Code to support a systems biology research program from inception through publication.  The methods focus on dimension reduction approaches to detect patterns in complex, multivariate experimental data and places an emphasis on informative visualizations.  The goal for this project is to create a package that will evolve over time, thereby remaining relevant and reflective of current methods and techniques.  As a result, we encourage suggested additions to the package, both methodological and graphical.
Read a table of fixed width formatted data of different types into
    a data.frame for each type.
Uses a transformed, rotated and optionally adapted n-dimensional
    grid of quadrature points to calculate the numerical integral of n multivariate
    normal distributed parameters.
Functions to plot and manipulate multigraphs, weighted multigraphs, and bipartite graphs with different layout options.
Several functions are presented in order to study data in a group structure,
     where the same set of variables are measured on different groups of
    individuals.
Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parameterizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version (since 2.1).
The functions in this package are designed to be used in the analysis of multilevel data by applied psychologists.  The package includes functions for estimating common within-group agreement and reliability indices. The package also contains basic data manipulation functions that facilitate the analysis of multilevel and longitudinal data.
Functions to estimate and visualize propensity
    score analysis for multilevel, or clustered, data.
Traditional and spatial capture-mark-recapture analysis with
    multiple non-invasive marks. The models implemented in 'multimark' combine
    encounter history data arising from two different non-invasive ``marks'',
    such as images of left-sided and right-sided pelage patterns of bilaterally
    asymmetrical species, to estimate abundance and related demographic
    parameters while accounting for imperfect detection. Bayesian models are
    specified using simple formulae and fitted using Markov chain Monte Carlo.
    Addressing deficiencies in currently available software, 'multimark' also
    provides a user-friendly interface for performing Bayesian multimodel
    inference using non-spatial or spatial capture-recapture data consisting of a single
    conventional mark or multiple non-invasive marks.
Allows running a meta-analysis of multivariate Genome Wide
    Association Studies (GWAS) and easily visualizing results through custom
    plotting functions. The multivariate setting implies that results for each
    single nucleotide polymorphism (SNP) include several effect sizes (also
    known as "beta coefficients", one for each trait), as well as related
    variance values, but also covariance between the betas. The main goal of
    the package is to provide combined beta coefficients across different
    cohorts, together with the combined variance/covariance matrix. The method
    is inverse-variance based, thus each beta is weighted by the inverse of its
    variance-covariance matrix, before taking the average across all betas. The
    default options of the main function \code{multi_meta} will work with files
    obtained from GEMMA multivariate option for GWAS (Zhou & Stephens, 2014).
    It will work with any other output, as soon as columns are formatted to
    have the according names. The package also provides several plotting
    functions for QQ-plots, Manhattan Plots and custom summary plots.
This is a likelihood approach for the regression analysis of overdispersed correlated count data with cluster varying covariates. The approach fits a multivariate negative binomial model by maximum likelihood and provides robust estimates of the regression coefficients.
Functions for the creation/generation and analysis of multilayer social networks.
An implementation of a method for building simultaneous
        confidence intervals for the probabilities of a multinomial
        distribution given a set of observations, proposed by Sison and
        Glaz in their paper Sison, C.P and J. Glaz. Simultaneous
        confidence intervals and sample size determination for
        multinomial proportions. Journal of the American Statistical
        Association, 90:366-369 (1995).  The method is an R translation
        of the SAS code implemented by May and Johnson in their paper:
        May, W.L. and W.D. Johnson. Constructing two-sided simultaneous
        confidence intervals for multinomial proportions for small
        counts in a large number of cells. Journal of Statistical
        Software 5(6) (2000). Paper and code available at
        http://www.jstatsoft.org/v05/i06
MNL and overdispersed multinomial regression using robust
        (LQD and tanh) estimation
A method for multivariate ordinal data generation given marginal distributions and correlation matrix based on the methodology proposed by Demirtas (2006).
Tools to create a layout for figures made of multiple panels, and
    to fill the panels with base, lattice and ggplot2 plots, grobs, and PNG,
    JPEG, and TIFF images.
Performs genetic association tests between SNPs
        (one-at-a-time) and multiple phenotypes (separately or in joint
        model).
Performs variable importance analysis using a causal inference approach. This is done by fitting Population Intervention Models. The default is to use a Targeted Maximum Likelihood Estimator (TMLE). The other available estimators are Inverse Probability of Censoring Weighted (IPCW), Double-Robust IPCW (DR-IPCW), and Graphical Computation (G-COMP) estimators. Inference can be obtained from the influence curve (plug-in) or by bootstrapping.
Fit Cox proportional hazard models with a weighted 
  partial likelihood. It handles one or multiple endpoints, additional matching 
  and makes it possible to reuse controls for other endpoints.
Algebraic procedures for the analysis of multiple social networks are delivered with 
	    this package. Among other things, it makes possible to create and manipulate multivariate 
	    network data with different formats, and there are effective ways available to treat multiple 
	    networks with routines that combine algebraic systems like the partially ordered semigroup or 
	    the semiring structure together with the relational bundles occurring in different types of 
	    multivariate network data sets. It also provides an algebraic approach for two-mode networks 
	    through Galois derivations between families of the pairs of subsets in the two domains.
Provides a new form of data frame backed by shared memory matrices
    and a way to manipulate them. Upon creation these data frames are shared across
    multiple local nodes to allow for simple parallel processing.
Various utilities to manipulate multivariate polynomials
Functions to calculate Unique Trait Combinations (UTC) and scaled
    Unique Trait Combinations (sUTC) as measures of multivariate richness. The
    package can also calculate beta-diversity for trait richness and can
    partition this into nestedness-related and turnover components. The code
    will also calculate several measures of overlap.
Calculates bias, precision, and power for multi-level random regressions. Random regressions are types of hierarchical models in which data are structured in groups and (regression) coefficients can vary by groups. Tools to estimate model performance are designed mostly for scenarios where (regression) coefficients vary at just one level. 'MultiRR' provides simulation and analytical tools (based on 'lme4') to study model performance for random regressions that vary at more than one level (multi-level random regressions), allowing researchers to determine optimal sampling designs.
Functions to perform sensitivity analysis on a model with multivariate output.
Implements two versions of the algorithm namely: stochastic and batch. The package determines also the best number of clusters and offers to the user the best clustering scheme from different results.
The multispatial convergent cross mapping algorithm can be used as a test for causal associations between pairs of processes represented by time series. This is a combination of convergent cross mapping (CCM), described in Sugihara et al., 2012, Science, 338, 496-500, and dew-drop regression, described in Hsieh et al., 2008, American Naturalist, 171, 71–80. The algorithm allows CCM to be implemented on data that are not from a single long time series. Instead, data can come from many short time series, which are stitched together using bootstrapping.
MultiSV is an R package for identification of structural
    variations in multiple populations based on whole genome resequencing. It
    fits linear mixed model and identifies structural variations in multiple
    populations using whole genome sequencing data. It could also be
    manipulated to use on RNA-seq data for differential gene expression
    (implementation in future releases). Main steps for analysis include
    generating read depth in bins using ComputeBinCounts. conversion of bins to
    MultiSV format using Bin2MultiSV. Finally, identification of structural
    variations using CallMultiSV.
Implements multitaper spectral analysis using discrete prolate spheroidal sequences (Slepians) and sine tapers. It includes an adaptive weighted multitaper spectral estimate, a coherence estimate, Thomson's Harmonic F-test, and complex demodulation. The Slepians sequences are generated efficiently using a tridiagonal matrix solution, and jackknifed confidence intervals are available for most estimates.
Models and predicts Multiple Features in single random forest considering the linear relation among the features.
A multivariate generalization of the emulator package
Computation of an estimation of the long-memory parameters and
     the long-run covariance matrix using a multivariate model
	 (Lobato (1999) <doi:10.1016/S0304-4076(98)00038-4>; Shimotsu (2007) <doi:10.1016/j.jeconom.2006.01.003>). Two semi-parametric methods are
	 implemented: a Fourier based approach (Shimotsu (2007) <doi:10.1016/j.jeconom.2006.01.003>) and a wavelet based
	 approach (Achard and Gannaz (2016) <doi:10.1111/jtsa.12170>).
Fits multi-way component models via alternating least squares algorithms with optional constraints (orthogonality, non-negativity, and structural). Fit models include Individual Differences Scaling, Parallel Factor Analysis (1 and 2), Simultaneous Component Analysis, and Tucker Factor Analysis.
Exports two functions implementing
    multi-way clustering using the method suggested by Cameron, Gelbach, &
    Miller (2011) and cluster (or block)
    bootstrapping for estimating variance-covariance matrices. Normal one and
    two-way clustering matches the results of other common statistical
    packages.  Missing values are handled transparently and rudimentary
    parallelization support is provided.
A collection of multivariate nonparametric methods, selected in
    part to support an MS level course in nonparametric statistical methods. Methods
    include adjustments for multiple comparisons, implementation of multivariate
    Mann-Whitney-Wilcoxon testing, inversion of these tests to produce a confidence
    region, some permutation tests for linear models, and some algorithms for
    calculating exact probabilities associated with one- and two- stage testing
    involving Mann-Whitney-Wilcoxon statistics.
Implementation of commonly used p-value-based and
        parametric multiple testing procedures (computation of adjusted
        p-values and simultaneous confidence intervals) and parallel
        gatekeeping procedures based on the methodology presented in
        the book "Multiple Testing Problems in Pharmaceutical
        Statistics" (edited by Alex Dmitrienko, Ajit C. Tamhane and
        Frank Bretz) published by Chapman and Hall/CRC Press 2009.
Preprocessing of high-throughput data (normalization and
        scalings); Principal Component Analysis with help tool for
        choosing best-separating principal components and automatic
        testing for outliers; automatic univariate analysis for
        parametric and non-parametric data, with generation of specific
        reports (volcano and box plots); partial least square
        discriminant analysis (PLS-DA); orthogonal partial least square
        discriminant analysis (OPLS-DA); Statistical Total Correlation
        Spectroscopy (STOCSY); Ratio Analysis Nuclear Magnetic
        Resonance (NMR) Spectroscopy (RANSY).
Model selection and model averaging based on information criteria
             (AICc and alike).
Multidimensional unfolding using Schoenemann's algorithm for metric
   and Procrustes rotation of unfolding results.
Provides easy access to, and manipulation of, the Munsell 
    colours. Provides a mapping between Munsell's 
    original notation (e.g. "5R 5/10") and hexadecimal strings suitable 
    for use directly in R graphics. Also provides utilities 
    to explore slices through the Munsell colour tree, to transform 
    Munsell colours and display colour palettes.
Methods for interpolating data in the Munsell color system following the ASTM D-1535 standard. Hues and chromas with decimal values can be interpolated and converted to/from the Munsell color system and CIE xyY, CIE XYZ, sRGB, CIE Lab or CIE Luv. Chromas can be odd and hue steps can be real numbers. Based on the work by Paul Centore, "The Munsell and Kubelka-Munk Toolbox".
Provides mailmerge methods for reading spreadsheets of addresses and other relevant information to create standardized but customizable letters.  Provides a method for mapping US ZIP codes, including those of letter recipients.  Provides a method for parsing and processing html code from online job postings of the American Political Science Association.
Data and code for the paper by Ehm, Gneiting, Jordan and Krueger ('Of Quantiles and Expectiles: Consistent Scoring Functions, Choquet Representations, and Forecast Rankings', 2015).
Provides functions allowing for (1) easy aggregation of multivariate time series into custom time scales, (2) comparison of statistical summaries between different data sets at multiple time scales (e.g. observed and bias-corrected data), (3) comparison of relations between variables and/or different data sets at multiple time scales (e.g. correlation of precipitation and temperature in control and scenario simulation) and (4) transformation of time series at custom time scales.
This package is a collection of function for converting and modifying mono dimensional nuclear magnetic resonance spectra.
Performs Wilcox rank sum test, Kruskal rank sum test,
        Friedman rank sum test and McNemar test.
The Mutoss package and accompanying mutossGUI package are
    designed to ease the application and comparison of multiple
    hypothesis testing procedures.
The mutossGUI package provides a graphical user interface for the MuToss Project.
Cancer cells accumulate DNA mutations as result of DNA damage and DNA repair processes. This computational framework is aimed at deciphering DNA mutational signatures operating in cancer. The input is a numeric matrix of DNA mutation counts detected in a panel of cancer samples. The framework performs Non-negative Matrix Factorization to extract the most likely signatures explaining the observed set of DNA mutations. The framework relies on parallelization and is optimized for use on multi-core systems. This framework is an R-based implementation of the original MATLAB WTSI framework by Alexandrov LB et al (2013) <DOI:10.1016/j.celrep.2012.12.008>.
An ensemble classifier for multiclass classification. This is a novel classifier that natively works as an ensemble. It projects data on a large number of matrices, and uses very simple classifiers on each of these projections. The results are then combined, ideally via Dempster-Shafer Calculus.
Functions, data sets, analyses and examples from the book 
  `An Introduction to Applied Multivariate Analysis with R' 
  (Brian S. Everitt and Torsten Hothorn, Springer, 2011).
A set of tools for displaying, modeling and analysing
        multivariate abundance data in community ecology. See
        'mvabund-package.Rd' for details of overall package organization.
        The package is implemented with the Gnu Scientific Library
        (http://www.gnu.org/software/gsl/) and Rcpp
        (http://dirk.eddelbuettel.com/code/rcpp.html) R / C++ classes.
Pacote para analise multivariada, que possui funcoes que executam analise de correspondencia simples (CA) e multipla (MCA), analise de componentes principais (PCA), analise de correlacao canonica (CCA), analise fatorial (FA), escalonamento multidimensional (MDS), analise de cluster hierarquico e nao hierarquico, regressao linear, analise de multiplos fatores (MFA) para dados quantitativos, qualitativos, de frequencia (MFACT) e dados mistos. Tambem possui outras funcoes uteis para a analise multivariada.
Fit log-linear model for multivariate Bernoulli
        distribution with mixed effect models and LASSO
Modelling Multivariate Binary Data with Blocks of Specific One-Factor Distribution. Variables are grouped into independent blocks. Each variable is described by two continuous parameters (its marginal probability and its dependency strength with the other block variables), and one binary parameter (positive or negative dependency). Model selection consists in the estimation of the repartition of the variables into blocks. It is carried out by the maximization of the BIC criterion by a deterministic (faster) algorithm or by a stochastic (more time consuming but optimal) algorithm. Tool functions facilitate the model interpretation.
Hierarchical workspace tree, code editing and backup, easy
        package prep, editing of packages while loaded, per-object
        lazy-loading, easy documentation, macro functions, and
        miscellaneous utilities. Needed by debug package.
An implementation of Multi-View Clustering (Bickel and Scheffer, 2004). Documents are generated by drawing word values from a categorical distribution for each word, given the cluster. This means words are not counted (multinomial, as in the paper), but words take on different values from a finite set of values (categorical). Thus, it implements Mixture of Categoricals EM (as opposed to Mixture of Multinomials developed in the paper), and Spherical k-Means. The latter represents documents as vectors in the categorical space.
Implementation of multi-view bi-clustering algorithms. When a sample is characterized by two or more sets of input features, it creates multiple data matrices for the same set of examples, each corresponding to a view. For instance, individuals who are diagnosed with a disorder can be described by their clinical symptoms (one view) and their genomic markers (another view). Rows of a data matrix correspond to examples and columns correspond to features. A multi-view bi-clustering algorithm groups examples (rows) consistently across the views and simultaneously identifies the subset of features (columns) in each view that are associated with the row groups. This mvcluster package includes three such methods. (1) MVSVDL1: multi-view bi-clustering based on singular value decomposition where the left singular vectors are used to identify row clusters and the right singular vectors are used to identify features (columns) for each row cluster. Each singular vector is regularized by the L1 vector norm.  (2) MVLRRL0: multi-view bi-clustering based on sparse low rank representation (i.e., matrix approximation) where the decomposed components are regularized by the so-called L0 vector norm (which is not really a vector norm). (3) MVLRRL1: multi-view bi-clustering based on sparse low rank representation (i.e., matrix approximation) where the decomposed components are regularized by the L1 vector norm.
Permutation tests for variance components for 2-level, 3-level and 4-level data with univariate or multivariate responses.
This package computes the continuous wavelet transform of
    irregularly sampled time series.
An open-source project implementation of latent variable methods and multivariate modeling tools. The focus is on exploratory analyses using dimensionality reduction methods including low dimensional embedding, classical multivariate statistical tools , and tools for enhanced interpretation of machine learning methods (i.e. intelligible models to provide important information for end-users).   Target domains include extension to dedicated applications e.g. for manufacturing process modeling, spectroscopic analyses, and data mining.
Maximum likelihood estimates are obtained via an EM algorithm with either a first-order or a fully exponential Laplace approximation.
Computes regression deletion diagnostics for multivariate linear models and provides some associated
	diagnostic plots.  The diagnostic measures include hat-values (leverages), generalized Cook's distance, and
	generalized squared 'studentized' residuals.  Several types of plots to detect influential observations are
	provided.
Allows a user to conduct multivariate multiple regression using analytic p-values rather than classic approximate F-tests.
Tools for analysing multivariate time series with wavelets. This includes: simulation of a multivariate locally stationary wavelet (mvLSW) process from a multivatriate evolutionary wavelet spectrum (mvEWS); estimation of the mvEWS, local coherence and local partial coherence; and, estimation of the asymptotic variance for mvEWS elements. See Park, Eckley and Ombao (2014) <doi:10.1109/TSP.2014.2343937> for details.
Define, manipulate and plot meshes on simplices, spheres, balls, rectangles and tubes.
 Directional and other multivariate histograms are provided.
Collection of functions to perform fixed and random-effects multivariate and univariate meta-analysis and meta-regression.
Fits multivariate (Brownian Motion, Early Burst, ACDC, Ornstein-Uhlenbeck and Shifts) models of continuous traits evolution on trees and time series.
Performs multivariate normality tests and graphical approaches and implements multivariate outlier detection and univariate normality of marginal distributions through plots and tests.
Computes the Nelson-Aalen estimator of the cumulative transition hazard for arbitrary Markov multistate models <ISBN:978-0-387-68560-1>.
Provides computationally efficient tools related to
    the multivariate normal and Student's t distributions. The main functionalities are:
    simulating multivariate random vectors, evaluating multivariate
    normal or Student's t densities and Mahalanobis distances. These tools are very efficient
    thanks to the use of C++ code and of the OpenMP API.
Package for  moving grid adjustment 
	      in plant breeding field trials.
Finds the maximum likelihood estimate of the mean vector
        and variance-covariance matrix for multivariate normal data
        with missing values.
Generalization of shapiro-wilk test for multivariate
        variables.
Given a vector of multivariate normal data, a matrix of
    covariates and the data covariance matrix, generate new multivariate normal
    samples that have the same covariance matrix based on permutations of
    the transformed data residuals.
Routines for assessing multivariate normality. Implements three Wald's type chi-squared tests; non-parametric Anderson-Darling and Cramer-von Mises tests; Doornik-Hansen test,  Royston test and Henze-Zirkler test.
Various Methods for Multivariate Outlier Detection.
Tools for high-dimensional peaks-over-threshold inference and simulation
  of spatial extremal processes.
Tools for estimating multivariate probit models,
   calculating conditional and unconditional expectations,
   and calculating marginal effects on conditional and unconditional
   expectations.
Computes orthant probabilities multivariate normal distribution.
Provides methods to construct multivariate grids, which can be used
    for multivariate quadrature. This grids can be based on different quadrature
    rules like Newton-Cotes formulas (trapezoidal-, Simpson's- rule, ...) or Gauss
    quadrature (Gauss-Hermite, Gauss-Legendre, ...). For the construction of the
    multidimensional grid the product-rule or the combination- technique can be
    applied.
This is a non-parametric method for joint adaptive mean-variance regularization and variance stabilization of high-dimensional data. It is suited for handling difficult problems posed by high-dimensional multivariate datasets (p >> n paradigm). Among those are that the variance is often a function of the mean, variable-specific estimators of variances are not reliable, and tests statistics have low powers due to a lack of degrees of freedom. Key features include:
            (i) Normalization and/or variance stabilization of the data,
            (ii) Computation of mean-variance-regularized t-statistics (F-statistics to follow),
            (iii) Generation of diverse diagnostic plots,
            (iv) Computationally efficient implementation using C/C++ interfacing and an option for parallel computing to enjoy a faster and easier experience in the R environment.
Mean, variance, and random variates for left/right truncated normal distributions.
Generalization of the Shapiro-Francia test for
        multivariate variables.
This package implements the generalization of the Shapiro-Wilk test
        for multivariate normality proposed by Villasenor-Alva and
        Gonzalez-Estrada (2009).
Fits multivariate Ornstein-Uhlenbeck types of models to continues trait data from species related by a common evolutionary history.
Estimates the multivariate skew-t and nested models, as described in the articles Liseo, B., Parisi, A. (2013). Bayesian inference for the multivariate skew-normal model: a population Monte Carlo approach. Comput. Statist. Data Anal. <doi:10.1016/j.csda.2013.02.007> and in Parisi, A., Liseo, B. Objective Bayesian analysis for the multivariate skew-t model (to appear).
Routines to perform estimation and inference under the multivariate t-distribution.
  Currently, the following methodologies are implemented: multivariate mean and covariance
  estimation, hypothesis testing about the mean, equicorrelation and homogeneity of variances,
  the Wilson-Hilferty transformation, QQ-plots with envelopes and random variate
  generation. Some auxiliary functions are also provided.
Fits a multivariate model of decision trees for multiple, continuous outcome variables. A model for each outcome variable is fit separately, selecting predictors that explain covariance in the outcomes.  Built on top of 'gbm', which fits an ensemble of decision trees to univariate outcomes.
This package contains functions to run fixed effects or
        random effects multivariate meta-analysis.
Computes multivariate normal and t probabilities, quantiles,
  random deviates and densities.
A function for plotting multivariate time series data
Matched Wake Analysis (mwa) grants insights into causal relationships in spatiotemporal event data.
Computes the Wavelet deconvolution estimate of a common signal
    present in multiple channels that have possible different levels of blur
    and long memory additive error.
The Moving-Window Lasso (MWLasso) method for genome-wide association studies. A window scans the design matrix. For predictors in the same window, their coefficients estimates are smoothed.
A two stage moving-window Ridge method for coefficients estimation and model prediction. In the first stage, moving-window penalty and L1 penalty are applied. In the second stage, ridge regression is applied.
mxkssd is a package that generates efficient balanced
        mixed-level k-circulant supersaturated designs by interchanging
        the elements of the generator vector. The package tries to
        generate a supersaturated design that has EfNOD efficiency more
        than user specified efficiency level (mef). The package also
        displays the progress of generation of an efficient mixed-level
        k-circulant design through a progress bar. The progress of 100
        per cent means that one full round of interchange is completed.
        More than one full round (typically 4-5 rounds) of interchange
        may be required for larger designs.
Feature selection methods for identifying minimal, statistically-equivalent and equally-predictive feature subsets. Bayesian network algorithms and related functions are also included. The package name 'MXM' stands for "Mens eX Machina", meaning "Mind from the Machine" in Latin.
The mycobacrvR package contains utilities to provide detailed information for B cell and T cell epitopes for predicted adhesins from various servers such as ABCpred, Bcepred, Bimas, Propred, NetMHC and IEDB. Please refer the URL below to download data files (data_mycobacrvR.zip) used in functions of this package.
Perform correlation and linear regression test
    among the numeric columns in a data frame automatically
    and make plots using pairs or lattice::parallelplot.
Useful functions for accessing MyEpisodes feeds and
        episode information as well as other tv episode related actions
        through www.myepisodes.com
Recommendation engine based on Myrrix. Myrrix is a complete,
    real-time, scalable clustering and recommender system, evolved from Apache
    Mahout. It uses Alternating Least Squares to build a recommendation engine.
External jars required for package Myrrix. Myrrix is a
    recommendation engine
Investigate the evolution of biological processes by capturing evolutionary signatures in transcriptomes. The aim of this tool is to provide a transcriptome analysis environment for answering questions regarding the evolution of biological processes.
Linear and logistic regression models for quantitative genetic analysis of data from monozygotic twins.
An R wrapper for 'libnabo', an exact or approximate k nearest
    neighbour library which is optimised for low dimensional spaces (e.g. 3D).
    'libnabo' has speed and space advantages over the 'ANN' library wrapped by
    package 'RANN'. 'nabor' includes a knn function that is designed as a 
    drop-in replacement for 'RANN' function nn2. In addition, objects which 
    include the k-d tree search structure can be returned to speed up repeated 
    queries of the same set of target points.
Contains methods described by Dennis R. Helsel in 
             his book "Nondetects And Data Analysis: Statistics 
             for Censored Environmental Data"
Constructs (non)additive genetic relationship matrices, and their inverses, from a pedigree to be used in linear mixed effect models (A.K.A. the 'animal model'). Also includes other functions to facilitate the use of animal models. Some functions have been created to be used in conjunction with the R package 'asreml' for the 'ASReml' software, which can be obtained upon purchase from 'VSN' international (http://www.vsni.co.uk/software/asreml).
Contains a sample of the 2005 Grade 8 Mathematics data from the National Assessment of Educational Progress (NAEP). This data set is called the NAEP Primer.
High performance implementation of the Naive Bayes algorithm.
Designed for association studies in nested association mapping (NAM) panels, also handling experimental and random panels. It includes tools for genome-wide associations of multiple populations, marker quality control, population genetics analysis, genome-wide prediction, solving mixed models and finding variance components through likelihood and Bayesian methods.
This package provides user-level functions to manage
        namespaces not (yet) available in base R: 'registerNamespace',
        'unregisterNamespace', 'makeNamespace', and
        'getRegisteredNamespace'

