS Cordillera measures the amount of 'clusteredness' in a numeric data matrix within a distance-density based framework for a given minimum number of points comprising a cluster, as described in Rusch, Hornik, Mair (2017) <doi:10.1080/10618600.2017.1349664>. There is an R native version and a version that uses 'ELKI', with methods for printing, summarizing, and plotting the result. There also is an interface to the reference implementation of OPTICS in 'ELKI'.
given a collection of intervals with integer start and end positions, find recurrently targeted regions and estimate the significance of finding. Randomization is implemented by parallel methods, either using local host machines, or submitting grid engine jobs.
Computed tomography (CT) imaging is a powerful tool for understanding the composition of sediment cores. This package streamlines and accelerates the analysis of CT data generated in the context of environmental science. Included are tools for processing raw DICOM images to characterize sediment composition (sand, peat, etc.). Root analyses are also enabled, including measures of external surface area and volumes for user-defined root size classes. For a detailed description of the application of computed tomography imaging for sediment characterization, see: Davey, E., C. Wigand, R. Johnson, K. Sundberg, J. Morris, and C. Roman. (2011) <DOI: 10.1890/10-2037.1>.
Core Hunter is a tool to sample diverse, representative subsets from large germplasm
    collections, with minimum redundancy. Such so-called core collections have applications in plant
    breeding and genetic resource management in general. Core Hunter can construct cores based on
    genetic marker data, phenotypic traits or precomputed distance matrices, optimizing one of many
    provided evaluation measures depending on the precise purpose of the core (e.g. high diversity,
    representativeness, or allelic richness). In addition, multiple measures can be simultaneously
    optimized as part of a weighted index to bring the different perspectives closer together.
    The Core Hunter library is implemented in Java 8 as an open source project (see
    <http://www.corehunter.org>).
A suite of machine learning algorithms written in C++ with the R 
 interface contains several learning techniques for classification and regression.
 Predictive models include e.g., classification and regression trees with
 optional constructive induction and models in the leaves, random forests, kNN, 
 naive Bayes, and locally weighted regression. All predictions obtained with these
 models can be explained and visualized with the 'ExplainPrediction' package.  
 This package is especially strong in feature evaluation where it contains several variants of
 Relief algorithm and many impurity based attribute evaluation functions, e.g., Gini, 
 information gain, MDL, and DKM. These methods can be used for feature selection 
 or discretization of numeric attributes.
 The OrdEval algorithm and its visualization is used for evaluation
 of data sets with ordinal features and class, enabling analysis according to the 
 Kano model of customer satisfaction. 
 Several algorithms support parallel multithreaded execution via OpenMP.  
 The top-level documentation is reachable through ?CORElearn.
Provides a minimal interface for applying
    annotators from the 'Stanford CoreNLP' java library. Methods
    are provided for tasks such as tokenisation, part of speech
    tagging, lemmatisation, named entity recognition, coreference
    detection and sentiment analysis.
Core functions for simulating quantities of interest
    from generalised linear models (GLM). This package will form the backbone of
    a series of other packages that improve the interpretation of GLM estimates.
Use to analysis case-parent trio sequencing studies. Test the compound heterozygous and recessive disease models
Fits a hidden rates model that allows different transition rate
    classes on different portions of a phylogeny by treating rate classes as hidden
    states in a Markov process and various other functions for evaluating models of
    binary character evolution.
Includes binning categorical variables into lesser number of categories based on t-test, converting categorical variables into continuous features 
	using the mean of the response variable for the respective categories, understanding the relationship between the response variable and predictor variables 
	using data transformations.
A matrix of agreement patterns and counts for record pairs is the input for the procedure.  An EM algorithm is used to impute plausible values for missing record pairs.  A second EM algorithm, incorporating possible correlations between per-field agreement, is used to estimate posterior probabilities that each pair is a true match - i.e. constitutes the same individual.
We proposed a new model-based clustering method, called the clustering of 
             regression models method(CORM), which groups genes that share a similar 
             relationship to the covariate(s). This method provides a unified approach 
             for a family of clustering procedures and can be applied to data collected 
             with various experimental designs. This package includes the implementation 
             for two such clustering procedures: (1) the Clustering of Linear Models 
             (CLM) method, and (2) the Clustering of Linear Mixed Models (CLMM) method.
Implements a James-Stein-type shrinkage estimator for 
  the covariance matrix, with separate shrinkage for variances and correlations.  
  The details of the method are explained in Schafer and Strimmer (2005) 
  <DOI:10.2202/1544-6115.1175> and Opgen-Rhein and Strimmer (2007) 
  <DOI:10.2202/1544-6115.1252>.  The approach is both computationally as well
  as statistically very efficient, it is applicable to "small n, large p" data, 
  and always returns a positive definite and well-conditioned covariance matrix.  
  In addition to inferring the covariance matrix the package also provides 
  shrinkage estimators for partial correlations and partial variances.  
  The inverse of the covariance and correlation matrix 
  can be efficiently computed, as well as any arbitrary power of the 
  shrinkage correlation matrix.  Furthermore, functions are available for fast 
  singular value decomposition, for computing the pseudoinverse, and for 
  checking the rank and positive definiteness of a matrix.
Utility functions and data sets for the statistical
        analysis of corpus frequency data, used in the SIGIL statistics
        course.
A set of functions used to compare co-occurrence between two corpora.
Text corpus data analysis, with full support for Unicode.  Functions for reading data from newline-delimited JSON files, for normalizing and tokenizing text, for searching for term occurrences, and for computing term occurrence frequencies (including n-grams).
Implementation of two-dimensional (2D) correlation analysis based
    on the Fourier-transformation approach described by Isao Noda (I. Noda
    (1993) <DOI:10.1366/0003702934067694>). Additionally there are two plot
    functions for the resulting correlation matrix: The first one creates
    colored 2D plots, while the second one generates 3D plots.
This package implements non-parametric analyses for clustered
    binary and multinomial data. The elements of the cluster are assumed
    exchangeable, and identical joint distribution (also known as marginal
    compatibility, or reproducibility) is assumed for clusters of different
    sizes. A trend test based on stochastic ordering is implemented.
Correcting area under ROC (AUC) for measurement error based on probit-shift model.
Analyzes and modifies metabolomics raw data (generated using GC-APCI-MS, Gas Chromatography-Atmospheric Pressure Chemical Ionization-Mass Spectrometry) to correct overloaded signals, i.e. ion intensities exceeding detector saturation leading to a cut-off peak. Data in xcmsRaw format are accepted as input and mzXML files can be processed alternatively. Overloaded signals are detected automatically and modified using an Gaussian or Isotopic-Ratio approach, QC plots are generated and corrected data are stored within the original xcmsRaw or mzXML respectively to allow further processing.
Linear regression based on a recursive structural equation model
    (explicit multiples correlations) found by a M.C.M.C. algorithm. It permits to face
    highly correlated variables. Variable selection is included (by lasso,
    elastic net, etc.). It also provides some graphical tools for basic
    statistics.
A collection of tools for correspondence regression, i.e. the
    correspondence analysis of the crosstabulation of a categorical variable Y
    in function of another one X, where X can in turn be made up of the
    combination of various categorical variables. Consequently, correspondence
    regression can be used to analyze the effects for a polytomous or
    multinomial outcome variable.
Calculates the probabilities of k successes given n trials of a binomial random variable with non-negative correlation across trials. The function takes as inputs the scalar values the level of correlation or association between trials, the success probability, the number of trials, an optional input specifying the number of bits of precision used in the calculation, and an optional input specifying whether the calculation approach to be used is from Witt (2014) <doi:10.1080/03610926.2012.725148> or from Kuk (2004) <doi:10.1046/j.1467-9876.2003.05369.x>. The output is a (trials+1)-dimensional vector containing the likelihoods of 0, 1, ..., trials successes.
Correlplot contains diverse routines for the construction of different plots for representing correlation matrices.
Calculates correlation of variables and displays the results
    graphically. Included panel functions can display points, shading, ellipses, and
    correlation values with confidence intervals.
In clinical practice and research settings in medicine and the behavioral sciences, it is often of interest to quantify the correlation of a continuous endpoint that was repeatedly measured (e.g., test-retest correlations, ICC, etc.). This package allows for estimating these correlations based on mixed-effects models. Part of this software has been developed using funding provided from the European Union's 7th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.
A graphical display of a correlation matrix or general matrix.
    It also contains some algorithms to do matrix reordering.
A tool for exploring correlations.
    It makes it possible to easily perform routine tasks when
    exploring correlation matrices such as ignoring the diagonal,
    focusing on the correlations of certain variables against others,
    or rearranging and visualising the matrix in terms of the
    strength of the correlations.
Statistical summary of Structure output.
Modeling the correlation transitions under specified distributional assumptions within the realm of discretization in the context of the latency and threshold concepts.
Set of methods to constrain numerical series and time series within
             arbitrary boundaries.
Designed for analysis of the results of a Genome Wide Association Study. Includes tools to pull lists of Chromosome number and SNP position below a certain significance threshold, refine gene networks (including data I/O for Cytoscape), and check SNP base pair changes.
Tools for generating and analyzing pedigrees.  Specifically, this has functions that will generate realistic pedigrees for the USA and China based on historical birth rates and family sizes.  It also has functions for analyzing these pedigrees when they include disease information including one based on counting meioses and another based on likelihood ratios.
To identify the globally most discriminative subnetwork from gene 
             expression profiles using an optimization model and genetic algorithm
cosinor is a set of simple functions that transforms longitudinal
    data to estimate the cosinor linear model as described in Tong (1976).
    Methods are given to summarize the mean, amplitude and acrophase, to
    predict the mean annual outcome value, and to test the coefficients.
Statistical procedures for calculating population–mean cosinor, non–stationary cosinor, estimation of best–fitting period, tests of population rhythm differences and more. See Cornélissen, G. (2014). <doi:10.1186/1742-4682-11-16>.
Package encapsulates standard expressions for distances,
        times, luminosities, and other quantities useful in
        observational cosmology, including molecular line observations.
        Currently coded for a flat universe only.
User-friendly interfaces to perform fast and reliable photometric
    redshift estimation. The code makes use of generalized linear models and
    can adopt gamma or inverse gaussian families, either from a frequentist or
    a Bayesian perspective. The code additionally provides a Shiny application
    providing a simple user interface.
COSSO is a new regularization method that automatically
        estimates and selects important function components by a
        soft-thresholding penalty in the context of smoothing spline
        ANOVA models. Implemented models include mean regression,
        quantile regression, logistic regression and the Cox regression
        models.
Contains functions that can determine whether a time series
	is second-order stationary or not (and hence evidence for
	locally stationarity). Given two non-stationary series (i.e.
	locally stationary series) this package can then discover
	time-varying linear combinations that are second-order stationary.
Density, distribution function, quantile function, random
    generation and survival function for the Cosine Weibull Distribution as defined
    by SOUZA, L. New Trigonometric Class of Probabilistic Distributions. 219 p.
    Thesis (Doctorate in Biometry and Applied Statistics) - Department of Statistics
    and Information, Federal Rural University of Pernambuco, Recife, Pernambuco,
    2015 (available at <http://www.openthesis.org/documents/New-trigonometric-classes-probabilistic-distributions-602633.html>) and BRITO, C. C. R. Method Distributions generator and
    Probability Distributions Classes. 241 p. Thesis (Doctorate in Biometry and
    Applied Statistics) - Department of Statistics and Information, Federal Rural
    University of Pernambuco, Recife, Pernambuco, 2014 (available upon request).
Implements cointegration/cotrending rank selection
        algorithm in Guo and Shintani(2011). Paper: "Consistant
        Cotrending rank selection when both stochastic and nonlinear
        deterministic trends are present", Preprint, Feb 2011.
Interface to the couchDB document database <http://couchdb.apache.org>.
Functions, data and code for Hilbe, J.M. 2011. Negative Binomial Regression, 2nd Edition (Cambridge University Press) and Hilbe, J.M. 2014. Modeling Count Data (Cambridge University Press).
Implements the estimation and inference methods for counterfactual analysis described in Chernozhukov, Fernandez-Val and Melly (2013) <DOI:10.3982/ECTA10582> "Inference on Counterfactual Distributions," Econometrica, 81(6). The counterfactual distributions considered are the result of changing either the marginal distribution of covariates related to the outcome variable of interest, or the conditional distribution of the outcome given the covariates. They can be applied to estimate quantile treatment effects and wage decompositions.
Flexible univariate count models based on renewal
    processes. The models may include covariates and can be specified
    with familiar formula syntax as in glm().
Standardize country names, convert them into one of
    eleven coding schemes, convert between coding schemes, and
    assign region descriptors.
Modeling under- and over-dispersed count data using extended Poisson process models (EPPM).
Quantifies United States flood impacts at the county level using
    United States Geological Service (USGS) River Discharge data for the USGS
    API. This package builds on R packages from the USGS, with the goal of
    creating county-level time series of flood status that can be more easily
    joined with county-level impact measurements, including health outcomes.
    This work was supported in part by grants from the National Institute of
    Environmental Health Sciences (R00ES022631), the Colorado Water Center,
    and the National Science Foundation, Integrative Graduate Education and
    Research Traineeship (IGERT) Grant No. DGE-0966346 "I-WATER: Integrated
    Water, Atmosphere, Ecosystems Education and Research Program" at
    Colorado State University.
Inputs date-times in Coordinated Universal Time (UTC)
    and converts to a local date-time and local date for US counties, based on
    each county's Federal Information Processing Standard (FIPS) code.
    This work was supported in part by grants from the National Institute of
    Environmental Health Sciences (R00ES022631) and the National Science
    Foundation (1331399).
Interacts with NOAA data sources (including the NCDC API at
  <http://www.ncdc.noaa.gov/cdo-web/webservices/v2> and ISD data) using
  functions from the 'rnoaa' package to obtain and compile weather time
  series for U.S. counties. This work was supported in part by grants from the
  National Institute of Environmental Health Sciences (R00ES022631) and the
  Colorado State University Water Center.
Contact prediction using shrinked covariance (COUSCOus). COUSCOus is a residue-residue contact detecting method approaching the contact inference using the glassofast implementation of Matyas and Sustik (2012, The University of Texas at Austin UTCS Technical Report 2012:1-3. TR-12-29.) that solves the L_1 regularised Gaussian maximum likelihood estimation of the inverse of a covariance matrix. Prior to the inverse covariance matrix estimation we utilise a covariance matrix shrinkage approach, the empirical Bayes covariance estimator, which has been shown by Haff (1980) <DOI:10.1214/aos/1176345010> to be the best estimator in a Bayesian framework, especially dominating estimators of the form aS, such as the smoothed covariance estimator applied in a related contact inference technique PSICOV.
Facilitates local polynomial regression for state dependent covariates in state-space models. The functionality can also be used from 'C++' based model builder tools such as 'Rcpp'/'inline', 'TMB', or 'JAGS'.
Tests on properties of space-time covariance functions.
    Tests on symmetry, separability and for assessing 
    different forms of non-separability are available. Moreover tests on 
    some classes of covariance functions, such that the classes of 
    product-sum models, Gneiting models and integrated product models have 
    been provided.
Allows Brownian motion, fractional Brownian motion,
    and integrated Ornstein-Uhlenbeck process components to
    be added to linear and non-linear mixed effects models
    using the structures and methods of the 'nlme' package.
Converts any word, sentence or speech into Trump's infamous
  "covfefe" format. Reference: <https://www.nytimes.com/2017/05/31/us/politics/covfefe-trump-twitter.html>.
  Inspiration thanks to: <https://codegolf.stackexchange.com/questions/123685/covfefify-a-string>.
Estimation of latent class models with covariate effects
        on underlying and measured variables. The measured variables
        are dichotomous or polytomous, all with the same number of
        categories.
We implement a collection of techniques for estimating covariance matrices. 
              Covariance matrices can be built using missing data. Stambaugh Estimation and 
              FMMC methods can be used to construct such matrices. Covariance matrices can 
              be built by denoising or shrinking the eigenvalues of a sample covariance 
              matrix. Such techniques work by exploiting the tools in Random Matrix Theory 
              to analyse the distribution of eigenvalues. Covariance matrices can also 
              be built assuming that data has many underlying regimes. Each regime is 
              allowed to follow a Dynamic Conditional Correlation model. Robust covariance 
              matrices can be constructed by multivariate cleaning and smoothing of noisy data.
Track and report code coverage for your package and (optionally)
    upload the results to a coverage service like 'Codecov' <http://codecov.io> or
    'Coveralls' <http://coveralls.io>. Code coverage is a measure of the amount of
    code being exercised by a set of tests. It is an indirect measure of test
    quality and completeness. This package is compatible with any testing
    methodology or framework and tracks coverage of both R code and compiled
    C/C++/FORTRAN code.
This package fits a simultaneous regression model for the mean vectors and covariance matrices of multivariate response variables, as described in Hoff and Niu (2012). The explanatory variables can be continuous or discrete. The current version of the package provides the Bayesian estimates.
The cov.nnve() function implements robust covariance estimation
        by the nearest neighbor variance estimation (NNVE) method of
        Wang and Raftery (2002) <DOI:10.1198/016214502388618780>.
Model-free selection of covariates under unconfoundedness for situations where the parameter of interest is an average causal effect. This package is based on  model-free backward elimination algorithms proposed in de Luna, Waernbaum and Richardson (2011). Marginal co-ordinate hypothesis testing is used in situations where all covariates are continuous while kernel-based smoothing appropriate for mixed data is used otherwise.
Model-free selection of covariates in high dimensions under unconfoundedness for situations where the parameter of interest is an average causal effect. This package is based on  model-free backward elimination algorithms proposed in de Luna, Waernbaum and Richardson (2011) <DOI:10.1093/biomet/asr041> and VanderWeele and Shpitser (2011) <DOI:10.1111/j.1541-0420.2011.01619.x>. Confounder selection can be performed via either Markov/Bayesian networks, random forests or LASSO.
Functions for testing if the covariance structure of 2-dimensional data
    (e.g. samples of surfaces X_i = X_i(s,t)) is separable, i.e. if covariance(X) = C_1 x C_2.
    A complete descriptions of the implemented tests can be found in the paper
    arXiv:1505.02023.
This package computes covariance test for the lasso.
Implements a specific form of segmented linear regression
    with two independent variables. The visualization of that function looks 
    like a quarter segment of a cowbell giving the package its name. 
    The package has been specifically constructed for the case where minimum 
    and maximum value of the dependent and two independent variables 
    are known a prior, which is usually the case
    when those values are derived from Likert scales.
Some helpful extensions and modifications to the 'ggplot2'
    package. In particular, this package makes it easy to combine multiple
    'ggplot2' plots into one and label them with letters, e.g. A, B, C, etc.,
    as is often required for scientific publications. The package also provides
    a streamlined and clean theme that is used in the Wilke lab, hence the
    package name, which stands for Claus O. Wilke's plot package.
Allows printing of character strings as messages/warnings/etc.
    with ASCII animals, including cats, cows, frogs, chickens, ghosts,
    and more.
This package provides routines for fitting Cox models by
        likelihood based boosting for a single endpoint or in presence
        of competing risks
Fits Cox-type models based on interval-censored data from a survival
             or illness-death process.
Cox proportional hazards models containing Gaussian random 
  effects, also known as frailty models.
Cox model regularized with net (L1 and Laplacian), elastic-net (L1 and L2) or lasso (L1) penalty, and their adaptive forms, such as adaptive lasso and net adjusting for signs of linked coefficients. Moreover, it treats the number of non-zero coefficients as another tuning parameter and simultaneously selects with the regularization parameter \code{lambda}. In addition, it fits a varying coefficient Cox model by kernel smoothing, incorporated with the aforementioned penalties. The package uses one-step coordinate descent algorithm and runs extremely fast by taking into account the sparsity structure of coefficients.
Implements Firth's penalized maximum likelihood bias reduction method  for Cox regression
  which has been shown to provide a solution in case of monotone likelihood (nonconvergence of likelihood function).
  The program fits profile penalized likelihood confidence intervals which were proved to outperform
  Wald confidence intervals.
Sparse estimation for Cox PH models is done via
    Minimum approximated Information Criterion (MIC) by Su, Wijayasinghe, 
    Fan, and Zhang (2016) <DOI:10.1111/biom.12484>. MIC mimics the best 
    subset selection using a penalized likelihood approach yet with no need 
    of a tuning parameter. The problem is further reformulated with a 
    re-parameterization step so that it reduces to one unconstrained non-convex
    yet smooth programming problem, which can be solved efficiently. Furthermore,
    the re-parameterization tactic yields an additional advantage in terms of
    circumventing post-selection inference.
Estimate coefficients of Cox proportional hazards model using stochastic gradient descent algorithm for batch data.
Implements weighted estimation in Cox regression as proposed by
             Schemper, Wakounig and Heinze (Statistics in Medicine, 2009, <doi:10.1002/sim.3623>). Weighted Cox regression
             provides unbiased average hazard ratio estimates also in case of non-proportional hazards.
             Approximated generalized concordance probability an effect size measure for clear-cut
             decisions can be obtained.
             The package provides options to estimate time-dependent effects conveniently by
             including interactions of covariates with arbitrary functions of time, with or without
             making use of the weighting option.
A high performance package estimating Cox Model when an even has more than one causes. It also supports random and fixed effects, tied events, and time-varying variables.
A package for fitting Cox models with penalized ridge-type partial likelihood. The package includes functions for fitting simple Cox models with all covariates controlled by a ridge penalty. The weight of the penalty is optimised by using a REML type-algorithm. Models with time varying effects of the covariates can also be fitted. Some of the covariates may be allowed to be fixed and thus not controlled by the penalty. There are three different penalty functions, ridge, dynamic and weighted dynamic. Time varying effects can be fitted without the need of an expanded dataset.
Fit robustly proportional hazards regression model
It fits a CoxSEI (Cox type Self-Exciting Intensity) model to right-censored counting process data.
Functions for calculating the conditional power
             for different models in survival time analysis
             within randomized clinical trials
             with two different treatments to be compared
             and survival as an endpoint.
Functions to check whether a vector of p-values respects the assumptions of FDR (false discovery rate) control procedures and to compute adjusted p-values.
The package includes functions to test and compare causal models.
A Bayesian meta-analysis method for studying cross-phenotype
  genetic associations. It uses summary-level data across multiple phenotypes to
  simultaneously measure the evidence of aggregate-level pleiotropic association and
  estimate an optimal subset of traits associated with the risk locus. CPBayes is based
  on a spike and slab prior and is implemented by Markov chain Monte Carlo technique Gibbs sampling.
This package contains methods to perform Common Principal
    Component Analysis (CPCA). The stepwise method by Trendafilov is published
    in the current version. Please see Trendafilov (2010). Stepwise estimation
    of common principal components. Computational Statistics & Data Analysis,
    54(12), 3446-3457. doi:10.1016/j.csda.2010.03.010
Functions to calculate concordance probability estimates
        in survival analysis
Is designed to test for association between methylation at CpG sites across the genome and a phenotype of interest, adjusting for any relevant covariates. The package can perform standard analyses of large datasets very quickly with no need to impute the data. It can also handle mixed effects models with chip or batch entering the model as a random intercept. Also includes tools to apply quality control filters, perform permutation tests, and create QQ plots, manhattan plots, and scatterplots for individual CpG sites.
Frequently used methods in genomic applications with emphasis on parallel computing (OpenMP).
 At its core, the package has a Gibbs Sampler that allows running univariate linear
 mixed models that have both, sparse and dense design matrices. The parallel sampling method
 in case of dense design matrices (e.g. Genotypes) allows running Ridge Regression or BayesA for
 a very large number of individuals. The Gibbs Sampler is capable of running Single Step Genomic Prediction models.
 In addition, the package offers parallelized functions for common tasks like genome-wide
 association studies and cross validation in a memory efficient way.
Filter CpGs based on Intra-class Correlation Coefficients (ICCs) when replicates are available. ICCs are calculated by fitting linear mixed effects models to all samples including the un-replicated samples. Including the large number of un-replicated samples improves ICC estimates dramatically. The method accommodates any replicate design.
This package computes the maximum likelihood estimator (MLE) of the shape-constrained hazard baseline and the effect parameters in the Cox proportional hazards model under IID sampling.  We assume that the data are continuous and allow for right censoring.  The function 'find.shapeMLE' allows for four different shape constraints: increasing, decreasing, unimodal, and u-shaped.
The package cpk provides simplified clinical pharmacokinetic functions for dose regimen design and modification at the point-of-care. Currently, the following functions are available: (1) ttc.fn for target therapeutic concentration, (2) dr.fn for dose rate, (3) di.fn for dosing interval, (4) dm.fn for maintenance dose, (5) bc.ttc.fn for back calculation, (6) ar.fn for accumulation ratio, (7) dpo.fn for orally administered dose, (8) cmax.fn for peak concentration, (9) css.fn for steady-state concentration, (10) cmin.fn for trough,(11) ct.fn for concentration-time predictions, (12) dlcmax.fn for calculating loading dose based on drug's maximum concentration, (13) dlar.fn for calculating loading dose based on drug's accumulation ratio, and (14) R0.fn for calculating drug infusion rate. Reference: Linares O, Linares A. Computational opioid prescribing: A novel application of clinical pharmacokinetics. J Pain Palliat Care Pharmacother 2011;25:125-135.
This is the R Interface to the C API of IBM ILOG CPLEX. It necessarily depends on IBM ILOG CPLEX (>= 12.1).
Likelihood-based and Bayesian methods for various compound Poisson linear models.
Sequential and batch change detection for univariate data streams, using the change point model framework. Functions are provided to allow nonparametric distribution-free change detection in the mean, variance, or general distribution of a given sequence of observations. Parametric change detection methods are also provided for Gaussian, Bernoulli and Exponential sequences. Both the batch (Phase I) and sequential (Phase II) settings are supported, and the sequences may contain either a single or multiple change points.
We propose to determine the correction of the significance level after multiple coding of an explanatory variable in Generalized Linear Model. The different methods of correction of the p-value are the Single step Bonferroni procedure, and resampling based methods developped by P.H.Westfall in 1993. Resampling methods are based on the permutation and the parametric bootstrap procedure. If some continuous, and dichotomous transformations are performed this package offers an exact correction of the p-value developped by B.Liquet & D.Commenges in 2005. The naive method with no correction is also available.
Implementation of the Control Polygon Reduction and Control Net
    Reduction methods for finding parsimonious B-spline regression models.
Permits to estimate the conditional probability function of a competing event, and to fit, using the temporal process regression or the pseudo-value approach, a proportional-odds model to the conditional probability function (or other models by specifying another link function). See <doi:10.1111/j.1467-9876.2010.00729.x>.
Nonparametric change point estimation for survival data based on p-values of exact binomial tests.
Non-parametric test for equality of multivariate distributions.  Trains a classifier to classify (multivariate) observations as coming from one of two distributions.  If the classifier is able to classify the observations better than would be expected by chance (using permutation inference), then the null hypothesis that the two distributions are equal is rejected.
Estimate quantile regression(QR) and composite quantile regression (cqr) and with adaptive lasso penalty using interior point (IP), majorize and minimize(MM), coordinate descent (CD), and alternating direction method of multipliers algorithms(ADMM).
Estimation, based on conditional maximum likelihood, of the quadratic exponential model proposed by Bartolucci, F. & Nigro, V. (2010, Econometrica) <DOI:10.3982/ECTA7531> and of a simplified and a modified version of this model. The quadratic exponential model is suitable for the analysis of binary longitudinal data when state dependence (further to the effect of the covariates and a time-fixed individual intercept) has to be taken into account. Therefore, this is an alternative to the dynamic logit model having the advantage of easily allowing conditional inference in order to eliminate the individual intercepts and then getting consistent estimates of the parameters of main interest (for the covariates and the lagged response). The simplified version of this model does not distinguish, as the original model does, between the last time occasion and the previous occasions. The modified version formulates in a different way the interaction terms and it may be used to test in a easy way state dependence as shown in Bartolucci, F., Nigro, V. & Pigini, C. (2013, Econometric Reviews) <DOI:10.1080/07474938.2015.1060039>. The package also includes estimation of the dynamic logit model by a pseudo conditional estimator based on the quadratic exponential model, as proposed by Bartolucci, F. & Nigro, V. (2012, Journal of Econometrics) <DOI:10.1016/j.jeconom.2012.03.004>.
This package contains R-functions to perform power
        calculation in a group sequential clinical trial with censored
        survival data and possibly unequal patient allocation between
        treatment and control groups. The fuctions can also be used to
        determine the study duration in a clinical trial with censored
        survival data as the sum of the accrual duration, which
        determines the sample size in a traditional sense, and the
        follow-up duration, which more or less controls the number of
        events to be observed. This package also contains R functions
        and methods to display the computed results.
R functions for cosmological research.
  The main functions are similar to the python library, cosmolopy.
Using a sampling-based approach (either sequential importance sampling or explicit Monte Carlo), this package can be used to perform a probabilistic damage tolerance for aircraft structures. It can model a single crack, or two simultaneously growing fatigue cracks (the so-called continuing damage problem). With a single crack, multiple types of future repairs are possible.
Provides R routine for the so called two-sample
        Cramer-Test.  This nonparametric two-sample-test on equality
        of the underlying distributions can be applied to 
        multivariate data as well as univariate data. It offers two 
        possibilities to approximate the critical value both of which 
        are included in this package.
Provides a data frame listing of known data-only and data-heavy
    packages available on CRAN.
Functions for completing and recalculating rankings.
A set of functions to manage 'CRAN'-like repositories
    efficiently.
'API' to the database of 'CRAN' package downloads from the
    'RStudio' 'CRAN mirror'. The database itself is at <http://cranlogs.r-pkg.org>,
    see <https://github.com/metacran/cranlogs.app> for the raw 'API'.
One of the strengths of R is its vast package ecosystem. Indeed, R packages extend from visualization to Bayesian inference and from spatial analyses to pharmacokinetics (<https://cran.r-project.org/web/views/>). There is probably not an area of quantitative research that isn't represented by at least one R package. At the time of this writing, there are more than 10,000 active CRAN packages. Because of this massive ecosystem, it is important to have tools to search and learn about packages related to your personal R needs. For this reason, we developed an RStudio addin capable of searching available CRAN packages directly within RStudio.
Various R tools for http://crantastic.org/
Fit continuous-time
    correlated random walk models with time indexed
    covariates to animal telemetry data. The model is fit using the Kalman-filter on
    a state space version of the continuous-time stochastic
    movement process.
Colored terminal output on terminals that support 'ANSI'
    color and highlight codes. It also works in 'Emacs' 'ESS'. 'ANSI'
    color support is automatically detected. Colors and highlighting can
    be combined and nested. New styles can also be created easily.
    This package was inspired by the 'chalk' 'JavaScript' project.
Implements a statistical test for comparing bar plots or
        histograms of categorical data derived from a randomized block
        repeated measures layout.
Different approaches to censored or truncated regression with 
  conditional heteroscedasticity are provided. First, continuous 
  distributions can be used for the (right and/or left censored or truncated)
  response with separate linear predictors for the mean and variance. 
  Second, cumulative link models for ordinal data
  (obtained by interval-censoring continuous data) can be employed for
  heteroscedastic extended logistic regression (HXLR). In the latter type of
  models, the intercepts depend on the thresholds that define the intervals.
A set of functions for computing the CreditMetrics risk model
Provides tools for pricing credit default swaps using
             C code for the International Swaps and Derivatives
             Association (ISDA) CDS Standard Model. See
             <http://www.cdsmodel.com/cdsmodel/documentation.html>
             for more information about the model and 
             <http://www.cdsmodel.com/cdsmodel/cds-disclaimer.html>
             for license details for the C code.
Functions for constructing simultaneous credible bands and identifying subsets via the "credible subsets" (also called "credible subgroups") method.
It provides functions to bootstrap Credit Curves from market quotes (Credit Default Swap - CDS - spreads) and price Credit Default Swaps - CDS.
Implements modeling and computational tools for conditional
    random fields (CRF) model as well as other probabilistic undirected
    graphical models of discrete data with pairwise and unary potentials.
Tools for analyzing performances of cricketers based on stats in
    ESPN Cricinfo Statsguru. The toolset can  be used for analysis of Tests,ODIs 
    and Twenty20 matches of both batsmen and bowlers.
This package fits discrete mixtures of Zero-Inflated
        Poisson (ZIP) models for analyzing criminal trajectories.
Statistical Methods for Crime Series Linkage. This package provides 
      code for criminal case linkage, crime series identification, crime series 
      clustering, and suspect identification.
Implements convex regression with interpretable sharp partitions
    (CRISP), which considers the problem of predicting an outcome variable on the basis of two covariates, using an interpretable yet non-additive model. CRISP partitions the covariate space into blocks in a data-adaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is fit using a non-greedy approach by solving a convex optimization problem, resulting in low-variance fits. More details are provided in Petersen, A., Simon, N., and Witten, D. (2016). Convex Regression with Interpretable Sharp Partitions. Journal of Machine Learning Research, 17(94): 1-31 <http://jmlr.org/papers/volume17/15-344/15-344.pdf>.
CRM simulator for Phase I Clinical Trials
Text mining client for 'Crossref' (<https://crossref.org>). Includes
    functions for getting getting links to full text of articles, fetching full
    text articles from those links or Digital Object Identifiers ('DOIs'),
    and text extraction from 'PDFs'.
Implements the Cross-contribution Compensating Multiple
    standard Normalization (CCMN) method and other normalization
    algorithms.
Implements a wide range of model-based dose
    escalation designs, ranging from classical and modern continual
    reassessment methods (CRMs) based on dose-limiting toxicity endpoints to
    dual-endpoint designs taking into account a biomarker/efficacy outcome. The
    focus is on Bayesian inference, making it very easy to setup a new design
    with its own JAGS code. However, it is also possible to implement 3+3
    designs for comparison or models with non-Bayesian estimation. The whole
    package is written in a modular form in the S4 class system, making it very
    flexible for adaptation to new models, escalation or stopping rules.
The crn package provides the core functions required to
        download and format data from the Climate Reference Network.
        Both daily and hourly data are downloaded from the ftp, a
        consolidated file of all stations is created, station metadata
        is extracted. In addition functions for selecting individual
        variables and creating R friendly datasets for them is
        provided.
Functions to help implement the extraction / subsetting / indexing
    function [ and replacement function [<- of custom matrix-like types (based
    on S3, S4, etc.), modeled as closely to the base matrix class as possible
    (with tests to prove it).
Create, edit, and remove 'cron' jobs on your unix-alike system. The package provides a set of easy-to-use wrappers
    to 'crontab'. It also provides an RStudio add-in to easily launch and schedule your scripts.
A device closing function which is able to crop graphics (e.g.,
  PDF, PNG files) on Unix-like operating systems with the required underlying
  command-line tools installed.
Provides peruvian agricultural production data from the Agriculture Minestry of Peru (MINAGRI). The first version includes
             6 crops: rice, quinoa, potato, sweet potato, tomato and wheat; all of them across 24 departments. Initially,  in excel files which has been transformed
             and assembled using tidy data principles, i.e. each variable is in a column, each observation is a row and each value is in a cell.
             The variables variables are sowing and harvest area per crop, yield, production and price per plot, every one year, from 2004 to 2014.
Computes a partial clustering algorithm that combines
    the Ward's minimum variance and Complete Linkage algorithms, providing
    automatic estimation of a suitable number of clusters and identification of
    outlier elements.
Contains functions for the construction of carryover
        balanced crossover designs. In addition contains functions to
        check given designs for balance.
This package performs a test for comparing two
        multivariate distributions by using the distance between
        observations.  The input is a distance matrix and the labels of
        the two groups to be compared, the output is the number of
        cross-matches and a p-value.
Package Crossover provides different crossover designs from
    combinatorial or search algorithms as well as from literature and a GUI to
    access them.
This package provides functions to calculate confidence intervals for crossover points of two simple linear regression lines using 
 the non-linear regression, the delta method, the Fieller method, and the bootstrap methods.
Cross-screening is a new method that uses both random halves of the sample to screen and test many hypotheses. It generally improves statistical power in observational studies when many hypotheses are tested simultaneously. References: 1. Qingyuan Zhao, Dylan S Small, and Paul R Rosenbaum. Cross-screening in observational studies that test many hypotheses. <arXiv:1703.02078>. 2. Qingyuan Zhao. On sensitivity value of pair-matched observational studies. <arXiv:1702.03442>.
Provides building blocks for allowing HTML widgets to communicate
    with each other, with Shiny or without (i.e. static .html files). Currently
    supports linked brushing and filtering.
Enables transformation of Verbal Autopsy data collected with the WHO 2016 questionnaire
  for automated coding of Cause of Death using different computer algorithms. Currently supports user-supplied mappings,
  and provides unvalidated mapping definitions to transform to InterVA4, Tariff 2, and InSilicoVA.
Contains generic functions for performing 
  cross validation and for computing diagnostic errors.
Defines classes and methods to cross-validate various
  binary classification algorithms used for "class prediction"
  problems.
Modelling credit risks based on the concept of "CreditRisk+", First Boston Financial Products, 1997 and "CreditRisk+ in the Banking Industry", Gundlach & Lehrbass, Springer, 2003.
In order to facilitate analysis of datasets hosted on the Crunch
    data platform <http://crunch.io/>, the 'crplyr' package implements 'dplyr'
    methods on top of the Crunch backend. The usual methods "select", "filter",
    "mutate", "group_by", and "summarize" are implemented in such a way as to
    perform as much computation on the server and pull as little data locally
    as possible.
Cross-recurrence quantification analysis 
    of two time-series, of either categorical or
    continuous values. It provides different methods
    for profiling cross-recurrence, i.e., only looking
    at the diagonal recurrent points, as well as more
    in-depth measures of the whole cross-recurrence plot,
    e.g., recurrence rate.
In competing risks regression, the proportional subdistribution hazards(PSH) model is popular for its direct assessment of covariate effects on the cumulative incidence function. This package allows for penalized variable selection for the PSH model. Penalties include LASSO, SCAD, MCP, and their group versions.
Extension of cmprsk to Stratified and Clustered data.
        Goodness of fit test for Fine-Gray model.
Performs forward and backwards stepwise regression for the Proportional subdistribution hazards model in competing risks (Fine & Gray 1999). Procedure uses AIC, BIC and BICcr as selection criteria. BICcr has a penalty of k = log(n*), where n* is the number of primary events.
Regression splines that handle a mix of continuous and categorical (discrete) data often encountered in applied settings. I would like to gratefully acknowledge support from  the Natural Sciences and Engineering Research Council of Canada (NSERC, <http://www.nserc-crsng.gc.ca>), the Social Sciences and Humanities Research Council of Canada (SSHRC, <http://www.sshrc-crsh.gc.ca>), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, <https://www.sharcnet.ca>).
Provides the implementation of analytical and graphical approaches for checking the assumptions of the Fine and Gray model.
Functions for nonlinear regression parameters estimation by algorithms based on Controlled Random Search algorithm.
  Both functions (crs4hc(), crs4hce()) adapt current search strategy by four heuristics competition. In addition, crs4hce() improves adaptability by adaptive stopping condition.
Provides wrapper functions for running classification and
    regression tests using different machine learning techniques, such as Random
    Forests and decision trees. The package provides standardized methods for
    preparing data to suit the algorithm's needs, training a model, making
    predictions, and evaluating results. Also, some functions are provided to run
    multiple instances of a test.
Implements a semi-parametric GEE estimator accounting for missing data with Inverse-probability weighting (IPW) and for imbalance in covariates with augmentation (AUG). The estimator IPW-AUG-GEE is Doubly robust (DR).
Sample size estimation in cluster (group) randomized trials.  Contains traditional power-based methods, empirical smoothing (Rotondi and Donner, 2009), and updated meta-analysis techniques (Rotondi and Donner, 2012).
A simple HTTP client, with tools for making HTTP requests,
    and mocking HTTP requests. The package is built on R6, and takes
    inspiration from Ruby's 'faraday' gem (<https://rubygems.org/gems/faraday>).
    The package name is a play on curl, the widely used command line tool
    for HTTP, and this package is built on top of the R package 'curl', an
    interface to 'libcurl' (<https://curl.haxx.se/libcurl>).
The Crunch.io service <http://crunch.io/> provides a cloud-based
    data store and analytic engine, as well as an intuitive web interface.
    Using this package, analysts can interact with and manipulate Crunch
    datasets from within R. Importantly, this allows technical researchers to
    collaborate naturally with team members, managers, and clients who prefer a
    point-and-click interface.
To facilitate building custom dashboards on the Crunch data
    platform <http://crunch.io/>, the 'crunchy' package provides tools for
    working with 'shiny'. These tools include utilities to manage authentication
    and authorization automatically and custom stylesheets to help match the
    look and feel of the Crunch web application.
Functions for reading in and manipulating CRU TS3.21: Climatic
    Research Unit (CRU) Time-Series (TS) Version 3.21 data.
Exploratory tools to identify closely related subjects using autosomal genetic marker data.
Performs cryptographic randomness tests on a sequence of random
    integers or bits. Included tests are greatest common divisor, birthday spacings,
    book stack, adaptive chi-square, topological binary, and three random walk
    tests. Tests except greatest common divisor and birthday spacings are not
    covered by standard test suites. In addition to the chi-square goodness-of-fit
    test, results of Anderson-Darling, Kolmogorov-Smirnov, and Jarque-Bera tests are
    also generated by some of the cryptographic randomness tests.
Functions to calculate the relative crystallinity of starch by X-ray Diffraction (XRD) and Infrared Spectroscopy (FTIR). Starch is biosynthesized by plants in the form of granules semicrystalline. For XRD, the relative crystallinity is obtained by separating the crystalline peaks from the amorphous scattering region. For FTIR, the relative crystallinity is achieved by setting of a Gaussian holocrystalline-peak in the 800-1300 cm-1 region of FTIR spectrum of starch which is divided into amorphous region and crystalline region. The relative crystallinity of native starch granules varies from 14 of 45 percent. This package was supported by FONDECYT 3150630 and CIPA Conicyt-Regional R08C1002 is gratefully acknowledged.
Monte Carlo conditional inference for the parameters of a
  linear nonnormal regression model
Germline and somatic locus data which contain the total read depth and B allele 
    read depth using Bayesian model (Dirichlet Process) to cluster. Meanwhile, the cluster 
    model can deal with the SNVs mutation and the CNAs mutation.
Mines contiguous sequential patterns in text.
cSFM is a method to model skewed functional data when considering covariates via a copula-based approach.
Package for CShapes, a GIS dataset of country boundaries (1946-today). Includes functions for data extraction and the computation of distance matrices and -lists.
Provides functions for computing the density
    and the log-likelihood function of closed-skew normal variates,
    and for generating random vectors sampled from this distribution.
    See Gonzalez-Farias, G., Dominguez-Molina, J., and Gupta, A. (2004).
    The closed skew normal distribution, 
    Skew-elliptical distributions and their applications: a journey beyond normality,
    Chapman and Hall/CRC, Boca Raton, FL, pp. 25-42.
Provides the Correlates of State Policy data set for easy use in R.
Includes two functions to evaluate the hypothesis of complete spatial randomness (csr) in point processes.  The function 'mwin' calculates quadrat counts to estimate the intensity of a spatial point process through the moving window approach proposed by Bailey and Gatrell (1995).  Event counts are computed within a window of a set size over a fine lattice of points within the region of observation.  The function 'pielou' uses the nearest neighbor test statistic and asymptotic distribution proposed by Pielou (1959) to compare the observed point process to one generated under csr.  The value can be compared to that given by the more widely used test proposed by Clark and Evans (1954).
Cell-type specific differential expression of a microarray
        experiment of heterogeneous tissue samples, using SAM.
A collection of tools for estimating a network from a random sample of cognitive social structure (CSS) slices. Also contains functions for evaluating a CSS in terms of various error types observed in each slice.
Selection of the number of clusters in cluster analysis using
    stability methods.
Functions that allow a researcher to examine the robustness of the substantive significance of their findings. Implements ideas set out in Esarey and Danneman (2014).
Reads and writes CSV with selected conventions.
 Uses the same generic function for reading and writing to promote consistent formats.
Functions for loading large (10M+ lines) CSV
    and other delimited files, similar to read.csv, but typically faster and
    using less memory than the standard R loader. While not entirely general,
    it covers many common use cases when the types of columns in the CSV file
    are known in advance. In addition, the package provides a class 'int64',
    which represents 64-bit integers exactly when reading from a file. The
    latter is useful when working with 64-bit integer identifiers exported from
    databases. The CSV file loader supports common column types including
    'integer', 'double', 'string', and 'int64', leaving further type
    transformations  to the user.
Support for import from and export to the CSVY file format. CSVY is a file format that combines the simplicity of CSV (comma-separated values) with the metadata of other plain text and binary formats (JSON, XML, Stata, etc.) by placing a YAML header on top of a regular CSV.
Estimation and inference methods for the continuous threshold expectile regression.
    It can fit the continuous threshold expectile regression and test the existence of change point,
    for the paper, "Feipeng Zhang and Qunhua Li (2016). A continuous threshold expectile regression, submitted."
Analysis of genetical genomic data to identify genetic loci associated with correlation changes in quantitative traits (CTL).
The CTM package is designed to solve problems of text mining and is specific for Chinese document.
Functions for estimating Markov generator matrices from discrete-time observations. The implemented approaches comprise diagonal adjustment, weighted adjustment and quasi-optimization of matrix logarithm based candidate solutions, an expectation-maximization algorithm as well as a Gibbs sampler.
Software to facilitates taking movement data in xyt format and pairing it with raster covariates within a continuous time Markov chain (CTMC) framework.  As described in Hanks et al. (2015) <DOI:10.1214/14-AOAS803> , this allows flexible modeling of movement in response to covariates (or covariate gradients) with model fitting possible within a Poisson GLM framework.
Functions for identifying, fitting, and applying continuous-space, continuous-time stochastic movement models to animal tracking data.
Estimation of quantile regression models for survival data.
Functions to fit continuous time autoregressive models with the Kalman filter.
A multivariate continuous (and discrete) time dynamic modelling
    package for panel and time series data, using linear stochastic differential
    equations. Contains a faster frequentist set of functions using OpenMx for
    single subject and mixed-effects (random intercepts only) structural
    equation models, or a hierarchical Bayesian implementation using Stan that
    allows for random effects over all model parameters. Allows for modelling of
    multiple noisy measurements of multiple stochastic processes, time varying
    input / event covariates, and time invariant covariates used to predict the
    parameters.
Interactive shiny application for running classical test theory (item analysis).
Infrastructure for task views to CRAN-style repositories: Querying task views and installing the associated
             packages (client-side tools), generating HTML pages and storing task view information in the repository
	     (server-side tools).
Estimate and test models for ordinal data concerning the family of
    CUB models and their extensions (where CUB stands for Combination of a 
	discrete Uniform and a shifted Binomial distributions).
R wrapper around the cubature C library of
    Steven G. Johnson for adaptive multivariate integration over hypercubes.
    This version provides both hcubature and pcubature routines in addition
    to a vector interface that results in substantial speed gains.
Estimating mutation and selection coefficients on synonymous
       codon bias usage based on models of ribosome overhead cost (ROC).
       Multinomial logistic regression and Markov Chain Monte Carlo are used to
       estimate and predict protein production rates with/without the presence
       of expressions and measurement errors. Work flows with examples for
       simulation, estimation and prediction processes are also provided
       with parallelization speedup. The whole framework is tested with
       yeast genome and gene expression data of Yassour, et al. (2009)
       <doi:10.1073/pnas.0812841106>.
Regression modeling using rules with added instance-based corrections.
Functions for MultiDimensional Feature Selection (MDFS):
    * calculating multidimensional information gains,
    * finding interesting tuples for chosen variables,
    * scoring variables,
	* finding important variables,
	* plotting selection results.
	CuCubes is also known as CUDA Cubes and it is a library that allows fast
	CUDA-accelerated computation of information gains in binary classification
	problems.
	This package wraps CuCubes and provides an alternative CPU version as well
	as helper functions for building MultiDimensional Feature Selectors.
Compute Unified Device Architecture (CUDA) is a software
        platform for massively parallel high-performance computing on
        NVIDIA GPUs. This package provides a CUDA implementation of a
        Bayesian multilevel model for the analysis of brain fMRI data.
        A fMRI data set consists of time series of volume data in 4D
        space. Typically, volumes are collected as slices of 64 x 64
        voxels. Analysis of fMRI data often relies on fitting linear
        regression models at each voxel of the brain. The volume of the
        data to be processed, and the type of statistical analysis to
        perform in fMRI analysis, call for high-performance computing
        strategies. In this package, the CUDA programming model uses a
        separate thread for fitting a linear regression model at each
        voxel in parallel. The global statistical model implements a
        Gibbs Sampler for hierarchical linear models with a normal
        prior. This model has been proposed by Rossi, Allenby and
        McCulloch in `Bayesian Statistics and Marketing', Chapter 3,
        and is referred to as `rhierLinearModel' in the R-package
        bayesm. A notebook equipped with a NVIDIA `GeForce 8400M GS'
        card having Compute Capability 1.1 has been used in the tests.
        The data sets used in the package's examples are available in
        the separate package cudaBayesregData.
FMRI data sets used in the examples of "cudaBayesreg".
        Data sets have been separated from the main package
        "cudaBayesreg" for convenience.
Reconstruct individual-level values from aggregate-level
        summaries.
Utility functions that provides wrapper to descriptive base functions
  like cor, mean and table.  It makes use of the formula interface to pass
  variables to functions.  It also provides operators to concatenate (%+%), to
  repeat (%n%) and manage character vectors for nice display.
Combining Univariate Association Test Results of Multiple Phenotypes for Detecting Pleiotropy.
Extends ddply to allow calculation of cumulative
        quantities.
Estimation of number and location of change points in
        mean-shift (piecewise constant) models. Particularly useful to
        model genomic sequences of continuous measurements.
Cumulative descriptive statistics for (arithmetic, geometric, harmonic) mean, median, mode, variance, skewness and kurtosis.
The curl() and curl_download() functions provide highly
    configurable drop-in replacements for base url() and download.file() with
    better performance, support for encryption (https, ftps), gzip compression,
    authentication, and other 'libcurl' goodies. The core of the package implements a
    framework for performing fully customized requests where data can be processed
    either in memory, on disk, or streaming via the callback or connection
    interfaces. Some knowledge of 'libcurl' is recommended; for a more-user-friendly
    web client see the 'httr' package which builds on this package with http
    specific tools and logic.
The currentSurvival package contains functions for the
        estimation of the current cumulative incidence (CCI) and the
        current leukaemia-free survival (CLFS). The CCI is the
        probability that a patient is alive and in any disease
        remission (e.g. complete cytogenetic remission in chronic
        myeloid leukaemia) after initiating his or her therapy (e.g.
        tyrosine kinase therapy for chronic myeloid leukaemia). The
        CLFS is the probability that a patient is alive and in any
        disease remission after achieving the first disease remission.
Partial application is the process of reducing the arity of a
    function by fixing one or more arguments, thus creating a new function
    lacking the fixed arguments. The curry package provides three different ways
    of performing partial function application by fixing arguments from either
    end of the argument list (currying and tail currying) or by fixing multiple
    named arguments (partial application). This package provides this
    functionality through the %<%, %-<%, and %><% operators which allows for
    a programming style comparable to modern functional languages. Compared
    to other implementations such a purrr::partial() the operators in curry
    composes functions with named arguments, aiding in autocomplete etc.
Computes the maximum likelihood estimator, the smoothed maximum likelihood
    estimator and pointwise bootstrap confidence intervals for the distribution 
    function under current status data. 
    Groeneboom and Hendrickx (2017) <arXiv:1701.07359>.
Test Equality of Curves with Homoscedastic or
        Heteroscedastic Errors.
Filtering, also known as gating, of flow cytometry samples using 
             the curvHDR method, which is described in Naumann, U., Luta, G. and 
             Wand, M.P. (2010) <DOI:10.1186/1471-2105-11-44>.
Cobb's maximum likelihood method for cusp-catastrophe modeling
        (Grasman, van der Maas, & Wagenmakers, 2009, JSS, 32:8;
        Cobb, L, 1981, Behavioral Science, 26:1, 75--78).
        Includes a cusp() function for model fitting, and several
        utility functions for plotting, and for comparing the
        model to linear regression and logistic curve models.
Customized training is a simple technique for transductive
    learning, when the test covariates are known at the time of training. The
    method identifies a subset of the training set to serve as the training set
    for each of a few identified subsets in the training set. This package
    implements customized training for the glmnet() and cv.glmnet() functions.
Computation of decision intervals (H) and average run lengths (ARL) for CUSUM charts.
This package provides a set of tools for spatio-temporal imputation 
          in R. It includes the implementation for then CUTOFF imputation method, 
          a useful cross-validation function that can be used not only by the 
          CUOTFF method but also by some other imputation  functions to help 
          choosing an optimal value for relevant parameters, such as the number 
          of k-nearest neighbors for the KNN imputation method, or the number of
          components for the SVD imputation method. It also contains tools for 
          simulating data with missing values with respect to some specific 
          missing pattern, for example, block missing. Some useful visualisation
          functions for imputation purposes are also provided in the package.
This package can be used to standardize abundance indices using the delta-GLM method and to model the English Channel cuttlefish stock using a two-stage biomass model
This package contains various tools for working with and evaluating cross-validated area under the ROC curve (AUC) estimators.  The primary functions of the package are ci.cvAUC and ci.pooled.cvAUC, which report cross-validated AUC and compute confidence intervals for cross-validated AUC estimates based on influence curves for i.i.d. and pooled repeated measures data, respectively.  One benefit to using influence curve based confidence intervals is that they require much less computation time than bootstrapping methods.  The utility functions, AUC and cvAUC, are simple wrappers for functions from the ROCR package.
Statistical inferences for estimating the calibration equation with error-in observations
Methods for color vision deficiencies (CVD), to help understanding and mitigating issues with CVDs and to generate tests for diagnosis and interpretation.
Contains functions for testing for significant differences between multiple coefficients of variation. Includes Feltz and Miller's (1996) <DOI:10.1002/(SICI)1097-0258(19960330)15:6%3C647::AID-SIM184%3E3.0.CO;2-P> asymptotic test and Krishnamoorthy and Lee's (2014) <DOI:10.1007/s00180-013-0445-2> modified signed-likelihood ratio test. See the vignette for more, including full details of citations.
The package uses majorization minimization by coordinate
        descent (MMCD) algorithm to compute the solution surface for
        concave penalized logistic regression model.  The SCAD and MCP
        (default) are two concave penalties considered in this
        implementation.  For the MCP penalty, the package also provides
        the local linear approximation by coordinate descant (LLA-CD)
        and adaptive rescaling algorithms for computing the solutions.
        The package also provides a Lasso-concave hybrid penalty for
        fast variable selection. The hybrid penalty applies the concave
        penalty only to the variables selected by the Lasso.  For all
        the implemented methods, the solution surface is computed along
        kappa, which is a more smooth fit for the logistic model.
        Tuning parameter selection method by k-fold cross-validated
        area under ROC curve (CV-AUC) is implemented as well.
The external prediction capability of quantitative structure-activity relationship (QSAR) models is often quantified using the predictive squared correlation coefficient. This value can be calculated with an external data set or by cross validation.
Perform canonical variate regression (CVR) for two sets of covariates and a univariate
            response, with regularization and weight parameters tuned by cross validation.
This package implements the fast cross-validation via sequential testing (CVST) procedure. CVST is an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating underperforming candidates quickly and keeping promising candidates as long as possible, the method speeds up the computation while preserving the capability of a full cross-validation. Additionally to the CVST the package contains an implementation of the ordinary k-fold cross-validation with a flexible and powerful set of helper objects and methods to handle the overall model selection process. The implementations of the Cochran's Q test with permutations and the sequential testing framework of Wald are generic and can therefore also be used in other contexts.
This package carries out level-dependent cross-validation
        method for the selection of thresholding value in wavelet
        shrinkage. This procedure is implemented by coupling a
        conventional cross validation with an imputation method due to
        a limitation of data length, a power of 2. It can be easily
        applied to classical leave-one-out and k-fold cross validation.
        Since the procedure is computationally fast, a level-dependent
        cross validation can be performed for wavelet shrinkage of
        various data such as a data with correlated errors.
Tools that allow developers to write functions for
        cross-validation with minimal programming effort and assist
        users with model selection.
This is a package for selecting tuning parameters based on cross-validation (CV) in regularized estimators of large covariance matrices. Four regularized methods are implemented: banding, tapering, hard-thresholding and soft-thresholding. Two types of matrix norms are applied: Frobenius norm and operator norm. Two types of CV are considered: K-fold CV and random CV. Usually K-fold CV use K-1 folds to train a model and the rest one fold to validate the model. The reverse version trains a model with 1 fold and validates with the rest with K-1 folds. Random CV randomly splits the data set to two parts, a training set and a validation set with user-specified sizes.
An iterative algorithm for solving a convex
    formulation of the biclustering problem.
Alternating Minimization Algorithm (AMA) and Alternating Direction
    Method of Multipliers (ADMM) splitting methods for convex clustering.
Miscellaneous useful or interesting functions. Some parameters of functions may have changed, so beware!
This package estimates gaussian cluster weighted linear regressions by EM algorithm.
extend cxxfunction by saving the dynamic shared objects
        for reusing across R sessions
A suite of functions for analysing cycling data.
Cyclomatic complexity is a software metric (measurement),
    used to indicate the complexity of a program. It is a quantitative
    measure of the number of linearly independent paths through a program's
    source code. It was developed by Thomas J. McCabe, Sr. in 1976.
Tools for calculating coordinate representations of
    hypocycloids, epicyloids, hypotrochoids, and epitrochoids
    (altogether called 'cycloids' here) with different scaling
    and positioning options. The cycloids can be visualised with
    any appropriate graphics function in R.
This model fitting tool incorporates cyclic coordinate descent and
    majorization-minimization approaches to fit a variety of regression models
    found in large-scale observational healthcare data.  Implementations focus
    on computational optimization and fine-scale parallelization to yield
    efficient inference in massive datasets.
A toolkit for querying 'Team Cymru' <http://team-cymru.org> 'IP'
    address, Autonomous System Number ('ASN'), Border Gateway Protocol ('BGP'), Bogon
    and 'Malware' Hash Data Services.
This library contains a primary function that divides
        chewing sequences in cycles and cycles into phases.  See
        get.all.breaks for an example.
The cystiSim package provides an agent-based model for Taenia solium transmission and control. cystiSim was developed within the framework of CYSTINET, the European Network on taeniosis/cysticercosis, COST ACTION TD1302.
Tools to interface with Cytobank's API via R, organized by various
    endpoints that represent various areas of Cytobank functionality. Learn more
    about Cytobank at <https://www.cytobank.org>.
Calculates ecological diversity indices for a microbial
        community
Given the hypothesis of a bimodal distribution of cells for each
 marker, the algorithm constructs a binary tree, the nodes of which are subpopulations of cells.
 At each node, observed cells and markers are modeled by both a family of
 normal distributions and a family of bimodal normal mixture distributions.
 Splitting is done according to a normalized difference of AIC between
 the two families.
The relationship between statistical dependency and causality lies
    at the heart of all statistical approaches to causal inference. The D2C
    package implements a supervised machine learning approach to infer the
    existence of a directed causal link between two variables in multivariate
    settings with n>2 variables. The approach relies on the asymmetry of some
    conditional (in)dependence relations between the members of the Markov
    blankets of two variables causally connected. The D2C algorithm predicts
    the existence of a direct causal link between two variables in a
    multivariate setting by (i) creating a set of of features of the
    relationship based on asymmetric descriptors of the multivariate dependency
    and (ii) using a classifier to learn a mapping between the features and the
    presence of a causal link
Creates interactive genome browser with 'R'. It joins the data analysis power of R and the visualization libraries of JavaScript in one package.
Create interactive heat maps that are usable from the R console, in
    the 'RStudio' viewer pane, in 'R Markdown' documents, and in 'Shiny' apps. Hover
    the mouse pointer over a cell to show details, drag a rectangle to zoom, and
    click row/column labels to highlight.
Two sample test based on Wasserstein metric. This is motivated from detection of differential DNA-methylation sites based on underlying distributions.
This packages is intended to make it easy to create D3 JavaScript
    network, tree, dendrogram, and Sankey graphs from R using data frames.
    !!! NOTE: Active development has moved to the networkD3 package. !!!
Plotting hierarchical plots in R such as Sunburst, Treemap, Circle
    Treemap, Partition Chart, collapsible indented tree and collapsible tree.
Helper functions for using 'd3.js' in R.
Create and customize interactive collapsible 'D3' trees using the 'D3'
    JavaScript library and the 'htmlwidgets' package. These trees can be used
    directly from the R console, from 'RStudio', in Shiny apps and R Markdown documents.
    When in Shiny the tree layout is observed by the server and can be used as a reactive filter
    of structured data.
Various data sets used in examples and exercises in the
        book Maindonald, J.H. and Braun, W.J. (2003, 2007, 2010) "Data
        Analysis and Graphics Using R".
Data sets and functions, for the display of gene expression array (microarray) data, and for demonstrations with such data.
various data sets used in additional exercises for
        the book Maindonald, J.H. and Braun, W.J. (3rd edn 2010)
        "Data Analysis and Graphics Using R", and for a
        'Data Mining' course.  Note that a number of datasets
        that were in earlier versions of this package have been
        transferred to the DAAG package.
The three-way data consists of a set of variables measured on several groups of individuals. To each group is associated an estimated probability density function. The package provides tools to manage such data and functional methods (principal component analysis, multidimensional scaling, cluster analysis, discriminant analysis...) for such probability densities.
The content falls into the following groupings: (i) Data, (ii)
    Factor manipulation functions, (iii) Design functions, (iv) ANOVA functions, (v)
    Matrix functions, (vi) Projector and canonical efficiency functions, and (vii)
    Miscellaneous functions. There is a vignette describing how to use the 
    Design functions for randomizing and assessing designs available in the file 
    'daeDesignNotes.pdf'. The ANOVA functions facilitate the extraction of 
    information when the 'Error' function has been used in the call to 'aov'.
Contains Data frames and functions used in the book "Design and Analysis of Experiments with R".
Diff, patch and merge for data frames. Document changes in data
    sets and use them to apply patches. Changes to data can be made visible by using
    render_diff. The V8 package is used to wrap the 'daff.js' JavaScript library
    which is included in the package.
Data and miscellanea to support the book "Introduction to
        Data analysis with R for Forensic Scientists", Curran, J.M.
        2010 CRC Press ISBN: 978-1-4200-8826-7
dagbag is a set of methods that learn DAGs via bootstrap aggregating.
Integrates the information from multiple linkage maps to
        create a consensus directed graph, which is then linearized to
        produce a consensus map.
A port of the web-based software 'DAGitty', available at 
    <http://dagitty.net>, for analyzing structural causal models 
    (also known as directed acyclic graphs or DAGs).
    This package computes covariate adjustment sets for estimating causal
    effects, enumerates instrumental variables, derives testable
    implications (d-separation and vanishing tetrads), generates equivalent
    models, and includes a simple facility for data simulation.
Functions to draw, manipulate, evaluate directed
        acyclic graphs and simulate corresponding data.
Several functions for evaluating the accuracy of
        classification models. The package provides the following
        performance measures: repeated k-fold cross-validation, 
		0.632 and 0.632+ bootstrap estimation of the misclassification rate, 
        sensitivity, specificity and AUC. If an application is 
        computationally intensive, parallel execution can be used 
        to reduce the computational effort.
Simulates and computes the (maximum) likelihood of a dynamical model of island biota assembly through speciation, immigration and extinction. See Valente et al. 2015. Ecology Letters 18: 844-852, <DOI:10.1111/ele.12461>.
Functions and an example dataset for the psychometric theory of
  knowledge spaces.  This package implements data analysis methods and
  procedures for simulating data and quasi orders and transforming different
  formulations in knowledge space theory.  See package?DAKS for an overview.
The DALY Calculator is a free, open-source Graphical User
             Interface (GUI) for stochastic disability-adjusted life year
             (DALY) calculation.
A collection of functions which aim to assist common computational workflow for analysis of matabolomic data..
Interactively train neural networks on Numerai, <https://numer.ai/>, data. Generate tournament predictions and write them to a CSV.
Miscellaneous set of functions I use in my teaching either at the University of Wisconsin-Milwaukee or the Inter-university Consortium for Political and Social Research Summer Program in Quantitative Methods.  Broadly, the functions help with presentation and interpretation of GLMs.
Simulates and computes (maximum) likelihood of a dynamical model of community assembly that takes into account phylogenetic history.
The single largest source of dams in the United States is the
    National Inventory of Dams (NID) <http://nid.usace.army.mil> from the US
    Army Corps of Engineers. Entire data from the NID cannot be obtained all at
    once and NID's website limits extraction of more than a couple of thousand
    records at a time. Moreover, selected data from the NID's user interface
    cannot not be saved to a file. In order to make the analysis of this data
    easier, all the data from NID was extracted manually. Subsequently, the raw
    data was checked for potential errors and cleaned. This package provides
    sample cleaned data from the NID and provides functionality to access the
    entire cleaned NID data.
Contains the function used to create the Dandelion Plot. Dandelion Plot is a visualization method for R-mode Exploratory Factor Analysis.
The darch package is built on the basis of the code from G. E.
    Hinton and R. R. Salakhutdinov (available under Matlab Code for deep belief
    nets). This package is for generating neural networks with many layers (deep
    architectures) and train them with the method introduced by the publications
    "A fast learning algorithm for deep belief nets" (G. E. Hinton, S. Osindero,
    Y. W. Teh (2006) <DOI:10.1162/neco.2006.18.7.1527>) and "Reducing the
    dimensionality of data with neural networks" (G. E. Hinton, R. R.
    Salakhutdinov (2006) <DOI:10.1126/science.1127647>). This method includes a
    pre training with the contrastive divergence method published by G.E Hinton
    (2002) <DOI:10.1162/089976602760128018> and a fine tuning with common known
    training algorithms like backpropagation or conjugate gradients.
    Additionally, supervised fine-tuning can be enhanced with maxout and
    dropout, two recently developed techniques to improve fine-tuning for deep
    learning.
The recovery of visual sensitivity in a dark environment is known
    as dark adaptation. In a clinical or research setting the recovery is typically
    measured after a dazzling flash of light and can be described by the Mahroo,
    Lamb and Pugh (MLP) model of dark adaptation. The functions in this package take
    dark adaptation data and use nonlinear regression to find the parameters of the
    model that 'best' describe the data. They do this by firstly, generating rapid
    initial objective estimates of data adaptation parameters, then a multi-start
    algorithm is used to reduce the possibility of a local minimum. There is also a
    bootstrap method to calculate parameter confidence intervals. The functions rely
    upon a 'dark' list or object. This object is created as the first step in the
    workflow and parts of the object are updated as it is processed.
Provides programmatic access to the Dark Sky API 
    <https://darksky.net/dev/docs>, which provides current or historical global 
    weather conditions.
Functions are provided that facilitate the import and analysis of
    snp and silicodart (presence/absence) data. The main focus is on data generated
    by DarT (Diversity Arrays Technology). However, once SNP or related fragment
    presence/absence data from any source is imported into a genlight object many
    of the functions can be used. Functions are available for input and output of
    SNP and silicodart data, for reporting on and filtering on various criteria
    (e.g. CallRate, Heterozygosity, Reproducibility, maximum allele frequency).
    Advanced filtering is based on Linkage Disequilibrium and HWE. Other functions
    are available for visualization after PCoA, or to facilitate transfer of data
    between genlight/genind objects and newhybrids, related, phylip packages etc.
Are you aiming at the right spot in darts? Maybe not! Use
        this package to find your optimal aiming location. For a better
        explanation, go to
        http://www-stat.stanford.edu/~ryantibs/darts/ or see the paper
        "A Statistician Plays Darts".
The dashboard package allows users to create web pages which display 
	interactive data visualizations working in a standard modern browser. It displays them locally 
	using the Rook server. Nor knowledge about web technologies nor Internet connection are 
	required. D3.js is a JavaScript library for manipulating documents based on data. 
	D3 helps the dashboard package bring data to life using HTML, SVG and CSS.
An implementation of common higher order functions with syntactic
    sugar for anonymous function. Provides also a link to 'dplyr' for common
    transformations on data frames to work around non standard evaluation by
    default.
Provides an interface to the C++ FILEVECTOR library
    facilitating analysis using large (giga- to tera-bytes) matrices.
    Matrix storage is organized in a way that either columns or rows
    are quickly accessible. DatABEL is primarily aimed to support
    genome-wide association analyses e.g. using GenABEL, MixABEL and
    ProbABEL.
Algorithms implementing populations of agents which interact with one another and sense their environment may exhibit emergent behavior such as self-organization and swarm intelligence. Here a swarm system, called databionic swarm (DBS), is introduced which is able to adapt itself to structures of high-dimensional data such as natural clusters characterized by distance and/or density based structures in the data space. The first module is the parameter-free projection method Pswarm, which exploits the concepts of self-organization and emergence, game theory, swarm intelligence and symmetry considerations. The second module is a parameter-free high-dimensional data visualization technique, which generates projected points on a topographic map with hypsometric colors based on the generalized U-matrix. The third module is the clustering method itself with non-critical parameters. The clustering can be verified by the visualization and vice versa. The term DBS refers to the method as a whole. DBS enables even a non-professional in the field of data mining to apply its algorithms for visualization and/or clustering to data sets with completely different structures drawn from diverse research fields.
Functions to check variables against a
    set of data quality rules. A rule file can be accompanied by look-up tables. In
    addition, there are some convenience functions that may
    serve as an example for defining clearer 'data rules'. An
    HTML based user interface facilitates initial exploration of the
    functionality.
Checks column names, classes, values, keys and joins in data frames.
    Also checks length, class and values of vectors and scalars.
    Returns an informative error message if user-defined conditions are not met.
Includes functions that researchers or practitioners may use to clean
    raw data, transferring html, xlsx, txt data file into other formats. And it
    also can be used to manipulate text variables, extract numeric variables from
    text variables and other variable cleaning processes. It is originated from a
    author's project which focuses on creative performance in online education
    environment. The resulting paper of that study will be published soon.
Tools for combining and cleaning data sets, particularly
    with grouped and time series data.
Easy comparison of two tabular data
    objects in R. Specifically designed to show differences between two sets of
    data in a useful way that should make it easier to understand the differences,
    and if necessary, help you work out how to remedy them. Aims
    to offer a more useful output than all.equal() when your two data sets do not
    match, but isn't intended to replace all.equal() as a way to test for equality.
Query for metrics from 'Datadog' (<https://www.datadoghq.com/>) via its API.
Methods for dividing data into subsets, applying analytical
    methods to the subsets, and recombining the results.  Comes with a generic
    MapReduce interface as well.  Works with key-value pairs stored in memory,
    on local disk, or on HDFS, in the latter case using the R and Hadoop
    Integrated Programming Environment (RHIPE).
This is a GUI application for defining
  attributes and setting valid values of variables, and then,
  entering questionnaire data in a data.frame.
Data exploration process for data analysis and model building, so
    that users could focus on understanding data and extracting insights. The
    package automatically scans through each variable and does data profiling.
    Typical graphical techniques will be performed for both discrete and
    continuous features.
Writes data frames to xls files. It supports 
        multiple sheets and basic formatting.
Our method automatically generates models of dynamic decision-
    making that both have strong predictive power and are interpretable in human
    terms. We use an efficient model representation and a genetic algorithm-based
    estimation process to generate simple deterministic approximations that explain
    most of the structure of complex stochastic processes. We have applied the
    software to empirical data, and demonstrated it's ability to recover known data-
    generating processes by simulating data with agent-based models and correctly
    deriving the underlying decision models for multiple agent models and degrees of
    stochasticity.
Functions to save either '.dtable' or '.dtbin' files that can be read by DataGraph, a graphing and analysis application for macOS. Can save a data frame, collection of data frames and sequences of data frames and individual vectors. For more information see <https://www.visualdatatools.com/DataGraph/Help/DataFiles/R/index.html>.
Functions to import multiple files of multiple data file types ('.xlsx', '.xls', '.csv', '.txt')
             from a given directory into R data frames.
Data cleaning is an important first step of any statistical
    analysis. dataMaid provides an extendable suite of test for common potential
    errors in a dataset. It produces a document with a thorough summary of the
    checks and the results that a human can use to identify possible errors.
datamap utilizes variable bindings and objects of class
        "UserDefinedDatabase" to provide a simple mapping system to
        foreign objects. Maps can be used as environments or attached
        to the search path, and changes to either are persistent.
        Mapped foreign objects are fetched in real-time and are never
        cached by the mapping system.
Provides an S4 infrastructure for unified handling
    of internal datasets and web based data sources. The package is
    currently in beta; things may break, change or go away without
    warning.
Designed to create a basic data dictionary and append to the original dataset's attributes list. The package makes use of a tidy dataset and creates a data frame that will serve as a linker that will aid in building the dictionary. The dictionary is then appended to the list of the original dataset's attributes. The user will have the option of entering variable and item descriptions by writing code or use alternate functions that will prompt the user to add these.
Post Global Financial Crisis derivatives reforms have lifted the 
  veil off over-the-counter (OTC) derivative markets. Swap Execution Facilities
  (SEFs) and Swap Data Repositories (SDRs) now publish data on swaps that are 
  traded on or reported to those facilities (respectively). This package provides
  you the ability to get this data from supported sources.
Provides read and write access to data and metadata from
    the DataONE network <https://www.dataone.org> of data repositories.  
    Each DataONE repository implements a consistent repository application 
    programming interface. Users call methods in R to access these remote 
    repository functions, such as methods to query the metadata catalog, get 
    access to metadata for particular data packages, and read the data objects 
    from the data repository. Users can also insert and update data objects on 
    repositories that support these methods.
Provides a flexible container to transport and
    manipulate complex sets of data. These data may consist of multiple data files and
    associated meta data and ancillary files. Individual data objects have
    associated system level meta data, and data files are linked together using
    the OAI-ORE standard resource map which describes the relationships between the files. 
    The OAI-ORE standard is described at <https://www.openarchives.org/ore>. Data packages 
    can be serialized and transported as structured files that have been created following 
    the BagIt specification. The BagIt specification is described at 
    <https://tools.ietf.org/html/draft-kunze-bagit-08>.
RStudio addins and R functions that make copy-pasting vectors and tables to text painless.
Do most of the painful data preparation for a data science project with a minimum amount of code; Take advantages of data.table efficiency and use some algorithmic trick in order to perform data preparation in a time and RAM efficient way.
The package performs variable level data quality checks including
    missing values, unique values, frequency tables, and generates summary
    statistics
Collection of functions to help retrieve U.S. Geological Survey
    (USGS) and U.S. Environmental Protection Agency (EPA) water quality and
    hydrology data from web services. USGS web services are discovered from 
    National Water Information System (NWIS) <https://waterservices.usgs.gov/> and <https://waterdata.usgs.gov/nwis>. 
    Both EPA and USGS water quality data are obtained from the Water Quality Portal <https://www.waterqualitydata.us/>.
For working with the DataRobot predictive modeling platform's <https://app.datarobot.com> API.
The Datasaurus Dozen is a set of datasets with the same summary statistics. They 
             retain the same summary statistics despite having radically different distributions.
             The datasets represent a larger and quirkier object lesson that is typically taught
             via Anscombe's Quartet (available in the 'datasets' package). Anscombe's Quartet
             contains four very different distributions with the same summary statistics and as 
             such highlights the value of visualisation in understanding data, over and above
             summary statistics. As well as being an engaging variant on the Quartet, the data
             is generated in a novel way. The simulated annealing process used to derive datasets 
             from the original Datasaurus is detailed in "Same Stats, Different Graphs: Generating 
             Datasets with Varied Appearance and Identical Statistics through Simulated Annealing" 
             <http://dx.doi.org/10.1145/3025453.3025912>.
Download and import time series from <http://www.dataseries.org>, a comprehensive and up-to-date collection of open data from Switzerland.
Visual interface for loading datasets in RStudio from all installed (unloaded) packages.
Implements several algorithms for Euclidean distance matrix completion,
  Sensor Network Localization, and sparse Euclidean distance matrix completion using
  the minimum spanning tree.
Based on a SAS data step. This allows for row-wise dynamic building
    of data, iteratively importing slices of existing dataframes, conducting
    analyses, and exporting to a results frame. This is particularly useful for
    differential or time-series analyses, which are often not well suited to vector-
    based operations.
Implementation of advanced data structures such as hashmaps,
    heaps, or queues. Advanced data structures are essential
    in many computer science and statistics problems, for example graph
    algorithms or string analysis. The package uses 'Boost' and 'STL' data
    types and extends these to R with 'Rcpp' modules.
It allows the user to retrieve the data from the systems of 
    DATASUS (SUS IT department related to the Brazilian Ministry of Health, 
    see <http://datasus.saude.gov.br/informacoes-de-saude/tabnet/> for more
    information) much in the same way that is done in the online portal.
    For now the package allows access to the SINASC and SIM's (ICD-10) 
    systems, that is, the 'Estatísticas Vitais'.
Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns, a fast friendly file reader and parallel file writer. Offers a natural and flexible syntax, for faster development.
Create tree structures from hierarchical data, and traverse the
    tree in various orders. Aggregate, cumulate, print, plot, convert to and from
    data.frame and more. Useful for decision trees, machine learning, finance,
    conversion from and to JSON, and many other applications.
Contains facilities such as getting the current timestamp in decimal seconds, computing interval w.r.t. a reference timestamp, and custom plotting with error bars.
Provides access to Dataverse version 4 APIs <https://dataverse.org/>, 
    enabling data search, retrieval, and deposit. For Dataverse versions <= 4.0, 
    use the deprecated 'dvn' package <https://cran.r-project.org/package=dvn>.
Tools for deciphering the contents of
	unknown objects or environments from within the terminal,
	a problem often encountered when working with unfamiliar packages or debugging complex functions.
	If working in xterm256 or ANSI terminals the output is coloured by default
	to improve readability (e.g. the standard Ubuntu terminal).
High-level tools for working with data.world data sets. data.world is a community 
    where you can find interesting data, store and showcase your own data and data projects, 
    and find and collaborate with other members. In addition to exploring, querying and 
    charting data on the data.world site, you can access data via 'API' endpoints and 
    integrations. Use this package to access, query and explore data sets, and to 
    integrate data into R projects. Visit <https://data.world>, for additional information.
Provides methods for working with nominal dates, times, and 
 durations. Base R has sophisticated facilities for handling time, but these 
 can give unexpected results if, for example, timezone is not handled properly. 
 This package provides a more casual approach to support cases which 
 do not require rigorous treatment.  It systematically deconstructs the 
 concepts origin and timezone, and de-emphasizes the display of seconds. It 
 also converts among nominal durations such as seconds, hours, days, and weeks.
 See '?datetime' and '?duration' for examples. Adapted from 'metrumrg' 
 <http://r-forge.r-project.org/R/?group_id=1215>.
Utilities for handling dates and times, such
   as selecting particular days of the week or month,
   formatting timestamps as required by RSS feeds, or
   converting timestamp representations of other software
   (such as 'MATLAB' and 'Excel') to R. The package is
   lightweight (no dependencies, pure R implementations) and
   relies only on R's standard classes to represent dates
   and times ('Date' and 'POSIXt'); it aims to provide
   efficient implementations, through vectorisation and the
   use of R's native numeric representations of timestamps
   where possible.
This package performs voxel-wise deconvolution analysis of
        DCE-MRI contrast agent concentration versus time data and
        generates the Impulse Response Function, which can be used to
        approximate commonly utilized kinetic parameters such as Ktrans
        and ve.  An interactive advanced voxel diagnosis tool (AVDT) is
        also provided to facilitate easy navigation of voxel-wise data.
A collection of functions accompanying the book "Data Analysis in Vegetation Ecology", Second edition. 2013, Wiley-Blackwell, Chichester
Various utilities for the Davies distribution.
In applications it is usual that some additional information is available. This package dawai (an acronym for Discriminant Analysis With Additional Information) performs linear and quadratic discriminant analysis with additional information expressed as inequality restrictions among the populations means. It also computes several estimations of the true error rate.
Fits Bayesian additive regression trees (BART) while allowing the updating of predictors or response so that BART can be incorporated as a conditional model in a Gibbs/MH sampler. Also serves as a drop-in replacement for package 'BayesTree'.
Using widgets provided by gWidgets this creates a nice
        usable interface to help facilitate the learning of SQL.  It
        also just gives a nice interface for more advanced users.  Not
        all features are provided in the GUI but it is possible for
        more advanced users to achieve these goals inside the GUI.
Goodness-of-fit and two sample comparison tests using sample entropy
A program for analyzing vegetation time series, with two algorithms: 1) change detection algorithm that detects trend changes, determines their type (abrupt or non-abrupt), and estimates their timing, magnitude, number, and direction; 2) generalization algorithm that simplifies the temporal trend into main features. The user can set the number of major breakpoints or magnitude of greatest changes of interest for detection, and can control the generalization process by setting an additional parameter of generalization-percentage.
A tool to ensure the validity of database writes.
  It provides a set of utilities to analyze and type check the properties
  of data frames that are to be written to databases with SQL support.
This package provides methods and examples to support a
        method of Gene Set Analysis (GSA).  DBGSA is a novel
        distance-based gene set enrichment analysis method. We consider
        that, the distance between 2 groups with different phenotype by
        focusing on the gene expression should be larger, if a certain
        gene functional set was significantly associated with a
        particular phenotype.
Client for programmatic access to the South Florida Water
  Management District's 'DBHYDRO' database at 
  <https://www.sfwmd.gov/science-data/dbhydro>, with functions
  for accessing hydrologic and water quality data.
A database interface definition for communication
    between R and relational database management systems.  All
    classes in this package are virtual and need to be extended by
    the various R/DBMS implementations.
A helper that tests 'DBI' back ends for conformity
    to the interface.
This package allows for nonparametric graduation of mortality rates using fixed or adaptive discrete beta kernel estimator.