  ('makeNamespaces' is extracted from the R 'base' package source code:
        src/library/base/R/namespace.R)
This software package implements functions to simulate spherical, ellipsoid and cubic polyatomic nanoparticles with arbitrary crystal structures and to calculate the associated pair-distribution function and X-ray/neutron total-scattering signals. It also provides a target function that can be used for simultaneous fitting of small- and wide-angle total scattering data in real and reciprocal spaces. The target function can be generated either as a sum of weighted residuals for individual datasets or as a vector of residuals suitable for optimization using multi-criteria algorithms (e.g. Pareto methods).
A set of tools for normalizing, diagnostics and visualization of NanoString nCounter data.
Full 64-bit resolution date and time support with resolution up
 to nanosecond granularity is provided, with easy transition to and from the
 standard 'POSIXct' type.
Enables the processing and normalisation of the standard mRNA data output from the Nanostring nCounter software.
Provides a near drop-in replacement for base::Sys.sleep() that allows more types of input
    to produce delays in the execution of code and can silence/prevent typical sources of error.
Provides functions to query and manipulate arrays
    of arbitrary dimensions.
Provides functions to access NASA's Earth Imagery and Assets API
    and the Earth Observatory Natural Event Tracker (EONET) webservice.
This package contains tidied data from the ASA 2006 data expo,
    as well as a number of useful other related data sets.
NeuroAnatomy Toolbox (nat) enables analysis and visualisation of 3D
    biological image data, especially traced neurons. Reads and writes 3D images
    in NRRD and 'Amira' AmiraMesh formats and reads surfaces in 'Amira' hxsurf
    format. Traced neurons can be imported from and written to SWC and 'Amira'
    LineSet and SkeletonGraph formats. These data can then be visualised in 3D
    via 'rgl', manipulated including applying calculated registrations, e.g.
    using the 'CMTK' registration suite, and analysed. There is also a simple
    representation for neurons that have been subjected to 3D skeletonisation
    but not formally traced; this allows morphological comparison between
    neurons including searches and clustering (via the 'nat.nblast' extension
    package).
Extends package 'nat' (NeuroAnatomy Toolbox) by providing a
    collection of NBLAST-related functions.
Interface to 'NatureServe' (<http://www.natureserve.org>).
    Includes methods to get data, image metadata, search taxonomic names,
    and make maps.
Extends package 'nat' (NeuroAnatomy Toolbox) by providing objects
    and functions for handling template brains.
Provides functions related to human natural ordering.
    It handles adjacent digits in a character sequence as a number so that
    natural sort function arranges a character vector by their numbers, not digit
    characters. It is typically seen when operating systems lists file names. For
    example, a sequence a-1.png, a-2.png, a-10.png looks naturally ordered because 1
    < 2 < 10 and natural sort algorithm arranges so whereas general sort algorithms
    arrange it into a-1.png, a-10.png, a-2.png owing to their third and fourth
    characters.
Utility functions that may be of general interest but are 
    specifically required by the 'NeuroAnatomy Toolbox' ('nat'). Includes
    functions to provide a basic make style system to update files based on
    timestamp information, file locking and 'touch' utility. Convenience 
    functions for working with file paths include 'abs2rel', 'split_path' 
    and 'common_path'. Finally there are utility functions for working with 
    'zip' and 'gzip' files including integrity tests.
Estimate the effective population size of a closed population using genetic data collected from two or more data points.
An implementation of the Naive Bayes Classifier (NBC) algorithm used for Verbal Autopsy (VA) built on code from Miasnikof et al (2015) <DOI:10.1186/s12916-015-0521-2>.
It provides 30 indexes for determining the optimal number of clusters in a data set and offers the best clustering scheme from different results to the user.
Calls the 'Jupyter'/'IPython' script 'nbconvert' to create vignettes from notebooks.
  Those notebooks ('.ipynb' files) are files containing rich text, code, and its output.
  Code cells can be edited and evaluated interactively.
  See <http://ipython.org/notebook.html> for more information.
The Dirichlet (aka NBD-Dirichlet) model describes the
        purchase incidence and brand choice of consumer products.  We
        estimate the model and summarize various theoretical quantities
        of interest to marketing researchers. Also provides functions
        for making tables that compare observed and theoretical
        statistics.
Perform non-bipartite matching and matched randomization. A
    "bipartite" matching utilizes two separate groups, e.g. smokers being
    matched to nonsmokers or cases being matched to controls. A "non-bipartite"
    matching creates mates from one big group, e.g. 100 hospitals being
    randomized for a two-arm cluster randomized trial or 5000 children who
    have been exposed to various levels of secondhand smoke and are being
    paired to form a greater exposure vs. lesser exposure comparison. At the
    core of a non-bipartite matching is a N x N distance matrix for N potential
    mates. The distance between two units expresses a measure of similarity or
    quality as mates (the lower the better). The 'gendistance()' and
    'distancematrix()' functions assist in creating this. The 'nonbimatch()'
    function creates the matching that minimizes the total sum of distances
    between mates; hence, it is referred to as an "optimal" matching. The
    'assign.grp()' function aids in performing a matched randomization. Note
    bipartite matching can be performed using the prevent option in
    'gendistance()'.
Negative Binomial (NB) models for two-group comparisons and
    regression inferences from RNA-Sequencing Data.
Performs a Necessary Condition Analysis (NCA). (Dul, J. 2016. Necessary Condition Analysis (NCA). ''Logic and Methodology of 'Necessary but not Sufficient' causality." Organizational Research Methods 19(1), 10-52)
  NCA identifies necessary (but not sufficient) conditions in datasets. Instead of drawing a regression line ''through the middle of the data'' in an xy-plot, NCA draws the ceiling line. The ceiling line y = f(x) separates the area with observations from the area without observations.
  (Nearly) all observations are below the ceiling line: y <= f(x). The empty zone is in the upper left hand corner of the xy-plot (with the convention that the x-axis is ''horizontal'' and the y-axis is ''vertical'' and that values increase ''upwards'' and ''to the right''). The ceiling line is a (piecewise) linear non-decreasing line: a linear step function or a straight line. It indicates which level of x (e.g. an effort or input) is necessary but not sufficient for a (desired) level of y (e.g. good performance or output).
Performs nonlinear calibration and curve fitting for data from Luminex, RT-PCR, ELISA, RPPA etc. Its precursor is Ruminex.
A flexible tool is presented here that can perform
    (i) traditional non-compartmental analysis (NCA) and
    (ii) simulation-based posterior predictive checks for a population
    pharmacokinetic (PK) model using NCA metrics.
Conduct a noncompartmental analysis as closely as possible to the most widely used commercial software for pharmacokinetic analysis, i.e. 'Phoenix(R) WinNonlin(R)' <https://www.certara.com/software/pkpd-modeling-and-simulation/phoenix-winnonlin/>.
             Some features include:
             1) Use CDISC SDTM PP domain terms and generate PP domains from DM, EX, and PC domains.
             2) Automatic slope selection with the same criterion of WinNonlin(R)
             3) Support both 'linear-up linear-down' and 'linear-up log-down' method
             4) Calculate partial(interval) AUC with 'linear' or 'log' interpolation method
             5) Perform a noncompartmental analysis of CDISC standardized pharmacokinetic dataset (.XPT)
             For more details on noncompartmental analysis, see the reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107)
             *) Acknowledgement: Author thanks for the careful review and valuable input of Dr. Jee Eun Lee.
making NCBI taxonomic data locally available and
        searchable as an R object
Provides a high-level R interface to 
 data files written using Unidata's
 netCDF library (version 4 or earlier), which are binary data files that are portable 
 across platforms and include metadata information in addition to the data sets.  
 Using this package, netCDF files (either version 4 
 or "classic" version 3) can be opened and data sets read in easily.
 It is also easy to create new netCDF dimensions, variables, and files,
 in either version 3 or 4 format, and manipulate existing netCDF files.
 This package replaces the former ncdf package, which only worked with
 netcdf version 3 files.  For various reasons the names of the functions
 have had to be changed from the names in the ncdf package.  The old ncdf package is still
 available at the URL given below, if you need to have backward compatibility.
 It should be possible to have both the ncdf and ncdf4 packages installed
 simultaneously without a problem.
 However, the ncdf package does not provide an interface for netcdf version 4 files.
This package contains a collection of helper functions for dealing
    with NetCDF files opened using ncdf4.
Set of tools to simplify the handling of 'NetCDF' files with the 'RNetCDF' package. 
 Most functions are wrappers of basic functions from the 'RNetCDF' package to easily run combinations of these 
 functions for frequently encountered tasks.
Computes the noncentral Dunnett's test distribution (pdf, cdf and quantile) and generates random numbers.
R functions for analyzing spatial (cross-)covariance: the
        nonparametric (cross-)covariance, the spline correlogram, the
        nonparametric phase coherence function, and related.
Computes the noncentral gamma function: pdf, cdf, quantile
        function and inverse for the noncentrality parameter.
A set of handy functions. Includes a versatile one line progress bar, one 
 line function timer with detailed output, time delay function, text histogram, object 
 preview, CRAN package search, simpler package installer, Linux command install check, 
 a flexible Mode function, top function, simulation of correlated data, and more.
Efficient algorithms for fitting regularization paths for linear or
 logistic regression models penalized by MCP or SCAD, with optional additional
 L2 penalty.
Streaming 'JSON' ('ndjson') has one 'JSON' record per-line and many modern
    'ndjson' files contain large numbers of records. These constructs may not be
    columnar in nature, but it's often useful to read in these files and "flatten"
    the structure out to work in an R data.frame-like context. Functions are provided that
    make it possible to read in plain 'ndjson' files or compressed ('gz') 'ndjson'
    files and either validate the format of the records or create "flat" data.table
    ('tbl_dt') structures from them.
Naive discriminative learning implements learning and
    classification models based on the Rescorla-Wagner equations and their
    equilibrium equations.
Renders dynamic network data from 'networkDynamic' objects as movies, interactive animations, or other representations of changing relational structures and attributes.
Performs network enrichment analysis against functional gene sets.
    Benchmarks networks. Renders raw gene profile matrices of dimensionality 'Ngenes
    x Nsamples' into the space of gene set (typically pathway) enrichment scores of
    dimensionality 'Npathways x Nsamples'.
Near-far matching is a study design technique for preprocessing observational data to mimic a pair-randomized trial.  Individuals are matched to be near on measured confounders and far on levels of an instrumental variable.
This package implements a path algorithm for Near-Isotonic
        Regression. For more details see the help files
Includes functions and examples to compute NEAT, a network-based test for gene enrichment analysis (Signorelli, Vinciotti and Wit, 2016, <DOI:10.1186/s12859-016-1203-6>).
NeatMap is a package to create heatmap like plots in 2 and
        3 dimensions, without the need for cluster analysis. Like the
        heatmap, the plots created by NeatMap display both a
        dimensionally reduced representation of the data as well as the
        data itself. They are intended to be used in conjunction with
        dimensional reduction techniques such as PCA.
A simple function for easier package loading and auto-installation.
needy is a small utility library designed to make testing function
    inputs less difficult. R is a dynamically typed language, but larger
    projects need input checking for scalabity. needy offers a single
    function, require_a( ), which lets you specify the traits an input object
    should have, such as class, size, numerical properties or number of
    parameters, while reducing boilerplate code and aiding debugging.
Effective population sizes (often abbreviated as "Neff") are essential in biodiversity monitoring and conservation. For the first time, calculating effective sizes with data obtained within less than a generation but considering demographic parameters is possible. This individual based model uses demographic parameters of a population to calculate annual effective sizes and effective population sizes (per generation). A defined number of alleles and loci will be used to simulate the genotypes of the individuals. Stepwise mutation rates can be included. Variations in life history parameters (sex ratio, sex-specific survival, recruitment rate, reproductive skew) are possible. These results will help managers to define existing populations as viable or not.
The Negative Binomial regression with mean and shape modeling and mean and variance modeling and Beta Binomial regression with mean and dispersion modeling.
Estimating the number of essential genes in a genome on the basis of data from a random transposon mutagenesis experiment, through the use of a Gibbs sampler.
Provides several direct search optimization algorithms based on the
  simplex method. The provided algorithms are direct search algorithms, i.e.
  algorithms which do not use the derivative of the cost function. They are
  based on the update of a simplex. The following algorithms are available: the
  fixed shape simplex method of Spendley, Hext and Himsworth (unconstrained
  optimization with a fixed shape simplex), the variable shape simplex method of
  Nelder and Mead (unconstrained optimization with a variable shape simplex
  made), and Box's complex method (constrained optimization with a variable
  shape simplex).
Access paleoecological datasets from the Neotoma Paleoecological
    Database using the published API (http://api.neotomadb.org/).  The functions
    in this package access various pre-built API functions and attempt to return
    the results from Neotoma in a usable format for researchers and the public.
Set of functions to estimate the Glomerular Filtration Rate (GFR) using different biomarkers and other phenotypes of interest for nephrology.
Package for a Network assisted algorithm for Epigenetic studies using mean and variance Combined signals: NEpiC. NEpiC combines both signals in mean and variance differences in methylation level between case and control groups searching for differentially methylated sub-networks (modules) using the protein-protein interaction network.
This tool set provides a set of functions to fit the nested Dirichlet process mixture of products of multinomial distributions (NDPMPM) model for nested categorical household data in the presence of impossible combinations. It has direct applications in generating synthetic nested household data.
Estimate hazard ratios, survival curves and attributable
        risks for cohorts with missing covariates, using Cox models or
        Kaplan-Meier estimated for strata.  This handles studies nested
        within cohorts, such as case-cohort studies with stratified
        sampling.  See
        http://www.r-project.org/doc/Rnews/Rnews_2008-1.pdf
Calculate a Mann-Whitney-Wilcoxon test for a difference between treatment levels using nested ranks.  This test can be used when observations are structured into several groups and each group has received both treatment levels. The p-value is determined via bootstrapping.  The nested ranks test is intended to be one possible mixed-model extension of the Mann-Whitney-Wilcoxon test, for which treatment is a fixed effect and group membership is a random effect.
Infers species associations from community matrices. Uses local and (optional) regional-scale co-occurrence data by comparing observed partial correlation coefficients between species to those estimated from regional species distributions. Extends Gaussian graphical models to a null modeling framework. Provides interface to a variety of inverse covariance matrix estimation methods.
netClass is an R package for network-based feature (gene)
        selection for biomarkers discovery via integrating biological
        information.  This package adapts the following 5 algorithms
        for classifying and predicting gene expression data using prior
        knowledge: 1) average gene expression of pathway (aep); 2)
        pathway activities classification (PAC); 3) Hub network
        Classification (hubc); 4) filter via top ranked genes (FrSVM);
        5) network smoothed t-statistic (stSVM).
Facilitates network clustering and evaluation of cluster
        configurations.
Model fitting procedures for regression with network cohesion effects, when a network connecting sample individuals is available in a regression problem. In the future, other commonly used statistical models will be added, such as gaussian graphical model.
Create interactive networked coincidences. It joins the data analysis power of R to study coincidences and the visualization libraries of JavaScript in one package.
This package contains functions to carry out high
        throughput data analysis and to conduct data set comparisons.
        Similarity matrices from high throughput phenotypic data
        containing uninformative (e.g. wild type) or missing data can
        be calculated to report similarity of response.  A suite of
        graph comparisons using an adjacency or correlation matrix
        format are included to facilitate quick network analysis.
This package contains all data needed for Dan McFarland's
        SNA R labs.
Empirical statistical analysis, visualization and simulation
    of diffusion and contagion processes on networks. The package implements
    algorithms for calculating network diffusion statistics such as transmission
    rate, hazard rates, exposure models, network threshold levels, infectiousness
    (contagion), and susceptibility. The package is inspired by work published in
    Valente, et al., (2015) <DOI:10.1016/j.socscimed.2015.10.001>; Valente (1995)
    <ISBN:9781881303213>, Myers (2000) <DOI:10.1086/303110>, Iyengar and others
    (2011) <DOI:10.1287/mksc.1100.0566>, Burt (1987) <DOI:10.1086/228667>; among
    others.
Methods for the generation of a wide range of network geographies,
    e.g., grid networks or clustered networks. Useful for the generation of
    benchmarking instances for the investigation of, e.g., Vehicle-Routing-Problems
    or Travelling Salesperson Problems.
Carry out Network-based Gene Set Analysis by incorporating external information about interactions among genes, as well as novel interactions learned from data.
Given a network (e.g. a food web), estimates several network indices. These include: Ascendency network indices, Direct and indirect dependencies, Effective measures, Environ network indices, General network indices, Pathway analysis, Network uncertainty indices and constraint efficiencies and the trophic level and omnivory indices of food webs.
A comprehensive set of functions providing frequentist methods for network meta-analysis and supporting Schwarzer et al. (2015) <DOI:10.1007/978-3-319-21416-0>, Chapter 8 "Network Meta-Analysis":
 - frequentist network meta-analysis following Rücker (2012) <DOI:10.1002/jrsm.1058>;
 - net heat plot and design-based decomposition of Cochran's Q according to Krahn et al. (2013) <DOI:10.1186/1471-2288-13-35>;
 - measures characterizing the flow of evidence between two treatments by König et al. (2013) <DOI:10.1002/sim.6001>;
 - ranking of treatments (frequentist analogue of SUCRA) according to Rücker & Schwarzer (2015) <DOI:10.1186/s12874-015-0060-8>;
 - automated drawing of network graphs described in Rücker & Schwarzer (2016) <DOI:10.1002/jrsm.1143>.
Performs network-based source estimation. Different approaches are available: effective distance median, recursive backtracking, and centrality-based source estimation. Additionally, we provide public transportation network data as well as methods for data preparation, source estimation performance analysis and visualization.
Package for the pre-processing and normalization of graphs.
Functions for assessing the replication/preservation of a network 
  module's topology across datasets through permutation testing.
NetSim allows to combine and simulate a variety of micro-models to research their impact on the macro-features of social networks.
A set of functions for studying network robustness, resilience and vulnerability.
A collection of network inference methods for co-expression networks, quantitative network distances and a novel framework for network stability analysis.
Implements various simple function utilities and flexible pipelines to generate circular images for visualizing complex genomic and network data analysis features.
Tools to create and modify network objects.  The network class can represent a range of relational data types, and supports arbitrary vertex/edge/graph attributes.
This permutation based hypothesis test, suited for Gaussian and
    binary data, assesses the difference between two networks based on several
    invariance measures (network structure invariance, global strength invariance,
    edge invariance). Network structures are estimated with l1-regularized partial
    correlations (Gaussian data) or with l1-regularized logistic regression (eLasso,
    binary data). Suited for comparison of independent and dependent samples
    (currently, only for one group measured twice).
Creates 'D3' 'JavaScript' network, tree, dendrogram, and Sankey
    graphs from 'R'.
Simple interface routines to facilitate the handling of network objects with complex intertemporal data. This is a part of the "statnet" suite of packages for network analysis.
A collection of dynamic network data sets from various sources and multiple authors represented as 'networkDynamic'-formatted objects.
Functions useful
    for producing estimates from data that were collected using network
    reporting techniques like network scale-up, indirect sampling,
    network reporting, and sibling history.
Implements some risk measures for (financial) networks, such as DebtRank, Impact Susceptibility, Impact Diffusion and Impact Fluidity.
Tools to simulate bipartite networks/graphs with the
        degrees of the nodes fixed and specified. 'networksis' is part
        of the 'statnet' suite of packages for network analysis.
networkTomography implements the methods developed and evaluated in
    Blocker and Airoldi (2011) and Airoldi and Blocker (2012). These include the
    authors' own dynamic multilevel model with calibration based upon a Gaussian
    state-space model in addition to implementations of the methods of Tebaldi &
    West (1998; Poisson-Gamma model with MCMC sampling), Zhang et al. (2002;
    tomogravity), Cao et al. (2000; Gaussian model with mean-variance relation),
    and Vardi (1996; method of moments). Data from the 1router network of Cao et
    al. (2000), the Abilene network of Fang et al. (2007), and the CMU network
    of Blocker and Airoldi (2011) are included for testing and reproducibility.
RBF and MLP neural networks with graphical user interface
Training of neural networks using backpropagation,
    resilient backpropagation with (Riedmiller, 1994) or without
    weight backtracking (Riedmiller and Braun, 1993) or the
    modified globally convergent version by Anastasiadis et al.
    (2005). The package allows flexible settings through
    custom-choice of error and activation function. Furthermore,
    the calculation of generalized weights (Intrator O & Intrator
    N, 1993) is implemented.
Visualization and analysis tools to aid in the interpretation of
    neural network models.  Functions are available for plotting,
    quantifying variable importance, conducting a sensitivity analysis, and
    obtaining a simple list of model weights.
Base package for 'Neuroconductor', which includes many helper functions 
    that interact with objects of class 'nifti', implemented by
    package 'oro.nifti', for reading/writing and also other manipulation functions.
Annotated neuroblastoma copy number profiles,
	     a benchmark data set for change-point detection algorithms.
A collection of data structures that represent
    volumetric brain imaging data. The focus is on basic data handling for 3D
    and 4D neuroimaging data. In addition, there are function to read and write
    NIFTI files and limited support for reading AFNI files.
Contains free datasets and statistical functions useful in psychology, neuropsychology and neuroscience.
The package allows users to generate fMRI time series or 4D data. Some high-level functions are created for fast data generation with only a few arguments and a diversity of functions to define activation and noise. For more advanced users it is possible to use the low-level functions and manipulate the arguments.
Computes the probability density function, cumulative distribution function, quantile function, random numbers and measures of inference for the following general families of  distributions (each family defined in terms of an arbitrary cdf G): Marshall Olkin G distributions, exponentiated G distributions, beta G distributions, gamma G distributions, Kumaraswamy G distributions, generalized beta G distributions, beta extended G distributions, gamma G distributions, gamma uniform G distributions, beta exponential G distributions, Weibull G distributions, log gamma G I distributions, log gamma G II distributions, exponentiated generalized G distributions, exponentiated Kumaraswamy G distributions, geometric exponential Poisson G distributions, truncated-exponential skew-symmetric G distributions, modified beta G distributions, and exponentiated exponential Poisson G distributions.
Indices, heuristics and strategies to help determine the number of factors/components to retain:
                1. Acceleration factor (af with or without Parallel Analysis);
                2. Optimal Coordinates (noc with or without Parallel Analysis);
                3. Parallel analysis (components, factors and bootstrap);
                4. lambda > mean(lambda) (Kaiser, CFA and related);
                5. Cattell-Nelson-Gorsuch (CNG);
                6. Zoski and Jurs multiple regression (b, t and p);
                7. Zoski and Jurs standard error of the regression coeffcient (sescree);
                8. Nelson R2;
                9. Bartlett khi-2;
               10. Anderson khi-2;
               11. Lawley khi-2 and
               12. Bentler-Yuan khi-2.
Numerical Formal Concept Analysis (nFCA) is a modern unsupervised learning tool for analyzing general numerical data. Given input data, this R package nFCA outputs two nFCA graphs: a H-graph and an I-graph that reveal systematic, hierarchical clustering and inherent structure of the data.
An implementation of the network fingerprint framework that introduced 
  in paper "Network fingerprint: a knowledge-based characterization of biomedical 
  networks" (Cui, 2015) <doi:10.1038/srep13286>. This method worked by making 
  systematic comparisons to a set of well-studied "basic networks", measuring 
  both the functional and topological similarity.  A biological could be
  characterized as a spectrum-like vector consisting of similarities to basic 
  networks. It shows great potential in biological network study.
An n-gram is a sequence of n "words" taken, in order, from a
    body of text.  This is a collection of utilities for creating,
    displaying, summarizing, and "babbling" n-grams.  The
    'tokenization' and "babbling" are handled by very efficient C
    code, which can even be built as its own standalone library.
    The babbler is a simple Markov chain.  The package also offers
    a vignette with complete example 'workflows' and information about
    the utilities offered in the package.
A simple n-gram (contiguous sequences of n items from a
    given sequence of text) tokenizer to be used with the 'tm' package with no
    'rJava'/'RWeka' dependency.
Provides tools for analyzing spatial data, especially non-Gaussian areal data. The current version supports the sparse
    spatial generalized linear mixed model of Hughes and Haran (2013) <DOI:10.1111/j.1467-9868.2012.01041.x> and the centered
	autologistic model of Caragea and Kaiser (2009) <DOI:10.1198/jabes.2009.07032>.
Body Shape and related measurements from the US National Health
    and Nutrition Examination Survey (NHANES, 1999-2004).  See
    http://www.cdc.gov/nchs/nhanes.htm for details.
Utility to retrieve data from the National Health and Nutrition
    Examination Survey (NHANES).
NHEMOtree performs cost-sensitive classification by
        solving the two-objective optimization problem of minimizing
        misclassification rate and minimizing total costs for
        classification. The three methods comprised in NHEMOtree are
        based on EMOAs with either tree representation or bitstring
        representation with an enclosed classification tree algorithm.
Compiling the NHL Real Time Scoring System Database for easy use in R
Bayesian HMM and NHMM modeling for multiple time series. The
    emission distribution can be mixtures of Gammas, Poissons, Normals and zero
    inflation is possible.
Calibration, simulation, validation of (non-)homogeneous Markov switching autoregressive models with Gaussian or von Mises innovations.  Penalization methods are implemented for Markov Switching Vector Autoregressive Models of order 1 only. Most functions of the package handle missing values.
Tools for modelling, ML estimation, validation analysis and simulation of non homogeneous Poisson processes in time.
Illustrate graphically the most common Null Hypothesis Significance Testing procedures. More specifically, this package provides functions to plot Chi-Squared, F, t (one- and two-tailed) and z (one- and two-tailed) tests, by plotting the probability density under the null hypothesis as a function of the different test statistic values. Although highly flexible (color theme, fonts, etc.), only the minimal number of arguments (observed test statistic, degrees of freedom) are necessary for a clear and useful graph to be plotted, with the observed test statistic and the p value, as well as their corresponding value labels. The axes are automatically scaled to present the relevant part and the overall shape of the probability density function. This package is especially intended for education purposes, as it provides a helpful support to help explain the Null Hypothesis Significance Testing process, its use and/or shortcomings.
Get or set UNIX priority (niceness) of running R process.
This package uses a probabilistic method to calculate niche
    regions and pairwise niche overlap using multidimensional niche indicator
    data (e.g., stable isotopes, environmental variables, etc.). The niche
    region is defined as the joint probability density function of the
    multidimensional niche indicators at a user-defined probability alpha
    (e.g., 95%).  Uncertainty is accounted for in a Bayesian framework, and the
    method can be extended to three or more indicator dimensions.  It provides
    directional estimates of niche overlap, accounts for species-specific
    distributions in multivariate niche space, and produces unique and
    consistent bivariate projections of the multivariate niche region.  A
    forthcoming article by Swanson et al. (Ecology, 2014) provides a detailed
    description of the methodology.  See the package vignette for a worked
    example using fish stable isotope data.
Computes and plots the boundary between night and day.
Miscellaneous R functions developed over the course of statistical
	     research. These include utilities that supplement the existing
	     idiosyncrasies of R; extend plotting functionality and aesthetics; 
	     provide alternative presentations of matrix decompositions; extend 
	     types of random variables supported for simulation; extend access
	     to command line tools and system information, making work on remote
	     systems easier.
Flexible application of algorithms to models specified in the BUGS
    language. Algorithms can be written in the NIMBLE language and made available to
    any model.
Japan-specific data is sometimes too unhandy for R users to manage. The utility functions and data in this package disencumber us from such an unnecessary burden.
Fast and Accurate Trisomy Prediction in Non-Invasive Prenatal
    Testing.
Datasets for testing nonlinear regression routines.
Fundamental physical constants (Quantity, Value, Uncertainty, Unit) for 
    SI (International System of Units) and non-SI units, plus unit conversions
    Based on the data from NIST (National Institute of Standards and Technology, USA)
Noninferiority tests for difference in failure rates at a prespecified control rate or prespecified time.
Optimization for nonlinear objective and constraint functions.  Linear or nonlinear equality and inequality constraints are allowed.  It accepts the input parameters as a constrained matrix.
Solve a system of nonlinear equations using a Broyden or a Newton method
             with a choice of global strategies such as line search and trust region.
             There are options for using a numerical or user supplied Jacobian,
             for specifying a banded numerical Jacobian and for allowing
             a singular or ill-conditioned Jacobian.
Fit and compare Gaussian linear and nonlinear mixed-effects models.
This package combines the odesolve and nlme packages for
        mixed-effects modelling using differential equations.
nlmeU: Datasets and utility  functions enhancing functionality of nlme package. Datasets, functions and scripts are described in book titled 'Linear Mixed-Effects Models:
 A Step-by-Step Approach' by Galecki and Burzykowski (2013). Package is under development.
Replacement for nls() tools for working with nonlinear least squares problems.
      The calling structure is similar to, but much simpler than, that of the nls()
      function. Moreover, where nls() specifically does NOT deal with small or zero
      residual problems, nlmrt is quite happy to solve them. It also attempts to be
      more robust in finding solutions, thereby avoiding 'singular gradient' messages
      that arise in the Gauss-Newton method within nls(). The Marquardt-Nash approach
      in nlmrt generally works more reliably to get a solution, though this may be 
      one of a set of possibilities, and may also be statistically unsatisfactory.
      Added print and summary as of August 28, 2012.
It includes three methods: K-profiles clustering, non-linear network reconstruction, and non-linear hierarchical clustering.
nloptr is an R interface to NLopt. NLopt is a free/open-source library for
    nonlinear optimization, providing a common interface for a number of
    different free optimization routines available online as well as original
    implementations of various other algorithms.
    See http://ab-initio.mit.edu/wiki/index.php/NLopt_Introduction for more
    information on the available algorithms. During installation on Unix the
    NLopt code is downloaded and compiled from the NLopt website.
Basic classes and methods for Natural Language Processing.
Utilities for Natural Language Processing.
Likelihood inference based on higher order approximations 
  for nonlinear models with possibly non constant variance
This is a package which can help you search for the root
        of a equation.
Estimate the non-linear odds ratio and plot it against a continuous exposure.
Adds brute force and multiple starting values to nls.
Estimation of structural equation models with nonlinear effects
  and underlying nonnormal distributions.
Non-linear shrinkage estimation of population eigenvalues and covariance
    matrices, based on publications by Ledoit and Wolf (2004, 2015, 2016).
Data sets and nonlinear regression models dedicated to predictive microbiology
Fit univariate non-linear scale mixture of
        skew-normal(NL-SMSN) regression.
Provides tools for working with nonlinear least squares problems.
      It is intended to eventually supersede the nls() function in the R distribution.
      For example, nls() specifically does NOT deal with small or zero
      residual problems. Its Gauss-Newton method frequently stops with 'singular 
	gradient' messages.
Fit biologically meaningful distribution functions to
  time-sequence data (phenology), estimate parameters to draw the cumulative
  distribution function and probability density function and calculate standard
  statistical moments and percentiles.
Several tools for assessing the quality of fit of a
        gaussian nonlinear model are provided.
Utilities and kinship information for behavior genetics and
    developmental research using the National Longitudinal Survey of Youth
    (NLSY; <http://www.bls.gov/nls/>).
Uses a modified lifting algorithm on which it builds the
        nondecimated lifting transform. It has applications in wavelet
        shrinkage.
R functions for (non)linear time series analysis. A core
        topic is order estimation through cross-validation.
Provides functions to calculate the normalised Lineage-Through-
    Time (nLTT) statistic, given two phylogenetic trees. The nLTT statistic measures
    the difference between two Lineage-Through-Time curves, where each curve is
    normalised both in time and in number of lineages.
Wald Test for nonlinear restrictions on model parameters and confidence
             intervals for nonlinear functions  of parameters using delta-method.  Applicable
             after ANY model, provided parameters estimates and their covariance matrix are
             available.
Without the need to know the true number of change-points in advance,
             this package provides a function for detecting multiple change-points
             using  a non-parametric maximum likelihood approach.
Provides a framework to perform Non-negative Matrix
    Factorization (NMF). The package implements a set of already published algorithms
    and seeding methods, and provides a framework to test, develop and plug
    new/custom algorithms. Most of the built-in algorithms have been optimized
    in C++, and the main interface function provides an easy way of performing
    parallel computations on multicore machines.
Wrapper package for the nmfgpu library, which implements several
    Non-negative Matrix Factorization (NMF) algorithms for CUDA platforms.
    By using the acceleration of GPGPU computing, the NMF can be used for
    real-world problems inside the R environment. All CUDA devices starting with
    Kepler architecture are supported by the library.
Calculates the normalized mutual information (NMI) of two community structures in network analysis.
Functions, examples and data from the book
 "Numerical Methods and Optimization in Finance" by M.
 'Gilli', D. 'Maringer' and E. Schumann (2011), ISBN
 978-0123756626.  The package provides implementations of
 several optimisation heuristics, such as Differential
 Evolution, Genetic Algorithms and Threshold Accepting.
 There are also functions for the valuation of financial
 instruments, such as bonds and options, and functions that
 help with stochastic simulations.
Software for feed-forward neural networks with a single
  hidden layer, and for multinomial log-linear models.
Computing prediction intervals of neural network models (e.g.backpropagation) at certain confidence level. It can take the output from models trained by other packages like 'nnet', 'neuralnet', 'RSNNS', etc.
Estimates of coefficients of lasso penalized linear regression and generalized linear models subject to non-negativity constraints on the parameters using multiplicative iterative algorithm. Entire regularization path for a sequence of lambda values can be obtained. Functions are available for creating plots of regularization path, cross validation and estimating coefficients at a given lambda value. There is also provision for obtaining standard error of coefficient estimates.
This is a package for Non-Negative Linear Models (NNLM). It implements
    fast sequential coordinate descent algorithms for non-negative linear regression
    and non-negative matrix factorization (NMF). It supports mean square error and Kullback-Leibler divergence loss.
    Many other features are also implemented, including missing value imputation, domain knowledge integration,
    designable W and H matrices and multiple forms of regularizations.
An R interface to the Lawson-Hanson implementation of an
        algorithm for non-negative least squares (NNLS).  Also allows
        the combination of non-negative and non-positive constraints.
Nonlinear nonparametric statistics using partial moments.  Partial moments are the elements of variance and asymptotically approximate the area of f(x).  These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences.  NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modelling, Normalization and Stochastic dominance.  All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).
Helps a clinical trial team  discuss
    the clinical goals  of a well-defined biomarker with a diagnostic,
    staging, prognostic, or predictive purpose.  From this discussion will
    come a statistical plan for a (non-randomized) validation trial.
    Both prospective and retrospective trials are supported. In a specific
    focused discussion, investigators should determine the range of
    "discomfort" for the NNT, number needed to treat.  The meaning of
    the discomfort range, [NNTlower, NNTupper], is that within this range
    most physicians would feel discomfort either in treating or withholding
    treatment. A pair of NNT values bracketing that range, NNTpos and NNTneg,
    become the targets of the study's design.  If the trial can demonstrate
    that a positive biomarker test yields an NNT less than NNTlower,
    and that a negative biomarker test yields an NNT less than NNTlower,
    then the biomarker may be useful for patients. A highlight of the package
    is visualization of a "contra-Bayes" theorem, which produces criteria for
    retrospective case-controls studies.
Node harvest is a simple interpretable tree-like estimator for high-dimensional regression and classification. A few nodes are selected from an initially large ensemble of nodes, each associated with a positive weight. New observations can fall into one or several nodes and predictions are the weighted average response across all these groups. The package offers visualization of the estimator. Predictions can return the nodes a new observation fell into, along with the mean response of training observations in each node, offering a simple explanation of the prediction.
An implementation of the nodiv algorithm, see Borregaard, M.K., Rahbek, C., Fjeldsaa, J., Parra, J.L., Whittaker, R.J. & Graham, C.H. 2014. Node-based analysis of species distributions. Methods in Ecology and Evolution 5(11): 1225-1235. <DOI:10.1111/2041-210X.12283>. Package for phylogenetic analysis of species distributions. The main function goes through each node in the phylogeny, compares the distributions of the two descendant nodes, and compares the result to a null model. This highlights nodes where major distributional divergence have occurred. The distributional divergence for these nodes is mapped using the SOS statistic.
The NOIA model, as described extensively in Alvarez-Castro & Carlborg (2007), is a framework facilitating the estimation of genetic effects and genotype-to-phenotype maps. This package provides the basic tools to perform linear and multilinear regressions from real populations (provided the phenotype and the genotype of every individuals), estimating the genetic effects from different reference points, the genotypic values, and the decomposition of genetic variances in a multi-locus, 2 alleles system. This package is presented in Le Rouzic & Alvarez-Castro (2008).
Functions to calculate estimates of intrinsic and extrinsic noise from the two-reporter single-cell experiment, as in Elowitz, M. B., A. J. Levine, E. D. Siggia, and P. S. Swain (2002) Stochastic gene expression in a single cell. Science, 297, 1183-1186.  Functions implement multiple estimators developed for unbiasedness or min Mean Squared Error (MSE) in Fu, A. Q. and Pachter, L. (2016). Estimating intrinsic and extrinsic noise from single-cell gene expression measurements.  <http://arxiv.org/abs/1601.03334>.
An extensive implementation of state-of-the-art and classical
    algorithms to preprocess label noise in classification problems.
Package for hierarchical clustering of objects characterized by
    nominal variables.
Analysis of a matrix of polytomous items using Nominal Logistic Biplots (NLB)
  according to Hernandez-Sanchez and Vicente-Villardon (2013). 
  The NLB procedure extends the binary logistic biplot to nominal (polytomous) data. 
  The  individuals are represented as points on a plane and the  variables are represented 
  as convex prediction regions rather than vectors as in a classical or binary biplot. 
  Using the methods from Computational Geometry, the set of prediction regions is converted to a set of points 
  in such a way that the prediction for each individual is established by its closest 
  "category point". Then interpretation is based on distances rather than on projections. 
  In this package we implement the geometry of such a representation and construct computational algorithms 
  for the estimation of parameters and the calculation of prediction regions.
A nomogram can not be easily applied,
    because it is difficult to calculate the points or even the survival probability.
  The package, including a function of nomogramEx(),
    is to extract the polynomial equations to calculate the points of each variable,
    and the survival probability corresponding to the total points.
A collection of various regional information determined by the
    U.S. Census Bureau along with demographic data.
Conduct a noncompartmental analysis as closely as possible to the most widely used commercial software for pharmacokinetic analysis, i.e. 'Phoenix(R) WinNonlin(R)' <https://www.certara.com/software/pkpd-modeling-and-simulation/phoenix-winnonlin/>.
             Some features include:
             1) Use CDISC SDTM PP domain terms.
             2) Automatic slope selection with the same criterion of WinNonlin(R)
             3) Support both 'linear-up linear-down' and 'linear-up log-down' method
             4) Calculate partial(interval) AUC with 'linear' or 'log' interpolation method
             For more details on noncompartmental analysis, see the reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107)
             *) Acknowledgement: Author thanks for the careful review and valuable input of Dr. Jee Eun Lee.
A finite-population significance test of the 'sharp' causal null hypothesis that
    treatment exposure X has no effect on final outcome Y, within the principal stratum of Compliers.
    A generalized likelihood ratio test statistic is used, and the resulting p-value is exact.
    Currently, it is assumed that there are only Compliers and Never Takers in the population.
Functions for nonlinear time series analysis. This package permits 
             the computation of the  most-used nonlinear statistics/algorithms
             including generalized correlation dimension, information dimension,
             largest Lyapunov exponent, sample entropy and Recurrence 
             Quantification Analysis (RQA), among others. Basic routines
             for surrogate data testing are also included. Part of this work 
             was based on the  book "Nonlinear time series analysis" by 
             Holger Kantz and Thomas Schreiber (ISBN: 9780521529020).
Loading NONMEM (NONlinear Mixed-Effect Modeling, <http://www.iconplc.com/innovation/solutions/nonmem/> ) output files and simulate with parameter uncertainty.
Testing non-nested models via theory supplied by Vuong (1989) <DOI:10.2307/1912557>.
    Includes tests of model distinguishability and of model fit that can be applied
    to both nested and non-nested models. Also includes functionality to obtain
    confidence intervals associated with AIC and BIC. This material is based on work
    supported by the National Science Foundation under Grant Number SES-1061334.
This package contains functions for measuring efficiency
        and productivity of decision making units (DMUs) under the
        framework of Data Envelopment Analysis (DEA) and its
        variations.
Provides tests of significance for covariates (or groups of covariates) in a fully nonparametric regression model and a variable (or group) selection procedure based on False Discovery Rate. In addition, it provides a function for local polynomial regression for any number of dimensions, using a bandwidth specified by the user or automatically chosen by cross validation or an adaptive procedure.
This package offers a comprehensive data analysis if
        stratification and matching by the propensity score is done.
        Several functions are implemented, starting from the selection
        of the propensity score model up to estimating propensity score
        based treatment or exposure effects. All functions can be
        applied separately as well as combined.
Screening a HRMS data set for peaks related by (1) isotope patterns, (2) different adducts of the same molecule and/or (3) homologue series. The resulting isotopic pattern and adduct groups can then be combined to so-called components, with homologue series information attached. Also allows plotting and filtering HRMS data for mass defects, frequent m/z distances and components vs. non-components.
Data sets for isotope pattern grouping of LC-HRMS peaks with package nontarget. Based on a vast set of unique PubChem molecular formulas, quantized (a) m/z, (b) m/z differences, (c) intensity ratios and (d) marker centroids of simulated centroid pairs are listed for different instrument resolutions.
A non-parametric test for multi-observer concordance and
    differences between concordances in (un)balanced data.
Estimation of party/candidate ideological positions
             that correspond to a Nash equilibrium along a 
             one-dimensional space.
Onedimensional Normal Mixture Models Classes, for, e.g.,
   density estimation or clustering algorithms research and teaching;
   providing the widely used Marron-Wand densities.  Now fitting to data
   by ML (Maximum Likelihood) or EM estimation.
The Nordklim dataset 1.0 is a unique and useful achievement for climate 
    analysis. It includes observations of twelve different climate elements from 
    more than 100 stations in the Nordic region, in time span over 100 years.
    The project contractors were NORDKLIM/NORDMET on behalf of the National 
    meteorological services in Denmark (DMI), Finland (FMI), Iceland (VI), 
    Norway (DNMI) and Sweden (SMHI).
Analysis of multivariate normal datasets with missing
        values
Functions for parameter estimation, Bayesian posterior simulation
        and multiple imputation from incomplete multivariate data under a
        normal model.
Builds general noise SVR models using Naive Online R Minimization Algorithm, NORMA, an optimization method based on classical stochastic gradient descent suitable for computing SVR models in an online setting.
The functions proposed in this package compute the density of the sum of a Gaussian and a gamma random variables, estimate the parameters and correct the noise effect in a gamma-signal and Gaussian-noise model. This package has been used to implement the background correction method for Illumina microarray data presented in Plancade S., Rozenholc Y. and Lund E. "Generalization of the normal-exponential model : exploration of a more accurate parameterization for the signal distribution on Illumina BeadArrays", BMC Bioinfo 2012, 13(329).
This package provides functions for the normal Laplace
        distribution. It is currently under development and provides
        only limited functionality. Density, distribution and quantile
        functions, random number generation, and moments are provided.
Collection of utilities referred to Exponential Power distribution, 
  also known as General Error Distribution (see Mineo, A.M. and Ruggieri, M. (2005), A software Tool for the Exponential Power Distribution: The normalp package. In Journal of Statistical Software, Vol. 12, Issue 4)
The robustness of many of the statistical techniques, such as factor analysis, applied in 
          the social sciences rests upon the assumption of item-level normality. However, when dealing 
          with real data, these assumptions are often not met. The Box-Cox transformation (Box & Cox, 1964)
          <http://www.jstor.org/stable/2984418> provides an optimal transformation for non-normal variables. Yet, for 
          large datasets of continuous variables, its application in current software programs is cumbersome
          with analysts having to take several steps to normalise each variable. We present an R package 
          'normalr' that enables researchers to make convenient optimal transformations of multiple variables
          in datasets. This R package enables users to quickly and accurately: (1) anchor all of their 
          variables at 1.00, (2) select the desired precision with which the optimal lambda is estimated, 
          (3) apply each unique exponent to its variable, (4) rescale resultant values to within their 
          original X1 and X(n) ranges, and (5) provide original and transformed estimates of skewness, 
          kurtosis, and other inferential assessments of normality.
Functions for normalizing psychometric test scores. The normalization aims at correcting the metrological properties of the psychometric tests such as the ceiling and floor effects and the curvilinearity (unequal interval scaling). Functions to compute and plot predictions in the natural scale of the psychometric test from the estimates of a linear mixed model estimated on the normalized scores are also provided.
Evaluates the probability density function of the sum of
        the Gaussian and Student's t density on 3 degrees of freedom.
        Evaluates the p.d.f. of the sphered Student's t density
        function.  Also evaluates the erf, and erfc functions on
        complex-valued arguments. Thanks to Krishna Myneni the function
        is calculates the Faddeeva function also!
Tests for the composite hypothesis of normality
Includes Omnibus Univariate and Multivariate Normality
        Tests (See Doornik and Hansen (1994)). One variation allows for
        the possibility of weak dependence rather than independence in
        the variable(s). Also included is an univariate white noise
        test where the null hypothesis is "white noise" rather than
        strict "white noise".
CIPW Norm (acronym from the surnames of the authors: Cross, Iddings, Pirrson and Washington)
             is the most commonly used calculation algorithm to estimate the standard mineral assemblages
             for igneous rocks from its geochemical composition. NORRRM (acronym from noRm, R language and
             Renee) is the highly consistent program to calculate the CIPW Norm.
An implementation of a specific method for generating
    n-dimensional random vectors with given marginal distributions and
    correlation matrix. The method uses the NORTA (NORmal To Anything)
    approach which generates a standard normal random vector and then
    transforms it into a random vector with specified marginal distributions
    and the RA (Retrospective Approximation) algorithm which is a generic
    stochastic root-finding algorithm. The marginals can be continuous or
    discrete. See the vignette of package for more details.
Five omnibus tests for testing the composite hypothesis of
        normality.
Tests the goodness-of-fit to the Normal distribution for the errors of an ARMA model.
The nose package consists of a collection of three
        functions for classifying sparseness in typical 2 x 2 data sets
        with at least one cell should have zero count. These functions
        are based on the three widely applied summary measures for 2 x
        2 categorical data viz, Risk Difference (RD), Relative Risk
        (RR), Odds Ratio (OR). This package helps to identify suitable
        continuity correction for zero cells when a multi centre
        analysis or a meta analysis is carried out. Further, it can be
        considered as a tool for sensitivity analysis for adding a
        continuity correction and to identify the presence of Simpson's
        paradox.
Provides functions to produce advanced ascii graphics, directly to the terminal window. This package utilizes the txtplot() function from the 'txtplot' package, to produce text-based histograms, empirical cumulative distribution function plots, scatterplots with fitted and regression lines, quantile plots, density plots, image plots, and contour plots.
Provides efficient implementation of the Narrowest-Over-Threshold methodology for detecting an unknown number of change-points occurring at unknown locations in one-dimensional data following 'deterministic signal + noise' model. Currently implemented scenarios are: piecewise-constant signal, piecewise-constant signal with a heavy-tailed noise, piecewise-linear signal, piecewise-quadratic signal, piecewise-constant signal and with piecewise-constant variance of the noise.
Send desktop notifications from R, on macOS, Windows and Linux.
Functions to flash your hue lights, or text yourself, from R. Designed to be used with long running scripts.
This Package provides a connection to the pushover.net API
        to send push notification to your smartphone directly from R.
        (ACCOUNT REQUIRED!)
Estimate Large correlation and covariance matrices and their inverses using 
 integration of the sample and thresholded correlation and covariance estimators.
The noweb system for source code, implemented in R.
The Nozzle package provides an API to generate HTML
        reports with dynamic user interface elements based on
        JavaScript and CSS (Cascading Style Sheets). Nozzle was
        designed to facilitate summarization and rapid browsing of
        complex results in data analysis pipelines where multiple
        analyses are performed frequently on big data sets. The package
        can be applied to any project where user-friendly reports need
        to be created.
This package provides a variety of nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered, and ordered factor data types. We would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC:www.nserc.ca), the Social Sciences and Humanities Research Council of Canada (SSHRC:www.sshrc.ca), and the Shared Hierarchical Academic Research Computing Network (SHARCNET:www.sharcnet.ca).
Computes interdaily stability (IS), intradaily variability (IV) & the relative amplitude (RA) from actigraphy data as described in Blume et al. (2016) <doi:10.1016/j.mex.2016.05.006> and van Someren et al. (1999) <doi:10.3109/07420529908998724>. Additionally, it also computes L5 (i.e. the 5 hours with lowest average actigraphy amplitude) and M10 (the 10 hours with highest average amplitude) as well as the respective start times. The flex versions will also compute the L-value for a user-defined number of minutes. IS describes the strength of coupling of a rhythm to supposedly stable zeitgebers. It varies between 0 (Gaussian Noise) and 1 for perfect IS. IV describes the fragmentation of a rhythm, i.e. the frequency and extent of transitions between rest and activity. It is near 0 for a perfect sine wave, about 2 for Gaussian noise and may be even higher when a definite ultradian period of about 2 hrs is present. RA is the relative amplitude of a rhythm. Note that to obtain reliable results, actigraphy data should cover a reasonable number of days.
With this package, it is possible to compute nonparametric simultaneous confidence intervals for relative contrast effects in the unbalanced one way layout. Moreover, it computes simultaneous p-values. The simultaneous confidence intervals can be computed using multivariate normal distribution, multivariate t-distribution with a Satterthwaite Approximation of the degree of freedom or using multivariate range preserving transformations with Logit or Probit as transformation function. 2 sample comparisons can be performed with the same methods described above. There is no assumption on the underlying distribution function, only that the data have to be at least ordinal numbers.
The package "nparLD" is designed to perform nonparametric
        analysis of longitudinal data in factorial experiments.
        Longitudinal data are those which are collected from the same
        subjects over time, and they frequently arise in biological
        sciences. Nonparametric methods do not require distributional
        assumptions, and are applicable to a variety of data types
        (continuous, discrete, purely ordinal, and dichotomous).  Such
        methods are also robust with respect to outliers and for small
        sample sizes.
Nonparametric Tests for Main Effects, Simple Effects and Interaction Effect with Censored Data and Two Factorial Influencing Variables.
These routines create multiple imputations of missing at random categorical data, with or without structural zeros. Imputations are based on Dirichlet process mixtures of multinomial distributions, which is a non-parametric Bayesian modeling approach that allows for flexible joint modeling.
A variety of functions for the best known and most innovative approaches to nonparametric boundary estimation. The selected methods are concerned with empirical, smoothed, unrestricted as well as constrained fits under both separate and multiple shape constraints. They cover robust approaches to outliers  as well as data envelopment techniques based on piecewise polynomials, splines, local linear fitting, extreme values and kernel smoothing. The package also seamlessly allows for Monte Carlo comparisons among these different estimation methods.  Its use is illustrated via a number of empirical applications and simulated examples.
An implementation of nonparametric combination of hypothesis tests.
    This package performs nonparametric combination (Pesarin and Salmaso 2010),
    a permutation-based procedure for jointly testing multiple hypotheses. The
    tests are conducted under the global "sharp" null hypothesis of no effects,
    and the component tests are combined on the metric of their p-values. A
    key feature of nonparametric combination is that it accounts for the
    dependence among tests under the null hypothesis. In addition to the
    "NPC" function, which performs nonparametric combination itself, the
    package also contains a number of helper functions, many of which calculate
    a test statistic given an input of data.
An array of nonparametric and parametric estimation methods for cognitive diagnostic models, including nonparametric classification of examinee attribute profiles, joint maximum likelihood estimation (JMLE) of examinee attribute profiles and item parameters, and nonparametric refinement of the Q-matrix, as well as conditional maximum likelihood estimation (CMLE) of examinee attribute profiles given item parameters and CMLE of item parameters given examinee attribute profiles. Currently the nonparametric methods in the package support both conjunctive and disjunctive models, and the parametric methods in the package support the DINA model, the DINO model, the NIDA model, the G-NIDA model, and the R-RUM model.
This package implements nonparametric smoothing methods for circular data.
A non parametric test for change points detection in the dependence between the components of multivariate data, with or without (multiple) changes in the marginal distributions.
Provides nonparametric tests for assessing whether possibly serially dependent univariate or multivariate observations have the same c.d.f. or not. In addition to tests focusing directly on the c.d.f., the package contains tests designed to be particularly sensitive to changes in the underlying copula, Spearman's rho or certain quantities that can be estimated using one-sample U-statistics of order two such as the variance, Gini's mean difference or Kendall's tau. The latest addition is a nonparametric test for detecting changes in the distribution of independent block maxima.
Routines to compute normalised prediction distribution
        errors, a metric designed to evaluate non-linear mixed effect
        models such as those used in pharmacokinetics and
        pharmacodynamics
Dirichlet process mixture of multivariate normal, skew normal or skew t-distributions
             modeling oriented towards flow-cytometry data pre-processing applications.
An R-package for calculating sample size of a survival trial with or without cure fractions
Returns nonparametric aligned rank tests for the interaction in two-way factorial designs, on R data sets with repeated measures in 'wide' format. Five ANOVAs tables are reported. A PARAMETRIC one on the original data, one for a CHECK upon the interaction alignments, and three aligned rank tests: one on the aligned REGULAR, one on the FRIEDMAN, and one on the KOCH ranks. In these rank tests, only the resulting values for the interaction are relevant.
This package provides routines for plotting
      linkage and association results along a chromosome,
      with marker names displayed along the top border.
      There are also routines for generating BED and BedGraph
      custom tracks for viewing in the UCSC genome browser.
      The data reformatting program Mega2 uses this
      package to plot output from a variety of
      programs.
Performing drug response analyses and IC50 estimations using n-Parameter logistic regression. Can also be applied to proliferation analyses.
Given a failure type, the function computes covariate-specific probability of failure over time and covariate-specific conditional hazard rate based on possibly right-censored competing risk data. Specifically, it computes the non-parametric maximum-likelihood estimates of these quantities and their asymptotic variances in a semi-parametric mixture model for competing-risks data, as described in Chang et al. (2007a).
Nonparametric maximum likelihood estimation or Gaussian
        quadrature for overdispersed generalized linear models and
        variance component models
The main method npmpm calculates bacterial concentrations
        during food manufacture after a contamination. Variability and
        uncertainty are included by use of probability distributions
        and Monte Carlo Simulation. The model aims at predicting
        possible bacterial concentrations at one certain point in time
        s, e.g. at the end of a process chain. The process steps of
        this process chain are run through in linear order.
        Experimental data that match current process step conditions
        are gathered, and one deterministic primary model is fitted to
        every series of measured values. From every fitted curve one
        concentration of bacteria at time s is computed, yielding a set
        of concentrations. This sample of possible contamination sizes
        is assumed to follow a certain probability distribution. After
        calculation of distribution parameters, one value is randomly
        drawn from this probability distribution. This value may be
        modified, and then serves as contamination for the next process
        step.
Fit multinomial logistic regression with a penalty on the nuclear
    norm of the estimated regression coefficient matrix, using proximal
    gradient descent.
Performs analysis of one-way multivariate data, for small samples using Nonparametric techniques. Using approximations for ANOVA Type, Wilks' Lambda, Lawley Hotelling, and Bartlett Nanda Pillai Test statics, the package compares the multivariate distributions for a single explanatory variable.  The comparison is also performed using a permutation test for each of the four test statistics. The package also performs an all-subsets algorithm regarding variables and regarding factor levels.
Nonparametric Multivariate Change Point Model
Implements a nonparametric statistical test for rank or
        score data from partially-balanced incomplete block-design
        experiments.
A method for obtaining nonparametric estimates of regression models
    with or without factor-by-curve interactions using local polynomial kernel
    smoothers or splines. Additionally, a parametric model (allometric model) can be
    estimated.
Tools for data-driven analytical statistical inference for Local Polynomial Regression estimators and Kernel Density Estimation.
Given a sample of class 0 and class 1 and a classification method, the package generates the corresponding Neyman-Pearson classifier with a pre-specified type-I error control and Neyman-Pearson Receiver Operating Characteristics.
Implements several nonparametric regression approaches for the inclusion of covariate information on the receiver operating characteristic (ROC) framework.
Small functions to make working with survey data in the context of
    a Net Promoter programme easier. Specifically, data transformation methods,
    some methods for examining the statistical properties of the NPS, such as
    its variance and standard errors, and some simple inferential testing
    procedures. Net Promoter and NPS are registered trademarks of Bain &
    Company, Satmetrix Systems and Fred Reichheld.
Provides a variety of tools for nonparametric and parametric efficiency measurement.
This package contains a collection of functions to to perform nonparametric deconvolution using simulation extrapolation (SIMEX). We propose an estimator that adopts the SIMEX idea but bypasses the simulation step in the original SIMEX algorithm. There is no bandwidth parameter and the estimate is determined by appropriately selecting "design points". See details in: Wang, X.F., Sun, J. and Fan, Z. (2011). Deconvolution density estimation with heteroscedastic errors using SIMEX.
Functions and datasets used in the book Nonparametric Statistical Methods Using R.
Multidimensional nonparametric spatio-temporal (geo)statistics.
    S3 classes and methods for multidimensional: linear binning,
    local polynomial kernel regression, density and variogram estimation.
    Nonparametric methods for trend and variogram inference.
Package 'npst' generalizes Hewitt's (1971) test for seasonality and
  Rogerson's (1996) extension based on Monte-Carlo simulation.
Contains functions for non-parametric survival analysis of
	     exact and interval-censored observations.
Calculating the net reclassification improvement (NRI) for risk prediction models with time to event and binary data.
Post-normalization of total copy-number estimates.
This package implements two algorithms for canonical correlation
    analysis (CCA) that are based on iterated regression
    steps. By choosing the appropriate regression algorithm for each data
    modality, it is possible to enforce sparsity, non-negativity or other kinds
    of constraints on the projection vectors. Multiple canonical variables are
    computed sequentially using a generalized deflation scheme, where the
    additional correlation not explained by previous variables is maximized.
    'nscancor' is used to analyze paired data from two domains, and has the same
    interface as the 'cancor' function from the 'stats' package (plus some extra
    parameters). 'mcancor' is appropriate for analyzing data from three or more
    domains.
Simulation and estimation for Neyman-Scott spatial cluster point process models and their extensions. For estimating parameters by the simplex method, parallel computation using OpenMP application programming interface is available.
Collection of functions designed to calculate numerical standard error (NSE) of univariate time series 
  as described in Ardia et al. (2016) <doi:10.2139/ssrn.2741587> and Ardia and Bluteau (2017) <doi:10.2139/ssrn.2741587>.
This package provide functions for box-constrained
        multiobjective optimization using the elitist non-dominated
        sorting genetic algorithm - NSGA-II. Fast non-dominated
        sorting, crowding distance, tournament selection, simulated
        binary crossover, and polynomial mutation are called in the
        main program, nsga2R, to complete the search.
A Gaussian process regression using a Gaussian kernel for both
    one-sample and two-sample cases. Includes non-stationary Gaussian kernel
    (exponential decay function) and several likelihood ratio tests for
    differential testing along target points.
Designed to replace the tables which were in the back of the first two editions of Hollander and Wolfe - Nonparametric Statistical Methods.  Exact procedures are performed when computationally possible.  Monte Carlo and Asymptotic procedures are performed otherwise.  For those procedures included in the base packages, our code simply provides a wrapper to standardize the output with the other procedures in the package.
This package implements two methods for performing a constrained
    principal component analysis (PCA), where non-negativity and/or sparsity
    constraints are enforced on the principal axes (PAs). The function
    'nsprcomp' computes one principal component (PC) after the other. Each PA
    is optimized such that the corresponding PC has maximum additional variance
    not explained by the previous components. In contrast, the function
    'nscumcomp' jointly computes all PCs such that the cumulative variance is
    maximal. Both functions have the same interface as the 'prcomp' function
    from the 'stats' package (plus some extra parameters), and both return the
    result of the analysis as an object of class 'nsprcomp', which inherits
    from 'prcomp'.
A collection of statistical tools for objective (non-supervised) applications 
             of the Regional Frequency Analysis methods in hydrology. 
             The package refers to the index-value method and, more precisely, helps the
             hydrologist to: (1) regionalize the index-value; (2) form homogeneous regions 
             with similar growth curves; (3) fit distribution functions to the 
             empirical regional growth curves.
A Bayesian framework for population group size estimation using the Network Scale Up Method (NSUM). Size estimates are based on a random degree model and include options to adjust for barrier and transmission effects.
Tools for 4D nucleome imaging. Quantitative analysis of the 3D nuclear landscape recorded with super-resolved fluorescence microscopy.
Modelling the vegetation, carbon, nitrogen and water dynamics of undisturbed open bog ecosystems in a temperate to sub-boreal climate. The test data can be downloaded from http://jeroenpullens.github.io/NUCOMBog_data/. And the executable of the model can downloaded from https://github.com/jeroenpullens/source_NUCOMBog-executable.
Tools for visual inference. Generate null data sets
    and null plots using permutation and simulation. Calculate distance metrics
    for a lineup, and examine the distributions of metrics.
Provides number-theoretic functions for factorization, prime numbers,
    twin primes, primitive roots, modular inverses, extended GCD, Farey 
    series and continuous fractions. Included are some divisor functions,
    or Euler's Phi function and Egyptian fractions.
Methods for calculating (usually) accurate
	numerical first and second order derivatives. Accurate calculations 
	are done using 'Richardson''s' extrapolation or, when applicable, a
	complex step derivative is available. A simple difference 
	method is also provided. Simple difference is (usually) less accurate
	but is much quicker than 'Richardson''s' extrapolation and provides a 
	useful cross-check. 
	Methods are provided for real scalar and vector valued functions.
Package for optimizing regular numeric problems in optically stimulated luminescence 
        dating, such as: equivalent dose calculation, dose rate determination, growth curve fitting,
        decay curve decomposition, statistical age model optimization, and statistical plot visualization.
This package contains data sets used as examples in the
        book "R in a Nutshell" from O'Reilly Media. For more
        information on this book, see
        http://shop.oreilly.com/product/0636920022008.do
This package contains the Audio Scrobbler data set used as
        an example in the book "R in a Nutshell" from O'Reilly Media.
        For more information about this book, see
        http://shop.oreilly.com/product/0636920022008.do
This package contains the baseball databank data set used
        as an example in the book "R in a Nutshell" from O'Reilly
        Media. For more information about this book, see
        http://shop.oreilly.com/product/0636920022008.do
Provides coordination and parallel execution facilities,
        as well as limited cross-language data exchange, using the
        netWorkSpaces server developed by REvolution Computing
Airline on-time data for all flights departing NYC in 2013.
    Also includes useful 'metadata' on airlines, airports, weather, and planes.
Convenient access to New Zealand election
    results by voting place.  Voting places have been matched to Regional Council,
    Territorial Authority, and Area Unit, to facilitate matching with additional data.
Datasets of driving offences and fines in New Zealand between 2009 and 2016.
    Originally published by the New Zealand Police at
    <http://www.police.govt.nz/about-us/publication/road-policing-driver-offence-data-january-2009-september-2016>.
Provides carefully chosen color palettes as used a.o. at OpenAnalytics <http://www.openanalytics.eu>.
A general purpose client to work with any 'OAI-PMH'
    (Open Archives Initiative Protocol for 'Metadata' Harvesting) service.
    The 'OAI-PMH' protocol is described at
    <http://www.openarchives.org/OAI/openarchivesprotocol.html>.
    Functions are provided to work with the 'OAI-PMH' verbs: 'GetRecord',
    'Identify', 'ListIdentifiers', 'ListMetadataFormats', 'ListRecords', and
    'ListSets'.
Harvest metadata using the Open Archives Initiative Protocol for Metadata
  Harvesting (OAI-PMH) version 2.0.
Interface to D-optimal design generation code of the Orthogonal Array package. Can generate D-optimal designs with specified number of runs and factors. The optimality of the designs is defined in terms of a user specified optimization function based on the D-efficiency and Ds-efficiency.
Offers a suite of functions for enhancing R plots.
Generalise the starting point of the array index
Trains and makes predictions from the OASIS method, described in
    detail in the paper "OASIS is Automated Statistical Inference for Segmentation,
    with applications to multiple sclerosis lesion segmentation in MRI" 
    <doi:10.1016/j.nicl.2013.03.002>. 
    OASIS is a method for multiple sclerosis (MS)
    lesion segmentation on structural magnetic resonance image (MRI) studies. OASIS
    creates probability maps of lesion presence using the FLAIR, T2, T1, and PD
    structural MRI volumes. This packages allows for training of the OASIS model
    and prediction of OASIS probability maps from a trained model with user supplied
    studies that have a gold standard lesion segmentation masks. The package will
    also create OASIS probability maps for MRI studies using the OASIS model from
    the OASIS paper if no gold standard lesion segmentation masks are available.
A set of indexes and tests for the analysis of social segregation.
An implementation of the Blinder-Oaxaca decomposition for linear regression models.
Data processing, visualisation and analysis of Limit Order Book
    event data.
Supports the definition of sets of properties on objects. Observers can listen to changes on individual properties or the set as a whole. The properties are meant to be fully self-describing. In support of this, there is a framework for defining enumerated types, as well as other bounded types, as S4 classes.
A mutable Signal object can report changes to its state,
    clients could register functions so that they are called whenever
    the signal is emited. The signal could be emited, disconnected,
    blocked, unblocked, and buffered.
This package conducts factor rotation techniques which
        intentd to identify a simple and well-clustered structure in a
        factor loading matrix.
Random forest with oblique decision trees for binary
        classification tasks. Discriminative node models in the tree
        are based on: ridge regression, partial least squares
        regression, logistic regression, linear support vector
        machines, or random coefficients.
Grows Oblique Trees to Classification Data.
The package includes two functions for measuring agreement. Raw Agreement Indices (RAI) to categorical data and Information-Based Measure of Disagreement (IBMD) to continuous data. It can be used for multiple raters and multiple readings cases.
Checks that a given dataset passes user-specified 
    rules. The main functions are observe_if() and inspect().
Implements the objective Bayesian methodology proposed in Consonni and Deldossi in order to choose the optimal experiment that better discriminate between competing models.
Observational studies are limited in that there could be
        an unmeasured variable related to both the response variable
        and the primary predictor.  If this unmeasured variable were
        included in the analysis it would change the relationship
        (possibly changing the conclusions).  Sensitivity analysis is a
        way to see how much of a relationship needs to exist with the
        unmeasured variable before the conclusions change.  This
        package provides tools for doing a sensitivity analysis for
        regression (linear, logistic, and cox) style models.
Estimates Optimal Classification scores from roll call
        votes supplied though a 'rollcall' object from package 'pscl'.
Computes optimal capital allocations based on some standard principles such as Haircut, Overbeck type II and the Covariance Allocation Principle. It also provides some shortcuts for obtaining the Value at Risk and the Expectation Shortfall, using both the normal and the t-student distribution, see Urbina and Guillén (2014)<doi:10.1016/j.eswa.2014.05.017> and Urbina (2013)<http://hdl.handle.net/2099.1/19443>.
This package provides a generic function for estimating
        positron emission tomography (PET) neuroreceptor occupancies
        from the total volumes of distribution of a set of regions of
        interest. Fittings methods include the simple 'reference
        region' and 'ordinary least squares' (sometimes known as
        occupancy plot) methods, as well as the more efficient
        'restricted maximum likelihood estimation'.
Supports the analysis of Oceanographic data, including 'ADCP'
    measurements, measurements made with 'argo' floats, 'CTD' measurements,
    sectional data, sea-level time series, coastline and topographic data, etc.
    Provides specialized functions for calculating seawater properties such as
    potential temperature in either the 'UNESCO' or 'TEOS-10' equation of state.
    Produces graphical displays that conform to the conventions of the Oceanographic
    literature.
This package contains a set of functions for manipulating and
    visualizing output from numerical ocean models. Many of the routines are
    targeted towards Lagrangian particle-tracking models with the data stored
    in NetCDF files. This package may be useful to spatial marine ecologists
    and biological or physical oceanographers.
Plotting toolbox for 2D oceanographic data (satellite data, sst, chla, ocean fronts & bathymetry). Recognized classes and formats include ncdf4, Raster, '.nc' and '.gz' files.
Functions for transforming and viewing 2-D and 3-D (oceanographic) data and model output.
Several important and Oceanographic datasets are provided. These are particularly useful to the Oce package, but can be helpful in a general context, as well.
Extends 'flexclust' with an R implementation of order constrained
  solutions in k-means clustering (Steinley and Hubert, 2008, <doi:10.1007/s11336-008-9058-z>).
Regression model where the response variable is a rank-indexed compositional vector (non-negative values that sum up to one and are ordered from the largest to the smallest). Parameters are estimated in the Bayesian framework using MCMC methods.
Helper methods for accessing data from web service based on OData Protocol.
  It provides several helper methods to access the service metadata, the data from datasets and to download some file resources (it only support CSV for now).
  For more information about OData go to <http://www.odata.org/documentation/>.
This package provides functions to create, connect, update
        and query HSQL databases embedded in Open Document Databases
        (.odb) files, as OpenOffice and LibreOffice do.
A DBI-compatible interface to ODBC databases.
Conversion between the most common odds types for sports betting.
    Hong Kong odds, US odds, Decimal odds, Indonesian odds, Malaysian odds, and raw
    Probability are covered in this package.
Simplified odds ratio calculation of GAM(M)s & GLM(M)s. 
    Provides structured output (data frame) of all predictors and their corresponding odds ratios and confident intervals
    for further analyses. It helps to avoid false references of predictors and increments by
    specifying these parameters in a list instead of using 'exp(coef(model))' 
    (standard approach of odds ratio calculation for GLMs) which just returns a plain numeric output. 
    For GAM(M)s, odds ratio calculation is highly simplified with this package since it takes care of
    the multiple 'predict()' calls of the chosen predictor while holding other predictors constant.
    Also, this package allows odds ratio calculation of percentage steps across the whole
    predictor distribution range for GAM(M)s. In both cases, confident intervals are returned additionally.
    Calculated odds ratio of GAM(M)s can be inserted into the smooth function plot.
Wraps the Boost odeint library for integration of differential
    equations.
Sweave processing of Open Document Format (ODF) files
Provides methods for odfTable() for objects in the survey
        package.
Transformation of 'ODM' (see <http://www.cdisc.org/odm>) files into R format, Microsoft Office format, 'CDA' format and vice versa. 'ODM' format is commonly used in clinical trials. Semantic annotation (such as 'UMLS', 'SNOMED' or 'LOINC' codes) are transformed accordingly.
Use The Observatory of Economic Complexity's API in R to download international trade data in csv and create and D3Plus visualizations.
Solves penalized least squares problems using the orthogonalizing EM algorithm of Xiong et al. (2016) <doi:10.1080/00401706.2015.1054436>. The main fitting function is oem() and the functions cv.oem() and xval.oem() are for cross validation, the latter being an accelerated cross validation function for linear models. The big.oem() function allows for out of memory fitting.
Ordered models such as ordered probit and ordered logit presume that the error variance is constant across observations. In the case that this assumption does not hold estimates of marginal effects are typically biased. This package allows for generalization of ordered probit and ordered logit models by allowing the user to specify a model for the variance. Furthermore, the package includes functions to calculate the marginal effects. Wrapper functions to estimate the standard limited dependent variable models are also included.
R Client for Ohmage 2 server. Implements basic R
    functions to retrieve and process data.
A collection of data sets from several sources that may be
        useful for teaching, practice, or other purposes. Functions
        have also been included to assist in the retrieval of table
        data from websites or in visualizing sample data.
Provides simple crosstab output with optional statistics (e.g.,
    Goodman-Kruskal Gamma, Somers' d, and Kendall's tau-b) as well as two-way
    and one-way tables. The package is used within the statistics component of
    the Masters of Science (MSc) in Social Science of the Internet at the Oxford
    Internet Institute (OII), University of Oxford, but the functions should be
    useful for general data analysis and especially for analysis of categorical and
    ordinal data.
Supplemental functions and data for the OpenIntro guide to the survival package in R.
Functions to calculate the Oja median, Oja signs and ranks and methods based upon them.
Cleaned profile data from "OkCupid Profile Data for Introductory
    Statistics and Data Science Courses" (Journal of Statistics Education 2015
    <http://www.amstat.org/publications/jse/v23n2/kim.pdf>).
okmesonet retrieves and summarizes Oklahoma (USA) Mesonet
 climatological data provided by the Oklahoma Climatological Survey. 
 Measurements are recorded every five minutes at approximately 120 stations
 throughout Oklahoma and are available in near real-time.
'Open Location Codes' <http://openlocationcode.com/>
    are a Google-created standard for identifying geographic locations. 'olctools' provides
    utilities for validating, encoding and decoding entries that follow this
    standard.
Calculate the theorical specificity of a system of
        multiple primers used for PCR, qPCR primers or degenerated
        primer design
Provides tools for more easily organizing and
    plotting individual ordinary least square (OLS) growth curve
    trajectories.
This package including two useful function, which can be
        used for filter the molecular descriptors matrix for QSAR.
It provides functions to generate a correlation matrix
    from a genetic dataset and to use this matrix to predict the phenotype of an
    individual by using the phenotypes of the remaining individuals through
    kriging. Kriging is a geostatistical method for optimal prediction or best
    unbiased linear prediction. It consists of predicting the value of a
    variable at an unobserved location as a weighted sum of the variable at
    observed locations. Intuitively, it works as a reverse linear regression:
    instead of computing correlation (univariate regression coefficients are
    simply scaled correlation) between a dependent variable Y and independent
    variables X, it uses known correlation between X and Y to predict Y.
A collection of functions to analyse '--omics' datasets such as DNA
    methylation and gene expression profiles.
Computing probabilistic tree models for oncogenesis based
        on genetic data using maximum likelihood.
Contains functions to construct and evaluate directed tree structures that
   model the process of occurrence of genetic alterations during carcinogenesis.
Purpose of this package is it to plan, monitor and evaluate
    oncological phase II studies. In general this kind of studies are single-arm
    trials with planned interim analysis and binary endpoint. To meet the resulting
    requirements, the package provides functions to calculate and evaluate 'Simon's
    two-stage designs' and 'so-called' 'subset designs'. If you are unfamiliar with
    this package a good starting point is to take a closer look at the functions
    getSolutions() and getSolutionsSub1().The web-based tool (https://imbi.shinyapps.io/phaseII-app/)
    extends the functionality of our R package by means of a proper dealing with over- and underrunning.
    The R function binom.test of the 'stats' R package and the package 'binom' might be 
    helpful to assess the performance of the corresponding one-stage design as a reference.
Analysis of molecular marker data from model (backcrosses,
        F2 and recombinant inbred lines) and non-model systems (i. e.
        outcrossing species). For the later, it allows statistical
        analysis by simultaneously estimating linkage and linkage
        phases (genetic map construction). All analysis are based on
        multipoint approaches using hidden Markov models.
Implements the One Rule (OneR) Machine Learning classification algorithm with enhancements for sophisticated handling of numeric data and missing values together with extensive diagnostic functions. It is useful as a baseline for machine learning models and the rules are often helpful heuristics.
Provides a series of functions designed to enable users to easily search and interact with occupational data from the O*NET API <www.onetonline.org>. The package produces parsed and listed XML data for custom interactions, or pre-packaged functions for easy extraction of specific data (e.g., Knowledge, Skills, Abilities, Work Styles, etc.).
In this package, we introduce an R function
        one_two_sample() which can deal with one and two (normal)
        samples. For one normal sample x, the function reports
        descriptive statistics, plot, interval estimation and test of
        hypothesis of x. For two normal samples x and y, the function
        reports descriptive statistics, plot, interval estimation and
        test of hypothesis of x and y, respectively. It also reports
        interval estimation and test of hypothesis of mu1-mu2 (the
        difference of the means of x and y) and sigma1^2 / sigma2^2
        (the ratio of the variances of x and y), tests whether x and y
        are from the same population, finds the correlation coefficient
        of x and y if x and y have the same length.
Two or more samples in one-way independent groups design are compared via different tests.
A collection of routines to manipulate and visualize quaternions and
  octonions.
Detects multiple changepoints in uni- or multivariate time series data. The algorithm is based on Bayesian methods and detects changes on-line; i.e. the model updates with every observation rather than relying on retrospective segmentation. However, the user may choose to use the algorithm off- or on-line.
Online PCA for multivariate and functional data using perturbation methods, low-rank incremental methods, and stochastic optimization methods.
Orthogonal Nonlinear Least-Squares Regression using Levenberg-Marquardt Minimization.
Functions for reading ontologies into R as lists and manipulating sets of ontological terms - 'ontologyX: A suite of R packages for working with ontological data', Greene et al 2017 <doi:10.1093/bioinformatics/btw763>.
Functions for visualising sets of ontological terms using the 'graphviz' layout system.
Functions for calculating semantic similarities between ontological terms or sets of ontological terms based on term information content and assessing statistical significance of similarity in the context of a collection of sets of ontological terms.
Implementation of optimistic optimization methods for global optimization of deterministic or stochastic functions. The algorithms feature guarantees of the convergence to a global optimum. They require minimal assumptions on the (only local) smoothness, where the smoothness parameter does not need to be known. They are expected to be useful for the most difficult functions when we have no information on smoothness and the gradients are unknown or do not exist. Due to the weak assumptions, however, they can be mostly effective only in small dimensions, for example, for hyperparameter tuning.
Contains tools for open assessment and modelling in Opasnet,
 a wiki-based web site and workspace for societal decision making
 (see <http://en.opasnet.org/w/Main_Page> for more information).
 The core principle of the workspace is maximal openness and modularity.
 Variables are defined on public wiki pages using wiki inputs/tables,
 databases and R code. This package provides the functionality to download and use these
 variables. It also contains health impact assessment tools such as
 spatial methods for exposure modelling.
Companion package to the book Option Pricing and
        Estimation of Financial Models in R, Wiley, Chichester. ISBN:
        978-0-470-74584-7.
'Openaddresses' (<https://openaddresses.io/>) client. Search,
    fetch data, and combine 'datasets'. Outputs are easy to visualize
    with base plots, 'ggplot2', or 'leaflet'.
Tools to analyse, interpret and understand air
    pollution data. Data are typically hourly time series
    and both monitoring data and dispersion model output
    can be analysed.  Many functions can also be applied to
    other data, including meteorological and traffic data.
Tool for accessing the OpenCage API, which provides forward
    geocoding (from placename to longitude and latitude) and reverse geocoding (from
    longitude and latitude to placename).
This package provides an interface to OpenCL, allowing R
        to leverage computing power of GPUs and other HPC accelerator
        devices.
The OpenCPU system exposes an http API for embedded scientific
    computing with R. The server can run either as a single-user development
    server within the interactive R session, or as a multi-user linux stack
    based on rApache and NGINX. The current R package implements the core of
    the system. When loaded in R, it automatically initiates the single-user
    server and displays the web address in the console. The OpenCPU website 
    has more detailed API documentation.
Incorporates functions for image preprocessing, filtering and image recognition. The package takes advantage of 'RcppArmadillo' to speed up computationally intensive functions. The histogram of oriented gradients descriptor is a modification of the 'findHOGFeatures' function of the 'SimpleCV' computer vision platform and the average_hash(), dhash() and phash() functions are based on the 'ImageHash' python library.
This package is a supplement to OpenIntro Statistics,
        which is a free textbook available at openintro.org (at cost
        paperbacks are also available for under $10 on Amazon). The
        package contains data sets used in the textbook along with
        custom plotting functions for reproducing book figures. Note
        that many functions and examples include color transparency.
        Some plotting elements may not show up properly (or at all) in
        some Windows versions.
'OpenML.org' is an online machine learning platform where 
    researchers can easily download and upload data sets, share machine learning 
    tasks and experiments and organize them online to work and collaborate more 
    effectively.
    We provide an R interface to the OpenML REST API in order to download and 
    upload data sets, tasks, flows and runs, see <http://www.openml.org/guide> 
    for more information.
The OpenMPController package provides a function
        'omp_set_num_threads()' to set the number of OpenMP threads to
        be used.  This may be useful, for example, when linking against
        a vendor optimised BLAS/LAPACK library (e.g. the AMD Core Math
        Library), since the defaults used by those libraries may not be
        highly performant.
Facilitates treatment of statistical model specifications as things
    that can be generated and manipulated programmatically. Structural equation
    models may be specified with reticular action model matrices or paths, linear
    structural relations matrices or paths, or directly in matrix algebra. Fit
    functions include full information maximum likelihood, maximum likelihood, and
    weighted least squares. Example models include confirmatory factor, multiple
    group, mixture distribution, categorical threshold, modern test theory,
    differential equations, state space, and many others.
An interface to the Apache OpenNLP tools (version 1.5.3).
  The Apache OpenNLP library is a machine learning based toolkit for the
  processing of natural language text written in Java.
  It supports the most common NLP tasks, such as tokenization, sentence
  segmentation, part-of-speech tagging, named entity extraction, chunking,
  parsing, and coreference resolution.
  See <http://opennlp.apache.org/> for more information.
Apache OpenNLP jars and basic English language models.
Bindings to OpenSSL libssl and libcrypto, plus custom SSH pubkey parsers.
    Supports RSA, DSA and EC curves P-256, P-384 and P-521. Cryptographic signatures
    can either be created and verified manually or via x509 certificates. AES can be
    used in cbc, ctr or gcm mode for symmetric encryption; RSA for asymmetric (public
    key) encryption or EC for Diffie Hellman. High-level envelope functions combine
    RSA and AES for encrypting arbitrary sized data. Other utilities include key
    generators, hash functions (md5, sha1, sha256, etc), base64 encoder, a secure
    random number generator, and 'bignum' math methods for manually performing
    crypto calculations on large multibyte integers.
Accesses high resolution raster maps using the OpenStreetMap
    protocol. Dozens of road, satellite, and topographic map servers are directly
    supported, including Apple, Mapnik, Bing, and stamen. Additionally raster maps
    may be constructed using custom tile servers.  Maps can be
    plotted using either base graphics, or ggplot2. This package is not affiliated
    with the OpenStreetMap.org mapping project.
opentraj uses the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) for computing simple air parcel trajectories. The functions in this package allow users to run HYSPLIT for trajectory calculations, as well as get its results, directly from R without using any GUI interface.
Implements multiple existing open-source algorithms for coding cause of death from verbal autopsies. It also provides tools for data manipulation tasks commonly used in Verbal Autopsy analysis and implements easy graphical visualization of individual and population level statistics.
Simplifies the creation of Excel .xlsx files by providing a
    high level interface to writing, styling and editing worksheets. Through
    the use of 'Rcpp', read/write times are comparable to the 'xlsx' and 'XLConnect'
    packages with the added benefit of removing the dependency on Java.
Misc methods to form online predictions, for regression-oriented time-series, by combining a finite set of forecasts provided by the user.
A set of binary operators for common tasks such as regex
        manipulation.
These utilities allow for programatically working with
    R's operators: translating between an operator and its underlying 
    function, inverting an operator, determining an operator's type, etc.
Implementation of the Open Perimetry Interface (OPI) for simulating and controlling visual field machines using R. The OPI is a standard for interfacing with visual field testing machines (perimeters). It specifies basic functions that allow many visual field tests to be constructed. As of February 2016 it is fully implemented on the Octopus 600 and Octopus 900 and partially on the Heidelberg Edge Perimeter, the Kowa AP 7000 and the CrewT imo. It also has a cousin: the R package visualFields, which has tools for analysing and manipulating visual field data.
Comparison of data by Pearson product-moment correlation
        coefficients is prone to outliers. The problem can be
        alleviated by normalizing data with outliers before computing
        the Pearson correlation coefficient. The sample provides such
        normalization by optimal power space transformation.
Searches for optimal linear combination of multiple
        diagnostic tests (markers) that maximizes the area under the
        receiver operating characteristic curve (AUC); performs an
        approximated cross-validation for estimating the AUC associated
        with the estimated coefficients.
Given a certain coverage level, obtains simultaneous confidence
    bands for the survival and cumulative hazard functions such that the area
    between is minimized. Produces an approximate solution based on local time
    arguments.
Computes A-, MV-, D- and E-optimal or near-optimal block designs for two-colour cDNA microarray experiments using the linear fixed effects and mixed effects models where the interest is in a comparison of all possible elementary treatment contrasts. The algorithms used in this package are based on the treatment exchange and array exchange algorithms of Debusho, Gemechu and Haines (2016, unpublished). The package also provides an optional method of using the graphical user interface (GUI) R package tcltk to ensure that it is user friendly.
Estimates optimal number of biomarkers for two-group
        classification based on microarray data
Cluster analysis using statistical and biological
	validation measures for both continuous and count data.
Compute optimal experimental designs
    that measure the slope divided by the intercept.
Tools to assist in safely applying user generated objective and 
   derivative function to optimization programs. These are primarily function 
   minimization methods with at most bounds and masks on the parameters.
   Provides a way to check the basic computation of objective functions that 
   the user provides, along with proposed gradient and Hessian functions, 
   as well as to wrap such functions to avoid failures when inadmissible parameters 
   are provided. Check bounds and masks. Check scaling or optimality conditions. 
   Perform an axial search to seek lower points on the objective function surface. 
   Includes forward, central and backward gradient approximation codes.
Functions to find near-optimal multi-stage designs for continuous outcomes.
Estimation of value and hedging strategy of call and put options, based on optimal hedging and Monte Carlo method, from Chapter 3 of 'Statistical Methods for Financial Engineering', by Bruno Remillard, CRC Press, (2013).
Likelihood based optimal partitioning for indicator
  species analysis. Finding the best binary partition for each species
  based on model selection, possibly controlling for modifying/confounding
  variables as described in Kemencei et al. (2014) <doi:10.1556/ComEc.15.2014.2.6>.
  The package also implements various measures of uncertainty based on
  binary partitions, optimal multinomial partitioning, and exploratory
  suitability indices, with native support for parallel computations.
A single function 'options.ifunset(...)' is contained herewith, which allows the user to set a global option ONLY if it is not already set. By this token, for package maintainers this function can be used in preference to the standard 'options(...)' function, making provision for THEIR end user to place 'options(...)' directives within their '.Rprofile' file, which will not be overridden at the point when a package is loaded.
Parse options from the command-line using a simple, clean syntax. 
    It requires little or no specification and supports short and long options,
    GNU-, Java- or Microsoft- style syntaxes, verb commands and more.
This package enables users to compute one or more optimal cutpoints for diagnostic tests or continuous markers. Various approaches for selecting optimal cutoffs have been implemented, including methods based on cost-benefit analysis and diagnostic test accuracy measures (Sensitivity/Specificity, Predictive Values and Diagnostic Likelihood Ratios). Numerical and graphical output for all methods is easily obtained.
Algorithms for D-, A- and IV-optimal designs of experiments. Some of the functions in this package require the 'gurobi' software and its accompanying R package. For their installation, please follow the instructions at <www.gurobi.com> and the file gurobi_inst.txt, respectively.
Computes confidence regions on the location of
    response surface optima.
Provides a set of commands to manage an abstract
        optimization method. The goal is to provide a building block
        for a large class of specialized optimization methods. This
        package manages: the number of variables, the minimum and
        maximum bounds, the number of non linear inequality
        constraints, the cost function, the logging system, various
        termination criteria, etc...
Flexible multi-purpose optimizer with numerous input specifications. It allows a very detailed parameterization and is therefore useful for specific and complex loss functions, like functions with discrete parameter space. Visualization tools for validation and analysis of the convergence are also included.
Provides a test of replacement and extension of the optim()
    function to unify and streamline optimization capabilities in R
    for smooth, possibly box constrained functions of several or
    many parameters. This version has a reduced set of methods and is
    intended to be on CRAN.
Provides a building block for optimization algorithms
        based on a simplex. The optimsimplex package may be used in the
        following optimization methods: the simplex method of Spendley
        et al., the method of Nelder and Mead, Box's algorithm for
        constrained optimization, the multi-dimensional search by
        Torczon, etc...
Provides a replacement and extension of the optim()
    function to unify and streamline optimization capabilities in R
    for smooth, possibly box constrained functions of several or
    many parameters. This is the CRAN version of the package.
Optimal two and three stage designs monitoring
        time-to-event endpoints at a specified timepoint
Efficient Monte Carlo Algorithms for the price and the sensitivities of Asian and European Options under Geometric Brownian Motion.
This fills the gaps credit analysts and loan modellers at
    Optimum Credit identify in the existing R code body.
    It allows for the production of documentation with less coding,
    replicates a number of Microsoft Excel functions useful for
    modelling loans (without rounding), and other helpful functions
    for producing charts and tables.  It also has some additional scales for
    use, including a GBP scale.
Tools for performing an optimal scaling transformation on a data
    vector
Computation of optimum contributions of selection candidates in animal breeding, genetic contributions, the native effective size, native genome equivalents, the diversity of native alleles, and pedigree completeness. Moreover, functions are provided for preparing and plotting pedigrees.
Code for optimal mixture weights in importance sampling. Workhorse
    functions penoptpersp() and penoptpersp.alpha.only() minimize estimated
    variances with and without control variates respectively. It can be used in
    adaptive mixture importance sampling, for example, function batch.estimation() does
    two stages, a pilot estimate of mixing alpha and a following importance
    sampling.
Distance based bipartite matching using the RELAX-IV minimum cost flow solver,
    oriented to matching of treatment and control groups in observational
    studies. Routines are provided to generate distances from generalised linear models (propensity
    score matching), formulas giving variables on which to limit matched distances, stratified or
    exact matching directives, or calipers, alone or in combination.
A command line parser inspired by Python's 'optparse' library to
    be used with Rscript to write "#!" shebang scripts that accept short and
    long flag/options.
Contains a set of algorithms for creating
        partitions and coverings of objects largely based on operations
        on (dis)similarity relations (or matrices). There are several
        iterative re-assignment algorithms optimizing different
        goodness-of-clustering criteria.  In addition, there are
        covering algorithms 'clique' which derives maximal cliques, and
        'maxpact' which creates a covering of maximally compact sets.
        Graphical analyses and conversion routines are also included.
Solves linear systems of form Ax=b via Gauss elimination, 
  LU decomposition, Gauss-Seidel, Conjugate Gradient Method (CGM) and Cholesky methods.
Finds optimal trees in weighted graphs. In
    particular, this package provides solving tools for minimum cost spanning
    tree problems, minimum cost arborescence problems, shortest path tree
    problems and minimum cut tree problem.
Provides a simple R interface to the OPUS Miner algorithm (implemented in C++) for finding the top-k productive, non-redundant itemsets from transaction data.  The OPUS Miner algorithm uses the OPUS search algorithm to efficiently discover the key associations in transaction data, in the form of self-sufficient itemsets, using either leverage or lift.  See <http://i.giwebb.com/index.php/research/association-discovery/> for more information in relation to the OPUS Miner algorithm.
Easy-to-use functions to explore Oracle databases and import data
  into R. User interface for the ROracle package.
Implements orbit counting using a fast combinatorial approach.
	Counts orbits of nodes and edges from edge matrix or data frame, or a
	graph object from the graph package.
Computes various confidence intervals for the odds ratio of two independent binomial proportions.
Functions to perform subspace clustering and
        classification.
Provides clustering of genes with similar 
  dose response (or time course) profiles. It implements the method 
  described by Lin et al. (2012).
Solve first order autocorrelation problems using an iterative method. This procedure estimates both autocorrelation and beta coefficients recursively until we reach the convergence (8th decimal). The residuals are computed after estimating Beta using EGLS approach and Rho is estimated using the previous residuals.
This package extends the Bradley-Terry-Luce model for fitting pair
    comparison models with an ordinal response. It is also possible to
    incorporate an order effect, or, equivalently, an effect for the home
    advantage.
Provides the setup and calculations needed
        to run a likelihood-based continual reassessment method (CRM)
        dose finding trial and performs simulations to assess design
        performance under various scenarios. 3 dose finding designs
        are included in this package: ordinal proportional odds model
        (POM) CRM, ordinal continuation ratio (CR) model CRM, and the
        binary 2-parameter logistic model CRM.
        These functions allow customization of design characteristics
        to vary sample size, cohort sizes, target dose-limiting
        toxicity (DLT) rates, discrete or continuous dose levels,
        combining ordinal grades 0 and 1 into one category, and
        incorporate safety and/or stopping rules.
        For POM and CR model designs, ordinal toxicity grades are
        specified by common terminology criteria for adverse events
        (CTCAE) version 4.0.
        Function 'pseudodata' creates the necessary starting models
        for these 3 designs, and function 'nextdose' estimates the
        next dose to test in a cohort of patients for a target DLT
        rate.
        We also provide the function 'crmsimulations' to assess the
        performance of these 3 dose finding designs under various
        scenarios.
Estimate location-shift models or rating-scale models accounting for response styles (RSRS) for the regression analysis of ordinal responses.
Computes ordinal, statistics and effect sizes as an
        alternative to mean comparison: Cliff's delta or success rate
        difference (SRD), Vargha and Delaney's A or the Area Under a
        Receiver Operating Characteristic Curve (AUC), the discrete
        type of McGraw & Wong's Common Language Effect Size (CLES) or
        Grissom & Kim's Probability of Superiority (PS), and the Number
        needed to treat (NNT) effect size. Moreover, comparisons to
        Cohen's d are offered based on Huberty & Lowman's Percentage of
        Group (Non-)Overlap considerations.
This package uses B-spline based nonparametric smooth
        estimators to estimate parent distributions given observations
        on multiple order statistics.
Functions for visualizing and retrieving data for the
        state of an orderbook at a particular period in time.
Ordered lasso and time-lag sparse regression. Ordered Lasso fits a
    linear model and imposes an order constraint on the coefficients. It writes
    the coefficients as positive and negative parts, and requires positive
    parts and negative parts are non-increasing and positive. Time-Lag Lasso
    generalizes the ordered Lasso to a general data matrix with multiple
    predictors. For more details, see Suo, X.,Tibshirani, R., (2014) 'An
    Ordered Lasso and Sparse Time-lagged Regression'.
In biomedical studies, researchers are often interested in assessing the association between one or more ordinal explanatory variables and an outcome variable, at the same time adjusting for covariates of any type. The outcome variable may be continuous, binary, or represent censored survival times. In the absence of a precise knowledge of the response function, using monotonicity constraints on the ordinal variables improves efficiency in estimating parameters, especially when sample sizes are small. This package implements an active set algorithm that efficiently computes such estimators.
Calculates ordinated diet breadth with some plotting functions.
Implementation of cumulative link (mixed) models also known
    as ordered regression models, proportional odds models, proportional
    hazards models for grouped survival times and ordered logit/probit/...
    models. Estimation is via maximum likelihood and mixed models are fitted
    with the Laplace approximation and adaptive Gauss-Hermite quadrature.
    Multiple random effect terms are allowed and they may be nested, crossed or
    partially nested/crossed. Restrictions of symmetry and equidistance can be
    imposed on the thresholds (cut-points/intercepts). Standard model
    methods are available (summary, anova, drop-methods, step,
    confint, predict etc.) in addition to profile methods and slice
    methods for visualizing the likelihood function and checking
    convergence.
A regression framework for response variables which are continuous
    self-rating scales such as the Visual Analog Scale (VAS) used in pain
    assessment, or the Linear Analog Self-Assessment (LASA) scales in quality
    of life studies. These scales measure subjects' perception of an intangible
    quantity, and cannot be handled as ratio variables because of their inherent
    non-linearity. We treat them as ordinal variables, measured on a continuous
    scale. A function (the g function) connects the scale with an underlying
    continuous latent variable. The link function is the inverse of the CDF of the
    assumed underlying distribution of the latent variable. Currently the logit
    link, which corresponds to a standard logistic distribution, is implemented.
Provides a function for fitting cumulative link, adjacent category, forward and backward continuation ratio, and stereotype ordinal response models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method.
Analysis of a matrix of polytomous items using Ordinal Logistic Biplots (OLB)
  The OLB procedure extends the binary logistic biplot to ordinal (polytomous) data. 
  The  individuals are represented as points on a plane and the variables are represented 
  as lines rather than vectors as in a classical or binary biplot, specifying the points
  for each of the categories of the variable. 
  The set of prediction regions is established by stripes perpendicular to the line 
  between the category points, in such a way that the prediction for each individual is given
  by its projection into the line of the variable.
Fits ordinal regression models with elastic net penalty. Supported
    models include cumulative logit, probit, cauchit, and complementary log-log. 
    The algorithm uses Fisher Scoring with Coordinate Descent updates.
Method for develops a classification model for ordinal responses based on logic regression.
We consider the problem of estimating two isotonic regression curves g1* and g2* under the constraint that they are ordered, i.e. g1* <= g2*. Given two sets of n data points y_1, ..., y_n and z_1, ..., z_n that are observed at (the same) deterministic design points x_1, ..., x_n, the estimates are obtained by minimizing the Least Squares criterion L(a, b) = sum_{i=1}^n (y_i - a_i)^2 w1(x_i) + sum_{i=1}^n (z_i - b_i)^2 w2(x_i) over the class of pairs of vectors (a, b) such that a and b are isotonic and a_i <= b_i for all i = 1, ..., n. We offer two different approaches to compute the estimates: a projected subgradient algorithm where the projection is calculated using a PAVA as well as Dykstra's cyclical projection algorithm.
Implementation of a procedure for generating samples from a mixed distribution of ordinal and normal random variables with pre-specified correlation matrix and marginal distributions.
Selection and/or smoothing of ordinally scaled independent variables using a group lasso or generalized ridge penalty.
Provides an alternative to R's built-in functionality for handling
    regular expressions, based on the Oniguruma library. Offers first-class
    compiled regex objects, partial matching and function-based substitutions,
    amongst other features.
A connector to ORES (<http://ores.wmflabs.org/>), an AI project to provide edit scoring for content
             on Wikipedia and other Wikimedia projects. This lets a researcher identify
             if edits are likely to be reverted, damaging, or made in good faith.
Organic/biological mass spectrometry data analysis.
Provides functionality to process text files created by Emacs' Org mode, and decompose the content to the smallest components (headlines, body, tag, clock entries etc).  Emacs is an extensible, customizable text editor and Org mode is for keeping notes, maintaining TODO lists, planning projects.  Allows users to analyze org files as data frames in R, e.g., to convieniently group tasks by tag into project and calculate total working hours.  Also provides some help functions like search.parent, gg.pie (visualise working hours in ggplot2) and tree.headlines (visualise headline stricture in tree format) to help user managing their complex org files.
ORIClust is a user-friendly R-based software package for
        gene clustering. Clusters are given by genes matched to
        prespecified profiles across various ordered treatment groups.
        It is particularly useful for analyzing data obtained from
        short time-course or dose-response microarray experiments.
Representations, conversions and display of orientation
        SO(3) data.  See the orientlib help topic for details.
Used primarily for estimates of allele frequency surfaces from point estimates.  
    It can also place individuals of unknown origin back onto the geographic map with great accuracy.  
    Additionally, it can place admixed individuals by estimating contributing fractions at each
    location on a map. Lastly, it can rank SNPs by their ability to differentiate populations.  
    See "Fast Spatial Ancestry via Flexible Allele Frequency Surfaces" (John Michael Ranola, John
    Novembre, Kenneth Lange) in Bioinformatics 2014 for more info.
Get species origin data (whether species is native/invasive) from the
    following sources on the web: Encyclopedia of Life (<http://eol.org>), Flora
    'Europaea' (<http://rbg-web2.rbge.org.uk/FE/fe.html>), Global Invasive Species
    Database (<http://www.iucngisd.org/gisd>), the Native Species Resolver
    (<http://bien.nceas.ucsb.edu/bien/tools/nsr/nsr-ws/>), Integrated Taxonomic
    Information Service (<http://www.itis.gov/>), and Global Register of 
    Introduced and Invasive Species (<http://www.griis.org/>).
This version of the package deals with the min-sum location
    problem, also known as Fermat--Weber problem. The min-sum location problem
    search for a point such that the weighted sum of the distances to the
    demand points are minimized.
Version espanola del paquete orloca que trata el problema
        de localizacion min-sum, tambien conocido como problema de
        Fermat--Weber. El problema de localizacion min-sum busca un
        punto tal que la suma ponderada de las distancias a los puntos
        de demanda sea minima.
Odds ratio based multifactor-dimensionality reduction
        method for detecting gene-gene interactions
Data input/output functions for data that conform to the 
  Digital Imaging and Communications in Medicine (DICOM) standard, part
  of the Rigorous Analytics bundle.
Functions for the input/output and visualization of
    medical imaging data that follow either the ANALYZE, NIfTI or AFNI
    formats.  This package is part of the Rigorous Analytics bundle.
Image analysis techniques for positron emission tomography 
  (PET) that form part of the Rigorous Analytics bundle.
Assess repeatability, accuracy and corss-platform
        agreement of titration microarray data based on order
        restricted inference procedures
A data set package with the "Orsi" fronts as a
    'SpatialLinesDataFrame' object. The Orsi et al. (1995) fronts are published at
    the Southern Ocean Atlas Database Page, please see package CITATION for details.
Convert the Odds Ratio to the Relative Risk in Cohort Studies with Partial Data Information.
Represents the basis functions for B-splines in a simple matrix
    formulation that facilitates, taking integrals, derivatives, and
    making orthogonal the basis functions.
Implements the orthogonal reparameterization
    approach recommended by Lancaster (2002) to estimate dynamic panel
    models with fixed effects (and optionally: panel specific
    intercepts). The approach uses a likelihood-based estimator and
    produces estimates that are asymptotically unbiased as N goes to
    infinity, with a T as low as 2.
A collection of functions to construct sets of orthogonal
        polynomials and their recurrence relations. Additional
        functions are provided to calculate the derivative, integral,
        value and roots of lists of polynomial objects.
Allows distance based spatial clustering of georeferenced data by implementing the City Clustering Algorithm - CCA. Multiple versions allow clustering for matrix, raster and single coordinates on a plain (euclidean distance) or on a sphere (great-circle or orthodromic distance).
Compound deconvolution for chromatographic data, including gas chromatography - mass spectrometry (GC-MS) and comprehensive gas chromatography - mass spectrometry (GCxGC-MS). The package includes functions to perform independent component analysis - orthogonal signal deconvolution (ICA-OSD), independent component regression (ICR), multivariate curve resolution (MCR-ALS) and orthogonal signal deconvolution (OSD) alone.
The osDesign serves for planning an observational study. Currently, functionality is focused on the two-phase and case-control designs. Functions in this packages provides Monte Carlo based evaluation of operating characteristics such as powers for estimators of the components of a logistic regression model.
A connector to the API maintained by the Open Source Initiative <https://api.opensource.org/licenses/>, which
             provides machine-readable metadata about a variety of open source software licenses.
This package provides infrastructure to access
    OpenStreetMap data from different sources, to work with the data
    in common R manner, and to convert data into available
    infrastructure provided by existing R packages (e.g., into sp and
    igraph objects).
Customisable images of OpenStreetMap (OSM) data and
    data visualisation using OSM objects.
Functionality to handle and project lat-long coordinates, easily download background maps
    and add a correct scale bar to 'OpenStreetMap' plots in any map projection.
An interface between R and the OSRM API. OSRM is a routing
    service based on OpenStreetMap data. See <http://project-osrm.org/> for more
    information. A public API exists but one can run its own instance. This package
    allows to compute distances (travel time and kilometric distance) between points
    and travel time matrices.
Functions for creating ensembles of optimal trees for regression, classification and class membership probability estimation are given. A few trees are selected from an initial set of trees grown by random forest for the ensemble on the basis of their individual and collective performance. Trees are assessed on out-of-bag data and on an independent training data set for individual and collective performance respectively. The prediction functions return estimates of the test responses and their class membership probabilities. Unexplained variations, error rates, confusion matrix, Brier scores, etc. are also returned for the test data.
Performs robust cluster analysis allowing for outliers and noise that cannot be fitted by any cluster. The data are modelled by a mixture of Gaussian distributions and a noise component, which is an improper uniform  distribution covering the whole Euclidean space. Parameters are estimated by  (pseudo) maximum likelihood. This is fitted by a EM-type algorithm. See Coretto and Hennig (2015) <https://arxiv.org/abs/1406.0808>, and Coretto and Hennig (2016) <https://arxiv.org/abs/1309.6895>.
A penalized regression framework that can simultaneously estimate the optimal treatment strategy and identify important variables. Appropriate for either censored or uncensored continuous response.
Analyses of OTU tables produced by 16S sequencing, as well as example data. It contains the data and scripts used in the paper Linz, et al. (2016) "Seasonal mixing as a barrier to the development of stable bacterial communities in bog lakes" (Manuscript in prep).
Fit and compare Ornstein-Uhlenbeck models for evolution along a phylogenetic tree.
Bayesian reconstruction of disease outbreaks using epidemiological
    and genetic information.
Empirical or simulated disease outbreak data, provided either as
    RData or as text files.
Implements basic tools for storing, handling and visualizing disease outbreak data, as well as simple analysis tools. OutbreakTools defines the new formal class obkData which can be used to store any case-base outbreak data, and provides summaries for these objects, alongside a range of functions for subsetting and data manipulation. It implements a range of graphics for visualising timelines, maps, contact networks and genetic analyses. It also includes a simple case-base outbreak simulation tool.
This package provides three algorithms to detect outlying observations for censored survival data.
Detecting outlying values such as genes, peptides or samples for multi-replicated high-throughput high-dimensional data
A collection of some tests commonly used for identifying
        outliers.
Functions to process ''outranking'' ELECTRE methods existing in the literature. See, e.g.,
			 <http://en.wikipedia.org/wiki/ELECTRE> about the outranking approach and the foundations of ELECTRE methods.
Calculates and compares rate differences of continuous character evolution under Brownian motion and a new set of Ornstein-Uhlenbeck-based Hansen models that allow the strength of selection and stochastic motion to vary across selective regimes.
Provides functions to fit kernel density functions to
 data on temporal activity patterns of animals; estimate coefficients
 of overlapping of densities for two species; and calculate bootstrap
 estimates of confidence intervals.
Functions for estimating the overlapping area of two or more empirical distributions (special unofficial edition).
Accesses OpenWeatherMap's (owm) <https://openweathermap.org/> API.
   'owm' itself is a service providing weather data in the past, in the future and now.
   Furthermore, 'owm' serves weather map layers usable in frameworks like 'leaflet'.
   In order to access the API, you need to sign up for an API key. There are free and paid plans.
   Beside functions for fetching weather data from 'owm', 'owmr' supplies
   tools to tidy up fetched data (for fast and simple access) and to show it on leaflet maps.
Tools for oxycline depth calculation from echogram matrices.
Provides utilities for processing of Oxy-Bisulfite microarray data 
  (e.g. via the Illumina Infinium platform, <http://www.illumina.com>) 
  with tandem arrays, one using conventional
  bisulfite conversion, the other using oxy-bisulfite conversion.
Functions for plotting Australia's coastline and state
  boundaries.
Conducts posterior predictive checks of coalescent models using gene and species trees generated by 'BEAST' or '*BEAST'. The functionality of P2C2M can be extended via two third-party R packages that are available from the author websites: 'genealogicalSorting' and 'phybase'. To use these optional packages, the installation of the Python libraries 'NumPy' (>= 1.9.0) and 'DendroPy' (= 3.12.0) is required.
The welfare's synthetic indicator provides an ideal tool
        for measuring multi-dimensional concepts such as welfare,
        development, living standards, etc. It enables information from
        the various indicators to be aggregated into a single synthetic
        measure.
Analyzing survival data from illness-death model
A package that provides tools for conducting performance attribution for equity portfolios. The package uses two methods: the Brinson method and a regression-based analysis.
Summarize physical activity (Mets, Proportion, Total, etc.) in the given time period from accelerometer data. This package has been tested by data exported from 'activPAL'. 'activPAL' is a wearable device for medical and healthcare research application developed by PAL Technologies, Glasgow, Scotland. A simulated accelerometer sample dataset is provided in this package. See https://github.com/YukunZhang/PAactivPAL for more information.
Pabon Lasso is a graphical method for monitoring the efficiency of different wards of a hospital or different hospitals.Pabon Lasso graph is divided into 4 parts which are created after drawing the average of BTR and BOR. The part in the left-down side is Zone I, left-up side is Zone II, Right-up side part is Zone III and the last part is Zone IV.
Implements Partition-Assisted Clustering and Multiple Alignments of Networks. It 1) utilizes partition-assisted clustering to find robust and accurate clusters and 2) discovers coherent relationships of clusters across multiple samples. It is particularly useful for analyzing single-cell data set.
A function for clustering online datasets. The number
    of cells is data-driven which need not to be chosen in advance by the user.
    The method is introduced and fully described in Le Li, Benjamin Guedj and Sebastien Loustau (2016), "PAC-Bayesian Online Clustering" (arXiv preprint: <https://arxiv.org/abs/1602.00522>).
This package is intended to perform estimation and
        prediction in high-dimensional additive models, using a sparse
        PAC-Bayesian point of view and a MCMC algorithm. The method is
        fully described in Guedj and Alquier (2013), 'PAC-Bayesian
        Estimation and Prediction in Sparse Additive Models',
        Electronic Journal of Statistics, 7, 264--291.
Functions to easily convert data to binary formats other programs/machines can understand.
Build a package documentation and function reference site and use it as the package vignette.
Allows to get and cache R package download log files
  from RStudio's CRAN mirror for analyzing package usage.
Simple algorithm to arrange non-overlapping circles within a rectangle.
This package comes to illustrate the book "Petit Manuel de
        Programmation Orientee Objet sous R"
Various useful functions for statisticians: describe data, plot Kaplan-Meier curves with numbers of subjects at risk, compare data sets, display spaghetti-plot, build multi-contingency tables...
Manage the R packages your project depends
    on in an isolated, portable, and reproducible way.
Illustration of the book "Petit Manuel de Programmation Orientee Objet sous R". The english version "A (Not so) Short Introduction to S4" is on CRAN, 'Contributed documentation'.
Tools to more conveniently perform tasks associated with
        add-on packages.  pacman conveniently wraps library and package
        related functions and names them in an intuitive and consistent
        fashion.  It seeks to combine functionality from lower level
        functions which can speed up workflow.
Procrustes analyses to infer co-phylogenetic
    matching between pairs of (ultrametric) phylogenetic trees.
Routines for two different test types, the Equal Correlation (ECORR) test and the Vectorial Independence (VI) test are provided. The tests can be applied to check whether a conditional copula coincides with its partial copula. Functions to test whether a regular vine copula satisfies the so-called simplifying assumption or to test a single copula within a regular vine copula to be a (j-1)-th order partial copula are available. The ECORR test comes with a decision tree approach to allow testing in high-dimensional settings.
A prediction-based approach to the analysis of data from
    randomized clinical trials is implemented. Based on response and
    covariate data from a randomized clinical trial comparing a new
    experimental treatment E versus a control C, the objective is to
    develop and internally validate a model that can identify subjects
    likely to benefit from E rather than C. Currently,
    survival and binary response types are permitted.
Given a vector of Taylor series coefficients of sufficient length as input, the function returns the numerator and denominator coefficients for the Padé approximant of appropriate order.
Transforms datetime data into a format ready for analysis.
    It offers two functionalities; aggregating data to a higher level interval
    (thicken) and imputing records where observations were absent (pad). It also
    offers a few functions that assist with filling missing values after padding.
Calculate unadjusted/adjusted attributable fraction function of a set of covariates for a censored survival outcome from a Cox model using the method proposed by Chen, Lin and Zeng (Biometrika 97, 713-726., 2010).
A framework for modelling and inferencing attachment mechanisms of temporal complex networks is implemented in this package. For estimating the preferential attachment (PA) function in isolation, we implement Jeong's method, the corrected Newman's method and the PAFit method. For jointly estimating the PA function and node fitnesses, we implement the PAFit method. The package also provides flexible methods to generate a wide range of temporal networks based on PA and fitness.
A simple way to add page numbers to base/ggplot/lattice graphics.
Pageview data from the 'Wikimedia' sites, such as
    'Wikipedia' <https://www.wikipedia.org/>, from entire projects to per-article
    levels of granularity, through the new RESTful API and data source <https://
    wikimedia.org/api/rest_v1/?doc>.
The package can identify the dysregulated KEGG pathways
        based on global influence from the internal effect of pathways
        and crosstalk between pathways. (1) The PAGI package can
        prioritize the pathways associated with two biological states
        by statistical significance or FDR. (2) The PAGI package can
        evaluated the global influence factor (GIF) score in the global
        gene-gene network constructed based on the relationships of
        genes extracted from each pathway in KEGG database and the
        overlapped genes between pathways.
Bayesian hierarchical methods for pathway analysis of genomewide association data: Normal/Bayes factors and Sparse Normal/Adaptive lasso. The Frequentist Fisher's product method is included as well.
Plots matrices of colours as grids of coloured squares - aka heatmaps, 
	guaranteeing legible row and column names, 
	without transformation of values, 
	without re-ordering rows or columns,
	and without dendrograms.
The package contains two functions: paired.Loc and
        paired.Scale. A parametric and nonparametric confidence
        interval can be computed for the ratio of locations
        (paired.Loc) and the ratio of scales (paired.Scale). The
        samples must be paired and expected values must be positive.
This package provides many datasets and a set of graphics
        (based on ggplot2), statistics, effect sizes and hypothesis
        tests for analysing paired data with S4 class.
A tool to compare two heatmaps and discover patterns
        within and across groups. In the context of biology, group can
        be defined based on gene ontology.
Creates an interactive scatterplot matrix using the D3 JavaScript library. See <http://d3js.org/> for more information on D3.
Eulerian tours and Hamiltonian decompositions of complete graphs are used to ameliorate order effects in statistical graphics.
Performs the explicit calculation
    -- not estimation! -- of the Rasch item parameters for dichotomous and
    polytomous item responses, using a pairwise comparison approach. Person
    parameters (WLE) are calculated according to Warm's weighted likelihood
    approach.
Calculation of the parametric, nonparametric confidence intervals
 for the difference or ratio of location parameters, nonparametric confidence interval
 for the Behrens-Fisher problem and for the difference, ratio and odds-ratio of binomial
 proportions for comparison of independent samples. Common wrapper functions to split 
 data sets and apply confidence intervals or tests to these subsets.
 A by-statement allows calculation of CI separately for the levels of further factors. 
 CI are not adjusted for multiplicity.
Tests if quantitative palaeoenvironmental reconstructions are statistically significant.
Includes 19 functions to wrap each endpoint of
    the PaleobioDB API, plus 8 functions to visualize and process the fossil
    data. The API documentation for the Paleobiology Database can be found in
    <http://paleobiodb.org/data1.1/>.
Tools to extract and analyse charcoal sedimentary data stored in
    the Global Charcoal Database. Main functionalities includes data extraction
    and sites selection, transformation and interpolation of the charcoal
    records as well as compositing.
Transfer functions and statistical operations for
        paleoecology
Fill missing symmetrical data with mirroring, calculate Procrustes alignments with or without scaling, and compute standard or vector correlation and covariance matrices (congruence coefficients) of 3D landmarks. Tolerates missing data for all analyses.
Provides tools for transforming, a posteriori time-scaling, and
    modifying phylogenies containing extinct (i.e. fossil) lineages. In particular,
    most users are interested in the functions timePaleoPhy(), bin_timePaleoPhy(),
    cal3TimePaleoPhy() and bin_cal3TimePaleoPhy(), which a posteriori time-scale cladograms of
    fossil taxa into dated phylogenies. This package also contains a large number
    of likelihood functions for estimating sampling and diversification rates from
    different types of data available from the fossil record (e.g. range data,
    occurrence data, etc). paleotree users can also simulate diversification and
    sampling in the fossil record using the function simFossilRecord(), which is a
    detailed simulator for branching birth-death-sampling processes composed of
    discrete taxonomic units arranged in ancestor-descendant relationships. Users
    can use simFossilRecord() to simulate diversification in incompletely sampled
    fossil records, under various models of morphological differentiation (i.e.
    the various patterns by which morphotaxa originate from one another), and
    with time-dependent, longevity-dependent and/or diversity-dependent rates of
    diversification, extinction and sampling. Additional functions allow users to
    translate simulated ancestor-descendant data from simFossilRecord() into standard
    time-scaled phylogenies or unscaled cladograms that reflect the relationships
    among taxon units.
Facilitates analysis of paleontological sequences of trait values from an evolving lineage.  Functions are provided to fit, using maximum likelihood, evolutionary models including unbiased random walks, directional evolution, stasis, Ornstein-Uhlenbeck, punctuated change, and evolutionary models in which traits track some measured covariate.
Use Pokemon(R) inspired palettes with additional 'ggplot2' scales.
    Palettes are the colours in each Pokemon's sprite, ordered by how common
    they are in the image. The first 386 Pokemon are currently provided.
R package to compute Incoming Solar Radiation (insolation) for palaeoclimate studies. Features three solutions: Berger (1978), Berger and Loutre (1991) and Laskar et al. (2004). Computes daily-mean, season-averaged and annual means for all latitudes.
Functions for the fitting of point process models using the Palm likelihood. First proposed by Tanaka, Ogata, and Stoyan (2008) <DOI:10.1002/bimj.200610339>, maximisation of the Palm likelihood can provide computationally efficient parameter estimation in situations where the full likelihood is intractable. This package is chiefly focused on Neyman-Scott point processes, but can also fit void processes. The development of this package was motivated by the analysis of capture-recapture surveys on which individuals cannot be identified---the data from which can conceptually be seen as a clustered point process. As such, some of the functions in this package are specifically for the estimation of cetacean density from two-camera aerial surveys.
Colour palettes for data, based on some well known public data
    sets.
A comprehensive collection of color palettes, colormaps, and tools to evaluate them.
Correspondence Analysis of Contingency Tables with Simple
        and Double Structures Superimposed Representations, Intra
        Blocks Correspondence Analysis (IBCA), Weighted Intra Blocks
        Correspondence Analysis (WIBCA).
Simulation functions to assess or explore the power of a dataset to estimates significant random effects (intercept or slope) in a mixed model. The functions are based on the "lme4" package.
Implements the Panel Data Approach Method for program evaluation as developed in Hsiao, Ching and Ki Wan (2012). pampe estimates the effect of an intervention by comparing the evolution of the outcome for a unit affected by an intervention or treatment to the evolution of the unit had it not been affected by the intervention.
Some functions for sample classification in microarrays
Multiple imputation for multivariate panel or clustered data.
Provides the tools needed to benchmark the R2 value corresponding to a certain acceptable noise level while also providing a rescaling function based on that noise level yielding a new value of R2 we refer to as R2k which is independent of both the number of degrees of freedom and the noise distribution function.
PANDA (Preferential Attachment based common Neighbor Distribution derived Associations) is designed to perform the following tasks in PPI networks: (1) identify significantly functionally associated protein pairs, (2) predict GO terms and KEGG pathways for proteins, (3) make a cluster of proteins based on the significant protein pairs, (4) identify subclusters whose members are enriched in KEGG pathways. For other types of biological networks, (1) and (3) can still be performed.
Contains some functions catching all messages, stdout and other
    useful information while evaluating R code and other helpers to return user
    specified text elements (like: header, paragraph, table, image, lists etc.)
    in pandoc's markdown or several type of R objects similarly automatically
    transformed to markdown format. Also capable of exporting/converting (the
    resulting) complex pandoc documents to e.g. HTML, PDF, docx or odt. This
    latter reporting feature is supported in brew syntax or with a custom
    reference class with a smarty caching backend.
The document converter 'pandoc' <http://pandoc.org/> is widely used
    in the R community. One feature of 'pandoc' is that it can produce and consume
    JSON-formatted abstract syntax trees (AST). This allows to transform a given
    source document into JSON-formatted AST, alter it by so called filters and pass
    the altered JSON-formatted AST back to 'pandoc'. This package provides functions
    which allow to write such filters in native R code. 
    Although this package is inspired by the Python package 'pandocfilters' 
    <https://github.com/jgm/pandocfilters/>, it provides additional convenience functions which make it simple to use the 'pandocfilters' package as a 
    report generator. Since 'pandocfilters' inherits most of it's functionality
    from 'pandoc' it can create documents in many formats 
    (for more information see <http://pandoc.org/>) but is also bound to the same
    limitations as 'pandoc'.
Aggregate Business Tendency Survey Data (and other qualitative
    surveys) to time series at various aggregation levels. Run aggregation of
    survey data in a speedy, re-traceable and a easily deployable way.
    Aggregation is substantially accelerated by use of data.table.
    This package intends to provide an interface that is less general and abstract than data.table but rather geared towards
    survey researchers.
The package estimates linear models on panel data structures in the presence of AR(1)-type autocorrelation as well as panel heteroskedasticity and/or contemporaneous correlation. First, AR(1)-type autocorrelation is addressed via a two-step Prais-Winsten feasible generalized least squares (FGLS) procedure, where the autocorrelation coefficients may be panel-specific. A number of common estimators for the autocorrelation coefficient are supported. In case of panel heteroskedasticty, one can choose to use a sandwich-type robust standard error estimator with OLS or a panel weighted least squares estimator after the two-step Prais-Winsten estimator. Alternatively, if panels are both heteroskedastic and contemporaneously correlated, the package supports panel-corrected standard errors (PCSEs) as well as the Parks-Kmenta FGLS estimator.
A high performance package implementing random effects and/or sample selection models for panel count data.
Linear models for panel data: the fixed effect model and the
    random effect model
Tools to interact with the 'Pangaea' Database (<https://
    www.pangaea.de>), including functions for searching for data, fetching 'datasets'
    by 'dataset' 'ID', and working with the 'Pangaea' 'OAI-PMH' service.
A methodology that makes use of the factor structure of large
    dimensional panels to understand the nature of nonstationarity inherent
    in data. This is referred to as PANIC, Panel Analysis of Nonstationarity
    in Idiosyncratic and Common Components. 
    PANIC (2004)<doi:10.1111/j.1468-0262.2004.00528.x> includes
    valid pooling methods that allow panel tests to be constructed.
    PANIC (2004) can detect whether the nonstationarity in a series is
    pervasive, or variable specific, or both.
    PANIC (2010) <doi:10.1017/s0266466609990478> includes
    two new tests on the idiosyncratic component that estimates the pooled
    autoregressive coefficient and sample moment, respectively. The PANIC
    model approximates the number of factors based on
    Bai and Ng (2002) <doi:10.1111/1468-0262.00273>.
A central decision in a parametric regression is how to specify the relation between an dependent variable and each explanatory variable. This package provides a semi-parametric tool for comparing different transformations of an  explanatory variables in a parametric regression. The functions is relevant in a situation, where you would use a box-cox or Box-Tidwell transformations.  In contrast to the classic power-transformations, the methods in this package allows for  theoretical driven user input and the possibility to compare with a non-parametric transformation.
Users pass images and objects of class 'nifti' from the 'oro.nifti'
    package to a Papaya, an interactive lightweight JavaScript viewer.
    Although many packages can view individual slices or projections of
    image and matrix data, this package allows for quick and easy
    interactive browsing of images.  The viewer is based off of the
    Mango software, which is a lightweight medical image viewer.
A toolbox for writing 'knitr', 'Sweave' or other 'LaTeX'- or 'markdown'-based
	     reports and to prettify the output of various estimated models.
This is a data only package, that provides distances from a paper plane experiment.
R package implementing random forest classification using parallel computing, built with Fortran and OpenMP in the backend.
Passing a given function name or a call to the
        parallelize/parallelize_call functions analyses and executes
        the code, if possible in parallel. Parallel code execution can
        be performed locally or on remote batch queuing systems.
Unified parallelization framework for multiple back-end,
  designed for internal package and interactive usage.
    The main operation is a parallel "map" over lists.
    Supports local, multicore, mpi and BatchJobs mode.
    Allows "tagging" of the parallel operation
    with a level name that can be later selected by the user to
    switch on parallel execution for exactly this operation.
Recent Bayesian Markov chain Monto Carlo (MCMC) methods have been developed for big data sets that are too large to be analyzed using traditional statistical methods. These methods partition the data into non-overlapping subsets, and perform parallel independent Bayesian MCMC analyses on the data subsets, creating independent subposterior samples for each data subset. These independent subposterior samples are combined through four functions in this package, including averaging across subset samples, weighted averaging across subsets samples, and kernel smoothing across subset samples. The four functions assume the user has previously run the Bayesian analysis and has produced the independent subposterior samples outside of the package; the functions use as input the array of subposterior samples. The methods have been demonstrated to be useful for Bayesian MCMC models including Bayesian logistic regression, Bayesian Gaussian mixture models and Bayesian hierarchical Poisson-Gamma models. The methods are appropriate for Bayesian hierarchical models with hyperparameters, as long as data values in a single level of the hierarchy are not split into subsets.
By sampling your data, running the provided classifier on these samples in parallel on your own machine and letting your models vote on a prediction, we return much faster predictions than the regular machine learning algorithm and possibly even more accurate predictions.
Parallelise constraint based causality discovery and causal inference methods. The parallelised algorithms in the package will generate the same results as that of the 'pcalg' package but will be much more efficient.
By sampling your data, running the Support-Vector-Machine algorithm on these samples in parallel on your own machine and letting your models vote on a prediction, we return much faster predictions than the regular Support-Vector-Machine and possibly even more accurate predictions.
Provides two functions: Group_function() and Parallel_Tree(). Group_function() applies a given function (e.g.,mean()) to input variable(s) by group across levels. Has additional data management options.
    Parallel_Tree() uses 'ggplot2' to create a parallel coordinate plots (technically a facsimile of parallel coordinate plots in a Cartesian coordinate system).
    Used in combination these functions can create parallel tree plots, a variant of parallel coordinate plots, which are useful for visualizing multilevel data.
Allows specification and fitting of some parameter
    estimation examples inspired by time-resolved spectroscopy via
    a Shiny GUI.
Functions for parameter descriptions and operations in black-box
    optimization, tuning and machine learning. Parameters can be described
    (type, constraints, defaults, etc.), combined to parameter sets and can in
    general be programmed on. A useful OptPath object (archive) to log function
    evaluations is also provided.
Parametric linkage analysis and other likelihood-based pedigree analyses based on the Elston-Stewart algorithm, including singlepoint LOD scores, power analysis and computation of marker genotype distributions. Multipoint analysis is provided through a convenient MERLIN wrapper. Includes many utility functions for creating, manipulating and plotting pedigrees, markers and linkage results.
An interface to simplify organizing parameters used in a package,
    using external configuration files. This attempts to provide a cleaner
    alternative to options().
paran is an implementation of Horn's technique for
        numerically and graphically evaluating the components or
        factors retained in a principle components analysis (PCA) or
        common factor analysis (FA). Horn's method contrasts
        eigenvalues produced through a PCA or FA on a number of random
        data sets of uncorrelated variables with the same number of
        variables and observations as the experimental or observational
        data set to produce eigenvalues for components or factors that
        are adjusted for the sample error-induced inflation. Components
        with adjusted eigenvalues greater than one are retained. paran
        may also be used to conduct parallel analysis following
        Glorfeld's (1995) suggestions to reduce the likelihood of
        over-retention.
Distributed gradient boosting based on the mboost package. The
    parboost package is designed to scale up component-wise functional
    gradient boosting in a distributed memory environment by splitting the
    observations into disjoint subsets, or alternatively using bootstrap
    samples (bagging). Each cluster node then fits a boosting model to its
    subset of the data. These boosting models are combined in an ensemble,
    either with equal weights, or by fitting a (penalized) regression
    model on the predictions of the individual models on the complete
    data.
The package estimates the matrix of partial correlations
        based on different regularized regression methods: lasso,
        adaptive lasso, PLS, and Ridge Regression. In addition, the
        package provides model selection for lasso, adaptive lasso and
        Ridge regression based on cross-validation.
Parallelized version of the "segment" function from Bioconductor package "DNAcopy", utilizing multi-core computation on host CPU
Conduct the Parent-Offspring Test Using Monomorphic SNP Markers. The similarity to the parents is computed for each offspring, and a plot of similarity for all offspring is produced. One can keep the offspring above some threshold for the similarity for further studies.
Statistical functions to describe a Pareto Positive Stable (PPS) 
    distribution and fit it to real data. Graphical and statistical tools to 
    validate the fits are included.
Fits Parametric Frailty Models by maximum marginal likelihood.
             Possible baseline hazards: Weibull, inverse Weibull, exponential, Gompertz, lognormal and loglogistic.
             Possible Frailty distributions: gamma, positive stable, inverse Gaussian and lognormal.
The package provides a number of easily parallelized
        functions from the fossil package. This package is designed to
        be used with some type of parallel computing backend, such as
        multicore, snow or MPI.
Provision of a set of models and methods for use in the allocation and management of capital in financial portfolios.
The package provides a parallel estimation of the mutual
        information based on entropy estimates from k-nearest neighbors
        distances and algorithms for the reconstruction of gene
        regulatory networks.
Model-based clustering and identifying informative features based on regularization methods. The package includes three regularization methods - PAirwise Reciprocal fuSE (PARSE) penalty proposed by Wang, Zhou and Hoeting (2016), the adaptive L1 penalty (APL1) and the adaptive pairwise fusion penalty (APFP). Heatmaps are included to shown the identification of informative features.
Implements basic partial order tools for multidimensional poverty evaluation with ordinal variables. Its main goal is to provide socio-economic scholars with an integrated set of elementary functions for multidimensional poverty evaluation, based on ordinal information. The package is organized in four main parts. The first two comprise functions for data management and basic partial order analysis; the third and the fourth are devoted to evaluation and implement both the poset-based approach and a more classical counting procedure.
Parse dates automatically, without the need of
    specifying a format. Currently it includes the git date parser.
    It can also recognize and parse all ISO 8601 formats.
Provides functions for parsing Thermo MSF files produced by Proteome Discoverer 1.4.x (see <https://thermofisher.com> for more information). This package makes it easy to view individual peptide information, including peak areas, and to map peptides to locations within the parent protein sequence. This package also estimates protein abundances from peak areas and across multiple technical replicates. The author of this package is not affiliated with ThermoFisher Scientific in any way.
A novel tool for generating a piecewise
        constant estimation list of increasingly complex predictors
        based on an intensive and comprehensive search over the entire
        covariate space.
Fits time series models which consist of a sum of a permanent and a transient component.
The "partially overlapping samples t-tests", for the comparison
    of means for two samples which include both paired observations and independent
    observations. [See Derrick, B., Russ, B., Toher, D. & White P (2017). Test
    statistics for the comparison of means for two samples which include both paired
    observations and independent observations. Journal of Modern Applied Statistical
    Methods, 16(1)].
Computes Odds Ratio adjusted for a vector of possibly
        continuous covariates
Low-dimensional embedding, using Random Forests for
        multiclass classification
partitionMetric computes a distance between two partitions
        of a set.
Additive partitions of integers.  Enumerates the
  partitions, unequal partitions, and restricted partitions of an
  integer; the three corresponding partition functions are also
  given.  Set partitions are now included.
Miscellaneous utilities for parallelizing large
   computations.  Alternative to MapReduce.
   File splitting and distributed operations such as sort and aggregate.
   "Software Alchemy" method for parallelizing most statistical methods,
   presented in N. Matloff, Parallel Computation for Data Science,
   Chapman and Hall, 2015.  Includes a debugging aid.
This package performs basic functions to fit and predict periodic autoregressive time series models. These models are discussed in the book P.H. Franses (1996) "Periodicity and Stochastic Trends in Economic Time Series", Oxford University Press. Data set analyzed in that book is also provided. NOTE: the package was orphaned during several years. It is now only maintained, but no major enhancement are expected, and the maintainer cannot provide any support.
A computational toolbox for recursive partitioning.
  The core of the package is ctree(), an implementation of
  conditional inference trees which embed tree-structured 
  regression models into a well defined theory of conditional
  inference procedures. This non-parametric class of regression
  trees is applicable to all kinds of regression problems, including
  nominal, ordinal, numeric, censored as well as multivariate response
  variables and arbitrary measurement scales of the covariates. 
  Based on conditional inference trees, cforest() provides an
  implementation of Breiman's random forests. The function mob()
  implements an algorithm for recursive partitioning based on
  parametric models (e.g. linear models, GLMs or survival
  regression) employing parameter instability tests for split
  selection. Extensible functionality for visualizing tree-structured
  regression models is available. The methods are described in
  Hothorn et al. (2006) <doi:10.1198/106186006X133933>,
  Zeileis et al. (2008) <doi:10.1198/106186008X319331> and 
  Strobl et al. (2007) <doi:10.1186/1471-2105-8-25>.
A toolkit with infrastructure for representing, summarizing, and
  visualizing tree-structured regression and classification models. This
  unified infrastructure can be used for reading/coercing tree models from
  different sources ('rpart', 'RWeka', 'PMML') yielding objects that share
  functionality for print()/plot()/predict() methods. Furthermore, new and improved
  reimplementations of conditional inference trees (ctree()) and model-based
  recursive partitioning (mob()) from the 'party' package are provided based
  on the new infrastructure.
Parviol combines parallel coordinates and violin plot
An R package for polygenic trait analysis
Provide summary table of daily physical activity and per-person/grouped heat map for accelerometer data from SenseWear Armband. See <https://templehealthcare.wordpress.com/the-sensewear-armband/> for more information about SenseWear Armband.
Pipeline and additional tools designed to handle aligned data from chromatin NGS experiments (less adapted for typical RNAseq applications). Specifically, starting from aligned reads after sequencing, the pipeline function aims at removing artefacts from datasets, estimate in-silico the fragments size, and generate WIG files with corrected values. The package also provides several tools for WIG files manipulations, scaling, merging and subtract (e.g. input).
To implement two methods, Kappa and PASS, for selecting
        tuning parameters in regularized procedures such as LASSO,
        SCAD, adaptive LASSO, aiming for variable selection in
        regularized regression
Create random passwords of letters, numbers and punctuation.
Intuitive and readable infix functions to paste strings together.
Regulation, decomposition and analysis of space-time series. The pastecs library is a PNEC-Art4 and IFREMER (Benoit Beliaeff <Benoit.Beliaeff@ifremer.fr>) initiative to bring PASSTEC 2000 (http://www.obs-vlfr.fr/~enseigne/anado/passtec/passtec.htm) functionalities to R.
A pre-processor for mrBayes that assimilates sequences, taxonomic
    information and tree constraints as per xxx.  The main functions of
    interest for most users will be pastis_simple, pastis_main and conch. The
    main analysis is conducted with pastis_simple or pastis_main followed by a
    manual execution of mrBayes (>3.2). The placement of taxa not contained in
    the tree constraint can be investigated using conch.
Data and functions for the book PROBABILITY and STATISTICS
        WITH R.
Functions and data sets for the text Probability and Statistics
    with R, Second Edition.
Functions to patch specials in .dvi files,
        or entries in .synctex files.  Works with "concordance=TRUE" 
        in Sweave or knitr to link sources to previews.
Functions to generate scatterplots with images patches
        instead of usual glyphs, with associated utilities.
This utility eases the debugging of literate documents
	     ('noweb' files) by patching the synchronization information
	     (the '.synctex(.gz)' file) produced by 'pdflatex' with
	     concordance information produced by 'Sweave' or 'knitr' and
	     'Sweave' or 'knitr' ; this allows for bilateral communication
	     between a text editor (visualizing the 'noweb' source) and
	     a viewer (visualizing the resultant 'PDF'), thus bypassing
	     the intermediate 'TeX' file.
An R tool suited to Affymetrix microarray data that combines three different statistical tests (Bootstrap, Fisher exact and Wilcoxon signed rank) to evaluate genetic pathway alterations.
pathClass is a collection of classification methods that
        use information about feature connectivity in a biological
        network as an additional source of information. This additional
        knowledge is incorporated into the classification a priori.
        Several authors have shown that this approach significantly
        increases the classification performance.
Implementation of simple functions to draw
    basic path diagrams just for visualization purposes.
pathmox, the cousin package of plspm, provides a very
    interesting solution for handling segmentation variables
    in PLS Path Modeling: segmentation trees in PLS Path Modeling.
Utilities for paths, files and directories.
Primarily for use with datasets containing only categorical variables, although continuous variables may be included as independent variables in paths. Using M Plus, backward variable selection is performed on all Total, Total Indirect, and then Direct effects until none of these effects have p-values greater than the specified target p-value. If there are missing values in the data, imputations are performed using the Mice package. Then selection is performed with the imputed data sets, and results are averaged.
Quickly and easily visualize longitudinal microbiome profiles using standard output from the QIIME microbiome analysis toolkit (see <http://qiime.org> for more information).
Provides tools for estimating composition and configuration parameters from a categorical (binary) landscape map (grid) and then simulates a selected number of statistically similar landscapes.  Class-focused pattern metrics are computed for each simulated map to produce empirical distributions against which statistical comparisons can be made. The code permits the analysis of single maps or pairs of maps.
Creates aesthetically pleasing and informative pie charts. 
             It can plot pie charts either in black and white or in colors, 
             with or without filled patterns. On the one hand, black and white pie charts filled 
			 with patterns are useful for publications, especially when an increasing 
			 number of journals only accept black and white figures or charge a 
			 significant amount for a color figure. On the other hand, colorful pie
			 charts with or without patterns are useful for print design, online publishing,
			 or poster and 'PowerPoint' presentations. 'patternplot' allows the flexibility of a
			 variety of combinations of patterns and colors to choose from. It also has the
			 ability to fill in the slices with any external images in 'png' and 'jpeg' formats.
			 In summary, 'patternplot' allows the users to be as creative as they can while
			 creating pie charts!
Implementation of a Bayesian active learning strategy to carry out sequential experimental design in the context of biochemical network kinetic parameter estimation. This package gathers functions and pre-computed data sets to reproduce results presented in Pauwels E. et. al published in BMC Systems Biology, 2014. Scripts are given to compute all results from scratch or to draw pictures based on pre-computed data sets.
A cohesive framework for parsing, analyzing and organizing color from spectral data.
This is a collection of functions to process, format and store accelerometer data.
Implementation of the Parallel Adaptive Wang-Landau
        algorithm. Also implemented for comparison: parallel adaptive
        Metropolis-Hastings,SMC sampler.
A lightweight package that adds
  progress bar to vectorized R functions
  ('*apply'). The implementation can easily be added
  to functions where showing the progress is
  useful (e.g. bootstrap). The type and style of the
  progress bar (with percentages or remaining time)
  can be set through options.
  Functions can exploit parallel processing resources.
This package provides data analysis via the pbat program,
        and an alternative internal implementation of the power
        calculations via simulation only.  For analysis, this package
        provides a frontend to the PBAT software, automatically reading
        in the output from the pbat program and displaying the
        corresponding figure when appropriate (i.e. PBAT-logrank). It
        includes support for multiple processes and clusters. For
        analysis, users must download PBAT (developed by Christoph
        Lange) and accept it's license, available on the PBAT webpage.
        Both the data analysis and power calculations have command line
        and graphical interfaces using tcltk.
Conducts maximum likelihood analysis and simulation of the protracted speciation model.
An interface to and extensions for the 'PBLAS' and
    'ScaLAPACK' numerical libraries.  This enables R to utilize
    distributed linear algebra for codes written in the 'SPMD' fashion.
    This interface is deliberately low-level and mimics the style of
    the native libraries it wraps.  For a much higher level way of
    managing distributed matrices, see the 'pbdDMAT' package.
A set of demos of 'pbdR' packages, together with a useful,
    unifying vignette.
A set of classes for managing distributed matrices, and
    a collection of methods for computing linear algebra and
    statistics.  Computation is handled mostly by routines from the
    'pbdBASE' package, which itself relies on the 'ScaLAPACK' and
    'PBLAS' numerical libraries for distributed computing.
An efficient interface to MPI by utilizing S4
        classes and methods with a focus on Single Program/Multiple Data
        ('SPMD')
        parallel programming style, which is intended for batch parallel
        execution.
This package adds collective parallel read and write capability
             to the R package ncdf4 version 1.8. Typical use is as a
             parallel NetCDF4 file reader in SPMD style programming. Each R
             process reads and writes its own data in a synchronized
             collective mode, resulting in faster parallel performance.
             Performance improvement is conditional on a parallel file system.
A very light implementation yet secure for remote procedure calls
        with unified interface via ssh (OpenSSH) or plink/plink.exe (PuTTY).
Utilizing scalable linear algebra packages mainly
        including BLACS, PBLAS, and ScaLAPACK in double precision via
        pbdMPI based on ScaLAPACK version 2.0.2.
'ZeroMQ' is a well-known library for high-performance
    asynchronous messaging in scalable, distributed applications.  This
    package provides high level R wrapper functions to easily utilize
    'ZeroMQ'. We mainly focus on interactive client/server programming
    frameworks. For convenience, a minimal 'ZeroMQ' library (4.1.0 rc1)
    is shipped with 'pbdZMQ', which can be used if no system installation
    of 'ZeroMQ' is available.  A few wrapper functions compatible with
    'rzmq' are also provided.
It constructs four series of PBIB designs and also assists in calculating the efficiencies of PBIB Designs with any number of associate classes. This will help the researchers in adopting a PBIB designs and calculating the efficiencies of any PBIB design very quickly and efficiently.
A set of datasets and functions used in the book
  'Modele liniowe i mieszane w R, wraz z przykladami w analizie danych'.
  Datasets either come from real studies or are created to be as similar 
  as possible to real studies.
Provides a vectorized R function for calculating
    probabilities from a standard bivariate normal CDF.
Test in mixed effects models.

   Attention is on mixed effects models as implemented in the
 'lme4' package.

   This package implements a parametric bootstrap test and a Kenward Roger 
 modification of F-tests for linear mixed effects models and a parametric bootstrap 
 test for generalized linear mixed models.
A light-weight package helps you track and visualize
  the progress of parallel version of vectorized R functions (mc*apply).
  The package works on *nix (Linux, Unix such as macOS) system only due to
  the lack of fork() functionality, which is essential for mc*apply, on Windows.
Implements permutation based non-parametric analysis of CRISPR (Clustered 
    Regularly Interspaced Short Palindromic Repeats) screen data.
Following the method of Bailey et al., computes for a collection
    of candidate models the probability of backtest overfitting, the
    performance degradation and probability of loss, and the stochastic
    dominance.
Adds different kinds of brackets to a plot, including braces, chevrons, parentheses or square brackets.
R Support for ADMB (Automatic Differentiation Model Builder)
Routines for solving systems of delay differential equations by
   interfacing numerical routines written by Simon N. Wood , with contributions
   by Benjamin J. Cairns. These numerical routines first appeared in Simon
   Wood's 'solv95' program. This package includes a vignette and a complete
   user's guide. 'PBSddesolve' originally appeared on CRAN under the name
   'ddesolve'. That version is no longer supported. The current name emphasizes
   a close association with other PBS packages, particularly 'PBSmodelling'.
This software has evolved from fisheries research conducted at the
   Pacific Biological Station (PBS) in `Nanaimo', British Columbia, Canada. It
   extends the R language to include two-dimensional plotting features similar
   to those commonly available in a Geographic Information System (GIS).
   Embedded C code speeds algorithms from computational geometry, such as
   finding polygons that contain specified point events or converting between
   longitude-latitude and Universal Transverse Mercator (UTM) coordinates.
   Additionally, we include `C++' code developed by Angus Johnson for the `Clipper'
   library. Also included are data for a global shoreline and other
   data sets in the public domain. The R directory `.../library/PBSmapping/doc'
   offers a complete user's guide, which should be consulted to use package
   functions effectively.
Provides software to facilitate the design, testing, and operation
   of computer models. It focuses particularly on tools that make it easy to
   construct and edit a customized graphical user interface (GUI). Although our
   simplified GUI language depends heavily on the R interface to the Tcl/Tk
   package, a user does not need to know Tcl/Tk. Examples illustrate models
   built with other R packages, including PBSmapping, PBSddesolve, and BRugs. 
   A complete user's guide `PBSmodelling-UG.pdf' shows how to use this package
   effectively.
Functions simplifying presentation of PCA models in a 3D interactive representation using 'rgl'.
To seek for a contemporaneous linear transformation for
    a multivariate time series such that the transformed series is segmented
    into several lower-dimensional subseries, and those subseries are
    uncorrelated with each other both contemporaneously and serially.
Draws a 2D principal component plot using the first 2 principal
  components from the original and bootstrapped data to give some sense of
  variability.
Methods to detect genetic markers involved in biological
    adaptation. 'pcadapt' provides statistical tools for outlier detection based on
    Principal Component Analysis. Implements the method described in (Luu, 2016)
    <DOI:10.1111/1755-0998.12592>.
Implementations of six methods for principal component analysis 
        using the L1 norm.  The package depends on COIN-OR Clp version >= 
        1.12.0.  The methods implemented are 
        PCA-L1 (Kwak 2008) <DOI:10.1109/TPAMI.2008.114>, 
        L1-PCA (Ke and Kanade 2003, 2005) <DOI:10.1109/CVPR.2005.309>, 
        L1-PCA* (Brooks, Dula, and Boone 2013) <DOI:10.1016/j.csda.2012.11.007>, 
        L1-PCAhp (Visentin, Prestwich and Armagan 2016) 
                 <DOI:10.1007/978-3-319-46227-1_37>, 
        wPCA (Park and Klabjan 2016), and
        awPCA (Park and Klabjan 2016).
Functions for causal structure
  learning and causal inference using graphical models. The main algorithms
  for causal structure learning are PC (for observational data without hidden
  variables), FCI and RFCI (for observational data with hidden variables),
  and GIES (for a mix of data from observational studies
  (i.e. observational data) and data from experiments
  involving interventions (i.e. interventional data) without hidden
  variables). For causal inference the IDA algorithm, the Generalized
  Backdoor Criterion (GBC) and the Generalized Adjustment Criterion (GAC)
  are implemented.
Principal Component Analysis, orthogonal rotation and multiple factor analysis for a mixture of quantitative and qualitative variables.
A set of functions to perform parallel analysis for
        principal components analysis intended mainly for large data
        sets. It performs a parallel analysis of continuous, ordered
        (including dichotomous/binary as a special case) or mixed type
        of data associated with a principal components analysis.
        Polychoric correlations among ordered variables, Pearson
        correlations among continuous variables and polyserial
        correlation between mixed type variables (one ordered and one
        continuous) are used. Whenever the use of polyserial or
        polychoric correlations yields a non positive definite
        correlation matrix, the resulting matrix is transformed into
        the nearest positive definite matrix. This is a continued work 
        based on a previous version developed at the Colombian Institute 
        for the evaluation of education - ICFES.
Provides functions for robust PCA by projection pursuit.
Method extends multivariate dynamic principal components to periodically correlated multivariate time series.
Semiparametric regression analysis of panel count data under the non-homogeneous Poisson
             process model with and without Gamma frailty using monotone splines.
Principal component of explained variance (PCEV) is a statistical tool for the analysis of a multivariate
    response vector. It is a dimension-reduction technique, similar to Principal
    component analysis (PCA), which seeks the maximize the proportion of
    variance (in the response vector) being explained by a set of covariates.
The package solves linear system of equations Ax=b by using Preconditioned Conjugate Gradient Algorithm where A is real symmetric positive definite matrix. A suitable preconditioner matrix may be provided by user. This can also be used to minimize quadratic function (x'Ax)/2-bx for unknown x.
Contains logic for computing the statistical association of variable groups, i.e., gene sets, with respect to the principal components of genomic data.
Using piecewise constant hazards models is a very flexible approach
    for the analysis of survival data. The time line is divided into sub-intervals;
    for each interval, a different hazard is estimated using Poisson regression.
This package implements a work-alike to R's POSIXct class
        which implements 360- and 365-day calendars in addition to the
        gregorian calendar.
Estimates the multidimensional polytomous Rasch model
    (Rasch, 1961) and the Continuous Rating Scale model (Mueller, 1987).
Apply Partial Correlation coefficient with Information
        Theory (PCIT) to a correlation matrix.
        The PCIT algorithm identifies meaningful correlations to define
        edges in a weighted network. The algorithm can be applied to
        any correlation-based network including but not limited to gene
        co-expression networks.
        To reduce compute time by making use of multiple compute cores,
        simply run PCIT on a computer with has multiple cores and also
        has the Rmpi package installed. PCIT will then auto-detect the
        multicore environment and run in parallel mode without the need
        to rewrite your scripts. This makes scripts, using PCIT, portable
        across single core (or no Rmpi package installed) computers
        which will run in serial mode and multicore (with Rmpi package
        installed) computers which will run in parallel mode.
Provides functions to perform arm-based network meta-analysis for datasets with binary, continuous, and count outcomes.
Computation of the Pedroni (1999) panel cointegration test statistics.  Reported are the empirical and the standardized values.
Analyzing regression data with many and/or highly collinear predictor variables, by simultaneously reducing the predictor variables to a limited number of components and regressing the criterion variables on these components. Several rotation options are provided in this package, as well as model selection options.
Set of functions for analysis of Principal Coordinates of
    Phylogenetic Structure (PCPS).
Implementing the Coalescent Approach to PCR Simulation.
Simulate the forensic DNA process: generate random or fixed DNA
    profiles, create forensic samples including mixtures of diploid and haploid
    cells, simulate DNA extraction, normalization, degradation, amplification
    including stutters and inter-locus balance, and capillary electrophoresis.
    DNA profiles are visualized as electropherograms and saved in tables.
    The command pcrsim() opens up a graphical user interface which allow the user
    to create projects, to enter, load, and save parameters required for the simulation.
    The simulation is transparent and the parameters used in each step of the simulation
    can be viewed in the result tables.
Given k populations (can be in thousands), what is the probability that a given subset of size t contains the true top t populations?  This package finds this probability and offers three tuning parameters (G, d, L) to relax the definition.
This package contains a function to estimate
        panel-corrected standard errors. Data may contain balanced or
        unbalanced panels.
Parallel Constraint Satisfaction (PCS) models are an increasingly
    common class of models in Psychology, with applications to reading and word
    recognition (McClelland & Rumelhart, 1981), judgment and decision making
    (Glöckner & Betsch, 2008; Glöckner, Hilbig, & Jekel, 2014), and several
    other fields (e.g. Read, Vanman, & Miller, 1997). In each of these fields,
    they provide a quantitative model of psychological phenomena, with precise
    predictions regarding choice probabilities, decision times, and often the degree
    of confidence. This package provides the necessary functions to create and
    simulate basic Parallel Constraint Satisfaction networks within R.
Permutation Distribution Clustering is a clustering method for time series. Dissimilarity of time series is formalized as the divergence between their permutation distributions. The permutation distribution was proposed as measure of the complexity of a time series.
The package performs cluster analysis via nonparametric density 
   estimation. Operationally, the kernel method is used throughout to estimate
   the density. Diagnostics methods for evaluating the quality of the clustering 
   are available. The package includes also a routine to estimate the 
   probability density function obtained by the kernel method, given a set of
   data with arbitrary dimensions.
Download economic and financial time series from public sources, 
  including the St Louis Fed's FRED system, Yahoo Finance, the US Bureau of Labor Statistics, 
  the US Energy Information Administration, the World Bank, Eurostat, the European Central Bank,
  the Bank of England, the UK's Office of National Statistics, and INSEE.
Includes functions for keyword search of pdf files. There is
    also a wrapper that includes searching of all files within a single
    directory.
Allows the user to convert PDF tables to formats more amenable to
  analysis ('.csv', '.xml', or '.xlsx') by wrapping the PDFTables API.
  In order to use the package, the user needs to sign up for an API account
  on the PDFTables website (<https://pdftables.com/pdf-to-excel-api>).
  The package works by taking a PDF file as input, uploading it to PDFTables,
  and returning a file with the extracted data.
Utilities based on libpoppler for extracting text,
    fonts, attachments and metadata from a pdf file. Also implements
    rendering of PDF to bitmaps on supported platforms.
Computes the euclidean distance between rows of a matrix X
        and rows of another matrix Y.  Previously, this could be done
        by binding the two matrices together and calling 'dist', but
        this creates unnecessary computation by computing the distances
        between a row of X and another row of X, and likewise for Y.
        pdist strictly computes distances across the two matrices, not
        within the same matrix, making computations significantly
        faster for certain use cases.
Measures real distances in pictures. With PDM() function, you can choose one '*.jpg' file, select the measure in mm of scale, starting and and finishing point in the graphical scale, the name of the measure, and starting and and finishing point of the measures. After, ask the user for a new measure.
This package provides a model of Pavlovian conditioning phenomena, such as response extinction and spontaneous recovery, and partial reinforcement extinction effects. Competing proximal and distal reward predictions, computed using fast and slow learning rates, combine according to their uncertainties and the recency of information. The resulting mean prediction drives the response rate.
Estimates panel data cointegrating relationships following the estimator of Mark and Sul (2003), <DOI:10.1111/j.1468-0084.2003.00066.x>.
A general framework for constructing partial dependence (i.e., 
  marginal effect) plots from various types machine learning models in R.
Looks for amino acid and/or nucleotide patterns coordinated to a given prosthetic centre. It also accounts for small molecule ligands. Files have to be in the local file system and contain the '.pdb' extension.
A collection of tools for approximating the 'PDQ' functions
    (respectively, the cumulative distribution, density, and quantile) of
    probability distributions via classical expansions involving moments and
    cumulants.
Threshold model, panel version of Hylleberg et al.(1990)<DOI:10.1016/0304-4076(90)90080-D> seasonal unit root tests, and panel unit root test of Chang(2002)<DOI:10.1016/S0304-4076(02)00095-7>.
A package to compute and tune some positive definite and
        sparse covariance estimators
Implementation of wavelet-based multivariate spectral density estimation and clustering methods in the Riemannian manifold of Hermitian and positive-definite matrices.
The original definition of the two and three dimensional Kolmogorov-Smirnov two-sample
             test statistics given by Peacock (1983) is implemented. Two R-functions: peacock2 and peacock3, 
             are provided to compute the test statistics in two and three dimensional spaces, respectively. 
             Note the Peacock test is different from the Fasano and Franceschini test (1987). The latter is 
             a variant of the Peacock test.
Calculates the periodogram of a time series, maximum-likelihood fits an Ornstein-Uhlenbeck state space (OUSS) null model and evaluates the statistical significance of periodogram peaks against the OUSS null hypothesis. The OUSS is a parsimonious model for stochastically fluctuating variables with linear stabilizing forces, subject to uncorrelated measurement errors. Contrary to the classical white noise null model for detecting cyclicity, the OUSS model can account for temporal correlations typically occurring in ecological and geological time series.
Biologically inspired methods for
    detecting peaks in one-dimensional data, such as time series or genomics data.
    The algorithms were originally designed by Weber, Ramachandran, and Henikoff,
    see documentation.
When working with big data sets, RAM conservation is critically
    important. However, it is not always enough to just monitor the
    size of the objects created. So-called "copy-on-modify" behavior,
    characteristic of R, means that some expressions or functions may
    require an unexpectedly large amount of RAM overhead. For example,
    replacing a single value in a matrix duplicates that matrix in the
    back-end, making this task require twice as much RAM as that used
    by the matrix itself. This package makes it easy to monitor the total
    and peak RAM used so that developers can quickly identify and
    eliminate RAM hungry code.
Spectrum manipulation: background estimation, Markov
        smoothing, deconvolution and peaks search functions. Ported
        from ROOT/TSpectrum class.
Package for estimating periodic autoregressive models.
        Datasets: monthly ozone and Fraser riverflow. Plots: periodic
        versions of boxplot, auto/partial correlations, moving-average
        expansion.
Supports maximum likelihood inference for the Pearson VII
    distribution with shape parameter 3/2 and free location and scale
    parameters. This distribution is relevant when estimating the velocity of
    processive motor proteins with random detachment.
Implementation of the Pearson distribution system, including full
  support for the (d,p,q,r)-family of functions for probability distributions 
  and fitting via method of moments and maximum likelihood method.
The Pearson-ICA algorithm is a mutual information-based
        method for blind separation of statistically independent source
        signals. It has been shown that the minimization of mutual
        information leads to iterative use of score functions, i.e.
        derivatives of log densities. The Pearson system allows
        adaptive modeling of score functions. The flexibility of the
        Pearson system makes it possible to model a wide range of
        source distributions including asymmetric distributions. The
        algorithm is designed especially for problems with asymmetric
        sources but it works for symmetric sources as well.
Validation of risk predictions obtained from survival models and
    competing risk models based on censored data using inverse weighting and
    cross-validation.
Contains functions for sensitivity and power analysis, for calculating statistics describing pedigrees from wild populations, and for viewing pedigrees
An implementation for association analysis with CNV data in R. It
    provides two methods for association study: first, the observed probe
    intensity measurement can be directly used to detect the association of CNV
    with phenotypes of interest. Second, the most probable copy number is
    estimated with the proposed likelihood and the association of the most
    probable copy number with phenotype is tested. This method can be applied
    to both the independent and correlated population.
Gene-level association tests with disease status for pedigree data: kernel and burden association statistics.
Functions to employ many of the tools and techniques used in the 
    field of pedometrics.
Provides functions to perform the peer performance
    analysis of funds' returns as described in Ardia and Boudt (2016) <doi:10.2139/ssrn.2000901>.
Functions for reading, writing, plotting, analysing, and manipulating allelic and haplotypic data, and for the analysis of population nucleotide sequences and micro-satellites including coalescence analyses.
The population proportion using group testing can be estimated by different methods. Four functions including p.mle(), p.gart(), p.burrow() and p.order() are provided to implement four estimating methods including the maximum likelihood estimate, Gart's estimate, Burrow's estimate, and order statistic estimate.
Several functions introduced in Aster et al.'s book on inverse theory. The functions are often translations of MATLAB code developed by the authors to illustrate concepts of inverse theory as applied to geophysics. Generalized inversion, tomographic inversion algorithms (conjugate gradients, 'ART' and 'SIRT'), non-linear least squares, first and second order Tikhonov regularization, roughness constraints, and procedures for estimating smoothing parameters are included. Includes a wrapper for the FORTRAN based 'LSQR' (Paige and Saunders) routine.
This package provides functions to perform multivariate Gaussian parameter estimation based on data with abundance-dependent missingness. It implements a penalized Expectation-Maximization (EM) algorithm. The package is tailored for but not limited to proteomics data applications, in which a large proportion of the data are often missing-not-at-random with lower values (or absolute values) more likely to be missing.
Utility functions for the handling, analysis and visualisation 
  of data from portable emissions measurement systems ('PEMS') and other 
  similar mobile activity monitoring devices. The package includes a dedicated 
  'pems' data class that manages many of the quality control, unit handling 
  and data archiving issues that can hinder efforts to standardise 'PEMS' 
  research.
Fitting possibly high dimensional penalized
        regression models. The penalty structure can be any combination
        of an L1 penalty (lasso and fused lasso), an L2 penalty (ridge) and a
        positivity constraint on the regression coefficients. The
        supported regression models are linear, logistic and Poisson
        regression and the Cox Proportional Hazards model.
        Cross-validation routines allow optimization of the tuning
        parameters.
Implements the penalized LDA proposal of "Witten and Tibshirani (2011), Penalized classification using Fisher's linear discriminant, to appear in Journal of the Royal Statistical Society, Series B".
This package provides feature selection SVM using penalty
        functions. The smoothly clipped absolute deviation (SCAD),
        'L1-norm', 'Elastic Net' ('L1-norm' and 'L2-norm') and 'Elastic
        SCAD' (SCAD and 'L2-norm') penalties are availible. The tuning
        parameters can be founf using either a fixed grid or a interval
        search.
Flexible copula density estimation with penalized hierarchical B-Splines.
A regularization approach for Cox Frailty Models by penalization methods is provided.
Estimation of univariate (conditional) densities using penalized B-splines with automatic selection of optimal smoothing parameter.
Flexible Pair-Copula Estimation in D-vines using Bivariate Penalized Splines.
Structured fusion Lasso penalized estimation of multi-state models with the penalty applied to absolute effects and absolute effect differences (i.e., effects on transition-type specific hazard rates).
Simulation of continuous, correlated high-dimensional data with time to event or binary response, and parallelized functions for Lasso, Ridge, and Elastic Net penalized regression with repeated starts and two-dimensional tuning of the Elastic Net.
Package peperr is designed for prediction error estimation
        through resampling techniques, possibly accelerated by parallel
        execution on a compute cluster. Newly developed model fitting
        routines can be easily incorporated.
This package provides a variety of methods for dealing
        with analysis of peptide library data, including clustering,
        motif finding, and QSAR model fitting.
Amino acid exchange based on single nucleotide variant (SNV) information and tryptic digestion on peptide sequence. Searching for homologous by comparison of tryptic digested peptide sequences.
An implementation of the data processing and data analysis portion
    of a pipeline named the PepSAVI-MS which is currently under development by
    the Hicks laboratory at the University of North Carolina.  The statistical
    analysis package presented herein provides a collection of software tools
    used to facilitate the prioritization of putative bioactive peptides from a
    complex biological matrix.  Tools are provided to deconvolute mass
    spectrometry features into a single representation for each peptide charge
    state, filter compounds to include only those possibly contributing to the
    observed bioactivity, and prioritize these remaining compounds for those
    most likely contributing to each bioactivity data set.
Evaluation of diversity in peptide libraries, including NNN, NNB,
    NNK/S, and 20/20 schemes. Custom encoding schemes can also be defined.
    Metrics for evaluation include expected coverage, relative efficiency, and
    the functional diversity of the library. Peptide-level inclusion
    probabilities are computable for both the native and custom encoding
    schemes.
Calculate physicochemical properties and indices from amino-acid sequences of peptides and proteins. Include also the option to read and plot output files from the 'GROMACS' molecular dynamics package.
Moderated regression with mean and residual centering and simple slopes analysis.
Identification, model fitting and estimation for time series with periodic structure.
    Additionally procedures for simulation of periodic processes
    and real data sets are included.
To find the certainty of dominance interactions with indirect
    interactions being considered.
Contains functions which allow the user to compute confidence intervals quickly using the double bootstrap-based percentile calibrated ('perc-cal') method for linear regression coefficients. 'perccal_interval()' is the primary user-facing function within this package.
Several person-fit statistics (PFSs) are offered. These statistics allow assessing whether
  individual response patterns to tests or questionnaires are (im)plausible given 
  the other respondents in the sample or given a specified item response theory model. Some PFSs apply to 
  dichotomous data, such as the likelihood-based PFSs (lz, lz*) and the group-based PFSs 
  (personal biserial correlation, caution index, (normed) number of Guttman errors, 
  agreement/disagreement/dependability statistics, U3, ZU3, NCI, Ht). PFSs suitable to polytomous data include
  extensions of lz, U3, and (normed) number of Guttman errors.
Package that implements different performance measures for classification and ranking tasks. AUC, precision at a given recall, F-score for single and multiple classes are available.
Collection of econometric functions for
    performance and risk analysis. This package aims to aid
    practitioners and researchers in utilizing the latest
    research in analysis of non-normal return streams.  In
    general, it is most tested on return (rather than
    price) data on a regular scale, but most functions will
    work with irregular return data as well, and increasing
    numbers of functions will work with P&L or price data
    where possible.
An infra-structure for estimating the predictive performance of
    predictive models. In this context, it can also be used to compare and/or select
    among different alternative ways of solving one or more predictive tasks. The
    main goal of the package is to provide a generic infra-structure to estimate
    the values of different metrics of predictive performance using different
    estimation procedures. These estimation tasks can be applied to any solutions
    (workflows) to the predictive tasks. The package provides easy to use standard
    workflows that allow the usage of any available R modeling algorithm together
    with some pre-defined data pre-processing steps and also prediction post-
    processing methods. It also provides means for addressing issues related with
    the statistical significance of the observed differences.
Provides tools for linkage mapping in polyploids.
    It implements the method PERGOLA, which is a fast, deterministic method to
    calculate the order of markers in a linkage group.
Provides a dataset containing properties for chemical elements.
    Helper functions are also provided to access some atomic properties.
Perform Exact or Asymptotic permutation tests
This version of the permutational algorithm generates a
        dataset in which event and censoring times are conditional on
        an user-specified list of covariates, some or all of which are
        time-dependent.
Includes functions to work with the Mallows and Generalized Mallows
    Models. The considered distances are Kendall's-tau, Cayley, Hamming and Ulam
    and it includes functions for making inference, sampling and learning such
    distributions, some of which are novel in the literature. As a by-product,
    PerMallows also includes operations for permutations, paying special attention
    to those related with the Kendall's-tau, Cayley, Ulam and Hamming distances. It
    is also possible to generate random permutations at a given distance, or with
    a given number of inversions, or cycles, or fixed points or even with a given
    length on LIS (longest increasing subsequence).
Can be used to carry out
        permutation resampling inference in the context of RNA
        microarray studies.
Permutational group-sequential tests for time-to-event data based on the log-rank test statistic. Supports exact permutation test when the censoring distributions are equal in the treatment and the control group and approximate imputation-permutation methods when the censoring distributions are different.
Can be used to carry out permutation based gene expression pathway analysis. This work was supported by a National Institute of Allergy and Infectious Disease/National Institutes of Health contract (No. HHSN272200900059C).
All the functions compiled in this package were created to perform permutation-based non-parametric analysis on microbiome data for biomarker discovery aims. This test executes thousands of comparisons in pairwise manner, after random shuffling of data into the different groups of study.
Manipulates invertible functions from a finite set to itself.  Can transform from word form to cycle form and back.
A set of restricted permutation designs for freely exchangeable, line transects (time series), and spatial grid designs plus permutation of blocks (groups of samples) is provided. 'permute' also allows split-plot designs, in which the whole-plots or split-plots or both can be freely-exchangeable or one of the restricted designs. The 'permute' package is modelled after the permutation schemes of 'Canoco 3.1' (and later) by Cajo ter Braak.
Tools that allow developers to write functions for
        prediction error estimation with minimal programming effort and
        assist users with model selection in regression problems.
Translate words from English to Persian (Over 67,000
        words)
Automated identification of printed array positions from high content 
    microscopy images and the export of those positions as individual images 
    written to output as multi-layered tiff files.
Visualizes treatment effects using person icons, similar to Cates (NNT) charts.
Provides a robust framework for analyzing the extent to which differential survival with respect to higher level trait variation is reducible to lower level variation. In addition to its primary test, it also provides functions for simulation-based power analysis, reading in common data set formats, and visualizing results. Temporarily contains an edited version of function hr.mcp() from package 'wild1', written by Glen Sargeant. For tutorial see: http://evolve.zoo.ox.ac.uk/Evolve/Perspectev.html.
"perturb" evaluates collinearity by adding random noise to
        selected variables. "colldiag" calculates condition numbers and
        variance decomposition proportions to test for collinearity and
        uncover its sources.
Single item and composite pesticide residue measurements
        of fifteen commodity-pesticide combinations plus analytical
        tools.
This package implements different analytic/direct and
        iterative reconstruction methods of Peter Toft. It also offer
        the possibility to simulate PET data.
In analysis of high-dimensional DNA methylation data, a penalized exponential tilt model can identify differentially methylated loci between cases and controls, using network based regularization. It is able to detect any differences in means only, in variances only or in both means and variances.
Functions for the construction of Petri Nets. Petri Nets can be replayed by firing enabled transitions.
     Silent transitions will be hidden by the execution handler. Also includes functionalities for the visualization of Petri Nets and
     export of Petri Nets to PNML (Petri Net Markup Language) files.
Reproducible, programmatic retrieval of survey datasets from the
    Pew Research Center.
Eco-phylogenetic and community phylogenetic analyses.
    Keeps community ecological and phylogenetic data matched up and
    comparable using 'comparative.comm' objects. Wrappers for common
    community phylogenetic indices ('pez.shape', 'pez.evenness',
    'pez.dispersion', and 'pez.dissimilarity' metrics). Implementation
    of Cavender-Bares (2004) correlation of phylogenetic and
    ecological matrices ('fingerprint.regression'). Phylogenetic
    Generalised Linear Mixed Models (PGLMMs; 'pglmm') following Ives &
    Helmus (2011) and Rafferty & Ives (2013). Simulation of null
    assemblages, traits, and phylogenies ('scape', 'sim.meta.comm').
Estimate the false discovery proportion (FDP) by Principal Factor Approximation method with general known and unknown covariance dependence.
This work is an extension of the state space model for
        Poisson count data, Poisson-Gamma model, towards a
        semiparametric specification. Just like the generalized
        additive models (GAM), cubic splines are used for covariate
        smoothing. The semiparametric models are fitted by an iterative
        process that combines maximization of likelihood and
        backfitting algorithm.
Fits penalized generalized estimating equations to longitudinal data with high-dimensional covariates.
Perform simultaneous estimation and variable selection for correlated bivariate
    mixed outcomes (one continuous outcome and one binary outcome per cluster) using
    penalized generalized estimating equations. In addition, clustered Gaussian and binary
    outcomes can also be modeled. The SCAD, MCP, and LASSO penalties are supported.
    Cross-validation can be performed to find the optimal regularization parameter(s).
A Group ICA Algorithm that can run in parallel on an SGE platform or multi-core PCs
Miscellaneous functions for data analysis in ecology, with special emphasis on spatial data.
Estimation of panel models for glm-like models: this includes binomial models (logit and probit) count models (poisson and negbin) and ordered models (logit and probit)
Based on the Generalized Least Square model for
        comparative Phylogenetics (ref).
Construction method of nested resolvable designs from 
    a projective geometry defined on Galois field of order 2. The obtained
    Resolvable designs are used to build uniform design. The presented results
    are based on <https://eudml.org/doc/219563> and A. Boudraa et al. (See references).
This is an R and C code implementation of the New-SP and New-JGL method of Gao et al. (2016) <DOI:10.1214/16-EJS1135> to perform model-based clustering and multiple graph estimation.
Carries out model-based clustering or classification using parsimonious Gaussian mixture models.
Evaluation of the pdf and the cdf of the univariate,
        noncentral, p-generalized normal distribution. Sampling from
        the univariate, noncentral, p-generalized normal distribution
        using either the p-generalized polar method, the p-generalized
        rejecting polar method, the Monty Python method, the Ziggurat
        method or the method of Nardon and Pianca. The package also
        includes routines for the simulation of the bivariate,
        p-generalized uniform distribution and the simulation of the
        corresponding angular distribution.
Implements a general framework for creating dependency graphs using projection. Both lasso and sparse additive model projections are implemented. Both Pearson correlation and distance covariance are used to generate the graph.
Provides functions to aid the identification of probable/possible
    duplicates in Plant Genetic Resources (PGR) collections using
    'passport databases' comprising of information records of each constituent
    sample. These include methods for cleaning the data, creation of a
    searchable Key Word in Context (KWIC) index of keywords associated with
    sample records and the identification of nearly identical records with
    similar information by fuzzy, phonetic and semantic matching of keywords.
Computation of mean squared errors of stereological predictors.
An implementation of Bayesian single-arm phase II
    design methods for binary outcome based on posterior
    probability and predictive probability.
Calculate the Bayesian posterior/predictive probability and
    determine the sample size and stopping boundaries for single-arm Phase II design.
Provide multinomial design methods under intersection-union test (IUT) and union-intersection test (UIT) scheme for Phase II trial. The design types include : Minimax (minimize the maximum sample size), Optimal (minimize the expected sample size), Admissible (minimize the Bayesian risk) and Maxpower (maximize the exact power level).
The phalen package contains (1) clustering and 
 partitioning algorithms; (2) penalty functions for numeric 
 vectors; (3) a ranking function; and (4) color palettes and 
 functions.
Phylogenetic analysis in R: Estimation of phylogenetic
        trees and networks using Maximum Likelihood, Maximum Parsimony,
        distance methods and Hadamard conjugation.
This package contains functions performing power calculations for mixed (sparse/dense sampled) pharmacokinetic study designs. The input data for these functions is tailored for NONMEM .phi files.
phaseR is an R package for the qualitative analysis of one and
   two dimensional autonomous ODE systems, using phase plane methods. Programs
   are available to identify and classify equilibrium points, plot the
   direction field, and plot trajectories for multiple initial conditions. In
   the one dimensional case, a program is also available to plot the phase
   portrait. Whilst in the two dimensional case, additionally a program is
   available to plot nullclines. Many example systems are provided for the
   user.
Functions to perform Bayesian inference on absorption time
        data for Phase-type distributions.  Plans to expand this to
        include frequentist inference and simulation tools.
phcfM is an R package for modelling anthropogenic
        deforestation. It was initially developed to obtain REDD+
        baseline scenarios of deforestation for the "programme
        holistique de conservation des forets a Madagascar" (from which
        the package is named after). Parameter inference is done in a
        hierarchical Bayesian framework. Markov chains Monte Carlo
        (MCMC) are coded in C++ using the Scythe statistical library to
        maximize computation efficiency.
Implementation of heatmaps that offers more control
    over dimensions and appearance.
An alternative to carrying out phenotypic adaptability and stability analyses, taking into account nonparametric statistics. Can be used as a robust approach, less sensitive to departures from common genotypic, environmental, and GxE effects data assumptions (e.g., normal distribution of errors).
Provides some easy-to-use functions for 
	spatial analyses of (plant-) phenological data 
	sets and satellite observations of vegetation.
Provides functions to estimate the size-controlled phenotypic integration index, a novel method by Torices & Méndez (2014) to solve problems due to individual size when estimating integration (namely, larger individuals have larger components, which will drive a correlation between components only due to resource availability that might obscure the observed measures of integration). In addition, the package also provides the classical estimation by Wagner (1984), bootstrapping and jackknife methods to calculate confidence intervals and a significance test for both integration indices.
Provides functions to preprocess phenological data, for modelling and result handling.
Provides some easy-to-use functions for time series
        analyses of (plant-) phenological data sets. These functions
        mainly deal with the estimation of combined phenological time
        series and are usually wrappers for functions that are already
        implemented in other R packages adapted to the special
        structure of phenological data and the needs of phenologists.
        Some date conversion functions to handle Julian dates are also
        provided.
High-throughput generation of genetic markers from molecular phenotypes for crosses between inbred strains. These markers can be use to saturate existing genetic map or creating a new one.
Functions used to fit and test the phenology of species based on counts.
Provides tools for the evaluation of the goodness of fit and the predictive capacity of the proportional hazards model.
Analysis of terms in linear, generalized and mixed linear models, 
	on the basis of multiple comparisons of factor contrasts.  Specially suited 
	for the analysis of interaction terms.
Fits proportional hazards model incorporating random effects using
    an EM algorithm using Markov Chain Monte Carlo at E-step.
Convert English letters to numbers or numbers to English letters as 
    on a telephone keypad. When converting letters to numbers, a character 
    vector is returned with "A," "B," or "C" becoming 2, "D," "E", or "F" 
    becoming 3, etc. When converting numbers to letters, a character vector is 
    returned with multiple elements (i.e., "2" becomes a vector of "A," "B," and 
    "C").
Provides a collection of phonetic algorithms including
    Soundex, Metaphone, NYSIIS, Caverphone, and others.
Tools for phoneticians and phonologists, including functions for normalization and plotting of vowels.
Contains tools for the organization, display, and analysis of the sorts of data frequently encountered in phonetics research and experimentation, including the easy creation of IPA vowel plots, and the creation and manipulation of WAVE audio files.
Definitions of classes, methods, operators and functions for use in
    photobiology and radiation meteorology and climatology. Calculation of
    effective (weighted) and not-weighted irradiances/doses, fluence rates,
    transmittance, reflectance, absorptance, absorbance and diverse
    ratios and other derived quantities from spectral data. Local maxima and
    minima. Conversion between energy- and photon-based units. Wavelength
    interpolation. Astronomical calculations related solar angles and day
    length. Colours and vision.
Functions for reading and writing foreign files containing
    spectral data from spectrometers and their associated software, and output
    from simulation models in common use. As well as functions for exchange
    of spectral data with other R packages.
Spectral emission data for some frequently used lamps excluding 
    LEDs. Original data for incandescent and different types of discharge lamps 
    are included.
Spectral emission data for some frequently used light emitting
    diodes.
Provides functions for quantifying visible ('VIS') and ultraviolet
    ('UV') radiation in relation to the 'photoreceptors' 'Phytochromes',
    'Cryptochromes', and 'UVR8' which are present in plants. It also
    includes data sets on the optical properties of plants.
Constructors of waveband objects for commonly used biological
    spectral weighting functions ('BSWFs') and for different wavebands describing
    named ranges of wavelengths in the ultraviolet ('UV'), visible ('VIS')
    and infrared ('IR') regions of the electromagnetic spectrum.
Simple noun phrase extraction using part-of-speech information.
    Takes a collection of un-processed documents as input and returns a set of noun
    phrases associated with those documents.
A geochemical modeling program developed by the US Geological
    Survey that is designed to perform a wide variety of aqueous geochemical
    calculations, including speciation, batch-reaction, one-dimensional
    reactive-transport, and inverse geochemical calculations.
The package provides estimation procedures for panel data with large dimensions n, T, and general forms of unobservable heterogeneous effects. Particularly, the estimation procedures are those of Bai (2009) and Kneip, Sickles, and Song (2012), which complement one another very well: both models assume the unobservable heterogeneous effects to have a factor structure. The method of Bai (2009) assumes that the factors are stationary, whereas the method of Kneip et al. (2012) allows the factors to be non-stationary. Additionally, the 'phtt' package provides a wide range of dimensionality criteria in order to estimate the number of the unobserved factors simultaneously with the remaining model parameters.
Assessment of habitat selection by means of the permutation-based combination of sign tests (Fattorini et al., 2014 <DOI:10.1007/s10651-013-0250-7>). To exemplify the application of this procedure, habitat selection is assessed for a population of European Brown Hares settled in central Italy.
A collection of several pharmacovigilance signal detection methods extended to the multiple comparison setting.
Calls 'Phoenix NLME' (non-linear mixed effects), a population
    modeling and simulation software, for pharmacokinetics and pharmacodynamics
    analyses and conducts post-processing of the results. This includes creation of
    various diagnostic plots, bootstrap and visual predictive checks. See <http://
    www.certara.com/software/pkpd-modeling-and-simulation/phoenix-nlme/> for more
    information about 'Phoenix NLME'.
Implementation the outbreak analysis method described by 
    Klinkenberg et al (2016) <doi:10.1101/069195>. 
    Simulate outbreaks, analyse datasets by creating samples from the 
    posterior distribution with a Markov-Chain Monte Carlo sampler, 
    and summarize the output.
Phylogenetic clustering (phyloclustering) is an evolutionary
        Continuous Time Markov Chain model-based approach to identify
        population structure from molecular data without assuming
        linkage equilibrium. The package phyclust (Chen 2011) provides a
        convenient implementation of phyloclustering for DNA and SNP data,
        capable of clustering individuals into subpopulations and identifying
        molecular sequences representative of those subpopulations. It is
        designed in C for performance, interfaced with R for visualization,
        and incorporates other popular open source programs including
        ms (Hudson 2002) <doi:10.1093/bioinformatics/18.2.337>,
        seq-gen (Rambaut and Grassly 1997)
        <doi:10.1093/bioinformatics/13.3.235>,
        Hap-Clustering (Tzeng 2005) <doi:10.1002/gepi.20063> and
        PAML baseml (Yang 1997, 2007) <doi:10.1093/bioinformatics/13.5.555>,
        <doi:10.1093/molbev/msm088>,
        for simulating data, additional analyses, and searching the best tree.
        See the phyclust website for more information, documentations and
        examples.
Based on (but not identical to) the no-longer-maintained package 'phyext', provides enhancements to 'phylobase' classes, specifically for use by package 'SigTree'; provides classes and methods which help users manipulate branch-annotated trees (as in 'SigTree'); also provides support for a few other extra features.
Enables rapid calculation of phylogenetic information content using the latest advances in phylogenetic informativeness based theory. These advances include modifications that incorporate uneven branch lengths and any model of nucleotide substitution to provide assessments of the phylogenetic utility of any given dataset or dataset partition. Also provides new tools for data visualization and routines optimized for rapid statistical calculations, including approaches making use of Bayesian posterior distributions and parallel processing. Users can apply these approaches toward screening datasets for phylogenetic/genomic information content.
The spatial interpolation of genetic distances between
	     samples is based on a modified kriging method that
	     accepts a genetic distance matrix and generates a map of
	     probability of lineage presence. This package also offers
	     tools to generate a map of  potential contact zones
	     between groups with user-defined thresholds in the tree
	     to account for old and recent divergence. Additionally,
	     it has functions for IDW interpolation using genetic data
	     and midpoints.
Provides a base S4 class for comparative methods, incorporating
    one or more trees and trait data.
Create and customize interactive phylogenetic trees using the 'phylocanvas' JavaScript library and the 'htmlwidgets' package. These trees can be used directly from the R console, from 'RStudio', in Shiny apps, and in R Markdown documents.  See <http://phylocanvas.org/>  for more information on the 'phylocanvas' library.
This package implements some recently developed methods in phyloclimatic modeling.
Tools for studying the evolution of high-dimensional traits
    (morphometric, function-valued, etc.) including ancestral state reconstruction,
    estimating phylogenetic signal, and assessing correlated trait evolution. Visit
    http://www.phylocurve.org for more information.
Implementation of the automatic shift detection method for
    Brownian Motion (BM) or Ornstein–Uhlenbeck (OU) models of trait evolution on
    phylogenies. Some tools to handle equivalent shifts configurations are also
    available.
Manipulation and analysis of phylogenetically simulated
        data sets and phylogenetically based analyses using GLS.
Phyloland package models a space colonization process mapped onto a phylogeny, it aims at estimating limited dispersal and ecological competitive exclusion in a Bayesian MCMC statistical phylogeographic framework (please refer to phyloland-package help for details.)
Provides functions for fitting phylogenetic linear models and phylogenetic generalized linear models. The computation uses an algorithm that is linear in the number of tips in the tree. The package also provides functions for simulating continuous or binary traits along the tree. Other tools include functions to test the adequacy of a population tree.
Given a phylogenetic tree T and an assemblage S of species represented as 
             a subset of tips in T, we want to compute a measure of the diversity 
             of the species in S with respect to T. The current package offers 
             efficient algorithms that can process large phylogenetic data for several such measures. 
             Most importantly, the package includes algorithms for computing 
             efficiently the standardized versions of phylogenetic measures and their p-values, which are 
             essential for null model comparisons. Among other functions, 
             the package provides efficient computation of richness-standardized versions 
             for indices such as the net relatedness index (NRI), 
             nearest taxon index (NTI), phylogenetic
             diversity index (PDI), and the corresponding indices of two-sample measures. 
             The package also introduces a new
             single-sample measure, the Core Ancestor Cost (CAC); the package provides
             functions for computing the value and the standardised index of the CAC and,
             more than that, there is an extra function available that can compute exactly 
             any statistical moment of the measure. The package supports computations
             under different null models, including abundance-weighted models.
Provides functions to estimate statistical errors of phylogenetic
    metrics particularly to detect binary trait influence on diversification, as
    well as a function to simulate trees with fixed number of sampled taxa and trait
    prevalence.
A comprehensive and easy to use R implementation of confirmatory
    phylogenetic path analysis as described by Von Hardenberg and Gonzalez-Voyer
    (2012) <doi:10.1111/j.1558-5646.2012.01790.x>.
A collection of tools to explore the phylogenetic signal in univariate and multivariate data. The package provides functions to plot traits data against a phylogenetic tree, different measures and tests for the phylogenetic signal, methods to describe where the signal is located and a phylogenetic clustering method.
An extensible object-oriented framework for the Monte Carlo simulation of sequence evolution written in 100 percent R. It is built on the top of the R.oo and ape packages and uses Gillespie's direct method to simulate substitutions, insertions and deletions.
Functions to read and write APE-compatible phylogenetic
  trees in NEXUS and Newick formats, while preserving annotations.
Building supermatrix for DNA barcodes using different
        genes, calculating the inequality among lineages and
        phylogenetic similarity for very large dataset using slicing
        methods by invoking Phylocom.
Tools for calculating and viewing topological properties of phylogenetic trees.
Use topological or taxonomic information to maximize the overlap of phylogenetic and comparative data.
Provides general linear model facilities (single y-variable, multiple x-variables with arbitrary mixture of continuous and categorical and arbitrary interactions) for cross-species data. The theory is in A. Grafen (1989, Proc. R. Soc. B 326, 119-157) and aims to cope with both recognised phylogeny (closely related species tend to be similar) and unrecognised phylogeny (a polytomy usually indicates ignorance about the true  sequence of binary splits).
Contains a function to categorize accelerometer readings collected in free-living (e.g., for 24 hours/day for 7 days), preprocessed and compressed as counts (unit-less value) in a specified time period termed epoch (e.g., 1 minute) as either bedrest (sleep) or active.  The input is a matrix with a timestamp column and a column with number of counts per epoch. The output is the same dataframe with an additional column termed bedrest. In the bedrest column each line (epoch) contains a function-generated classification 'br' or 'a' denoting bedrest/sleep and activity, respectively.  The package is designed to be used after wear/nonwear marking function in the 'PhysicalActivity' package.
This package contains functions to classify monitor wear and nonwear time intervals in accelerometer data collected to assess physical activity in free-living condition. The package also contains functions to make plot for accelerometer data, and to obtain the summary of daily monitor wear time and the mean of monitor wear time during valid days. A monitored day is considered valid if the total minutes of classified monitor wear time per day is greater than a user defined cutoff.
A variety of formulae are provided for estimation of height,
    weight and fluid compartments of adults and children. Each formula is
    referenced to the original publication. Warnings can be given for
    estimation based on input data outside of normal ranges. Future functions
    will cover more material with a focus on anaesthesia, critical
    care and peri-operative medicine.
Screens and sorts phylogenetic trees in both traditional and
    extended Newick format. Allows for the fast and flexible screening (within
    a tree) of Exclusive clades that comprise only the target taxa and/or Non-
    Exclusive clades that includes a defined portion of non-target taxa.
Fits PE and RLC data to one of a four published PE models.
	Simulates incident irradiance as a function of time and space.
	Calculates phytoplankton production by transposing modeled PE or RLC data 
	to a water column with a user-defined theoretical in-situ irradiance field.
Methods for estimating the proportion of true null hypotheses, i.e., the pi0, when a very large number of hypotheses are simultaneously tested, especially for the purpose of (local) false discovery rate control for microarray data. It also contains functions to estimate the distribution of noncentrality parameters from a large number of parametric tests.
Phylocom integration, community analyses, null-models, traits and evolution in R
Computationally efficient tools for fitting generalized linear model with convex or non-convex penalty. Users can enjoy the superior statistical property of non-convex penalty such as SCAD and MCP which has significantly less estimation error and overfitting compared to convex penalty such as lasso and ridge. Computation is handled by multi-stage convex relaxation and the PathwIse CAlibrated Sparse Shooting algOrithm (PICASSO) which exploits warm start initialization, active set updating, and strong rule for coordinate preselection to boost computation, and attains a linear convergence to a unique sparse local optimum with optimal statistical properties. The computation is memory-optimized using the sparse matrix output.
A collection of scripts and data files for the statistics text: 
 "Process Improvement using Data". The package contains code for designed 
 experiments, data sets and other convenience functions used in the book.
Implements piecewise structural equation models.
Extension of the ARTP package for gene- and pathway-environment
    interaction
Fits models of gene expression evolution to expression data from
    coregulated groups of genes, assuming inverse gamma distributed rate
    variation.
Global univariate minimization of Lipschitz functions is performed by using Pijavski method, which was published in Pijavski (1972) <DOI:10.1016/0041-5553(72)90115-2>.
Fit a probabilistic index model as described in 
    Thas et al <doi:10.1111/j.1467-9868.2011.01020.x>. The interface to the 
    modeling function has changed in this new version. The old version is
    still available at R-Forge. You can install the old package
    using install.packages('pimold', repos = 'http://R-Forge.R-project.org').
Utilities for fast and stable estimation of the probability of 
 informed trading (PIN) in the model introduced by Easley et al. (2002) 
 <DOI:10.1111/1540-6261.00493> are implemented. Since the basic model developed 
 by Easley et al. (1996) <DOI:10.1111/j.1540-6261.1996.tb04074.x> is nested in the 
 former due to equating the intensity of uninformed buys and sells, functions 
 can also be applied to this simpler model structure, if needed. 
 State-of-the-art factorization of the model likelihood function as well as 
 most recent algorithms for generating initial values for optimization routines are implemented. 
 In total, two likelihood factorizations and three methodologies for 
 starting values are included. 
 Furthermore, functions for simulating datasets of daily aggregated buys and sells, 
 calculating confidence intervals for the probability of informed trading and posterior probabilities 
 of trading days' conditions are available.
Genomic data for the plant pathogen "Phytophthora infestans." It
    includes a variant file ('VCF'), a sequence file ('FASTA') and an annotation file
    ('GFF'). This package is intended to be used as example data for packages that
    work with genomic data.
Check if a remote computer is up. It can either
    just call the system ping command, or check a specified
    TCP port.
An R interface to the Pinnaclesports.com API.
Implements the Product of Independent beta Probabilities dose Escalation (PIPE) design for dual-agent Phase I trials as described in Mander AP, Sweeting MJ (2015) <DOI:10.1002/sim.6434>.
To take nested function calls and convert them to a more readable form using pipes from package 'magrittr'.
Code for various permutation p-values estimation methods for gene set test. The description of corresponding methods can be found in the dissertation of Yu He(2016) "Efficient permutation P-value estimation for gene set tests" <https://searchworks.stanford.edu/view/11849351>. One of the methods also corresponds to the paper "Permutation p-value approximation via generalized Stolarsky invariance" <https://arxiv.org/abs/1603.02757>.
A framework for defining 'pipelines' of functions for applying data transformations, 
  model estimation and inverse-transformations, resulting in predicted value generation (or 
  model-scoring) functions that automatically apply the entire pipeline of functions required to go
  from input to predicted output.
Provides various styles of function chaining methods: Pipe
    operator, Pipe object, and pipeline function, each representing a distinct
    pipeline model yet sharing almost a common set of features: A value can be
    piped to the first unnamed argument of a function and to dot symbol in an
    enclosed expression. The syntax is designed to make the pipeline more
    readable and friendly to a wide range of operations.
Generate Predicted Interval Plots.  Simulate and plot
        confidence intervals of an effect estimate given observed data
        and a hypothesis about the distribution of future data.
An implementation of the generated effect modifier (GEM) method. This method constructs composite variables by linearly combining pre-treatment scalar patient characteristics to create optimal treatment effect modifiers in linear models. The optimal linear combination is called a GEM. Treatment is assumed to have been assigned at random. For reference, see E Petkova, T Tarpey, Z Su, and RT Ogden. Generated effect modifiers (GEMs) in randomized clinical trials. Biostatistics (First published online: July 27, 2016, <doi:10.1093/biostatistics/kxw035>).
With 'pitchRx', one can easily obtain Major League Baseball Advanced
    Media's 'Gameday' data (as well as store it in a remote database). The
    'Gameday' website hosts a wealth of data in XML format, but perhaps most
    interesting is 'pitchfx'. Among other things, 'pitchfx' data can be used to
    recreate a baseball's flight path from a pitcher's hand to home plate. With
    pitchRx, one can easily create animations and interactive 3D 'scatterplots'
    of the baseball's flight path. 'pitchfx' data is also commonly used to
    generate a static plot of baseball locations at the moment they cross home
    plate. These plots, sometimes called strike-zone plots, can also refer to a
    plot of event probabilities over the same region. 'pitchRx' provides an easy
    and robust way to generate strike-zone plots using the 'ggplot2' package.
Provides an R interface for the Pivotal Data stack
    running on 'PostgreSQL', 'Greenplum' or 'Apache HAWQ (incubating)'
    databases with parallel and distributed computation ability for big data
    processing. 'PivotalR' provides an R interface to various database
    operations on tables or views. These operations are almost the same as
    the corresponding native R operations. Thus users of R do not need
    to learn 'SQL' when they operate on objects in the database. It also
    provides a wrapper for 'Apache MADlib (incubating)', which is an open-
    source library for parallel and scalable in-database analytics.
The introduction of the broom package has made converting model
    objects into data frames as simple as a single function. While the broom
    package focuses on providing tidy data frames that can be used in advanced
    analysis, it deliberately stops short of providing functionality for reporting
    models in publication-ready tables. pixiedust provides this functionality with
    a programming interface intended to be similar to ggplot2's system of layers
    with fine tuned control over each cell of the table. Options for output include
    printing to the console and to the common markdown formats (markdown, HTML, and
    LaTeX). With a little pixiedust (and happy thoughts) tables can really fly.
Functions for import, export, plotting and other
        manipulations of bitmapped images.
Estimation of pharmacokinetic parameters using non-compartmental theory.
Set configuration options on a per-package basis.
    Options set by a given package only apply to that package,
    other packages are unaffected.
"Copy" local R package information to a temporary cloud space and "paste" your favorite R packages to a new environment.
Provides a function kitten() which creates cute little 
 packages which pass R package checks. This sets it apart from 
 package.skeleton() which it calls, and which leaves imperfect files 
 behind. As this is not exactly helpful for beginners, kitten() offers 
 an alternative.
This package provides some low-level utilities to use for package
    development. It currently provides managers for multiple package specific
    options and registries, vignette, unit test and bibtex related utilities.
    It serves as a base package for packages like NMF, RcppOctave, doRNG, and
    as an incubator package for other general purposes utilities, that will
    eventually be packaged separately.
    It is still under heavy development and changes in the interface(s) are
    more than likely to happen.
PKgraph provides a graphical user interface for population
        pharmacokinetic model diagnosis. It also provides an integrated
        and comprehensive platform for the analysis of pharmacokinetic
        data including exploratory data analysis, goodness of model
        fit, model validation and model comparison. Results from a
        variety of modeling fitting software, including NONMEM,
        Monolix, SAS and R, can be used. PKgraph is programmed in R,
        and uses the R packages lattice, ggplot2 for static graphics,
        and rggobi for interactive graphics.
PKI functions such as verifying certificates, RSA encription and signing which can be used to build PKI infrastructure and perform cryptographic tasks.
We implement two least-squares estimators under k-monotony constraint using a method based on the Support Reduction Algorithm from Groeneboom et al (2008) <DOI:10.1111/j.1467-9469.2007.00588.x>. The first one is a projection estimator on the set of k-monotone discrete functions. The second one is a projection on the set of k-monotone discrete probabilities. This package provides functions to generate samples from the spline basis from Lefevre and Loisel (2013) <DOI:10.1239/jap/1378401239>, and from mixtures of splines.
Compute standard Non-Compartmental Analysis (NCA)
    parameters and summarize them.  In addition to this core work, it
    also provides standardized plotting routines, basic assessments
    for biocomparison or drug interaction, and model-based estimation
    routines for calculating doses to reach specific values of AUC or
    Cmax.
Provides functions to evaluate common
        pharmacokinetic/pharmacodynamic models and their gradients.
PKreport aims to 1) provide automatic pipeline for users
        to visualize data and models. It creates a flexible R framework
        with automatically generated R scripts to save time and cost
        for later usage; 2) implement an archive-oriented management
        tool for users to store, retrieve and modify figures. 3) offer
        powerful and convenient service to generate high-quality graphs
        based on two R packages: lattice and ggplot2.
Fitting and testing probabilistic knowledge structures,
  especially the basic local independence model (BLIM, Doignon & Flamagne,
  1999), using the minimum discrepancy maximum likelihood (MDML) method.
Parallel Line Assays: Completely randomized design,
 Randomized Block design, and Latin squares design.
 Balanced data are fitted as described in the Ph.Eur.
 In the presence of missing values complete data analysis can be
 performed (with computation of Fieller's confidence intervals for
 the estimated potency), or imputation of values can be applied.
 The package contains a script such that a pdf-document with a
 report of an analysis of an assay can be produced from an input file
 with data of the assay. Here no knowledge of R is needed by the user.
A semi-parametric estimation method for the Cox model
    with left-truncated data using augmented information
    from the marginal of truncation times.
The main functions in this package are drive_time
	(used for calculating distances between physical addresses or coordinates) and
	geocode_url (used for estimating the lat/long coordinates
	of a physical address). Optionally, it generates the cryptographic signatures necessary
	for making API calls with a Google for Work/Premium account within the geocoding process.
	These accounts have larger quota limits than the "standard_api" and, thus, this package
	may be useful for individuals seeking to submit large batch jobs within R to the Google Maps API.
	Placement also provides methods for accessing the standard API using a (free) Google API key
	(see: <https://developers.google.com/maps/documentation/javascript/get-api-key#get-an-api-key>).
Supports the creation of burndown charts and gantt diagrams.
Solves the electromagnetic problem of reflection and transmission at a planar multilayer interface. Also computed are the decay rates and emission profile for a dipolar emitter.
Calculates an acceptance sampling plan, (sample size and acceptance number) based in MIL STD 105E, Dodge  Romig and MIL STD 414 tables and procedures. The arguments for each function are related to lot size, inspection level and quality level. The specific plan operating curve (OC), is calculated by the binomial distribution.
Automatic generation of regular factorial designs, including fractional designs, orthogonal block designs, row-column designs and split-plots.
Coupled leaf gas exchange model, A-Ci curve simulation and
    fitting, Ball-Berry stomatal conductance models, 
    leaf energy balance using Penman-Monteith, Cowan-Farquhar
    optimization, humidity unit conversions.
Estimation, prediction, thresholding, and plotting for partially linear additive quantile regression.  Intuitive functions for fitting and plotting partially linear additive quantile regression models.  Uses and works with functions from the 'quantreg' package.
Aims at detecting single nucleotide variation
    (SNV) and insertion/deletion (INDEL) in circulating tumor DNA (ctDNA), used
    as a surrogate marker for tumor, at each base position of an Next Generation
    Sequencing (NGS) analysis. Mutations are assessed by comparing the minor-allele
    frequency at each position to the measured PER in control samples.
Contains functions developed to combine the results of querying a plasmid database using
    short-read sequence typing with the results of a blast analysis against the query results.
Tools for interacting with data from experiments done in microtiter
    plates. Easily read in plate-shaped data and convert it to tidy format, 
    combine plate-shaped data with tidy data, and view tidy data in plate shape.
Collection of functions for working with multi-well microtitre
    plates, mainly 96, 384 and 1536 well plates.
Implements schemes for estimating player or 
  team skill based on dynamic updating. Implemented methods include 
  Elo, Glicko and Stephenson. Contains pdf documentation of a 
  reproducible analysis using approximately two million chess 
  matches.
A GTK+ graphical user interface for editing and
        interacting with R plots.
Provides a set of functions for interacting with the
    'Pleiades' 'API', including getting status data, places data, and
    creating a 'GeoJSON' based map on 'GitHub' 'gists'.
Perform tests for pleiotropy of multiple traits on genotypes for a genetic marker.
Functions for estimating probabilistic latent feature models with a disjunctive or a conjunctive mapping rule on (aggregated) binary three-way data.
Provides a graphical user interface for viewing and designing various types of graphs of the data. The graphs can be saved in different formats of an image.
Sequential Monte Carlo inference for fully Bayesian
  Gaussian process (GP) regression and classification models by
  particle learning (PL).  The sequential nature of inference
  and the active learning (AL) hooks provided facilitate thrifty 
  sequential design (by entropy) and optimization
  (by improvement) for classification and
  regression models, respectively.
  This package essentially provides a generic
  PL interface, and functions (arguments to the interface) which
  implement the GP models and AL heuristics.  Functions for 
  a special, linked, regression/classification GP model and 
  an integrated expected conditional improvement (IECI) statistic 
  is provides for optimization in the presence of unknown constraints.
  Separable and isotropic Gaussian, and single-index correlation
  functions are supported.
  See the examples section of ?plgp and demo(package="plgp") 
  for an index of demos
PLIS is a multiple testing procedure for testing several
        groups of hypotheses. Linear dependency is expected from the
        hypotheses within the same group and is modeled by hidden
        Markov Models. It is noted that, for PLIS, a smaller p value
        does not necessarily imply more significance because of
        dependency among the hypotheses. A typical application of PLIS
        is to analyze genome wide association studies datasets, where
        SNPs from the same chromosome are treated as a group and
        exhibit strong linear genomic dependency.
A set of estimators and tests for panel data.
A set of tools for identifying genes whose differential
        expression is associated with measurements of other covariates
        on a continuous scale.  These methods rely on generalized
        additive partially linear models which can be fitted
        efficiently using a B-spline basis approximation.  Still under
        development: methods for interfacing with objects extending the
        eSet class and a function to pass linear models in edgeR and
        DEseq format.
Fit finite mixtures of Plackett-Luce models for partial top rankings/orderings within the Bayesian framework. It provides MAP point estimates via EM algorithm and posterior MCMC simulations via Gibbs Sampling. It also fits MLE as a special case of the noninformative Bayesian analysis with vague priors.
This package fits the partially linear mixed effects model
        (semiparametric random intercept model) using kernel
        regression, without distributional assumptions for the random
        terms. Estimation procedure is an iterative generalized least
        squares type. A nonparametric heteroskedastic variance function
        is allowed for the regression error. Bootstrap resampling is
        provided for inference. The package implements bandwidth
        selection by an alternative cross validation for correlated
        data.
Performs bivariate composite likelihood and full
        information maximum likelihood estimation for polytomous
        logit-normit (graded logistic) item response theory (IRT)
        models.
A simple header-only logging library for C++.
  Add 'LinkingTo: plogr' to 'DESCRIPTION', and '#include <plogr.h>' in your C++ modules to use it.
Multivariate ordered probit model, i.e. the extension of the scalar ordered probit model where the observed variables have dimension greater than one. Estimation of the parameters is done via maximization of the pairwise likelihood, a special case of the composite likehood obtained as product of bivariate marginal distributions. The package uses the Fortran 77 subroutine SADMVN by Alan Genz, with minor adaptations made by Adelchi Azzalini in his "mvnormt" package for evaluating the two-dimensional Gaussian integrals involved in the pairwise log-likelihood. Optimization of the latter objective function  is performed via quasi-Newton box-constrained optimization algorithm, as implemented in nlminb.
Functions for viewing 2-D and 3-D data, including perspective plots, slice plots, surface plots, scatter plots, etc. Includes data sets from oceanography.
The 'rgl' implementation of plot3D functions.
When analyzing data, plots are a helpful tool for visualizing data and interpreting statistical models. This package provides a set of simple tools for building plots incrementally, starting with an empty plot region, and adding bars, data points, regression lines, error bars, gradient legends, density distributions in the margins, and even pictures. The package builds further on R graphics by simply combining functions and settings in order to reduce the amount of code to produce for the user. As a result, the package does not use formula input or special syntax, but can be used in combination with default R plot functions. Note: Most of the functions were part of the package 'itsadug', which is now split in two packages: 1. the package 'itsadug', which contains the core functions for visualizing and evaluating nonlinear regression models, and 2. the package 'plotfunctions', which contains more general plot functions.
Provides an interactive plot device for handling the geographic data for web browsers, designed for the automatic creation of web maps as a combination of users' data and Google Maps layers.
Writes sp-class, spacetime-class, raster-class and similar spatial and spatio-temporal objects to KML following some basic cartographic rules.
Examines the characteristics of a data frame and a formula to
    automatically choose the most suitable type of plot out of the following supported
    options: scatter, violin, box, bar, density, hexagon bin, spine plot, and
    heat map. The aim of the package is to let the user focus on what to plot,
    rather than on the "how" during exploratory data analysis. It also automates
    handling of observation weights, logarithmic axis scaling, reordering of
    factor levels, and overlaying smoothing curves and median lines. Plots are
    drawn using 'ggplot2'.
Easily translate 'ggplot2' graphs to an interactive web-based version and/or create custom web-based visualizations directly from R. Once uploaded to a 'plotly' account, 'plotly' graphs (and the data behind them) can be viewed and modified in a web browser.
Markov chain Monte Carlo diagnostic plots. The purpose of the
  package is to combine existing tools from the 'coda' and 'lattice' packages,
  and make it easy to adjust graphical details.
Plot marginal effects for interactions estimated
    from linear models.
Plot model surfaces for a wide variety of models
        using partial dependence plots and other techniques.
        Also plot model residuals and other information on the model.
Plot principal component histograms around a bivariate
        scatter plot.
Useful set of tools for plotting network diagrams in any kind of project.
Creates an envelope around a set of plotted points. The
        envelope is compact with a boundary that is continuous, smooth
        and convex. Each point is represented as a circle and the
        circles and connecting lines are the solution to the multiple
        pulley problem. This method can be used to highlight regions in
        a two-dimensional space.
Lots of plots, various labeling, axis and color scaling functions.
Most ROC curve plots obscure the cutoff values and inhibit
    interpretation and comparison of multiple curves. This attempts to address
    those shortcomings by providing plotting and interactive tools. Functions
    are provided to generate an interactive ROC curve plot for web use, and
    print versions. A Shiny application implementing the functions is also
    included.
Functions for making visual exploratory data analysis easier.
Contains a graphical user interface to generate the diagnostic
    plots proposed by Bauer (2005) and Pek & Chalmers (2015) to investigate
    nonlinear bivariate relationships in latent regression models using structural
    equation mixture models (SEMMs).
Small self-contained plots for use in larger plots or to
    delegate plotting in other functions. Also contains a number of
    alternative color palettes and HSL color space based tools to modify
    colors or palettes.
Fit Log Linear by Linear Association models and Rasch family models by pseudolikelihood estimation
This package provides statistical inference tools applied to Partial Linear Regression (PLR)
 models. Specifically, point estimation, confidence intervals estimation, bandwidth selection, goodness-of-fit tests and analysis of
 covariance are considered. 
 Kernel-based methods, combined with ordinary least squares estimation, are used and time series 
 errors are allowed. In addition, these techniques are also implemented for both parametric (linear) 
 and nonparametric regression models.
Multivariate regression methods
	Partial Least Squares Regression (PLSR), Principal Component
	Regression (PCR) and Canonical Powered Partial Least Squares (CPPLS).
Principal Component Analysis (PCA) biplots, Covariance monoplots
    and biplots, Partial Least Squares (PLS) biplots, Partial Least Squares for
    Generalized Linear Model (PLS-GLM) biplots, Sparse Partial Least Squares
    (SPLS) biplots and Sparse Partial Least Squares for Generalized Linear
    Model (SPLS-GLM) biplots.
plsdepot contains different methods for PLS analysis of
        one or two data tables such as Tucker's Inter-Battery, NIPALS,
        SIMPLS, SIMPLS-CA, PLS Regression, and PLS Canonical Analysis.
        The main reference for this software is the awesome book (in
        French) 'La Regression PLS: Theorie et Pratique' by Michel
        Tenenhaus.
The plsdof package provides Degrees of Freedom estimates
        for Partial Least Squares (PLS) Regression. Model selection for
        PLS is based on various information criteria (aic, bic, gmdl)
        or on cross-validation. Estimates for the mean and covariance
        of the PLS regression coefficients are available. They allow
        the construction of approximate confidence intervals and the
        application of test procedures. Further, cross-validation
        procedures for Ridge Regression and Principal Components
        Regression are available.
Routines for PLS-based genomic analyses,
        implementing PLS methods for classification with
        microarray data and prediction of transcription factor
        activities from combined ChIP-chip analysis. The >=1.2-1
        versions include two new classification methods for microarray
        data: GSIM and Ridge PLS. The >=1.3 versions includes a
        new classification method combining variable selection and
        compression in logistic regression context: RIRLS-SPLS; and
        an adaptive version of the sparse PLS.
Partial Least Squares Path Modeling (PLS-PM)
    analysis for both metric and
    non-metric data, as well as REBUS analysis.
The main objective is to make easy the PLS Path Modeling with R using the package 'plspm'. It compute automatically the inner matrix and the outer list the 'plspm' function need simply by specify the model using formulas.
Computation of sensitivity indexes by using a method based on a truncated Polynomial Chaos Expansion of the response and regression PLS, for computer models with correlated continuous inputs, whatever the input distribution. The truncated Polynomial Chaos Expansion is built from the multivariate Legendre orthogonal polynomials. 
 The number of runs (rows) can be smaller than the number of monomials. It is possible to select only the most significant monomials. 
 Of course, this package can also be used if the inputs are independent. Note that, when they are  independent and uniformly distributed, the package 'polychaosbasics' is more appropriate.
Provides Partial least squares Regression for (weighted) beta regression models and k-fold cross-validation of such models using various criteria. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.
Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models in high dimensional settings.
Provides (weighted) Partial least squares Regression for generalized linear models and repeated k-fold cross-validation of such models using various criteria. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.
Interfaces and methods for variable selection in Partial Least
    Squares. The methods include filter methods, wrapper methods and embedded
    methods. Both regression and classification is supported.
Kernel density estimation with global bandwidth selection
	     via "plug-in".
Gives the ability to automatically generate and serve an HTTP API
    from R functions using the annotations in the R documentation around your
    functions.
The base R data.frame, like any vector, is
    copied upon modification. This behavior is at odds with
    that of GUIs and interactive graphics. To rectify this,
    plumbr provides a mutable, dynamic tabular data model.
    Models may be chained together to form the complex
    plumbing necessary for sophisticated graphical
    interfaces. Also included is a
    general framework for linking datasets; an typical
    use case would be a linked brush.
plusser provides an API interface to Google+ so that posts,
    profiles and pages can be automatically retrieved.
A set of tools that solves a common set of problems: you
    need to break a big problem down into manageable pieces, operate on each
    piece and then put all the pieces back together.  For example, you might
    want to fit a model to each spatial location or time point in your study,
    summarise data by panels or collapse high-dimensional arrays to simpler
    summary statistics. The development of 'plyr' has been generously supported
    by 'Becton Dickinson'.
Performs Penalized Multivariate Analysis: a penalized
        matrix decomposition, sparse principal components analysis, and
        sparse canonical correlation analysis, described in the
        following papers: (1) Witten, Tibshirani and Hastie (2009) A
        penalized matrix decomposition, with applications to sparse
        principal components and canonical correlation analysis.
        Biostatistics 10(3):515-534. (2) Witten and Tibshirani (2009)
        Extensions of sparse canonical correlation analysis, with
        applications to genomic data. Statistical Applications in
        Genetics and Molecular Biology 8(1): Article 28.
Monte Carlo based model choice for applied phylogenetics of
    continuous traits. Method described in  Carl Boettiger, Graham Coop,
    Peter Ralph (2012) Is your phylogeny informative? Measuring
    the power of comparative methods, Evolution 66 (7)
    2240-51. doi:10.1111/j.1558-5646.2011.01574.x.
Parsimonious Mixtures of Contaminated Gaussian Distributions
Aims to utilize model-based clustering (unsupervised)
        for high dimensional and ultra large data, especially in a distributed
        manner. The code employs pbdMPI to perform a
        expectation-gathering-maximization algorithm
        for finite mixture Gaussian
        models. The unstructured dispersion matrices are assumed in the
        Gaussian models. The implementation is default in the single program
        multiple data programming model. The code can be executed
        through pbdMPI and independent to most MPI applications.
        See the High Performance
        Statistical Computing website for more information, documents
        and examples.
The Kruskal and Wallis one-way analysis of variance by ranks 
	     or van der Waerden's normal score test can be employed, 
	     if the data do not meet the assumptions 
	     for one-way ANOVA. Provided that significant differences 
	     were detected by the omnibus test, one may be interested 
	     in applying post-hoc tests for pairwise multiple comparisons 
	     (such as Nemenyi's test, Dunn's test, Conover's test,
	     van der Waerden's test). Similarly, one-way ANOVA with repeated 
	     measures that is also referred to as ANOVA with unreplicated 
	     block design can also be conducted via the Friedman-Test 
	     or the Quade-test. The consequent post-hoc pairwise 
	     multiple comparison tests according to Nemenyi, Conover and Quade
	     are also provided in this package. Finally Durbin's test for 
	     a two-way balanced incomplete block design (BIBD) is also given
	     in this package.
Routines for state estimate in a linear
    Gaussian state space model and a simple stochastic volatility model using
    particle filtering. Parameter inference is also carried out in these models
    using the particle Metropolis-Hastings algorithm that includes the particle
    filter to provided an unbiased estimator of the likelihood. This package is 
    a collection of minimal working examples of these algorithms and is only 
    meant for educational use and as a start for learning to them on your own.
Allows to build and train simple Process Mining (PM) models. The aim is to support PM specifically for the clinical domain from both administrative and clinical data.
Extends the approach proposed by Firth (1993) for bias
        reduction of MLEs in exponential family models to the
        multinomial logistic regression model with general covariate
        types.  Modification of the logistic regression score function
        to remove first-order bias is equivalent to penalizing the
        likelihood by the Jeffreys prior, and yields penalized maximum
        likelihood estimates (PLEs) that always exist.  Hypothesis
        testing is conducted via likelihood ratio statistics.  Profile
        confidence intervals (CI) are constructed for the PLEs.
The Predictive Model Markup Language (PMML) is an XML-based
    language which provides a way for applications to define statistical and
    data mining models and to share models between PMML compliant applications.
    More information about PMML and the Data Mining Group can be found at http://
    www.dmg.org. The generated PMML can be imported into any PMML consuming
    application, such as the Zementis ADAPA and UPPI scoring engines which allow for
    predictive models built in R to be deployed and executed on site, in the cloud
    (Amazon, IBM, and FICO), in-database (IBM Netezza, Pivotal, Sybase IQ, Teradata
    and Teradata Aster) or Hadoop (Datameer and Hive).
Allows for data to be transformed before using
    it to construct models. Builds structures to allow functions in
    the PMML package to output transformation details in
    addition to the model in the resulting PMML file.
Descriptive statistics (mean rank, pairwise frequencies, and marginal matrix), Analytic Hierarchy Process models (with Saaty's and Koczkodaj's inconsistencies), probability models (Luce models, distance-based models, and rank-ordered logit models) and visualization with multidimensional preference analysis for ranking data are provided. Current, only complete rankings are supported by this package.
This package provides an easy and simple way to read, write and display bitmap images stored in the PNG format. It can read and write both files and in-memory raw vectors.
An R package for Probit-Normal Marginalized Transition
        Random Effects Models
The program pnn implements the algorithm proposed by
        Specht (1990).  It is written in the R statistical language. It
        solves a common problem in automatic learning. Knowing a set of
        observations described by a vector of quantitative variables,
        we classify them in a given number of groups. Then, the
        algorithm is trained with this datasets and should guess
        afterwards the group of any new observation. This neural
        network has the main advantage to begin generalization
        instantaneously even with a small set of known observations. It
        is delivered with four functions (learn, smooth, perf and
        guess) and a dataset. The functions are documented with
        examples and provided with unit tests.
Provides functions to implement and simulate the partial order continual reassessment method (PO-CRM) for use in Phase I trials of combinations of agents. Provides a function for generating a set of initial guesses (skeleton) for the toxicity probabilities at each combination that correspond to the set of possible orderings of the toxicity probabilities specified by the user.
Estimate large covariance matrices in approximate factor
        models by thresholding principal orthogonal complements.
Bayesian variable selection for regression models of under-reported
    count data as well as for (overdispersed) Poisson, negative binomal and
    binomial logit regression models using spike and slab priors.
The data sets used in the online course ,,PogromcyDanych''. You can process data in many ways. The course Data Crunchers will introduce you to this variety. For this reason we will work on datasets of different size (from several to several hundred thousand rows), with various level of complexity (from two to two thousand columns) and prepared in different formats (text data, quantitative data and qualitative data). All of these data sets were gathered in a single big package called PogromcyDanych to facilitate access to them. It contains all sorts of data sets such as data about offer prices of cars, results of opinion polls, information about changes in stock market indices, data about names given to newborn babies, ski jumping results or information about outcomes of breast cancer patients treatment.
This package implements both the exact and approximation
        methods for computing the cdf of the Poisson binomial
        distribution. It also provides the pmf, quantile function, and
        random number generation for the Poisson binomial distribution.
Implements the methods described in the paper, Witten (2011) Classification and Clustering of Sequencing Data using a Poisson Model, Annals of Applied Statistics 5(4) 2493-2518.
Functions for obtaining the density, random deviates 
             and maximum likelihood estimates of the Poisson lognormal 
             distribution and the bivariate Poisson lognormal distribution.
For every spatial point in a list, calculates the density 
    and temporal tendency (average age) of points within a user defined 
    neighborhood; also supports point density and temporal tendency 
    visualization using 'ggmap'.
Functions to calculate and plot event and pointer years as well as components of resilience. Designed for dendroecological applications, but also suitable to analyze patterns in other ecological time series.
Read and write PO and POT files, for package translations.
Generation of multiple count, binary and continuous variables simultaneously 
             given the marginal characteristics and association structure. Throughout the package,
             the word 'Poisson' is used to imply count data under the assumption of Poisson distribution.
Generation of multiple count, binary and ordinal variables simultaneously 
             given the marginal characteristics and association structure. Throughout the package,
             the word 'Poisson' is used to imply count data under the assumption of Poisson distribution.
Generation of a chosen number of count, binary, ordinal, and continuous (via Fleishman polynomials) random variables, with specified correlations and marginal properties.
Generation of multiple count, binary, ordinal and normal variables 
 simultaneously given the marginal characteristics and association structure.
Functions to create confidence intervals for ratios of Poisson
    rates under misclassification using double sampling.
Generation of count (assuming Poisson distribution) and continuous data (using Fleishman polynomials) simultaneously.
Generates multivariate data with count and continuous variables with a pre-specified correlation matrix. The count and continuous variables are assumed to have Poisson and normal marginals, respectively. The data generation mechanism is a combination of the normal to anything principle and a connection between Poisson and normal correlations in the mixture.
Contains functions and classes for simulating, plotting and analysing homogenous and non-homogenous Poisson processes.
High dimensional mixtures of Poisson Generalized Linear models with three different parameterizations of Poisson means are considered. Moreover, partitioning the response variables into a set of blocks is possible. The package estimates parameters via EM algorithm. For an efficient initialization, a random splitting small-EM is introduced.
This package implements a method for normalization,
        testing, and false discovery rate estimation for RNA-sequencing
        data. The description of the method is in Li J, Witten DM,
        Johnstone I, Tibshirani R (2012). Normalization, testing, and
        false discovery rate estimation for RNA-sequencing data.
        Biostatistics 13(3): 523-38. We estimate the sequencing depths
        of experiments using a new method based on Poisson
        goodness-of-fit statistic, calculate a score statistic on the
        basis of a Poisson log-linear model, and then estimate the
        false discovery rate using a modified version of permutation
        plug-in method. A more detailed instruction as well as sample
        data is available at
        http://www.stanford.edu/~junli07/research.html.  In this
        version, we changed the way of calculating log foldchange for
        two-class data. The FDR estimation part remains unchanged.
Latent class analysis and latent class regression models 
    for polytomous outcome variables.  Also known as latent structure analysis.
This package provides easy access to various political data APIs
    directly from R. For example, you can access Google Civic Information API
    (https://developers.google.com/civic-information/) or Sunlight Congress API
    (https://sunlightlabs.github.io/congress/) for US Congress data, and POPONG
    API (http://data.popong.com/) for South Korea National Assembly data.
Bundles the datasets and functions used in the book by Philip Pollock and Barry Edwards, An R Companion to Essentials of Political Analysis, Second Edition.
A set of functions for working with aerobiological data (e.g. calculation of pollen season limits, replacement of outliers).
Client for the HuffPost Pollster API, which provides
    access to U.S. polls on elections and political opinion.
Tools for corpus analysis using the CWB as an efficient backend.
    The package offers basic functionality to flexibly create subcorpora and to
    carry out and basic statistical operations. Beyond that, it is intended to
    serve as an interface to packages implementing advanced statistical
    procedures.
Routines for the polynomial spline fitting routines
  hazard regression, hazard estimation with flexible tails, logspline,
  lspec, polyclass, and polymars, by C. Kooperberg and co-authors.
Functions for evaluating the mass density, cumulative distribution function, quantile function and random variate generation for the Polya-Aeppli distribution, also known as the geometric compound Poisson distribution.
Simulate via Markov chain Monte Carlo (hit-and-run algorithm)
    a Dirichlet distribution conditioned to satisfy a finite set of linear
    equality and inequality constraints (hence to lie in a convex polytope
    that is a subset of the unit simplex).
Computation of sensitivity indexes by using a method based on a truncated Polynomial Chaos Expansions of the response.
 The necessary condition of the method is: the inputs must be uniformly and independently sampled. Since the inputs are uniformly distributed, the truncated Polynomial Chaos Expansion is built from the multivariate Legendre orthogonal polynomials.
R port of Angus Johnson's open source library Clipper. Performs polygon clipping operations (intersection, union, set minus, set difference) for polygonal regions of arbitrary complexity, including holes. Computes offset polygons (spatial buffer zones, morphological dilations, Minkowski dilations) for polygonal regions and polygonal lines. Computes Minkowski Sum of general polygons. There is a function for removing self-intersections from polygon data.
Computes polychoric and polyserial correlations by quick "two-step" methods or ML, 
  optionally with standard errors; tetrachoric and biserial correlations are special cases.
The following methods for cubature (numerical integration)
             over polygonal domains are currently implemented:
             the two-dimensional midpoint rule as a simple wrapper around
             as.im.function() from package spatstat (Baddeley and Turner, 2005),
             the product Gauss cubature by Sommariva and Vianello (2007),
             an adaptive cubature for isotropic functions via line integrate()
             along the boundary (Meyer and Held, 2014),
             and quasi-exact methods specific to the integration of the
             bivariate Gaussian density over polygonal and circular domains
             (based on formulae from the Abramowitz and Stegun (1972) handbook).
             For cubature over simple hypercubes, the packages "cubature" and
             "R2Cuba" are more appropriate.
Implements a Gibbs sampling algorithm to perform Bayesian inference
    on biallelic SNP frequencies, genotypes and heterozygosity (observed and
    expected) in a population of autopolyploids. See the published paper in
    Molecular Ecology Resources: Blischak et al. (2016) <doi:10.1111/1755-0998.12493>.
A collection of functions to implement a class for univariate
  polynomial manipulations.
Implements univariate polynomial operations in R
Functions to perform paternity exclusion via allele
    matching, in autopolyploid species having ploidy 4, 6, or 8. The
    marker data used can be genotype data (copy numbers known) or
    'allelic phenotype data' (copy numbers not known).
A collection of tools to handle microsatellite data of
 any ploidy (and samples of mixed ploidy) where allele copy number is not
 known in partially heterozygous genotypes.  It can import and export data in
 ABI 'GeneMapper', 'Structure', 'ATetra', 'Tetrasat'/'Tetra', 'GenoDive', 'SPAGeDi',
 'POPDIST', 'STRand', and binary presence/absence formats.  It can calculate
 pairwise distances between individuals using a stepwise mutation model or
 infinite alleles model, with or without taking ploidies and allele frequencies
 into account.  These distances can be used for the calculation of clonal
 diversity statistics or used for further analysis in R.  Allelic diversity
 statistics are also available.  polysat can assist the user in estimating the
 ploidy of samples, and it can estimate allele frequencies in
 populations, calculate pairwise Fst values based on those frequencies, and
 export allele frequencies to 'SPAGeDi' and 'adegenet'.  Functions are also
 included for assigning alleles to isoloci in cases where one pair of
 microsatellite primers amplifies alleles from two or more independently
 segregating isoloci.
Perform classic chi-squared tests and Ripol et al(1999)
             binomial confidence interval approach for autopolyploid
             dominant markers. Also, dominant markers may be generated
             for families of offspring where either one or both of the
             parents possess the marker. Missing values and
             misclassified markers may be generated at random.
Fits Bayesian mixture models to estimate marker dosage for dominant markers on autopolyploids using JAGS (1.0 or greater) as outlined in Baker et al (2010). May be used in conjunction with polySegratio for simulation studies and comparison with standard methods.
This algorithm classifies the trends into linear, quadratic, cubic, concealed and no-trend types. The "concealed trends" are those trends that possess quadratic or cubic forms, but the net change from the start of the time period to the end of the time period hasn't been significant. The "no-trend" category includes simple linear trends with statistically in-significant slope coefficient.
Routines for flexible functional form estimation via basis
    regression, with model selection via the adaptive LASSO or SCAD to prevent
    overfitting.
This package fits a patch occupancy model
Calculations of an information criterion are proposed to check the quality of simulations results of ABM/IBM or other non-linear rule-based models. The POMDEV measure is based on the KL divergence and likelihood theory. It basically indicates the deviance of simulation results from field observations. Once POMDEV scores and metropolis-hasting sampling on different model versions are effectuated, POMIC scores can be calculated. This method is still under development and further work are needed for the incorporation of multiple patterns assessment.
Tools for working with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models).  The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods.  It is also a versatile platform for implementation of inference methods for general POMP models.
Finds equivalence classes corresponding to a symmetric relation
    or undirected graph.  Finds total order consistent with partial order
    or directed graph (so-called topological sort).
Population dynamic models underpin a range of analyses and applications in ecology and epidemiology. The various approaches for analysing population dynamics models (MPMs, IPMs, ODEs, POMPs, PVA) each require the model to be defined in a different way. This makes it difficult to combine different modelling approaches and data types to solve a given problem. 'pop' aims to provide a flexible and easy to use common interface for constructing population dynamic models and enabling to them to be fitted and analysed in lots of different ways.
Construct and analyze projection matrix models from a demography study of marked individuals classified by age or stage. The package covers methods described in Matrix Population Models by Caswell (2001) and Quantitative Conservation Biology by Morris and Doak (2002).
Tools for modelling populations and demography using matrix projection 
    models (MPMs). Designed to build on similar tools already available in 'popbio'. 
    Specific foci are on indices of transient dynamics and use of control theory 
    approaches, but 'popdemo' may also be useful for other implementations of MPMs, 
    or matrix models in a more general sense.
Optimal experimental designs for both population and individual
    studies based on nonlinear mixed-effect models. Often this is based on a
    computation of the Fisher Information Matrix. This package was developed
    for pharmacometric problems, and examples and predefined models are available
    for these types of systems.
Enables computation of epidemiological statistics where e.g. 
    counts or mortality rates of the reference population are used. Currently 
    supported: excess hazard models, rates, mean survival times, relative 
    survival, as well as standardized incidence and mortality ratios (SIRs/SMRs), 
    all of which can be easily adjusted for e.g. age. 
    Fast splitting and aggregation of 'Lexis' objects (from package 'Epi') 
    and other computations achieved using 'data.table'.
There are two main purposes to this package. The first is
        to allow batch conversion of Genepop (Rousset 2008) input files
        for use with Arlequin (Excoffier and Lischer 2010), which has a
        simple GUI to analyze batch files. Two commonly used simulation
        software, BottleSim (Kuo & Janzen 2003) and Easypop (Balloux
        2001) produce Genepop output files that can be analyzed this
        way. There are also functions to convert to and from BottleSim
        format, to quickly produce allele frequency tables or to
        convert a file directly for use in ordination analyses (e.g.
        principal component analysis). This package also includes
        functions to calculate allele rarefaction curves, confidence
        intervals on heterozygosity and allelic richness with
        resampling strategies (bootstrap and jackknife).
Provides efficient tools for population genomics data analysis,
	able to process individual loci, large sets of loci, or whole genomes. PopGenome not only 
	implements a wide range of population genetics statistics, but also facilitates the easy 
	implementation of new algorithms by other researchers. PopGenome is optimized for speed via 
	the seamless integration of C code.
Provides beginner friendly framework to analyse population genetic
    data. Based on 'adegenet' objects it uses 'knitr' to create comprehensive reports on spatial genetic data. 
    For detailed information how to use the package refer to the comprehensive
    tutorials or visit <http://www.popgenreport.org/>.
This is a generic package that produces "Population Graphs"
    a graph-theoretic topology based upon conditional genetic covariance.  This
    functionality used to be within the gstudio package, but has been taken out
    to focus on spatial integration of graph topologies with existing packages
    like sp, raster, and ggplot2.
Provides a suite of tools for various methods of estimating
    confidence intervals for the mean of selected populations.
Provides objects and accompanying methods which facilitates populating and querying SQLite databases.
Population genetic analyses for hierarchical analysis of partially
    clonal populations built upon the architecture of the 'adegenet' package.
GenAlEx is a popular Excel macro for genetic analysis and the
    'poppr' R package allows import of GenAlEx formatted CSV data for genetic
    data analysis in R. This package allows for the import of GenAlEx formatted
    Excel files, serving as a small 'poppr' add on for those who have trouble or
    simply do not want to export their data into CSV format.
Implements the Bayesian hierarchical model described by Wheldon, Raftery, Clark and Gerland (see: http://www.csss.washington.edu/Papers/wp108.pdf) for simultaneously estimating age-specific population counts, fertility rates, mortality rates and net international migration flows, at the national level.
State of the art functions for constructing and evaluating self-organizing maps (SOMs).
Functions to estimate and plot smooth or linear population trends, or population indices, 
    from animal or plant count survey data.
Run population simulations using an Individual-Based Model (IBM) compiled in C.
The main attribute of 'PopVar' is the prediction of genetic variance in bi-parental populations, from which the package derives its name. 'PopVar' contains a set of functions that use phenotypic and genotypic data from a set of candidate parents to 1) predict the mean, genetic variance, and superior progeny value of all, or a defined set of pairwise bi-parental crosses, and 2) perform cross-validation to estimate genome-wide prediction accuracy of multiple statistical models. More details are available in Mohammadi, Tiede, and Smith (2015). Crop Sci. doi:10.2135/cropsci2015.01.0030. A dataset 'think_barley.rda' is included for reference and examples.
Simulate the dynamic of wolf populations using a specific Individual-Based Model (IBM) compiled in C.
This package contains a set of portmanteau diagnostic
        checks for univariate and multivariate time series.
Classes for analysing and implementing equity portfolios.
Portfolio optimization and analysis routines and graphics.
R interface to PortfolioEffect cloud service for estimating
    high frequency price variance, quarticity, microstructure noise variance, 
    and other metrics in both aggregate and rolling window flavors. 
    Constructed estimators could use client-side market data or access 
    HF intraday price history for all major US Equities.
    See <https://www.portfolioeffect.com/> for more information on the 
    PortfolioEffect high frequency portfolio analytics platform.
R interface to PortfolioEffect cloud service for backtesting
    high frequency trading (HFT) strategies, intraday portfolio analysis
    and optimization. Includes auto-calibrating model pipeline for market
    microstructure noise, risk factors, price jumps/outliers, tail risk
    (high-order moments) and price fractality (long memory). Constructed
    portfolios could use client-side market data or access HF intraday price
    history for all major US Equities. See <https://www.portfolioeffect.com/>
    for more information on the PortfolioEffect high frequency portfolio
    analytics platform.
Classes that serve as a framework for designing equity
        portfolio simulations.
Risk Attribution of a portfolio with Volatility Risk Analysis.
In linear LS regression, calculate for a given design matrix
  the multiplier K of coefficient standard errors such that the
  confidence intervals [b - K*SE(b), b + K*SE(b)] have a
  guaranteed coverage probability for all coefficient estimates
  b in any submodels after performing arbitrary model selection.
Functions to convert geometry and 'hstore' data types from
    'PostgreSQL' into standard R objects, as well as to simplify
    the import of R data frames (including spatial data frames) into 'PostgreSQL'.
Some functions useful to perform a Peak Over Threshold
        analysis in univariate and bivariate cases. A user's guide is
        available.
Does Markov chain Monte Carlo (MCMC) simulation of Potts models
   and composite likelihood inference for Potts models.
A package including several functions related to the Potts models.
Optimizes a function using Powell's UObyQA algorithm.
Functions for the computation of power and level tables for hypothesis tests, in LaTeX format, functions to build explanatory graphs for studying power of test statistics.
Contains functions to obtain the operational characteristics of 
    bioequivalence studies with 2-stage designs (TSD) via simulations.
Basic functions for power analysis and effect size calculation.
Functions for bootstrapping the power of ANOVA designs
    based on estimated means and standard deviations of the conditions.
    Please refer to the documentation of the boot.power.anova() function
    for further details.
Power and sample size calculation for eQTL analysis
             based on ANOVA or simple linear regression. It can also calculate power/sample size 
             for testing the association of a SNP to a continuous type phenotype.
Analytical power calculations for GxE and GxG interactions for case-control studies of candidate genes and genome-wide association studies (GWAS). This includes power calculation for four two-step screening and testing procedures.  It can also calculate power for GxE and GxG without any screening.
An implementation of maximum likelihood estimators for a variety
    of heavy tailed distributions, including both the discrete and continuous
    power law distributions. Additionally, a goodness-of-fit based approach is
    used to estimate the lower cut-off for the scaling region.
Functions to 
        calculate power and sample size for testing
        (1) mediation effects; 
        (2) the slope in a simple linear regression; 
        (3) odds ratio in a simple logistic regression;
        (4) mean change for longitudinal study with 2 time points;
        (5) interaction effect in 2-way ANOVA; and
        (6) the slope in a simple Poisson regression.
(1) To estimate the power of testing for linkage using an
        affected sib pair design, as a function of the recurrence risk
        ratios. We will use analytical power formulae as implemented in
        R. These are based on a Mathematica notebook created by Martin
        Farrall. (2) To examine how the power of the transmission
        disequilibrium test (TDT) depends on the disease allele
        frequency, the marker allele frequency, the strength of the
        linkage disequilibrium, and the magnitude of the genetic
        effect. We will use an R program that implements the power
        formulae of Abel and Muller-Myhsok (1998). These formulae allow
        one to quickly compute power of the TDT approach under a
        variety of different conditions. This R program was modeled on
        Martin Farrall's Mathematica notebook.
Computation of matrix and scalar exponentiation.
Functions to calculate power and
                sample size for testing main effect or interaction effect in
                the survival analysis of epidemiological studies
                (non-randomized studies), taking into account the 
                correlation between the covariate of the
                interest and other covariates. Some calculations also take 
                into account the competing risks and stratified analysis. 
                This package also includes
                a set of functions to calculate power and sample size
                for testing main effect in the survival analysis of 
                randomized clinical trials.
Statistical power analysis tools for designing multilevel randomized experiments.
 Includes functions to calculate statistical power (1 - type II error), minimum detectable effect size (MDES),
 minimum required sample size (MRSS), functions to solve constrained optimal sample allocation (COSA) problems,
 and to visualize duo or trio relationships between statistical power, MDES, MRSS, and a component of COSA.
The PP package includes estimation of (MLE, WLE, MAP, EAP, ROBUST)
    person parameters for the 1,2,3,4-PL model and the GPCM (generalized
    partial credit model). The parameters are estimated under the assumption
    that the item parameters are known and fixed. The package is useful e.g. in
    the case that items from an item pool / item bank with known item parameters
    are administered to a new population of test-takers and an ability
    estimation for every test-taker is needed.
Calculates partial and semi-partial
        (part) correlations along with p-value.
Computing similarities between proteins based on their GO annotation, KEGG annotation and PPI network topology. It integrates seven features (TCSS, IntelliGO, Wang, KEGG, Jaccard, RA and AA) to predict PPIs using an SVM classifier. Some internal functions to calculate GO semantic similarities are re-used from R package GOSemSim authored by Guangchuang Yu.
This package contains linear and nonlinear regression
        methods based on Partial Least Squares and Penalization
        Techniques. Model parameters are selected via cross-validation,
        and confidence intervals ans tests for the regression
        coefficients can be conducted via jackknifing.
Toolkit for fitting point process models with sequences of LASSO penalties ("regularisation paths"). Regularisation paths of Poisson point process models or area-interaction models can be fitted with LASSO, adaptive LASSO or elastic net penalties. A number of criteria are available to judge the bias-variance tradeoff.
The pps package contains functions to select samples using
        PPS (probability proportional to size) sampling. It also
        includes a function for stratified simple random sampling, a
        function to compute joint inclusion probabilities for
        Sampford's method of PPS sampling, and a few utility functions.
        The user's guide pps-ug.pdf is included.
Projection pursuit classification tree using LDA, Lr or PDA projection pursuit index
Tools for exploring projection pursuit classification tree using
    various projection pursuit indexes.
This package allows users to calculate the number of
    under-five child deaths caused by consumption of poor quality
    antimalarials across 39 sub-Saharan nations. The package supports one
    function, that starts an interactive web tool created using
    the shiny R package. The web tool runs locally on the user's machine.
    The web tool allows users to set input parameters (prevalence of poor
    quality antimalarials, case fatality rate of children who take poor
    quality antimalarials, and sample size) which are then used to perform
    an uncertainty analysis following the Latin hypercube
    sampling scheme. Users can download the output figures as PDFs, and the
    output data as CSVs. Users can also download their input parameters
    for reference. This package was designed to accompany the analysis
    presented in:
    J. Patrick Renschler, Kelsey Walters, Paul Newton, Ramanan Laxminarayan
    "Estimated under-five deaths associated with poor-quality
    antimalarials in sub-Saharan Africa", 2014. Paper submitted.
Distance-based parametric bootstrap tests for clustering with 
  spatial neighborhood information. Some distance measures, 
  Clustering of presence-absence, abundance and multilocus genetical data 
  for species delimitation, nearest neighbor 
  based noise detection. Try package?prabclus for on overview.
Provides a large number of functions from numerical analysis and
    linear algebra, numerical optimization, differential equations,
    time series, plus some well-known special mathematical functions.
    Uses 'MATLAB' function names where appropriate to simplify porting.
Contains functions for sample size calculation for survey samples using stratified or clustered one-, two-, and three-stage sample designs. Other functions compute variance components for multistage designs and sample sizes in two-phase designs. A number of example datasets are included.
pragma allows for the use of pragma (also sometimes called
        directives or keywords. These allow assigning arbitrary
        functionality to a word without requiring the standard function
        call syntax i.e. with parens.
The Prais-Winsten estimation procedure takes into account serial correlation of type AR(1) in a linear model. The procedure is an iterative method that recursively estimates the beta coefficients and the error autocorrelation of the specified model until convergence of rho, i.e. the AR(1) coefficient, is attained. All estimates are obtained by OLS.
Build friendly R packages that
    praise their users if they have done something
    good, or they just need it to feel better.
Kasulikud funktsioonid kvantitatiivsete mudelite kursuse
    (SHPH.00.004) jaoks
Estimation, prediction and testing for analyzing serial dilution assay data using paired response curve.
A testing workbench for evaluating precision-recall curves under various conditions.
Clustering is unsupervised and exploratory in nature. Yet, it can be performed through penalized regression with grouping pursuit. In this package, we provide two algorithms for fitting the penalized regression-based clustering (PRclust) with non-convex grouping penalties, such as group truncated lasso, MCP and SCAD. One algorithm is based on quadratic penalty and difference convex method. Another algorithm is based on difference convex and ADMM, called DC-ADD, which is more efficient. Generalized cross validation and stability based method were provided to select the tuning parameters. Rand index, adjusted Rand index and Jaccard index were provided to estimate the agreement between estimated cluster memberships and the truth.
While person-centered analysis is increasingly common in psychology, education, and related fields, carrying it out is challenging. The prcr package provides an easy-to-use yet adaptable set of tools to conduct person-center analysis.
Derives prediction rule ensembles (PREs). Largely follows the 
  procedure for deriving PREs as described in Friedman & Popescu (2008; 
  <DOI:10.1214/07-AOAS148>), with several adjustments and improvements. The 
  main function pre() derives a prediction rule ensemble. Functions coef() and 
  importance() can be used to inspect the generated ensemble. Function predict() 
  generates predicted values. Singeplot() and pairplot() depict dependence of the 
  output on specified predictor variables. Function cvpre() performs full cross 
  validation of a pre to calculate the expected prediction error. Functions 
  interact() and bsnullinteract() can be used to assess interaction effects of 
  predictor variables.
It contains functions to analyze the precipitation
    intensity, concentration and anomaly.
Accurate calculations and visualization of precision-recall and ROC (Receiver Operator Characteristics)
    curves.
Allows the user to check and find (allegedly) predatory journals based on Beall's list available at <https://scholarlyoa.com/>.
   As part of a research project, the data from the website has been scraped with web scraping algorithms and manual work. 
   The package includes a search function and direct access to this database of predatory journals. The use of this tool
   should facilitate the detection of predatory publications by researchers and librarians.
PredictABEL includes functions to assess the performance of
 risk models. The package contains functions for the various measures that are
 used in empirical studies, including univariate and multivariate odds ratios
 (OR) of the predictors, the c-statistic (or area under the receiver operating
 characteristic (ROC) curve (AUC)), Hosmer-Lemeshow goodness of fit test,
 reclassification table, net reclassification improvement (NRI) and
 integrated discrimination improvement (IDI). Also included are functions
 to create plots, such as risk distributions, ROC curves, calibration plot,
 discrimination box plot and predictiveness curves. In addition to functions
 to assess the performance of risk models, the package includes functions to
 obtain weighted and unweighted risk scores as well as predicted risks using
 logistic regression analysis. These logistic regression functions are
 specifically written for models that include genetic variables, but they
 can also be applied to models that are based on non-genetic risk factors only.
 Finally, the package includes function to construct a simulated dataset with 
 genotypes, genetic risks, and disease status for a hypothetical population, which 
 is used for the evaluation of genetic risk models.
A one-function package containing 'prediction()', a type-safe alternative to 'predict()' that always returns a data frame.
A common problem faced by journal reviewers and authors is the question of
     whether the results of a replication study are consistent with the original
     published study. One solution to this problem is to examine the effect size
     from the original study and generate the range of effect sizes that could
     reasonably be obtained (due to random sampling) in a replication attempt
     (i.e., calculate a prediction interval). This package has functions that calculate
     the prediction interval for the correlation (i.e., r),
     standardized mean difference (i.e., d-value), and mean.
Three prediction algorithms described in the paper
        "On-line predictive linear regression" Annals of Statistics 37,
        1566 - 1590 (2009)
This package provides functions to diagnose 
  and make inferences from various linear models, such as those obtained from 'aov', 
  'lm', 'glm', 'gls', 'lme', and 'lmer'. Inferences include predicted 
  means and standard errors, contrasts, multiple comparisons, permutation tests and graphs.
Provides a Testbench for comparison of prediction models. This
    package is inspired from 'imputeTestbench' package
    <https://cran.r-project.org/package=imputeTestbench>. It compares prediction
    models with reference to RMSE, MAE or MAPE parameters. It allows to add new
    proposed methods to test bench and to compare with other methods. The function
    'prediction_append()' allows to add multiple numbers of methods to the existing
    methods available in test bench. One/two step ahead prediction is also possible
    in the testbench.
"train_predict_mix" predicts the binary response with
        binary features
Recent years have seen an increased interest in novel methods
    for analyzing quantitative data from experimental psychology. Currently, however, they lack an
    established and accessible software framework. Many existing implementations provide no guidelines,
    consisting of small code snippets, or sets of packages. In addition, the use of existing packages
    often requires advanced programming experience. 'PredPsych' is a user-friendly toolbox based on
    machine learning predictive algorithms. It comprises of multiple functionalities for multivariate
    analyses of quantitative behavioral data based on machine learning models.
Generates design matrix for analysing real paired comparisons and derived paired comparison data (Likert type items/ratings or rankings) using a loglinear approach. Fits loglinear Bradley-Terry model (LLBT) exploiting an eliminate feature. Computes pattern models for paired comparisons, rankings, and ratings. Some treatment of missing values (MCAR and MNAR). Fits latent class (mixture) models for paired comparison, rating and ranking patterns using a non-parametric ML approach.
Dynamize headers or R code within 'Rmd' files to prevent proliferation of 'Rmd' files for similar reports. Add in external HTML document within 'rmarkdown' rendered HTML doc.
Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically linking a response vector to covariate data through cluster membership. The package allows Bernoulli, Binomial, Poisson, Normal, survival and categorical response, as well as Normal and discrete covariates. It also allows for fixed effects in the response model, where a spatial CAR (conditional autoregressive) term can be also included. Additionally, predictions may be made for the response, and missing values for the covariates are handled. Several samplers and label switching moves are implemented along with diagnostic tools to assess convergence. A number of R functions for post-processing of the output are also provided. In addition to fitting mixtures, it may additionally be of interest to determine which covariates actively drive the mixture components. This is implemented in the package as variable selection.
Prepares data for statistical analysis (e.g., analysis of variance
  ;ANOVA) by enabling the user to easily and quickly merge (using the
  file_merge() function) raw data files into one merged table and then
  aggregate the merged table (using the prep() function) into a finalized
  table while keeping track and summarizing every step of the preparation.
  The finalized table contains several possibilities for dependent measures of
  the dependent variable. Most suitable when measuring variables in an
  interval or ratio scale (e.g., reaction-times) and/or discrete values such
  as accuracy. Main functions included are file_merge() and prep(). The
  file_merge() function vertically merges individual data files (in a long
  format) in which each line is a single observation to one single dataset.
  The prep() function aggregates the single dataset according to any
  combination of grouping variables (i.e., between-subjects and
  within-subjects independent variables, respectively), and returns a data
  frame with a number of dependent measures for further analysis for each cell
  according to the combination of provided grouping variables. Dependent
  measures for each cell include among others means before and after rejecting
  all values according to a flexible standard deviation criteria, number of
  rejected values according to the flexible standard deviation criteria,
  proportions of rejected values according to the flexible standard deviation
  criteria, number of values before rejection, means after rejecting values
  according to procedures described in Van Selst & Jolicoeur (1994; suitable
  when measuring reaction-times), standard deviations, medians, means according
  to any percentile (e.g., 0.05, 0.25, 0.75, 0.95) and harmonic means. The data
  frame prep() returns can also be exported as a txt file to be used for
  statistical analysis in other statistical programs.
Preprocessing is often the most time-consuming phase in data analysis
    and preprocessing transformations interdependent in unexpected
    ways. This package helps to make preprocessing faster and more effective. It
    provides an S4 framework for creating and evaluating preprocessing combinations
    for classification, clustering and outlier detection. The framework supports
    adding of user-defined preprocessors and preprocessing phases. Default preprocessors
    can be used for low variance removal, missing value imputation, scaling, outlier
    removal, noise smoothing, feature selection and class imbalance correction.
Data quality simulation can be used to check the robustness of data
    analysis findings and learn about the impact of data quality contaminations on
    classification. This package helps to add contaminations (noise, missing values,
    outliers, low variance, irrelevant features, class swap (inconsistency), class
    imbalance and decrease in data volume) to data and then evaluate the simulated
    data sets for classification accuracy. As a lightweight solution simulation runs
    can be set up with no or minimal up-front effort.
Data quality issues such as missing values and outliers are often
    interdependent, which makes preprocessing both time-consuming and leads to
    suboptimal performance in knowledge discovery tasks. This package supports
    preprocessing decision making by visualizing interdependent data quality issues
    through means of feature construction. The user can define his own application
    domain specific constructed features that express the quality of a data point
    such as number of missing values in the point or use nine default features.
    The outcome can be explored with plot methods and the feature constructed data
    acquired with get methods.
The R Markdown templates in this package are based on the Center
    for Open Science Preregistration Challenge and the 'AsPredicted.org' questions.
    They are, thus, particularly suited to draft preregistration documents for
    these programs but can also be used for internally.
This package provides a set of functions useful when
        evaluating the results of presence-absence models. Package
        includes functions for calculating threshold dependent measures
        such as confusion matrices, pcc, sensitivity, specificity, and
        Kappa, and produces plots of each measure as the threshold is
        varied. It will calculate optimal threshold choice according to
        a choice of optimization criteria. It also includes functions
        to plot the threshold independent ROC curves along with the
        associated AUC (area under the curve).
Makes output files from select PreSens Fiber Optic Oxygen
    Transmitters easier to work with in R. See <http://www.presens.de> for more
    information about PreSens (Precision Sensing GmbH). Note: this package is
    neither created nor maintained by PreSens.
Estimating the number of species represented at least r times in a random sample.
Computes probability-scale residuals and residual correlations
    for continuous, ordinal, binary, count, and time-to-event data.
Functions to assess the effects of different text preprocessing decisions on the inferences drawn from the resulting document-term matrices they generate.
Replace the standard print method for functions with one that
    performs syntax highlighting, using ANSI colors, if the terminal
    supports them.
Creating tiny yet beautiful documents and vignettes from R
    Markdown. The package provides the 'html_pretty' output format as an
    alternative to the 'html_document' and 'html_vignette' engines that
    convert R Markdown into HTML pages. Various themes and syntax highlight
    styles are supported.
prettyGraphs contains simple, crisp graphics. Graphics produced by prettyGraphs are publication-quality.
Automates the process of creating a scale bar and north arrow in
    any package that uses base graphics to plot in R. Bounding box tools help find
    and manipulate extents. Finally, there is a function to automate the process
    of setting margins, plotting the map, scale bar, and north arrow, and resetting
    graphic parameters upon completion.
Functions for conventionally formatting descriptive stats,
 reshaping data frames and formatting R output as HTML.
Pretty, human readable formatting of quantities.
    Time intervals: 1337000 -> 15d 11h 23m 20s.
    Vague time intervals: 2674000 -> about a month ago.
    Bytes: 1337 -> 1.34 kB.
The prevalence package provides Frequentist and Bayesian methods for prevalence assessment studies. IMPORTANT: the truePrev functions in the prevalence package call on JAGS (Just Another Gibbs Sampler), which therefore has to be available on the user's system. JAGS can be downloaded from http://mcmc-jags.sourceforge.net/.
It provides functions for both likelihood-based
    and Bayesian analysis of spatially referenced prevalence data, and is
    also an extension of the 'geoR' package.
Spatial estimation of a prevalence surface
    or a relative risks surface, using data from a Demographic and Health
    Survey (DHS) or an analog survey.
Estimate False Discovery Rates (FDRs) for importance metrics from
    random forest runs.
Patient Rule Induction Method (PRIM) for bump hunting in high-dimensional data.
Use Prime Factorization for simplifying computations,
    for instance for ratios of large factorials.
Functions are primarily functions for systems of ordinary
        differential equations, difference equations, and eigenanalysis
        and projection of demographic matrices; data are for examples.
Identifies potential target sequences for a given set of primers
  and generates taxonomically annotated phylogenetic trees with the predicted
  amplification products.
Functions to test whether a number is prime and generate the prime numbers within a specified range. Based around
             an implementation of Wilson's theorem for testing for an integer's primality.
Performs a unified treatment of Bump Hunting by Patient Rule Induction Method (PRIM) in Survival, Regression and Classification settings (SRC). The current version is a development release that only implements the case of a survival response. New features will be added soon as they are available.
fits a principal curve to a data matrix in arbitrary
        dimensions
Provides capabilities beyond principal components
    analysis to focus on finding structure in low variability
    subspaces. Constructs and plots simple basis vectors for
    pre-defined and user-defined measures of simplicity.
Allows users to access the Oregon State Prism climate data. Using the web service API data can easily downloaded in bulk and loaded into R for spatial analysis. Some user friendly visualizations are also provided.
The PRISMA package is capable of loading and processing huge text
    corpora processed with the sally toolbox (http://www.mlsec.org/sally/).
    sally acts as a very fast preprocessor which splits the text files into
    tokens or n-grams. These output files can then be read with the PRISMA
    package which applies testing-based token selection and has some
    replicate-aware, highly tuned non-negative matrix factorization and
    principal component analysis implementation which allows the processing of
    very big data sets even on desktop machines.
Plot a PRISMA <http://prisma-statement.org/> flow chart
    describing the identification, screening, eligibility and inclusion or studies in
    systematic reviews. PRISMA is an evidence-based minimum set of items for reporting
    in systematic reviews and meta-analyses. PRISMA focuses on the reporting of reviews
    evaluating randomized trials, but can also be used as a basis for reporting
    systematic reviews of other types of research, particularly evaluations of
    interventions.
PrivateLR implements two differentially private algorithms for 
  estimating L2-regularized logistic regression coefficients. A randomized
  algorithm F is epsilon-differentially private (C. Dwork, Differential
  Privacy, ICALP 2006), if 
     |log(P(F(D) in S)) - log(P(F(D') in S))| <= epsilon
  for any pair D, D' of datasets that differ in exactly one element, any
  set S, and the randomness is taken over the choices F makes.
Estimation of prevalence ratios using logistic models and confidence intervals with delta and bootstrap methods.
Optogenetics is a new tool to study neuronal circuits that have been genetically modified to allow stimulation by flashes of light.  This package implements the methodological framework, Point-process Response model for Optogenetics (PRO), for analyzing data from these experiments.  This method provides explicit nonlinear transformations to link the flash point-process with the spiking point-process.  Such response functions can be used to provide important and interpretable scientific insights into the properties of the biophysical process that governs neural spiking in response to optogenetic stimulation.
A framework for performing elementary probability
 calculations on finite sample spaces, which may be represented by data frames
 or lists.  Functionality includes setting up sample spaces, counting tools,
 defining probability spaces, performing set algebra, calculating probability
 and conditional probability, tools for simulation and checking the law of
 large numbers, adding random variables, and finding marginal distributions.
 Characteristic functions for all base R distributions are included.
Contains functions that are useful for probing moderation effects (or interactions) including techniques such as pick-a-point (also known as spotlight analysis) and Johnson-Neyman (also known as floodlight analysis). Plot function is also provided to facilitate visualization of results from each of these techniques.
Probabilistic Fisher discriminant analysis (pFDA) is a probabilistic version of the popular and powerful Fisher linear discriminant analysis for dimensionality reduction and classification.
The ProbForecastGOP package contains a main function,
        called ProbForecastGOP and other functions, to produce
        probabilistic weather forecasts of weather fields using the
        Geostatistical Output Perturbation (GOP) method of Gel,
        Raftery, and Gneiting (JASA, 2004).
Binomial Spatial Probit models for big data.
This package provides multiclass conditional probability
        estimation for the SVM, which is distributional assumption
        free.
Confidence intervals and point estimation for R under various parametric model assumptions; likelihood inference based on classical first-order approximations and higher-order asymptotic procedures.
Tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). (Partial) area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.
Package for processing downloaded MODIS Calibrated radiances
    Product HDF files. Specifically, MOD02 calibrated radiance product files, and
    the associated MOD03 geolocation files (for MODIS-TERRA). The package will be
    most effective if the user installs MRTSwath (MODIS Reprojection Tool for swath
    products; <https://lpdaac.usgs.gov/tools/modis_reprojection_tool_swath>, and
    adds the directory with the MRTSwath executable to the default R PATH by editing
    ~/.Rprofile.
Generate time series chart for individual values with mean and +/-
    3 standard deviation lines and the corresponding mR chart with the upper control
    limit. Also execute the 8 Shewhart stability run tests and display the violations.
A direct and flexible method for estimating an ICA model.
        This approach estimates the densities for each component
        directly via a tilted gaussian. The tilt functions are
        estimated via a GAM poisson model. Details can be found in
        "Elements of Statistical Learning (2nd Edition)" Section 14.7.4
Create a project directory structure, along with typical files
    for that project.  This allows projects to be quickly and easily created,
    as well as for them to be standardized. Designed specifically with scientists
    in mind (mainly bio-medical researchers, but likely applies to other fields).
Fast and user friendly implementation of nonparametric estimators
    for censored event history (survival) analysis. Kaplan-Meier and
    Aalen-Johansen method.
Various transitive measures of productivity and profitability, in levels and changes, are computed. 
  In addition to the classic Malmquist productivity index, the 'productivity' package contains also the 
  multiplicatively complete and transitive Färe-Primont and Lowe indices.
  These indices are also decomposed into different components providing insightful information on 
  the sources of productivity and profitability improvements. 
  In the use of Malmquist productivity index, the technological change index is further decomposed 
  into bias technological change components.
  For the transitive Färe-Primont and Lowe measures, it is possible to rule out technological change.
  The package also allows to prohibit negative technological change.
  All the estimations are based on the nonparametric Data Envelopment Analysis (DEA) and several 
  assumptions regarding returns to scale are available (i.e. CRS, VRS, NIRS, NDRS).
  The package allows parallel computing by default, depending on the user's computer configuration.
Framework for visualising tables of counts, proportions
  and probabilities. The framework is called product plots, alluding to
  the computation of area as a product of height and width, and the
  statistical concept of generating a joint distribution from the
  product of conditional and marginal distributions. The framework,
  with extensions, is sufficient to encompass over 20 visualisations
  previously described in fields of statistical graphics and 'infovis',
  including bar charts, mosaic plots, 'treemaps', equal area plots and
  fluctuation diagrams.
The package can prioritize the candidate disease metabolites based on the assumption 
       that functionally related metabolites tend to associate with the same or similar 
       diseases in the context of metabolic pathway. The PROFANCY package (1) prioritizes the 
       disease metabolites from global functional similarity and local modularity of the 
       metabolic network; (2) allows users to select default metabolites or input their 
       interested metabolites as seed nodes or candidate nodes (3) can prioritize the candidate 
       metabolites in KEGG or EHMN metabolic network.
This package facilitates profile inference (inference at
        the posterior mode) for a class of product partition models
        (PPM). The Dirichlet process mixture is currently the only
        available member of this class. These methods search for the
        maximum posterior (MAP) estimate for the data partition in a
        PPM.
Programs to determine student grades and create
  examinations from Question banks.  Programs will create numerous
  multiple choice exams, randomly shuffled, for different versions of same question list.
This package provides profile likelihoods for a parameter of interest in commonly used statistical models. The models include linear models, generalized linear models, proportional odds models, linear mixed-effects models, and linear models for longitudinal responses fitted by generalized least squares. The package also provides plots for normalized profile likelihoods as well as the maximum profile likelihood estimates and the kth likelihood support intervals.
profileModel provides tools that can be used to calculate, evaluate, plot and use for inference the profiles of *arbitrary* inference functions for *arbitrary* 'glm'-like fitted models with linear predictors.
A suite of multivariate methods and data visualization 
      tools to implement profile analysis and cross-validation techniques described in 
      Davison & Davenport (2002) <DOI: 10.1037/1082-989X.7.4.468>, Bulut (2013), and other published and unpublished resources.
      The package includes routines to perform criterion-related profile analysis, profile analysis 
      via multidimensional scaling, moderated profile analysis, profile analysis by group, and a 
      within-person factor model to derive score profiles.
Allows users to quickly and reliably profile data in R
    using convenience functions. The profiled data returns as a data.frame
    and provides a wealth of common and uncommon summary statistics.
Get data / Define model / ??? / ProFit! ProFit is a Bayesian galaxy fitting tool that uses a fast C++ image generation library and a flexible interface to a large number of likelihood samplers.
A simple and light-weight API for memory profiling of R expressions.  The profiling is built on top of R's built-in memory profiler ('utils::Rprofmem()'), which records every memory allocation done by R (also native code).
profr provides an alternative data structure
    and visual rendering for the profiling information
    generated by Rprof.
Tools for examining Rprof profile output.
An alternative data structure for the profiling information
    generated by Rprof().
Interactive visualizations for profiling R code.
Implementing the Progeny Clustering algorithm, the 'progenyClust' package assesses the clustering stability and identifies the optimal clustering number for a given data matrix. It uses k-means clustering as a default, provides a tailored hierarchical clustering function, and can be customized to work with other clustering algorithms and different parameter settings. The package includes a main function progenyClust(), plot and summary methods for 'progenyClust' object, a function hclust.progenyClust() for hierarchical clustering, and two example datasets (test and cell) for testing.
sample code, appendices and functions for the text
    Programming GUIs in R
Prognostic ROC curve is an alternative graphical approach to represent the discriminative capacity of the marker: a receiver operating characteristic (ROC) curve by plotting 1 minus the survival in the high-risk group against 1 minus the survival in the low-risk group. This package contains functions to assess prognostic ROC curve. The user can enter the survival according to a model previously estimated or the user can also enter individual survival data for estimating the prognostic ROC curve by using Kaplan-Meier estimator. The area under the curve (AUC) corresponds to the probability that a patient in the low-risk group has a longer lifetime than a patient in the high-risk group. The prognostic ROC curve provides complementary information compared to survival curves. The AUC is assessed by using the trapezoidal rules. When survival curves do not reach 0, the prognostic ROC curve is incomplete and the extrapolations of the AUC are performed by assuming pessimist, optimist and non-informative situations.
Configurable Progress bars, they may include percentage,
    elapsed time, and/or the estimated completion time. They work in
    terminals, in 'Emacs' 'ESS', 'RStudio', 'Windows' 'Rgui' and the
    'macOS' 'R.app'. The package also provides a 'C++' 'API', that works
    with or without 'Rcpp'.
A simple interface to lat/long projection and datum
        transformation of the PROJ.4 cartographic projections library.
        It allows transformation of geographic coordinates from one
        projection and/or datum to another.
Provides functions to
    automatically build a directory structure for a new R
    project. Using this structure, 'ProjectTemplate'
    automates data loading, preprocessing, library
    importing and unit testing.
Provides functions for calculating and analyzing the proliferative 
    index (PI) from an RNA-seq dataset. As described in Ramaker & Lasseigne, 
    et al. bioRxiv, 2016 <doi:10.1101/063057>.
High-throughput experiments are now widely used in biological researches, which improves both the quality and quantity of omics data. Network-based presentation of these data has become a popular way in data analyses. This package mainly provides functions for biological network construction, visualization and analyses. Networks can be constructed either from experimental data or from a set of proteins and integrated PPI database. Based on them, users can perform traditional visualization, along with the subcellular localization based ones for Homo sapiens and Arabidopsis thaliana. Furthermore, analyses including topological statistics, functional module clustering and go profiling can also be achieved.
Propagation of uncertainty using higher-order Taylor expansion and Monte Carlo simulation.
Computes two-sample confidence intervals for single, paired and independent proportions
This package implements propensity clustering and
        decomposition. Propensity decomposition can be viewed on the
        one hand as a generalization of the eigenvector-based
        approximation of correlation networks, and on the other hand as
        a generalization of random multigraph models and
        conformity-based decompositions.
The prop.comb.RR package analyzes combination of proportions and relative risk.
The properties package allows to parse Java properties files
  in the context of R Service Bus applications.
Implements a procedure for forecasting time series data based on
  an additive model where non-linear trends are fit with yearly and weekly
  seasonality, plus holidays.  It works best with daily periodicity data with
  at least one year of historical data.  Prophet is robust to missing data,
  shifts in the trend, and large outliers.
A package for selecting the most relevant features (genes) in the high-dimensional binary classification problems. The discriminative features are identified using analyzing the overlap between the expression values across both classes. The package includes functions for measuring the proportional overlapping score for each gene avoiding the outliers effect. The used measure for the overlap is the one defined in the "Proportional Overlapping Score (POS)" technique for feature selection. A gene mask which represents a gene's classification power can also be produced for each gene (feature). The set size of the selected genes might be set by the user. The minimum set of genes that correctly classify the maximum number of the given tissue samples (observations) can be also produced.
The bioinformatic evaluation of gene co-expression often begins with
    correlation-based analyses. However, this approach lacks statistical validity
    when applied to relative count data. This includes, for example, biological data
    produced by high-throughput RNA-sequencing, chromatin immunoprecipitation (ChIP),
    ChIP-sequencing, Methyl-Capture sequencing, and other techniques. Two metrics of
    proportionality, phi [Lovell et al (2015) <DOI:10.1371/journal.pcbi.1004075>]
    and rho [Erb and Notredame (2016) <DOI:10.1007/s12064-015-0220-8>], both derived
    from compositional data analysis, a branch of math dealing specifically with
    relative data, represent novel alternatives to correlation. This package
    introduces a programmatic framework for calculating feature dependence through
    proportionality, as discussed in the cited publications.
This package contains functions to run propensity-biased allocation to balance covariate distributions in sequential trials and propensity-constrained randomization to balance covariate distributions in trials with known baseline covariates at time of randomization.  Currently this package only supports trials comparing two groups.
The package provides functions for pretreatment and sample
    selection of visible and near infrared diffuse reflectance spectra
An implementation of protein descriptors in R. These descriptors
    combine the advantages of being fixed length and including partial sequential
    effect: Various length of protein sequences are described with fixed length
    vectors that are suitable for machine learning algorithms, and still includes
    partial sequential effect.
This package provides functions to identify the optimal
        solution that maximizes numbers of detectable differentiated
        proteins from a multi-stage clinical proteomic study.
Provides methods for making inference in isobaric labelled
    LC-MS/MS experiments, i.e. iTRAQ experiments. It provides a function that
    reasonably parses a CSV-export from Proteome Discoverer(TM) into a data
    frame that can be easily handled in R. Functions and methods are provided
    for quality control, filtering, norming, and the calculation of response
    variables for further analysis. The merging of multiple iTRAQ experiments
    with respect to a reference is also covered.
Method for protein quantification based on identified and
    quantified peptides. protiq can be used for absolute and relative protein
    quantification. Input peptide abundance scores can come from various
    sources, including SRM transition areas and intensities or spectral counts
    derived from shotgun experiments. The package is still being extended to
    also include the model for protein identification, MIPGEM, presented in
    Gerster, S., Qeli, E., Ahrens, C.H. and Buehlmann, P. (2010). Protein and
    gene model inference based on statistical modeling in k-partite graphs.
    Proceedings of the National Academy of Sciences 107(27):12101-12106.
An object oriented system using object-based, also
	called prototype-based, rather than class-based object oriented ideas.
Greedy algorithm described in Bien and Tibshirani (2011)
        Prototype Selection for Interpretable Classification. Annals of
        Applied Statistics. 5(4). 2403-2424
Performs minimax linkage hierarchical clustering.  Every cluster
    has an associated prototype element that represents that cluster as
    described in Bien, J., and Tibshirani, R. (2011), "Hierarchical Clustering
    with Prototypes via Minimax Linkage," accepted for publication in The
    Journal of the American Statistical Association, DOI:
    10.1198/jasa.2011.tm10183.
PROTOLIDAR package contains functions for analyze the
        LIDAR scan of plants (grapevine) and make 3D maps in GRASS GIS.
Optimized C++ implementations for reading and writing protocol-buffers.
    Currently supports 'rexp.proto' for serializing R objects and 'geobuf.proto' for
    geojson data. This lightweight package is complementary to the much larger
    'RProtoBuf' package which provides a full featured toolkit for working with
    protocol-buffers in R.
'The Proton Game' is a console-based data-crunching game for younger and older data scientists.
  Act as a data-hacker and find Slawomir Pietraszko's credentials to the Proton server.
  You have to solve four data-based puzzles to find the login and password.
  There are many ways to solve these puzzles. You may use loops, data filtering, ordering, aggregation or other tools.
  Only basics knowledge of R is required to play the game, yet the more functions you know, the more approaches you can try.
  The knowledge of dplyr is not required but may be very helpful.
  This game is linked with the ,,Pietraszko's Cave'' story available at http://biecek.pl/BetaBit/Warsaw.
  It's a part of Beta and Bit series.
  You will find more about the Beta and Bit series at http://biecek.pl/BetaBit.
Procedures for testing for group-wide signal in clusters of variables. Tests can be performed for single groups in isolation (univariate) or multiple groups together (multivariate). Specific tests include the exact and approximate (un)selective likelihood ratio tests described in Reid et al (2015), the selective F test and marginal screening prototype test of Reid and Tibshirani (2015). User may pre-specify columns to be included in prototype formation, or allow the function to select them itself. A mixture of these two is also possible. Any variable selection is accounted for using the selective inference framework. Options for non-sampling and hit-and-run null reference distributions.
Comprehensive toolkit for generating various numerical
    features of protein sequences described in Xiao et al. (2015)
    <DOI:10.1093/bioinformatics/btv042>. For full functionality,
    the software 'ncbi-blast+' is needed, see
    <https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastDocs&DOC_TYPE=Download>
    for more information.
'ProTracker' is a popular music tracker to sequence
    music on a Commodore Amiga machine. This package offers the
    opportunity to import, export, manipulate and play 'ProTracker'
    module files. Even though the file format could be considered
    archaic, it still remains popular to this date. This package
    intends to contribute to this popularity and therewith
    keeping the legacy of 'ProTracker' and the Commodore Amiga
    alive.
This R package helps with quality checks, visualizations 
    and analysis of mass spectrometry data, coming from proteomics 
    experiments. The package is developed, tested and used at the Functional 
    Genomics Center Zurich. We use this package mainly for prototyping, 
    teaching, and having fun with proteomics data. But it can also be 
    used to do data analysis for small scale data sets.
Bundles a number of established statistical methods to facilitate
    the visual interpretation of large datasets in sedimentary geology. Includes
    functionality for adaptive kernel density estimation, multidimensional scaling,
    generalised procrustes analysis and individual differences scaling using a
    variety of dissimilarity measures. Univariate provenance proxies, such as
    single-grain ages or (isotopic) compositions are compared with the Kolmogorov-
    Smirnov dissimilarity and Sircombe-Hazelton L2-norm. Categorical provenance
    proxies, such as mineralogical, petrographic or chemical compositions are
    compared with the Aitchison and Bray-Curtis distances. Also included are tools
    to plot compositional data on ternary diagrams, to calculate the sample size
    required for specified levels of statistical precision, and to assess the
    effects of hydraulic sorting on detrital compositions. Includes an intuitive 
    query-based user interface for users who are not proficient in R.
Provides an extensible framework for the efficient calculation of auto- and cross-proximities, along with implementations of the most popular ones.
Determine minimal protein set explaining peptide spectrum matches.
Computes the areas under the precision-recall (PR) and ROC curve for weighted (e.g., soft-labeled) and unweighted data. In contrast to other implementations, the interpolation between points of the PR curve is done by a non-linear piecewise function. In addition to the areas under the curves, the curves themselves can also be computed and plotted by a specific S3-method.
Tests periodicity in short time series using response surface regression.
Useful tools to pry back the covers of R and understand the
    language at a deeper level.
Data sets and functions used in the polish book 
    "Przewodnik po pakiecie R" (The Hitchhiker's Guide to the R). 
    See more at <http://biecek.pl/R>. Among others you will find here 
    data about housing prices, cancer patients, running times and many others.
Bootstrapping for propensity score analysis and matching.
A collection of functions that primarily produce graphics
        to aid in a Propensity Score Analysis (PSA).  Functions
        include: cat.psa and box.psa to test balance within strata of
        categorical and quantitative covariates, circ.psa for a
        representation of the estimated effect size by stratum,
        loess.psa that provides a graphic and loess based effect size
        estimate, and various balance functions that provide measures
        of the balance achieved via a PSA in a categorical covariate.
Algorithms for fitting penalized parametric and semiparametric Bayesian survival models with shrinkage and grouping priors.
Segmentation of allele-specific DNA copy number data and detection of regions with abnormal copy number within each parental chromosome.  Both tumor-normal paired and tumor-only analyses are supported.
Bayesian analysis of item-response theory (IRT) models,
	     roll call analysis; computing highest density regions; maximum
	     likelihood estimation of zero-inflated and hurdle models for count
	     data; goodness-of-fit measures for GLMs; data sets used
	     in writing	and teaching at the Political Science
	     Computational Laboratory; seats-votes curves.
Provides a number of functions to
  simplify and automate the scoring, comparison, and evaluation of
  different ways of creating composites of data.  It is particularly
  aimed at facilitating the creation of physiological composites of
  metabolic syndrome symptom score (MSSS) and allostatic load (AL).
Produces power spectral density estimates through iterative
    refinement of the optimal number of sine-tapers at each frequency. This
    optimization procedure is based on the method of Riedel and Sidorenko
    (1995), which minimizes the Mean Square Error (sum of variance and bias)
    at each frequency, but modified for computational stability.
This R package includes functions for gathering commonly used and
    regularly maintained data set in political science. It also includes
    functions for combining components from these data sets into variables that
    have been suggested in the literature, but are not regularly maintained.
Functions for creating Latin Hypercubes with
    prescribed correlations and performing parameter space exploration.
    Also implements the PLUE method.
    Based on the package sensitivity, by Gilles Pujol,
    Bertrand Iooss & Alexandre Janon.
Various functions for computing pseudo-observations for
        censored data regression
Contains the core methods for the evaluation of principal
    surrogates in a single clinical trial. Provides a flexible interface for
    defining models for the risk given treatment and the surrogate, the models
    for integration over the missing counterfactual surrogate responses, and the
    estimation methods. Estimated maximum likelihood and pseudo-score can be used
    for estimation, and the bootstrap for inference. A variety of post-estimation
    summary methods are provided, including print, summary, plot, and testing.
Pattern Sequence Based Forecasting (PSF) takes univariate
    time series data as input and assist to forecast its future values.
    This algorithm forecasts the behavior of time series
    based on similarity of pattern sequences. Initially, clustering is done with the
    labeling of samples from database. The labels associated with samples are then
    used for forecasting the future behaviour of time series data. The further
    technical details and references regarding PSF are discussed in Vignette.
Implements projected sparse Gaussian process kriging for the intamap package
This package contains functions to calculate the Specificity Index statistic, which can be used for comparative quantitative analysis to identify genes enriched in specific cell populations across a large number of profiles, as well as perform numerous post-processing operations. NOTE:Supplementary data (human & mouse expression sets, calculated pSI datasets, etc.) can be found in pSI.data package located at the following URL: http://genetics.wustl.edu/jdlab/psi_package/
Makes it easy to build panel data in wide format from Panel Survey
    of Income Dynamics (PSID) delivered raw data. Deals with data downloaded and
    pre-processed by 'Stata' or 'SAS', or can optionally download directly from
    the PSID server using the 'SAScii' package. 'psidR' takes care of merging
    data from each wave onto a cross-period index file, so that individuals can be
    followed over time. The user must specify which years they are interested in,
    and the PSID variable names (e.g. ER21003) for each year (they differ in each
    year). There are different panel data designs and sample subsetting criteria
    implemented ("SRC", "SEO", "immigrant" and "latino" samples).
Suite of R functions for the estimation of local false discovery rate (LFDR) using several methods.
This package provides functions for estimation of linear
        and non-linear mixed-effects models using stochastic
        differential equations. Moreover it provides functions for
        finding smoothed estimates of model states and for simulation.
        The package allows for any multivariate non-linear time-variant
        model to be specified, and it also handles multidimensional
        input, co-variates, missing observations and specification of
        dosage regimen.
The package provides an implementation of PSO consistent
        with the standard PSO 2007/2011 by Maurice Clerc et al.
        Additionally a number of ancillary routines are provided for
        easy testing and graphics.
Particle swarm optimization - a basic variant.
Spearman's rank correlation test with precomputed exact
        null distribution for n <= 22.
Smoothing splines with penalties on order m derivatives.
Estimates parameters of a piecewise exponential model for time to progression and time from progression to death with interval censoring of the time to progression and covariates for each distribution using proportional hazards.
Provides a framework for analysing state sequences with probabilistic suffix trees (PST), the construction that stores variable length Markov chains (VLMC). Besides functions for learning and optimizing VLMC models, the PST library includes many additional tools to analyse sequence data with these models: visualization tools, functions for sequence prediction and artificial sequences generation, as well as for context and pattern mining. The package is specifically adapted to the field of social sciences by allowing to learn VLMC models from sets of individual sequences possibly containing missing values, and by accounting for case weights. The  library also allows to compute probabilistic divergence between two models, and to fit segmented VLMC, where sub-models fitted to distinct strata of the learning sample are stored in a single PST. This software results from research work executed within the framework of the Swiss National Centre of Competence in Research LIVES, which is financed by the Swiss National Science Foundation. The authors are grateful to the Swiss National Science Foundation for its financial support.
The propensity score is one of the most widely used tools in studying the causal effect
    of a treatment, intervention, or policy. Given that the propensity score is usually unknown,
    it has to be estimated, implying that the reliability of many treatment effect estimators depends
    on the correct specification of the (parametric) propensity score. This package provides
    data-driven nonparametric diagnostic tools for detecting propensity score misspecification.
Functions to simulate Poisson or Normally distributed responses relative to 
  a baseline and compute achieved significance level and powers for tests on the
  simulated responses.
Kappa, ICC, Cronbach alpha, screeplot, mtmm
A general purpose toolbox for personality, psychometric theory and experimental psychology.   Functions are primarily for multivariate analysis and scale construction using factor analysis, principal component analysis, cluster analysis and reliability analysis, although others provide basic descriptive statistics. Item Response Theory is done using  factor analysis of tetrachoric and polychoric correlations. Functions for analyzing data at multiple levels include within and between group statistics, including correlations and factor analysis.   Functions for simulating and testing particular item and test structures are included. Several functions  serve as a useful front end for structural equation modeling.  Graphical displays of path diagrams, factor analysis and structural equation models are created using basic graphics. Some of the functions are written to support a book on psychometric theory as well as publications in personality research. For more information, see the personality-project.org/r web page.
Contains functions useful for correlation theory,
        meta-analysis (validity-generalization), reliability, item
        analysis, inter-rater reliability, and classical utility
Psychometric mixture models based on 'flexmix' infrastructure. At the moment Rasch mixture models
  with different parameterizations of the score distribution (saturated vs. mean/variance specification)
  and Bradley-Terry mixture models are implemented. Both mixture models can be estimated with or without
  concomitant variables. See vignette('raschmix', package = 'psychomix') for details on the Rasch mixture
  models.
Infrastructure for psychometric modeling such as data classes
  (for item response data and paired comparisons), basic model fitting
  functions (for Bradley-Terry, Rasch, partial credit, rating scale,
  multinomial processing tree models), extractor functions for different types
  of parameters (item, person, threshold, discrimination), unified inference
  and visualizations, and various datasets for illustration. Intended as a
  common lightweight and efficient toolbox for psychometric modeling and a
  common building block for fitting psychometric mixture models in package
  "psychomix" and trees based on psychometric models in package "psychotree".
Recursive partitioning based on psychometric models,
  employing the general MOB algorithm (from package partykit) to obtain
  Bradley-Terry trees, Rasch trees, rating scale and partial credit trees, and
  MPT trees.
An assortment of functions that could be useful in analyzing data from psychophysical experiments. It includes functions for calculating d' from several different experimental designs, links for m-alternative forced-choice (mafc) data to be used with the binomial family in glm (and possibly other contexts) and self-Start functions for estimating gamma values for CRT screen calibrations.
Produces tables conforming to "psychological style" (i.e. APA style) based on standard R output. The resulting tables can be exported to '.rtf', '.html' or '.doc' format.
A multiway method to decompose a tensor (array) of any order, as a generalisation of SVD also supporting non-identity metrics and penalisations. 2-way SVD with these extensions is also available. The package includes also some other multiway methods: PCAn (Tucker-n) and PARAFAC/CANDECOMP with these extensions.
A tool to draw inference in personalized medicine regimes
Implements p-value computations using an approximation to the cumulative distribution function for a variety of tests for periodicity. These tests include harmonic regression tests with normal and double exponential errors as well as modifications of Fisher's g test. An accompanying vignette illustrates the application of these tests.
Function 'pip3d' tests whether a point in 3D space is
  within, exactly on, or outside an enclosed surface defined by a triangular mesh.
  Function 'pip2d' tests whether a point in 2D space is within, exactly on, or outside a polygon.
Fits and analyses time dependent marked point process models with an emphasis on earthquake modelling. For a more detailed introduction to the package, see the topic "PtProcess". A list of recent changes can be found in the topic "Change Log".
Wraps a collection of stemming algorithms for the Portuguese
    Language.
Parametric Time Warping aligns patterns, i.e. it aims to
        put corresponding features at the same locations. The algorithm
        searches for an optimal polynomial describing the warping. It
        is possible to align one sample to a reference, several samples
        to the same reference, or several samples to several
        references. One can choose between calculating individual
        warpings, or one global warping for a set of samples and one
        reference. Two optimization criteria are implemented: RMS (Root
        Mean Square error) and WCC (Weighted Cross Correlation). Both
	warping of peak profiles and of peak lists are supported.
Contains a dataset of words used in 15.000 randomly extracted pages
    from the Portuguese Wikipedia (<https://pt.wikipedia.org/>).
Generates Proteomics (PTX) quality control (QC) reports for shotgun LC-MS data analyzed with the 
             MaxQuant software suite (see <http://www.maxquant.org>).
             Reports are customizable (target thresholds, subsetting) and available in HTML or PDF format.
             Published in J. Proteome Res., Proteomics Quality Control: Quality Control Software for MaxQuant Results (2015) <doi:10.1021/acs.jproteome.5b00780>.
Bayesian variable selection for linear regression models using hierarchical
  priors. There is a prior that combines information across responses and one
  that combines information across covariates, as well as a standard spike and
  slab prior for comparison. An MCMC samples from the marginal posterior
  distribution for the 0-1 variables indicating if each covariate belongs to the
  model for each response.
Text mining of PubMed Abstracts (text and XML) from <http://www.ncbi.nlm.nih.gov/pubmed>.
Create a word cloud using the abstract of publications from 'Pubmed'.
Takes the output of statistical tests and transforms it in a
    publish-friendly pattern. Currently only APA (American Psychological
    Association) style is supported with output to HTML, LaTeX, Markdown and
    plain text. It is easily customizable, extendable and can be used well
    with 'knitr'.  Additionally 'pubprint' offers a memory system that allows
    to save and retrieve results of computations.
R Interface to Pullword Service for natural language processing
    in Chinese. It enables users to extract valuable words from text by deep learning models. 
    For more details please visit the official site (in Chinese) http://pullword.com/.
Model selection for penalized graphical models using the Stability Approach to Regularization Selection ('StARS'), with options for speed-ups including Bounded StARS (B-StARS), batch computing, and other stability metrics (e.g., graphlet stability G-StARS).
R package to query and get data out of a Pumilio sound archive system (http://ljvillanueva.github.io/pumilio/).
PurBayes is an MCMC-based algorithm that uses
        next-generation sequencing data to estimate tumor purity and
        clonality for paired tumor-normal data.
Enables the removal of training data from fitted R models while
    retaining predict functionality. The purged models are more portable as their
    memory footprints do not scale with the training sample size.
Make your pure functions purr with the 'purrr' package. This
    package completes R's functional programming tools with missing features
    present in other programming languages.
Send push notifications to mobile devices or the desktop using Pushover. These notifications can display job status, results, scraped web data, or any other text or numeric data.
Likelihood based population viability analysis in the
  presence of observation error and missing data.
  The package can be used to fit, compare, predict,
  and forecast various growth model types using data cloning.
The calculation of p-variation of the finite sample data.
Computes nonparametric p-values for the potential class
        memberships of new observations as well as cross-validated
        p-values for the training data. The p-values are based on
        permutation tests applied to an estimated Bayesian likelihood
        ratio, using a plug-in statistic for the Gaussian model, 'k
        nearest neighbors', 'weighted nearest neighbors' or
        'penalized logistic regression'.
        Additionally, it provides graphical displays and quantitative
        analyses of the p-values.
An implementation of multiscale bootstrap resampling for
             assessing the uncertainty in hierarchical cluster analysis.
             It provides AU (approximately unbiased) p-value as well as
             BP (bootstrap probability) value for each cluster in a dendrogram.
Computes rank correlations and their p-values with various options for tied ranks.
The pvsR package facilitates data retrieval from Project
    Vote Smart's rich online data base on US politics via the Project Vote
    Smart application programming interface (PVS API). The functions in this
    package cover most PVS API classes and methods and return the
    requested data in a data frame.
Contains functions which allow the user to perform time series regression quickly using the Power Weighted Densities (PWD) approach. alphahat_LR_one_Rcpp() is the main workhorse function within this package.
Calculates various functions needed for design and monotoring survival trials
    accounting for complex situations such as delayed treatment effect, treatment crossover, non-uniform accrual,
    and different censoring distributions between groups. The event time distribution is assumed to be
    piecewise exponential (PWE) distribution and the entry time is assumed to be piecewise uniform distribution.
This R package contains open source implementations
  of several p-value weighting methods, including Spjotvoll, exponential
  and Bayes weights. These are methods for improving power in multiple testing
  via the use of prior information.
Power analysis functions along the lines of Cohen (1988).
Tools the evaluation of interim analysis plans for sequentially
 monitored trials on a survival endpoint; tools to construct efficacy and 
 futility boundaries, for deriving power of a sequential design at a specified
 alternative, template for evaluating the performance of candidate plans at a 
 set of time varying alternatives.
Statistical power simulation for testing the Rasch Model based on a three-way analysis of variance design with mixed classification.
The Penn World Table provides purchasing power parity and
	national income accounts converted to international prices for
	189 countries for some or all of the years 1950-2010.
The Penn World Table 8.x provides information on relative levels of
	income, output, inputs, and productivity for 167 countries
	between 1950 and 2011.
The Penn World Table 9.x provides information on relative levels of
	income, output, inputs, and productivity for 182 countries
	between 1950 and 2014.
Provides a set of functions for reading and writing PC-Axis files, used by different statistical organizations around the globe for data dissemination.
Generic interface for the PX-Web/PC-Axis API. The PX-Web/PC-Axis
    API is used by organizations such as Statistics Sweden and Statistics
    Finland to disseminate data. The R package can interact with all
    PX-Web/PC-Axis APIs to fetch information about the data hierarchy, extract
    metadata and extract and parse statistics to R data.frame format. PX-Web is
    a solution to disseminate PC-Axis data files in dynamic tables on the web.
    Since 2013 PX-Web contains an API to disseminate PC-Axis files.
Given a SpatialPolygonsDataFrame and a set of populations for each polygon,
 compute a population density estimate based on Tobler's pycnophylactic interpolation
 algorithm. The result is a SpatialGridDataFrame.
Drawing population pyramid using (1) data.frame or (2) vectors.
	 The former is named as pyramid() and the latter pyramids(), as wrapper
	 function of pyramid().  pyramidf() is the function to draw population
	 pyramid within the specified frame.
String operations the Python way - a package for those of us who
    miss Python's string methods while we're working in R.
Implements heuristics for the Quadratic Assignment Problem (QAP). Currently only a simulated annealing heuristic is available.
Functions for a scientific quality assurance of meteorological data.
An extensive set of functions to perform Qualitative Comparative Analysis:
             crisp sets ('csQCA'), temporal ('tQCA'), multivalue ('mvQCA')
             and fuzzy sets ('fsQCA'), using a GUI - graphical user interface.
             'QCA' is a methodology that bridges the qualitative and quantitative divide
             in social science research. It uses a Boolean algorithm that results in a
             minimal causal combination which explains a given phenomenon.
A set of functions for Qualitative Comparative Analysis (QCA). It can be used for various types of QCA (csQCA, mvQCA fsQCA and crisp set TQCA  and time-serious QCA). It has methods for simplifying assumption, contradictory simplifying assumption.
Implements tests for Type I error in Qualitative Comparative Analysis (QCA) that take into account the multiple hypothesis tests inherent in the procedure. Tests can be carried out on three variants of QCA: crisp-set QCA (csQCA), multi-value QCA (mvQCA) and fuzzy-set QCA (fsQCA). For fsQCA, the fsQCApermTest() command implements a permutation test that provides 95% confidence intervals for the number of counterexamples and degree of consistency, respectively. The distributions of permuted values can be plotted against the observed values. For csQCA and mvQCA, simple binomial tests are implemented in csQCAbinTest() and mvQCAbinTest(), respectively.
This is an obsolete version of the package QCAGUI, which is now merged with package QCA.
The 'QCApro' package provides professional functionality for performing configurational comparative research with Qualitative Comparative Analysis (QCA), including crisp-set, multi-value, and fuzzy-set QCA. It also offers advanced tools for sensitivity diagnostics and methodological evaluations of QCA.
Helper functions for Qualitative Comparative Analysis: evaluate and
    plot Boolean formulae on fuzzy set score data, apply Boolean operations, compute
    consistency and coverage measures.
Shewhart quality control charts for continuous, attribute and count data. Cusum and EWMA charts. Operating characteristic curves. Process capability analysis. Pareto chart and cause-and-effect chart. Multivariate control charts.
Tools for (automated and manual) quality control of
  the results of Epigenome-Wide Association Studies.
Tools for (automated and manual) quality control of
  the results of Genome Wide Association Studies
Robust estimation of Gaussian mixture models fitted by modified EM algorithm, robust clustering and classification.
Allows to generate Shewhart-type charts and to obtain
    numerical results of interest for a process quality control
    (involving  continuous, attribute or count data).
    This package provides basic functionality for univariable and multivariable
    quality control analysis, including: xbar, xbar-one, S, R, ewna, cusum,
    mewna, mcusum  and T2 charts. Additionally have nonparametric
    control  charts multivariate. Parametric and nonparametric Process Capability Indices.
Simulates a 5 qubit Quantum Computer and evaluates quantum circuits with 1,2
             qubit quantum gates.
Quantile correlation-sure independence screening (QC-SIS) and composite quantile correlation-sure independence screening (CQC-SIS) for ultrahigh-dimensional data.
Automates many of the tasks associated with quantitative
        discourse analysis of transcripts containing discourse
        including frequency counts of sentence types, words, sentences,
        turns of talk, syllables and other assorted analysis tasks. The
        package provides parsing tools for preparing transcript data.
        Many functions enable the user to aggregate data by any number
        of grouping variables, providing analysis and seamless
        integration with other R packages that undertake higher level
        analysis and visualization of text.  This affords the user a
        more efficient and targeted analysis.  'qdap' is designed for
        transcript analysis, however, many functions are applicable to
        other areas of Text Mining/Natural Language Processing.
A collection of dictionaries and word lists for use with
        the 'qdap' package.
A collection of regular expression tools associated with the 'qdap' package
          that may be useful outside of the context of discourse analysis. Tools include
          removal/extraction/replacement of abbreviations, dates, dollar amounts, email
          addresses, hash tags, numbers, percentages, citations, person tags, phone
          numbers, times, and zip codes.
A collection of tools associated with the 'qdap' package
        that may be useful outside of the context of text analysis.
This package provides different specifications of a Quadrilateral
    Dissimilarity Model which can be used to fit same-different judgments
    in order to get a predicted matrix that satisfies regular minimality
    [Colonius & Dzhafarov, 2006, Measurement and representations of
    sensations, Erlbaum]. From such a matrix, Fechnerian distances can be
    computed.
Accurate estimates of the diets of predators are required
    in many areas of ecology, but for many species current methods are
    imprecise, limited to the last meal, and often biased. The diversity
    of fatty acids and their patterns in organisms, coupled with the
    narrow limitations on their biosynthesis, properties of digestion in
    monogastric animals, and the prevalence of large storage reservoirs of
    lipid in many predators, led us to propose the use of quantitative
    fatty acid signature analysis (QFASA) to study predator diets.
An implementation of Quantitative Fatty Acid Signature
    Analysis (QFASA) in R.  QFASA is a method of estimating the diet
    composition of predators.  The fundamental unit of information in
    QFASA is a fatty acid signature (signature), which is a vector of
    proportions describing the composition of fatty acids within lipids.
    Signature data from at least one predator and from samples of all
    potential prey types are required.  Calibration coefficients, which
    adjust for the differential metabolism of individual fatty acids by
    predators, are also required. Given those data inputs, a predator
    signature is modeled as a mixture of prey signatures and its diet
    estimate is obtained as the mixture that minimizes a measure of
    distance between the observed and modeled signatures.  A variety of
    estimation options and simulation capabilities are implemented.
    Please refer to the vignette for additional details and references.
Option pricing (financial derivatives) techniques mainly following textbook 'Options, Futures and Other Derivatives', 9ed by John C.Hull, 2014. Prentice Hall. Implementations are via binomial tree option model (BOPM), Black-Scholes model, Monte Carlo simulations, etc. 
  This package is a result of Quantitative Financial Risk Management course (STAT 449 and STAT 649) at Rice University, Houston, TX, USA, taught by Oleg Melnikov, statistics PhD student, as of Spring 2015.
Compute various quantitative genetics parameters from a Generalised Linear Mixed Model (GLMM) estimates. Especially, it yields the observed phenotypic mean, phenotypic variance and additive genetic variance.
Can be used to visualize data as networks as well as provides an interface for visualizing weighted graphical models.
Two linear mixed model approaches: REML(restricted maximum likelihood) and MINQUE (minimum norm quadratic unbiased estimation) approaches and several resampling techniques are integrated for various quantitative genetics analyses. With these two types of approaches, various unbalanced data structures, missing data, and any irregular genetic  mating designs can be analyzed and statistically tested. This package also offers fast computations for many large data sets. Other functions will be added to this R tool in the future.
Multivariate description of the state changes of a qualitative variable by  
             Correspondence Analysis and Clustering. See:
             Deville, J.C., & Saporta, G. (1983). 
                 Correspondence analysis, with an extension towards nominal time series. 
                 Journal of econometrics, 22(1-2), 169-189.
             Corrales, M.L., & Pardo, C.E. (2015) <doi:10.15332/s2027-3355.2015.0001.01>. 
                 Analisis de datos longitudinales cualitativos con analisis de correspondencias y clasificacion. 
                 Comunicaciones en Estadistica, 8(1), 11-32.
Extremely fast algorithm "QICD", Iterative Coordinate 
              Descent Algorithm for High-dimensional Nonconvex Penalized Quantile 
              Regression. This algorithm combines the coordinate descent algorithm 
              in the inner iteration with the majorization minimization step 
              in the outside step. For each inner univariate minimization problem, 
              we only need to compute a one-dimensional weighted median, 
              which ensures fast computation. Tuning parameter selection is based 
              on two different method: the cross validation and BIC for 
              quantile regression model. Details are described in the Peng,B and Wang,L. (2015) 
              linked to via the URL below with <DOI:10.1080/10618600.2014.913516>.
Functions for making run charts and  basic Shewhart control
    charts for measure and count data.
    The main function, qic(), creates run and control charts and has a
    simple interface with a rich set of options to control data analysis
    and plotting, including options for automatic data aggregation by
    subgroups, easy analysis of before-and-after data, exclusion of one
    or more data points from analysis, and splitting charts into
    sequential time periods.
    Missing values and empty subgroups are handled gracefully.
Open QIIME output files in R, compute statistics, and
    create plots from the data.
Qiita is a technical knowledge sharing and collaboration platform for programmers.
  See <https://qiita.com/api/v2/docs> for more information.
This is a collection of functions to read, recode, and transcode data.
Extension of the functionality of the Matrix package for using sparse matrices. Some of the functions are very general, while other are highly specific for special data format as used for quantitative language comparison (QLC).
Collection of visualizations as used in quantitative language comparison.
Functions to implement Q-learning for estimating optimal
        dynamic treatment regimes from two stage sequentially
        randomized trials, and to perform inference via m-out-of-n
        bootstrap for parameters indexing the optimal regime.
Empirical adjustment of the distribution of variables originating from (regional) climate model simulations using quantile mapping.
Analysis of Q methodology, used to identify distinct perspectives existing within a group.
  This methodology is used across social, health and environmental sciences to understand diversity of attitudes, discourses, or decision-making styles (for more information, see http://qmethod.org).
  A single function runs the full analysis. Each step can be run separately using the corresponding functions: for automatic flagging of Q-sorts (manual flagging is optional), for statement scores, for distinguishing and consensus statements, and for general characteristics of the factors.
  Additional functions are available to import and export data, to print and plot, to import raw data from individual *.CSV files, and to make printable cards.
  The package also offers functions to print Q cards and to generate Q distributions for study administration.
  The package uses principal components and it allows manual or automatic flagging, a number of mathematical methods for rotation, and a number of correlation coefficients for the initial correlation matrix.
  See further details in the package documentation, and in the web pages below, which include a cookbook, guidelines for more advanced analysis (how to perform manual flagging or change the sign of factors), data management, and a beta graphical user interface for online and offline use.
Basic functions for building parsers, with an application to PC-AXIS format files.
As a newly emerged research area, RNA epigenetics has drawn increasing 
             attention recently for the participation of RNA methylation and other 
             modifications in a number of crucial biological processes. Thanks to high 
             throughput sequencing techniques, such as m6A-Seq, transcriptome-wide RNA 
             methylation profile is now available in the form of count-based data, with 
             which it is often of interests to study the dynamics in epitranscriptomic 
             layer. However, the sample size of RNA methylation experiment is usually 
             very small due to its costs; and additionally, there usually exist a large 
             number of genes whose methylation level cannot be accurately estimated due 
             to their low expression level, making differential RNA methylation analysis 
             a difficult task.
             We present QNB, a statistical approach for differential RNA methylation 
             analysis with count-based small-sample sequencing data. The method is based 
             on 4 independent negative binomial dis-tributions with their variances and 
             means linked by local regressions. QNB showed improved performance on 
             simulated and real m6A-Seq datasets when compared with competing algorithms. 
             And the QNB model is also applicable to other datasets related RNA 
             modifications, including but not limited to RNA bisulfite sequencing, 
             m1A-Seq, Par-CLIP, RIP-Seq, etc.Please don't hesitate to contact 
            <liulian19860905@163.com> if you have any questions.
To generate the scores of the EORTC QLQ-C30 questionnaire and supplementary modules and to determine the time to health-related quality of life score deterioration in longitudinal analysis.
Model fitting, optimal model selection and calculation of various features that are essential in the analysis of quantitative real-time polymerase chain reaction (qPCR).
use 2^ddCT methods calculate the relative gene expression,
        data file can be export from bio-rad qpcr machine, the results
        can be plot with errorbar. ver 1.1 add GroupPlot function, can
        plot all the groups once.
Tools to 1) simulate and visualize stochastic differential
    equations and 2) determine stability of equilibria using the ordered-upwind
    method to compute the quasi-potential.
Provides users the necessary utility functions to generate permutation-based QQ plots and also estimate inflation factor based on the empirical NULL distribution. While it has general utility, it is particularly helpful when the skewness of the Fisher's Exact test in sparse data situations with imbalanced case-control sample sizes renders the reliance on the uniform chi-square expected distribution inappropriate.
Provides the function qqtest which incorporates uncertainty in its
    qqplot display(s) so that the user might have a better sense of the
    evidence against the specified distributional hypothesis.  qqtest draws a
    quantile quantile plot for visually assessing whether the data come from a
    test distribution that has been defined in one of many ways.  The vertical
    axis plots the data quantiles, the horizontal those of a test distribution.
    The default behaviour generates 1000 samples from the test distribution and
    overlays the plot with shaded pointwise interval estimates for the ordered
    quantiles from the test distribution.  A small number of independently
    generated exemplar quantile plots can also be overlaid.  Both the interval
    estimates and the exemplars provide different comparative information to
    assess the evidence provided by the qqplot for or against the hypothesis
    that the data come from the test distribution (default is normal or
    gaussian).  Finally, a visual test of significance (a lineup plot) can also
    be displayed to test the null hypothesis that the data come from the test
    distribution.
Presents an explanatory animation of normal quantile-quantile plots based on a water-filling analogy.  The animation presents a normal QQ plot as the parametric plot of the water levels in vases defined by two distributions.  The distributions decorate the axes in the normal QQ plot and are optionally shown as vases adjacent to the plot.  The package draws QQ plots for several distributions, either as samples or continuous functions.
Upload raster data and easily create interactive quantitative risk analysis 'QRA' visualizations. Select
    from numerous color palettes, base-maps, and different configurations.
Tools that create D3 JavaScript force directed graph from R. D3 JavaScript was created by Michael Bostock. See http://d3js.org/ and, more specifically for Force Directed Graph https://github.com/mbostock/d3/wiki/Force-Layout.
A Quantile Rank-score based test for the identification of expression quantitative trait loci.
Parametric modeling of quantile regression coefficient functions.
    Can be used with censored and truncated data.
Quantile regression in varying-coefficient models (VCM) using one particular nonparametric technique called P-splines. The functions can be applied on three types of VCM; (1) Homoscedastic VCM, (2) Simple heteroscedastic VCM, and (3) General heteroscedastic VCM.
Quick Response codes (QR codes) are a type of matrix bar code and can be
    used to authenticate transactions, provide access to multi-factor authentication
    services and enable general data transfer in an image. QR codes use four standardized 
    encoding modes (numeric, alphanumeric, byte/binary, and kanji) to efficiently store 
    data. Matrix barcode generation is performed efficiently in C via the included
    'libqrencoder' library created by Kentaro Fukuchi.
The qrfactor package simultaneously runs both Q and R  mode factor analyses. The package contains only one function called qrfactor() that can perform PCA, R-mode Factor Analysis, Q-mode Factor Analysis, Simultaneous R- and Q-mode Factor Analysis, Principal Coordinate Analysis, as wells as Multidimensional Scaling  (MDS). Loadings and scores can easily be computed from the  simulation.The plot.qrfactor() function offers several annotated  biplots for all possible combinations of eigenvectors, loadings, and scores. Input data includes shapefiles, tables and dataframe
Joint estimation of quantile specific intercept and slope parameters in a linear regression setting.
Quantile regression (QR) for Linear 
             Mixed-Effects Models via the asymmetric Laplace distribution (ALD). 
             It uses the Stochastic Approximation of the EM (SAEM) algorithm for 
             deriving exact maximum likelihood estimates and full inference results 
             for the fixed-effects and variance components. 
             It also provides graphical summaries for assessing the algorithm 
             convergence and fitting results.
Accompanying package to the book
 Quantitative Risk Management: Concepts, Techniques and Tools by
 Alexander J. McNeil, Rüdiger Frey, and Paul Embrechts.
Various data sets (stocks, stock indices, constituent data, FX,
 zero-coupon bond yield curves, volatility, commodities) for Quantitative
 Risk Management practice.
Functions and data sets for reproducing selected results from
  the book "Quantitative Risk Management: Concepts, Techniques and Tools".
  Furthermore, new developments and auxiliary functions for Quantitative
  Risk Management practice.
Functionality for generating (randomized) quasi-random numbers in
  high dimensions.
Quantile regression (QR) for Nonlinear
             Mixed-Effects Models via the asymmetric Laplace distribution (ALD). 
             It uses the Stochastic Approximation of the EM (SAEM) algorithm for 
             deriving exact maximum likelihood estimates and full inference results 
             for the fixed-effects and variance components.
             It also provides graphical summaries for assessing the algorithm
             convergence and fitting results.
Fit a quantile regression neural network with optional
        left censoring using a variant of the finite smoothing
        algorithm.
Molecular descriptors and outcomes for several public domain data sets
Dynamic bindings to the Qt library for calling Qt
    methods and extending Qt classes from R. Other packages build upon 'qtbase'
    to provide special-purpose high-level interfaces to specific parts of Qt.
Provides several methods for computing the Quantile Treatment Effect (QTE) and Quantile Treatment Effect on the Treated (QTET).  The main cases covered are (i) Treatment is randomly assigned, (ii) Treatment is as good as randomly assigned after conditioning on some covariates (also called conditional independence or selection on observables), (iii) Identification is based on a Difference in Differences assumption (several varieties are available in the package).
Analysis of experimental crosses to identify genes
  (called quantitative trait loci, QTLs) contributing to variation in
  quantitative traits.
Datasets for the book, A Guide to QTL Mapping with R/qtl.
Densitometric evaluation of the photo-archived quantitative thin-layer chromatography (TLC) plates.
Web-based interactive charts (using D3.js) for the analysis of
    experimental crosses to identify genetic loci (quantitative trait
    loci, QTL) contributing to variation in quantitative traits.
Functions to infer co-mapping trait hotspots and causal models
Provides tools for joint analysis of multiple traits in a backcross (BC) or recombinant inbred lines (RIL) population. It can be used to select an optimal subset of traits for multiple-trait mapping, analyze multiple traits via the SURE model, which can associate different QTL with different traits, and perform multiple-trait composite multiple-interval mapping.
Functions to Simultaneously Infer Causal Graphs and Genetic Architecture
This software provides tools for quantitative trait mapping in populations such as advanced intercross lines where relatedness among individuals should not be ignored. It can estimate background genetic variance components, impute missing genotypes, simulate genotypes, perform a genome scan for putative quantitative trait loci (QTL), and plot mapping results. It also has functions to calculate identity coefficients from pedigrees, especially suitable for pedigrees that consist of a large number of generations, or estimate identity coefficients from genotypic data in certain circumstances.
This is a collection of functions for unconditional and conditional quantiles. These include methods for transformation-based quantile regression, quantile-based measures of location, scale and shape, methods for quantiles of discrete variables, quantile-based multiple imputation, and restricted quantile regression.
Low-level interface to functionality in Qt for efficiently drawing
             dynamic graphics and handling basic user input.
Quantitative Analysis of Complex Networks. This package offers a set of topological network measures to analyze complex Networks structurally.
This package gives you the exact first four permutation moments for the most commonly used quadratic form statistics, which need not be positive definite. The extension of this work to quadratic forms greatly expands the utility of density approximations for these problems, including for high-dimensional applications, where the statistics must be extreme in order to exceed stringent testing thresholds. Approximate p-values are obtained by matching the exact moments to the Pearson family of distributions using the PearsonDS package.
Gridded data need not be regular, quadmesh creates a mesh from rasters. Future versions may be more general.
This package contains routines and documentation for
        solving quadratic programming problems.
This package fits classical sparse regression models with
    efficient active set algorithms by solving quadratic problems. Also
    provides a few methods for model selection purpose (cross-validation,
    stability selection).
Exact one-sided p-values and confidence intervals for an outcome variable defined on an interval measurement scale with only qualitative and ordinal information available.
Used for testing for qualitative interactions between
    treatment effects and patient subgroups. Here the term treatment effect
    means a comparison result of two treatments within each patient subgroup.
    Models included in this package are Gaussian, binomial and Cox models.
    Methods included here are interval based graphical approach and Gail Simon
    LRT.
Contains methods associated with the Define, Measure, Analyze, Improve and Control (i.e. DMAIC) cycle of the Six Sigma Quality Management methodology.It covers distribution fitting, normal and non-normal process capability indices, techniques for Measurement Systems Analysis especially gage capability indices and Gage Repeatability (i.e Gage RR) and Reproducibility studies, factorial and fractional factorial designs as well as response surface methods including the use of desirability functions. Improvement via Six Sigma is project based strategy that covers 5 phases: Define - Pareto Chart; Measure - Probability and Quantile-Quantile Plots, Process Capability Indices for various distributions and Gage RR Analyze i.e. Pareto Chart, Multi-Vari Chart, Dot Plot; Improve - Full and fractional factorial, response surface and mixture designs as well as the desirability approach for simultaneous optimization of more than one response variable. Normal, Pareto and Lenth Plot of effects as well as Interaction Plots; Control - Quality Control Charts can be found in the 'qcc' package. The focus is on teaching the statistical methodology used in the Quality Sciences.
Automatic generation of distinct qualitative color palettes,
    optionally adapted to color blindness. It takes a subspace of the HSL color
    space as input and projects it to the DIN99d color space where it selects
    and return colors that are maximally distinct.
Qualtrics <https://www.qualtrics.com/about/> 
    allows users to collect online data through surveys.
    This package contains convenience functions to pull
    survey results straight into R using the Qualtrics
    API. See <https://api.qualtrics.com/> for more 
    information about the Qualtrics API.
Qualitative methods for the validation of dynamic models.
    It contains (i) an orthogonal set of deviance measures for absolute,
    relative and ordinal scale and (ii) approaches accounting for time
    shifts. The first approach transforms time to take time delays and speed
    differences into account. The second divides the time series into
    interval units according to their main features and finds the longest
    common subsequence (LCS) using a dynamic programming algorithm.
Implements indices of qualitative variation proposed by Wilcox (1973).
Functions for interacting directly with the Quandl API to offer
    data in a number of formats usable in R, downloading a zip with all data from a
    Quandl database, and the ability to search. This R package uses the Quandl API.
    For more information go to https://www.quandl.com/docs/api. For more help on the
    package itself go to https://www.quandl.com/help/r.
Methods which streamline the descriptive analysis of quantitative
    matrices. Matrix columns are samples while rows are features i.e. proteins, genes.
Statistical evaluation of calibration curves by different
        regression techniques: ordinary, weighted, robust (up to 4th
        order polynomial).  Log-log and Box-Cox transform, estimation
        of optimal power and weighting scheme. Tests for
        heteroscedascity and normality of residuals. Different kinds of
        plots commonly used in illustrating calibrations. Easy "inverse
        prediction" of concentration by given responses and statistical
        evaluation of results (comparison of precision and accuracy by
        common tests).
A fast, flexible toolset for for the management, processing, and
    quantitative analysis of textual data in R.
Provides different functions for quantifying qualitative survey data. It supports the Carlson-Parkin method, the regression approach, the balance approach and the conditional expectations method.
Estimation of conditional quantiles using optimal quantization.
    Construction of an optimal grid of N quantizers, estimation of conditional
    quantiles and data driven selection of the size N of the grid. Graphical
    illustrations for the selection of N and of resulting estimated curves or
    surfaces when the dimension of the covariate is one or two.
Code for centroid, median and quantile classifiers.
Specify, build, trade, and analyse quantitative financial trading strategies.
Contains functions useful for data screening, testing
        moderation, mediation and estimating power.
Estimation and inference methods for models of conditional quantiles: 
  Linear and nonlinear parametric and non-parametric (total variation penalized) models 
  for conditional quantiles of a univariate response and several methods for handling
  censored survival data.  Portfolio selection methods based on expected shortfall
  risk are also included.
Quantile Regression Forests is a tree-based ensemble
        method for estimation of conditional quantiles. It is
        particularly well suited for high-dimensional data. Predictor
        variables of mixed classes can be handled. The package is
        dependent on the package 'randomForest', written by Andy Liaw.
Fits non-crossing regression quantiles as a function of linear covariates and a smooth terms via B-splines with difference penalties.
Implements the nonparametric quantile regression method developed by Belloni, Chernozhukov, and Fernandez-Val (2011) to partially linear quantile models. Provides point estimates of the conditional quantile function and its derivatives based on series approximations to the nonparametric part of the model. Provides pointwise and uniform confidence intervals using analytic and resampling methods.
Methods to determine, smooth and plot quantile periodograms for
    univariate and multivariate time series.
Download and organize historical market data from multiple sources like Yahoo (<https://finance.yahoo.com>), Google (<https://www.google.com/finance>), Finam (<https://www.finam.ru/profile/moex-akcii/sberbank/export/>), MOEX (<https://www.moex.com/en/derivatives/contracts.aspx>) and IQFeed (<https://www.iqfeed.net/symbolguide/index.cfm?symbolguide=lookup>). Code your trading algorithms in modern C++11 with powerful event driven tick processing API including trading costs and exchange communication latency and transform detailed data seamlessly into R. In just few lines of code you will be able to visualize every step of your trading model from tick data to multi dimensional heat maps.
Using HTS data, clusters mutations in order to recreate putative
    clones from the data provided. It requires genotype at the location of the
    variant as well as the depth of coverage and number of reads supporting the
    mutation. Additional information may be provided, such as the contamination
    in the tumor sample. This package also provides a function QuantumCat() which
    simulates data obtained from tumor sequencing.
An implementation of two interaction indices between extractive
    activity and groundwater resources based on hazard and vulnerability
    parameters used in the assessment of natural hazards. One index is based
    on a discrete choice model and the other is relying on an artificial
    neural network.
Identify differentially expressed genes in RNA-seq count data using quasi-Poisson or quasi-negative binomial models with 'QL', 'QLShrink' and 'QLSpline' methods (Lund, Nettleton, McCarthy, and Smyth, 2012).
Set of functions to make the processing and analysis of
    surveys easier : interactive shiny apps and addins for data recoding, 
    contingency tables, dataset metadata handling, and several convenience 
    functions.
Implementation of a computationally efficient method for
    simulating queues with arbitrary arrival and service times.
It provides versatile tools for analysis of birth and death based Markovian Queueing Models
    and Single and Multiclass Product-Form Queueing Networks.
    It implements M/M/1, M/M/c, M/M/Infinite, M/M/1/K, M/M/c/K, M/M/c/c, M/M/1/K/K, M/M/c/K/K, M/M/c/K/m, M/M/Infinite/K/K,
    Multiple Channel Open Jackson Networks, Multiple Channel Closed Jackson Networks,
    Single Channel Multiple Class Open Networks, Single Channel Multiple Class Closed Networks
    and Single Channel Multiple Class Mixed Networks.
    Also it provides a B-Erlang, C-Erlang and Engset calculators.
    This work is dedicated to the memory of D. Sixto Rios Insua.
This calculates the Quandle, Rack and Degenerate Homology groups of
    Racks and Biracks (as well as Quandles and Biquandles). In addition, a test is
    provided to ascertain if a given set with one or two given functions is indeed a
    biquandle or not.
Use Newton's method and coordinate descent to solve the
        regularized inverse covariance matrix estimation problem.
        Please refer to: Sparse Inverse Covariance Matrix Estimation
        Using Quadratic Approximation, Cho-Jui Hsieh, Matyas A. Sustik,
        Inderjit S. Dhillon, Pradeep Ravikumar, Advances in Neural
        Information Processing Systems 24, 2011, p. 2330--2338.
While analyzing geospatial data, easy visualization is often
    needed that allows for quick plotting, and simple, but easy interactivity.
    Additionally, visualizing geospatial data in projected coordinates is also
    desirable. The 'quickmapr' package provides a simple method to visualize 'sp'
    and 'raster' objects, allows for basic zooming, panning, identifying,
    labeling, selecting, and measuring spatial objects.  Importantly, it does 
    not require that the data be in geographic coordinates.
Quickly fits and plots psychometric functions (normal, logistic,
    Weibull or any or any function defined by the user) for multiple groups.
A set of functions to extract results from regression models and
 plot the effect size using 'ggplot2' seamlessly. While 'broom' is useful to
 convert statistical analysis objects into tidy data frames, 'coefplot' is adept at showing
 multivariate regression results. With specific outcome, this package could build regression models
 automatically, extract results into a data frame and provide a quicker way to summarize
 models' statistical findings using 'ggplot2'.
Grows a qualitative interaction tree. Quint is a tool for subgroup analysis, suitable for data from a two-arm randomized controlled trial.
Gene banks increasingly use molecular markers for routine
    characterization of plant collections and farmer managed diversity. The
    gene bank of the International Potato Center presently uses a
    micro-satellite marker kit to produce molecular profiles for potato
    accessions. We have been searching for a compact graphical representation
    that shows both molecular diversity and accession characteristics - thus
    permitting biologists and collection curators to have a simple way to
    interpret molecular data. Inspired by the ancient Andean data recording
    system we devised a graph that allows for standardized representation while
    leaving room for updates of the marker kit and the collection of
    accessions. The graph has been used in several catalogs of potatoes.
Functions to compute confidence statistics
	     for ordering population quantiles.
Selection of a threshold parameter for GLM-lasso and Square-root lasso to obtain a sparse model 
 with a good compromise between high true positive rate and low false discovery rate.
For a given data matrix A and cluster centers/prototypes collected in the matrix P, the functions described here select a subset of statistic variables Q that mostly explains/justifies P as prototypes. The functions are useful to reduce the data dimension for classification and to discard masking variables for clustering.
Functions to compute quasi variances and associated measures of approximation error.
Implement a multivariate analysis interface for questionnaire validation of Likert-type scale variables.
A collection of (wrapper) functions the creator found useful
    for quickly placing data summaries and formatted regression results into
    '.Rnw' or '.Rmd' files. Functions for generating commonly used graphics,
    such as receiver operating curves or Bland-Altman plots, are also provided
    by 'qwraps2'.  'qwraps2' is a updated version of a package 'qwraps'. The
    original version 'qwraps' was never submitted to CRAN but can be found at
    <https://github.com/dewittpe/qwraps/>. The implementation and limited scope
    of the functions within 'qwraps2' <https://github.com/dewittpe/qwraps2/> is
    fundamentally different from 'qwraps'.
Generalized eigenvalues and QZ decomposition
        (generalized Schur form) for an N-by-N non-symmetric
        matrix A or paired matrices (A,B) with eigenvalues reordering
        mechanism. The package is mainly based complex*16 and double
        precision of LAPACK library (version 3.4.2.)
Estimation of reproduction numbers for disease outbreak, based on
    incidence data. The R0 package implements several documented methods. It is
    therefore possible to compare estimations according to the methods used.
    Depending on the methods requested by user, basic reproduction number
    (commonly denoted as R0) or real-time reproduction number (referred to as
    R(t)) is computed, along with a 95% Confidence Interval. Plotting outputs
    will give different graphs depending on the methods requested : basic
    reproductive number estimations will only show the epidemic curve
    (collected data) and an adjusted model, whereas real-time methods will also
    show the R(t) variations throughout the outbreak time period. Sensitivity
    analysis tools are also provided, and allow for investigating effects of
    varying Generation Time distribution or time window on estimates.
Utilities for sparse signal recovery suitable for compressed sensing. L1, L2 and TV penalties, DFT basis matrix, simple sparse signal generator, mutual cumulative coherence between two matrices and examples, Lp complex norm, scaling back regression coefficients.
A series of functions to call 'AD Model Builder' (i.e.,
    compile and run models) from within R, read the results back
    into R as 'admb' objects, and provide standard accessors (i.e.
    coef(), vcov(), etc.)
An R interface to estimate structured additive regression (STAR) models with 'BayesX'.
It is a wrapper around the Cuba-1.6 library by Thomas Hahn available from the URL http://www.feynarts.de/cuba/. Implement four general-purpose multidimensional integration algorithms: Vegas, Suave, Divonne and Cuhre.
This package provides generic functions to analyze the distribution
  of two continuous variables: 'conf2d' to calculate a smooth empirical
  confidence region, and 'freq2d' to calculate a frequency distribution.
2dRUE is a methodology to make a diagnostic of land
        condition in a large territory during a given time period. The
        following projects have funded this package: DeSurvey IP (EC
        FP6 Integrated Project contract No. 003950), DesertWatch (ESA
        DUE contract No. 18487/04/I-LG) and MesoTopos (Junta de
        Andalucia PE ref. RNM-4023).
Converting R CRAN outputs into Google Earth.
The model R squared and semi-partial R squared for the linear and
    generalized linear mixed model (LMM and GLMM) are computed with confidence
    limits. The R squared measure from Edwards et.al (2008) <DOI:10.1002/sim.3429>
    is extended to the GLMM using penalized quasi-likelihood (PQL) estimation
    (see Jaeger et al. 2016 <DOI:10.1080/02664763.2016.1193725>). Three methods
    of computation are provided and described as follows. Firstly, The
    Kenward-Roger approach. Due to some inconsistency between the 'pbkrtest'
    package and the 'glmmPQL' function, the Kenward-Roger approach in the
    'r2glmm' package is limited to the LMM. Secondly, The method introduced
    by Nakagawa and Schielzeth (2013) <DOI:10.1111/j.2041-210x.2012.00261.x>
    and later extended by Johnson (2014) <DOI:10.1111/2041-210X.12225>.
    The 'r2glmm' package only computes marginal R squared for the LMM and does
    not generalize the statistic to the GLMM; however, confidence limits and
    semi-partial R squared for fixed effects are useful additions. Lastly, an
    approach using standardized generalized variance (SGV) can be used for
    covariance model selection. Package installation instructions can be found
    in the readme file.
Wrapper functions for GUESS, a GPU-enabled sparse Bayesian variable
             selection method for linear regression based analysis of possibly
             multivariate/correlated outcomes.
Includes HTML function and methods to write in an HTML
        file. Thus, making HTML reports is easy. Includes a function
        that allows redirection on the fly, which appears to be very
        useful for teaching purpose, as the student can keep a copy of
        the produced output to keep all that he did during the course.
        Package comes with a vignette describing how to write HTML
        reports for statistical analysis. Finally, a driver for 'Sweave'
        allows to parse HTML flat files containing R code and to
        automatically write the corresponding outputs (tables and
        graphs).
Providing wrapper functions to implement Bayesian analysis in JAGS.  Some major features include monitoring convergence of a MCMC model using Rubin and Gelman Rhat statistics, automatically running a MCMC model till it converges, and implementing parallel processing of a MCMC model for multiple chains.
generate univariate and bivariate analyses in LaTeX or
        HTML formats
An R command interface to the 'MLwiN' multilevel
    modelling software package.
Using this package,
 it is possible to call a BUGS model, summarize inferences and
 convergence in a table and graph, and save the simulations in arrays for easy access
 in R.
R2PPT provides a simple set of wrappers to easily use rcom
        or RDCOMClient for generating Microsoft PowerPoint
        presentations.
R2STATS is a gWidgetsRGtk2 GUI for fitting and comparing GLM or GLMM (based on Douglas Bates' lme4 package) in R. It is designed to make comparisons between numerous models easy, both numerically and graphically, which may be useful for teaching. Relevant plots are automatically produced for each model family. R2STATS is *not* a generic graphical interface for R, but a GUI for statistical modelling in a model comparison approach.
r2stl, R package for visualizing data using a 3D printer
        Package r2stl converts R data to STL (stereolithography) files
        that can be used to feed a 3-dimensional printer.  The
        3-dimensional output from an R function can be materialized
        into a solid surface in a plastic material, therefore allowing
        more detailed examination. There are many possible uses for
        this new R tool, such as to examine mathematical expressions
        with very irregular shapes, to aid teaching people with
        impaired vision, to create raised relief maps from digital
        elevation maps (DEMs), to bridge the gap between mathematical
        tools and rapid prototyping, and many more.  Ian Walker created
        the function "r2stl" and Jose' Gama assembled the package.
Using the Ming library
    (http://www.libming.org/) to create Flash animations.
    Users can either use the SWF device swf() to generate SWF file
    directly through plotting functions like plot() and lines(),
    or convert images of other formats (SVG, PNG, JPEG) into SWF.
This package uses either the statconnDCOM server (via the
        rcom package) or the RDCOMClient to communicate with MS-Word
        via the COM interface.
Invoke a 'BUGS' model in 'OpenBUGS' or 'WinBUGS', a class "bugs" for 'BUGS' 
  results and functions to work with that class.
  Function write.model() allows a 'BUGS' model file to be written.  
  The class and auxiliary functions could be used with other MCMC programs, including 'JAGS'.
This is a collection of useful functions and data for
        Stats 330
Provides a collection of functions for basic
    database and document management operations such as add, get, list or
    delete. Every cdbFunction() gets and returns a list() containing the
    connection setup.
    Such a list (in the documentation mostly called cdb) can be generated by
    cdb <- cdbIni(). Then, the list cdb provides some functions respectively
    functionality e.g cdb$baseUrl() or cdb$getDocRev().
This package provides an R interface with 2-part 4dfp MRI images
  (.4dfp.ifh and .4dfp.img files.)
A collection of R functions for use with Stock Synthesis, a
    fisheries stock assessment modeling platform written in ADMB by Dr. Richard
    D.  Methot at the NOAA Northwest Fisheries Science Center. The functions
    include tools for summarizing and plotting results, manipulating files,
    visualizing model parameterizations, and various other common stock
    assessment tasks.
The R6 package allows the creation of classes with reference
    semantics, similar to R's built-in reference classes. Compared to reference
    classes, R6 classes are simpler and lighter-weight, and they are not built
    on S4 classes so they do not require the methods package. These classes
    allow public and private members, and they support inheritance, even when
    the classes are defined in different packages.
Provides a R6 "frame" around data which allows one to create more
    complex objects/operations based on the underlying data.
Solves the bioenergetic balance for different aquaculture sea fish (Sea Bream and Sea Bass) and shellfish (Mussel and Clam) both at individual and population scale.
Implementation of some racing methods for the empirical
        selection of the best. If the R package `rpvm' is installed
        (and if PVM is available, properly configured, and
        initialized), the evaluation of the candidates are performed in
        parallel on different hosts.
Fit a variety of models to Rank Abundance Data
Implements import, export, manipulation, visualization, and downstream
             (post-clustering) analysis of RADseq data, integrating with the 'pyRAD' package by Deren Eaton.
It has tools for normalization of rank abundance
     distributions (RAD) to a desired number of ranks using MaxRank
     Normalization method.
     RADs are commonly used in biology/ecology and mathematically equivalent
     to complementary cumulative distributions (CCDFs) which are used in
     physics, linguistics and sociology and more generally in data science.
Fundamental formulas for Radar, for attenuation, range, velocity,
    effectiveness, power, scatter, doppler, geometry, radar equations, etc.
    Based on Nick Guy's Python package PyRadarMet
Create interactive radar charts using the 'Chart.js' 'JavaScript' library
    and the 'htmlwidgets' package. 'Chart.js' <http://www.chartjs.org/> is a 
    lightweight library that supports several types of simple chart using the 'HTML5' 
    canvas element. This package provides an R interface specifically to the 
    radar chart, sometimes called a spider chart, for visualising multivariate data.
A platform-independent browser-based interface for business
    analytics in R, based on the 'shiny' package. The application combines the
    functionality of 'radiant.data', 'radiant.design', 'radiant.basics',
    'radiant.model', and 'radiant.multivariate'.
The Radiant Basics menu includes interfaces for probability calculation, central limit theorem simulation, comparing means and proportions, goodness-of-fit testing, cross-tabs, and correlation. The application extends the functionality in radiant.data.
A platform-independent browser-based interface for business analytics in R, based on the Shiny package.
The Radiant design menu includes interfaces for design of experiments, sampling, and sample size calculation. The application extends the functionality in radiant.data.
The Radiant Model menu includes interfaces for linear and logistic regression, Neural Networks, model evaluation, decision analysis, and simulation. The application extends the functionality in radiant.data.
The Radiant Multivariate menu includes interfaces for perceptual mapping, factor analysis, cluster analysis, and conjoint analysis. The application extends the functionality in radiant.data.
Functions to extract first and second order statistics from
    images.
RadioSonde is a collection of programs for reading and
        plotting SKEW-T,log p diagrams and wind profiles for data
        collected by radiosondes (the typical weather balloon-borne
        instrument), which we will call "flights", "sondes", or
        "profiles" throughout the associated documentation.  The raw
        data files are in a common format that has a header followed by
        specific variables.  Use "help(ExampleSonde)" for the full
        explanation of the data files.
Radioactive doses estimation using individual chromosomal aberrations information. See Higueras M, Puig P, Ainsbury E, Rothkamm K. (2015) <DOI:10.1098/rspa.2014.0588>.
Designed for the import, analysis, and visualization of dosimetric and volumetric data in Radiation Oncology, the tools herein enable import of dose-volume histogram information from multiple treatment planning system platforms and 3D structural representations and dosimetric information from 'DICOM-RT' files.  These tools also enable subsequent visualization and statistical analysis of these data.
Contains 4 different functions for radon and soil gas transport in a porous medium.
An implementation of the radviz projection in R. It enables the visualization of
    multidimensional data while maintaining the relation to the original dimensions.
    This package provides functions to create and plot radviz projections, and a number of summary
    plots that enable comparison and analysis. For reference see Ankerst et al. (1996) 
    <http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.68.1811> for original implementation, 
    see Di Caro et al. (2010) <DOI:10.1007/978-3-642-13672-6_13> for the original method for dimensional
    anchor arrangements.
Aims at loading Google Adwords data into R. Adwords is an online
    advertising service that enables advertisers to display advertising copy to web
    users. Therefore the package implements three main features. First, the package
    provides an authentication process for R with the Google Adwords API via OAUTH2.
    Second, the package offers an interface to apply the Adwords query language in
    R and query the Adwords API with ad-hoc reports. Third, the received data are
    transformed into suitable data formats for further data processing and data
    analysis.
A series of shortcuts for routine tasks originally developed by Rafael A. Irizarry to facilitate data exploration.
Proper L2-penalized ML estimators for the
  precision matrix as well as supporting functions to employ these estimators
  in a graphical modeling setting.
Ridge maximum likelihood estimation of vector auto-regressive processes and supporting functions for their exploitation.
Algorithms to price American and European
    equity options, convertible bonds and a
    variety of other financial derivatives. It uses an
    extension of the usual Black-Scholes model in which
    jump to default may occur at a probability specified
    by a power-law link between stock price and hazard
    rate as found in the paper by Takahashi, Kobayashi,
    and Nakagawa (2001) <doi:10.3905/jfi.2001.319302>.  We
    use ideas and techniques from Andersen and
    Buffum (2002) <doi:10.2139/ssrn.355308> and
    Linetsky (2006) <doi:10.1111/j.1467-9965.2006.00271.x>.
Data fusion filters for Attitude Heading Reference System (AHRS) based on
    Vlad Maximov's GyroLib AHRS library (quaternion based linearized/extended/unscented Kalman filter,
    Euler based LKF, gyro-free with vector matching, SVD calibration and EKF calibration),
    Sebastian O.H. Madgwick AHRS algorithms and Sebastian O.H. Madgwick implementation of Mayhony et al AHRS algorithm.
Functions and data sets for functional data display and outlier detection.
The Climate Prediction Center's (CPC) rainfall data for the
    world (1979 to present, 50 km resolution) and the USA (1948 to
    present, 25 km resolution), is one of the few high quality, long
    term, observation based, daily rainfall products available for free.
    Although raw data is available at CPC's ftp site, obtaining,
    processing and visualizing the data is not straightforward. There are
    more than 12,000 files for the world and about 24,000 files for the USA.
    Moreover, file formats and file extensions have not been consistent.
    This package provides functionality to download, process and visualize
    over 35 years of global rainfall data and over 65 years of USA rainfall
    data from CPC.
Estimates of rainfall at desired frequency (e.g., 1% annual
    chance or 100-year return period) and desired duration (e.g.,
    24-hour duration) are often required in the design of dams and other
    hydraulic structures, catastrophe risk modeling, environmental
    planning and management. One major source of such estimates for the
    USA is the NOAA National Weather Service's (NWS) division of
    Hydrometeorological Design Studies Center (HDSC). Raw data from
    NWS-HDSC is available at 1-km resolution and comes as a huge number
    of GIS files. This package provides functionality to easily access
    and analyze the 1-km GIS files provided by NWS' PF Data Server for
    the entire USA. This package also comes with datasets on record point
    rainfall measurements provided by NWS-HDSC.
Functions for performing spatial microsimulation ('raking')
    in R.
Programmatic interface to the Altmetric.com API.
Characterizing environmental microbiota diversity using amplicon-based next generation sequencing (NGS) data. Functions are developed to manipulate operational taxonomic unit (OTU) table, perform descriptive and inferential statistics, and generate publication-quality plots.
Parser generator for R using combinatory parsers. It
    is inspired by combinatory parsers developed in Haskell.
Estimate the parameters, the number of classes and cluster vertices of a random network into groups with homogeneous connection profiles. The clustering is performed for directed graphs with typed edges (edges are assumed to be drawn from multinomial distributions) for which a partition of the vertices is available.
API for using 'AmCharts' Library. Based on 'htmlwidgets', it
    provides a global architecture to generate 'JavaScript' source code for charts.
    Most of classes in the library have their equivalent in R with S4 classes;
    for those classes, not all properties have been referenced but can easily be
    added in the constructors. Complex properties (e.g. 'JavaScript' object) can
    be passed as named list. See examples at <http:// datastorm- open.github.io/
    introduction_ramcharts/>. and <http://www.amcharts.com/> for more information
    about the library. The package includes the free version of 'AmCharts'
    Library. Its only limitation is a small link to the web site displayed on
    your charts. If you enjoy this library, do not hesitate to refer to this
    page <http://www.amcharts.com/online-store/> to purchase a licence, and thus
    support its creators and get a period of Priority Support. See also <http://
    www.amcharts.com/about/> for more information about 'AmCharts' company.
Function for adapting the shape of the random walk Metropolis proposal
    as specified by robust adaptive Metropolis algorithm by Vihola (2012) <DOI:10.1007/s11222-011-9269-5>. 
    Package also includes fast functions for rank-one Cholesky update and downdate.
    These functions can be used directly from R or the corresponding C++ header files 
    can be easily linked to other R packages.
Offers a link to some of the functionality of the
    AMIDST toolbox <http://www.amidsttoolbox.com> for handling data streams.
    More precisely, the package provides inference and concept drift detection
    using hybrid Bayesian networks.
Additional matrix functionality for R including: (1) wrappers for 
    the base matrix function that allow matrices to be created from character
    strings and lists (the former is especially useful for creating block
    matrices), (2) better printing of large matrices via the generic "pretty" 
    print function, and (3) a number of convenience functions for users more
    familiar with other scientific languages like 'Julia', 'Matlab'/'Octave', or
    'Python'+'NumPy'.
Provides an efficient procedure for fitting the entire solution
    path for high-dimensional regularized quadratic generalized linear models with
    interactions effects under the strong or weak heredity constraint.
We rewrite of RAMpath software developed by John McArdle and Steven Boker as an R package. In addition to performing regular SEM analysis through the R package lavaan, RAMpath has unique features.  First, it can generate path diagrams according to a given model. Second, it can display path tracing rules through path diagrams and decompose total effects into their respective direct and indirect effects as well as decompose variance and covariance into individual bridges. Furthermore, RAMpath can fit dynamic system models automatically based on latent change scores and generate vector field plots based upon results obtained from a bivariate dynamic system. Starting version 0.4, RAMpath can conduct power analysis for both univariate and bivariate latent change score models.
Bayesian geostatistical modeling of Gaussian processes using a reparameterized and marginalized posterior sampling (RAMPS) algorithm designed to lower autocorrelation in MCMC samples.  Package performance is tuned for large spatial datasets.
Provides a solution path for Reinforced Angle-based Multicategory Support Vector Machines, with linear learning, polynomial learning, and Gaussian kernel learning.
The deterministic part of the Fortuna cryptographic
        pseudorandom number generator, described by Schneier & Ferguson
        "Practical Cryptography"
Generate random positions (latitude/longitude), 
    Well-known text (WKT) points or polygons, or 'GeoJSON' points or 
    polygons.
Generates random names with additional information including fake
    SSNs, gender, location, zip, age, address, and nationality.
The true random number service provided by the RANDOM.ORG
 website created by Mads Haahr samples atmospheric noise via radio tuned to
 an unused broadcasting frequency together with a skew correction algorithm
 due to John von Neumann.  More background is available in the included
 vignette based on an essay by Mads Haahr.  In its current form, the package
 offers functions to retrieve random integers, randomized sequences and
 random strings.
Simple methods to generate attractive random colors. The random
  colors are from a wrapper of 'randomColor.js'
  <https://github.com/davidmerfield/randomColor>. In addition, it also generates
  optimally distinct colors based on k-means (inspired by 'IWantHue'
  <https://github.com/medialab/iwanthue>).
Methods for the inference on and the simulation of Gaussian fields are provided, as well as methods for the simulation of extreme value random fields.
Various utilities are provided that might be used in spatial statistics and elsewhere. It delivers a method for solving linear equations that checks the sparsity of the matrix before any algorithm is used. Furthermore, it includes the Struve functions.
Classification and regression based on a forest of trees
        using random inputs.
Distributed training and prediction of random forest models based upon 'randomForest' package using 'ddR' (Distributed Data Structures) API in the 'ddR' package.
A unified treatment of Breiman's random forests for survival, regression and classification problems based on Ishwaran and Kogalur's random survival forests (RSF) package.  The package runs in both serial and parallel (OpenMP) modes. Now extended to include multivariate and unsupervised forests.
The package implements a bagging predictor based on
        general linear models
Allows the user to conduct randomization-based inference for a wide variety of experimental scenarios. The package leverages a potential outcomes framework to output randomization-based p-values and null intervals for test statistics geared toward any estimands of interest, according to the specified null and alternative hypotheses. Users can define custom randomization schemes so that the randomization distributions are accurate for their experimental settings. The package also creates visualizations of randomization distributions and can test multiple test statistics simultaneously.
Contains a function to randomize subjects, patients in groups of 
   sequences (treatment sequences).
   If a blocksize is given, the randomization will be done within blocks.
   The randomization may be controlled by a Wald-Wolfowitz runs test.
   Functions to obtain the p-value of that test are included.
   The package is mainly intended for randomization of bioequivalence studies
   but may be used also for other clinical crossover studies.
   Contains two helper functions sequences() and williams() to get the sequences 
   of commonly used designs in BE studies.
This tool enables the user to choose a randomization procedure
    based on sound scientific criteria. It comprises the generation of
    randomization sequences as well the assessment of randomization procedures
    based on carefully selected criteria. Furthermore, randomizeR provides a
    function for the comparison of randomization procedures.
Generates random assignments for common experimental
    designs: simple, complete, blocked, and clustered.
Fits standard and random effects latent class models. The single level random effects model is described in Qu et al <doi:10.2307/2533043> and the two level random effects model in Beath and Heller <doi:10.1177/1471082X0800900302>. Examples are given for their use in diagnostic testing.
Function for generating random gender and ethnicity correct first and/or last names. Names are chosen proportionally based upon their probability of appearing in a large scale data base of real names.
The Function performs a parallel analysis using simulated polychoric correlation matrices. The nth-percentile of the eigenvalues distribution obtained from both the randomly generated and the real data polychoric correlation matrices is returned. A plot comparing the two types of eigenvalues (real and simulated) will help determine the number of real eigenvalues that outperform random data. The function is based on the idea that if real data are non-normal and the polychoric correlation matrix is needed to perform a Factor Analysis, then the Parallel Analysis method used to choose a non-random number of factors should also be based on randomly generated polychoric correlation matrices and not on Pearson correlation matrices. Random data sets are simulated assuming or a uniform or a multinomial distribution or via the bootstrap method of resampling (i.e., random permutations of cases). Also Multigroup Parallel analysis is made available for random (uniform and multinomial distribution and with or without difficulty factor) and bootstrap methods. An option to choose between default or full output is also available as well as a parameter to print Fit Statistics (Chi-squared, TLI, RMSEA, RMR and BIC) for the factor solutions indicated by the Parallel Analysis.
Ensemble model, for classification, regression
	and unsupervised learning, based on a forest of unpruned 
	and randomized binary decision trees. Each tree is grown 
	by sampling, with replacement, a set of variables at each node. 
	Each cut-point is generated randomly, according to the continuous 
	Uniform distribution. For each tree, data are either bootstrapped 
	or subsampled. The unsupervised mode introduces clustering, dimension reduction
	and variable importance, using a three-layer engine. Random Uniform Forests are mainly 
	aimed to lower correlation between trees (or trees residuals), to provide a deep analysis 
	of variable importance and to allow native distributed and incremental learning.
Performs random projection using Johnson-Lindenstrauss (JL) Lemma (see William B.Johnson and Joram Lindenstrauss (1984) <doi:10.1090/conm/026/737400>). Random Projection is a technique, where the data in the high dimensional space is projected into the low dimensional space using JL transform. The original high dimensional data matrix is multiplied with the low dimensional random matrix which results in reduced matrix. The random matrix can be generated by Gaussian matrix or sparse matrix.
Generate random strings of a dictated size of symbol set and
    distribution of the lengths of strings.
Several non parametric randomness tests for numeric sequences
Provides (1) pseudo random generators - general linear congruential generators, multiple recursive generators and generalized feedback shift register (SF-Mersenne Twister algorithm and WELL generators); (2) quasi random generators - the Torus algorithm, the Sobol sequence, the Halton sequence (including the Van der Corput sequence) and (3) some RNG tests - the gap test, the serial test, the poker test. The package depends on rngWELL package but it can be provided without this dependency on demand to the maintainer. For true random number generation, use the 'random' package, for Latin Hypercube Sampling (a hybrid QMC method), use the 'lhs' package. A number of RNGs and tests for RNGs are also provided by 'RDieHarder', all available on CRAN. There is also a small stand-alone package 'rngwell19937' for the WELL19937a RNG.
Implements random variables by means of S4 classes and methods.
Provides tools for filtering occurrence records, generating alpha-hull-derived range polygons and mapping species richness.
Tools for easy generation of (life-history) traits maps based on
    species range (extent-of-occurrence) maps.
Generates expected values of species richness, with continuous or
              scattered ranges, for data across one or two dimensions.
A fast implementation of Random Forests, particularly suited for high dimensional
          data. Ensembles of classification, regression, survival and probability prediction
          trees are supported. Data from genome-wide association studies can be analyzed
          efficiently. In addition to data frames, datasets of class 'gwaa.data' (R package
          'GenABEL') can be directly analyzed.
This package performs aggregation of ordered lists based
        on the ranks using several different algorithms: Borda count,
        Cross-Entropy Monte Carlo algorithm, Genetic algorithm, and a
        brute force algorithm (for small problems)
Implementation of a model-based clustering algorithm for
    ranking data. Multivariate rankings as well as partial rankings are taken
    into account. This algorithm is based on an extension of the Insertion
    Sorting Rank (ISR) model for ranking data, which is a meaningful and
    effective model parametrized by a position parameter (the modal ranking,
    quoted by mu) and a dispersion parameter (quoted by pi). The heterogeneity
    of the rank population is modelled by a mixture of ISR, whereas conditional
    independence assumption is considered for multivariate rankings.
Implements distance based probability models for ranking data. 
    The supported distance metrics include Kendall distance, Spearman distance, Footrule distance, Hamming distance,
    , Weighted-tau distance and Weighted Kendall distance.
    Phi-component model and mixture models are also supported.
The rankFD() function calculates the Wald-type statistic (WTS) and the ANOVA-type
	     statistic (ATS) for nonparametric factorial designs, e.g., for count, ordinal or score data
	     in a crossed design with an arbitrary number of factors.
Rank-hazard plots Karvanen and Harrell (2009) <DOI:10.1002/sim.3591> visualize the relative importance of covariates in a proportional hazards model. The key idea is to rank the covariate values and plot the relative hazard as a function of ranks scaled to interval [0,1]. The relative hazard is plotted in respect to the reference hazard, which can bee.g. the hazard related to the median of the covariate.
Methods for ranking responses of a single response question or a multiple response question
Implementation of Kernelized score functions and other semi-supervised learning algorithms for node label ranking in biomolecular networks. RANKS can be easily applied to a large set of different relevant problems in computational biology, ranging from automatic protein function prediction, to gene disease prioritization and drug repositioning, and more in general to any bioinformatics problem that can be formalized as a node label ranking problem in a graph. The modular nature of the implementation allows to experiment with different score functions and kernels and to easily compare the results with baseline network-based methods such as label propagation and random walk algorithms, as well as to enlarge the algorithmic scheme by adding novel user-defined score functions and kernels.
Finds the k nearest neighbours for every point in a given dataset
    in O(N log N) time using Arya and Mount's ANN library (v1.1.3). There is
    support for approximate as well as exact searches, fixed radius searches
    and bd as well as kd trees.
Finds the k nearest neighbours for every point in a given dataset
    in O(N log N) time using Arya and Mount's ANN library (v1.1.3). There is
    support for approximate as well as exact searches, fixed radius searches
    and 'bd' as well as 'kd' trees. The distance is computed using the L1
    (Manhattan, taxicab) metric. Please see package 'RANN' for the same
    functionality using the L2 (Euclidean) metric.
To find the reversal association between variables.
Access services specified in OpenAPI (formerly Swagger) format.
  It is not a code generator. Client is generated dynamically as a list of R 
  functions.
Facilitates efficient polygon search using kd trees.
    Coordinate level spatial data can be aggregated to higher geographical
    identities like census blocks, ZIP codes or police district boundaries.
    This process requires mapping each point in the given data set to a
    particular identity of the desired geographical hierarchy. Unless efficient
    data structures are used, this can be a daunting task. The operation
    point.in.polygon() from the package sp is computationally expensive.
    Here, we exploit kd-trees as efficient nearest neighbor search algorithm
    to dramatically reduce the effective number of polygons being searched.
Package to perform non-invasive fetal testing for aneuploidies
    using sequencing count data from cell-free DNA
This package provides other packages with access to the internal 
 R serialization code. Access to this code is provided at the C function
 level by using the registration of native function mechanism. Client
 packages simply include a single header file RApiSerializeAPI.h provided by
 this package.