Use EM algorithm to compute the NPMLE of CDF and also the
        two censoring distributions. For doubly censored data (as
        described in Chang and Yang (1987) Ann. Stat. 1536-47). You can
        also specify a constraint, it will return the constrained NPMLE
        and the -2 log empirical likelihood ratio. This can be used to
        test the hypothesis about the constraint and find confidence
        intervals for probability or quantile via empirical likelihood
        ratio theorem. Influence function of hat F may also be
        calculated (but may be slow).
Simple computation of spatial statistic functions of distance to characterize the spatial structures of mapped objects, including classical ones (Ripley's K and others) and more recent ones used by spatial economists (Duranton and Overman's Kd, Marcon and Puech's M). Relies on 'spatstat' for some core calculation.
A 'dplyr' back end for databases that allows you to work with 
    remote database tables as if they are in-memory data frames. Basic features
    works with any database that has a 'DBI' back end; more advanced features 
    require 'SQL' translation to be provided by the package author.
A fast reimplementation of several density-based algorithms of
    the DBSCAN family for spatial data. Includes the DBSCAN (density-based spatial
    clustering of applications with noise) and OPTICS (ordering points to identify
    the clustering structure) clustering algorithms HDBSCAN (hierarchical DBSCAN) and the LOF (local outlier
    factor) algorithm. The implementations uses the kd-tree data structure (from
    library ANN) for faster k-nearest neighbor search. An R interface to fast kNN
    and fixed-radius NN search is also provided.
Prediction methods where explanatory information is coded as a matrix of distances between individuals. Distances can either be directly input as a distances matrix, a squared distances matrix, an inner-products matrix or computed from observed predictors.
Performs differential network analysis to infer disease specific gene networks.
Finding dominant latent signals that regulate dynamic correlation between many pairs of variables.
Functions for analyzing dichotomous choice contingent valuation (CV) data. It provides 
   functions for estimating parametric and nonparametric models for single-, one-and-one-half-, 
   and double-bounded CV data.
A collection of routines and documentation that allows one to
  perform voxel-wise quantitative analysis of dynamic contrast-enhanced MRI 
  (DEC-MRI) and diffusion-weighted imaging (DWI) data, with emphasis on 
  oncology applications.
Data cloud geometry (DCG) applies random walks in finding community structures for social networks.
Functions for 1) gene filtration; 2) link filtration; 3) differential co-expression analysis: DCG (Differential Coexpressed
        Gene) identification and DCL (Differentially Coexpressed Link)
        identification; and 4) differential regulation analysis: DRG (Differential Regulated
        Gene) identification, DRL (Differential Regulated Link)
        identification, DRL visualization and regulator ranking.
There lacks a package for analysing domain-centric ontologies and annotations, particularly those in the dcGO database. The dcGO (http://supfam.org/SUPERFAMILY/dcGO) is a comprehensive domain-centric database for annotating protein domains using a panel of ontologies including Gene Ontology. With the package, users are expected to analyse and visualise domain-centric ontologies and annotations. Supported analyses include but are not limited to: easy access to a wide range of ontologies and their domain-centric annotations; able to build customised ontologies and annotations; domain-based enrichment analysis and visualisation; construction of a domain (semantic similarity) network according to ontology annotations; significance analysis for estimating a contact (statistical significance) network via Random Walk with Restart; and high-performance parallel computing. The new functionalities are: 1) to create domain-centric ontologies; 2) to predict ontology terms for input protein sequences (precisely domain content in the form of architectures) plus to assess the predictions; 3) to reconstruct ancestral discrete characters using maximum likelihood/parsimony.
Functions for reading DCP and CDF.bin files generated by the dChip software.
Statistical modelling and forecasting in claims reserving in non-life insurance under the Double Chain Ladder framework by Martinez-Miranda, Nielsen and Verrall (2012).
Low level functions for implementing
    maximum likelihood estimating procedures for
    complex models using data cloning and Bayesian
    Markov chain Monte Carlo methods.
    Sequential and parallel MCMC support
    for JAGS, WinBUGS and OpenBUGS.
A set of functions for the detection of spatial clusters
        of disease using count data. Bootstrap is used to estimate
        sampling distributions of statistics.
Model-based methods for the detection of disease clusters
  using GLMs, GLMMs and zero-inflated models.
Data Converter Module (DCM) converts the dataset format from split into stack and to the reverse.
S4 classes around infrastructure provided by the
    'coda' and 'dclone' packages to make package development easy as a breeze
    with data cloning for hierarchical models.
Analysis of dichotomous response data to obtain attribute profile
    estimates for respondents using Diagnostic Classification Model (DCM) and
    Markov Chain Monte Carlo (MCMC) method. The estimation procedure uses a
    loglinear cognitive diagnostic modeling (LDCM) framework that allows for
    the estimation of a host of DCMs such as NIDO, NIDA, NC-RUM etc.
Traversal graph algorithm for listing linear n-peptide constraints for overlapping protein regions. (Lebre and Gascuel, The combinatorics of overlapping genes, freely available from arXiv at : http://arxiv.org/abs/1602.04971).
Computing and plotting the distance covariance and correlation
             function of a univariate or a multivariate time series. Both versions
             of biased and unbiased estimators of distance covariance and correlation are provided.
             Test statistics for testing pairwise independence are also implemented. 
             Some data sets are also included.
This package performs several conventional
        Cross-validation statistical methods for climate-growth model
        in the climate reconstruction from tree rings, including Sign
        Test statistic, Reduction of Error statistic, Product Mean
        Test, Durbin-Watson statistic etc. This package is at its
        primary stage, the functions have not been tested exhaustively
        and more functions would be added in the comming days.
Contains procedures for depth-based supervised learning, which are entirely non-parametric, in particular the DDalpha-procedure (Lange, Mosler and Mozharovskyi, 2014). The training data sample is transformed by a statistical depth function to a compact low-dimensional space, where the final classification is done. It also offers an extension to functional data and routines for calculating certain notions of statistical depth functions. 50 multivariate and 5 functional classification problems are included.
Implements maximum likelihood and bootstrap methods based on the diversity-dependent birth-death process to test whether speciation or extinction are diversity-dependent, under various models including various types of key innovations.
   See Etienne et al. 2012, Proc. Roy. Soc. B 279: 1300-1309, <DOI:10.1098/rspb.2011.1439>, Etienne & Haegeman 2012, Am. Nat. 180: E75-E89, <DOI:10.1086/667574> and Etienne et al. 2016. Meth. Ecol. Evol. 7: 1092-1099, <DOI:10.1111/2041-210X.12565>.
   Also contains functions to simulate the diversity-dependent process.
A wrapper for the Duke Analytics model deployment API. See <http://www.dukeanalytics.com/products/dukedeployR.php> for more details.
Contains the normalizing and variance stabilizing
	Data-Driven Haar-Fisz algorithm. Also contains related algorithms
	for simulating from certain microarray gene intensity models and
	evaluation of certain transformations. Contains cDNA and shipping
	credit flow data.
This package provides useful functions for various DDI (Data Documentation Initiative) related outputs.
A set of three two-census methods to the estimate the degree of death registration coverage for a population. Implemented methods include the Generalized Growth Balance method (GGB), the Synthetic Extinct Generation method (SEG), and a hybrid of the two, GGB-SEG. Each method offers automatic estimation, but users may also specify exact parameters or use a graphical interface to guess parameters in the traditional way if desired.
An interface to explore, analyze, and visualize droplet digital PCR
    (ddPCR) data in R. This is the first non-proprietary software for analyzing
    two-channel ddPCR data. An interactive tool was also created and is available
    online to facilitate this analysis for anyone who is not comfortable with
    using R.
Provides distributed data structures and simplifies
    distributed computing in R.
Provides an implementation of the framework of reversed graph embedding (RGE) which projects data into a reduced dimensional space while constructs a principal tree which passes through the middle of the data simultaneously. DDRTree shows superiority to alternatives (Wishbone, DPT) for inferring the ordering as well as the intrinsic structure of the single cell genomics data. In general, it could be used to reconstruct the temporal progression as well as bifurcation structure of any datatype.
Smooth testing of goodness of fit. These tests are data
    driven (alternative hypothesis is dynamically selected based on data). In this
    package you will find various tests for exponent, Gaussian, Gumbel and uniform
    distribution.
Statistical deadband algorithms are based on the Send-On-Delta concept as in Miskowicz(2006,<doi:10.3390/s6010049>). A collection of functions compare effectiveness and fidelity of sampled signals using statistical deadband algorithms.
Bayesian networks with continuous and/or discrete
        variables can be learned and compared from data.
deamer provides deconvolution algorithms for the
        non-parametric estimation of the density f of an error-prone
        variable x with additive noise e. The model is y = x + e where
        the noisy variable y is observed, while x is unobserved.
        Estimation may be performed for i) a known density of the error
        ii) with an auxiliary sample of pure noise and iii) with an
        auxiliary sample of replicate (repeated) measurements.
        Estimation is performed using adaptive model selection and
        penalized contrasts.
A Bayesian framework for parameter inference in differential equations.
    This approach offers a rigorous methodology for parameter inference as well as
    modeling the link between unobservable model states and parameters, and
    observable quantities. Provides templates for the DE model, the
    observation model and data likelihood, and the model parameters and their prior
    distributions. A Markov chain Monte Carlo (MCMC) procedure processes these inputs
    to estimate the posterior distributions of the parameters and any derived
    quantities, including the model trajectories. Further functionality is provided
    to facilitate MCMC diagnostics and the visualisation of the posterior distributions
    of model parameters and trajectories.
Debugger for R functions, with code display, graceful
        error recovery, line-numbered conditional breakpoints, access
        to exit code, flow control, and full keyboard input.
Specify debug messages as special string constants, and
    control debugging of packages via environment variables.
Calculates various estimates for measures of educational
        differentials, the relative importance of primary and secondary
        effects in the creation of such differentials and compares the
        estimates obtained from two datasets.
Contains a function called dmur() which accepts four parameters like possible values, probabilities of the values, selling cost and preparation cost. The dmur() function generates various numeric decision parameters like MEMV (Maximum (optimum) expected monitory value), best choice, EPPI (Expected profit with perfect information), EVPI (Expected value of the perfect information), EOL (Expected opportunity loss), which facilitate effective decision-making.
Supporting the quantitative analysis of binary welfare based
    decision making processes using Monte Carlo simulations. Decision support
    is given on two levels: (i) The actual decision level is to choose between
    two alternatives under probabilistic uncertainty. This package calculates
    the optimal decision based on maximizing expected welfare. (ii) The meta
    decision level is to allocate resources to reduce the uncertainty in the
    underlying decision problem, i.e to increase the current information to
    improve the actual decision making process. This problem is dealt with
    using the Value of Information Analysis. The Expected Value of
    Information for arbitrary prospective estimates can be calculated as
    well as Individual Expected Value of Perfect Information.
    The probabilistic calculations are done via Monte Carlo
    simulations. This Monte Carlo functionality can be used on its own.
Integrated differential expression (DE) and differential co-expression (DC) analysis on gene expression data based on DECODE (DifferEntial CO-expression and Differential Expression) algorithm.
Main function "decode" is used to decode coded key values to plain
    text. Function "code" can be used to code plain text to code if there is a
    1:1 relation between the two. The concept relies on 'keyvalue' objects used
    for translation. There are several 'keyvalue' objects included in the areas
    of geographical regional codes, administrative health care unit codes,
    diagnosis codes and more. It is also easy to extend the use by arbitrary 
    code sets.
Predict future values with hybrid combinations of Pattern Sequence based
        Forecasting (PSF), Autoregressive Integrated Moving Average (ARIMA), Empirical Mode
        Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) methods based
        hybrid methods.
Two global-value-chain decompositions are implemented. Firstly, the
    Wang-Wei-Zhu (Wang, Wei, and Zhu, 2013) algorithm splits bilateral gross exports
    into 16 value-added components. Secondly, the Leontief decomposition (default)
    derives the value added origin of exports by country and industry, which is also
    based on Wang, Wei, and Zhu (Wang, Z., S.-J. Wei, and K. Zhu. 2013. "Quantifying
    International Production Sharing at the Bilateral and Sector Levels.").
This package contains a collection of functions to deal
        with nonparametric measurement error problems using
        deconvolution kernel methods. We focus two measurement error
        models in the package: (1) an additive measurement error model,
        where the goal is to estimate the density or distribution
        function from contaminated data; (2) nonparametric regression
        model with errors-in-variables. The R functions allow the
        measurement errors to be either homoscedastic or
        heteroscedastic. To make the deconvolution estimators
        computationally more efficient in R, we adapt the "Fast Fourier
        Transform" (FFT) algorithm for density estimation with
        error-free data to the deconvolution kernel estimation. Several
        methods for the selection of the data-driven smoothing
        parameter are also provided in the package. See details in:
        Wang, X.F. and Wang, B. (2011). Deconvolution estimation in
        measurement error models: The R package decon. Journal of
        Statistical Software, 39(10), 1-24.
Takes sample information in the form of the fraction of mutations
    in each of 96 trinucleotide contexts and identifies the weighted combination
    of published signatures that, when summed, most closely reconstructs the
    mutational profile.
Empirical Bayes methods for learning prior distributions from data.
    An unknown prior distribution (g) has yielded (unobservable) parameters, each of
    which produces a data point from a parametric exponential family (f). The goal
    is to estimate the unknown prior ("g-modeling") by deconvolution and Empirical
    Bayes methods.
DECORATE (Diverse Ensemble Creation by Oppositional Relabeling
    of Artificial Training Examples) builds an ensemble of J48 trees by recursively
    adding artificial samples of the training data ("Melville, P., & Mooney, R. J. (2005). Creating diversity in ensembles using artificial data. Information Fusion, 6(1), 99-111. <doi:10.1016/j.inffus.2004.04.001>").
An intuitive, cross-platform graphical data analysis system. It uses menus and dialogs to guide the user efficiently through the data manipulation and analysis process, and has an excel like spreadsheet for easy data frame visualization and editing. Deducer works best when used with the Java based R GUI JGR, but the dialogs can be called from the command line. Dialogs have also been integrated into the Windows Rgui.
Added functionality for Deducer. This package includes
        additional dialogs for calculating distribution function
        values, cluster analysis and more.
A example GUI plug-in package to serve as a template.
A Deducer plug-in for factor analysis, reliability
        analysis and discriminant analysis, using psych, GPArotation
        and mvnormtest packages.
A Deducer plug-in for spatial data analysis. Includes The
        ability to plot and explore open street map and Bing satellite
        images.
Adds Kaplan-Meier Survival Analysis to the Deducer GUI
A collection of methods for automated data cleaning where all actions are logged.
Attempt to repair inconsistencies and missing values in data 
    records by using information from valid values and validation rules 
    restricting the data.
Provides deep boosting models training, evaluation, predicting and
 hyper parameter optimising using grid search and cross validation.
 Based on Google's Deep Boosting algorithm, and Google's C++ implementation.
 Cortes, C., Mohri, M., & Syed, U. (2014) <http://machinelearning.wustl.edu/mlpapers/papers/icml2014c2_cortesb14>.
Implement some deep learning architectures and neural network
    algorithms, including BP,RBM,DBN,Deep autoencoder and so on.
Tests for, and describe differences in event count profiles in groups of reconstructed cophylogenies
A simple syntax to change the default values for function
  arguments, whether they are in packages or defined locally.
Numerical quadrature of functions of one variable over a finite or infinite interval
	with double exponential formulas.
Provides a workflow for combining amphibian taxonomic data sets from different sources that use different taxonomic nomenclature, by suggesting the most current nomenclature used by the Amphibian Species of the World: an Online Reference, Frost (2017) <http://research.amnh.org/herpetology/amphibia/index.html> for a given input query of species names. It can also be used to extract specific systematic information (e.g. species downstream of a given taxonomic level).
Detection of differentially expressed genes between two
        distinct groups of samples.
Likelihood-based inference for skewed count distributions used in network modeling. "degreenet" is a part of the "statnet" suite of packages for network analysis.
Performs reference based multiple imputation of recurrent event data
    based on a negative binomial regression model, as described
    by Keene et al (2014) <doi:10.1002/pst.1624>.
Provides probability mass, distribution, quantile, random-variate generation, and method-of-moments parameter-estimation functions for the Delaporte distribution. The Delaporte is a discrete probability distribution which can be considered the convolution of a negative binomial distribution with a Poisson distribution. Alternatively, it can be considered a counting distribution with both Poisson and negative binomial components. It has been studied in actuarial science as a frequency distribution which has more variability than the Poisson, but less than the negative binomial.
Calculates the Delaunay triangulation and the Dirichlet
	or Voronoi tessellation (with respect to the entire plane) of
	a planar point set. Plots triangulations and tessellations in
	various ways.  Clips tessellations to sub-windows. Calculates
	perimeters of tessellations.  Summarises information about the
	tiles of the tessellation.
Implements the DeLorean model to estimate pseudotimes for
    single cell expression data. The DeLorean model uses a Gaussian process
    latent variable model to model uncertainty in the capture time of
    cross-sectional data.
We implement methods for estimating multivariate densities.
  We include a discretized kernel estimator,
  an adaptive histogram (a greedy histogram and a CART-histogram), 
  stagewise minimization, and bootstrap aggregation.
Measure of agreement Delta was originally by Martín & Femia (2004) <DOI:10.1348/000711004849268>. 
    Since then has been considered as agreement measure for different 
	fields, since their behavior is usually better than the usual Kappa index
	by Cohen (1960) <DOI:10.1177/001316446002000104>. The main issue with Delta 
	is that can not be computed by hand contrary to Kappa.
The deltaPlotR package proposes an implementation of Angoff's Delta Plot method to detect dichotomous DIF. Several detection thresholds are included, either from multivariate normality assumption or by prior determination. Item purification is supported.
Functions to calculate pairwise relatedness on diploid genetic datasets. Different estimators for relatedness can be combined with information on geographical distances. Information on heterozygosity, allele- and genotype diversity as well as genetic F-statistics are provided for each population.
This package allows to calculate the fixation index Gst
  (Nei, 1973) and the differentiation index D (Jost, 2008) pairwise
  between or averaged over several populations.  P-values, stating the
  significance of differentiation, and 95 percent confidence intervals
  can be estimated using bootstrap resamplings. In the case that more
  than two populations are compared pairwise, the p-values are
  adjusted by bonferroni correction and in several other ways due to
  the multiple comparison from one data set.
Implementation of the DEMI method for the analysis of high-density microarray data.
Generalized Deming regression, Theil-Sen regression and Passing-Bablock regression functions.
Construction and analysis of matrix population models in R.
Functions for demographic analysis including lifetable
        calculations; Lee-Carter modelling; functional data analysis of
        mortality rates, fertility rates, net migration numbers; and
        stochastic population forecasting.
Demonstration code showing how (univariate) kernel
	     density estimates are computed, at least conceptually,
	     and allowing users to experiment with different kernels,
	     should they so wish.  NOTE: the density function in the
	     stats package should be used for computational efficiency.
Tool for the development of multi-linear QSPR/QSAR models (Quantitative structure-property/activity relationship). Theses models are used in chemistry, biology and pharmacy to find a relationship between the structure of a molecule and its property (such as activity, toxicology but also physical properties). The various functions of this package allows: selection of descriptors based of variances, intercorrelation and user expertise; selection of the best multi-linear regression in terms of correlation and robustness; methods of internal validation (Leave-One-Out, Leave-Many-Out, Y-scrambling) and external using test sets.
Offers a set of functions for extending
    'dendrogram' objects in R, letting you visualize and compare trees of
    'hierarchical clusterings'. You can (1) Adjust a tree's graphical parameters
    - the color, size, type, etc of its branches, nodes and labels. (2)
    Visually and statistically compare different 'dendrograms' to one another.
Provides novel dendroclimatological methods, primarily used by the
    Tree-ring research community. The core function is daily_response(), which 
    finds the optimal sequence of days that are linearly or nonlinearly related
    to one or more tree-ring proxy records.
Provides extra functions to manipulate dendrograms
    that build on the base functions provided by the 'stats' package. The main
    functionality it is designed to add is the ability to colour all the edges
    in an object of class 'dendrogram' according to cluster membership i.e. each
    subtree is coloured, not just the terminal leaves. In addition it provides
    some utility functions to cut 'dendrogram' and 'hclust' objects and to 
    set/get labels.
Various functions to import, verify, process and plot high-resolution dendrometer using daily and stem-cycle approaches.
Provides functions for the calculation and plotting of synchrony in 
      tree growth from tree-ring width chronologies (TRW index). It combines
      variance-covariance (VCOV) mixed modelling with functions that quantify 
      the degree to which the TRW chronologies contain a common temporal 
      signal. It also implements temporal trends in spatial synchrony using a 
      moving window. These methods can also be used with other kind of ecological
      variables that have temporal autocorrelation corrected.
Re-arranges a dendrogram to optimize visualisation-based cost functions
An implementation of functions to optimize ordering of nodes in
    a dendrogram, without affecting the meaning of the dendrogram. A dendrogram can
    be sorted based on the average distance of subtrees, or based on the smallest
    distance value. These sorting methods improve readability and interpretability
    of tree structure, especially for tasks such as comparison of different distance
    measures or linkage types and identification of tight clusters and outliers. As
    a result, it also introduces more meaningful reordering for a coupled heatmap
    visualization.
Provides functions for parameter estimation and real-time predictions  of a single dengue 
 outbreak taking into account model uncertainty using  model averaging.
Given count data from two conditions, it determines which transcripts are differentially expressed across the two conditions using Bayesian inference of the parameters of  a bottom-up model for PCR amplification. This model is  developed in Ndifon Wilfred, Hilah Gal, Eric Shifrut, Rina Aharoni, Nissan Yissachar, Nir Waysbort, Shlomit Reich Zeliger, Ruth Arnon, and Nir Friedman (2012), <http://www.pnas.org/content/109/39/15865.full>, and results in a distribution for the counts that is a superposition of the binomial and negative binomial distribution.
Estimate a low rank matrix from noisy data using singular values
    thresholding and shrinking functions. Impute missing values with matrix completion.
An integrated toolset for the analysis of de novo (sporadic)
    genetic sequence variants. denovolyzeR implements a mutational model that
    estimates the probability of a de novo genetic variant arising in each human
    gene, from which one can infer the expected number of de novo variants in a
    given population size. Observed variant frequencies can then be compared against
    expectation in a Poisson framework. denovolyzeR provides a suite of functions
    to implement these analyses for the interpretation of de novo variation in human
    disease.
We provide tools to 
  (1) visualize multivariate density functions and density estimates 
      with level set trees,
  (2) visualize level sets with shape trees,
  (3) visualize multivariate data with tail trees, 
  (4) visualize scales of multivariate density estimates with 
      mode graphs and branching maps, and
  (5) visualize anisotropic spread with 2D volume functions and
      2D probability content functions.
  Level set trees visualize mode structure,
  shape trees visualize shapes of level sets of unimodal densities,
  and tail trees visualize connected data sets.
  The kernel estimator is implemented
  but the package may  also be applied for visualizing other density estimates.
Estimation of functional linear mixed models for densely sampled data based on functional principal component analysis.
An implementation of the clustering algorithm described by Alex
    Rodriguez and Alessandro Laio (Science, 2014 vol. 344), along with tools to
    inspect and visualize the results.
Implementation in the density framework of the non-combinatorial algorithm and its greedy version, introduced by Magalhães and Rozenholc (2014), for T-estimation Hold-Out proposed in Birgé (2006, Section 9). The package provide an implementation which uses several families of estimators (regular and irregular histograms, kernel estimators) which may be used alone or combined. As a complement, provides also a comparison with other Held-Out derived from least-squares and maximum-likelihood. This package implements also the T-estimation Hold-Out derived from the test introduced in Baraud (2011).
Provide a Dens-based method for estimating functional connection in large scale brain networks using partial correlation.
Density ratio estimation.
    The estimated density ratio function can be used in many applications such as
    the inlier-based outlier detection, covariate shift adaptation and etc.
Graphical methods for compactly illustrating probability distributions, including density strips, density regions, sectioned density plots and varying width strips.
Implements the differential evolution algorithm for global optimization of a real-valued function of a real-valued parameter vector.
Differential Evolution (DE) stochastic algorithms for global
  optimization of problems with and without constraints.
  The aim is to curate a collection of its state-of-the-art variants that
  (1) do not sacrifice simplicity of design,
  (2) are essentially tuning-free, and
  (3) can be efficiently implemented directly in the R language.
  Currently, it only provides an implementation of the 'jDE' algorithm by
  Brest et al. (2006) <doi:10.1109/TEVC.2006.872133>.
Suppose that one can observe bivariate random variables (X, Y) only when X<=Y holds. Data (Xj, Yj), subject to Xj<=Yj, for all j=1,...,n, are called truncated data. For truncated data, several different approaches are implemented for statistical inference on (X, Y), when X and Y are dependent. Also included is truncated data on the number of deaths at each year (1963-1980) for Japanese male centenarians.
Traditional phasing programs are limited to diploid organisms.
 Our method modifies Li and Stephens algorithm with Markov chain Monte Carlo
 (MCMC) approaches, and builds a generic framework that allows haplotype searches
 in a multiple infection setting. This package is primarily developed as part of
 the Pf3k project, which is a global collaboration using the latest
 sequencing technologies to provide a high-resolution view of natural variation
 in the malaria parasite Plasmodium falciparum. Parasite DNA are extracted from
 patient blood sample, which often contains more than one parasite strain, with
 unknown proportions. This package is used for deconvoluting mixed haplotypes,
 and reporting the mixture proportions from each sample.
Fits (multigroup) mixtures of latent or hidden Markov models on mixed categorical and continuous (timeseries) data. The Rdonlp2 package can optionally be used for optimization of the log-likelihood and is available from R-forge.
Fits latent (hidden) Markov models on mixed categorical and continuous (time series) data, otherwise known as dependent mixture models.
Tools for depth functions methodology applied 
        to multivariate analysis. Besides allowing calculation 
        of depth values and depth-based location estimators, the package
        includes functions or drawing contour plots and perspective plots
        of depth functions. Euclidian and spherical depths are supported.
Could be used to obtain spatial depths, spatial ranks and outliers of multivariate random variables. Could also be used to visualize DD-plots (a multivariate generalization of QQ-plots).
Data depth concept offers a variety of powerful and user friendly
    tools for robust exploration and inference for multivariate data. The offered
    techniques may be successfully used in cases of lack of our knowledge on
    parametric models generating data due to their nature. The
    package consist of among others implementations of several data depth techniques
    involving multivariate quantile-quantile plots, multivariate scatter estimators,
    multivariate Wilcoxon tests and robust regressions.
depthTools is a package that implements different
        statistical tools for the description and analysis of gene
        expression data based on the concept of data 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. All these techniques are based on the Modified
        Band Depth, which is a recent notion of depth with a low
        computational cost, what renders it very appropriate for high
        dimensional data such as gene expression data.
Queues, stacks, and 'deques' are list-like, abstract data types. 
    These are meant to be very cheap to "grow", or insert new objects into.
    A typical use case involves storing data in a list in a streaming fashion,
    when you do not necessarily know how may elements need to be stored.
    Unlike R's lists, the new data structures provided here are not
    necessarily stored contiguously, making insertions and deletions at the
    front/end of the structure much faster.  The underlying implementation
    is new and uses a head/tail doubly linked list; thus, we do not rely on R's
    environments or hashing.  To avoid unnecessary data copying, most operations
    on these data structures are performed via side-effects.
R-based solution for symbolic differentiation. It admits
    user-defined function as well as function substitution
    in arguments of functions to be differentiated. Some symbolic
    simplification is part of the work.
A set of pricing and expository functions that should
    be useful in teaching a course on financial derivatives.
Tools to read, write, create, and manipulate DESCRIPTION files.
    It is intended for packages that create or manipulate other packages.
Decompose a time series into seasonal, trend and irregular components using transformations to amplitude-frequency domain.
Weighted frequency and contingency tables of categorical
       variables and of the comparison of the mean value of a numerical
       variable by the levels of a factor, and methods to produce xtable
       objects of the tables and to plot them. There are also functions to
       facilitate the character encoding conversion of objects, to quickly
       convert fixed width files into csv ones, and to export a data.frame to
       a text file with the necessary R and SPSS codes to reread the data.
Produce publication quality graphics from output of GGobi's
    describe display plugin.
Allows users to quickly and easily describe data using
    common descriptive statistics.
Generate descriptive statistics such as measures of location,
    dispersion, frequency tables, cross tables, group summaries and multiple
    one/two way tables. Visualize and compute percentiles/probabilities of 
    normal, t, f, chi square and binomial distributions.
Easily create descriptive and comparative tables.
    It makes use and integrates directly with the tidyverse family of packages, and pipes.
    Tables are produced as data frames/lists of data frames for easy manipulation after creation,
    and ready to be saved as csv, or piped to DT::datatable() or pander::pander() to integrate into reports.
A collection of miscellaneous basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The 'camel style' was consequently applied to functions borrowed from contributed R packages as well.
'RStudio' as of recently offers the option to define addins and assign shortcuts to them. This package contains AddIns for a few most used functions in an analysts (at least mine) daily work (like str(), example(), plot(), head(), view(), Desc()). Most of these functions will get the current selection in the editor window and send the specific command to the console while instantly executing it. Assigning shortcuts to these AddIns will spare you quite a few keystrokes.
Deseasonalize daily or monthly time series.
The package provides R scripts for designing genetical
        genomics experiments.
Use simulated annealing to find optimal designs for Poisson regression models with blocks.
Includes functions for the construction of matched samples that are balanced and representative by design.  Among others, these functions can be used for matching in observational studies with treated and control units, with cases and controls, in related settings with instrumental variables, and in discontinuity designs.  Also, they can be used for the design of randomized experiments, for example, for matching before randomization.  By default, 'designmatch' uses the 'GLPK' optimization solver, but its performance is greatly enhanced by the 'Gurobi' optimization solver and its associated R interface.  For their installation, please follow the instructions at <http://user.gurobi.com/download/gurobi-optimizer> and <http://www.gurobi.com/documentation/7.0/refman/r_api_overview.html>.  We have also included directions in the gurobi_installation file in the inst folder.
Functions for (1) ranking, selecting, and prioritising  genes,
    proteins, and metabolites from high dimensional biology experiments, (2)
    multivariate hit calling in high content screens, and (3) combining data
    from diverse sources.
S3 classes for multivariate optimization using the desirability function by Derringer and Suich (1980).
Harrington and Derringer-Suich type desirability functions
This package implements a statistical data mining method to
    compare whole genome gene expression profiles, with respect to the presence
    of a recurrent genetic disturbance event, to identify the affected target
    genes.
Functions that solve initial value problems of a system
        of first-order ordinary differential equations ('ODE'), of
        partial differential equations ('PDE'), of differential
        algebraic equations ('DAE'), and of delay differential
        equations.  The functions provide an interface to the FORTRAN
        functions 'lsoda', 'lsodar', 'lsode', 'lsodes' of the 'ODEPACK'
        collection, to the FORTRAN functions 'dvode', 'zvode' and 'daspk'
	and a C-implementation of solvers of the 'Runge-Kutta' family with
        fixed or variable time steps.  The package contains routines
        designed for solving 'ODEs' resulting from 1-D, 2-D and 3-D
        partial differential equations ('PDE') that have been converted
        to 'ODEs' by numerical differencing.
Several estimators of the diagonal elements of a sparse precision
	(inverse covariance) matrix from a sample of Gaussian vectors for a
	given matrix of estimated marginal regression coefficients. Moreover, a robust estimator
  of the precision matrix is proposed.
	To install package 'gurobi', instructions at 
	<http://user.gurobi.com/download/gurobi-optimizer> and 
	<http://www.gurobi.com/documentation/6.0/refman/r_api_overview.html>.
A function for plotting maps of agricultural field experiments that
    are laid out in grids.
Models for analyzing site occupancy and count data models
  with detection error, including single-visit based models,
  conditional distance sampling and time-removal models.
  Package development was supported by the
  Alberta Biodiversity Monitoring Institute (www.abmi.ca)
  and the Boreal Avian Modelling Project (borealbirds.ca).
Allows users to quickly and easily detect data containing
    Personally Identifiable Information (PII) through convenience functions.
Solvers and test set for stiff and non-stiff differential equations, and 
  differential algebraic equations.
Implementation of DetMCD, a new algorithm for robust and deterministic estimation of location and scatter. The benefits of robust and deterministic estimation are explained in Hubert, Rousseeuw and Verdonck (2012) <doi:10.1080/10618600.2012.672100>.
DetLTS, DetMM (and DetS) Algorithms for Deterministic, Robust
    Linear Regression.
Simple GUI to perform some standard tree-ring analyses.
In the new era of population genomics, surveys of genetic
        polymorphism ("genome scans") offer the opportunity to
        distinguish locus-specific from genome wide effects at many
        loci. Identifying presumably neutral regions of the genome that
        are assumed to be influenced by genome-wide effects only, and
        excluding presumably selected regions, is therefore critical to
        infer population demography and phylogenetic history reliably.
        Conversely, detecting locus-specific effects may help identify
        those genes that have been, or still are, targeted by natural
        selection. The software package DetSel has been developed to
        identify markers that show deviation from neutral expectation
        in pairwise comparisons of diverging populations.
Compare detrital zircon suites by uploading univariate,
      U-Pb age, or bivariate, U-Pb age and Lu-Hf data, in a 'shiny'-based
      user-interface. Outputs publication quality figures using 'ggplot2', and
      tables of statistics currently in use in the detrital zircon geochronology
      community.
Data sets and sample analyses from Jay L. Devore (2008),
        "Probability and Statistics for Engineering and the Sciences
        (7th ed)", Thomson.
A set of functions to quantify the relationship between development
    rate and temperature. The package comprises a set of models and estimated parameters
    borrowed from a literature review in ectotherms (mostly arthropods).
Collection of package development tools.
A system for the management, assessment, and psychometric analysis of data from educational and psychological tests. Developed at Cito, The Netherlands, with subsidy from the Dutch Ministry of Education, Culture, and Science.
It handles numerics, characters, factors, and logicals.
Phase I/II adaptive dose-finding design for combination
   studies. Several methods are proposed depending on the type of
   combinations: (1) the combination of two cytotoxic agents, and (2)
   combination of a molecularly targeted agent with a cytotoxic agent.
Compares two dataframes with a common key
            and returns the delta records. The package will return
            three dataframes that contain the added, changed,
            and deleted records.
This package provides functions to run the CRM and
        TITE-CRM in phase I trials and calibration tools for trial
        planning purposes.
Quickly and graphically show missing values and classes of each variable and each observation of a data.frame. The aim is to show pattern of missing values and if there is wrong class attribution.
A set of functions to perform Raju, van der Linden and Fleer's
    (1995, doi:10.1177/014662169501900405) Differential Functioning of Items
    and Tests (DFIT) analyses. It includes functions to use the Monte Carlo Item
    Parameter Replication approach (Oshima, Raju, & Nanda, 2006, doi:10.1111/j.
    1745-3984.2006.00001.x) for obtaining the associated statistical significance
    tests cut-off points. They may also be used for a priori and post-hoc power
    calculations (Cervantes, 2017, doi:10.18637/jss.v076.i05).
Phase I/II adaptive dose-finding design for single-agent
   Molecularly Targeted Agent (MTA), according to the paper "Phase
   I/II Dose-Finding Design for Molecularly Targeted Agent: Plateau
   Determination using Adaptive Randomization".
Derivative-Free optimization algorithms. These algorithms do not require gradient information. More importantly, they can be used to solve non-smooth optimization problems.
Decision forest: combining the predictions of
    multiple independent decision tree models.
A unified method for designing and analysing dose-finding trials in paediatrics, while bridging information from adults, is proposed in the dfped package. The dose range can be calculated under three extrapolation methods: linear, allometry and maturation adjustment, using pharmacokinetic (PK) data. To do this, it is assumed that target exposures are the same in both populations. The working model and prior distribution parameters of the dose-toxicity and dose-efficacy relationships can be obtained using early phase adult toxicity and efficacy data at several dose levels through dfped package. Priors are used into the dose finding process through a Bayesian model selection or adaptive priors, to facilitate adjusting the amount of prior information to differences between adults and children. This calibrates the model to adjust for misspecification if the adult and paediatric data are very different. User can use his/her own Bayesian model written in Stan code through the dfped package. A template of this model is proposed in the examples of the corresponding R functions in the package. Finally, in this package you can find a simulation function for one trial or for more than one trial.
Statistical methods for retrospectively detecting changes in location and/or dispersion of univariate and multivariate variables. Data values are assumed to be independent, can be individual (one observation at each instant of time) or subgrouped (more than one observation at each instant of time). Control limits are computed, often using a permutation approach, so that a prescribed false alarm probability is guaranteed  without making any parametric assumptions on the stable (in-control) distribution.
Statistical methods involving PK measures are provided, in the dose allocation 
						process during a Phase I clinical trials. These methods enter pharmacokinetics 
						(PK) in the dose finding designs in different ways, including covariates models,
						dependent variable or hierarchical models. 
						This package provides functions to generate data from several scenarios and functions 
						to run simulations which their objective is to determine the maximum tolerated dose (MTD).
Performs Bayesian model averaging for capture-recapture. This includes code to stratify records, check the strata for suitable overlap to be used for capture-recapture, and some functions to plot the estimated population size.
Feature selection from high dimensional data using a diploid
    genetic algorithm with Incomplete Dominance for genotype to phenotype mapping
    and Random Assortment of chromosomes approach to recombination.
Performs differential correlation analysis on input
    matrices, with multiple conditions specified by a design matrix. Contains
    functions to filter, process, save, visualize, and interpret differential
    correlations of identifier-pairs across the entire identifier space, or with
    respect to a particular set of identifiers (e.g., one). Also contains several
    functions to perform differential correlation analysis on clusters (i.e., modules)
    or genes. Finally, it contains functions to generate empirical p-values for the
    hypothesis tests and adjust them for multiple comparisons. Although the package
    was built with gene expression data in mind, it is applicable to other types of
    genomics data as well, in addition to being potentially applicable to data from
    other fields entirely. It is described more fully in the manuscript
    introducing it, freely available at <doi:10.1186/s12918-016-0349-1>.
Spatial analyses involving binning require that every bin have the same area, but this is impossible using a rectangular grid laid over the Earth or over any projection of the Earth. Discrete global grids use hexagons, triangles, and diamonds to overcome this issue, overlaying the Earth with equally-sized bins. This package provides utilities for working with discrete global grids, along with utilities to aid in plotting such data.
Differential geometric least angle regression method for fitting 
	sparse generalized linear models. In this version of the package, the user can fit
	models specifying Gaussian, Poisson, Binomial, Gamma and Inverse Gaussian family. 
	Furthermore, several link functions can be used to model the relationship between the 
	conditional expected value of the response variable and the linear predictor. The 
	solution curve can be computed using an efficient predictor-corrector or a cyclic 
	coordinate descent algorithm, as described in the paper linked to via the URL below.
Model fitting and evaluation tools for double generalized linear
    models (DGLMs). This class of models uses one generalized linear model (GLM)
    to fit the specified response and a second GLM to fit the deviance of the first
    model.
A set of functions have been implemented to generate random data to perform Monte Carlo simulations on structural models with formative constructs and interaction and nonlinear effects (Two-Step PLS Mode B structural models). The setup of the true model considers a simple structure with three formative exogenous constructs related to one formative endogenous construct. The routines take into account the interaction and nonlinear effects of the exogenous constructs on the endogenous construct.
Fit dynamic group-level IRT and MRP models from individual or
    aggregated item response data. This package handles common preprocessing
    tasks and extends functions for inspecting results, poststratification, and
    quick iteration over alternative models.
Provides data used by package 'dgo' in examples and vignettes.
This package contains a proposed revision to the
        stats::ks.test() function and the associated ks.test.Rd help
        page.  With one minor exception, it does not change the
        existing behavior of ks.test(), and it adds features necessary
        for doing one-sample tests with hypothesized discrete
        distributions.  The package also contains cvm.test(), for doing
        one-sample Cramer-von Mises goodness-of-fit tests.
The 'DHARMa' package uses a simulation-based approach to create
    readily interpretable scaled (quantile) residuals for fitted generalized linear mixed
    models. Currently supported are generalized linear mixed models from 'lme4' 
    (classes 'lmerMod', 'glmerMod'), generalized additive models ('gam' from 'mgcv'), 
    'glm' (including 'negbin' from 'MASS', but excluding quasi-distributions) and 'lm' model
    classes. Alternatively, externally created simulations, e.g. posterior predictive simulations 
    from Bayesian software such as 'JAGS', 'STAN', or 'BUGS' can be processed as well. 
    The resulting residuals are standardized to values between 0 and 1 and can be interpreted 
    as intuitively as residuals from a linear regression. The package also provides a number of 
    plot and test functions for typical model misspecification problems, such as 
    over/underdispersion, zero-inflation, and residual spatial and temporal autocorrelation.
Identification of hub genes in a gene co-expression network from gene expression data. The differential network analysis for two contrasting conditions leads to the identification of various types of hubs like Housekeeping, Unique to stress (Disease) and Unique to control (Normal) hub genes.
Double hierarchical generalized linear models in which the mean, dispersion parameters for variance of random effects, and residual variance (overdispersion) can be further modeled as random-effect models.
Contains an implementation of the
	     d-variable Hilbert Schmidt independence criterion
	     and several hypothesis tests based on it, as described
	     in Pfister et al. (2017) <doi:10.1111/rssb.12235>.
A set of utilities for calculating the Deficit (frailty) Index (DI) in gerontological studies. 
             The deficit index was first proposed by Arnold Mitnitski and Kenneth Rockwood 
             and represents a proxy measure of aging and also can be served as
             a sensitive predictor of survival. For more information, see 
             (i)"Accumulation of Deficits as a Proxy Measure of Aging" 
             by Arnold B. Mitnitski et al. (2001), 
             The Scientific World Journal 1, <DOI:10.1100/tsw.2001.58>;
             (ii) "Frailty, fitness and late-life mortality in relation to chronological and biological age" 
             by Arnold B Mitnitski et al. (2001), 
             BMC Geriatrics2002 2(1), <DOI:10.1186/1471-2318-2-1>.
Fast functions for effective sample size, weighted multivariate mean and variance computation, 
    and weight diagnostic plot for generic importance sampling type results.
Several tools for handling block-matrix diagonals and similar constructs are implemented. Block-diagonal matrices can be extracted or removed using two small functions implemented here. In addition, non-square matrices are supported. Block diagonal matrices occur when two dimensions of a data set are combined along one edge of a matrix. For example, trade-flow data in the 'decompr' and 'gvc' packages have each country-industry combination occur along both edges of the matrix.
Visualises simple graphs (networks) based on a transition matrix, utilities to plot flow diagrams, 
     visualising webs, electrical networks, etc.
     Support for the book "A practical guide to ecological modelling -
     using R as a simulation platform"
     by Karline Soetaert and Peter M.J. Herman (2009), Springer.
     and the book "Solving Differential Equations in R"
     by Karline Soetaert, Jeff Cash and Francesca Mazzia (2012), Springer.
     Includes demo(flowchart), demo(plotmat), demo(plotweb).
Graph and network visualization using tabular data.
Allows for export of DiagrammeR Graphviz objects to SVG.
Performs Diallel Analysis with R using Griffing's and Hayman's approaches. Four different methods (1: Method-I (Parents + F1's + reciprocals); 2: Method-II (Parents and one set of F1's); 3: Method-III (One set of F1's and reciprocals); 4: Method-IV (One set of F1's only)) and two methods (1: Fixed Effects Model; 2: Random Effects Model) can be applied using Griffing's approach.
Visualize one-factor data frame. 
  Beads plot consists of diamonds of each factor of each data series. 
  A diamond indicates average and range. 
  Look over a data frame with many numeric columns and a factor column.
This package provides utilities to calculate the probabilities of various dice-rolling events, such as the probability of rolling a four-sided die six times and getting a 4, a 3, and either a 1 or 2 among the six rolls (in any order); the probability of rolling two six-sided dice three times and getting a 10 on the first roll, followed by a 4 on the second roll, followed by anything but a 7 on the third roll; or the probabilities of each possible sum of rolling five six-sided dice, dropping the lowest two rolls, and summing the remaining dice.
A Web crawler for <http://www.dice.com>. The function getjobs() automatically crawls Dice.com and downloads job descriptions based on the supplied parameters. The job data is returned via a data frame. JobSearch API supplied by Dice.com is used for retrieving job data.
Space-Filling Designs and Uniformity Criteria.
Estimation, validation and prediction of models of different types : linear models, additive models, MARS,PolyMARS and Kriging.
Estimation, validation and prediction of kriging models.
        Important functions : km, print.km, plot.km, predict.km.
Efficient Global Optimization (EGO) algorithm and adaptations for
  parallel infill (multipoint EI), problems with noise, and problems with
  constraints.
Performs cluster analysis using an ensemble clustering framework.
    Results from a diverse set of algorithms are pooled together using methods
    such as majority voting, K-Modes, LinkCluE, and CSPA. There are options to
    compare cluster assignments across algorithms using internal and external
    indices, visualizations such as heatmaps, and significance testing for the
    existence of clusters.
View 2D/3D sections or contours of computer experiments designs, surrogates or test functions.
Collapse red-green or green-blue distinctions to simulate
        the effects of different types of color-blindness.
This package contains the dictionaries for reading microdata
        from IBGE (Brazilian Institute of Geography and Statistics)
        surveys PNAD, PME and POF.
A basic, clear implementation of tree-based gradient boosting
    designed to illustrate the core operation of boosting models. Tuning
    parameters (such as stochastic subsampling, modified learning rate, or
    regularization) are not implemented. The only adjustable parameter is the
    number of training rounds. If you are looking for a high performance boosting
    implementation with tuning parameters, consider the 'xgboost' package.
This hosts the findRFM function which generates RFM scores on a 1-5 point scale for
             customer transaction data. The function consumes a data frame with Transaction Number,
             Customer ID, Date of Purchase (in date format) and Amount of Purchase as the attributes.
             The function returns a data frame with RFM data for the sales information.
Physical constants. Gold, silver and glass permittivities,
    together with spline interpolation functions.
A wrapper for the ZEIT ONLINE Content API, available at <http://developer.zeit.de>. 'diezeit' gives access to articles and corresponding metadata from the ZEIT archive and from ZEIT ONLINE. A personal API key is required for usage.
Performs detection of Differential Item Functioning using the method DIFboost as proposed in Schauberger and Tutz (2015): Detection of Differential item functioning in Rasch models by boosting techniques, British Journal of Mathematical and Statistical Psychology.
Estimation of DIFferential COexpressed NETworks using diverse and user metrics.
			 This package is basically used for three functions related to the estimation
			 of differential coexpression. 
			 First, to estimate differential coexpression where
			 the coexpression is estimated, by default, by Spearman correlation. For this,
			 a metric to compare two correlation distributions is needed. The package includes
			 6 metrics. Some of them needs a threshold. A new metric can also be specified as
			 a user function with specific parameters (see difconet.run). The significance is
			 be estimated by permutations.
			 Second, to generate datasets with controlled differential correlation data. This 
			 is done by either adding noise, or adding specific correlation structure.
			 Third, to show the results of differential correlation analyses. Please see
			 <http://bioinformatica.mty.itesm.mx/difconet> for further information.
A method for identifying pattern changes between 2 experimental
         conditions in correlation networks (e.g., gene co-expression networks),
         which builds on a commonly used association measure, such as Pearson's
         correlation coefficient. This package includes functions to calculate
         correlation matrices for high-dimensional dataset and to test
         differential correlation, which means the changes in the correlation
         relationship among variables (e.g., genes and metabolites) between 2
         experimental conditions.
The package includes functions to calculate confidence
        intervals for the difference of dependent proportions. There
        are two functions implemented to edit the data (dichotomising
        with the help of cutpoints, counting accordance and discordance
        of two tests or situations). For the calculation of the
        confidence intervals entries of the fourfold table are needed.
Functions and examples from the book Solving Differential 
   Equations in R by Karline Soetaert, Jeff R Cash and Francesca Mazzia.
   Springer, 2012.
Metrics of difference for comparing pairs of maps representing real or categorical variables at original and multiple resolutions.
Package to fit diffusion-based IRT models to response and 
	response time data. Models are fit using marginal maximum 
	likelihood. Parameter restrictions (fixed value and equality 
	constraints) are possible. In addition, factor scores (person drift 
	rate and person boundary separation) can be estimated. Model fit 
	assessment tools are also available. The traditional diffusion model 
	can be estimated as well.
This R function implements the nonstationary Kriging model proposed by Tuo, Wu and Yu (2014) <DOI:10.1080/00401706.2013.842935> for analyzing multi-fidelity computer outputs. This function computes the maximum likelihood estimates for the model parameters as well as the predictive means and variances of the exact solution (i.e., the conceptually highest fidelity).
Provides an implementation of statistically significant 
    differential sub-network analysis for paired biological networks.
Generate a colorized diff of two R objects for an intuitive
    visualization of their differences.
An implementation of major general-purpose mechanisms for privatizing
    statistics, models, and machine learners, within the framework of differential
    privacy of Dwork et al. (2006) <doi:10.1007/11681878_14>. Example mechanisms
    include the Laplace mechanism for releasing numeric aggregates, and the 
    exponential mechanism for releasing set elements. A sensitivity sampler 
    (Rubinstein & Alda, 2017) <arXiv:1706.02562> permits sampling target 
    non-private function sensitivity; combined with the generic mechanisms, it 
    permits turn-key privatization of arbitrary programs.
An R interface to the 'codediff' JavaScript library (a copy of which is included in the package,
  see <https://github.com/danvk/codediff.js> for information).
  Allows for visualization of the difference between 2 files, usually text files or R scripts, in a browser.
Residual-based baseline identification and peak decomposition for x-ray diffractograms as introduced in Davies/Gather/Mergel/Meise/Mildenberger (2008).
Provides data structures and methods for measuring, coding, 
    and analysing text within text corpora. The package allows for manual as 
    well computer aided coding on character, token and text pair level.
Interactive visualizations and tabulations for diffrprojects. 
    All presentations are based on the htmlwidgets framework allowing for 
    interactivity via HTML and Javascript, Rstudio viewer integration, 
    RMarkdown integration, as well as Shiny compatibility.
Implements diffusion map method of data
          parametrization, including creation and visualization of
          diffusion map, clustering with diffusion K-means and
	  regression using adaptive regression model.
Tools for performing inference and analysis on a class of quadratic diffusion processes for both scalar and bivariate diffusion systems. For scalar diffusions, a module is provided for solving first passage time problems for both time-homogeneous and time-inhomogeneous GQDs.
Tools for performing inference and analysis using a data-imputation scheme and the method of lines.
Tools for performing inference and analysis on a class of quadratic jump diffusion processes.
Implementation of network diffusion algorithms such as insulated
  heat propagation or Markov random walks. Network diffusion algorithms generally
  spread information in the form of node weights along the edges of a graph to other nodes.
  These weights can for example be interpreted as temperature, an initial amount
  of water, the activation of neurons in the brain, or the location of a random
  surfer in the internet. The information (node weights) is iteratively propagated
  to other nodes until a equilibrium state or stop criterion occurs.
Performs DIFlasso, a method to detect DIF (Differential Item Functioning) in Rasch Models. It can handle settings with many variables and also metric variables.
Detection of DIF among dichotomously scored items and DDF among unscored items with non-linear regression procedures based on generalized logistic regression models.
Provides a collection of standard methods to detect differential item functioning among dichotomously scored items. Methods for uniform and non-uniform DIF, based on test-score or IRT methods, for comparing two or more than two groups of respondents, are available (Magis, Beland, Tuerlinckx and De Boeck,A General Framework and an R Package for the Detection of Dichotomous Differential Item Functioning, Behavior Research Methods, 42, 2010, 847-862 <doi:10.3758/BRM.42.3.847>).
Item focused recursive partitioning for simultaneous selection of
    items and variables that induce Differential Item Functioning (DIF) based on the
    Rasch Model or the Logistic Regression Approach for DIF detection.
Implementation of a function 'digest()' for the creation 
 of hash digests of arbitrary R objects (using the 'md5', 'sha-1', 'sha-256', 
 'crc32', 'xxhash' and 'murmurhash' algorithms) permitting easy comparison of R
 language objects, as well as a function 'hmac()' to create hash-based
 message authentication code. Please note that this package is not meant to
 be deployed for cryptographic purposes for which more comprehensive (and
 widely tested) libraries such as 'OpenSSL' should be used.
Digiroo2 is an R package developed by researchers at the
        University of Queensland to investigate association patterns
        and social structure in wild animal populations. Proximity
        between individuals is generally considered to be an
        appropriate proxy for associations and pairwise association
        indices are the most widely used technique for analysing animal
        social structure. However, little attention is given to
        identifying how patterns of spatial overlap affect these
        association patterns. For example, do individuals associate
        randomly with others with whom they share home ranges, or do
        some individuals go out of their way to associate with or avoid
        particular individuals? This program builds a null model of
        random associations based on an individual's space use
        determined using home range methodologies. Random points may be
        generated within a specified home range contour or according to
        the Utilization Distribution (UD). Expected associations of
        individuals are extracted based on probability of occurrence
        and the proximity between home range weighted random points.
        Association matrices can be generated from multiple
        permutations for analysis using SOCPROG 2.4 (Whitehead 2009) to
        create 'expected' pairwise half-weight association indices
        (HWIs). These may be compared with the 'observed' HWIs from
        field observations to reveal whether pairs of animals associate
        more (= attraction) or less (= avoidance) than expected by
        chance.
The assay sensitivity is the minimum number of copies that the digital PCR assay can detect. Users provide serial dilution results in the format of counts of positive and total reaction wells. The output is the estimated assay sensitivity and the copy number per well in the initial dilute.
Import data from a digital image; it requires user input for
    calibration and to locate the data points. The end result is similar to
    'DataThief' and other other programs that 'digitize' published plots or
    graphs.
Combine multiple-relationship networks into a single weighted
    network.  The approach is similar to factor analysis in the that
    contribution from each constituent network varies so as to maximize the
    information gleaned from the multiple-relationship networks.
    This implementation uses Principal Component Analysis calculated using
    'prcomp' with bootstrap subsampling.  Missing links are imputed using
    the method of Chen et al.
    (2012).
A robust differential identification method that considers an ensemble of finite mixture models combined with a local false discovery rate (fdr) to analyze ChIP-seq (high-throughput genomic)data comparing two samples allowing for flexible modeling of data.
A collection of dimensionality reduction
    techniques from R packages and provides a common
    interface for calling the methods.
Estimate the Deterministic Input, Noisy "And" Gate (DINA)
    cognitive diagnostic model parameters using the Gibbs sampler described
    by Culpepper (2015) <doi:10.3102/1076998615595403>.
This function implements the DiNAMIC procedure for
        assessing the statistical significance of recurrent DNA copy
        number aberrations (Bioinformatics (2011) 27(5) 678 - 685).
Compute Hartigan's dip test statistic for unimodality /
 multimodality and provide a test with simulation based p-values,  where
 the original public code has been corrected.
A Bayesian clustering method for replicated time series or replicated measurements from multiple experimental conditions, e.g., time-course gene expression data.  It estimates the number of clusters directly from the data using a Dirichlet-process prior.  See Fu, A. Q., Russell, S., Bray, S. and Tavare, S. (2013) Bayesian clustering of replicated time-course gene expression data with weak signals. The Annals of Applied Statistics. 7(3) 1334-1361. <doi:10.1214/13-AOAS650>.
A collection of functions for directional data analysis. Hypothesis testing, discriminant and regression analysis, MLE of distributions and more are included. The standard textbook for such data is the "Directional Statistics" by Mardia, K. V. and Jupp, P. E. (2000).
An extensible framework
 for automatically placing direct labels onto multicolor 'lattice' or
 'ggplot2' plots.
 Label positions are described using Positioning Methods
 which can be re-used across several different plots.
 There are heuristics for examining "trellis" and "ggplot" objects
 and inferring an appropriate Positioning Method.
Direction analysis is a set of tools designed to identify
    combinatorial effects of multiple treatments and/or perturbations on pathways
    and kinases profiled by microarray, RNA-seq, proteomics, or phosphoproteomics
    data.
Calculate adjusted means and proportions of a variable by groups defined by another variable by direct standardisation, standardised to the structure of the dataset.
Implements Dirichlet regression models in R.
Implementation of Directional Metropolis Hastings Algorithm for
    MCMC.
Estimate parameters in Dirichlet-Multinomial and compute
        profile log-likelihoods.
Discrete smoothing of probability mass function is performed using three discrete associated kernels: Dirac Discrete Uniform (DiracDU), Binomial and Discrete Triangular. Two automatic bandwidth selection procedures are implemented: the cross-validation method for the three kernels and the local Bayesian approach for Binomial kernel. Note that DiracDU is used for categorical data, Binomial kernel is appropriate for count data with small or moderate sample sizes, and Discrete Triangular kernel is recommended for count data with large sample sizes.
Client for the Discourse 'API'. Discourse is a open source
    discussion forum platform (<http://www.discourse.org/>). It comes with 'RESTful'
    API access to an installation. This client requires that you are authorized
    to access a Discourse installation, either yours or another.
Discrete Laplace exponential family for models such as a generalized linear model
Make inference in a mixture of discrete Laplace distributions using the EM algorithm. This can e.g. be used for modelling the distribution of Y chromosomal haplotypes as described in [1, 2] (refer to the URL section).
DiscML performs rate estimation using maximum likelihood with the 
 options to correct for unobservable data, to implement a Gamma-distribution 
 for rate variation, and to estimate the prior root probabilities from the 
 empirical data.
Concordance and discordance of homologous gene regulation allows comparing
  reaction to stimuli in different organisms, 
  for example human patients and animal models of a disease. The package
  contains functions to calculate discordance and concordance score
  for homologous gene pairs, identify concordantly or
  discordantly regulated transcriptional modules and visualize the results.
  It is intended for analysis of transcriptional data.
Functions for discordant kinship modeling (and other sibling-based quasi-experimental designs). Currently, the package contains data restructuring functions; functions for generating genetically- and environmentally-informed data for kin pairs.
Structure learning of Bayesian network using coordinate-descent
    algorithm. This algorithm is designed for discrete network assuming a multinomial data set,
    and we use a multi-logit model to do the regression.
    The algorithm is described in Gu, Fu and Zhou (2016) <http://arxiv.org/abs/1403.2310>.
Probability mass function, distribution function, quantile function, random generation and parameter estimation for the discrete inverse Weibull distribution.
Probability mass function, distribution function, quantile function, random generation and estimation for the skew discrete Laplace distributions.
Multiple testing procedures for discrete test statistics,
        that use the known discrete null distribution of the p-values
        for simultaneous inference.
Create, manipulate, transform, and simulate from discrete random
    variables. The syntax is modeled after that which is used in mathematical
    statistics and probability courses, but with powerful support for more
    advanced probability calculations. This includes the creation of joint
    random variables, and the derivation and manipulation of their conditional
    and marginal distributions.
Probability mass function, distribution function, quantile function, random generation and parameter estimation for the type I and III discrete Weibull distributions.
This package is a collection of supervised discretization
        algorithms. It can also be grouped in terms of top-down or
        bottom-up, implementing the discretization algorithms.
'discrimARTs' discriminates with explicit confidence the alternative reproductive tactics (ARTs) in dimorphic systems with bimodal traits by computing the maximum likelihood estimate of a mixture of distributions of a measured ARTs trait. Supported distributions include a mixture of 2 normal distributions and a mixture of 2 facing gamma distributions.
Functions for Discriminant Analysis and Classification purposes
    covering various methods such as descriptive, geometric, linear, quadratic,
    PLS, as well as qualitative discriminant analyses
Provides data transformations, estimation utilities,
    predictive evaluation measures and simulation functions for discrete time
    survival analysis.
Formatting of population and case data, calculation of Standardized
    Incidence Ratios, and fitting the BYM model using INLA.
Model cell type heterogeneity of bulk renal cell carcinoma. The observed gene expression in bulk tumor sample is modeled by a log-normal distribution with the location parameter structured as a linear combination of the component-specific gene expressions.
Implement the methods proposed by Ahmad & Dey (2007) <doi:10.1016/j.datak.2007.03.016> in calculating the dissimilarity matrix at the presence of mixed attributes. This Package includes functions to discretize quantitative variables, calculate conditional probability for each pair of attribute values, distance between every pair of attribute values, significance of attributes, calculate dissimilarity between each pair of objects.
A pipeline to analyze photographs of disk diffusion plates. This removes the need to analyze the plates themselves, and thus analysis can be done separate from the assay. Furthermore, diskImageR removes potential researcher bias, by quantitative assessment of drug resistance as the zone diameter at multiple cutoff values of growth inhibition. This method also extends the disk diffusion assay by measuring drug tolerance (in addition to drug resistance) as the fraction of the subpopulation that is able to grow above the resistance point ("FoG"), and drug sensitivity as the rate of change from no growth to full growth ("slope").
Functions for species distribution modeling, that is, predicting entire geographic distributions form occurrences at a number of sites and the environment at these sites.
An implementation of two exact algorithms for testing the
        Hausdorff and simplex dispersion orderings
The disparity filter algorithm is a network reduction technique to
    identify the 'backbone' structure of a weighted network without destroying
    its multi-scale nature. The algorithm is documented in M. Angeles Serrano,
    Marian Boguna and Alessandro Vespignani in "Extracting the multiscale
    backbone of complex weighted networks", Proceedings of the National Academy
    of Sciences 106 (16), 2009. This implementation of the algorithm supports
    both directed and undirected networks.
A package containing R functions for displaying data and
        results from high-throughput screening experiments.
Description: Functions for modelling dispersion in GLM.
Create disposable R packages for testing.
    You can create, install and load multiple R packages with a single
    function call, and then unload, uninstall and destroy them with another
    function call. This is handy when testing how some R code or an R package
    behaves with respect to other packages.
This package has extensible C++ code for computing dissimilarities between vectors. It also has a number of C++ functions for assembling collections of dissimilarities. In particular, it lets you find a matrix of dissimilarities between the rows of two input matrices. There are also functions for finding the nearest neighbors of each row of a matrix, either within the matrix itself or within another matrix.
A simple way of fitting detection functions to distance sampling
    data for both line and point transects. Adjustment term selection, left and
    right truncation as well as monotonicity constraints and binning are
    supported. Abundance and density estimates can also be calculated (via a
    Horvitz-Thompson-like estimator) if survey area information is provided.
Provides tools for constructing, manipulating and using distance metrics.
Calculates the study size (either number of detections, or
    proportion of region that should be covered) to achieve a target precision for
    the estimated abundance. The calculation allows for the penalty due to unknown
    detection function, and for overdispersion. The user must specify a guess at the
    true detection function.
Implement DiSTATIS and CovSTATIS (three-way multidimensional scaling). For the  analysis of  multiple distance/covariance matrices collected on the same set of observations
Implementing algorithms and fitting models when sites (possibly remote) share
  computation summaries rather than actual data over HTTP with a master R process (using
  'opencpu', for example). A stratified Cox model and a singular value decomposition are
  provided. The former makes direct use of code from the R 'survival' package. (That is,
  the underlying Cox model code is derived from that in the R 'survival' package.)
  Sites may provide data via several means: CSV files, Redcap API, etc. An extensible
  design allows for new methods to be added in the future. Web applications are provided
  (via 'shiny') for the implemented methods to help in designing and deploying the
  computations.
Download data from the FlorKart database of the floristic field mapping in Germany in a convenient way. The database incorporates distribution data for plants in Germany on the basis of quadrants on a topographical map with a resolution of 1 : 25000 (TK 25). The data is owned and provided by the German Federal Agency for Nature Conservation (BfN) and the Network Phytodiversity in Germany (NetPhyD). For further information please visit <http://www.floraweb.de/pflanzenarten/hintergrundtexte_florkart_organisation.html>. The author of this package is in no way associated with the BfN or NetPhyD.
An R package that is developed for constructing confidence
        regions without the need to know the sampling distribution of
        bivariate data.
Some very simple method functions for confidence interval calculation, bootstrap resampling, and to distill pertinent information from a potentially complex object; primarily used in common with packages extRemes and SpatialVx.
Geodesic distance between phylogenetic trees and
        associated functions.
S4-classes and methods for distributions.
Provides documentation in form of a common vignette to packages 'distr', 'distrEx',
        'distrMod', 'distrSim', 'distrTEst', 'distrTeach', and 'distrEllipse'.
Distribution (S4-)classes for elliptically contoured distributions (based on
        package 'distr').
Extends package 'distr' by functionals, distances, and conditional distributions.
A different way for calculating pdf/pmf, cdf, quantile and random data such that the user is able to consider the name of related distribution as an argument and so easily can changed by a changing argument by user. It must be mentioned that the core and computation base of package 'DISTRIB' is package 'stats'. Although similar functions are introduced previously in package 'stats', but the package 'DISTRIB' has some special applications in some special computational programs.
This package contains utilities which are of use in the
        packages I have developed for dealing with distributions.
        Currently these packages are GeneralizedHyperbolic,
        VarianceGamma, and SkewHyperbolic and NormalLaplace. Each of
        these packages requires DistributionUtils. Functionality
        includes sample skewness and kurtosis, log-histogram, tail
        plots, moments by integration, changing the point about which a
        moment is calculated, functions for testing distributions using
        inversion tests and the Massart inequality. Also includes an
        implementation of the incomplete Bessel K function.
Implements S4 classes for probability models based on packages 'distr' and
        'distrEx'.
Estimation for a multinomial logistic regression factorized into independent  Poisson log regressions.  See the textir package for applications in multinomial inverse regression analysis of text.
S4-distribution classes based on package distr for distributions from packages
        'fBasics' and 'fGarch'.
S4-classes for setting up a coherent framework for simulation within the distr
        family of packages.
Provides flexible examples of LLN and CLT for teaching purposes in secondary
        school.
Evaluation (S4-)classes based on package distr for evaluating procedures
        (estimators/tests) at data/simulation in a unified way.
Provides tools for quality checks of georeferenced plant
        species accessions.
R-package DivE contains functions for the DivE estimator (Laydon, D. et al., Quantification of HTLV-1 clonality and TCR diversity, PLOS Comput. Biol. 2014). The DivE estimator is a heuristic approach to estimate the number of classes or the number of species (species richness) in a population.
Utilities to represent, visualize, filter, analyse, and summarize
	     time-depth recorder (TDR) data.  Miscellaneous functions for
	     handling location data are also provided.
Computes the most common diversity measures used in social and other sciences, and includes new measures from interdisciplinary research.
Contains a number of comparative 'phylogenetic' methods,
  mostly focusing on analysing diversification and character
  evolution.  Contains implementations of 'BiSSE' (Binary State
  'Speciation' and Extinction) and its unresolved tree extensions,
  'MuSSE' (Multiple State 'Speciation' and Extinction), 'QuaSSE',
  'GeoSSE', and 'BiSSE-ness' Other included methods include Markov
  models of discrete and continuous trait evolution and constant rate
  'speciation' and extinction.
Allows the calculation of both genetic diversity partition 
  statistics, genetic differentiation statistics, and locus informativeness 
  for ancestry assignment. 
  It also provides users with various option to calculate 
  bootstrapped 95\% confidence intervals both across loci, 
  for pairwise population comparisons, and to plot these results interactively. 
  Parallel computing capabilities and pairwise results without 
  bootstrapping are provided. 
  Also calculates F-statistics from Weir and Cockerham (1984). 
  Various plotting features are provided, as well as Chi-square tests of 
  genetic heterogeneity. 
  Functionality for the calculation of various diversity parameters is 
  possible for RAD-seq derived SNP data sets containing thousands of marker loci.
  A shiny application for the development of microsatellite multiplexes is 
  also available.
Predictions of alpha diversity are fitted from presence data, first abundance is modeled from occupancy models and then, several diversity indices are calculated and finally GLM models are used to predict diversity in different environments and select priority areas.
There are two functions in this package, which can be used
        together to estimate the Shannon's Diversity index at different
        levels of sample size. A Monte-Carlo procedure is used to
        re-sample a given observation at each level of sampling. The
        expectation being that the mean of the re-sampling will
        approach Shannon's diversity index at that sample level.
Provides tools to convert DICOM-format files to NIfTI-1 format.
This package has tools for analyzing DNA melting data to
        generate HRM scores, the DNA diversity measure output of the
        HRM Diversity Assay.  For additional documentation visit
        http://code.google.com/p/divmelt/.
A set of tools for empirical analysis of diversity (a number and frequency of different types in population) and similarity (a number and frequency of shared types in two populations) in biological or ecological systems.
Function to test spatial segregation and association based
        in contingency table analysis of nearest neighbour counts
        following Dixon (2002). Some fortran code has been included to
        the original dixon2002 function of the ecespa package to
        improve speed.
Implements various decision support tools related to the new product development.
             Subroutines include correlation reliability test, Mahalanobis distance measure for outlier detection, combinatorial search (all possible subset regression), non-parametric efficiency analysis measures: DDF (directional distance function), DEA (data envelopment analysis), HDF (hyperbolic distance function), SBM (slack-based measure), and SF (shortage function), benchmarking, risk analysis, technology adoption model, new product target setting, etc.
Compute diffusion kernels on DNA polymorphisms, including SNP and bi-allelic genotypes.
Provides time series regression models with one predictor using finite distributed lag models, polynomial (Almon) distributed lag models, geometric distributed lag models with Koyck transformation, and autoregressive distributed lag models. It also consists of functions for computation of h-step ahead forecasts from these models. See Baltagi (2011) <doi:10.1007/978-3-642-20059-5> for more information.
An implementation of the differentiable lasso (dlasso) and SCAD (dSCAD) using iterative ridge algorithm. This package allows selecting the tuning parameter by AIC, BIC, GIC and GIC.
Interface for 'Rcpp' users to 'dlib' <http://dlib.net> which is a
    'C++' toolkit containing machine learning algorithms and computer vision tools.
    It is used in a wide range of domains including robotics, embedded devices,
    mobile phones, and large high performance computing environments. This package
    allows R users to use 'dlib' through 'Rcpp'.
Maximum likelihood, Kalman filtering and smoothing, and Bayesian
  analysis of Normal linear State Space models, also known as 
  Dynamic Linear Models
QTL mapping in a mixed model framework with separate
        detection and localization stages. The first stage detects the
        number of QTL on each chromosome based on the genetic variation
        due to grouped markers on the chromosome; the second stage uses
        this information to determine the most likely QTL positions.
        The mixed model can accommodate general fixed and random
        effects, including spatial effects in field trials and pedigree
        effects. Applicable to backcrosses, doubled haploids,
        recombinant inbred lines, F2 intercrosses, and association
        mapping populations.
dlmodeler is a set of user-friendly functions to simplify the state-space modelling, fitting, analysis and forecasting of Generalized Dynamic Linear Models (DLMs). It includes functions to name and extract individual components of a DLM, build classical seasonal time-series models (monthly, quarterly, yearly, etc. with calendar adjustments) and provides a unified interface compatible with other state-space packages including: dlm, FKF and KFAS.
Development, simulation testing, and implementation of management
    procedures for data-limited fisheries 
    (see Carruthers et al (2014) <doi:10.1016/j.fishres.2013.12.014>).
Collection of functions for distributed lag linear and non-linear models.
Inference functionalities for distributed-lag linear structural equation models. Endpoint-constrained quadratic, quadratic decreasing and gamma lag shapes are available.
Monthly download stats of 'CRAN' and 'Bioconductor' packages.
	     Download stats of 'CRAN' packages is from the 'RStudio' 'CRAN mirror', see <http://cranlogs.r-pkg.org>.
	     'Bioconductor' package download stats is at <https://bioconductor.org/packages/stats/>.
Dynamic model averaging for binary and continuous
        outcomes.
The state-of-the-art algorithms for distance metric learning, including global and local methods such as Relevant Component Analysis, Discriminative Component Analysis, Local Fisher Discriminant Analysis, etc. These distance metric learning methods are widely applied in feature extraction, dimensionality reduction, clustering, classification, information retrieval, and computer vision problems.
Dyadic mixed model analysis with multi-trait responses and
  pedigree-based partitioning of individual variation into a range of
  environmental and genetic variance components for individual and 
  maternal effects.
Implements the daily based Morgan-Morgan-Finney (DMMF) soil erosion model (Choi et al., 2017 <doi:10.3390/w9040278>) for estimating surface runoff and sediment budgets from a field or a catchment on a daily basis.
The framework provides functions to generate ODEs of reaction
    networks, parameter transformations, observation functions, residual functions,
    etc. The framework follows the paradigm that derivative information should be
    used for optimization whenever possible. Therefore, all major functions produce
    and can handle expressions for symbolic derivatives.
A backward selection procedure called delete or merge
        regressors (DMR) combines deleting continuous variables with
        merging levels of factors. The method assumes greedy search
        among linear models with set of constraints of two types:
        either a parameter for a continuous variable is set to zero or
        parameters corresponding to two levels of a factor are
        compared. DMR is a stepwise regression procedure, where in each
        step a new constraint is added according to ranking of the
        hypotheses based on squared t-statistics. As a result a nested
        family of linear models is obtained and the final decision is
        made according to minimization of the generalized information
        criterion (GIC, default BIC). The main function of the package
        is DMR, which is based on hierarchical clustering. Moreover,
        other functions for extensions of DMR method are given, such as
        stepDMR which is based on recalculation of t-statistics in each
        step and function DMR4glm for generalized linear models.
Perform differential analysis for
        methylation array data. Detect differentially
        methylated regions (DMRs) from array M-values. 
        The core is a Non-homogeneous Hidden Markov Model
        for estimating spatial correlation and a novel Constrained 
        Gaussian Mixture Model for modeling the M-value pairs of each individual locus.
Work within the 'dplyr' workflow to add random variates to your data frame. 
  Variates can be added at any level of an existing column.  Also, bounds can be specified 
  for simulated variates.
This package includes functions and data accompanying the book 
	     "Data Mining with R, learning with case studies" by Luis Torgo, CRC Press 2010.
Functions and data accompanying the second edition of the book  "Data Mining with R, learning with case studies" by Luis Torgo, published by CRC Press.
Package for conducting differential network analysis from
        microarray data.
Generates DNA sequences based on Markov model techniques for matched sequences. This can be generalized to several sequences. The sequences (taxa) are then arranged in an evolutionary tree (phylogenetic tree) depicting how taxa diverge from their common ancestors. This gives the tests and estimation methods for the parameters of different models. Standard phylogenetic methods assume stationarity, homogeneity and reversibility for the Markov processes, and  often impose further restrictions on the parameters.
Computationally efficient tools for comparing all pairs of profiles
    in a DNA database. The expectation and covariance of the summary statistic
    is implemented for fast computing. Routines for estimating proportions of
    close related individuals are available. The use of wildcards (also called F-
    designation) is implemented. Dedicated functions ease plotting the results.
Community detection for dynamic networks, i.e., networks measured repeatedly over a sequence of discrete time points, using a latent space approach.
The DnE package involves functions to analyse the distribution of a set of given data. The basic idea of the analysis is chi-squared test. Functions which have the form as "is.xxdistribution" are used to analyse whether the data obeys the xxdistrbution. If you do not know which distribution to judge, use function is.dt().
The focus of the dnet is to make sense of omics data (such as gene expression and mutations) from different angles including: integration with molecular networks, enrichments using ontologies, and relevance to gene evolutionary ages. Integration is achieved to identify a gene subnetwork from the whole gene network whose nodes/genes are labelled with informative data (such as the significant levels of differential expression or survival risks). To help make sense of identified gene networks, enrichment analysis is also supported using a wide variety of pre-compiled ontologies and phylostratific gene age information in major organisms including: human, mouse, rat, chicken, C.elegans, fruit fly, zebrafish and arabidopsis. Add-on functionalities are supports for calculating semantic similarity between ontology terms (and between genes) and for calculating network affinity based on random walk; both can be done via high-performance parallel computing.
Using Local Moran's I for detection of differential network local consistency.
Discriminant Non-Negative Matrix Factorization aims to extend the Non-negative Matrix Factorization algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. It refers to three article, Zafeiriou, Stefanos, et al. "Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification." Neural Networks, IEEE Transactions on 17.3 (2006): 683-695. Kim, Bo-Kyeong, and Soo-Young Lee. "Spectral Feature Extraction Using dNMF for Emotion Recognition in Vowel Sounds." Neural Information Processing. Springer Berlin Heidelberg, 2013. and Lee, Soo-Young, Hyun-Ah Song, and Shun-ichi Amari. "A new discriminant NMF algorithm and its application to the extraction of subtle emotional differences in speech." Cognitive neurodynamics 6.6 (2012): 525-535.
Provides Frequentist (EM) and Bayesian  (MCMC) Methods for Inference of  Birth-Death-Immigration Markov Chains.
The facilities can roughly be grouped as: 

   1) Facilities for groupwise computations of summary statistics and
 other facilities for working with grouped data: 'do' something to data 
 stratified 'by' some variables.  

   2) LSmeans (least-squares means), general linear contrasts.

   3) Miscellaneous other utilities.
Allows accessing 'Docker' 'SDK' from 'R' via the 'Docker' 'Python' 'SDK' using the 'reticulate' package.
  This is a very thin wrapper that tries to do very little and get out of the way.
  The user is expected to know how to use the 'reticulate' package to access 'Python' modules, and how the 'Docker' 'Python' 'SDK' works.
Define a command-line interface by just giving it
    a description in the specific format.
A direct approach to optimal designs for copula models based on
    the Fisher information. Provides flexible functions for building joint PDFs,
    evaluating the Fisher information and finding optimal designs. It includes an
    extensible solution to summation and integration called 'nint', functions for
    transforming, plotting and comparing designs, as well as a set of tools for
    common low-level tasks.
Provides the ability to display something analogous to
    Python's docstrings within R.  By allowing the user to document
    their functions as comments at the beginning of their function
    without requiring putting the function into a package we allow
    more users to easily provide documentation for their functions.
    The documentation can be viewed just like any other help files
    for functions provided by packages as well.
Production of R packages from tagged comments introduced within the code 
             and a minimum of additional documentation files.
Have you ever been tempted to create 'roxygen2'-style documentation
    comments for one of your functions that was not part of one of your
    packages (yet)?
    This is exactly what this package is about: running 'roxygen2' on
    (chunks of) a single code file.
Connect to the 'DocuSign' Rest API <https://www.docusign.com/p/RESTAPIGuide/RESTAPIGuide.htm>, 
  which supports embedded signing, and sending of documents.
A set of helper functions for using R Markdown to create documents
    in docx format, especially documents for use in a classroom or workshop
    setting.
Microsoft Word docx files provide an XML structure that is fairly
    straightforward to navigate, especially when it applies to Word tables and
    comments. Tools are provided to determine table count/structure, comment count
    and also to extract/clean tables and comments from Microsoft Word docx documents.
Various sampling plans are able to be compared using evaluations of their OC, AOQ, ATI etc.
Detect Differences in rhythmic time series. Using linear
    least squares and the robust semi-parametric rfit() method. Differences in
    harmonic fitting could be detected as well as differences in scale of the
    noise distribution.
Package DoE.base creates full factorial experimental designs and designs based on orthogonal arrays for (industrial) experiments. Additionally, it provides utility functions for the class design, which is also used by other packages for designed experiments.
Rare variant association test integrating variant position information. It aims to identify the presence of clusters of disease-risk variants in specific gene regions. For more details, please read the publication from Persyn et al. (2017)  <doi:10.1371/journal.pone.0179364>.
This package creates various kinds of designs for
        (industrial) experiments. It uses, and sometimes enhances,
        design generation routines from other packages. 
        So far, response surface designs from package rsm, latin hypercube
        samples from packages lhs and DiceDesign, and 
        D-optimal designs from package AlgDesign have been implemented.
Provides a '%dopar%' adaptor such that any type of futures can
    be used as backends for the 'foreach' framework.
Provides a parallel backend for the %dopar% function using
        the multicore functionality of the parallel package.
Can calculate ADI (Average Dominance Index), FDI (Frequency based Dominance Indexand) and can build social network graphs with dual directions, can build a Music Notation Graph.
A wrapper on top of the 'Domino Command-Line Client'. It lets you
    run 'Domino' commands (e.g., "run", "upload", "download") directly from your
    R environment. Under the hood, it uses R's system function to run the 'Domino'
    executable, which must be installed as a prerequisite. 'Domino' is a service
    that makes it easy to run your code on scalable hardware, with integrated
    version control and collaboration features designed for analytical workflows
    (see <http://www.dominodatalab.com> for more information).
Provides a parallel backend for the %dopar% function using
        the Rmpi package.
Provides a parallel backend for the %dopar% function using
        the parallel package.
A Redis parallel backend for the %dopar% function
Provides functions to perform
    reproducible parallel foreach loops, using independent
    random streams as generated by L'Ecuyer's combined
    multiple-recursive generator [L'Ecuyer (1999), <DOI:10.1287/opre.47.1.159>].
    It enables to easily convert standard %dopar% loops into
    fully reproducible loops, independently of the number
    of workers, the task scheduling strategy, or the chosen
    parallel environment and associated foreach backend.
The DoseFinding package provides functions for the design and analysis
	     of dose-finding experiments (with focus on pharmaceutical Phase
	     II clinical trials). It provides functions for: multiple contrast
	     tests, fitting non-linear dose-response models (using Bayesian and
	     non-Bayesian estimation), calculating optimal designs and an
	     implementation of the MCPMod methodology.
Provides a parallel backend for the %dopar% function using
        Luke Tierney's snow package.
It consists of a collection of
    functions to estimate dose-response relations from summarized dose-response
    data and to combine them according to principles of (multivariate)
    random-effects model.
A small package containing helper utilities for creating function
    for computing statistics.
Renders DOT diagram markup language in R and also provides the possibility to
    export the graphs in PostScript and SVG (Scalable Vector Graphics) formats.
    In addition, it supports literate programming packages such as 'knitr' and
    'rmarkdown'.
Calculation of cluster typicality coefficients as being generated by fuzzy k-means clustering.
An alternative version of .C() and .Fortran() supporting long vectors and 64-bit integer type arguments. The provided interface .C64() features mechanisms the avoid unnecessary copies of read-only or write-only arguments. This makes it a convenient and fast interface to C/C++ and Fortran code.
Load configuration from a '.env' file, that is
    in the current working directory, into environment variables.
Quick and easy dot-and-whisker plots of regression results.
Performs hypothesis tests concerning a regression function in a least-squares model, where the null is a parametric function, and the alternative is the union of large-dimensional convex polyhedral cones.
Differential exon usage test for RNA-Seq data via an empirical Bayes shrinkage method for the dispersion parameter the utilizes inclusion-exclusion data to analyze the propensity to skip an exon across groups. The input data consists of two matrices where each row represents an exon and the columns represent the biological samples. The first matrix is the count of the number of reads expressing the exon for each sample. The second matrix is the count of the number of reads that either express the exon or explicitly skip the exon across the samples, a.k.a. the total count matrix. Dividing the two matrices yields proportions representing the propensity to express the exon versus skipping the exon for each sample.
Local linear estimator for the univariate hazard (hazard rate) and bandwidth parameter selection using the do-validation method and the standard least squares cross-validation method.
'Kevin Dowd's' book Measuring Market Risk is a widely read book 
          in the area of risk measurement by students and 
          practitioners alike. As he claims, 'MATLAB' indeed might have been the most 
          suitable language when he originally wrote the functions, but,
          with growing popularity of R it is not entirely 
	  valid. As 'Dowd's' code was not intended to be error free and were mainly 
	  for reference, some functions in this package have inherited those 
	  errors. An attempt will be made in future releases to identify and correct 
	  them. 'Dowd's' original code can be downloaded from www.kevindowd.org/measuring-market-risk/. 
          It should be noted that 'Dowd' offers both
          'MMR2' and 'MMR1' toolboxes. Only 'MMR2' was ported to R. 'MMR2' is more 
          recent version of 'MMR1' toolbox and they both have mostly similar 
          function. The toolbox mainly contains different parametric and non 
	  parametric methods for measurement of market risk as well as 
	  backtesting risk measurement methods.
Provides a wrapper for the download.file function,
    making it possible to download files over HTTPS on Windows, Mac OS X, and
    other Unix-like platforms. The 'RCurl' package provides this functionality
    (and much more) but can be difficult to install because it must be compiled
    with external dependencies. This package has no external dependencies, so
    it is much easier to install.
A set of functions that downscales species occupancy at
    coarse grain sizes to predict species occupancy at fine grain sizes.
Toggles the test and production versions of a large workflow.
A GUI or command-line operated data analysis tool, for
        analyzing time-dependent simulation data in which multiple
        instantaneous or time-lagged relations are assumed. This
        package uses Structural Equation Modeling (the sem package). It
        is aimed to deal with time-dependent data and estimate whether
        a causal diagram fits data from an (agent-based) simulation
        model.
A Scannerless GLR parser/parser generator.  Note that GLR standing for "generalized LR", where L stands for "left-to-right" and
   R stands for "rightmost (derivation)".  For more information see <https://en.wikipedia.org/wiki/GLR_parser>. This parser is based on the Tomita
   (1987) algorithm. (Paper can be found at <http://acl-arc.comp.nus.edu.sg/archives/acl-arc-090501d3/data/pdf/anthology-PDF/J/J87/J87-1004.pdf>).
   The original dparser package documentation can be found at <http://dparser.sourceforge.net/>.  This allows you to add mini-languages to R (like
   RxODE's ODE mini-language Wang, Hallow, and James 2015 <DOI:10.1002/psp4.12052>) or to parse other languages like NONMEM to automatically translate
   them to R code.   To use this in your code, add a LinkingTo 'dparser' in your DESCRIPTION file and instead of using '#include <dparse.h>' use
   '#include <dparser.h>'.  This also provides a R-based port of the make_dparser <http://dparser.sourceforge.net/d/make_dparser.cat> command called
   'mkdparser'.  Additionally you can parse an arbitrary grammar within R using the 'dparse' function.
Beta-binomial Mixture Model is used to infer the pattern from count data.
		It can be used for clustering of RNA methylation sequencing data.
Analysis, visualisation and simulation of digital polymerase chain
    reaction (dPCR). Supports data formats of commerical systems (Bio-Rad QX100 and
    QX200; Fluidigm BioMark) and other systems.
fits the primal graphical lasso, via one-at-a-time
        block-coordinate descent.
Compares distributions with one another in terms of their fit to each sample in a dataset that contains multiple samples, as described in Joo, Aguinis, and Bradley (in press). Users can examine the fit of seven distributions per sample: pure power law, lognormal, exponential, power law with an exponential cutoff, normal, Poisson, and Weibull. Automation features allow the user to compare all distributions for all samples with a single command line, which creates a separate row containing results for each sample until the entire dataset has been analyzed.
Perform tree-ring analyses such as detrending, chronology
        building, and cross dating.  Read and write standard file formats
        used in dendrochronology.
The concordance method is a non-parametric method based on bootstrapping that is used to test the hypothesis that two subsets of time series are similar in terms of mean, variance or both. This method was developed to address a concern within dendroclimatology that young trees may produce a differing climate response to older more established trees. Details of this method are available in  Pirie, M. (2013). The Climate of New Zealand reconstructed from kauri tree rings: Enhancement through the use of novel statistical methodology. PhD. Dissertation, School of Environment and Department of Statistics, University of Auckland, New Zealand. This package also produces a figure with 3 panels, each panel is for a different climate variable. An example of this figure in included in "On the influence of tree size on the climate - growth relationship of New Zealand kauri (Agathis australis): insights from annual, monthly and daily growth patterns. J Wunder, AM Fowler, ER Cook, M Pirie, SPJ McCloskey. Trees 27 (4), 937-948". For further R functions for loading your own dendroclimatology datasets and performing dendrochronology analysis refer to the R package "dplR: Dendrochronology Program Library in R". The concordance procedure is intended to add to the standard dendrochronology techniques provided in "dplR".
A fast, consistent tool for working with data frame like objects,
    both in memory and out of memory.
The package implements a Dirichlet Process Mixture (DPM)
        model for clustering and image segmentation.  The DPM model is
        a Bayesian nonparametric methodology that relies on MCMC
        simulations for exploring mixture models with an unknown number
        of components.  The code implements conjugate models with
        normal structure (conjugate normal-normal DP mixture model).
        The package's applications are oriented towards the
        classification of magnetic resonance images according to tissue
        type or region of interest.
Create, install, and summarise data packages that follow
    the Open Knowledge Foundation's Data Package Protocol.
This package contains functions to perform inference via
        simulation from the posterior distributions for Bayesian
        nonparametric and semiparametric models. Although the name of
        the package was motivated by the Dirichlet Process prior, the
        package considers and will consider other priors on functional
        spaces. So far, DPpackage includes models considering Dirichlet
        Processes, Dependent Dirichlet Processes, Dependent Poisson-
        Dirichlet Processes, Hierarchical Dirichlet Processes, Polya
        Trees, Linear Dependent Tailfree Processes, Mixtures of
        Triangular distributions, Random Bernstein polynomials priors
        and Dependent Bernstein Polynomials.  The package also includes
        models considering Penalized B-Splines. Currently the package
        includes semiparametric models for marginal and conditional
        density estimation, ROC curve analysis, interval censored data,
        binary regression models, generalized linear mixed models, IRT
        type models, and generalized additive models. The package also
        contains functions to compute Pseudo-Bayes factors for model
        comparison, and to elicitate the precision parameter of the
        Dirichlet Process. To maximize computational efficiency, the
        actual sampling for each model is done in compiled FORTRAN. The
        functions return objects which can be subsequently analyzed
        with functions provided in the coda package.
Data preprocessing techniques for classification. Functions for normalization, handling of missing values,discretization, outlier detection, feature selection, and data visualization are included.
Use Dirichlet process Weibull mixture model and dependent Dirichlet process Weibull mixture model for survival data with and without competing risks. Dirichlet process Weibull mixture model is used for data without covariates and dependent Dirichlet process model is used for regression data. The package is designed to handle exact/right-censored/ interval-censored observations without competing risks and exact/right-censored observations for data with competing risks. Inside each cluster of Dirichlet process, we assume a multiplicative effect of covariates as in Cox model and Fine and Gray model. In addition, we provide a wrapper for DPdensity() function from the R package 'DPpackage'. This wrapper automatically uses Low Information Omnibus prior and can model one and two dimensional data with Dirichlet mixture of Gaussian distributions.
Functions, methods, and datasets for fitting dimension
 reduction regression, using slicing (methods SAVE and SIR), Principal
 Hessian Directions (phd, using residuals and the response), and an
 iterative IRE.  Partial methods, that condition on categorical
 predictors are also available.  A variety of tests, and stepwise
 deletion of predictors, is also included.  Also included is
 code for computing permutation tests of dimension.  Adding additional
 methods of estimating dimension is straightforward.
 For documentation, see the vignette in the package.   With version 3.0.4,
 the arguments for dr.step have been modified.
A solution for reproducible code and 
    high-performance computing.
Creation and use of R Repositories via helper functions 
 to insert packages into a repository, and to add repository information 
 to the current R session. Two primary types of repositories are support:
 gh-pages at GitHub, as well as local repositories on either the same machine
 or a local network. Drat is a recursive acronym: Drat R Archive Template.
Graphical display of R expression, showing the
        interpretation of an expression by R and the various kind of R
        data structure. The steps of the interpretation of an
        expression are obtained through the parsed tree.
We present DRaWR, a network-based method for ranking genes or
    properties related to a given gene set. Such related genes or properties are
    identified from among the nodes of a large, heterogeneous network of biological
    information. Our method involves a random walk with restarts, performed on
    an initial network with multiple node and edge types, preserving more of the
    original, specific property information than current methods that operate
    on homogeneous networks. In this first stage of our algorithm, we find the
    properties that are the most relevant to the given gene set and extract a
    subnetwork of the original network, comprising only the relevant properties. We
    then rerank genes by their similarity to the given gene set, based on a second
    random walk with restarts, performed on the above subnetwork.
Feed longitudinal data into a Bayesian Latent Factor Model to obtain a low-rank representation. Parameters are estimated using a Hamiltonian Monte Carlo algorithm with STAN. See G. Weinrott, B. Fontez, N. Hilgert and S. Holmes, "Bayesian Latent Factor Model for Functional Data Analysis", Actes des JdS 2016.
Analysis of dose-response data is made available through a suite of flexible and versatile model fitting and after-fitting functions.
A penalized/non-penalized implementation for dynamic regression in the presence of autocorrelated residuals (DREGAR) using iterative penalized/ordinary least squares. It applies Mallows CP, AIC, BIC and GCV to select the tuning parameters.
A somewhat outdated package of basic and easy-to-use functions for
  fitting dose-response curves to continuous dose-response data, calculating some
  toxicological parameters and plotting the results. Please consider using
  the more powerful and actively developed 'drc' package.  Functions that are
  fitted are the cumulative density function of the log-normal distribution
  ('probit' fit), of the logistic distribution ('logit' fit), of the Weibull
  distribution ('weibull' fit) and a linear-logistic model ('linlogit' fit),
  derived from the latter, which is used to describe data showing stimulation at
  low doses (hormesis). In addition, functions checking, plotting and retrieving
  dose-response data retrieved from a database accessed via 'RODBC' are included.
  As an alternative to the original fitting methods, the algorithms from the 'drc'
  package can be used.
Fit restricted mean models for the conditional association
  between an exposure and an outcome, given covariates. Three methods
  are implemented: O-estimation, where a nuisance model for the
  association between the covariates and the outcome is used;
  E-estimation where a nuisance model for the association
  between the covariates and the exposure is used, and doubly robust (DR)
  estimation where both nuisance models are used. In DR-estimation,
  the estimates will be consistent when at least one of the nuisance
  models is correctly specified, not necessarily both.
Provides a R driver for Apache Drill<https://drill.apache.org>, which could connect to the Apache Drill cluster<https://drill.apache.org/docs/installing-drill-on-the-cluster> or drillbit<https://drill.apache.org/docs/embedded-mode-prerequisites> and get result(in data frame) from the SQL query and check the current configuration status. This link <https://drill.apache.org/docs> contains more information about Apache Drill.
R codes for imputing dropout events. Many statistical methods in cell type identification, visualization and lineage reconstruction do not account for dropout events ('PCAreduce', 'SC3', 'PCA', 't-SNE', 'Monocle', 'TSCAN', etc). 'DrImpute' can improve the performance of such software by imputing dropout events.
This is a collection of functions for discontinuous regression
	     analysis and image processing.
Contains quality control routines for multiplex immunoassay data, 
    including several approaches for: treating the background noise of the assay,  
    fitting the dose-response curves and estimating the limits of quantification.
Likelihood-based marginal regression and association
        modelling for repeated, or otherwise clustered, categorical
        responses using dependence ratio as a measure of the
        association
Dual empirical likelihood (DEL) inference under semiparametric density ratio models (DRM) in the presence of multiple samples, including population cumulative distribution function estimation, quantile estimation and comparison, density estimation, composite hypothesis testing for DRM parameters which encompasses testing for changes in population distribution functions as a special case, etc.
Drop out analysis for psychologists in a R based web application.
    Shiny is used to visualize and analyze drop outs tailored to the methods of
    online survey methodology. Concept and app presented at the SCIP Conference
    in Long Beach, California.
An Implementation of Dimensionality Reduction
    via Regression using Kernel Ridge Regression.
Provides tools for assessing the shape of a dose-response
    curve by testing linearity and non-linearity at user-defined cut-offs. It
    also provides two methods of estimating a threshold dose, or the dose at
    which the dose-response function transitions to significantly increasing:
    bi-linear (based on pkg 'segmented') and smoothed with splines (based on
    pkg 'mgcv').
Targeted minimum loss-based estimators of counterfactual means and causal effects that are doubly-robust with respect both to consistency and asymptotic normality (van der Laan (2014), <doi:10.1515/ijb-2012-0038>).
An implementation of a Machine Learning Framework for prediction of new drugs Side Effects.
    Firstly drugs are clustered with respect to their features description and secondly predictions are made, according to Bayesian scores.
    Moreover it can perform protein enrichment considering the proteins clustered together in the first step of the algorithm.
    This last tool is of extreme interest for biologist and drug discovery purposes, given the fact that it can be used either as a validation of the clusters obtained, as well as for the possible discovery of new interactions between certain side effects and non targeted pathways.
    Clustering of the drugs in the feature space can be done using K-Means, PAM or K-Seeds (a novel clustering algorithm proposed by the author).
The package performs various analyzes of descriptive statistics, including correlations
A collection of Shiny apps that allow for the simulation and
    exploration of various infectious disease transmission dynamics scenarios.
    The purpose of the package is to help individuals learn 
    about infectious disease epidemiology from a dynamical systems perspective.
    All apps include explanations of the underlying models and instructions on
    what to do with the models.
Two discretization-based Monte Carlo algorithms, namely the Fu-Wang algorithm and the Wang-Lee algorithm, are provided for random sample generation from a high dimensional distribution of complex structure.  The normalizing constant of the target distribution needs not to be known.
Calculate posterior modes and credible intervals of parameters of the Dixon-Simon model for subgroup analysis (with binary covariates) in clinical trials.
Tools for multivariate, linear, time-invariant,
	time series models. This includes ARMA and state-space representations,
	and methods for converting between them. It also includes simulation
	methods and several estimation functions. The package has functions 
	for looking at model roots, stability, and forecasts at different 
	horizons. The ARMA model representation is general, so that VAR, VARX, 
	ARIMA, ARMAX, ARIMAX can all be considered to be special cases. Kalman
	filter and smoother estimates can be obtained from the state space
	model, and state-space model reduction techniques are implemented. 
	An introduction and User's Guide is available in a vignette.
An abstract DList class helps storing large list-type objects in a distributed manner. Corresponding high-level functions and methods for handling distributed storage (DStorage) and lists allows for processing such DLists on distributed systems efficiently. In doing so it uses a well defined storage backend implemented based on the DStorage class.
Datasets and functions that facilitate data analysis labs in data science courses and workshops.
Dynamic slicing is a method designed for dependency detection between a categorical variable and a continuous variable. It could be applied for non-parametric hypothesis testing and gene set enrichment analysis.
Density surface modelling of line transect data. A Generalized
    Additive Model-based approach is used to calculate spatially-explicit estimates
    of animal abundance from distance sampling (also presence/absence and strip
    transect) data. Several utility functions are provided for model checking,
    plotting and variance estimation.
An expressive language to facilitate simulation and visualization
    of two-dimensional dynamical systems. The basic elements of the language are
    a model wrapping around a function(x,y) which outputs a list(x
    = xprime, y = yprime), and a range. The language supports three
    types of visual objects: visualizations, features, and backgrounds. Visualizations, including dots and arrows,
    depict the behavior of the dynamical system over the entire range.
    Features display
    user-defined curves and points, and their images under the system.
    Backgrounds define and color regions of interest, such as basins of attraction.
    The language
    can also approximate attractors and their basins through simulation.
Fits inhomogeneous Poisson process spatial models
    to line transect sampling data and provides estimate of
    abundance within a region.
Perform a test of a simple null hypothesis about a 
    directly standardized rate and obtain the matching confidence 
    interval using a choice of methods.
Performs distance sampling simulations.It repeatedly generates
    instances of a user defined population within a given survey region, generates
    realisations of a survey design (currently these must be generated using
    Distance software in advance <http://distancesampling.org/>) and simulates
    the detection process. The data are then analysed so that the results can
    be compared for accuracy and precision across all replications. This will
    allow users to select survey designs which will give them the best accuracy
    and precision given their expectations about population distribution. Any
    uncertainty in population distribution or population parameters can be
    included by running the different survey designs for a number of different
    population descriptions. An example simulation can be found in the help file for
    make.simulation.
This package allows you to make basic probability assignments on a set of
    possibilities (events) and combine these events with Dempster's rule of combination.
A collection of functions to estimate parameters of a diffusion model via a D*M analysis. Build in models are: the Ratcliff diffusion model, the RWiener diffusion model, and Linear Ballistic Accumulator models. Custom models functions can be specified as long as they have a density function.
Building discrete-time survival trees and bagged trees based on
    the functionalities of the rpart package. Splitting criterion maximizes the
    likelihood of a covariate-free logistic discrete time hazard model.
Functions for direct surrogate variable analysis, which can identify hidden factors in high-dimensional biomedical data.
Elucidation of human disease similarities has emerged as an active research area, which is highly relevant to etiology, disease classification, and drug repositioning. This package was designed and implemented for identifying disease similarities. It contains five functions which are 'DCEA', 'DCpathway', 'DS', 'comDCGL' and 'comDCGLplot'. In 'DCEA' function, differentially co-expressed genes and differentially co-expressed links are extracted from disease vs. health samples. Then 'DCpathway' function assigns differential co-expression values of pathways to be the average differential co-expression value of their component genes. Then 'DS' employs partial correlation coefficient of pathways as the disease similarity for each disease pairs. And 'DS' contains a permutation process for evaluating the statistical significant of observed disease partial correlation coefficients. At last, 'comDCGL' and 'comDCGLplot' sort out shared differentially co-expressed genes and differentially co-expressed links with regulation information and visualize them.
Data objects in R can be rendered as HTML tables using the
    JavaScript library 'DataTables' (typically via R Markdown or Shiny). The
    'DataTables' library has been included in this R package. The package name
    'DT' is an abbreviation of 'DataTables'.
Towards automation of descriptive frequencies and statistics tables.
Deconvolving cell types from high-throughput gene profiling data.
This package contains functions to compare the accuracy of two binary diagnostic tests in a "paired" study design, i.e. when each test is applied to each subject in the study.
This package implements different algorithms for analyzing
        randomly truncated data, one-sided and two-sided (i.e. doubly)
        truncated data. Two real data sets are included.
Diffusion Weighted Imaging (DWI) is a Magnetic Resonance Imaging
             modality, that measures diffusion of water in tissues like the human 
             brain. The package contains R-functions to process diffusion-weighted 
             data. The functionality includes diffusion tensor imaging (DTI),
             diffusion kurtosis imaging (DKI), modeling for high angular resolution 
             diffusion weighted imaging (HARDI) using Q-ball-reconstruction and 
             tensor mixture models, several methods for structural adaptive 
             smoothing including POAS and msPOAS, and a streamline fiber tracking 
             for tensor and tensor mixture models.
             The package provides functionality to manipulate and visualize results 
             in 2D and 3D.
This package was created to analyze multi-level one-way
        experimental designs. It is designed to handle vectorized
        observation and factor data where there are unequal sample
        sizes and population variance homogeneity can not be assumed.
        To conduct the Dunnett modified Tukey-Kramer test (a.k.a. the
        T3 Procedure), create two vectors: one for your observations
        and one for the factor level of each observation. The function,
        gl.unequal, provides a means to more conveniently produce a
        factor vector with unequal sample sizes. Next, use the DTK.test
        function to conduct the test and save the output as an object
        to input into the DTK.plot function, which produces a
        confidence interval plot for each of the pairwise comparisons.
        Lastly, the function TK.test conducts the original Tukey-Kramer
        test.
A series of functions which aid in both simulating and
        determining the properties of finite, discrete-time, discrete
        state markov chains.  Two functions (DTMC, MultDTMC) produce n
        iterations of a Markov Chain(s) based on transition
        probabilities and an initial distribution.  The function FPTime
        determines the first passage time into each state.  The
        function statdistr determines the stationary distribution of a
        Markov Chain.
This implements the data table back-end for 'dplyr' so that you
    can seamlessly use data table and 'dplyr' together.
Estimation and comparison of survival distributions of dynamic treatment regimes (DTRs) from sequentially randomized clinical trials.
Combines various decision tree algorithms, plus both
             linear regression and ensemble methods into one package.
             Allows for the use of both continuous and categorical outcomes.
             An optional feature is to quantify the (in)stability to the
             decision tree methods, indicating when results can be trusted
             and when ensemble methods may be preferential.
Dynamic treatment regimens (DTRs) are sequential decision rules tailored at each stage by time-varying subject-specific features and intermediate outcomes observed in previous stages. This package implements three methods: O-learning (Zhao et. al. 2012,2014), Q-learning (Murphy et. al. 2007; Zhao et.al. 2009) and P-learning (Liu et. al. 2014, 2015) to estimate the optimal DTRs.
Dynamic treatment regime estimation and inference via G-estimation,
    dynamic weighted ordinary least squares (dWOLS) and Q-learning. Inference
    via bootstrap and (for G-estimation) recursive sandwich estimation.
This package provides functions for 1D and 2D Discrete
        Cosine Transform (DCT), Discrete Sine Transform (DST) and
        Discrete Hartley Transform (DHT).
A comprehensive implementation of dynamic time warping (DTW) algorithms in R.  DTW computes the optimal (least cumulative distance) alignment between points of two time series.  Common DTW variants covered include local (slope) and global (window) constraints, subsequence matches, arbitrary distance definitions, normalizations, minimum variance matching, and so on.  Provides cumulative distances, alignments, specialized plot styles, etc.
Time series clustering along with optimized techniques related
    to the Dynamic Time Warping distance and its corresponding lower bounds.
    Implementations of partitional, hierarchical, fuzzy, k-Shape and TADPole
    clustering are available. Functionality can be easily extended with
    custom distance measures and centroid definitions. Implementations of
    DTW barycenter averaging and a distance based on global alignment kernels
    are also provided. All included distance functions have custom loops
    optimized for the calculation of cross-distance matrices, including
    parallelization support. Several cluster validity indices are included.
Provides an implementation of the Time-Weighted Dynamic Time
    Warping (TWDTW) method for land cover mapping using satellite image time series.
    TWDTW is based on the Dynamic Time Warping technique and has achieved high
    accuracy for land cover classification using satellite data. The method is
    based on comparing unclassified satellite image time series with a set of known
    temporal patterns (e.g. phenological cycles associated with the vegetation).
    Using 'dtwSat' the user can build temporal patterns for land cover types, apply
    the TWDTW analysis for satellite datasets, visualize the results of the time
    series analysis, produce land cover maps, create temporal plots for land cover
    change, and compute accuracy assessment metrics.
Functions to analyze multiple choice data using Dual Scaling
Programmatic access to the DuckDuckGo Instant Answer API <https://api.duckduckgo.com/api>.
Expands factors, characters and other eligible classes
        into dummy/indicator variables.
Efficiently create dummies of all factors and character vectors in a data frame. Support is included for learning the categories on one data set (e.g., a training set) and deploying them on another (e.g., a test set).
For the implementation of the step-down or step-up Dunnett testing procedures, the package includes R functions to calculate critical constants and R functions to calculate adjusted P-values of the test statistics. In addition, the package also contains functions to evaluate testing powers and hence the necessary sample sizes specially for the classical problem of comparisons of several treatments with a control.
Computes Dunn's test (1964) for stochastic dominance and reports the results among multiple pairwise comparisons after a Kruskal-Wallis test for stochastic dominance among k groups (Kruskal and Wallis, 1952). The interpretation of stochastic dominance requires an assumption that the CDF of one group does not cross the CDF of the other. 'dunn.test' makes k(k-1)/2 multiple pairwise comparisons based on Dunn's z-test-statistic approximations to the actual rank statistics. The null hypothesis for each pairwise comparison is that the probability of observing a randomly selected value from the first group that is larger than a randomly selected value from the second group equals one half; this null hypothesis corresponds to that of the Wilcoxon-Mann-Whitney rank-sum test. Like the rank-sum test, if the data can be assumed to be continuous, and the distributions are assumed identical except for a difference in location, Dunn's test may be understood as a test for median difference. 'dunn.test' accounts for tied ranks.
Inference of population sizes using a binomial likelihood and least informative discrete uniform priors.
Hurst exponent estimation of a fractional Brownian motion
        by using discrete variations methods in presence of outliers
        and/or an additive noise
Functionality for analyzing dose-volume histograms (DVH)
        in radiation oncology: Read DVH text files, calculate DVH
        metrics as well as generalized equivalent uniform dose (gEUD),
        biologically effective dose (BED), equivalent dose in 2 Gy
        fractions (EQD2), normal tissue complication probability
        (NTCP), and tumor control probability (TCP). Show DVH
        diagrams, check and visualize quality assurance constraints
        for the DVH. Includes web-based graphical user interface.
Provides access to Dataverse version 3 APIs, enabling access to archived data (and metadata), and the ability to create and manipulate studies in a user's dataverse(s). For Dataverse server versions >= 4.0, please use the dataverse package <https://github.com/IQSS/dataverse-client-r> instead.
A set of wrapper functions for data.world's REST API endpoints.
Infers networks with hubs using degree weighted Lasso method.
Regression for a discrete response, where the conditional distribution is modelled via a discrete Weibull distribution.
An R interface to the 'dygraphs' JavaScript charting library
    (a copy of which is included in the package). Provides rich facilities
    for charting time-series data in R, including highly configurable
    series- and axis-display and interactive features like zoom/pan and
    series/point highlighting.
Add a "Did You Mean" feature to the R interactive. With this
    package, error messages for misspelled input of variable names or package names
    suggest what you really want to do in addition to notification of the mistake.
Time series regression.  The dyn class interfaces ts,
        irts(), zoo() and zooreg() time series classes to lm(), glm(),
        loess(), quantreg::rq(), MASS::rlm(), MCMCpack::MCMCregress(),
        quantreg::rq(), randomForest::randomForest() and other regression
        functions allowing those functions to be used with time series
        including specifications that may contain lags, diffs and
        missing values.
The package is aimed at dynamically visualizing probability
    distributions and their moments and all the commonly used distributions are
    included.
Interactive graphical tool for manipulating graphs
Contains functions that lets you fit dynamic hazard models with binary 
  outcomes using state space models. The methods are originally described in  
  Fahrmeir (1992) <doi:10.1080/01621459.1992.10475232> and Fahrmeir (1994) 
  <doi:10.1093/biomet/81.2.317>. The functions also provide an extension hereof where the 
  Extended Kalman filter is replaced by an Unscented Kalman filter. Models are 
  fitted with the regular coxph() like formula.
Contains methods for detection of clusters in hierarchical clustering dendrograms.
A native R implementation and enhancement of the Dynamic TOPMODEL
    semi-distributed hydrological model. Includes some pre-processsing and
    output routines.
Inference by sequential Monte Carlo for 
  dynamic tree regression and classification models
  with hooks provided for sequential design and optimization, 
  fully online learning with drift, variable selection, and 
  sensitivity analysis of inputs.  Illustrative 
  examples from the original dynamic trees paper are facilitated
  by demos in the package; see demo(package="dynaTree").
A GUI to solve dynamic biplots and classical biplot. Try matrices
    of 2-way and 3-way. The GUI can be run in multiple languages.
DynClust is a two-stage procedure for the denoising and clustering of stack of noisy images acquired over time. Clustering only assumes that the data contain an unknown but small number of dynamic features. The method first denoises the signals using local spatial and full temporal information. The clustering step uses the previous output to aggregate voxels based on the knowledge of their spatial neighborhood. Both steps use a single keytool based on the statistical comparison of the difference of two signals with the null signal. No assumption is therefore required on the shape of the signals. The data are assumed to be normally distributed (or at least follow a symmetric distribution) with a known constant variance. Working pixelwise, the method can be time-consuming depending on the size of the data-array but harnesses the power of multicore cpus.
Computes dynamical correlation estimates and percentile
        bootstrap confidence intervals for pairs of longitudinal
        responses, including consideration of lags and derivatives.
An anonymization algorithm to resist neighbor label attack in a dynamic network.
Fit dynamic intervention model using the arima() function.
Dynamic linear models and time series regression.
Demonstrate the results of a statistical model object as a dynamic nomogram in an RStudio panel or web browser. Also, the generic DNbuilder() function in this package provides a simple and straightforward way to build and publish a dynamic nomogram on the web to use the app independent of R. 'DynNom' supports a variety of model objects; lm(), glm(), coxph() models and also ols(), Glm(), lrm(), cph() models in the 'rms' package.
Implements the fast Fourier transform to estimate delays of expression initiation between trajectories to integrate and
    analyse time course omics data.
Computes the first stage GMM estimate of a dynamic linear model with p lags of the dependent variables.
The dynpred package contains functions for dynamic prediction in survival analysis.
Intensive longitudinal data have become increasingly prevalent in
    various scientific disciplines. Many such data sets are noisy, multivariate,
    and multi-subject in nature. The change functions may also be continuous, or
    continuous but interspersed with periods of discontinuities (i.e., showing
    regime switches). The package 'dynr' (Dynamic Modeling in R) is an R package
    that implements a set of computationally efficient algorithms for handling a
    broad class of linear and nonlinear discrete- and continuous-time models with
    regime-switching properties under the constraint of linear Gaussian measurement
    functions. The discrete-time models can generally take on the form of a state-
    space or difference equation model. The continuous-time models are generally
    expressed as a set of ordinary or stochastic differential equations. All
    estimation and computations are performed in C, but users are provided with the
    option to specify the model of interest via a set of simple and easy-to-learn
    model specification functions in R. Model fitting can be performed using single-
    subject time series data or multiple-subject longitudinal data.
Improves the concept of multivariate range boxes, which is highly susceptible for outliers and does not consider the distribution of the data. The package uses dynamic range boxes to overcome these problems.
Dynamic stochastic block model that combines a stochastic block model (SBM) for its static part with independent Markov chains for the evolution of the nodes groups through time, developed in  Matias and Miele (2016) <doi:10.1111/rssb.12200>.
Dynamic simulations and graphical depictions of autoregressive
    relationships.
Functions to fit time-varying coefficient models for interval
    censored and right censored survival data. Three major approaches are
    implemented: 1) Bayesian Cox model with time-independent, time-varying or
    dynamic coefficients for right censored and interval censored data; 2)
    Spline based time-varying coefficient Cox model for right censored data; 3)
    Transformation model with time-varying coefficients for right censored data
    using estimating equations.
Methods to estimate dynamic treatment regimes using Interactive Q-Learning, Q-Learning, weighted learning, and value-search methods based on Augmented Inverse Probability Weighted Estimators and Inverse Probability Weighted Estimators.
Functions for dyadic binary/dichotomous sequence analyses are implemented in this contribution.
             The focus is on estimating actor-partner-interaction models using various approaches, for  
             instances the approach of Bakeman & Gottman's (1997) <DOI:10.1017/cbo9780511527685>,
             generalized multi-level models, and basic Markov models. Moreover, coefficients of one model 
             can be translated into those of the other models. Finally, simulation-based power analyses are
             provided.
A collection of functions designed to estimate and predict skewed spatial processes, and a real data set.
Functions for latent class analysis, short time Fourier
	     transform, fuzzy clustering, support vector machines,
	     shortest path computation, bagged clustering, naive Bayes
	     classifier, ...
Plots of the empirical attainment function for two objectives.
An implementation of multiple-locus association mapping on a genome-wide scale. 'Eagle' can handle inbred or outbred study populations, populations of arbitrary unknown complexity, and data larger than the memory capacity of the computer. Since 'Eagle' is based on linear mixed models, it is best suited to the analysis of data on continuous traits. However, it can tolerate non-normal data. 'Eagle' reports, as its findings, the best set of snp in strongest association with a trait. To perform an analysis, run 'OpenGUI()'. This  opens a web browser to the menu-driven user interface for the input of data, and for performing genome-wide analysis.
A 'Shiny' web application for energy industry analytics.
      Take an overview of the industry, measure Key Performance Indicators,
      identify changes in the industry over time, and discover new relationships in the data.
The Early-Warning-Signals Toolbox provides methods for estimating
    statistical changes in timeseries that can be used for identifying nearby
    critical transitions. Based on Dakos et al (2012) Methods for Detecting
    Early Warnings of Critical Transitions in Time Series Illustrated Using
    Simulated Ecological Data. PLoS ONE 7(7):e41010
Build regression models using the techniques in Friedman's
    papers "Fast MARS" and "Multivariate Adaptive Regression
    Splines". (The term "MARS" is trademarked and thus not used in
    the name of the package.)
Downloads a satellite image via Google Maps/Earth (these are
    originally from a variety of aerial photography sources), 
    translates the image into a perceptually uniform color space,
    runs one of a few different clustering algorithms on the colors in the image 
    searching for a user-supplied number of colors,
    and returns the resulting color palette.
Enables launching a series of simulations of a computer code from the R session, and to retrieve the simulation outputs in an appropriate format for post-processing treatments. Five sequential sampling schemes and three coupled-to-MCMC schemes are implemented.
Given the scores from decision makers, the analytic hierarchy process can be conducted easily.
Perform analysis of variance and other important complementary analyzes. The functions are easy to use. Performs analysis in various designs, with balanced and unbalanced data.
Allows users to easily read multiple comma separated tables and create a data frame under the same name.
    Is able to read multiple comma separated tables from a local directory, a zip file or a zip file on a remote directory.
Descriptive analysis is essential for publishing medical articles.
  This package provides an easy way to conduct the descriptive analysis.
  1. Both numeric and factor variables can be handled. For numeric variables, normality test will be applied to choose the parametric and nonparametric test.
  2. Both two or more groups can be handled. For groups more than two, the post hoc test will be applied, 'Tukey' for the numeric variables and 'FDR' for the factor variables.
  3. T test, ANOVA or Fisher test can be forced to apply.
Builds format strings that can be used with strptime() and sprintf().
It is a package that can be used to send HTML reports easily.
Contains a few utility functions for working with capture-history data, a function for simulating capture-history data, and a function to fit this data, using a Gibbs sampler.
Easily build and evaluate machine learning models on a dataset. Machine 
    learning models supported include penalized linear models, penalized linear models 
    with interactions, random forest, support vector machines, neural networks, 
    and deep neural networks.
Utilities for building certain kinds of common matrices and models in 
    the extended structural equation modeling package, OpenMx.
Set of wrappers for the 'ncdf4' package to simplify and extend its reading/writing capabilities into/from multidimensional R arrays.
The package fit and plot some nonlinear models
Easily load and install multiple packages from different sources, 
    including CRAN and GitHub. The libraries function allows you to load or attach 
    multiple packages in the same function call. The packages function will load one 
    or more packages, and install any packages that are not installed on your system 
    (after prompting you). Also included is a from_import function that allows you 
    to import specific functions from a package into the global environment.
Power analysis is used in the estimation of sample sizes for
    experimental designs. Most programs and R packages will only output the highest
    recommended sample size to the user. Often the user input can be complicated
    and computing multiple power analyses for different treatment comparisons can
    be time consuming. This package simplifies the user input and allows the user
    to view all of the sample size recommendations or just the ones they want to see.
    The calculations used to calculate the recommended sample sizes are from the
    'pwr' package.
Query NCBI Entrez and retrieve PubMed records in XML or text format. Process PubMed records by extracting and aggregating data from selected fields. A large number of records can be easily downloaded via this simple-to-use interface to the NCBI PubMed API.
Performs analysis of regression in simple designs with quantitative treatments, 
             including mixed models and non linear models. Plot graphics (equations and data).
The main function, ProtectTable(), performs table suppression according to a 
 frequency rule with a data set as the only required input. Within this function, 
 protectTable(), protectLinkedTables() or runArgusBatchFile() in package 'sdcTable' is called. 
 Lists of level-hierarchy (parameter 'dimList') and other required input to these functions 
 are created automatically. 
 The function, PTgui(), starts a graphical user interface based on the shiny package.
This is a pipelining tool that facilitates 
  evaluation and visualisation of stratified genome-wide 
  association meta-analyses (GWAMAs) results data. It 
  provides (i) statistical methods to test and to account 
  for between-strata difference and to clump genome-wide
  results into independent loci and (ii) extended graphical 
  features (e.g., Manhattan, Miami and QQ plots) tailored 
  for stratified GWAMA results.
Set of tools to simplify application of atomic forecast
    verification metrics for (comparative) verification of ensemble forecasts
    to large data sets. The forecast metrics are imported from the
    'SpecsVerification' package, and additional forecast metrics are provided
    with this package. Alternatively, new user-defined forecast scores can be
    implemented using the example scores provided and applied using the
    functionality of this package.
Fitting and testing multi-attribute probabilistic choice
  models, especially the Bradley-Terry-Luce (BTL) model (Bradley &
  Terry, 1952; Luce, 1959), elimination-by-aspects (EBA) models
  (Tversky, 1972), and preference tree (Pretree) models (Tversky &
  Sattath, 1979).
Package implements entropy balancing, a data preprocessing procedure that allows users to reweight a dataset such that the covariate distributions in the reweighted data satisfy a set of user specified moment conditions. This can be useful to create balanced samples in observational studies with a binary treatment where the control group data can be reweighted to match the covariate moments in the treatment group. Entropy balancing can also be used to reweight a survey sample to known characteristics from a target population.
We propose a new sample size calculation method for trial-based
    cost-effectiveness analyses. Our strategy is based on the value of perfect
    information that would remain after the completion of the study.
Empirical Bayes thresholding using the methods developed
    by I. M. Johnstone and B. W. Silverman. The basic problem is to
    estimate a mean vector given a vector of observations of the mean
    vector plus white noise, taking advantage of possible sparsity in
    the mean vector. Within a Bayesian formulation, the elements of
    the mean vector are modelled as having, independently, a
    distribution that is a mixture of an atom of probability at zero
    and a suitable heavy-tailed distribution. The mixing parameter can
    be estimated by a marginal maximum likelihood approach. This leads
    to an adaptive thresholding approach on the original data.
    Extensions of the basic method, in particular to wavelet
    thresholding, are also implemented within the package.
Infer the adjacency matrix of a
	network from time course data using an empirical Bayes
	estimation procedure based on Dynamic Bayesian Networks.
Provides the Empirical Bayesian Elastic Net for handling multicollinearity in generalized linear regression models.  As a special case of the 'EBglmnet'
  package (also available on CRAN), this package encourages a grouping effects to select relevant variables and estimate the corresponding non-zero effects.
Genotyping the population using next generation sequencing data is essentially important for the rare variant detection. In order to distinguish the genomic structural variation from sequencing error, we propose a statistical model which involves the genotype effect through a latent variable to depict the distribution of non-reference allele frequency data among different samples and different genome loci, while decomposing the sequencing error into sample effect and positional effect. An ECM algorithm is implemented to estimate the model parameters, and then the genotypes and SNPs are inferred based on the empirical Bayes method.
Provides empirical Bayesian lasso and elastic net algorithms for variable selection and effect estimation. Key features include sparse variable selection and effect estimation via generalized linear regression models, high dimensionality with p>>n, and significance test for nonzero effects. This package outperforms other popular methods such as lasso and elastic net methods in terms of power of detection, false discovery rate, and power of detecting grouping effects.
Functions for querying the EBI Metagenomics Portal (https://www.ebi.ac.uk/metagenomics/). The current main focus is on taxa abundance data, but the intention is that this package should evolve into a general purpose package for working with EBI Metagenomics data using R.
Empirical Bayes ranking applicable to parallel-estimation settings where the estimated parameters are asymptotically unbiased and normal, with known standard errors.  A mixture normal prior for each parameter is estimated using Empirical Bayes methods, subsequentially ranks for each parameter are simulated from the resulting joint posterior over all parameters (The marginal posterior densities for each parameter are assumed independent). Finally, experiments are ordered by expected posterior rank, although computations minimizing other plausible rank-loss functions are also given.
Performs an exact Bayesian segmentation on data and returns the probabilities of breakpoints, an ICL criteria, comparison of change-point location, etc.
Genotyping and SNP calling tool for single-sample next generation sequencing data analysis using an empirical Bayes method.
Provides an interface to the European Central Bank's Statistical
    Data Warehouse API, allowing for programmatic retrieval of a vast quantity
    of statistical data.
Draw sample paths for endpoint-conditioned continuous time Markov chains via modified rejection sampling or uniformization.
An implementation of the univariate elliptic distribution, and
    lambda option pricing model. It provides detailed functionality and data
    sets for the distribution and modelling. Especially, it contains functions
    for the computation of density, probability, quantile, fitting procedures,
    option prices, volatility smile. It also comes with sample financial data,
    and plotting routines.
Computes and plots a transformed empirical CDF (ecdf) as a
    diagnostic for heavy tailed data, specifically data with power law decay on the
    tails.  Routines for annotating the plot, comparing data to a model, fitting a
    nonparametric model, and some multivariate extensions are given.
Some wrappers, functions and data sets for for spatial point pattern analysis (mainly based on spatstat), used in the book "Introduccion al Analisis Espacial de Datos en Ecologia y Ciencias Ambientales: Metodos y Aplicaciones".
Testing the equality of several covariance functions of functional data. Four different methods are implemented: L2-norm with W-S naive, L2-norm with W-S bias-reduced, L2-norm (Zhang 2013) <ISBN:9781439862735>, and sup-norm with resampling (Guo et al. 2017) <arXiv:1609.04232>.
Functions to update data sets in Ecdat and to create,
          manipulate, plot and analyze those and similar data sets.
Embed interactive charts to their Shiny applications. These charts will be generated by ECharts library developed by Baidu (<http://echarts.baidu.com/>). Current version supports line chart, bar chart, pie chart, scatter plot, gauge, word cloud, radar chart, tree map, and heat map.
Easily import multi-frequency acoustic data stored in 'HAC' files (see <http://biblio.uqar.ca/archives/30005500.pdf> for more information on the format), and produce echogram visualisations with predefined or customized color palettes. It is also possible to merge consecutive echograms; mask or delete unwanted echogram areas; model and subtract background noise; and more important, develop, test and interpret different combinations of frequencies in order to perform acoustic filtering of the echogram's data.
Provides a function that quickly computes the fine structure
    isotope patterns of a set of chemical formulas to a given degree of
    accuracy (up to the limit set by errors in floating point arithmetic). A
    data-set comprising the masses and isotopic abundances of individual
    elements is also provided.
Companion package to the paper: An analytic approach for 
    interpretable predictive models in high dimensional data, in the presence of 
    interactions with exposures. Bhatnagar, Yang, Khundrakpam, Evans, Blanchette, Bouchard, Greenwood (2017) <DOI:10.1101/102475>. 
    This package includes an algorithm for clustering high dimensional data that can be affected by an environmental factor.
Functions for easy building of error correction models (ECM) for time series regression.
Implements the Bayesian and likelihood methods proposed 
  in Imai, Lu, and Strauss (2008 <DOI: 10.1093/pan/mpm017>) and 
  (2011 <DOI:10.18637/jss.v042.i05>) for ecological inference in 2 
  by 2 tables as well as the method of bounds introduced by Duncan and 
  Davis (1953).  The package fits both parametric and nonparametric 
  models using either the Expectation-Maximization algorithms (for 
  likelihood models) or the Markov chain Monte Carlo algorithms (for 
  Bayesian models).  For all models, the individual-level data can be 
  directly incorporated into the estimation whenever such data are available.
  Along with in-sample and out-of-sample predictions, the package also
  provides a functionality which allows one to quantify the effect of data
  aggregation on parameter estimation and hypothesis testing under the
  parametric likelihood models.
Dissimilarity-based analysis functions including ordination and Mantel test functions, intended for use with spatial and community data.
The ecoengine ('ecoengine';
    <https://ecoengine.berkeley.edu/>). provides access to more than 5 million georeferenced
    specimen records from the University of California, Berkeley's Natural History
    Museums.
Management and exploratory analysis of spatial data in population biology. Easy integration of information from multiple sources with 'ecogen' objects.
Simulates the soil water balance (soil moisture, evapotranspiration, leakage and runoff), rainfall series by using the marked Poisson process and the vegetation growth through the normalized difference vegetation index (NDVI). Please see Souza et al. (2016) <doi:10.1002/hyp.10953>.
This package provides a flexible foundation for scientists, 
  engineers, and policy makers to base teaching exercises as well as for 
  more applied use to model complex eco-hydrological interactions.
Calculates several indices, such as of diversity, fluctuation, etc., and they are used to estimate ecological indicators.
Figures, data sets and examples from the book "A practical guide to ecological modelling - using R as a simulation platform" by Karline Soetaert and Peter MJ Herman (2009). Springer.
  All figures from chapter x can be generated by "demo(chapx)", where x = 1 to 11. 
  The R-scripts of the model examples discussed in the book are in subdirectory "examples", ordered per chapter.
  Solutions to model projects are in the same subdirectories.
Coalescent-Based Simulation of Ecological Communities as proposed
    by Munoz et al. (2017) <doi:10.13140/RG.2.2.31737.26728>. The package includes
    a tool for estimating parameters of community assembly by using Approximate 
    Bayesian Computation.
Tools for general properties including price, quantity, elasticity, convexity, marginal revenue and manifold of various economics demand systems including Linear, Translog, CES, LES and CREMR.
Estimating individual-level covariate-outcome associations 
 using aggregate data ("ecological inference") or a combination of 
 aggregate and individual-level data ("hierarchical related regression").
Creates an R interface to the Bacen <http://api.bcb.gov.br/> and Sidra <http://api.sidra.ibge.gov.br> APIs and IPEA data <http://www.ipeadata.gov.br/Default.aspx>.
Classes and methods for implementing aquatic ecosystem models,
  for running these models, and for visualizing their results.
Given a site by species interaction matrix, users can make inferences about species interactions by performance hypothesis comparing test statistics against a null distribution. The current package provides algorithms and metrics for niche-overlap, body size ratios and species co-occurrence. Users can also integrate their own algorithms and metrics within these frameworks or completely novel null models. Detailed explanations about the underlying assumptions of null model analysis in ecology can be found at http://ecosimr.org.
R interface to the Embedded COnic Solver (ECOS), an efficient
	     and robust C library for convex problems. Conic and equality
	     constraints can be specified in addition to integer and
	     boolean variable constraints for mixed-integer problems. This
	     R interface is inspired by the python interface and has
	     similar calling conventions.
Implements stochastic simulations of community assembly (ecological
    diversification) using customizable ecospace frameworks (functional trait
    spaces). Provides a wrapper to calculate common ecological disparity and
    functional ecology statistical dynamics as a function of species richness.
    Functions are written so they will work in a parallel-computing environment.
Collection of R functions and data sets for the support of spatial ecology analyses with a focus on pre-, core and post- modelling analyses of species distribution, niche quantification and community assembly. Written by current and former members and collaborators of the ecospat group of Antoine Guisan, Department of Ecology and Evolution (DEE) & Institute of Earth Surface Dynamics (IDYST), University of Lausanne, Switzerland.
Implementation of the EPA's Ecological Exposure Research Division (EERD) tools (discontinued in 1999) for Probit and Trimmed Spearman-Karber Analysis.
    Probit and Spearman-Karber methods from Finney's book "Probit analysis a statistical treatment of the sigmoid response curve" with options for most accurate results or identical results to the book.
    Probit and all the tables from Finney's book (code-generated, not copied) with the generating functions included.
    Control correction: Abbott, Schneider-Orelli, Henderson-Tilton, Sun-Shepard.
    Toxicity scales: Horsfall-Barratt, Archer, Gauhl-Stover, Fullerton-Olsen, etc.
EcoTroph is an approach and software for modelling marine and freshwater ecosystems. It is articulated entirely around trophic levels. EcoTroph's key displays are bivariate plots, with trophic levels as the abscissa, and biomass flows or related quantities as ordinates. Thus, trophic ecosystem functioning can be modelled as a continuous flow of biomass surging up the food web, from lower to higher trophic levels, due to predation and ontogenic processes. Such an approach, wherein species as such disappear, may be viewed as the ultimate stage in the use of the trophic level metric for ecosystem modelling, providing a simplified but potentially useful caricature of ecosystem functioning and impacts of fishing. This version contains catch trophic spectrum analysis (CTSA) function and corrected versions of the mf.diagnosis and create.ETmain functions.
Functions for evaluating and visualizing
  ecological assessment procedures for surface waters
  containing physical, chemical and biological assessments
  in the form of value functions.
Computer simulations of classical ecological models as a
    learning resource.
Implements various procedures for finding 
	     multiple change-points. Two methods make use of dynamic 
	     programming and probabilistic pruning, with no distributional 
	     assumptions other than the existence of certain absolute 
	     moments in one method. Hierarchical and exact search methods 
	     are included. All methods return the set of estimated change-
	     points as well as other summary information.
Framework for building evolutionary algorithms for both single- and multi-objective continuous or discrete optimization problems. A set of predefined evolutionary building blocks and operators is included. Moreover, the user can easily set up custom objective functions, operators, building blocks and representations sticking to few conventions. The package allows both a black-box approach for standard tasks (plug-and-play style) and a much more flexible white-box approach where the evolutionary cycle is written by hand.
Functions useful for exploratory data analysis
    using random forests which can be used to compute multivariate partial
    dependence, observation, class, and variable-wise marginal and joint permutation
    importance as well as observation-specific measures of distance 
    (supervised or unsupervised). All of the aforementioned functions are
    accompanied by 'ggplot2' plotting functions.
This package provides a unified approach for Economic
        Design of Control Charts. The main purpose of this package is
        to find out the optimal parameters to minimize the ECH
        (Expected Cost per Hour) of the process.
Detection of edge points in images based on the difference
        of two asymmetric M-kernel estimators. Linear and circular
        regression clustering based on redescending M-estimators.
        Detection of linear edges in images.
Functions for exploratory and descriptive analysis of event based data. Provides methods for describing and selecting process data, and for preparing event log data for process mining. Builds on the S3-class for event logs implemented in the package 'bupaR'.
This package will allow to connect with email server through Internet Message Access Protocol (IMAP) and extract header information e.g. from, to, cc, subject, date and time. User will supply their email address and password along with other options. Initially this package is developed only for Gmail. To run the functions from this package user have to have IMAP enabled Gmail account.
An implementation of maximum entropy sampling for spatial
  data is provided. An exact branch-and-bound algorithm as well as greedy and
  dual greedy heuristics are included.
Import physiologic data stored in
    the European Data Format (EDF and EDF+) into R.
    Both EDF and EDF+ files are supported. Discontinuous
    EDF+ files are not yet supported.
Functions for reading in data sets of prey and predator isotopic measurements and producing estimates for discrimination factors.
Reads European Data Format files EDF and EDF+, see <http://www.edfplus.info>,
    BioSemi Data Format files BDF, see <http://www.biosemi.com/faq/file_format.htm>,
    and BDF+ files, see <http://www.teuniz.net/edfbrowser/bdfplus%20format%20description.html>.
    The files are read in two steps: first the header is read
    and then the signals (using the header object as a parameter).
Easily creating empirical distribution functions from data: 'dfun', 'pfun',
    'qfun' and 'rfun'.
In the USA, firms file different forms with the U.S. Securities and
    Exchange Commission (SEC) through EDGAR (Electronic Data Gathering, Analysis,
    and Retrieval system). EDGAR automated system collects all the different
    necessary filings and make it publicly available. Secondly, it validates
    collected filings, then perform indexing and accepting of these submitted
    forms. Investors, regulators, and researchers often require these forms for
    various purposes. This package helps in data gathering, management, and 
	visualization in this regard. It downloads SEC EDGAR quarterly master
    indexes, daily master indexes, and filings from SEC.org site and perform 
    sentiment analysis of these filing.
A set of methods to access and parse live filing information from the
    U.S. Securities and Exchange Commission (SEC - <https://sec.gov>) including
    company and fund filings along with all associated metadata.
Generates interactive circle plots with the nodes around the
    circumference and linkages between the connected nodes using hierarchical
    edge bundling via the D3 JavaScript library. See <http://d3js.org/> for more
    information on D3.
Facilitates basic spatial edge correction to point pattern data.
Extends edgeR functionality by improving on exactTest using an unconditional exact test of negative binomial means.
Package EDISON (Estimation of Directed Interactions from
    Sequences Of Non-homogeneous gene expression) runs an MCMC
    simulation to reconstruct networks from time series data, using
    a non-homogeneous, time-varying dynamic Bayesian network.
    Networks segments and changepoints are inferred concurrently,
    and information sharing priors provide a reduction of the
    inference uncertainty.
An integrated editing and imputation method for continuous microdata under linear constraints is implemented. It relies on a Bayesian nonparametric hierarchical modeling approach as described in Kim et al. (2015) <DOI:10.1080/01621459.2015.1040881>. In this approach, the joint distribution of the data is estimated by a flexible joint probability model. The generated edit-imputed data are guaranteed to satisfy all imposed edit rules, whose types include ratio edits, balance edits and range restrictions.
Facilitates reading and manipulating (multivariate) data restrictions
    (edit rules) on numerical and categorical data. Rules can be defined with common R syntax
    and parsed to an internal (matrix-like format). Rules can be manipulated with
    variable elimination and value substitution methods, allowing for feasibility checks
    and more. Data can be tested against the rules and erroneous fields can be found based
    on Fellegi and Holt's generalized principle. Rules dependencies can be visualized with 
    using the igraph package.
Perform Dynamic Model Averaging with grid search as in Dangl and Halling (2012) <doi:10.1016/j.jfineco.2012.04.003> using parallel computing.
R client for Empirical Data Platform. More information is at <https://empirical.com>. For support, contact support@empirical.com.
The library contains R-functions to estimate the effective
        dimension reduction space in 'multi-index' regression models.
This package comes to illustrate the articles "A graphical
        tool for selecting the number of slices and the dimension of
        the model in SIR and SAVE approaches" and "Comparison of sliced
        inverse regression approaches for underdetermined cases"
Provides convenience functions and pre-programmed Stan models
    related to item response theory. Its purpose is to make fitting
    common item response theory models using Stan easy.
Read in and analysis functions for education surveys and
             assessments data from the National Center for Education Statistics
             (NCES) <https://nces.ed.gov/>, including the National Assessment
             of Educational Progress (NAEP)
             data <https://nces.ed.gov/nationsreportcard/>.
Easily compute education inequality measures and the distribution 
  of educational attainments for any group of countries, using the data set 
  developed in Jorda, V. and Alonso, JM. (2017) <DOI:10.1016/j.worlddev.2016.10.005>. 
  The package offers the possibility to compute not only the Gini index, but 
  also generalized entropy measures for different values of the sensitivity 
  parameter. In particular, the package includes functions to compute the 
  mean log deviation, which is more sensitive to the bottom part of the 
  distribution; the Theil’s entropy measure, equally sensitive to all parts 
  of the distribution; and finally, the GE measure when the sensitivity 
  parameter is set equal to 2, which gives more weight to differences in 
  higher education. The decomposition of these measures in the components 
  between-country and within-country inequality is also provided. Two 
  graphical tools are also provided, to analyse the evolution of the
  distribution of educational attainments: The cumulative distribution 
  function and the Lorenz curve.
Data from the 2013 and 2016 Australian Federal Election (House of
    Representatives) and the 2011 Australian Census. Includes tools for
    visualizing and analysing the data. This package incorporates
    data that is copyright Commonwealth of Australia (Australian
    Electoral Commission) 2016.
Provides tools for analysing education trials. Making different
    methods accessible in a single place is essential for sensitivity analysis
    of education trials, particularly the implication of the different methods in
    analysing simple randomised trials, cluster randomised trials and multisite
    trials.
Analysis and visualization tools for electroencephalography (EEG) data. Includes functions for plotting (a) EEG caps, (b) single- and multi-channel EEG time courses, and (c) EEG spatial maps. Also includes smoothing and Independent Component Analysis functions for EEG data analysis, and a function for simulating event-related potential EEG data.
Contains the example EEG data used in the package eegkit. Also contains code for easily creating larger EEG datasets from the EEG Database on the UCI Machine Learning Repository.
Compute the extended empirical log likelihood ratio (Tsao & Wu, 2014) for the mean and parameters defined by estimating equations.
Read raw EEM data and prepares them for further analysis.
Provides various tools for preprocessing Emission-Excitation-Matrix (EEM) for Parallel Factor Analysis (PARAFAC). Different
  methods are also provided to calculate common metrics such as humification index and fluorescence index.
Collection of convenience functions to make working with
    administrative records easier and more consistent. Includes functions to
    clean strings, and identify cut points. Also includes three example data 
    sets of administrative education records for learning how to process records 
    with errors.
Provides functions to create simulated time series of environmental
    exposures (e.g., temperature, air pollution) and health outcomes for use in
    power analysis and simulation studies in environmental epidemiology. This
    package also provides functions to evaluate the results of simulation studies
    based on these simulated time series. This work was supported by a grant
    from the National Institute of Environmental Health Sciences (R00ES022631) and
    a fellowship from the Colorado State University Programs for Research and
    Scholarly Excellence.
A number of utility function for exploratory
    factor analysis are included in this package. In particular, it computes standard errors for parameter estimates and factor correlations under a variety of conditions.
Enhanced False Discovery Rate (EFDR) is a tool to detect anomalies
    in an image. The image is first transformed into the wavelet domain in
    order to decorrelate any noise components, following which the coefficients
    at each resolution are standardised. Statistical tests (in a multiple
    hypothesis testing setting) are then carried out to find the anomalies. The
    power of EFDR exceeds that of standard FDR, which would carry out tests on
    every wavelet coefficient: EFDR choose which wavelets to test based on a
    criterion described in Shen et al. (2002). The package also provides
    elementary tools to interpolate spatially irregular data onto a grid of the
    required size. The work is based on Shen, X., Huang, H.-C., and Cressie, N.
    'Nonparametric hypothesis testing for a spatial signal.' Journal of the
    American Statistical Association 97.460 (2002): 1122-1140.
Variable selection and Bayesian effect fusion for categorical predictors in linear regression models. Effect fusion aims at the question which categories have a similar effect on the response and therefore can be fused to obtain a sparser representation of the model. Effect fusion and variable selection can be obtained either with a prior that has an interpretation as spike and slab prior on the level effect differences or with a sparse finite mixture prior on the level effects. The regression coefficients are estimated with a flat uninformative prior after model selection or model averaged. For posterior inference, an MCMC sampling scheme is used that involves only Gibbs sampling steps.
Use structural equation modeling to estimate average and
    conditional effects of a treatment variable on an outcome variable, taking into
    account multiple continuous and categorical covariates.
Graphical and tabular effect displays, e.g., of interactions, for 
  various statistical models with linear predictors.
Functions to test for changes of a response to a stimulus grouping relative 
  to a background or baseline response.
Notice: The package EffectStars2 provides a more up-to-date implementation of effect stars! EffectStars provides functions to visualize regression models with categorical response. The effects of the variables are plotted with star plots in order to allow for an optical impression of the fitted model.
Provides functions for the method of effect star. Effect stars can be used to visualize estimates of parameters corresponding to different groups, for example in multinomial logit models. Beside the main function 'effectstars' there exist methods for special objects, for example for 'vglm' objects from the 'VGAM' package.
In personalized medicine, one wants to know, for a given patient and his or her outcome for a predictor (pre-treatment variable), how likely it is that a treatment will be more beneficial than an alternative treatment. This package allows for the quantification of the predictive causal association (i.e., the association between the predictor variable and the individual causal effect of the treatment) and related metrics. Part of this software has been developed using funding provided from the European Union's 7th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.
An implementation for using efficient initials to compute the
    maximal eigenpair in R. It provides two algorithms to find the efficient
    initials under two cases: the tridiagonal matrix case and the general matrix
    case. Besides, it also provides algorithms for the next to the maximal eigenpair under
    these two cases.
Fitting a causal loglinear model and calculating the causal effects for a causal loglinear model with the multiplicative interaction or without the multiplicative interaction, obtaining the natural direct, indirect and the total effect. It calculates also the cell effect, which is a new interaction effect.
A collection of functions to compute the standardized 
  effect sizes for experiments (Cohen d, Hedges g, Cliff delta, Vargha-Delaney A). 
  The computation algorithms have been optimized to allow efficient computation even 
  with very large data sets.
The European Eddy Fluxes Database Cluster distributes fluxes of different Green House Gases measured mainly using the eddy covariance technique acquired in sites involved in EU projects but also single sites in Europe, Africa and others continents that decided to share their measurements in the database <http://gaia.agraria.unitus.it>. The package provides two functions to load and row-wise bind CSV files distributed by the database. Currently only L2, L3, and L4 (L=Level), half-hourly and daily (aggregation) files are supported.
Provides a function to check the
  importance of a feature based on a dependent classification
  variable. An ensemble of feature selection methods
  is used to determine the normalized importance value of
  all features. Combining these methods in one function
  (building the cumulative importance values) provides a 
  stable feature selection tool. This selection
  can also be viewed in a barplot using the barplot_fs() function
  and proved using the evaluation function efs_eval().
Functions for assigning Clarke or Parkes (Consensus) error grid
    zones to blood glucose values, and for plotting both types of error grids
    in both mg/mL and mmol/L units.
An easy-to-use implementation of the Engle-Granger
  two-step procedure for identifying pairs of cointegrated series.  It is geared towards 
  the analysis of pairs of securities.  Summary and plot functions are provided, 
  and the package is able to fetch closing prices of securities from Yahoo.
  A variety of unit root tests are supported, and an improved unit root test is included.
An implementation of hierarchical models
    for faecal egg count data to assess anthelmintic
    efficacy. Bayesian inference is done via MCMC sampling using Stan.
A small tool for Social Network Analysis, dealing with
        ego-centric network measures, including Burt's effective size
        and aggregate constraint and an import code suitable for a
        large number of adjacency matrices. A free web application is
        also available on http://www.egonet.associazionerospo.org
Statistics and graphics for streamflow history,
    water quality trends, and the statistical modeling algorithm: Weighted
    Regressions on Time, Discharge, and Season (WRTDS).
Collection of functions to evaluate uncertainty of results from
    water quality analysis using the Weighted Regressions on Time Discharge and
    Season (WRTDS) method. This package is an add-on to the EGRET package that
    performs the WRTDS analysis.
Sampling of risk sets in Cox regression, selections in 
        the Lexis diagram, bootstrapping. Parametric proportional 
        hazards fitting with left truncation and right censoring for 
        common families of distributions, piecewise constant hazards, 
        and discrete models. AFT regression for left truncated and 
        right censored data. Binary and Poisson regression for 
        clustered data, fixed and random effects with bootstrapping.
Extended and enhanced hierarchical logistic regression models (called Huisman-Olff-Fresco in biology, see Huisman et al. 1993 JVS <doi:10.1111/jvs.12050>) models. Response curves along one-dimensional gradients including no response, monotone, plateau, unimodal and bimodal models.
Software accompanying Gary King's book: A Solution to the Ecological Inference Problem. (1997). Princeton University Press. ISBN 978-0691012407.
An R wrapper to allow the user to query categories and Series IDs, and import data, from the EIA's API.
Compares estimates from three ecological inference routines, based on King (1997) <ISBN: 0691012407>, <http://gking.harvard.edu/eicamera/kinroot.html>; King et. al. (2004) <ISBN: 0521542804>, <http://gking.harvard.edu/files/abs/ecinf04-abs.shtml>.
Solves the ``inverse eigenvalue problem'' which is to
        generate a real-valued matrix that has the specified real
        eigenvalue spectrum.  It can generate infinitely many dense
        matrices, symmetric or asymmetric, with the given set of
        eigenvalues.  Algorithm can also generate stochastic and doubly
        stochastic matrices.
This package estimates the parameters of a model for
        symmetric relational data (e.g., the above-diagonal part of a
        square matrix), using a model-based eigenvalue decomposition
        and regression. Missing data is accomodated, and a posterior
        mean for missing data is calculated under the assumption that
        the data are missing at random. The marginal distribution of
        the relational data can be arbitrary, and is fit with an
        ordered probit specification.
Computes confidence intervals for the proportion explained by the first 1,2,k principal components, and computes confidence intervals for each eigenvalue. Both computations are done via nonparametric bootstrap.
Implementation of Efficient Inference of Local Ancestry 
	     using fused quantile regression and k-means classifier
Provides methods for analyzing RxC ecological contingency
        tables using the extreme case analysis, ecological regression,
        and Multinomial-Dirichlet ecological inference models.  Also
        provides tools for manipulating higher-dimension data objects.
EIVE performs a compact genetic algorithm search to reduce errors-in-variables bias in linear regression.
This package allows to use the hybrid Multinomial-Dirichlet-Model
    of Ecological Inference for estimating inner Cells of RxC-Tables. This was
    already implemented in the eiPack-package. eiwild-package now has the
    possibility to use individual level data to support the aggregate level
    data and using different Hyperpriori-Distributions.
Empirical likelihood (EL) inference for two-sample problems. The following statistics are included: the difference of two-sample means, smooth Huber estimators, quantile (qdiff) and cumulative distribution functions (ddiff), probability-probability (P-P) and quantile-quantile (Q-Q) plots as well as receiver operating characteristic (ROC) curves.
Functions for computing critical values and implementing the one-sided/two-sided EL tests.
Performs some enhanced variable selection algorithms 
  based on the least absolute shrinkage and selection operator for regression model.
O pacote desponibiliza funções para estimar modelos de componentes 
  não observados e determinar intervenções automaticamente. Com especial
  atenção para a replicação dos modelos utilizados na metodologia de calculo
  do resultado estrutural da SPE/MF.
  The package provides simple ways to estimates general unobserved components models
  and automatically detects intervenctions. It is specially useful to
  replicate Brazilian Ministry of Finance methodology to estimate income-output gap
  elasticities.
Connect to 'Elasticsearch', a 'NoSQL' database built on the 'Java'
    Virtual Machine. Interacts with the 'Elasticsearch' 'HTTP' 'API'
    (<https://www.elastic.co/products/elasticsearch>), including functions for
    setting connection details to 'Elasticsearch' instances, loading bulk data,
    searching for documents with both 'HTTP' query variables and 'JSON' based body
    requests. In addition, 'elastic' provides functions for interacting with 'APIs'
    for 'indices', documents, nodes, clusters, an interface to the cat 'API', and
    more.
Description: Uses k-fold cross-validation and elastic-net regularization to estimate the
              Ising model on binary data. Produces 3D plots of the cost function as a function
              of the tuning parameter in addition to the optimal network structure.
This package provides functions for fitting the entire
        solution path of the Elastic-Net and also provides functions
        for estimating sparse Principal Components. The Lasso solution
        paths can be computed by the same function. First version:
        2005-10.
A lightweight R interface to 'Elasticsearch' - a NoSQL search-engine and 
    column store database (see <https://www.elastic.co/products/elasticsearch> for more 
    information). This package implements a simple Domain-Specific Language (DSL) for indexing, 
    deleting, querying, sorting and aggregating data using 'Elasticsearch'.
This is a bizzare collection of functions written to do
        various sorts of statistical election audits.  There are also
        functions to generate simulated voting data, and simulated
        "truth" so as to do simulations to check charactaristics of
        these methods.
An extension of the elec package intended for use on
        election audits using stratified random samples.  Includes
        functions to obtain conservative and exact p-values, and
        functions that give sample sizes that may make election audits
        more efficient.
Offers a set of functions to easily download and clean 
    Brazilian electoral data from the Superior Electoral Court website. 
    Among others, the package retrieves data on local and
    federal elections for all positions (city councilor, mayor, state deputy,
    federal deputy, governor, and president) aggregated by
    state, city, and electoral zones.
Aims to facilitate the reduction of elemental microchemistry data from solid-phase LA-ICPMS analysis (laser ablation inductive coupled plasma mass spectrometry). The elementR package provides a reactive and user friendly interface for conducting all steps needed for an optimal data reduction while leaving maximum control for user.
Useful when reading the book above mentioned, in the
        documentation referred to as `the book'.
Several web services are available that provide access to elevation
             data. This package provides access to several of those services and 
             returns elevation data either as a SpatialPointsDataFrame from 
             point elevation services or as a raster object from raster 
             elevation services.  Currently, the package supports access to the
             Mapzen Elevation Service <https://mapzen.com/documentation/elevation/elevation-service/>, 
             Mapzen Terrain Service <https://mapzen.com/documentation/terrain-tiles/>,
             Amazon Web Services Terrain Tiles <https://aws.amazon.com/public-datasets/terrain/> and the USGS
             Elevation Point Query Service <http://ned.usgs.gov/epqs/>.
Provides R access to election results data. Wraps elex (https://github.com/newsdev/elex/), a Python package and command line tool for fetching and parsing Associated Press election results.
A tool to draw samples from a Empirical Likelihood Bayesian posterior
   of parameters using Hamiltonian Monte Carlo.
Correlation chart of two set (x and y) of data.  
  Using Quantiles.  Visualize the effect of factor.
This package contains various routines for drawing
        ellipses and ellipse-like confidence regions, implementing the
        plots described in Murdoch and Chow (1996), A graphical display
        of large correlation matrices, The American Statistician 50,
        178-180. There are also routines implementing the profile plots
        described in Bates and Watts (1988), Nonlinear Regression
        Analysis and its Applications.
A suite of elliptic and related functions including Weierstrass and
 Jacobi forms.  Also includes various tools for manipulating and
 visualizing complex functions.
Training and predict functions for SLFN ( Single
        Hidden-layer Feedforward Neural Networks ) using the ELM
        algorithm. ELM algorithm differs from the traditional
        gradient-based algorithms for very short training times ( it
        doesn't need any iterative tuning, this makes learning time
        very fast ) and there is no need to set any other parameters
        like learning rate, momentum, epochs, etc.
Training and prediction functions are provided for the Extreme Learning Machine algorithm (ELM). The ELM use a Single Hidden Layer Feedforward Neural Network (SLFN) with random generated weights and no gradient-based backpropagation. The training time is very short and the online version allows to update the model using small chunk of the training set at each iteration. The only parameter to tune is the hidden layer size and the learning function.
Perform model based gene clustering for genomics data generated from paired/matched design based on mixture of extended lognormal normal Bayesian hierarchical models (See Li Y, Morrow J, Raby B, Tantisira K, Weiss ST, Huang W, Qiu W. (2017), <doi:10.1371/journal.pone.0174602>).
A flexible framework for calculating Elo ratings and resulting
    rankings of any two-team-per-matchup system (chess, sports leagues, 'Go',
    etc.). This implementation is capable of evaluating a variety of matchups,
    Elo rating updates, and win probabilities, all based on the basic Elo
    rating system.
Allows calculating global scores for characteristics of visual stimuli. Stimuli are presented as sequence of pairwise comparisons ('contests'), during each of which a rater expresses  preference for one stimulus over the other. The algorithm for calculating global scores is based on Elo rating, which updates individual scores after each single pairwise contest. Elo rating is widely used to rank chess players according to their performance. Its core feature is that dyadic contests with expected outcomes lead to smaller changes of participants' scores than outcomes that were unexpected. As such, Elo rating is an efficient tool to rate individual stimuli when a large number of such stimuli are paired against each other in the context of experiments where the goal is to rank stimuli according to some characteristic of interest.
Calculate Elo ratings as means to describe animal dominance hierarchies
elrm implements a Markov Chain Monte Carlo algorithm to
        approximate exact conditional inference for logistic regression
        models. Exact conditional inference is based on the
        distribution of the sufficient statistics for the parameters of
        interest given the sufficient statistics for the remaining
        nuisance parameters. Using model formula notation, users
        specify a logistic model and model terms of interest for exact
        inference.
Flexible implementation of Elston-Stewart algorithm
Empirical likelihood ratio tests for the Yang and Prentice (short/long term hazards ratio) models. 
             Empirical likelihood tests within a Cox model, for parameters defined via 
			 both baseline hazard function and regression parameters.
We propose both a clear analysis strategy and a selection of tools to investigate microarray gene expression data. The most usual and relevant existing R functions were discussed, validated and gathered in an easy-to-use R package (EMA) devoted to gene expression microarray analysis. These functions were improved for ease of use, enhanced visualisation and better interpretation of results.
Do data management functions common in real-time monitoring (also called: ecological momentary assessment, experience sampling, micro-longitudinal) data, including creating power curves for multilevel data, centering on participant means and merging event-level data into momentary data sets where you need the events to correspond to the nearest data point in the momentary data. This is VERY early release software, and more features will be added over time.
Unsupervised, multivariate, binary clustering for meaningful annotation of data, taking into account the uncertainty in the data. A specific constructor for trajectory movement analysis yields behavioural annotation of trajectories based on estimated local measures of velocity and turning angle, eventually with solar position covariate as a daytime indicator, ("Expectation-Maximization Binary Clustering for Behavioural Annotation").
Tools to analyze the embryo growth and the sexualisation thermal reaction norms.
random walk Metropolis, Metropolis Hasting, parallel tempering, evolutionary Monte Carlo, temperature ladder construction and placement
Evolutionary Monte Carlo methods for clustering, temperature
        ladder construction and placement. This package implements methods
        introduced in Goswami, Liu and Wong (2007) <doi:10.1198/106186007X255072>.
        The paper above introduced probabilistic genetic-algorithm-style crossover
        moves for clustering. The paper applied the algorithm to several clustering
        problems including Bernoulli clustering, biological sequence motif
        clustering, BIC based variable selection, mixture of Normals clustering,
        and showed that the proposed algorithm performed better both as a sampler
        and as a stochastic optimizer than the existing tools, namely, Gibbs sampling,
        ``split-merge'' Metropolis-Hastings algorithm, K-means clustering, and the
        MCLUST algorithm (in the package 'mclust').
Computes and visualizes empirical joint distribution of multivariate data with optimized algorithms and multi-thread computation. There is a faster algorithm using dynamic programming to compute the whole empirical joint distribution of a bivariate data. There are optimized algorithms for computing empirical joint CDF function values for other multivariate data. Visualization is focused on bivariate data. Levelplots and wireframes are included.
EM algorithms and several efficient
        initialization methods for model-based clustering of finite
        mixture Gaussian distribution with unstructured dispersion
        in both of unsupervised and semi-supervised learning.
This package carries out empirical mode decomposition and Hilbert spectral
        analysis. For usage of EMD, see Kim and Oh, 2009 (Kim, D and Oh, H.-S. (2009) EMD: A Package for Empirical 
        Mode Decomposition and Hilbert Spectrum, The R Journal, 1, 40-46).
Auxiliary functions and data sets for "Ecological Models and Data", a book presenting maximum likelihood estimation and related topics for ecologists (ISBN 978-0-691-12522-0).
Functions that support estimating, assessing and mapping regional
    disaggregated indicators. So far, estimation methods comprise direct estimation
    and the model-based approach Empirical Best Prediction (see "Small area
    estimation of poverty indicators" by Molina and Rao (2010) <doi:10.1002/cjs.10051>), 
    as well as their precision estimates. The assessment of the used model
    is supported by a summary and diagnostic plots. For a suitable presentation of
    estimates, map plots can be easily created. Furthermore, results can easily be
    exported to excel.
Package providing calculation of Earth Mover's Distance
        (EMD).
Provides basic distribution functions for a mixture model of a Gaussian and exponential distribution.
A toolbox for designing and evaluating predictive models with
    resampling methods. The aim of this package is to provide a simple and
    efficient general framework for working with any type of prediction
    problem, be it classification, regression or survival analysis, that is
    easy to extend and adapt to your specific setting. Some commonly used
    methods for classification, regression and survival analysis are included.
Various Expectation-Maximization (EM) algorithms are implemented for item response theory
				(IRT) models. The current implementation includes IRT models for binary and ordinal
				responses, along with dynamic and hierarchical IRT models with binary responses. The
				latter two models are derived and implemented using variational EM.  Subsequent edits
				also include variational network and text scaling models.
The EmiStatR provides a fast and parallelised calculator to estimate combined wastewater emissions. 
    It supports the planning and design of urban drainage systems, without the requirement of 
    extensive simulation tools. The EmiStatR package implements modular R methods. This enables 
    to add new functionalities through the R framework. Furthermore, EmiStatR was implemented 
    with an interactive user interface with sliders and input data exploration.
Parse and serialize Ecological Metadata Language ('EML', see
    <https://en.wikipedia.org/wiki/Ecological_Metadata_Language> for
    more information) files into S4 objects.
Log-linear modeling is a popular method for the analysis of contingency table data. When the table is sparse, the data can fall on the boundary of the convex support, and we say that "the MLE does not exist" in the sense that some parameters cannot be estimated. However, an extended MLE always exists, and a subset of the original parameters will be estimable. The 'eMLEloglin' package determines which sampling zeros contribute to the non-existence of the MLE. These problematic zero cells can be removed from the contingency table and the model can then be fit (as far as is possible) using the glm() function.
The evolutionary model-based multiresponse approach (EMMA)
        is a novel methodology to process optimisation and product
        improvement. The approach is suitable to contexts in which the
        experimental cost and/or time limit the number of implementable
        trials.
End-member modelling analysis of grain-size data.
This package includes functions to read and write to an
        EMME/2 databank
For forming contrasts in the mixed effects for mixtures of linear mixed models fitted to the gene profiles.
Functions to fit finite mixture of multivariate canonical fundamental skew t (FM-CFUST) distributions, random sample generation, 2D and 3D contour plots.
EM algorithm for Fitting Mixture of Multivariate Skew Normal and Skew t Distributions. An implementation of the algorithm described in Wang, Ng, and McLachlan (2009) <doi:10.1109/DICTA.2009.88>.
Functions to fit finite mixture of unrestricted multivariate skew t (FM-uMST) model, random sample generation, discriminant analysis, 2D and 3D contour plots
Fit models of modularity to morphological landmarks. Perform model 
  selection on results. Fit models with a single within-module correlation or
  with separate within-module correlations fitted to each module.
The main functions are 'emmreml', and 'emmremlMultiKernel'. 'emmreml' solves a mixed model with known covariance structure using the 'EMMA' algorithm.  'emmremlMultiKernel' is a wrapper for 'emmreml' to handle multiple random components with known covariance structures. The function 'emmremlMultivariate' solves a multivariate gaussian mixed model with known covariance structure using the 'ECM' algorithm.
Collection of building blocks for the design and analysis
        of evolutionary multiobjective optimization algorithms.
An implementation of using emoji and fontawesome for using in both
    base and 'ggplot2' graphics.
Statistical tools for environmental and ecological surveys.
    Simulation-based power and precision analysis; detection probabilities from
    different survey designs; visual fast count estimation.
Fixation and saccade detection in eye movement recordings. This package implements a dispersion-based algorithm (I-DT) proposed by Salvucci & Goldberg (2000) which detects fixation duration and position.
Functions for estimating EMP (Expected Maximum Profit Measure) in Credit Risk Scoring and Customer Churn Prediction, according to Verbraken et al (2013, 2014) <DOI:10.1109/TKDE.2012.50>, <DOI:10.1016/j.ejor.2014.04.001>.
Routines for performing empirical calibration of observational
    study estimates. By using a set of negative control hypotheses we can
    estimate the empirical null distribution of a particular observational
    study setup. This empirical null distribution can be used to compute a
    calibrated p-value, which reflects the probability of observing an
    estimated effect size when the null hypothesis is true taking both random
    and systematic error into account.
Auxiliary functions for the DESeq2 package to simulate read counts according to the null hypothesis (i.e., with empirical sample size factors, per-gene total counts and dispersions, but without effects of predictor variables) and to compute the empirical false discovery rate.
Empirical likelihood ratio tests for means/quantiles/hazards
 	from possibly censored and/or truncated data. Now does regression too.
	This version contains some C code.
Calculates the p-value for a mean-type hypothesis (or multiple mean-type hypotheses) based on two samples with censored data.
Collection of functions for data analysis and
    editing. Most of them are related to benchmarking with prediction models.
Provides the analysis of variance table including the expected mean squares (EMS) for various types of experimental design. When some variables are random effects or we use special experimental design such as nested design, repeated-measures design, or split-plot design, it is not easy to find the appropriate test, especially denominator for F-statistic which depends on EMS.
Background correction of spectral like data. Handles variations in
  scaling, polynomial baselines, interferents, constituents and replicate variation.
  Parameters for corrections are stored for further analysis, and spectra are corrected
  accordingly.
The package provides functions to carry out a
        Goodness-of-fit test for discrete multivariate data. It is
        tested if a given observation is likely to have occurred under
        the assumption of an ab-initio model. A p-value can be
        calculated using different distance measures between observed
        and expected frequencies. A Monte Carlo method is provided to
        make the package capable of solving high-dimensional problems.
This package allows one to estimate the output of a computer program,
 as a function of the input parameters, without actually running it.