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

    The R Core group is the original author of the serialization code made
 available by this package.
Bindings to various methods in the kernel for enforcing security
    restrictions. AppArmor can apply mandatory access control (MAC) policies on
    a given task (process) via security profiles with detailed ACL definitions. 
    In addition the package has kernel bindings for setting the process hardware
    resource limits (rlimit), uid, gid, affinity and priority. The high level R
    function 'eval.secure' builds on these methods to do dynamic sandboxing:
    it evaluates a single R expression within a temporary fork which acts as a 
    sandbox by enforcing fine grained restrictions without affecting the main R 
    process. Recent versions on this package can also be installed on systems
    without libapparmor, in which case some features are automatically disabled.
An easy way to determine which directories on the users computer
    you should use to save data, caches and logs. A port of Python's 'Appdirs'
    (\url{https://github.com/ActiveState/appdirs}) to R.
Facilitating the creation of reproducible statistical
    report templates. Once created, rapport templates can be exported to
    various external formats (HTML, LaTeX, PDF, ODT etc.) with pandoc as the
    converter backend.
Helper functions that act as wrappers to more advanced statistical
    methods with the advantage of having sane defaults for quick reporting.
Biodiversity is in crisis. The overarching aim of conservation 
    is to preserve biodiversity patterns and processes. To this end, protected 
    areas are established to buffer species and preserve biodiversity processes. 
    But resources are limited and so protected areas must be cost-effective. This 
    package contains tools to generate plans for protected areas (prioritizations),
    using spatially explicit targets for biodiversity patterns and processes. 
    To obtain solutions in a feasible amount  of time, this package uses the 
    commercial 'Gurobi' software package (obtained from <http://www.gurobi.com/>). 
    Additionally, the 'rgurobi' package can also be installed to provide extra 
    functionality (obtained from <http://github.com/jeffreyhanson/rgurobi>).
This package uses the functions written by Daniel 
 Morissette <danmo@videotron.ca> to read geographical information in Arc/Info 
 V 7.x format and E00 files to import the coverages into R variables.
Tests gene-environment interaction for rare genetic variants using Sequence Kernel Association Test (SKAT) type gene-based tests. Includes two tests for the interaction term only, and one joint test for genetic main effects and gene-environment interaction.
Randomization tests for the statistical comparison of \emph{i} = two or more individual-based, sample-based or coverage-based rarefaction curves. The ecological null hypothesis is that the \emph{i} samples were all drawn randomly from a single assemblage, with (necessarily) a single underlying species abundance distribution. The biogeographic null hypothesis is that the \emph{i} samples were all drawn from different assemblages that, nonetheless, share similar species richness and species abundance distributions
Allows calculation of rarity weights for species and indices of rarity for assemblages of species according to different methods (Leroy et al. 2012, Insect. Conserv. Divers. 5:159-168 <doi:10.1111/j.1752-4598.2011.00148.x>; Leroy et al. 2013, Divers. Distrib. 19:794-803 <doi:10.1111/ddi.12040>).
Previously an R wrapper of the 'ARPACK' library
    <http://www.caam.rice.edu/software/ARPACK/>, and now a shell of the
    R package 'RSpectra', an R interface to the 'Spectra' library
    <http://yixuan.cos.name/spectra/> for solving large scale
    eigenvalue/vector problems. The current version of 'rARPACK'
    simply imports and exports the functions provided by 'RSpectra'.
    New users of 'rARPACK' are advised to switch to the 'RSpectra' package.
MCMC based sampling of binary matrices with fixed margins as used in exact Rasch model tests.
Software to perform supervised and pixel based raster image classification. It has been designed to facilitate land-cover analysis. Five classification algorithms can be used: Maximum Likelihood Classification, Multinomial Logistic Regression, Neural Networks, Random Forests and Support Vector Machines. The output includes the classified raster and standard classification accuracy assessment such as the accuracy matrix, the overall accuracy and the kappa coefficient. An option for in-sample verification is available.
The rase package primarily implements the Range Ancestral State Estimation for phylogeography described in Quintero, I., Keil, P., Jetz, W., & Crawford, F. W. (2015). Historical Biogeography Using Species Geographical Ranges. Systematic biology, 64(6), 1059-1073. It also includes Bayesian inference of ancestral states under a Brownian Motion model of character evolution and Maximum Likelihood estimation of rase for n-dimensional data. Visualizing functions in 3D are implemented using the rgl package.
Reading, writing, manipulating, analyzing and modeling of gridded spatial data. The package implements basic and high-level functions. Processing of very large files is supported.
This is a wrapper function for image(), which makes reasonable
    raster plots with nice axis and other useful features.
Performs kernel based estimates on in-memory raster images 
  from the raster package.  These kernel estimates include local means
  variances, modes, and quantiles.  All results are in the form of 
  raster images, preserving original resolution and projection attributes.
Methods for enhanced visualization and interaction with raster data. It implements visualization methods for quantitative data and categorical data, both for univariate and multivariate rasters. It also provides methods to display spatiotemporal rasters, and vector fields. See the website for examples.
An implementation of routines for solving rate-distortion problems.
  Rate-distortion theory is a field within information theory that
  examines optimal lossy compression. That is, given that some
  information must be lost, how can a communication channel be designed
  that minimizes the cost of communication error? Rate-distortion
  theory is concerned with the optimal  (minimal cost) solution to such
  tradeoffs. An important tool for solving rate-distortion problems is
  the Blahut algorithm, developed by Richard Blahut and described in:

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

    This package implements the basic Blahut algorithm, and additionally contains a number of `helper' functions, including a routine for searching for an information channel that minimizes cost subject to a constraint on information rate.
Allows to limit the rate at which one or more functions can be called.
A function which performs exact rate ratio tests and returns an object of class htest.
The kappa statistic implemented by Fleiss is a very popular index for assessing the reliability of agreement among multiple observers. It is used both in the psychological and in the psychiatric field. Other fields of application are typically medicine, biology and engineering. Unfortunately,the kappa statistic may behave inconsistently in case of strong agreement between raters, since this index assumes lower values than it would have been expected. We propose a modification kappa implemented by Fleiss in case of nominal and ordinal variables. Monte Carlo simulations are used both to testing statistical hypotheses and to calculating percentile bootstrap confidence intervals based on proposed statistic in case of nominal and ordinal data.
Computes confidence intervals for the rate (or risk)
    difference ("RD") or ratio ("RR") for independent binomial
    proportions or Poisson rates, or for odds ratio ("OR", binomial
    only). Also confidence intervals for a single binomial or
    Poisson rate. Includes asymptotic score methods including
    skewness corrections, which have been developed from Miettinen
    & Nurminen (1985) <doi:10.1002/sim.4780040211> and Gart & Nam (1988)
    <doi:10.2307/2531848>. Also includes MOVER methods
    (Method Of Variance Estimates Recovery), derived from the
    Newcombe method but using equal-tailed Jeffreys intervals. 
    Also methods for stratified calculations (e.g. meta-analysis),
    either assuming fixed effects or incorporating stratum
    heterogeneity.
Reduces items in a rating scale by a new procedure of reducing items in a rating scale.
This package provides several functions to
    manipulate rational functions, including basic
    arithmetic operators, derivatives and integrals with
    EXPLICIT forms.
This package provide an easy way to produce atmospheric profiles of Pressure,
 Temperature and Density according to the standard atmosphere 1976. 
 It provides also profiles of molecular volume backscatter coefficients for standard atmosphere,
 and approximated estimations of sunset, sunrise and solar zenith angle.
The R Analytic Tool To Learn Easily (Rattle) provides a 
  Gnome (RGtk2) based interface to R functionality for data mining. 
  The aim is to provide a simple and intuitive interface 
  that allows a user to quickly load data from a CSV file 
  (or via ODBC), transform and explore the data, 
  build and evaluate models, and export models as PMML (predictive
  modelling markup language) or as scores. All of this with knowing little 
  about R. All R commands are logged and commented through the log tab. Thus they
  are available to the user as a script file or as an aide for the user to 
  learn R or to copy-and-paste directly into R itself. 
  Rattle also exports a number of utility 
  functions and the graphical user interface, invoked as rattle(),  does 
  not need to be run to deploy these.
Parameter estimation for averaging models of Anderson's Information Integration Theory
Interface to <http://proyectoavis.com> database. 
    It provides means to download data filtered by species, order,
    family, and several other criteria. Provides also basic functionality to
    plot exploratory maps of the datasets.
In order to facilitate R instruction for actuaries, we have organized several 
  sets of publicly available data of interest to non-life actuaries. In addition, we suggest 
  a set of packages, which most practicing actuaries will use routinely. Finally, there is 
  an R markdown skeleton for basic reserve analysis.
Provides an interface to functions of the 'SAMtools' C-Library by Heng Li.
A Pure R implementation of Bayesian Global Optimization with Gaussian Processes.
Basic R package to access data structures offered by any
    BEFdata portal instance.
rbenchmark is inspired by the Perl module Benchmark, and
        is intended to facilitate benchmarking of arbitrary R code. The
        library consists of just one function, benchmark, which is a
        simple wrapper around system.time.  Given a specification of
        the benchmarking process (counts of replications, evaluation
        environment) and an arbitrary number of expressions, benchmark
        evaluates each of the expressions in the specified environment,
        replicating the evaluation as many times as specified, and
        returning the results conveniently wrapped into a data frame.
An implementation of robust bent line regression. It can fit the bent line regression and test the existence of change point,
    for the paper, "Feipeng Zhang and Qunhua Li (2016). Robust bent line regression, submitted."
Oracle Berkeley DB -
    <http://www.oracle.com/technetwork/database/database-technologies/berkeleydb/overview/index.html> -
    is a family of embedded key-value database
    libraries providing scalable high-performance data management services
    to applications. The Berkeley DB products use simple function-call APIs
    for data access and management. This package provides an R interface to
    the Berkeley DB API.
The package contains functions to generate random numbers
        from the beta distribution and random vectors from the
        Dirichlet distribution.
Facilities for working with Atlantis box-geometry model (BGM) 
 files. Atlantis is a deterministic, biogeochemical, whole-of-ecosystem model. 
 Functions are provided to read from BGM files directly, preserving their 
 internal topology, as well as helper functions to generate 'Spatial' objects.
 This functionality aims to simplify the creation and modification of box 
 and geometry as well as the ability to integrate with other data sources.
Interface to 'Biodiversity' 'Heritage' Library ('BHL') 
    (<http://www.biodiversitylibrary.org/>) 'API' 
    (<http://www.biodiversitylibrary.org/api2/docs/docs.html>). 'BHL' is a 
    repository of 'digitized' literature on 'biodiversity'
    studies, including 'floras', research papers, and more.
Provides a complete R interface to LibBi, a library for Bayesian inference (see <http://libbi.org> for more information). This includes functions for manipulating LibBi models, for reading and writing LibBi input/output files, for converting LibBi output to provide traces for use with the coda package, and for running LibBi from R.
To facilitate retrieving and processing biochemical reaction data such as Rhea, MetaCyc, KEGG and Unipathway, the package provides the functions to download and parse data, instantiate generic reaction and check mass-balance. The package aims to construct an integrated metabolic network and genome-scale metabolic model.
Functions for connecting to BioUML server, querying BioUML repository and launching BioUML analyses.
Interface to the 'USGS' 'BISON' 'API', a 'database' for
    species occurrence data on the web. Data comes from species in the
    United States from participating data providers. You can get
    data via 'taxonomic' and location based queries. A simple function
    is provided to help visualize data.
Utilities related to Bitcoin. Unified markets API interface
    (bitstamp, kraken, btce, bitmarket). Both public and private API calls.
    Integration of data structures for all markets. Support SSL. Read Rbitcoin
    documentation (command: ?btc) for more information.
An R package for the BitCoinCharts.com API.
An R Interface to 'Bloomberg' is provided via the 'Blp API'.
Creation, manipulation, simulation of linear Gaussian Bayesian
             networks from text files and more...
The Biological Magnetic Resonance Data Bank (BMRB,<http://www.bmrb.wisc.edu/>) collects, annotates, archives, and disseminates (worldwide in the public domain) the important spectral and quantitative data derived from NMR(Nuclear Magnetic Resonance) spectroscopic investigations of biological macromolecules and metabolites. This package provides an interface to BMRB database for easy data access and includes a minimal set of data visualization functions. Users are encouraged to make their own data visualizations using BMRB data.
A native R plotting library that provides a flexible declarative interface for creating interactive web-based graphics, backed by the Bokeh visualization library <http://bokeh.pydata.org/>.
Scalable decision tree training and prediction.
Takes matched and unmatched data and calculates Rosenbaum bounds for the treatment effect.  Calculates bounds for binary outcome data, Hodges-Lehmann point estimates, Wilcoxon signed-rank test for matched data and matched IV estimators, Wilcoxon sum rank test, and for data with multiple matched controls. Package is also designed to work with the Matching package and operate on Match() objects.
The RBP curve is a visual tool to assess the
    performance of prediction models.
Functions to prepare files needed for running BUGS in
        batch-mode, and running BUGS from R. Support for Linux and
        Windows systems with OpenBugs is emphasized.
Rbundler manages a project-specific library for dependency
    package installation. By specifying dependencies in a DESCRIPTION file
    in a project's root directory, one may install and use dependencies
    in a repeatable fashion without requiring manual maintenance.
    rbundler creates a project-specific R library in
    `PROJECT_ROOT/.Rbundle` (by default) and a project-specific
    `R_LIBS_USER` value, set in `PROJECT_ROOT/.Renviron`. It supports
    dependency management for R standard "Depends", "Imports",
    "Suggests", and "LinkingTo" package dependencies. rbundler also
    attempts to validate and install versioned dependencies, such
    as ">=", "==", "<=". Note that, due to the way R manages package
    installation, differing nested versioned dependencies are not
    allowed. For example, if your project depends on packages A (== 1),
    and B (== 2), but package A depends on B (== 1), then a nested
    dependency violation will cause rbundler to error out.
Implements the Ranking-Based Variable Selection
    algorithm for variable selection in high-dimensional data.
Relational Class Analysis (RCA) is a method for detecting
        heterogeneity in attitudinal data (as described in Goldberg
        A., 2011, Am. J. Soc, 116(5)).
Memoization can be used to speed up repetitive and computational expensive function calls.  The first time a function that implements memoization is called the results are stored in a cache memory.  The next time the function is called with the same set of parameters, the results are momentarily retrieved from the cache avoiding repeating the calculations.  With this package, any R object can be cached in a key-value storage where the key can be an arbitrary set of R objects.  The cache memory is persistent (on the file system).
Calculate the flow of particles between polygons by two integration methods: integration by a cubature method and integration on a grid of points.
Provides an interface to the National Topographic System (NTS),
    which is the way in which a number of freely available Canadian datasets are
    organized. CanVec and CanVec+ datasets, which include all data used to create
    Canadian topographic maps, are two such datasets that are useful in creating
    vector-based maps for locations across Canada. This packages searches CanVec
    data by location, plots it using pretty defaults, and exports it to human-
    readable shapefiles for use in another GIS.
Estimation of abundance and other of demographic parameters for closed 
             populations, open populations and the robust design in capture-recapture experiments 
             using loglinear models.
This package makes some maps using shapefiles and
        dataframes. Five kinds of maps are available : proportionnal
        circles, proportionnal circles colored by a discretized
        quantitative variable, proportionnal circles colored by the
        modalities of a qualitative variable, choropleth and typology.
This packages provides a direct interface (without the
	     use of Java) to the most basic functionality of Apache
	     Cassanda such as login, updates and queries.
A set of tools to help with species conservation assessments (Red List threat assessments). Includes tool for Extent of occurrence, Area of Occupancy, Minimum Enclosing Rectangle, a geographic Projection Wizard and Species batch processing.
Provides implementations of rule pruning algorithms based on the "Classification Based on Associations" (CBA). It can be used for building classification models from association rules. Rules are pruned in the order of precedence given by the sort criteria and a default rule is added. CBA was originally proposed by Liu, B. Hsu, W. and Ma, Y (1998). Integrating Classification and Association Rule Mining. Proceedings KDD-98, New York, 27-31 August. AAAI. pp80-86.
Tools for large, sparse optimal matching of treated units
	and control units in observational studies.  Provisions are
	made for refined covariate balance constraints, which include
	fine and near-fine balance as special cases.  Matches are 
	optimal in the sense that they are computed as solutions to
	network optimization problems rather than greedy algorithms.
Tools for optimal subset matching of treated units
	and control units in observational studies, with support
	for refined covariate balance constraints, (including
	fine and near-fine balance as special cases). A close 
	relative is the 'rcbalance' package.
Identify, convert and handle dates as used within the Swedish cancer register and associated cancer quality registers in Sweden. Especially the cancer register sometimes use nonstandard date variables where day and/or month can be "00" or were the date format is a mixture of"%Y-%m-%d", "%Y%m%d" and "%y%V" (two digit year and week number according to ISO 8601,which is not completely supported by R).These dates must be approximated to valid dates before being used in for example survival analysis. The package also includes some convenient functions for calculating "lead times" (relying on 'difftime') and introduce a "year" class with relevant S3-methods to handle yearly cohort.
Functions either required by other Swedish Regional Cancer Center packages or standalone functions outside the scope of other packages.
R interface to (some of) cddlib
    (http://www.ifor.math.ethz.ch/~fukuda/cdd_home/cdd.html).
    Converts back and forth between two representations of a convex polytope:
    as solution of a set of linear equalities and inequalities and as
    convex hull of set of points and rays.
    Also does linear programming and redundant generator elimination
    (for example, convex hull in n dimensions).  All functions can use exact
    infinite-precision rational arithmetic.
Allows the user to access functionality in the
    CDK, a Java framework for chemoinformatics. This allows the user to load
    molecules, evaluate fingerprints, calculate molecular descriptors and so on.
    In addition the CDK API allows the user to view structures in 2D.
An R interface to the Chemistry Development Kit, a Java library
    for chemoinformatics. Given the size of the library itself, this package is
    not expected to change very frequently. To make use of the CDK within R, it is
    suggested that you use the rcdk package. Note that it is possible to directly
    interact with the CDK using rJava. However rcdk exposes functionality in a more
    idiomatic way. The CDK library itself is released as LGPL and the sources can be
    obtained from https://github.com/cdk/cdk.
RCEIM is a package implementing a stochastic heuristic method for performing multidimensional function optimization. The method is inspired in the Cross-Entropy Method. It does not relies on derivatives, neither imposes particularly strong requirements into the function to be optimized. Additionally, it takes profit from multicore processing to enable optimization of time-consuming functions.
Example dataset for 'Rcell' package. Contains images and cell data object.
To facilitate using 'cereal' with Rcpp.
    'cereal' is a header-only C++11 serialization library.
    'cereal' takes arbitrary data types and reversibly turns them into
    different representations, such as compact binary encodings, XML,
    or JSON. 'cereal' was designed to be fast, light-weight, and easy
    to extend - it has no external dependencies and can be easily
    bundled with other code or used standalone. Please see
    <http://uscilab.github.io/cereal> for more information.
Conjugate gradient minimization of nonlinear functions
        with box constraints incorporating the Dai/Yuan update. This
        implementation should be used in place of the "CG" algorithm
        of the optim() function.
A simple data science challenge system using R Markdown and Dropbox <https://www.dropbox.com/>.
    It requires no network configuration, does not depend on external platforms
    like e.g. Kaggle <https://www.kaggle.com/> and can be easily installed on a personal computer.
R package for chess validations, pieces movements and check
    detection. Also integrates functions to plot chess boards given a
    Forsyth Edwards and Portable Game notations.
An implementation of simulated maximum likelihood method for the
    estimation of Binary (Probit and Logit), Ordered (Probit and Logit) and
    Poisson models with random parameters for cross-sectional and longitudinal
    data.
Collection of portable choice dialog widgets
Provides a list of functions for the statistical analysis and the post-processing of the Markov Chains simulated by ChronoModel (see <http://www.chronomodel.fr> for more information).  ChronoModel is a friendly software to construct a chronological model in a Bayesian framework.  Its output is  a sampled Markov chain from the posterior distribution of dates component  the chronology. The functions can also be applied  to the  analyse of mcmc output generated by Oxcal software.
Functions to generate stimuli and analyze data of reverse correlation image classification experiments (psychophysical tasks aimed at visualizing cognitive mental representations of faces).
A simple and flexible way to generate Circos 2D track plot images for genomic data visualization is implemented in this package. The types of plots include: heatmap, histogram, lines, scatterplot, tiles and plot items for further decorations include connector, link (lines and ribbons), and text (gene) label. All functions require only R graphics package that comes with R base installation.
The Environment Canada climate archives <http://climate.weather.gc.ca/>
  are an important source of data for climate researchers in Canada and world wide.
  The repository contains temperature, precipitation, and wind data for more than
  8,000 locations. The functions in this package simplify the process of downloading,
  subsetting, and manipulating these data for the purposes of more efficient workflows
  in climate research.
This package contains wrapper functions and methods which allow to
    use "climdex.pcic" and "RMAWGEN" packages.  With this simple approach it is
    possible to calculate climate change indices, suggested by the WMO-CCL,
    CLIVAR, ETCCDMI(http://www.climdex.org),on stochastic generations of
    temperature and precipitation time series, obtained by the application of
    RMAWGEN.  Each index can be applied to both observed data and to synthetic
    time series produced by the Weather Generator, over a reference period
    (e.g. 1981-2010, as in the example). It contains also functions and methods
    to evaluate the generated time series of climate change indices consistency
    by statistical tests.Bugs/comments/questions/collaboration of any kind are
    warmly welcomed.
R tools for integrating with the www.clinicalcodes.org web
    repository
ClinicalTrials.gov is a registry and results database of publicly
    and privately supported clinical studies of human participants conducted
    around the world (see <https://clinicaltrials.gov/> for more information). 
    Users can search for information about and results from
    those trials. This provides a set of functions to interact with the search
    and download features. Results are downloaded to temporary directories and
    returned as R objects.
R version of 'GenClone' (a computer program to analyse genotypic data, test for clonality and describe spatial clonal organization, Arnaud-Haond & Belkhir 2007, <http://wwz.ifremer.fr/clonix/content/download/68205/903914/file/GenClone2.0.setup.zip>), this package allows clone handling as 'GenClone' does, plus the possibility to work with several populations, MultiLocus Lineages (MLL) custom definition and use, and p-value calculation for psex statistic (probability of originating from distinct sexual events) and psex_Fis statistic (taking account of Hardy-Weinberg equilibrium departure) as 'MLGsim'/'MLGsim2' (a program for detecting clones using a simulation approach, Stenberg et al. 2003).
Provide flexible native clustering routines that can be
        linked against in downstream packages.
Tool for providing access to the Java version 'CMAEvolutionStrategy' of
    Nikolaus Hansen. 'CMA-ES' is the Covariance Matrix Adaptation Evolution Strategy,
    see https://www.lri.fr/~hansen/cmaes_inmatlab.html#java.
Run 'R CMD check' from 'R' programmatically, and capture the
    results of the individual checks.
A platform-independent basic-statistics GUI (graphical user interface) for R, based on the tcltk package.
Various statistical, graphics, and data-management functions used by the Rcmdr package in the R Commander GUI for R.
An Rcmdr "plug-in" to accompany the book Customer and 
		Business Analytics: Applied Data Mining for Business Decision
		Making Using R by Daniel S. Putler and Robert E. Krider.
A plug-in for R Commander ('Rcmdr'). The package is a Graphical
    User Interface (GUI) in which several biclustering methods can be executed,
    followed by diagnostics and plots of the results. Further, the GUI also has
    the possibility to connect the methods to more general diagnostic packages for
    biclustering. Biclustering methods from 'biclust', 'fabia', 's4vd', 'iBBiG',
    'isa2', 'BiBitR', 'rqubic' and 'BicARE' are implemented. Additionally, 'superbiclust' and
    'BcDiag' are also implemented to be able to further investigate results. The
    GUI also provides a couple of extra utilities to export, save, search through
    and plot the results. 'RcmdrPlugin.BiclustGUI' also provides a very specific
    framework for biclustering in which new methods, diagnostics and plots can be
    added. Scripts were prepared so that R-package developers can freely design
    their own dialogs in the GUI which can then be added by the maintainer of
    'RcmdrPlugin.BiclustGUI'. These scripts do not required any knowledge of 'tcltk'
    and 'Rcmdr' and are easy to fill in.
This package provides a Rcmdr "plug-in" based on coin (Conditional Inference Procedures in a Permutation Test Framework).
This package provides an Rcmdr plug-in based on the
        depthTools package, which implements different robust
        statistical tools for the description and analysis of gene
        expression data based on the Modified Band Depth, namely, the
        scale curves for visualizing the dispersion of one or various
        groups of samples (e.g. types of tumors), a rank test to decide
        whether two groups of samples come from a single distribution
        and two methods of supervised classification techniques, the DS
        and TAD methods.
The package provides a platform-independent GUI for design of experiments.
 It is implemented as a plugin to the R-Commander, which is a more general 
 graphical user interface for statistics in R based on tcl/tk. 
 DoE functionality can be accessed through the menu Design that is added to the 
 R-Commander menus.
This package provides an Rcmdr "plug-in" based on the
        Design of experiments class Stat 4309
Este paquete proporciona una interfaz grafica de usuario (GUI) para algunos de los procedimientos estadisticos detallados en un curso de 'Estadistica aplicada a las Ciencias Sociales mediante el programa informatico R' (EACSPIR). LA GUI se ha desarrollado como un Plugin del programa R-Commander.
Rcmdr plug-in GUI extension for Evidence Based Medicine medical indicators calculations (Sensitivity, specificity, absolute risk reduction, relative risk, ...).
A Rcmdr "plug-in" for the EcoVirtual package, designed primarily for teaching ecological models using simulations.
This package provides an Rcmdr "plug-in" based on the time
        series functions.  Contributors: G. Jay Kerns, John Fox, and
        Richard Heiberger.
Export Rcmdr output to LaTeX or HTML code. The
        plug-in was originally intended to facilitate exporting Rcmdr
        output to formats other than ASCII text and to provide R
        novices with an easy-to-use, easy-to-access reference on
        exporting R objects to formats suited for printed output. The
        package documentation contains several pointers on creating
        reports, either by using conventional word processors or
        LaTeX/LyX.
EZR (Easy R) adds a variety of statistical functions, including survival analyses, ROC analyses, metaanalyses, sample size calculation, and so on, to the R commander. EZR enables point-and-click easy access to statistical functions, especially for medical statistics. EZR is platform-independent and runs on Windows, Mac OS X, and UNIX. Its complete manual is available only in Japanese (Chugai Igakusha, ISBN: 978-4-498-10901-8 or Nankodo, ISBN: 978-4-524-26158-1), but an report that introduced the investigation of EZR was published in Bone Marrow Transplantation (Nature Publishing Group) as an Open article. This report can be used as a simple manual. It can be freely downloaded from the journal website as shown below.
Rcmdr Plugin for the 'FactoMineR' package.
The R Commander Plug-in for Fuzzy Clustering Methods. This Plug-
    in provide Graphical User Interface of 2 methods of Fuzzy Clustering (Fuzzy C-
    Means /FCM and Gustafson Kessel-Babuska). For validation of clustering, this plug-
    in use Xie Beni Index, MPC index, and CE index. For statistical test (test
    of significant differences of grouping/clustering), this plug-in use MANOVA
    analysis with Pillai trace statistics. For stabilize the result, this package provide
    soft voting cluster ensemble function. Visualization of result are provided via plugin that must be load in Rcmdr file.
Provides an Rcmdr plug-in based on the 'GWRM' package.
Rcmdr menu support for many of the functions in the HH package.
        The focus is on menu items for functions we use in our introductory
        courses.
This package is an R Commander plugin that accompanies IPSUR, an Introduction to Probability and Statistics Using R.
A GUI front-end for 'ggplot2' supports Kaplan-Meier plot, histogram,
    Q-Q plot, box plot, errorbar plot, scatter plot, line chart, pie chart,
    bar chart, contour plot, and distribution plot.
Provides an Rcmdr "plug-in" based on the lfstat package for low flow analysis.
Easy to use interface for conducting meta-analysis in R. This
    package is an Rcmdr-plugin, which allows the user to conduct analyses in a
    menu-driven, graphical user interface environment (e.g., CMA, SPSS). It
    uses recommended procedures as described in The Handbook of Research
    Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009).
Rcmdr menu items to display mosaic and assoc plots Allows
        to visually restructure the underlying structables Developed
        after extended discussions with Rich Heiberger
Extends R Commander with a unified menu of new and pre-existing 
        statistical functions related to public management and policy analysis 
        statistics. Functions and menus have been renamed according to the 
        usage in PMGT 630 in the Master of Public Administration program at
        Brigham Young University.
An R Commander "plug-in" extending functionality of linear models
    and providing an interface to Partial Least Squares Regression and Linear and
    Quadratic Discriminant analysis. Several statistical summaries are extended,
    predictions are offered for additional types of analyses, and extra plots, tests
    and mixed models are available.
This package provides a GUI for the orloca package, it is
        developed as an Rcmdr plug-in.
The R commander plug-in for robust principal component analysis. The Graphical User Interface for Principal Component Analysis (PCA) with Hubert Algorithm method.
Rcmdr menu support for some of the graphics by group in
        the lattice package
This package provides a Rcmdr "plug-in" to analyze questionnaire data.
This package provides an Rcmdr "plug-in" based on the
        Quality control class Stat 4300
Mixed Treatment Comparison is a methodology to compare directly and/or indirectly health strategies (drugs, treatments, devices). This package provides an 'Rcmdr' plugin to perform Mixed Treatment Comparison for binary outcome using BUGS code from Bristol University (Lu and Ades).
Rcmdr GUI extension plug-in for Receiver Operator Characteristic tools from pROC and ROCR packages. Also it ads a Rcmdr GUI extension for Hosmer and Lemeshow GOF test from the package ResourceSelection.
This package includes tools for calculating sample sizes and 
 selecting samples using various sampling designs. This package is an extension
 of RcmdrPlugin.EHESsampling which was developed as part of the EHES pilot project.
 The EHES Pilot project has received funding from the European Commission and 
 DG Sanco. The views expressed here are those of the authors and they do not represent 
 Commission's official position.
Provides a GUI for the SCVA, SCRT and SCMA packages. The package is written as an Rcmdr plugin.
Supports the text book Acevedo, M.F (2013) "Data Analysis
        and Statistics for Geography, Environmental Science, and
        Engineering" CRC Press
This package provides a GUI for the SLC package, it is
        written as an Rcmdr plug-in.
This package provides an Rcmdr "plug-in" for studying
        sport management data.
Rcmdr interface to the 'sos' package. The plug-in renders
  the 'sos' searching functionality easily accessible via the Rcmdr
  menus. It also simplifies the task of performing multiple searches and 
  subsequently obtaining the union or the intersection of the results.
This package provides a GUI for the steepness package, it
        is written as an Rcmdr plug-in.
An R Commander plug-in for the survival
  package, with dialogs for Cox models, parametric survival regression models,
  estimation of survival curves, and testing for differences in survival
  curves, along with data-management facilities and a variety of tests, 
  diagnostics and graphs.
Provides an Rcmdr "plug-in" based on the TeachingDemos package, and is primarily for illustrative purposes.
An 'R Commander' plug-in providing an integrated solution to perform
    a series of text mining tasks such as importing and cleaning a corpus, and
    analyses like terms and documents counts, vocabulary tables, terms
    co-occurrences and documents similarity measures, time series analysis,
    correspondence analysis and hierarchical clustering. Corpora can be imported
    from spreadsheet-like files, directories of raw text files, 'Twitter' queries,
    as well as from 'Dow Jones Factiva', 'LexisNexis', 'Europresse' and 'Alceste' files.
Some extension to Rcmdr (R Commander), randomness test and predictions using active model, made by R-UCA project and used in teaching statistics at University of Cadiz (UCA).
Working with CMIP5 data can be tricky, forcing scientists to write
    custom scripts and programs. The `RCMIP5` package aims to ease this
    process, providing a standard, robust, and high-performance set of scripts
    to (i) explore what data have been downloaded, (ii) identify missing data,
    (iii) average (or apply other mathematical operations) across experimental
    ensembles, (iv) produce both temporal and spatial statistical summaries,
    and (v) produce easy-to-work-with graphical and data summaries.
Functions for co-clustering with C code illustrating two diagonal approaches, 
             (Ailem & Role & Nadif, 2016, <doi:10.1016/j.knosys.2016.07.002>), 
             (Laclau & Nadif, 2016, <doi:10.1016/j.neucom.2016.02.003>). 
             A relevant initialization and a suitable setting of the parameters are required.
             For a dimensionality reduction via random projection, a function is included.
Provides programmatic access to Colombos, a web based
    interface for exploring and analyzing comprehensive organism-specific
    cross-platform expression compendia of bacterial organisms.
Provides color schemes for maps (and other graphics)
        designed by Cynthia Brewer as described at http://colorbrewer2.org
Functions and datasets to support "Summary and Analysis of
             Extension Education Program Evaluation in R" and "An R
             Companion for the Handbook of Biological Statistics". 
             Vignettes are available at <http://rcompanion.org>.
Solve some conic related problems (intersection of conics with lines and conics, arc length of an ellipse, polar lines, etc.).
A collection of small text corpora of interesting data.
    It contains all data sets from https://github.com/dariusk/corpora.
    Some examples:
    names of animals: birds, dinosaurs, dogs; foods: beer categories,
    pizza toppings; geography: English towns, rivers, oceans;
    humans: authors, US presidents, occupations; science: elements,
    planets; words: adjectives, verbs, proverbs, US president quotes.
R interface to CPLEX solvers for linear, quadratic, and (linear and quadratic) mixed integer programs. Support for quadratically constrained programming is available. See the file "INSTALL" for details on how to install the Rcplex package in Linux/Unix-like and Windows systems. Support for sparse matrices is provided by an S3-style class "simple_triplet_matrix" from package slam and by objects from the Matrix package class hierarchy.
Identifies regions of common (species) profiles (RCPs), possibly when sampling artefacts are present.  Within a region the probability of sampling all species remains approximately constant.  This is performed using mixtures-of-experts models.  The package also contains associated methods, such as diagnostics.
The 'Rcpp' package provides R functions as well as C++ classes which
 offer a seamless integration of R and C++. Many R data types and objects can be
 mapped back and forth to C++ equivalents which facilitates both writing of new
 code as well as easier integration of third-party libraries. Documentation 
 about 'Rcpp' is provided by several vignettes included in this package, via the 
 'Rcpp Gallery' site at <http://gallery.rcpp.org>, the paper by Eddelbuettel and 
 Francois (2011, JSS), and the book by Eddelbuettel (2013, Springer); see 
 'citation("Rcpp")' for details on these last two.
Rcpp11 includes a header only C++11 library that facilitates 
  integration between R and modern C++.
'Annoy' is a small C++ library for Approximate Nearest Neighbors 
 written for efficient memory usage as well an ability to load from / save to
 disk. This package provides an R interface by relying on the 'Rcpp' package,
 exposing the same interface as the original Python wrapper to 'Annoy'. See
 <https://github.com/spotify/annoy> for more on 'Annoy'. 'Annoy' is released
 under Version 2.0 of the Apache License. Also included is a small Windows
 port of 'mmap' which is released under the MIT license.
The 'APT Package Management System' provides Debian and
 Debian-derived Linux systems with a powerful system to resolve package
 dependencies. This package offers access directly from R.
'Armadillo' is a templated C++ linear algebra library (by Conrad
 Sanderson) that aims towards a good balance between speed and ease of use. Integer,
 floating point and complex numbers are supported, as well as a subset of
 trigonometric and statistics functions. Various matrix decompositions are
 provided through optional integration with LAPACK and ATLAS libraries.
 The 'RcppArmadillo' package includes the header files from the templated
 'Armadillo' library. Thus users do not need to install 'Armadillo' itself in
 order to use 'RcppArmadillo'. 'Armadillo' is licensed under the MPL 2.0, while
 'RcppArmadillo' (the 'Rcpp' bindings/bridge to Armadillo) is licensed under the
 GNU GPL version 2 or later, as is the rest of 'Rcpp'.  Note that Armadillo
 requires a fairly recent compiler; for the g++ family at least version 4.6.*
 is required.
This package provides R with access to Boost Date_Time
 functionality by using Rcpp modules. 

    Functionality from Boost Date_Time for dates, durations (both for days 
 and datetimes), timezones, and posix time ("ptime") is provided. The posix
 time implementation can support high-resolution of up to nano-second
 precision by using 96 bits (instead of R's 64) to present a ptime object.
'Blaze' is an open-source, high-performance C++ math library
    for dense and sparse arithmetic. With its state-of-the-art Smart Expression
    Template implementation Blaze combines the elegance and ease of use of a
    domain-specific language with HPC-grade performance, making it one of the most
    intuitive and fastest C++ math libraries available. The Blaze library offers:
    - high performance through the integration of BLAS libraries and manually
    tuned HPC math kernels - vectorization by SSE, SSE2, SSE3, SSSE3, SSE4, AVX,
    AVX2, AVX-512, FMA, and SVML - parallel execution by OpenMP, C++11 threads and
    Boost threads (Boost threads is disables in RcppBlaze) - the intuitive and easy
    to use API of a domain specific language - unified arithmetic with dense and
    sparse vectors and matrices - thoroughly tested matrix and vector arithmetic -
    completely portable, high quality C++ source code The RcppBlaze package includes
    the header files from the Blaze library with disabling some functionalities
    related to link to the thread and system libraries which make RcppBlaze be
    a header-only library. Therefore, users do not need to install Blaze and the
    dependency Boost. Blaze is licensed under the New (Revised) BSD license, while
    RcppBlaze (the Rcpp bindings/bridge to Blaze) is licensed under the GNU GPL
    version 2 or later, as is the rest of Rcpp. Note that since latest version of
    Blaze (3.0) commit to C++14 which does not used by most R users, we will use the
    version 2.6 of Blaze which is C++98 compatible to support the most compilers and
    system.
'Rcpp' Access to the 'CCTZ' timezone library is provided. 'CCTZ' is
 a C++ library for translating between absolute and civil times using the rules
 of a time zone. The 'CCTZ' source code, released under the Apache 2.0 License,
 is included in this package. See <https://github.com/google/cctz> for more
 details.
The RcppClassic package provides a deprecated C++ library which
 facilitates the integration of R and C++. 

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

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

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

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

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

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

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

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

    The package is still in an early state. Unless you know what you are doing,
 sticking with the generators provided by R may be a good idea as these have
 been extremely diligently tested.
Implements Corpus Query Protocol functions based on the
        CWB software. Rely on CWB (GPL v2), PCRE (BSD licence), glib2
        (LGPL).
Aims at loading Criteo online advertising campaign data into R.
    Criteo <http://www.criteo.com/> is an online advertising service that enables
    advertisers to display commercial ads to web users. The package provides
    an authentication process for R with the Criteo API <http://kb.criteo.com/
    advertising/content/5/27/en/api.html>. Moreover, the package features an
    interface to query campaign data from the Criteo API. The data can be downloaded
    and will be transformed into a R data frame.
Client for various 'CrossRef' APIs, including 'metadata' search
    with their old and newer search APIs, get 'citations' in various formats
    (including 'bibtex', 'citeproc-json', 'rdf-xml', etc.), convert 'DOIs'
    to 'PMIDs', and 'vice versa', get citations for 'DOIs', and get links to
    full text of articles when available.
Provides easy symmetric file encryption using GPG with
    cryptographically strong defaults. Only symmetric encryption is 
    supported. GPG is pre-installed with most Linux distributions. 
    Windows users will need to install 'Gpg4win' (http://www.gpg4win.org/). 
    OS X users will need to install 'GPGTools' (https://gpgtools.org/).
Basic access to market infomation from the public API for the Crypto-Currency exchange Cryptsy.
R interface to the CSDP semidefinite programming library. Installs version 6.1.1 of CSDP from the COIN-OR website if required. An existing installation of CSDP may be used by passing the proper configure arguments to the installation command. See the INSTALL file for further details.
The numerical treatment of optimal switching problems in a finite time setting when the state evolves as a controlled Markov chain consisting of a uncontrolled continuous component following linear dynamics and a controlled Markov chain taking values in a finite set. The reward functions are assumed to be convex and Lipschitz continuous in the continuous state. The action set is finite.
Functions and objects for CUR matrix decomposition.
Implements robust cure models for survival analysis by incorporate
    a weakly informative prior in the logistic part of cure models. Estimates
    prognostic accuracy, i.e. AUC, k-index and c-index, with bootstrap confidence
    interval for cure models.
A wrapper for 'libcurl' <http://curl.haxx.se/libcurl/>
	Provides functions to allow one to compose general HTTP requests
        and provides convenient functions to fetch URIs, get & post
        forms, etc. and process the results returned by the Web server.
        This provides a great deal of control over the HTTP/FTP/...
        connection and the form of the request while providing a
        higher-level interface than is available just using R socket
        connections.  Additionally, the underlying implementation is
        robust and extensive, supporting FTP/FTPS/TFTP (uploads and
        downloads), SSL/HTTPS, telnet, dict, ldap, and also supports
        cookies, redirects, authentication, etc.
Functions to convert Rd to roxygen documentation. It can parse an
    Rd file to a list, create the roxygen documentation and update the original
    R script (e.g. the one containing the definition of the function)
    accordingly. This package also provides utilities which can help developers
    build packages using roxygen more easily. The formatR package can be used
    to reformat the R code in the examples sections so that the code will be
    more readable.
Shrunken Centroids Regularized Discriminant Analysis for
        the classification purpose in high dimensional data.
Provides basic functionalities for writing a module
    for http://datacanvas.io. The http://datacanvas.io is a big data
    analytics platform that helps data scientists to build, manage
    and share data pipelines.
Client for the web service methods provided
    by 'DataCite' (<https://www.datacite.org/>), including functions to interface with
    their 'OAI-PMH' 'metadata' service, and a 'RESTful' search 'API'. The 'API'
    is backed by 'SOLR', allowing expressive queries, including faceting,
    statistics on variables, and 'more-like-this' queries.
Fetches data from DataMarket.com, either as
    timeseries in zoo form (dmseries) or as long-form data
    frames (dmlist). Metadata including dimension structure
    is fetched with dminfo, or just the dimensions with
    dmdims.
Provides the tools to undertake estimation in
    Regression Discontinuity Designs. Both sharp and fuzzy designs are
    supported. Estimation is accomplished using local linear regression.
    A provided function will utilize Imbens-Kalyanaraman optimal
    bandwidth calculation. A function is also included to test the
    assumption of no-sorting effects.
Set of functions for Regression Discontinuity Design ('RDD'), for
    data visualisation, estimation and testing.
Data Envelopment Analysis for R, estimating robust DEA scores without and with environmental variables and doing returns-to-scale tests.
Get data from DEFRA's UK-AIR website. It basically scraps the HTML content.
The package provides functions for estimating the relevant
        dimension of a data set in feature spaces, applications to
        model selection, graphical illustrations and prediction.
Functions for creating plots and image files in a unified way
    regardless of output format (EPS, PDF, PNG, SVG, TIFF, WMF, etc.). Default
    device options as well as scales and aspect ratios are controlled in a uniform
    way across all device types. Switching output format requires minimal changes
    in code. This package is ideal for large-scale batch processing, because it
    will never leave open graphics devices or incomplete image files behind, even on
    errors or user interrupts.
A client library for 'The Guardian' (https://www.guardian.com/)
    and their API, this package allows users to search for Guardian articles and
    retrieve both the content and metadata.
A collection of functions to simulate
  dice rolls and the like. In particular, experiments and exercises can
  be performed looking at combinations and permutations of values in dice
  rolls and coin flips, together with the corresponding frequencies of
  occurrences. When applying each function, the user has to input the
  number of times (rolls, flips) to toss the dice. Needless to say, the more
  the tosses, the more the frequencies approximate the actual probabilities.
  Moreover, the package provides functions to generate non-transitive sets
  of dice (like Efron's) and to check whether a given set of dice is non-transitive
  with given probability.
The package has many function that helps to perform
        various quality check on the data.It basically provides many
        function that helps in performing Extrapolative data analysis.
The RDieHarder packages provides an R interface to 
  the dieharder suite of random number generators and tests that 
  was developed by Robert G. Brown and David Bauer, extending 
  earlier work by George Marsaglia and others.
Analysis of distance sampling data collected on line transect surveys.  Estimates distance-based detection functions and abundances.
Imports real-time thermo cycler (qPCR) data from Real-time PCR
    Data Markup Language (RDML) format files and transforms to the appropriate
    formats of the 'qpcR' and 'chipPCR' packages. Contains a dendrogram
    visualization for the structure of RDML object and GUI for RDML editing.
Control the Java software Discourse Network Analyzer (DNA) from 
  within R. Network matrices, statement frequency time series and attributes 
  of actors can be transferred directly into R.
Comprehensive toolkit for generating various numerical                                 representation schemes of DNA sequence. The descriptors and similarity
    scores included are extensively used in bioinformatics and chemogenomics.
A wrapper for the 'Deutsche Nationalbibliothek (German National
    Library) API', available at <http://www.dnb.de>. The German National Library is
    the German central archival library, collecting, archiving, bibliographically
    classifying all German and German-language publications, foreign
    publications about Germany, translations of German works, and the works of
    German-speaking emigrants published abroad between 1933 and 1945. A personal
    access token is required for usage.
Wraps around the default help functionality in R. Instead of plain documentation files, documentation will now show up as it does on 'RDocumentation.org', a platform that shows R documentation from CRAN, GitHub and Bioconductor, together with informative stats to assess the package quality and possibilities to discuss packages.
Get the category of content hosted by a domain. Use Shallalist <http://shalla.de/>,  
    Virustotal (which provides access to lots of services) <https://www.virustotal.com/>, 
    McAfee <https://www.trustedsource.org/>, Alexa <https://aws.amazon.com/awis/>, 
    DMOZ <http://www.dmoz.org/>, or validated machine learning classifiers based on 
    Shallalist data to learn about the kind of content hosted by a domain.
An R API Client for Valve's Dota2. RDota2 can be easily used 
    to connect to the Steam API and retrieve data for Valve's popular video 
    game Dota2. You can find out more about Dota2 at 
    <http://store.steampowered.com/app/570/>.
Functions for manipulation of Rd objects, including
             function reprompt() for updating existing Rd
             documentation for functions, methods and classes and
             function rebib() for import of references from 'bibtex'
             files. There is also a macro for importing 'bibtex'
             references which an be used in Rd files and 'roxygen'
             comments without importing this package.
Interact with the Digital Public Library of America
    <https://dp.la> ('DPLA') 'REST' 'API'
    <https://dp.la/info/developers/codex/> from R, including search
    and more.
Regression-discontinuity (RD) designs are quasi-experimental research designs popular in social, behavioral and natural sciences. The RD design is usually employed to study the (local) causal effect of a treatment, intervention or policy. This package provides tools for data-driven graphical and analytical statistical inference in RD	designs: rdrobust() to construct local-polynomial point estimators and robust confidence intervals for average treatment effects at the 	cutoff in Sharp, Fuzzy and Kink RD settings, rdbwselect() to perform bandwidth selection for the different procedures implemented, and rdplot() to conduct exploratory data analysis (RD plots).
Provides full programmatic access to the Dropbox file hosting platform (dropbox.com), including support for all standard file operations.
Interface to the Dryad Solr API, their OAI-PMH service, and
    fetch datasets.
Provides functionality for carrying out estimation
    with data collected using Respondent-Driven Sampling. This includes
    Heckathorn's RDS-I and RDS-II estimators as well as Gile's Sequential
    Sampling estimator. The package is part of the "RDS Analyst" suite of
    packages for the analysis of respondent-driven sampling data.
R interface to DSDP semidefinite programming library. The DSDP software is a free open source implementation of an interior-point method for semidefinite programming. It provides primal and dual solutions, exploits low-rank structure and sparsity in the data, and has relatively low memory requirements for an interior-point method.
Provides a threads-type programming environment for R.
   The package gives the R programmer the clearer, more concise
   shared memory world view, and in some cases gives superior
   performance as well.  In addition, it enables parallel processing on
   very large, out-of-core matrices.
This package provides an R interface to Pete Warden's Data
        Science Toolkit.  See www.datasciencetoolkit.org for more
        information.  The source code for this package can be found at
        github.com/rtelmore/RDSTK Happy hacking!
A tree bootstrap method for estimating uncertainty in respondent-driven samples (RDS). Quantiles are estimated by multilevel resampling in such a way that preserves the dependencies of and accounts for the high variability of the RDS process.
Implementation of density tracking by quadrature (DTQ) algorithms for stochastic differential equations (SDEs).  DTQ algorithms numerically compute the density function of the solution of an SDE with user-specified drift and diffusion functions.  The calculation does not require generation of sample paths, but instead proceeds in a deterministic fashion by repeatedly applying quadrature to the Chapman-Kolmogorov equation associated with a discrete-time approximation of the SDE.  The DTQ algorithm is provably convergent.  For several practical problems of interest, we have found the DTQ algorithm to be fast, accurate, and easy to use.
The rDVR package allows you to start stop and save a video server
    from within R. It does this by way of a REST interface to a JAVA service.
    The jar binary relies on the screen recorder included in the Monte Media
    Library (CC BY 3.0 licence) developed by Werner Randelshofer
    (http://http://www.randelshofer.ch/monte/). The REST interface was modified
    from https://github.com/tuenti/VideoRecorderService which has an Apache
    licence.
Handle climate data from the 'DWD' ('Deutscher Wetterdienst', see 
             <https://www.dwd.de/EN/climate_environment/cdc/cdc.html> for more information).
             Choose files with 'selectDWD()', download and process data sets with 'dataDWD()' and 'readDWD()'.
Make it easy to use 'react' in R with helper
              dependency functions, embedded 'Babel' transpiler,
              and examples.  Please note the separate 'react'
              BSD license <https://github.com/facebook/react/blob/master/LICENSE>
              when using 'react' in your projects.
Routines for developing models that describe reaction and advective-diffusive transport in one, two or three dimensions.
  Includes transport routines in porous media, in estuaries, and in bodies with variable shape.
Calculate readability scores by grouping variables.  Readability is
        an approximation of the ease with which a reader parses and comprehends a
        written text.  These scores use text attributes such as syllable counts,
        number of words, and number of characters to calculate an approximate
        grade level reading ease for the text.  The readability scores that are
        calculated include: Flesch Kincaid, Gunning Fog Index, Coleman Liau,
        SMOG, and Automated Readability Index.
Identifies and reads Windows BMP, JPEG and PNG format bitmap
    images. Identification defaults to the use of the magic number embedded in
    the file rather than the file extension. Reading of JPEG and PNG image
    depends on libjpg and libpng libraries. See file INSTALL for details if
    necessary.
Reads data files acquired by Bruker Daltonics' matrix-assisted
    laser desorption/ionization-time-of-flight mass spectrometer of the *flex
    series.
Combine multiple data files from a common directory.
    The data files will be read into R and bound together, creating a
    single large data.frame. A general function is provided along with
    a specific function for data that was collected using the open-source
    experiment builder 'OpenSesame' <http://osdoc.cogsci.nl/>.
Functions for reading and decompressing the DBC (compressed DBF) files. Please note that this is the file format used by the Brazilian Ministry of Health (DATASUS) to publish healthcare datasets. It is not related to the FoxPro or CANdb DBC file formats.
A set of functions to simplify reading data from files. The main function, reader(), should read most common R datafile types without needing any parameters except the filename. Other functions provide simple ways of handling file paths and extensions, and automatically detecting file format and structure.
Import data written in the JCAMP-DX format. This is an instrument-independent format used in the field of spectroscopy. Examples include IR, NMR, and Raman spectroscopy. See the vignette for background and supported formats.  The official JCAMP-DX site is <http://www.jcamp-dx.org/>.
Functions for reading data sets in different formats
  for testing machine learning tools are provided. This allows to run
  a loop over several data sets in their original form, for example
  if they are downloaded from UCI Machine Learning Repository.
  The data are not part of the package and have to be downloaded
  separately.
Functions for reading mass spectrometry data in mzXML format.
Import ODS (OpenDocument Spreadsheet) into R as a data frame. Also support writing data frame into ODS file.
Read flat/tabular text files from disk (or a connection).
Function to read and write the 'Stata' file format.
Import excel files into R. Supports '.xls' via the embedded
    'libxls' C library (http://sourceforge.net/projects/libxls/) and '.xlsx' via
    the embedded 'RapidXML' C++ library (http://rapidxml.sourceforge.net). Works on
    Windows, Mac and Linux without external dependencies.
The RealVAMs package fits a multivariate value-added model (VAM) (see Broatch and Lohr 2012) with normally distributed test scores and a binary outcome indicator. This material is based upon work supported by the National Science Foundation under grants DRL-1336027 and DRL-1336265.
Resampling methods for adaptive linear model selection.
        These can be thought of as extensions of the Akaike information
        criterion that account for searching among candidate models.
The rearrangement operator (Hardy,
        Littlewood, and Polya 1952) for univariate, bivariate, and
        trivariate point estimates of monotonic functions. The package
        additionally provides a function that creates simultaneous
        confidence intervals for univariate functions and applies the
        rearrangement operator to these confidence intervals.
Collection of models and analysis methods used in regional and urban economics and (quantitative) economic geography, e.g. measures of inequality, regional disparities and convergence, regional specialization as well as accessibility and spatial interaction models.
Kiefer-Wolfowitz maximum likelihood estimation for mixture models
    and some other density estimation and regression methods based on convex
    optimization.
A programmatic client for the eBird database, including functions
    for searching for bird observations by geographic location (latitude,
    longitude), eBird hotspots, location identifiers, by notable sightings, by
    region, and by taxonomic name.
R functions for random univariate and multivariate finite mixture model generation, estimation, clustering and classification. Variables can be continuous, discrete, independent or dependent and may follow normal, lognormal, Weibull, gamma, binomial, Poisson or Dirac parametric families.
Build regular expressions piece by piece using human readable code.
    This package is designed for interactive use.  For package development, use
    the rebus.* dependencies.
Build regular expressions piece by piece using human readable code.
    This package contains core functionality, and is primarily intended to be
    used by package developers.
Build regular expressions piece by piece using human readable code.
    This package contains date and time functionality, and is primarily intended
    to be used by package developers.
Build regular expressions piece by piece using human readable code.
    This package contains number-related functionality, and is primarily intended
    to be used by package developers.
Build regular expressions piece by piece using human readable code.
    This package contains Unicode functionality, and is primarily intended to be
    used by package developers.
Relevant Component Analysis (RCA) tries to find a linear
    transformation of the feature space such that the effect of irrelevant
    variability is reduced in the transformed space.
A toolset for 3D reconstruction and analysis of excavations. It provides methods to reconstruct natural and artificial surfaces based on field measurements. This allows to spatially contextualize documented subunits and features. Intended to be part of a 3D visualization workflow.
The 'Echo nest' <http://the.echonest.com> is the industry's leading
    music intelligence company, providing developer with deepest understanding of
    music content and music fans. This package can be used to access artist's data
    including songs, blogs, news, reviews etc. Song's data including audio summary,
    style, danceability, tempo etc can also be accessed.
Pathways in a database could have many redundancies among
        them. This package allows the user to set a maximum value for
        the proportion of these redundancies.
Beta-diversity indices provide dissimilarity matrices with particular distribution of data requiring specific treatment. For example the high frequency of ties and zero values in turnover indices produces hierarchical cluster dendrograms whose topology and bootstrap supports are affected by the order of rows in the original matrix. Moreover, biogeographical regionalization can be facilitated by a combination of hierarchical clustering and multi-dimensional scaling. The recluster package provides robust techniques to analyze pattern of similarity in species composition.
Provides an interface and a C++ implementation of the RecMap MP2
  construction heuristic (see 'citation("recmap")' for details). This algorithm
  draws maps according to a given statistical value (e.g. election results,
  population or epidemiological data). The basic idea of the RecMap algorithm is
  that each map region (e.g. different countries) is represented by a
  rectangle. The area of each rectangle represents the statistical value given
  as input (maintain zero cartographic error). Documentation about RecMap is
  provided by a vignette included in this package.
Simple, easy to use, and flexible functionality for recoding variables. It allows for simple piecewise definition of transformations.
Provides a research infrastructure to test and develop
    recommender algorithms including UBCF, IBCF, FunkSVD and association
    rule-based algorithms.
Provides the Book-Crossing Dataset for the package recommenderlab.
Provides the Jester Dataset for package recommenderlab.
Functions to reconstruct sessions from web log or other user trace data
             and calculate various metrics around them, producing tabular,
             output that is compatible with 'dplyr' or 'data.table' centered processes.
Provides functions for linking and de-duplicating data sets.
  Methods based on a stochastic approach are implemented as well as 
  classification algorithms from the machine learning domain.
Functions for generating k-record values and k-record
        times
R wrapper of the 'libmf' library
    (http://www.csie.ntu.edu.tw/~cjlin/libmf/) for recommender
    system using matrix factorization. It is typically used to
    approximate an incomplete matrix using the product of two
    matrices in a latent space. Other common names for this task
    include "collaborative filtering", "matrix completion",
    "matrix recovery", etc. High performance multi-core parallel
    computing is supported in this package.
Includes algorithms to facilitate the assessment of extinction risk of species according to the IUCN (International Union for Conservation of Nature, see <http://www.iucn.org> for more information) red list criteria.
Functions that fit gamma frailty model with spline or piecewise
    constant baseline rate function for recurrent event data, compute and
    plot parametric mean cumulative function (MCF) from a fitted model
    as well as nonparametric sample MCF (Nelson-Aalen estimator) are provided.
    Most functions are S4 methods that produce S4 class objects.
Provides functions used in the 'R: Einführung durch angewandte Statistik' (second edition).
Access data stored in REDCap databases using the Application
    Programming Interface (API).  REDCap (Research Electronic Data CAPture) is
    a web application for building and managing online surveys and databases
    developed at Vanderbilt University.  The API allows users to access data
    and project meta data (such as the data dictionary) from the web
    programmatically.  The redcapAPI package facilitates the process of
    accessing data with options to prepare an analysis-ready data set
    consistent with the definitions in a database's data dictionary.
Encapsulates functions to streamline calls from R to the REDCap
    API.  REDCap (Research Electronic Data CAPture) is a web application for
    building and managing online surveys and databases developed at Vanderbilt
    University.  The Application Programming Interface (API) offers an avenue
    to access and modify data programmatically, improving the capacity for
    literate and reproducible programming.
Reddit is an online bulletin board and a social networking website
    where registered users can submit and discuss content. This package uses
    Reddit API to extract Reddit data using Reddit API. Note that due to the API
    limitations, the number of comments that can extracted is limited to 500 per
    thread. The package consists of 4 functions, one for extracting relevant URLs,
    one for extracting features out of given URLs, one that does both together and
    one that constructs graphs based on the structure of a thread.
Computes quality control for daily precipitation datasets, reconstructs the original series by estimating precipitation in missing values, creates new series in a specified pair of coordinates and creates grids.
Enables researchers to sample redistricting plans from a pre-specified target distribution using a Markov Chain Monte Carlo algorithm. The package allows for the implementation of various constraints in the redistricting process such as geographic compactness and population parity requirements.  The algorithm also can be used in combination with efficient simulation methods such as simulated and parallel tempering algorithms. Tools for analysis such as inverse probability reweighting and plotting functionality are included. The package implements methods described in Fifield, Higgins, Imai and Tarr (2016) ``A New Automated Redistricting Simulator Using Markov Chain Monte Carlo,'' working paper available at <http://http://imai.princeton.edu/research/files/redist.pdf>.
Provides methods to parse, query and serialize information
    stored in the Resource Description Framework (RDF). RDF is described at <http://www.w3.org/TR/rdf-primer>.
    This package supports RDF by implementing an R interface to the Redland RDF C library,
    described at <http://librdf.org/docs/api/index.html>. In brief, RDF provides a structured graph
    consisting of Statements composed of Subject, Predicate, and Object Nodes.
A new implementation of EDM algorithms based on research software previously developed for internal use in the Sugihara Lab (UCSD/SIO). Contains C++ compiled objects that use time delay embedding to perform state-space reconstruction and nonlinear forecasting and an R interface to those objects using 'Rcpp'. It supports both the simplex projection method from Sugihara & May (1990) <DOI:10.1038/344734a0> and the S-map algorithm in Sugihara (1994) <DOI:10.1098/rsta.1994.0106>. In addition, this package implements convergent cross mapping as described in Sugihara et al. (2012) <DOI:10.1126/science.1227079> and multiview embedding as described in Ye & Sugihara (2016) <DOI:10.1126/science.aag0863>.
Provide color schemes for maps (and other graphics) based on the
    color palettes of several Microsoft(r) products. Forked from 'RColorBrewer' v1.1-2.
This package estimates regression trees with random
        effects as a way to use data mining techniques to describe
        longitudinal or panel data.
small package with functions for creating references,
        reading from and writing to references and a memory efficient
        refdata type that transparently encapsulates matrices and
        data.frames
This is a collection of tools to allow the medical professional to
 calculate appropriate reference ranges (intervals) with confidence intervals around
 the limits for diagnostic purposes.
Reference-free method for conducting EWAS while deconvoluting DNA methylation arising as mixtures of cell types.  
   The older method (Houseman et al., 2014) is similar to surrogate variable analysis (SVA and ISVA), except that it makes additional use of a biological mixture assumption.
   The newer method (Houseman et al., 2016) is similar to non-negative matrix factorization, with additional constraints and additional utilities.
Contains functionality for import and managing of downloaded genome annotation Data from Ensembl genome browser (European Bioinformatics Institute) and from UCSC genome browser (University of California, Santa Cruz) and annotation routines for genomic positions and splice site positions.
Provides wrapper functions around the UK Research
    Excellence Framework 2014 Impact Case Studies Database API
    <http://impact.ref.ac.uk/>. The database contains relevant publication and
    research metadata about each case study as well as several paragraphs of
    text from the case study submissions. Case studies in the database are
    licenced under a CC-BY 4.0 licence
    <http://creativecommons.org/licenses/by/4.0/legalcode>.
Provides tools for importing and working with bibliographic
    references. It greatly enhances the 'bibentry' class by providing a class
    'BibEntry' which stores 'BibTeX' and 'BibLaTeX' references, supports 'UTF-8'
    encoding, and can be easily searched by any field, by date ranges, and by
    various formats for name lists (author by last names, translator by full names,
    etc.). Entries can be updated, combined, sorted, printed in a number of styles,
    and exported. 'BibTeX' and 'BibLaTeX' '.bib' files can be read into 'R' and
    converted to 'BibEntry' objects. Interfaces to 'NCBI Entrez', 'CrossRef', and
    'Zotero' are provided for importing references and references can be created
    from locally stored 'PDF' files using 'Poppler'. Includes functions for citing
    and generating a bibliography with hyperlinks for documents prepared with
    'RMarkdown' or 'RHTML'.
A tool for refining data frame with formulas.
Provides subsets with reference semantics, i.e. subsets
    which automatically reflect changes in the original object, and which
    optionally update the original object when they are changed.
Methods for regression for functional
    data, including function-on-scalar, scalar-on-function, and
    function-on-function regression. Some of the functions are applicable to
    image data.
Interactive plotting for functional data analyses.
Methods for regressing scalar responses on functional or image predictors, via transformation to the wavelet domain and back.
Contains basic tools for visualizing, interpreting, and building regression models.  It has been designed for use with the book Introduction to Regression and Modeling with R by Adam Petrie, Cognella Publishers, ISBN: 978-1-63189-250-9 <https://titles.cognella.com/behavioral-and-social-sciences/general-social-sciences/introduction-to-regression-and-modeling-with-r-9781631892509.html>.
This package clusters regression coefficients using the methods of clustering through linear regression models (CLM) (Qin and Self 2006). Maximum likelihood approach is used to infer the parameters for each cluster. Bayesian information criterion (BIC) combined with Bootstrapped maximum volume (BMV) criterion are used to determine the number of clusters.
Detecting mislabeled samples in genetic data.
Produces population distribution of disease risk and statistical risk categories, and predicts risks for individuals with genotype information.
Provides wrappers around base::grep() where the first argument
    is standardized to take the data object. This makes it less of a pain to use
    regular expressions with 'magrittr' or other pipe operators.
An R framework for constructing and managing human
        readable regular expressions.  It aims to provide tools that
        enable the user to write regular expressions in a way that is
        similar to the ways R code is written. The tools allow the user
        to (1) write in smaller, modular, named, sub-expressions, (2)
        write top to bottom, rather than a single string (3) comment
        individual chunks, (4) indent expressions to clearly present
        regular expression groups, (5) add vertical line spaces and R
        comments (i.e., #), and (6) test the validity of the
        concatenated expression and the modular sub-expressions.
Provides a generic infrastructure for creating and using registries.
Regularized (polychotomous) logistic regression 
  by Gibbs sampling. The package implements subtly different 
  MCMC schemes with varying efficiency depending on the data type 
  (binary v. binomial, say) and the desired estimator (regularized maximum
  likelihood, or Bayesian maximum a posteriori/posterior mean, etc.) through a 
  unified interface.
Tools are provided for
  (1) nonparametric regression (kernel, local linear),
  (2) semiparametric regression (single index, additive models), and
  (3) quantile regression (linear, kernel).
Functions to fit Gaussian linear model by maximising the
        residual log likelihood where the covariance structure can be
        written as a linear combination of known matrices.  Can be used
        for multivariate models and random effects models.  Easy
        straight forward manner to specify random effects models,
        including random interactions. Code now optimised to use
        Sherman Morrison Woodbury identities for matrix inversion in
        random effects models. We've added the ability to fit models
        using any kernel as well as a function to return the mean and
        covariance of random effects conditional on the data (BLUPs).
The expander functions rely on the mathematics developed for the Hessian-definiteness invariance theorem for linear projection transformations of variables, described in authors' paper, to generate the full, high-dimensional gradient and Hessian from the lower-dimensional derivative objects. This greatly relieves the computational burden of generating the regression-function derivatives, which in turn can be fed into any optimization routine that utilizes such derivatives. The theorem guarantees that Hessian definiteness is preserved, meaning that reasoning about this property can be performed in the low-dimensional space of the base distribution. This is often a much easier task than its equivalent in the full, high-dimensional space. Definiteness of Hessian can be useful in selecting optimization/sampling algorithms such as Newton-Raphson optimization or its sampling equivalent, the Stochastic Newton Sampler. Finally, in addition to being a computational tool, the regression expansion framework is of conceptual value by offering new opportunities to generate novel regression problems.
Performs Random Subspace Method (RSM) for high-dimensional linear regression to obtain variable importance measures. The final model is chosen based on validation set or Generalized Information Criterion.
Functions for fitting linear and generalized linear models with variable selection. The functions can automatically do Stepwise Regression, Lasso or Elastic Net as variable selection methods. Lasso and Elastic net are improved and handle factors better (they can either include or exclude all factor levels).
Uses both ridge and lasso penalties (and extensions) to penalize
    specific parameters in structural equation models. The package offers additional
    cost functions, cross validation, and other extensions beyond traditional SEM.
Computes linear Bayesian spectral estimates from multirate
	data for second-order stationary time series. Provides credible
	intervals and methods for plotting various spectral estimates.
For a sequence of event occurence times, we are interested in
  finding subsequences in it that are too "regular". We define regular as being
  significantly different from a homogeneous Poisson process. The departure from
  the Poisson process is measured using a L1 distance. See Di and Perlman 2007
  for more details.
Functions for unary and binary regression tests
Tools for linear, nonlinear and nonparametric regression
             and classification.  Parametric fit assessment using
             nonparametric methods. One vs. All and All vs. All
             multiclass classification.  Nonparametric regression for
             general dimension, locally-linear option.  Nonlinear 
             regression with Eickert-White method for dealing with 
             heteroscedasticity, k-NN for general dimension and 
             general descriptive functions.
Functions for the detection of footprints of selection on
        dense SNP data using Extended Homozygosity Haplotype (EHH)
        based tests. The package includes computation of EHH, iHS
        (within population) and Rsb or XP-EHH (across pairs of populations)
        statistics. Various plotting functions are also included to
        facilitate visualization and interpretation of the results.
Contains example data for the 'rehh' package.
Functions from the book "Reinsurance: Actuarial and Statistical Aspects" (Wiley, 2017) 
             by Hansjoerg Albrecher, Jan Beirlant and Jef Teugels.
Search R files for not installed packages and run install.packages.
Derives point estimates with confidence intervals for Bennett et als S, Cohen's kappa, Conger's kappa, Fleiss' kappa, Gwet's AC, Krippendorff's alpha, Scott's pi, and weighted kappa.
Item analysis with alpha standard error and principal axis
        factoring for continuous variable scales (with plots).
In confirmatory factor analysis (CFA), structural constraints
    typically ensure that the model is identified up to all possible reflections,
    i.e., column sign changes of the matrix of loadings. Such reflection invariance
    is problematic for Bayesian CFA when the reflection modes are not well separated
    in the posterior distribution. Imposing rotational constraints -- fixing
    some loadings to be zero or positive in order to pick a factor solution that
    corresponds to one reflection mode -- may not provide a satisfactory solution
    for Bayesian CFA. The function 'relabel' uses the relabeling algorithm of
    Erosheva and Curtis to correct for sign invariance in MCMC draws from CFA
    models. The MCMC draws should come from Bayesian CFA models that are fit without
    rotational constraints.
relaimpo provides several metrics for assessing relative importance in linear models. These can be printed, plotted and bootstrapped. The recommended metric is lmg, which provides a decomposition of the model explained variance into non-negative contributions. There is a version of this package available that additionally provides a new and also recommended metric called pmvd. If you are a non-US user, you can download this extended version from Ulrike Groempings web site.
Inference of relatedness coefficients from a bi-allelic genotype matrix using a Maximum Likelihood estimation.
Data structures and algorithms for k-ary relations with
  arbitrary domains, featuring relational algebra, predicate functions,
  and fitters for consensus relations.
package relax contains some functions for report
    writing, presentation, and programming: relax(), tangleR(),
    weaveR(), (g)slider(). "relax" is written in R and Tcl/Tk.
    relax creates a new window (top level Tcl/Tk widget) that consists
    of two text fields and some buttons and menus.
    Text (chunks) and code (chunks) are inserted in the upper text field (report field).
    Code chunks are evaluated by clicking on EvalRCode.
    Results are shown in the lower text field (output field) and
    will be transferred to the report field by pressing on Insert.
    In this way you get correct reports. These reports can be
    loaded again in cause of presentation, modification and result checking.
    tangleR() and weaveR() implement a plain kind of tangling
    and weaving. gslider() and slider() are designed to define sliders for interactive
    experiments in a simple way.
    The syntax rules of code chunks and text chunks are defined by 
    the noweb system proposed by Norman Ramsey
    (http://www.eecs.harvard.edu/~nr/noweb/intro.html).
Extends the glmnet package with "relaxation", done by running glmnet once on the entire predictor matrix, then again on each different subset of variables from along the regularization path. Relaxation may lead to improved prediction accuracy for truly sparse data generating models, as well as fewer false positives (i.e. fewer noncontributing predictors in the final model). Penalty may be lasso (alpha = 1) or elastic net (0 < alpha < 1). For this version, family may be "gaussian" or "binomial" only. Takes advantage of fast FORTRAN code from the glmnet package.
Relaxed Lasso is a generalisation of the Lasso shrinkage
        technique for linear regression. Both variable selection and
        parameter estimation is achieved by regular Lasso, yet both
        steps do not necessarily use the same penalty parameter. The
        results include all standard Lasso solutions but allow often
        for sparser models while having similar or even slightly better
        predictive performance if many predictor variables are present.
        The package depends on the LARS package.
Tools for the comparison of distributions. This includes nonparametric estimation of the relative distribution PDF and CDF and numerical summaries as described in "Relative Distribution Methods in the Social Sciences" by Mark S. Handcock and Martina Morris, Springer-Verlag, 1999, Springer-Verlag, ISBN 0387987789.
This function computes the relative entropy (H) as an index for qualitative variation of a factor.
Functions for estimating parameters in software reliability models.
             Only infinite failure models are implemented so far.
A variety of tools useful for performing structural
        reliability analysis, such as with structure function and
        system signatures.  Plans to expand more widely.
A collection of utilities for some reliability
        models/probability distributions.
Functions to facilitate inference on the relative importance of predictors in a linear or generalized linear model, and a couple of useful Tcl/Tk widgets.
Makes relationship inference involving DNA mixtures with unknown profiles.
A set of tools to explore the behaviour statistics used for forensic DNA interpretation when close relatives are involved. The package also offers some useful tools for exploring other forensic DNA situations.
Various functions for relative survival analysis.
Classes and functions for analyzing the performance of portfolios relative to a benchmark.
Calculate endogenous network effects in event sequences and fit relational event models (REM): Using network event sequences (where each tie between a sender and a target in a network is time-stamped), REMs can measure how networks form and evolve over time. Endogenous patterns such as popularity effects, inertia, similarities, cycles or triads can be calculated and analyzed over time.
A small wrapper on 'regexpr' to extract the matches and
    captured groups from the match of a regular expression to a character
    vector.
remix provides remix, a quick and easy function for
        describing datasets. It can be view as a mix of cast (in
        package reshape) and summary.formula (in package Hmisc).
Implements TRACDS (Temporal Relationships 
    between Clusters for Data Streams), a generalization of 
    Extensible Markov Model (EMM). TRACDS adds a temporal or order model
    to data stream clustering by superimposing a dynamically adapting
    Markov Chain. Also provides an implementation of EMM (TRACDS on top of tNN 
    data stream clustering). Development of this 
    package was supported in part by NSF IIS-0948893 and R21HG005912 from 
    the National Human Genome Research Institute.
remMap is developed for fitting multivariate response regression models under the high-dimension-low-sample-size setting
Empirical orthogonal teleconnections in R.
    'remote' is short for 'R(-based) EMpirical Orthogonal TEleconnections'.
    It implements a collection of functions to facilitate empirical
    orthogonal teleconnection analysis. Empirical Orthogonal Teleconnections
    (EOTs) denote a regression based approach to decompose spatio-temporal
    fields into a set of independent orthogonal patterns. They are quite
    similar to Empirical Orthogonal Functions (EOFs) with EOTs producing
    less abstract results. In contrast to EOFs, which are orthogonal in both
    space and time, EOT analysis produces patterns that are orthogonal in
    either space or time.
A set of utilities for controlling a remote R session
    from a local one.  Simply set up a server (see package vignette
    for more details) and connect to it from your local R session,
    including 'RStudio'.  Network communication is handled
    by the 'ZeroMQ' library by way of the 'pbdZMQ' package.  The 
    client/server framework is a custom 'REPL'.
Download and install R packages stored in 'GitHub',
    'BitBucket', or plain 'subversion' or 'git' repositories. This package
    is a lightweight replacement of the 'install_*' functions in 'devtools'.
    Indeed most of the code was copied over from 'devtools'.
Fits linear models with endogenous regressor using latent
    instrumental variable approaches. The methods included in the package are Lewbel's (1997)
    higher moments approach as well as Lewbel's (2012) heteroskedasticity approach, Park and Gupta's (2012) joint estimation method that uses Gaussian
    copula and Kim and Frees's (2007) multilevel generalized method of moment approach that deals with endogeneity in a multilevel setting. 
    These are statistical techniques to address the endogeneity problem where no external instrumental variables are needed.
Peaks Over Threshold (POT) or 'methode du renouvellement'. The distribution for the exceedances can be chosen, and heterogeneous data (including historical data or block data) can be used in a Maximum-Likelihood framework.
Provides an R interface to the NCBI's EUtils API
    allowing users to search databases like GenBank and PubMed, process the
    results of those searches and pull data into their R sessions.
An R interface to the Encyclopedia of Life API. Includes functions for downloading and extracting information off the EOL pages.
Tools for performing the leaf reordering for the dendrogram that preserves the hierarchical clustering result and at the same time tries to group instances from the same class together.
Performs genome-wide association studies on individuals that are both related and have repeated measurements.
Various functions to fit models for non-normal repeated
    measurements.
Global tests for expression data of high-dimensional sets of
    molecular features.
Estimation of Bayes and local Bayes false discovery rates for
    replicability analysis.
Supplies classes and routines to convert data to and from EpiJSON files. This package provides conversion functions for data.frame, sp and obkData.
Test for effects of  both individual factors and their interaction on  replicated spatial patterns in a two factorial design.
A common problem faced by journal reviewers and authors is the question of
     whether the results of a replication study are consistent with the original
     published study. One solution to this problem is to examine the effect size
     from the original study and generate the range of effect sizes that could
     reasonably be obtained (due to random sampling) in a replication attempt
     (i.e., calculate a replication interval). If a replication effect size falls
     outside the replication interval, then that effect likely did not occur
     due to the effects of sampling error alone. Alternatively, if a replication
     effect size falls within the replication interval, then the replication
     effect could have reasonably occurred due to the effects of sampling error
     alone. This package has functions that calculate the replication interval for 
     the correlation (i.e., r), standardized mean difference (i.e., d-value), and mean. 
     The calculations used in version 2.0.0 and onward differ from past calculations 
     due to feedback during the journal review process. The new calculations allow 
     for a more precise interpretation of the replication interval.
Methods to get a grip on working with remote 'tbl' sources ('SQL' databases,
    'Spark') through 'dplyr'.  Adds convenience functions to make such tasks more like
    working with an in-memory 'data.frame'.  Results do depend on which 'dplyr' data service you use.
Tools to load 'R' packages
    and automatically generate BibTeX files citing them as well as load and
    cache plain-text and 'Excel' formatted data stored on 'GitHub', and
    from other sources.
A data manager meant to avoid manual storage/retrieval of
    data to/from the file system. It builds one (or more) centralized
    repository where R objects are stored with annotations, tags,
    dependency notes, provenance traces. It also provides navigation
    tools to easily locate, load and edit previously stored resources.
Fits linear models to repeated ordinal scores using GEE methodology.
Create 'Microsoft Word' document (>=2007) and 
    'Microsoft PowerPoint' document (>=2007) from R. There are
    several features to let you format and present R outputs ; e.g. Editable
    Vector Graphics, functions for complex tables reporting, reuse of corporate
    template document. You can use the package as a tool for fast reporting
    and as a tool for reporting automation. The package does not require
    any installation of Microsoft product to be able to write Microsoft files.
External jars required for package ReporteRs. ReporteRs is an 
	R package for creating Microsoft Word document (>=2007), Microsoft 
	Powerpoint document (>=2007) and HTML documents from R.
Provides a system for reporting messages, which provides certain useful features over the standard R system, such as the incorporation of output consolidation, message filtering, expression substitution, automatic generation of stack traces for debugging, and conditional reporting based on the current "output level".
reportRx is a set of tools that integrates with LaTeX and knitr to
    automatically generate generate reproducible clinical reports. Fuctions to
    automatically produce demoraphic tables, outcome summaries, univariate and
    multivariate analysis results and more are included.
Assists in writing reports and
        presentations by providing a frame work that brings together
        existing R, LaTeX/.docx and Pandoc tools. The package is
        designed to be used with RStudio, MiKTex/Tex Live/LibreOffice,
        knitr, knitcitations, Pandoc and pander. The user will want to
        download these free programs/packages to maximize the
        effectiveness of the reports package.  Functions with two
        letter names are general text formatting functions for copying
        text from articles for inclusion as a citation.
These functions are especially helpful when writing reports of data analysis using Sweave.
An R Interface to 'EPP-lab' v1.0. 'EPP-lab' is a Java program for
    projection pursuit using genetic algorithms written by Alain Berro and S. Larabi
    Marie-Sainte and is included in the package. The 'EPP-lab' sources are available
    under https://github.com/fischuu/EPP-lab.git.
String and binary representations of objects for several formats /
    mime types.
Contains workhorse function jrparams(), as well as two
        helper functions Mboxtest() and JRsMahaldist(), and four
        example data sets.
Reproducible research tools automates the creation of an analysis directory structure and work flow. There are R markdown
  skeletons which encapsulate typical analytic work flow steps. Functions will create appropriate modules which may
  pass data from one step to another.
Convenience wrapper that uses the 'rmarkdown' package to render
  small snippets of code to target formats that include both code and output.
  The goal is to encourage the sharing of small, reproducible, and runnable
  examples on code-oriented websites, such as <http://stackoverflow.com> and
  <https://github.com>, or in email. 'reprex' also extracts clean, runnable R
  code from various common formats, such as copy/paste from an R session.
Includes data analysis functions (e.g., to calculate effect sizes and 95% Confidence Intervals (CI) on Standardised Effect Sizes (d) for ABBA cross-over repeated-measures experimental designs), data presentation functions (e.g., density curve overlaid on histogram), and the data sets analyzed in different research papers in software engineering (e.g., related to software defect prediction or multi-site experiment concerning the extent to which structured abstracts were clearer and more complete than conventional abstracts) to streamline reproducible research in software engineering.
Predicting regulatory DNA elements based on epigenomic signatures. This package is more of a set of building blocks than a direct solution. REPTILE regulatory prediction pipeline is built on this R package. See <https://github.com/yupenghe/REPTILE> for more information.
This package contains the function run.eqs() which calls
        an EQS script file, executes the EQS estimation, and, finally,
        imports the results as R objects. These two steps can be
        performed separately: call.eqs() calls and executes EQS,
        whereas read.eqs() imports existing EQS outputs as objects into
        R. It requires EQS 6.2 (build 98 or higher).
High level and easy 'HTTP' client for 'R'. Provides functions for
    building 'HTTP' queries, including query parameters, body requests, headers,
    authentication, and more.
Modularizes source code. Keeps the global environment clean,
    explicifies interdependencies. Inspired by 'RequireJS'<http://requirejs.org/>.
General purpose R client for 'ERDDAP' servers. Includes
    functions to search for 'datasets', get summary information on
    'datasets', and fetch 'datasets', in either 'csv' or 'netCDF' format.
    'ERDDAP' information: http://upwell.pfeg.noaa.gov/erddap/information.html.
Executes a post-rotation algorithm that REorders and/or REflects FACTors (REREFACT) for each replication of a simulation study with exploratory factor analysis.
A collection of regression models for recurrent event process and failure time.
Bootstrap, permutation tests, and other resampling functions,
	featuring easy-to-use syntax.
Package of data sets from "Mathematical Statistics
    with Resampling in R" (2011) by Laura Chihara and Tim Hesterberg.
Implementation of functions for spectral similarity/dissimilarity
    analysis and memory-based learning (MBL) for non-linear modeling
    in complex spectral datasets. In chemometrics MBL is also known as local modeling.
Measure single-storage water supply system performance using resilience,
    reliability, and vulnerability metrics; assess storage-yield-reliability
    relationships; determine no-fail storage with sequent peak analysis; optimize
    release decisions for water supply, hydropower, and multi-objective reservoirs
    using deterministic and stochastic dynamic programming; generate inflow
    replicates using parametric and non-parametric models; evaluate inflow
    persistence using the Hurst coefficient.
Flexibly restructure and aggregate data using 
  just two functions: melt and cast.
Flexibly restructure and aggregate data using just two
    functions: melt and 'dcast' (or 'acast').
A tool for learning how to use the functions, melt,
        acast/dcast, and ddply.
electrical properties of resistor networks.
Resource Selection (Probability) Functions
  for use-availability wildlife data
  based on weighted distributions as described in
  Lele and Keim (2006) <doi:10.1890/0012-9658(2006)87[3021:WDAEOR]2.0.CO;2>,
  Lele (2009) <doi:10.2193/2007-535>,
  and Solymos & Lele (2016) <doi:10.1111/2041-210X.12432>.
Provides tools to enable the researcher to more precisely conduct
    respirometry experiments. Strong emphasis is on aquatic respirometry. Tools
    focus on helping the researcher setup and conduct experiments. Analysis of the
    resulting data is not a focus since analyses are often specific to a particular
    setup, and thus are better created by the researcher individually. This
    package provides tools for intermittent, flow-through, and closed respirometry
    techniques.
Contains three functions that query AuriQ Systems' Essentia Database and return the results in R. 'essQuery' takes a single Essentia command and captures the output in R, where you can save the output to a dataframe or stream it directly into additional analysis. 'read.essentia' takes an Essentia script and captures the output csv data into R, where you can save the output to a dataframe or stream it directly into additional analysis. 'capture.essentia' takes a file containing any number of Essentia commands and captures the output of the specified statements into R dataframes. Essentia can be downloaded for free at http://www.auriq.com/documentation/source/install/index.html.
Contains easy scripts which can be used to quickly create GUI windows for 'Rcmdr' Plugins. No knowledge about Tcl/Tk is required to make use of these scripts (These scripts are a generalisation of the template scripts in the 'RcmdrPlugin.BiclustGUI' package).
Models a RESTful service as if it were a nested R list.
Provides the user with functions to develop their trading strategy,
    uncover actionable trading ideas, and monitor consensus shifts with
    crowdsourced earnings and economic estimate data directly from
    <www.estimize.com>. Further information regarding the web services this
    package invokes can be found at <www.estimize.com/api>.
The restlos package provides algorithms for robust estimation of location (mean and mode) and scatter based on minimum spanning trees (pMST), self-organizing maps (Flood Algorithm), Delaunay triangulations (RDELA), and nested minimum volume convex sets (MVCH). The functions are also suitable for outlier detection.
Debugging with restore points instead of break points. A restore
    point stores all local variables when called inside a function. The stored
    values can later be retrieved and evaluated in a modified R console that
    replicates the function's environment. To debug step by step, one can
    simply copy & paste the function body from the R script. Particularly
    convenient in combination with RStudio. See the github page for a tutorial.
A fast Gibbs sampler for multivariate normal with affine constraints.
Using a database, LaTeX and R easily build attractive resumes.
Reaction time analysis by maximum likelihood
Reconstructs retinae by morphing a flat surface with
    cuts (a dissected flat-mount retina) onto a curvilinear surface (the a
    standard retinal shape). It can estimate the position of a point on the
    intact adult retina to within 8 degrees of arc (3.6% of nasotemporal axis).
    The coordinates in reconstructed retinae can be transformed to visuotopic
    coordinates.
An interface to NCBI databases such as PubMed, GenBank, or GEO
    powered by the Entrez Programming Utilities (EUtils). The nine EUtils
    provide programmatic access to the NCBI Entrez query and database
    system for searching and retrieving biological data.
Simplified scenario testing and sensitivity analysis with R via a
    generalized function for one-factor-at-a-time (OFAT) sensitivity analysis,
    evaluation of parameter sets and (sampled) parameter permutations. Options
    for formatting output and parallel processing are also provided.
Provides functions for the Bayesian analysis of extreme value
    models.  The 'rust' package <https://cran.r-project.org/package=rust> is
    used to simulate a random sample from the required posterior distribution.
    The functionality of 'revdbayes' is similar to the 'evdbayes' package
    <https://cran.r-project.org/package=evdbayes>, which uses Markov Chain
    Monte Carlo (MCMC) methods for posterior simulation.  See the 'revdbayes' 
    website for more information, documentation and examples.
Computation of (direct and indirect) revealed preferences, fast non-parametric tests of rationality axioms (WARP, SARP, GARP), simulation of axiom-consistent data, and detection of axiom-consistent subpopulations.
R Markdown format for 'reveal.js' presentations, a framework 
  for easily creating beautiful presentations using HTML.
An implementation of the reverse ecology framework. Reverse ecology
    refers to the use of genomics to study ecology with no a priori assumptions
    about the organism(s) under consideration, linking organisms to their
    environment. It allows researchers to reconstruct the metabolic networks and
    study the ecology of poorly characterized microbial species from their
    genomic information, and has substantial potentials for microbial community
    ecological analysis.
Adjusts the weights of survey respondents so that the
        marginal distributions of certain variables fit more closely to
        those from a more precise source (e.g. Census Bureau's data).
A friendly interface for the construction of regular expressions.
Provides convenience functions to communicate with
    an Experigen server: Experigen (<http://github.com/aquincum/experigen>)
    is an online framework for creating  linguistic experiments,
    and it stores the results on a dedicated server. This package can be
    used to retrieve the results from the server, and it is especially
    helpful with registered experiments, as authentication with the server
    has to happen.
This package wraps some of the matrix exponentiation utilities from EXPOKIT (http://www.maths.uq.edu.au/expokit/), a FORTRAN library that is widely recommended for matrix exponentiation (Sidje RB, 1998. "Expokit: A Software Package for Computing Matrix Exponentials." ACM Trans. Math. Softw. 24(1): 130-156).  EXPOKIT includes functions for exponentiating both small, dense matrices, and large, sparse matrices (in sparse matrices, most of the cells have value 0).  Rapid matrix exponentiation is useful in phylogenetics when we have a large number of states (as we do when we are inferring the history of transitions between the possible geographic ranges of a species), but is probably useful in other ways as well.
Provides an interface to the Facebook API.
A collection of fast (utility) functions for data analysis. Column- and row- wise means, medians, variances, minimums, maximums, many t, F and G-square tests, many regressions (normal, logistic, Poisson), Mahalanobis distance are some of the many fast functions.
Returns environmental data such as air temperature,
        precipitation rate and wind speed from the FetchClimate Web service (<http://fetchclimate.org/>)
        based on user specified arguments such as geographical regions or coordinates and time bounds.
This package facilitates searching for and downloading seismic time series in miniSEED format (a minimalist version of the Standard for the Exchange of Earthquake Data) from International Federation of Digital Seismograph Networks repositories. This package can also be used to gather information about seismic networks (stations, channels, locations, etc) and find historical earthquake data (origins, magnitudes, etc).
An R implementation of the random ferns classifier by Ozuysal et
    al., modified for generic and multi-label classification and featuring OOB error
    approximation and importance measure.
RFGLS  uses a generalized least-squares method to perform single-marker association analysis, 
  in datasets of nuclear families containing parents, twins, and/or adoptees
Variable selection tools for groups of variables and functional data based on a new grouped variable importance with random forests.
An interface to 'figshare' (http://figshare.com), a scientific repository to archive and assign 'DOIs' to data, software, figures, and more.
A file set refers to a set of files located in one or more directories on the file system.  This package provides classes and methods to locate, setup, subset, navigate and iterate such sets.  The API is designed such that these classes can be extended via inheritance to provide a richer API for special file formats.  Moreover, a specific name format is defined such that filenames and directories can be considered to have full names which consists of a name followed by comma-separated tags.  This adds additional flexibility to identify file sets and individual files.  NOTE: This package's API should be considered to be in an beta stage.  Its main purpose is currently to support the aroma.* packages, where it is one of the main core components; if you decide to build on top of this package, please contact the author first.
Japanese stock market from Yahoo!-finance-Japan
A set of add on tools for the 'randomForest' package.
A programmatic interface to http://www.fishbase.org, re-written
    based on an accompanying 'RESTful' API. Access tables describing over 30,000
    species of fish, their biology, ecology, morphology, and more. This package also
    supports experimental access to http://www.sealifebase.org data, which contains
    nearly 200,000 species records for all types of aquatic life not covered by
    'FishBase.'
A programmatic interface to 'openfisheries.org'. This package is
    part of the 'rOpenSci' suite (http://ropensci.org).
R estimation and inference for linear models.  Estimation
        is for general scores and a library of commonly used score
        functions is included.
Basic R interface for the 'FLANN' C++ library version 1.8.4 written by Marius Muja
             and David Lowe. K-nearest neighbours searching and radius searching.
RFLPtools provides functions to analyse DNA fragment samples 
  (i.e. derived from RFLP-analysis) and standalone BLAST report files 
  (i.e. DNA sequence analysis).
A collection of tools for multivariate analysis of metabolomics
    data, which includes several preprocessing methods (normalization, scaling)
    and various exploration and data visualization techniques (Principal
    Components Analysis and Multi Dimensional Scaling). The core of the package
    is the Random Forest algorithm used for the construction, optimization and
    validation of classification models with the aim of identifying potentially
    relevant biomarkers.
Functionality required to efficiently use R with MarkLogic NoSQL Database Server, <http://www.marklogic.com/what-is-marklogic/>. Many basic and complex R operations are pushed down into the database, which removes the main memory boundary of R and allows to make full use of MarkLogic server. In order to use the package you need a MarkLogic Server version 8 or higher.
Various tools for handling fuzzy measures, calculating Shapley value and Interaction index,  Choquet and Sugeno integrals, as well as fitting fuzzy measures to empirical data are provided. Construction of fuzzy measures from empirical data is done by solving a linear programming problem by using 'lpsolve' package, whose source in C adapted to the R environment 
  is included. The description of the basic theory of fuzzy measures is in the manual in the Doc folder in this package.
R access to the 'FOAAS' (F... Off As A Service) web service is provided.
Graphics for statistics on a sphere, as applied to geological fault data, crystallography, earthquake focal mechanisms, radiation patterns, ternary plots and geographical/geological maps.  Non-double couple plotting of focal spheres and source type maps are included for statistical analysis of moment tensors.
Insert, update,
    retrieve, delete and bulk operate datasets with a SaaS based CRM
    Salesforce.com and a PaaS based application platform Force.com from R.
Contains all the code examples in the book "R for Dummies" (1st
    edition). You can view the table of contents as well as the sample code for each
    chapter.
A modified version (with great help from Ian J. Wilson) of Ian J. Wilson's program BATWING for calculating forensic trace-suspect match probabilities.
Set of tools designed for forest inventory analysis.
The R Formatter formats R source code. It is very much based on
    formatR, but tries to improve it by heuristics. For example, spaces can be
    forced around the division operator "/".
Estimate significance of importance metrics
    for a Random Forest model by permuting the response
    variable. Produces null distribution of importance
    metrics for each predictor variable and p-value of
    observed. Provides summary and visualization functions for 'randomForest' 
    results.
An R interface to a modified version of the Free Evolutionary Algorithm Kit FrEAK. FrEAK is a toolkit written in Java to design and analyze evolutionary algorithms. Both the R interface and an extended version of FrEAK are contained in the RFreak package. For more information on FrEAK see http://sourceforge.net/projects/freak427/.
Uses the lm() and glm() functions to fit models
    generated from a feasible solution algorithm. The feasible solution algorithm comes up with model forms of a
    specific type that can have fixed variables, higher order interactions and their
    lower order terms.
An efficient C++ based implementation of "Follow The (Proximally) Regularized Leader" online learning algorithm.
  This algorithm was proposed in McMahan et al. (2013) <DOI:10.1145/2487575.2488200>.
Utilities for Random Forest model selection, class balance
    correction, significance test, cross validation and partial dependency
    plots.
Provides functions for accessing and retrieving data from the
    Google Analytics APIs (https://developers.google.com/analytics/). Supports
    OAuth 2.0 authorization. Package provides access to the Management, Core
    Reporting, Multi-Channel Funnels Reporting, Real Time Reporting and
    Metadata APIs. Access to all the Google Analytics accounts which the user
    has access to. Auto-pagination to return more than 10,000 rows of the
    results by combining multiple data requests. Also package provides
    shiny app to explore the core reporting API dimensions and metrics.
An Interface to the GA4GH API that allows users to easily GET responses and POST requests to
  GA4GH Servers. See <http://ga4gh.org> for more information about the GA4GH project.
This package was created to analyze multi-level one-way
        experimental designs. It is designed to handle vectorized
        observation and factor data where there are unequal sample
        sizes and population variance homogeneity can not be assumed.
        To conduct the Gabriel test, create two vectors: one for your 
        observations and one for the factor level of each observation. 
        The function, rgabriel, conduct the test and save the output as
        a vector to input into the gabriel.plot function, which produces 
        a confidence interval plot for Multiple Comparison.
This package implements a Gamma convolution model for
        background correction.
A programmatic interface to the Web Service methods
    provided by the Global Biodiversity Information Facility ('GBIF'; 
    <http://www.gbif.org/developer/summary>). 'GBIF' is a database 
    of species occurrence records from sources all over the globe. 
    'rgbif' includes functions for searching for taxonomic names, 
    retrieving information on data providers, getting species occurrence 
    records, and getting counts of occurrence records.
We utilize  approximate Bayesian machinery to fit two-level conjugate hierarchical models on overdispersed Gaussian, Poisson, and Binomial data and evaluates whether the resulting approximate Bayesian interval estimates for random effects meet the nominal confidence levels via frequency coverage evaluation. The data that Rgbp assumes comprise observed sufficient statistic for each random effect, such as an average or a proportion of each group, without population-level data.  The approximate Bayesian tool equipped with the adjustment for density maximization produces approximate point and interval estimates for model parameters including second-level variance component, regression coefficients, and random effect. For the Binomial data, the package provides an option to produce  posterior samples of all the model parameters via the acceptance-rejection method. The package provides a quick way to evaluate coverage rates of the resultant Bayesian interval estimates for random effects via a parametric bootstrapping, which we call frequency method checking.
Multi-block data analysis concerns the analysis of several sets
    of variables (blocks) observed on the same group of individuals. The main aims
    of the RGCCA package are: (i) to study the relationships between blocks and
    (ii) to identify subsets of variables of each block which are active in their
    relationships with the other blocks.
Thin plate spline fitting and prediction
Provides bindings to Frank Warmerdam's Geospatial Data Abstraction Library (GDAL) (>= 1.6.3) and access to projection/transformation operations from the PROJ.4 library. The GDAL and PROJ.4 libraries are external to the package, and, when installing the package from source, must be correctly installed first. Both GDAL raster and OGR vector map data can be imported into R, and GDAL raster data and OGR vector data exported. Use is made of classes defined in the sp package. Windows and Mac Intel OS X binaries (including GDAL, PROJ.4 and Expat) are provided on CRAN.
A method 'generate()' is implemented in this package for the random
    generation of vector time series according to models obtained by 'RMAWGEN',
    'vars' or other packages.  This package was created to generalize the
    algorithms of the 'RMAWGEN' package for the analysis and generation of any
    environmental vector time series.
The method 'generate()' is extended for spatial multi-site
    stochastic generation of daily precipitation. It generates precipitation
    occurrence in several sites using logit regression (Generalized Linear
    Models) and D.S. Wilks' approach (Journal of Hydrology, 1998).
A genetic algorithm plus derivative optimizer.
Provides access to information from 
        <https://api.gouv.fr/explorer/geoapi/> about French 
        "Communes", "Departements" and "Regions".
Connectors to online and offline sources for taking IP addresses
    and geolocating them to country, city, timezone and other geographic ranges. For
    individual connectors, see the package index.
Interface to Geometry Engine - Open Source (GEOS) using the C API for topology operations on geometries. The GEOS library is external to the package, and, when installing the package from source, must be correctly installed first. Windows and Mac Intel OS X binaries are provided on CRAN.
Provides native wrappers for the functions available
    via the spatial quality REST API. See <http://bit.ly/bioinformatics_btw057>
    for more information on the API.
Create, read and write GEXF (Graph Exchange XML Format) graph files (used in Gephi and others). Using the XML package, it allows the user to easily build/read graph files including attributes, GEXF viz attributes (such as color, size, and position), network dynamics (for both edges and nodes) and edge weighting. Users can build/handle graphs element-by-element or massively through data-frames, visualize the graph on a web browser through "sigmajs" (a javascript library) and interact with the igraph package.
A command-line interface to 'GGobi', an interactive and dynamic
    graphics package. 'Rggobi' complements the graphical user interface of
    'GGobi' providing a way to fluidly transition between analysis and
    exploration, as well as automating common tasks.
Access WHO Global Health Observatory
    (<http://www.who.int/gho/>)
    data from R via the Athena web service
    (<http://apps.who.int/gho/data/node.resources.api>),
    an application program interface providing
    a simple query interface to the World
    Health Organization's data and statistics content.
This package provides some functions to create quizzes
  in the GIFT format. This format is used by several Virtual Learning
  Environments such as Moodle.
Provides medium to high level functions for 3D interactive graphics, including
    functions modelled on base graphics (plot3d(), etc.) as well as functions for
    constructing representations of geometric objects (cube3d(), etc.).  Output
    may be on screen using OpenGL, or to various standard 3D file formats including
    WebGL, PLY, OBJ, STL as well as 2D image formats, including PNG, Postscript, SVG, PGF.
A programmatic interface to the web service methods
    provided by Global Biotic Interactions (GloBI). GloBI provides 
    access to spatial-temporal species interaction records from 
    sources all over the world. rglobi provides methods to search 
    species interactions by location, interaction type, and 
    taxonomic name. In addition, it supports Cypher, a graph query
    language, to allow for executing custom queries on the GloBI 
    aggregate species interaction data set.
R interface to the GNU Linear Programming Kit.
  GLPK is open source software for solving large-scale linear programming (LP),
  mixed integer linear programming (MILP) and other related problems.
The contents of this package have
  been merged into rgl, so it is no longer needed.
Interface for gnuplot
        Based on gnuplot_i version 1.11, the GPL code from Nicolas Devillard.
Provides functions for accessing and retrieving data from the
    Google Analytics API
It fires a query to the API to get the unsampled data in R for Google Analytics Premium Accounts. It retrieves data from the Google drive document and stores it into the local drive. The path to the excel file is returned by this package. The user can read data from the excel file into R using read.csv() function.
Provides interface to Google Fit REST API v1 (see <https://developers.google.com/fit/rest/v1/reference/>).
Serves two purposes: (i) Provide a
        comfortable R interface to query the Google server for static
        maps, and (ii) Use the map as a background image to overlay
        plots within R. This requires proper coordinate scaling.
RGP is a simple modular Genetic Programming (GP) system build in
    pure R. In addition to general GP tasks, the system supports Symbolic
    Regression by GP through the familiar R model formula interface. GP
    individuals are represented as R expressions, an (optional) type system
    enables domain-specific function sets containing functions of diverse
    domain- and range types. A basic set of genetic operators for variation
    (mutation and crossover) and selection is provided.
RGP UI provides as modern web-based user interface to the modular
    Genetic Programming (GP) system RGP.
Geological Survey of Canada (GSC) functions for exploratory data analysis with applied geochemical data, with special application to the estimation of background ranges and identification of outliers, 'anomalies', to support mineral exploration and environmental studies.  Additional functions are provided to support analytical data QA/QC, ANOVA for investigations of field sampling and analytical variability, and utility tasks.  NOTE: function caplot() for concentration-area plots employs package 'akima', however, 'akima' is only licensed for not-for-profit use.  Therefore, not-for-profit users of 'rgr' will have to independently make package 'akima' available through library(....); and use of function caplot() by for-profit users will fail.
Data and Functions from the book R Graphics, Second Edition. There is a function to produce each figure in the book, plus several functions, classes, and methods defined in Chapter 8.
Interpreted interface between GRASS 7 geographical 
    information system and R, based on starting R from within the GRASS GIS
    environment, or running free-standing R in a temporary GRASS location;
    the package provides facilities for using all GRASS commands from the 
    R command line. This package may not be used for GRASS 6, for which
    spgrass6 should be used.
Integrates the Groovy scripting language with the R Project for Statistical Computing.
Facilities in the R language for programming
  graphical interfaces using Gtk, the Gimp Tool Kit.
Implementation of the affine-invariant method of Goodman & Weare (2010) <DOI:10.2140/camcos.2010.5.65>, a method of producing Monte-Carlo samples from a target distribution.
DBI/RJDBC interface to h2 database. h2 version 1.3.175 is included.
An R interface to the 'Handsontable' JavaScript library, which is a
    minimalist Excel-like data grid editor.  See <https://handsontable.com/> for details.
An R interface for the HealthData.gov data API.  For each data resource, you can filter results (server-side) to select subsets of data.
Performs the random heteroscedastic nested error regression model described in Kubokawa, Sugasawa, Ghosh and Chaudhuri (2016) <doi:10.5705/ss.202014.0070>.
Rho is used to test the generalization of inter rater reliability
    (IRR) statistics. Calculating rho starts by generating a large number of
    simulated, fully-coded data sets: a sizable collection of hypothetical
    populations, all of which have a kappa value below a given threshold -- which
    indicates unacceptable agreement. Then kappa is calculated on a sample from
    each of those sets in the collection to see if it is equal to or higher than
    the kappa in then real sample. If less than five percent of the distribution
    of samples from the simulated data sets is greater than actual observed kappa,
    the null hypothesis is rejected and one can conclude that if the two raters had
    coded the rest of the data, we would have acceptable agreement (kappa above the
    threshold).
Evaluating risk (that a patient arises a side effect) during hospitalization is the main purpose of this package. Several methods (Parametric, non parametric and De Vielder estimation) to estimate the risk constant (R) are implemented in this package. There are also functions to simulate the different models of this issue in order to quantify the previous estimators. It is necessary to read at least the first six pages of the report to understand the topic.
Function of apply style using 'MPI' provides better 'HPC' environment on R.
 and this package supports long vector, can deal with slightly big data.
Control the number of threads on 'BLAS' (Aka 'GotoBLAS', 'ACML' and 'MKL').
  and possible to control the number of threads in 'OpenMP'.
  get a number of logical cores and physical cores if feasible.
rHpcc is an R package providing an Interface between R and
        HPCC.Familiarity with ECL (Enterprise Control Language) is a
        must to use this package.HPCC is a massive parallel-processing
        computing platform that solves Big Data problems.ECL is the
        Enterprise Control Language designed specifically for huge data
        projects using the HPCC platform.Its extreme scalability comes
        from a design that allows you to leverage every query you
        create for re-use in subsequent queries as needed. To do this,
        ECL takes a dictionary approach to building queries wherein
        each ECL definition defines an Attribute. Each previously
        defined Attribute can then be used in succeeding ECL Attribute
        definitions as the language extends itself as you use it.
Allows users to import data files containing heartbeat	positions in the most broadly used formats, to remove outliers or points with unacceptable physiological values present in the time series, to plot HRV data, and to perform time domain, frequency domain and nonlinear HRV analysis.
This package offers functions to perform regularized
        Hotelling's T-square test for pathway or gene set analysis. The
        package is tailored for but not limited to proteomics data, in
        which sample sizes are often small, a large proportion of the
        data are missing and/or correlations may be present.
DEPRECATED. Do not start building new projects based on this package. Cross-platform alternatives are the following packages: bigmemory (CRAN), ff (CRAN), BufferedMatrix (Bioconductor).  The main usage of it was inside the aroma.affymetrix package. (The package currently provides a class representing a matrix where the actual data is stored in a binary format on the local file system.  This way the size limit of the data is set by the file system and not the memory.)
This package provides a set of tools for conducting exact
        or approximate randomization-based inference for experiments of
        arbitrary design. The primary functionality of the package is
        in the generation, manipulation and use of permutation matrices
        implied by given experimental designs. Among other features,
        the package facilitates estimation of average treatment
        effects, constant effects variance estimation, randomization
        inference for significance testing against sharp null
        hypotheses and visualization of data and results.
Computes attributable effects based confidence interval, permutation
    test confidence interval, or asymptotic confidence interval for the
    the average treatment effect on a binary outcome.
The Diceware method can be used to generate strong passphrases.
  In short, you roll a 6-faced dice 5 times in a row, the number obtained is
  matched against a dictionary of easily remembered words. By combining together 7
  words thus generated, you obtain a password that is relatively easy to remember,
  but would take several millions years (on average) for a powerful computer to guess.
Computes rarefaction curves, cumulated and mean species
        richness. Compares these estimates by means of randomization
        tests.
This package implements ridge fusion methodology for inverse covariance matrix estimation for use in quadratic discriminant analysis.  The package also contains function for model based clustering using ridge fusion for inverse matrix estimation, as well as tuning parameter selection functions.  We have also implemented QDA using joint inverse covariance estimation.
An interface to iDigBio's search API that allows downloading
  specimen records. Searches are returned as a data.frame. Other functions
  such as the metadata end points return lists of information. iDigBio is a US
  project focused on digitizing and serving museum specimen collections on the
  web. See <https://www.idigbio.org> for information on iDigBio.
An extension of the Kruskal-Wallis Test that allow
        selection of arbitrary reference group. Also provide Mean Ridit
        for each group. Mean Ridit of a group is an estimate of
        probability a random observation from that group will be
        greater than or equal to a random observation from reference
        group.
Allows to get weather data from Automated Surface Observing System (ASOS) stations (airports) in the
    whole world thanks to the Iowa Environment Mesonet website.
RIFS package provides functionality for generating &
        plotting prefractals in R^n with various protofractal sets and
        partition coefficient for iterative segments
Provides functions to read 'ImageJ' (http://imagej.nih.gov/ij/)
    Region of Interest (ROI) files, to plot the ROIs and to convert them to
    'spatstat' (http://spatstat.org/) spatial patterns.
A pure R implementation of the median cut algorithm.
    Extracts the dominant colors from an image, and turns them into
    a scale for use in plots or for fun!
Cloudera 'Impala', which is a massively parallel processing (MPP) SQL query engine runs natively in Apache Hadoop. 'RImpala' facilitates the connection and execution of distributed queries through 'R'. 'Impala' supports JDBC integration which 'RImpala' utilizes to establish the connection between 'R' and 'Impala'. Thanks to Mu Sigma for their continued support throughout the development of the package.
A programmatic interface to the
    API provided by the iNaturalist website.
Index structures allow quickly accessing elements from
        large collections. While btree are optimized for disk databases
        and ttree for ram databases we use hybrid static indexing which
        is quite optimal for R.
C++ classes to embed R in C++ applications
 The 'RInside' packages makes it easier to have "R inside" your C++ application
 by providing a C++ wrapper class providing the R interpreter.