 The computer program is assumed to be a Gaussian process, whose
 parameters are estimated using Bayesian techniques that give a PDF of
 expected program output.  This PDF is conditional on a ``training set''
 of runs, each consisting of a point in parameter space and the model
 output at that point.  The emphasis is on complex codes that take
 weeks or months to run, and that have a large number of undetermined
 input parameters; many climate prediction models fall into this
 class.  The emulator essentially determines Bayesian posterior
 estimates of the PDF of the output of a model, conditioned on results
 from previous runs and a user-specified prior linear model.  A 
 working example is given in the help page for function `interpolant()',
 which should be the first point of reference.
Provides the next iteration of the EMU Speech 
    Database Management System (EMU-SDMS) with database management, data 
    extraction, data preparation and data visualization facilities.
Contains logic for the data-driven optimization of annotations via minimization of the entropy of variable group members over discrete variable clusters (see Frost, HR and Moore, JH (2014) <doi:10.1093/bioinformatics/btu110>).
Provides algorithms for the analysis of ecological networks.
Interconverts between ordered lists and compact string notation.  
 Useful for capturing code lists, and pair-wise codes and decodes, for text storage.
 Analogous to factor levels and labels. Generics encode() and decode()
 perform interconversion, while codes() and decodes() extract components of an encoding.
 The function encoded() checks whether something is interpretable as an encoding.
Likelihood-based approaches to estimate linear regression parameters and treatment effects in the presence of endogeneity. Specifically, this package includes James Heckman's classical simultaneous equation models-the sample selection model for outcome selection bias and hybrid model with structural shift for endogenous treatment. For more information, see the seminal paper of Heckman (1978) <DOI:10.3386/w0177> in which the details of these models are provided. This package accommodates repeated measures on subjects with a working independence approach. The hybrid model further accommodates treatment effect modification.
endogMNP is an R package that fits a Bayesian multinomial
        probit model with endogenous selection, which is sometimes
        called an endogenous switching model.  This can be used to
        model discrete choice data when respondents select themselves
        into one of several groups.  This package is based on the MNP
        package by Kosuke Imai and David A. van Dyk.  This package
        modifies their code.
Fit the hierarchical and non-hierarchical Bayesian measurement models proposed by Bullock, Imai, and Shapiro (2011) <DOI:10.1093/pan/mpr031> to analyze endorsement experiments.  Endorsement experiments are a survey methodology for eliciting truthful responses to sensitive questions.  This methodology is helpful when measuring support for socially sensitive political actors such as militant groups.  The model is fitted with a Markov chain Monte Carlo algorithm and produces the output containing draws from the posterior distribution.
Computes the expectation of the number of transmissions and receptions considering an End-to-End transport model with limited number of retransmissions per packet. It provides theoretical results and also estimated values based on Monte Carlo simulations.
E-statistics (energy) tests and statistics for multivariate and univariate inference,
             including distance correlation, one-sample, two-sample, and multi-sample tests for
             comparing multivariate distributions, are implemented. Measuring and testing
             multivariate independence based on distance correlation, partial distance correlation,
             multivariate goodness-of-fit tests, clustering based on energy distance, testing for
             multivariate normality, distance components (disco) for non-parametric analysis of
             structured data, and other energy statistics/methods are implemented.
Data published by the United States Federal Energy Regulatory Commission including
    electric company financial data, natural gas company financial data, 
    hydropower plant data, liquified natural gas plant data, oil company financial data
    natural gas company financial data, and natural gas storage field data.
Allow numbers to be presented in an English language
        version, one, two, three, ...
Datasets from Nelson, Coffin and Copeland "Introductory
        Statistics for Engineering Experimentation" (Elsevier, 2003)
        with sample code.
Analyzing English & European soccer results data from 1871-2016.
The company 'Enigma' (<http://enigma.io>) holds many public
    'datasets' from governments, companies, universities, and organizations.
    'Enigma' provides an 'API' for data, 'metadata', and statistics on each of
    the 'datasets'. 'enigma' is a client to interact with the 'Enigma' 'API',
    including getting the data and 'metadata' for 'datasets' in 'Enigma', as 
    well as collecting statistics on 'datasets'. In addition, you can download 
    a 'gzipped' 'csv' file of a 'dataset' if you want the whole 'dataset'. An 
    'API' key from 'Enigma' is required to use 'enigma'.
A set of tools for the analysis of ecological niche of species and calculation of habitat suitability maps.
Automatically partitions data into evaluation bins, executes ecological niche models across a range of settings, and calculates a variety of evaluation statistics.  Current version only implements ENMs with Maxent (Phillips et al. 2006)<doi:10.1016/j.ecolmodel.2005.03.026>.
The ENmisc package contains utility function for different
        purposes: mtapply and mlapply (multivariate version of tapply
        and lapply), wtd.boxplot (a boxplot with weights), and a visual
        interface to restructuring mosaic plots.
An algorithmic framework for measuring feature importance,
    outlier detection, model applicability domain evaluation,
    and ensemble predictive modeling with (sparse)
    partial least squares regressions.
An R package for joint statistical modelling of ChIP-seq data, accounting for technical/biological replicates, multiple conditions and different ChIP efficiencies of the individual experiments.
Provides an R interface to all 'Enrichr' databases, a web-based tool for analysing gene sets and returns any enrichment of common annotated biological functions. <http://amp.pharm.mssm.edu/Enrichr/>.
These programs are used for calculating enrichment
        factors, drawing enrichment curves to evaluate virtual
        screening approaches.
Provides the "enrich" method to enrich list-like R objects with new, relevant components. The current version has methods for enriching objects of class 'family', 'link-glm', 'lm' and 'glm'. The resulting objects preserve their class, so all methods associated with them still apply. The package also provides the 'enriched_glm' function that has the same interface as 'glm' but results in objects of class 'enriched_glm'. In addition to the usual components in a `glm` object, 'enriched_glm' objects carry an object-specific simulate method and functions to compute the scores, the observed and expected information matrix, the first-order bias, as well as model densities, probabilities, and quantiles at arbitrary parameter values. The package can also be used to produce customizable source code templates for the structured implementation of methods to compute new components and enrich arbitrary objects.
An implementation of the clustering methods of categorical data
    discussed in Amiri, S., Clarke, B., and Clarke, J. (2015). Clustering categorical 
    data via ensembling dissimilarity matrices.  Preprint <arXiv:1506.07930>.
Extensible S4 classes and methods for batch training of regression and classification algorithms such as Random Forest, Gradient Boosting Machine, Neural Network, Support Vector Machines, K-Nearest Neighbors, Penalized Regression (L1/L2), and Bayesian Additive Regression Trees. These algorithms constitute a set of 'base learners', which can subsequently be combined together to form ensemble predictions. This package provides cross-validation wrappers to allow for downstream application of ensemble integration techniques, including best-error selection. All base learner estimation objects are retained, allowing for repeated prediction calls without the need for re-training. For large problems, an option is provided to save estimation objects to disk, along with prediction methods that utilize these objects. This allows users to train and predict with large ensembles of base learners without being constrained by system RAM.
Bayesian Model Averaging to create probabilistic forecasts
        from ensemble forecasts and weather observations.
Extends the base classes and methods of EnsembleBase package for cross-validation-based integration of base learners. Default implementation calculates average of repeated CV errors, and selects the base learner / configuration with minimum average error. The package takes advantage of the file method provided in EnsembleBase package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. The package can be extended, e.g. by adding variants of the current implementation.
Ensemble Model Output Statistics to create probabilistic
        forecasts from ensemble forecasts and weather observations.
Extends the base classes and methods of 'EnsembleBase' package for Principal-Components-Regression-based (PCR) integration of base learners. Default implementation uses cross-validation error to choose the optimal number of PC components for the final predictor. The package takes advantage of the file method provided in 'EnsembleBase' package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. Users and developers can extend the package by extending the generic methods and classes provided in 'EnsembleBase' package as well as this package.
Extending the base classes and methods of EnsembleBase package for Penalized-Regression-based (Ridge and Lasso) integration of base learners. Default implementation uses cross-validation error to choose the optimal lambda (shrinkage parameter) for the final predictor. The package takes advantage of the file method provided in EnsembleBase package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. Users and developers can extend the package by extending the generic methods and classes provided in EnsembleBase package as well as this package.
Functions to use ensembles of several machine learning models
    specified in caret package.
Add simple runtime contracts to R values. These ensure that values
    fulfil certain conditions and will raise appropriate errors if they do not.
Measurement and partitioning of diversity, based on Tsallis entropy.
This package implements various estimators of entropy, such
  as the shrinkage estimator by Hausser and Strimmer, the maximum likelihood 
  and the Millow-Madow estimator, various Bayesian estimators, and the 
  Chao-Shen estimator.  It also offers an R interface to the NSB estimator.
  Furthermore, it provides functions for estimating Kullback-Leibler divergence,
  chi-squared, mutual information, and chi-squared statistic of independence.
  In addition there are functions for discretizing continuous random variables.
Contains methods for the estimation of Shannon's entropy, variants of Renyi's entropy, mutual information, Kullback-Leibler divergence, and generalized Simpson's indices. The estimators used have a bias that decays exponentially fast.
Rows of two matrices are compared for Shannon entropy,
     coefficient of variation, and expression. P-values can be requested for all metrics.
Tools for automatic model selection and diagnostics for Climate and Environmental data.  In particular the envcpt() function does automatic model selection between a variety of trend, changepoint and autocorrelation models.  The envcpt() function should be your first port of call.
Prints out information about the R working environment
    (system, R version,loaded and attached packages and versions) from a single
    function "env_doc()".  Optionally adds information on git repository,
    tags, commits and remotes (if available).
A collection of functions for microbial ecology and other
   applications of genomics and metagenomics. Companion package for the
   Enveomics Collection (Rodriguez-R, L.M. and Konstantinidis, K.T., 2016
   <DOI:10.7287/peerj.preprints.1900v1>).
Gas/Liquid Chromatography-Mass Spectrometer(GC/LC-MS) Data Analysis for Environmental Science. This package covered topics such as raw data process, molecular isotope ratio, matrix effects, batch correction, mass defect filter, etc. in environmental analysis.
Fast and very memory-efficient calculation of isotope patterns,
    subsequent convolution to theoretical envelopes (profiles) plus valley
    detection and centroidization or intensoid calculation. Batch processing,
    resolution interpolation, wrapper, adduct calculations and molecular
    formula parsing.
Sequential partitioning, clustering and peak detection of
    centroided LC-MS mass spectrometry data (.mzXML). Interactive result and raw
    data plot.
Generation of bioclimatic rasters that will be particularly 
	useful for species distribution modeling.
Methods to perform a Probabilistic Environmental Risk assessment from exposure to toxic substances - i.e. USEPA (1997) <https://www.epa.gov/risk/guiding-principles-monte-carlo-analysis> -.
Functions and datasets to support the book by Nhu D Le and James V Zidek, Springer (2006).
Envelope methodology and aster modeling are combined to provide users with precise estimation of expected Darwinian fitness.
A plot overlying the niche of multiple species is obtained: 1) to determine the niche conditions which favor a higher species richness, 2) to create a box plot with the range of environmental variables of the species, 3) to obtain a list of species in an area of the niche selected by the user and, 4) to estimate niche overlap among the species.
Graphical and statistical analyses of environmental data, with 
  focus on analyzing chemical concentrations and physical parameters, usually in 
  the context of mandated environmental monitoring.  Major environmental 
  statistical methods found in the literature and regulatory guidance documents, 
  with extensive help that explains what these methods do, how to use them, 
  and where to find them in the literature.  Numerous built-in data sets from 
  regulatory guidance documents and environmental statistics literature.  Includes 
  scripts reproducing analyses presented in the book "EnvStats:  An R Package for 
  Environmental Statistics" (Millard, 2013, Springer, ISBN 978-1-4614-8455-4, 
  <http://www.springer.com/book/9781461484554>).
A collection of nice plotting functions directly from a
        data.frame with limited customisation possibilities.
Analyzing censored variables usually requires the use of optimization algorithms. This package provides an alternative algebraic approach to the task of determining the expected value of a random censored variable with a known censoring point. Likewise this approach allows for the determination of the censoring point if the expected value is known. These results are derived under the assumption that the variable follows an Epanechnikov kernel distribution with known mean and range prior to censoring. Statistical functions related to the uncensored Epanechnikov distribution are also provided by this package.
Reads water network simulation data in 'Epanet' text-based
    '.inp' and '.rpt' formats into R. Also reads results from 'Epanet-msx'.
    Provides basic summary information and plots.  The README file has a 
    quick introduction. See <http://www2.epa.gov/water-research/epanet>
    for more information on the 'Epanet' software for modeling
    hydraulic and water quality behavior of water piping systems.
The main functions compute the expectation propagation approximation of a Bayesian probit/logit models with Gaussian prior. More information can be found in Chopin and Ridgway (2015). More models and priors should follow.
Functions for demographic and epidemiological analysis in
  the Lexis diagram, i.e. register and cohort follow-up data, in
  particular representation, manipulation and simulation of multistate 
  data - the Lexis suite of functions, which includes interfaces to
  'mstate', 'etm' and 'cmprsk' packages. 
  Also contains functions for Age-Period-Cohort and Lee-Carter
  modeling and a function for interval censored data and some useful
  functions for tabulation and plotting, as well as a number of
  epidemiological data sets.
This package contains elementary tools for analysis of
        common epidemiological problems, ranging from sample size
        estimation, through 2x2 contingency table analysis and basic
        measures of agreement (kappa, sensitivity/specificity).
        Appropriate print and summary statements are also written to
        facilitate interpretation wherever possible.  Source code is
        commented throughout to facilitate modification.  The target
        audience includes advanced undergraduate and graduate students
        in epidemiology or biostatistics courses, and clinical
        researchers.
Hierarchical Bayesian models for use in disease 
		freedom and disease prevalence studies, designed 
		with epidemiological applications in mind. The models 
		themselves are in the spirit of those presented in Branscum 
		et al. (2006) (see package documentation for full 
		reference). The helper functions and methods were designed 
		to make implementation and processing of the complex model 
		output relatively simple in application.
A collection of tools for representing epidemiological contact data, composed of case line lists and contacts between cases. Also contains procedures for data handling, interactive graphics, and statistics.
Routines for epidemiological contact tracing
    and visualisation of network of contacts.
Creates simple or stacked epidemic curves for hourly, daily, weekly or monthly incomes data.
The Economic Policy Institute (<http://www.epi.org/>) provides
    researchers, media, and the public with easily accessible, up-to-date, and
    comprehensive historical data on the American labor force. It is compiled
    from Economic Policy Institute analysis of government data sources. Use
    it to research wages, inequality, and other economic indicators over time
    and among demographic groups. Data is usually updated monthly.
Package for data exploration and result presentation.
             Full 'epicalc' package with data management functions is available 
             at the author's repository.
Mathematical models of infectious diseases in humans and animals.
    Both, deterministic and stochastic models can be simulated and plotted.
This package provides tools to quantify transmissibility
        throughout an epidemic from the analysis of time series of
        incidence.
Provides flexible model fitting used in epidemiological data
    analysis by a unified model specification, along with some data manipulation
    functions. This package covers fitting of variety models including Cox
    regression models, linear regression models, Poisson regression models, logistic
    models and others whose likelihood is expressed in negative binomial, gamma and
    Weibull distributions.
Provides tools for simulating from discrete-time individual level models for infectious disease data analysis. This epidemic model class contains spatial and contact-network based models with two disease types: Susceptible-Infectious (SI) and Susceptible-Infectious-Removed (SIR).
Tools for simulating mathematical models of infectious disease. 
    Epidemic model classes include deterministic compartmental models, stochastic 
    agent-based models, and stochastic network models. Network models use the
    robust statistical methods of exponential-family random graph models (ERGMs) 
    from the Statnet suite of software packages in R. Standard templates for epidemic 
    modeling include SI, SIR, and SIS disease types. EpiModel features 
    an easy API for extending these templates to address novel scientific research aims.
A collection of epidemic/network-related tools. Simulates transmission of diseases through contact networks. Performs Bayesian inference on network and epidemic parameters, given epidemic data.
Tools for the analysis of epidemiological data. Contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, and computing confidence intervals around incidence risk and incidence rate estimates. Miscellaneous functions for use in meta-analysis, diagnostic test interpretation, and sample size calculations.
Basic sensitivity analysis of the observed relative risks adjusting
    for unmeasured confounding and misclassification of the
    exposure/outcome, or both. It follows the bias analysis methods and
    examples from the book by Lash T.L, Fox M.P, and Fink A.K.
    "Applying Quantitative Bias Analysis to Epidemiologic Data",
    ('Springer', 2009).
A collection of R functions supporting the text book
  Modern Epidemiology, Second Edition, by Kenneth J.Rothman and Sander Greenland.
  ISBN 13: 978-0781755641  See <http://www.krothman.org/> for more information.
Produce one-dimensional density estimates using 
 exponential epi-splines. The user may incorporate soft information, by 
 imposing constraints that (i) require unimodality; (ii) require that the 
 density be monotone non-increase or non-decreasing; (iii) put upper bounds
 on first or second moments; (iv) bound the density's values at mesh points;
 (v) require that the estimate be continuous or continuously differentiable;
 and more.
An efficient multi-core package to reconstruct an underlying network of
             genomic signatures of high-dimensional epistatic selection from 
             partially observed genotype data. The phenotype that we consider is viability. 
			 The network captures the conditional dependent short- and long-range linkage 
			 disequilibrium structure of genomes and thus reveals aberrant marker-marker 
			 associations that are due to epistatic selection. We target on high-dimensional
			 genotype data where number of variables (markers) is larger than number of 
			 sample sizes (p >> n). The computations is memory-optimized using the sparse 
			 matrix output.
Constructing an epistemic model such that, for every player i and for every choice c(i) which is optimal, there is one type that expresses common belief in rationality.
Tools for training and practicing epidemiologists including methods for two-way and multi-way contingency tables.
Users can easily derive the calendar dates from epidemiological weeks, and vice versa.
Aim: Adjust the graphical parameters to create nicer
    longitudinal series plots. The default set of graphical parameters is very
    general, and can be improved upon when we are interested in plotting data
    points observed over time. Functions facilitate plotting those kind of
    series, univariate plots, bivariate plots (with vertical axis on both left
    and right hand sides), multivariate plots and plots which allow to examine
    whether a new observation is 'unusual' via construction and visualization
    of prediction intervals around it.
Evaluate a function over a data frame of expressions.
Estimates sparse matrices A or G using fast lasso regression from mRNA transcript levels Y and CNA profiles U. Two models are provided, EPoC A where
      AY + U + R = 0
  and EPoC G where
      Y = GU + E,
  the matrices R and E are so far treated as noise. For details see the reference and the manual page of `lassoshooting'.
The package performs analysis of polynomial regression in simple designs with quantitative treatments
Import data from 'Epidata' XML files '.epx' and convert it to R data structures.
Computation of the EQL for a given family of variance
        functions, Saddlepoint-approximations and related auxiliary
        functions (e.g. Hermite polynomials)
Transitioning from EQS to R for structural equation modeling (SEM)
    is made easier with a set of functions to convert .out files into R code.
    The EQS output can be converted into lavaan syntax and run in the R
    environment.  Other functions parse descriptive statistics and the covariance matrix
    from an EQS .out file.  A heat map plot of a covariance matrix is also
    included.
Analysis of experimental crosses to identify genes (called
        quantitative trait loci, QTLs) contributing to variation in
        quantitative traits.
Functions for examining measurement invariance via equivalence testing along with adjusted RMSEA(root mean square error of approximation; Steiger & Lind, 1980) cutoff values. In particular, a projection-based method is implemented to test the equality of latent factor means across groups without assuming the equality of intercepts.
Contains methods for observed-score linking
  and equating under the single-group, equivalent-groups,
  and nonequivalent-groups with anchor test(s) designs.
  Equating types include identity, mean, linear, general
  linear, equipercentile, circle-arc, and composites of
  these. Equating methods include synthetic, nominal
  weights, Tucker, Levine observed score, Levine true
  score, Braun/Holland, frequency estimation, and chained
  equating. Plotting and summary methods, and methods for
  multivariate presmoothing and bootstrap error estimation
  are also provided.
Computation of direct, chain and average (bisector) equating coefficients with standard errors using Item Response Theory (IRT) methods for dichotomous items. Test scoring can be performed by true score equating and observed score equating methods.
Provides statistical tests and graphics for assessing tests
        of equivalence.  Such tests have similarity as the alternative
	hypothesis instead of the null.  Sample data sets are included.
Provides an equivalent measure of partial correlation coefficients for high-dimensional Gaussian Graphical Models to learn and visualize the underlying relationships between variables from single or multiple datasets. You can refer to <doi:10.1080/01621459.2015.1012391> for more detail. Based on this method, the package also provides the method for constructing networks for Next Generation Sequencing Data.
Automated compound deconvolution, alignment across samples, and identification of metabolites by spectral library matching in Gas Chromatography - Mass spectrometry (GC-MS) untargeted metabolomics. Outputs a table with compound names, matching scores and the integrated area of the compound for each sample.
Expectile regression is a nice tool for estimating the conditional expectiles of a response variable given a set of covariates. This package implements a regression tree based gradient boosting estimator for nonparametric multiple expectile regression.
Provides a methodology simple and trustworthy for the analysis of extreme values and multiple threshold tests for a generalized Pareto distribution, together
    with an automatic threshold selection algorithm. See del Castillo, J, Daoudi, J and Lockhart, R (2014) <doi:10.1111/sjos.12037>.
Functions, datasets, and sample codes related to the book of 'Empirical Research in Economics: Growing up with R' by Dr. Changyou Sun are included. Marginal effects for binary or ordered choice models can be calculated. Static and dynamic Almost Ideal Demand System (AIDS) models can be estimated. A typical event analysis in finance can be conducted with several functions included.
An integrated set of tools to analyze and simulate networks based on exponential-family random graph models (ERGMs). 'ergm' is a part of the Statnet suite of packages for network analysis.
A set of extensions for the 'ergm' package to fit weighted networks whose edge weights are counts.
Utilities for managing egocentrically sampled network data and a wrapper around the 'ergm' package to facilitate ERGM inference and simulation from such data.
Integrates graphlet statistics based model terms for use in exponential-family random graph models ('ergm') as part of the 'statnet' suite of packages.
Data for use with the Sage Introduction to Exponential
        Random Graph Modeling text by Jenine K. Harris.  Network data
        set consists of 1283 local health departments and the
        communication links among them along with several attributes.
A set of extensions for the 'ergm' package to fit weighted networks whose edge weights are ranks.
A template package to demonstrate the use of user-specified statistics for use in "ergm" models as part of the "statnet" suite of packages.
Fits Rasch models (RM), linear logistic test models (LLTM), rating scale model (RSM), linear rating scale models (LRSM), partial credit models (PCM), and linear partial credit models (LPCM).  Missing values are allowed in the data matrix.  Additional features are the ML estimation of the person parameters, Andersen's LR-test, item-specific Wald test, Martin-Löf-Test, nonparametric Monte-Carlo Tests, itemfit and personfit statistics including infit and outfit measures, various ICC and related plots, automated stepwise item elimination, simulation module for various binary data matrices.
The functions provided in the package ERP are designed for the significance analysis of ERP data in a linear model framework. The possible procedures are either the collection of FDR or FWER controlling methods available in the generic function p.adjust, the same collection combined with a factor modeling of the time dependence among tests (see Sheu, Perthame, Lee and Causeur, 2016) and the Guthrie and Buchwald (1991) test.
A set of user-friendly functions to aid in organizing, plotting
    and analyzing event-related potential (ERP) data.  Provides an easy-to-learn
    method to explore ERP data. Should be useful to those without a background
    in computer programming, and to those who are new to ERPs (or new to the
    more advanced ERP software available). Emphasis has been placed on highly
    automated processes using functions with as few arguments as possible.
    Expects processed (cleaned) data.
This package is dedicated to the analysis of event-related potentials (ERPs). Event-related potentials are the measured brain responses associated with a specific sensory, cognitive, or motor event and are obtained from electroencephalographic (EEG) signal. The erpR package contains a series of functions for importing ERP data, computing traditional ERP measures, exploratory ERP analyses and plotting.
Builds and analyzes error intervals for a particular model predictions assuming different distributions for noise in the data.
Provides a function to convert existing R functions into "errorized" versions 
    with added logging and handling functionality when encountering errors or warnings. 
    The errorize function accepts an existing R function as its first argument and 
    returns a R function with the exact same arguments and functionality. However, 
    if an error or warning occurs when running that "errorized" R function, it will save a 
    .Rds file to the current working directory with the relevant objects and information 
    required to immediately recreate the error.
Errors in data can be located and removed using validation rules from package 'validate'.
Support for painless automatic error propagation in numerical operations.
Implementation of the Edge Selection Algorithm
These functions estimate the latent factors of a given matrix, no matter it is high-dimensional or not. It tries to first estimate the number of factors using bi-cross-validation and then estimate the latent factor matrix and the noise variances. For more information about the method, see Art B. Owen and Jingshu Wang 2015 archived article on factor model (http://arxiv.org/abs/1503.03515).
Tools for fitting the Extended Empirical Saddlepoint (EES) density.
Implementation of the web-based 'Practical Meta-Analysis Effect Size
    Calculator' from David B. Wilson (<http://www.campbellcollaboration.org/escalc/html/EffectSizeCalculator-Home.php>)
    in R. Based on the input, the effect size can be returned as standardized mean 
    difference, Hedges' g, correlation coefficient r or Fisher's transformation z, 
    odds ratio or log odds effect size.
The package can identify the dysregulated canonical pathways by investigating the changes of biological relationships of pathways in the context of gene expression data. (1) The ESEA package constructs a background set of edges by extracting pathway structure (e.g. interaction, regulation, modification, and binding etc.) from the seven public databases (KEGG; Reactome; Biocarta; NCI; SPIKE; HumanCyc; Panther) and the edge sets of pathways for each of the above databases. (2) The ESEA package can can quantify the change of correlation between genes for each edge based on gene expression data with cases and controls. (3) The ESEA package uses the weighted Kolmogorov-Smirnov statistic to calculate an edge enrichment score (EES), which reflects the degree to which a given pathway is associated the specific phenotype. (4) The ESEA package can provide the visualization of the results.
The package presents a "Scenarios" class containing
        general parameters, risk parameters and projection results.
        Risk parameters are gathered together into a ParamsScenarios
        sub-object. The general process for using this package is to
        set all needed parameters in a Scenarios object, use the
        customPathsGeneration method to proceed to the projection, then
        use xxx_PriceDistribution() methods to get asset prices.
Toolkit for Monte Carlo simulations of financial assets and
    interest rates models, involved in an Economic Scenario Generator (ESG).
    The underlying simulation loops have been implemented in C++.
Computes shrinkage estimators for regression problems. Selects
    penalty parameter by minimizing bias and variance in the effect estimate, where bias and variance are estimated from the posterior predictive distribution.
Functions for classification and group membership probability estimation are given. 
             The issue of non-informative features in the data is addressed by utilizing the ensemble method. 
             A few optimal models are selected in the ensemble from an initially large set of base k-nearest neighbours (KNN) models, generated on subset of features from the training data.
             A two stage assessment  is applied in selection of optimal models for the ensemble in the training function. 
             The prediction functions for classification and class membership probability estimation returns class outcomes and class membership probability estimates for the test data. 
             The package includes measure of classification error and brier score, for classification and probability estimation tasks respectively.
Misc functions programmed by Eduard Szöcs. 
    Provides read_regnie() to read gridded precipitation data from German Weather 
    Service  (DWD, see <http://www.dwd.de/> for more information).
Simultaneous modeling of the quantile and the expected shortfall of a response variable given 
    a set of covariates, see Dimitriadis and Bayer (2017) <arXiv:1704.02213>.
Provides an interface to e-Stat API, the one-stop service for official statistics of the Japanese government.
Estimates item and person parameters for the Samejima's Continuous Response Model (CRM), computes item fit residual statistics, draws empirical 3D item category response curves, draws theoretical 3D item category response curves, and generates data under the CRM for simulation studies.
Our method is a variable selection method to select active components in sparse linear mixed models in order to estimate the heritability. The selection allows us to reduce the size of the data sets which improves the accuracy of the estimations. Our package also provides a confidence interval for the estimated heritability.
Provides tools for determining estimability of linear functions of regression coefficients, 
  and 'epredict' methods that handle non-estimable cases correctly.
Can be used to simultaneously estimate networks (Gaussian Graphical Models) in data from different groups or classes via Joint Graphical Lasso. Tuning parameters are selected via information criteria (AIC / BIC / eBIC) or crossvalidation.
This package is intended to speedup the process of
        creating model-comparing tables common in Macroeconomics. The
        function collection stores the estimates of several models and
        formats it to a table of the form estimate starred and std.err.
        below. The default output is LaTeX but output to CSV for later
        editing in a spreadsheet tool is possible as well. It works for
        linear models (lm) and panel models from the "plm"-package
        (plm). Two further implemented functions "descsto" and
        "desctab" enable you to export descriptive statistics of
        data-frames and single variables to LaTeX and CSV.
Estimation of production functions by the Olley-Pakes and Levinsohn-Petrin methodologies. 
    The package aims to reproduce the results obtained with the Stata's user written opreg <http://www.stata-journal.com/article.html?article=st0145> and levpet <http://www.stata-journal.com/article.html?article=st0060> commands. 
    The first was originally proposed by Olley, G.S. and Pakes, A. (1996) <doi:10.2307/2171831>.
    And the second by Levinsohn, J. and Petrin, A. (2003) <doi:10.1111/1467-937X.00246>.
This package deals with the estimation of the jump rate for piecewise-deterministic Markov processes (PDMPs), from only one observation of the process within a long time. The main functions provide an estimate of this function. The state space may be discrete or continuous. The associated paper has been published in Scandinavian Journal of Statistics and is given in references. Other functions provide a method to simulate random variables from their (conditional) hazard rate, and then to simulate PDMPs.
A table function for descriptive statistics in tabular format, using variables in a data.frame. You can create simple or highly customized tables.
Fits the space-time Epidemic Type Aftershock Sequence
    (ETAS) model to earthquake catalogs using a stochastic declustering 
    approach. The ETAS model is a spatio-temporal marked point process
    model and a special case of the Hawkes process. The package is based 
    on a Fortran program by Jiancang Zhuang
    (available at <http://bemlar.ism.ac.jp/zhuang/software.html>),
    which is modified and translated into C++ and C such that it 
    can be called from R. Parallel computing with OpenMP is possible 
    on supported platforms.
Estimation of the components of an ETAS (Epidemic Type Aftershock Sequence) model for earthquake description. Non-parametric background seismicity can be estimated through FLP (Forward Likelihood Predictive), while parametric components are estimated through maximum likelihood. The two estimation steps are alternated until convergence is obtained. For each event the probability of being a background event is estimated and used as a weight for declustering steps. Many options to control the estimation process are present, together with some diagnostic tools. Some descriptive functions for earthquakes catalogs are  present; also plot, print, summary, profile methods are defined for main output (objects of class 'etasclass').
The package allows selecting those treatments of a one-way layout
 being equivalent to a control. Bonferroni adjusted "two one-sided t-tests"
 (TOST) and related simultaneous confidence intervals are given for both
 differences or ratios of means of normally distributed data. For the case of
 equal variances and balanced sample sizes for the treatment groups, the
 single-step procedure of Bofinger and Bofinger (1995) can be chosen. For
 non-normal data, the Wilcoxon test is applied.
Reliable and rapid ethnicity annotation from whole exome sequencing data.
A framework for loading medium-sized data from
    the Internet to a local or remote relational database management system.
    This package itself does not do much more than provide a toy example and set up
    the method structure. Packages that depend on this package will facilitate the
    construction and maintenance of their respective databases.
Provides functions to facilitate the use of the 'ff' package in
    interaction with big data in 'SQL' databases (e.g. in
    'Oracle', 'MySQL', 'PostgreSQL', 'Hive') by allowing easy importing directly into 'ffdf'
    objects using 'DBI', 'RODBC' and 'RJDBC'. Also contains some basic utility
    functions to do fast left outer join merging based on 'match', factorisation of data and a basic
    function for re-coding vectors.
Matrix of transition probabilities for any time-inhomogeneous multistate model with finite state space
Traditional meta-regression based method has been developed for using meta-analysis data, but it faced the challenge of inconsistent estimates. This package purpose a new statistical method to detect epistasis using incomplete information summary, and have proven it not only successfully let consistency of evidence, but also increase the power compared with traditional method (Detailed tutorial is shown in website).
Computes moments of univariate truncated t distribution.
    There is only one exported function, e_trunct(), which should be seen for details.
Client to interact with the 'etcd' 'key-value' data store
    <https://github.com/coreos/etcd>. Functions included for managing
    directories, keys, nodes, and getting statistics.
An eulerian path is a path in a graph which visits every edge exactly once. This package provides methods to handle eulerian paths or cycles.
If possible, generates exactly area-proportional Euler diagrams,
    or otherwise approximately proportional diagrams using numerical
    optimization. A Euler diagram is a generalization of a Venn diagram,
    relaxing the criterion that all interactions need to be represented.
An R Client for the Europe PubMed Central RESTful Web Service 
    (see <https://europepmc.org/RestfulWebService> for more information). It
    gives access to both metadata on life science literature and open access
    full texts. Europe PMC indexes all PubMed content and other literature
    sources including Agricola, a bibliographic database of citations to the
    agricultural literature, or Biological Patents. In addition to bibliographic
    metadata, the client allows users to fetch citations and reference lists.
    Links between life-science literature and other EBI databases, including
    ENA, PDB or ChEMBL are also accessible. No registration or API key is
    required. See the vignettes for usage examples.
This dataset contains population estimates of all European cities 
    with at least 10,000 inhabitants during the period 1500-1800. These data are
    adapted from Jan De Vries, "European Urbanization, 1500-1800" (1984).
Frequentist and Bayesian single arm trial design and sample size software. 
	Designs cover one and two binary endpoints with both single and multi-stage 
	methodology. The research leading to these results has received funding from the 
	European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement 
	number 278742 (Eurosarc).
Tools to download data from the Eurostat database
    <http://ec.europa.eu/eurostat> together with search and
    manipulation utilities.
Goodness-of-fit tests for selection of r in the r-largest order
    statistics (GEVr) model. Goodness-of-fit tests for threshold selection in the
    Generalized Pareto distribution (GPD). Random number generation and density functions
    for the GEVr distribution. Profile likelihood for return level estimation
    using the GEVr and Generalized Pareto distributions. P-value adjustments for
    sequential, multiple testing error control. Non-stationary fitting of GEVr and
    GPD.
Provides functions for evaluating (time series) model
	estimation methods. These facilitate Monte Carlo experiments of repeated
	simulations and estimations. Also provides methods for
	looking at the distribution of the results from these experiments,
	including model roots (which are an equivalence class invariant).
Parsing and evaluation tools that make it easy to recreate the
    command line behaviour of R.
Provides Some of the most important evaluation measures for evaluating a model. Just by giving the real and predicted class, measures such as accuracy, sensitivity, specificity, ppv, npv, fmeasure, mcc and ... will be returned.
An open source information security strategic risk analysis 
  toolkit based on the OpenFAIR taxonomy 
  <https://www2.opengroup.org/ogsys/catalog/C13K> and risk assessment standard 
  <https://www2.opengroup.org/ogsys/catalog/C13G>. Empowers an organization to 
  perform a quantifiable, repeatable, and data-driven review of its security 
  program.
Uses data and constants to calculate potential evapotranspiration (PET) and actual evapotranspiration (AET) from 21 different formulations including Penman, Penman-Monteith FAO 56, Priestley-Taylor and Morton formulations.
Different evidential distance-based classifiers, which provide
    outputs in the form of Dempster-Shafer mass functions. The methods are: the
    evidential K-nearest neighbor rule and the evidential neural network.
Various clustering algorithms that produce a credal partition,
    i.e., a set of Dempster-Shafer mass functions representing the membership of objects
    to clusters. The mass functions quantify the cluster-membership uncertainty of the objects.
    The algorithms are: Evidential c-Means (ECM), Relational Evidential c-Means (RECM),
    Constrained Evidential c-Means (CECM), EVCLUS and EK-NNclus.
Package for combining pieces of evidence
Extends simulation, distribution, quantile and density
        functions to univariate and multivariate parametric extreme
        value distributions, and provides fitting functions which
        calculate maximum likelihood estimates for univariate and
        bivariate maxima models, and for univariate and bivariate
        threshold models.
Provides functions for the bayesian analysis of extreme
        value models, using MCMC methods.
Compare lists (from 2 to infinite) and plot the results in a Venn diagram if (N<=4) with regulation details. It allows to produce a complete annotated file, merging the annotations of the compared lists. It is also possible to compute an overlaps table to show the overlaps proportions of all the couples of lists and draw proportional Venn diagrams.
Functions for setting up and analyzing event history data.
Event dataset repository including both real-life and artificial event logs. They can be used in combination with functionalities provided by the 'bupaR' packages 'edeaR', 'processmapR', etc.
Functions for analysis of rate changes in sequential events.
Stores, manipulates, aggregates and otherwise messes with event
    data from KEDS/TABARI or any other extraction tool with similar output
An R package for conducting event studies and a platform
             for methodological research on event studies.
Perform Event Studies from through our <http://EventStudyTools.com> Application Programming Interface, parse the results, visualize it, and / or use the results in further analysis.
Integrated Sensitivity Analysis of Evidence Factors in Observational Studies.
Functions for extreme value theory, which may be divided
        into the following groups; exploratory data analysis, block
        maxima, peaks over thresholds (univariate and bivariate), point
        processes, gev/gpd distributions.
The usual distribution functions, maximum likelihood inference and
    model diagnostics for univariate stationary extreme value mixture models
    are provided. Kernel density estimation including various boundary
    corrected kernel density estimation methods and a wide choice of kernels,
    with cross-validation likelihood based bandwidth estimator.
    Reasonable consistency with the base functions in the 'evd' package is
    provided, so that users can safely interchange most code.
Comparative analysis of continuous traits influencing discrete states, and utility tools to facilitate comparative analyses.  Implementations of ABBA/BABA type statistics to test for introgression in genomic data. Wright-Fisher, phylogenetic tree, and statistical distribution Shiny interactive simulations for use in teaching.
Provides functions for covariance matrix comparisons, estimation
    of repeatabilities in measurements and matrices, and general evolutionary
    quantitative genetics tools.
An implementation of the evolvability parameters defined in Hansen and Houle (2008).
Evolutionary process simulation using geometric morphometric data. Manipulation of landmark data files (TPS), shape plotting and distances plotting functions.
The EvoPER, Evolutionary Parameter Estimation for 'Repast Simphony'
    Agent-Based framework (<https://repast.github.io/>), provides optimization
    driven parameter estimation methods based on evolutionary computation
    techniques which could be more efficient and require, in some cases,
    fewer model evaluations than other alternatives relying on experimental design.
Uses maximum likelihood to estimate rates of trait evolution across environmental gradients.
Computes extreme value index (EVI) estimate for heavy tailed models by Mean of order p (MOP) 
	     and peaks over random threshold (PORT) Hill methodologies. 
             Besides, also computes moment, generalised Hill and mixed moment estimates for EVI. 
             Compute high quantile or value-at-risk (VaR) based on above EVI estimates.
Commonly used classification and regression tree methods like the CART algorithm
             are recursive partitioning methods that build the model in a forward stepwise search.
	     Although this approach is known to be an efficient heuristic, the results of recursive
	     tree methods are only locally optimal, as splits are chosen to maximize homogeneity at
	     the next step only. An alternative way to search over the parameter space of trees is
	     to use global optimization methods like evolutionary algorithms. The 'evtree' package
	     implements an evolutionary algorithm for learning globally optimal classification and
	     regression trees in R. CPU and memory-intensive tasks are fully computed in C++ while
	     the 'partykit' package is leveraged to represent the resulting trees in R, providing
	     unified infrastructure for summaries, visualizations, and predictions.
An implementation of a large number of the goodness-of-fit tests for the exponential and Weibull distributions classified into families: the tests based on the empirical distribution function, the tests based on the probability plot, the tests based on the normalized spacings, the tests based on the Laplace transform and the likelihood-based tests.
Performs unconditional exact tests and power calculations for 2x2 contingency tables. Unconditional exact tests are often more powerful than conditional exact tests and asymptotic tests.
Calculates conditional exact tests (Fisher's exact test, Blaker's exact test, or  exact McNemar's test) and unconditional exact tests (including score-based tests on differences in proportions, ratios of proportions, and odds ratios, and Boshcloo's test) with appropriate matching confidence intervals, and provides power and sample size calculations. Also gives melded confidence intervals for the binomial case.
Calculates exact tests and confidence intervals for one-sample binomial and one- or two-sample Poisson cases.
This is a package for exact Confidence Intervals for the
        difference between two independent or dependent proportions.
Monte Carlo and MCMC goodness of fit tests for log-linear
        models
Perform exact fixed effect meta analysis for rare events data without the need of artificial continuity correction.
ExactPath implements an algorithm for exact LASSO
        solution. Two methods are provided to print and visualize the
        whole solution paths. Use ?ExactPath to see an introduction.
        Packages ncvreg and lars are required so that their data sets
        can be used in examples.
Computes exact conditional p-values and quantiles using an
 implementation of the Shift-Algorithm by Streitberg & Roehmel.
Take the examples written in your documentation of functions and 
    use them to create shells (skeletons which must be manually completed by
    the user) of test files to be tested with the 'testthat' package.
Automatic generation of exams based on exercises in Markdown or LaTeX format,
	possibly including R code for dynamic generation of exercise elements.
	Exercise types include single-choice and multiple-choice questions, arithmetic problems,
	string questions, and combinations thereof (cloze). Output formats include standalone
	files (PDF, HTML, Docx, ODT, ...), Moodle XML, QTI 1.2 (for OLAT/OpenOLAT), QTI 2.1,
	Blackboard, ARSnova, and TCExam. In addition to fully customizable PDF exams, a
	standardized PDF format is provided that can be printed, scanned, and automatically evaluated.
Tools for constructing confidence regions for exceedance regions
    and contour lines.
Allows access to data in running instance of Microsoft Excel
    (e. g. 'xl[a1] = xl[b2]*3' and so on). Graphics can be transferred with
    'xl[a1] = current.graphics()'. There is an Excel workbook with examples of
    calling R from Excel in the 'doc' folder. It tries to keep things as
    simple as possible - there are no needs in any additional
    installations besides R, only 'VBA' code in the Excel workbook.
    Microsoft Excel is required for this package.
This is an R interface to the
    python package 'excerpts' (<https://pypi.python.org/pypi/excerpts>).
Implementation of a function which calculates the empirical excess mass 
	for given \eqn{\lambda} and given maximal number of modes (excessm()). Offering 
	powerful plot features to visualize empirical excess mass (exmplot()). This 
	includes the possibility of drawing several plots (with different maximal 
	number of modes / cut off values) in a single graph.
Interactive tools to explore topographic-like data
    sets.  Such data sets take the form of a matrix in which the rows and
    columns provide location/frequency information, and the matrix elements
    contain altitude/response information.  Such data is found in cartography,
    2D spectroscopy and chemometrics.  The functions in this package create
    interactive web pages showing the contoured data, possibly with
    slices from the original matrix parallel to each dimension. The interactive
    behavior is created using the 'D3.js' 'JavaScript' library by Mike Bostock.
Functions that compute probabilistic excursion sets, contour credibility regions, contour avoiding regions, and simultaneous confidence bands for latent Gaussian random processes and fields. The package also contains functions that calculate these quantities for models estimated with the INLA package.
Extracts Exchangeable Image File Format (EXIF) metadata, such as camera make and model, ISO speed and the date-time
             the picture was taken on, from JPEG images. Incorporates the 'easyexif' (https://github.com/mayanklahiri/easyexif)
             library.
Reads EXIF data using ExifTool <http://www.sno.phy.queensu.ca/~phil/exiftool/>
    and returns results as a data frame.
    ExifTool is a platform-independent Perl library plus a command-line
    application for reading, writing and editing meta information in a wide variety
    of files. ExifTool supports many different metadata formats including EXIF,
    GPS, IPTC, XMP, JFIF, GeoTIFF, ICC Profile, Photoshop IRB, FlashPix, AFCP and
    ID3, as well as the maker notes of many digital cameras by Canon, Casio, FLIR,
    FujiFilm, GE, HP, JVC/Victor, Kodak, Leaf, Minolta/Konica-Minolta, Motorola, Nikon,
    Nintendo, Olympus/Epson, Panasonic/Leica, Pentax/Asahi, Phase One, Reconyx, Ricoh,
    Samsung, Sanyo, Sigma/Foveon and Sony.
Calls copy number variants (CNVs) from targeted sequence data, typically exome sequencing experiments designed to identify the genetic basis of Mendelian disorders.
For a given metabolic model with well formed Gene-Protein-Reaction (GPR) associations and an expressionSet with their associated gene expression values, this package converts gene expression values to the FBA boundaries for each reaction based in the boolean rules described in its associated GPR.
Generates feature matrix outputs from R object inputs
    using a variety of expansion functions.  The generated
    feature matrices have applications as inputs
    for a variety of machine learning algorithms.
    The expansion functions are based on coercing the input
    to a matrix, treating the columns as features and
    converting individual columns or combinations into blocks of
    columns.
    Currently these include expansion of columns by
    efficient sparse embedding by vectors of lags,
    quadratic expansion into squares and unique products,
    powers by vectors of degree,
    vectors of orthogonal polynomials functions,
    and block random affine projection transformations (RAPTs).
    The transformations are
    magrittr- and cbind-friendly, and can be used in a
    building block fashion.  For instance, taking the cos() of
    the output of the RAPT transformation generates a
    stationary kernel expansion via Bochner's theorem, and this
    expansion can then be cbind-ed with other features.
    Additionally, there are utilities for replacing features,
    removing rows with NAs,
    creating matrix samples of a given distribution,
    a simple wrapper for LASSO with CV,
    a Freeman-Tukey transform,
    generalizations of the outer function,
    matrix size-preserving discrete difference by row,
    plotting, etc.
Expanding Ploidy and Allele Frequency on Nested Subpopulations (expands) characterizes coexisting subpopulations in a single tumor sample using copy number and allele frequencies derived from exome- or whole genome sequencing input data (<http://www.ncbi.nlm.nih.gov/pubmed/24177718>). The model detects coexisting genotypes by leveraging run-specific tradeoffs between depth of coverage and breadth of coverage. This package predicts the number of clonal expansions, the size of the resulting subpopulations in the tumor bulk, the mutations specific to each subpopulation, tumor purity and phylogeny. The main function runExPANdS() provides the complete functionality needed to predict coexisting subpopulations from single nucleotide variations (SNVs) and associated copy numbers.  The robustness of subpopulation predictions increases with the number of mutations provided. It is recommended that at least 200 mutations are used as input to obtain stable results. Updates in version 2.0 include: (i) copy-neutral LOH are now modelled; (ii) more robust calculation of cell frequency probabilities from kernel density estimates instead of Gaussian mixtures. Further documentation and FAQ available at <http://dna-discovery.stanford.edu/software/expands>.
Modular implementation of the Differential Evolution algorithm for
    experimenting with different types of operators.
Package for analysis of simple experimental designs (CRD,
        RBD and LSD), experiments in double factorial schemes (in CRD
        and RBD), experiments in a split plot in time schemes (in CRD
        and RBD), experiments in double factorial schemes with an
        additional treatment (in CRD and RBD), experiments in triple
        factorial scheme (in CRD and RBD) and experiments in triple
        factorial schemes with an additional treatment (in CRD and
        RBD), performing the analysis of variance and means comparison
        by fitting regression models until the third power
        (quantitative treatments) or by a multiple comparison test,
        Tukey test, test of Student-Newman-Keuls (SNK), Scott-Knott,
        Duncan test, t test (LSD) and Bonferroni t test (protected LSD)
        - for qualitative treatments.
Pacote destinado a analise de delineamentos experimentais
        simples (DIC, DBC e DQL), experimentos em esquema de fatorial
        duplo (em DIC e DBC), experimentos em esquema de parcelas
        subdivididas no tempo (em DIC e DBC), experimentos em esquema
        de fatorial duplo com um tratamento adicional (em DIC e DBC),
        experimentos em esquema de fatorial triplo (em DIC e DBC) e
        experimentos em esquema de fatorial triplo com um tratamento
        adicional (em DIC e DBC); realizando a analise de variancia e
        comparacao de medias pelo ajuste de modelos de regressao ate o
        terceiro grau (tratamentos quantitativos) ou por testes de
        comparacao multipla: teste de Tukey, teste de
        Student-Newman-Keuls (SNK), teste de Scott-Knott, teste de
        Duncan, teste t (LSD), teste t de Bonferroni (LSD protegido) e
        teste Bootstrap - tratamentos qualitativos.
Expectile and quantile regression of models with nonlinear effects
  e.g. spatial, random, ridge using least asymmetric weighed squares / absolutes
  as well as boosting; also supplies expectiles for common distributions.
The package provides various statistical methods for
        designing and analyzing randomized experiments. One main
        functionality of the package is the implementation of
        randomized-block and matched-pair designs based on possibly
        multivariate pre-treatment covariates. The package also
        provides the tools to analyze various randomized experiments
        including cluster randomized experiments, randomized
        experiments with noncompliance, and randomized experiments with
        missing data.
Expert opinion (or judgment) is a body of techniques to
  estimate the distribution of a random variable when data is scarce
  or unavailable. Opinions on the quantiles of the distribution are
  sought from experts in the field and aggregated into a final
  estimate. The package supports aggregation by means of the Cooke,
  Mendel-Sheridan and predefined weights models.
The exponential integrals E_1(x), E_2(x), E_n(x) and
  Ei(x), and the incomplete gamma function G(a, x) defined for
  negative values of its first argument. The package also gives easy
  access to the underlying C routines through an API; see the package
  vignette for details. A test package included in sub-directory
  example_API provides an implementation. C routines derived from the
  GNU Scientific Library <https://www.gnu.org/software/gsl/>.
Generates explanations for classification and regression models and visualizes them.
 Explanations are generated for individual predictions as well as for models as a whole. Two explanation methods
 are included, EXPLAIN and IME. The EXPLAIN method is fast but might miss explanations expressed redundantly
 in the model. The IME method is slower as it samples from all feature subsets.
 For the EXPLAIN method see Robnik-Sikonja and Kononenko (2008) <doi:10.1109/TKDE.2007.190734>, 
 and the IME method is described in Strumbelj and Kononenko (2010, JMLR, vol. 11:1-18).
 All models in package 'CORElearn' are natively supported, for other prediction models a wrapper function is provided 
 and illustrated for models from packages 'randomForest', 'nnet', and 'e1071'.
Shiny interfaces and graphical functions for multivariate analysis results exploration.
Simplifies some complicated and labor intensive processes involved in exploring and explaining data.  Allows you to quickly and efficiently visualize the interaction between variables and simplifies the process of discovering covariation in your data.  Also includes some convenience features designed to remove as much redundant typing as possible.
Computation of the matrix exponential, logarithm, sqrt,
  and related quantities.
An R-interface to the Fortran package Expokit.
ExPosition is for descriptive (i.e., fixed-effects) multivariate analysis with the singular value decomposition.
Calculates the density dependent likelihood of a phylogenetic tree. It takes branching and sampling times as an argument and integrates the likelihood function over the whole tree.
Tools and data to accompany Schlicht, Valcu and Kempenaers "Spatial patterns of extra-pair paternity: beyond paternity gains and losses"
Allows to calculate the probabilities  of occurrences of an event in a 
 great number of repetitions of Bernoulli experiment, through the application of  
 the local and the integral theorem of De Moivre Laplace, and the theorem of Poisson. 
 Gives the possibility to show the results graphically and analytically, and to compare
 the results obtained by the application of the above  theorems with those calculated by
 the direct application of the Binomial formula. Is basically useful for educational
 purposes.
Supervised machine learning has an increasingly important role in biological
    studies. However, the sheer complexity of classification pipelines poses a significant
    barrier to the expert biologist unfamiliar with machine learning. Moreover,
    many biologists lack the time or technical skills necessary to establish their own
    pipelines. This package introduces a framework for the rapid implementation of
    high-throughput supervised machine learning built with the biologist user in mind.
    Written by biologists, for biologists, this package provides a user-friendly interface
    that empowers investigators to execute state-of-the-art binary and multi-class
    classification, including deep learning, with minimal programming
    experience necessary.
Data sets from the book "Forecasting with exponential smoothing: the state space approach" by 
	Hyndman, Koehler, Ord and Snyder (Springer, 2008).
Package provides tabulation functions with support for 'SPSS'-style 
        labels, multiple / nested banners, weights, multiple-response variables 
        and significance testing. There are facilities for nice output of tables 
        in 'knitr', R notebooks, 'Shiny' and 'Jupyter' notebooks. Proper methods 
        for labelled variables add value labels support to base R functions and to 
        some functions from other packages. Additionally, the package offers 
        useful functions for data processing in marketing research / social 
        surveys - popular data transformation functions from 'SPSS' Statistics 
        ('RECODE', 'COUNT', 'COMPUTE', 'DO IF', etc.) and 'Excel' ('COUNTIF', 
        'VLOOKUP', etc.). Package is intended to help people to move data 
        processing from 'Excel'/'SPSS' to R.
Tests for the composite hypothesis of exponentiality
Analysis of experimental results and automatic report generation in both interactive HTML and LaTeX. This package ships with a rich interface for data modeling and built in functions for the rapid application of statistical tests and generation of common plots and tables with publish-ready quality.
Estimation for high conditional quantiles based on quantile regression.
The package provides tools for botanists, plant taxonomists,
    curators of plant genebanks and perhaps other biological collections.
A consistent, unified and extensible
    framework for estimation of parameters for probability distributions, including 
    parameter estimation procedures that allow for weighted samples; the current set of distributions included are: the standard beta, The four-parameter beta, Burr, gamma, Gumbel, Johnson SB and SU, Laplace, logistic, normal, symmetric truncated normal, truncated normal, symmetric-reflected truncated beta, standard symmetric-reflected truncated beta, triangular, uniform, and Weibull distributions; decision criteria and selections based on these decision criteria.
If 'dplyr' is a grammar for data manipulation, 'extdplyr' is like
    a short paragraph written in 'dplyr'. 'extdplyr' extends 'dplyr' and
    'tidyr' verbs to some common "routines" that manipulate data sets. It uses
    the same interface and preserves all the features from 'dplyr', has good 
    performance, and supports various data sources.
If one treated group is matched to one control reservoir in two different ways to produce two sets of treated-control matched pairs, then the two control groups may be entwined, in the sense that some control individuals are in both control groups.  The exterior match is used to compare the two control groups.
This is a package containing the function extfunnel()
        which produces a funnel plot including additional augmentations
        such as statistical significance contours and heterogeneity
        contours.
The package estimates coefficients of extended LASSO penalized linear regression and generalized linear models. Currently lasso and elastic net penalized linear regression and generalized linear models are considered. The package currently utilizes an accurate approximation of L1 penalty and then a modified Jacobi algorithm to estimate the coefficients. There is provision for plotting of the solutions and predictions of coefficients at given values of lambda. The package also contains functions for cross validation to select a suitable lambda value given the data. The package also provides a function for estimation in fused lasso penalized linear regression.
This package tests for differences in minor allele
        frequency between groups and is based on an extra-binomial
        variation model for pooled sequencing data.
Categorical Data Analysis and Visualization.
Density, distribution function, quantile function
    and random generation for a number of univariate
    and multivariate distributions. This package implements the
    following distributions: Bernoulli, beta-binomial, beta-negative
    binomial, beta prime, Bhattacharjee, Birnbaum-Saunders,
    bivariate normal, bivariate Poisson, categorical, Dirichlet,
    Dirichlet-multinomial, discrete gamma, discrete Laplace,
    discrete normal, discrete uniform, discrete Weibull, Frechet,
    gamma-Poisson, generalized extreme value, Gompertz,
    generalized Pareto, Gumbel, half-Cauchy, half-normal, half-t,
    Huber density, inverse chi-squared, inverse-gamma, Kumaraswamy,
    Laplace, logarithmic, Lomax, multivariate hypergeometric,
    multinomial, negative hypergeometric, non-standard t,
    non-standard beta, normal mixture, Poisson mixture, Pareto,
    power, reparametrized beta, Rayleigh, shifted Gompertz, Skellam,
    slash, triangular, truncated binomial, truncated normal,
    truncated Poisson, Tukey lambda, Wald, zero-inflated binomial,
    zero-inflated negative binomial, zero-inflated Poisson.
Tools to using fonts other than the standard PostScript fonts.
    This package makes it easy to use system TrueType fonts and with PDF or
    PostScript output files, and with bitmap output files in Windows. extrafont
    can also be used with fonts packaged specifically to be used with, such as
    the fontcm package, which has Computer Modern PostScript fonts with math
    symbols. See https://github.com/wch/extrafont for instructions and
    examples.
Package for holding the database for the extrafont package
Classification and regression based on an ensemble of decision trees. The package also provides extensions of ExtraTrees to multi-task learning and quantile regression. Uses Java implementation of the method.
An implementation of Extreme Bounds Analysis (EBA), a global sensitivity analysis that examines the robustness of determinants in regression models. The package supports both Leamer's and Sala-i-Martin's versions of EBA, and allows users to customize all aspects of the analysis.
Extreme value theory, nonparametric kernel estimation, tail
    conditional probabilities, extreme conditional quantile, adaptive estimation,
    quantile regression, survival probabilities.
Functions for performing extreme value analysis.
Code to fit, plot and compare several (extreme value)
    distribution functions. Can also compute (truncated) distribution quantile estimates and
    draw a plot with return periods on a linear scale.
Detect outliers in one-dimensional data.
Estimation of the sample univariate, cross and return time extremograms. The package can also adds empirical confidence bands to each of the extremogram plots via a permutation procedure under the assumption that the data are independent. Finally, the stationary bootstrap allows us to construct credible confidence bands for the extremograms.
It implements the subjectively censored Weibull MLE and censored Weibull mixture methods for the lower quantile estimation. Quantile estimates from these two methods are robust to model misspecification in the lower tail. It also includes functions to evaluation the standard error of the resulting quantile estimates. Also, the methods here can be used to fit the Weibull or Weibull mixture for the Type-I or Type-II right censored data.
Eyelink eye trackers output a horrible mess, typically under
    the form of a '.asc' file. The file in question is an assorted collection of
    messages, events and raw data. This R package will attempt to make sense of it.
Misc function for working with eyetracking data
A set of tools that address tasks along the pipeline from raw
    data to analysis and visualization for eye-tracking data. Offers several
    popular types of analyses, including linear and growth curve time analyses,
    onset-contingent reaction time analyses, as well as several non-parametric
    bootstrapping approaches.
Facilitates easy analysis of factorial experiments, including
    purely within-Ss designs (a.k.a. "repeated measures"), purely between-Ss
    designs, and mixed within-and-between-Ss designs. The functions in this package
    aim to provide simple, intuitive and consistent specification of data analysis
    and visualization. Visualization functions also include design visualization for
    pre-analysis data auditing, and correlation matrix visualization. Finally, this
    package includes functions for non-parametric analysis, including permutation
    tests and bootstrap resampling. The bootstrap function obtains predictions
    either by cell means or by more advanced/powerful mixed effects models, yielding
    predictions and confidence intervals that may be easily visualized at any level
    of the experiment's design.
Because fungicide resistance is an important phenotypic trait for
    fungi and oomycetes, it is necessary to have a standardized method of
    statistically analyzing the Effective Concentration (EC) values. This
    package is designed for those who are not terribly familiar with R to be
    able to analyze and plot an entire set of isolates using the 'drc' package.
This package implements a simplified version of least
        squares, and logistic regression for efficiently selecting the
        significant non-additive interactions between two variables.
An extension of 'knitr' that adds flexibility in several
    ways. One common source of frustration with 'knitr' is that it assumes
    the directory where the source file lives should be the working directory,
    which is often not true. 'ezknitr' addresses this problem by giving you
    complete control over where all the inputs and outputs are, and adds several
    other convenient features to make rendering markdown/HTML documents easier.
ezsim provides a handy way to run simulation and examine its result
Functions that simplify the process of generating print-ready data summary using 'dplyr' syntax.
Frequentist assisted by Bayes (FAB) confidence interval
    construction. See 'Adaptive multigroup confidence intervals with constant
    coverage' by Yu and Hoff <https://arxiv.org/abs/1612.08287>.
Fast covariance estimation for sparse functional data or longitudinal data for the paper Xiao et al., Stat. Comput., <doi:10.1007/s11222-017-9744-8>.
Provides an interface to the Facebook API and builds collections of elements that reflects the graph architecture of Facebook.
  See <https://developers.facebook.com/docs/graph-api> for more information.
This method tries to explain the gene-wise treatment response ratios in terms of the latent pathways. It uses bayesian sparse factor modeling to infer the loadings (weights) of each pathway on its associated probesets as well as the latent factor activity levels for each treatment.
The package estimates Factor Mixture Analysis via the EM
        algorithm
Perform Maximum Likelihood Factor analysis on a covariance matrix or data matrix.
Multivariate exploration of a data table with factorial
        analysis and cluster methods.
Provides some easy-to-use functions to extract and visualize the
    output of multivariate data analyses, including 'PCA' (Principal Component
    Analysis), 'CA' (Correspondence Analysis), 'MCA' (Multiple Correspondence
    Analysis), 'FAMD' (Factor Analysis of Mixed Data), 'MFA' (Multiple Factor Analysis) and 'HMFA' (Hierarchical Multiple
    Factor Analysis) functions from different R packages. It contains also functions
    for simplifying some clustering analysis steps and provides 'ggplot2' - based
    elegant data visualization.
Brings a set of tools to help and automatically realise the description of principal component analyses (from 'FactoMineR' functions). Detection of existing outliers, identification of the informative components, graphical views and dimensions description are performed threw dedicated functions. The Investigate() function performs all these functions in one, and returns the result as a report document (Word, PDF or HTML).
Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis.
Identifies change-points in the common and the idiosyncratic components via factor modelling.
Implementation of three machine learning approaches: Support Vector Machines (SVM) with a linear kernel, second-order Factorization Machines (FM), and higher-order Factorization Machines (HoFM).
A set of tools to support results of post-hoc testing and enable to extract hierarchical structure of factors. Work on this package was financially supported by the 'NCN Opus grant 2016/21/B/ST6/02176'.
Methods to calculate, print, summarize and plot pairwise differences from GLMs, GLHT or Multinomial Logit models.
Package to fit Bayesian quantile regression models that
        assume a factor structure for at least part of the design
        matrix.
It identifies the factors significantly related to species richness, and their relative contribution, using multiple regressions and support vector machine models. It uses an output file of 'ModestR' (<http://www.ipez.es/ModestR>) with data of richness of the species and environmental variables in a cell size defined by the user. The residuals of the support vector machine model are shown on a map. Negative residuals may be potential areas with undiscovered and/or unregistered species, or areas with decreased species richness due to the negative effect of anthropogenic factors.
Markov chain Monte Carlo (MCMC) sampler for fully Bayesian
             estimation of latent factor stochastic volatility models.
             Sparsity can be achieved through the usage of Normal-Gamma priors
             on the factor loading matrix.
Perform factorial analysis with a menu and draw graphs interactively thanks to 'FactoMineR' and a Shiny application.
Provides functions to score the Functional Assessment of Cancer
    Therapy (FACT) and Functional Assessment of Chronic Illness Therapy (FACIT)
    family of patient-reported outcome (PRO) measures.  The questionnaires 
    themselves can be downloaded from www.FACIT.org.  For most of the FACIT 
    questionnaires, FACIT.org provides scoring syntax for use with commercial 
    statistical software (SAS and SPSS).  The FACTscorer R package is intended 
    to serve as a free, reliable alternative for those without access to SAS or 
    SPSS.  Additionally, it will allow R users to both score and analyze the 
    FACT and FACIT scales in R, avoiding the time-consuming and and error-prone 
    process of transferring data back-and-forth between statistical software.
    Finally, use of the FACTscorer package will prevent many sources of scoring
    error common when using SAS and/or SPSS syntax (e.g., copy-paste errors and
    other accidental modifications to the syntax).
Per the Factual.com website, "Factual is a platform where
        anyone can share and mash open, living data on any subject."
        The data is in the form of tables and is accessible via REST
        API.  The factualR package is a thin wrapper around the
        Factual.com API, to make it even easier for people working with
        R to explore Factual.com data sets.
The functions provided in the FADA (Factor Adjusted Discriminant Analysis) package aim at performing supervised classification of high-dimensional and correlated profiles. The procedure combines a decorrelation step based on a  
   factor modeling of the dependence among covariates and a classification method. The available methods are Lasso regularized logistic model
    (see Friedman et al. (2010)), sparse linear discriminant analysis (see
    Clemmensen et al. (2011)), shrinkage linear and diagonal discriminant
    analysis (see M. Ahdesmaki et al. (2010)). More methods of classification can be used on the decorrelated data provided by the package FADA.
Probability distributions that are sometimes useful in hydrology.
Data and functions for the book "Multivariate Statistical 
              Modelling Based on Generalized Linear Models", first edition, by 
              Ludwig Fahrmeir and Gerhard Tutz.  Useful when using the book.
More comfortable interface to work with R data or source files
    in a key-value fashion.
This package estimates factor analysis models using a
        genetic algorithm, which permits a general mechanism for
        restricted optimization with arbitrary restrictions that are
        chosen at run time with the help of a GUI. Importantly,
        inequality restrictions can be imposed on functions of multiple
        parameters, which provides a new avenues for testing and
        generating theories with factor analysis models. This package
        also includes an entirely new estimator of the common factor
        analysis model called semi-exploratory factor analysis, which
        is a general alternative to exploratory and confirmatory factor
        analysis. Finally, this package integrates a lot of other
        packages that estimate sample covariance matrices and thus
        provides a lot of alternatives to the traditional sample
        covariance calculation. Note that you need to have the Gtk run
        time library installed on your system to use this package; see
        the URL below for detailed installation instructions. Most
        users would only need to understand the first twenty-four pages
        of the PDF manual.
It is used for systematic analysis of decisions based on attributes and its levels.
Generates fake data from a dataset of different variable types.
    The package contains the functions simulate_dataset and simulate_dataset_ts 
    to simulate time-independent and time-dependent data. It randomly samples 
    character and factor variables from contingency tables and  numeric and 
    ordered factors from a multivariate normal distribution. It currently supports the 
    simulation of stationary and zero-inflated count time series.
This is a method for Allele-specific DNA Copy Number Profiling using Next-Generation Sequencing.  Given the allele-specific coverage at the variant loci, this program segments the genome into regions of homogeneous allele-specific copy number.  It requires, as input, the read counts for each variant allele in a pair of case and control samples. For detection of somatic mutations, the case and control samples can be the tumor and normal sample from the same individual.
This is a method for Allele-specific DNA Copy Number profiling for whole-Exome sequencing data.  Given the allele-specific coverage and site biases at the variant loci, this program segments the genome into regions of homogeneous allele-specific copy number.  It requires, as input, the read counts for each variant allele in a pair of case and control samples, as well as the site biases. For detection of somatic mutations, the case and control samples can be the tumor and normal sample from the same individual.  The implemented method is based on the paper: Chen, H., Jiang, Y., Maxwell, K., Nathanson, K. and Zhang, N. (under review). Allele-specific copy number estimation by whole Exome sequencing.
Functionality provided for conditional simulation, likelihoods and plotting of pedigrees, mostly as a wrapper for 'paramlink'. Users typically start by exporting from the Windows version of 'Familias'.
Simulates age-at-onset traits associated with a segregating major gene in family data 
     obtained from population-based, clinic-based, or multi-stage designs. Appropriate ascertainment 
     correction is utilized to estimate age-dependent penetrance functions either parametrically from 
     the fitted model or nonparametrically from the data. The Expectation and Maximization algorithm 
     can infer missing genotypes and carrier probabilities estimated from family's genotype and
     phenotype information or from a fitted model. Plot functions include pedigrees of simulated 
     families and predicted penetrance curves based on specified parameter values.
An interface to the core Familias functions (www.familias.name), 
 which are programmed in C++.
Fits penalized linear and logistic regression models with pairwise interaction terms.
Estimate parameters of univariate probability distributions 
  with maximum likelihood and Bayesian methods.
The method proposed in this package takes into account the impact of dependence on the multiple testing procedures for high-throughput data as proposed by Friguet et al. (2009). The common information shared by all the variables is modeled by a factor analysis structure. The number of factors considered in the model is chosen to reduce the false discoveries variance in multiple tests. The model parameters are estimated thanks to an EM algorithm. Adjusted tests statistics are derived, as well as the associated p-values. The proportion of true null hypotheses (an important parameter when controlling the false discovery rate) is also estimated from the FAMT model. Graphics are proposed to interpret and describe the factors.
Computes the penalized maximum likelihood estimates of factor loadings and unique variances for various tuning parameters. The pathwise coordinate descent along with EM algorithm is used.  This package also includes a new graphical tool which outputs path diagram, goodness-of-fit indices and model selection criteria for each regularization parameter. The user can change the regularization parameter by manipulating scrollbars, which is helpful to find a suitable value of regularization parameter.
This package contains a collection of R functions to
        perform nonparametric analysis of covariance for regression
        curves or surfaces. Testing the equality or parallelism of
        nonparametric curves or surfaces is equivalent to analysis of
        variance (ANOVA) or analysis of covariance (ANCOVA) for
        one-sample functional data. Three different testing methods are
        available in the package, including one based on L-2 distance,
        one based on an ANOVA statistic, and one based on variance
        estimators.
Provides the function fancycut() which is like cut() except
  you can mix left open and right open intervals with point values,
  intervals that are closed on both ends and intervals that are open on both ends.
Estimation and plotting of a function's FANOVA graph to identify the interaction structure and fitting, prediction and simulation of a Kriging model modified by the identified structure. The interactive function plotManipulate() can only be run on the RStudio IDE with RStudio's package 'manipulate' loaded. RStudio is freely available (www.rstudio.org), and includes package 'manipulate'. The equivalent function plotTk() bases on CRAN Repository packages only.
Visualise sequential distributions using a range of plotting
    styles. Sequential distribution data can be input as either simulations or
    values corresponding to percentiles over time. Plots are added to
    existing graphic devices using the fan function. Users can choose from four
    different styles, including fan chart type plots, where a set of coloured
    polygon, with shadings corresponding to the percentile values are layered
    to represent different uncertainty levels.
A list of functions to download statistics from FAOSTAT (database
    of the Food and Agricultural Organization of the United Nations) and WDI
    (database of the World Bank), and to perform some harmonization operations.
Tools for detecting and summarize influential cases that
    can affect exploratory and confirmatory factor analysis models as well as
    structural equation models more generally.
Modelizations and previsions functions for
   Functional AutoRegressive processes using
   nonparametric methods: functional kernel,
   estimation of the covariance operator in
   a subspace, ...
Books are "Practical Regression and ANOVA in R" on CRAN, "Linear Models with R" published 1st Ed. August 2004, 2nd Ed. July 2014 by CRC press, ISBN 9781439887332, and "Extending the Linear Model with R" published by CRC press in 1st Ed. December 2005 and 2nd Ed. March 2016, ISBN 9781584884248.
Reads and writes 'ARFF' files. 'ARFF' (Attribute-Relation File Format) files are like 'CSV' files, with a little bit of added meta information in a header and standardized NA values. They are quite often used for machine learning data sets and were introduced for the 'WEKA' machine learning 'Java' toolbox. See <http://weka.wikispaces.com/ARFF> for further info on 'ARFF' and for <http://www.cs.waikato.ac.nz/ml/weka/> for more info on 'WEKA'. 'farff' gets rid of the 'Java' dependency that 'RWeka' enforces, and it is at least a faster reader (for bigger files). It uses 'readr' as parser back-end for the data section of the 'ARFF' file. Consistency with 'RWeka' is tested on 'Github' and 'Travis CI' with hundreds of 'ARFF' files from 'OpenML'. Note that the 'OpenML' package is currently only available from 'Github' at: <https://github.com/openml/openml-r>.
Environment for teaching "Financial Engineering and
        Computational Finance"
Allow numbers to be presented in an persian language
        version
This is an R implementation of "A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models" (FASJEM). The FASJEM algorithm can be used to estimate multiple related precision matrices. For instance, it can identify context-specific gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogonous data sets, this tool  can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(fasjem) to learn the basic functions provided by this package. For more details, please see <http://proceedings.mlr.press/v54/wang17e/wang17e.pdf>.
Environment for teaching 
    "Financial Engineering and Computational Finance".
The Fourier Amplitude Sensitivity Test (FAST) is a method to determine global sensitivities of a model on parameter changes with relatively few model runs. This package implements this sensitivity analysis method.
Implements Adaboost based on C++ backend code.
             This is blazingly fast and especially useful for large, in memory data sets. 
             The package uses decision trees as weak classifiers. Once the classifiers
             have been trained, they can be used to predict new data. 
             Currently, we support only binary classification tasks.
             The package implements the Adaboost.M1 algorithm and the real
             Adaboost(SAMME.R) algorithm.
Fast and numerically stable estimation of a covariance matrix by banding the Cholesky factor using a modified Gram-Schmidt algorithm implemented in RcppArmadilo. See <http://stat.umn.edu/~molst029> for details on the algorithm.
Provides a method of recovering the precision matrix efficiently 
        and solving for the dantzig selector by applying the parametric 
        simplex method.  The computation is based on a linear optimization
        solver. It also contains a generic LP solver and a parameterized LP 
        solver using parametric simplex method.
This is a two-in-one package which provides interfaces to
        both R and Python. It implements fast hierarchical, agglomerative
        clustering routines. Part of the functionality is designed as drop-in
        replacement for existing routines: linkage() in the SciPy package
        'scipy.cluster.hierarchy', hclust() in R's 'stats' package, and the
        'flashClust' package. It provides the same functionality with the
        benefit of a much faster implementation. Moreover, there are
        memory-saving routines for clustering of vector data, which go beyond
        what the existing packages provide. For information on how to install
        the Python files, see the file INSTALL in the source distribution.
A method which uses the Cochran-Mantel-Haenszel test with significant pattern mining to detect intervals in binary genotype data which are significantly associated with a particular phenotype, while accounting for categorical covariates.
Provides an R interface to Bob Jenkin's streaming, 
 non-cryptographic 'SpookyHash' hash algorithm for use in digest-based 
 comparisons of R objects. 'fastdigest' plugs directly into R's internal 
 serialization machinery, allowing digests of all R objects the serialize() 
 function supports, including reference-style objects via custom hooks. Speed is
 high and scales linearly by object size; memory usage is constant and 
 negligible.
Creates dummy columns from columns that have categorical variables (character or factor types). You can also specify which columns to make dummies out of, or which columns to ignore. This package provides a significant speed increase from creating dummy variables through model.matrix().
If there is a need to recognise edges on a raster image or a bitmap or any kind of a matrix, one can find packages
    that does only 90 degrees vectorization. Typically the nature of artefact images is linear and can be vectorized in much more
    efficient way than draw a series of 90 degrees lines. The fasteraster package does recognition of lines using only one pass.
    It also allows to calculate mass and the mass centers for the recognized zones or polygons.
Contains Rcpp and RcppEigen implementations of matrix operations useful for Gaussian process models, such as the inversion of a symmetric Toeplitz matrix, sampling from multivariate normal distributions, evaluation of the log-density of a multivariate normal vector, and Bayesian inference for latent variable Gaussian process models with elliptical slice sampling (Murray, Adams, and MacKay 2010).
Provides functionality to produce graphs of probability density functions and cumulative distribution functions with few keystrokes, allows shading under the curve of the probability density function to illustrate concepts such as p-values and critical values, and fits a simple linear regression line on a scatter plot with the equation as the main title.
The FastHCS algorithm of Schmitt and Vakili (2014) for high-dimensional, robust PCA modelling and associated outlier detection and diagnostic tools.
It implements HICA (Hierarchical Independent Component Analysis) algorithm. This approach, obtained through the integration between treelets and Independent Component Analysis, is able to provide a multi-scale non-orthogonal data-driven basis, whose elements have a phenomenological interpretation according to the problem under study.
Implementation of FastICA algorithm to perform Independent
        Component Analysis (ICA) and Projection Pursuit.
TrainFastImputation() uses training data to describe a
    multivariate normal distribution that the data approximates or
    can be transformed into approximating and stores this information
    as an object of class 'FastImputationPatterns'. FastImputation()
    function uses this 'FastImputationPatterns' object to impute (make
    a good guess at) missing data in a single line or a whole data frame
    of data.  This approximates the process used by 'Amelia'
    <http://gking.harvard.edu/amelia/> but is much faster when
    filling in values for a single line of data.
This 'Rcpp'-based package implements highly efficient functions for the calculation of the Jonckheere-Terpstra statistic. It can be used for a variety of applications, including feature selection in machine learning problems, or to conduct genome-wide association studies (GWAS) with multiple quantitative phenotypes. The code leverages 'OpenMP' directives for multi-core computing to reduce overall processing time.
A computationally efficient and statistically rigorous fast Kernel Machine method for multi-kernel analysis. The approach is based on a low-rank approximation to the nuisance effect kernel matrices. The algorithm is applicable to continuous, binary, and survival traits and is implemented using the existing single-kernel analysis software 'SKAT' and 'coxKM'. 'coxKM' can be obtained from http://www.hsph.harvard.edu/xlin/software.html.
Compute labels for a test set according to the k-Nearest Neighbors classification. This is a fast way to do k-Nearest Neighbors classification because the distance matrix -between the features of the observations- is an input to the function rather than being calculated in the function itself every time.
Implements a Fellegi-Sunter probabilistic record linkage model that allows for missing data
    and the inclusion of auxiliary information. This includes functionalities to conduct a merge of two datasets under the Fellegi-Sunter model
    using the Expectation-Maximization algorithm. In addition, tools for preparing, adjusting, and summarizing
    data merges are included. The package implements methods described in Enamorado, Fifield,
    and Imai (2017) ''Using a Probabilistic Model to Assist Merging of Large-scale Administrative
    Records'', available at <http://imai.princeton.edu/research/linkage.html>.
The package implements the new algorithm for fast computation of M-scatter matrices using a partial Newton-Raphson procedure for several estimators.
Package providing a fast match() replacement for cases
	that require repeated look-ups. It is slightly faster that R's
	built-in match() function on first match against a table, but
	extremely fast on any subsequent lookup as it keeps the hash
	table in memory.
We present an implementation of the algorithms required to simulate large-scale social networks and retrieve their most relevant metrics.
The FastPCS algorithm of Vakili and Schmitt (2014) for robust estimation of multivariate location and scatter and multivariate outliers detection.
Computes pseudo-observations for survival analysis on right-censored data based on restricted mean survival time.
'FASTQC' is the most widely used tool for evaluating the quality of high throughput sequencing data.  
    It produces, for each sample, an html report and a compressed file containing the raw data. 
    If you have hundreds of samples, you are not going to open up each 'HTML' page. 
    You need some way of looking at these data in aggregate. 
    'fastqcr' Provides helper functions to easily parse, aggregate and analyze 
    'FastQC' reports for large numbers of samples. It provides a convenient solution for building 
    a 'Multi-QC' report, as well as, a 'one-sample' report with result interpretations.
Data sets and utilities to accompany
    "Foundations and Applications of Statistics: an Introduction
    using R" (R Pruim, published by AMS, 2011), a text covering
    topics from probability and mathematical statistics at an advanced
    undergraduate level.  R is integrated throughout, and access to all
    the R code in the book is provided via the snippet function.
The FastRCS algorithm of Vakili and Schmitt (2014) for robust fit of the multivariable linear regression model and outliers detection.
Infrastrcture for creating rich, dynamic web content using R scripts while maintaining very fast response time.
An implementation of the fast structural filtering with L0 penalty. It includes an adaptive polynomial estimator by minimizing the least squares error with constraints on the number of breaks in their (k + 1)-st discrete derivative, for a chosen integer k >= 0. It also includes generalized structure sparsity constraint, i.e., graph trend filtering. This package is implemented via the primal dual active set algorithm, which formulates estimates and residuals as primal and dual variables, and utilizes efficient active set selection strategies based on the properties of the primal and dual variables.
Functions for computing spillover measures, especially spillover
    tables and spillover indices, as well as their average, minimal, and maximal
    values.
An interface to the 'fastText' library
	<https://github.com/facebookresearch/fastText>. The package
	can be used for text classification and to learn word vectors.
	The install folder contains the 'PATENTS' file.
	An example how to use 'fastTextR' can be found in the 'README' file.
Fast functions for timestamp
	manipulation that avoid system calls and take shortcuts
	to facilitate operations on very large data.
Performs family-based association tests with a polytomous outcome under 2-locus and 1-locus models
             defined by some design matrix.
Kiener distributions K1, K2, K3, K4 and K7 to characterize
    distributions with left and right, symmetric or asymmetric fat tails in market
    finance, neuroscience and other disciplines. Two algorithms to estimate with
    a high accuracy distribution parameters, quantiles, value-at-risk and expected
    shortfall. Include power hyperbolas and power hyperbolic functions.
HTTP error helpers. Methods included for general purpose HTTP 
    error handling, as well as individual methods for every HTTP status
    code, both via status code numbers as well as their descriptive names.
    Supports ability to adjust behavior to stop, message or warning.
    Includes ability to use custom whisker template to have any configuration
    of status code, short description, and verbose message. Currently 
    supports integration with 'crul', 'curl', and 'httr'.
A dataset of favourite numbers, selected from an online poll of over 30,000 people by Alex Bellos
             (http://pages.bloomsbury.com/favouritenumber).
Provides functions and datasets from the book "Forest Analytics with R".
This is a simple package for Flux Balance Analysis and related
    metabolic modelling techniques. Functions are provided for: parsing
    models in tabular format, converting parsed metabolic models to input
    formats for common linear programming solvers, and
    evaluating and applying gene-protein-reaction mappings. In addition, there
    are wrappers to parse a model, select a solver, find the metabolic fluxes,
    and return the results applied to the original model. Compared to other
    packages in this field, this package puts a much heavier focus on
    providing reusable components that can be used in the design of new
    implementation of new techniques, in particular those that involve large
    parameter sweeps.
Environment for teaching 
	"Financial Engineering and Computational Finance".
Does family-based gene by environment interaction tests, joint gene, gene-environment interaction test, and a test of a set of genes conditional on another set of genes.
We propose an objective Bayesian algorithm for searching the space of Gaussian directed acyclic graph (DAG) models. The algorithm proposed makes use of moment fractional Bayes factors (MFBF) and thus it is suitable for learning sparse graph. The algorithm is implemented by using Armadillo: an open-source C++ linear algebra library.
Normalizes the data from a file containing the raw values
        of the SNP probes of microarrray data by using the FISH probes
        and their corresponding CNs.
Wrapper functions around the Facebook Marketing 'API' to create, read, update and delete custom audiences, images, campaigns, ad sets, ads and related content.
This package uses time dependent Poisson regression and a record of goals scored in matches to rank teams via estimated attack and defense strengths.  The statistical model is based on Dixon and Coles (1997) Modeling Association Football Scores and Inefficiencies in the Football Betting Market, Applied Statistics, Volume 46, Issue 2, 265-280.  The package has a some webscrapers to assist in the development and updating of a match database.  If the match database contains unconnected clusters (i.e. sets of teams that have only played each other and not played teams from other sets), each cluster is ranked separately relative to the median team strength in the cluster.  The package contains functions for predicting and simulating tournaments and leagues from estimated models. The package allows fitting via the glm(), speedglm(), and glmnet() functions.  The latter allows fast and efficient fitting of very large numbers of teams.  The fitting algorithm will analyze the match data and determine which teams form a cluster (a set of teams where there is a path of matches connecting every team) and fit each cluster separately.
Implements a very fast C++ algorithm to quickly bootstrap receiver
    operating characteristics (ROC) curves and derived performance metrics,
    including the area under the curve (AUC) and the partial area under the curve as well as 
    the true and false positive rate. The analysis of paired receiver operating curves is supported as well,
    so that a comparison of two predictors is possible. You can also plot the
    results and calculate confidence intervals. On a typical desktop computer the time needed for 
    the calculation of 100000 bootstrap replicates given 500 observations requires time on the
    order of magnitude of one second.
Efficient procedures for community detection in network studies, especially for sparse networks with not very obvious community structure. The algorithms impose penalties on the differences of the coordinates which represent the community labels of the nodes.
Collection of pricing by duplication methods for popular structured products ("Zertifikate").
Fatigue Crack Growth in Reliability estimates the distribution
             of material lifetime due to mechanical fatigue efforts. The FCGR
             package provides simultaneous crack growth curves fitting to 
             different specimens in materials under mechanical stress efforts. 
             Linear mixed-effects models (LME) with smoothing B-Splines 
             and the linearized Paris-Erdogan law are applied. Once defined
             the fail for a determined crack length, the distribution function
             of failure times to fatigue is obtained. The density function is
             estimated by applying nonparametric binned kernel density estimate 
             (bkde) and the kernel estimator of the distribution function (kde). 
             The results of Pinheiro and Bates method based on nonlinear 
             mixed-effects regression (nlme) can be also retrieved. The package 
             contains the crack.growth, PLOT.cg, IB.F, and Alea.A (database) functions.
Change point estimation and detection methods for functional data are implemented using dimension reduction via functional principal component analysis and a fully-functional (norm-based) method. Detecting and dating structural breaks for both dependent and independent functional samples is illustrated along with some basic functional data generating processes.
Algorithms for fuzzy clustering, cluster validity indices and plots for cluster validity and visualizing fuzzy clustering results.
Provides a selection of 3 different inference rules (including additionally the clamped types of the referred inference rules) and 4 threshold functions in order to obtain the inference of the FCM (Fuzzy Cognitive Map). Moreover, the 'fcm' package returns a data frame of the concepts' values of each state after the inference procedure. Fuzzy cognitive maps were introduced by Kosko (1986) <doi:10.1002/int.4550010405> providing ideal causal cognition tools for modeling and simulating dynamic systems.
Provides several functions to create and manipulate fuzzy
    cognitive maps. It is based on 'FCMapper' for Excel, distributed at <http://
    www.fcmappers.net/joomla/>, developed by Michael Bachhofer and Martin Wildenberg.
    Maps are inputted as adjacency matrices. Attributes of the maps and the
    equilibrium values of the concepts (including with user-defined constrained
    values) can be calculated. The maps can be graphed with a function that calls
    'igraph'. Multiple maps with shared concepts can be aggregated.
Provides an interface to kernel routines from the FCNN C++ library.
    FCNN is based on a completely new Artificial Neural Network representation that
    offers unmatched efficiency, modularity, and extensibility. FCNN4R provides
    standard teaching (backpropagation, Rprop, simulated annealing, stochastic
    gradient) and pruning algorithms (minimum magnitude, Optimal Brain Surgeon),
    but it is first and foremost an efficient computational engine. Users can
    easily implement their algorithms by taking advantage of fast gradient computing
    routines, as well as network reconstruction functionality (removing weights
    and redundant neurons, reordering inputs, merging networks). Networks can be
    exported to C functions in order to integrate them into virtually any software
    solution.
Environment for teaching
	"Financial Engineering and Computational Finance".
A fold change rank based method is presented to search for genes with changing
             expression and to detect recurrent chromosomal copy number aberrations. This 
             method may be useful for high-throughput biological data (micro-array, sequencing, ...).
             Probabilities are associated with genes or probes in the data set and there is no
             problem of multiple tests when using this method. For array-based comparative genomic
             hybridization data, segmentation results are obtained by merging the significant
             probes detected.
Automatically suggests a correction when a typo occurs.
FD is a package to compute different multidimensional FD indices. It implements a distance-based framework to measure FD that allows any number and type of functional traits, and can also consider species relative abundances. It also contains other useful tools for functional ecology.
These functions were developed to support functional data
        analysis as described in Ramsay, J. O. and Silverman, B. W.
        (2005) Functional Data Analysis. New York: Springer.  They were
        ported from earlier versions in Matlab and S-PLUS.  An
        introduction appears in Ramsay, J. O., Hooker, Giles, and
        Graves, Spencer (2009) Functional Data Analysis with R and
        Matlab (Springer). The package includes data sets and script
        files working many examples including all but one of the 76
        figures in this latter book.  Matlab versions of the code and
        sample analyses are no longer distributed through CRAN, as they
        were when the book was published.  For those, ftp from
        <http://www.psych.mcgill.ca/misc/fda/downloads/FDAfuns/>
        There you find a set of .zip files containing the functions and
        sample analyses, as well as two .txt files giving instructions for
        installation and some additional information.
        The changes from Version 2.4.1 are fixes of bugs in density.fd and
        removal of functions create.polynomial.basis, polynompen, and 
        polynomial. These were deleted because the monomial basis
        does the same thing and because there were errors in the code.
It performs simultaneously clustering and alignment of a multidimensional or unidimensional functional dataset by means of k-mean alignment.
Likelihood based analysis of 1-dimension functional data
        in a mixed-effects model framework. Matrix computation are
        approximated by semi-explicit operator equivalents with linear
        computational complexity.
Provides implementation of various methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this package is Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely or densely sampled random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm or numerical integration. PACE is useful for the analysis of data that have been generated by a sample of underlying (but usually not fully observed) random trajectories. It does not rely on pre-smoothing of trajectories, which is problematic if functional data are sparsely sampled. PACE provides options for functional regression and correlation, for Longitudinal Data Analysis, the analysis of stochastic processes from samples of realized trajectories, and for the analysis of underlying dynamics. The core computational algorithms are implemented using the 'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".
An implementation of regression models with partial differential regularizations, making use of the Finite Element Method. The models efficiently handle data distributed over irregularly shaped domains and can comply with various conditions at the boundaries of the domain. A priori information about the spatial structure of the phenomenon under study can be incorporated in the model via the differential regularization.
Performs alignment, PCA, and modeling of multidimensional and
            unidimensional functions using the square-root velocity framework
            (Srivastava et al., 2011 <arXiv:1103.3817> and
            Tucker et al., 2014 <DOI:10.1016/j.csda.2012.12.001>). This framework
            allows for elastic analysis of functional data through phase and
            amplitude separation.
Implementation of the Interval Testing Procedure for functional data in different frameworks (i.e., one or two-population frameworks, functional linear models) by means of different basis expansions (i.e., B-spline, Fourier, and phase-amplitude Fourier). The current version of the package requires functional data evaluated on a uniform grid; it automatically projects each function on a chosen functional basis; it performs the entire family of multivariate tests; and, finally, it provides the matrix of the p-values of the previous tests and the vector of the corrected p-values. The functional basis, the coupled or uncoupled scenario, and the kind of test can be chosen by the user. The package provides also a plotting function creating a graphical output of the procedure: the p-value heat-map, the plot of the corrected p-values, and the plot of the functional data.
Routines for exploratory and descriptive analysis of functional data such as depth measurements, atypical curves detection, regression models, supervised classification, unsupervised classification and functional analysis of variance.
Regression models for functional data, i.e., scalar-on-function,
    function-on-scalar and function-on-function regression models, are fitted
    by a component-wise gradient boosting algorithm.
Provides a variety of tools for the analysis of covariance operators.
FDG copulas are a class of copulas featuring an interesting balance between flexibility and tractability. This package provides tools to construct, calculate the pairwise dependence coefficients of, simulate from, and fit FDG copulas. The acronym FDG stands for 'one-Factor with Durante Generators', as an FDG copula is a one-factor copula -- that is, the variables are independent given a latent factor -- whose linking copulas belong to the Durante class of bivariate copulas (also referred to as exchangeable Marshall-Olkin or semilinear copulas).
It allows to estimate Dynamic Model Averaging, Dynamic Model Selection and Median Probability Model. The original methods (see References) are implemented, as well as, selected further modifications of these methods. In particular the User might choose between recursive moment estimation and exponentially moving average for variance updating. Inclusion probabilities might be modified in a way using Google Trends. The code is written in a way which minimises the computational burden (which is quite an obstacle for Dynamic Model Averaging if many variables are used). For example, this package allows for parallel computations under Windows machines. Additionally, the User might reduce a set of models according to a certain algorithm. The package is designed in a way that is hoped to be especially useful in economics and finance. (Research funded by the Polish National Science Centre grant under the contract number DEC-2015/19/N/HS4/00205.)
Contains methods of analysis of forest databases, the purpose of the analyzes is to evaluate 
    the quality of the data present in the databases focusing on the dimensions of consistency, punctuality 
    and completeness. Databases can range from forest inventory data to growth model data. The package has 
    methods to work with large volumes of data quickly, in addition in certain analyzes it is possible to 
    generate the graphs for a better understanding of the analysis and reporting of the analyzed analysis.
FDR functions for permutation-based estimators, including pi0 as well as FDR
             confidence intervals. The confidence intervals account for dependencies between
             tests by the incorporation of an overdispersion parameter, which is estimated
             from the permuted data.
It is known that current false discovery rate (FDR) procedures can be very conservative
             when applied to p-values (and test statistics) with discrete (and heterogeneous) null distributions.
             This package implements the more powerful weighted generalized FDR procedure that adapts to these two features of the discrete paradigm for multiple testing. 
             The package takes in the original data set rather than the p-values in order to carry out the adjustments needed for multiple testing in this paradigm. The methodology applies also to multiple testing where the null p-values are uniformly distributed.
             The package implements the method for three types of test statistics and their p-values:
             (a) binomial test on if two independent Poisson distributions have the same means, (b) Fisher's exact test on if the conditional distribution is the same as the marginal distribution for two binomial distributions,
             (c) the exact negative binomial test on if two independent negative binomial distributions with the same size parameter have the same means. 
             It depends on the R packages ``MCMCpack'' to use its function ``dnoncenhypergeom'' for hypergeometric distributions, and edgeR to uses its normalization techniques for data that follow negative binomial distributions.
Tools for FDR problems, including false discovery rate regression.
    See corresponding paper: "False discovery rate regression: application to
    neural synchrony detection in primary visual cortex."  James G. Scott, Ryan
    C. Kelly, Matthew A. Smith, Robert E. Kass.
Defines a collection of functions to compute average power and sample size for studies that use the false discovery rate as the final measure of statistical significance.
Estimates both tail area-based false 
   discovery rates (Fdr) as well as local false discovery rates (fdr) for a 
   variety of null models (p-values, z-scores, correlation coefficients,
   t-scores).  The proportion of null values and the parameters of the null 
   distribution are adaptively estimated from the data.  In addition, the package 
   contains functions for non-parametric density estimation (Grenander estimator), 
   for monotone regression (isotonic regression and antitonic regression with weights),
   for computing the greatest convex minorant (GCM) and the least concave majorant (LCM), 
   for the half-normal and correlation distributions, and for computing
   empirical higher criticism (HC) scores and the corresponding decision threshold.
Perform frequency distribution tables, associated histograms
             and polygons from vector, data.frame and matrix objects for
             numerical and categorical variables.
For each feature, a score is computed that can be useful
        for feature selection. Several random subsets are sampled from
        the input data and for each random subset, various linear
        models are fitted using lars method. A score is assigned to
        each feature based on the tendency of LASSO in including that
        feature in the models.Finally, the average score and the models
        are returned as the output. The features with relatively low
        scores are recommended to be ignored because they can lead to
        overfitting of the model to the training data. Moreover, for
        each random subset, the best set of features in terms of global
        error is returned. They are useful for applying Bolasso, the
        alternative feature selection method that recommends the
        intersection of features subsets.
Read and write feather files, a lightweight binary columnar
    data store designed for maximum speed.
Local inferential feature significance for multivariate kernel density estimation.
Feature hashing, also called as the hashing trick, is a method to transform 
  features of a instance to a vector. Thus, it is a method to transform a real dataset to a matrix. 
  Without looking up the indices in an associative array, 
  it applies a hash function to the features and uses their hash values as indices directly.
  The method of feature hashing in this package was proposed in Weinberger et al. (2009). 
  The hashing algorithm is the murmurhash3 from the digest package. 
  Please see the README in https://github.com/wush978/FeatureHashing for more information.
Discretely-sampled function is first smoothed.  Features
        of the smoothed function are then extracted.  Some of the key
        features include mean value, first and second derivatives,
        critical points (i.e. local maxima and minima), curvature of
        cunction at critical points, wiggliness of the function, noise
        in data, and outliers in data.
A collection of functions that would help one to build features based on external data. Very useful for Data Scientists in data to day work. Many functions create features using parallel computation. Since the nitty gritty of parallel computation is hidden under the hood, the user need not worry about creating clusters and shutting them down.
Functions and example datasets for Fechnerian scaling of discrete
  object sets.  User can compute Fechnerian distances among objects representing
  subjective dissimilarities, and other related information.  See
  package?fechner for an overview.
Calculates fishing effort following the DG MARE Ad-Hoc Workshops on Transversal Variables in Zagreb (2015) and Nicosia (2016).
Functions to automate downloading geospatial data available from
    several federated data sources (mainly sources maintained by the US Federal
    government). Currently, the package enables extraction from six datasets:
    The National Elevation Dataset digital elevation models (1 and 1/3 arc-second;
    USGS); The National Hydrography Dataset (USGS); The Soil Survey Geographic
    (SSURGO) database from the National Cooperative Soil Survey (NCSS), which is
    led by the Natural Resources Conservation Service (NRCS) under the USDA; the
    Global Historical Climatology Network (GHCN), coordinated by National Climatic
    Data Center at NOAA; the Daymet gridded estimates of daily weather parameters 
    for North America, version 3, available from the Oak Ridge National Laboratory's
    Distributed Active Archive Center (DAAC); and the International Tree Ring Data Bank.
Access data from the Federal Register API <https://www.federalregister.gov/developers/api/v1>.
Analysis of fragmented time directionality to investigate feedback in time series. Tools provided by the package allow the analysis of feedback for a single time series and the analysis of feedback for a set of time series collected across a spatial domain.
Efficient estimation of fixed-effect maximum likelihood models with, possibly, non-linear right hand sides.
Frailty Exponential Random Graph Models estimated through pseudo likelihood with frailty terms estimated using 'Stan' as per Box-Steffensmeier et. al (2017) <doi:10.7910/DVN/K3D1M2>.
    Goodness of fit for Frailty Exponential Random Graph Models is also available, with easy visualizations for comparison to fit Exponential Random Graph Models.
Data from various catalogs of astrophysical gamma-ray sources
    detected by NASA's Large Area Telescope (The Astrophysical Journal, 697, 1071,
    2009 June 1), on board the Fermi gamma-ray satellite. More information on
    Fermi and its data products is available from the Fermi Science Support Center
    (http://fermi.gsfc.nasa.gov/ssc/).
Wind fetch is the unobstructed length of water over which wind can
    blow from a certain direction. The wind fetch is typically calculated for many directions
    around the compass rose for a given location, which can then be incorporated 
    into a larger model (such as the InVEST coastal vulnerability model;
    <http://data.naturalcapitalproject.org/invest-releases/documentation/2_2_0/coastal_vulnerability.html>),
    or simply averaged for a reasonable measure of 
    the overall wind exposure for a specific marine location. The process of calculating
    wind fetch can be extremely time-consuming and tedious, particularly if a large
    number of fetch vectors are required at many locations. The 'fetchR' package
    calculates wind fetch and summarises the information efficiently. There are 
    also plot methods to help visualise the wind exposure at the various
    locations, and methods to output the fetch vectors to a KML file for further
    investigation.
Collection of pricing by duplication and Monte Carlo methods for Express Certificates products (also known as Autocallables)
Extends the out of memory vectors of 'ff' with
    statistical functions and other utilities to ease their usage.
Functions, S4 classes/methods and a graphical user interface (GUI) to design surveys to substantiate freedom from disease using a modified hypergeometric function (see Cameron and Baldock, 1997). Herd sensitivities are computed according to sampling strategies "individual sampling" or "limited sampling" (see M. Ziller, T. Selhorst, J. Teuffert, M. Kramer and H. Schlueter, 2002). Methods to compute the a-posteriori alpha-error are implemented. Risk-based targeted sampling is supported.
Force field simulation of interaction of set of points.
        Very useful for placing text labels on graphs, such as
        scatterplots.
This package performs general linear modeling with
        multiple responses (MANCOVA).  An overall p-value for each
        model term is calculated by the 50-50 MANOVA method, which
        handles collinear responses.  Rotation testing is used to
        compute adjusted single response p-values according to
        familywise error rates and false discovery rates.
An implementation of the adaptive forgetting factor scheme described in Bodenham and Adams (2016) <doi:10.1007/s11222-016-9684-8> which adaptively estimates the mean and variance of a stream in order to detect multiple changepoints in streaming data. The implementation is in C++ and uses Rcpp. Additionally, implementations of the fixed forgetting factor scheme from the same paper, as well as the classic CUSUM and EWMA methods, are included.
Create, visualize, and test fast-and-frugal decision trees (FFTs). FFTs are very simple decision trees for
    binary classification problems. FFTs can be preferable to more complex algorithms because they are easy to communicate, require very little information, and are
    robust against overfitting.
Provides a simple and efficient wrapper around the fastest
        Fourier transform in the west (FFTW) <http://www.fftw.org/> library.
Provides a wrapper for several 'FFTW' functions. This package provides access to the two-dimensional 'FFT', the multivariate 'FFT', and the one-dimensional real to complex 'FFT' using the 'FFTW3' library. The package includes the functions fftw() and mvfftw() which are designed to mimic the functionality of the R functions fft() and mvfft(). The 'FFT' functions have a parameter that allows them to not return the redundant complex conjugate when the input is real data.
Bi-variate data fitting is done by two stochastic
        components: the marginal distributions and the dependency
        structure. The dependency structure is modeled through a
        copula. An algorithm was implemented considering seven families
        of copulas (Generalized Archimedean Copulas), the best fitting
        can be obtained looking all copula's options (totally positive
        of order 2 and stochastically increasing models).
This is a package for implementation of Flury-Gautschi
        algorithms.
Environment for teaching "Financial Engineering and
        Computational Finance".
Growth models and forest production require existing data
    manipulation and the creation of new data, structured from basic forest
    inventory data. The purpose of this package is provide functions to support
    these activities.
Exact MLE and Whittle MLE estimation for power law decay models.
Goodness-of-fit test with multiplier or parametric
        bootstrap.
A permutation technique to explore and control for spatial autocorrelation. This package contains low level functions for performing permutations and calculating statistics as well as higher level functions. Higher level functions are an easy to use function for performing spatially restricted permutation tests and summarize and plot results.
Implement algorithms for feature grouping and selection over an undirected graph, solves problems like graph fused lasso, graph OSCAR and so on.
Rapidly create a GUI interface for a function you created
        by automatically creating widgets for arguments of the
        function. Automatically parses help routines for
        context-sensative help to these arguments. The interface
        essentially a wrapper to some tcltk routines to both simplify
        and facilitate GUI creation. More advanced tcltk routines/GUI
        objects can be incorporated into the interface for greater
        customization for the more experienced.
Impute general multivariate missing data with the fractional hot deck imputation.
Functions to compare two or more survival curves with:
       a) The Fleming-Harrington test for right-censored data based on permutations and on counting processes.
       b) An extension of the Fleming-Harrington test for interval-censored data based on a permutation distribution and on a score vector distribution.
Provide functions for forest inventory calculations.
        Common volumetric equations (Smalian, Newton and Huber) as well
        stacking factor and form
Useful functions for fMRI preprocessing.
Algorithms for classical symmetric and deflation-based FastICA, reloaded deflation-based FastICA algorithm and an algorithm for adaptive deflation-based FastICA using multiple nonlinearities.
For curve, surface and function fitting with an emphasis
 on splines, spatial data and spatial statistics. The major methods
 include cubic, and thin plate splines, Kriging, and compactly supported
 covariance functions for large data sets. The splines and Kriging methods are
 supported by functions that can determine the smoothing parameter
 (nugget and sill variance) and other covariance function parameters by cross
 validation and also by restricted maximum likelihood. For Kriging
 there is an easy to use function that also estimates the correlation
 scale (range parameter).  A major feature is that any covariance function
 implemented in R and following a simple format can be used for
 spatial prediction. There are also many useful functions for plotting
 and working with spatial data as images. This package also contains
 an implementation of sparse matrix methods for large spatial data
 sets and currently requires the sparse matrix (spam) package. Use
 help(fields) to get started and for an overview.  The fields source
 code is deliberately commented and provides useful explanations of
 numerical details as a companion to the manual pages. The commented
 source code can be viewed by expanding  source code version
 and looking in the R subdirectory. The reference for fields can be generated
 by the citation function in R and has DOI <doi:10.5065/D6W957CT>.
Tools for random fields and bridges simulations.
A very flexible framework for building server side logic in R. The 
    framework is unopinionated when it comes to how HTTP requests and WebSocket
    messages are handled and supports all levels of app complexity; from serving
    static content to full-blown dynamic web-apps. Fiery does not hold your hand
    as much as e.g. the shiny package does, but instead sets you free to create
    your web app the way you want.
Functions and datasets that can be used for data cleanup (e.g., functions for eliminating all but a few columns from a dataset, selecting a range of columns, quickly editing column names), plotting/presenting data (prism-like reproductions, spearman plots for ordinal data, making colored tables, plotting interactions with quantitative variables), and analyses common to biostatistics (e.g., random forest, multiple comparisons with chi square tests). See the package vignette for a brief introduction to many of the main functions.
A simple data package to ease the process of creating choropleths
    in 'ggplot2' with all fifty U.S. states and Washington D.C.,
    including Alaska and Hawaii as insets.
Implements a simple key-value style database where character string keys
  are associated with data values that are stored on the disk. A simple interface is provided for inserting,
  retrieving, and deleting data from the database. Utilities are provided that allow 'filehash' databases to be
  treated much like environments and lists are already used in R. These utilities are provided to encourage
  interactive and exploratory analysis on large datasets. Three different file formats for representing the
  database are currently available and new formats can easily be incorporated by third parties for use in the
  'filehash' framework.
Simple key-value database using SQLite as the backend
Interface for working with large matrices stored in files,
  not in computer memory. Supports multiple non-character
  data types (double, integer, logical and raw) of
  various sizes (e.g. 8 and 4 byte real values).
  Access to parts of the matrix is done by indexing, 
  exactly as with usual R matrices.
  Supports very large matrices.
  Tested on multi-terabyte matrices.
  Allows for more than 2^32 rows or columns.
  Allows for quick addition of extra columns to a filematrix.
  Cross-platform as the package has R code only.
Create descriptive file names with ease. New file names are
    automatically (but optionally) time stamped and placed in date stamped
    directories. Streamline your analysis pipeline with input and output file
    names that have informative tags and proper file extensions.
Perform chunk processing or split-apply-combine on data in a
    delimited file (example: CSV) and Distributed Dataframes (DDF) across multiple
    cores of a single machine with low memory footprint. These functions are a
    convenient wrapper over the versatile package 'datadr'.
Functions for printing the contents of a folder as columns in a ragged-bottom data.frame and for
  viewing the details (size, time created, time modified, etc.) of a folder's top level contents.
Convenient functions for moving files, deleting directories, 
    and a variety of string operations that facilitate manipulating file names 
    and extracting information from strings.
Functions for financial analysis and financial modeling, 
             including batch graphs generation, beta calculation, 
             descriptive statistics, annuity calculation, bond pricing 
             and financial data download.
Time value of money, cash flows and other financial
        functions.
Infrastructure for defining meta-data and
    relationships for financial instruments.
Contains financial math functions and introductory derivative functions included in the Society of Actuaries and Casualty Actuarial Society 'Financial Mathematics' exam, and some topics in the 'Models for Financial Economics' exam.
This package accomplishes two tasks: a) it classifies
        implicit trading activity from quotes in OTC markets using the
        algorithm of Lee and Ready (1991); b) based on information for
        trade initiation, the package computes the probability of
        informed trading of Easley and O'Hara (1987).
Package for time value of money calculation, time series analysis and computational finance.
Parse and create Darwin Core (<http://rs.tdwg.org/dwc/>) Simple 
    and Archives. Functionality includes reading and parsing all the 
    files in a Darwin Core Archive, including the datasets and metadata; 
    read and parse simple Darwin Core files; and validation of Darwin 
    Core Archives.
Estimation and regularization for covariance matrix of asset
    returns. For covariance matrix estimation, three major types of factor
    models are included: macroeconomic factor model, fundamental factor model and
    statistical factor model. For covariance matrix regularization, four regularized
    estimators are included: banding, tapering, hard-thresholding and soft-
    thresholding. The tuning parameters of these regularized estimators are selected
    via cross-validation.
Find all root(s) of the equation,including complex
        roots;Find root(s) of the equation by dichotomy.Besides,in
        dichotomy, more than one interval can be given at a time.
The heterogeneous treatment effect estimation procedure 
        proposed by Imai and Ratkovic (2013)<DOI: 10.1214/12-AOAS593>.  
        The proposed method is applicable, for
        example, when selecting a small number of most (or least)
        efficacious treatments from a large number of alternative
        treatments as well as when identifying subsets of the
        population who benefit (or are harmed by) a treatment of
        interest. The method adapts the Support Vector Machine
        classifier by placing separate LASSO constraints over the
        pre-treatment parameters and causal heterogeneity parameters of
        interest. This allows for the qualitative distinction between
        causal and other parameters, thereby making the variable
        selection suitable for the exploration of causal heterogeneity. 	
        The package also contains a class of functions, CausalANOVA, 
        which estimates the average marginal interaction effects (AMIEs)
        by a regularized ANOVA as proposed by Egami and Imai (2016+). 
        It contains a variety of regularization techniques to facilitate 
	analysis of large factorial experiments.
Creates models from all combinations of a list of variables and sorts by minimum IC (information criterion).
Package designed to find an acceptable python binary.
A tool to explore wide data sets, by detecting, ranking
    and plotting groups of statistically dependent columns.
Statistical tool set for population genetics. The package provides following functions: 1) empirical Bayes estimator of Fst and other measures of genetic differentiation, 2) regression analysis of environmental effects on genetic differentiation using bootstrap method, 3) interfaces to read and manipulate 'GENEPOP' format data files and allele/haplotype frequency format files.
A S4 class to represent binary 'fingerprints' and methods to manipulate 
 fingerprint objects. Internally, the 'fingerprint' class models a binary fingerprint
 as a vector of integers, such
 that each element represents the position in the fingerprint that is set to 1.
 The bitwise logical functions in R are overridden so that they can be used directly
 with 'fingerprint' objects. A number of distance metrics are also
 available (many contributed by Michael Fadock). Fingerprints 
 can be converted to Euclidean vectors (i.e., points on the unit hypersphere) and
 can also be folded using OR.  Arbitrary fingerprint formats can be handled via line
 handlers. Currently handlers are provided for CDK, MOE and BCI fingerprint data.
Fingertips (<http://fingertips.phe.org.uk/>) contains data for many indicators of public health in England. The
    underlying data is now more easily accessible by making use of the API.
In the Cramér–Lundberg risk process perturbed by a Wiener
    process, this packages provides approximations to the probability of
    ruin within a finite time horizon.  Currently, there are three methods
    implemented: The first one uses saddlepoint approximation (two
    variants are provided), the second one uses importance sampling and
    the third one is based on the simulation of a dual process.  This last
    method is not very accurate and only given here for completeness.
Download and display company financial data from the U.S. Securities
    and Exchange Commission's EDGAR database. It contains a suite of functions with
    web scraping and XBRL parsing capabilities that allows users to extract data from EDGAR 
    in an automated and scalable manner. See <https://www.sec.gov/edgar/searchedgar/companysearch.html>
    for more information.
The FisherEM package provides an efficient algorithm for
        the unsupervised classification of high-dimensional data. This
        FisherEM algorithm models and clusters the data in a
        discriminative and low-dimensional latent subspace. It also
        provides a low-dimensional representation of the clustered
        data. A sparse version of Fisher-EM algorithm is also provided.
fisheyeR provides tools for creating Interactive Data
        Visualizations by implementing ideas from Furnas, Munzner,
        Costa and Venturini.
FisHiCal integrates Hi-C and FISH data, offering a modular and easy-to-use tool for chromosomal spatial analysis.
Dataset of 302 measurements of 11 fish species to accompany the
    manuscript "Length-weight relationships of six freshwater fish species
    from lake Kirkkojarvi, Finland".
Fishery science methods and models from published literature and contributions from colleagues.
Fits models to catch and effort data. Single-species models are 1) delta log-normal, 2) Tweedie, or 3) Poisson-gamma (G)LMs.
Functions to predict fish movement parameters plotting leptokurtic fish dispersal kernels (see Radinger and Wolter, 2014: Patterns and predictors of fish dispersal in rivers. Fish and Fisheries. 15:456-473.)
Calculates metabolic rate of fish and other aquatic organisms measured
    using an intermittent-flow respirometry approach. The tool is used to
    run a set of graphical QC tests of raw respirometry data, correct it for
    background respiration and chamber effect, filter and extract target
    values of absolute and mass-specific metabolic rate. Experimental design
    should include background respiration tests and measuring of one or two
    metabolic rate traits. The package allows a user to import raw respirometry
    data obtained from 'AutoResp' (see <http://www.loligosystems.com> for more
    information) or other oxygen logger software.
Provides functionality for constructing
    statistical models of transcriptomic dynamics in field conditions.
    It further offers the function to predict expression of a gene given 
    the attributes of samples and meteorological data.
Comprehensive model building function for identification,
        estimation and diagnostic checking for AR and subset AR models.
        Two types of subset AR models are supported.  One family of
        subset AR models, denoted by ARp, is formed by taking subet of
        the original AR coefficients and in the other, denoted by ARz,
        subsets of the partial autocorrelations are used. The main
        advantage of the ARz model is its applicability to very large
        order models.
Implements fast maximum likelihood algorithm for fitting ARMA time series. Uses S3 methods print, summary, fitted, residuals. Fast exact Gaussian ARMA simulation.
Scrapes data from Fitbit <http://www.fitbit.com>. This does not use the official
    API, but instead uses the API that the web dashboard uses to generate the graphs
    displayed on the dashboard after login at <http://www.fitbit.com>.
A pure R package for decoding activity files written in the FIT ("Flexible and Interoperable Data Transfer") format. A format that is fast becoming the standard for recording running and cycling data. Details of the FIT protocol can be found at <https://www.thisisant.com/resources/fit>.
Extends the fitdistr() function (of the MASS package) with several functions to help the fit of a parametric distribution to non-censored or censored data. Censored data may contain left censored, right censored and interval censored values, with several lower and upper bounds. In addition to maximum likelihood estimation (MLE), the package provides moment matching (MME), quantile matching (QME) and maximum goodness-of-fit estimation (MGE) methods (available only for non-censored data). Weighted versions of MLE, MME and QME are available.
Fits Density Ratio Classes to elicited
        probability-quantile points or intervals
The fit.models function and its associated methods (coefficients,
  print, summary, plot, etc.) were originally provided in the robust package to
  compare robustly and classically fitted model objects. The aim of the fit.models
  package is to separate this fitted model object comparison functionality from
  the robust package and to extend it to support fitting methods (e.g., classical,
  robust, Bayesian, regularized, etc.) more generally.
Fits Weibull or sigmoidal models to percent loss conductivity (plc) curves as a function of plant water potential, computes confidence intervals of parameter estimates and predictions with bootstrap or parametric methods, and provides convenient plotting methods.
Utilities to read and write files in the FITS (Flexible
  Image Transport System) format, a standard format in astronomy (see
  e.g. <https://en.wikipedia.org/wiki/FITS> for more information).
  Present low-level routines allow: reading, parsing, and modifying
  FITS headers; reading FITS images (multi-dimensional arrays);
  reading FITS binary and ASCII tables; and writing FITS images
  (multi-dimensional arrays).  Higher-level functions allow: reading
  files composed of one or more headers and a single (perhaps
  multidimensional) image or single table; reading tables into
  data frames; generating vectors for image array axes; scaling and
  writing images as 16-bit integers.  Known incompletenesses are
  reading random group extensions, as well as
  bit, complex, and array descriptor data types in binary tables.
Package fitTetra contains three functions that can be used
        to assign genotypes to a collection of tetraploid samples based
        on bialleleic marker assays. Functions fitTetra (to fit several
        models for one marker from the data and select the best
        fitting) or saveMarkerModels (calls fitTetra for multiple
        markers and saves the results to files) will probably be the
        most convenient to use. Function CodomMarker offers more
        control and fits one specified model for a given marker.
Wrapper for computing parameters and then assigning to distribution
    function families.
Datasets and code published by the data journalism website 
    'FiveThirtyEight' available at <https://github.com/fivethirtyeight/data>. 
    Note that while we received guidance from editors at 'FiveThirtyEight', this 
    package is not officially published by 'FiveThirtyEight'.
Distribution functions and test for over-representation of short
    distances in the Liland distribution. Simulation functions are included for
    comparison.
Several generalized / directional Fixed Sequence Multiple Testing
    Procedures (FSMTPs) are developed for testing a sequence of pre-ordered
    hypotheses while controlling the FWER, FDR and Directional Error (mdFWER).
    All three FWER controlling generalized FSMTPs are designed under arbitrary
    dependence, which allow any number of acceptances. Two FDR controlling
    generalized FSMTPs are respectively designed under arbitrary dependence and
    independence, which allow more but a given number of acceptances. Two mdFWER
    controlling directional FSMTPs are respectively designed under arbitrary
    dependence and independence, which can also make directional decisions based
    on the signs of the test statistics. The main functions for each proposed
    generalized / directional FSMTPs are designed to calculate adjusted p-values
    and critical values, respectively. For users' convenience, the functions also
    provide the output option for printing decision rules.
An implementation of the Fizz Buzz algorithm, as defined e.g. in <https://en.wikipedia.org/wiki/Fizz_buzz>. 
    It provides the standard algorithm with 3 replaced by Fizz and 5 replaced by Buzz, with the option of specifying start 
    and end numbers, step size and the numbers being replaced by fizz and buzz, respectively. This package gives 
    interviewers the optional answer of "I use fizzbuzzR::fizzbuzz()" when interviewing rather than having to write an algorithm
    themselves.
This is a fast and flexible implementation of the Kalman
        filter, which can deal with NAs. It is entirely written in C
        and relies fully on linear algebra subroutines contained in
        BLAS and LAPACK. Due to the speed of the filter, the fitting of
        high-dimensional linear state space models to large datasets
        becomes possible. This package also contains a plot function
        for the visualization of the state vector and graphical
        diagnostics of the residuals.
Contains tools and features, which can be used for an Exploratory
    Landscape Analysis (ELA) of single-objective continuous optimization problems.
    Those features are able to quantify rather complex properties, such as the
    global structure, separability, etc., of the optimization problems.
Implements the fused lasso additive model as proposed in Petersen, A., Witten, D., and Simon, N. (2015). Fused Lasso Additive Model. To appear in the Journal of Computational and Graphical Statistics.
Tools for fluctuations analysis of mutant cells counts.
The package "flare" provides the implementation of a family of Lasso variants including Dantzig Selector, LAD Lasso, SQRT Lasso, Lq Lasso for estimating high dimensional sparse linear model. We adopt the alternating direction method of multipliers and convert the original optimization problem into a sequential L1 penalized least square minimization problem, which can be efficiently solved by linearization algorithm. A multi-stage screening approach is adopted for further acceleration. Besides the sparse linear model estimation, we also provide the extension of these Lasso variants to sparse Gaussian graphical model estimation including TIGER and CLIME using either L1 or adaptive penalty. Missing values can be tolerated for Dantzig selector and CLIME. The computation is memory-optimized using the sparse matrix output.
Variable selection algorithm for functional linear regression with scalar response variable and mixed scalar/functional predictors.
Fast implementation of hierarchical clustering
The main function kcca implements a general framework for
        k-centroids cluster analysis supporting arbitrary distance
        measures and centroid computation. Further cluster methods
        include hard competitive learning, neural gas, and QT
        clustering. There are numerous visualization methods for
        cluster results (neighborhood graphs, convex cluster hulls,
        barcharts of centroids, ...), and bootstrap methods for the
        analysis of cluster stability.
Allows for maximum likelihood fitting of cluster-weighted models, a class of mixtures of regression models with random covariates.
Format for converting an R Markdown document to a grid oriented
  dashboard. The dashboard flexibly adapts the size of it's components to the
  containing web page.
Provides tools to work with the Flexible Dirichlet
    distribution. The main features are an E-M algorithm for computing the maximum
    likelihood estimate of the parameter vector and a function based on conditional
    bootstrap to estimate its asymptotic variance-covariance matrix. It contains
    also functions to plot graphs, to generate random observations and to handle
    compositional data.
A general framework for finite mixtures of regression models
  using the EM algorithm is implemented. The package provides the E-step
  and all data handling, while the M-step can be supplied by the user to
  easily define new models. Existing drivers implement mixtures of standard
  linear models, generalized linear models and model-based clustering.
Model selection tools and 'selfStart' functions to fit parametric curves in 'nls', 'nlsList' and 'nlme' frameworks.
Estimation of flexible parametric models for survival data.
Package for parametric relative survival analyses. It allows to model non-linear and 
        non-proportional effects using splines (B-spline and truncated power basis). It also includes 
        both non proportional and non linear effects of 
			Remontet, L. et al. (2007) <DOI:10.1002/sim.2656> and 
			Mahboubi, A. et al. (2011) <DOI:10.1002/sim.4208>.
Flexible parametric models for time-to-event data,
    including the Royston-Parmar spline model, generalized gamma and
    generalized F distributions.  Any user-defined parametric
    distribution can be fitted, given at least an R function defining
    the probability density or hazard. There are also tools for
    fitting and predicting from fully parametric multi-state models.
Flexible parametric mixture and non-mixture cure models for time-to-event data.
Create pretty tables for 'Microsoft Word', 'Microsoft PowerPoint' and 'HTML' documents. 
  Functions are provided to let users create tables, modify and format their content. 
  It extends package 'officer' that does not contain any feature for customized tabular reporting. 
  Function tabwid() produces an 'htmlwidget' ready to be used in 'Shiny' or 'R Markdown (*.Rmd)' documents.
Functions to create and manipulate 
    FIFO (First In First Out), LIFO (Last In First Out), and NINO (Not In or Never Out) 
    stacks in R.
Estimate positions of animal from data collected by solar geolocation archival tags.
FLIM fits linear models for the observed increments in a longitudinal dataset, and imputes missing values according to the models.
It implements many univariate and multivariate permutation (and rotation) tests. Allowed tests: the t one and two samples, ANOVA, linear models, Chi Squared test, rank tests (i.e. Wilcoxon, Mann-Whitney, Kruskal-Wallis), Sign test and McNemar. Test on Linear Models are performed also in presence of covariates (i.e. nuisance parameters). The permutation and the rotation methods to get the null distribution of the test statistics are available. It also implements methods for multiplicity control such as Westfall-Young minP procedure and Closed Testing (Marcus, 1976) and k-FWER. Moreover, it allows to test for fixed effects in mixed effects models.
The lipid scrambling activity of protein extracts and purified
    scramblases is often determined using a fluorescence-based assay involving
    many manual steps. flippant offers an integrated solution for the analysis
    and publication-grade graphical presentation of dithionite scramblase
    assays, as well as a platform for review, dissemination and extension of the
    strategies it employs. The package's name derives from a play on the fact
    that lipid scrambling is also sometimes referred to as 'flipping'.
Fits the Fused Lasso Latent Feature model, which is used for modeling multi-sample aCGH data to identify regions of copy number variation (CNV).  Produces a set of features that describe the patterns of CNV and a set of weights that describe the composition of each sample.  Also provides functions for choosing the optimal tuning parameters and the appropriate number of features, and for estimating the false discovery rate.
Implements synchronization between R processes (spawned by using the "parallel" package for instance) using file locks. Supports both exclusive and shared locking.
Includes several statistical methods for the estimation of parameters and high quantiles of river flow distributions. The focus is on regional estimation based on homogeneity assumptions and computed from multivariate observations (multiple measurement stations).
	For details see Kinsvater et al. (2017) <arXiv:1701.06455>.
Tools to quickly compile taxonomic and distribution data from
    the Brazilian Flora 2020 at http://floradobrasil.jbrj.gov.br/.
Concatenates some 'flowWorkspace', 'flowCore', 'vegan' and 'gdata' packages functionalities to import 'FlowJo' workspaces and calculates ecological diversity indices for gated populations, based on two dimensions cytograms.
Flowering is an important life history trait of flowering plants. It has been mainly analyzed with respect to flowering onset and duration of flowering. This tools provide some functions to compute the temporal distribution of an flowering individual related to other population members. fCV() measures the temporal variation in flowering. RIind() measures the rank order of flowering for individual plants within a population. SI(), SI2(), SI3(), and SI4() calculate flowering synchrony with different methods.
Flow field forecasting draws information from an interpolated flow field of the observed time series to incrementally build a forecast.
This framework allows you to design and implement complex
    pipelines, and deploy them on your institution's computing cluster. This has
    been built keeping in mind the needs of bioinformatics workflows. However, it is
    easily extendable to any field where a series of steps (shell commands) are to
    be executed in a (work)flow.
Selections on flow matrices, statistics on selected flows, map and
    graph visualisations.
Screens daily streamflow time series for temporal trends and
    change-points. This package has been primarily developed for assessing the
    quality of daily streamflow time series. It also contains tools for plotting
    and calculating many different streamflow metrics. The package can be used to
    produce summary screening plots showing change-points and significant temporal
    trends for high flow, low flow, and/or baseflow statistics, or it can be used
    to perform more detailed hydrological time series analyses. The package was
    designed for screening daily streamflow time series from Water Survey Canada
    and the United States Geological Survey but will also work with streamflow time
    series from many other agencies.
This package implements a path algorithm for the Fused
        Lasso Signal Approximator. For more details see the help files
A novel algorithm for solving the subset sum problem with bounded error in multidimensional real domain and its application to the general-purpose knapsack problem.
Contains data sets from Bernard Flury (1997) A First
        Course in Multivariate Statistics, Springer NY
Functions for the calculation of greenhouse gas flux rates 
	from closed chamber concentration measurements. The package follows 
	a modular concept: Fluxes can be calculated in just two simple steps 
	or in several steps if more control in details is wanted. Additionally 
	plot and preparation functions as well as functions for modelling
	gpp and reco are provided.
All data sets from "Forecasting: methods and applications" by
             Makridakis, Wheelwright & Hyndman (Wiley, 3rd ed., 1998).
Implements basic financial market objects like currencies, currency
  pairs, interest rates and interest rate indices. You will be able to use
  Benchmark instances of these objects which have been defined using their most
  common conventions or those defined by International Swap Dealer Association
  (ISDA, <http://www2.isda.org>) legal documentation.
Generate cost effective minimally changed run sequences 
              for symmetrical as well as asymmetrical factorial 
              designs.
Implements common date calculations relevant for specifying
  the economic nature of financial market contracts that are typically defined
  by International Swap Dealer Association (ISDA, <http://www2.isda.org>) legal
  documentation. This includes methods to check whether dates are business
  days in certain locales, functions to adjust and shift dates and time length
  (or day counter) calculations.
Provides functions to help in fitting models to data, to
  perform Monte Carlo, sensitivity and identifiability analysis. It is
  intended to work with models be written as a set of differential
  equations that are solved either by an integration routine from
  package 'deSolve', or a steady-state solver from package
  'rootSolve'. However, the methods can also be used with other types of
  functions.
Estimates Filtered Monotonic Polynomial IRT Models as described by Liang and Browne (2015) <DOI:10.3102/1076998614556816>.
Contains R-functions to perform an fMRI analysis as described in
             Tabelow et al. (2006) <DOI:10.1016/j.neuroimage.2006.06.029>,
             Polzehl et al. (2010) <DOI:10.1016/j.neuroimage.2010.04.241>,
             Tabelow and Polzehl (2011) <DOI:10.18637/jss.v044.i11>.
Provides parameter estimation as well as variable selection in
    Finite Mixture of Accelerated Failure Time Regression and Finite
    Mixture of Regression Models.
    Furthermore, this package provides Ridge Regression and Elastic Net.
Several utility functions for the book entitled 
	"Practices of Medical and Health Data Analysis using R"
	(Pearson Education Japan, 2007) with Japanese demographic
	data and some demographic analysis related functions.
Fit linear regression models where the random errors follow a finite mixture of of Skew Heavy-Tailed Errors.
This package implements some basic procedures for dealing
        with log maximally skew stable distributions, which are also
        called finite moment log stable distributions.
This package computes posterior residual variances to be
        used in the denominator of a moderated t-statistic from a
        linear model analysis of microarray data.  It is an extension
        of the moderated t-statistic original proposed by Smyth (2004).
        LOESS local regression and empirical Bayesian method are used
        to estimate gene specific prior degrees of freedom and prior
        variance based on average gene intensity level. The posterior
        residual variance in the denominator is a weighted average of
        prior and residual variance and the weights are prior degrees
        of freedom and residual variance degrees of freedom. The
        degrees of freedom of the moderated t-statistic is simply the
        sum of prior and residual variance degrees of freedom.
Environment for teaching 
	"Financial Engineering and Computational Finance"
Cover-tree and kd-tree fast k-nearest neighbor search algorithms and related applications
        including KNN classification, regression and information measures are implemented.
foba is a package that implements forward, backward, and foba sparse learning algorithms for ridge regression, described in the paper "Adaptive Forward-Backward Greedy Algorithm for Learning Sparse Representations".
Takes a distance matrix and plots it as an 
    interactive graph. One point is focused at the center of the graph,
    around which all other points are plotted in their exact distances as
    given in the distance matrix. All other non-focus points are plotted 
    as best as possible in relation to one another. Double click on any 
    point to choose a new focus point, and hover over points to see their
    ID labels. If color label categories are given, hover over colors in 
    the legend to highlight only those points and click on colors to 
    highlight multiple groups. For more information on the rationale 
    and mathematical background, as well as an interactive introduction,
    see <https://lea-urpa.github.io/focusedMDS.html>.
The CRAN check results in your R terminal.
Defines a compact data format that includes metadata. 
 The function fold() creates the format by converting 
 from data.frame, and unfold() converts back. The predictability
 of the folded format supports reusability of data processing tools,
 while the presence of embedded metadata improves portability, 
 interpretability, and efficiency.
Provides fonts licensed under the 'Bitstream Vera Fonts'
    license for the 'fontquiver' package.
Computer Modern font for use with extrafont package
Provides 'ggplot2' themes based on the 'Hind' fonts.
  'Hind' is an open source 'typeface' supporting the 'Devanagari' and Latin scripts.
  Developed explicitly for use in User Interface design, the 'Hind' font family includes five styles.
  More information about the font can be found at <https://fonts.google.com/specimen/Hind>.
A placeholder for the Liberation fontset intended for the
    `fontquiver` package. This fontset covers the 12 combinations of
    families (sans, serif, mono) and faces (plain, bold, italic, bold
    italic) supported in R graphics devices.
Provides 'ggplot2' themes based on the 'M+' fonts.
  The 'M+' fonts are a font family under a free license. The font family provides
  multilingual glyphs. The fonts provide 'Kana', over 5,000 'Kanji', Basic Latin,
  Latin-1 Supplement, Latin Extended-A, and 'IPA' Extensions glyphs. Most of the Greek,
  Cyrillic, Vietnamese, and extended glyphs and symbols are included too.
  So the fonts are in conformity with ISO-8859-1, 2, 3, 4, 5, 7, 9, 10, 13, 14, 15, 16,
  Windows-1252, T1, and VISCII encoding.
  More information about the fonts can be found at <http://mplus-fonts.osdn.jp/about-en.html>.
Provides a set of fonts with permissive licences. This is
    useful when you want to avoid system fonts to make sure your
    outputs are reproducible.
Calculates twelve commonly-used, basic measures of food
        web network structure from binary, predator-prey matrices:
        species richness, connectance, total number of links, link
        density, number of trophic positions, predator:prey ratio, and
        fraction of carnivores, herbivores, top species and
        intermediate species.  Employs food web language in the code
        and output, translates between a couple of common food web
        formats, can handle food webs consisting of multiple levels,
        and can automate the analysis for a large number of webs. The
        program produces 3-dimensional graphs of high quality that can
        be customized by the user.
Environment for teaching 
	"Financial Engineering and Computational Finance".
	Pricing and Evaluating Basic Options.
SHE, FORAM Index and ABC Method analyses and custom plot
        functions for community data.
Helpers for reordering factor levels (including moving
    specified levels to front, ordering by first appearance, reversing, and
    randomly shuffling), and tools for modifying factor levels (including
    collapsing rare levels into other, 'anonymising', and manually 'recoding').
Support for the foreach looping construct.  Foreach is an
        idiom that allows for iterating over elements in a collection,
        without the use of an explicit loop counter.  This package in
        particular is intended to be used for its return value, rather
        than for its side effects.  In that sense, it is similar to the
        standard lapply function, but doesn't require the evaluation
        of a function.  Using foreach without side effects also
        facilitates executing the loop in parallel.
Implementation of Forecastable Component Analysis ('ForeCA'),
    including main algorithms and auxiliary function (summary, plotting, etc.) to
    apply 'ForeCA' to multivariate time series data. 'ForeCA' is a novel dimension
    reduction (DR) technique for temporally dependent signals. Contrary to other
    popular DR methods, such as 'PCA' or 'ICA', 'ForeCA' takes time dependency
    explicitly into account and searches for the most ''forecastable'' signal.
    The measure of forecastability is based on the Shannon entropy of the spectral
    density of the transformed signal.
Methods and tools for displaying and analysing
             univariate time series forecasts including exponential smoothing
             via state space models and automatic ARIMA modelling.
Aim: Supports the most frequently used methods to combine forecasts. Among others: Simple average, Ordinary Least Squares, Least Absolute Deviation, Constrained Least Squares, Variance-based, Best Individual model, Complete subset regressions and Information-theoretic (information criteria based).
Create modular models.  Quickly prototype models whose input includes (multiple) time series data.  Create pieces of model use cases separately, and swap out particular models as desired. Create modeling competitions, data processing pipelines, and re-useable models.
Convenient functions for ensemble forecasts in R combining
    approaches from the 'forecast' package. Forecasts generated from auto.arima(), ets(),
    thetam(), nnetar(), stlm(), and tbats() can be combined with equal weights, weights
    based on in-sample errors, or CV weights. Cross validation for time series data
    and user-supplied models and forecasting functions is also supported to evaluate model accuracy.
Methods to compute linear h-step ahead prediction coefficients based
    on localised and iterated Yule-Walker estimates and empirical mean squared
    and absolute prediction errors for the resulting predictors. Also, functions
    to compute autocovariances for AR(p) processes, to simulate tvARMA(p,q) time
    series, and to verify an assumption from Kley et al. (2017),
    Preprint <http://personal.lse.ac.uk/kley/forecastSNSTS.pdf>.
Routines for forecasting univariate time series using Theta Models. Contains several cross-validation routines.
The implemented algorithm performs a floating-point genetic algorithm search with a statistical forecasting operator that generates offspring which probably will be generated in future generations. Use of this operator enhances the search capabilities of floating-point genetic algorithms because offspring generated by usual genetic operators rapidly forecasted before performing more generations.
Reading and writing data stored by some versions of
	'Epi Info', 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka',
	and for reading and writing some 'dBase' files.
The statistical evaluation of DNA mixtures, DNA profile
        match probability
Statistical methods and simulation tools for the
        interpretation of forensic DNA mixtures
Form visualizations of high dimensional mapping structures of random forests and feature contributions.
Extensive global and small-area estimation procedures for multiphase
    forest inventories under the design-based Monte-Carlo approach are provided.
    The implementation includes estimators for simple and cluster sampling
    published by Daniel Mandallaz in 2007 (<DOI:10.1201/9781584889779>),
    2013 (<DOI:10.1139/cjfr-2012-0381>, <DOI:10.1139/cjfr-2013-0181>,
    <DOI:10.1139/cjfr-2013-0449>, <DOI:10.3929/ethz-a-009990020>)
    and 2016 (<DOI:10.3929/ethz-a-010579388>). It provides point estimates,
    their external- and design-based variances as well as confidence intervals.
    The procedures have also been optimized for the use of remote sensing data
    as auxiliary information.
Produces forest plots using 'ggplot2' from models produced by functions
    such as stats::lm(), stats::glm() and survival::coxph().
A forest plot that allows for
    multiple confidence intervals per row,
    custom fonts for each text element,
    custom confidence intervals,
    text mixed with expressions, and more.
    The aim is to extend the use of forest plots beyond meta-analyses.
    This is a more general version of the original 'rmeta' package's forestplot()
    function and relies heavily on the 'grid' package.
Provides tools for analyzing remotely sensed forest data, including functions for detecting treetops from canopy models, outlining tree crowns and generating spatial statistics.
Imputation of missing values in datasets of ordinal variables through a forward imputation algorithm
This package provides estimates of tree volume and biomass from
    Italian NFI models
Implemented, addition, subtracking, multiplication,
        division in formal series rings of any number of variables
        (except division is only to 3 variables). Also are available
        "[" "[<-" operators.
Provides a function tidy_source() to format R source code. Spaces
    and indent will be added to the code automatically, and comments will be
    preserved under certain conditions, so that R code will be more
    human-readable and tidy. There is also a Shiny app as a user interface in
    this package (see tidy_app()).
Provides functions to create formattable vectors and data frames.
    'Formattable' vectors are printed with text formatting, and formattable
    data frames are printed with multiple types of formatting in HTML
    to improve the readability of data presented in tabular form rendered in
    web pages.
Infrastructure for extended formulas with multiple parts on the
             right-hand side and/or multiple responses on the left-hand side
	     (see <DOI:10.18637/jss.v034.i01>).
These utilities facilitate the programmatic manipulations of
    formulas, expressions, calls, names, symbols and other objects. These 
    objects all share the same structure: a left-hand side operator and 
    right-hand side. This packages provides methods for accessing and modifying
    the structures as well as extracting names and symbols from these objects.
A collection of fortunes from the R community.
Forward search approach to robust analysis in linear and
        generalized linear regression models.
Anonymized data from surveys conducted by Forwards <http://forwards.github.io/>, the R Foundation task force on women and other under-represented groups. Currently, a single data set of responses to a survey of attendees at useR! 2016 <http://user2016.org/>, the R user conference held at Stanford University, Stanford, California, USA, June 27 - June 30 2016.
Forward Search analysis of time series regressions. Implements the asymptotic theory developed in Johansen and Nielsen (2013, 2014).
A set of analytical tools useful in analysing ecological
        and geographical data sets, both ancient and modern. The
        package includes functions for estimating species richness
        (Chao 1 and 2, ACE, ICE, Jacknife), shared species/beta
        diversity, species area curves and geographic distances and
        areas.
Computes Fourier integrals of functions of one and two variables using the Fast Fourier transform. The Fourier transforms must be evaluated on a regular grid.
Estimate Barton & Lord's (1981) <doi:10.1002/j.2333-8504.1981.tb01255.x> 
    four parameter IRT model with lower and upper asymptotes using Bayesian
    formulation described by Culpepper (2016) <doi:10.1007/s11336-015-9477-6>.
A game for two players: Who gets first four in a row
        (horizontal, vertical or diagonal) wins. Published by Milton
        Bradley, designed by Howard Wexler and Ned Strongin.
Spatio-temporal Fixation Pattern Analysis (FPA) is a new method of analyzing eye 
  movement data, developed by Mr. Jinlu Cao under the supervision of Prof. Chen Hsuan-Chih at 
  The Chinese University of Hong Kong, and Prof. Wang Suiping at the South China Normal 
  Univeristy. The package "fpa" is a R implementation which makes FPA analysis much easier. 
  There are four major functions in the package: ft2fp(), get_pattern(), plot_pattern(), and 
  lineplot(). The function ft2fp() is the core function, which can complete all the preprocessing 
  within moments. The other three functions are supportive functions which visualize the eye 
  fixation patterns.
Various methods for clustering and cluster validation.
  Fixed point clustering. Linear regression clustering. Clustering by 
  merging Gaussian mixture components. Symmetric 
  and asymmetric discriminant projections for visualisation of the 
  separation of groupings. Cluster validation statistics
  for distance based clustering including corrected Rand index. 
  Cluster-wise cluster stability assessment. Methods for estimation of 
  the number of clusters: Calinski-Harabasz, Tibshirani and Walther's 
  prediction strength, Fang and Wang's bootstrap stability. 
  Gaussian/multinomial mixture fitting for mixed 
  continuous/categorical variables. Variable-wise statistics for cluster
  interpretation. DBSCAN clustering. Interface functions for many 
  clustering methods implemented in R, including estimating the number of
  clusters with kmeans, pam and clara. Modality diagnosis for Gaussian
  mixtures. For an overview see package?fpc.
A geometric approach to MLE for functional principal
        components
Compute the two dimension functional principal component scores for a series of two dimension images.
Comparisons of floating point numbers are problematic due to errors
    associated with the binary representation of decimal numbers.
    Despite being aware of these problems, people still use numerical methods
    that fail to account for these and other rounding errors (this pitfall is
    the first to be highlighted in Circle 1 of Burns (2012)
    [The R Inferno](http://www.burns-stat.com/pages/Tutor/R_inferno.pdf)).
    This package provides new relational operators useful for performing
    floating point number comparisons with a set tolerance.
Probabilistic distance clustering (PD-clustering) is an iterative, distribution free, probabilistic clustering method. PD-clustering assigns units to a cluster according to their probability of membership, under the constraint that the product of the probability and the distance of each point to any cluster centre is a constant. PD-clustering is a flexible method that can be used with non-spherical clusters, outliers, or noisy data. Facto PD-clustering (FPDC) is a recently proposed factor clustering method that involves a linear transformation of variables and a cluster optimizing the PD-clustering criterion. It works on high dimensional datasets.
Given the values of sampled units and selection probabilities 
             the desraj function  in the package computes the estimated value of the total
             as well as estimated variance.
Returns the noncentrality parameter of the noncentral F
        distribution if probability of type I and type II error,
        degrees of freedom of the numerator and the denominator are
        given.  It may be useful for computing minimal detectable
        differences for general ANOVA models.  This program is
        documented in the paper of A. Baharev, S. Kemeny, On the
        computation of the noncentral F and noncentral beta
        distribution; Statistics and Computing, 2008, 18 (3), 333-340.
All data sets required for the examples and exercises in
        the book "Forecasting: principles and practice" by Rob J
        Hyndman and George Athanasopoulos. All packages required to run
        the examples are also loaded.
All data sets required for the examples and exercises 
  in the book "Forecasting: principles and practice" 
  by Rob J Hyndman and George Athanasopoulos <http://OTexts.org/fpp2/>. 
  All packages required to run the examples are also loaded.
Efficient approximation of first-passage-time densities for diffusion processes based on the First-Passage-Time Location (FPTL) function.
Maximum likelihood estimation of the parameters of a
        fractionally differenced ARIMA(p,d,q) model (Haslett and
        Raftery, Appl.Statistics, 1989).
Functions for fitting data to a quiescent growth model,
        i.e. a growth process that involves members of the population
        who stop dividing or propagating.
Stochastic fractal and deterministic chaotic time series
        analysis.
Implements various methods for estimating fractal dimension of time series and 2-dimensional data.
The parameters p and q are estimated with the aid of a randomized Sierpinski Carpet which is built on a [p-p-p-q]-model. Thereby, for three times a simulation with a p-value and once with a q-value is assumed. Hence, these parameters are estimated and displayed. Moreover, a function for simulating random Sierpinski-Carpets is included.
The basic principle driving fractal generation of time
        series is that data is generated iteratively based on
        increasing levels of resolution.  The initial series is defined
        by a so-called initiator pattern and then generators are used
        to replace each segment of the initial pattern. Regular,
        repeatable patterns can be produced by using the same seed and
        generators.  By using a set of generators, non-repeatable time
        series can be produced.  This technique is the basis of the
        fractal time series process in this package.
This is the package which can help you turn
        numeric,dataframe,matrix into fraction form.
The main function of this package allows numerical vector objects to
  be displayed with their values in vulgar fractional form.  This is convenient if
  patterns can then be more easily detected.  In some cases replacing the components
  of a numeric vector by a rational approximation can also be expected to remove
  some component of round-off error.  The main functions form a re-implementation
  of the functions 'fractions' and 'rational' of the MASS package, but using a
  radically improved programming strategy.
Implements and extends the fragility index calculation for
    dichotomous results as described in Walsh, Srinathan, McAuley,
    Mrkobrada, Levine, Ribic, Molnar, Dattani, Burke, Guyatt,
    Thabane, Walter, Pogue, and Devereaux (2014)
    <DOI:10.1016/j.jclinepi.2013.10.019>.
Performs fragment analysis using genetic data coming from capillary electrophoresis machines. These are files with FSA extension which stands for FASTA-type file, and .txt files from Beckman CEQ 8000 system, both contain DNA fragment intensities read by machinery. In addition to visualization, it performs automatic scoring of SSRs (Sample Sequence Repeats; a type of genetic marker very common across the genome) and other type of PCR markers (standing for Polymerase Chain Reaction) in biparental populations such as F1, F2, BC (backcross), and diversity panels (collection of genetic diversity).
Contains functions for fitting shared frailty models with a semi-parametric
    baseline hazard with the Expectation-Maximization algorithm. Supported data formats 
    include clustered failures with left truncation and recurrent events in gap-time
    or Andersen-Gill format. Several frailty distributions, such as the the gamma, positive stable
    and the Power Variance Family are supported.
The frailtyHL package implements the h-likelihood
        estimation procedures for frailty models. The package fits
        Cox's proportional hazards models with random effects (or
        frailties). For the frailty distribution lognormal and gamma
        are allowed. The h-likelihood uses the Laplace approximation
        when the numerical integration is intractable, giving a
        statistically efficient estimation in frailty models.
Simulates and fits semiparametric shared frailty models under a
    wide range of frailty distributions using a consistent and
    asymptotically-normal estimator. Currently supports: gamma, power variance
    function, log-normal, and inverse Gaussian frailty models.
Tools to support sensible statistics for functional response analysis.
Point and interval estimation in dual frame surveys. In contrast
    to classic sampling theory, where only one sampling frame is considered,
    dual frame methodology assumes that there are two frames available for
    sampling and that, overall, they cover the entire target population. Then,
    two probability samples (one from each frame) are drawn and information
    collected is suitably combined to get estimators of the parameter of
    interest.
With no external dependencies and
    support for 335 languages; all languages spoken by
    more than one million speakers. 'Franc' is a port
    of the 'JavaScript' project of the same name,
    see <https://github.com/wooorm/franc>.
Accompanying package of the book 'Financial Risk Modelling
        and Portfolio Optimisation with R', second edition. The data sets used in the book are contained in this package.
This package performs robust inference based on applying
        Fast and Robust Bootstrap on robust estimators. Available
        methods are multivariate regression, PCA and Hotelling tests.
An implementation of various learning algorithms based on fuzzy rule-based systems (FRBSs) for dealing with classification and regression tasks. Moreover, it allows to construct an FRBS model defined by human experts. 
    FRBSs are based on the concept of fuzzy sets, proposed by Zadeh in 1965, which aims at
    representing the reasoning of human experts in a set of IF-THEN rules, to
    handle real-life problems in, e.g., control, prediction and inference, data
    mining, bioinformatics data processing, and robotics. FRBSs are also known
    as fuzzy inference systems and fuzzy models. During the modeling of an
    FRBS, there are two important steps that need to be conducted: structure
    identification and parameter estimation. Nowadays, there exists a wide
    variety of algorithms to generate fuzzy IF-THEN rules automatically from
    numerical data, covering both steps. Approaches that have been used in the
    past are, e.g., heuristic procedures, neuro-fuzzy techniques, clustering
    methods, genetic algorithms, squares methods, etc. Furthermore, in this
    version we provide a universal framework named 'frbsPMML', which is adopted
    from the Predictive Model Markup Language (PMML), for representing FRBS
    models. PMML is an XML-based language to provide a standard for describing
    models produced by data mining and machine learning algorithms. Therefore,
    we are allowed to export and import an FRBS model to/from 'frbsPMML'.
    Finally, this package aims to implement the most widely used standard
    procedures, thus offering a standard package for FRBS modeling to the R
    community.
This package implements the functions associated with Fast
        Regularized Canonical Correlation Analysis.
Despike, ustar filtering, plotting, footprint modelling and general post-processing of a standard EddyPro full output file (LI-COR Inc 2011-2015 <https://www.licor.com/env/products/eddy_covariance/eddypro.html>).
This package is for fitting free-knot splines for data
        with one independent variable and one dependent variable.  Four
        free-knot spline algorithms are provided for the case where the
        number of knots is known in advance.  A knot-search algorithm
        is provided for the case where the number of knots is not known
        in advance.  In addition, methods are available to compute the
        fitted values, the residuals, and the coefficients of the
        splines, and to plot the results, along with a method to
        summarize the results.
Provides tools for describing and analysing free sorting data. Main methods are computation of consensus partition and factorial analysis of the dissimilarity matrix between stimuli (using multidimensional scaling approach).
A collections of useful statistical functions used in Columbia
    course W4240/W4400.
Wrapper functions that interface with 'Freesurfer' (https://
    surfer.nmr.mgh.harvard.edu/), a powerful and commonly-used 'neuroimaging'
    software, using system commands. The goal is to be able to interface with
    'Freesurfer' completely in R, where you pass R objects of class 'nifti',
    implemented by package 'oro.nifti', and the function executes an 'Freesurfer'
    command and returns an R object of class 'nifti' or necessary output.
Fast regional association analysis of quantitative traits for family-based and population studies.
Real capture frequencies will be fitted to various distributions which provide the basis of estimating population sizes, their standard error, and symmetric as well as asymmetric confidence intervalls.
Generates a frequency distribution. The frequency
    distribution includes raw frequencies, percentages in each category, and
    cumulative frequencies. The frequency distribution can be stored as a data
    frame.
Methods for the analysis of multivariate time series using frequency domain techniques. Implementations of dynamic principle components analysis (DPCA) and estimators of operators in lagged regression. Examples of usage in functional data analysis setup.
A frequency moving average plot (MAP) is estimated from a
        multinomial data and a continuous covariate. The frequency MAP
        is a moving average estimate of category frequencies, where
        frequency means and posterior bounds are estimated. Comparisons
        of two frequency MAPs as well as odds ratios can be plotted.
New approaches to parallel coordinates plots for
   multivariate data visualization, including applications to clustering,
   outlier hunting and regression diagnostics.  Includes general functions
   for multivariate nonparametric density and regression estimation, 
   using parallel computation.
Tools for generating an informative type of line graph, the frequency profile, 
    which allows single behaviors, multiple behaviors, or the specific behavioral patterns 
    of individual subjects to be graphed from occurrence/nonoccurrence behavioral data.
Provides functions to create frequency tables which display both counts
    and rates.
Accompanies a paper (Barunik, Krehlik (2017) <doi:10.2139/ssrn.2627599>) dedicated to spectral decomposition of connectedness measures and their interpretation. We implement all the developed estimators as well as the historical counterparts. For more information, see the help or GitHub page (<https://github.com/tomaskrehlik/frequencyConnectedness>) for relevant information.
The frequency of a particular data value is the number of times it
    occurs. A frequency table is a table of values with their corresponding
    frequencies. Frequency weights are integer numbers that indicate how many
    cases each case represents. This package provides some functions to work
    with such type of collected data.
Contains a set of utilities for building and testing formula-based models (linear, logistic or COX) for Computer Aided Diagnosis/Prognosis applications. Utilities include data adjustment, univariate analysis, model building, model-validation, longitudinal analysis, reporting and visualization.
Regular and non-regular Fractional Factorial 2-level designs 
        can be created. Furthermore, analysis tools for Fractional
        Factorial designs with 2-level factors are offered (main
        effects and interaction plots for all factors simultaneously,
        cube plot for looking at the simultaneous effects of three
        factors, full or half normal plot, alias structure in a more
        readable format than with the built-in function alias).
This package provides catalogues of resolution IV regular
        fractional factorial designs in 128 runs for up to 33 2-level
        factors. The catalogues are complete, excluding resolution IV
        designs without 5-letter words, because these do not add value
        for a search for clear designs. The previous package version
        1.0 with complete catalogues up to 24 runs (24 runs and a
        namespace added later) can be downloaded from the authors
        website.
Fixed Rank Kriging is a tool for spatial/spatio-temporal modelling
    and prediction with large datasets. The approach, discussed in Cressie and
    Johannesson (2008) <DOI:10.1111/j.1467-9868.2007.00633.x>, decomposes the field, 
    and hence the covariance function, using a fixed set of n basis functions, 
    where n is typically much smaller than the number of data points (or polygons) m. 
    The method naturally allows for non-stationary, anisotropic covariance functions 
    and the use of observations with varying support (with known error variance). The 
    projected field is a key building block of the Spatial Random Effects (SRE) model, 
    on which this package is based. The package FRK provides helper functions to model, 
    fit, and predict using an SRE with relative ease.
Estimation and specification analysis of one- and two-part fractional regression models and calculation of partial effects.
Estimation and specification analysis of fractional regression models with neglected heterogeneity and/or endogenous covariates.
Estimation of panel data regression models for fractional responses.
A collection of R and C++ functions to work with the
        generalized hyperbolic distribution, related distributions and
        their applications in financial risk management and
        quantitative analysis.
Fast computation of moments via 'Rcpp'. Supports computation on
   vectors and matrices, and Monoidal append of moments.
Maximum Likelihood Estimation of
   Stochastic Frontier Production and Cost Functions.
   Two specifications are available:
   the error components specification with time-varying efficiencies
   (Battese and Coelli, 1992)
   and a model specification in which the firm effects are directly 
   influenced by a number of variables (Battese and Coelli, 1995).
It calculates the alpha-quantile and order-m efficiency score in multi-dimension and computes several summaries and representation of the associated frontiers in 2d and 3d.
A variety of simple fish stock assessment methods.
    Detailed vignettes are available on the fishR website <http://derekogle.com/fishR/>.
The datasets to support the Fish Stock Assessment ('FSA') package.
Automated feature selection using variety of models
        provided by 'caret' package.
        This work was funded by Poland-Singapore bilateral cooperation
        project no 2/3/POL-SIN/2012.
Functions for selecting attributes from a given dataset. Attribute
    subset selection is the process of identifying and removing as much of the
    irrelevant and redundant information as possible.
Import data of tests and questionnaires from FormScanner. FormScanner is an open source software that converts scanned images to data using optical mark recognition (OMR) and it can be downloaded from <http://sourceforge.net/projects/formscanner/>. The spreadsheet file created by FormScanner is imported in a convenient format to perform the analyses provided by the package. These analyses include the conversion of multiple responses to binary (correct/incorrect) data, the computation of the number of corrected responses for each subject or item, scoring using weights,the computation and the graphical representation of the frequencies of the responses to each item and the report of the responses of a few subjects.
Performs fast detection of interactions in large-scale data using the
    method of random intersection trees introduced in
    Shah, R. D. and Meinshausen, N. (2014) <http://www.jmlr.org/papers/v15/shah14a.html>. 
    The algorithm finds potentially high-order interactions in high-dimensional binary
    two-class classification data, without requiring lower order interactions
    to be informative.  The search is particularly fast when the matrices of
    predictors are sparse.  It can also be used to perform market basket analysis
    when supplied with a single binary data matrix.  Here it will find collections
    of columns which for many rows contain all 1's.
Wrapper functions that interface with 'FSL' 
    <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/>, a powerful and commonly-used 'neuroimaging'
    software, using system commands. The goal is to be able to interface with 'FSL'
    completely in R, where you pass R objects of class 'nifti', implemented by
    package 'oro.nifti', and the function executes an 'FSL' command and returns an R
    object of class 'nifti' if desired.
Fuzzy set ordination is a multivariate analysis used in
        ecology to relate the composition of samples to possible
        explanatory variables.  While differing in theory and method,
        in practice, the use is similar to 'constrained ordination.'
        The package contains plotting and summary functions as well as
        the analyses
Social Relations Analysis with roles ("Family SRM") are computed,
    using a structural equation modeling approach. Groups ranging from three members
    up to an unlimited number of members are supported and the mean structure can
    be computed. Means and variances can be compared between different groups of
    families and between roles.
Read and write data frames at high speed. Compress your data with fast and efficient type-optimized algorithms that allow for random access of stored data frames (columns and rows).
Functions for sequencing studies allowing for multiple functional annotation scores. Score type tests and an efficient perturbation method are used for individual gene/large gene-set/genome wide analysis. Only summary statistics are needed.
A wrapper for the API of the Danish Parliament. It makes it 
    possible to get data from the API easily into a data frame. Learn more at 
    <http://www.ft.dk/dokumenter/aabne_data>.
This package was developed partially with funding from the
        NIH Training Program in Biomolecular Technology
        (2-T32-GM08799).
The package contains R-functions to perform the methods in
        nonparametric regression and density estimation, described in
        Davies, P. L. and Kovac, A. (2001) Local Extremes, Runs,
        Strings and Multiresolution (with discussion) Annals of
        Statistics. 29. p1-65 Davies, P. L. and Kovac, A. (2004)
        Densities, Spectral Densities and Modality Annals of
        Statistics. Annals of Statistics. 32. p1093-1136 Kovac, A.
        (2006) Smooth functions and local extreme values. Computational
        Statistics and Data Analysis (to appear) D\"umbgen, L. and
        Kovac, A. (2006) Extensions of smoothing via taut strings
        Davies, P. L. (1995) Data features. Statistica Neerlandica
        49,185-245.
Implementation of Follow The Regularized Leader (FTRL) Proximal algorithm, proposed by McMahan et al. (2013) <doi:10.1145/2487575.2488200>, used for online training of large scale regression models using a mixture of L1 and L2 regularization.
Functions for visualizing, modeling, forecasting and hypothesis testing of functional time series.
Functions for estimating spectral density operator of functional
    time series (FTS) and comparing the spectral density operator of two
    functional time series, in a way that allows detection of differences of
    the spectral density operator in frequencies and along the curve length.
Fuel economy data from the EPA, 1985-2015, conveniently
    packaged for consumption by R users.
This is an evolutionary algorithm for fuzzy systems, a
        genetic algorithm is used to construct a fuzzy system able to
        fit the given training data.  This fuzzy system can then be
        used as a prediction model, it's composed of fuzzy logic rules
        that provide a good lingustic representation.
We facilitate the analysis of full factorial mating designs with mixed-effects models. The observed data functions extract the variance explained by random and fixed effects and provide their significance. We then calculate the additive genetic, nonadditive genetic, and maternal variance components explaining the phenotype. In particular, we integrate nonnormal error structures for estimating these components for nonnormal data types. The resampled data functions are used to produce bootstrap confidence intervals, which can then be plotted using a simple function. This package will facilitate the analyses of full factorial mating designs in R, especially for the analysis of binary, proportion, and/or count data types and for the ability to incorporate additional random and fixed effects and power analyses. The paper associated with the package including worked examples is: Houde ALS, Pitcher TE (2016) <doi:10.1002/ece3.1943>.
Provides a single interface to many sources of full text
    'scholarly' data, including 'Biomed Central', Public Library of
    Science, 'Pubmed Central', 'eLife', 'F1000Research', 'PeerJ',
    'Pensoft', 'Hindawi', 'arXiv' 'preprints', and more. Functionality
    included for searching for articles, downloading full or partial
    text, downloading supplementary materials, converting to various
    data formats used in and outside of R.
This is a collection of R games and other funny stuff,
        such as the classical Mine sweeper and sliding puzzles.
A set of functions, some subset of which I use in every .R file I write. Examples are table2(), which adds useful functionalities to base table (sorting, built-in proportion argument, etc.); lyx.xtable(), which converts xtable() output to a format more easily copy-pasted into LyX; pdf2(), which writes a plot to file while also displaying it in the RStudio plot window; and abbr_to_colClass(), which is a much more concise way of feeding many types to a colClass argument in a data reader.
Statistical hypothesis testing methods for
 non-parametric functional dependencies using asymptotic
 chi-square or exact distributions. Functional chi-squares are
 asymmetric and functionally optimal, unique from other related
 statistics. Tests in this package reveal evidence for causality 
 based on the causality-by-functionality principle. They include
 asymptotic functional chi-square tests, an exact functional
 test, a comparative functional chi-square test, and also a
 comparative chi-square test. The normalized non-constant
 functional chi-square test was used by Best Performer
 NMSUSongLab in HPN-DREAM (DREAM8) Breast Cancer Network
 Inference Challenges. For continuous data, these tests offer an
 advantage over regression analysis when a parametric functional
 form cannot be assumed; for categorical data, they provide a
 novel means to assess directional dependencies not possible with
 symmetrical Pearson's chi-square or Fisher's exact tests.
FunCluster performs a functional analysis of microarray
        expression data based on Gene Ontology & KEGG functional
        annotations. From expression data and functional annotations
        FunCluster builds classes of putatively co-regulated biological
        processes through a specially designed clustering procedure.
This packages proposes a model-based clustering algorithm for
    multivariate functional data. The parametric mixture model, based on the
    assumption of normality of the principal components resulting from a
    multivariate functional PCA, is estimated by an EM-like algorithm. The main
    advantage of the proposed algorithm is its ability to take into account the
    dependence among curves.
Analyzes the function calls in an R package and creates a hive plot of the calls, dividing them among functions that only make outgoing calls (sources), functions that have only incoming calls (sinks), and those that have both incoming calls and make outgoing calls (managers).  Function calls can be mapped by their absolute numbers, their normalized absolute numbers, or their rank.  FuncMap should be useful for comparing packages at a high level for their overall design.  Plus, it's just plain fun.  The hive plot concept was developed by Martin Krzywinski (www.hiveplot.com) and inspired this package.  Note: this package is maintained for historical reasons. HiveR is a full package for creating hive plots.
Curry, Compose, and other higher-order functions
R package providing functions to perform gene and gene set network inference.
Extends functional programming in R by
    providing support to the usual higher order functional
    suspects (Map, Reduce, Filter, etc.).
Unified framework to cluster functional data according to one of
    seven models. All models are based on the projection of the curves onto a basis.
    The main function funcit() calls wrapper functions for the existing algorithms,
    so that input parameters are the same. A list is returned with each entry
    representing the same or extended output for the corresponding method. Method
    specific as well as general visualization tools are available.
S4 classes for univariate and multivariate functional data with
    utility functions.
The funFEM algorithm (Bouveyron et al., 2014) allows to cluster functional data by modeling the curves within a common and discriminative functional subspace.
Computes fungible coefficients and Monte Carlo data.  
    Underlying theory for these functions is described in the following publications:
    Waller, N. (2008). Fungible Weights in Multiple Regression. Psychometrika, 73(4), 691-703, <DOI10.1007/s11336-008-9066-z>. 
    Waller, N. & Jones, J. (2009). Locating the Extrema of Fungible Regression Weights. 
    Psychometrika, 74(4), 589-602, <DOI10.1007/s11336-008-9087-7>.
    Waller, N. G. (2016). Fungible Correlation Matrices: 
    A Method for Generating Nonsingular, Singular, and Improper Correlation Matrices for 
    Monte Carlo Research. Multivariate Behavioral Research, 51(4), 554-568, <DOI10.1080/00273171.2016.1178566>. 
    Jones, J. A. & Waller, N. G. (2015). The normal-theory and asymptotic distribution-free (ADF) 
    covariance matrix of standardized regression coefficients: theoretical extensions 
    and finite sample behavior. Psychometrika, 80, 365-378, <DOI10.1007/s11336-013-9380-y>.
The package provides the funHDDC algorithm (Bouveyron & Jacques, 2011) which allows to cluster functional data by modeling each group within a specific functional subspace.
A tool for fitting latent Dirichlet allocation models to genomic annotation data.
Around 10% of almost any predictive modeling project is spent in predictive modeling, 'funModeling' and the book Data Science Live Book (<http://livebook.datascienceheroes.com/>) are intended to cover remaining 90%: data preparation, profiling, selecting best variables 'dataViz', assessing model performance and other functions.