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

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

    Doxygen-generated documentation of the C++ classes is available at the
 'RInside' website as well.
Functions to calculate several ecological indices of individual and population niche width
   (Araujo's E, clustering and pairwise similarity among individuals, IS, Petraitis' W, and Roughgarden's
   WIC/TNW) to assess individual specialization based on data of resource use. Resource use can be
   quantified by counts of categories, measures of mass/lenght or proportions. Monte Carlo resampling
   procedures are available for hypothesis testing against multinomial null models.
A wrapper for the 'Intro.js' library (For more info: <http://www.introjs.com>). 
  This package makes it easy to include step-by-step introductions, and clickable hints in a 'Shiny' 
  application. It supports both static introductions in the UI, and programmatic introductions from 
  the server-side.
Streamlined data import and export by making assumptions that
    the user is probably willing to make: 'import()' and 'export()' determine
    the data structure from the file extension, reasonable defaults are used for
    data import and export (e.g., 'stringsAsFactors=FALSE'), web-based import is
    natively supported (including from SSL/HTTPS), compressed files can be read
    directly without explicit decompression, and fast import packages are used where
    appropriate. An additional convenience function, 'convert()', provides a simple
    method for converting between file types.
Functions for the analysis of Quaternary science data, including
         constrained clustering, WA, WAPLS, IKFA, MLRC and MAT transfer 
         functions, and stratigraphic diagrams.
Utility functions and S3 classes for IPv4 and IPv6 addresses, including 
             conversion to and from binary representation.
A package including various functions for image processing and analysis. With this package is possible to process and analyse RGB, LAN (multispectral) and AVIRIS (hyperspectral) images. This packages also provides functions for reading JPEG files, extracted from the archived 'rimage' package.
Fits an Ising model to a binary dataset using L1 regularized
    logistic regression and extended BIC. Also includes a fast lasso logistic
    regression function for high-dimensional problems. Uses the 'libLBFGS'
    optimization library by Naoaki Okazaki.
Collection of functions designed to compute risk-based portfolios as described 
    in Ardia et al. (2016) <doi:10.2139/ssrn.2650644> and Ardia et al. (2017) <doi:10.21105/joss.00171>.
Computes risk measures from data, as well as performs risk management procedures.
Risk regression models for survival analysis with and without
    competing risks.
Compute time-dependent Incident/dynamic accuracy measures
        (ROC curve, AUC, integrated AUC )from censored survival data
        under proportional or non-proportional hazard assumption of
        Heagerty & Zheng (Biometrics, Vol 61 No 1, 2005, PP 92-105).
Implements efficient simulation procedures to estimate tail loss probabilities and conditional excess for a stock portfolio. The log-returns are assumed to follow a t-copula model with generalized hyperbolic or t marginals.
A set of tools to extract bibliographic content from the National
    Center for Biotechnology Information (NCBI) databases, including PubMed. The
    name RISmed is a portmanteau of RIS (for Research Information Systems, a common
    tag format for bibliographic data) and PubMed.
Implements the simulation and regression of
        integrated Isothermal Titration Calorimetry (ITC) data using
        the most commonly used one-to-one binding reaction model.
A simple yet powerful script editor built natively in R with tcltk.
An interface to the Integrated Taxonomic Information System ('ITIS')
    (<https://www.itis.gov>). Includes functions to work with the 'ITIS' REST
    'API' methods (<https://www.itis.gov/ws_description.html>), as well as the
    'Solr' web service (<https://www.itis.gov/solr_documentation.html>).
Finds a robust instrumental variables estimator using a
             high breakdown point S-estimator of multivariate location
             and scatter matrix.
Reads river network shape files and computes network distances.
    Also included are a variety of computation and graphical tools designed 
    for fisheries telemetry research, such as minimum home range, kernel density 
    estimation, and clustering analysis using empirical k-functions with 
    a bootstrap envelope.  Tools are also provided for editing the river 
    networks, meaning there is no reliance on external software.
Functions for reading, analysing and plotting river networks.
  For this package, river networks consist of sections and nodes with associated attributes, 
  e.g. to characterise their morphological, chemical and biological state.
  The package provides functions to read this data from text files, to analyse the network
  structure and network paths and regions consisting of sections and nodes that fulfill
  prescribed criteria, and to plot the river network and associated properties.
Sankey plots are a type of diagram that is convenient to
    illustrate how flow of information, resources etc. separates and joins,
    much like observing how rivers split and merge. For example, they can be
    used to compare different clusterings.
This R package is a flexible and efficient tool to 
    visualise both quantitative and qualitative data from river surveys. 
    It can be used to produce diagrams with the topological structure of 
    the river network.
This package is devoted to the IVIVC linear level A with
        numerical deconvolution method. The latter is working for
        inequal and incompatible timepoints between impulse and
        response curves. A numerical convolution method is also
        available. Application domains include pharamaceutical industry
        QA/QC and R&D together with academic research.
A tool for undergraduate and graduate courses in open-channel
    hydraulics. Provides functions for computing normal and critical depths,
    steady-state water surface profiles (e.g. backwater curves) and unsteady flow
    computations (e.g. flood wave routing).
Bayesian analysis of CGH microarrays fitting Hidden Markov
        Chain models. The selection of the number of states is made via
        their posterior probability computed by Reversible Jump Markov
        Chain Monte Carlo Methods. Also returns probabilistic common
        regions for gains/losses.
Jade is a high performance template engine heavily influenced by
    Haml and implemented with JavaScript for node and browsers.
Low-level interface to Java VM very much like .C/.Call and friends. Allows creation of objects, calling methods and accessing fields.
RJDBC is an implementation of R's DBI interface using JDBC as a back-end. This allows R to connect to any DBMS that has a JDBC driver.
A series of useful functions, some available in different
  forms in other packages, but which have been extended, sped up, or
  otherwise modified in some way considered useful to the author.
Performs the JIVE decompositions on a list of data sets when the data share a dimension, returning low-rank matrices that capture the joint and individual structure of the data. It provides two methods of rank selection when the rank is unknown, a permutation test and a BIC selection algorithm. Also included in the package are three plotting functions for visualizing the variance attributed to each data source: a bar plot that shows the percentages of the variability attributable to joint and individual structure, a heatmap that shows the structure of the variability, and principal component plots.
R-interface to gene drop programs from Alun Thomas' Java Programs for Statistical Genetics and Computational Statistics (JPSGCS)
R interface to statistical database organized by Japanese government (http://statdb.nstac.go.jp/)
Provides functions to retrieve data and metadata from providers 
			 that disseminate data by means of SDMX web services. 
			 SDMX (Statistical Data and Metadata eXchange) is a standard that 
			 has been developed with the aim of simplifying the exchange of 
			 statistical information. 
			 More about the SDMX standard and the SDMX Web Services 
			 can be found at: http://sdmx.org .
Converts R object into JSON objects and vice-versa
Consumer for APIs that Follow the JSON API Specification
    (<http://jsonapi.org/>). Package mostly consumes data - with experimental
    support for serving JSON API data.
This is a package that allows conversion to and from 
  data in Javascript object notation (JSON) format.
  This allows R objects to be inserted into Javascript/ECMAScript/ActionScript code
  and allows R programmers to read and convert JSON content to R objects.
  This is an alternative to rjson package. Originally, that was too slow for converting large R objects to JSON
  and was not extensible.  rjson's performance is now similar to this package, and perhaps slightly faster in some cases.
  This package uses methods and is readily extensible by defining methods for different classes, 
  vectorized operations, and C code and callbacks to R functions for deserializing JSON objects to R. 
  The two packages intentionally share the same basic interface. This package (RJSONIO) has many additional
  options to allow customizing the generation and processing of JSON content.
  This package uses libjson rather than implementing yet another JSON parser. The aim is to support
  other general projects by building on their work, providing feedback and benefit from their ongoing development.
Read and write the 'JSON-stat' format (http://json-stat.org) to and
    from (lists of) R data frames. Not all features are supported, especially
    the extensive metadata features of 'JSON-stat'.
R interface to Python via Jython allowing R to call python
        code.
Apache 'Kafka' is an open-source message broker project developed by the Apache Software Foundation which can be thought of as a distributed, partitioned, replicated commit log service.At a high level, producers send messages over the network to the 'Kafka' cluster which in turn serves them up to consumers.See <http://kafka.apache.org/> for more information.Functions included in this package enable:1.Creating 'Kafka' producer 2.Writing messages to a topic 3.Closing 'Kafka' producer 4.Creating 'Kafka' consumer 5.Reading messages from a topic 6.Closing 'Kafka' consumer. The jars required for this package are included in a separate package 'rkafkajars'.Thanks to Mu Sigma for their continued support throughout the development of the package.
The 'rkafkajars' package collects all the external jars required for the 'rkafka' package.
An R interface to KEA (Version 5.0).
  KEA (for Keyphrase Extraction Algorithm) allows for extracting
  keyphrases from text documents. It can be either used for free
  indexing or for indexing with a controlled vocabulary. For more
  information see <http://www.nzdl.org/Kea/>.
External jars required for package RKEA.
KEEL is a popular Java software for a large number of different knowledge data discovery tasks.
    This package takes the advantages of KEEL and R, allowing to use KEEL algorithms in simple R code.
    The implemented R code layer between R and KEEL makes easy both using KEEL algorithms in R as implementing new algorithms for 'RKEEL' in a very simple way.
    It includes more than 100 algorithms for classification, regression, association rules and preprocess, which allows a more complete experimentation process.
    For more information about KEEL, see <http://www.keel.es/>.
KEEL is a popular Java software for a large number of different knowledge data discovery tasks. Furthermore, RKEEL is a package with a R code layer between R and KEEL, for using KEEL in R code. This package includes the datasets from KEEL in .dat format for its use in RKEEL package. For more information about KEEL, see <http://www.keel.es/>.
KEEL is a popular Java software for a large number of different knowledge data discovery tasks. Furthermore, 'RKEEL' is a package with a R code layer between R and KEEL, for using KEEL in R code. This package downloads and install the .jar files necessary for 'RKEEL' algorithms execution. For more information about KEEL, see <http://www.keel.es/>.
Applies methods used to estimate animal homerange, but
    instead of geospatial coordinates, we use isotopic coordinates. The estimation
    methods include: 1) 2-dimensional bivariate normal kernel utilization density
    estimator, 2) bivariate normal ellipse estimator, and 3) minimum convex polygon
    estimator, all applied to stable isotope data. Additionally, functions to
    determine niche area, polygon overlap between groups and levels (confidence
    contours) and plotting capabilities.
An interface of R to Klout API v2. It fetches Klout Score for a Twitter Username/handle in real time. Klout is a website and mobile app that uses social media analytics to rank its users according to online social influence via the "Klout Score", which is a numerical value between 1 and 100.  In determining the user score, Klout measures the size of a user's social media network and correlates the content created to measure how other users interact with that content.
Random knn classification and regression are implemented. Random knn based feature selection methods are also included. The approaches are mainly developed for high-dimensional data with small sample size.
Contains functions for the topological analysis of polymers, with a focus on protein structures.
Contains function rkt which computes the Mann-Kendall test (MK) and the Seasonal and the Regional Kendall Tests for trend (SKT and RKT) and  Theil-Sen's slope estimator.
This package provides functionality to read files
    containing observations which consist of arbitrary key/value
    pairs.
Functions and Datasets Required for ST370 class
The LabKey client library for R makes it easy for R users to
    load live data from a LabKey Server, <http://www.labkey.com/>,
    into the R environment for analysis, provided users have permissions
    to read the data. It also enables R users to insert, update, and
    delete records stored on a LabKey Server, provided they have appropriate
    permissions to do so.
Standardized methods for calculating common important derived
    physical features of lakes including water density based based on
    temperature, thermal layers, thermocline depth, lake number, Wedderburn
    number, Schmidt stability and others.
Read and write 'las' and 'laz' binary file formats. The LAS file format is a public file format for the interchange of 3-dimensional point cloud data between data users. The LAS specifications are approved by the American Society for Photogrammetry and Remote Sensing. The LAZ file format is an open and lossless compression scheme for binary LAS format versions 1.0 to 1.3.
Analyzes data from repeated low-dose challenge experiments and provide vaccine efficacy estimates. In addition, this package can provide guidance to design repeated low-dose challenge studies.
Linguistic Descriptions of Complex Phenomena (LDCP) is an architecture and methodology that allows us to model complex phenomena, interpreting input data, and generating automatic text reports customized to the user needs (see <doi:10.1016/j.ins.2016.11.002> and <doi:10.1007/s00500-016-2430-5>). The proposed package contains a set of methods that facilitates the development of LDCP systems. It main goal is increasing the visibility and practical use of this research line.
Display spatial data with interactive maps powered by the 
  open-source JavaScript library 'Leaflet' (see <http://leafletjs.com/>). Maps can be rendered in a web browser or
  displayed in the HTML viewer pane of 'RStudio'. This package is designed to be easy to use and
  can create complex maps with vector and raster data, web served map tiles and interface elements.
Provides an interface to the C implementation of the
        random number generator with multiple independent streams
        developed by L'Ecuyer et al (2002). The main purpose of this
        package is to enable the use of this random number generator in
        parallel R applications.
An R interface for C library libeemd for performing the ensemble
    empirical mode decomposition (EEMD), its complete variant (CEEMDAN) or the
    regular empirical mode decomposition (EMD).
Set of tools for reading, processing and visualizing small set 
	of LiDAR (Light Detection and Ranging) data for forest inventory applications.
An interface to LINDO API. Supports Linear, Integer, Quadratic, Conic, General Nonlinear, Global, and Stochastic Programming models. To download the trial version LINDO API, please visit www.lindo.com/rlindo.
A series of functions that allow users
    to access the 'LinkedIn' API to get information about connections,
    search for people and jobs, share updates with their network,
    and create group discussions.  For more information about using
    the API please visit <https://developer.linkedin.com/>.
Provides a set of functions for data manipulation with
    list objects, including mapping, filtering, grouping, sorting,
    updating, searching, and other useful functions. Most functions
    are designed to be pipeline friendly so that data processing with
    lists can be chained.
Robust fitting of linear model which can take response in matrix form.
Utilities for writing to 'LibreOffice Writer'
  (see  <https://www.libreoffice.org/discover/writer/> for more information)
   documents using the 'Python-UNO' bridge.
R parallel implementation of Local Outlier Factor(LOF) which uses multiple CPUs to significantly speed up the LOF computation for large datasets. (Note: The overall performance depends on the computers especially the number of the cores).It also supports multiple k values to be calculated in parallel, as well as various distance measures in addition to the default Euclidean distance.
Processing logical operations such as AND/OR/NOT operations
    dynamically. It also handles nesting in the operations.
Rapid, simulation-based exact (restricted) likelihood ratio tests
    for testing the presence of variance components/nonparametric terms for
    models fit with nlme::lme(),lme4::lmer(), lmeTest::lmer(), gamm4::gamm4(),
    mgcv::gamm() and SemiPar::spm().
R interface to LTP-Cloud service for Natural Language Processing
    in Chinese. For more details please visit http://www.ltp-cloud.com/. Visit 
    https://github.com/hetong007/rLTP for up-to-date version.
A collection of functions to simulate luminescence signals in the
    mineral quartz based on published models.
A collection of 'shiny' applications for the R package
    'Luminescence'. These mainly, but not exclusively, include applications for
    plotting chronometric data from e.g. luminescence or radiocarbon dating. It
    further provides access to bootstraps tooltip and popover functionality and
    contains the 'jscolor.js' library with a custom 'shiny' output binding.
R implementation of the common parsing tools 'lex' and 'yacc'.
RM2 is a simple package that implements functions used in
        revenue management and pricing environments.
Uses refined moving average filter based on the optimal and data-driven moving average lag q or smoothing spline to estimate trend and seasonal components, as well as irregularity (residuals) for univariate time series or data.
An EM algorithm to fit Mallows' Models to full or partial
        rankings, with or without ties.
An implementation of an algorithm family for continuous
    optimization called memetic algorithms with local search chains
    (MA-LS-Chains). Memetic algorithms are hybridizations of genetic
    algorithms with local search methods. They are especially suited
    for continuous optimization.
Edit and simplify 'geojson' and 'Spatial' objects.
    This is wrapper around the 'mapshaper' 'javascript' library
    <https://github.com/mbloch/mapshaper/> to perform topologically-aware
    polygon simplification, as well as other operations such as clipping,
    erasing, dissolving, and converting 'multi-part' to 'single-part' geometries.
    It relies on the 'geojsonio' package for working with 'geojson' objects,
    and the 'sp' and 'rgdal' packages for working with 'Spatial' objects.
Given a time series of daily temperatures, the package provides tools
    to detect extreme thermal events, including marine heat waves, and to
    calculate the exceedances above or below specified threshold values.
    It outputs the properties of all detected events and exceedances.
An interface to the software package MARK that constructs input
    files for MARK and extracts the output. MARK was developed by Gary White
    and is freely available at <http://www.phidot.org/software/mark/downloads/>
    but is not open source.
Convert R Markdown documents into a variety of formats.
rmatio is a package for reading and writing Matlab MAT
    files from R. The rmatio package supports reading MAT version 4,
    MAT version 5 and MAT compressed version 5. The rmatio package can
    write version 5 MAT files and version 5 files with variable
    compression.
Methods readMat() and writeMat() for reading and writing MAT files.  For user with MATLAB v6 or newer installed (either locally or on a remote host), the package also provides methods for controlling MATLAB (trademark) via R and sending and retrieving data between R and MATLAB.
S3 and S4 functions are implemented for spatial multi-site
    stochastic generation of daily time series of temperature and
    precipitation. These tools make use of Vector AutoRegressive models (VARs).
    The weather generator model is then saved as an object and is calibrated by
    daily instrumental "Gaussianized" time series through the 'vars' package
    tools. Once obtained this model, it can it can be used for weather
    generations and be adapted to work with several climatic monthly time
    series.
Functions for fitting, diagnosing and predicting from a
        class of Markov models.
MCFS-ID (Monte Carlo Feature Selection and Interdependency Discovery) is a 
  Monte Carlo method-based tool for feature selection. It also allows for the discovery of interdependencies between the relevant features. MCFS-ID is particularly suitable for the analysis of high-dimensional, 'small n large p' transactional and biological data.
Compute the repeated measures correlation, a statistical technique
    for determining the overall within-individual relationship among paired measures
    assessed on two or more occasions, first introduced by Bland and Altman (1995).
    Includes functions for diagnostics, p-value, effect size with confidence
    interval including optional bootstrapping, as well as graphing. Also includes
    several example datasets.
HTML formats and templates for 'rmarkdown' documents, with some extra
    features such as automatic table of contents, lightboxed figures, dynamic
    crosstab helper.
A series of functions to aid in repeated tasks for Rmd documents. All details are to my personal preference, though I am happy to add flexibility if there are use cases I am missing. I will continue updating with new functions as I add utility functions for myself.
'R' 'Markdown' format for 'shower' presentations, see
   <https://github.com/shower/shower>.
We provide functions to compute confidence
    intervals (CIs) for a well-defined nonlinear function of the model
    parameters (e.g., product of k coefficients) in single--level and
    multilevel structural equation models.
Functions for simple fixed and random effects
        meta-analysis for two-sample comparisons and cumulative
        meta-analyses. Draws standard summary plots, funnel plots, and
        computes summaries and tests for association and heterogeneity
An interface between R and the metasim simulation engine.
    The simulation environment is documented in: "Strand, A.(2002) <DOI:10.1046/j.1471-8286.2002.00208.x> Metasim 1.0: an individual-based environment for simulating population genetics of 
    complex population dynamics. Mol. Ecol. Notes. 
    Please see the vignettes CreatingLandscapes and Simulating to get some ideas on how to use the packages.  
    See the rmetasim vignette to get an overview and to see important changes to the 
    code in the most recent version.
Methods that simplify the setup of S3 generic functions and S3 methods.  Major effort has been made in making definition of methods as simple as possible with a minimum of maintenance for package developers.  For example, generic functions are created automatically, if missing, and naming conflict are automatically solved, if possible.  The method setMethodS3() is a good start for those who in the future may want to migrate to S4.  This is a cross-platform package implemented in pure R that generates standard S3 methods.
Feasible multivariate GARCH models including DCC, GO-GARCH and Copula-GARCH.
Facilitates the use of data mining algorithms in classification and regression (including time series forecasting) tasks by presenting a short and coherent set of functions. Versions: 1.4.2 new NMAE metric, "xgboost" and "cv.glmnet" models (16 classification and 18 regression models); 1.4.1 new tutorial and more robust version; 1.4 - new classification and regression models/algorithms, with a total of 14 classification and 15 regression methods, including: Decision Trees, Neural Networks, Support Vector Machines, Random Forests, Bagging and Boosting; 1.3 and 1.3.1 - new classification and regression metrics (improved mmetric function); 1.2 - new input importance methods (improved Importance function); 1.0 - first version.
The Rmisc library contains many functions useful for data analysis
    and utility operations.
Interface of MIXMOD software for supervised, unsupervised and semi-Supervised classification with MIXture MODelling.
The Rmixmod package provides model-based clustering by fitting a mixture model (e.g. Gaussian components for quantitative continuous data) to the data and identifying each cluster with one of its components. The number of components can be determined from the data, typically using the BIC criterion. In practice, however, individual clusters can be poorly fitted by Gaussian distributions, and in that case model-based clustering tends to represent one non-Gaussian cluster by a mixture of two or more Gaussian components. If the number of mixture components is interpreted as the number of clusters, this can lead to overestimation of the number of clusters. This is because BIC selects the number of mixture components needed to provide a good approximation to the density. This package, RmixmodCombi, according to \emph{Combining Mixture Components for Clustering} by J.P. Baudry, A.E. Raftery, G. Celeux, K. Lo, R. Gottardo, combines the components of the EM/BIC solution (provided by Rmixmod) hierarchically according to an entropy criterion. This yields a clustering for each number of clusters less than or equal to K. These clusterings can be compared on substantive grounds, and we also provide a way of selecting the number of clusters via a piecewise linear regression fit to the (possibly rescaled) entropy plot.
Provides an interface to many endpoints of Mixpanel's Data Export, Engage and JQL API. The R functions allow for event and profile data export as well as for segmentation, retention, funnel and addiction analysis. Results are always parsed into convenient R objects. Furthermore it is possible to load and update profiles.
Sundry discrete probability distributions and helper functions.
A collection of miscellaneous functions for medical data analysis. Visualization
 of multidimensional data, diagnostic test calibration, pairwise tests for qualitative variables and outcome simulation.
Connect R with MOA (Massive Online Analysis -
    http://moa.cms.waikato.ac.nz) to build classification models and
    regression models on streaming data or out-of-RAM data
External jars required for package RMOA. RMOA is a framework to
    build data stream models on top of MOA (Massive Online Analysis -
    http://moa.cms.waikato.ac.nz)
MongoDB Database interface for R. The interface is provided via Java calls to the mongo-java-driver.
An interface to the MOSEK optimization library designed to
        solve large-scale mathematical optimization problems. Supports
        linear, quadratic and second order cone optimization
        with/without integer variables, in addition to the more general
        separable convex problems.  Trial and free academic licenses
        available at http://www.mosek.com.
Performs probability mass function estimation with nonparametric mixtures of rounded kernels.
Arithmetic (via S4 classes and methods) for
  arbitrary precision floating point numbers, including transcendental
  ("special") functions.  To this end, Rmpfr interfaces to
  the LGPL'ed MPFR (Multiple Precision Floating-Point Reliable) Library
  which itself is based on the GMP (GNU Multiple Precision) Library.
An interface (wrapper) to MPI APIs. It also 
	     provides interactive R manager and worker environment.
Analysis of oxygen consumption data generated by Loligo (R) Systems respirometry equipment. The package includes a function for loading data output by Loligo's 'AutoResp' software (get.witrox.data()), functions for calculating metabolic rates over user-specified time intervals, extracting critical points from data using broken stick regressions based on Yeager and Ultsch (<DOI:10.1086/physzool.62.4.30157935>), and easy functions for converting between different units of barometric pressure.
This package contains functions and S3 methods for spatial
    multi-site stochastic generation of daily precipitation. It generates
    precipitation occurrence in several sites using Wilks' Approach (1998).
    Bugs/comments/questions/collaboration of any kind are warmly welcomed.
Regression modeling, testing, estimation, validation,
	graphics, prediction, and typesetting by storing enhanced model design
	attributes in the fit.  'rms' is a collection of functions that
	assist with and streamline modeling.  It also contains functions for
	binary and ordinal logistic regression models, ordinal models for
  continuous Y with a variety of distribution families, and the Buckley-James
	multiple regression model for right-censored responses, and implements
	penalized maximum likelihood estimation for logistic and ordinary
	linear models.  'rms' works with almost any regression model, but it
	was especially written to work with binary or ordinal regression
	models, Cox regression, accelerated failure time models,
	ordinary linear models,	the Buckley-James model, generalized least
	squares for serially or spatially correlated observations, generalized
	linear models, and quantile regression.
Display a randomly selected quote about Richard M. Stallman
 based on the collection in the 'GNU Octave' function 'fact()' which was
 aggregated by Jordi Gutiérrez Hermoso based on the (now defunct) site
 stallmanfacts.com (which is accessible only via <http://archive.org>).
This package can be used to test any simple null
        hypothesis using the root-mean-square goodness of fit test.
        Monte Carlo estimation is used to calculate the associated
        P-value.
An algorithm which can be used to determine an objective threshold for signal-noise separation in large random matrices (correlation matrices, mutual information matrices, network adjacency matrices) is provided. The package makes use of the results of Random Matrix Theory (RMT). The algorithm increments a suppositional threshold monotonically, thereby recording the eigenvalue spacing distribution of the matrix. According to RMT, that distribution undergoes a characteristic change when the threshold properly separates signal from noise. By using the algorithm, the modular structure of a matrix - or of the corresponding network - can be unraveled.
Functions for working with the Tracy-Widom laws and other distributions 
    related to the eigenvalues of large Wishart matrices.
    The tables for computing the Tracy-Widom densities and distribution
    functions were computed by Momar Dieng's MATLAB package "RMLab"
    (formerly available on his homepage at 
    http://math.arizona.edu/~momar/research.htm ).
    This package is part of a collaboration between Iain Johnstone, 
    Zongming Ma, Patrick Perry, and Morteza Shahram.  It will soon be
    replaced by a package with more accuracy and built-in support for
    relevant statistical tests.
Some basic features of MUMPS (MUltifrontal Massively Parallel
	sparse direct Solver) are wrapped in a Rmumps class. It can be used
	for storing a sparse matrix and sequentially solving corresponding linear system
	with one or many right hand sides (in their turn dense or sparse).
	There is a possibility to do separately symbolic analysis, LU factorization and system solving.
A toolkit of functions for nonlinear regression and repeated
    measurements not to be used by itself but called by other Lindsey packages such
    as 'gnlm', 'stable', 'growth', 'repeated', and 'event' 
    (available at <http://www.commanster.eu/rcode.html>).
Rasch model and extensions for survey data, using Conditional Maximum likelihood (CML).
Implements 'DBI' Interface to 'MySQL' and 'MariaDB' Databases.
This package is designed to streamline several of the common steps for RNA-seq
              analysis, including differential expression and variant discovery.
              For the development build, or to contribute changes to this package,
              please see our repository at https://bitbucket.org/petersmp/rnaseqwrapper/
GUI to explore high dimensional data (including image data) using graphs as navigational infrastructure.
Image data used as examples in the RnavGraph R package.
        See the demos in the RnavGraph package.
Provides a set of functions to access a data visualization web service. For more information and a tutorial on how to use it, see https://navicell.curie.fr/pages/nav_web_service.html and https://github.com/sysbio-curie/RNaviCell.
Access to data held by the National Biodiversity Network (NBN,
    www.nbn.org.uk). The NBN acts as a data warehouse for biological records data in
    the UK and is the UK node of GBIF (Global Biodiversity Information Facility). In
    this package NBN data is accessed via its web-services.
Provides the libraries of the EUtils operations for the
        RNCBI package.
Contains functions to retrieve, organize, and visualize weather data from the NCEP/NCAR Reanalysis (http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html) and NCEP/DOE Reanalysis II (http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis2.html) datasets.  Data are queried via the Internet and may be obtained for a specified spatial and temporal extent or interpolated to a point in space and time.  We also provide functions to visualize these weather data on a map.  There are also functions to simulate flight trajectories according to specified behavior using either NCEP wind data or data specified by the user.
An interface to the Nexus Class Library which allows parsing
    of NEXUS, Newick and other phylogenetic tree file formats. It provides
    elements of the file that can be used to build phylogenetic objects
    such as ape's 'phylo' or phylobase's 'phylo4(d)'. This functionality
    is demonstrated with 'read_newick_phylo()' and 'read_nexus_phylo()'.
Extract the implied risk neutral density from options using various methods.
Using as input a 'LaTeX' file with a multiple choice exam, this package will produce several versions with randomized contents of the same exam. Functions for grading and testing for cheating are also available.
Neo4j, a graph database, allows users to store their data as a property graph. A graph consists of nodes that are connected by relationships; both nodes and relationships can have properties, or key-value pairs. RNeo4j is Neo4j's R driver. It allows users to read and write data from and to Neo4j directly from their R environment by exposing an interface for interacting with nodes, relationships, paths, and more. Most notably, it allows users to retrieve Cypher query results as R data frames, where Cypher is Neo4j's graph query language. Visit <http://www.neo4j.com> to learn more about Neo4j.
Within this package the XML-RPC API to NEOS <https://neos-server.org/neos/> is implemented. This enables the user to pass optimization problems to NEOS and retrieve results within R.
It provides functions to compute the modularity and modularity-related roles in networks. It is a wrapper around the rgraph library (Guimera & Amaral, 2005, doi:10.1038/nature03288).
An interface to the NetCDF file format designed by Unidata
  for efficient storage of array-oriented scientific data and descriptions.
  The R interface is closely based on the C API of the NetCDF library,
  and it includes calendar conversions from the Unidata UDUNITS library.
  The current implementation supports all operations on NetCDF datasets
  in classic and 64-bit offset file formats, and NetCDF4-classic format
  is supported for reading and modification of existing files.
Interface to use and access Wilensky's NetLogo (Wilensky 1999) from R using either headless (no GUI) or interactive GUI mode. Provides functions to load models, execute commands, and get values from reporters. Mostly analogous to the NetLogo Mathematica Link <https://github.com/NetLogo/Mathematica-Link>.
A collection of tools for measuring the similarity of news content and tracing the flow of (news) messages over
    time and across media.
Provides access to phyloinformatic data in 'NeXML' format.  The
    package should add new functionality to R such as the possibility to
    manipulate 'NeXML' objects in more various and refined way and compatibility
    with 'ape' objects.
A function setVectorSeed() is provided. Its argument
        is a numeric vector of an arbitrary nonzero length, whose
        components have integer values from [0, 2^32-1]. The input
        vector is transformed using AES (Advanced Encryption Standard)
        algorithm into an initial state of Mersenne-Twister random
        number generator. The function provides a better alternative
        to the R base function set.seed(), if the input vector is
        a single integer. Initializing a stream of random numbers
        with a vector is a convenient way to obtain several streams,
        each of which is identified by several integer indices.
This package contains a set of functions for working with
    Random Number Generators (RNGs). In particular, it defines a generic
    S4 framework for getting/setting the current RNG, or RNG data
    that are embedded into objects for reproducibility.
    Notably, convenient default methods greatly facilitate the way current
    RNG settings can be changed.
It is a dedicated package to WELL pseudo random generators, which were introduced in Panneton et al. (2006), ``Improved Long-Period Generators Based on Linear Recurrences Modulo 2'', ACM Transactions on Mathematical Software. But this package is not intended to be used directly, you are strongly __encouraged__ to use the 'randtoolbox' package, which depends on this package.
Long period linear random number generator WELL19937a by
        F. Panneton, P. L'Ecuyer and M. Matsumoto. The initialization
        algorithm allows to seed the generator with a
        numeric vector of an arbitrary length and uses MRG32k5a by
        P. L'Ecuyer to achieve good quality of the initialization. The
        output function may be set to provide numbers from the interval
        (0,1) with 53 (the default) or 32 random bits. WELL19937a is of
        similar type as Mersenne Twister and has the same period.
        WELL19937a is slightly slower than Mersenne Twister, but has
        better equidistribution and "bit-mixing" properties and faster
        recovery from states with prevailing zeros than Mersenne
        Twister. All WELL generators with orders 512, 1024, 19937 and
        44497 can be found in randtoolbox package.
Tools for downloading and analyzing CDC NHANES data, with a focus
    on analytical laboratory data.
Provides very fast access to images stored in the NIfTI-1 file
    format <http://nifti.nimh.nih.gov/nifti-1>, with seamless synchronisation
    between compiled C and interpreted R code. Not to be confused with
    'RNiftyReg', which provides tools for image registration.
Provides an 'R' interface to the 'NiftyReg' image registration tools
    <http://sourceforge.net/projects/niftyreg/>. Linear and nonlinear registration
    are supported, in two and three dimensions.
An implementation of robust nonnegative matrix factorization (rNMF). The rNMF algorithm decomposes a nonnegative high dimension data matrix into the product of two low rank nonnegative matrices, while detecting and trimming outliers. The main function is rnmf(). The package also includes a visualization tool, see(), that arranges and prints vectorized images.
Implementation of a Recurrent Neural Network in R.
Client for many 'NOAA' data sources including the 'NCDC' climate
    'API' at <https://www.ncdc.noaa.gov/cdo-web/webservices/v2>, with functions for
    each of the 'API' 'endpoints': data, data categories, data sets, data types,
    locations, location categories, and stations. In addition, we have an interface
    for 'NOAA' sea ice data, the 'NOAA' severe weather inventory, 'NOAA' Historical
    Observing 'Metadata' Repository ('HOMR') data, 'NOAA' storm data via 'IBTrACS',
    tornado data via the 'NOAA' storm prediction center, and more.
An interface to the National Oceanic and Atmospheric Administration's Operational Model Archive and Distribution System (NOMADS, see <http://nomads.ncep.noaa.gov/> for more information) that allows R users to quickly and efficiently download global and regional weather model data for processing.  rNOMADS currently supports a variety of models ranging from global weather data to an altitude of 40 km, to high resolution regional weather models, to wave and sea ice models. It can also retrieve archived NOMADS models.  rNOMADS can retrieve binary data in grib format as well as import ascii data directly into R by interfacing with the GrADS-DODS system.
Programmatic interface to the
    Web Service methods provided by the National 'Phenology' Network
    (<https://usanpn.org/>), which includes data on various life history
    events that occur at specific times.
Utility functions to retrieve data from the UK National River Flow Archive (http://nrfa.ceh.ac.uk/). The package contains R wrappers to the UK NRFA data temporary-API. There are functions to retrieve stations falling in a bounding box, to generate a map and extracting time series and general information.
A collection of methods for the robust analysis of univariate and
    multivariate functional data, possibly in high-dimensional cases, and hence
    with attention to computational efficiency and simplicity of use.
Provides an interface to the OAuth 1.0 specification
        allowing users to authenticate via OAuth to the
        server of their choice.
Base S4-classes and functions for robust asymptotic statistics.
Includes 'sysdata.rda' file for packages of the 'RobASt' - family of packages; is
        currently used by package 'RobExtremes' only.
Methods for analysis of compositional data including robust
    methods, imputation, methods to replace rounded zeros, (robust) outlier
    detection for compositional data, (robust) principal component analysis for
    compositional data, (robust) factor analysis for compositional data, (robust)
    discriminant analysis for compositional data (Fisher rule), robust regression
    with compositional predictors and (robust) Anderson-Darling normality tests for
    compositional data as well as popular log-ratio transformations (addLR, cenLR,
    isomLR, and their inverse transformations). In addition, visualisation and
    diagnostic tools are implemented as well as high and low-level plot functions
    for the ternary diagram.
Robust pairwise correlations based on estimates of scale,
  particularly on "FastQn" one-step M-estimate.
Locations problems, M-estimates of coefficients and scale
        in linear regression, Weights for bounded influence regression,
        Covariance matrix of the coefficient estimates, Asymptotic
        relative efficiency of regression M-estimates, Robust testing
        in linear models, High breakdown point regression, M-estimates
        of covariance matrices, M-estimates for discrete generalized
        linear models.
We provide an outlier robust alternative of the function ets() in the 'forecast' package of Hyndman and Khandakar (2008)<DOI:10.18637/jss.v027.i03>. For each method of a class of exponential smoothing variants we made a robust alternative. The class includes methods with a damped trend and/or seasonal components. The robust method is developed by robustifying every aspect of the original exponential smoothing variant. We provide robust forecasting equations, robust initial values, robust smoothing parameter estimation and a robust information criterion. The method is described in more detail in Crevits and Croux (2016)<DOI:10.13140/RG.2.2.11791.18080>.
A set of functions to filter time series based on concepts
        from robust statistics.
Functions for the determination of optimally robust influence curves and
        estimators in case of normal location and/or scale.
Functions for the determination of optimally robust influence curves and
        estimators for preprocessing omics data, in particular gene expression data.
Perform mediation analysis via a bootstrap test.
Provides functions to download and parse 'robots.txt' files.
        Ultimately the package makes it easy to check if bots
        (spiders, scrapers, ...) are allowed to access specific
        resources on a domain.
Calculates periodograms based on (robustly) fitting periodic functions to light curves (irregularly observed time series, possibly with measurement accuracies, occurring in astroparticle physics). Three main functions are included: RobPer() calculates the periodogram. Outlying periodogram bars (indicating a period) can be detected with betaCvMfit(). Artificial light curves can be generated using the function tsgen(). For more details see the corresponding article: Thieler, Fried and Rathjens (2016), Journal of Statistical Software 69(9), 1-36, <doi:10.18637/jss.v069.i09>.
Three-step regression and inference for cellwise and casewise contamination.
Functions for the determination of optimally robust influence curves in case of
        linear regression with unknown scale and standard normal distributed errors where the
        regressor is random.
This package provides the function to calculate SVD, regularized SVD, robust SVD and robust regularized SVD method. The robust SVD methods use alternating iteratively reweighted least squares methods. The regularized SVD uses generalized cross validation to choose the optimal smoothing parameters.
Data management in hydrology and other fields is facilitated with functions to enter and modify data in a database according to the Observations Data Model (ODM) standard by CUASHI (Consortium of Universities for the Advancement of Hydrologic Science). While this data model has been developed in hydrology, it is also useful for other fields. RObsDat helps in the setup of the database within one of the free database systems MariaDB, PostgreSQL or SQLite. It imports the controlled water vocabulary from the CUASHI web service and provides a smart interface between the analyst and the database: Already existing data entries are detected and duplicates avoided. The data import function converts different data table designs to make import simple. Cleaning and modifications of data are handled with a simple version control system. Variable and location names are treated in a user friendly way, accepting and processing multiple versions. When querying data from the database, it is stored in a spacetime objects within R for subsequent processing.
Functions for conducting robust variance estimation (RVE) meta-regression using both large and small sample RVE estimators under various weighting schemes. These methods are distribution free and provide valid point estimates, standard errors and hypothesis tests even when the degree and structure of dependence between effect sizes is unknown. Also included are functions for conducting sensitivity analyses under correlated effects weighting and producing RVE-based forest plots.
R functions for the computation of the truncated maximum
	     likelihood and the robust accelerated failure time regression 
	     for gaussian and log-Weibull case.
"Essential" Robust Statistics.
 Tools allowing to analyze data with robust methods.  This includes
 regression methodology including model selections and multivariate
 statistics where we strive to cover the book "Robust Statistics,
 Theory and Methods" by 'Maronna, Martin and Yohai'; Wiley 2006.
Robust mixture discriminant analysis (RMDA, Bouveyron & Girard, 2009) allows to build a robust supervised classifier from learning data with label noise. The idea of the proposed method is to confront an unsupervised modeling of the data with the supervised information carried by the labels of the learning data in order to detect inconsistencies. The method is able afterward to build a robust classifier taking into account the detected inconsistencies into the labels.
The Spatial-EM is a new robust EM algorithm for the finite  
    mixture learning procedures. The algorithm utilizes median- 
    based location and rank-based scatter estimators to replace sample
    mean and sample covariance matrix in each M step, hence enhancing 
    stability and robustness of the algorithm. To understand more about 
    this algorithm, read the article
    ''Yu, K., Dang, X., Bart Jr, H. and Chen, Y. (2015). Robust Model-
    based Learning via Spatial-EM Algorithm. IEEE Transactions on 
    Knowledge and Data Engineering, 27(6), 1670-1682.   
    doi:10.1109/TKDE.2014.2373355''.
Testing homogeneity for generalized exponential tilt model. This package includes a collection of functions for (1) implementing methods for testing homogeneity for generalized exponential tilt model; and (2) implementing existing methods under comparison.
An object oriented solution for robust factor analysis. In the solution, new S4 classes "Fa", "FaClassic", "FaRobust", "FaCov", "SummaryFa" are created.
This package provides robust estimation for generalized
        additive models. It implements a fast and stable algorithm in
        Wong, Yao and Lee (2013). The implementation also contains
        three automatic selection methods for smoothing parameter. They
        are designed to be robust to outliers. For more details, see
        Wong, Yao and Lee (2013).
Robust parameter estimation and prediction of Gaussian stochastic process emulators.
             Important functions : rgasp(), predict.rgasp().
Robust methods for high-dimensional data, in particular linear
    model selection techniques based on least angle regression and sparse
    regression.
A method to fit linear mixed effects models robustly.
    Robustness is achieved by modification of the scoring equations
    combined with the Design Adaptive Scale approach.
Robust estimation of the generalized log gamma model is provided using Quantile Tau estimator, Weighted Likelihood estimator and Truncated Maximum Likelihood estimator. Functions for regression and censored data are also available.
Implements several rank-based tests, including the modified Wilcoxon-Mann-Whitney two sample location test, also known as the Fligner-Policello test.
Methods for aggregating ranked lists, especially lists of
        genes. It implements the Robust Rank Aggregation (Kolde et. al
        in preparation) and some other simple algorithms for the task.
        RRA method uses a probabilistic model for aggregation that is
        robust to noise and also facilitates the calculation of
        significance probabilities for all the elements in the final
        ranking.
A collection of functions to compute the Rao-Stirling diversity index
	(Porter and Rafols, 2009) <DOI:10.1007/s11192-008-2197-2> and its extension to
	acknowledge missing data (i.e.,	uncategorized references) by calculating its
	interval of uncertainty using	mathematical optimization as proposed in Calatrava
	et al. (2016) <DOI:10.1007/s11192-016-1842-4>.
	The Rao-Stirling diversity index is a well-established bibliometric indicator
	to measure the interdisciplinarity of scientific publications. Apart from the
	obligatory dataset of publications with their respective references and	a
	taxonomy of disciplines that categorizes references as well as a measure of
	similarity between the disciplines, the Rao-Stirling diversity index requires
	a complete categorization of all references of a publication into disciplines.
	Thus, it fails for a incomplete categorization; in this case, the robust
	extension has to be used, which encodes the uncertainty caused by missing
	bibliographic data as an uncertainty interval.
	Classification / ACM - 2012: Information systems ~ Similarity measures,
	Theory of computation ~ Quadratic	programming, Applied computing ~ Digital
	libraries and archives.
Linear regression functions using Huber and bisquare psi functions. Optimal weights are calculated using IRLS algorithm.
Functions for Robust Bayesian Small Area Estimation.
Robust Estimation of Variance Component Models by classic and composite robust procedures. The composite procedures are robust against outliers generated by the Independent Contamination Model.
Robustness -- 'eXperimental', 'eXtraneous', or 'eXtraordinary'
  Functionality for Robust Statistics.  In other words, methods which are not
  yet well established, often related to methods in package 'robustbase'.
This package computes traditional ROC curves and time-dependent ROC curves using the cross-validation, the 0.632 and the 0.632+ estimators. The 0.632+ estimator of time-dependent ROC curve is useful to estimate the predictive accuracy of prognostic signature based on high-dimensional data. For instance, it outperforms the other approaches, especially the cross-validation solution which is often used. The 0.632+ estimators correct the area under the curve in order to adequately estimate the prognostic capacities regardless of the overfitting level. This package also allows for the construction of diagnostic or prognostic scoring systems (penalized regressions). The methodology is adapted to complete data (penalized logistic regression associated with ROC curve) or incomplete time-to-event data (penalized Cox model associated with time-dependent ROC curve).
Functions for a classification method based on receiver
        operating characteristics (ROC). Briefly, features are selected
        according to their ranked AUC value in the training set. The
        selected features are merged by the mean value to form a
        metagene. The samples are ranked by their metagene value and
        the metagene threshold that has the highest accuracy in
        splitting the training samples is determined. A new sample is
        classified by its metagene value relative to the threshold. In
        the first place, the package is aimed at two class problems in
        gene expression data, but might also apply to other problems.
Cross validate large genetic data while specifying clinical variables that should always be in the model using the function cv(). An ROC plot from the cross validation data with AUC can be obtained using rocplot(), which also can be used to compare different models.
A collection of functions for interpretation and presentation
    of regression analysis.  These functions are used
    to produce the statistics lectures in
    http://pj.freefaculty.org/guides. Includes regression
    diagnostics, regression tables, and plots of interactions and
    "moderator" variables. The emphasis is on "mean-centered" and
    "residual-centered" predictors. The vignette 'rockchalk' offers a
    fairly comprehensive overview.  The vignette 'Rstyle' has advice
    about coding in R.  The package title 'rockchalk' refers to our
    school motto, 'Rock Chalk Jayhawk, Go K.U.'.
Provides functions to complete three-dimensional rock fabric and strain analyses following the Rf Phi, Fry, and normalized Fry methods. Also allows for plotting of results and interactive 3D visualization functionality.
Non-inferiority test and diagnostic test are very important in clinical trails.
  This package is to get a p value from the non-inferiority test for ROC curves from diagnostic test.
The 'rococo' package provides a robust gamma rank correlation
	     coefficient along with a permutation-based rank correlation test.
	     The rank correlation coefficient and the test are explicitly
	     designed for dealing with noisy numerical data.
ROC graphs, sensitivity/specificity curves, lift charts,
  and precision/recall plots are popular examples of trade-off
  visualizations for specific pairs of performance measures. ROCR is a
  flexible tool for creating cutoff-parameterized 2D performance curves
  by freely combining two from over 25 performance measures (new
  performance measures can be added using a standard interface).
  Curves from different cross-validation or bootstrapping runs can be
  averaged by different methods, and standard deviations, standard
  errors or box plots can be used to visualize the variability across
  the runs. The parameterization can be visualized by printing cutoff
  values at the corresponding curve positions, or by coloring the
  curve according to cutoff. All components of a performance plot can
  be quickly adjusted using a flexible parameter dispatching
  mechanism. Despite its flexibility, ROCR is easy to use, with only
  three commands and reasonable default values for all optional
  parameters.
Plots the Receiver Operating Characteristics Surface for high-throughput class-skewed data, calculates the Volume under the Surface (VUS) and the FDR-Controlled Area Under the Curve (FCAUC), and conducts tests to compare two ROC surfaces. Computes eROC curve and the corresponding AUC for imperfect reference standard.
Contains functions in order to estimate diagnostic and prognostic capacities of continuous markers. More precisely, one function concerns the estimation of time-dependent ROC (ROCt) curve, as proposed by Heagerty et al. (2000) <doi:10.1111/j.0006-341X.2000.00337.x>. One function concerns the adaptation of the ROCt theory for studying the capacity of a marker to predict the excess of mortality of a specific population compared to the general population. This last part is based on additive relative survival models and the work of Pohar-Perme et al. (2012) <doi:10.1111/j.1541-0420.2011.01640.x>. We also propose two functions for cut-off estimation in medical decision making by maximizing time-dependent expected utility function. Finally, we propose confounder-adjusted estimators of ROC and ROCt curves by using the Inverse Probability Weighting (IPW) approach.  For the confounder-adjusted ROC curve (without censoring), we also proposed the implementation of the estimator based on placement values proposed by Pepe and Cai (2004) <doi:10.1111/j.0006-341X.2004.00200.x>.
Function to estimate the ROC Curve of a continuous-scaled
        diagnostic test with the help of a second imperfect diagnostic
        test with binary responses.
'ODAM' (Open Data for Access and Mining) is a framework that implements a simple way to make research data broadly accessible and fully available for reuse, including by a script language such as R. The main purpose is to make a data set accessible online with a minimal effort from the data provider, and to allow any scientists or bioinformaticians to be able to explore the data set and then extract a subpart or the totality of the data according to their needs. The Rodam package has only one class, 'odamws', that provides methods to allow you to retrieve online data using 'ODAM' Web Services. This obviously requires that data are implemented according the 'ODAM' approach , namely that the data subsets were deposited in the suitable data repository in the form of TSV files associated with  their metadata also described  in TSV files. See <http://www.slideshare.net/danieljacob771282/odam-open-data-access-and-mining>.
An implementation of R's DBI interface using ODBC package as a
    back-end. This allows R to connect to any DBMS that has a ODBC driver.
An extension for RODBC package adding support for parameterized
    queries.
A collection of functions for numerical construction of optimal discriminating designs. At the current moment T-optimal designs (which maximize the lower bound for the power of F-test for regression model discrimination), KL-optimal designs (for lognormal errors) and their robust analogues can be calculated with the package.
Provides an R6 class and several utility methods to
    facilitate the implementation of models based on ordinary
    differential equations. The heart of the package is a code generator
    that creates compiled 'Fortran' (or 'R') code which can be passed to
    a numerical solver. There is direct support for solvers contained
    in packages 'deSolve' and 'rootSolve'.
Fetch and process Hillary Rodham Clinton's "personal" emails.
This package implements an interface to Oracle Data Mining
        (ODM). It provides an ideal environment for rapid development
        of demos and proof of concept data mining studies. It
        facilitates the prototyping of vertical applications and makes
        ODM and the RDBMS environment easily accessible to
        statisticians and data analysts familiar with R but not fluent
        in SQL or familiar with the database environment. It also
        facilitates the benchmarking and testing of ODM functionality
        including the production of summary statistics, performance
        metrics and graphics. It enables the scripting and control of
        production data mining methodologies from a high-level
        environment. Oracle Data Mining (ODM) is an option of Oracle
        Relational Database Management System (RDBMS) Enterprise
        Edition (EE). It contains several data mining and data analysis
        algorithms for classification, prediction, regression,
        clustering, associations, feature selection, anomaly detection,
        feature extraction, and specialized analytics. It provides
        means for the creation, management and operational deployment
        of data mining models inside the database environment. For more
        information consult the entry for "Oracle Data Mining" in
        Wikipedia (en.wikipedia.org).
The R Optimization Infrastructure (ROI) is a
    sophisticated framework for handling optimization problems in R.
A collection of 'ROI' optimization problems based on the 'NETLIB-LP' collection.
  'Netlib' is a software repository, which amongst many other software for scientific computing contains a collection of linear programming problems. The purpose of this package is to make 
  this problems easily accessible from 'R' as 'ROI' optimization problems.
Enhances the R Optimization Infrastructure (ROI) package by registering
	     the CPLEX commercial solver. It allows for solving mixed integer quadratically
	     constrained programming (MIQPQC) problems as well as all
	     variants/combinations of LP, QP, QCP, IP.
Enhances the 'R' Optimization Infrastructure ('ROI') package
	     with the Embedded Conic Solver ('ECOS') for solving conic optimization problems.
Enhances the R Optimization Infrastructure (ROI) package by registering
	     the free GLPK solver. It allows for solving mixed integer linear programming (MILP)
	     problems as well as all variants/combinations of LP, IP.
Enhances the R Optimization Infrastructure ('ROI') package 
	     by registering the ipop solver from package 'kernlab'.
Enhances the 'R' Optimization Infrastructure ('ROI') package
             with the 'lp_solve' solver.
Enhances the R Optimization Infrastructure ('ROI') package
        with the 'NLopt' solver for solving nonlinear optimization problems.
Enhances the R Optimization Infrastructure (ROI) package
	     by registering the quadprog solver. It allows for solving quadratic programming (QP)
	     problems.
Enhances the 'R' Optimization Infrastructure ('ROI') package
         with the 'SCS' solver for solving convex cone problems.
Enhances the R Optimization Infrastructure (ROI) package by registering
	     the SYMPHONY open-source solver from the COIN-OR suite. It allows for
	     solving mixed integer linear programming (MILP) problems as well as all
	     variants/combinations of LP, IP.
Parallel functions for computing rolling statistics of time-series
    data.
Reports errors and messages to Rollbar, the error tracking platform <https://rollbar.com>.
Apply a function in a moving window, then
    combine the results in a data frame.
Provides the possibility to plot Lexis surface maps (heat maps) of rates of mortality improvement. Raw data to be plotted can be read from the Human Mortality Database using code originally written by Tim Riffe. The European Research Council has provided financial support under the European Community's Seventh Framework Programme (FP7/2007-2013) / ERC grant agreement no. 263744.
Methods and classes for object-oriented programming in R with or without references.  Large effort has been made on making definition of methods as simple as possible with a minimum of maintenance for package developers.  The package has been developed since 2001 and is now considered very stable.  This is a cross-platform package implemented in pure R that defines standard S3 classes without any tricks.
This package contains the Rook specification and
 convenience software for building and running Rook applications. To
 get started, be sure and read the 'Rook' help file first.
Implementation of the Taylor Regression Estimator method described in Christopoulos (2014,<https://www.researchgate.net/publication/261562841>)
 for finding the root, extreme or inflection point of a curve, when we only have a set of probably noisy xy points for it. The method uses a suitable
 polynomial regression in order to find the coefficients of the relevant Taylor polynomial for the function that has generated our data.
Routines to find the root of nonlinear functions, and to perform steady-state and equilibrium analysis of ordinary differential equations (ODE). 
  Includes routines that: (1) generate gradient and jacobian matrices (full and banded),
  (2) find roots of non-linear equations by the 'Newton-Raphson' method, 
  (3) estimate steady-state conditions of a system of (differential) equations in full, banded or sparse form, using the 'Newton-Raphson' method, or by dynamically running,
  (4) solve the steady-state conditions for uni-and multicomponent 1-D, 2-D, and 3-D partial differential equations, that have been converted to ordinary differential equations
    by numerical differencing (using the method-of-lines approach).
  Includes fortran code.
Functions for hypothesis testing in single and double Wishart
    settings, based on Roy's largest root. This test statistic is especially
    useful in multivariate analysis. The computations are based on results by
    Chiani (2014) <DOI:10.1016/j.jmva.2014.04.002> and Chiani (2016)
    <DOI:10.1016/j.jmva.2015.10.007>. They use the fact that the CDF is related
    to the Pfaffian of a matrix that can be computed in a finite number of
    iterations. This package takes advantage of the Boost and Eigen C++ libraries
    to perform multi-precision linear algebra.
Selects one model with variable selection FDR controlled at a
    specified level. A q-value for each potential variable is also returned. The
    input, variable selection counts over many bootstraps for several levels of
    penalization, is modeled as coming from a beta-binomial mixture
    distribution.
Allows access to air quality data from the API of the OpenAQ platform <https://docs.openaq.org/>, with the different services the API offers (getting measurements for a given query, getting latest measurements, getting lists of available countries/cities/locations).
Provide a simple interface to Bloomberg's OpenFIGI API. Please
    see <https://openfigi.com> for API details and registration. You may be
    eligible to have an API key to accelerate your loading process.
An R package for the OpenSecrets.org web services API.
OpenWeatherMap (OWM) <http://openweathermap.org/api> is a service providing weather related data.
           This package can be used to access current weather data for one location or several locations.
           It can also be used to forecast weather for 5 days with data for every 3 hours.
Optimally robust estimation in general smoothly parameterized models using S4 
        classes and methods.
Optimally robust estimation using S4 classes and methods. Old version still needed
        for current versions of ROptRegTS and RobRex.
R package extracts optimizely test results and test information using Optimizely REST API. Only read functionality is supported for analysis and reporting.
Optimally robust estimation for regression-type models using S4 classes and
        methods
Oracle Database interface (DBI) driver for R.
   This is a DBI-compliant Oracle driver based on the OCI.
Client for the 'Orcid.org' 'API' (http://orcid.org/).
    Functions included for searching for people, searching by 'DOI',
    and searching by 'Orcid' 'ID'.
Implementation of Robust Ordinal Regression for multiple criteria value-based sorting with preference information provided in form of possibly imprecise assignment examples, assignment-based pairwise comparisons, and desired class cardinalities [Kadzinski et al. 2015, <doi:10.1016/j.ejor.2014.09.050>].
The package provides functions to deal with binary classification
  problems in the presence of imbalanced classes. Synthetic balanced samples are  
  generated according to ROSE (Menardi and Torelli, 2013).  
  Functions that implement more traditional remedies to the class imbalance
  are also provided, as well as different metrics to evaluate a learner accuracy.
  These are estimated by holdout, bootstrap or cross-validation methods.
Rosette is an API for multilingual text analysis and information
    extraction. More information can be found at <https://developer.rosette.com>.
Download and plot Open Street Map <http://www.openstreetmap.org/>,
    Mapquest <http://www.mapquest.com/>, Bing Maps <http://www.bing.com/maps> and
    other tiled map sources in a way that works seamlessly with plotting from the
    'sp' package. Use to create high-resolution basemaps and add hillshade to vector
    based maps.
Implementation of robust sparse PCA using the ROSPCA algorithm 
             of Hubert et al. (2016) <DOI:10.1080/00401706.2015.1093962>.
Fit and deploy rotation forest models ("Rodriguez, J.J., Kuncheva, L.I., 2006. Rotation forest: A new classifier ensemble method. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1619-1630") for binary classification. Rotation forest is an ensemble method where each base classifier (tree) is fit on the principal components of the variables of random partitions of the feature set.
Tools for working with rotational data, including
    simulation from the most commonly used distributions on SO(3),
    methods for different Bayes, mean and median type estimators for
    the central orientation of a sample, confidence/credible
    regions for the central orientation based on those estimators and
    a novel visualization technique for rotation data.  Most recently,
    functions to identify potentially discordant (outlying) values
    have been added.
R build of Rothermel's (1972) model for surface fire rate of spread with some additional utilities (uncertainty propagation, standard fuel model selection, fuel model optimization by genetic algorithm) and sample datasets.
An interface to the 'Open Tree of Life' API to retrieve
    phylogenetic trees, information about studies used to assemble the synthetic
    tree, and utilities to match taxonomic names to 'Open Tree identifiers'. The
    'Open Tree of Life' aims at assembling a comprehensive phylogenetic tree for all
    named species.
A set of functions to support Xiong K, 'Roughened Random Forests for Binary Classification' (2014). The functions include RRFA, RRFB, RRFC1-RRFC7, RRFD and RRFE. RRFB and RRFC6 are usually recommended. RRFB is much faster than RRFC6.
Rough Sets were introduced by Zdzislaw Pawlak on his book "Rough Sets: Theoretical Aspects of Reasoning About Data". Rough Sets provide a formal method to approximate crisp sets when the set-element belonging relationship is either known or undetermined. This enables the use of Rough Sets for reasoning about incomplete or contradictory knowledge. A decision table is a prescription of the decisions to make given some conditions. Such decision tables can be reduced without losing prescription ability. This package provides the classes and methods for knowledge reduction from decision tables as presented in the chapter 7 of the aforementioned book. This package provides functions for calculating the both the discernibility matrix and the essential parts of decision tables.
Implementations of algorithms for data analysis
    based on the rough set theory (RST) and the fuzzy rough set theory (FRST). We
    not only provide implementations for the basic concepts of RST and FRST but also
    popular algorithms that derive from those theories. The methods included in the
    package can be divided into several categories based on their functionality:
    discretization, feature selection, instance selection, rule induction and classification
    based on nearest neighbors. RST was introduced by Zdzisław Pawlak in 1982
    as a sophisticated mathematical tool to
    model and process imprecise or incomplete information. By using
    the indiscernibility relation for objects/instances, RST does not require
    additional parameters to analyze the data. FRST is an extension of RST. The
    FRST combines concepts of vagueness and indiscernibility that are expressed
    with fuzzy sets (as proposed by Zadeh, in 1965) and RST.
Provides utilities which interact with all R objects as
    if they were arranged in rows.  It allows more consistent and predictable 
    output to common functions, and generalizes a number of utility functions to
    to be failsafe with any number and type of input objects.
Generate your Rd documentation, 'NAMESPACE' file, and collation 
    field using specially formatted comments. Writing documentation in-line
    with code makes it easier to keep your documentation up-to-date as your
    requirements change. 'Roxygen2' is inspired by the 'Doxygen' system for C++.
Performs a multivariate normality test based on Royston's H test
Implements macroevolutionary analyses on phylogenetic trees. See Morlon et al. (2010) <DOI:10.1371/journal.pbio.1000493>, Morlon et al. (2011) <DOI:10.1073/pnas.1102543108>, Condamine et al. (2013) <DOI:10.1111/ele.12062>, Morlon et al. (2014) <DOI:10.1111/ele.12251>, Manceau et al. (2015) <DOI:10.1111/ele.12415>, Lewitus & Morlon (2016) <DOI:10.1093/sysbio/syv116>, and Drury et al. (2016) <DOI:10.1093/sysbio/syw020>.
rpanel provides a set of functions to build simple 
        GUI controls for R functions.  These are built on the tcltk package. 
        Uses could include changing a parameter on a graph by animating it 
        with a slider or a "doublebutton", up to more sophisticated control 
        panels.
        Some functions for specific graphical tasks, referred to as 'cartoons',
        are provided.
Recursive partitioning for classification, 
  regression and survival trees.  An implementation of most of the 
  functionality of the 1984 book by Breiman, Friedman, Olshen and Stone.
Provides algorithims for randomly sampling a feasible set defined
    by a given total and number of elements using integer partitioning.
Plot 'rpart' models. Extends plot.rpart() and text.rpart()
             in the 'rpart' package.
This package contains functions that allow to build
        classification trees for ordinal responses within the CART
        framework. The trees are grown using the Generalized Gini
        impurity function, where the misclassification costs are given
        by the absolute or squared differences in scores assigned to
        the categories of the response. Pruning is based on the total
        misclassification rate or on the total misclassification cost.
This package contains additional tools for working with rpart
    objects.  Most importantly, it includes methods for converting rpart rules
    into a series of structured tables sufficient for executing the decision
    tree completely in SQL.
Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Candes, E. J., Li, X., Ma, Y., & Wright, J. (2011). Robust principal component analysis?. Journal of the ACM (JACM), 58(3), 11. prove that we can recover each component individually under some suitable assumptions. It is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the L1 norm. This package implements this decomposition algorithm resulting with Robust PCA approach.
Allows users to identify relevant clinical codes and
    automate the construction of clinical code lists for primary care database
    studies. This package is analogous to the Stata command pcdsearch.
This package implements the R-PCLR (Random-Penalized
        Conditional Logistic Regression) algorithm for obtaining
        variable importance. The algorithm is applicable for the
        analysis of high dimensional data from matched case-control
        studies.
Provides tools to read, write, visualize PDB files and perform some structural manipulations.
Monthly Pacific Decadal Oscillation (PDO) index
    values from January 1900 to November 2016 as the pdo tibble.
    The download_pdo() function scrapes the latest values from 
    <http://research.jisao.washington.edu/pdo/PDO.latest>.
The quantitative measurement and detection of molecules in HPLC should be carried out by an accurate description of chromatographic peaks. In this package non-linear fitting using a modified Gaussian model with a parabolic variance (PVMG) has been implemented to obtain the retention time and height at the peak maximum. This package also includes the traditional Van Deemter approach and two alternatives approaches to characterize chromatographic column.
Implements the methodology of "Cannings, T. I. and Samworth, R. J. (2015) Random projection ensemble classification. http://arxiv.org/abs/1504.04595". The random projection ensemble classifier is a general method for classification of high-dimensional data, based on careful combination of the results of applying an arbitrary base classifier to random projections of the feature vectors into a lower-dimensional space. The random projections are divided into non-overlapping blocks, and within each block the projection yielding the smallest estimate of the test error is selected. The random projection ensemble classifier then aggregates the results of applying the base classifier on the selected projections, with a data-driven voting threshold to determine the final assignment.
The purpose of this package is to factor out logic and math common
    to Item Factor Analysis fitting, diagnostics, and analysis. It is
    envisioned as core support code suitable for more specialized IRT packages
    to build upon. Complete access to optimized C functions are made available
    with R_RegisterCCallable().
Allows ad hoc queries and reading and
    writing data frames to and from a database.
Provides an R interface to the PHAST software
    (Phylogenetic Analysis with Space/Time Models).  It can be used for
    many types of analysis in comparative and evolutionary genomics,
    such as estimating models of evolution from sequence data, scoring
    alignments for conservation or acceleration, and predicting
    elements based on conservation or custom phylogenetic hidden Markov
    models.  It can also perform many basic operations on multiple
    sequence alignments and phylogenetic trees.
Rphylip provides an R interface for the PHYLIP package. All users
    of Rphylip will thus first have to install the PHYLIP phylogeny methods
    program package (Felsenstein 2013). See http://www.phylip.com for more 
	information about installing PHYLIP.
Tools for performing phylogenetic comparative methods for datasets with with multiple observations per species (intraspecific variation or measurement error) and/or missing data. Performs ancestral state reconstruction and missing data imputation on the estimated evolutionary model, which can be specified as Brownian Motion, Ornstein-Uhlenbeck, Early-Burst, Pagel's lambda, kappa, or delta, or a star phylogeny.
Get information (boards, pins and
    users) from the Pinterest <http://www.pinterest.com>
    API.
Build powerful pivot tables (aka Pivot Grid, Pivot Chart, Cross-
    Tab) and dynamically slice & dice / drag 'n' drop your data. 'rpivotTable' is a
    wrapper of 'pivottable', a powerful open-source Pivot Table library implemented
    in 'JavaScript' by Nicolas Kruchten. Aligned to 'pivottable' v1.6.3.
Provides an interface to the the many computational resources iPlant offers through their RESTful application programming interface, see <http://www.iplantcollaborative.org/> for more information about iPlant, and <http://www.iplantcollaborative.org/ci/iplant-science-apis> for its APIs.  Currently, 'rPlant' functions interact with the iPlant Agave API, the Taxonomic Name Resolution Service API, and the Phylotastic Taxosaurus API. Before using 'rPlant', users will have to register with the iPlant Collaborative.  See <http://agaveapi.co/> for more information on the Agave API and <http://user.iplantcollaborative.org/> to register with iPlant.
Efficiently read and analyze 'PLEXOS' solutions by converting
    them into 'SQLite' databases that can be easily queried. It supports collation
    of solutions that may have been divided into different time partitions, as
    well as the comparison across different scenarios. 'PLEXOS' is a power systems
    production cost model, created and distributed by Energy Exemplar (see <http://energyexemplar.com/software/plexos-desktop-edition>
    for more information).
A programmatic interface to the 'SOLR' based
    search 'API' (<http://api.plos.org/>) provided by the Public
    Library of Science journals to search their articles.
    Functions are included for searching for articles, retrieving
    articles, making plots, doing 'faceted' searches,
    'highlight' searches, and viewing results of 'highlighted'
    searches in a browser.
Generate basic charts either by custom applications, or from a small script launched from the system console, or within the R console. Two ASCII text files are necessary:
  (1) The graph parameters file, which name is passed to the function 'rplotengine()'.
    The user can specify the titles, choose the type of the graph, graph output formats
    (e.g. png, eps), proportion of the X-axis and Y-axis, position of the legend,
    whether to show or not a grid at the background, etc.
  (2) The data to be plotted, which name is specified as a parameter ('data_filename')
    in the previous file. This data file has a tabulated format, with a single character
    (e.g. tab) between each column, and a headers line located in the first row.
    Optionally, the file could include data columns for showing confidence intervals.
Really Poor Man's Graphical User Interface, used to create interactive R analysis sessions with simple R commands.
Recursively Partitioned Mixture Model for Beta and Gaussian Mixtures.  
    This is a model-based clustering algorithm that returns a hierarchy
    of classes, similar to hierarchical clustering, but also similar to
    finite mixture models.
Fits a linear model to survey data in each node obtained by 
    recursively partitioning the data.  The splitting variables and splits
    selected are obtained using a procedure which adjusts for complex sample
    design features used to obtain the data. Likewise the model fitting
    algorithm produces design-consistent coefficients to the least squares
    linear model between the dependent and independent variables.
    The first stage of the design is accounted for in the provided variance 
    estimates. The main function returns the resulting binary tree with the 
    linear model fit at every end-node. The package provides a number of 
    functions and methods for these trees.
Pure R implementation of a simple (Reverse) Polish Notation (RPN) interpreter
  and converter.
A set of functions to analyze and print the development of a
    commodity using the Point and Figure (P&F) approach. A P&F processor can be used
    to calculate daily statistics for the time series. These statistics can be used
    for deeper investigations as well as to create plots. Plots can be generated as
    well known X/O Plots in plain text format, and additionally in a more graphical
    format.
PDF tools based on the Poppler PDF rendering library.
  See <http://poppler.freedesktop.org/> for more information on Poppler.
A collection of tools used to generate
        various types of random portfolios.  The weights of these
        portfolios are random variables derived from truncated
        continuous random variables.
Provides an interface between R and
    'PostGIS'-enabled 'PostgreSQL' databases to transparently transfer
    spatial data. Both vector (points, lines, polygons) and raster
    data are supported in read and write modes. Also provides
    convenience functions to execute common procedures in
    'PostgreSQL/PostGIS'.
Integrates R and the 'PostgreSQL/PostGIS' database 
    system to build and manage animal trajectory (movement) data sets. 
    The package relies on 'ltraj' objects from the R package 'adehabitatLT',
    building the analogous 'pgtraj' data structure in 'PostGIS'. Functions
    allow users to seamlessly transfer between 'ltraj' and 'pgtraj', as
    well as build new 'pgtraj' directly from location data stored in the 
    database.
Database interface and PostgreSQL driver for R This
        package provides a Database Interface (DBI) compliant driver
        for R to access PostgreSQL database systems.

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

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

   A wiki and issue tracking system for the package are available at
        Google Code at https://code.google.com/p/rpostgresql/ .
The significance of mean difference tests in clinical trials is established if at least r null hypotheses are rejected among m that are simultaneously tested. This package enables one to compute necessary sample sizes for single-step (Bonferroni) and step-wise procedures (Holm and Hochberg). These three procedures control the q-generalized family-wise error rate (probability of making at least q false rejections). Sample size is computed (for these single-step and step-wise procedures) in a such a way that the r-power (probability of rejecting at least r false null hypotheses, i.e. at least r significant endpoints among m) is above some given threshold, in the context of tests of difference of means for two groups of continuous endpoints (variables). Various types of structure of correlation are considered. It is also possible to analyse data (i.e., actually test difference in means) when these are available. The case r equals 1 is treated in separate functions that were used in Lafaye de Micheaux et al. (2014) <doi:10.1080/10543406.2013.860156>.
Using some association schemes to obtain a new series of resolvable partially pairwise balanced designs (RPPBD) and space-filling designs.
Reads in sample description and slide description files and
        annotates the expression values taken from GenePix results files
	(text file format used by many microarray scanner and software providers). 
	After normalization data can be visualized as boxplot, heatmap or dotplot.
Performs joint selection in Generalized Linear Mixed Models (GLMMs) using penalized likelihood methods. Specifically, the Penalized Quasi-Likelihood (PQL) is used as a loss function, and penalties are then "added on" to perform simultaneous fixed and random effects selection. Regularized PQL avoids the need for integration (or approximations such as the Laplace's method) during the estimation process, and so the full solution path for model selection can be constructed relatively quickly.
Routines to select and visualize the maxima for a given strict
    partial order. This especially includes the computation of the Pareto
    frontier, also known as (Top-k) Skyline operator, and some
    generalizations (database preferences).
Implements a 'DBI' compliant interface to Presto. Presto is
    an open source distributed SQL query engine for running interactive
    analytic queries against data sources of all sizes ranging from
    gigabytes to petabytes: <https://prestodb.io/>.
Estimates disease prevalence for a given index date, using existing
    registry data extended with Monte Carlo simulations.
'Eprime' is a set of programs for administering psychological
    experiments by computer. This package provides functions for loading,
    parsing, filtering and exporting data in the text files produced by
    'Eprime' experiments.
Provides a set of functions to facilitate building formatted strings
    under various replacement rules: C-style formatting, variable-based formatting,
    and number-based formatting. C-style formatting is basically identical to built-in
    function 'sprintf'. Variable-based formatting allows users to put variable names
    in a formatted string which will be replaced by variable values. Number-based
    formatting allows users to use index numbers to represent the corresponding
    argument value to appear in the string.
Robust, reliable and flexible paths to files below a
    project root. The 'root' of a project is defined as a directory
    that matches a certain criterion, e.g., it contains a certain
    regular file.
Protocol Buffers are a way of encoding structured data in an
 efficient yet extensible format. Google uses Protocol Buffers for almost all
 of its internal 'RPC' protocols and file formats.  Additional documentation
 is available in two included vignettes one of which corresponds to our paper
 in the Journal of Statistical Software (2016, v71i02). Either version 2 or 3
 of the 'Protocol Buffers' 'API' is supported.
Implements methods described by the paper Robins and Tsiatis (1991) <DOI:10.1080/03610929108830654>. These use g-estimation to estimate the causal effect of a treatment in a two-armed randomised control trial where non-compliance exists and is measured, under an assumption of an accelerated failure time model and no unmeasured confounders.
The rpsychi offers a number of functions for psychiatry, psychiatric nursing, clinical psychology. Functions are primarily for statistical significance testing using published work. For example, you can conduct a factorial analysis of variance (ANOVA), which requires only the mean, standard deviation, and sample size for each cell, rather than the individual data. This package covers fundamental statistical tests such as t-test, chi-square test, analysis of variance, and multiple regression analysis. With some exceptions, you can obtain effect size and its confidence interval. These functions help you to obtain effect size from published work, and then to conduct a priori power analysis or meta-analysis, even if a researcher do not report effect size in a published work.
Performs goodness of fits tests for both high and low-dimensional linear models.
    It can test for a variety of model misspecifications including nonlinearity and heteroscedasticity.
    In addition one can test the significance of potentially large groups of variables, and also
    produce p-values for the significance of individual variables in high-dimensional linear
    regression.
R functions for estimating repeatability (intra-class
    correlation) from gaussian, binary, proportion and count data.
Access PubChem data (compounds, substance, assays) using R.
 Structural information is provided in the form of SMILES strings. 
 It currently only provides access to a subset of the 
 precalculated data stored by PubChem. Bio-assay data can be accessed to 
 obtain descriptions as well as the actual data. It is also possible to search for assay ID's by keyword.
Client for accessing data journalism APIs from ProPublica <http://www.propublica.org/>.
An R interface to the Pushbullet messaging service which
 provides fast and efficient notifications (and file transfer) between
 computers, phones and tablets.  An account has to be registered at the site
 http://www.pushbullet.com site to obtain a (free) API key.
Provide access to (virtually any) ArcGIS Geoprocessing
        tool from within R by running Python geoprocessing scripts
        without writing Python code or touching ArcGIS. Requires ArcGIS
        >=9.2, a suitable version of Python (for ArcGIS 9.2: Python
        2.4; for ArcGIS 10.0: 2.6), and Windows.
Run Python code, make function calls,  assign and retrieve variables, etc. from R.
Current version only supports plain text, but it can import PDF highlights if package 'rjpod' (<https://r-forge.r-project.org/projects/rqda/>) is installed.
Establishes an interface between R and 'QGIS', i.e. it allows
    the user to access 'QGIS' functionalities from the R console. It achieves this
    by using the 'QGIS' Python API via the command line. Hence, RQGIS extends R's 
    statistical power by the incredible vast geo-functionality of 'QGIS' (including 
    also 'GDAL', 'SAGA'- and 'GRASS'-GIS among other third-party providers). This in turn 
    creates a powerful environment for advanced and innovative (geo-)statistical
    geocomputing. 'QGIS' is licensed under GPL version 2 or greater and is available from
    <http://www.qgis.org/en/site/>.
Performs penalized quantile regression for LASSO, SCAD and MCP functions including group penalties. Provides a function that automatically generates lambdas and evaluates different models with cross validation or BIC, including a large p version of BIC.
Hypocenter estimation and analysis of seismic data collected continuously, or in trigger mode. The functions organize other functions from RSEIS and GEOmap to help researchers pick, locate, and store hypocenters for detailed seismic investigation.
The 'RQuantLib' package makes parts of 'QuantLib' accessible from R
 The 'QuantLib' project aims to provide a comprehensive software framework
 for quantitative finance. The goal is to provide a standard open source library
 for quantitative analysis, modeling, trading, and risk management of financial
 assets.
Enables researchers to conduct multivariate statistical analyses
    of survey data with randomized response technique items from several designs,
    including mirrored question, forced question, and unrelated question. This
    includes regression with the randomized response as the outcome and logistic
    regression with the randomized response item as a predictor. In addition,
    tools for conducting power analysis for designing randomized response items
    are included. The package implements methods described in Blair, Imai, and Zhou
    (2015) ''Design and Analysis of the Randomized Response Technique,'' Journal
    of the American Statistical Association 
    <http://graemeblair.com/papers/randresp.pdf>.
Analyzes and predicts from matrix population models in the way of the Ramas (c) software
An implementation of the Real-time Adaptive Penalization (RAP) algorithm through which to iteratively update a regularization parameter in a streaming context.
Replication Rate (RR) is the probability of replicating a statistically significant association in genome-wide association studies. This R-package provide the estimation method for replication rate which makes use of the summary statistics from the primary study. We can use the estimated RR to determine the sample size of the replication study, and to check the consistency between the results of the primary study and those of the replication study.
Software for genomic prediction with the RR-BLUP mixed model. One application is to estimate marker effects by ridge regression; alternatively, BLUPs can be calculated based on an additive relationship matrix or a Gaussian kernel.
rrBlupMethod6 -- Re-parametrization of mixed model
        formulation to allow for a fixed residual variance when using
        RR-BLUP for genomwide estimation of marker effects and linear
        transformation of the adjusted means proposed by Piepho et
        al.(2011)
Robust Location and Scatter Estimation and Robust
        Multivariate Analysis with High Breakdown Point.
Robust multivariate methods for high dimensional data including
        outlier detection, PCA, PLS and classification.
Robust Location and Scatter Estimation and Robust
        Multivariate Analysis with High Breakdown Point for Incomplete
        Data.
Make use of the hardware random number accessed by the 'RdRand'
 instruction in recent Intel CPUs (Ivy Bridge and later).
 'DRNG' is "Digital Random Number Generator".
Provides implementations of several popular recommendation systems. They can process standard recommendation datasets (user/item matrix) as input and generate rating predictions and recommendation lists.  Standard algorithm implementations included in this package are: Global/Item/User-Average baselines, Item-Based KNN, FunkSVD, BPR and weighted ALS. They can be assessed according to the standard offline evaluation methodology for recommender systems using measures such as MAE, RMSE, Precision, Recall, AUC, NDCG, RankScore and coverage measures. The package is intended for rapid prototyping of recommendation algorithms and education purposes.
R client interface to the "Redis" key-value database.
'IUCN' Red List (<http://apiv3.iucnredlist.org/api/v3/docs>) client.
    The 'IUCN' Red List is a global list of threatened and endangered species.
    Functions cover all of the Red List 'API' routes. An 'API' key is required.
Superclasses 'PostgreSQL' connection to help enable full 'dplyr' functionality on 'Redshift'.
'OpenRefine' (formerly 'Google Refine') is a popular, open source data cleaning software. This package enables users to programmatically trigger data transfer between R and 'OpenRefine'. Available functionality includes project import, export and deletion.
An R and Repast integration tool for running individual-based
    (IbM) simulation models developed using 'Repast Simphony' Agent-Based framework
    directly from R code. This package integrates 'Repast Simphony' models within
    R environment, making easier the tasks of running and analyzing model output
    data for automated parameter calibration and for carrying out uncertainty and
    sensitivity analysis using the power of R environment.
Feature Selection with Regularized Random Forest. This
    package is based on the 'randomForest' package by Andy Liaw.
    The key difference is the RRF() function that builds a
    regularized random forest.
Collection of functions for fitting distributions to given data or
    by known quantiles. Two main functions fit.perc() and fit.cont() provide
    users a GUI that allows to choose a most appropriate distribution without
    any knowledge of the R syntax. Note, this package is a part of the 'rrisk'
    project.
This package offers methods to perform robust regularized
        linear discriminant analysis.
Functions for creating and manipulating RNA secondary structure plots.
Reduced-rank regression, diagnostics and graphics.
Univariate and multivariate methods to analyze
    randomized response (RR) survey designs (e.g., Warner, S. L. (1965).
    Randomized response: A survey technique for eliminating evasive answer
    bias. Journal of the American Statistical Association, 60, 63–69).
    Besides univariate estimates of true proportions, RR variables can be used
    for correlations, as dependent variable in a logistic regression (with or
    without random effects), as predictors in a linear regression, or as
    dependent variable in a beta-binomial ANOVA. For simulation and bootstrap
    purposes, RR data can be generated according to several models.
The RSP markup language makes any text-based document come alive.  RSP provides a powerful markup for controlling the content and output of LaTeX, HTML, Markdown, AsciiDoc, Sweave and knitr documents (and more), e.g. 'Today's date is <%=Sys.Date()%>'.  Contrary to many other literate programming languages, with RSP it is straightforward to loop over mixtures of code and text sections, e.g. in month-by-month summaries.  RSP has also several preprocessing directives for incorporating static and dynamic contents of external files (local or online) among other things.  Functions rstring() and rcat() make it easy to process RSP strings, rsource() sources an RSP file as it was an R script, while rfile() compiles it (even online) into its final output format, e.g. rfile('report.tex.rsp') generates 'report.pdf' and rfile('report.md.rsp') generates 'report.html'.  RSP is ideal for self-contained scientific reports and R package vignettes.  It's easy to use - if you know how to write an R script, you'll be up and running within minutes.
Point and interval estimation of linear parameters with data
    obtained from complex surveys (including stratified and clustered samples)
    when randomization techniques are used. The randomized response technique
    was developed to obtain estimates that are more valid when studying
    sensitive topics. Estimators and variances for 14 randomized response
    methods for qualitative variables and 7 randomized response methods for
    quantitative variables are also implemented. In addition, some data sets
    from surveys with these randomization methods are included in the package.
Advanced response surface analysis. The main function RSA computes
    and compares several nested polynomial regression models (full polynomial,
    shifted and rotated squared differences, rising ridge surfaces, basic
    squared differences). The package provides plotting functions for 3d
    wireframe surfaces, interactive 3d plots, and contour plots. Calculates
    many surface parameters (a1 to a4, principal axes, stationary point,
    eigenvalues) and provides standard, robust, or bootstrapped standard errors
    and confidence intervals for them.
The package contains all the data sets related to the book
        written by the maintainer of the package.
Robust Small Area Estimation. Robust Basic Unit- and Area-Level Models
Provides access to geocomputing and terrain analysis
    functions of the geographical information system (GIS) 'SAGA' (System for
    Automated Geoscientific Analyses) from within R by running the command 
    line version of SAGA. This package furthermore provides several R functions
    for handling ASCII grids, including a flexible framework for applying local
    functions (including predict methods of fitted models) and focal functions to
    multiple grids. SAGA GIS is available under GPLv2 / LGPLv2 licence from
    http://sourceforge.net/projects/saga-gis/.
Performs parameter estimation for geostatistical data using a resampling-based stochastic approximation (RSA) method.
Contains functions that are useful for pre- and post-processing the tree output from the MCMC-based C++ program sampletrees. The program sampletrees is used to sample gene genealogies conditional on either phased or unphased SNP genotype data. More information about sampletrees can be found at http://stat.sfu.ca/statgen/research/sampletrees.html . Pre-processing tasks involve creating the settings file, the haplotype estimation files (if input data is genotype), and error checking the settings. Post-processing involves reading in the MCMC output from a run of sampletrees and computing summary statistics.
Resampling Stats (http://www.resample.com) is an add-in for
    running randomization tests in Excel worksheets. The workflow is (1) to define
    a statistic of interest that can be calculated from a data table, (2) to
    randomize rows ad/or columns of a data table to simulate a null hypothesis
    and (3) and to score the value of the statistic from many randomizations. The
    relative frequency distribution of the statistic in the simulations is then
    used to infer the probability of the observed value be generated by the null
    process (probability of Type I error). This package intends to translate this
    logic for R for teaching purposes. Keeping the original workflow is favored over
    performance.
Tools to create, evaluate, critique,
    revise, track progress, and communicate a detailed wilderness or urban
    search plan to management. This package uses and creates csv files in the R
    working directory to document inputs and results. It also creates a series
    of PDF and PNG files to accomplish communication of the plan. The program
    creates and revises search plans using Bayesian models. The package
    includes functions bestsearch(), searchstatus() and searchme() to model the
    number of searchers and hours required to search an area, calculate the
    probability of detection, probability of success, and project the best plan
    given limited resources.
Implements the Gibbs sampling algorithm to randomly sample association rules with one pre-chosen item as the consequent from a transaction dataset. The Gibbs sampling algorithm was proposed in G. Qian, C.R. Rao, X. Sun and Y. Wu (2016) <DOI:10.1073/pnas.1604553113>.
SaTScan(TM) (http://www.satscan.org) is software for finding regions in 
    Time, Space, or Time-Space that have excess risk, based on scan statistics, and 
	uses Monte Carlo hypothesis testing to generate P-values for these regions.  The 
	rsatscan package provides functions for writing R data frames in 
	SaTScan-readable formats, for setting SaTScan parameters, for running SaTScan in 
	the OS, and for reading the files that SaTScan creates.
Retrieve, update, delete job information from <https://saucelabs.com/>. Poll the 'SauceLabs' services
    current status and access supported platforms. Send and retrieve files from 'SauceLabs' and manage tunnels associated
    with 'SauceConnect'.
This package implements a statistical tool for modeling multivariate relationships using a stepwise cluster analysis (SCA) method.
Performs the Rao-Scott Cochran-Armitage by Slices trend test (RSCABS) used 
	in analysis of histopathological endpoints. It has functions for both command line 
	operations along with a built in GUI.
The Scala interpreter is embedded in R and callbacks to R from the embedded interpreter are supported.  Conversely, the R interpreter is embedded in Scala.  Scala versions in the 2.10.x, 2.11.x, and 2.12.x series are supported.
The SciMark 2.0 benchmark was originally developed in Java as a benchmark for numerical and scientific computational performance. It measures the performance of several computational kernels which are frequently occurring in scientific applications. This package is a simple wrapper around the ANSI C implementation of the benchmark.
Client for Rserve, allowing to connect to Rserve instances and issue commands.
Programmatic deployment interface for 'RPubs', 'shinyapps.io', and
    'RStudio Connect'. Supported content types include R Markdown documents,
    Shiny applications, plots, and static web content.
Uses Elsevier 'Scopus' 'API'
    <https://dev.elsevier.com/sc_apis.html> to download 
    information about authors and their citations.
A method to download Department of Education College
     Scorecard data using the public API
     <https://collegescorecard.ed.gov/data/documentation/>. It is based on
     the 'dplyr' model of piped commands to select and filter data in a
     single chained function call.  An API key from the U.S. Department of
     Education is required.
rscproxy library provides an interface to R used by third
        party applications, most notable, but not limited to,
        statconnDCOM, ROOo and other systems.
Symbolic Data Analysis (SDA) was proposed by professor Edwin Diday in 1987, the main purpose of SDA is to substitute the set of rows (cases) in the data table for  a concept (second order statistical unit). This package implements, to the symbolic case, certain techniques of automatic classification, as well as some linear models.
Ray Shooting Depth functions are provided for bivariate analysis.
Set of classes and methods to read data and metadata documents
  exchanged through the Statistical Data and Metadata Exchange (SDMX) framework,
  currently focusing on the SDMX XML standard format (SDMX-ML).
An implementation of the analysis about seed components from Borenstein et.al. 2008.
Estimate the percentage of seeds in a seedlot that contain stacks
    of genetically modified traits.  Estimates are calculated using a
    multinomial group testing model with maximum likelihood estimation of the
    parameters.
Multiple interactive codes to view and analyze seismic data, via spectrum analysis, wavelet transforms, particle motion, hodograms.  Includes general time-series tools, plotting, filtering, interactive display.
Provides a set of R bindings for the 'Selenium 2.0 WebDriver'
    (see <https://seleniumhq.github.io/docs/wd.html>
    for more information) using the 'JsonWireProtocol' (see
    <https://github.com/SeleniumHQ/selenium/wiki/JsonWireProtocol> for more
    information). 'Selenium 2.0 WebDriver' allows driving a web browser
    natively as a user would either locally or on a remote machine using
    the Selenium server it marks a leap forward in terms of web browser
    automation. Selenium automates web browsers (commonly referred to as
    browsers). Using RSelenium you can automate browsers locally or
    remotely.
A robust procedure is implemented to estimate means and covariance matrix of multiple variables with missing data using Huber weight and then to estimate a structural equation model.
Analyses sentiment of a sentence in English and assigns score to it. It can classify sentences to the following categories of sentiments:- Positive, Negative, very Positive, very negative, 
              Neutral or Sarcasm. For a vector of sentences, it counts the number of sentences in each
              category of sentiment.In calculating the score, negation and various degrees
              of adjectives are taken into consideration. It deals only with English sentences.
Rserve acts as a socket server (TCP/IP or local sockets) 
	     which allows binary requests to be sent to R. Every
	     connection has a separate workspace and working
	     directory. Client-side implementations are available
	     for popular languages such as C/C++ and Java, allowing
	     any application to use facilities of R without the need of
	     linking to R code. Rserve supports remote connection,
	     user authentication and file transfer. A simple R client
	     is included in this package as well.
Slow Feature Analysis in R, ported to R based on the
    matlab versions SFA toolkit 1.0 by Pietro Berkes and SFA toolkit
    2.8 by Wolfgang Konen for matlab.
This package provides functions for calculating
        associations between two genes with five correlation
        methods(e.g., the Gini correlation coefficient [GCC], the
        Pearson's product moment correlation coefficient [PCC], the
        Kendall tau rank correlation coefficient [KCC], the Spearman's
        rank correlation coefficient [SCC] and the Tukey's biweight
        correlation coefficient [BiWt], and three non-correlation
        methods (e.g., mutual information [MI] and the maximal
        information-based nonparametric exploration [MINE], and the
        euclidean distance [ED]). It can also been implemented to
        perform the correlation and clustering analysis of
        transcriptomic data profiled by microarray and RNA-Seq
        technologies. Additionally, this package can be further applied
        to construct gene co-expression networks (GCNs).
Robust estimation of sparse inverse covariance matrix via the gamma-divergence.
Functions for estimating models using a Hierarchical Bayesian (HB) framework. The flexibility comes in allowing the user to specify the likelihood function directly instead of assuming predetermined model structures. Types of models that can be estimated with this code include the family of discrete choice models (Multinomial Logit, Mixed Logit, Nested Logit, Error Components Logit and Latent Class) as well ordered response models like ordered probit and ordered logit. In addition, the package allows for flexibility in specifying parameters as either fixed (non-varying across individuals) or random with continuous distributions. Parameter distributions supported include normal, positive/negative log-normal, positive/negative censored normal, and the Johnson SB distribution. Kenneth Train's Matlab and Gauss code for doing Hierarchical Bayesian estimation has served as the basis for a few of the functions included in this package. These Matlab/Gauss functions have been rewritten to be optimized within R. Considerable code has been added to increase the flexibility and usability of the code base. Train's original Gauss and Matlab code can be found here: http://elsa.berkeley.edu/Software/abstracts/train1006mxlhb.html See Train's chapter on HB in Discrete Choice with Simulation here: http://elsa.berkeley.edu/books/choice2.html; and his paper on using HB with non-normal distributions here: http://eml.berkeley.edu//~train/trainsonnier.pdf.
Fits models to longitudinal network data
Robust and efficient feature selection algorithm to
    identify important features for predicting survival risk.
    The method is based on subsampling and averaging linear models
    obtained from the (preconditioned) Lasso algorithm, with an extra 
    shrinking procedure to reduce the size of signatures. An 
    evaluation procedure using subsampling is also provided.
This package provides a way to transform an image into a mosaic composed from a set of smaller images (tiles). It also contains a simple function for creating the tiles from a folder of images directly through R, without the need of any external code. At this moment only the JPEG format is supported, either as input (image and tiles) or output (mosaic transformed image).
Makes  operations with raster images, such as map
    viewing in time series, export values in time series for specific, total or limited
    within a polygon locations. Makes data processing of remote sensing of climatic variables distributed in the space (maps 2D) and the time (time series).
Functions for interacting with the Adobe Analytics API V1.4
    (https://api.omniture.com/admin/1.4/rest/).
This RSKC package contains a function RSKC which runs the robust sparse K-means clustering algorithm.
Implements the "Stemming Algorithm for the Portuguese Language" <DOI:10.1109/SPIRE.2001.10024>.
Functions that simplify the R interface the 'SLURM' cluster workload 
    manager, and automate the process of dividing a parallel calculation across 
    cluster nodes.
Provides functions to generate response-surface designs, 
    fit first- and second-order response-surface models, 
    make surface plots, obtain the path of steepest ascent, 
    and do canonical analysis.
Aims at loading Facebook and Instagram advertising data from
    'Smartly.io' into R. 'Smartly.io' is an online advertising service that enables
    advertisers to display commercial ads on social media networks (see <http://www.smartly.io/> for more information).
    The package offers an interface to query the 'Smartly.io' API and loads data directly into R for further data processing and data analysis.
It manages snow and weather local time series as provided by MeteoIO (<http://models.slf.ch/docserver/meteoio/html/index.html>, <http://www.slf.ch/ueber/organisation/schnee_permafrost/projekte/MeteoIO/index_EN>, <https://www.openhub.net/p/MeteoIO>). MeteoIO is a C/C++ Open Source library which "has been designed to accomodate both the needs of carefully crafted simulations for a specific purpose/study and for the needs of operational simulations that run automatically and unattended". It is integrated in physical spatially-distributed models and tackles several issues with weather input/output data. Here a SMET S4 class object is defined and can be imported from/ exported to a SMET ini files of MeteoIO , allowing interoperability from R to MeteoIO and other SMET-compliant software.
Read and analyse Root System Markup Language (RSML) files, used to
    store plant root system architecture data. More information can be found
    at the address <http://rootsystemml.github.io/>.
The Stuttgart Neural Network Simulator (SNNS) is a library
    containing many standard implementations of neural networks. This
    package wraps the SNNS functionality to make it available from
    within R. Using the 'RSNNS' low-level interface, all of the
    algorithmic functionality and flexibility of SNNS can be accessed.
    Furthermore, the package contains a convenient high-level
    interface, so that the most common neural network topologies and
    learning algorithms integrate seamlessly into R.
A programmatic interface to various 'SNP' 'datasets'
    on the web: 'OpenSNP' (<https://opensnp.org>), 'NBCIs' 'dbSNP' database 
    (<https://www.ncbi.nlm.nih.gov/projects/SNP>), and Broad Institute 'SNP'
    Annotation and Proxy Search 
    (<http://archive.broadinstitute.org/mpg/snap/ldsearch.php>). Functions 
    are included for searching for 'SNPs' for the Broad Institute and 'NCBI'. 
    For 'OpenSNP', functions are included for getting 'SNPs', and data for 
    'genotypes', 'phenotypes', annotations, and bulk downloads of data by user.
An implementation of the use of efficient score statistics
  in genome-wide SNP set analysis with complex traits. Three standard score statistics
  (Cox, binomial, and Gaussian) are provided, but the package is easily extensible to
  include others. Code implementing the inferential procedure is primarily written in C++ and
  utilizes parallelization of the analysis to reduce runtime. A supporting function offers
  simple computation of observed, permutation, and FWER and FDR adjusted p-values.
Provides easier interaction with
    Socrata open data portals http://dev.socrata.com.
    Users can provide a 'Socrata' data set resource URL,
    or a 'Socrata' Open Data API (SoDA) web query,
    or a 'Socrata' "human-friendly" URL,
    returns an R data frame. Converts dates to 'POSIX'
    format and manages throttling by 'Socrata'.
    Users can upload data to Socrata portals directly
    from R.
General Non-linear Optimization Using Augmented Lagrange Multiplier Method.
Somoclu is a massively parallel implementation of self-organizing maps.  It exploits multicore CPUs and it can be accelerated by CUDA. The topology of the map can be planar or toroid and the grid of neurons can be rectangular or hexagonal .
Based on (optionally sparse) quadratic optimization with the main algorithms implemented in C. Includes features for easy processing of many (smaller) records. The algorithm has been tested on fairly large optimization problems with up to a few million variables and several hundred thousand restrictions.
Conducts a spatially-explicit, simulation-based power analysis for detecting trends in population abundance through occupancy-based modeling. Applicable for evaluating monitoring designs in conservation and ecological settings.
An extension package for 'sparklyr' that provides an R interface to
    H2O Sparkling Water machine learning library (see <https://github.com/h2oai/sparkling-water> for more information).
R interface to the 'Spectra' library
    <http://yixuan.cos.name/spectra/> for large scale eigenvalue and SVD
    problems. It is typically used to compute a few
    eigenvalues/vectors of an n by n matrix, e.g., the k largest eigenvalues,
    which is usually more efficient than eigen() if k << n. This package
    provides the 'eigs()' function which does the similar job as in 'Matlab',
    'Octave', 'Python SciPy' and 'Julia'. It also provides the 'svds()' function
    to calculate the largest k singular values and corresponding
    singular vectors of a real matrix. Matrices can be given in either dense
    or sparse form.
Conversion between attitude representations: DCM, Euler angles, Quaternions, and Euler vectors.
    Plus conversion between 2 Euler angle set types (xyx, yzy, zxz, xzx, yxy, zyz, xyz, yzx, zxy, xzy, yxz, zyx).
    Fully vectorized code, with warnings/errors for Euler angles (singularity, out of range, invalid angle order), 
    DCM (orthogonality, not proper, exceeded tolerance to unity determinant) and Euler vectors(not unity).
    Also quaternion and other useful functions.
    Based on SpinCalc by John Fuller and SpinConv by Paolo de Leva.
Provides functions for estimating power or sample size for RNA-Seq studies. Empirical approach is used and the data is assumed to be count in nature. The underlying distribution of data is assumed to be Poisson or negative binomial. The package contains 6 function; 4 functions provide estimates of sample size or power for Poisson and Negative Binomial distribution; 2 functions provide plots of power for given sample size or sample size for given power.
Calculates a newly defined coefficient of determination, aka R^2, and coefficient of partial determination, aka partial R^2 by Zhang (2016) for generalized linear models (including quasi models with well defined variance functions). It also avoids overstatement of variation proportion explained by the model or a group of covariates.
Embeds the 'SQLite' database engine in R and
    provides an interface compliant with the 'DBI' package. The
    source for the 'SQLite' engine (version 3.8.8.2) is included.
Methods and tools for Singular Spectrum Analysis including decomposition, forecasting and gap-filling for univariate and multivariate time series.
A collection of implementations of semi-supervised classifiers and methods to evaluate their performance. The package includes implementations of, among others, Implicitly Constrained Learning, Moment Constrained Learning, the Transductive SVM, Manifold regularization, Maximum Contrastive Pessimistic Likelihood estimation, S4VM and WellSVM.
Reservoir Systems Standard Operation Policy. A system for simulation of supply reservoirs. It proposes functionalities for plotting and evaluation of supply reservoirs systems.
An extremely simple stack data type, implemented with 'R6'
    classes. The size of the stack increases as needed, and the amortized
    time complexity is O(1). The stack may contain arbitrary objects.
Provides fast, persistent (side-effect-free) stack, queue and
    deque (double-ended-queue) data structures. While deques include a superset
    of functionality provided by queues, in these implementations queues are
    more efficient in some specialized situations. See the documentation for
    rstack, rdeque, and rpqueue for details.
User-facing R functions are provided to parse, compile, test, 
    estimate, and analyze Stan models by accessing the header-only Stan library 
    provided by the 'StanHeaders' package. The Stan project develops a
    probabilistic programming language that implements full Bayesian statistical 
    inference via Markov Chain Monte Carlo, rough Bayesian inference via 'variational'
    approximation, and (optionally penalized) maximum likelihood estimation via 
    optimization. In all three cases, automatic differentiation is used to quickly 
    and accurately evaluate gradients without burdening the user with the need 
    to derive the partial derivatives.
Estimates pre-compiled regression models using the 'rstan' package, which provides
    the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the
    customary R syntax with a formula and data.frame plus some additional arguments for priors.
Provides various tools for developers of R packages interfacing
    with 'Stan' <http://mc-stan.org>, including functions to set up the required 
    package structure, S3 generics and default methods to unify function naming 
    across 'Stan'-based R packages, and a vignette with recommendations for 
    developers.
Access to the Digital Universe Data set API pulls data on
    exoplants, stars, and galaxies.
A simple R -> Stata interface allowing the user to
    execute Stata commands (both inline and from a .do file)
    from R.
R interface for china national data http://data.stats.gov.cn/, 
    some convenient functions for accessing the national data are provided.
This package simulates random orthonormal matrices from linear and quadratic exponential family distributions on the Stiefel manifold. The most general type of distribution covered is the matrix-variate  Bingham-von Mises-Fisher distribution. Most of the simulation methods are presented in Hoff(2009) "Simulation of the Matrix Bingham-von Mises-Fisher Distribution, With Applications to Multivariate and Relational Data."
Toolbox for remote sensing image processing and analysis such as
    calculating spectral indices, principal component transformation, unsupervised
    and supervised classification or fractional cover analyses.
While streaming processing provides opportunities to deal with extremely large and ever growing data sets in (near) real time, the development of streaming algorithms for complex models is often cumbersome: the software packages that facilitate streaming processing in production environments do not provide statisticians with the simulation, estimation, and plotting tools they are used to. Developers of streaming algorithms would thus benefit from the flexibility of [R] to create, plot and compute data while developing streaming algorithms. Package RStorm implements a streaming architecture modeled on Storm for easy development and testing of streaming algorithms in [R]. RStorm is not intended as a production package, but rather a development tool for streaming algorithms.
R implementation of generalized survival models, where g(S(t|x))=eta(t,x) for a link function g, survival S at time t with covariates x and a linear predictor eta(t,x). The main assumption is that the time effect(s) are smooth. For fully parametric models, this re-implements Stata's 'stpm2' function, which are flexible parametric survival models developed by Royston and colleagues. We have extended the parametric models to include any smooth parametric smoothers for time. We have also extended the model to include any smooth penalized smoothers from the 'mgcv' package, using penalized likelihood. These models include left truncation, right censoring, interval censoring, gamma frailties and normal random effects.
Unified object oriented interface for multiple independent streams of random numbers from different sources.
A convenience interface for communicating with the Stripe payment processor to accept payments online. See <https://stripe.com> for more information.
Access the RStudio API (if available) and provide informative error
    messages when it's not.
A collection of efficient and effective tools and
	algorithms for subgroup discovery and analytics. The package
	integrates an R interface to the org.vikamine.kernel library
	of the VIKAMINE system (http://www.vikamine.org) implementing
	subgroup discovery, pattern mining and analytics in Java.
This package provides an interface for the package of
        nonlinear differential algebraic equation solvers that comprise
        SUNDIALS. ODEs are expressed as R functions or as compiled
        code.
Interface to three 'Sunlight' Foundation 'APIs' (http://
    sunlightfoundation.com/api/) for government data, including the
    Congress 'API' 'v3', the Capitol Words 'API', and the Open States
    'API'. 'Sunlight' Foundation is a 'nonpartison' 'nonprofit' that collects and
    provides data on government activities, and those that influence government.
    Functions are provided to interact with each of the three 'APIs'.
Provides functions to estimate the proportion of treatment effect on the primary outcome that is explained by the treatment effect on the surrogate marker.
A range of functions for the design and
    analysis of disease surveillance activities. These functions were
    originally developed for animal health surveillance activities but can be
    equally applied to aquatic animal, wildlife, plant and human health
    surveillance activities. Utilities are included for sample size calculation
    and analysis of representative surveys for disease freedom, risk-based
    studies for disease freedom and for prevalence estimation.
A geographic information system (GIS) graphical user interface (GUI) that
  provides data viewing, management, and analysis tools.
Randomized singular value decomposition (rsvd) is a very fast probabilistic algorithm that can be used to compute the near optimal low-rank singular value decomposition of massive data sets with high accuracy. SVD plays a central role in data analysis and scientific computing. SVD is also widely used for computing (randomized) principal component analysis (PCA), a linear dimensionality reduction technique. Randomized PCA (rpca) uses the approximated singular value decomposition to compute the most significant principal components. This package also includes a function to compute (randomized) robust principal component analysis (RPCA). In addition several plot functions are provided.
Renders vector-based svg images into high-quality custom-size bitmap
    arrays using 'librsvg2'. The resulting bitmap can be written to e.g. png, jpeg
    or webp format. In addition, the package can convert images directly to various
    formats such as pdf or postscript.
A graphics device for R that uses the w3.org xml standard
        for Scalable Vector Graphics.
A graphics device for R that uses the w3.org xml standard
             for Scalable Vector Graphics.  This version supports
             tooltips with 1 to 3 lines, hyperlinks, and line styles.
An R interface to the SYMPHONY solver for mixed-integer linear programs.
Access SymPy computer algebra system from R via Jython
Play the classic game of tic-tac-toe (naughts and crosses).
Provides tabular reporting functionalities to work with 'ReporteRs'
    package: 'as.FlexTable' methods are available for 'ftable' and 'xtable' objects,
    function 'FlexPivot' is producing a pivot table and 'freqtable' a percentage table,
    a 'knitr' print method and a 'shiny' render function are provided for 'FlexTable' objects.
Contains functions for random generation of R x C and 2 x 2 x K contingency tables. In addition to the generation of contingency tables over predetermined intraclass-correlated clusters, it is possible to generate contingency tables without intraclass correlations under product multinomial, multinomial, and Poisson sampling plans. It also consists of a function for generation of random data from a given discrete probability distribution function.
Storing huge data in RData format causes problems because
        of the necessity to load the whole file to the memory in order
        to access and manipulate objects inside such file; rtape is a
        simple solution to this problem. The package contains several
        wrappers of R built-in serialize/unserialize mechanism allowing
        user to quickly append objects to a tape-like file and later
        iterate over them requiring only one copy of each stored object
        to reside in memory a time.
Discretize AR(1) process following Tauchen (1986) <http://www.sciencedirect.com/science/article/pii/0165176586901680>. A discrete Markov chain that approximates in the sense of weak convergence a continuous-valued univariate Autoregressive process of first order is generated. It is a popular method used in economics and in finance.
Tools for analyzing sales report files of iTunes Connect.
Robust tail dependence estimation for bivariate models. This package is based on two papers by the authors:'Robust and bias-corrected estimation of the coefficient of tail dependence' and 'Robust and bias-corrected estimation of probabilities of extreme failure sets'. This work was supported by a research grant (VKR023480) from VILLUM FONDEN and an international project for scientific cooperation (PICS-6416).
Provides response time distributions (density/PDF, distribution
    function/CDF, quantile function, and random generation): (a) Ratcliff diffusion
    model based on C code by Andreas and Jochen Voss and (b) linear ballistic
    accumulator (LBA) with different distributions underlying the drift rate.
Exploit controlled vocabularies organized on tematres servers.
A set of tools for creation, manipulation, and modeling
    of tensors with arbitrary number of modes. A tensor in the context of data
    analysis is a multidimensional array. rTensor does this by providing a S4
    class 'Tensor' that wraps around the base 'array' class. rTensor
    provides common tensor operations as methods, including matrix unfolding,
    summing/averaging across modes, calculating the Frobenius norm, and taking
    the inner product between two tensors. Familiar array operations are
    overloaded, such as index subsetting via '[' and element-wise operations.
    rTensor also implements various tensor decomposition, including CP, GLRAM,
    MPCA, PVD, and Tucker. For tensors with 3 modes, rTensor also implements
    transpose, t-product, and t-SVD, as defined in Kilmer et al. (2013). Some
    auxiliary functions include the Khatri-Rao product, Kronecker product, and
    the Hamadard product for a list of matrices.
For natural language processing and analysis of qualitative text
    coding structures which provide a way to bind together text and text data
    are fundamental. The package provides such a structure and accompanying
    methods in form of R6 objects. The 'rtext' class allows for text handling
    and text coding (character or regex based) including data updates on
    text transformations as well as aggregation on various levels.
    Furthermore, the usage of R6 enables inheritance and passing by reference
    which should enable 'rtext' instances to be used as back-end for R based
    graphical text editors or text coding GUIs.
Reorder sentences for Korean text using TextRank algorithm.
RTextTools is a machine learning package for automatic
        text classification that makes it simple for novice users to
        get started with machine learning, while allowing experienced
        users to easily experiment with different settings and
        algorithm combinations. The package includes nine algorithms
        for ensemble classification (svm, slda, boosting, bagging,
        random forests, glmnet, decision trees, neural networks,
        maximum entropy), comprehensive analytics, and thorough
        documentation.
This package contains several functions for calculation of texture metrics for Grey Level Co-occurrence matrices
A set of R functions to output Rich Text Format (RTF) files with high resolution tables and graphics that may be edited with a standard word processor such as Microsoft Word.
Identifies and scores possible Transcription Factor
    Binding Sites and allows for FDR analysis and pruning.  It supports
    splitting of sequences based on size or a specified GFF, grouping
    by G+C content, and specification of Markov model order.  The heavy
    lifting is done in C while all results are made available via R.
A suite of custom R Markdown formats and templates for
  authoring journal articles and conference submissions.
Calculates tide heights based on tide station harmonics.
    It includes the harmonics data for 637 US stations.
    The harmonics data was converted from harmonics-dwf-20151227-free,
    NOAA web site data processed by David Flater for XTide.
    The code to calculate tide heights from the harmonics is based on XTide.
Reads and writes TIFF format images and
        returns them as a pixmap object. Because the resulting object
        can be very large for even modestly sized TIFF images, images
        can be reduced as they are read for improved performance.  This
        package is a wrapper around libtiff (www.libtiff.org), on which
        it depends (i.e. the libtiff shared library must be on your
        PATH for the binary to work, and tiffio.h must be on your
        system to build the package from source). By using libtiff's
        highlevel TIFFReadRGBAImage function, this package inherently
        supports a wide range of image formats and compression schemes.
        This package also provides an implementation of the Ridler
        Autothresholding algorithm for easy generation of binary masks.
Interface to Congress, Campaign Finance, Article Search, and
    Geographic 'APIs' from the New York Times. Documentation for New York 
    Times 'APIs' (http://developer.nytimes.com/docs/). This client covers 
    a subset of the New York Times 'APIs'.
R tools to measure and compare inequality, welfare and poverty using the EU statistics on income and living conditions surveys.
Allows normalization of very large abundance tables, from metagenomics applications or other sources, to an equal depth. Additionally diversity measures, such as richness, Shannon diversity, chao1, chao2 and others are calculated on the fly. The results may be plotted using the provided plotting functions.
STK++ (http://www.stkpp.org) is a collection of
    C++ classes for statistics, clustering, linear algebra, arrays (with an
    Eigen-like API), regression, dimension reduction, etc. The integration of
    the library to R is using Rcpp.

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

       STK++ is licensed under the GNU LGPL version 2 or later. rtkore
    (the stkpp integration into R) is licensed under the
    GNU GPL version 2 or later. See file LICENSE.note for details.
Truncated Newton function minimization with bounds constraints
	based on the 'Matlab'/'Octave' codes of Stephen Nash.
Aimed at seismic tomography, the package
  plots tomographic images, and allows one to interact and query
  three-dimensional tomographic models.
  Vertical cross-sectional cuts can be extracted by mouse click.
  Geographic information can be added easily.
Geostatistical interpolation of data with irregular spatial support such as runoff related data or data from administrative units.
Analyze download logs from the CRAN RStudio mirror 
            (<http://cran.rstudio.com/>). 
            This CRAN mirror is the default one used in RStudio.
            The available data is the result of parsed and anonymised raw log data from
            that CRAN mirror.
This is a port of Jonathan Shewchuk's Triangle library to
    R. From his description: "Triangle generates exact Delaunay
    triangulations, constrained Delaunay triangulations, conforming
    Delaunay triangulations, Voronoi diagrams, and high-quality
    triangular meshes. The latter can be generated with no small or
    large angles, and are thus suitable for finite element analysis."
A simple R-based implementation of a Trie (a.k.a. digital tree/radix tree/prefix tree)
    A trie, also called digital tree and sometimes radix tree or prefix tree is a kind of search tree.
    This ordered tree data structure  is used to store a dynamic set or associative array where the keys are usually strings.
The TRIM model is widely used for estimating growth and decline of
    animal populations based on (possibly sparsely available) count data. The
    current package is a reimplementation of the original TRIM software developed
    at Statistics Netherlands by Jeroen Pannekoek. See
    <https://www.cbs.nl/en-gb/society/nature-and-environment/indices-and-trends--trim-->
    for more information about TRIM.
This framework aims to provide classes and methods for manipulating and processing of raster time series data (e.g. a time series of satellite images).
An R wrapper around the fast T-distributed Stochastic
    Neighbor Embedding implementation by Van der Maaten.
TSON, short for Typed JSON, is a binary-encoded serialization of
    JSON like document that support JavaScript typed data (https://github.com/tercen/TSON).
Contains the program 'ttf2pt1', for use with the
    'extrafont' package. This product includes software developed by the 'TTF2PT1'
    Project and its contributors.
Convert text into speech (voice file in 'wav' format) with API offered by ITRI TTS (Text-To-Speech service, Industrial Technology Research Institute, Taiwan. http://tts.itri.org.tw/). One main function is given, tts_ITRI(). English and Chinese (both traditional and simplified) are supported, and user can specify the speaker accent, speed and volume. Using this package requires internet connection.
Contains functions for launching R Tutorial & Plotting Apps. The R Tutorial app
    provides a set of most commonly performed data manipulation tasks in R. The
    app structures the contents into different topics and provides an interactive & dynamic interface
    for navigation.
    The plotting app provides an automated interface for generating plots using the 'ggplot2' package.
    Current version of this app supports 10 different plot types along with options to manipulate specific
    aesthetics and controls related to each plot type.
The 't-walk' is a general-purpose MCMC sampler for
      arbitrary continuous distributions that requires no tuning.
An implementation of calls designed to extract and organize Twitter data via
    Twitter's REST and stream APIs. Functions formulate and send API requests, convert
    response objects to more user friendly data structures---e.g., data frames---and
    provide some aesthetically pleasing visualizations for exploring the data.
A strong type system for R which supports
    symbol declaration and assignment with type checking
    and condition checking.
An R interface to the 'typeform' <https://typeform.com> application program interface.
  Also provides functions for downloading your results.
R port of 'universalchardet', that is the encoding detector
    library of Mozilla.
Unobserved Components Models (introduced in Harvey, A. (1989),
    Forecasting, structural time series models and the Kalman filter, Cambridge
    New York: Cambridge University Press) decomposes a time series into
    components such as trend, seasonal, cycle, and the regression effects due
    to predictor series which captures the salient features of the series to
    predict its behavior.
R bindings for functions from the UCR Suite by Rakthanmanon et al. (2012) <DOI:10.1145/2339530.2339576>, which enables ultrafast subsequence
      search for a best match under Dynamic Time Warping and Euclidean Distance.
ARFIMA, in-mean, external regressors and various GARCH flavors, with methods for fit, forecast, simulation, inference and plotting.
Contains data and visualization functions for USA unemployment
    data. Data comes from the US Bureau of Labor Statistics (BLS). State data
    is in ?df_state_unemployment and covers 2000-2013. County data is in
    ?df_county_unemployment and covers 1990-2013. Choropleth maps of the data
    can be generated with ?state_unemployment_choropleth() and
    ?county_unemployment_choropleth() respectively.
R functions implementing a standard Unit Testing
        framework, with additional code inspection and report
        generation tools.
Automatically convert a file or package worth of 'RUnit' test
    functions into 'testthat' tests.
This package contains some functions for converting R
        objects to Java style variables and XML. Generated Java code is
        interpretable by dynamic Java libraries such as Beanshell.
        Calling R externally and handling the Java or XML output is an
        other way to call R from other languages without native
        interfaces. For a Java implementation of this approach visit
        http://www.mhsatman.com/rcaller.php and
        http://stdioe.blogspot.com/search/label/rcaller
User-friendly interface utilities for MCMC models via
    Just Another Gibbs Sampler (JAGS), facilitating the use of parallel
    (or distributed) processors for multiple chains, automated control
    of convergence and sample length diagnostics, and evaluation of the
    performance of a model using drop-k validation or against simulated
    data. Template model specifications can be generated using a standard
    lme4-style formula interface to assist users less familiar with the
    BUGS syntax.  A JAGS extension module provides additional distributions
    including the Pareto family of distributions, the DuMouchel prior and
    the half-Cauchy prior.
Interface to the UNU.RAN library for Universal Non-Uniform RANdom variate generators. 
	     Thus it allows to build non-uniform random number generators from quite arbitrary
	     distributions. In particular, it provides an algorithm for fast numerical inversion
	     for distribution with given density function.
	     In addition, the package contains densities, distribution functions and quantiles
	     from a couple of distributions.
This package provides a GUI (Graphical User Interface) for
        the UNU.RAN random variate generators. Thus it allows to build
        non-uniform random number generators interactively for quite
        arbitrary distributions. In addition, R code for the required
        calls from package Runuran can be displayed and stored for
        later use. Some basic analysis like goodness-of-fit tests can
        be performed.
An interface to the web service methods provided by the United States Department of Agriculture (USDA). The Agricultural Research Service (ARS) provides a large set of databases. The current version of the package holds interfaces to the Systematic Mycology and Microbiology Laboratory (SMML), which consists of four databases: Fungus-Host Distributions, Specimens, Literature and the Nomenclature database. It provides functions for querying these databases. The main function is \code{associations}, which allows searching for fungus-host combinations.
Uses the generalised ratio-of-uniforms (RU) method to simulate
    from univariate and (low-dimensional) multivariate continuous distributions.
    The user specifies the log-density, up to an additive constant. The RU
    algorithm is applied after relocation of mode of the density to zero, and
    the user can choose a tuning parameter r. For details see Wakefield, Gelfand
    and Smith (1991) <DOI:10.1007/BF01889987>, Efficient generation of random
    variates via the ratio-of-uniforms method, Statistics and Computing (1991)
    1, 129-133.  A Box-Cox variable transformation can be used to make the input
    density suitable for the RU method and to improve efficiency.  In the
    multivariate case rotation of axes can also be used to improve efficiency.
    See the rust website for more information, documentation and examples.
Utility functions useful when programming and developing R packages.
Implements the 'RUV' (Remove Unwanted Variation) algorithms.  These algorithms attempt to adjust for systematic errors of unknown origin in high-dimensional data.  The algorithms were originally developed for use with genomic data, especially microarray data, but may be useful with other types of high-dimensional data as well.  These algorithms were proposed by Gagnon-Bartsch and Speed (2012), and by Gagnon-Bartsch, Jacob and Speed (2013).  The algorithms require the user to specify a set of negative control variables, as described in the references.  The algorithms included in this package are 'RUV-2', 'RUV-4', 'RUV-inv', and 'RUV-rinv', along with various supporting algorithms.
Contains diverse more or less complicated functions, written to simplify user's life: simplifications of existing functions, basic but not implemented tests, easy-to-use tools, bridges between functions of different packages... All functions are presented in the French book 'Aide-memoire de statistique appliquee a la biologie', written by the same author and available on CRAN.
A collection of functions for computing "r-values" from various
    kinds of user input such as MCMC output or a list of effect size estimates
    and associated standard errors. Given a large collection of measurement units,
    the r-value, r, of a particular unit is a reported percentile that may be
    interpreted as the smallest percentile at which the unit should be placed in the
    top r-fraction of units.
Operations on triangular meshes based on 'VCGLIB'. This package
    integrates nicely with the R-package 'rgl' to render the meshes processed by
    'Rvcg'. The Visualization and Computer Graphics Library (VCG for short) is
    an open source portable C++ templated library for manipulation, processing
    and displaying with OpenGL of triangle and tetrahedral meshes. The library,
    composed by more than 100k lines of code, is released under the GPL license,
    and it is the base of most of the software tools of the Visual Computing Lab of
    the Italian National Research Council Institute ISTI <http://vcg.isti.cnr.it>,
    like 'metro' and 'MeshLab'. The 'VCGLIB' source is pulled from trunk
    <svn://svn.code.sf.net/p/vcg/code/trunk/vcglib> and patched to work with options
    determined by the configure script as well as to work with the header files
    included by 'RcppEigen'.
Check latest release version of R and R package (both in 'CRAN' or 'Bioconductor').
Query the main 'R' 'SVN' repository to find the
    versions 'r-release' and 'r-oldrel' refer to, and also all
    previous 'R' versions and their release dates.
Retrieve, map and summarize data from the 'VertNet.org' archives.
    Functions allow searching by many parameters, including 'taxonomic' names,
    places, and dates. In addition, there is an interface for conducting spatially
    delimited searches, and another for requesting large 'datasets' via email.
Wrappers around the 'xml2' and 'httr' packages to make it easy to
    download, then manipulate, HTML and XML.
The RVFam package provides functions to perform single SNP association analyses and gene-based tests for continuous, binary and survival traits against sequencing data (e.g. exome chip) using family data.
Vector Graphics devices for 'SVG', 'DrawingML' for
  Microsoft Word, PowerPoint and Excel.
Test suite for non-uniform pseudo-random number generators.
To detecting rare variants for binary traits using general pedigrees, the pedigree disequilibrium tests are proposed by collapsing rare haplotypes/variants with/without weights. To run the test, MERLIN is needed in Linux for haplotyping.
Play Video Poker with R, complete with a graphical user
        interface. So far, only "Jacks or Better" is implemented.
'ViennaCL' is a free open-source linear algebra library 
  for computations on many-core architectures (GPUs, MIC) and 
  multi-core CPUs. The library is written in C++ and supports 'CUDA', 
  'OpenCL', and 'OpenMP' (including switches at runtime). 
  I have placed these libraries in this package as a more efficient 
  distribution system for CRAN. The idea is that you can write a package 
  that depends on the 'ViennaCL' library and yet you do not need to 
  distribute a copy of this code with your package.
Variable metric nonlinear function minimization with bounds constraints.
The 'Vowpal Wabbit' project is a fast out-of-core learning
 system sponsored by Microsoft Research (having started at Yahoo! Research)
 and written by John Langford along with a number of contributors. This R
 package does not include the distributed computing implementation of the
 cluster/ directory of the upstream sources. Use of the software as a network
 service is also not directly supported as the aim is a simpler direct call
 from R for validation and comparison.
This is a collection of the five region-based
    rare-variant genetic association tests. The following tests are
    currently implemented: ASKAT, ASKAT-Normalized, VC-C1, VC-C2 and
    VC-C3.
When a gene or a genetic region is significantly associated with a disease or a trait, the rare variant selection procedure is able to distinguish causal (risk or protective) rare variants from noncausal rare variants located within the same gene or the same genetic region.
Computes estimates of the probability of related individuals sharing a rare variant.
Used to compute population controls weighted rare variants transmission distortion test
Use multiple regression methods to test rare variants
        association with disease traits.
Provides functions to retrieve and reformat data from the 'Star Wars' API (SWAPI) <https://swapi.co/>.
A set of R functions which provide an
        environment for the Time-Frequency analysis of 1-D signals (and
        especially for the wavelet and Gabor transforms of noisy
        signals). It was originally written for Splus by Rene Carmona,
        Bruno Torresani, and Wen L. Hwang, first at the University of
        California at Irvine and then at Princeton University.  Credit
        should also be given to Andrea Wang whose functions on the
        dyadic wavelet transform are included. Rwave is based on the
        book: "Practical Time-Frequency Analysis: Gabor and Wavelet
        Transforms with an Implementation in S", by Rene Carmona, Wen
        L. Hwang and Bruno Torresani, Academic Press, 1998.
This package will download model predictions from 15 different global circulation models in 20 year intervals from the world bank.  Users can also access historical data, and create maps at 2 different spatial scales.
a Bipartite graph and is constructed based on the spatial and/or non-spatial attributes of the spatial objects in the dataset. Secondly, RW techniques are utilized on the graphs to compute the outlierness for each point (the differences between spatial objects and their spatial neighbours). The top k objects with higher outlierness are recognized as outliers.
RWebLogo is a wrapper for the WebLogo python package
    that allows generating of customised sequence logos. Sequence logos are
    graphical representations of the sequence conservation of nucleotides (in a
    strand of DNA/RNA) or amino acids (in protein sequences). Each logo
    consists of stacks of symbols, one stack for each position in the sequence.
    The overall height of the stack indicates the sequence conservation at that
    position, while the height of symbols within the stack indicates the
    relative frequency of each amino or nucleic acid at that position. In
    general, a sequence logo provides a richer and more precise description of,
    for example, a binding site, than would a consensus sequence.
An R interface to Weka (Version 3.9.1).
   Weka is a collection of machine learning algorithms for data mining
   tasks written in Java, containing tools for data pre-processing,
   classification, regression, clustering, association rules, and
   visualization.  Package 'RWeka' contains the interface code, the
   Weka jar is in a separate package 'RWekajars'.  For more information
   on Weka see <http://www.cs.waikato.ac.nz/ml/weka/>.
External jars required for package 'RWeka'.
Communications simulation package supporting forward error correction.
This package provides Wiener process distribution functions,
  namely the Wiener first passage time density, CDF, quantile and random
  functions.
Provides an interface with the 'Wildbook' mark-recapture ecological database framework. It 
    helps users to pull data from the 'Wildbook' framework and format data for further analysis
    with mark-recapture applications like 'Program MARK' (which can be accessed via the 'RMark' package in 'R').
    Further information on the 'Wildbook' framework is available at: <http://www.wildbook.org/doku.php>.
Tools for downloading, editing and transforming wind data from Global Forecast System (GFS, see <https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/global-forcast-system-gfs>) of the USA's National Weather Service (NWS, see <http://www.weather.gov/>).
A plug in for using 'WinEdt' as an editor for R.
The Rwinsteps package facilitates communication between R
        and the Rasch modeling software Winsteps. The package currently
        includes functions for reading and writing command files,
        sending them to Winsteps, reading and writing data according to
        command file specifications, reading output into R, and
        plotting various results.
A communications simulation package supporting common wireless modulation
    formats including BPSK, QPSK, 8-PSK, 16-PSK, 16-QAM and 64-QAM. The package includes AWGN
    noise generation, and raised cosine and square root raised cosine pulse shaping. It also
    includes convenience functions for plotting constellation diagrams, density plots, stem
    plots and eye diagrams. Together these functions enable the evaluation of bit error and
    symbol error rates, evaluation of pulse shaping and inter-symbol interference and support
    visualization of the respective signals and noise in a variety of formats.
Enables mapping of country level and gridded user datasets.
High resolution vector country boundaries derived from
        Natural Earth data, can be plotted in rworldmap.
Provides a set of functions for performing digital signal
    processing.
Implements various tests, visualizations, and metrics
    for diagnosing convergence of MCMC chains in phylogenetics.  It implements
    and automates many of the functions of the AWTY package in the R
    environment.
Tools for getting historical weather information and forecasts from wunderground.com.  Historical weather and forecast data includes, but is not limited to, temperature, humidity, windchill, wind speed, dew point, heat index.  Additionally, the weather underground weather API also includes information on sunrise/sunset, tidal conditions, satellite/webcam imagery, weather alerts, hurricane alerts and historical record high/low temperatures.
Fits the R x C inference model described in Greiner and
        Quinn (2009).  Allows incorporation of survey results.
Visualize your favorite XKCD comic strip directly from
        R. XKCD web comic content is provided under the Creative
        Commons Attribution-NonCommercial 2.5 License.
Functions which allow to read many XMCDA tags and transform them into R variables which are then usable in MCDA algorithms written in R. It also allows to write certain R variables into XML files respecting the XMCDA standard.
Methods to compute chemical similarity between two or more reactions and molecules. Allows masking of chemical substructures for weighted similarity computations. Uses packages 'rCDK' and 'fingerprint' for cheminformatics functionality.
Facilities for running simulations from ordinary 
   differential equation (ODE) models, such as pharmacometrics and other 
   compartmental models.  A compilation manager translates the ODE model 
   into C, compiles it, and dynamically loads the object code into R for 
   improved computational efficiency.  An event table object facilitates 
   the specification of complex dosing regimens (optional) and sampling 
   schedules.  NB: The use of this package requires both C and 
   Fortran compilers, for details on their use with R please see 
   Section 6.3, Appendix A, and Appendix D in the "R Administration and 
   Installation" manual.
Analysis of combined total and allele specific reads from the reciprocal cross study with RNA-seq data.
Identify and display TRACEs for a specified shrinkage path and determine
  the extent of shrinkage most likely, under normal distribution theory, to produce an
  optimal reduction in MSE Risk in estimates of regression (beta) coefficients.
An interface to the yacas computer algebra system.
'Yandex Translate' (https://translate.yandex.com/) is a statistical machine translation system.
	The system translates separate words, complete texts, and webpages.
	This package can be used to detect language from text and to translate it to supported target language.
	For more info: https://tech.yandex.com/translate/doc/dg/concepts/About-docpage/ .
You can use this package to translate thousands of words. The Youdao translation open API is applied in this package. But, it just translates less than 1000 English words into Chinese.
Package contains some data and functions that 
    are used in my forthcoming "R you ready?" book.
A programmatic interface to web-services of YouTheria. YouTheria is
    an online database of mammalian trait data <http://www.utheria.org/>.
This package is a port to R of the SysGran program, written in Delphi by Camargo (2006). It contains functions for the analysis of grain size samples (in logarithmic (phi) and geometric (micrometers) scale) based on various methods, like Folk & Ward (1957) and Methods of Moments (Tanner, 1995), among others; textural classifications and distribution of unconsolidated sediments are shown in histograms, bivariated plots and ternary diagrams of Shepard (1954) and Pejrup (1988). English and Portuguese languages are supported in outputs
R is very powerful but it lacks some of the functionalities found in
  Stata or SPSS to manage survey data. The 'memisc' package provides these
  (variable labels, value labels, definable missing values and so on), but to
  efficiently work these functions need a graphical interface to allow the user
  to get an overview of the data. This package provides such a graphical interface,
  similar in fashion to SPSS's Variable View and data managing system. It uses the
  'memisc' package as its backend. Additionally, 'Rz' has a powerful plot assistant
  interface based on 'ggplot2'.
R interface to the 'Zabbix API' data <https://www.zabbix.com/documentation/3.0/manual/api/reference>. Enables easy and direct communication with 'Zabbix API' from 'R'.
Interface to the 'ZeroMQ' lightweight messaging kernel (see <http://www.zeromq.org/> for more information).
R bindings for Google's s2 library for geometric calculations on
    the sphere.
A set of functions used in teaching STATS 201/208 Data Analysis at the University of Auckland. The functions are designed to make parts of R more accessible to a large undergraduate population who are mostly not statistics majors.
Set of tools to verify forecasts through the computation of typical prediction scores against one or more observational datasets or reanalyses (a reanalysis being a physical extrapolation of observations that relies on the equations from a model, not a pure observational dataset). Intended for seasonal to decadal climate forecasts although can be useful to verify other kinds of forecasts. The package can be helpful in climate sciences for other purposes than forecasting.
Fit a spatial instrumental-variable regression by two-stage least squares.
The main function s4vd() performs a biclustering via sparse
        singular value decomposition with a nested stability selection.
        The results is an biclust object and thus all methods of the
        biclust package can be applied.
Perform joint segmentation on two signal dimensions derived from 
    total read depth (intensity) and allele specific read depth (intensity) for 
    whole genome sequencing (WGS), whole exome sequencing (WES) and SNP array data.
A collection of baseball analytics functions for sabermetrics
    purposes. Among these functions include popular metrics such as FIP, wOBA, and
    runs created and other advanced metrics.
Semiparametric empirical likelihood ratio
    based test of changepoint with one-change or epidemic alternatives
    with data-based model diagnostic
Functions for detecting eye fixations in raw eye-tracking
    data.  The detection is done using a velocity-based algorithm for
    saccade detection proposed by Ralf Engbert and Reinhold Kliegl in
    2003.  The algorithm labels segments as saccades when the velocity of
    the eye movement exceeds a certain threshold.  Anything between two
    saccades is considered a fixation.  Thus the algorithm is not
    appropriate for data containing episodes of smooth pursuit eye
    movements.
Computes the Exposure-At-Default based on standardized approach
    of the Basel III Regulatory framework (SA-CCR). Currently, trade types of all
    the five major asset classes have been created and, given the inheritance-
    based structure of the application, the addition of further trade types
    is straightforward. The application returns a list of trees (one per CSA) after
    automatically separating the trades based on the CSAs, the hedging sets, the
    netting sets and the risk factors. The basis and volatility transactions are
    also identified and treated in specific hedging sets whereby the corresponding 
    penalty factors are applied. All the examples appearing on the
    regulatory paper (including the margined and the un-margined workflow) have been
    implemented.
Performs constrained optimization for expensive black-box problems.
We analyzed the nucleotide composition of genes with a special emphasis on stability of DNA sequences. Besides, in a variety of different organisms unequal use of synonymous codons, or codon usage bias, occurs which also show variation among genes in the same genome. Seemingly, codon usage bias is affected by both selective constraints and mutation bias which allows and enables us to examine and detect changes in these two evolutionary forces between genomes or along one genome. Therefore, we determined the codon adaptation index (CAI), effective number of codons (ENC) and codon usage analysis with calculation of the relative synonymous codon usage (RSCU), and subsequently predicted the translation efficiency and accuracy through GC-rich codon usages. Furthermore, we estimated the relative stability of the DNA sequence following calculation of the average free energy (Delta G) and Dimer base-stacking energy level.
Provides the density, distribution, quantile and generation
    functions of some obscure probability distributions, including the doubly non-
    central t, F, Beta, and Eta distributions; Lecoutre's lambda-prime and K-prime;
    the upsilon distribution; the (weighted) sum of non-central chi-squares to a
    power; the (weighted) sum of log non-central chi-squares; the product of non-
    central chi-squares to powers; the product of doubly non-central F variates.
Maximum likelihood tools to fit and compare models of species
    abundance distributions and of species rank-abundance distributions.
Time series models for small area estimation based on area-level models.
The SAEMIX package implements the Stochastic Approximation EM algorithm for parameter estimation in (non)linear mixed effects models. The SAEM algorithm: - computes the maximum likelihood estimator of the population parameters, without any approximation of the model (linearisation, quadrature approximation,...), using the Stochastic Approximation Expectation Maximization (SAEM) algorithm, - provides standard errors for the maximum likelihood estimator - estimates the conditional modes, the conditional means and the conditional standard deviations of the individual parameters, using the Hastings-Metropolis algorithm. Several applications of SAEM in agronomy, animal breeding and PKPD analysis have been published by members of the Monolix group (http://group.monolix.org/).
An implementation of a stacked sparse autoencoder for dimension reduction of features and pre-training of feed-forward neural networks
		with the 'neuralnet' package is contained within this package. The package also includes a predict function for the stacked autoencoder object to generate the compressed
		representation of new data if required. For the purposes of this package, 'stacked' is defined in line with http://ufldl.stanford.edu/wiki/index.php/Stacked_Autoencoders .
		The underlying sparse autoencoder is defined in the documentation of 'autoencoder'.
Methods to fit robust alternatives to commonly used models used in
    Small Area Estimation. The methods here used are based on best linear
    unbiased predictions and linear mixed models. At this time available models
    include area level models incorporating spatial and temporal correlation in
    the random effects.
A complete set of functions to calculate several EBLUP (Empirical Best Linear Unbiased Predictor) estimators and their mean squared errors. All estimators are based on an area-level linear mixed model introduced by Rao and Yu in 1994 (see documentation). The REML method is used for fitting this model.
Tools for the simulation of data in the context of small area
    estimation. Combine all steps of your simulation - from data generation
    over drawing samples to model fitting - in one object. This enables easy
    modification and combination of different scenarios. You can store your
    results in a folder or start the simulation in parallel.
The aim of the package is to provide some basic functions
        for doing statistics with one dimensional Fuzzy Data (in the
        form of polygonal fuzzy numbers). In particular, the package
        contains functions for the basic operations on the class of
        fuzzy numbers (sum, scalar product, mean, median, Hukuhara difference) 
        as well as for calculating (Bertoluzza) distance,
        sample variance, sample covariance, and the
        Dempster-Shafer (levelwise) histogram. Moreover a function to
        simulate fuzzy random variables, bootstrap tests for the
        equality of means, and a function to do linear regression given
        trapezoidal fuzzy data is included. Version 1.0 fixes some bugs
        of previous versions.
Functions for Generalized and Safe-Bayesian Ridge and Lasso Regression models with both fixed and varying variance.
Overloads the glm function in the stats package so that
  a test for the existence of the maximum likelihood estimate is included
  in the fitting procedure for binary regression models.
Tools for the statistical analysis and visualization of (relative
    and absolute) quantitative (LFQ,TMT,HRM) Proteomics data.
Design and sensitivity analysis for computer experiments with scalar-valued output and functional input, e.g. over time or space. The aim is to explore the behavior of the sensitivity over the functional domain.
Implements an information theory approach to the analysis of line
    cross data providing model averaged results of parameter estimates and
    unconditional standard errors.  Also includes functions to provide a
    visualization of models space, custom plots of multi-model inference results, and traditional
    line cross analysis plots.
Computes the Akaike information criterion for the generalized linear models (logistic regression, Poisson regression, and Gaussian graphical models) estimated by the lasso.
A coordinate descent algorithm for computing the solution path of the sparse and coupled sparse asymmetric least squares, including the elastic net and (adaptive) Lasso penalized SALES and COSALES regressions.
The SALTSampler package facilitates Monte Carlo Markov Chain (MCMC)
    sampling of random variables on a simplex. A Self-Adjusting Logit Transform
    (SALT) proposal is used so that sampling is still efficient even in difficult
    cases, such as those in high dimensions or with parameters that differ by orders
    of magnitude. Special care is also taken to maintain accuracy even when some
    coordinates approach 0 or 1 numerically. Diagnostic and graphic functions are
    included in the package, enabling easy assessment of the convergence and mixing
    of the chain within the constrained space.
The package SAM targets at high dimensional predictive
        modeling (regression and classification) for complex data
        analysis. SAM is short for sparse additive modeling, and adopts
        the computationally efficient basis spline technique. We solve
        the optimization problems by various computational algorithms
        including the block coordinate descent algorithm, fast
        iterative soft-thresholding algorithm, and newton method. The
        computation is further accelerated by warm-start and active-set
        tricks.
Programs for fitting Gaussian linear mixed models (LMM).
A framework for running sets of MCMC samplers on sets of
  distributions with a variety of tuning parameters, along with plotting
  functions to visualize the results of those simulations.  See sc-intro.pdf
  for an introduction.
Two-step estimation
   and maximum likelihood estimation
   of Heckman-type sample selection models:
   standard sample selection models (Tobit-2)
   and endogenous switching regression models (Tobit-5).
Computes sample size for Student's t-test and for the Wilcoxon-Mann-Whitney test for categorical data. The t-test function allows paired and unpaired (balanced / unbalanced) designs as well as homogeneous and heterogeneous variances. The Wilcoxon function allows for ties.
Computes the required sample size using the optimal designs with multiple constraints proposed in Mayo et al.(2010). This optimal method is designed for two-arm, randomized phase II clinical trials, and the required sample size can be optimized either using fixed or flexible randomization allocation ratios.
Computes the required sample size for estimation of totals, means
    and proportions under complex sampling designs.
To determine sample size for case-control studies to be analyzed using logistic regression.
A set of R functions for calculating sample size
        requirements using three different Bayesian criteria in the
        context of designing an experiment to estimate a normal mean or
        the difference between two normal means.  Functions for
        calculation of required sample sizes for the Average Length
        Criterion, the Average Coverage Criterion and the Worst Outcome
        Criterion in the context of normal means are provided.
        Functions for both the fully Bayesian and the mixed
        Bayesian/likelihood approaches are provided.
A set of R functions for calculating sample size
        requirements using three different Bayesian criteria in the
        context of designing an experiment to estimate the difference
        between two binomial proportions. Functions for calculation of
        required sample sizes for the Average Length Criterion, the
        Average Coverage Criterion and the Worst Outcome Criterion in
        the context of binomial observations are provided. In all
        cases, estimation of the difference between two binomial
        proportions is considered. Functions for both the fully
        Bayesian and the mixed Bayesian/likelihood approaches are
        provided.
Functions for drawing and calibrating samples.
Sampling procedures from the book 'Stichproben - Methoden
        und praktische Umsetzung mit R' by Goeran Kauermann and Helmut
        Kuechenhoff (2010).
Package provides the possibility to sampling complete datasets 
  from a normal distribution to simulate cluster randomized trails for different study designs.
Functions to estimate from survey data. This package is a user-friendly wrapper of the samplingVarEst package. It considers that the user is more familiar with practical survey data rather than with research on survey sampling (variance estimation). More functionalities are on the way.
In the field of stratified sampling design, this package
        offers an approach for the determination of the best
        stratification of a sampling frame, the one that ensures the
        minimum sample cost under the condition to satisfy precision
        constraints in a multivariate and multidomain case. This
        approach is based on the use of the genetic algorithm: each
        solution (i.e. a particular partition in strata of the sampling
        frame) is considered as an individual in a population; the
        fitness of all individuals is evaluated applying the
        Bethel-Chromy algorithm to calculate the sampling size
        satisfying precision constraints on the target estimates.
        Functions in the package allows to: (a) analyse the obtained
        results of the optimisation step; (b) assign the new strata
        labels to the sampling frame; (c) select a sample from the new
        frame accordingly to the best allocation. 
        Functions for the execution of the genetic algorithm are a modified 
        version of the functions in the 'genalg' package.
Functions to calculate some point estimators and estimating their variance under unequal probability sampling without replacement. Single and two stage sampling designs are considered. Some approximations for the second order inclusion probabilities are also available (sample and population based). A variety of Jackknife variance estimators are implemented.
Sampling surface simulation is useful in the comparison of different areal sampling methods
             in forestry, ecology and natural resources. The sampSurf package allows the simulation 
			 of numerous sampling methods for standing trees and downed woody debris in a spatial context.
			 It also provides an S4 class and method structure that facilitates the addition of new sampling
			 methods.
Augmenting a matched data set by generating multiple stochastic, matched samples from the data using a
  multi-dimensional histogram constructed from dropping the input matched data into a multi-dimensional grid built on
  the full data set. The resulting stochastic, matched sets will likely provide a collectively higher coverage of the full
  data set compared to the single matched set. Each stochastic match is without duplication, thus allowing downstream
  validation techniques such as cross-validation to be applied to each set without concern for overfitting.
This package contains R functions to gauge the impact of unpublished studies upon the meta-analytic summary effect of a set of published studies. (Credits: The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 282574.)
Data sets for the book 'Statistical Analysis of 
  Network Data with R'.
Model-robust standard error estimators for cross-sectional, time series, and longitudinal data.
Recent gcc and clang compiler versions provide functionality to
 memory violations and other undefined behaviour; this is often referred to as
 "Address Sanitizer" (or SAN) and "Undefined Behaviour Sanitizer" (UBSAN).
 The Writing R Extension manual describes this in some detail in Section 4.9.