A small utility which wraps Rscript and provides access to all R
    functions from the shell.
Computes functional rarity indices as proposed by Violle et al.
    (2017) <doi:10.1016/j.tree.2017.02.002>. Various indices can be computed
    using both regional and local information. Functional Rarity combines both
    the functional aspect of rarity as well as the extent aspect of rarity.
Performs functional regression, and some related
    approaches, for intensive longitudinal data (see the book by Walls & Schafer, 
    2006, Models for Intensive Longitudinal Data, Oxford) when such data is not
    necessarily observed on an equally spaced grid of times.  The
    approach generally follows the ideas of Goldsmith, Bobb, Crainiceanu,
    Caffo, and Reich (2011)<DOI:10.1198/jcgs.2010.10007> and the approach taken in their sample code, but
    with some modifications to make it more feasible to use with long rather
    than wide, non-rectangular longitudinal datasets with unequal and
    potentially random measurement times.  It also allows easy plotting of the
    correlation between the smoothed covariate and the outcome as a function of
    time, which can add additional insights on how to interpret a functional
    regression.  Additionally, it also provides several permutation tests for
    the significance of the functional predictor.  The heuristic interpretation
    of ``time'' is used to describe the index of the functional predictor, but
    the same methods can equally be used for another unidimensional continuous
    index, such as space along a north-south axis.  The development of this
    package was part of a research project supported by Award R03 CA171809-01
    from the National Cancer Institute and Award P50 DA010075 from the National
    Institute on Drug Abuse. The content is solely the responsibility of the
    authors and does not necessarily represent the official views of the
    National Institute on Drug Abuse, the National Cancer Institute, or the
    National Institutes of Health.
Computes the functional tangential angle pseudo-depth and its robustified version from the paper by Kuhnt and Rehage (2016). See Kuhnt, S.; Rehage, A. (2016): An angle-based multivariate functional pseudo-depth for shape outlier detection, JMVA 146, 325-340, <doi:10.1016/j.jmva.2015.10.016> for details.
Includes non-parametric estimators and tests for time series analysis. The functions are to test for presence of possibly non-monotonic trends and for synchronism of trends in multiple time series, using modern bootstrap techniques and robust non-parametric difference-based estimators.
Contains two main functions (i.e., two pieces of furniture):
    table1() which produces a well-formatted table of descriptives common as Table 1
    in research articles and washer() which is helpful in cleaning up the data. In addition,
    long() and wide(), built on the stats::reshape(), can help reformat the data.
    These furniture functions are designed to simplify common tasks in 
    quantitative analysis. Other data summary and cleaning tools are also available.
Efficient procedures for community detection in network studies, especially for sparse networks. The algorithms impose penalties on the differences of the coordinates which represent the community labels of the nodes.
Enables high-dimensional penalized regression across heterogeneous 
    subgroups. Fusion penalties are used to share information about the linear 
    parameters across subgroups. The underlying model is described 
    in detail in Dondelinger and Mukherjee (2017) <arXiv:1611.00953>.
This utility package provides polymorphism over common operations and is now subsumed by lambda.tools.
Provides a simple yet powerful logging utility. Based loosely on
    log4j, futile.logger takes advantage of R idioms to make logging a
    convenient and easy to use replacement for cat and print statements.
A collection of functions for manipulating matrices and generating
    ensembles of random matrices. Used primarily to identify the cutoff point
    for the noise portion of the eigenvalue spectrum.
Provides dispatching implementations suitable for
        functional programming paradigms. The framework provides a
        mechanism for attaching guards to functions similar to Erlang,
        while also providing the safety of assertions reminiscent of
        Eiffel.
Randomized clinical trials commonly follow participants for a time-to-event efficacy endpoint for a fixed period of time. Consequently, at the time when the last enrolled participant completes their follow-up, the number of observed endpoints is a random variable. Assuming data collected through an interim timepoint, simulation-based estimation and inferential procedures in the standard right-censored failure time analysis framework are conducted for the distribution of the number of endpoints--in total as well as by treatment arm--at the end of the follow-up period. The future (i.e., yet unobserved) enrollment, endpoint, and dropout times are generated according to mechanisms specified in the simTrial() function in the 'seqDesign' package. A Bayesian model for the endpoint rate is used for generating future data (see the vignette for details).
The purpose of this package is to provide a lightweight and
    unified Future API for sequential and parallel processing of R
    expression via futures.  The simplest way to evaluate an expression
    in parallel is to use `x %<-% { expression }` with `plan(multiprocess)`.
    This package implements sequential, multicore, multisession, and
    cluster futures.  With these, R expressions can be evaluated on the
    local machine, on in parallel a set of local machines, or distributed
    on a mix of local and remote machines.
    Extensions to this package implements additional backends for
    processing futures via compute cluster schedulers etc.
    Because of its unified API, there is no need to modify code in order
    switch from sequential on the local machine to, say, distributed
    processing on a remote compute cluster.
    Another strength of this package is that global variables and functions
    are automatically identified and exported as needed, making it
    straightforward to tweak existing code to make use of futures.
Implements of the Future API on top of the 'BatchJobs' package.
    This allows you to process futures, as defined by the 'future' package,
    in parallel out of the box, not only on your local machine or ad-hoc
    cluster of machines, but also via high-performance compute ('HPC') job
    schedulers such as 'LSF', 'OpenLava', 'Slurm', 'SGE', and 'TORQUE' / 'PBS',
    e.g. 'y <- future_lapply(files, FUN = process)'.
Implements of the Future API on top of the 'batchtools' package.
    This allows you to process futures, as defined by the 'future' package,
    in parallel out of the box, not only on your local machine or ad-hoc
    cluster of machines, but also via high-performance compute ('HPC') job
    schedulers such as 'LSF', 'OpenLava', 'Slurm', 'SGE', and 'TORQUE' / 'PBS',
    e.g. 'y <- future_lapply(files, FUN = process)'.
Inputs a directory of climate projection files and, for each,
    identifies and characterizes heat waves for specified study locations. The
    definition used to identify heat waves can be customized. Heat wave
    characterizations include several metrics of heat wave length, intensity,
    and timing in the year. The heat waves that are identified can be
    explored using a function to apply user-created functions across all
    generated heat wave files.This work was supported in part by grants from
    the National Institute of Environmental Health Sciences (R00ES022631), the
    National Science Foundation (1331399), and the Colorado State University
    Vice President for Research.
Test function arguments with a wide array of inputs, and produce
  reports summarizing messages, warnings, errors, and returned values.
Calculation of AHP (Analytic Hierarchy Process -
    <http://en.wikipedia.org/wiki/Analytic_hierarchy_process>)
    with classic and fuzzy weights based on Saaty's pairwise
    comparison method for determination of weights.
Exact calculation of fuzzy decision rules for multiple
	testing. Choose to control FDR (false discovery rate) using the
	Benjamini and Hochberg method, or FWER (family wise error rate)
	using the Bonferroni method. Kulinsakaya and Lewin (2007).
Fuzzy forests, a new algorithm based on random forests,
    is designed to reduce the bias seen in random forest feature selection
    caused by the presence of correlated features.  Fuzzy forests uses
    recursive feature elimination random forests to select
    features from separate blocks of correlated features where the
    correlation within each block of features is high
    and the correlation between blocks of features is low.
    One final random forest is fit using the surviving features.
    This package fits random forests using the 'randomForest' package and
    allows for easy use of 'WGCNA' to split features into distinct blocks.
Join tables together based not on whether columns
  match exactly, but whether they are similar by some comparison.
  Implementations include string distance and regular expression
  matching.
Provides methods to solve Fuzzy Linear Programming Problems with
             fuzzy constraints (following different approaches proposed by 
             Verdegay, Zimmermann, Werners and Tanaka), fuzzy costs, and fuzzy
             technological matrix.
Implementation of several MCDM methods for fuzzy data (triangular
    fuzzy numbers) for decision making problems. The methods that are implemented in
    this package are Fuzzy TOPSIS (with two normalization procedures), Fuzzy VIKOR,
    Fuzzy Multi-MOORA and Fuzzy WASPAS. In addition, function MetaRanking() calculates
    a new ranking from the sum of the rankings calculated, as well as an aggregated ranking.
S4 classes and methods
    to deal with fuzzy numbers. With them you can compute any arithmetic
    operations (e.g. by using the Zadeh extension principle),
    perform approximation of arbitrary FNs by trapezoidal and piecewise
    linear FNs, prepare plots of FNs for publications, calculate 
    possibility and necessity values for comparisons, etc.
One can easily draw the membership function of f(x,y) by package 'FuzzyNumbers.Ext.2' in which f(.,.) is supposed monotone and x and y are two fuzzy numbers. This work is possible using function f2apply() which is an extension of function fapply() from Package 'FuzzyNumbers' for two-variable monotone functions. Moreover, this package has the ability of computing the core, support and alpha-cuts of the fuzzy-valued final result.
The main goal of this package is drawing the membership function of the fuzzy p-value which is defined as a fuzzy set on the unit interval for three following problems: (1) testing crisp hypotheses based on fuzzy data, (2) testing fuzzy hypotheses based on crisp data, and (3) testing fuzzy hypotheses based on fuzzy data. In all cases, the fuzziness of data or/and the fuzziness of the boundary of null fuzzy hypothesis transported via the p-value function and causes to produce the fuzzy p-value. If the p-value is fuzzy, it is more appropriate to consider a fuzzy significance level for the problem. Therefore, the comparison of the fuzzy p-value and the fuzzy significance level is evaluated by a fuzzy ranking method in this package.
Design and simulate fuzzy logic systems using Type 1 Fuzzy Logic.
    This toolkit includes with graphical user interface (GUI) and an adaptive neuro-
    fuzzy inference system (ANFIS). This toolkit is a continuation from the previous
    package ('FuzzyToolkitUoN'). Produced by the Intelligent Modelling & Analysis
    Group, University of Nottingham.
Does fuzzy tests and confidence intervals (following Geyer
    and Meeden, Statistical Science, 2005, <doi:10.1214/088342305000000340>)
    for sign test and Wilcoxon signed rank and rank sum tests.
An implementation of a method for computing fuzzy numbers representing stationary probabilities of an unknown Markov chain,
        from which a sequence of observations along time has been obtained. The algorithm is based on the proposal presented by James Buckley 
        in his book on Fuzzy probabilities (Springer, 2005), chapter 6. Package 'FuzzyNumbers' is used to represent the output probabilities.
The aim of the package is to provide some basic functions
 for doing statistics with trapezoidal fuzzy numbers. In particular,
  the package contains several functions for simulating trapezoidal 
  fuzzy numbers, as well as for calculating some central tendency 
  measures (mean and two types of median), some scale measures 
  (variance, ADD, MDD, Sn, Qn, Tn and some M-estimators) and 
  one diversity index and one inequality index. Moreover, 
  functions for calculating the 1-norm distance, the mid/spr 
  distance and the (phi,theta)-wabl/ldev/rdev distance between 
  fuzzy numbers are included, and a function to calculate the 
  value phi-wabl given a sample of trapezoidal fuzzy numbers.
A custom framework for working with Type 1 Fuzzy Logic,
        produced by the University of Nottingham IMA Group.
Fuzzy string matching implementation of the 'fuzzywuzzy' <https://github.com/seatgeek/fuzzywuzzy> 'python' package. It uses the Levenshtein Distance <https://en.wikipedia.org/wiki/Levenshtein_distance> to calculate the differences between sequences.
fwdmsa performs the Forward Search for Mokken scale
        analysis. It detects outliers, it produces several types of
        diagnostic plots.
A simple method
    to select the best model or best subset of variables using
    different types of data (binary, Gaussian or Poisson) and
    applying it in different contexts (parametric or non-parametric).
Provides three functions to calculate the outputs of the two main components of the Canadian Forest Fire Danger Rating System (CFFDRS): the Fire Weather Index (FWI) System and the Fire Behaviour Prediction (FBP) System.
Simulates a population under the Fisher-Wright model (fixed or stochastic population size) with a one-step neutral mutation process (stepwise mutation model, logistic mutation model and exponential mutation model supported). The stochastic population sizes are random Poisson distributed and different kinds of population growth are supported. For the stepwise mutation model, it is possible to specify locus and direction specific mutation rate (in terms of upwards and downwards mutation rate). Intermediate generations can be saved in order to study e.g. drift.
Exchange rate regression and structural change tools
             for estimating, testing, dating, and monitoring
	     (de facto) exchange rate regimes.
G1DBN performs DBN inference using 1st order conditional
        dependencies.
For a given metabolic network, this package finds the gaps (metabolites not produced or not consumed in any other reaction), and fills it from the stoichiometric reactions of a reference metabolic reconstruction using a weighting function. Also the option to download all the set of gene-associated stoichiometric reactions for a specific organism from the KEGG database <http://www.genome.jp/kegg/> is available.
Full descriptive statistics, physical description of sediment,
            metric or phi sieves.
An R package for optimisation using genetic algorithms. The package provides a flexible general-purpose set of tools for implementing genetic algorithms search in both the continuous and discrete case, whether constrained or not. Users can easily define their own objective function depending on the problem at hand. Several genetic operators are available and can be combined to explore the best settings for the current task. Furthermore, users can define new genetic operators and easily evaluate their performances. Local search using general-purpose optimisation algorithms can be applied stochastically to exploit interesting regions. GAs can be run sequentially or in parallel, using an explicit master-slave parallelisation or a coarse-grain islands approach.
This is a Genetic Algorithm package for the determination
        of the stratum boundaries and sample sizes of each stratum in
        stratified sampling
Scale abbreviation using Genetic Algorithms that maximally capture the variance in the original data.
Generalized subspace pattern mining in data arrays, using a genetic algorithm framework.
This package analyses complex ANOVA models with any
        combination of orthogonal/nested and fixed/random factors, as
        described by Underwood (1997). There are two restrictions: (i)
        data must be balanced; (ii) fixed nested factors are not
        allowed. Homogeneity of variances is checked using Cochran's C
        test and 'a posteriori' comparisons of means are done using
        Student-Newman-Keuls (SNK) procedure.
Sparse large Directed Acyclic Graphs learning with a combination of a convex program and a tailored genetic algorithm (see Champion et al. (2017) <https://hal.archives-ouvertes.fr/hal-01172745v2/document>).
In this package we consider Gaussian Diffusion processes and smooth thresholds. After evaluating the mean of the process to check the subthreshold regimen hypothesis, the FPT density function is reconstructed via the numerical quadrature of the integral equation in (Buonocore 1987); first passage times are also generated by the method in (Buonocore 2014) and results are compared. The timestep of the simulations can iteratively be refined. User should provide the functional form for the drift and the infinitesimal variance in the script 'userfunc.R' and for the threshold in the script 'userthresh.R'. All the parameters required by the implementation are to be set in the script 'userparam.R'. Example scripts for common drifts and thresholds are given.
Manipulate, assemble, export <http://www.gadm.org> shapefiles. Create choropleth, heatmaps, dots plot, proportional dots and more.
A group of sample points are evaluated against a
        user-defined expression, the sample points are lists of
        parameters with values that may be substituted into that
        expression. The genetic algorithm attempts to make the result
        of the expression as low as possible (usually this would be the
        sum of residuals squared).
Tools for using genetic markers, stable isotope data, and habitat
    suitability data to calculate posterior probabilities of breeding origin of
    migrating birds.
This package constructs gains tables and lift charts for prediction algorithms. Gains tables and lift charts are commonly used in direct marketing applications.
GAIPE implements graphical representation of accuracy in
        parameter estimation (AIPE) on RMSEA for sample size planning
        in structural equation modeling. Sample sizes suggested by
        RMSEA with AIPE method and power analysis approach can also be
        obtained separately using the provided functions.
This package includes the ga.lts function that estimates
        LTS (Least Trimmed Squares) parameters using genetic algorithms
        and C-steps. ga.lts() constructs a genetic algorithm to form a
        basic subset and iterates C-steps as defined in Rousseeuw and
        van-Driessen (2006) to calculate the cost value of the LTS
        criterion. OLS(Ordinary Least Squares) regression is known to
        be sensitive to outliers. A single outlying observation can
        change the values of estimated parameters. LTS is a resistant
        estimator even the number of outliers is up to half of the
        data. This package is for estimating the LTS parameters with
        lower bias and variance in a reasonable time. Version 1.3
        included the function medmad for fast outlier detection in
        linear regression.
Functions for fitting and working with generalized
		additive models, as described in chapter 7 of "Statistical Models in
		S" (Chambers and Hastie (eds), 1991), and "Generalized Additive
		Models" (Hastie and Tibshirani, 1990).
Data sets and scripts used in the book "Generalized
        Additive Models: An Introduction with R", Wood (2006) CRC.
Fits unimodal and multimodal gambin distributions to species-abundance distributions
    from ecological data. 'gambin' is short for 'gamma-binomial'. The main function
    is fit_abundances(), which estimates the 'alpha' parameter(s) of the gambin distribution
    using maximum likelihood. Functions are also provided to generate the gambin distribution 
    and for calculating likelihood statistics.
This package provides routines for fitting generalized
        linear and and generalized additive models by likelihood based
        boosting, using penalized B-splines
Boosting models for fitting generalized additive models for
  location, shape and scale ('GAMLSS') to potentially high dimensional
  data.
Provides features to use function gamboost() from  package mboost for estimation of multistate models
Functions and data are provided that support a course that emphasizes statistical
  issues of inference and generalizability.  Attention is restricted to a relatively small
  number of methods, often (misleadingly in my view) referred to as algorithms.
Implementation of various inference and simulation tools to
    apply generalized additive models to bivariate dependence structures and
    non-simplified vine copulas.
Ensemble classifiers based upon generalized additive models for binary
    classification (De Bock et al. (2010) <DOI:10.1016/j.csda.2009.12.013>). The ensembles
    implement Bagging (Breiman (1996) <DOI:10.1023/A:1018054314350>), the Random Subspace Method (Ho (1998) <DOI:10.1109/34.709601>), or
    both, and use Hastie and Tibshirani's (1990) generalized additive models (GAMs)
    as base classifiers. Once an ensemble classifier has been trained, it can
    be used for predictions on new data. A function for cross validation is also
    included.
Provides estimation and analysis functions for
    strategic statistical models.
Finds adaptive strategies for sequential symmetric 
    games using a genetic algorithm. Currently, any symmetric two by two matrix
    is allowed, and strategies can remember the history of an opponent's play
    from the previous three rounds of moves in iterated interactions between
    players. The genetic algorithm returns a list of adaptive strategies given
    payoffs, and the mean fitness of strategies in each generation.
Implementation of a common set of punctual solutions  for Cooperative Game Theory.
Many situations can be modeled as game theoretic situations. Some procedures are included in this package to calculate the most important allocations rules in Game Theory: Shapley value, Owen value or nucleolus, among other. First, we must define as an argument the value of the unions of the envolved agents with the characteristic function.
The gamma lasso algorithm provides regularization paths corresponding to a range of non-convex cost functions between L0 and L1 norms.  As much as possible, usage for this package is analogous to that for the glmnet package (which does the same thing for penalization between L1 and L2 norms).  For details see: Taddy (2015), One-Step Estimator Paths for Concave Regularization, http://arxiv.org/abs/1308.5623.
Functions for fitting Generalized Additive Models for Location Scale and Shape.
Interface for extra smooth functions including tensor products, neural networks and decision trees.
Density, distribution function, quantile function and random generation for the bimodal skew symmetric normal distribution of Hassan and El-Bassiouni (2016) <doi:10.1080/03610926.2014.882950>.
This is an add-on package to GAMLSS. The purpose of this
        package is to allow users to fit interval response variables in
        GAMLSS models. The main function gen.cens() generates a
        censored version of an existing GAMLSS family distribution.
Demos for smoothing and gamlss.family distributions.
The different distributions used for the response variables in Generalized Additive Models for Location Scale and Shape.
This is an add-on package to 'gamlss'. The purpose of this package is to allow users to fit GAMLSS (Generalised Additive Models for Location Scale and Shape) models when the response variable is defined either in the intervals [0,1), (0,1] and [0,1] (inflated at zero and/or one distributions), or in the positive real line including zero (zero-adjusted distributions). The mass points at zero and/or one are treated as extra parameters with the possibility to include a linear predictor for both. The package also allows transformed or truncated distributions from the GAMLSS family to be used for the continuous part of the distribution. Standard methods and GAMLSS diagnostics can be used with the resulting fitted object.
The main purpose of this package is to allow fitting of
        mixture distributions with GAMLSS models.
This is an add on package to GAMLSS. It allows one extra
        method for fitting GAMLSS models. The main function nlgamlss()
        can fit any parametric (up to four parameter) GAMLSS
        distribution.
It allows us to fit Gaussian Markov Random Field within the
    Generalized Additive Models for Location Scale and Shape algorithms.
This is an add on package to GAMLSS. The purpose of this
        package is to allow users to defined truncated distributions in
        GAMLSS models. The main function gen.trun() generates truncated
        version of an existing GAMLSS family distribution.
Extra functions for GAMLSS and others models.
Estimate generalized additive mixed models via a version of
         function gamm() from 'mgcv', using 'lme4' for estimation.
This package performs gamma regression, where both mean and shape parameters follows lineal regression structures.
Uses a slice sampling-based Markov chain Monte Carlo to
        conduct Bayesian fitting and inference for generalized additive
        mixed models (GAMM).  Generalized linear mixed models and
        generalized additive models are also handled as special cases
        of GAMM.
Robust regression via gamma-divergence with L1, elastic net and ridge.
To calculate the relative risk (RR) for the generalized additive model.
Using overlap grouped lasso penalties, gamsel selects whether a term in a gam is nonzero, linear, or a non-linear spline (up to a specified max df per variable). It fits the entire regularization path on a grid of values for the overall penalty lambda, both for gaussian and binomial families.
This package implements a network-based gene weighting
        algorithm for pathways, as well as a gene-weighted gene set
        analysis approach for microarray data pathway analysis.
This is a dataset package for GANPA, which implements a
        network-based gene weighting approach to pathway analysis. This
        package includes data useful for GANPA, such as a functional
        association network, pathways, an expression dataset and
        multi-subunit proteins.
Performs a Genetic Algorithm Optimization, given a
        real-based or permutation-based function and the associated
        search space.
It is designed as an integrated package for genetic data
        analysis of both population and family data. Currently, it
        contains functions for sample size calculations of both
        population-based and family-based designs, probability of
        familial disease aggregation, kinship calculation, statistics
        in linkage analysis, and association analysis involving genetic
        markers including haplotype analysis with or without environmental
        covariates.
Methodology that combines feature selection, model tuning, and parsimonious model selection with Genetic Algorithms (GA) proposed in {Martinez-de-Pison} (2015) <DOI:10.1016/j.asoc.2015.06.012>. To this objective, a novel GA selection procedure is introduced based on separate cost and complexity evaluations.
Tools to fill missing values in satellite data and to develop new
    gap-fill algorithms. The methods are tailored to data (images) observed
    at equally-spaced points in time. The package is illustrated with MODIS
    NDVI data.
The gap encodes the distance between clusters and improves
    interpretation of cluster heatmaps. The gaps can be of the same
    distance based on a height threshold to cut the dendrogram. Another
    option is to vary the size of gaps based on the distance between
    clusters.
An excerpt of the data available at Gapminder.org. For each of 142
    countries, the package provides values for life expectancy, GDP per capita,
    and population, every five years, from 1952 to 2007.
The functions included are used to obtain initial authentication with Google Analytics as well as simple and organized data retrieval from the API. Allows for retrieval from multiple profiles at once.
Simulate, estimate and forecast using univariate and multivariate GAS models 
  as described in Ardia et al. (2016) <https://ssrn.com/abstract=2825380>.
Provides a genetic algorithm for finding variable
    subsets in high dimensional data with high prediction performance. The
    genetic algorithm can use ordinary least squares (OLS) regression models or
    partial least squares (PLS) regression models to evaluate the prediction
    power of variable subsets. By supporting different cross-validation
    schemes, the user can fine-tune the tradeoff between speed and quality of
    the solution.
Functions for greenhouse gas flux calculation from chamber
    measurements.
Fits gastric emptying time series from MRI or scintigraphic measurements
   using nonlinear mixed-model population fits with 'nlme' and Bayesian methods with 
   Stan; computes derived parameters such as t50 and AUC.
Manipulation of genetic data (SNPs), computation of Genetic Relationship Matrix, Linkage Disequilibrium, etc. Efficient algorithms for Linear Mixed Model (AIREML, diagonalization trick).
Allows user to choose/gate a region on the plot and returns points
    within it.
Fits a Gaussian process model to data. Gaussian processes
 are commonly used in computer experiments to fit an interpolating model.
 The model is stored as an 'R6' object and can be easily updated with new 
 data. There are options to run in parallel (not for Windows), and 'Rcpp'
 has been used to speed up calculations. Other R packages that perform
 similar calculations include 'laGP', 'DiceKriging', 'GPfit', and 'mlegp'.
A collection difference measures for multivariate Gaussian
        probability density functions, such as the Euclidea mean, the
        Mahalanobis distance, the Kullback-Leibler divergence, the
        J-Coefficient, the Minkowski L2-distance, the Chi-square
        divergence and the Hellinger Coefficient.
Display a random fact about Carl Friedrich Gauss
 based the on collection curated by Mike Cavers via the
 <http://gaussfacts.com> site.
A collection of functions to perform Gaussian quadrature
        with different weight functions corresponding to the orthogonal
        polynomials in package orthopolynom.  Examples verify the
        orthogonality and inner products of the polynomials.
Eye-tracking data must be transformed into fixations and saccades before it can be analyzed. This package provides a non-parametric speed-based approach to do this on a trial basis. The method is especially useful when there are large differences in data quality, as the thresholds are adjusted accordingly. The same pre-processing procedure can be applied to all participants, while accounting for individual differences in data quality.
This package collects algorithms and functions for fitting data to a generalized lambda distribution via moment matching methods, and generalized bootstrapping.
Package GB2 explores the Generalized Beta distribution of the second kind. Density, cumulative distribution function, quantiles and moments of the distributions are given. Functions for the full log-likelihood, the profile log-likelihood and the scores are provided. Formulas for various indicators of inequality and poverty under the GB2 are implemented. The GB2 is fitted by the methods of maximum pseudo-likelihood estimation using the full and profile log-likelihood, and non-linear least squares estimation of the model parameters. Various plots for the visualization and analysis of the results are provided. Variance estimation of the parameters is provided for the method of maximum pseudo-likelihood estimation. A mixture distribution based on the compounding property of the GB2 is presented (denoted as "compound" in the documentation). This mixture distribution is based on the discretization of the distribution of the underlying random scale parameter. The discretization can be left or right tail. Density, cumulative distribution function, moments and quantiles for the mixture distribution are provided. The compound mixture distribution is fitted using the method of maximum pseudo-likelihood estimation. The fit can also incorporate the use of auxiliary information. In this new version of the package, the mixture case is complemented with new functions for variance estimation by linearization and comparative density plots.
An implementation of extensions to Freund and
        Schapire's AdaBoost algorithm and Friedman's gradient boosting
        machine. Includes regression methods for least squares,
        absolute loss, t-distribution loss, quantile regression,
        logistic, multinomial logistic, Poisson, Cox proportional
        hazards partial likelihood, AdaBoost exponential loss,
        Huberized hinge loss, and Learning to Rank measures
        (LambdaMart).
Writes SAS code to get predicted values from every tree of a gbm.object.
Basic infrastructure and several algorithms for 1d-4d bin packing
    problem. This package provides a set of c-level classes and solvers for
    1d-4d bin packing problem, and an r-level solver for 4d bin packing problem,
    which is a wrapper over the c-level 4d bin packing problem solver.
    The 4d bin packing problem solver aims to solve bin packing problem, a.k.a
    container loading problem, with an additional constraint on weight.
    Given a set of rectangular-shaped items, and a set of rectangular-shaped bins
    with weight limit, the solver looks for an orthogonal packing solution
    such that minimizes the number of bins and maximize volume utilization.
    Each rectangular-shaped item i = 1, .. , n is characterized by length l_i,
    depth d_i, height h_i, and weight w_i, and each rectangular-shaped bin
    j = 1, .. , m is specified similarly by length l_j, depth d_j, height h_j,
    and weight limit w_j.
    The item can be rotated into any orthogonal direction, and no further
    restrictions implied.
Provides utilities for processing Rd objects and files.
        Extract argument descriptions and other parts of the help pages
        of functions.
Functions for inference of ploidy from (Genotyping-by-sequencing) GBS data, including a function to infer allelic ratios and allelic proportions in a Bayesian framework.
An implementation of hyperparameter optimization for Gradient
    Boosted Trees on binary classification and regression problems. The current
    version provides two optimization methods: Bayesian optimization and random
    search. Instead of giving the single best model, the final output is an 
    ensemble of Gradient Boosted Trees constructed via the method of ensemble 
    selection.
Simulate real and complex numbers from distributions of
       their magnitude and arguments. Optionally, the magnitudes and/or
       arguments may be fixed in almost arbitrary ways.  Small
       programming utilities: check if an object is identical to NA,
       count positional arguments in a call, set intersection of more
       than two sets, check if an argument is unnamed.
Many inherited biases and effects exists in RNA-seq due to both biological and technical effects. We observed the biological variance of testing target transcripts can influence the yield of sequencing reads, which might indicate a resource competition existing in RNA-seq. We developed this package to capture the bias depending on local sequence and perform the correction of this type of bias by borrowing information from spike-in measurement.
Aligns chromatography peaks with a three step algorithm: (1) Linear
    transformation of retention times to maximise shared peaks among samples
    (2) Align peaks within a certain error-interval (3) Merges rows that are likely
    representing the same substance (i.e. no sample shows peaks in both rows and
    the rows have similar retention time means).
    The method was first described in Stoffel et al. (2015) <doi:10.1073/pnas.1506076112>.
These are two-sample tests for categorical data utilizing similarity information among the categories.  They are useful when there is underlying structure on the categories.
'GPU'/CPU Benchmarking on Debian-package based systems
 This package benchmarks performance of a few standard linear algebra
 operations (such as a matrix product and QR, SVD and LU decompositions)
 across a number of different 'BLAS' libraries as well as a 'GPU' implementation.
 To do so, it takes advantage of the ability to 'plug and play' different
 'BLAS' implementations easily on a Debian and/or Ubuntu system.  The current
 version supports
  - 'Reference BLAS' ('refblas') which are un-accelerated as a baseline
  - Atlas which are tuned but typically configure single-threaded
  - Atlas39 which are tuned and configured for multi-threaded mode
  - 'Goto Blas' which are accelerated and multi-threaded
  - 'Intel MKL' which is a commercial accelerated and multithreaded version.
 As for 'GPU' computing, we use the CRAN package
  - 'gputools'
 For 'Goto Blas', the 'gotoblas2-helper' script from the ISM in Tokyo can be
 used. For 'Intel MKL' we use the Revolution R packages from Ubuntu 9.10.
Contains the Global Charcoal database data. Data include charcoal
    series (age, depth, charcoal quantity, associated units and methods) and
    informations on sedimentary sites (localisation, depositional environment, biome, etc.).
This package implements a generalized coordinate descent (GCD) algorithm for computing the solution path of the hybrid Huberized support vector machine (HHSVM) and its generalization, including the elastic net penalized least squares, the elastic net penalized SVM with the squared hinge loss and the elastic net penalized logistic regression.
Generalized competing event model based on Cox PH model and Fine-Gray model.
    This function is designed to develop optimized risk-stratification methods for competing
    risks data, such as described in:
    1. Carmona R, Gulaya S, Murphy JD, Rose BS, Wu J, Noticewala S,McHale MT, Yashar CM, Vaida F,
    and Mell LK (2014) <DOI:10.1016/j.ijrobp.2014.03.047>.
    2. Carmona R, Zakeri K, Green G, Hwang L, Gulaya S, Xu B, Verma R, Williamson CW, Triplett DP, Rose
    BS, Shen H, Vaida F, Murphy JD, and Mell LK (2016) <DOI:10.1200/JCO.2015.65.0739>.
    3. Lunn, Mary, and Don McNeil (1995) <DOI:10.2307/2532940>.
Scrapes Google Citation pages and creates data frames of 
    citations over time.
Provides a variety of functions to analyze and model
    geostatistical count data with Gaussian copulas, including
 1) data simulation and visualization; 
 2) correlation structure assessment (here also known as the Normal To Anything); 
 3) calculate multivariate normal rectangle probabilities; 
 4) likelihood inference and parallel prediction at predictive locations.
Orders panels in scatterplot matrices and parallel
        coordinate displays by some merit index. Package contains
        various indices of merit, ordering functions, and enhanced
        versions of pairs and parcoord which color panels according to
        their merit level.
Likelihood inference in Gaussian copula marginal
        regression models.
Estimates a sparse inverse covariance matrix from a convex
    pseudo-likelihood function with L1 penalty
This package contains data sets used in the book "R
        Graphics Cookbook" by Winston Chang, published by O'Reilly
        Media.
Analyze the default risk of credit portfolios. Commonly known models, 
		like CreditRisk+ or the CreditMetrics model are implemented in their very basic settings.
		The portfolio loss distribution can be achieved either by simulation or analytically 
		in case of the classic CreditRisk+ model. Models are only implemented to respect losses
		caused by defaults, i.e. migration risk is not included. The package structure is kept
		flexible especially with respect to distributional assumptions in order to quantify the
		sensitivity of risk figures with respect to several assumptions. Therefore the package
		can be used to determine the credit risk of a given portfolio as well as to quantify
		model sensitivities.
Datasets used in the book 'Graphical Data Analysis with R' (Antony Unwin, CRC Press 2015).
Wrappers for the Geospatial Data Abstraction Library (GDAL)
    Utilities.
Various R programming tools for data manipulation, including:
   - medical unit conversions ('ConvertMedUnits', 'MedUnits'),
   - combining objects ('bindData', 'cbindX', 'combine', 'interleave'),
   - character vector operations ('centerText', 'startsWith', 'trim'),
   - factor manipulation ('levels', 'reorder.factor', 'mapLevels'),
   - obtaining information about R objects ('object.size', 'elem', 'env',
     'humanReadable', 'is.what', 'll', 'keep', 'ls.funs',
     'Args','nPairs', 'nobs'),
   - manipulating MS-Excel formatted files ('read.xls',
     'installXLSXsupport', 'sheetCount', 'xlsFormats'),
   - generating fixed-width format files ('write.fwf'),
   - extricating components of date & time objects ('getYear', 'getMonth',
     'getDay', 'getHour', 'getMin', 'getSec'),
   - operations on columns of data frames  ('matchcols', 'rename.vars'),
   - matrix operations ('unmatrix', 'upperTriangle', 'lowerTriangle'),
   - operations on vectors ('case', 'unknownToNA', 'duplicated2', 'trimSum'),
   - operations on data frames ('frameApply', 'wideByFactor'),
   - value of last evaluated expression ('ans'), and
   - wrapper for 'sample' that ensures consistent behavior for both
     scalar and vector arguments ('resample').
Create and maintain delayed-data packages (ddp's).  Data stored in
  a ddp are available on demand, but do not take up memory until requested.
  You attach a ddp with g.data.attach(), then read from it and assign to it in
  a manner similar to S-PLUS, except that you must run g.data.save() to
  actually commit to disk.
Contains functions for 'specific' MCA (Multiple Correspondence Analysis), 
	'class specific' MCA, computing and plotting structuring factors and concentration ellipses, 
	Multiple Factor Analysis, 'standardized' MCA, inductive tests and others tools for Geometric Data Analysis. It also provides functions
	for the translation of logit models coefficients into percentages, weighted contingency tables and an association 
        measure - i.e. Percentages of Maximum Deviation from Independence (PEM).
The GDELT data set is over 60 GB now and growing 100 MB a month.
    The number of source articles has increased over time and unevenly across
    countries. This package makes it easy to download a subset of that data,
    then normalize that data to facilitate valid time series analysis.
Diffusion anisotropy has been used to characterize
 white matter neuronal pathways in the human brain, and infer global
 connectivity in the central nervous system. The package implements
 algorithms to estimate and visualize the orientation of neuronal
 pathways in model-free methods (q-space imaging methods).
 For estimating fibre orientations two methods have been
 implemented.  One method implements fibre orientation detection
 through local maxima extraction. A second more robust method
 is based on directional statistical clustering of ODF voxel data.
 Fibre orientations in multiple fibre voxels are estimated using
 a mixture of von Mises-Fisher (vMF) distributions. This statistical
 estimation procedure is used to resolve crossing fibre
 configurations.
 Reconstruction of orientation distribution function (ODF)
 profiles may be performed using the standard generalized
 q-sampling imaging (GQI) approach, Garyfallidis' GQI (GQI2)
 approach, or Aganj's variant of the Q-ball imaging (CSA-QBI)
 approach. Procedures for the visualization of RGB-maps,
 line-maps and glyph-maps of real diffusion magnetic resonance
 imaging (dMRI) data-sets are included in the package.
A set of psychometric tools for cognitive diagnostic analyses for both dichotomous and polytomous responses. Various cognitive diagnosis models can be estimated, include the generalized deterministic inputs, noisy and gate (G-DINA) model by de la Torre (2011) <DOI:10.1007/s11336-011-9207-7>, the sequential G-DINA model by Ma and de la Torre (2016) <DOI:10.1111/bmsp.12070>, and many other models they subsume. Joint attribute distribution can be saturated, higher-order or structured. Q-matrix validation, item and model fit statistics, model comparison at test and item level and differential item functioning can also be conducted. A graphical user interface is also provided.
Calculate distances and routes on geographic grids.
A toolkit with functions to fit, plot, and summarize Generalized Dissimilarity Models (Ferrier et al. 2007, <doi:10.1111/j.1472-4642.2007.00341.x>).
Manage and analyze high-dimensional SNP data from chips with multiple densities.
To address the problem of insecurity of UDP-based DNS requests,
    Google Public DNS offers DNS resolution over an encrypted HTTPS
    connection. DNS-over-HTTPS greatly enhances privacy and security
    between a client and a recursive resolver, and complements DNSSEC
    to provide end-to-end authenticated DNS lookups. Functions that enable
    querying individual requests that bulk requests that return detailed
    responses and bulk requests are both provided. Support for reverse
    lookups is also provided. See <https://developers.google.com/speed/public-dns/docs/dns-over-https> 
    for more information.
Functions to compute the Generalized Dynamic Principal Components
    introduced in Peña and Yohai (2016) <DOI:10.1080/01621459.2015.1072542>.
Contains a function called gds() which accepts three input
    parameters like lower limits, upper limits and the frequencies of the
    corresponding classes. The gds() function calculate and return the values
    of mean ('gmean'), median ('gmedian'), mode ('gmode'), variance ('gvar'), standard
    deviation ('gstdev'), coefficient of variance ('gcv'), quartiles ('gq1', 'gq2', 'gq3'),
    inter-quartile range ('gIQR'), skewness ('g1'), and kurtosis ('g2') which facilitate
    effective data analysis. For skewness and kurtosis calculations we use moments.
Useful tools for writing vector graphics devices.
Implements common geostatistical methods in a clean,
    straightforward, efficient manner. A quasi reboot of the SpatialTools R package.
Fit joint mean-covariance models for longitudinal data within the 
    framework of (weighted) generalised estimating equations (GEE/WGEE). The 
    models and their components are represented using S4 classes and methods. 
    The core computational algorithms are implemented using the 'Armadillo' C++ 
    library for numerical linear algebra and 'RcppArmadillo' glue.
Provides adaptive association tests for SNP level, gene level and pathway level analyses.
GEE estimation of the parameters in mean structures with possible
    correlation between the outcomes. User-specified mean link and variance
    functions are allowed, along with observation weighting. The "M" in the name
    "geeM" is meant to emphasize the use of the Matrix package, which allows for an
    implementation based fully in R.
Causal mediation analysis for a single exposure/treatment and a
    single mediator, both allowed to be either continuous or binary. The package
    implements the difference method and provide point and interval estimates as
    well as testing for the natural direct and indirect effects and the mediation
    proportion.
Generalized estimating equations solver for parameters in
        mean, scale, and correlation structures, through mean link,
        scale link, and correlation link. Can also handle clustered
        categorical responses.
Generalized estimating equations with the original sandwich variance estimator proposed by Liang and Zeger (1986), and eight types of more recent modified variance estimators for improving the finite small-sample performance.
Functions to compute generalized eigenvalues and eigenvectors,
             the generalized Schur decomposition and
             the generalized Singular Value Decomposition of a matrix pair,
             using Lapack routines.
Methods for fitting macroevolutionary models to phylogenetic trees.
First major functionality is to compute the bias in misspecified linear gene-environment interaction models. The most 
	generalized function for this objective is GE_bias().  However GE_bias() requires specification of many
	higher order moments of covariates in the model.  If users are unsure about how to calculate/estimate
	these higher order moments, it may be easier to use GE_bias_normal_squaredmis().  This function places
	many more assumptions on the covariates (most notably that they are all jointly generated from a multivariate
	normal distribution) and is thus able to automatically calculate many of the higher order moments automatically,
	necessitating only that the user specify some covariances.  There are also functions to solve for the bias 
	through simulation and non-linear equation solvers, these can be used to check your work. Second major functionality
	is to implement the Bootstrap Inference with Correct Sandwich (BICS) testing procedure, which we have found to provide better finite-sample
	performance than other inference procedures for testing GxE interaction.  More details on these functions 
	are available in Sun, Carroll, Christiani, and Lin, "Testing for Gene-Environment Interaction Under Exposure Misspecification"
	(In Revision).
Implements several extensions of the elastic net regularization
    scheme. These extensions include individual feature penalties for the L1 term,
    feature-feature penalties for the L2 term, as well as translation coefficients
    for the latter.
Reads data files from the 'Gem infrasound logger' for analysis and converts to segy format (which is convenient for reading with traditional seismic analysis software). The Gem infrasound logger is an in-development low-cost, lightweight, low-power instrument for recording infrasound in field campaigns; email the maintainer for more information.
Simulate and analyze multistate models with general hazard
    functions. gems provides functionality for the preparation of hazard functions
    and parameters, simulation from a general multistate model and predicting future
    events. The multistate model is not required to be a Markov model and may take
    the history of previous events into account. In the basic version, it allows
    to simulate from transition-specific hazard function, whose parameters are
    multivariable normally distributed.
Network meta-analyses (mixed treatment comparisons) in the Bayesian
    framework using JAGS. Includes methods to assess heterogeneity and
    inconsistency, and a number of standard visualizations.
An R package providing the Java JAR for the gemtc package
a package for genome-wide association analysis between 
             quantitative or binary traits and single-nucleotide
             polymorphisms (SNPs).
GenABEL.data package consists of a data set used by GenABEL functions
R based genetic algorithm for binary and floating point
        chromosomes.
Defines classes and methods that can be used
  to implement genetic algorithms for feature selection.  The idea is
  that we want to select a fixed number of features to combine into a
  linear classifier that can predict a binary outcome, and can use a
  genetic algorithm heuristically to select an optimal set of features.
genasis package contains methods for air pollution assessment. Concerned on persistent organic pollutants, the package allows to compute trends of their concentrations, compare different datasets and estimate relations between values from active and passive air samplers.
Density, distribution function, quantile function and random generation for the Generalized Binomial Distribution. Functions to compute the Clopper-Pearson Confidence Interval and the required sample size. Enhanced model for burn-in studies, where failures are tackled by countermeasures.
Implementation of the genetic class level association testing (GenCAT) method from SNP level association data. Refer to: "Qian J, Nunez S, Reed E, Reilly MP, Foulkes AS (2016) <DOI:10.1371/journal.pone.0148218> A Simple Test of Class-Level Genetic Association Can Reveal Novel Cardiometabolic Trait Loci. PLoS ONE 11(2): e0148218".
Engines for cross-validation of many types of regression and class prediction models are provided. These engines include built-in support for 'glmnet', 'lars', 'plus', 'MASS', 'rpart', 'C50' and 'randomforest'. It is easy for the user to add other regression or classification algorithms. The 'parallel' package is used to improve speed. Several data generation algorithms for problems in regression and classification are provided.
Set of functions to create datasets using a correlation matrix.
Encodes gender based on names and dates of birth using historical
    datasets. By using these datasets instead of lists of male and female names,
    this package is able to more accurately guess the gender of a name, and it
    is able to report the probability that a name was male or female.
Utilizes the 'genderize.io' Application Programming Interface 
    to predict gender from first names extracted from a text vector. 
    The accuracy of prediction could be controlled by two parameters: 
    counts of a first name in the database and probability of prediction.
Computes the probability density function (pdf), cumulative distribution function (cdf), quantile function (qf) and generates random values (rg) for the following general models : mixture models, composite models, folded models, skewed symmetric models and arc tan models.
Functions and analytics for GENEA-compatible accelerometer
        data into R objects. See topic 'GENEAread' for an introduction
        to the package.
Functions for clustering time-course gene expression and reconstructing of gene regulatory network based on Dynamic Bayesian Network.
The GeneCycle package implements the approaches of Wichert
        et al. (2004), Ahdesmaki et al. (2005) and Ahdesmaki et al.
        (2007) for detecting periodically expressed genes from gene
        expression time series data.
This package implements several generalized F-statistics.
        The current version includes a generalized F-statistic based on
        the flexible isotonic/monotonic regression or order restricted
        hypothesis testing.
GeneFeST is a genome scan method to detect outlier loci (genes) which are under balancing or directional selection.
"geneListPie" package is for mapping a gene list to
        function categories defined in GOSlim or Kegg. The results can
        be plotted as a pie chart to provide a quick view of the genes
        distribution of the gene list among the function categories.
        The gene list must contain a list of gene symbols. The package
        contains a set of pre-processed gene sets obtained from Gene
        Ontology and MSigDB including human, mouse, rat and yeast. To
        provide a high level concise view, only GO slim and kegg are
        provided. The gene sets are regulared updated. User can also
        use customized gene sets. User can use the R Pie() or Pie3D()
        function for plotting the pie chart. Users can also choose to
        output the gene function mapping results and use external
        software such as Excel(R) for ploting.
Using simple input, this package creates plots of gene models. Users can create plots of alternatively spliced gene variants and the positions of mutations and other gene features.
Analyzes gene expression
  (time series) data with focus on the inference of gene networks.
  In particular, GeneNet implements the methods of Schaefer and 
  Strimmer (2005a,b,c) and Opgen-Rhein and Strimmer (2006, 2007)
  for learning large-scale gene association networks (including
  assignment of putative directions).
Belief propagation methods in genotype-phenotype networks (Conditional Gaussian and Discrete Bayesian Networks) to propagate phenotypic evidence through the network.
Functions for Genetic Epi Methods Developed at MSKCC
Makes the Genepop software available in R. This software implements a mixture of traditional population genetic methods and some more focused developments: it computes exact tests for Hardy-Weinberg equilibrium, for population differentiation and for genotypic disequilibrium among pairs of loci; it computes estimates of F-statistics, null allele frequencies, allele size-based statistics for microsatellites, etc.; and it performs analyses of isolation by distance from pairwise comparisons of individuals or population samples.
Since causal paths from data are important for all sciences, the
     package provides sophisticated functions. The idea is simply 
     that if X causes Y (path: X to Y) then non-deterministic variation in X
     is more "original or independent" than similar variation in Y.  We compare 
     two flipped kernel regressions: X=f(Y, Z) and Y=g(X,Z), where Z are control 
     variables. Our first two criteria compare absolute gradients (Cr1) and 
     absolute residuals (Cr2), both quantified by stochastic dominance of four 
     orders (SD1 to SD4). Our third criterion (Cr3) expects X to be better able 
     to predict Y than the other way around using generalized partial correlation 
     If |r*(x|y)|> |r*(y|x)| it suggests that y is more likely
    the "kernel cause" of x. The usual partial correlations are generalized for 
    the asymmetric matrix of r*'s developed here.
    Partial correlations help asses effect of x on y after removing the effect of a
    set of variables. 
    The package provides additional tools for causal assessment,
    for printing the causal directions in a clear, comprehensive compact summary form,
    for matrix algebra, for "outlier detection", and for numerical integration by the
    trapezoidal rule, stochastic dominance, etc. 
    The package has functions for bootstrap-based statistical inference and one 
    for a heuristic t-test.
Functions to assess the goodness of fit of binary, multinomial and ordinal logistic models.
	Included are the Hosmer-Lemeshow tests (binary, multinomial and ordinal) and the Lipsitz and 
	Pulkstenis-Robinson tests (ordinal).
This package provides functions for the hyperbolic and
        related distributions. Density, distribution and quantile
        functions and random number generation are provided for the
        hyperbolic distribution, the generalized hyperbolic
        distribution, the generalized inverse Gaussian distribution and
        the skew-Laplace distribution. Additional functionality is
        provided for the hyperbolic distribution, normal inverse
        Gaussian distribution and generalized inverse Gaussian
        distribution, including fitting of these distributions to data.
        Linear models with hyperbolic errors may be fitted using
        hyperblmFit.
Projections from a high-dimensional data space onto a two-dimensional plane are used to detect structures, such as clusters, in multivariate data. The generalized Umatrix is able to visualize errors of these two-dimensional scatter plots by using a 3D topographic map.
Perform the Blinder-Oaxaca decomposition for generalized linear 
   model with bootstrapped standard errors. The twofold and threefold 
   decomposition are given, even the generalized linear model output in each group.
Allows users to quickly and easily generate fake data containing
    Personally Identifiable Information (PII) through convenience functions.
GeneReg is an R package for inferring time delay gene
        regulatory network using time course gene expression profiles.
        The main idea of time delay linear model is to fit a linear
        regression model using a set of putative regulators to estimate
        the transcription pattern of a specific target gene.
A tool for finding an ensemble gene-signature by a steepest ascending algorithm partially supervised by survival time data.
Genome-wide association study (GWAS) performed with SLOPE,
    short for Sorted L-One Penalized Estimation, a
    method for estimating the vector of coefficients in linear model.
    In the first step of GWAS, SNPs are clumped according to their correlations and
    distances. Then, SLOPE is performed on data where each clump has
    one representative.
Classes and methods for handling genetic data. Includes
        classes to represent genotypes and haplotypes at single markers
        up to multiple markers on multiple chromosomes. Function
        include allele frequencies, flagging homo/heterozygotes,
        flagging carriers of certain alleles, estimating and testing
        for Hardy-Weinberg disequilibrium, estimating and testing for
        linkage disequilibrium, ...
Finds subsets of sets of genotypes with a high Heterozygosity, and Mean of Transformed Kinships (MTK), measures that can indicate a subset would be beneficial for rare-trait discovery and genome-wide association scanning, respectively.
Implementation of spatial graph-theoretic genetic gravity models.
    The model framework is applicable for other types of spatial flow questions.
    Includes functions for constructing spatial graphs, sampling and summarizing
    associated raster variables and building unconstrained and singly constrained
    gravity models.
Two methods based on the Forward Imputation approach are implemented for the imputation of quantitative missing data. One method alternates Nearest Neighbour Imputation and Principal Component Analysis (function 'ForImp.PCA'), the other uses Nearest Neighbour Imputation with the Mahalanobis distance (function 'ForImp.Mahala').
A new hierarchical clustering linkage criterion:
    the Genie algorithm links two clusters in such a way that a chosen
    economic inequity measure (e.g., the Gini index) of the cluster
    sizes does not increase drastically above a given threshold. Benchmarks
    indicate a high practical usefulness of the introduced method:
    it most often outperforms the Ward or average linkage in terms of
    the clustering quality while retaining the single linkage speed,
    see (Gagolewski et al. 2016a <DOI:10.1016/j.ins.2016.05.003>,
    2016b <DOI:10.1007/978-3-319-45656-0_16>)
    for more details.
This package computes the solution path for generalized lasso problems. Important use cases are the fused lasso over an arbitrary graph, and trend fitting of any given polynomial order. Specialized implementations for the latter two subproblems are given to improve stability and speed.
Genealogical data analysis including descriptive statistics (e.g., kinship and inbreeding coefficients) and gene-dropping simulations.
Implements the Mode Oriented Stochastic Search (MOSS) algorithm as well as a simple moving window approach to look for combinations of SNPs that are associated with a response.
This is a genMOSS package with additional datafile preprocessing functions. Performs genome-wide analysis of dense SNP array data using the mode oriented stochastic search (MOSS) algorithm in a case-control design. The MOSS algorithm is a Bayesian variable selection procedure that is applicable to GWAS data. It identifies combinations of the best predictive SNPs associated with the response. It also performs a hierarchical log-linear model search to identify the most relevant associations among the resulting subsets of SNPs. This package also includes preprocessing of the data from Plink format to the format required by the MOSS algorithm.
Plot values of markers(SNPs, expression, genes, RNA,...) for all chromosomes.
Implements the genomic mating approach in the recently published article: Akdemir, D., & Sanchez, J. I. (2016). Efficient Breeding by Genomic Mating. Frontiers in Genetics, 7. <DOI:10.3389/fgene.2016.00210>.
Circular genomic permutation approach uses GWAS results to establish the significance of pathway/gene-set associations whilst accounting for genomic structure. All SNPs in the GWAS are placed in a 'circular genome' according to their location. Then the complete set of SNP association p-values are permuted by rotation with respect to the SNPs' genomic locations. Two testing frameworks are available: permutations at the gene level, and permutations at the SNP level. The permutation at the gene level uses fisher's combination test to calculate a single gene p-value, followed by the hypergeometric test. The SNP count methodology maps each SNP to pathways/gene-sets and calculates the proportion of SNPs for the real and the permutated datasets above a pre-defined threshold. Genomicper requires a matrix of GWAS association p-values. The SNPs annotation and pathways annotations can be performed within the package or provided by the user.
A loose collection of tools for the analysis of expression and genotype data, currently with the main focus on (e)QTL and MDR analysis.
Draws gene or genome maps and comparisons between these, in a 
   publication-grade manner. Starting from simple, common files, it will 
   draw postscript or PDF files that can be sent as such to journals.
A gaussian copula based procedure for generating samples from discrete random variables with prescribed correlation matrix and marginal distributions.
We implement a common genotyping workflow with a standardized software interface. 'genotypeR' designs genotyping markers from vcf files, outputs markers for multiplexing suitability on various platforms (Sequenom and Illumina GoldenGate), and provides various QA/QC and analysis functions. We developed this package to analyze data in Stevison LS, SA Sefick, CA Rushton, and RM Graze. Accepted. Invited Review: Recombination rate plasticity: revealing mechanisms by design. Philosophical Transactions Royal Society London B Biol Sci. <DOI:10.1098/rstb.2015.0615>.
Provides a very interesting solution for
    handling segmentation variables in complex statistical methodology. It
    contains en extended version of the "Pathmox" algorithm (Lamberti, Sanchez
    and Aluja,(2016)<doi:10.1002/asmb.2168>) in the context of
    Partial Least Squares Path Modeling including the F-block
    test (to detect the responsible latent endogenous equations of the
    difference), the F-coefficient (to detect the path coefficients responsible
    of the difference) and the "invariance" test (to realize a comparison
    between the sub-models' latent variables). Furthermore, the package
    contains a generalized version of the "Pathmox" algorithm to approach
    different methodologies: linear regression and least absolute regression
    models.
The genridge package introduces generalizations of the standard univariate
	ridge trace plot used in ridge regression and related methods.  These graphical methods
	show both bias (actually, shrinkage) and precision, by plotting the covariance ellipsoids of the estimated
	coefficients, rather than just the estimates themselves.  2D and 3D plotting methods are provided,
	both in the space of the predictor variables and in the transformed space of the PCA/SVD of the
	predictors.
Performs search for global minimum of a very complex non-linear objective function with a very large number of optima.
Generalized ensemble methods allowing arbitrary underlying
        models to be used. Currently only bagging is supported.
Define and compute with generalized spherical distributions - multivariate probability
 laws that are specified by a star shaped contour (directional behavior) and a radial component.
Generation of survival data with one (binary)
        time-dependent covariate.  Generation of survival data arising
        from a progressive illness-death model.
Defines window or bin boundaries for the analysis of genomic data.
    Boundaries are based on the inflection points of a cubic smoothing spline
    fitted to the raw data. Along with defining boundaries, a technique to
    evaluate results obtained from unequally-sized windows is provided.
    Applications are particularly pertinent for, though not limited to, genome
    scans for selection based on variability between populations (e.g. using
    Wright's fixations index, Fst, which measures variability in subpopulations
    relative to the total population).
Used by Hafro staff to draw maps showing the distribution of
  fishing intensity and catches, and of survey data for Icelandic fish stocks.
  Potentially useful for others.
Split 'geospatial' objects into pieces. Includes
    support for some spatial object inputs, 'Well-Known Text', and
    'GeoJSON'.
Functions to fit geostatistical data. The data can be
        continuous, binary or count data and the models implemented are
        flexible. Conjugate priors are assumed on some parameters while
        inference on the other parameters can be done through a full
        Bayesian analysis of by empirical Bayes methods.
Make geographic box plot as detailed in Willmott et al. (2007).
This package provides a variety of functions to analyze and model geostatistical count data with generalized linear spatial models, including
 1) simulate and visualize the data;
 2) posterior sampling with robust MCMC algorithms (in serial or parallel way);
 3) perform prediction for unsampled locations;
 4) conduct Bayesian model checking procedure to evaluate the goodness of fitting;
 5) conduct transformed residual checking procedure.
 In the package, seamlessly embedded C++ programs and parallel computing techniques are implemented to speed up the computing processes.
Given expression data this package calculate a multivariate
    geometrical characterization of the differential expression and can also
    perform gene-set enrichment.
Visualizes two-dimensional geoelectric resistivity measurement profiles in three dimensions.
Provides geofaceting functionality for 'ggplot2'. Geofaceting arranges a sequence of plots of data for different geographical entities into a grid that preserves some of the geographical orientation.
Kriging based methods are used for predicting functional data 
             (curves) with spatial dependence.
A model building procedure to build parsimonious geoadditive model from a large number of covariates. Continuous, binary and ordered categorical responses are supported. The model building is based on component wise gradient boosting with linear effects, smoothing splines and a smooth spatial surface to model spatial autocorrelation. The resulting covariate set after gradient boosting is further reduced through backward elimination and aggregation of factor levels. The package provides a model based bootstrap method to simulate prediction intervals for point predictions. A test data set of a soil mapping case study in Berne (Switzerland) is provided.
Quantification of the effect of geographic versus environmental isolation on genetic differentiation
Provides tools to encode lat/long pairs into geohashes, decode those geohashes,
             and identify their neighbours.
Classes for 'GeoJSON' to make working with 'GeoJSON' easier.
    Includes S3 classes for 'GeoJSON' classes with brief summary output,
    and a few methods such as extracting and addin bounding boxes, 
    properties, and coordinate reference systems; linting through 
    the geojsonlint package; and serializing to/from Geobuf binary 'GeoJSON'
    format.
Convert data to 'GeoJSON' or 'TopoJSON' from various R classes,
    including vectors, lists, data frames, shape files, and spatial classes.
    'geojsonio' does not aim to replace packages like 'sp', 'rgdal', 'rgeos', 
    but rather aims to be a high level client to simplify conversions of data 
    from and to 'GeoJSON' and 'TopoJSON'.
Tools for linting 'GeoJSON'. Includes tools for interacting with the
    online tool <http://geojsonlint.com>, the 'Javascript' library 'geojsonhint'
    (<https://www.npmjs.com/package/geojsonhint>), and validating against a
    'GeoJSON' schema via the 'Javascript' library
    (<https://www.npmjs.com/package/is-my-json-valid>). Some tools work locally
    while others require an internet connection.
Includes functions for processing GeoJson objects <https://en.wikipedia.org/wiki/GeoJSON> relying on 'RFC 7946' <https://tools.ietf.org/pdf/rfc7946.pdf>. The geojson encoding is based on 'json11', a tiny JSON library for 'C++11' <https://github.com/dropbox/json11>. Furthermore, the source code is exported in R through the 'Rcpp' and 'RcppArmadillo' packages.
Processes gridded datasets found on the U.S. Geological Survey
    Geo Data Portal web application or elsewhere, using a web-enabled workflow
    that eliminates the need to download and store large datasets that are reliably
    hosted on the Internet. The package provides access to several data subset and
    summarization algorithms that are available on remote web processing servers.
Provides basic functions for global
    positioning based on light intensity measurements over time.
    Positioning process includes the determination of sun events, a
    discrimination of residency and movement periods, the
    calibration of period-specific data and, finally, the
    calculation of positions.
Set of routines for making Map Projections (forward and inverse), Topographic Maps, Perspective plots, Geological Maps, geological map symbols, geological databases, interactive plotting and selection of focus regions.
Set of data for use in package GEOmap.  Includes world
        map, USA map, Coso map, Japan Map, ETOPO5
Provides eigenvector-based (geometric) forecast
    combination methods; also includes simple approaches (simple average,
    median, trimmed and winsorized mean, inverse rank method) and regression-based
    combination. Tools for data pre-processing are available in order to deal with 
    common problems in forecast combination (missingness, collinearity).
The Genomic Observatory Metadatabase (GeOMe Database) is an open access repository for
    geographic and ecological metadata associated with sequenced samples. This package is used to retrieve
    GeOMe data for analysis. See <http://www.geome-db.org> for more information regarding GeOMe.
Geospatial data integration framework that merges raster, spatial polygon, and (dynamic) spatial points data into a spatial (panel) data frame at any geographical resolution.
Provides facilities to handle reading and writing of geographic metadata 
 defined with OGC/ISO 19115 and 19139 (XML) standards.
Makes the qhull library (www.qhull.org)
    available in R, in a similar manner as in Octave and MATLAB. Qhull
    computes convex hulls, Delaunay triangulations, halfspace
    intersections about a point, Voronoi diagrams, furthest-site
    Delaunay triangulations, and furthest-site Voronoi diagrams. It
    runs in 2-d, 3-d, 4-d, and higher dimensions. It implements the
    Quickhull algorithm for computing the convex hull. Qhull does not
    support constrained Delaunay triangulations, or mesh generation of
    non-convex objects, but the package does include some R functions
    that allow for this. Currently the package only gives access to
    Delaunay triangulation and convex hull computation.
Network visualization in the 'ggplot2' framework. Network
    functionality is provided in a single 'ggplot2' layer by calling the geom 'net'.
    Layouts are calculated using the 'sna' package, example networks are included.
Utilizes methods of the 'PyMongo' 'Python' library to initialize, insert and query 'GeoJson' data (see <https://api.mongodb.com/python/current/#> for more information on 'PyMongo'). Furthermore, it allows the user to validate 'GeoJson' objects and to use the console for 'MongoDB' (bulk) commands. The 'reticulate' package provides the 'R' interface to 'Python' modules, classes and functions.
Read, manipulate, and digitize landmark data, generate shape
    variables via Procrustes analysis for points, curves and surfaces, perform
    shape analyses, and provide graphical depictions of shapes and patterns of
    shape variation.
Code for querying the web service at www.geonames.org
A wrapper for the Geoparser.io API version 0.4.0 (see <https://geoparser.io/>), which is a web service
    that identifies places mentioned in text, disambiguates those places, and
    returns detailed data about the places found in the text. Basic, limited
    API access is free with paid plans to accommodate larger workloads.
Codes for analyzing various problems of Geophysics, Continuum Mechanics and  Mogi Models.
Geostatistical analysis including traditional, likelihood-based and Bayesian methods.
Calculates and analyzes six measures of geographic range from a set of longitudinal and latitudinal occurrence data. Measures included are minimum convex hull area, minimum spanning tree distance, longitudinal range, latitudinal range, maximum pairwise great circle distance, and number of X by X degree cells occupied.
Functions for inference in generalised linear spatial models. The posterior and predictive inference is based on Markov chain Monte Carlo methods. Package geoRglm is an extension to the package geoR, which must be installed first.
Provides functions for efficiently fitting linear models with 
         spatially correlated errors by robust and Gaussian (Restricted) 
         Maximum Likelihood and for computing robust and customary point 
         and block external-drift Kriging predictions, along with utility
         functions for variogram modelling in ad hoc geostatistical analyses, 
         model building, model evaluation by cross-validation and for 
         unbiased back-transformation of Kriging predictions of 
         log-transformed data.
Provides an R interface to the GeoServer REST API, allowing to upload 
 and publish data in a GeoServer web-application and expose data to OGC Web-Services. 
 The package currently supports all CRUD (Create,Read,Update,Delete) operations
 on GeoServer workspaces, namespaces, datastores (stores of vector data), featuretypes,
 layers, styles, as well as vector data upload operations. For more information about 
 the GeoServer REST API, see <http://docs.geoserver.org/stable/en/user/rest/>.
Function for adding the geological timescale to bivariate plots.
Generates distance matrices from shape files and represents spatially weighted multilevel analysis results (see 'spacom')
Provides S4 classes and data import, preprocessing, graphing, 
    manipulation and export methods for geo-Spectral datasets (datasets with space/time/spectral 
    dimensions). These type of data are frequently collected within earth observation projects 
    (remote sensing, spectroscopy, bio-optical oceanography, mining, agricultural, atmospheric, 
    environmental or similar branch of science).
Spherical trigonometry for geographic applications. That is, compute distances and related measures for angular (longitude/latitude) locations.
Estimation of the variogram through trimmed mean, radial basis 
        functions (optimization, prediction and cross-validation), summary
        statistics from cross-validation, pocket plot, and design of
        optimal sampling networks through sequential and simultaneous
        points methods.
Spatio-temporal: Inverse Distance Weighting (IDW) and radial basis functions; optimization, prediction, summary statistics from leave-one-out cross-validation, adjusting distance-based linear regression model and generation of the principal coordinates of a new individual from Gower's distance.
Geostatistical modelling facilities using Raster and SpatialPoints
    objects are provided. Non-Gaussian models are fit using INLA, and Gaussian
    geostatistical models use Maximum Likelihood Estimation.
A compilation of functions for performing calculations and
    creating plots that commonly arise in geotechnical engineering and soil
    mechanics. The types of calculations that are currently included are:
    (1) phase diagrams and index parameters, (2) grain-size distributions, 
    (3) plasticity, (4) soil classification, (5) compaction, (6) groundwater,
    (7) subsurface stresses (geostatic and induced), (8) Mohr circle analyses,
    (9) consolidation settlement and rate, (10) shear strength, (11) bearing
    capacity, (12) lateral earth pressures, (13) slope stability, and (14)
    subsurface explorations. Geotechnical engineering students, educators,
    researchers, and practitioners will find this package useful.
The basic idea of this package is provides some tools to help the researcher to work with geostatistics. Initially, we present a collection of functions that allow the researchers to deal with spatial data using bootstrap procedure.There are five methods available and two ways to display them: bootstrap confidence interval - provides a two-sided bootstrap confidence interval; bootstrap plot - a graphic with the original variogram and each of the B bootstrap variograms.
It analyzes raster maps and other information as input/output
    files from the Hydrological Distributed Model GEOtop. It contains functions
    and methods to import maps and other keywords from geotop.inpts file. Some
    examples with simulation cases of GEOtop 2.0 are presented in the package.
    Any information about the GEOtop Distributed Hydrological Model source code
    is available on https://github.com/geotopmodel or
    https://github.com/se27xx/GEOtop. Technical details about the model are
    available in Endrizzi et al, 2014
    (http://www.geosci-model-dev.net/7/2831/2014/gmd-7-2831-2014.html).
GeoXp is a tool for researchers in spatial statistics,
        spatial econometrics, geography, ecology etc allowing to link
        dynamically statistical plots with elementary maps. This
        coupling consists in the fact that the selection of a zone on
        the map results in the automatic highlighting of the
        corresponding points on the statistical graph or reversely the
        selection of a portion of the graph results in the automatic
        highlighting of the corresponding points on the map. GeoXp
        includes tools from different areas of spatial statistics
        including geostatistics as well as spatial econometrics and
        point processes. Besides elementary plots like boxplots,
        histograms or simple scatterplot, GeoXp also couples with maps
        Moran scatterplots, variogram cloud, Lorentz Curves,...In order
        to make the most of the multidimensionality of the data, GeoXp
        includes some dimension reduction techniques such as PCA.
Geometric objects defined in 'geozoo' can be simulated or displayed in the R package 'tourr'.
Encode and decode the Google Encoded Polyline Algorithm Format (<https://developers.google.com/maps/documentation/utilities/polylinealgorithm>).
Estimation and diagnosis of the convergence of Generalized
    Exponential Random Graph Models via Gibbs sampling or Metropolis
    Hastings with exponential down weighting.
Different types of seed indexes, rates and visualization techniques
    are used to provide a robust approach for germination data analysis. The package
    aims to make available germination seed indexes and graphical functions to
    analyze germination seed data.
Fit a variety of component-based structural equation models.
Implements the gene-based segregation test(GESE) and the weighted GESE test for identifying genes with causal variants of large effects for family-based sequencing data. The methods are described in Qiao, D. Lange, C., Laird, N.M., Won, S., Hersh, C.P., et al. (2017). <DOI:10.1002/gepi.22037>. Gene-based segregation method for identifying rare variants for family-based sequencing studies. Genet Epidemiol 41(4):309-319. More details can be found at <http://scholar.harvard.edu/dqiao/gese>.
Provides programmatic access to the GESIS - Leibniz-Institute for
    the Social Sciences Data Catalogue/Datenbestandkatalog (DBK), which
    maintains a large repository of data sets related to the social sciences.
    See <http://www.gesis.org> for more information.
The Gene Expression State Transformation (GESTr) models
        the states of expression of genes across a compendium of
        samples in order to provide a universal scale of gene
        expression for all genes. TranSAM is a modification of the SAM
        approach designed to utilise GESTr-transformed gene expression
        data.
Provides functions that automate downloading and importing
    University of East Anglia Climate Research Unit (CRU) CL v. 2.0 climatology
    data into R, facilitates the calculation of minimum temperature and maximum
    temperature and formats the data into a tidy data frame as a tibble or a 
    list of raster stack objects for use in an R session.  CRU CL v. 2.0 data 
    are a gridded climatology of 1961-1990 monthly means released in 2002 and
    cover all land areas (excluding Antarctica) at 10 arcminutes
    (0.1666667 degree) resolution.  For more information see the description of
    the data provided by the University of East Anglia Climate Research Unit,
    <https://crudata.uea.ac.uk/cru/data/hrg/tmc/readme.txt>.
Downloads and aggregates high frequency trading data for Brazilian instruments directly from Bovespa ftp site <ftp://ftp.bmf.com.br/MarketData/>.
Get Landsat 8 Data from Amazon Web Services ('AWS')
    public data sets (<https://aws.amazon.com/public-data-sets/landsat/>).
    Includes functions for listing images and fetching them, and handles
    caching to prevent unnecessary additional requests.
Hydrologic models often require users to collect and format input meteorological data. This package contains functions for sourcing, formatting, and
    editing meteorological data for hydrologic models.
Quantifying systematic heterogeneity in meta-analysis using R.
    The M statistic aggregates heterogeneity information across multiple
    variants to, identify systematic heterogeneity patterns and their direction
    of effect in meta-analysis. It's primary use is to identify outlier studies,
    which either show "null" effects or consistently show stronger or weaker
    genetic effects than average across, the panel of variants examined in a
    GWAS meta-analysis. In contrast to conventional heterogeneity metrics
    (Q-statistic, I-squared and tau-squared) which measure random heterogeneity
    at individual variants, M measures systematic (non-random)
    heterogeneity across multiple independently associated variants. Systematic
    heterogeneity can arise in a meta-analysis due to differences in the study
    characteristics of participating studies. Some of the differences may
    include: ancestry, allele frequencies, phenotype definition, age-of-disease
    onset, family-history, gender, linkage disequilibrium and quality control
    thresholds. See <https://magosil86.github.io/getmstatistic/> for statistical
    statistical theory, documentation and examples.
Package designed to be used with Rscript to write
    ``#!'' shebang scripts that accept short and long flags/options.
    Many users will prefer using instead the packages optparse or argparse
    which add extra features like automatically generated help option and usage,
    support for default values, positional argument support, etc.
This is yet another command-line argument parser which wraps the 
    powerful Perl module Getopt::Long and with some adaptation for easier use
	in R. It also provides a simple way for variable interpolation in R.
A micro-package for reading "passwords", i.e.  reading
    user input with masking, so that the input is not displayed as it 
    is typed.  Currently we have support for 'RStudio', the command line
    (every OS), and any platform where 'tcltk' is present.
The GetR package calculates Guttman error trees, which can
        be used to find homogeneous subgroups regarding Guttman errors.
Automated General-to-Specific (GETS) modelling of the mean and variance of a regression, and indicator saturation methods for detecting and testing for structural breaks in the mean.
Downloads and aggregates data for Brazilian government issued bonds directly from the website of Tesouro Direto <http://www.tesouro.fazenda.gov.br/tesouro-direto-balanco-e-estatisticas>.
Getting to the Bottom accompanies the "Getting to
    the Bottom" optimization methods series at Statisticsviews.com.  It
    contains data and code to reproduce the examples in the articles.
A function to retrieve the system timezone on Unix systems
 which has been found to find an answer when 'Sys.timezone()' has failed.
 It is based on an answer by Duane McCully posted on 'StackOverflow', and
 adapted to be callable from R. The package also builds on Windows, but
 just returns NULL.
Implements a flexible nonlinear modelling framework for nonstationary
        generalized extreme value analysis in hydroclimatology.
Package for simulation and estimation of ARMA-GARCH/APARCH models with GEV and stable distributions.
Perform statistical tests to unveil genomic clusters,
        produces garphical interpretations of the statistical results
        in pdf files, perform a Gene Ontology analysis and produces
        graphic results in pdf files
Factor analysis implementation for multiple data sources, i.e., for groups of variables. The whole data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The model group factor analysis (GFA) is inferred with Gibbs sampling, and it has been presented originally by Virtanen et al. (2012), and extended in Klami et al. (2015) <DOI:10.1109/TNNLS.2014.2376974> and Bunte et al. (2016) <DOI:10.1093/bioinformatics/btw207>; for details, see the citation info.
The gfcanalysis package supports analyses using the Global
    Forest Change dataset released by Hansen et al. gfcanalysis was
    written for the Tropical Ecology Assessment and Monitoring (TEAM) Network
    (http://www.teamnetwork.org). For additional details on the Global Forest
    Change dataset, see: Hansen, M. et al. 2013. "High-Resolution Global Maps
    of 21st-Century Forest Cover Change." Science 342 (15 November): 850-53.
    The forest change data and more information on the product is available at
    http://earthenginepartners.appspot.com.
Implemented are the Wald-type statistic,
    a permuted version thereof as well as the ANOVA-type statistic
    for general factorial designs, even with non-normal error terms
    and/or heteroscedastic variances, for crossed designs with an
    arbitrary number of factors and nested designs with up to three factors.
Focuses on data collecting, analyzing and visualization in green finance and environmental 
  risk research and analysis. Main function includes environmental data collecting from 
  official websites such as MEP (Ministry of Environmental Protection of China, <http://www.mep.gov.cn>), water 
  related projects identification and environmental data visualization.
Software to implement methodology to preform automatic response category
  combinations in multinomial logistic regression. There are functions for both cross validation
  and AIC for model selection.  The method provides regression coefficient estimates
  that may be useful for better understanding the true probability distribution of
  multinomial logistic regression when category probabilities are similar. These methods are not
  recommended for a large number of predictor variables.
The R package 'ggplot2' is a plotting system based on the grammar of graphics.
    'GGally' extends 'ggplot2' by adding several functions
    to reduce the complexity of combining geometric objects with transformed data.
    Some of these functions include a pairwise plot matrix, a two group pairwise plot
    matrix, a parallel coordinates plot, a survival plot, and several functions to
    plot networks.
A compendium of new geometries, coordinate systems, statistical 
    transformations, scales and fonts for 'ggplot2', including splines, 1d and 2d densities, 
    univariate average shifted histograms, a new map coordinate system based on the 
    'PROJ.4'-library along with geom_cartogram() that mimics the original functionality of 
    geom_map(), formatters for "bytes", a stat_stepribbon() function, increased 'plotly'
    compatibility and the 'StateFace' open source font 'ProPublica'. Further new 
    functionality includes lollipop charts, dumbbell charts, the ability to encircle
    points and coordinate-system-based text annotations.
Provides two methods of plotting categorical scatter plots such
    that the arrangement of points within a category reflects the density of
    data at that region, and avoids over-plotting.
Provides a Chernoff face geom for 'ggplot2'. Maps multivariate data
    to human-like faces.
We provide two primary resources in 'ggCompNet'. The first is a function to compare the speed of network drawing using several different packages. The second is the vignette folder which contains two vignettes that provide code for reproducing examples comparing the three network visualization packages 'geomnet', 'ggnetwork', and the ggnet2() function from the 'GGally' package.
The 'ggcorrplot' package can be used to visualize easily a
    correlation matrix using 'ggplot2'. It provides a solution for reordering the
    correlation matrix and displays the significance level on the plot. It also
    includes a function for computing a matrix of correlation p-values.
This is a set of tools for dendrograms and
    tree plots using 'ggplot2'.  The 'ggplot2' philosophy is to
    clearly separate data from the presentation.
    Unfortunately the plot method for dendrograms plots
    directly to a plot device without exposing the data.
    The 'ggdendro' package resolves this by making available
    functions that extract the dendrogram plot data. The package
    provides implementations for tree, rpart, as well as diana and agnes
    cluster diagrams.
A fast engine for computing hierarchical Bayesian model
    implemented in the Dynamic Model of Choice.
Create biplots for GGE (genotype plus genotype-by-environment) and
    GGB (genotype plus genotype-by-block-of-environments) models.
Description: A GUI with which to construct and interact with GGE biplots.
Genotype plus genotype-by-environment (GGE) biplots rendered using 'ggplot2'. Provides a command line interface to all of the functionality contained within 'GGEBiplotGUI'.
Interactively edit 'ggplot2' layer and theme aesthetics definitions.
Compute marginal effects at the mean or average marginal effects from 
             statistical models and returns the result as tidy data frames. These 
             data frames are ready to use with the 'ggplot2'-package.
             Marginal effects can be calculated for many different models. Interaction
             terms, splines and polynomial terms are also supported. The two main
             functions are 'ggpredict()' and 'ggaverage()', however, there are
             some convenient wrapper-functions especially for polynomials or
             interactions. There is a generic 'plot()'-method to plot the results
             using 'ggplot2'.
Methods for searching through genealogical data and displaying the results. Plotting algorithms assist with data exploration and publication-quality image generation. Includes interactive genealogy visualization tools. Provides parsing and calculation methods for variables in descendant branches of interest. Uses the Grammar of Graphics.
Collection of functions and layers to enhance 'ggplot2'. The main
    function is ggMarginal(), which can be used to add marginal
    histograms/boxplots/density plots to 'ggplot2' scatterplots.
Provides 'ggplot2' geoms to fit text into a box by growing, shrinking
    or wrapping the text.
The aim of 'ggplot2' is to aid in visual data investigations. This
    focus has led to a lack of facilities for composing specialised plots.
    'ggforce' aims to be a collection of mainly new stats and geoms that fills
    this gap. All additional functionality is aimed to come through the official
    extension system so using 'ggforce' should be a stable experience.
Provides a formula interface to 'ggplot2' graphics.
Unified plotting tools for statistics commonly used, such as GLM,
    time series, PCA families, clustering and survival analysis. The package offers
    a single plotting interface for these analysis results and plots in a unified
    style using 'ggplot2'.
Provides a 'ggplot2' geom and helper functions for drawing gene
  arrow maps.
Creates a reproducible 'ggplot2' object by storing the data and calls.
Utilities for Creating Guitar Tablature using tidyverse packages.
Reproduce the halfnorm() function found in the 'faraway' package 
    using the 'ggplot2' API.
Supports image files and graphic objects to be visualized in
    'ggplot2' graphic system.
A tool to process and analyse data collected with wearable raw acceleration sensors. The package has been developed and tested for binary data from GENEActiv and GENEA devices, .csv-export data from  Actigraph devices, and .cwa and .wav-format data from Axivity. These devices are currently widely used in research on human daily physical activity.
Create interactive 'ggplot2' graphics using 'htmlwidgets'.
Collection of functions to enhance 'ggplot2' and 'ggiraph'. Provides functions for exploratory plots.
    All plot can be a 'static' plot or an 'interactive' plot using 'ggiraph'.
Joyplots provide a convenient way of visualizing changes in distributions over 
    time or space. This package enables the creation of such plots in 'ggplot2'.
This package implements a unified algorithm, blockwise-majorization-decent (BMD), for efficiently computing the solution paths of the group-lasso penalized least squares, logistic regression, Huberized SVM and squared SVM.
Visualize sequences in (modified) logo plots. The design choices
    used by these logo plots allow sequencing data to be more easily analyzed.
    Because it is integrated into the 'ggplot2' geom framework, these logo plots
    support native features such as faceting.
Pass a data frame and mapping aesthetics to ggloop() in order
    to create a list of 'ggplot2' plots. The way x-y and dots are paired together
    is controlled by the remapping arguments. Geoms, themes, facets, and other
    features can be added with the special %L+% (L-plus) operator.
Functions and datasets for maximum likelihood fitting of some classes of graphical Markov models.
A collection of functions to visualize spatial data and models
    on top of static maps from various online sources (e.g Google Maps and Stamen
    Maps). It includes tools common to those tasks, including functions for
    geolocation and routing.
Tools for assessing and diagnosing convergence of
    Markov Chain Monte Carlo simulations, as well as for graphically display
    results from full MCMC analysis. The package also facilitates the graphical
    interpretation of models by providing flexible functions to plot the
    results against observed variables.
Mosaic plots in the 'ggplot2' framework. Mosaic plot functionality
    is provided in a single 'ggplot2' layer by calling the geom 'mosaic'.
Estimation of partial correlation matrix using ridge penalty followed by thresholding and reestimation. Under multivariate Gaussian assumption, the matrix constitutes an Gaussian graphical model (GGM).
Graph estimation in Gaussian Graphical Models. The main functions return the adjacency matrix of an undirected graph estimated from a data matrix.
Geometries to plot network objects with 'ggplot2'.
Create hammock plots, parallel sets, and common angle plots
    with 'ggplot2'.
A system for 'declaratively' creating graphics,
    based on "The Grammar of Graphics". You provide the data, tell 'ggplot2'
    how to map variables to aesthetics, what graphical primitives to use,
    and it takes care of the details.
A dataset about movies. This was previously contained in ggplot2,
   but has been moved its own package to reduce the download size of ggplot2.
Easily explore data by creating ggplots through a (shiny-)GUI. R-code to recreate graph provided.
Extensions to 'ggplot2' respecting the grammar of graphics
    paradigm. Provides new statistics to locate and tag peaks and valleys in 2D
    plots, a statistics to add a label with the equation of a polynomial fitted
    with lm(), or R^2 or adjusted R^2 or information criteria for any model
    fitted with function lm(). Additional statistics give access to functions
    in package 'broom'. Provides a function for flexibly converting time
    series to data frames suitable for plotting with ggplot(). In addition
    provides statistics and ggplot geometries useful for diagnosing what data
    are passed to compute_group() and compute_panel() functions and to
    geometries.
Tools for working with polygons with holes in 'ggplot2', with a
    new 'geom' for drawing a 'polypath' applying the 'evenodd' or 'winding'
    rules.
'ggplot2' is an excellent and flexible package for elegant data
    visualization in R. However the default generated plots requires some formatting
    before we can send them for publication. Furthermore, to customize a 'ggplot',
    the syntax is opaque and this raises the level of difficulty for researchers
    with no advanced R programming skills. 'ggpubr' provides some easy-to-use
    functions for creating and customizing 'ggplot2'- based publication ready plots.
Automatically performs desired statistical tests (e.g. wilcox.test(), t.test()) to compare between groups, 
    and adds the resulting p-values to the plot with an annotation bar.
    Visualizing group differences are frequently performed by boxplots, bar plots, etc.
    Statistical test results are often needed to be annotated on these plots. 
    This package provides a convenient function that works on 'ggplot2' objects, 
    performs the desired statistical test between groups of interest and annotates the test results on the plot.
Plot single and faceted type quality control charts
  within the grammar of graphics plotting framework.
An extension of 'ggplot2' to provide quiver plots to visualise vector fields. 
    This functionality is implemented using a geom to produce a new graphical layer, which
    allows aesthetic options. This layer can be overlaid on a map to improve visualisation
    of mapped data.
Graphic elements for exploring Random Forests using the 'randomForest' or
    'randomForestSRC' package for survival, regression and classification forests and
    'ggplot2' package plotting.
The grammar of graphics as implemented in ggplot2 is a poor fit for
    graph and network visualizations due to its reliance on tabular data input.
    ggraph is an extension of the ggplot2 API tailored to graph visualizations
    and provides the same flexible approach to building up plots layer by layer.
Intended for both technical and non-technical users to create
    interactive data visualizations through a web browser GUI without writing any
    code.
Provides text and label geoms for 'ggplot2' that help to avoid overlapping
    text labels. Labels repel away from each other and away from the data
    points.
A collection of 'ggplot2' color palettes inspired by
    plots in scientific journals, data visualization libraries,
    science fiction movies, and TV shows.
Provides 'ggplot2' 'stats' that estimate seasonally adjusted series 
    and rolling summaries such as rolling average on the fly for time series.
The extensive range of functions provided by this package makes it possible to draw highly versatile sequence logos. Features include, but not limited to, modifying colour schemes and fonts used to draw the logo, generating multiple logo plots, and aiding the visualisation with annotations. Sequence logos can easily be combined with other plots 'ggplot2' plots.
Enrich your 'ggplots' with group-wise comparisons.
  This package provides an easy way to indicate if two groups are significantly different.
  Commonly this is shown by a bracket on top connecting the groups of interest which itself is annotated with the level of significance (NS, *, **, ***).
  The package provides a single layer (geom_signif()) that takes the groups for comparison and the test (t.test(), wilcox.text() etc.) as arguments and adds the annotation
  to the plot.
Adds north symbols (18 options) and scale bars in kilometers to
    maps in geographic or metric coordinates created with 'ggplot2' or 'ggmap'.
Spatial data plus the power of the ggplot2 framework means easier mapping when input 
  data are already in the form of Spatial* objects.
Additional annotations, and statsfor plotting "light"
    spectra with 'ggplot2', together with specializations of ggplot()
    and plot() methods for spectral data stored in objects of the classes
    defined in package 'photobiology' and a plot() method for objects of
    class "waveband", also defined in package 'photobiology'.
A 'ggplot2' extension that provides flipped components:
    horizontal versions of 'Stats' and 'Geoms', and vertical versions
    of 'Positions'.
Offers various swiss maps as data frames and 'ggplot2' objects and gives the
    possibility to add layers of data on the maps. Data are publicly available
    from the swiss federal statistical office.
Extends the functionality of 'ggplot2', providing the capability
    to plot ternary diagrams for (subset of) the 'ggplot2' geometries. Additionally,
    'ggtern' has implemented several NEW geometries which are unavailable to the
    standard 'ggplot2' release. For further examples and documentation, please
    proceed to the 'ggtern' website.
Rstudio add-in that delivers a graphical interface for editing 'ggplot2' theme elements.
Some extra themes, geoms, and scales for 'ggplot2'.
    Provides 'ggplot2' themes and scales that replicate the look of plots
    by Edward Tufte, Stephen Few, 'Fivethirtyeight', 'The Economist', 'Stata',
    'Excel', and 'The Wall Street Journal', among others.
    Provides 'geoms' for Tufte's box plot and range frame.
A collection of datasets for the upcoming book "Graficas versatiles con ggplot: Analisis visuales de datos", by Raymond L. Tremblay and Julian Hernandez-Serano.
An implementation of an interactive grammar of graphics, taking the
    best parts of 'ggplot2', combining them with the reactive framework of
    'shiny' and drawing web graphics using 'vega'.
Minimal client to access the 'GitHub' 'API'.
Colour palettes inspired by Studio Ghibli films, ported to R for your enjoyment.
A lightweight, vectorized drop-in replacement for
    'devtools::install_github()' that uses native git and R methods to clone and
    install a package from GitHub. From v0.2.15, also includes an analogue for 
    'install_bitbucket()'.
The GHQ function can be used to obtain the quadrature points and weights to approximate an integral in two or more dimensions. This function uses the pruning approach to eliminate that points that do not contribute to the approximation of the integral and increases computational cost. The advantage to conducting this elimination of points is the decrease in the number of times that the function of interest is evaluated. This advantage is crucial in mixed models in which we must address several integrations within an iterative process to obtain model parameters.
Detailed functionality for working
        with the univariate and multivariate Generalized Hyperbolic
        distribution and its special cases (Hyperbolic (hyp), Normal
        Inverse Gaussian (NIG), Variance Gamma (VG), skewed Student-t
        and Gaussian distribution). Especially, it contains fitting
        procedures, an AIC-based model selection routine, and functions
        for the computation of density, quantile, probability, random
        variates, expected shortfall and some portfolio optimization
        and plotting routines as well as the likelihood ratio test. In
        addition, it contains the Generalized Inverse Gaussian
        distribution.
Toolbox for various enrichment analysis methods and quantification of uncertainty of gene sets.
Gibbs sampler for one-way linear mixed-effects models
        (ANOVA, ANCOVA) with homoscedasticity of errors and uniform
        priors.
This package provides two generic functions for performing
        Markov chain sampling in a naive way for a user-defined target
        distribution, which involves only continuous variables. The
        function "gibbs_met" performs Gibbs sampling with each
        1-dimensional distribution sampled with Metropolis update using
        Gaussian proposal distribution centered at the previous state.
        The function "met_gaussian" updates the whole state with
        Metropolis method using independent Gaussian proposal
        distribution centered at the previous state. The sampling is
        carried out without considering any special tricks for
        improving efficiency. This package is aimed at only routine
        applications of MCMC in moderate-dimensional problems.
Functions to read in the geometry format under the 
    'Neuroimaging' 'Informatics' Technology Initiative ('NIfTI'), called 
    'GIFTI' <https://www.nitrc.org/projects/gifti/>. 
    These files contain surfaces of brain imaging data.
Generator and density function for the
  Generalized Inverse Gaussian (GIG) distribution.
GillespieSSA provides a simple to use, intuitive, and
        extensible interface to several stochastic simulation
        algorithms for generating simulated trajectories of finite
        population continuous-time model. Currently it implements
        Gillespie's exact stochastic simulation algorithm (Direct
        method) and several approximate methods (Explicit tau-leap,
        Binomial tau-leap, and Optimized tau-leap). The package also
        contains a library of template models that can be run as demo
        models and can easily be customized and extended. Currently the
        following models are included, decaying-dimerization reaction
        set, linear chain system, logistic growth model, Lotka
        predator-prey model, Rosenzweig-MacArthur predator-prey model,
        Kermack-McKendrick SIR model, and a metapopulation SIRS model.
Automated identification and estimation of group- and
    individual-level relations in time series data from within a structural
    equation modeling framework.
We provide a set of functions to retrieve information about GIMMS NDVI3g files currently available online; download (and re-arrange, in the case of NDVI3g.v0) the half-monthly data sets; import downloaded files from ENVI binary (NDVI3g.v0) or NetCDF format (NDVI3g.v1) directly into R based on the widespread 'raster' package; conduct quality control; and generate monthly composites (e.g., maximum values) from the half-monthly input data. As a special gimmick, a method is included to conveniently apply the Mann-Kendall trend test upon 'Raster*' images, optionally featuring trend-free pre-whitening to account for lag-1 autocorrelation.
Performs image segmentation in fruit or seeds pictures in order to measure physical features in a high-throughput manner for genome-wide association (GWAS) and genomic selection programs.
Gini-based coefficients and plot of the ordinary and generalized curve of maximum inequality in the presence of weighted and negative attributes.
Maximum likelihood estimation under relational models, with or without the overall effect.
An interface to the 'API' of 'Giphy', a popular index-based search 
    engine for 'GIFs' and animated stickers (see <http://giphy.com/faq> and 
    <https://github.com/Giphy/GiphyAPI> for more information about 'Giphy' and 
    its 'API') . This package also provides a 'RStudio Addin', which can help 
    users easily search and download 'GIFs' and insert them to a 'rmarkdown' 
    presentation.
Allows calculation on, and
 sampling from Gibbs Random Fields, and more precisely general 
 homogeneous Potts model. The primary tool is the exact computation of
 the intractable normalising constant for small rectangular lattices. 
 Beside the latter function, it contains method that give exact sample from the likelihood
 for small enough rectangular lattices or approximate sample from the 
 likelihood using MCMC samplers for large lattices.
Supply classes and methods to represent and manipulate
        graphs
Some mapping and spatial data manipulation tools - in particular
    drawing choropleth maps with nice looking legends,  and aggregation of point
    data to polygons.
Work with 'GitHub' 'gists' from 'R' (e.g., 
    <http://en.wikipedia.org/wiki/GitHub#Gist>, 
    <https://help.github.com/articles/about-gists/>). A 'gist'
    is simply one or more files with code/text/images/etc. This package allows
    the user to create new 'gists', update 'gists' with new files, rename files,
    delete files, get and delete 'gists', star and 'un-star' 'gists', fork 'gists',
    open a 'gist' in your default browser, get embed code for a 'gist', list
    'gist' 'commits', and get rate limit information when 'authenticated'. Some
    requests require authentication and some do not. 'Gists' website: 
    <https://gist.github.com/>.
Interface to the 'libgit2' library, which is a pure C
    implementation of the 'Git' core methods. Provides access to 'Git'
    repositories to extract data and running some basic 'Git'
    commands.
An Rstudio addin for version control that allows users to clone
    repositories, create and delete branches, and sync forks on GitHub, GitLab, etc.
    Furthermore, the addin uses the GitLab API to allow instructors to create
    forks and merge requests for all students/teams with one click of a button.
Provides an helpful way to install packages hosted on GitHub.
Provides R functions to access the API of the project and
    repository management web application gitlab. For many common tasks (repository
    file access, issue assignment and status, commenting) convenience wrappers
    are provided, and in addition the full API can be used by specifying request
    locations. Gitlab is open-source software and can be self-hosted or used on
    gitlab.com.
The goal of this package is to allow users to robustly
    and quickly grid and quantify sizes of pinned colonies in plate images.
    gitter works by first finding the grid of colonies from a preprocessed image and then locating the bounds of each colony separately.
    It includes several image pre-processing techniques, such
    as autorotation of plates, noise removal, contrast adjustment and image
    resizing.
Functions to assess the calibration of logistic regression models with the GiViTI (Gruppo Italiano per la Valutazione degli interventi in Terapia Intensiva, Italian Group for the Evaluation of the Interventions in Intensive Care Units - see <http://www.giviti.marionegri.it/>) approach. The approach consists in a graphical tool, namely the GiViTI calibration belt, and in the associated statistical test. These tools can be used both to evaluate the internal calibration (i.e. the goodness of fit) and to assess the validity of an externally developed model.
`Giza' offers a simple way of creating multiple pyramid
        plots in one graphics window, exploiting the power of the
        lattice package. It is a handy way of visualizing longitudinal
        grouped (i.e.: age- and education-structured) data.
Analyzes joint attribute data (e.g., species abundance) that are combinations of continuous and discrete data with Gibbs sampling.
Routines for fitting various joint regression models, with several types of covariate effects, in the presence of associated error equations, endogeneity, non-random sample selection or partial observability.
Functions for the g-and-k and generalised g-and-h distributions.
Implementations of the treatment effect estimators for hybrid (self-selection) experiments, as developed by Brian J. Gaines and James H. Kuklinski, (2011), "Experimental Estimation of Heterogeneous Treatment Effects Related to Self-Selection," American Journal of Political Science 55(3): 724-736.
Imports the 'gkmSVM' v2.0 functionalities into R (www.beerlab.org/gkmsvm). It also uses the 'kernlab' library (separate R package by different authors) for various SVM algorithms.
Efficient design matrix free procedure for penalized estimation
    in large scale 2 and 3-dimensional generalized linear array models. Currently
    either Lasso or SCAD penalized estimation is possible for the followings models:
    The Gaussian model with identity link, the Binomial model with logit link, the
    Poisson model with log link and the Gamma model with log link.
Functions are provided for estimation, testing, diagnostic checking and forecasting of generalized linear autoregressive moving average (GLARMA) models for discrete valued time series with regression variables.  These are a class of observation driven non-linear non-Gaussian state space models. The state vector consists of a linear regression component plus an observation driven component consisting of an autoregressive-moving average (ARMA) filter of past predictive residuals. Currently three distributions (Poisson, negative binomial and binomial) can be used for the response series. Three options (Pearson, score-type and unscaled) for the residuals in the observation driven component are available. Estimation is via maximum likelihood (conditional on initializing values for the ARMA process) optimized using Fisher scoring or Newton Raphson iterative methods. Likelihood ratio and Wald tests for the observation driven component allow testing for serial dependence in generalized linear model settings. Graphical diagnostics including model fits, autocorrelation functions and probability integral transform residuals are included in the package. Several standard data sets are included in the package.
Analyses response times and accuracies from psychological experiments with the linear ballistic accumulator (LBA) model from Brown and Heathcote (2008). The LBA model is optionally fitted with explanatory variables on the parameters such as the drift rate, the boundary and the starting point parameters. A log-link function on the linear predictors can be used to ensure that parameters remain positive when needed.
Enables calculation of image textures derived from grey-level
    co-occurrence matrices (GLCMs). Supports processing images that cannot
    fit in memory.
The  generalised lambda distribution, or Tukey lambda distribution, provides a wide variety of shapes with one functional form.   
  This package provides random numbers, quantiles, probabilities, densities and density quantiles for four different parameterisations of the distribution. 
  It provides the density function, distribution function, and Quantile-Quantile plots.  
  It implements a variety of estimation methods for the distribution, including diagnostic plots.  
  Estimation methods include the starship (all 4 parameterisations) and a number of methods for only the FKML parameterisation.  
  These include maximum likelihood, maximum product of spacings, Titterington's method, Moments, L-Moments, Trimmed L-Moments and Distributional Least Absolutes.
The fitting algorithms considered in this package have two major objectives. One is to provide a smoothing device to fit distributions to data using the weight and unweighted discretised approach based on the bin width of the histogram. The other is to provide a definitive fit to the data set using the maximum likelihood and quantile matching estimation. Other methods such as moment matching, starship method, L moment matching are also provided. Diagnostics on goodness of fit can be done via qqplots, KS-resample tests and comparing mean, variance, skewness and kurtosis of the data with the fitted distribution.
Owing to the rich shapes of Generalised Lambda Distributions (GLDs), GLD standard/quantile regression is a competitive flexible model compared to standard/quantile regression.  The proposed method has some major advantages: 1) it provides a reference line which is very robust to outliers with the attractive property of zero mean residuals and 2) it gives a unified, elegant quantile regression model from the reference line with smooth regression coefficients across different quantiles. The goodness of fit of the proposed model can be assessed via QQ plots and Kolmogorov-Smirnov tests and data driven smooth test, to ensure the appropriateness of the statistical inference under consideration. Statistical distributions of coefficients of the GLD regression line are obtained using simulation, and interval estimates are obtained directly from simulated data.
Fits a generalized linear density ratio model (GLDRM).
    A GLDRM is a semiparametric generalized linear model.
    In contrast to a GLM, which assumes a particular exponential family distribution, 
    the GLDRM uses a semiparametric likelihood to estimate the reference distribution. 
    The reference distribution may be any discrete, continuous, or mixed exponential 
    family distribution. The model parameters, which include both the regression 
    coefficients and the cdf of the unspecified reference distribution, are estimated 
    by maximizing a semiparametric likelihood. Regression coefficients are estimated 
    with no loss of efficiency, i.e. the asymptotic variance is the same as if the 
    true exponential family distribution were known.
Functions evaluate global and individual tests for direct effects in Mendelian randomization studies.
Group-Lasso INTERaction-NET. Fits linear pairwise-interaction models that satisfy strong hierarchy: if an interaction coefficient is estimated to be nonzero, then its two associated main effects also have nonzero estimated coefficients. Accommodates categorical variables (factors) with arbitrary numbers of levels, continuous variables, and combinations thereof. Implements the machinery described in the paper "Learning interactions via hierarchical group-lasso regularization" (JCGS 2015, Volume 24, Issue 3). Michael Lim & Trevor Hastie (2015) <DOI:10.1080/10618600.2014.938812>.
Routines for log-linear models of incomplete contingency tables,
             including some latent class models, via EM and Fisher scoring
             approaches.  Allows bootstrapping.
Fits generalized linear models using the same model specification as glm in the stats package, but with a modified default fitting method that provides greater stability for models that may fail to converge using glm
Several tests for high dimensional generalized linear models have been proposed recently. In this package, we implemented a new test called adaptive  sum of powered score (aSPU) for high dimensional generalized linear models, which is often more powerful than the existing methods in a wide scenarios. We also implemented permutation based version of several existing methods for research purpose. We recommend users use the aSPU test for their real testing problem. You can learn more about the tests implemented in the package via the following papers: 1. Pan, W., Kim, J., Zhang, Y., Shen, X. and Wei, P. (2014) <DOI:10.1534/genetics.114.165035> A powerful and adaptive association test for rare variants, Genetics, 197(4). 2. Guo, B., and Chen, S. X. (2016) <DOI:10.1111/rssb.12152>. Tests for high dimensional generalized linear models. Journal of the Royal Statistical Society: Series B. 3. Goeman, J. J., Van Houwelingen, H. C., and Finos, L. (2011) <DOI:10.1093/biomet/asr016>. Testing against a high-dimensional alternative in the generalized linear model: asymptotic type I error control. Biometrika, 98(2).
Find all hierarchical models of specified generalized linear
    model with information criterion (AIC, BIC, or AICc) within specified
    cutoff of minimum value.  Alternatively, find all such graphical models.
    Use branch and bound algorithm so we do not have to fit all models.
Implements the Bayesian paradigm
    for fractional polynomials in generalized linear
    models, described by Held, Gravestock, Sabanes Bove (2015) <doi:10.1214/14-STS510>.
    See package 'bfp' for the treatment of normal
    models.
Fits generalized linear models where the parameters are
        subject to linear constraints. The model is specified by giving
        a symbolic description of the linear predictor, a description
        of the error distribution, and a matrix of constraints on the
        parameters.
Distributed training and prediction of generalized linear models using 'ddR' (Distributed Data Structures) API in the 'ddR' package.
This package contains functions to perform generalized
        linear mixed Dirichlet models using posterior simulation.
Recursive partitioning based on (generalized) linear mixed models
    (GLMMs) combining lmer()/glmer() from lme4 and lmtree()/glmtree() from partykit.
We propose to use sparse regression model to achieve variable selection while accounting for graph-constraints among coefficients. Different linear combination of a sparsity penalty(L1) and a smoothness(MCP) penalty has been used, which induces both sparsity of the solution and certain smoothness on the linear coefficients.
Efficient algorithms for fitting regularization paths for
        linear or logistic regression models penalized by LEP.
Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approximation. Then maximizes the likelihood approximation to return maximum likelihood estimates, observed Fisher information, and other model information.
Automates running Generalized Linear Mixed Models, including
    spatial models, with WinBUGS, OpenBUGS and JAGS. Models are specified with
    formulas, with the package writings model files, arranging unbalanced data
    in ragged arrays, and creating starting values. The model is re-parameterized,
    and functions are provided for converting model outputs to the original
    parameterization.
A variable selection approach for generalized linear mixed models by L1-penalized estimation is provided.
Binomial and Poisson regression for clustered data, fixed
        and random effects with bootstrapping.
Generalized Linear Mixed Model (GLMM) for Binary Randomized Response Data.
    Includes Cauchit, Compl. Log-Log, Logistic, and Probit link functions for Bernoulli Distributed RR data.
    RR Designs: Warner, Forced Response, Unrelated Question, Kuk, Crosswise, and Triangular.
Conduct inference about generalized linear mixed models, with a
    choice about which method to use to approximate the likelihood. In addition
    to the Laplace and adaptive Gaussian quadrature approximations, which are
    borrowed from 'lme4', the likelihood may be approximated by the sequential
    reduction approximation, or an importance sampling approximation. These
    methods provide an accurate approximation to the likelihood in some
    situations where it is not possible to use adaptive Gaussian quadrature.
Fit linear and generalized linear mixed models with various
    extensions, including zero-inflation. The models are fitted using maximum
    likelihood estimation via 'TMB' (Template Model Builder). Random effects are
    assumed to be Gaussian on the scale of the linear predictor and are integrated
    out using the Laplace approximation. Gradients are calculated using automatic
    differentiation.
Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial regression. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper linked to via the URL below.
This packages includes functions for restructuring an ordinal response dataset for fitting continuation ratio models for datasets   
 where the number of covariates exceeds the sample size or when there is collinearity among the covariates. This package uses the glmnet package  
 fitting algorithm.
Provides a formula interface for the 'glmnet' package for
    elasticnet regression, a method for cross-validating the alpha parameter,
    and other quality-of-life tools.
A path-following algorithm for L1 regularized generalized
        linear models and Cox proportional hazards model
Provides a function for fitting a penalized constrained continuation ratio model using the glmpath algorithm and methods for extracting coefficient estimates, predicted class, class probabilities, and plots.
Functions to calculate predicted values and the difference between
    the two cases with confidence interval for glm(), glm.nb(), polr() and multinom().
Automated model selection and model-averaging. Provides a
        wrapper for glm and other functions, automatically generating
        all possible models (under constraints set by the user) with
        the specified response and explanatory variables, and finding
        the best models in terms of some Information Criterion (AIC,
        AICc or BIC). Can handle very large numbers of candidate
        models. Features a Genetic Algorithm to find the best models
        when an exhaustive screening of the candidates is not feasible.
Variable selection deviation (VSD) measures and instability tests for high-dimensional model selection methods such as LASSO, SCAD and MCP, etc., to decide whether the sparse patterns identified by those methods are reliable.
Extended techniques for generalized linear models (GLMs), especially for binary responses,
             including parametric links and heteroskedastic latent variables.
'globalboosttest' implements a permutation-based testing
        procedure to globally test the (additional) predictive value of
        a large set of predictors given that a small set of predictors
        is already available. Currently, 'globalboosttest' supports
        binary outcomes (via logistic regression) and survival outcomes
        (via Cox regression). It is based on boosting regression as
        implemented in the package 'mboost'.
permutation based global test with deviance as test statistic
Initial metabolic networks often inaccurately predict in-silico growth or non-growth if compared to in-vivo data. This package refines metabolic network models by making networks changes (i.e., removing, adding, changing reversibility of reactions; adding and removing biomass metabolites) and simultaneously matching sets of experimental growth and non-growth data (e.g., KO-mutants, mutants grown under different media conditions,...)
Implementation of three different Gene set analysis (GSA) algorithms for combining the individual pvalues of a set of genetic variats (SNPs) in a gene level pvalue. The implementation includes the selection of the best inheritance model for each SNP.
It provides more controls on the option values such as validation
    and filtering on the values, making options invisible or private.
This package makes available 50 objective functions for benchmarking the performance of global optimization algorithms
Identifies global ("unknown" or "free") objects in R expressions
    by code inspection using various strategies, e.g. conservative or liberal.
    The objective of this package is to make it as simple as possible to
    identify global objects for the purpose of exporting them in distributed
    compute environments.
Basic functions for plotting 2D and 3D views of a sphere, by default the Earth with its major coastline, and additional lines and points.
Tools for the generalized logistic distribution (Type I,
             also known as skew-logistic distribution), encompassing
	     basic distribution functions (p, q, d, r, score), maximum
	     likelihood estimation, and structural change methods.
R Interface to C API of GLPK, needs GLPK Version >= 4.42
Functions to conduct four generalized logrank tests and a score test under a proportional hazards model
Implements the likelihood ratio test for genome-wide association
    under genetic heterogeneity as described in Qian and Shao (2013) <doi:10.1111/ahg.12005>.
Performs linear regression with correlated predictors, responses and correlated measurement errors in predictors and responses, correcting for biased caused by these.
An implementation of interpreted string literals, inspired by
  Python's Literal String Interpolation <https://www.python.org/dev/peps/pep-0498/> and Docstrings
  <https://www.python.org/dev/peps/pep-0257/> and Julia's Triple-Quoted String Literals
  <https://docs.julialang.org/en/stable/manual/strings/#triple-quoted-string-literals>.
Useful utilities in N-glycan data analysis. This package tries
    to fill the gap in N-glycan data analysis by providing easy
    to use functions for basic operations on data
    (see https://en.wikipedia.org/wiki/Glycomics for more
    details on Glycomics). At the moment glycanr is mostly oriented
    to data obtained by UPLC and LCMS analysis of Plasma and IgG glycome.
Performs genomic mediation
    analysis with adaptive confounding adjustment (GMAC) proposed by Yang et al. (2017) <doi:10.1101/078683>. It implements large scale
    mediation analysis and adaptively selects potential confounding variables to
    adjust for each mediation test from a pool of candidate confounders. The package
    is tailored for but not limited to genomic mediation analysis (e.g., cis-gene
    mediating trans-gene regulation pattern where an eQTL, its cis-linking gene
    transcript, and its trans-gene transcript play the roles as treatment, mediator
    and the outcome, respectively), restricting to scenarios with the presence of
    cis-association (i.e., treatment-mediator association) and random eQTL (i.e.,
    treatment).
An interface to the Gmail RESTful API.  Allows access to your
    Gmail messages, threads, drafts and labels.
Get distance and travel time between two points from Google Maps.
    Four possible modes of transportation (bicycling, walking, driving and
    public transportation).
A general framework for utilizing R to harness the power of NVIDIA GPU's. The "gmatrix" and "gvector" classes allow for easy management of the separate device and host memory spaces. Numerous numerical operations are implemented for these objects on the GPU. These operations include matrix multiplication, addition, subtraction, the kronecker product, the outer product, comparison operators, logical operators, trigonometric functions, indexing, sorting, random number generation and many more.
Unsupervised Clustering and Meta-analysis using Gaussian Mixture
    Copula Models.
Functions and a graphical user interface for graphical described multiple test procedures.
GMD is a package for non-parametric distance measurement between
    two discrete frequency distributions.
A template for a geometallurgical database and a fast and easy
    interface for accessing it is provided in this package.
Group method of data handling (GMDH) - type neural network algorithm is the heuristic self-organization method for modelling the complex systems. In this package, GMDH-type neural network algorithms are applied to make short term forecasting for a univariate time series.
Fast algorithms  for robust estimation with large samples of multivariate observations. Estimation of the geometric median, robust k-Gmedian clustering, and robust PCA based on the Gmedian covariation matrix.
Current version of this R package conducts mediation path analysis for multiple mediators in two stages.
An implementation of an all-in-one function for a wide range of meta-analysis problems. It contains a single function gmeta() that unifies all standard meta-analysis methods and also several newly developed ones under a framework of combining confidence distributions (CDs). Specifically, the package can perform classical p-value combination methods (such as methods of Fisher, Stouffer, Tippett, etc.), fit meta-analysis fixed-effect and random-effects models, and synthesizes 2x2 tables. Furthermore, it can perform robust meta-analysis, which provides protection against model-misspecifications, and limits the impact of any unknown outlying studies. In addition, the package implements two exact meta-analysis methods from synthesizing 2x2 tables with rare events (e.g., zero total event). A plot function to visualize individual and combined CDs through extended forest plots is also available.
Tools for making the descriptive "Table 1" used in medical
    articles, a transition plot for showing changes between categories, a method for
    variable selection based on the SVD, Bézier lines with arrows complementing the
    ones in the 'grid' package, and more.
It is a complete suite to estimate models based on moment
        conditions. It includes the two step Generalized method of
        moments (Hansen 1982; <doi:10.2307/1912775>), the iterated GMM and continuous
        updated estimator (Hansen, Eaton and Yaron 1996; <doi:10.2307/1392442>) and several
        methods that belong to the Generalized Empirical Likelihood
        family of estimators (Smith 1997; <doi:10.1111/j.0013-0133.1997.174.x>,
        Kitamura 1997; <doi:10.1214/aos/1069362388>, Newey and Smith 2004; <doi:10.1111/j.1468-0262.2004.00482.x>,
	and Anatolyev 2005 <doi:10.1111/j.1468-0262.2005.00601.x>).
Likelihood-based Boosting for Generalized mixed models
An implementation of maximum simulated likelihood method for the
    estimation of multinomial logit models with random coefficients.
    Specifically, it allows estimating models with continuous heterogeneity
    such as the mixed multinomial logit and the generalized multinomial logit.
    It also allows estimating models with discrete heterogeneity such as the
    latent class and the mixed-mixed multinomial logit model.
Various R programming tools for model fitting.
Make 2D plots of the polyeder of a LP or IP problem, including
    integer points and iso profit curve. Can also make a plot of a bi-objective
    criterion space.
Multiple Precision Arithmetic (big integers and rationals,
 prime number tests, matrix computation), "arithmetic without limitations"
 using the C library GMP (GNU Multiple Precision Arithmetic).
Integrates game theory and ecological theory to construct 
    social-ecological models that simulate the management of populations and 
    stakeholder actions. These models build off of a previously developed 
    management strategy evaluation (MSE) framework to simulate all aspects of 
    management: population dynamics, manager observation of populations, manager
    decision making, and stakeholder responses to management decisions. The 
    newly developed generalised management strategy evaluation (GMSE) 
    framework uses genetic algorithms to mimic the decision-making process of 
    managers and stakeholders under conditions of change, uncertainty, and 
    conflict. All simulations can be run using the gmse() function.
Interface between the GMT map-making software and R, enabling the
  user to manipulate geographic data within R and call GMT commands to draw and
  annotate maps in postscript format. The gmt package is about interactive data
  analysis, rapidly visualizing subsets and summaries of geographic data, while
  performing statistical analysis in the R console.
Direct R interface to Support Vector Machine libraries ('LIBSVM' and 'SVMLight') and efficient C++ implementations of Growing Neural Gas and models developed by 'GMUM' group (Cross Entropy Clustering and 2eSVM).
Generalized Mann-Whitney type tests based on probabilistic
        indices and new diagnostic plots.
Provide functions to compute standard and generalized Nash Equilibria. Optimization methods are available nonsmooth reformulation, fixed-point formulation, minimization problem and constrained-equation reformulation.
A variety of functions to fit linear and nonlinear
    regression with a large selection of distributions.
Functions to specify and fit generalized nonlinear models, including models with multiplicative interaction terms such as the UNIDIFF model from sociology and the AMMI model from crop science, and many others.  Over-parameterized representations of models are used throughout; functions are provided for inference on estimable parameter combinations, as well as standard methods for diagnostics etc.
This package performs generalized non-negative matrix factorization based on Renyi divergence.
Read data files readable by 'gnumeric' into 'R'. Can read
        whole sheet or a range, from several file formats, including
        the native format of 'gnumeric'. Reading is done by using
        'ssconvert' (a file converter utility included in the 'gnumeric'
        distribution <http://projects.gnome.org/gnumeric/>) to convert
        the requested part to CSV. From 'gnumeric' files (but not other
        formats) can list sheet names and sheet sizes or read all
        sheets.
A collection of functions to solve weighted and lexicographical
        goal programming problems as specified by Lee (1972) and Ignizio (1976).
A collection of functions useful in (vegetation) community analyses and ordinations, mainly to facilitate plotting and interpretation. Includes automatic species selection for ordination diagrams, species response curves and rank-abundance curves.
Implementation of model-checking techniques for generalized linear
    models and linear structural equation models based on cumulative residuals
Harness the power of 'quanteda', 'data.table' & 'stringi'
        to quickly generate 'tm' DocumentTermMatrix and
        TermDocumentMatrix data structures.
Several GoF tests for Copulae are provided. A new hybrid test is implemented which supports all of the individual tests. Estimation methods for the margins are provided. All the tests support parameter estimation and predefined values. The parameters are estimated by pseudo maximum likelihood but if it fails the estimation switches automatically to inversion of Kendall's tau. All the tests support automatized parallelization of the bootstrapping tasks.
Tests of goodness-of-fit based on a kernel smoothing of the data.
Consider a goodness-of-fit(GOF) problem of testing whether a random sample comes from one sample location-scale model where location and scale parameters are unknown. It is well known that Khmaladze martingale transformation method provides asymptotic distribution free test for the GOF problem. This package contains one function: KhmaladzeTrans(). In this version, KhmaladzeTrans() provides test statistic and critical value of GOF test for normal, Cauchy, logistic, Gamma, Weibull, Gumbel, and Rayleigh distributions.
Goodness-of-fit metrics, such as R-Squared, RMSE, etc., share a sensitivity to noise, dependent on the degrees of freedom.  Some metrics, such as R-Squared, decrease with increasing dof and some, such as RMSE, increase with increasing dof.  This package calculates the noise baseline (ceiling) by random sampling, calculating the metric’s value for each sample and counting the number of samples below a desired level, 95% by default. If one’s measure is above (below) the calculation corresponding to the desired level, then the measurement is distinguishable from noise.  In addition, the ratio of the measurement to the calculated level provides a way to compare measurements of different degrees of freedom.
Goodness-of-fit tests for  gamma, inverse Gaussian, lognormal, Weibull, Frechet, Gumbel, univariate normal, multivariate normal, Cauchy, Laplace or double exponential, exponential and generalized Pareto distributions. Parameter estimators for gamma, inverse Gaussian and generalized Pareto distributions.
Cramer-Von Mises and Anderson-Darling tests of goodness-of-fit
	     for continuous univariate distributions, using
	     efficient algorithms.
Extension of 'gof' package to survival models.
Easy and flexible interface for manipulating go game (weiqi, baduk) data.
    The package features a reader function for SGF (smart go format) text files,
    and a set of plotting functions that draw go board images.
Accounting for genes' functional-non-equivalence within pathways in classical Gene-set-analysis.
Implementation of the GO-GARCH model class
A tool for identifying multiple word key terms in free text with application to Gene Ontology labels.
A zero-inflated quasi-Poisson factor model to display similarity between samples visually in a low (2 or 3) dimensional space.
Association analysis between categorical
    variables using the Goodman and Kruskal tau measure. This asymmetric association
    measure allows the detection of asymmetric relations between categorical
    variables (e.g., one variable obtained by re-grouping another).
R library for interacting with the Google Analytics 
  Reporting API v3 and v4.
Create R functions that interact with OAuth2 Google APIs easily,
    with auto-refresh and Shiny compatibility.
Interact with Google Cloud Storage API in R. Part of the 'cloudyr' project.
Interact with the Google Compute Engine API in R. Lets you create, 
  start and stop instances in the Google Cloud.  Support for preconfigured instances, 
  with templates for common R needs.
GET and POST data to Google Forms; an API to Google Forms,
    allowing users to POST data securely to Google Forms without needing authentication or permissioning.
Provides a collection of functions designed for working with 'Google Public Data Explorer'. Automatically builds up the corresponding DSPL (XML) metadata files and CSV files; compressing all the files and leaving them ready to be published on the 'Public Data Explorer'.
R interface to Google's chart tools, allowing users
    to create interactive charts based on data frames. Charts
    are displayed locally via the R HTTP help server. A modern
    browser with an Internet connection is required and for some
    charts a Flash player. The data remains local and is not
    uploaded to Google.
Provides a mechanism to plot a Google Map from R and overlay
    it with shapes and markers. Also provides access to Google Maps APIs,
    including places, directions, roads, distances, geocoding, elevation and
    timezone.
Implementation of multilayered visualizations for enhanced
    graphical representation of functional analysis data. It combines and integrates
    omics data derived from expression and functional annotation enrichment
    analyses. Its plotting functions have been developed with an hierarchical
    structure in mind: starting from a general overview to identify the most
    enriched categories (modified bar plot, bubble plot) to a more detailed one
    displaying different types of relevant information for the molecules in a given
    set of categories (circle plot, chord plot, cluster plot, Venn diagram, heatmap).
Generalized Odds Rate Mixture Cure (GORMC) model is a flexible model of fitting survival data with a cure fraction, including the Proportional Hazards Mixture Cure (PHMC) model and the Proportional Odds Mixture Cure Model as special cases. This package fit the GORMC model with interval censored data.
Generalized Order-Restricted Information Criterion (GORIC) value for a set of hypotheses in multivariate regression models.
This package purposes to deal with public survey data of
        Japanese government via their Application Programming Interface
        (http://statdb.nstac.go.jp/)
Compute Gower's distance (or similarity) coefficient between records. Compute 
    the top-n matches between records. Core algorithms are executed in parallel on systems
    supporting OpenMP.
Produces a generalized pairs (gpairs) plot.
Gaussian process regression models, a.k.a. Kriging models, are applied to global multi-objective optimization of black-box functions. Multi-objective Expected Improvement and Step-wise Uncertainty Reduction sequential infill criteria are available. A quantification of uncertainty on Pareto fronts is provided using conditional simulations.
Gradient Projection Algorithm Rotation for Factor Analysis. See ?GPArotation.Intro for more details.
Functions that compute the distribution functions for the Generalized Poisson Binomial distribution, which provides the cdf, pmf, quantile function, and random number generation for the distribution.
A generalized polynomial chaos expansion of a model taking as input independent random variables is achieved. A statistical and a global sensitivity analysis of the model are also carried out.
This package implements guided principal components analysis for the detection of batch effects in high-throughput data.
General polygon clipping routines for R based on Alan
        Murta's C library
This package implements an extension of principal component analysis (PCA) tailored to handle multiple data tables. It can handle Big Data in the sense that the variation in massive data can be described by intervals [a, b] and multiple tables.
Functions implement collocation-inference for
    stochastic process driven by distributed delay differential equations.
    They also provide tools for selecting the lags for distributed delay
    using shrinkage methods, estimating time-varying coefficients,
    and tools for inference and prediction.
This package computes the bootstrap goodness-of-fit test
        for the generalized Pareto distribution by Villasenor-Alva and
        Gonzalez-Estrada (2009). The null hypothesis includes heavy and
        non-heavy tailed gPd's. A function for fitting the gPd to data
        using the parameter estimation methods proposed in the same
        article is also provided.
Use functional regression as the mean structure and Gaussian Process as the covariance structure.
A computationally stable approach of fitting a Gaussian Process (GP) model to a deterministic simulator.
Bindings to GnuPG for working with OpenGPG (RFC4880) cryptographic methods.
    Includes utilities for public key encryption, creating and verifying digital signatures,
    and managing your local keyring. Note that some functionality depends on the version of 
    GnuPG that is installed on the system. On Windows this package can be used together with
    'GPG4Win' which provides a GUI for managing keys and entering passphrases.
Sequential strategies for finding game equilibria are proposed in a black-box setting (expensive pay-off evaluations, no derivatives). The algorithm handles noiseless or noisy evaluations. Two acquisition functions are available. Graphical outputs can be generated automatically.
Collection of datasets as prepared by Profs. A.P. Gore, S.A. Paranjape, and M.B. Kulkarni of Department of Statistics, Poona University, India. With their permission, first letter of their names forms the name of this package, the package has been built by me and made available for the benefit of R users. This collection requires a rich class of models and can be a very useful building block for a beginner.
Provides functions for estimating a generalized partial
	     linear model, a semiparametric variant of the generalized linear model
	     (GLM) which replaces the linear predictor by the sum of a linear
	     and a nonparametric function.
Various R programming tools for plotting data, including:
  - calculating and plotting locally smoothed summary function as
    ('bandplot', 'wapply'),
  - enhanced versions of standard plots ('barplot2', 'boxplot2',
    'heatmap.2', 'smartlegend'),
  - manipulating colors ('col2hex', 'colorpanel', 'redgreen',
    'greenred', 'bluered', 'redblue', 'rich.colors'),
  - calculating and plotting two-dimensional data summaries ('ci2d',
    'hist2d'),
  - enhanced regression diagnostic plots ('lmplot2', 'residplot'),
  - formula-enabled interface to 'stats::lowess' function ('lowess'),
  - displaying textual data in plots ('textplot', 'sinkplot'),
  - plotting a matrix where each cell contains a dot whose size
    reflects the relative magnitude of the elements ('balloonplot'),
  - plotting "Venn" diagrams ('venn'),
  - displaying Open-Office style plots ('ooplot'),
  - plotting multiple data on same region, with separate axes
    ('overplot'),
  - plotting means and confidence intervals ('plotCI', 'plotmeans'),
  - spacing points in an x-y plot so they don't overlap ('space').
Combining a generalized linear model with an additional tree part 
          on the same scale. A four-step procedure is proposed to fit the model and test 
          the joint effect of the selected tree part while adjusting on confounding factors. 
          We also proposed an ensemble procedure based on the bagging to improve prediction 
          accuracy and computed several scores of importance for variable selection.
This package contains tools for studying genotype-phenotype (GP) maps for bi-allelic loci underlying quantitative phenotypes. The 0.1 version is released in connection with the publication of Gjuvsland et al. (2003) and implements basic line plots and the monotonicity measures for GP maps presented in the paper. Reference: Gjuvsland AB, Wang Y, Plahte E and Omholt SW (2013) Monotonicity is a key feature of genotype-phenotype maps. Front. Genet. 4:216. doi: 10.3389/fgene.2013.00216 [\href{http://www.frontiersin.org/Journal/10.3389/fgene.2013.00216/full}{link}]
Platform dedicated to the Global Modelling technique.
This package provides a minimalistic functionality necessary to apply Gaussian Process in R. They provide a selection of functionalities of GPML Matlab library.
Implements a Gaussian process (GP)-based ranking method
    which can be used to rank multiple time series according to their
    temporal activity levels. An example is the case when expression
    levels of all genes are measured over a time course and the main
    concern is to identify the most active genes, i.e. genes which
    show significant non-random variation in their expression levels.
    This is achieved by computing Bayes factors for each time series
    by comparing the marginal likelihoods under time-dependent and
    time-independent GP models. Additional variance information from
    pre-processing of the observations is incorporated into the GP
    models, which makes the ranking more robust against model
    overfitting. The package supports exporting the results to
    'tigreBrowser' for visualisation, filtering or ranking.
Functional enrichment analysis, gene identifier conversion and
    mapping homologous genes across related organisms via the 'g:Profiler' toolkit
    (http://biit.cs.ut.ee/gprofiler/).
Some functions for modeling sequence read counts as a
        generalized poisson model and to use this model for detecting
        differentially expressed genes in different conditions and
        differentially spliced exons.
The gptk package implements a general-purpose toolkit for Gaussian
  process regression with a variety of covariance functions (e.g. RBF, Mattern, polynomial, etc).
  Based on a MATLAB implementation by Neil D. Lawrence. See inst/doc/index.html for more details.
Provides GPU enabled functions for R objects in a simple and
    approachable manner.  New gpu* and vcl* classes have been provided to
    wrap typical R objects (e.g. vector, matrix), in both host and device
    spaces, to mirror typical R syntax without the need to know OpenCL.
Provides R interfaces to a handful of common
        functions implemented using the Nvidia CUDA toolkit. Some of the
        functions require at least GPU Compute Capability 1.3. 
        Thanks to Craig Stark at UC Irvine for donating time on his lab's Mac.
An EM algorithm, Karl et al. (2013) <doi:10.1016/j.csda.2012.10.004>, is used to estimate the generalized, variable, and complete persistence models, Mariano et al. (2010) <doi:10.3102/1076998609346967>. These are multiple-membership linear mixed models with teachers modeled as "G-side" effects and students modeled with either "G-side" or "R-side" effects.
Server implementation of 'GraphQL' <http://facebook.github.io/graphql/>,
    a query language created by Facebook for describing data requirements on complex application
    data models.  Visit <http://graphql.org> to learn more about 'GraphQL'.
Genomic biology is not limited to the confines of the canonical B-
    forming DNA duplex, but includes over ten different types of other secondary
    structures that are collectively termed non-B DNA structures. Of these non-B
    DNA structures, the G-quadruplexes are highly stable four-stranded structures
    that are recognized by distinct subsets of nuclear factors. This package
    provide functions for predicting intramolecular G quadruplexes. In addition, 
    functions for predicting other intramolecular nonB DNA structures are included.
Use the graph-constrained estimation (Grace) procedure (Zhao and Shojaie, 2016 <doi:10.1111/biom.12418>) to estimate graph-guided linear regression coefficients and use the Grace/GraceI/GraceR tests to perform graph-guided hypothesis tests on the association between the response and the predictors.
An implementation of various learning algorithms based on Gradient Descent for dealing with regression tasks. 
	The variants of gradient descent algorithm are :
	Mini-Batch Gradient Descent (MBGD), which is an optimization to use training data partially to reduce the computation load.
	Stochastic Gradient Descent (SGD), which is an optimization to use a random data in learning to reduce the computation load drastically.
	Stochastic Average Gradient (SAG), which is a SGD-based algorithm to minimize stochastic step to average.
	Momentum Gradient Descent (MGD), which is an optimization to speed-up gradient descent learning.
	Accelerated Gradient Descent (AGD), which is an optimization to accelerate gradient descent learning.
	Adagrad, which is a gradient-descent-based algorithm that accumulate previous cost to do adaptive learning.
	Adadelta, which is a gradient-descent-based algorithm that use hessian approximation to do adaptive learning.
	RMSprop, which is a gradient-descent-based algorithm that combine Adagrad and Adadelta adaptive learning ability.
	Adam, which is a gradient-descent-based algorithm that mean and variance moment to do adaptive learning.
Provides functions for matching student-answers to teacher answers for a variety of data types.
Functions to fit, visualise and compare Gaussian random field species distribution models.
Probability propagation in graphical independence networks, also
    known as Bayesian networks or probabilistic expert systems.
A native R implementation of grammatical evolution (GE). GE facilitates the discovery of programs that can achieve a desired goal. This is done by performing an evolutionary optimisation over a population of R expressions generated via a user-defined context-free grammar (CFG) and cost function.
Represents metagenomic samples on the Euclidean space to examine similarity amongst samples by studying clusters in the model. Given the matrix of metagenomic counts for samples, this package (1) quantifies dissimilarity between samples using Kendall's tau-distance, (2) constructs multidimensional models of different dimension, and (3) plots the models for visualization and comparison.
Repository based tools for department and analysis level
    reproducibility. 'GRANBase' allows creation of custom branched, continuous
    integration-ready R repositories, including incremental testing of only packages
    which have changed versions since the last repository build.
Gene-Ranking Analysis of Pathway Expression (GRAPE) is a tool for
   summarizing the consensus behavior of biological pathways in the form of a
   template, and for quantifying the extent to which individual samples deviate
   from the template. GRAPE templates are based only on the relative rankings
   of the genes within the pathway and can be used for classification of tissue
   types or disease subtypes. GRAPE can be used to represent gene-expression
   samples as vectors of pathway scores, where each pathway score indicates the
   departure from a given collection of reference samples. The resulting pathway-
   space representation can be used as the feature set for various applications,
   including survival analysis and drug-response prediction.
   ----------------------------------------------------------------------------------------------
       GRAPE is a generalization and extension of DIRAC, originally implemented in:
   Eddy, J.A., et al. (2010) <DOI:10.1371/journal.pcbi.1000792>. As a result, some
   of the software below may have been previously published in Matlab by the DIRAC
   authors and can be found at:
   https://price.systemsbiology.org/pricelab-resources/software/.
Turn arbitrary functions into binary operators.
gRapfa is for modelling discrete longitudinal data using acyclic probabilistic finite automata (APFA). The package contains functions for constructing APFA models from a given data using penalized likelihood methods. For graphical display of APFA models, gRapfa depends on 'igraph package'. gRapfa also contains an interface function to Beagle software that implements an efficient model selection algorithm.
gRapHD is designed for efficient selection of high-dimensional undirected 
             graphical models. The package provides tools for selecting trees, forests 
             and decomposable models minimizing information criteria such as AIC or BIC, 
             and for displaying the independence graphs of the models. It has also some 
             useful tools for analysing graphical structures. It supports the use of 
             discrete, continuous, or both types of variables.
A multi-platform user interface for drawing highly customizable graphs in R. It aims to be a valuable help to quickly draw publishable graphs without any knowledge of R commands. Six kinds of graph are available: histogram, box-and-whisker plot, bar plot, pie chart, curve and scatter plot.
A Network Implementation of Fuzzy Sets: Build Network Objects from Multivariate Flat Files. For more information on fuzzy sets, refer to: Zadeh, L.A. (1964) <DOI:10.1016/S0019-9958(65)90241-X>.
Estimates within and between time point interactions in experience sampling data, using the Graphical VAR model in combination with LASSO and EBIC.
Functions to generate
             graphics files, compare them with "model" files,
             and report the results, including visual and textual
             diffs of any differences.
A fast C++ implementation for computing various graph kernels including (1) simple kernels between vertex and/or edge label histograms, (2) random walk kernels (popular baselines), and (3) the Weisfeiler-Lehman graph kernel (state-of-the-art).
Efficient methods for constructing confidence intervals of monotone
    graph invariants, as well as testing for monotone graph properties. Many
    packages are available to estimate precision matrices, this package serves as a
    tool to extract structural properties from their induced graphs. By iteratively
    bootstrapping on only the relevant edge set, we are able to obtain the optimal
    interval size.
Bindings to the 'libgraphqlparser' C++ library. Currently parses
    GraphQL and exports the AST in JSON format.
Multiple scan statistic with variable window for one dimension data and scan statistic based on connected components in 2D or 3D.
Allows building an edge table from data frame of tweets, 
  also provides function to build nodes and another create a temporal graph.
Optimizing a function F(U), where U is a semi-orthogonal matrix and F is invariant under an orthogonal transformation of U
Create graticule lines and labels for maps. Control the creation
    of lines by setting their placement (at particular meridians and parallels)
    and extent (along parallels and meridians). Labels are created independently of
    lines.
A series of functions focused on costing and evaluating Australian tax policy in support of the Grattan Institute's Australian Perspectives program. For access to the taxstats package, please run install.packages("taxstats", repos = "https://hughparsonage.github.io/drat/", type = "source"). N.B. The taxstats package is approximately 50 MB.
The 'gRbase' package provides general features
    which are used by other graphical modelling packages, in particular
    by the packages 'gRain', 'gRim' and 'gRc'.
    'gRbase' contains several data sets relevant for use in connection with
    graphical models. Almost all data sets used in the book Graphical
    Models with R (2012) are contained in 'gRbase'.
    'gRbase' implements several graph algorithms (based mainly on
    representing graphs as adjacency matrices - either in the form
    of a standard matrix or a sparse matrix). Some graph
    algorithms are:
    (i) maximum cardinality search (for marked and unmarked graphs).
    (ii) moralize.
    (iii) triangulate.
    (iv) junction tree.
    'gRbase' facilitates array operations,
    'gRbase' implements functions for testing for conditional independence.
    'gRbase' illustrates how hierarchical log-linear models may be
    implemented and describes concept of graphical meta
    data. These features, however, are not maintained anymore and
    remains in 'gRbase' only because there exists a paper describing
    these facilities: A Common Platform for Graphical Models in R:
    The 'gRbase' Package, Journal of Statistical Software, Vol 14,
    No 17, 2005.
    NOTICE Proper functionality of 'gRbase' requires that the packages graph,
    'Rgraphviz' and 'RBGL' are installed from 'bioconductor'; for
    installation instructions please refer to the web page given below.
Estimation, model selection and other aspects of
        statistical inference in Graphical Gaussian models with edge
        and vertex symmetries (Graphical Gaussian models with colours).
We implement two main functions.
    The first function uses a given grouped and/or
    right-censored grouping scheme and empirical data to infer parameters,
    and implements chi-square goodness-of-fit tests.
    The second function searches for the global optimal grouping
    scheme of grouped and/or right-censored count responses in surveys.
Provides algorithms for detection of spatial patterns from oceanographic data using image processing methods based on Gradient Recognition.
Summarises a collection of partitions into a single optimal partition. The objective function is the expected posterior loss, and the minimisation is performed through a greedy algorithm described in Rastelli, R. and Friel, N. (2016) "Optimal Bayesian estimators for latent variable cluster models" <arXiv:1607.02325>.
Computes experimental designs for a
    two-arm experiment with covariates by greedily optimizing a
    balance objective function. This optimization provides lower
    variance for the treatment effect estimator (and higher power) 
    while preserving a design that is close to complete randomization.
    We return all iterations of the designs for use in a permutation test.
Methods for manipulating regression models and for describing these in a style adapted for medical journals. 
  Contains functions for generating an HTML table with crude and adjusted estimates, plotting hazard ratio, plotting model 
  estimates and confidence intervals using forest plots, extending this to comparing multiple models in a single forest plots. 
  In addition to the descriptives methods, there are addons for the robust covariance matrix provided by the sandwich
  package, a function for adding non-linearities to a model, and a wrapper around the Epi package's Lexis functions for
  time-spliting a dataset when modeling non-proportional hazards in Cox regressions.
Contains many functions useful for
    monitoring and reporting the results of clinical trials and other
    experiments in which treatments are compared.  LaTeX is
    used to typeset the resulting reports, recommended to be in the
    context of 'knitr'. The 'Hmisc', 'ggplot2', and 'lattice' packages are used
    by 'greport' for high-level graphics.
Write statistical models in R and fit them by MCMC on CPUs and GPUs, using Google TensorFlow (see <https://goldingn.github.io/greta> for more information).
Convert 'Ensembl' gene identifiers from Genotype-Tissue
    Expression (GTEx) data to identifiers in other annotation systems,
    including 'Entrez', 'HGNC', and 'UniProt'.
Allows one to classify patients into low, intermediate, and high risk groups for disease progression based on a continuous marker that is associated with progression-free survival. It uses a latent class model to link the marker and survival outcome and produces two cutoffs for the marker to divide patients into three groups. See the References section for more details.
A pluggable package for forest-based statistical estimation and inference.
    GRF currently provides methods for non-parametric least-squares regression,
    quantile regression, and treatment effect estimation (optionally using instrumental
    variables). This package is currently in beta, and we expect to make continual
    improvements to its performance and usability.
The functionality of this package has been deprecated;
    please use the Grid2Polygons function in the inlmisc package instead.
    Converts a spatial object from class sp::SpatialGridDataFrame to
    sp::SpatialPolygonsDataFrame.
Functions for drawing scene trees representing 
              scenes that have been drawn using grid graphics.
Provides a number of user-level functions to work with "grid" graphics, notably to arrange multiple grid-based plots on a page, and draw tables.
Functions to convert a page of plots drawn with the 
  graphics package into identical output drawn with the grid package.
  The result looks like the original graphics-based plot, but consists
  of grid grobs and viewports that can then be manipulated with 
  grid functions (e.g., edit grobs and revisit viewports).
Functions for drawing node-and-edge graphs that have been 
              laid out by graphviz.  This provides an alternative 
              rendering to that provided by the 'Rgraphviz' package, with
              two main advantages:  the rendering provided by 'gridGraphviz'
              should be more similar to what 'graphviz' itself would draw;
              and rendering with 'grid' allows for post-hoc customisations
              using the named viewports and grobs that 'gridGraphviz'
              produces.
Multi-stage cluster surveys of households are commonly performed by 
	governments and programmes to monitor population-level demographic, social, 
	economic, and health outcomes. Generally, communities are sampled from 
	subpopulations (strata) in a first stage, and then households are listed and 
	sampled in a second stage. In this typical two-stage design, sampled communities 
	are the Primary Sampling Units (PSUs) and households are the Secondary Sampling 
	Units (SSUs). Census data typically serve as the sample frame from which PSUs 
	are selected. However, if census data are outdated inaccurate, or too 
	geographically course, gridded population data (such as <http://www.worldpop.org.uk>) 
	can be used as a sample frame instead. GridSample generates PSUs from gridded 
	population data according to user-specified complex survey design characteristics 
	and household sample size. In gridded population sampling, like census sampling, 
	PSUs are selected within each stratum using a serpentine sampling method, and can 
	be oversampled in urban or rural areas to ensure a minimum sample size in each of 
	these important sub-domains. Furthermore, because grid cells are uniform in size 
	and shape, gridded population sampling allows for samples to be representative of
	both the population and of space, which is not possible with a census sample frame.
Simulation tool to facilitate determination of
    required sample size to achieve category saturation
    for studies using multiple repertory grids in conjunction with
    content analysis.
Functions to export graphics drawn with package grid to SVG
  format.  Additional functions provide access to SVG features that
  are not available in standard R graphics, such as hyperlinks, 
  animation, filters, masks, clipping paths, and gradient and pattern fills.
The function GriegSmith accepts either quadrat count data,
        a point process object(ppp) or a matrix of x and y coordinates.
        The function calculates a nested analysis of variance and
        simulation envelopes.
Provides the following types of models: Models for for contingency
    tables (i.e. log-linear models) Graphical Gaussian models for multivariate
    normal data (i.e. covariance selection models) Mixed interaction models.
Functions for converting, importing, and drawing PostScript 
              pictures in R plots.
The program GRNN implements the algorithm proposed by
        Specht (1991).
Workbench for testing genomic regression accuracy on (optionally noisy) phenotypes.
Robust multiple or multivariate linear regression, nonparametric regression on orthogonal components, classical or robust partial least squares models.
The package was developed to fit fit many growth curves
        obtained under different conditions in order to derive a
        conclusive dose-response curve, for instance for a compound
        that potentially affects growth. grofit fits data to different
        parametric models (function gcFitModel) and in addition
        provides a model free spline fit (function gcFitSpline) to
        circumvent systematic errors that might occur within
        application of parametric methods.
Computing Gromov-Hausdorff type l^p distances for labeled metric spaces. These distances were introduced in V.Liebscher, Gromov meets Phylogenetics - new Animals for the Zoo of Metrics on Tree Space. preprint  arXiv:1504.05795,  for phylogenetic trees but may apply to much more situations.
Subsetting methods for balanced cross-validation, time series windowing,
    and general grouping and splitting of data.
Regression models for grouped and coarse data, under the
        Coarsened At Random assumption.
An implementation of the GroupRemMap penalty for fitting regularized multivariate response regression models under the high-dimension-low-sample-size setting. When the predictors naturally fall into groups, the GroupRemMap penalty encourages procedure to select groups of predictors, while control for the overall sparsity of the final model.
A graphical user interface to compute group sequential designs
    based on normally distributed test statistics, particularly critical
    boundaries, power, drift, and confidence intervals of such designs. All
    computations are based on the alpha spending approach by Lan-DeMets with
    various alpha spending functions being available to choose among.
Group subset selection for linear regression models is provided in this package. Given response variable, and explanatory variables, which are organised in groups, group subset selection selects a small number of groups to explain response variable linearly using least squares.
Contains functions for a two-stage multiple testing procedure for grouped hypothesis, aiming at controlling both the total posterior false discovery rate and within-group false discovery rate.
Functional denoising and functional ANOVA through wavelet-domain 
  Markov groves. Fore more details see: Ma L. and Soriano J. (2016) 
  Efficient functional ANOVA through wavelet-domain Markov groves. 
  <arXiv:1602.03990v2 [stat.ME]>.
Employs a non-parametric formulation for by-subject random effect
    parameters to borrow strength over a constrained number of repeated
    measurement waves in a fashion that permits multiple effects per subject.
    One class of models employs a Dirichlet process (DP) prior for the subject
    random effects and includes an additional set of random effects that
    utilize a different grouping factor and are mapped back to clients through
    a multiple membership weight matrix; e.g. treatment(s) exposure or dosage.
    A second class of models employs a dependent DP (DDP) prior for the subject
    random effects that directly incorporates the multiple membership pattern.
Estimates a collection of time-indexed functions under
    either of Gaussian process (GP) or intrinsic Gaussian Markov
    random field (iGMRF) prior formulations where a Dirichlet process
    mixture allows sub-groupings of the functions to share the same
    covariance or precision parameters.  The GP and iGMRF formulations
    both support any number of additive covariance or precision terms,
    respectively, expressing either or both of multiple trend and
    seasonality.
Functions for fitting various normal theory (growth
    curve) and elliptically-contoured repeated measurements models
    with ARMA and random effects dependence.
This is a simple package that fits the logistic equation to
    microbial growth curve data (e.g., repeated absorbance measurements
    taken from a plate reader over time). From this fit, a variety of
    metrics are provided, including the maximum growth rate,
    the doubling time, the carrying capacity, the area under the logistic
    curve, and the time to the inflection point.
A compilation of nonlinear growth models used in many areas
A nonparametric empirical Bayes method for recovering
        gradients (or growth velocities) from observations of smooth
        functions (e.g., growth curves) at isolated time points.
A collection of methods to determine growth rates from
    experimental data, in particular from batch experiments and
    plate reader trials.
Fits user specified (GLM-) models with Group Lasso penalty
The package implements the concave 1-norm and 2-norm group penalty in linear and logistic regression.	        
	The concave 1-norm group penalty includes 1-norm group SCAD and 1-norm group MCP. 
	The concave 1-norm group penalty has bi-level selection features. That is it selects variables at group and individual levels with proper tuning parameters. 
	The concave 1-norm group penalty is robust to mis-specified group information.
	The concave 2-norm group penalty includes 2-norm group SCAD and 2-norm group MCP. The concave 2-norm group penalty select variable at group level only. 
	The package can also fit group Lasso, which is a special case of concave 2-norm group penalty when the regularization parameter kappa equals zero. 
	The highly efficient (block) coordinate descent algorithm (CDA) is used to compute the solutions for both penalties in linear models. 
	The highly stable and efficient (block) CDA and minimization-majorization approach are used to compute the solution for both penalties in logistic models. 
	In the computation of solution surface, the solution path along kappa is implemented. 
	This provides a better solution path compared to the solution path along lambda. 
	The package also provides a tuning parameter selection method based on cross-validation for both linear and logistic models.
Efficient algorithms for fitting the regularization path of
 linear or logistic regression models with grouped penalties.  This
 includes group selection methods such as group lasso, group MCP, and
 group SCAD as well as bi-level selection methods such as the group
 exponential lasso, the composite MCP, and the group bridge.
Fit the regularization path of linear, logistic or Cox models with 
	overlapping grouped covariates based on the latent group lasso approach. Latent 
	group MCP/SCAD as well as bi-level selection methods, namely the group exponential 
	lasso and the composite MCP are also available. This package serves as an 
	extension of R package 'grpreg' (by Dr. Patrick Breheny <patrick-breheny@uiowa.edu>)
	for grouped variable selection involving overlaps between groups.
Group SLOPE is a penalized linear regression method that is used
    for adaptive selection of groups of significant predictors in a
    high-dimensional linear model.
    The Group SLOPE method can control the (group) false discovery rate at a
    user-specified level (i.e., control the expected proportion of irrelevant
    among all selected groups of predictors).
Contains the tools to screen grouped variables, and select screened grouped variables afterwards. The main function grpss() can perform the grouped variables screening as well as selection for ultra-high dimensional data with group structure. The screening step is primarily used to reduce the dimensions of data so that the selection procedure can easily handle the moderate or low dimensions instead of ultra-high dimensions.
Methods include converting series of event names to strings, finding common patterns
    in a group of strings, discovering featured patterns when comparing two groups of strings as well
    as the number and starting position of each pattern in each string, obtaining transition matrix, 
    computing transition entropy, statistically comparing the difference between two groups of strings,
    and clustering string groups. Event names can be any action names or labels such as events in log
    files or areas of interest (AOIs) in eye tracking research.
Alternative implementations of some base R functions, including sort, order, and match.  Functions are simplified but can be faster or have other advantages.
Computational resources for test proposed by Gibbons, Ross, Shanken (1989)<DOI:10.2307/1913625>.
Functions to generate and analyze data for psychology experiments based on the General Recognition Theory.
Offers functions for the comparison of Gutenberg-Richter 
	b-values. Several functions in GRTo are helpful for the assessment of the
	quality of seismicity catalogs.
GSAgm is an R package that completes a self-contained gene set analysis (GSA) for RNA-seq and SNP data using the Gamma Method.
This package contains utility functions used by the Genome Analysis Toolkit (GATK) to load tables and plot data. The GATK is a toolkit for variant discovery in high-throughput sequencing data.
Computation of Quantitative Trait Loci hits in the selected gene set. Performing gene set validation with Quantitative Trait Loci information. Performing gene set enrichment analysis with available Quantitative Trait Loci data and computation of statistical significance value from gene set analysis. Obtaining the list of Quantitative Trait Loci hit genes along with their overlapped Quantitative Trait Loci names.
Write SARIMA models in (finite) AR representation and simulate 
	generalized multiplicative seasonal autoregressive moving average (time) series 
	with Normal / Gaussian, Poisson or negative binomial distribution.
Group Sequential Operating Characteristics for Clinical,
        Bayesian two-arm Trials with known Sigma and Normal Endpoints.
Derives group sequential designs and describes their properties.
Robust Estimation of Multivariate Location and Scatter in the
        Presence of Cellwise and Casewise Contamination and Missing Data.
R-interface to C++ implementation of the rank/score permutation based GSEA test (Subramanian et al 2005 <doi: 10.1073/pnas.0506580102>).
Provides function to apply "Group sequential enrichment design incorporating subgroup selection" (GSED) method proposed by Magnusson and Turnbull (2013) <doi:10.1002/sim.5738>.
Using an approach based on similarity graph to estimate change-point(s) and the corresponding p-values.  Can be applied to any type of data (high-dimensional, non-Euclidean, etc.) as long as a reasonable similarity measure is available.
Conducts a semi-gSEM statistical analysis (semi-supervised generalized structural equation modeling) on a data frame of coincident observations of multiple predictive or intermediate variables and a final continuous, outcome variable, via two functions sgSEMp1() and sgSEMp2(), representing fittings based on two statistical principles. Principle 1 determines all sensible univariate relationships in the spirit of the Markovian process. The relationship between each pair of variables, including predictors and the final outcome variable, is determined with the Markovian property that the value of the current predictor is sufficient in relating to the next level variable, i.e., the relationship is independent of the specific value of the preceding-level variables to the current predictor, given the current value. Principle 2 resembles the multiple regression principle in the way multiple predictors are considered simultaneously. Specifically, the relationship of the first-level predictors (such as Time and irradiance etc) to the outcome variable (such as, module degradation or yellowing)  is fit by a supervised additive model. Then each significant intermediate variable is taken as the new outcome variable and the other variables (except the final outcome variable) as the predictors in investigating the next-level multivariate relationship by a supervised additive model. This fitting process is continued until all sensible models are investigated.
calculate equivalence and futility boundaries based on the exact bivariate $t$ test statistics for group sequential designs in studies with equivalence hypotheses.
gsg (gam selection gradients) provides a unified approach to the regression analysis of selection from longitudinal data collected from natural populations.
Simple package to download Google Sheets using just the sharing
    link. Spreadsheets can be downloaded as a data frame, or as plain text to parse
    manually. Google Sheets is the new name for Google Docs Spreadsheets.
Global Soil Information Facilities - tools (standards and functions) and sample datasets for global soil mapping.
Family based association test via GEE Kernel Machine score test
An R wrapper for the special functions and quasi random number
 generators of the Gnu Scientific Library
 (http://www.gnu.org/software/gsl/).  See gsl-package.Rd for details of 
 overall package organization, and Misc.Rd for some functions that are
 widely used in the package, and some tips on installation.
Contains published data sets for global benthic d18O data for 
    0-5.3 Myr <doi:10.1029/2004PA001071> and global sea levels based 
    on marine sediment core data for 0-800 ka <doi:10.5194/cp-12-1-2016>.
Implementation of a Bayesian approach for estimating a mixture of gamma distributions in which the mixing occurs over the shape parameter. This family provides a flexible and novel approach for modeling heavy-tailed distributions, it is computationally efficient, and it only requires to specify a prior distribution for a single parameter.
Tools rewritten in C for various smoothing tasks
Estimating trait heritability and handling overfitting. This package includes a collection of functions for (1) estimating genetic variance-covariances and calculate trait heritability; and (2) handling overfitting by calculating the variance components and the heritability through cross validation.
Provides automated downloading, parsing, cleaning, unit conversion
    and formatting of Global Surface Summary of the Day (GSOD) weather data from
    the from the USA National Centers for Environmental Information (NCEI) for
    use in R. Units are converted from from United States Customary System
    (USCS) units to International System of Units (SI). Stations may be 
    individually checked for number of missing days defined by the user, where
    stations with too many missing observations are omitted. Only stations with 
    valid reported latitude and longitude values are permitted in the final 
    data. Additional useful elements, saturation vapour pressure (es), actual 
    vapour pressure (ea) and relative humidity are calculated from the original 
    data and included in the final data set. The resulting data include station
    identification information, state, country, latitude, longitude, elevation,
    weather observations and associated flags. Data may be automatically saved 
    to disk. File output may be returned as a comma-separated values (CSV) or 
    GeoPackage (GPKG) file. Additional data are included with this R package: a 
    list of elevation values for stations between -60 and 60 degrees latitude 
    derived from the Shuttle Radar Topography Measuring Mission (SRTM). For 
    information on the GSOD data from NCEI, please see the  GSOD readme.txt file
    available from, <http://www1.ncdc.noaa.gov/pub/data/gsod/readme.txt>.
Approaches a group sparse solution of an underdetermined linear system. It implements the proximal gradient algorithm to solve a lower regularization model of group sparse learning. For details, please refer to the paper "Y. Hu, C. Li, K. Meng, J. Qin and X. Yang. Group sparse optimization via l_{p,q} regularization. Journal of Machine Learning Research, to appear, 2017".
Pipeline to read and analyze raw SNP array data.
    The data is preprocessed and normalized.
    Genotypes and CNVs are called.
    Synteny blocks are calculated and translocations detected.
    The results can be plotted with special functions.
A gate-keeping procedure to test a primary and a secondary endpoint in a group sequential design with multiple interim looks. Computations related to group sequential primary and secondary boundaries. Refined secondary boundaries are calculated for a gate-keeping test on a primary and a secondary endpoint in a group sequential design with multiple interim looks. The choices include both the standard boundaries and the boundaries using error spending functions. Version 1.0.0 was released on April 12, 2017. See Tamhane et al. (2017+) "A gatekeeping procedure to test a primary and a secondary endpoint in a group sequential design with multiple interim looks", Biometrics, to appear.
A comprehensive package for structural multivariate
        function estimation using smoothing splines.
A collection of routines for the estimation of copula density
        and 2-D hazard function using smoothing splines.
We propose a fully efficient sieve maximum likelihood method to estimate genotype-specific distribution of time-to-event outcomes under a nonparametric model. We can handle missing genotypes in pedigrees.  We estimate the time-dependent hazard ratio between two genetic mutation groups using B-splines, while applying nonparametric maximum likelihood estimation to the reference baseline hazard function.  The estimators are calculated via an expectation-maximization algorithm.
Variogram modelling; simple, ordinary and universal point or block (co)kriging; spatio-temporal kriging; sequential Gaussian or indicator (co)simulation; variogram and variogram map plotting utility functions.
gsubfn is like gsub but can take a replacement function
   or certain other objects instead of the replacement string.
   Matches and back references are input to the replacement function and 
   replaced by the function output.   gsubfn can be used to split strings 
   based on content rather than delimiters and for quasi-perl-style string 
   interpolation. The package also has facilities for translating formulas 
   to functions and allowing such formulas in function calls instead of 
   functions.  This can be used with R functions such as apply, sapply,
   lapply, optim, integrate, xyplot, Filter and any other function that 
   expects another function as an input argument or functions like cat
   or sql calls that may involve strings where substitution is desirable.
Provides an interface to the Gibbs 'SeaWater' ('TEOS-10') C
    library, version 3.05-4 (commit '5b4d959e54031f9e972f3e863f63e67fa4f5bfec', dated 2017-08-07,
    available at <https://github.com/TEOS-10/GSW-C>, which stems from
    'Matlab' and other code written by members of Working Group 127 of
    'SCOR'/'IAPSO' (Scientific Committee on Oceanic Research /
    International Association for the Physical Sciences of the Oceans).
Estimation of the cutpoint defined by the Generalized Symmetry point in a binary classification setting based on a continuous diagnostic test or marker. Two methods have been implemented to construct confidence intervals for this optimal cutpoint, one based on the Generalized Pivotal Quantity and the other based on Empirical Likelihood. Numerical and graphical outputs for these two methods are easily obtained.
Generalized synthetic control method: causal inference with interactive fixed-effect models. It imputes counterfactuals for each treated unit using control group information based on a linear interactive fixed effects model that incorporates unit-specific intercepts interacted with time-varying coefficients. This method generalizes the synthetic control method to the case of multiple treated units and variable treatment periods, and improves efficiency and interpretability. Data must be in form of a balanced panel with a dichotomous treatment.
Provides tools to measure the reliability of an Information Retrieval test collection.
  It allows users to estimate reliability using Generalizability Theory and map those estimates onto
  well-known indicators such as Kendall tau correlation or sensitivity.
Tools to make it easier to work with "tables" of 'grobs'.
This package provides functions to calculate the
        efficiencies (expected tests per unit) of hierarchical and
        matrix group testing procedures.  Efficiencies can be
        calculated in the presense of correlated responses under
        multiple arrangements of clustesrs.  Efficiencies can also be
        evaluated in the presense of test error.
Generalized Turnbull's estimator proposed by Dehghan and Duchesne
    (2011).
Three graph-based tests are provided for testing whether two samples are from the same distribution.
Estimates variance components, generalizability coefficients,
    universe scores, and standard errors when observed scores contain variation from
    one or more measurement facets (e.g., items and raters).
Functions to assist in R programming, including:
  - assist in developing, updating, and maintaining R and R packages ('ask', 'checkRVersion',
    'getDependencies', 'keywords', 'scat'),
  - calculate the logit and inverse logit transformations ('logit', 'inv.logit'),
  - test if a value is missing, empty or contains only NA and NULL values ('invalid'),
  - manipulate R's .Last function ('addLast'),
  - define macros ('defmacro'),
  - detect odd and even integers ('odd', 'even'),
  - convert strings containing non-ASCII characters (like single quotes) to plain ASCII ('ASCIIfy'),
  - perform a binary search ('binsearch'),
  - sort strings containing both numeric and character components ('mixedsort'),
  - create a factor variable from the quantiles of a continuous variable ('quantcut'),
  - enumerate permutations and combinations ('combinations', 'permutation'),
  - calculate and convert between fold-change and log-ratio ('foldchange',
    'logratio2foldchange', 'foldchange2logratio'),
  - calculate probabilities and generate random numbers from Dirichlet distributions
    ('rdirichlet', 'ddirichlet'),
  - apply a function over adjacent subsets of a vector ('running'),
  - modify the TCP\_NODELAY ('de-Nagle') flag for socket objects,
  - efficient 'rbind' of data frames, even if the column names don't match ('smartbind'),
  - generate significance stars from p-values ('stars.pval'),
  - convert characters to/from ASCII codes.
In hierarchical time series (HTS) forecasting, the hierarchical relation between multiple time series is exploited to make better forecasts. This hierarchical relation implies one or more aggregate consistency constraints that the series are known to satisfy. Many existing approaches, like for example bottom-up or top-down forecasting, therefore attempt to achieve this goal in a way that guarantees that the forecasts will also be aggregate consistent. This package provides with an implementation of the Game-Theoretically OPtimal (GTOP) reconciliation method proposed in van Erven and Cugliari (2015), which is guaranteed to only improve any given set of forecasts. This opens up new possibilities  for constructing the forecasts. For example, it is not necessary to assume that bottom-level forecasts are unbiased, and aggregate forecasts may be constructed by regressing both on bottom-level forecasts and on other covariates that may only be available at the aggregate level.
An interface for retrieving and displaying the information returned
    online by Google Trends is provided. Trends (number of hits) over the time as
    well as geographic representation of the results can be displayed.
Assorted tools for genetic association analyses.  The
        current focus is on implementing (either exactly or
        approximately) regression analyses using summary statistics
        instead of using subject-specific data.  So far, functions
        exist to support multi-SNP risk score analyses, multi-SNP
        conditional regression analyses, and multi-phenotype analyses,
        using summary statistics.  There are helper functions for
        reading and manipulating subject-specific genotype data, which
        provide a platform for calculating the summary statistics, or
        for using R to conduct other analyses not supported by specific
        GWAS analysis tools.
Provides an interface to the Open Platform's Content API of the Guardian Media Group. It retrieves content from news outlets The Observer, The Guardian, and guardian.co.uk from 1999 to current day.
This package comprises maps of France in 1830, multivariate data from A.-M. Guerry and others, and statistical and 
	graphic methods related to Guerry's "Moral Statistics of France". The goal is to facilitate the exploration and
	development of statistical and graphic methods for multivariate data in a geo-spatial context of historical interest.
Adjust Estimates of Learning for Guessing. The package provides 
    standard guessing correction, and a latent class model that leverages
    informative pre-post transitions. For details of the latent class model,
    see <http://gsood.com/research/papers/guess.pdf>.
A nice GUI for financial DErivatives in R.
A graphical user interface for the R package Gems.  
    Apart from the functionality of Gems package in the Graphical User interface, GUIgems
    allows adding states to a defined model, merging states for the analysis and plotting 
    progression paths between states based on the simulated cohort.
    There is also a module in the GUIgems which allows to compare costs and QALYs between different cohorts.
A collection of sampling formulas for the unified neutral model of biogeography and biodiversity. Alongside the sampling formulas, it includes methods to perform maximum likelihood optimization of the sampling formulas, methods to generate data given the neutral model, and methods to estimate the expected species abundance distribution. Sampling formulas included in the GUILDS package are the Etienne Sampling Formula (Etienne 2005), the guild sampling formula, where guilds are assumed to differ in dispersal ability (Janzen et al. 2015), and  the guilds sampling formula conditioned on guild size (Janzen et al. 2015).
Show graphically the results of profiling R functions by tracking their execution time.
Provides probability functions (cumulative distribution and density functions), simulation function (Gumbel copula multivariate simulation) and estimation functions (Maximum Likelihood Estimation, Inference For Margins, Moment Based Estimation and Canonical Maximum Likelihood).
Generalized UniFrac distance for comparing microbial
        communities. Permutational multivariate analysis of variance
        using multiple distance matrices.
Statistical analysis of monthly background checks of gun purchases for the New York Times 
 story "What Drives Gun Sales: Terrorism, Obama and Calls for Restrictions" at 
 <http://www.nytimes.com/interactive/2015/12/10/us/gun-sales-terrorism-obama-restrictions.html?> is provided.
Download and process public domain works in the Project
    Gutenberg collection <http://www.gutenberg.org/>. Includes metadata for
    all Project Gutenberg works, so that they can be searched and retrieved.
Given exposure and survival time series as well as parameter values, GUTS allows for the fast calculation of the survival probabilities as well as the logarithm of the corresponding likelihood.
Several tools for Global Value Chain ('GVC') analysis are
    implemented.
Generalized structured regression models with regularized categorical effects, categorical effect modifiers, continuous effects and smooth effects.
Methods from the paper: Pena, EA and Slate, EH, "Global Validation of Linear Model Assumptions," J. American Statistical Association, 101(473):341-354, 2006.
Functions for genome-wide association/interaction analysis and rare variant analysis on a continuous/dichotomous trait using family data, and for making genome-wide p-value plot and QQ plot.
This package contains a function to do exact
        Hardy-Weinburg testing (using Fisher's test) for SNP genotypes
        as typically obtained in a Genome Wide Association Study
        (GWAS).
This is a Shiny application intended to provide better understanding of how geometrically-weighted degree terms function in exponential random graph models of networks. It contains just one user function, gwdegree(), which launches the Shiny application.
This package provides functions to calculate the
        significance threshold for controlling the type I error rate in
        mixed-model association mapping analyses.
Performs Geographically Weighted Fractal Analysis (GWFA) to calculate the local fractal dimension of a set of points. GWFA mixes the Sandbox multifractal algorithm and the Geographically Weighted Regression. Unlike fractal box-counting algorithm, the sandbox algorithm avoids border effects because the boxes are adjusted on the set of points. The Geographically Weighted approach consists in applying a kernel that describes the way the neighbourhood of each estimated point is taken into account to estimate its fractal dimension. GWFA can be used to discriminate built patterns of a city, a region, or a whole country.
Based on calculations of 758 women this package calculates
        positive predictive values (PPV) and negative predictive values
        (NPV) for inadequate and excessive gestational weight gain
        (GWG) for different prevalences for different BMI categories.
gWidgets provides a toolkit-independent API for building interactive GUIs. At least one of the 'gWidgetsXXX packages', such as gWidgetstcltk, needs to be installed. Some icons are on loan from the scigraphica project http://scigraphica.sourceforge.net.
Re-implementation of the 'gWidgets' API. The API is defined in this
    package. A second, toolkit-specific package is required to use it. There
    are three in development: 'gWidgets2RGtk2', 'gWidgets2Qt', and 'gWidgets2tcltk'.
Implements the 'gWidgets2' API for 'RGtk2.'
Port of the 'gWidgets2' API for the 'tcltk' package.
Port of the gWidgets API to the RGtk2 toolkit.
Port of the gWidgets API to the tcltk package. Requires Tk 8.5 or greater.
Fit a geographically weighted logistic elastic net regression.
In GWmodel, we introduce techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. GWmodel includes functions to calibrate: GW summary statistics, GW principal components analysis, GW discriminant analysis and various forms of GW regression; some of which are provided in basic and robust (outlier resistant) forms.
Fits Weighted Quantile Sum (WQS) regressions for continuous or binomial outcomes.
Statistical functions to fit, validate and describe a Generalized
    Waring Regression Model (GWRM).
Fits geographically weighted regression (GWR) models and
        has tools to diagnose and remediate collinearity in the GWR
        models. Also fits geographically weighted ridge regression
        (GWRR) and geographically weighted lasso (GWL) models.
The correlations and linkage disequilibrium between tests can vary as a function of minor allele frequency thresholds used to filter variants, and also varies with different choices of test statistic for region-based tests. Appropriate genome-wide significance thresholds can be estimated empirically through permutation on only a small proportion of the whole genome.
Quantifying and testing gene-by-measured-environment interaction in behavior genetic designs.
OpenAI Gym is a open-source Python toolkit for developing and comparing
    reinforcement learning algorithms. This is a wrapper for the OpenAI Gym API,
    and enables access to an ever-growing variety of environments.
    For more details on OpenAI Gym, please see here: <https://github.com/openai/gym>.
    For more details on the OpenAI Gym API specification, please see here:
    <https://github.com/openai/gym-http-api>.
SNP-set association testing for censored phenotypes in the presence of intrafamilial correlation.
R scripting functionality for H2O, the open source
    math engine for big data that computes parallel distributed
    machine learning algorithms such as generalized linear models,
    gradient boosting machines, random forests, and neural networks
    (deep learning) within various cluster environments.
S4 Interface to the 'HDF5' library supporting fast storage and
    retrieval of R-objects like vectors, matrices and arrays to binary files in
    a language independent format. The 'HDF5' format can therefore be used as
    an alternative to R's save/load mechanism. Since h5 is able to access only
    subsets of stored data it can also handle data sets which do not fit into
    memory.
A Haar-Fisz algorithm for Poisson intensity estimation
Package provides the estimation of the structure and the parameters, sampling methods and structural plots of Hierarchical Archimedean Copulae (HAC).
Provides a framework for writing map/reduce scripts for
        use in Hadoop Streaming. Also facilitates operating on data in
        a streaming fashion, without Hadoop.
Read data from the City of Portland's 'HYDRA' <http://or.water.usgs.gov/precip/> rainfall datasets within R.
Various functions and algorithms are provided here for solving optimal matching tasks in the context of preclinical cancer studies. Further, various helper and plotting functions are provided for unsupervised and supervised machine learning as well as longitudinal mixed-effects modeling of tumor growth response patterns.
An S4 implementation of Eq. (3) and Eq. (7) by David J. Hand and 
    Robert J. Till (2001) <DOI:10.1023/A:1010920819831>.
Provides an R routine for a Cramer-von Mises type two sample test which is based on empirical Hankel transforms of the non-negative sample variables. The test is non-parametric and not distribution free. The exact value of the test statistic for univariate data as well as the p-value and the critical value are computed.
Provides functions to download data from the <http://www.data.parliament.uk/> APIs. Because of the structure of the API, there is a named function for each type of available data for ease of use, as well as some functions designed to retrieve specific pieces of commonly used data. Functions for each new API will be added as and when they become available.
The following R functions are used for inference of trait
        associations with haplotypes and other covariates in
        generalized linear models.  The functions are developed
        primarily for data collected in cohort or cross-sectional
        studies. They can accommodate uncertain haplotype phase and
        handle missing genotypes at some SNPs.
The multi-locus stepwise regression (MSR) combines the advantages of stepwise regression and haplotype-based analysis. The MSR can be used to identify informative combinations of single nucleotide polymorphisms (SNPs) from unlinked SNPs (allele combinations) or SNPs within a chromosomal region (haplotypes).
The package provides a set of functions whose aim is to
        propose 4 methods of QTL detection. HAPimLD is an
        interval-mapping method designed for unrelated individuals with
        no family information that makes use of linkage disequilibrium.
        HAPimLDL is an interval-mapping method for design of half-sib
        families. It combines linkage analysis and linkage
        disequilibrium. HaploMax is based on an analysis of variance
        with a dose haplotype effect. HaploMaxHS is based on an
        analysis of variance with a sire effect and a dose haplotype
        effect in half-sib family design.  Fundings for the package
        development were provided to the LDLmapQTL project by the ANR
        GENANIMAL program and APIS-GENE.
Performs genetic association analyses of case-parent triad (trio) data with multiple markers. It can also incorporate complete or incomplete control triads, for instance independent control children. Estimation is based on haplotypes, for instance SNP haplotypes, even though phase is not known from the genetic data. Haplin estimates relative risk (RR + conf.int.) and p-value associated with each haplotype. It uses maximum likelihood estimation to make optimal use of data from triads with missing genotypic data, for instance if some SNPs has not been typed for some individuals. Haplin also allows estimation of effects of maternal haplotypes and parent-of-origin effects, particularly appropriate in perinatal epidemiology. Haplin allows special models, like X-inactivation, to be fitted on the X-chromosome. A GxE analysis allows testing interactions between environment and all estimated genetic effects.
'haplo.ccs' estimates haplotype and covariate relative
        risks in case-control data by weighted logistic regression.
        Diplotype probabilities, which are estimated by EM computation
        with progressive insertion of loci, are utilized as weights.
A set of utilities for querying 
             'HaploReg' <http://archive.broadinstitute.org/mammals/haploreg/haploreg.php> 
             and 'RegulomeDB' <http://www.regulomedb.org> web-based tools. The package connects to 
             'HaploReg' or 'RegulomeDB', searches and downloads results, without 
             opening web pages, directly from R environment. 
             Results are stored in a data frame that can be directly used in various 
             kinds of downstream analyses.
Reconstruction of founder haplotype blocks from time series data.
Simulate haplotypes through meioses. Allows specification
        of population parameters.
Routines for the analysis of indirectly measured haplotypes. The statistical methods assume that all subjects are unrelated and that haplotypes are ambiguous (due to unknown linkage phase of the genetic markers). The main functions are: haplo.em(), haplo.glm(), haplo.score(), and haplo.power(); all of which have detailed examples in the vignette.
Function to  identify  haplotypes
  within QTL (Quantitative Trait Loci). One haplotype is a combination of SNP
  (Single Nucleotide Polymorphisms) within the QTL. This function groups
  together all individuals of a population with the same haplotype.
  Each group contains individual with the same allele in each SNP,
  whether or not missing data. Thus, haplotyper groups individuals,
  that to be imputed, have a non-zero probability of having the same alleles
  in the entire sequence of SNP's. Moreover, haplotyper calculates such
  probability from relative frequencies.
Provides S4 classes and methods for reading and manipulating aligned DNA sequences, supporting an indel coding methods (only simple indel coding method is available in the current version), showing base substitutions and indels, calculating absolute pairwise distances between DNA sequences, and inferring haplotypes from DNA sequences or user provided absolute character difference matrix.  This package also includes S4 classes and methods for estimating genealogical relationships among haplotypes using statistical parsimony.
Software creation is a pretty big deal!  A repository's initial commit date can be thought of as its birthday.  Next time, drop in and wish a developer (any Github username) a happy birthday of their repo(s).  Or maybe just toast to the upcoming anniversary of your own software!  The software life cycle is too short not to celebrate!
Functions to perform ROR for sequence-based association
        analysis
Package for haplotype-based genotype simulations. Haplotypes are
        generated such that their allele frequencies and linkage
        disequilibrium coefficients match those estimated from an input
        data set.
Contains tools for exploring Hardy-Weinberg equilibrium for
  diallelic genetic marker data. All classical tests (chi-square, exact,
  likelihood-ratio and permutation tests) for Hardy-Weinberg equilibrium
  are included in the package, as well as functions for power computation and
  for the simulation of marker data under equilibrium and disequilibrium.
  Routines for dealing with markers on the X-chromosome are included.
  Functions for testing equilibrium in the presence of missing data by
  using multiple imputation are also provided. Implements several graphics
  for exploring the equilibrium status of a large set of diallelic markers: 
  ternary plots with acceptance regions, log-ratio plots and Q-Q plots.
The harmonic mean p-value (HMP) test simply and instantly combines p-values and corrects for multiple testing while controlling the family-wise error rate in a way that is more powerful than common alternatives including Bonferroni and Simes procedures, more stringent than controlling the false discovery rate, and is robust to positive correlations between tests and unequal weights. It is a multi-level test in the sense that a superset of one or more significant tests is almost certain to be significant and conversely when the superset is non-significant, the constituent tests are almost certain to be non-significant. It is based on MAMML (model averaging by mean maximum likelihood), a frequentist analogue to Bayesian model averaging, and is theoretically grounded in generalized central limit theorem.
Fits the first harmonics in a Fourier expansion to one or more time series. Trend elimination can be performed.  Computed values include estimates of amplitudes and phases, as well as confidence intervals and p-values for the null hypothesis of Gaussian noise.
Harriet was Charles Darwin's pet tortoise (possibly). 'harritr'
    implements some function to manipulate distance matrices and phylogenetic trees
    to make it easier to plot with 'ggplot2' and to manipulate using 'tidyverse'
    tools.
The goal of 'HARtools' is to provide a simple set of functions
    to read/parse, write and visualise HTTP Archive ('HAR') files in R.
Functions for easy and reproducible simulation.
Aimed at applying the Harvest classification tree algorithm, modified algorithm of classic classification tree.The harvested tree has advantage of deleting redundant rules in trees, leading to a simplify and more efficient tree model.It was firstly used in drug discovery field, but it also performs well in other kinds of data, especially when the region of a class is disconnected. This package also improves the basic harvest classification tree algorithm by extending the field of data of algorithm to both continuous and categorical variables. To learn more about the harvest classification tree algorithm, you can go to http://www.stat.ubc.ca/Research/TechReports/techreports/220.pdf for more information.
This package implements a data structure similar to hashes
        in Perl and dictionaries in Python but with a purposefully R
        flavor.  For objects of appreciable size, access using hashes
        outperforms native named lists and vectors.
This package provides common non-cryptographic hash
        functions for R. For example, SpookyHash, Murmur3Hash, Google
        CityHash.
An R port of the hashids library.  hashids generates YouTube-like hashes from integers or vector of integers.  Hashes generated from integers are relatively short, unique and non-seqential.  hashids can be used to generate unique ids for URLs and hide database row numbers from the user.  By default hashids will avoid generating common English cursewords by preventing certain letters being next to each other.  hashids are not one-way: it is easy to encode an integer to a hashid and decode a hashid back into an integer.
Provides a hash table class for fast
    key-value storage of atomic vector types.
    Internally, 'hashmap' makes extensive use of 'Rcpp', 'boost::variant',
    and 'boost::unordered_map' to achieve high performance, type-safety,
    and versatility, while maintaining compliance with the C++98 standard.
Apply the SuperFastHash algorithm to any R object. Hash whole R objects or, 
     for vectors or lists, hash R objects to obtain a set of hash values that is stored 
     in a structure equivalent to the input.
Drawing Hasse diagram - visualization of transitive reduction of a finite partially ordered set.
Import foreign statistical formats into R via the embedded
    'ReadStat' C library, <https://github.com/WizardMac/ReadStat>.
The package allows to simulate Hawkes process both in univariate and multivariate settings. It gives functions to compute different moments of the number of jumps of the process on a given interval, such as mean, variance or autocorrelation of process jumps on time intervals separated by a lag.
Damage Functions (DFs), also known as
    Vulnerability Functions, associate the physical damage
    to a building or a structure (and also its contents and
    inventory) from natural disasters to financial damage.
    The Federal Emergency Management Agency (FEMA) in USA
    developed several thousand DFs and these serve as a
    benchmark in natural catastrophe modeling, both in
    academia and industry. However, these DFs and their
    documentation are buried within the HAZUS software are
    not easily accessible for analysis and visualization.
    This package provides more than 1300 raw DFs used by FEMA's
    HAZUS software and also functionality to extract and
    visualize DFs specific to the flood hazard. The vignette
    included with this package demonstrates its use.
Fit an array of decision-making tasks with computational models in
    a hierarchical Bayesian framework. Can perform hierarchical Bayesian analysis of
    various computational models with a single line of coding.
Implements our hierarchical Bayesian design for randomized clinical trials.
Convenient and efficient functions for performing 2-level hierarchical Bayesian regression analysis for multi-group data. The lowest level may belong to the generalized linear model (GLM) family while the prior level, which effects pooling, allows for linear regression on lower level covariates. Constraints on all or part of the parameter set maybe specified with ease. A rich set of methods is included to visualize and analyze results.
Calculate expected relative risk and proportion protected assuming normally distributed log10 transformed antibody dose for several component vaccine. Uses Hill models for each component which are combined under Bliss independence.
A package for building hierarchical block matrices from association matrices and for performing multi-scale analysis. It specifically targets chromatin contact maps, generated from high-throughput chromosome conformation capture data, such as 5C and Hi-C, and provides methods for detecting movements and for computing chain hierarchy and region communicability across scales.
Contains functions for fitting hierarchical versions of
        EVSD, UVSD, DPSD, DPSD with d' restricted to be positive, and
        our gamma signal detection model to recognition memory
        confidence-ratings data.
Hi-C dataset processing algorithm introduced by Chao He (2016) <doi.org/10.1101/083576>.
   The optimized and flexible pipeline for analyzing the
   folding of whole chromosome and interactions between some
   specific sites from the Hi-C raw sequencing reads to the
   partially processed datasets. The other complex genetic and
   epigenetic datasets from public sources such as GWAS, ENCODE
   consortiums etc. will also easily be integrated into HBP,
   hence the final output results of HBP could provide a
   comprehensive in-depth understanding for the specific
   chromatin interactions, potential molecular mechanisms and
   biological significance. We believe that HBP is a reliable
   tool for the rapidly analysis of Hi-C data and will be very
   useful for a wide range of researchers, particularly those
   who lack of background in computational biology.
Functions to compute small area estimates based on a basic
        area or unit-level model. The model is fit using restricted
        maximum likelihood, or in a hierarchical Bayesian way. In the
        latter case numerical integration is used to average over the
        posterior density for the between-area variance.  The output
        includes the model fit, small area estimates and corresponding
        MSEs, as well as some model selection measures. Additional
        functions provide means to compute aggregate estimates and
        MSEs, to minimally adjust the small area estimates to
        benchmarks at a higher aggregation level, and to graphically
        compare different sets of small area estimates.
This package fits Hierarchical Bayesian space-Time models for Gaussian data. Furthermore, its functions have been implemented for analysing the fitting qualities of those models.
A new diagnostic check for model adequacy in regression
        and generalized linear models is implemented.
This package calculates the interval estimates for the parameters of 
 linear models heteroscedastic regression using bootstrap - (Wild Bootstrap) and double
 bootstrap-t (Wild Bootstrap). It is also possible to calculate confidence intervals using
 the percentile bootstrap and percentile bootstrap double. It is possible to calculate
 consistent estimates of the covariance matrix of the parameters of linear regression models
 with heteroskedasticity of unknown form. The package also provides function to calculate
 consistently the covariance matrix of the parameters of linear models with heteroskedasticity
 of unknown form.
Estimation of parameters in 3-segment (i.e. 2 change-point)
    regression models with heteroscedastic variances is provided based on both
    likelihood and hybrid Bayesian approaches, with and without continuity
    constraints at the change points.
Functions to perform dimensionality reduction for classification if the covariance matrices of the classes are unequal.
Fits regression models on high dimensional data to estimate coefficients and use bootstrap method to obtain confidence intervals. Choices for regression models are Lasso, Lasso+OLS, Lasso partial ridge, Lasso+OLS partial ridge.
Discriminant analysis and data clustering methods for high
    dimensional data, based on the assumption that high-dimensional data live in
    different subspaces with low dimensionality proposing a new parametrization of
    the Gaussian mixture model which combines the ideas of dimension reduction and
    constraints on the model.
Determine the sample size requirement to achieve the target probability of correct classification (PCC) for studies employing high-dimensional features.  The package implements functions to 1) determine the asymptotic feasibility of the classification problem; 2) compute the upper bounds of the PCC for any linear classifier; 3) estimate the PCC of three design methods given design assumptions; 4) determine the sample size requirement to achieve the target PCC for three design methods.
Cross-validated linear discriminant calculations determine
  the optimum number of features. Test and training scores from
  successive cross-validation steps determine, via a principal
  components calculation, a low-dimensional global space onto which test
  scores are projected, in order to plot them. Further functions are
  included that serve didactic purposes.
Facilitates discovery and handling of hydrological data, access to catalogues and databases.
A flexible and hierarchical framework for comparing categorical map composition and configuration (spatial pattern) along spatial, thematic, or external grouping variables.  Comparisons are based on measures of mutual information between thematic classes (colours) and location (spatial partitioning).  Results are returned in textual, tabular, and graphical forms.
Test the significance of coefficients in high dimensional generalized linear models.
Implementation of multiple approaches to perform inference in high-dimensional models.
A generic function and a set of methods to calculate highest density intervals for a variety of classes of objects which can specify a probability density distribution, including MCMC output, fitted density objects, and functions.
Mimics the lm() function found in the package stats to fit
        high dimensional regression models with point estimates,
        standard errors, and p-values. Methods for printing and
        summarizing the results are given.
Implementation of selected high-dimensional statistical and
    econometric methods for estimation and inference. Efficient estimators and
    uniformly valid confidence intervals for various low-dimensional causal/
    structural parameters are provided which appear in high-dimensional
    approximately sparse models. Including functions for fitting heteroscedastic
    robust Lasso regressions with non-Gaussian errors and for instrumental variable
    (IV) and treatment effect estimation in a high-dimensional setting. Moreover,
    the methods enable valid post-selection inference and rely on a theoretically
    grounded, data-driven choice of the penalty.
Creates nomogram visualizations for penalized Cox regression
    models, with the support of reproducible survival model building,
    validation, calibration, and comparison for high-dimensional data.
An implementation of an algorithm for outlier detection that can handle a) data with a mixed categorical and continuous variables, b) many columns of data, c) many rows of data, d) outliers that mask other outliers, and e) both unidimensional and multidimensional datasets. Unlike ad hoc methods found in many machine learning papers, HDoutliers is based on a distributional model that uses probabilities to determine outliers. See <https://www.cs.uic.edu/~wilkinson/Publications/outliers.pdf>.
In high-dimensional settings:
	Estimate the number of distant spikes based on the Generalized Spiked Population (GSP) model.
	Estimate the population eigenvalues, angles between the sample and population eigenvectors, correlations between the sample and population PC scores, and the asymptotic shrinkage factors.
	Adjust the shrinkage bias in the predicted PC scores.
Algorithms for lasso and fused-lasso problems: implementation of
    the lars algorithm for lasso and fusion penalization and EM-based
    algorithms for (logistic) lasso  and fused-lasso penalization.
Computation of highest density regions in one and two dimensions,
    kernel estimation of univariate density functions conditional on one covariate,
    and multimodal regression.
Functions for calculating the hazard discrimination summary and its
    standard errors, as described in Liang and Heagerty (2016) <doi:10.1111/biom.12628>.
High dimensional testing procedures on mean, covariance and white noises.
This package implements a iteratively reweighed least square (IRLS) strategy that incorporates a blockwise majorization decent (BMD) method, for efficiently computing the solution paths of the (grouped) lasso and the (grouped) elastic net for the Tweedie model.
A machine learning toolbox tailored to healthcare data.
    Aids in data cleaning, model development, hyperparameter tuning, and model
    deployment in a production SQL environment. Algorithms currently supported
    are Lasso, Random Forest, and Linear Mixed Model.
Timeseries analysis is conducted using Korean mortality and environmental variables
The heatex package calculates heat storage in the body and
        the components of heat exchange (conductive, convective,
        radiative, and evaporative) between the body and the
        environment during physical activity based on the principles of
        partitional calorimetry. The program enables heat exchange
        calculations for a range of environmental conditions when
        wearing various clothing ensembles.
An improved heatmap package. Completely
    compatible with the original R function 'heatmap',
    and provides more powerful and convenient features.
Generates a fit plot for diagnosing misspecification in models of
    binary dependent variables, and calculates the related heatmap fit
    statistic described in Esarey and Pierce (2012) <DOI:10.1093/pan/mps026>.
Create interactive cluster 'heatmaps' that can be saved as a stand-
    alone HTML file, embedded in 'R Markdown' documents or in a 'Shiny' app, and
    available in the 'RStudio' viewer pane. Hover the mouse pointer over a cell to
    show details or drag a rectangle to zoom. A 'heatmap' is a popular graphical
    method for visualizing high-dimensional data, in which a table of numbers
    are encoded as a grid of colored cells. The rows and columns of the matrix
    are ordered to highlight patterns and are often accompanied by 'dendrograms'.
    'Heatmaps' are used in many fields for visualizing observations, correlations,
    missing values patterns, and more. Interactive 'heatmaps' allow the inspection
    of specific value by hovering the mouse over a cell, as well as zooming into
    a region of the 'heatmap' by dragging a rectangle around the relevant area.
    This work is based on the 'ggplot2' and 'plotly.js' engine. It produces
    similar 'heatmaps' as 'heatmap.2' or 'd3heatmap', with the advantage of speed
    ('plotly.js' is able to handle larger size matrix), the ability to zoom from
    the 'dendrogram' panes, and the placing of factor variables in the sides of the
    'heatmap'.
Allows heatmap matrix to have non-identical X- and
        Y-dimensions.  Allows multiple tracks of annotation for
        RowSideColors and ColSideColors.
Functions to perform robust estimation considering heavy-tailed distributions.
  Currently, the package includes routines for linear regression, linear mixed-effect models,
  multivariate location and scatter estimation, multivariate regression, penalized splines
  and random variate generation.
Health Economic Evaluation Modelling:
    decision trees and cohort simulations. Provides a simple
    and consistent interface for Markov models specification,
    comparison, sensitivity and probabilistic analysis, input of
    survival models, etc. Models with time varying properties
    (non-homogeneous Markov models and semi-Markov models)
    are supported.
Calculates Healthy Eating Index (HEI) scores for National Health and Nutrition Examination Survey (NHANES) data sets to facilitate analysis of
    demographic and dietary differences. For more information on the HEI metric, refer to Guenther et al. (2014) <doi:10.3945/jn.113.183079>.
Decode elements of the Australian Higher Education Information Management System (HEIMS) data for clarity and performance. HEIMS is the record system of the Department of Education, Australia to record enrolments and completions in Australia's higher education system, as well as a range of relevant information. For more information, including the source of the data dictionary, see <http://heimshelp.education.gov.au/sites/heimshelp/dictionary/pages/data-element-dictionary>.
Create pretty visualizations of alpha-helical
    peptide sequences in R. The amino acid type (polar, hydrophobic, etc.) is
    denoted by color. This package allows creation of helical wheels, and the
    newer Wenxiang diagrams. Future versions aim to include increased
    customizability of the two existing visualizations, as well as a
    visualization for the helical net diagram.
Base R's default setting for 'stringsAsFactors' within
    'data.frame()' and 'as.data.frame()' is supposedly the most often complained
    about piece of code in the R infrastructure. The 'hellno' package provides
    an explicit solution without changing R itself or having to mess around with
    options. It tries to solve this problem by providing alternative
    'data.frame()' and 'as.data.frame()' functions that are in fact simple
    wrappers around base R's 'data.frame()' and 'as.data.frame()' with
    'stringsAsFactors' option set to 'HELLNO' ( which in turn equals FALSE )
    by default.
A dummy package to demonstrate how to interface to a jar
        file that resides inside an R package.
Contains many functions useful for managing 'NetCDF' files (see <http://en.wikipedia.org/wiki/NetCDF>), get tide levels on any point of the globe, get moon phase and time for sun rise and fall, analyse and reconstruct periodic time series of temperature with irregular sinusoidal pattern, show scales and wind rose in plot with change of color of text, Metropolis-Hastings algorithm for Bayesian MCMC analysis, plot graphs or boxplot with error bars, search files in disk by there names or their content, read the contents of all files from a folder at one time.
Tools for accessing various open data sources in the Helsinki
    region in Finland. Current data sources include
    the Real Estate Department (<http://ptp.hel.fi/avoindata/>),
    Service Map API (<http://api.hel.fi/servicemap/v1/>),
    Linked Events API (<http://api.hel.fi/linkedevents/v0.1/>),
    Helsinki Region Infoshare statistics API (<https://dev.hel.fi/stats/>).
An implementation of Hierarchical Ensemble Methods for DAGs: 'HTD-DAG' (Hierarchical Top Down) and 'TPR-DAG' (True Path Rule). 'HEMDAG' can be used to enhance the predictions of virtually any flat learning method, by taking into account the hierarchical nature of the classes of a bio-ontology. 'HEMDAG' is specifically designed for exploiting the hierarchical relationships of DAG-structured taxonomies, such as the Human Phenotype Ontology (HPO) or the Gene Ontology (GO), but it can be also safely applied to tree-structured taxonomies (as FunCat), since trees are DAGs. 'HEMDAG' scale nicely both in terms of the complexity of the taxonomy and in the cardinality of the examples. (Marco Notaro, Max Schubach, Peter N. Robinson and Giorgio Valentini, Prediction of Human Phenotype Ontology terms by means of Hierarchical Ensemble methods, BMC Bioinformatics 2017).
Provides HE plot and other functions for visualizing hypothesis
    tests in multivariate linear models. HE plots represent sums-of-squares-and-
    products matrices for linear hypotheses and for error using ellipses (in two
    dimensions) and ellipsoids (in three dimensions). The related 'candisc' package
    provides visualizations in a reduced-rank canonical discriminant space when
    there are more than a few response variables.
Constructs paths to your project's files.
    The 'here()' function uses a reasonable heuristics to find your project's
    files, based on the current working directory at the time when the package
    is loaded. Use it as a drop-in replacement for 'file.path()', it will always
    locate the files relative to your project root.
Hierarchical exponential-family random graph models with local dependence.
Implements marker-based estimation of heritability when observations on genetically identical replicates are available. These can be either observations on individual plants or plot-level data in a field trial. Heritability can then be estimated using a mixed model for the individual plant or plot data. For comparison, also mixed-model based estimation using genotypic means and estimation of repeatability with ANOVA are implemented. For illustration the package contains several datasets for the model species Arabidopsis thaliana.
Statistical framework to analyze heritability of gene expression 
    based on next-generation sequencing data and simulating sequencing reads. 
    Variance partition coefficients (VPC) are computed using linear mixed effects 
    and generalized linear mixed effects models. Compound Poisson and negative 
    binomial models are included.
Probability functions and other utilities for the generalized Hermite distribution.
Assess the presence of statistical heterogeneity and quantify its impact in the context of meta-analysis. It includes test for heterogeneity as well as other statistical measures (R_b, I^2, R_I).
Functions for the fitting and summarizing of heteroscedastic t-regression.
An implementation of White's Test for Heteroskedasticity
        as outlined in Doornik (1996).
Implements various heuristics like Take The Best and
    unit-weight linear, which do two-alternative choice: which of
    two objects will have a higher criterion?  Also offers functions
    to assess performance, e.g. percent correct across all row pairs
    in a data set and finding row pairs where models disagree.
    New models can be added by implementing a fit and predict function--
    see vignette.
Binning and plotting functions for hexagonal bins. Now
        uses and relies on grid graphics and formal (S4) classes and
        methods.
Helper functions for creating reproducible hexagon sticker purely
    in R.
Display hexagonally binned scatterplots for multi-class data, using coloured triangles to show class proportions.
Functions to view files in raw binary form like in a hex editor.  Additional functions to specify and read arbitrary binary formats.
A data only package containing commercial domestic flights that
    departed Houston (IAH and HOU) in 2011.
High-dimensional additive models as introduced by Meier,
        van der Geer and Buehlmann (2009).
Implements the hub graphical lasso and hub covariance graph proposal by Tan, KM., London, P., Mohan, K., Lee, S-I., Fazel, M., and Witten, D. (2014). Learning graphical models with hubs. To appear in Journal of Machine Learning Research. arXiv.org/pdf/1402.7349.pdf.
Procedures for fitting hierarchical generalized linear models (HGLM). It can be used for linear mixed models and generalized linear mixed models with random effects for a variety of links and a variety of distributions for both the outcomes and the random effects. Fixed effects can also be fitted in the dispersion part of the mean model.
This data-only package was created for distributing data used in the examples of the hglm package.
The holonomic gradient method (HGM, hgm) gives a way to evaluate normalization
  constants of unnormalized probability distributions by utilizing holonomic 
  systems of differential or difference equations. The holonomic gradient descent (HGD, hgd) gives a method
  to find maximal likelihood estimates by utilizing the HGM.
Contains functions for
 identifying and correcting HGNC gene symbols which have been converted
 to date format by Excel, for reversibly converting between HGNC
 symbols and valid R names, identifying invalid HGNC symbols and
 correcting synonyms and outdated symbols which can be mapped to an
 official symbol.
Support software for Statistical Analysis and Data Display (Second Edition, Springer, ISBN 978-1-4939-2121-8, 2015) and (First Edition, Springer, ISBN 0-387-40270-5, 2004) by Richard M. Heiberger and Burt Holland.  This contemporary presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis.  The second edition includes redesigned graphics and additional chapters. The authors emphasize how to construct and interpret graphs, discuss principles of graphical design, and show how accompanying traditional tabular results are used to confirm the visual impressions derived directly from the graphs. Many of the graphical formats are novel and appear here for the first time in print.  All chapters have exercises.  All functions introduced in the book are in the package.  R code for all examples, both graphs and tables, in the book is included in the scripts directory of the package.
Heller-Heller-Gorfine ('HHG') tests are a set of powerful statistical
    tests of multivariate k-sample homogeneity and independence. For the univariate
    case, the package also offers implementations of the 'MinP DDP' and 'MinP ADP'
    tests, which are consistent against all continuous alternatives but are
    distribution-free, and are thus much faster to apply.
Meyer and Held (2017) <doi:10.1093/biostatistics/kxw051> present an
    age-structured spatio-temporal model for infectious disease counts. The
    approach is illustrated in a case study on norovirus gastroenteritis in
    Berlin, 2011-2015, by age group, city district and week, using additional
    contact data from the POLYMOD survey. This package contains the data and
    code to reproduce the results from the paper, see 'demo("hhh4contacts")'.
Builds on the EMD package to provide additional tools for empirical mode decomposition (EMD) and Hilbert spectral analysis. It also implements the ensemble empirical decomposition (EEMD) and the complete ensemble empirical mode decomposition (CEEMD) methods to avoid mode mixing and intermittency problems found in EMD analysis.  The package comes with several plotting methods that can be used to view intrinsic mode functions, the HHT spectrum, and the Fourier spectrum.
Simulation from distributions supported by nested
        hyperplanes, using the algorithm described in Petris &
        Tardella, "A geometric approach to transdimensional Markov
        chain Monte Carlo", Canadian Journal of Statistics, v.31, n.4,
        (2003).  Also random direction multivariate Adaptive Rejection
        Metropolis Sampling.
Utilising the 'Have I been pwned?' API (see <https://haveibeenpwned.com/API/v2>
    for more information), check whether email addresses and/or user names have been present
    in a publicly disclosed data breach.
Here we propose a model to systematically analyze the roles of architectural proteins and functional elements in blocking long-range contacts between loci. The proposed model does not rely on topologically associating domain (TAD) mapping from Hi-C data. Instead of testing the enrichment or influence of protein binding at TAD borders, the model directly estimates the blocking effect of proteins on long-range contacts between flanking loci, making the model intuitive and biologically meaningful.
We propose a multiple logistic regression model to assess the influences of genomic features such as DNA-binding proteins and functional elements on topological domain borders.
We propose a generalized linear regression with higher-order interaction terms to assess the influences of genomic features such as DNA-binding proteins and functional elements on long-range contacts from Hi-C experiments.
A tool for Hierarchical Climate Regionalization applicable to any correlation-based clustering.
             It adds several features and a new clustering method (called, 'regional' linkage) to hierarchical 
             clustering in R ('hclust' function in 'stats' library): data regridding, coarsening spatial resolution,
             geographic masking (by continents, regions, or countries), data filtering by mean and/or variance 
             thresholds, data preprocessing (detrending, standardization, and PCA), faster correlation function
             with preliminary big data support, different clustering methods, hybrid hierarchical clustering, 
             multi-variate clustering (MVC), cluster validation, and visualization of region maps.
This package allows you to detect domains in HiC data by rephrasing this problem as a two-dimensional segmentation issue.
Computes the ACMIF test and Bonferroni-adjusted p-value of interaction in two-factor studies.  Produces corresponding interaction plot and analysis of variance tables and p-values from several other tests of non-additivity.
Contains functions for the analysis of Discrete Time Hidden Markov Models, Markov Modulated GLMs and the Markov Modulated Poisson Process. It includes functions for simulation, parameter estimation, and the Viterbi algorithm. See the topic "HiddenMarkov" for an introduction to the package, and "Change Log" for a list of recent changes. The algorithms are based of those of Walter Zucchini.
Performs linear discriminant analysis in high dimensional
        problems based on reliable covariance estimators for problems
        with (many) more variables than observations. Includes routines
        for classifier training, prediction, cross-validation and
        variable selection.
Inference of high dimensional max-stable
 distributions, from the paper "Likelihood based inference for
 high-dimensional extreme value distributions", by A. Bienvenüe
 and C. Robert, arXiv:1403.0065 [stat.AP].
Functions for performing hierarchical analysis of distance
    sampling data, with ability to use an areal spatial ICAR model on
    top of user supplied covariates to get at variation in abundance
    intensity.  The detection model can be specified as a function of
    observer and individual covariates, where a parametric model is
    supposed for the population level distribution of covariate values.
    The model uses data augmentation and a reversible jump MCMC
    algorithm to sample animals that were never observed.  Also
    included is the ability to include point independence (increasing
    correlation multiple observer's observations as a function of
    distance, with independence assumed for distance=0 or first
    distance bin), as well as the ability to model species
    misclassification rates using a multinomial logit formulation on data
    from double observers.  New in version 2.1 is the ability to
    include zero inflation, but this is only recommended for cases where
    sample sizes and spatial coverage of the survey are high.
Pure set data visualization approaches are often limited in
    scalability due to the combinatorial explosion of distinct set families as
    the number of sets under investigation increases. hierarchicalSets applies
    a set centric hierarchical clustering of the sets under investigation and
    uses this hierarchy as a basis for a range of scalable visual
    representations. hierarchicalSets is especially well suited for collections
    of sets that describe comparable comparable entities as it relies on the
    sets to have a meaningful relational structure.
Implementation of the convex banding procedure (using a
    hierarchical group lasso penalty) for covariance estimation that is
    introduced in Bien, Bunea, Xiao (2015) Convex Banding of the Covariance
    Matrix. Accepted for publication in JASA.
Hierarchical group-wise partitioning of phenotypic diversity into 
    within-group (alpha), among-group (beta), and pooled-total (gamma) 
    components using Hill numbers.
    Turnover and overlap are also calculated as standardized alternatives to
    beta diversity. Hierarchical bootstrapping is used to approximate 
    uncertainty around each diversity component.
Determine paths and states that social networks develop over
    time to form social hierarchies. Based upon algorithms described in
    W. Brent Lindquist & Ivan D. Chase (2009) <DOI:10.1007/s11538-008-9371-9>.
Allows the estimation of hierarchical F-statistics from haploid or diploid genetic data 
    with any numbers  of levels in the hierarchy, following the algorithm of Yang (Evolution, 1998, 52(4):950-956; 
    <DOI:10.2307/2411227>. Functions are also given to test via randomisations the significance of each F and variance components,  
	using the likelihood-ratio statistics G.
Fits sparse interaction models for continuous and binary responses subject to the strong (or weak) hierarchy restriction that an interaction between two variables only be included if both (or at least one of) the variables is included as a main effect.  For more details, see Bien, J., Taylor, J., Tibshirani, R., (2013) "A Lasso for Hierarchical Interactions." Annals of Statistics. 41(3). 1111-1141.
HierO is a graphical user interface (GUI) tool for calculating optimal statistical power and sample size for hierarchical data structure. HierO constructs a user defined sample size optimization problem to GAMS (General Algebraic Modeling System)form and uses Rneos package to send the problem to NEOS server for solving.
Variance partition of a multivariate data set
Implementation of the convex hierarchical testing (CHT) procedure
    introduced in Bien, Simon, and Tibshirani (2015) Convex Hierarchical Testing
    of Interactions. Annals of Applied Statistics. Vol. 9, No. 1, 27-42.
Uses likelihood to estimate ancestry and heterozygosity.
        Evaluates simple hybrid classifications (parentals, F1, F2,
        backcrosses). Estimates genomic clines.
A wrapper for the 'Highcharts' library including
  shortcut functions to plot R objects. 'Highcharts' 
  <http://www.highcharts.com/> is a charting library offering
  numerous chart types with a simple configuration syntax.
Performs the generalized component test from Gregory et al (2015), as well as the tests from Chen and Qin (2010), Srivastava and Kubokawa (2013), and Cai, Liu, and Xia (2014) for equality of two population mean vectors when the length of the vectors exceeds the sample size.
Three high-dimensional outlier detection algorithms and a outlier unification scheme are implemented in this package. The angle-based outlier detection (ABOD) algorithm is based on the work of Kriegel, Schubert, and Zimek [2008]. The subspace outlier detection (SOD) algorithm is based on the work of Kriegel, Kroger, Schubert, and Zimek [2009]. The feature bagging-based outlier detection (FBOD) algorithm is based on the work of Lazarevic and Kumar [2005]. The outlier unification scheme is based on the work of Kriegel, Kroger, Schubert, and Zimek [2011].
Provide functionality to manage, clean and match highfrequency
    trades and quotes data, calculate various liquidity measures, estimate and
    forecast volatility, and investigate microstructure noise and intraday
    periodicity.
Syntax highlighter for R code based 
	on the results of the R parser. Rendering in HTML and latex 
	markup. Custom Sweave driver performing syntax highlighting 
	of R code chunks.
A tool to highlight specific cells in an HTML table or more 
    generally text from an HTML document. This may be helpful for those 
    using markdown to create reproducible documents. In addition, the ability
    to compile directly from R markdown files is also possible using the 'knitr' 
    package.
Provides various tests for comparing high-dimensional mean vectors in two sample populations.
Provides syntax highlighting for R source code. Currently it
    supports LaTeX and HTML output. Source code of other languages is supported
    via Andre Simon's highlight package (http://www.andre-simon.de).
Functions for determining and evaluating high-risk zones and
    simulating and thinning point process data, as described in 'Determining
    high risk zones using point process methodology - Realization by building
    an R package' Seibold (2012) <http://highriskzone.r-forge.r-project.org/Bachelorarbeit.pdf> 
    and 'Determining high-risk zones for unexploded World War II bombs by using point 
    process methodology', Mahling et al. (2013) <doi:10.1111/j.1467-9876.2012.01055.x>.
Can be used to carry out extraction, normalization, quality control (QC), candidate hits identification and visualization for plate based essays, in drug discovery. This project was funded by the Division of Allergy, Immunology, and Transplantation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under contract No. HHSN272201400054C entitled "Adjuvant Discovery For Vaccines Against West Nile Virus and Influenza", awarded to Duke University and lead by Drs. Herman Staats and Soman Abraham.
Implements the method developed by Cao and Kosorok (2011) for the significance analysis of thousands of features in high-dimensional biological studies. It is an asymptotically valid data-driven procedure to find critical values for rejection regions controlling the k-familywise error rate, false discovery rate, and the tail probability of false discovery proportion.
Generate occupancy patterns based on arrival and departure timestamps
Estimation of heritability with confidence intervals in linear mixed models.
H(x) is the h-index for the past x years. Here, the h(x) of a scientist/department/etc. can be calculated using the exported excel file from a Web of Science citation report of a search. Also calculated is the year of first publication, total number of publications, and sum of times cited for the specified period. Therefore, for h-10: the date of first publication, total number of publications, and sum of times cited in the past 10 years are calculated. Note: the excel file has to first be saved in a .csv format.
Hypergeometric Intersection distributions are a broad group of distributions that describe the probability of picking intersections when drawing independently from two (or more) urns containing variable numbers of balls belonging to the same n categories.
Based on hierarchical modeling, this package provides a
        few practical functions to find and present the optimal designs
        for a pooled NGS design.
Implementation of a class of hierarchical item response
  theory (IRT) models where both the mean and the variance of latent preferences
  (ability parameters) may depend on observed covariates. The current
  implementation includes both the two-parameter latent trait model for binary data and the
  graded response model for ordinal data. Both are fitted via the Expectation-Maximization (EM)
  algorithm. Asymptotic standard errors are derived from the observed information
  matrix.
Methods for hierarchical semiparametric regression models for test statistics are implemented in this package. Specifically, test statistics given the null/alternative hypotheses are modeled parametrically, whereas the unobservable status of null/alternative hypotheses are modeled using nonparametric additive logistic regression over covariates.
Sets up and executes a HiSSE model (Hidden State Speciation and Extinction) on a phylogeny and character sets to test for hidden shifts in trait dependent rates of diversification.
The 'HistData' package provides a collection of small data sets
    that are interesting and important in the history of statistics and data
    visualization. The goal of the package is to make these available, both for
    instructional use and for historical research. Some of these present interesting
    challenges for graphics or analysis in R.
In the framework of Symbolic Data Analysis, a relatively new
    approach to the statistical analysis of multi-valued data, we consider
    histogram-valued data, i.e., data described by univariate histograms. The
    methods and the basic statistics for histogram-valued data are mainly based
    on the L2 Wasserstein metric between distributions, i.e., a Euclidean metric
    between quantile functions. The package contains unsupervised classification
    techniques, least square regression and tools for histogram-valued data and for
    histogram time series.
Using the MDL principle, it is possible to estimate
	parameters for a histogram-like model. The package contains
	the implementation of such an estimation method.
Automatic construction of regular and irregular histograms as described in Rozenholc/Mildenberger/Gather (2010).
Provides a number of utility functions useful for manipulating large histograms.  This includes methods to trim, subset, merge buckets, merge histograms, convert to CDF, and calculate information loss due to binning.  It also provides a protocol buffer representations of the default R histogram class to allow histograms over large data sets to be computed and manipulated in a MapReduce environment.
These sample data sets are intended for historians
    learning R. They include population, institutional, religious,
    military, and prosopographical data suitable for mapping,
    quantitative analysis, and network analysis.
Hierarchical inference testing (HIT) for (generalized) linear models with 
    correlated covariates applicable to high-dimensional settings.
The "Hit and Run" Markov Chain Monte Carlo method for sampling uniformly from convex shapes defined by linear constraints, and the "Shake and Bake" method for sampling from the boundary of such shapes. Includes specialized functions for sampling normalized weights with arbitrary linear constraints.
Hadoop InteractiVE facilitates distributed 
             computing via the MapReduce paradigm through R and Hadoop. An easy to use 
             interface to Hadoop, the Hadoop Distributed File System (HDFS), 
	     and Hadoop Streaming is provided.
Creates and plots 2D and 3D hive plots. Hive plots are a unique method of displaying networks of many types in which node properties are mapped to axes using meaningful properties rather than being arbitrarily positioned.  The hive plot concept was invented by Martin Krzywinski at the Genome Science Center (www.hiveplot.net/).  Keywords: networks, food webs, linnet, systems biology, bioinformatics.
The functions in this package produce a complete set of mortality rates as a function of a combination of HIV prevalence and either life expectancy at birth (e0), child mortality (5q0), or child mortality with adult mortality (45q15)
This is a collection of functions for converting coordinates between WGS84UTM, WGS84GEO, HK80UTM, HK80GEO and HK1980GRID Coordinate Systems used in Hong Kong SAR, based on the algorithms described in Explanatory Notes on Geodetic Datums in Hong Kong by Survey and Mapping Office Lands Department, Hong Kong Government (1995).
Implements an ensemble algorithm for clustering combining a k-means and a hierarchical clustering approach.
Several procedures around a particular hierarchical model for extreme value: the HKEVP of Reich and Shaby (2012) <DOI:10.1214/12-AOAS591>. Simulation, estimation and spatial extrapolation of this model are available for extreme value data. A special case of this process is also handled: the Latent Variable Model of Davison et al. (2012) <DOI:10.1214/11-STS376>.
A set of functions helps to retrieve data from HKEX (Hong Kong Stock Exchange), see <https://www.hkex.com.hk/> for more information. In addition, a function generates insert SQL statements from a dataframe.
Methods to make inference about the Hurst-Kolmogorov and the AR(1) process.
A suite of diagnostic tools for hierarchical
    (multilevel) linear models. The tools include
    not only leverage and traditional deletion diagnostics (Cook's
    distance, covratio, covtrace, and MDFFITS) but also 
    convenience functions and graphics for residual analysis. Models
    can be fit using either lmer in the 'lme4' package or lme in the 'nlme' package,
    but only two-level models fit using lme are currently supported.
Implements Hierarchical Latent Space Network Model (HLSM) for ensemble of networks as described in Sweet et. al. (2012). <DOI:10.3102/1076998612458702>.
Utilities for reading data from the Human Mortality Database (<www.mortality.org>), Human Fertility Database (<www.humanfertility.org>), and similar databases from the web or locally into an R session as data.frame objects. These are the two most widely used sources of demographic data to study basic demographic change, trends, and develop new demographic methods. Other supported databases at this time include the Human Fertility Collection (<http://www.fertilitydata.org/>), The Japanese Mortality Database (<http://www.ipss.go.jp/p-toukei/JMD>), and the Canadian Human Mortality Database (<http://www.bdlc.umontreal.ca/chmd/>). Arguments and data are standardized.
Scalar performance metrics, including the H-measure, based
        on classification scores for several classifiers applied to the
        same dataset.
Runs single level and multilevel imputation models. The user just has to pass the data to the main function and, optionally, his analysis model. Basically the package then translates this analysis model into commands to impute the data according to it with functions from 'mice', 'MCMCglmm' or routines build for this package.
Contains many functions useful for data
	analysis, high-level graphics, utility operations, functions for
	computing sample size and power, importing and annotating datasets,
	imputing missing values, advanced table making, variable clustering,
	character string manipulation, conversion of R objects to LaTeX and html code,
	and recoding variables.
Easy to use library to setup, apply and make inference
        with discrete time and discrete space Hidden Markov Models
The package includes the functions designed to analyse continuous observations processes with the Hidden Markov Model approach. They include Baum-Welch and Viterbi algorithms and additional visualisation functions. The observations are assumed to have Gaussian distribution and to be weakly stationary processes. The package was created for analyses of financial time series, but can also be applied to any continuous observations processes.
Fits hidden Markov models with discrete non-parametric 
        observation distributions to data sets.  Simulates data
	from such models.  Finds most probable underlying hidden
	states, the most probable sequences of such states, and the
	log likelihood of a collection of observations given the
	parameters of the model.
Functions for specifying and fitting marginal models for contingency tables proposed 
	by Bergsma and Rudas (2002) here called hierarchical multinomial marginal models (hmmm) and their extensions presented by Bartolucci et al. 
	(2007); multinomial Poisson homogeneous (mph) models and homogeneous linear predictor (hlp) models for contingency
 	tables proposed by Lang (2004) and (2005); hidden Markov models where the distribution of the observed variables 
	is described by a marginal model. 
	Inequality constraints on the parameters are allowed and can be tested.
Analysing time-series accelerometer data to quantify length and intensity of physical activity.
Using Dirichlet-Multinomial distribution to provide several functions for formal hypothesis testing, power and sample size calculations for human microbiome experiments.
Tools to model, compare, and visualize populations of taxonomic tree objects.
Statistical analysis of static chamber concentration data for trace gas flux estimation.
Implements an S3 class for storing and formatting time-of-day
    values, based on the 'difftime' class.
Perform association test between a group of variable and the outcome.
Hierarchical non-negative matrix factorization
	for tumor segmentation based on multi-parametric MRI data. 
	Several NMF algorithms are available.
Generates (half-)normal plots with simulation envelopes using different diagnostics from a range of different fitted models. A few example datasets are included.
Performs likelihood-based inference for a wide range of regression models. Provides higher-order approximations for inference based on extensions  of saddlepoint type arguments as discussed in the book Applied Asymptotics: Case Studies in Small-Sample Statistics by Brazzale, Davison, and Reid (2007).
Cross-species identification of novel gene candidates using the NCBI web service is provided. Further, sets of miRNA target genes can be identified by using the targetscan.org API.
Suite of tools for managing cached files, targeting
    use in other R packages. Uses 'rappdirs' for cross-platform paths.
    Provides utilities to manage cache directories, including targeting
    files by path or by key; cached directories can be compressed and
    uncompressed easily to save disk space.
Simulates hands and tournaments of Texas Holdem, 
	the most popular form of poker.
	For examples of probability problems involving Texas Holdem 
	and a brief explanation of the game see Schoenberg, F. (2011).
	An Introduction to Probability with Texas Hold'em Examples.
	Taylor and Francis, New York, ISBN-13: 978-1439827680.
Contains trading holiday and half-day data that is automatically registered when loaded.
Performs a homogeneity analysis (multiple correspondence analysis) and various extensions. Rank restrictions on the category quantifications can be imposed (nonlinear PCA). The categories are transformed by means of optimal scaling with options for nominal, ordinal, and numerical scale levels (for rank-1 restrictions). Variables can be grouped into sets, in order to emulate regression analysis and canonical correlation analysis.
A collection of functions useful for the analysis of
    building physics experiments.
A simple implementation of doughnut plots - pie charts with a blank center. The package is named after Homer Simpson - arguably the best-known lover of doughnuts.
Provides methods for closed testing using Simes local tests. In particular, calculates adjusted p-values for Hommel's multiple testing method, and provides lower confidence bounds for true discovery proportions. A robust but more conservative variant of the closed testing procedure that does not require the assumption of Simes inequality is also implemented. The methods have been described in detail in Meijer et al (2016) <arXiv:1611.06739>.
Homomorphic computations in R for privacy-preserving applications. Currently only
             the Paillier Scheme is implemented.
A theoretical model to simulate  radical polymerization. Material, energy and population balances are integrated for batch, semi batch and continuous process in a ideally mixed reactor. Limitations: single monomer (i.e.homo polymer), one phase (organic, aqueous). Datasets with chemical-physical data for the most common monomers is included. Some background in polymer science is suggested for its use. Graphical interface for a quick and friendly use is available.
A collection of homogeneity tests described in: Viglione
        A., Laio F., Claps P. (2007) ``A comparison of homogeneity
        tests for regional frequency analysis'', Water Resources
        Research, 43, W03428, doi:10.1029/2006WR005095.  More on
        Regional Frequency Analysis can be found in package nsRFA.
Computes the expectation of the number of transmissions and receptions considering a Hop-by-Hop transport model with limited number of retransmissions per packet. It provides the theoretical results shown in Palma et. al.(2016) <DOI:10.1109/TLA.2016.7555237> and also estimated values based on Monte Carlo simulations.
Calculates horizon elevation angle and sky view factor from a digital terrain model.
Supplement for the book "Handbook of Regression Methods" by D. S. Young.  Some datasets used in the book are included and documented.  Wrapper functions are included that simplify the examples in the textbook, such as code for constructing a regressogram and expanding ANOVA tables to reflect the total sum of squares.
A stand-alone function that generates a user specified number of random datasets and computes eigenvalues using the random datasets (i.e., implements Horn's [1965, Psychometrika] parallel analysis). Users then compare the resulting eigenvalues (the mean or the specified percentile) from the random datasets (i.e., eigenvalues resulting from noise) to the eigenvalues generated with the user's data. Can be used for both principal components analysis (PCA) and common/exploratory factor analysis (EFA). The output table shows how large eigenvalues can be as a result of merely using randomly generated datasets. If the user's own dataset has  actual eigenvalues greater than the corresponding eigenvalues, that lends support to retain that factor/component. In other words, if the i(th) eigenvalue from the actual data was larger than the percentile of the (i)th eigenvalue generated using randomly generated data, empirical support is provided to retain that factor/component. 
    Horn, J. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 32, 179-185.
Implementation of the HorseRule model a flexible tree based Bayesian regression method for linear and nonlinear regression and classification described in Nalenz & Villani (2017) <arXiv:1702.05008>.
Contains functions for applying the horseshoe prior to high-
    dimensional linear regression, yielding the posterior mean and credible
    intervals, amongst other things. The key parameter tau can be equipped with
    a prior or estimated via maximum marginal likelihood estimation (MMLE).
    The main function, horseshoe, is for linear regression. In addition, there
    are functions specifically for the sparse normal means problem, allowing
    for faster computation of for example the posterior mean and posterior
    variance. Finally, there is a function available to perform variable
    selection, using either a form of thresholding, or credible intervals.
Performs multiple hot-deck imputation of categorical and continuous variables in a data frame.
Hot deck imputation methods to resolve missing data.
A set of R functions which implements Hotelling's T^2 test and some variants of it. Functions are also included for Aitchison's additive log ratio and centred log ratio transformations.
Contains data for software hotspot analysis, along with a function performing the analysis itself.
Calculates a hot spot cutoff and associated analyses for
        statistical populations
Monthly median home listing, sale price per square foot, and number of units sold for 2984 counties in the contiguous United States From 2008 to January 2016.  Additional data sets containing geographical information and links to Wikipedia are also included.
When testing multiple hypotheses simultaneously, this
        package provides functionality to calculate a lower bound for
        the number of correct rejections (as a function of the number
        of rejected hypotheses), which holds simultaneously -with high
        probability- for all possible number of rejections.  As a
        special case, a lower bound for the total number of false null
        hypotheses can be inferred.  Dependent test statistics can be
        handled for multiple tests of associations. For independent
        test statistics, it is sufficient to provide a list of
        p-values.
This package provides all the functions necessary to
        estimate the 8 parameters of the Heligman Pollard mortality
        model using a Bayesian Melding procedure with IMIS as well as
        to convert those parameters into age-specifc probabilities of
        death and a corresponding life table
Probabilistic models describing the behavior 
	of workload and queue on a High Performance Cluster and computing GRID 
	under FIFO service discipline basing on modified Kiefer-Wolfowitz 
	recursion. Also sample data for inter-arrival times, service times, 
	number of cores per task and waiting times of HPC of Karelian 
	Research Centre are included, measurements took place from 06/03/2009 to 02/30/2011.
	Functions provided to import/export workload traces in Standard Workload Format (swf).
	Stability condition of the model may be verified either exactly, or approximately.
Collection of functions for manipulating sets of HPO terms and
    plotting them with a various options.
This package contains some miscellaneous convenience functions, 
	to create a matrix of dummy columns from a factor, 
	to determine whether x lies in range [a,b], 
	to add a rectangular bracket to an existing plot, 
	and to convert frequencies between Hz, semitones, mel and Bark. 
	This package also contains an example data set of a stratified sample
	of 80 talkers of Dutch.
Efficient algorithms for fitting regularization paths for lasso or elastic-net penalized regression models with Huber loss, quantile loss or squared loss.
A compilation of extra 'ggplot2' themes, scales and utilities, including a 
    spell check function plot label fields and an overall emphasis on typography. 
    A copy of the 'Google' font 'Roboto Condensed' <https://github.com/google/roboto/> 
    is also included to support one of the typography-oriented themes.
Methods for testing main and interaction effects in possibly
    high-dimensional repeated measures in factorial designs. The observations
    of the subjects are assumed to be multivariate normal.
    It is possible to use up to 2 whole- and 3 subplot factors.
Offers tools and modelling approaches for binomial data with overdispersion, with particular interest in Health Related Quality of Life (HRQoL) questionnaires regression analysis.
Print beautiful horizontal rules in your R scripts.
A library of the Hierarchical Spatial Autoregressive Model (HSAR), based on a Bayesian Markov Chain Monte Carlo (MCMC) algorithm.
Functions, data sets, analyses and examples from the book 
  ''A Handbook of Statistical Analyses Using R'' (Brian S. Everitt and Torsten
  Hothorn, Chapman & Hall/CRC, 2006). The first chapter
  of the book, which is entitled ''An Introduction to R'', 
  is completely included in this package, for all other chapters,
  a vignette containing all data analyses is available.
Functions, data sets, analyses and examples from the 
  second edition of the book 
  ''A Handbook of Statistical Analyses Using R'' (Brian S. Everitt and Torsten
  Hothorn, Chapman & Hall/CRC, 2008). The first chapter
  of the book, which is entitled ''An Introduction to R'', 
  is completely included in this package, for all other chapters,
  a vignette containing all data analyses is available. In addition,
  the package contains Sweave code for producing slides for selected
  chapters (see HSAUR2/inst/slides).
Functions, data sets, analyses and examples from the 
  third edition of the book 
  ''A Handbook of Statistical Analyses Using R'' (Torsten Hothorn and Brian S.
  Everitt, Chapman & Hall/CRC, 2014). The first chapter
  of the book, which is entitled ''An Introduction to R'', 
  is completely included in this package, for all other chapters,
  a vignette containing all data analyses is available. In addition,
  Sweave source code for slides of selected chapters is included in 
  this package (see HSAUR3/inst/slides). The publishers web page is 
  '<http://www.crcpress.com/product/isbn/9781482204582>'.
hSDM is an R package for estimating parameters of hierarchical Bayesian species distribution models. Such models allow interpreting the observations (occurrence and abundance of a species) as a result of several hierarchical processes including ecological processes (habitat suitability, spatial dependence and anthropogenic disturbance) and observation processes (species detectability). Hierarchical species distribution models are essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results.
Canonical correlation analysis that extracts nonlinear
        correlation through the use of Hilbert Schmidt Independence
        Criterion and Centered Kernel Target Alignment.
Implementation of the block coordinate descent procedure for
    solving the proximal function of latent group Lasso, highlighted by
    decomposing a DAG into several non-overlapping path graphs, and getting
    closed-form solution for each path graph. The procedure was introduced
    as Algorithm 4 in Yan and Bien (2015) <https://arxiv.org/abs/1512.01631>
    "Hierarchical Sparse Modeling: A Choice of Two Regularizers", and the
    closed-form solution for each path graph is solved in Algorithm 3 of
    that paper.
A package for computation of hidden semi markov models
Identification of recombination events, haplotype reconstruction, sire imputation and pedigree reconstruction using half-sib family SNP data
Implements a model for joint meta-analysis of sensitivity and specificity of the diagnostic test under evaluation, while taking into account the possibly imperfect sensitivity and specificity of the reference test. This hierarchical model accounts for both within and between study variability. Estimation is carried out using a Bayesian approach, implemented via a Gibbs sampler. The model can be applied in situations where more than one reference test is used in the selected studies.
A data mining tool for discovering subgroups of patients and genes that simultaneously display unusual levels of variability compared to other genes and patients. Based on sparse singular value decomposition (SSVD), the method can detect both mean and variance biclusters in the presence of heterogeneous residual variance.
Provides bindings to the National Geodetic Survey (NGS) Horizontal
 Time Dependent Positioning (HTDP) utility, v3.2.5, written by Richard Snay,
 Chris Pearson, and Jarir Saleh of NGS. HTDP is a utility that allows users to
 transform positional coordinates across time and between spatial reference
 frames. See <https://www.ngs.noaa.gov/TOOLS/Htdp/Htdp.shtml> for more
 information.
HTML tables are a valuable data source but extracting and recasting
    these data into a useful format can be tedious. This package allows to collect
    structured information from HTML tables. It is similar to readHTMLTable()
    of the XML package but provides three major advantages. First, the function
    automatically expands row and column spans in the header and body cells.
    Second, users are given more control over the identification of header and body
    rows which will end up in the R table, including semantic header information
    that appear throughout the body. Third, the function preprocesses table code,
    corrects common types of malformations, removes unneeded parts and so helps to
    alleviate the need for tedious post-processing.
Tables with state-of-the-art layout elements such as row spanners,
    column spanners, table spanners, zebra striping, and more. While allowing
    advanced layout, the underlying css-structure is simple in order to maximize
    compatibility with word processors such as 'MS Word' or 'LibreOffice'. The package
    also contains a few text formatting functions that help outputting text
    compatible with HTML/'LaTeX'.
HTML documents can be beautiful and pristine. They can also be
    wretched, evil, malformed demon-spawn. Now, you can tidy up that HTML and XHTML
    before processing it with your favorite angle-bracket crunching tools, going beyond
    the limited tidying that 'libxml2' affords in the 'XML' and 'xml2' packages and
    taming even the ugliest HTML code generated by the likes of Google Docs and Microsoft
    Word. It's also possible to use the functions provided to format or "pretty print"
    HTML content as it is being tidied. Utilities are also included that make it  
    possible to view formatted and "pretty printed" HTML/XML
    content from HTML/XML document objects, nodes, node sets and plain character HTML/XML
    using 'vkbeautify' (by Vadim Kiryukhin) and 'highlight.js' (by Ivan Sagalaev).
    Also (optionally) enables filtering of nodes via XPath or viewing an HTML/XML document
    in "tree" view using 'xml-viewer' (by Julian Gruber). See
    <https://github.com/vkiryukhin/vkBeautify> and 
    <https://github.com/juliangruber/xml-viewer> for more information about 'vkbeautify'
    and 'xml-viewer', respectively.
Facilitates automated HTML report creation, in particular
        framed HTML pages and dynamically sortable tables.
A framework for creating HTML widgets that render in various
    contexts including the R console, 'R Markdown' documents, and 'Shiny'
    web applications.
Historical regression trees are an extension of standard trees, 
	producing a non-parametric estimate of how the response depends on 
	all of its prior realizations as well as that of any time-varying predictor 
	variables. The method applies equally to regularly as well as irregularly 
	sampled data. The package implements random forest and boosting ensembles 
	based on historical regression trees, suitable for longitudinal data.
Methods for analysing and forecasting hierarchical and grouped time
    series.
A Poisson mixture model is implemented to cluster genes from high-
    throughput transcriptome sequencing (RNA-seq) data. Parameter estimation is
    performed using either the EM or CEM algorithm, and the slope heuristics are
    used for model selection (i.e., to choose the number of clusters).
Functions for analyzing high throughput sequencing 
    stable isotope probing (HTS-SIP) data.
    Analyses include high resolution stable isotope probing (HR-SIP),
    multi-window high resolution stable isotope probing (MW-HR-SIP), 
    and quantitative stable isotope probing (q-SIP).
Functions and data tables for simulation and statistical analysis of chemical toxicokinetics ("TK") using data obtained from relatively high throughput, in vitro studies. Both physiologically-based ("PBTK") and empirical (e.g., one compartment) "TK" models can be parameterized for several hundred chemicals and multiple species. These models are solved efficiently, often using compiled (C-based) code. A Monte Carlo sampler is included for simulating biological variability and measurement limitations. Functions are also provided for exporting "PBTK" models to "SBML" and "JARNAC" for use with other simulation software. These functions and data provide a set of tools for in vitro-in vivo extrapolation ("IVIVE") of high throughput screening data (e.g., ToxCast) to real-world exposures via reverse dosimetry (also known as "RTK").
In order to improve performance for HTTP API clients, 'httpcache'
    provides simple tools for caching and invalidating cache. It includes the
    HTTP verb functions GET, PUT, PATCH, POST, and DELETE, which are drop-in
    replacements for those in the 'httr' package. These functions are cache-aware
    and provide default settings for cache invalidation suitable for RESTful
    APIs; the package also enables custom cache-management strategies.
    Finally, 'httpcache' includes a basic logging framework to facilitate the
    measurement of HTTP request time and cache performance.
Find and explain the meaning of 'HTTP' status codes.
    Functions included for searching for codes by full or partial number,
    by message, and get appropriate dog and cat images for many
    status codes.
A suite of functions to ping 'URLs' and to time
    'HTTP' 'requests'. Designed to work with 'httr'.
HTTP Request protocols. Implements the GET, POST and multipart POST request.
Testing code and packages that communicate with remote servers can
    be painful. Dealing with authentication, bootstrapping server state,
    cleaning up objects that may get created during the test run, network
    flakiness, and other complications can make testing seem too costly to
    bother with. But it doesn't need to be that hard. This package enables one
    to test all of the logic on the R sides of the API in your package without
    requiring access to the remote service. Importantly, it provides three test
    contexts that mock the network connection in different ways, and it offers
    additional expectations to assert that HTTP requests were--or were
    not--made. Using these tools, one can test that code is making the intended
    requests and that it handles the expected responses correctly, all without
    depending on a connection to a remote API.
Provides low-level socket and protocol support for handling
    HTTP and WebSocket requests directly from within R. It is primarily
    intended as a building block for other packages, rather than making it
    particularly easy to create complete web applications using httpuv alone.
    httpuv is built on top of the libuv and http-parser C libraries, both of
    which were developed by Joyent, Inc. (See LICENSE file for libuv and
    http-parser license information.)
Useful tools for working with HTTP organised by HTTP verbs
    (GET(), POST(), etc). Configuration functions make it easy to control
    additional request components (authenticate(), add_headers() and so on).
Provides a general framework for
        high-dimensional undirected graph estimation. It integrates
        data preprocessing, neighborhood screening, graph estimation,
        and model selection techniques into a pipeline. In
        preprocessing stage, the nonparanormal(npn) transformation is
        applied to help relax the normality assumption. In the graph
        estimation stage, the graph structure is estimated by
        Meinshausen-Buhlmann graph estimation or the graphical lasso,
        and both methods can be further accelerated by the lossy
        screening rule preselecting the neighborhood of each variable
        by correlation thresholding. We target on high-dimensional data
        analysis usually d >> n, and the computation is
        memory-optimized using the sparse matrix output. We also
        provide a computationally efficient approach, correlation
        thresholding graph estimation. Three
        regularization/thresholding parameter selection methods are
        included in this package: (1)stability approach for
        regularization selection (2) rotation information criterion (3)
        extended Bayesian information criterion which is only available
        for the graphical lasso.
Tools for computing HUM (Hypervolume Under the Manifold) value to estimate features ability
             to discriminate the class labels, visualizing the ROC curve for two or three class labels.
Format quantities of time or bytes into human-friendly strings.
Human names are complicated and nonstandard things. Humaniformat,
    which is based on Anthony Ettinger's 'humanparser' project (https://github.com/
    chovy/humanparser) provides functions for parsing human names, making a best-
    guess attempt to distinguish sub-components such as prefixes, suffixes, middle
    names and salutations.
Utilises GRanges, data.frame or IRanges objects. Integrates gene annotation for ImmunoChip (or your custom chip) with function calls. Intuitive wrappers for annotation lookup (gene lists, exon ranges, etc) and conversion (e.g, between build 36 and 37 coordinates). Conversion between ensembl and HGNC gene ids, chip ids to rs-ids for SNP-arrays. Retrieval of chromosome and position for gene, band or SNP-ids, or reverse lookup. Simulation functions for ranges objects.
Vapor pressure, absolute humidity, specific humidity, and mixing ratio are commonly used water vapor measures in meteorology. This R package provides functions for calculating saturation vapor pressure (hPa), partial water vapor pressure (Pa), absolute humidity (kg/m^3), specific humidity (kg/kg), and mixing ratio (kg/kg) from temperature (K) and relative humidity (%).
A spell checker and morphological analyzer library designed for
    languages with rich morphology and complex word compounding or character
    encoding. The package can check and analyze individual words as well as
    search for incorrect words within a text, latex, html or xml document. Use
    the 'devtools' package to spell check R documentation with 'hunspell'.
Scraped dataset of the Hurricane Research Division's Hurricane 
    Re-Analysis Project known as HURDAT. Storm details are available for most 
    known hurricanes and tropical storms for the Atlantic and northeastern 
    Pacific ocean (northwestern hemisphere). See <http://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html> 
    for more information.
When considering count data, it is often the case that many more zero counts than would be expected of some given distribution are observed. It is well established that data such as this can be reliably modelled using zero-inflated or hurdle distributions, both of which may be applied using the functions in this package. Bayesian analysis methods are used to best model problematic count data that cannot be fit to any typical distribution. The package functions are flexible and versatile, and can be applied to varying count distributions, parameter estimation with or without explanatory variable information, and are able to allow for multiple hurdles as it is also not uncommon that count data have an abundance of large-number observations which would be considered outliers of the typical distribution. In lieu of throwing out data or misspecifying the typical distribution, these extreme observations can be applied to a second, extreme distribution. With the given functions of this package, such a two-hurdle model may be easily specified in order to best manage data that is both zero-inflated and over-dispersed.
Allows users to create time series of tropical storm
    exposure histories for chosen counties for a number of hazard metrics (wind, 
    rain, distance from the storm, etc.). This package interacts with data available 
    through the 'hurricaneexposuredata' package, which is available in a 'drat'
    repository. To access this data package, run 'install.packages("hurricaneexposuredata", 
    repos = "https://geanders.github.io/drat/", type = "source")'. The size of the 
    'hurricaneexposuredata' package is approximately 25 MB. This work was supported in 
    part by grants from the National Institute of Environmental Health Sciences 
    (R00ES022631), the National Science Foundation (1331399), and a NASA Applied 
    Sciences Program/Public Health Program Grant (NNX09AV81G).
Provides utility functions for, and drawing on, the 'data.table' package. The package also collates useful miscellaneous functions extending base R not available elsewhere. The name is a portmanteau of 'utils' and the author.
Creates HTML and LaTeX tables. Provides similar 
  functionality to 'xtable', but does more, with a simpler interface.
Fits models for genotypic disequilibria, as described in
  Huttley and Wilson (2000), Weir (1996) and Weir and Wilson (1986).
  Contrast terms are available that account for first order interactions
  between loci.  Also implements, for a single locus in a single
  population, a conditional exact test for Hardy-Weinberg equilibrium.
Estimation and testing of HWE using Bayesian methods.
        Three models are currently considered: HWE, a model
        parameterized in terms of the allele frequencies and a single
        inbreeding coefficient f, and the saturated model. Testing is
        based on Bayes factors.
General (multi-allelic) Hardy-Weinberg equilibrium problem from an objective Bayesian testing standpoint. This aim is achieved through the identification of a class of priors specifically designed for this testing problem. A class of intrinsic priors under the full model is considered. This class is indexed by a tuning quantity, the training sample size, as discussed in Consonni, Moreno and Venturini (2010). These priors are objective, satisfy Savage's continuity condition and have proved to behave extremely well for many statistical testing problems.
HW.pval calculates plain and fully conditional
        root-mean-square, chi-square, and log likelihood-ratio P-values
        for the user-provided genotypic counts to be consistent with
        the Hardy-Weinberg equilibrium model.  For further information
        on the Hardy-Weinberg equilibrium model and the pseudocode,
        refer to the paper "Testing Hardy-Weinberg equilibrium with a
        simple root-mean-square statistic" by Rachel Ward.
Easy-to-use and versatile functions to output R objects in
        HTML format
Provides methods to test whether time series is consistent
	with white noise.
Tests whether a set of genotype counts fits the HW expectations.
    Exact tests performed by an efficient algorithm. Included test statistics
    are likelihood ratio, probability, U-score and Pearson's X2.
Functions to build and deploy a hybrid ensemble consisting of eight different sub-ensembles: bagged logistic regressions, random forest, stochastic boosting, kernel factory, bagged neural networks, bagged support vector machines, rotation forest, and bagged k-nearest neighbors. Functions to cross-validate the hybrid ensemble and plot and summarize the results are also provided. There is also a function to assess the importance of the predictors.
Hybrid hierarchical clustering via mutual clusters.  A mutual cluster is a set of points closer to each other than to all other points.  Mutual clusters are used to enrich top-down hierarchical clustering.
This package is an R implementation of the Hybrid Monte
        Carlo and Multipoint Hybrid Monte Carlo sampling techniques
        described in Liu (2001): "Monte Carlo Strategies in Computing".
Simulates stochastic hybrid models for transmission of infectious
    diseases in dynamic networks.
Facilities for easy implementation of hybrid Bayesian networks
    using R. Bayesian networks are directed acyclic graphs representing joint
    probability distributions, where each node represents a random variable and
    each edge represents conditionality. The full joint distribution is therefore
    factorized as a product of conditional densities, where each node is assumed
    to be independent of its non-descendents given information on its parent nodes.
    Since exact, closed-form algorithms are computationally burdensome for inference
    within hybrid networks that contain a combination of continuous and discrete
    nodes, particle-based approximation techniques like Markov Chain Monte Carlo
    are popular. We provide a user-friendly interface to constructing these networks
    and running inference using the 'rjags' package. Econometric analyses (maximum
    expected utility under competing policies, value of information) involving
    decision and utility nodes are also supported.
Package providing tools for hydrological applications and models developed for regional analysis in Northwestern Italy.
Contains one function for drawing Piper diagrams (also
    called Piper-Hill diagrams) of water analyses for major ions.
S3 functions implementing both statistical and graphical goodness-of-fit measures between observed and simulated values, mainly oriented to be used during the calibration, validation, and application of hydrological models. Missing values in observed and/or simulated values can be removed before computations. Comments / questions / collaboration of any kind are very welcomed.
This package is version 2 of HydroMe v.1 package. It
        estimates the parameters in infiltration and water retention
        models by curve-fitting method. The models considered are those
        that are commonly used in soil science. It has new models for
        water retention characteristic curve and debugging of errors in
        HydroMe v.1
This package implements a state-of-the-art version of the Particle Swarm Optimisation (PSO) algorithm (SPSO-2011 and SPSO-2007 capable). hydroPSO can be used as a replacement of the 'optim' R function for (global) optimization of non-smooth and non-linear functions. However, the main focus of hydroPSO is the calibration of environmental and other real-world models that need to be executed from the system console. hydroPSO is model-independent, allowing the user to easily interface any computer simulation model with the calibration engine (PSO). hydroPSO  communicates with the model through the model's own input and output files, without requiring access to the model's source code. Several PSO variants and controlling options are included to fine-tune the performance of the calibration engine to different calibration problems. An advanced sensitivity analysis function together with user-friendly plotting summaries facilitate the interpretation and assessment of the calibration results. hydroPSO is parallel-capable, to alleviate the computational burden of complex models with "long" execution time. Bugs reports/comments/questions are very welcomed (in English, Spanish or Italian).
Calculates a suite of hydrologic indices for daily time series data that are widely used in hydrology and stream ecology.
S3 functions for management, analysis, interpolation and plotting of time series used in hydrology and related environmental sciences. In particular, this package is highly oriented to hydrological modelling tasks. The focus of this package has been put in providing a collection of tools useful for the daily work of hydrologists (although an effort was made to optimise each function as much as possible, functionality has had priority over speed). Bugs / comments / questions / collaboration of any kind are very welcomed, and in particular, datasets that can be included in this package for academic purposes.
Focuses on data processing and visualization in hydrology and
    climate forecasting. Main function includes data extraction, data downscaling,
    data resampling, gap filler of precipitation, bias correction of forecasting
    data, flexible time series plot, and spatial map generation. It is a good pre-
    processing and post-processing tool for hydrological and hydraulic modellers.
A suite of routines for the hyperdirichlet distribution; supersedes the hyperdirichlet package for most purposes.
This package provides functions for the hyperbolic and
        related distributions. Density, distribution and quantile
        functions and random number generation are provided for the
        hyperbolic distribution, the generalized hyperbolic
        distribution, the generalized inverse Gaussian distribution and
        the skew-Laplace distribution. Additional functionality is
        provided for the hyperbolic distribution, including fitting of
        the hyperbolic to data.
Includes two main high level codes for hyperplane fitting (hyper.fit) and visualising (hyper.plot2d / hyper.plot3d). In simple terms this allows the user to produce robust 1D linear fits for 2D x vs y type data, and robust 2D plane fits to 3D x vs y vs z type data. This hyperplane fitting works generically for any N-1 hyperplane model being fit to a N dimension dataset. All fits include intrinsic scatter in the generative model orthogonal to the hyperplane.
Performs (exact) hypergeometric tests on IxJ and 2x2x2 contingency tables using parallelised C code.
The Gaussian hypergeometric function for complex numbers.
Implements an algorithm for efficient and exact calculation of hypergeometric 
    and binomial probabilities using Chebyshev polynomials, while other algorithm use an 
    approximation when N is large. A useful applications is also considered in this package 
    for the construction of attribute sampling plans which is an important field of statistical
    quality control. The quantile, and the confidence limit for the attribute sampling plan are
    also implemented in this package. The hypergeometric distribution can be represented in 
    terms of Chebyshev polynomials. This representation is particularly useful in the calculation
    of exact values of hypergeometric variables.
Machine learning supervised method to learn rare genomic features in imbalanced genetic data sets. This method can be also applied to classify or rank examples characterized by a high imbalance between the minority and majority class. hyperSMURF adopts a hyper-ensemble (ensemble of ensembles) approach, undersampling of the majority class and oversampling of the minority class to learn highly imbalanced data. Both single-core and parallel multi-core version of hyperSMURF are implemented.
Comfortable ways to work with hyperspectral data sets.
    I.e. spatially or time-resolved spectra, or spectra with any other kind
    of information associated with each of the spectra. The spectra can be data
    as obtained in XRF, UV/VIS, Fluorescence, AES, NIR, IR, Raman, NMR, MS,
    etc. More generally, any data that is recorded over a discretized variable,
    e.g. absorbance = f (wavelength), stored as a vector of absorbance values
    for discrete wavelengths is suitable.
Estimates the shape and volume of high-dimensional datasets and performs set operations: intersection / overlap, union, unique components, inclusion test, and hole detection. Uses stochastic geometry approach to high-dimensional kernel density estimation, support vector machine delineation, and convex hull generation. Applications include modeling trait and niche hypervolumes and species distribution modeling.
Identifying hyphenation points in strings can be useful for both
    text processing and display functions. The 'Hunspell' hyphenation library
    <https://github.com/hunspell/hyphen> provides tools to perform hyphenation
    using custom language rule dictionaries. Many hyphenation rules
    dictionaries are included. Words can be hyphenated directly or split into
    hyphenated component strings for further processing.
A Bay Area high level phylogenetic analysis package mostly
        using the birth-death process. Analysis of species tree
        branching times and simulation of species trees under a number
        of different time variable birth-death processes.  Analysis of
        gene tree species tree reconciliations and simulations of gene
        trees in species trees.
A Multi-Hypothesis CSV Parser. Stresses your computer not you.
Compute the confidence interval of the population mean
        with one sample or of the difference of population means of two
        samples from normal distributions or t-distributions.Compute
        the confidence interval of population variance with one sample
        or of the difference of population variances of two samples by
        chi-square tests.Test for population mean or the differences of
        two normal samples under normality with the given null
        hypothesis H0,which depends on the user,so that he can know if
        he can reject H0 or not at the significance level alpha.Do the
        chi-square tests with one or two samples which have multinomial
        distributions by using an approximate chi-square distribution
        when n is large enough.
Interact with the application programming interface for the web
    annotation service 'Hypothes.is' (See <http://hypothes.is> for more
    information.) Allows users to download data about public annotations, and
    create, retrieve, update, and delete their own annotations.
Methods for addressing uncertainty in risk assessments using hybrid representations of uncertainty (probability distributions, fuzzy numbers, intervals, probability distributions with imprecise parameters). The uncertainty propagation procedure combines random sampling using Monte Carlo method with fuzzy interval analysis of Baudrit et al. (2007) <doi:10.1109/TFUZZ.2006.876720>. The sensitivity analysis is based on the pinching method of Ferson and Tucker (2006) <doi:10.1016/j.ress.2005.11.052>.
Fit, summarize and plot sinusoidal hysteretic processes using:
    two-step simple harmonic least squares, ellipse-specific non-linear least
    squares, the direct method, geometric least squares or linear least squares.
A collection of tools for modeling the shape of 1 dimensional clines.
In randomized-controlled trials, interim analyses are often planned for possible early 
   trial termination to claim superiority or futility of a new therapy. Blinded data also have 
   information about the potential treatment difference between the groups. We developed a blinded 
   data monitoring tool that enables investigators to predict whether they observe such an unblinded 
   interim analysis results that supports early termination of the trial. Investigators may skip 
   some of the planned interim analyses if an early termination is unlikely. 
   This tool will provide reference information about N: Sample size at interim analysis, and T: Total 
   number of responders at interim analysis for decision on performing an interim analysis.
Calculate false ring proportions from data frames of intra annual 
  density fluctuations.
Synthesizes information across collaborating research. Created specifically for Integrative Analysis of Longitudinal Studies of Aging (IALSA).
Functions for Water and Steam Properties based on the IAPWS Formulation
    1995 for the Thermodynamic Properties of Ordinary Water Substance for General and
    Scientific Use and on the releases for viscosity, conductivity, surface tension and
    melting pressure.
Calculate AIC's and AICc's of unimodal model (one normal distribution) and bimodal model(a mixture of two normal distributions) which fit the distribution of indices of asymmetry (IAS), and plot their density, to help determine IAS distribution is unimodal or bimodal.
Implements the standard D-Scoring algorithm
    (Greenwald, Banaji, & Nosek, 2003) for Implicit Association Test (IAT)
    data and includes plotting capabilities for exploring raw IAT data.