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

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

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

    The code samples are based on the examples from the Address Sanitizer
 Wiki at https://code.google.com/p/address-sanitizer/wiki/AddressSanitizer.
Sankey plots illustrate the flow of information or material.
There are several functions to implement the method for analysis in a randomized clinical trial with strata with following key features. A stratified Mann-Whitney estimator addresses the comparison between two randomized groups for a strictly ordinal response variable. The multivariate vector of such stratified Mann-Whitney estimators for multivariate response variables can be considered for one or more response variables such as in repeated measurements and these can have missing completely at random (MCAR) data. Non-parametric covariance adjustment is also considered with the minimal assumption of randomization. The p-value for hypothesis test and confidence interval are provided.
Software for the book Spectral Analysis for Physical
        Applications, Donald B. Percival and Andrew T. Walden,
        Cambridge University Press, 1993.
Functions for statistical analysis of point processes.
Functions, classes and methods for time series modelling with ARIMA
    and related models. The aim of the package is to provide consistent
    interface for the user. For example, a single function
    autocorrelations() computes various kinds of
    theoretical and sample autocorrelations. This is work in progress,
    see the documentation and vignettes for the current functionality.
Provides a set of basic functions for creating Moodle XML
 output files suited for importing questions in Moodle (a learning
 management system, see <https://moodle.org/> for more information).
Read SAS files in the sas7bdat data format.
Using any importation code designed for SAS users to read
        ASCII files into sas7bdat files, the SAScii package parses
        through the INPUT block of a (.sas) syntax file to design the
        parameters needed for a read.fwf function call.  This allows
        the user to specify the location of the ASCII (often a .dat)
        file and the location of the .sas syntax file, and then load
        the data frame directly into R in just one step.
Data sets and sample lmer analyses corresponding
  to the examples in Littell, Milliken, Stroup and Wolfinger
  (1996), "SAS System for Mixed Models", SAS Institute.
A statistical method for significant analysis of
        comparative proteomics based on LC-MS/MS Experiments
Functions for reading, listing
    the contents of, and writing 'SAS' 'xport' format files.
    The functions support reading and writing of either
    individual data frames or sets of data frames.  Further,
    a mechanism has been provided for customizing how
    variables of different data types are stored.
This smorgasbord provides a variety of functions which are useful 
    for handling, manipulating and visualizing remote sensing data.
Bayesian inference of graphical model structures using spanning trees.
Implements Bayesian statistical methodology for the 
		analysis of complex computer models.
		It allows for the emulation, calibration, and validation of computer models, 
		following methodology described in Bayarri et al 2007, Technometrics.
The purpose of this package is to be able to save and load only
    the needed variables/columns of a dataframe in special binary files (tar
    archives) - which seems to be a lot faster method than loading the whole
    binary object (RData files) via load() function, or than loading columns
    from SQLite/MySQL databases via SQL commands (see vignettes). Performance
    gain on SSD drives is a lot more sensible compared to basic load()
    function. The performance improvement gained by loading only the chosen
    variables in binary format can be useful in some special cases (e.g. where
    merging data tables is not an option and very different datasets are needed
    for reporting), but be sure if using this package that you really need
    this, as non-standard file formats are used!
Tests coefficients with sandwich estimator of variance and with small samples. Regression types supported are gee, linear regression, and conditional logistic regression.
Smooth Backfitting for additive models using
        Nadaraya-Watson estimator
An MCMC algorithm for simultaneous feature selection and classification, 
    and visualization of the selected features and feature interactions. 
    An implementation of SBFC by Krakovna, Du and Liu (2015), <http://arxiv.org/abs/1506.02371>.
This package estimates parameters of a Gaussian copula,
        treating the univariate marginal distributions as nuisance
        parameters as described in Hoff(2007). It also provides a
        semiparametric imputation procedure for missing multivariate
        data.
Computes the sBIC for various singular model collections including:
    binomial mixtures, factor analysis models, Gaussian mixtures,
    latent forests, latent class analyses, and reduced rank regressions.
sbioPN is a package suited to perform simulation of deterministic and stochastic systems of biochemical reaction
  networks with spatial effects.
  Models are defined using a subset of Petri Nets, in a way that is close at how chemical reactions are defined.
  For deterministic solutions, sbioPN creates the associated system of differential equations "on the fly", and
  solves it with a Runge Kutta Dormand Prince 45 explicit algorithm.
  For stochastic solutions, sbioPN offers two variants of Gillespie algorithm, or SSA.
  For hybrid deterministic/stochastic,
  it employs the Haseltine and Rawlings algorithm, that partitions the system in fast and slow reactions.
  sbioPN algorithms are developed in C to achieve adequate performance.
An ADMM implementation of SDP-1, a semidefinite programming relaxation of the maximum likelihood estimator for fitting a block model. SDP-1 has a tendency to produce equal-sized blocks and is ideal for producing a form of network histogram approximating a nonparametric graphon model. Alternatively, it can be used for community detection. (This is experimental code, proceed with caution.)
The package uses fitting axes-aligned rectangles to a time series in order to find structural breaks. The algorithm enclose the time series in a number of axes-aligned rectangles and tries to minimize their area and number. As these are conflicting aims, the user has to specify a parameter alpha in [0.0,1.0]. Values close to 0 result in more breakpoints, values close to 1 in fewer. The left edges of the rectangles are the breakpoints. The package supplies two  methods, computeBreakPoints(series,alpha) which returns the indices of the break points and computeRectangles(series,alpha) which returns the rectangles. The algorithm is randomised; it uses a genetic algorithm. Therefore, the break point sequence found can be different in different executions of the method on the same data, especially when used on  longer series of some thousand observations. The algorithm uses a range-tree as background data structure which makes i very fast and suited to analyse series with millions of observations. A detailed description can be found in Paul Fischer, Astrid Hilbert, Fast detection  of structural breaks, Proceedings of Compstat 2014.
An implementation of Scalable Bayesian Rule Lists Algorithm.
Tools for interacting with U.S. Geological Survey ScienceBase 
    <https://www.sciencebase.gov> interfaces. ScienceBase is a data cataloging and
    collaborative data management platform. Functions included for querying
    ScienceBase, and creating and fetching datasets.
Simple Component Analysis (SCA) often provides much more
   interpretable components than Principal Components (PCA) while still
   representing much of the variability in the data.
Calculates graph theoretic scagnostics. Scagnostics
        describe various measures of interest for pairs of variables,
        based on their appearance on a scatterplot.  They are useful
        tool for discovering interesting or unusual scatterplots from a
        scatterplot matrix, without having to look at every individual
        plot.
Provides the Scale class and corresponding functions, in order to facilitate data input for scale construction. Reverse items and alternative orders of administration are dealt with by the program. Computes reliability statistics, confirmatory, single factor loadings. It suggests item deletions and produces basic text output in English, for incorporation in reports. Returns list objects of all relevant functions from other packages (see Depends).
Graphical scales map data to aesthetics, and provide
    methods for automatically determining breaks and labels
    for axes and legends.
Algorithms for fitting scaled sparse linear regression and estimating precision matrices
Routines for generalized additive modelling under shape
        constraints on the component functions of the linear predictor
        (Pya and Wood, 2015) <doi:10.1007/s11222-013-9448-7>.
        Models can contain multiple shape constrained (univariate
        and/or bivariate) and unconstrained terms. The routines of gam() 
        in package 'mgcv' are used for setting up the model matrix,  
        printing and plotting the results.  Penalized likelihood
        maximization based on Newton-Raphson method is used to fit a
        model with multiple smoothing parameter selection by GCV or
        UBRE/AIC.
A collection of procedures for analysing single-case data of an AB-design. Some procedures support multiple-baseline designs.
Detection of anomalous space-time clusters using the scan 
    statistics methodology. Focuses on prospective surveillance of data streams, 
    scanning for clusters with ongoing anomalies. Hypothesis testing is made 
    possible by the generation of Monte Carlo p-values.
Import, plot, and diagnose results from statistical
  catch-at-age models, used in fisheries stock assessment.
This package computes the maximum likelihood estimator of the generalised additive and index regression with shape constraints. Each additive component function is assumed to obey one of the nine possible shape restrictions: linear, increasing, decreasing, convex, convex increasing, convex decreasing, concave, concave increasing, or concave decreasing.
scaRabee is a port of the Scarabee toolkit originally
        written as a Matlab-based application. It provides a framework
        for simulation and optimization of pharmacokinetic-pharmacodynamic 
        models at the individual and population level. It is built on top of the
        neldermead package, which provides the direct search algorithm proposed 
        by Nelder and Mead for model optimization.
Creates 'D3' 'JavaScript' scatterplots from 'R' with interactive
    features : panning, zooming, tooltips, etc.
Creates scatterpie plots, especially useful for plotting pies on a map.
Plots a three dimensional (3D) point cloud.
Statistical methods for estimating and inferring the mean of functional data. The methods include simultaneous confidence bands, local polynomial fitting,  bandwidth selection by plug-in and cross-validation, goodness-of-fit tests for parametric models, equality tests for two-sample problems, and plotting functions.
Provides a set of tools for estimating hierarchical linear
    models and effect sizes based on data from single-case designs. 
    Functions are provided for calculating standardized mean difference effect sizes that 
    are directly comparable to standardized mean differences estimated from between-subjects randomized experiments,
    as described in Hedges, Pustejovsky, and Shadish (2012) <DOI:10.1002/jrsm.1052>; 
    Hedges, Pustejovsky, and Shadish (2013) <DOI:10.1002/jrsm.1086>; and 
    Pustejovsky, Hedges, and Shadish (2014) <DOI:10.3102/1076998614547577>. 
    Includes an interactive web interface.
Uses the neural gas algorithm to construct
    a scenario tree for use in multi-stage stochastic programming.
    The primary input is a set of initial scenarios or realizations
    of a disturbance. The scenario tree nodal structure must be
    predefined using a scenario tree nodal partition matrix.
SCEPtER pipeline for estimating the stellar age, mass, and radius
        given observational 
        effective temperature, [Fe/H], and astroseismic
	parameters. The results are obtained adopting a maximum likelihood
	technique over a grid of pre-computed stellar models.
SCEPtER pipeline for estimating the stellar age for double-lined detached binary systems. The observational constraints adopted in the recovery are the effective temperature, the metallicity [Fe/H], the mass, and the radius of the two stars. The results are obtained adopting a maximum likelihood technique over a grid of pre-computed stellar models.
The Fisher Scoring Algorithm is extended so as to combine Partial
    Least Squares regression with Generalized Linear Model estimation in the
    multivariate context.
Provides schema-on-read tools including a single function call (e.g., schemaOnRead('filename')) that reads text ('TXT'), comma separated value ('CSV'), raster image ('BMP', 'PNG', 'GIF', 'TIFF', and 'JPG'), R data ('RDS'), HDF5 ('H5'), NetCDF ('CS'), spreadsheet ('XLS', 'XLSX', 'ODS', and 'DIF'), Weka Attribute-Relation File Format ('ARFF'), Epi Info ('REC'), SPSS ('SAV'), Systat ('SYS'), and Stata ('DTA') files. It also recursively reads folders (e.g., schemaOnRead('folder')), returning a nested list of the contained elements.
Provides functions to extract citation data from Google
    Scholar.  Convenience functions are also provided for comparing
    multiple scholars and predicting future h-index values.
This package contains functions and datasets for math
        taught in school. A main focus is set to prime-calculation
Offers a helping hand to psychologists and other behavioral scientists who routinely deal with experimental data from factorial experiments. It includes several functions to format output from other R functions according to the style guidelines of the APA (American Psychological Association). This formatted output can be copied directly into manuscripts to facilitate data reporting. These features are backed up by a toolkit of several small helper functions, e.g., offering out-of-the-box outlier removal. The package lends its name to Georg "Schorsch" Schuessler, ingenious technician at the Department of Psychology III, University of Wuerzburg.
This is a shape preserving spline which is guaranteed
    to be monotonic and concave or convex if the data is monotonic
    and concave or convex. It does not use any optimisation and is
    therefore quick and smoothly converges to a fixed point in
    economic dynamics problems including value function iteration.
    It also automatically gives the first two derivatives of the
    spline and options for determining behaviour when evaluated
    outside the interpolation domain.
This package provides detailed functionality for working with the Schwartz 1997 two-factor commodity model. Essentially, it contains pricing formulas for futures and European options and the standard d/p/q/r functions for the distribution of the state variables and futures prices. In addition, a parameter estimation procedure is contained together with many utilities as filtering and plotting functionality. This package is accompanied by futures data of ten commodities.
Functions for generating Standardized Climate Indices (SCI). 
		SCI is a transformation of (smoothed) climate (or environmental)
		time series that  removes seasonality and forces the data to
		take values of the standard normal distribution. SCI was 
		originally developed for precipitation. In this case it is 
		known as the Standardized Precipitation Index (SPI).
An R interface to the SciDB array database (http://scidb.org).
Provide functions for analyzing elections and political behavior
    data, including measures of political fragmentation, seat apportionment, and
    small data visualization graphs.
It involves bibliometric indicators calculation from bibliometric data.It also deals pattern analysis using the text part of bibliometric data.The bibliometric data are obtained from mainly Web of Science and Scopus.
Sparse Column-wise Inverse Operator for estimating the inverse covariance matrix.  Note that this is a preliminary version accompanying the arXiv paper (arXiv:1203.3896) in 2012.   This version contains only the minimal set of functions for estimation and cross validation.
A collection of functions that creates graphs with error
        bars for data collected from one-way or higher factorial
        designs.
Functions to install SciViews additions to R, and more (various) tools
Provides functions to measure growth patterns and align
    sampling spots in chronologically deposited materials. The package is
    intended for the fields of sclerochronology, dendrochronology and geology.
An R package for single-case meta-analysis.
Given a matrix with results of different algorithms for different
    problems, the package uses statistical tests and corrections to assess the
    differences between algorithms.
Provides routines for scoring behavioral questionnaires. Includes scoring procedures for the 'International Physical Activity Questionnaire (IPAQ)' <http://www.ipaq.ki.se>. Compares physical functional performance to the age- and gender-specific normal ranges.
Estimate GGUM Person Parameters Using Pre-Calibrated Item Parameters and Binary or Graded Disagree-Agree Responses
A set of tools for quickly scoring models in data science and
    machine learning. This toolset is written in C++ for blazing fast performance.
This package contains the functions necessary to run the SCORER 2.0 algorithm. SCORER 2.0 can be used to differentiate between parallel dimeric and trimeric coiled-coil sequence, which are the two most more frequent coiled-coil structures observed naturally. As such, SCORER 2.0 is particularly useful for researchers looking to characterize novel coiled-coil sequences. It may also be used to assist in the structural characterization of synthetic coiled-coil sequences. Also included in this package are functions that allows the user to retrain the SCORER 2.0 algorithm using user-defined training data.
Evaluating probabilistic forecasts via proper scoring rules.  scoring implements the beta, power, and pseudospherical families of proper scoring rules, along with ordered versions of the latter two families.  Included among these families are popular rules like the Brier (quadratic) score, logarithmic score, and spherical score.  For two-alternative forecasts, also includes functionality for plotting scores that one would obtain under specific scoring rules.
Dictionary-like reference for computing scoring rules in a wide
    range of situations. Covers both parametric forecast distributions (such as
    mixtures of Gaussians) and distributions generated via simulation.
Division of an ANOVA experiment treatment means into
        homogeneous distinct groups using the clustering method of
        Scott & Knott
An enhancement of the Scott-Knott test (which cluster distributions
    into statistically distinct ranks) that takes effect size into consideration.
Implements the Scout method for regression, described in "Covariance-regularized regression and classification for high-dimensional problems", by Witten and Tibshirani (2008),  Journal of the Royal Statistical Society, Series B 71(3): 615-636.
The package implements different inventory models, the
        bullwhip effect and other supply chain performance variables.
Phase variants within genes using allele counts from single-cell
    RNA-seq data.
Given a word will produce that word's scrabble score. Unlike many naive implementations this package takes into consideration the distribution of letter in scrabble. So a word like 'zzz' will be scored '10' rather than '30'.
Tools for Scraping Data from Web-Based Documents
Routines for a collection of screen-and-clean type
        variable selection procedures, including UPS and GS.
Tools for the analysis of high-dimensional data developed/implemented
  at the group "Statistical Complexity Reduction In Molecular Epidemiology" (SCRIME).
  Main focus is on SNP data. But most of the functions can also be applied to other
  types of categorical data.
Support for using .Rt (transcript) tests
  in the tests directory of a package.  Provides more
  convenience and features than the standard .R/.Rout.save
  tests.  Tests can be run under R CMD check and also
  interactively.  Provides source.pkg() for quickly loading
  code, DLLs, and data from a package for use in an
  edit/compile/test development cycle.
A coalescent simulator that allows the rapid simulation of
    biological sequences under neutral models of evolution. Different to other
    coalescent based simulations, it has an optional approximation parameter that
    allows for high accuracy while maintaining a linear run time cost for long
    sequences. It is optimized for simulating massive data sets as produced by Next-
    Generation Sequencing technologies for up to several thousand sequences.
Contains four functions used in the DIC-tau_g procedure. SCRSELECT() and SCRSELECTRUN() uses Stochastic Search Variable Selection to select important
    covariates in the three hazard functions of a semi-competing risks model. These functions perform the Gibbs sampler for variable selection and a Metropolis-Hastings-Green sampler for the number of split points and parameters for the
    three baseline hazard function. The function SCRSELECT() returns the posterior sample of all quantities sampled in the Gibbs sampler after a burn-in period to a desired
    file location, while the function SCRSELECTRUN() returns posterior values of important quantities to the DIC-Tau_g procedure in a list.
    The function DICTAUG() returns a list containing the DIC values for the unique models visited by the DIC-Tau_g grid search.
    The function ReturnModel() uses SCRSELECTRUN() and DICTAUG() to return a summary of the posterior coefficient vectors for the optimal model along with saving this posterior sample to a desired path location.
A package for designing single-case phase, alternation and multiple-baseline experiments, as well as for conducting randomization tests on data gathered by means of such designs.
Clean biological occurrence records. Includes functionality
    for cleaning based on various aspects of spatial coordinates,
    unlikely values due to political 'centroids', coordinates based on
    where collections of specimens are held, and more.
scrypt is an R package for working with scrypt. Scrypt is a
    password-based key derivation function created by Colin Percival. The
    algorithm was specifically designed to make it costly to perform
    large-scale custom hardware attacks by requiring large amounts of memory.
Solves convex cone programs via operator splitting. Can solve:
    linear programs (LPs), second-order cone programs (SOCPs), semidefinite programs
    (SDPs), exponential cone programs (ECPs), and power cone programs (PCPs), or
    problems with any combination of those cones.    SCS uses AMD (a set of routines for permuting sparse matrices prior to factorization) and LDL (a sparse LDL' factorization and solve package) from 'SuiteSparse' (<http://www.suitesparse.com>).
Code for describing and manipulating scuba diving profiles 
	(depth-time curves) and decompression models, 
        for calculating the predictions of decompression models,
	for calculating maximum no-decompression time and decompression tables,
	and for performing mixed gas calculations.
A package to make graphical representations of single case data and to transform graphical displays back to raw data. The package also includes tools for visually analyzing single-case data, by displaying central location, variability and trend.
Alternating Minimization Algorithm (AMA) and Alternating Direction Method of Multipliers (ADMM) splitting methods for sparse convex clustering.
Provides an efficient framework for 
   high-dimensional linear and diagonal discriminant analysis with 
   variable selection.  The classifier is trained using James-Stein-type 
   shrinkage estimators and predictor variables are ranked using 
   correlation-adjusted t-scores (CAT scores).  Variable selection error 
   is controlled using false non-discovery rates or higher criticism.
Functions and Datasets from Lohr, S. (1999), Sampling:
        Design and Analysis, Duxbury.
Test the global null in linear models using marginal approach.
Data from statistical agencies and other institutions are mostly
    confidential. This package can be used for the generation of anonymized
    (micro)data, i.e. for the creation of public- and scientific-use files. In
    addition, various risk estimation methods are included. Note that the package
    includes a graphical user interface that allows to use various methods of this
    package.
Methods for statistical disclosure control in
    tabular data such as primary and secondary cell suppression are covered in
    this package.
Classes and methods to calculate and evaluate target matrices for
    statistical disclosure control.
Allows for  computing (and by default plotting) different types of serial dependence diagrams.
Compares the evolution of an original network X to an augmented network Y by counting the number of Shortcuts, Detours, Dead Ends (SDDE), equal paths and disconnected nodes.
The semidiscrete decomposition (SDD) approximates a matrix
        as a weighted sum of outer products formed by vectors with
        entries constrained to be in the set {-1, 0, 1}.
Companion package to the book Simulation and Inference for
        Stochastic Differential Equations With R Examples, ISBN
        978-0-387-75838-1, Springer, NY.
Performs two tests to evaluate if the
        experiments are associated and returns a list of interesting
        features common to all the experiments.
Implementation of evolutionary fuzzy systems for the data mining task called
    "subgroup discovery". It also provide a Shiny App
    for make the analysis easier. The algorithms works with data sets provided in
    KEEL, ARFF and CSV format and also with data.frame objects.
An extensible R framework for developing species distribution
    models using individual and community-based approaches, generate ensembles of
    models, evaluate the models, and predict species potential distributions in
    space and time.
Functions provided by this pedagogic package allow to compute models with two popular machine learning approaches, BRT (Boosted Regression Trees) and MaxEnt (Maximum Entropy) applied on sets of marine biological and environmental data. They include the possibility of managing the main parameters for the construction of the models. Classic tools to evaluate model performance are provided (Area Under the Curve, omission rate and confusion matrix, map standard deviation) and are completed with tools to perform null models. The biological dataset includes original occurrences of two species of the class Echinoidea (sea urchins) present on the Kerguelen Plateau and that show contrasted ecological niches. The environmental dataset includes the corresponding statistics for 15 abiotic and biotic descriptors summarized for the Kerguelen Plateau and for different periods in a raster format. The package can be used for practicals to teach and learn the basics of species distribution modelling. Maps of potential distribution can be produced based on the example data included in the package, which brings prior observations of the influence of spatial and temporal heterogeneities on modelling performances. The user can also provide his own datasets to use the modelling functions.
This packages provides a set of tools for post processing the
    outcomes of species distribution modeling exercises. It includes novel
    methods for comparing models and tracking changes in distributions through
    time. It further includes methods for visualizing outcomes, selecting
    thresholds, calculating measures of accuracy and landscape fragmentation
    statistics, etc.. This package was made possible in part by financial
    support from the Australian Research Council & ARC Research Network for
    Earth System Science.
A software package help user to create virtual species for species distribution modelling. It includes
    several methods to help user to create virtual species distribution map.
    Those maps can be used for Species Distribution Modelling (SDM) study. SDM use
    environmental data for sites of occurrence of a species to predict all the sites
    where the environmental conditions are suitable for the species to persist, and
    may be expected to occur.
Fitting discrete Bayesian networks using soft-discretized data.  Soft-discretization is based on mixture of normal distributions.  Also implemented is a supervised Bayesian network learning employing Kullback-Leibler divergence.
Utility functions for scale-dependent and alternative hyperpriors.
Based on the compound Poisson risk process that is perturbed by
    a Brownian motion, saddlepoint approximations to some measures of risk are
    provided. Various approximation methods for the probability of ruin are
    also included. Furthermore, exact values of both the risk measures as well
    as the probability of ruin are available if the individual claims follow
    a hypo-exponential distribution (i. e., if it can be represented as a sum
    of independent exponentially distributed random variables with different
    rate parameters). For more details see Gatto and Baumgartner (2014)
    <doi:10.1007/s11009-012-9316-5>.
Routines for drawing samples, focusing on spatially balanced algorithms. Draws Halton Lattice (HAL), Balanced Acceptance Samples (BAS), Generalized Random Tesselation Stratified (GRTS), Simple Systematic Samples (SSS) and Simple Random Samples (SRS) from point, line, and polygon resources.  Frames are 'SpatialPoints', 'SpatialLines', or 'SpatialPolygons' objects from package 'sp'.
Implements algorithms to help with scenario discovery - currently only modified version of the the Patient Rule Induction Method.
Formulates a sparse distance weighted discrimination (SDWD) for high-dimensional classification and implements a very fast algorithm for computing its solution path with the L1, the elastic-net, and the adaptive elastic-net penalties.
Calculates parameters of the seawater carbonate system and assists the design of ocean acidification perturbation experiments.
Standard error adjusted adaptive lasso (SEA-lasso) is a version of the adaptive lasso, which incorporates OLS standard error to the L1 penalty weight. This method is intended for variable selection under linear regression settings (n > p). This new weight assignment strategy is especially useful when the collinearity of the design matrix is a concern.
Provides functionality for searching / subsetting and slicing named
    objects using 'stringr/i'-style modifiers by case (in)sensitivity,
    regular expressions or fixed expressions; searches uses the standard '['
    operator and allows specification of default search behavior to either the
    search target (named object) and/or the search pattern.
Provides an interface with the Google Search Console,
    formally called Google Webmaster Tools.
This package provides an implementation of the QuadTree
        data structure. It uses this to implement fast k-Nearest
        Neighbor and Rectangular range lookups in 2 dimenions. The
        primary target is high performance interactive graphics.
Capable of deriving seasonal statistics, such as "normals", and
  analysis of seasonal data, such as departures. This package also has
  graphics capabilities for representing seasonal data, including boxplots for
  seasonal parameters, and bars for summed normals. There are many specific
  functions related to climatology, including precipitation normals,
  temperature normals, cumulative precipitation departures and precipitation
  interarrivals. However, this package is designed to represent any
  time-varying parameter with a discernible seasonal signal, such as found
  in hydrology and ecology.
This package conducts Score Equity Assessment (SEA; Dorans, 2004) by calculating and plotting multiple SEA indices as introduced by a variety of authors and summarized by Huggins and Penfield (2012).
Routines for the seasonal analysis of health data,
    including regression models, time-stratified case-crossover,
    plotting functions and residual checks. Thanks to Yuming Guo
    for checking the case-crossover code.
Easy-to-use interface to X-13-ARIMA-SEATS, the seasonal adjustment
    software by the US Census Bureau. It offers full access to almost all
    options and outputs of X-13, including X-11 and SEATS, automatic ARIMA model
    search, outlier detection and support for user defined holiday variables,
    such as Chinese New Year or Indian Diwali. A graphical user interface can be
    used through the 'seasonalview' package. Uses the X-13-binaries from the
    'x13binary' package.
A graphical user interface to the 'seasonal' package and
  'X-13ARIMA-SEATS', the U.S. Census Bureau's seasonal adjustment software. 
  Unifies the code base of <http://www.seasonal.website> and the GUI in the
  'seasonal' package.
A model and utilities for analyzing trends in chemical concentrations in streams with a seasonal wave (seawave) and adjustment for streamflow (Q) and other ancillary variables
Displays state-event charts, for graphical presentation of longitudinal data.
Density, distribution function, quantile function, random
    generation and survival function for the Secant Kumaraswamy Weibull Distribution
    as defined by SOUZA, L. New Trigonometric Class of Probabilistic Distributions.
    219 p. Thesis (Doctorate in Biometry and Applied Statistics) - Department of
    Statistics and Information, Federal Rural University of Pernambuco, Recife,
    Pernambuco, 2015 (available at <http://www.openthesis.org/documents/New-trigonometric-classes-probabilistic-distributions-602633.html>) and BRITO, C. C. R. Method Distributions generator and
    Probability Distributions Classes. 241 p. Thesis (Doctorate in Biometry and
    Applied Statistics) - Department of Statistics and Information, Federal Rural
    University of Pernambuco, Recife, Pernambuco, 2014 (available upon request).
SECP package provides functionality for estimating
        parameters of site clusters on 2D & 3D square lattice with
        various lattice sizes, relative fractions of accessible sites
        (occupation probability), iso- & anisotropy, von Neumann &
        Moore (1,d)-neighborhoods
Functions to estimate the density and size of a spatially distributed animal population sampled with an array of passive detectors, such as traps, or by searching polygons or transects. Models incorporating distance-dependent detection are fitted by maximizing the likelihood. Tools are included for data manipulation and model selection.
Tools are provided for designing spatially explicit capture-recapture studies of animal populations. This is primarily a simulation manager for package 'secr'.
Tools for spatially explicit capture-recapture analysis of animal populations in linear habitats, extending package 'secr'
Provides a wrapper to access data from the SeeClickFix
  web API for R. SeeClickFix is a central platform employed by many cities
  that allows citizens to request their city's services. This package
  creates several functions to work with all the built-in calls to the
  SeeClickFix API. Allows users to download service request data from
  numerous locations in easy-to-use dataframe format manipulable in
  standard R functions.
Suite of functions for the simulation, visualisation and analysis of bacterial evolution within- and between-host.
Supports the text book "Data Analysis and Statistics for
        Geography, Environmental Science, and Engineering"
Supports Simulation of Ecological and Environmental Models
Creates SEER (Surveillance, Epidemiology and End Results) and A-bomb data binaries 
             from ASCII sources and provides tools for estimating SEER second cancer risks.
Functions for analysing, manipulating, displaying, editing and synthesizing time waves (particularly sound).  This package processes time analysis (oscillograms and envelopes), spectral content, resonance quality factor, entropy, cross correlation and autocorrelation, zero-crossing, dominant frequency, analytic signal, frequency coherence, 2D and 3D spectrograms and many other analyses.
A package that provides functions for measuring spatial 
        segregation. The methods implemented in this package include 
        White's P index (1983), Morrill's D(adj) (1991), Wong's D(w)
        and D(s) (1993), and Reardon and O'Sullivan's set of spatial 
        segregation measures (2004).
Performs correlation matrix segmentation and applies a test
    procedure to detect highly correlated regions in gene expression.
Contains functions that help to determine event
    boundaries in event segmentation experiments by bootstrapping a critical
    segmentation magnitude under the null hypothesis that all key presses were
    randomly distributed across the experiment. Segmentation magnitude is
    defined as the sum of Gaussians centered at the times of the segmentation
    key presses performed by the participants. Within a participant, the maximum
    of the overlaid Gaussians is used to prevent an excessive influence of a
    single participant on the overall outcome (e.g. if a participant is pressing
    the key multiple times in succession). Further functions are included, such
    as plotting the results.
Given a regression model, segmented `updates' the model by adding one or more segmented (i.e., piecewise-linear) relationships. Several variables with multiple breakpoints are allowed.
Performs a fast exact segmentation on data and allows for use of various cost functions.
An implementation of self-exciting point process model for information cascades, which occurs when many people engage in the same acts after observing the actions of others (e.g. post resharings on Facebook or Twitter). It provides functions to estimate the infectiousness of an information cascade and predict its popularity given the observed history. See http://snap.stanford.edu/seismic/ for more information and datasets.
Fast versions of seismic analysis functions that 'roll' over a
    vector of values. See the RcppRoll package for alternative
    versions of basic statistical functions such as rolling mean,
    median, etc.
Set of functions that access information about deputies and votings
    in Polish diet from webpage <http://www.sejm.gov.pl/>. The package was developed
    as a result of an internship in MI2 Group - <http://mi2.mini.pw.edu.pl/>,
    Faculty of Mathematics and Information Science, Warsaw University of Technology.
Sejong(http://www.sejong.or.kr/) corpus and
        Hannanum(http://semanticweb.kaist.ac.kr/home/index.php/HanNanum)
        dictionaries for KoNLP
This package implements a novel method for fitting a
        bounded probability distribution to quantiles (for example
        stated by an expert), see Bornkamp and Ickstadt (2009) for
        details.  For this purpose B-splines are used, and the density
        is obtained by penalized least squares based on a Brier entropy
        penalty.  The package provides methods for fitting the
        distribution as well as methods for evaluating the underlying
        density and cdf. In addition methods for plotting the
        distribution, drawing random numbers and calculating quantiles
        of the obtained distribution are provided.
For determining species probabilities that satisfy a given
    functional trait profile. Restoring resilient ecosystems requires a
    flexible framework for selecting assemblages that are based on the functional
    traits of species. However, current trait-based models have been biased toward
    algorithms that can only select species by optimising specific trait values,
    and could not elegantly accommodate the common desire among restoration ecologists
    to produce functionally diverse assemblages. We have solved this problem by
    applying a non-linear optimisation algorithm that optimises Rao’s Q, a closed-form
    functional diversity index that incorporates species abundances, subject to other
    linear constraints. This framework generalises previous models that could only
    optimise the entropy of the community, and can optimise both functional diversity
    and entropy simultaneously.
A collection of functions for correcting biased estimates under
    selection (range restriction).
Multi-stage selection is practiced in numerous fields of life and social sciences and particularly in breeding. A special characteristic of multi-stage selection is that candidates are evaluated in successive stages with increasing intensity and effort, and only a fraction of the superior candidates is selected and promoted to the next stage. For the optimum design of such selection programs, the selection gain plays a crucial role. It can be calculated by integration of a truncated multivariate normal (MVN) distribution. While mathematical formulas for calculating the selection gain and the variance among selected candidates were developed long time ago, solutions for numerical calculation were not available. This package can also be used for optimizing multi-stage selection programs for a given total budget and different costs of evaluating the candidates in each stage.
New tools for post-selection inference, for use
    with forward stepwise regression, least angle regression, the
    lasso, and the many means problem. The lasso function implements  Gaussian,  logistic and Cox survival models.
Publication bias, the fact that studies identified for inclusion in a meta analysis do not represent all studies on the topic of interest, is commonly recognized as a threat to the validity of the results of a meta analysis. One way to explicitly model publication bias is via selection models or weighted probability distributions. In this package we provide implementations of several parametric and nonparametric weight functions. The novelty in Rufibach (2011) is the proposal of a non-increasing variant of the nonparametric weight function of Dear & Begg (1992). The new approach potentially offers more insight in the selection process than other methods, but is more flexible than parametric approaches. To maximize the log-likelihood function proposed by Dear & Begg (1992) under a monotonicity constraint we use a differential evolution algorithm proposed by Ardia et al (2010a, b) and implemented in Mullen et al (2009). In addition, we offer a method to compute a confidence interval for the overall effect size theta, adjusted for selection bias as well as a function that computes the simulation-based p-value to assess the null hypothesis of no selection as described in Rufibach (2011, Section 6).
Translates a CSS3 selector into an equivalent XPath
  expression. This allows us to use CSS selectors when working with
  the XML package as it can only evaluate XPath expressions. Also
  provided are convenience functions useful for using CSS selectors on
  XML nodes. This package is a port of the Python package 'cssselect'
  (<https://pythonhosted.org/cssselect/>).
Fit and selects point pattern models based on minimum contrast, AIC and and goodness of fit.
Detection of outliers and influential errors using a latent variable model.
The W3C WebDriver specification defines a way for out-of-process
    programs to remotely instruct the behaviour of web browsers. It is detailed
    at <https://w3c.github.io/webdriver/webdriver-spec.html>. This package provides
    an R client implementing the W3C specification.
Functions using Cohen's effect sizes (Cohen, Jacob. Statistical power analysis for the behavioral sciences. Academic press, 2013) are provided for reliable feature selection in biology data analysis.  In addition to Cohen's effect sizes, p-values are calculated and adjusted from quasi-Poisson GLM, negative binomial GLM and Normal distribution ANOVA.  Significant features (genes, RNAs or proteins) are selected by adjusted p-value and minimum Cohen's effect sizes, calculated to keep certain level of statistical power of biology data analysis given p-value threshold and sample size.
A probability tree allows to compute probabilities of
	     complex events, such as genotype probabilities in intermediate generations of inbreeding
	     through recurrent self-fertilization (selfing). This package implements functionality to compute
	     probability trees for two- and three-marker genotypes in the F2 to F7 selfing
	     generations. The conditional probabilities are derived automatically
	     and in symbolic form. The package also provides functionality to
	     extract and evaluate the relevant probabilities.
Performs a regularization approach to variable selection in the
             model-based clustering and classification frameworks.
             First, the variables are arranged in order with a lasso-like procedure. 
             Second, the method of Maugis, Celeux, and Martin-Magniette (2009, 2011)
             <doi:10.1016/j.csda.2009.04.013>, <doi:10.1016/j.jmva.2011.05.004> 
             is adapted to define the role of variables in the two frameworks.
Functions for fitting general linear structural
    equation models (with observed and latent variables) using the RAM approach, 
    and for fitting structural equations in observed-variable models by two-stage least squares.
Outlier and leverage diagnostics for SEM.
Fits a multidimensional scaling (MDS) model for three-way data. It integrates concepts from structural equation models (SEM) by assuming an underlying, latent dissimilarity matrix. The methods uses an alternating estimation procedure in which the unknown symmetric dissimilarity matrix is estimated in a SEM framework while the objects are represented in a low-dimensional space. As a special case it can also handle asymmetric input dissimilarities.
This is an add-on package which provides fourteen
        goodness-of-fit indexes for structural equation models using
        'sem' package.
Package semiArtificial contains methods to generate and evaluate semi-artificial data sets. 
 Based on a given data set different methods learn data properties using machine learning algorithms and
 generate new data with the same properties.
 The package currently includes the following data generator:
  -a RBF network based generator using rbfDDA from RSNNS package,
  -a Random Forest based generator for both classification and regression problems
  -a density forest based generator for unsupervised data
 Data evaluation support tools include:
  -single attribute based statistical evaluation: mean, median, standard deviation, skewness, kurtosis, medcouple, L/RMC, KS test, Hellinger distance
  -evaluation based on clustering using Adjusted Rand Index (ARI) and FM
  -evaluation based on classification performance with various learning models, eg, random forests.
Parametric and semi-parametric analyses of semi-competing risks/univariate survival data. For semi-competing risks data, the package contains implementations of hierarchical models for independent data (Lee et al., 2015; <dio:10.1111/rssc.12078>) and cluster-correlated data (Lee et al., 2016; <dio:10.1080/01621459.2016.1164052>).
Provides routines to check identifiability or non-identifiability
    of linear structural equation models as described in Drton, Foygel &
    Sullivant (Ann. Statist., 2011) and Foygel, Draisma & Drton (Ann. Statist.,
    2012). The routines are based on the graphical representation of
    structural equation models by a path diagram/mixed graph.
Functions for fitting multi-state semi-Markov models to longitudinal data. A parametric maximum likelihood estimation method adapted to deal with Exponential, Weibull and Exponentiated Weibull distributions is considered. Right-censoring can be taken into account and both constant and time-varying covariates can be included using a Cox proportional model.
Functions for semiparametric regression analysis, to
        complement the book: Ruppert, D., Wand, M.P. and Carroll, R.J.
        (2003). Semiparametric Regression. Cambridge University Press.
Routines for fitting several classes of copula regression models, with several types of covariate effects, in the presence of associated error equations, endogeneity, non-random sample selection or partial observability.
Routine for fitting continuous or discrete response copula sample selection models with semi-parametric predictors, including linear and nonlinear effects.
Functions to compute Gaussian and p-Gaussian kernels,
        include supervision in these kernels, and perform kernel PCA
        projections.
Conduct tests of difference in fit for mean and covariance
        structure models as in structural equation modeling (SEM)
Path diagrams and visual analysis of various SEM packages' output
Fits structural equation models using partial least
        squares (PLS). The PLS approach is referred to as
        'soft-modeling' technique requiring no distributional
        assumptions on the observed data.
Semiparametric Estimation of Stochastic Frontier Models following a two step procedure: in the first step semiparametric or nonparametric regression techniques are used to relax parametric restrictions of the functional form representing technology and in the second step variance parameters are obtained by pseudolikelihood estimators or by method of moments.
Provides useful tools for structural equation modeling packages.
SEM Trees and SEM Forests -- an extension of model-based decision
    trees and forests to Structural Equation Models (SEM). SEM trees hierarchically
    split empirical data into homogeneous groups sharing similar data patterns
    with respect to a SEM by recursively selecting optimal predictors of these
    differences. SEM forests are an extension of SEM trees. They are ensembles of 
    SEM trees each built on a random sample of the original data. By aggregating over 
    a forest, we obtain measures of variable importance that are more robust than 
    measures from single trees.
Tools and functions for parsing, rendering and operating on
    semantic version strings. Semantic versioning is a simple set of rules
    and requirements that dictate how version numbers are assigned and
    incremented as outlined at <http://semver.org>.
Package contains a simple SMTP client which provides a
        portable solution for sending email, including attachment, from
        within R.
An implementation of sensitivity analysis in phylogenetic regression models,
    for both linear and logistic phylogenetic regressions. The package is an umbrella
    of statistical and graphical methods that estimate and report different types of
    uncertainty in PGLS models:
    (i) Species Sampling uncertainty (sample size; influential species and clades).
    (ii) Phylogenetic uncertainty (different topologies and/or branch lengths).
    (iii) Data uncertainty (intraspecific variation and measurement error).
A collection of functions for factor screening, global sensitivity analysis and reliability sensitivity analysis. Most of the functions have to be applied on model with scalar output, but several functions support multi-dimensional outputs.
Performs exact or approximate adaptive or nonadaptive Cochran-Mantel-Haenszel-Birch tests and sensitivity analyses for one or two 2x2xk tables in observational studies.
This package performs sensitivity analysis for case-control studies in which some cases may meet a more narrow definition of being a case compared to other cases which only meet a broad definition.  The sensitivity analyses are described in Small, Cheng, Halloran and Rosenbaum (2013, "Case Definition and Sensitivity Analysis", Journal of the American Statistical Association, 1457-1468).  The functions sens.analysis.mh and sens.analysis.aberrant.rank provide sensitivity analyses based on the Mantel-Haenszel test statistic and aberrant rank test statistic as described in Rosenbaum (1991, "Sensitivity Analysis for Matched Case Control Studies", Biometrics); see also Section 1 of Small et al.  The function adaptive.case.test provides adaptive inferences as described in Section 5 of Small et al.  The function adaptive.noether.brown provides a sensitivity analysis for a matched cohort study based on an adaptive test.  The other functions in the package are internal functions.
Sensitivity to unmeasured biases in an observational study that is a full match.
Sensitivity analysis in observational studies, including evidence factors and amplification, using the permutation distribution of Huber-Maritz M-statistics, including the permutational t-test.
Sensitivity analysis analysis in matched observational studies with multiple controls using weighted M-statistics to increase design sensitivity.
This package provides functions to perform principal stratification sensitivity analyses on datasets.
Functions that facilitate analysis of 
              Sensory as well as Consumer data within a mixed effects model 
              framework. The so-called mixed assessor models, 
              that correct for the scaling effect are implemented.
              The generation of the d-tilde plots forms part of the package.
              The shiny application provides GUI for the functionalities.
Contains the function CUUimpute() which performs model-based clustering and imputation simultaneously.
Provides methods for sensory discrimination methods;
  duotrio, tetrad, triangle, 2-AFC, 3-AFC, A-not A, same-different,
  2-AC and degree-of-difference.
  This enables the calculation of d-primes, standard errors of
  d-primes, sample size and power computations, and
  comparisons of different d-primes. Methods for profile likelihood
  confidence intervals and plotting are included.
Collection of datasets from Sen & Srivastava: "Regression
        Analysis, Theory, Methods and Applications", Springer.  Sources
        for individual data files are more fully documented in the
        book.
Provides access and analytic functions for Sensus data.
Calculate text polarity sentiment at the sentence level
        and optionally aggregate by rows or grouping variable(s).
It combines maximum likelihood estimation of the parameters
    of matrix and 3rd-order tensor normal distributions with unstructured
    factor variance-covariance matrices, two procedures, and unbiased
    modified likelihood ratio testing of simple and double separability
    for variance-covariance structures, two procedures.
Functions to generate separation plots for evaluation of
        model fit.
Potential randomization schemes are prospectively evaluated when
    units are assigned to treatment arms upon entry into the experiment. The schemes
    are evaluated for balance on covariates and on predictability (i.e., how well
    could a site worker guess the treatment of the next unit enrolled).
This is a method for DNA Copy Number Profiling using
        Next-Generation Sequencing. It has new model and test
        statistics based on non-homogeneous Poisson Processes with
        change point models. It uses an adaptation of Circular Binary
        Segmentation. Also included are methods for point-wise Bayesian
        Confidence Interval and model selection method for the
        change-point model. A case and a control sample reads (normal
        and tumor) are required.
A modification of the preventive vaccine efficacy trial design of Gilbert, Grove et al. (2011, Statistical Communications in Infectious Diseases) is implemented, with application generally to individual-randomized clinical trials with multiple active treatment groups and a shared control group, and a study endpoint that is a time-to-event endpoint subject to right-censoring. The design accounts for the issues that the efficacy of the treatment/vaccine groups may take time to accrue while the multiple treatment administrations/vaccinations are given; there is interest in assessing the durability of treatment efficacy over time; and group sequential monitoring of each treatment group for potential harm, non-efficacy/efficacy futility, and high efficacy is warranted. The design divides the trial into two stages of time periods, where each treatment is first evaluated for efficacy in the first stage of follow-up, and, if and only if it shows significant treatment efficacy in stage one, it is evaluated for longer-term durability of efficacy in stage two. The package produces plots and tables describing operating characteristics of a specified design including an unconditional power for intention-to-treat and per-protocol/as-treated analyses; trial duration; probabilities of the different possible trial monitoring outcomes (e.g., stopping early for non-efficacy); unconditional power for comparing treatment efficacies; and distributions of numbers of endpoint events occurring after the treatments/vaccinations are given, useful as input parameters for the design of studies of the association of biomarkers with a clinical outcome (surrogate endpoint problem). The code can be used for a single active treatment versus control design and for a single-stage design.
Provides user friendly methods for the identification of sequence patterns that are statistically significantly associated with a property of the sequence. For instance, SeqFeatR allows to identify viral immune escape mutations for hosts of given HLA types. The underlying statistical method is Fisher's exact test, with appropriate corrections for multiple testing, or Bayes. Patterns may be point mutations or n-tuple of mutations. SeqFeatR offers several ways to visualize the results of the statistical analyses.
The SeqGrapheR package provide interactive GUI for visualization of DNA sequence clusters. Details and principles of usage are described in user manual and (2010 BMC Bioinformatics 11:378). For full functionality installed NCBI blast is required.
Designed for fitting hidden (latent)
    Markov models and mixture hidden Markov models for social
    sequence data and other categorical time series. Also some more restricted
    versions of these type of models are available: Markov models, mixture
    Markov models, and latent class models. The package supports models for one or
    multiple subjects with one or multiple parallel sequences (channels). External
    covariates can be added to explain cluster membership in mixture models. The
    package provides functions for evaluating and comparing models, as well as
    functions for easy plotting of multichannel sequence data and hidden Markov
    models. Models are estimated using maximum likelihood via the EM algorithm and/or
    direct numerical maximization with analytical gradients.
    All main algorithms are written in C++ with support for parallel computation.
Exploratory data analysis and data visualization
  for biological sequence (DNA and protein) data. Includes also
  utilities for sequence data management under the ACNUC system.
A network module-based generalized linear model for differential expression analysis with the count-based sequence data from RNA-Seq.
Computes necessary information to meta analyze region-based
    tests for rare genetic variants (e.g. SKAT, T1) in individual studies, and
    performs meta analysis.
Integrate sequencing data (Variant call format, e.g. VCF or BCF) or meta-analysis results in R.
S4 class object for creating and managing group sequential designs. It calculates the efficacy and futility boundaries at each look. It allows modifying the design and tracking the design update history.
User inputs a range of values r1 and r2, as well as a
        number of columns v, in the function sq.pe(r1,r2,v). The
        returned statement is a permutation matrix.
This package includes functions for handling DNA
        sequences, especially simulated RFLP and TRFLP pattern based on
        selected restriction enzyme and DNA sequences.
Sequential triangular test for the arithmetic mean in one- and two-
    samples, proportions in one- and two-samples, and the Pearson's correlation
    coefficient.
Provides: 1) By Giving UniProtKB, the Protein Sequence will be returned from UniProt database. 2)By Giving UniProtKB, the Nucleotide Sequence will be returned from EBI database. 3) Amino acid Composition is calculated by four different methods: a) Twenty-two independent categories are considered, with one amino acid for each category. B) Five categories (Nonpolar Aliphatic, Nonpolar Aromatic, Polar Uncharged, Polar Positively Charged, Polar Negatively Charged) are considered according to the standard chemical structures of amino acids. c) Six categories (Nonpolar Aliphatic, Nonpolar Aromatic, Polar Uncharged, Polar Positively Charged, Polar Negatively Charged, Special cases) are considered which Cysteine, Selenocysteine, Glycine and Proline are placed in Special cases group. d)Eight categories are clustered via k-means algorithm on Physicochemical index of amino acids. 4) GC Content: Percentage of nucleotide g and c in sequence. 5) Codon usage: Frequency of occurrence of synonymous codons. 6) Stacking Energy: The NN model for nucleic acids assumes that the stability of a given base pair depends on the identity and orientation of neighboring base pairs. Stacking Energy = DeltaG(total) = Sigma (n(i)*DeltaG(i)) + DeltaG(init) + DeltaG(end) + DeltaG(sym), which DeltaG for i, init and end is obtained by Unified NN free energy parameter. Symmetry of self-complementary duplexes is also included by DeltaG(sym) equals to +0.43 (kcal/mol) if the duplex is self-complementary and zero if it is non-self-complementary. 7) Complement of desired nucleotide sequence. 8) Reverse of desired nucleotide sequence. 9) Reverse-Complement of desired nucleotide sequence. 10) Protein, Gene and Organism of desired UniProt ID. 11) Converting nucleotide sequence to protein sequence. 12) Getting Localization From Gene Ontology Cellular Component inside Uniprot. 13) All related codons of desired amino acid.
Educational package used in R courses to illustrate
       	     object-oriented programming and package
       	     development. Using biological sequences (DNA and RNA) as
       	     a working example.
Functions to calculate exact critical values, statistical power, expected time to signal and required sample sizes for performing exact sequential analysis. All these	calculations can be done for either Poisson or binomial data, for continuous or group sequential analyses, and for different types of rejection boundaries. In case of group sequential analyses, the group sizes do not have to be specified in advance and the alpha spending can be arbitrarily settled.
Tools to analyze genomic sequencing data from
 paired normal-tumor samples, including cellularity and ploidy estimation; mutation
 and copy number (allele-specific and total copy number) detection, quantification 
 and visualization.
Fast multi-generational pedigree inference from incomplete data on
    hundreds of SNPs, including parentage assignment and sibship clustering.
    See article "Pedigree reconstruction from SNP data: Parentage assignment,
    sibship clustering, and beyond" (Mol Ecol Res, under review) for more
    information.
Provides functionality for the use of the internal hardware for
             RS232/RS422/RS485 and any other virtual serial interfaces of the
             computer.
Infrastructure for seriation with an implementation of several
    seriation/sequencing techniques to reorder matrices, dissimilarity
    matrices, and dendrograms. Also provides (optimally) reordered heatmaps,
    color images and clustering visualizations like dissimilarity plots, and
    visual assessment of cluster tendency plots (VAT and iVAT).
Translates antibody levels measured in a (cross-sectional)
    population sample into an estimate of the frequency with which
    seroconversions (infections) occur in the sampled population.
Start an HTTP server in R to serve static files, or dynamic
    documents that can be converted to HTML files (e.g., R Markdown) under a
    given directory.
Structural equation modeling is a powerful statistical approach for the testing of networks of direct and indirect theoretical causal relationships in complex data sets with inter-correlated dependent and independent variables. Here we implement a simple method for spatially explicit structural equation modeling based on the analysis of variance co-variance matrices calculated across a range of lag distances. This method provides readily interpreted plots of the change in path coefficients across scale.
Utility functions for interacting with R processes from
        external programs. This package includes functions to save and
        restore session information (including loaded packages, and
        attached data objects), as well as functions to evaluate
        strings containing R commands and return the printed results or
        an execution transcript.
This initiated as a package companion to the book by C. Q.
        Schneider and C. Wagemann "Set-Theoretic Methods for the Social
        Sciences", Cambridge University Press. It grew to include functions 
	for performing set-theoretic multi-method research, QCA for clustered 
	data, theory evaluation, and Enhanced Standard Analysis. Additionally 
	it includes data to replicate the examples in the book and in the online 
	appendix.
Tests gene expression data from a biological pathway for biologically meaningful differences in the eigenstructure between two classes.  Specifically, it tests the null hypothesis that the two classes' leading eigenvalues and sums of eigenvalues are equal.  A pathway's leading eigenvalue arguably represents the total variability due to variability in pathway activity, while the sum of all its eigenvalues represents the variability due to pathway activity and to other, unregulated causes. Implementation of the method described in Danaher (2015), "Covariance-based analyses of biological pathways".
Implements an algorithm to conduct advanced
    gene set enrichment analysis on the results of genomics experiments.
SetRNG provides utilities to help set and record the setting of
	the seed and the uniform and normal generators used when a random
	experiment is run. The utilities can be used in other functions 
	that do random experiments to simplify recording and/or setting all the 
	necessary information for reproducibility. 
	See the vignette and reference manual for examples.
Data structures and basic operations for ordinary sets,
             generalizations such as fuzzy sets, multisets, and
             fuzzy multisets, customizable sets, and intervals.
Mutators to set attributes of variables, that work well in a pipe
    (much like stats::setNames()).
It provides cumulative distribution function (CDF),
    quantile, p-value, statistical power calculator and random number generator
    for a collection of group-testing procedures, including the Higher Criticism
    tests, the one-sided Kolmogorov-Smirnov tests, the one-sided Berk-Jones tests,
    the one-sided phi-divergence tests, etc. The input are a group of p-values.
    The null hypothesis is that they are i.i.d. Uniform(0,1). In the context of
    signal detection, the null hypothesis means no signals. In the context of the
    goodness-of-fit testing, which contrasts a group of i.i.d. random variables to
    a given continuous distribution, the input p-values can be obtained by the CDF
    transformation. The null hypothesis means that these random variables follow the
    given distribution. For reference, see Hong Zhang, Jiashun Jin and Zheyang Wu. "Distributions and
    Statistical Power of Optimal Signal Detection Methods in Finite Samples",
    submitted.
Provides option settings management that goes
    beyond R's default 'options' function. With this package, users can define
    their own option settings manager holding option names, default values and 
    (if so desired) ranges or sets of allowed option values that will be 
    automatically checked. Settings can then be retrieved, altered and reset 
    to defaults with ease. For R programmers and package developers it offers 
    cloning and merging functionality which allows for conveniently defining 
    global and local options, possibly in a multilevel options hierarchy. See 
    the package vignette for some examples concerning functions, S4 classes, 
    and reference classes. There are convenience functions to reset par() 
    and options() to their 'factory defaults'.
This package contains functions for calculating severity
        and generating severity curves. Specifically, the simple case
        of the one-parameter Normal distribution (i.e., with known
        variance) is considered.
This package combines several sighting based estimators of historical extinction, allowing them to be run simultaneously or individually. Code for this package was contributed by Ben Collen, Gene Hunt and Tracy Rout. Additional code was taken from McPherson & Myers (2009).
Support for simple features, a standardized way to encode spatial data, with bindings 
    to GDAL, GEOS and Proj.4.
Stochastic Frontier Analysis
  introduced by Aigner, Lovell and Schmidt (1976)
  and Battese and Coelli (1992, 1995).
Provides a function sfc() to compute the substance flow
    with the input files --- "data" and "model". If sample.size is
    set more than 1, uncertainty analysis will be executed while
    the distributions and parameters are supplied in the file "data".
Gives design points from a sequential full factorial-based
 Latin hypercube design, as described in Duan, Ankenman, Sanchez,
 and Sanchez (2015, Technometrics,
 <doi:10.1080/00401706.2015.1108233>).
The straightforward filtering index (SFINX) identifies true positive
    protein interactions in a fast, user-friendly, and highly accurate way.
    It is not only useful for the filtering of affinity purification -
    mass spectrometry (AP-MS) data, but also for similar types of data
    resulting from other co-complex interactomics technologies, such as TAP-MS,
    Virotrap and BioID. SFINX can also be used via the website interface at
    <http://sfinx.ugent.be>.
Useful utilities ['goodies'] from Seminar fuer Statistik ETH
  Zurich, quite a few related to graphics; many ported from S-plus times.
This package contains a series of tools for analyzing Systems Factorial Technology data.  This includes functions for plotting and statistically testing capacity coefficient functions and survivor interaction contrast functions.
Graph based clustering summaries for spatial point patterns.
    Includes Connectivity function, Cumulative connectivity function and clustering
    function, plus the triangle/triplet intensity function T.
A fast and flexible set of tools for large scale estimation. It
    features many stochastic gradient methods, built-in models, visualization
    tools, automated hyperparameter tuning, model checking, interval estimation,
    and convergence diagnostics.
Stagewise techniques implemented with Generalized Estimating Equations to handle individual, group, and bi-level selection.
An Object-oriented Framework for Geostatistical Modeling in S+ 
  containing functions for variogram estimation, variogram fitting and kriging
  as well as some plot functions. Written entirely in S, therefore works only
  for small data sets in acceptable computing time.
Fit a regularized generalized linear model via penalized
        maximum likelihood.  The model is fit for a path of values of
        the penalty parameter. Fits linear, logistic and Cox models.
RCON(V, E) models (Højsgaard, et al., 2008) are a kind of restriction of the Gaussian Graphical Models defined by a set of equality constraints on the entries of the concentration matrix. 'sglasso' package implements the structured graphical lasso (sglasso) estimator proposed in Abbruzzo et al. (2014) for the weighted l1-penalized RCON(V, E) model. Two cyclic coordinate algorithms are implemented to compute the sglasso estimator, i.e. a cyclic coordinate minimization (CCM) and a cyclic coordinate descent (CCD) algorithm.
Fast generic solver for sparse group lasso optimization problems.
    The loss (objective) function must be defined in a C++ module. The optimization
    problem is solved using a coordinate gradient descent algorithm. Convergence of
    the algorithm is established (see reference) and the algorithm is applicable to
    a broad class of loss functions. Use of parallel computing for cross validation
    and subsampling is supported through the 'foreach' and 'doParallel' packages.
    Development version is on GitHub, please report package issues on GitHub.
Functions for computing power and boundaries for pre-licensure vaccine trials using the Generalized Likelihood Ratio tests proposed by Shih, Lai, Heyse and Chen
Seven different methods for multiple testing problems. The SGoF-type methods and the BH and BY false discovery rate controlling procedures.
Functions to calculate student growth percentiles and percentile growth projections/trajectories for students using large scale,
        longitudinal assessment data.  Functions use quantile regression to estimate the conditional density associated
        with each student's achievement history.  Percentile growth projections/trajectories are calculated using the coefficient matrices derived from
	the quantile regression analyses and specify what percentile growth is required for students to reach future achievement targets.
Functions for computing sparse generalized principal components, including functions for modeling structured correlation
Data sets utilized by the SGP Package as exemplars for users to conduct their own SGP analyses.
The Sparse Group Partial Least Square package (sgPLS) provides sparse, group, and sparse group versions of partial least square regression models.
The package for Sample Generation by Replacement simulations (SGR; Lombardi & Pastore, 2014; Pastore & Lombardi, 2014). The package can be used to perform fake data analysis according to the sample generation by replacement approach. It includes functions for making simple inferences about discrete/ordinal fake data. The package allows to study the implications of fake data for empirical results.
Provides functions to implement sgRSEA (single-guide RNA Set Enrichment Analysis), which is a robust test for identification of essential genes from genetic screening data using CRISPR (clustered regularly interspaced short palindromic repeats) and Cas9 (CRISPR-associated nuclease 9) system.
Density, distribution function, quantile function and random generation for the skewed generalized t distribution. This package also provides a function that can fit data to the skewed generalized t distribution using maximum likelihood estimation.
Functions for easily manipulating colours, creating colour scales and calculating colour distances.
Functions for calculating (1) shade height at a single point, (2) shaded proportion of a building facade; (3) a polygonal layer of shade footprints on the ground; and (4) Sky View Factor value at a single point. Typical inputs include a polygonal layer of buildings outline along with the height of each building, sun azimuth and sun elevation. The package also provides functions for related preliminary calculations: converting polygons to line segments, finding segment azimuth, shifting segments by azimuth and distance, and constructing the footprint of a line of sight between an observer and the sun.
Functions for plotting graphical shapes
  such as ellipses, circles, cylinders, arrows, ...
In a scatterplot where the response variable is Gaussian, Poisson or binomial, we consider the case in which the mean function is smooth with a change-point, which is a mode, an inflection point or a jump point. The main routine estimates the mean curve and the change-point as well using shape-restricted B-splines. An optional subroutine delivering a bootstrap confidence interval for the change-point is incorporated in the main routine.
Functions to read and write ESRI shapefiles
An evolving and growing collection of tools for the quantification, assessment, and comparison of planar shape and pattern. The current flagship functionality is in the spatial decomposition of planar shapes using 'ShrinkShape' to incrementally shrink shapes to extinction while computing area, perimeter, and number of parts at each iteration of shrinking. The spectra of results are returned in graphic and tabular formats. Additional utility tools for handling data are provided and this package will be added to as more tools are created, cleaned-up, and documented.
Studies otolith shape variation among fish populations. 
  Otoliths are calcified structures found in the inner ear of teleost fish and their shape has 
  been known to vary among several fish populations and stocks, making them very useful in taxonomy, 
  species identification and to study geographic variations. The package extends previously described 
  software used for otolith shape analysis by allowing the user to automatically extract closed 
  contour outlines from a large number of images, perform smoothing to eliminate pixel noise, 
  choose from conducting either a Fourier or wavelet transform to the outlines and visualize 
  the mean shape. The output of the package are independent Fourier or wavelet coefficients 
  which can be directly imported into a wide range of statistical packages in R. The package 
  might prove useful in studies of any two dimensional objects.
Routines for the statistical analysis of landmark 
  shapes, including Procrustes analysis, graphical displays, principal
  components analysis, permutation and bootstrap tests, thin-plate 
  spline transformation grids and comparing covariance matrices. 
  see Dryden, I.L. and Mardia, K.V. (2016). Statistical shape analysis, 
      with Applications in R (2nd Edition), Wiley.
Landsat satellites collect important data about global forest conditions. Documentation about Landsat's role in forest disturbance estimation is available at the site <http://landsat.gsfc.nasa.gov/?p=9513>. By constrained quadratic B-splines, this package delivers an optimal shape-restricted trajectory to a time series of Landsat imagery for the purpose of modeling annual forest disturbance dynamics to behave in an ecologically sensible manner assuming one of seven possible "shapes", namely, flat, decreasing, one-jump (decreasing, jump up, decreasing), inverted vee (increasing then decreasing), vee (decreasing then increasing), linear increasing, and double-jump (decreasing, jump up, decreasing, jump up, decreasing). The main routine selects the best shape according to the minimum Bayes information criterion (BIC) or the cone information criterion (CIC), which is defined as the log of the estimated predictive squared error. The package also provides parameters summarizing the temporal pattern including year(s) of inflection, magnitude of change, pre- and post-inflection rates of growth or recovery. In addition, it contains routines for converting a flat map of disturbance agents to time-series disturbance maps and a graphical routine displaying the fitted trajectory of Landsat imagery.
A collection of tools for analyzing significance of trading
    strategies, based on the Sharpe ratio and overfit of the same.
An efficient moment-free estimator of the Sharpe ratio, or signal-to-noise ratio, for heavy-tailed data (see <https://arxiv.org/abs/1505.01333>).
Miscellaneous soil data management, summary, visualization, and conversion utilities to support soil survey.
Hierarchical models for the analysis of species-area 
  relationships (SARs) by combining several data sets and covariates; 
  with a global data set combining individual SAR studies; 
  as described in Solymos and Lele 
  (2012, Global Ecology and Biogeography 21, 109-120).
Provides a computational framework for Bayesian estimation of
    antigen-driven selection in immunoglobulin (Ig) sequences, providing an
    intuitive means of analyzing selection by quantifying the degree of
    selective pressure. Also provides tools to profile mutations in Ig
    sequences, build models of somatic hypermutation (SHM) in Ig sequences,
    and make model-dependent distance comparisons of Ig repertoires.
Implements various methods for eliciting a probability distribution
    for a single parameter from an expert or a group of experts. The expert
    provides a small number of probability judgements, corresponding
    to points on his or her cumulative distribution function. A range of parametric
    distributions can then be fitted and displayed, with feedback provided in the
    form of fitted probabilities and percentiles. A graphical interface for the roulette elicitation
    method is also provided. For multiple experts, a weighted linear pool can be
    calculated. Also includes functions for eliciting beliefs about population distributions.
Makes it incredibly easy to build interactive web
    applications with R. Automatic "reactive" binding between inputs and
    outputs and extensive pre-built widgets make it possible to build
    beautiful, responsive, and powerful applications with minimal effort.
Ace editor bindings to enable a rich text editing environment
    within Shiny.
Provides Bootstrap 2 web components for use with the Shiny
    package. With versions of Shiny prior to 0.11, these Bootstrap 2 components
    were included as part of the package. Later versions of Shiny include
    Bootstrap 3, so the Bootstrap 2 components have been moved into this
    package for those uses who rely on features specific to Bootstrap 2.
Adds additional Twitter Bootstrap components to Shiny.
Create dashboards with 'Shiny'. This package provides
    a theme on top of 'Shiny', making it easy to create attractive dashboards.
Add functionality to create drag and drop div elements in shiny.
Easily display user feedback next to Shiny inputs.  The feedback message is displayed when the feedback condition evaluates to TRUE.
Provides functionality for client-side navigation of
    the server side file system in shiny apps. In case the app is running
    locally this gives the user direct access to the file system without the
    need to "download" files to a temporary location. Both file and folder
    selection as well as file saving is available.
Interactive shiny application for analysis of educational tests and
    their items.
Perform common useful JavaScript operations in Shiny apps that will
    greatly improve your apps without having to know any JavaScript. Examples
    include: hiding an element, disabling an input, resetting an input back to
    its original value, delaying code execution by a few seconds, and many more
    useful functions for both the end user and the developer. 'shinyjs' can also
    be used to easily call your own custom JavaScript functions from R.
Provides functions that wrap HTML Bootstrap
    components code to enable the design and layout of informative landing home
    pages for Shiny applications. This can lead to a better user experience for
    the users and writing less HTML for the developer.
Shiny wrappers for the RGL package. This package exposes RGL's
    ability to export WebGL visualization in a shiny-friendly format.
A graphical user interface for interactive Markov chain Monte
    Carlo (MCMC) diagnostics and plots and tables helpful for analyzing a
    posterior sample. The interface is powered by RStudio's Shiny web
    application framework and works with the output of MCMC programs written
    in any programming language (and has extended functionality for Stan models
    fit using the rstan and rstanarm packages).
It's my experience that working with 'shiny' is intuitive once you're
    into it, but can be quite daunting at first. Several common mistakes are fairly
    predictable, and therefore we can control for these. The functions in this
    package help match up the assets listed in the UI and the SERVER files, and
    Visualize the ad hoc structure of the 'shiny' App.
Themes for use with Shiny. Includes several Bootstrap themes
    from <http://bootswatch.com/>, which are packaged for use with Shiny
    applications.
Provides a time input widget for Shiny. This widget allows intuitive time input in the
    '[hh]:[mm]:[ss]' or '[hh]:[mm]' (24H) format by using a separate numeric input for each time
    component. The interface with R uses 'DateTimeClasses' objects. See the project page for more
    information and examples.
Browser notifications in 'Shiny' apps, using
    'toastr': <https://github.com/CodeSeven/toastr#readme>.
Exposes bindings to jsTree -- a JavaScript library
    that supports interactive trees -- to enable a rich, editable trees in
    Shiny.
The SHIP-package allows the estimation of various types of
        shrinkage covariance matrices. These types differ in terms of
        the so-called covariance target (to be chosen by the user), the
        highly structured matrix which the standard unbiased sample
        covariance matrix is shrunken towards and which optionally
        incorporates prior biological knowledge extracted from the
        database KEGG. The shrinkage intensity is obtained via an
        analytical procedure.
A statistical method designed to take advantage of population genetics and microevolutionary theory, specifically by testing the association between haplotype sharing length and trait of interest.
Block-diagonal covariance selection for high dimensional Gaussian
    graphical models. The selection procedure is based on the slope heuristics.
An interface to the API of the E-commerce service Shopify
    (http://docs.shopify.com/api)
Analyzes shooting data with respect to group shape,
        precision, and accuracy. This includes graphical methods,
        descriptive statistics, and inference tests using standard,
        but also non-parametric and robust statistical methods.
        Implements distributions for radial error in bivariate normal
        variables. Works with files exported by OnTarget PC/TDS or
        Taran, as well as with custom data files in text format.
        Supports inference from range statistics like extreme spread.
        Includes a set of web-based graphical user interfaces.
Making it easy to use various types of fonts ('TrueType',
    'OpenType', Type 1, web fonts, etc.) in R graphs, and supporting most output
    formats of R graphics including PNG, PDF and SVG. Text glyphs will be converted
    into polygons or raster images, hence after the plot has been created, it no
    longer relies on the font files. No external software such as 'Ghostscript' is
    needed to use this package.
Providing font files that are needed by the 'showtext' package.
Functions for converting network data from a
        SpatialLinesDataFrame object to a "igraph-class" object.
The predictive value of a statistical model can often be improved
    by applying shrinkage methods. This can be achieved, e.g., by regularized
    regression or empirical Bayes approaches. Various types of shrinkage factors can
    also be estimated after a maximum likelihood. While global shrinkage modifies
    all regression coefficients by the same factor, parameterwise shrinkage factors
    differ between regression coefficients. With variables which are either highly
    correlated or associated with regard to contents, such as several columns of a
    design matrix describing a nonlinear effect, parameterwise shrinkage factors are
    not interpretable and a compromise between global and parameterwise shrinkage,
    termed 'joint shrinkage', is a useful extension. A computational shortcut to
    resampling-based shrinkage factor estimation based on DFBETA residuals can be
    applied. Global, parameterwise and joint shrinkage for models fitted by lm(),
    glm(), coxph(), or mfp() is available.
Provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in high dimensional settings, that is when the number of variables is larger than the sample size.
Implementation of the shuffle estimator, a non-parametric estimator for signal and noise variance under mild noise correlations.
This package takes data on organism isotopes and fits a
        Bayesian model to their dietary habits based upon a Gaussian
        likelihood with a mixture dirichlet-distributed prior on the
        mean. It also includes SiBER metrics. See siardemo() for an
        example. Version 4.1.2 contains bug fixes to allow more than
        isotope numbers other than 2. Version 4.2 fixes a bug that
        stopped siar working on 64-bit systems
Fits bi-variate ellipses to stable isotope data using Bayesian
    inference with the aim being to describe and compare their isotopic
    niche.
The code computes the structural intervention distance (SID) between a true directed acyclic graph (DAG) and an estimated DAG. Definition and details about the implementation can be found in J. Peters and P. Bühlmann: "Structural intervention distance (SID) for evaluating causal graphs", Neural Computation 27, pages 771-799, 2015.
This package has many purposes: first, it gives to the
        community an R implementation of each known side channel attack
        and countermeasures as well as data to test it, second it
        allows to implement a side channel attack quickly and easily.
Provides function to apply "Subgroup Identification based on Differential Effect Search" (SIDES) method proposed by Lipkovich et al. (2011) <doi:10.1002/sim.4289>.
Evolutionary reconstruction based on substitutions and insertion-deletion (indels) analyses in a distance-based framework.
Functions for making sieve test reports. Sieve test is widely used to obtain particle-size distribution of powders or granular materials.
Print function signatures and find overly complicated code.
SigClust is a statistical method for testing the
        significance of clustering results. SigClust can be applied to
        assess the statistical significance of splitting a data set
        into two clusters. For more than two clusters, SigClust can be
        used iteratively.
Uses logistic regression to model the probability of detection as a function of covariates. 
             This model is then used with observational survey data to estimate population size, while
             accounting for uncertain detection.  See Steinhorst and Samuel (1989).
A collection of tools for estimating the location of a transmitter signal from radio telemetry studies using the maximum likelihood estimation (MLE) approach described in Lenth (1981).
Several different sigmoid functions are implemented, including a wrapper function, SoftMax preprocessing and inverse functions.
A set of signal processing functions originally written for 'Matlab' and 'Octave'.
  Includes filter generation utilities, filtering functions,
  resampling routines, and visualization of filter models. It also
  includes interpolation functions.
Predicts the presence of signal peptides in eukaryotic protein
    using hidden semi-Markov models. The implemented algorithm can be accessed from
    both the command line and GUI.
Provide a significant variable selection procedure with different directions (forward, backward, stepwise) based on diverse criteria (Mallows' Cp, AIC, BIC, adjusted r-square, p-value). The algorithm selects a final model with only significant variables based on a correction choice of False Discovery Rate, Bonferroni, or no correction.
Perform sign test on one-sample data, which is one of the oldest non-parametric statistical methods. Assume that X comes from a continuous distribution with median = v ( unknown ). Test the null hypothesis H0: median of X v = mu ( mu is the location parameter and is given in the test ) v.s. the alternative hypothesis H1: v > mu ( or v < mu or v != mu ) and calculate the p-value. When the sample size is large, perform the asymptotic sign test. In both ways, calculate the R-estimate of location of X and the distribution free confidence interval for mu.
Pathway Analysis is the process of statistically linking observations on the molecular level to biological processes or pathways on the systems (organism, organ, tissue, cell) level.  
    Traditionally, pathway analysis methods regard pathways as collections of single genes and treat all genes in a pathway as equally informative.  This can lead to identification of spurious (misleading) pathways as statistically significant, since components are often shared amongst pathways. 
    SIGORA seeks to avoid this pitfall by focusing on genes or gene-pairs that are (as a combination) specific to a single pathway.  In relying on such pathway gene-pair signatures (Pathway-GPS), SIGORA inherently uses the status of other genes in the experimental context to identify the most relevant pathways. 
    The current version allows for pathway analysis of human and mouse data sets and contains pre-computed Pathway-GPS data for pathways in the KEGG and Reactome pathway repositories as well as mechanisms for extracting GPS for user supplied repositories.
Succinctly format significance summaries of
    various models and tests.  The main purpose is unified reporting and planning
    of experimental results, working around issue such as the difficulty of
    extracting model summary facts (such as with 'lm'/'glm').  This package also
    includes empirical tests, such as bootstrap estimates.
Provides tools to identify and visualize branches in a phylogenetic tree that are significantly responsive to some intervention, taking as primary inputs a phylogenetic tree (of class phylo) and a data frame (or matrix) of corresponding tip (OTU) labels and p-values.
This package calculates ANSI S3.5-1997 Speech Intelligibility Index
   (SII), a standard method for computing the intelligibility of
   speech from acoustical measurements of speech, noise, and hearing
   thresholds. This package includes data frames corresponding to
   Tables 1 - 4 in the ANSI standard as well as a function utilizing
   these tables and user-provided hearing threshold and noise level
   measurements to compute the SII score.  The methods implemented
   here extend the standard computations to allow calculation of SII
   when the measured frequencies do not match those required by the
   standard by applying interpolation to obtain values for the
   required frequencies
   -- 
   Development of this package was funded by the Center for Bioscience
   Education and Technology (CBET) of the Rochester Institute of
   Technology (RIT).
Besides functions for the calculation of similarity and
        multiple plot similarity measures with binary data (for
        instance presence/absence species data) the package contains
        some simple wrapper functions for reshaping species lists into
        matrices and vice versa and some other functions for further
        processing of similarity data (Mantel-like permutation
        procedures) as well as some other useful stuff for vegetation
        analysis.
Package simboot provides estimation of simultaneous bootstrap and asymptotic confidence intervals for diversity indices, namely the Shannon and the Simpson index. Several pre--specified multiple comparison types are available to choose. Further user--defined contrast matrices are applicable. In addition, simboot estimates adjusted as well as unadjusted p--values for two of the three proposed bootstrap methods. Further simboot allows for comparing biological diversities of two or more groups while simultaneously testing a user-defined selection of Hill numbers of orders q, which are considered as appropriate and useful indices for measuring diversity.
A flexible tool for simulating complex longitudinal data using
    structural equations, with emphasis on problems in causal inference.
    Specify interventions and simulate from intervened data generating
    distributions. Define and evaluate treatment-specific means, the average
    treatment effects and coefficients from working marginal structural models.
    User interface designed to facilitate the conduct of transparent and
    reproducible simulation studies, and allows concise expression of complex
    functional dependencies for a large number of time-varying nodes. See the
    package vignette for more information, documentation and examples.
Simultaneous tests and confidence intervals are provided for one-way experimental designs with one or many normally distributed, primary response variables (endpoints). Differences (Hasler and Hothorn, 2011) or ratios (Hasler and Hothorn, 2012) of means can be considered. Various contrasts can be chosen, unbalanced sample sizes are allowed as well as heterogeneous variances (Hasler and Hothorn, 2008) or covariance matrices (Hasler, 2014).
Simulates correlated multinomial responses conditional on a marginal model specification.
Algorithms for the implementation and evaluation of Monte Carlo tests, as well as for their use in multiple testing procedures.
Provides tools to help safely and efficiently organize Monte Carlo simulations in R.
    The package controls the structure and back-end of Monte Carlo simulations
    by utilizing a general generate-analyse-summarise strategy. The functions provided control
    common simulation issues such as re-simulating non-convergent results, support parallel
    back-end and MPI distributed computations, save and restore temporary files,
    aggregate results across independent nodes, and provide native support for debugging.
Provides the functions for simulating and modeling of stochastic differential equations (SDE's). Statistical analysis and simulation of the solution of SDE's enabled many searchers in different domains to use these equations to modeling practical problems, in financial and actuarial modeling and other areas of application. For example, modeling and simulate of dispersion in shallow water using the attractive center (Boukhetala K, 1996).
An object oriented framework to simulate
  ecological (and other) dynamic systems. It can be used for
  differential equations, individual-based (or agent-based) and other
  models as well. The package helps to organize scenarios (to avoid copy
  and paste) and aims to improve readability and usability of code.
Estimation of function and index vector in single index model with and without shape constraints including different smoothness conditions.
Implementation of the SIMEX-Algorithm by Cook & Stefanski
        and MCSIMEX by Küchenhoff, Mwalili & Lesaffre
Implement of the Simulation-Extrapolation (SIMEX) algorithm for the accelerated failure time (AFT) with covariates subject to measurement error.
A general framework for statistical simulation.
Generate power for the Cox proportional hazards model by simulating survival events data with time dependent exposure status for subjects. A dichotomous exposure variable is considered with a single transition from unexposed to exposed status during the subject's time on study.
Functions to run and assist four
  different similarity measures. The similarity
  measures included are: longest common
  subsequence (LCSS), Frechet distance, edit distance
  and dynamic time warping (DTW). Each of these
  similarity measures can be calculated from two
  n-dimensional trajectories, both in matrix form.
Allows a Simile model saved as a compiled binary to be
  loaded, parameterized, executed and interrogated. This version works 
  with Simile v5.97 on.
Livestock movements are important for the spread of many
    infectious diseases between herds. The package provides an
    efficient and flexible framework for stochastic disease spread
    modelling that integrates within-herd disease dynamics as
    continuous-time Markov chains and livestock movements between
    herds as scheduled events. The core simulation solver is
    implemented in C and uses 'OpenMP' (if available) to divide work
    over multiple processors. The package contains template models and
    can be extended with user defined models.
Provides methods for simulation and analysis of a very general fatigue
			 lifetime model for (metal matrix) composite materials.
A process-oriented and trajectory-based Discrete-Event Simulation
    (DES) package for R. Designed to be a generic framework, it leverages the
    power of 'Rcpp' to boost the performance, turning DES in R feasible. As a
    noteworthy characteristic, 'simmer' exploits the concept of trajectory: a
    common path in the simulation model for entities of the same type.
A set of plotting methods for 'simmer' trajectories and simulations.
Fits a stable isotope mixing model via JAGS in R. The package allows for any number of isotopes or sources, as well as concentration dependencies.
Algorithms to create prognostic biomarkers using biological networks.
Simulation of event histories with possibly non-linear baseline hazard rate functions, non-linear (time-varying) covariate effect functions, and dependencies on the past of the history. Random generation of event histories is performed using inversion sampling on the cumulative all-cause hazard rate functions.
Implements the inference of
    co-expression networks based on partial correlation
    coefficients from either steady-state or time-course
    transcriptomic data. Note that with both type of data this
    package can deal with samples collected in different
    experimental conditions and therefore not identically
    distributed. In this particular case, multiple but related
    networks are inferred on one simone run.
Simulate data from a Gaussian graphical model or a Gaussian Bayesian network in two conditions. Given a covariance matrix of a reference condition simulate plausible dysregulations.
Simulates and plots quantities of interest (relative
    hazards, first differences, and hazard ratios) for linear coefficients,
    multiplicative interactions, polynomials, penalised splines, and
    non-proportional hazards, as well as stratified survival curves from Cox
    Proportional Hazard models. It also simulates and plots marginal effects
    for multiplicative interactions.
Provides functions to simulate single or multiple, independent or correlated phenotype(s) with additive, dominance effects and their interactions. Also includes functions to generate phenotype(s) with specific heritability. Flexible and user-friendly options for simulation.
Simple classic graph algorithms for simple graph classes.
    Graphs may possess vertex and edge attributes. 'simplegraph' has
    so dependencies and it is written entirely in R, so it is easy to
    install.
Trains neural networks (multilayer perceptrons with one hidden layer) for bi- or multi-class classification.
Simple result caching in R based on Henrik
    Bengtsson's R.cache. The global environment is not considered when caching
    results simplifying moving files between multiple instances of R. Relies on
    more base functions than R.cache (e.g. cached results are saved using saveRDS() 
    and readRDS()).
When working across multiple machines and, similarly for
    reproducible research, it can be time consuming to ensure that you have
    all of the needed packages installed and loaded and that the correct working
    directory is set. 'simpleSetup' provides simple functions for making these
    tasks more straightforward.
SimpleTable provides a series of methods to conduct
        Bayesian inference and sensitivity analysis for causal effects
        from 2 x 2 and 2 x 2 x K tables when unmeasured confounding is
        present or suspected.
Simplex density, distribution, quantile functions as well as random variable
   	generation of the simplex distribution are given. Regression analysis of proportional data
   	using various kinds of simplex models is available. In addition, GEE method can be applied 
	to longitudinal data to model the correlation. Residual analysis is also involved. Some 
	subroutines are written in C with GNU Scientific Library (GSL) so as to facilitate the 
	computation.
Provides methods to integrate functions over m-dimensional simplices
 in n-dimensional Euclidean space.  There are exact methods for polynomials and
 adaptive methods for integrating an arbitrary function.  Dirichlet probabilities
 are calculated in certain cases.
Basic tools for symbolic expression simplification, e.g. simplify(x*1) => x, or simplify(sin(x)^2+cos(x)^2) => 1. Based on the "Expression v3" (Ev3) 1.0 system by Leo Liberti.
Tools and methods to simulate populations for surveys based
    on auxiliary data. The tools include model-based methods, calibration and
    combinatorial optimization algorithms. The package was developed with support of
    the International Household Survey Network, DFID Trust Fund TF011722 and funds from the World bank.
This package detects instances of Simpson's Paradox in
        datasets. It examines subpopulations in the data, either
        user-defined or by means of cluster analysis, to test whether a
        regression at the level of the group is in the opposite
        direction at the level of subpopulations.
Easy to use interfaces to a number of imputation methods
        that fit in the not-a-pipe operator of the 'magrittr' package.
Calculate power for generalised linear mixed models, using
    simulation. Designed to work with models fit using the 'lme4' package.
Provides a number of functions to simulate restriction enzyme digestion, library construction and fragments size selection to predict the number of loci expected from most of the Restriction site Associated DNA (RAD) and Genotyping By Sequencing (GBS) approaches. SimRAD estimates the number of loci expected from a particular genome depending on the protocol type and parameters allowing to assess feasibility, multiplexing capacity and the amount of sequencing required.
Functions for performing Bayesian similarity regression,
    and evaluating the probability of association between sets of ontological terms
    and binary response vector. A random model is compared with one in which
    the log odds of a true response is linked to the semantic similarity
    between terms and a latent characteristic ontological profile.
Facilitates data simulation from a random regression model where the data properties can be controlled by a few input parameters. The data simulation is based on the concept of relevant latent components and relevant predictors, and was developed for the purpose of testing methods for variable selection for prediction. Included are also functions for designing computer experiments in order to investigate the effects of the data properties on the performance of the tested methods. The design is constructed using the Multi-level Binary Replacement (MBR) design approach which makes it possible to set up fractional designs for multi-factor problems with potentially many levels for each factor.
Provides functions to generate and analyze individual-based spatially-explicit
  simulations of multi-state movements in heterogeneous landscapes, based on "resistance"
  rasters. Although originally conceived and designed to fill the gap of softwares simulating
  spatially-explicit trajectories of species constrained to linear, dendritic habitats
  (e.g., river networks), the simulation algorithm is built to be highly flexible and can be
  applied to any (aquatic, semi-aquatic or terrestrial) organism. Thus, the user will be able
  to use the package to simulate movements either in homogeneous landscapes, heterogeneous
  landscapes (e.g. semi-aquatic animal in a riverscape), or even in highly contrasted
  landscapes (e.g. fish in a river network). The algorithm and its input parameters are
  the same for all cases, so that results are comparable. Simulated trajectories can then
  be used as null models to test e.g. for species site fidelity and other movement ecology
  hypotheses, or to build predictive, mechanistic movement models, among other things. The
  package should thus be relevant to explore a broad spectrum of ecological phenomena, such
  as those at the interface of animal behaviour, landscape, spatial and movement ecology,
  disease and invasive species spread, and population dynamics. This is the first released
  experimental version; do test before using in production.
Tools for setting up ("design"), conducting, and evaluating
  large-scale simulation studies with graphics and tables, including
  parallel computations.
Contains two functions for simulating survival data from piecewise exponential hazards with a proportional hazards adjustment for covariates. The first function SimUNIVPiecewise simulates univariate survival data based on a piecewise exponential hazard, covariate matrix and true regression vector. The second function SimSCRPiecewise semi-competing risks data based on three piecewise exponential hazards, three true regression vectors and three matrices of patient covariates (which can be different or the same). This simulates from the Semi-Markov model of Lee et al (2015) given patient covariates, regression parameters, patient frailties and baseline hazard functions.
Provides an easy framework for Monte Carlo simulation in structural equation modeling, which can be used for various purposes, such as such as model fit evaluation, power analysis, or missing data handling and planning.
RNA sequencing analysis methods are often derived by relying on hypothetical parametric models for read counts that are not likely to be precisely satisfied in practice. Methods are often tested by analyzing data that have been simulated according to the assumed model. This testing strategy can result in an overly optimistic view of the performance of an RNA-seq analysis method. We develop a data-based simulation algorithm for RNA-seq data. The vector of read counts simulated for a given experimental unit has a joint distribution that closely matches the distribution of a source RNA-seq dataset provided by the user. Users control the proportion of genes simulated to be differentially expressed (DE) and can provide a vector of weights to control the distribution of effect sizes. The algorithm requires a matrix of RNA-seq read counts with large sample sizes in at least two treatment groups. Many datasets are available that fit this standard.
Simulates data sets in order to explore modeling techniques or
    better understand data generating processes. The user specifies a set of
    relationships between covariates, and generates data based on these
    specifications. The final data sets can represent data from randomized
    control trials, repeated measure (longitudinal) designs, and cluster
    randomized trials. Missingness can be generated using various
    mechanisms (MCAR, MAR, NMAR).
simSummary is a small utility package which eases the
        process of summarizing simulation results. Simulations often
        produce intermediate results - some focal statistics that need
        to be summarized over several scenarios and many replications.
        This step is in principle easy, but tedious. The package
        simSummary fills this niche by providing a generic way of
        summarizing the focal statistics of simulations. The useR must
        provide properly structured input, holding focal statistics,
        and then the summary step can be performed with one line of
        code, calling the simSummary function.
The simTool package is designed for statistical simulations that
    have two components. One component generates the data and the other one
    analyzes the data. The main aims of the simTool package are the reduction
    of the administrative source code (mainly loops and management code for the
    results) and a simple applicability of the package that allows the user to
    quickly learn how to work with the simTool package. Parallel computing is
    also supported. Finally, convenient functions are provided to summarize the
    simulation results.
This package provides simulation process of 4 selection methods in predicting potent compounds
A framework for performing simulations such as those common in
    methodological statistics papers.  The design principles of this package
    are described in greater depth in Bien, J. (2016) "The simulator: An Engine
    to Streamline Simulations," which is available at
    <http://faculty.bscb.cornell.edu/~bien/simulator.pdf>.
This package performs classical correspondence analysis (CA) and
     simultaneous analysis (SA). Simultaneous analysis is a factorial
     methodology developed for the joint treatment of a set of several
     contingency tables. In SA tables having the same rows, are concatenated 
     row-wise.
     In this version of the package a multiple option have been included for 
     the simultaneous analysis
     of tables having the same columns, concatenated column-wise.
     This way, a MSA allows to perform the analysis of an indicator matrix 
     where the rows represent individuals.
     In this package, functions for computation, summaries
     and graphical visualization in two dimensions are provided, including
     options to display partial and supplementary points.
This package provides routines to perform SIN model selection
  as described in Drton & Perlman (2004, 2008).  The selected models are
  represented in the format of the 'ggm' package, which allows in
  particular parameter estimation in the selected model.
The sinaplot is a data visualization chart suitable for plotting
    any single variable in a multiclass data set. It is an enhanced jitter strip
    chart, where the width of the jitter is controlled by the density
    distribution of the data within each class.
Density, distribution function, quantile function, random
    generation and survival function for the Sine Inverse Weibull Distribution as
    defined by SOUZA, L. New Trigonometric Class of Probabilistic Distributions.
    219 p. Thesis (Doctorate in Biometry and Applied Statistics) - Department of
    Statistics and Information, Federal Rural University of Pernambuco, Recife,
    Pernambuco, 2015 (available at <http://www.openthesis.org/documents/New-trigonometric-classes-probabilistic-distributions-602633.html>) and BRITO, C. C. R. Method Distributions generator and
    Probability Distributions Classes. 241 p. Thesis (Doctorate in Biometry and
    Applied Statistics) - Department of Statistics and Information, Federal Rural
    University of Pernambuco, Recife, Pernambuco, 2014 (available upon request).
A platform for experimenting with spatially explicit individual-based vegetation models.
Calculating daily global solar radiation at horizontal surface using several well-known models (i.e. Angstrom-Prescott, Supit-Van Kappel, Hargreaves, Bristow and Campbell, and Mahmood-Hubbard), and model calibration based on ground-truth data, and (3) model auto-calibration. The FAO Penmann-Monteith equation to calculate evapotranspiration is also included.
This package fits single-index (quadratic) response surface models.
Supplementary item response theory models to complement existing 
    functions in R, including multidimensional compensatory and
    noncompensatory IRT models, MCMC for hierarchical IRT models and 
    testlet models, NOHARM, Rasch copula model, faceted and 
    hierarchical rater models, ordinal IRT model (ISOP), 
    DETECT statistic, local structural equation modeling (LSEM),
    mean and covariance structure modelling for multivariate normally
    distributed data.
Variable selection techniques are essential tools for model
    selection and estimation in high-dimensional statistical models. Through this
    publicly available package, we provide a unified environment to carry out
    variable selection using iterative sure independence screening (SIS) and all
    of its variants in generalized linear models and the Cox proportional hazards
    model.
Implements the SISAL algorithm by Tikka and Hollmén. It is
        a sequential backward selection algorithm which uses a linear
        model in a cross-validation setting. Starting from the full
        model, one variable at a time is removed based on the
        regression coefficients. From this set of models, a
        parsimonious (sparse) model is found by choosing the model with
        the smallest number of variables among those models where the
        validation error is smaller than a threshold. Also implements
        extensions which explore larger parts of the search space
        and/or use ridge regression instead of ordinary least squares.
An interval fusion procedure for functional data in the
             semiparametric framework of SIR. Standard ridge and sparse SIR are 
             also included in the package.
SISUS for source partitioning using stable isotopes.
  SISUS reads in a specific Excel-like workbook and performs an IsoSource-type analysis by
    returning a sample of feasible solutions to p in the non-over constrained linear systems, b=Ap.
  Edit \file{sisus_*_template.xls} and input data values and run parameters.
  Run \code{\link{sisus.run}(filename)}. See output in current working directory.
Selects invalid instruments amongst a candidate of potentially bad instruments. The algorithm selects potentially invalid instruments and provides an estimate of the causal effect between exposure and outcome.
Functions for fitting and plotting SITAR (Super Imposition by
    Translation And Rotation) growth curve models. SITAR is a shape-invariant model
    with a regression B-spline mean curve and subject-specific random effects on
    both the measurement and age scales.
Provided within is a high quality and fast PPRNG that is able to be used in an 'OpenMP' parallel
    environment compiled under either C++98 or C++11. The objective of this package release is to consolidate
    the distribution of the 'sitmo' library on CRAN by enabling others to link to the 'sitmo' header file instead 
    of including a copy of 'sitmo' within their individual package. Lastly, the package contains example 
    implementations using 'sitmo' and two accompanying vignette that provide additional information.
Format a number (or a list of numbers) to a string (or a
        list of strings) with SI prefix. Use SI prefixes as constants
        like (4 * milli)^2
Sensitivity indices with dependent correlated inputs, using a
    method based on PLS regression.
Functions and utilities to perform
    Statistical Analyses in the Six Sigma way.
    Through the DMAIC cycle (Define, Measure, Analyze, Improve, Control),
    you can manage several Quality Management studies:
    Gage R&R, Capability Analysis, Control Charts, Loss Function Analysis,
    etc. Data frames used in the books "Six Sigma with R" (Springer, 2012)
    and "Quality Control with R" (Springer, 2015)
    are also included in the package.
This function develops an algorithm for presenting a Bayesian hierarchical model for estimating the sizes of local and national drug injected populations in Bangladesh. The model incorporates multiple commonly used data sources including mapping data, surveys, interventions, capture-recapture data, estimates or guesstimates from organizations, and expert opinion.
Contains functions to estimate morphometric and gonadal size at sexual maturity for organisms, usually fish and invertebrates. It includes methods for classification based on relative growth (using principal components analysis, hierarchical clustering, discriminant analysis), logistic regression (Frequentist or Bayes), parameters estimation and some basic plots.
Calculates and plots the SiZer map for scatterplot data.
        A SiZer map is a way of examining when the p-th derivative of a
        scatterplot-smoother is significantly negative, possibly zero
        or significantly positive across a range of smoothing
        bandwidths.
Provides a database-independent JDBC interface.
Collection of miscellaneous utility functions (especially intended
    for people coming from other statistical software packages like 'SPSS', and/
    or who are new to R), supporting following common tasks when working with
    data : 1) Reading and writing data between R and other statistical software
    packages like 'SPSS', 'SAS' or 'Stata' and working with labelled data; this
    includes easy ways to get and set label attributes, to convert labelled
    vectors into factors (and vice versa), or to deal with multiple declared
    missing values etc. 2) Data transformation tasks like recoding,
    dichotomizing or grouping variables, setting and replacing missing values.
    The data transformation functions also support labelled data.
Collection of plotting and table output functions for data
    visualization. Results of various statistical analyses (that are commonly used
    in social sciences) can be visualized using this package, including simple and
    cross tabulated frequencies, histograms, box plots, (generalized) linear models,
    mixed effects models, PCA and correlation matrices, cluster analyses, scatter
    plots, Likert scales, effects plots of regression models (including interaction
    terms) and much more. This package supports labelled data.
Collection of convenient functions for common statistical computations,
             which are not directly provided by R's base or stats packages.
             This package aims at providing, first, shortcuts for statistical
             measures, which otherwise could only be calculated with additional
             effort (like standard errors or root mean squared errors). Second,
             these shortcut functions are generic (if appropriate), and can be
             applied not only to vectors, but also to other objects as well
             (e.g., the Coefficient of Variation can be computed for vectors,
             linear models, or linear mixed models; the r2()-function returns
             the r-squared value for 'lm', 'glm', 'merMod' or 'lme' objects).
             The focus of most functions lies on summary statistics or fit
             measures for regression models, including generalized linear
             models and mixed effects models. However, some of the functions
             also deal with other statistical measures, like Cronbach's Alpha,
             Cramer's V, Phi etc.
Functions for kernel-regression-based association tests including Burden test, SKAT and SKAT-O. These methods aggregate individual SNP score statistics in a SNP set and efficiently compute SNP-set level p-values.
Sparse (Multicategory) Kernel Discriminant Analysis does variable selection for nonparametric classification
A shiny interface and supporting tools to guide users in choosing
    appropriate simulations, setting parameters, calculating summary genetic
    statistics, and organizing data output, all within the R environment. In
    addition to supporting existing forward and reverse-time simulators, new
    simulators can be integrated into the environment relatively easily.
A tool for bootstrapping new packages with useful defaults,
    including a test suite outline that passes checks and helpers for running
    tests, checking test coverage, building vignettes, and more. Package
    skeletons it creates are set up for pushing your package to
    'GitHub' and using other hosted services for building and test automation.
Functions for the Skellam distribution, including: density
    (pmf), cdf, quantiles and regression.
Functions are provided for the density function,
        distribution function, quantiles and random number generation
        for the skew hyperbolic t-distribution. There are also
        functions that fit the distribution to data. There are
        functions for the mean, variance, skewness, kurtosis and mode
        of a given distribution and to calculate moments of any order
        about any centre. To assess goodness of fit, there are
        functions to generate a Q-Q plot, a P-P plot and a tail plot.
Density, distribution function, quantile function and
        random generation for the skewed t distribution of Fernandez
        and Steel.
A generalization of the statistic used in Friedman's ANOVA method and in Durbin's rank test. This nonparametric statistical test is useful for the data obtained from block designs with missing observations occurring randomly. A resulting p-value is based on the chi-squared distribution and Monte Carlo method.
Algorithms for solving selective k-means problem,
    which is defined as finding k rows in an m x n matrix such that 
    the sum of each column minimal is minimized. 
    In the scenario when m == n and each cell value in matrix is a 
    valid distance metric, this is equivalent to a k-means problem. 
    The selective k-means extends the k-means problem in the sense 
    that it is possible to have m != n, often the case m < n which 
    implies the search is limited within a small subset of rows. 
    Also, the selective k-means extends the k-means problem in the 
    sense that the instance in row set can be instance not seen in 
    the column set, e.g., select 2 from 3 internet service provider
    (row) for 5 houses (column) such that minimize the overall cost 
    (cell value) - overall cost is the sum of the column minimal of
    the selected 2 service provider.
Algorithms to compute spherical k-means partitions.
  Features several methods, including a genetic and a fixed-point
  algorithm and an interface to the CLUTO vcluster program.
Provides an alternative to manually process hemispherical pictures. The algorithm processes each picture one by one to determine the proportion of sky pixel. The algorithm uses the Ridler and Calvard method (Ridler and Calvard 1978).
Query and download satellite imagery and climate/atmospheric datasets using the SkyWatch API. 
     Search datasets by wavelength (band), cloud cover, resolution, location, date, etc.
     Get the query results as data frame and as HTML. To learn more about the SkyWatch API, see <https://github.com/skywatchspaceapps/api>.
Provides directly interpretable estimated coefficients
    for four models in connection with the two-group straight line
    ANCOVA problem: (A) the full model, which requires the fitting of
    two intercepts and two slopes; (B) a reduced model, which requires
    the fitting of a single intercept and single slope; (C) a reduced
    model, which requires the fitting of two separate intercepts and a
    single, common slope; and (D) a reduced model, which requires the
    fitting of a single, common intercept and two separate slopes.  The
    summary function provides tests of fit for the (null) hypotheses of:
    (1) equivalent data sets, (2) equivalent slopes, and
    (3) equivalent intercepts.
'Slack' <http://slack.com/> provides a service for teams to
    collaborate by sharing messages, images, links, files and more. Functions are provided
    that make it possible to interact with the 'Slack' platform 'API'. When
    you need to share information or data from R, rather than resort to copy/
    paste in e-mails or other services like 'Skype' <http://www.skype.com/>, you
    can use this package to send well-formatted output from multiple R objects and
    expressions to all teammates at the same time with little effort. You can also
    send images from the current graphics device, R objects, and upload files.
Data structures and algorithms for sparse arrays and matrices,
  based on index arrays and simple triplet representations, respectively.
Estimates the slope and level change present in data after
        removing phase A trend. Represents graphically the original and
        the detrended data.
The skew logistic distribution is a quantile-defined generalisation
 of the logistic distribution (van Staden and King 2015).  Provides random 
 numbers, quantiles, probabilities, densities and density quantiles for the distribution.
 It provides Quantile-Quantile plots and method of L-Moments estimation 
 (including asymptotic standard errors) for the distribution.
Calculates maximum likelihood estimate, exact and asymptotic confidence intervals, and exact and asymptotic goodness of fit p-values for infectious units per million (IUPM) from serial limiting dilution assays. This package uses the likelihood equation, exact PGOF, and exact confidence intervals described in Meyers et al. (1994) <http://jcm.asm.org/content/32/3/732.full.pdf>.
Compute Time series Resistant Smooth 4253H, twice smoothing method.
Data sets from Ramsey, F.L. and Schafer, D.W. (2002), "The
    Statistical Sleuth: A Course in Methods of Data Analysis (2nd
    ed)", Duxbury.
Data sets from Ramsey, F.L. and Schafer, D.W. (2013), "The
    Statistical Sleuth: A Course in Methods of Data Analysis (3rd
    ed)", Cengage Learning.
Set of tools to find coherent patterns in microarray data
    using a Bayesian sparse latent factor model (Duarte and Mayrink 2015 -
    http://link.springer.com/chapter/10.1007%2F978-3-319-12454-4_15).
    Considerable effort has been put into making slfm fast and memory efficient,
    turning it an interesting alternative to simpler methods in terms
    of execution time. It implements versions of the SLFM using both type
    of mixtures: using a degenerate distribution or a very concentrated
    normal distribution for the spike part of the mixture. It also implements
    additional functions to help pre-process the data and fit the model
    for a large number of arrays.
Generate the optimal Latin Hypercube Designs (LHDs) for computer experiments with quantitative factors and the optimal Sliced Latin Hypercube Designs (SLHDs) for computer experiments with both quantitative and qualitative factors. Details of the algorithm can be found in Ba, S., Brenneman, W. A. and Myers, W. R. (2015), "Optimal Sliced Latin Hypercube Designs," Technometrics. Important function in this package is "maximinSLHD".
Fits singular linear models to longitudinal data. Singular linear
    models are useful when the number, or timing, of longitudinal observations
    may be informative about the observations themselves. They are described
    in Farewell (2010) <doi:10.1093/biomet/asp068>, and are extensions of the
    linear increments model of Diggle et al. (2007)
    <doi:10.1111/j.1467-9876.2007.00590.x> to general longitudinal data.
Efficient procedures for Sorted L1 Penalized Estimation (SLOPE).
    The sorted L1 norm is useful for statistical estimation and testing,
    particularly for variable selection in the linear model.
Interface for creation of 'slp' class smoother objects for 
             use in Generalized Additive Models (as implemented by packages 
             'gam' and 'mgcv').
This is software linked to the book
  'Applied Smoothing Techniques for Data Analysis -
  The Kernel Approach with S-Plus Illustrations' Oxford University Press.
Implementation of the Stochastic Multi-Criteria Acceptability Analysis (SMAA) family of Multiple Criteria Decision Analysis (MCDA) methods.
This package provides a solution path for L1-penalized angle-based classification. Three loss functions are implemented in smac, including the deviance loss in logistic regression, the exponential loss in boosting, and the proximal support vector machine loss.
Provides the following approaches for multidimensional scaling (MDS) based on stress minimization by means of majorization (smacof): basic MDS on symmetric dissimilarity matrices,
    MDS with external constraints on the configuration, individual difference scaling (idioscal, indscal, and friends), MDS with spherical restrictions, and unfolding. The MDS type can be ratio, interval, ordinal, and monotone splines. 
    Various tools and extensions like jackknife MDS, permutation tests, gravity models, inverse MDS, unidimensional scaling, classical scaling, and Procrustes.
Various statistical methods for analyzing case-control point data.
    The methods available closely follow those in chapter 6 of Applied Spatial
    Statistics for Public Health Data by Waller and Gotway (2004).
Inference techniques for Fay Herriot Model.
Animal movement models including moving-resting process
    with embedded Brownian motion, Brownian motion with measurement error.
Facilitates programmatic access to NASA Soil Moisture Active
    Passive (SMAP) data with R. It includes functions to search for, acquire,
    and extract SMAP data.
The package "smart" provides a general framework for
        analyzing (including estimation, feature selection and
        prediction) and visualize big data. It integrates several
        novel, efficient and robust data analysis tools, including
        Transelliptical Component Analysis (TCA), Transelliptical
        Correlation Estimation (TCE) and Group Nearest Shrunken
        Centroids (gnsc). We target on high dimensional data
        analysis(usually d >> n), and exploit computationally
        efficiently approaches. Results are organized to be visualized
        properly for users.
Tools for accessing and processing datasets prepared by the Foundation SmarterPoland.pl. Among all: access to API of Google Maps, Central Statistical Office of Poland, MojePanstwo, Eurostat, WHO and other sources.
Addressing the problem of outlier detection from the viewpoint of statistical learning theory. This method is proposed by Yamanishi, K., Takeuchi, J., Williams, G. et al. (2004) <DOI:10.1023/B:DAMI.0000023676.72185.7c>. It learns the probabilistic model (using a finite mixture model) through an on-line unsupervised process. After each datum is input, a score will be given with a high one indicating a high possibility of being a statistical outlier.
Introduces a fast and efficient Surrogate Variable Analysis algorithm that captures variation of unknown sources (batch effects) for high-dimensional genomic data. The algorithm is based on the popular 'SVA' package and proposes a revision on it that achieves more than 10 times faster running time while trading no accuracy loss in return.
This package provides methods of fitting bivariate lines in allometry using the major axis (MA) or standardised major axis (SMA), and for making inferences about such lines. The available methods of inference include confidence intervals and one-sample tests for slope and elevation, testing for a common slope or elevation amongst several allometric lines, constructing a confidence interval for a common slope or elevation, and testing for no shift along a common axis, amongst several samples.
The main purpose of the package is to categorize a numeric variable into bins
    mapped to a binary target variable for its ulterior usage in scoring modeling. 
    This functionality reduces dramatically the time consuming process of finding the optimal 
    cut points for a given numeric variable, quickly calculates the Information Value, either for
    one variable at the time or all at once in one line of code; and also outputs 'SQL' codes, 
    tables, and plots used throughout the development stage.
    The package also allows the user to understand the data via exploratory data analysis 
    in one step, establish customized cut points for numeric characteristics, and run the analysis 
    for categorical variables.
particle filtering, auxiliary particle filtering and sequential Monte Carlo algorithms
Implements multiple imputation of missing covariates by
    Substantive Model Compatible Fully Conditional Specification.
    This is a modification of the popular FCS/chained equations
    multiple imputation approach, and allows imputation of missing
    covariate values from models which are compatible with the user
    specified substantive model.
This package is for optimizing non-linear complex
        functions based on Monte Carlo random sampling.
A package containing functions for genome-wide association
        studies.
Data Sets for Kumar and Petersen (2012).
    Statistical Methods in Customer Relationship Management,
    Wiley: New York.
An R-package for Estimating Semiparametric PH and AFT
        Mixture Cure Models
Provides resampling functions (stratified, residual,
        multinomial, systematic, and branching), measures of weight
        uniformity (coefficient of variation, effective sample size,
        and entropy), and a weight renormalizing function.
Contains data files to accompany Smithson & Merkle (2013), Generalized Linear Models for Categorical and Continuous Limited Dependent Variables.
This package provides similarity among documents.
Symbolic multidimensional scaling for interval-valued dissimilarities. The hypersphere model and the hyperbox model are available.
A package for fitting smoothing-splines mixed-effects models to replicated functional
  data sets.
Provides statistical methods for the analysis of data areal data, with a focus on cluster detection.
R functions and data from Chapter 5 of 'Statistical
        Methods for Financial Engineering', by Bruno Remillard, CRC
        Press, (2013).
This package contains code and data for modelling and simulation of stochastic kinetic biochemical network models. It contains the code and data associated with the second edition of the book Stochastic Modelling for Systems Biology, published by Chapman & Hall/CRC Press, November 2011.
Smooth multivariate interpolation for gridded and scattered data
    with possibly large datasets. The code for scattered data is taken from the
    'SHEPPACK' Fortran package.
This package accompanies Aitkin et al, Statistical
        Modelling in R, OUP, 2009. The package contains some functions
        and datasets used in the text.
This tiny package contains one function smirnov() which
        calculates two scaled taxonomic coefficients, Txy (coefficient
        of similarity) and Txx (coefficient of originality). These two
        characteristics may be used for the analysis of similarities
        between any number of taxonomic groups, and also for assessing
        uniqueness of giving taxon. It is possible to use smirnov()
        output as a distance measure: convert it to distance by
        "as.dist(1 - smirnov(x))".
A collection of functions for statistical computing and data manipulation in R. 
    Includes routines for data ingestion, operating on dataframes and matrices, conversion to and 
    from lists, converting factors, filename manipulation, programming utilities, parallelization, plotting, 
    statistical and mathematical operations, and time series.
Constructs a yield curve by the Smith-Wilson method from a
        table of LIBOR and SWAP rates
The SML package is a collection of statistical machine learning methods.
Local Correlation Integral (LOCI) method for outlier identification is implemented here. The LOCI method developed here is invented in Breunig, et al. (2000), see <doi:10.1145/342009.335388>.
Fit univariate right, left or interval censored regression model under the scale mixture of normal distributions
Fits flexible additive models to data on stream networks, taking account of flow-connectivity of the network.  Models are fitted using penalised least squares.
Provides generators for a high number of both single- and multi-
    objective test functions which are frequently used for the benchmarking of
    (numerical) optimization algorithms. Moreover, it offers a set of convenient
    functions to generate, plot and work with objective functions.
The set of smoothing functions used for time series analysis and in forecasting.
             Currently the package includes exponential smoothing models and SARIMA in state-space
             form + several simulation functions.
The implemented method uses for smoothing bivariate thin plate splines, bivariate lasso-type regularization, and allows for both period and cohort effects. Thus the mortality rates are modelled as the sum of four components: a smooth bivariate function of age and time, smooth one-dimensional cohort effects, smooth one-dimensional period effects and random errors.
A collection of methods for smoothing numerical data, commencing with a port of the Matlab gaussian window smoothing function. In addition, several functions typically used in smoothing of financial data are included.
SmoothHazard is a package for fitting illness-death (and survival)
    model with possibly interval-censored data for transition toward transient
    state. Left-truncated and right-censored data are also allowed.
    Proportional intensities regression models allow for covariates on each of
    the 3 transitions. We use either a parametric approach with Weibull
    baseline intensities or a semi-parametric approach with M-splines
    approximation of baseline intensities in order to obtain smooth estimates.
    Estimation of parameters is conducted by likelihood maximization in the
    parametric approach or penalized likelihood maximization in the
    semi-parametric approach.
Provides flexible hazard ratio curves allowing non-linear
        relationships between continuous predictors and survival. To
        better understand the effects that each continuous covariate
        has on the outcome, results are ex pressed in terms of hazard
        ratio curves, taking a specific covariate value as reference.
        Confidence bands for these curves are also derived.
Functions to smooth two-dimensional fields using FFT and the convolution theorem
Some M-estimators for 1-dimensional location (Bisquare, ML
        for the Cauchy distribution, and the estimators from
        application of the smoothing principle introduced in Hampel,
        Hennig and Ronchetti (2011) to the above, the Huber
        M-estimator, and the median, main function is smoothm), and
        Pitman estimator.
Contains, as a main contribution, a function to fit
             a regression model with possibly right, left or interval
             censored observations and with the error distribution
             expressed as a mixture of G-splines. Core part
             of the computation is done in compiled C++ written
             using the Scythe Statistical Library Version 0.3.
Given independent and identically distributed observations X(1), ..., X(n) from a Generalized Pareto distribution with shape parameter gamma in [-1,0], offers several estimates to compute estimates of gamma. The estimates are based on the principle of replacing the order statistics by quantiles of a distribution function based on a log--concave density function. This procedure is justified by the fact that the GPD density is log--concave for gamma in [-1,0].
A collection of various oversampling techniques developed from SMOTE is provided. SMOTE is a oversampling technique which synthesizes a new minority instance between a pair of one minority instance and one of its K nearest neighbor. (see <https://www.jair.org/media/953/live-953-2037-jair.pdf> for more information) Other techniques adopt this concept with other criteria in order to generate balanced dataset for class imbalance problem.
This package contains the datasets and a few functions for
        use with the practicals outlined in Appendix A of the book
        Statistical Models (Davison, 2003, Cambridge University Press).
        The practicals themselves can be found at
        http://statwww.epfl.ch/davison/SM/
Computes the studentized midrange distribution (pdf, cdf and quantile) and generates random numbers
Produce small area population estimates by fitting census data to
    survey data.
Datasets used in "Statistical Methods for the Social Sciences"
    (SMSS) by Alan Agresti and Barbara Finlay.
Implements the structural model for variances in order to
        detect differentially expressed genes from gene expression data
Build and manipulate probability distributions of the skew-normal 
  family and some related ones, notably the skew-t family, and provide related
  statistical methods for data fitting and diagnostics, in the univariate and 
  the multivariate case.
A range of tools for social network analysis, including node and graph-level indices, structural distance and covariance methods, structural equivalence detection, network regression, random graph generation, and 2D/3D network visualization.
Run 'Python' code, make function calls, assign and retrieve variables, etc. from R.
    A fork from 'rPython' which uses 'jsonlite', 'Rcpp' and has several fixes and improvements.
Functions for reading and writing Gadget N-body snapshots. The Gadget code is popular in astronomy for running N-body / hydrodynamical cosmological and merger simulations. To find out more about Gadget see the main distribution page at www.mpa-garching.mpg.de/gadget/
Similarity Network Fusion takes multiple views of a network and fuses them together to construct an overall status matrix. The input to our algorithm can be feature vectors, pairwise distances, or pairwise similarities. The learned status matrix can then be used for retrieval, clustering, and classification.
Implementation of robust and non-robust Standard Normal Homogeneity
    Test (SNHT) for changepoint detection. This test statistic is equal sided,
    as proposed in "Homogenization of Radiosonde Temperature Time Series Using
    Innovation Statistics" by Haimberger, L., 2007. However, the statistic contains
    an estimate of sigma^2 in the denominator instead of sigma, which seems to
    be a more appropriate value (based on the paper "Homogenization of Swedish
    temperature data. Part I: Homogeneity test for linear trends." by Alexandersson,
    H., and A. Moberg, 1997).
Implement K-nearest neighbor classifier, weighted nearest neighbor classifier, bagged nearest neighbor classifier, optimal weighted nearest neighbor classifier and stabilized nearest neighbor classifier, and perform model selection via 5 fold cross-validation for them. This package also provides functions for computing the classification error and classification instability of a classification procedure.
Support for simple parallel computing in R.
An R interface to the C libstemmer library that implements
  Porter's word stemming algorithm for collapsing words to a common
  root to aid comparison of vocabulary. Currently supported languages are
  Danish, Dutch, English, Finnish, French, German, Hungarian, Italian,
  Norwegian, Portuguese, Romanian, Russian, Spanish, Swedish
  and Turkish.
Functions for analysis of network objects, which are imported or
    simulated by the package. The non-parametric methods of analysis center
    around snowball and bootstrap sampling.
Usability wrapper around snow for easier development of
        parallel R programs. This package offers e.g. extended error
        checks, and additional functions. All functions work in
        sequential mode, too, if no cluster is present or wished.
        Package is also designed as connector to the cluster management
        tool sfCluster, but can also used without it.
Extension of the snow package supporting fault tolerant and reproducible applications, as well as supporting easy-to-use parallel programming - only one function is needed.
contains several supplementary non-parametric statistics methods including quantile test, Cox-Stuart trend test, runs test, normal score test, kernel PDF and CDF estimation, kernel regression estimation and kernel Kolmogorov-Smirnov test.
This package carries out most common analysis when performing whole genome association studies. These analyses include descriptive statistics and exploratory analysis of missing values, calculation of Hardy-Weinberg equilibrium, analysis of association based on generalized linear models (either for quantitative or binary traits), and analysis of multiple SNPs (haplotype and epistasis analysis). Permutation test and related tests (sum statistic and truncated product) are also implemented. Max-statistic and genetic risk-allele score exact distributions are also possible to be estimated.
Implements classes and methods for large scale SNP enrichment analysis (e.g. SNPs associated with genes expression in a GWAS signal).
A set of functions to create SQL tables of gene and SNP information and compose them into a SNP Set, for example for use with the RSNPset package, or to export to a PLINK set.
Tools for performing variable selection in three-way data using N-PLS in combination with L1 penalization.
This package implements asymptotic methods related to
        maximally selected statistics, with applications to SNP data.
Bivariate Gaussian genotype clustering and calling for Illumina
    microarrays, building on the package 'mclust'. Pronounced snip-em-clust.
Creates plots of p-values using single SNP and/or haplotype data.
    Main features of the package include options to display a linkage
    disequilibrium (LD) plot and the ability to plot multiple datasets
    simultaneously. Plots can be created using global and/or individual
    haplotype p-values along with single SNP p-values. Images are created as
    either PDF/EPS files.
A modification of Breiman and Cutler's classification random forests modified for SNP (Single Nucleotide Polymorphism) data (based on randomForest v4.6-7) to prevent X-chromosome SNP variable importance bias compared to autosomal SNPs by simulating the process of X chromosome inactivation. Classification is based on a forest of trees using random subsets of SNPs and other variables as inputs.
Write snpStats objects to disk in formats suitable for reading by
    snphap, phase, mach, IMPUTE, beagle, and (almost) anything else that
    expects a rectangular format.
This package queries the SNPs data sets and makes plots of
        genes and SNPs.  This package allows users to access these data
        sets once they have been compiled into one file and indexed
        with Tabix (http://samtools.sourceforge.net/tabix.shtml). There
        are functions to retrieve SNPs, subset them for specific
        strains, obtain only SNPs that are polymorphic for a subset of
        strains, plot SNPs and look for certain allele patterns in
        SNPs. We have also added functions to access indels and
        structural variants.
Stochastic Newton Sampler (SNS) is a Metropolis-Hastings-based, Markov Chain Monte Carlo sampler for twice differentiable, log-concave probability density functions (PDFs) where the proposal density function is a multivariate Gaussian resulting from a second-order Taylor-series expansion of log-density around the current point. The mean of the Gaussian proposal is the full Newton-Raphson step from the current point. A Boolean flag allows for switching from SNS to Newton-Raphson optimization (by choosing the mean of proposal function as next point). This can be used during burn-in to get close to the mode of the PDF (which is unique due to concavity). For high-dimensional densities, mixing can be improved via 'state space partitioning' strategy, in which SNS is applied to disjoint subsets of state space, wrapped in a Gibbs cycle. Numerical differentiation is available when analytical expressions for gradient and Hessian are not available. Facilities for validation and numerical differentiation of log-density are provided.
Scan statistics applied in social network data can be used to test the cluster characteristics among a social network.
Contains functions to perform various models and 
	methods for test equating. It currently implements the traditional 
	mean, linear and equipercentile equating methods, as well as the 
	mean-mean, mean-sigma, Haebara and Stocking-Lord IRT linking methods. 
	It also supports newest methods such that local equating, kernel 
	equating (using Gaussian, logistic and uniform kernels) with presmoothing, 
	and IRT parameter linking methods based on asymmetric item characteristic 
	functions. Functions to obtain both standard error of equating (SEE) 
	and standard error of equating difference between two equating 
	functions (SEED) are also implemented for the kernel method of 
	equating.
Allows objects to be stored on disc and automatically
       		recalled into memory, as required, by delayed assignment.
Specific and class specific multiple correspondence analysis on
    survey-like data. Soc.ca is optimized to the needs of the social scientist and
    presents easily interpretable results in near publication ready quality.
A suite of tools for collecting and constructing networks from
    social media data. Provides easy-to-use functions for collecting data across
    popular platforms (Instagram, Facebook, Twitter, and YouTube) and generating
    different types of networks for analysis.
Social media search and analytic tool that takes one or multiple URL(s) and returns the information about the popularity and reach of the URL(s) on social media. The function get_socialmedia() retrieves the number of shares, likes, pins, and hits on Facebook (<http://www.facebook.com>), Pinterest (<http://www.pinterest.com>), StumbleUpon (<http://www.stumbleupon.com>), LinkedIn (<http://www.linkedin.com>), and Reddit (<http://www.reddit.com>). The package also includes dedicated functions for each social network platform and a function to resolve shortened URLs.
Generates social networks using either of two
    approaches: using either pairwise distances or territorial area intersections.
Provides to sociologists (and related scientists) a toolbox to facilitate the construction of social position indicators from survey data. Social position indicators refer to what is commonly known as social class and social status. There exists in the sociological literature many theoretical conceptualisation and empirical operationalization of social class and social status. This first version of the package offers tools to construct the International Socio-Economic Index of Occupational Status (ISEI) and the Oesch social class schema. It also provides tools to convert several occupational classifications (PCS82, PCS03, and ISCO08) into a common one (ISCO88) to facilitate data harmonisation work, and tools to collapse (i.e. group) modalities of social position indicators.
SOD (Self-Organising-Deltoids) provides multidimensional scaling by gradually reducing the dimensionality of an initial space and using the resulting stress in the configuration to re-arrange nodes. Stress is calculated from the errors in the inter-node distances, and the sum of the stresses at each node is combined to create N-dimensional force vectors that direct the movement of nodes as the dimensionality is iteratively reduced.
Functions, examples and other software related to the book
        "Software for Data Analysis: Programming with R". See
        package?SoDA for an overview.
Variable and interaction selection are essential to classification in high-dimensional setting. In this package, we provide the implementation of SODA procedure, which is a forward-backward algorithm that selects both main and interaction effects under quadratic discriminant analysis and logistic regression model.
To implement two clustering methods, ODC and SODC, for
        clustering datasets using optimal scoring, can also be used as
        an dimension reduction tool.
Bindings to libsodium: a modern, easy-to-use software library for
    encryption, decryption, signatures, password hashing and more. Sodium uses
    curve25519, a state-of-the-art Diffie-Hellman function by Daniel Bernstein,
    which has become very popular after it was discovered that the NSA had
    backdoored Dual EC DRBG.
Provides an interface to the 'NoSQL' database 'CouchDB'
    (<http://couchdb.apache.org>). Methods are provided for managing
    databases within 'CouchDB', including creating/deleting/updating/transferring,
    and managing documents within databases. One can connect with a local
    'CouchDB' instance, or a remote 'CouchDB' databases such as 'Cloudant'
    (<https://cloudant.com>). Documents can be inserted directly from
    vectors, lists, data.frames, and 'JSON'. Targeted at 'CouchDB' v2 or
    greater.
Este paquete tiene la finalidad de ayudar a aprender de una forma interactiva, teniendo ejemplos y la posibilidad de resolver nuevos al mismo tiempo. Apuntes de clase interactivos.
Software that leverages the capabilities of Circos by manipulating data, preparing configuration files, and running the Perl-native Circos directly from the R environment with minimal user intervention. Circos is a novel software that addresses the challenges in visualizing genetic data by creating circular ideograms composed of tracks of heatmaps, scatter plots, line plots, histograms, links between common markers, glyphs, text, and etc. Please see <http://www.circos.ca>.
An extension of sensitivity, specificity, positive and negative
    predictive value to continuous predicted and reference memberships in
    [0, 1].
It contains soft clustering algorithms, in particular approaches derived from rough set theory: Lingras & West original rough k-means, Peters' refined rough k-means, and PI rough k-means. It also contains classic k-means and a corresponding illustrative demo.
Iterative methods for matrix completion that use nuclear-norm regularization. There are two main approaches.The one approach uses iterative soft-thresholded svds to impute the missing values. The second approach uses alternating least squares. Both have an "EM" flavor, in that at each iteration the matrix is completed with the current estimate. For large matrices there is a special sparse-matrix class named "Incomplete" that efficiently handles all computations. The package includes procedures for centering and scaling rows, columns or both, and for computing low-rank SVDs on large sparse centered matrices (i.e. principal components)
Implementation of 'softmax' regression and classification models with multiple layer neural network. It can be used for many tasks like word embedding based document classification, 'MNIST' dataset handwritten digit recognition and so on. Multiple optimization algorithm including 'SGD', 'Adagrad', 'RMSprop', 'Moment', 'NAG', etc are also provided.
Sparsity Oriented Importance Learning (SOIL) provides an objective and informative profile of variable importances for high dimensional regression and classification models.
A collection of functions for reading data from USDA-NCSS soil databases.
Basic and model-based soil physical analyses.
This package provides functions to graphically represent 
	     soil properties. Morphological data gathered in the field 
	     such as horizon boundaries, root abundance and dimensions,
	     skeletal shape, abundance and dimension as well as	
	     meaningful soil color may be represented via the plot
	     function. A lattice-based plot.element function has been 
	     designed to represent depth function of a given variable.
This package contains functions for modeling Soil Organic
        Matter decomposition in terrestrial ecosystems.
Methods and classes for processing and analyzing soil and plant infrared (MIR, alpha-MIR and VISNIR) spectroscopy readings based on the Africa Soil Information Services (AfSIS) project data.
"The Soil Texture Wizard" is a set of R functions designed to produce texture triangles (also called texture plots, texture diagrams, texture ternary plots), classify and transform soil textures data. These functions virtually allows to plot any soil texture triangle (classification) into any triangle geometry (isosceles, right-angled triangles, etc.). This set of function is expected to be useful to people using soil textures data from different soil texture classification or different particle size systems. Many (> 15) texture triangles from all around the world are predefined in the package. A simple text based graphical user interface is provided: soiltexture_gui().
It is a set of R implementations of parametric formulas of soil water
    retention or conductivity curve. At the moment, only Van Genuchten (for
    soil water retention curve) and Mualem (for hydraulic conductivity) were
    implemented. See reference
    \url{http://en.wikipedia.org/wiki/Water_retention_curve}.
Calculation methods of solar radiation and performance of photovoltaic systems from daily and intradaily irradiation data sources.
SOLAR is the standard software program to perform linkage and
    association mappings of the quantitative trait loci (QTLs) in pedigrees of
    arbitrary size and complexity. This package allows the user to exploit the
    variance component methods implemented in SOLAR. It automates such routine
    operations as formatting pedigree and phenotype data. It also parses the model
    output and contains summary and plotting functions for exploration of the
    results. In addition, solarius enables parallel computing of the linkage and
    association analyses, that makes the calculation of genome-wide scans more
    efficient. See <http://solar.txbiomedgenetics.org/> for more information about
    SOLAR.
Calculation of solar zenith and azimuth angles.
Predicted solid earth tide displacements in the meridional,
        zonal and vertical directions. Based on "Solid" from Dennis Milbert, 
        modified from "dehanttideinelMJD" by V. Dehant, S. Mathews, J. Gipson, 
        and C. Bruyninx.
Provides a set of functions for querying and parsing
    data from Solr endpoints (local and remote), including search, faceting,
    highlighting, stats, and 'more like this'.
Provides a set of functions for querying and parsing data
    from 'Solr' (<http://lucene.apache.org/solr>) 'endpoints' (local and 
    remote), including search, 'faceting', 'highlighting', 'stats', and 
    'more like this'. In addition, some functionality is included for 
    creating, deleting, and updating documents in a 'Solr' 'database'.
R language bindings for SolveBio's API.
    SolveBio is a biomedical knowledge hub that enables life science
    organizations to collect and harmonize the complex, disparate
    "multi-omic" data essential for today's R&D and BI needs.
    For more information, visit <https://www.solvebio.com>.
Self-Organizing Map (with application in gene clustering).
This package provides an R implementation of the Self-Organising Migrating Algorithm, a general-purpose, stochastic optimisation algorithm. The approach is similar to that of genetic algorithms, although it is based on the idea of a series of ``migrations'' by a fixed set of individuals, rather than the development of successive generations. It can be applied to any cost-minimisation problem with a bounded parameter space, and is robust to local minima.
The stochastic (also called on-line) version of the Self-Organising
             Map (SOM) algorithm is provided. Different versions of the 
             algorithm are implemented, for numeric and relational data and for
             contingency tables. The package also contains many plotting 
             features (to help the user interpret the results) and a graphical
             user interface based on shiny.
some Brownian motions simulation functions
This package collects some procedures controlling the
        Generalized Familywise Error Rate.
It's a collection of functions for Multiplicity Correction and Multiple Testing.
Multivariate mixed model solver for multiple random effects allowing the specification of variance covariance structures. ML/REML estimates are obtained using the Average Information (AI), Expectation-Maximization (EM), Newton-Raphson (NR), or Efficient Mixed Model Association (EMMA) algorithms. Designed for genomic prediction and genome wide association studies (GWAS) to include additive, dominance and epistatic relationship structures or other covariance structures in R, but also functional as a regular mixed model program. Multivariate models (multiple responses) can be fitted currently with NR, AI and EMMA algorithms allowing multiple random effects as well. Covariance structures for the residual component is currently supported only for balanced univariate Newton Raphson models.
A topological version of k-NN: An abstract model is build
             as 2-dimensional self-organising map. Samples of unknown
             class are predicted by mapping them on the SOM and analysing
             class membership of neurons in the neighbourhood.
The package provides the plot function som.plot() to
        create high quality visualisations of hexagonal Kohonen maps
        (self-organising maps).
Formulas for calculating sound velocity, water pressure, depth, 
    density, absorption and sonar equations.
Estimate the abundance of cell clones from the
	     distribution of lengths of DNA fragments (as created by
	     sonication, whence `sonicLength').  The algorithm in
	     "Estimating abundances of retroviral insertion sites from
	     DNA fragment length data" by Berry CC, Gillet NA, Melamed
	     A, Gormley N, Bangham CR, Bushman FD. Bioinformatics;
	     2012 Mar 15;28(6):755-62 is implemented.  The
	     experimental setting and estimation details are described
	     in detail there. Briefly, integration of new DNA in a
	     host genome (due to retroviral infection or gene therapy)
	     can be tracked using DNA sequencing, potentially allowing
	     characterization of the abundance of individual cell
	     clones bearing distinct integration sites. The locations
	     of integration sites can be determined by fragmenting the
	     host DNA (via sonication or fragmentase), breaking the
	     newly integrated DNA at a known sequence, amplifying the
	     fragments containing both host and integrated DNA,
	     sequencing those amplicons, then mapping the host
	     sequences to positions on the reference genome. The
	     relative number of fragments containing a given position
	     in the host genome estimates the relative abundance of
	     cells hosting the corresponding integration site, but
	     that number is not available and the count of amplicons
	     per fragment varies widely.  However, the expected number
	     of distinct fragment lengths is a function of the
	     abundance of cells hosting an integration site at a given
	     position and a certain nuisance parameter. The algorithm
	     implicitly estimates that function to estimate the
	     relative abundance.
Sonification (or audification) is the process of representing data by sounds in the audible range. This package provides the R function sonify() that transforms univariate data, sampled at regular or irregular intervals, into a continuous sound with time-varying frequency. The ups and downs in frequency represent the ups and downs in the data. Sonify provides a substitute for R's plot function to simplify data analysis for the visually impaired.
Collection of different single objective test functions
        useful for benchmarks and algorithm development.
Load Russian economic indicators from the Archive of Economic and Social Data <http://sophist.hse.ru/>.
Provides functions to non-parametrically estimate the off-pulse interval of a source
    function originating from a pulsar. The technique is based on a sequential application of P-values
    obtained from goodness-of-fit tests for the uniform distribution, such as the Kolmogorov-Smirnov,
    Cramer-von Mises, Anderson-Darling and Rayleigh goodness-of-fit tests.
Used to construct the URLs and parameters of 'Socrata Open Data API' <https://dev.socrata.com> calls, using the API's 'SoQL' parameter format. Has method-chained and sensical syntax. Plays well with pipes.
Estimation for longitudinal data following outcome dependent sampling using the sequential offsetted regression technique.  Includes support for binary, count, and continuous data.  The first regression is a logistic regression, which uses a known ratio (the probability of being sampled given that the subject/observation was referred divided by the probability of being sampled given that the subject/observation was no referred) as an offset to estimate the probability of being referred given outcome and covariates.  The second regression uses this estimated probability to calculate the mean population response given covariates.
SortableHTMLTables writes a data frame to an HTML file
        that contains a sortable table. The sorting is done using the
        jQuery plugin Tablesorter. The appearance is controlled through
        a CSS file and several GIF's.
sortinghat is a classification framework to streamline the
    evaluation of classifiers (classification models and algorithms) and seeks
    to determine the best classifiers on a variety of simulated and benchmark
    data sets. Several error-rate estimators are included to evaluate the
    performance of a classifier. This package is intended to complement the
    well-known 'caret' package.
Algorithms for Finnish open government data.
Search contributed R packages, sort by package.
sos4R is a client for Sensor Observation Services (SOS) as
        specified by the Open Geospatial Consortium (OGC).  It allows
        users to retrieve metadata from SOS web services and to
        interactively create requests for near real-time observation
        data based on the available sensors, phenomena, observations et
        cetera using thematic, temporal and spatial filtering.
Tools to download data from the Sotkanet open data portal.
Basic functions for dealing with wav files and sound samples.
Functions to calculate indices for soundscape ecology and other ecology research that uses audio recordings.
The SoundexBR package provides an algorithm for decoding names
    into phonetic codes, as pronounced in Portuguese. The goal is for
    homophones to be encoded to the same representation so that they can be
    matched despite minor differences in spelling. The algorithm mainly encodes
    consonants; a vowel will not be encoded unless it is the first letter. The
    soundex code resultant consists of a four digits long string composed by
    one letter followed by three numerical digits: the letter is the first
    letter of the name, and the digits encode the remaining consonants.
Construct a ranking of a set of treatments/groups by
    gathering together information coming from several response variables.
    It can be used with both balanced and unbalanced experiments
    (with almost all test statistics) as well as in presence of either
    continuous covariates or a stratifying (categorical) variable.
Analogous to source(), but works when given a Gist URL or
        ID.
Implements a non-parametric source attribution model
    to attribute cases of disease to sources in Bayesian framework with source and
    type effects. Type effects are clustered using a Dirichlet Process. Multiple
    times and locations are supported.
Tools for the reading and tokenization of R code. The
    'sourcetools' package provides both an R and C++ interface for the tokenization
    of R code, and helpers for interacting with the tokenized representation of R
    code.
Genomic and multi-environmental soybean data. Soybean Nested
    Association Mapping (SoyNAM) project dataset funded by the United Soybean Board
    (USB), pre-formatted for general analysis and genome-wide association analysis
    using the NAM package.
Classes and methods for spatial
  data; the classes document where the spatial location information
  resides, for 2D or 3D data. Utility functions are provided, e.g. for
  plotting data as maps, spatial selection, as well as methods for
  retrieving coordinates, for subsetting, print, summary, etc.
Provides methods for generating, exploring and executing seamless Phase II-III designs of Lai, Lavori and Shih using generalized likelihood ratio statistics. Includes pdf and source files that describe the entire R implementation with the relevant mathematical details.
Test the results of any given investment/expense combinations for a series of sliding-window periods of the S&P500 from 1950 to the present.
Implements the Sequential Predictions Algorithm
The package implements the model-based kernel machine
        method for detecting gene-centric gene-gene interactions of Li
        and Cui (2012).
Miscellaneous functions for analysing species association
        and niche overlap.
Genomic Region Detection via Spatial Convex Clustering. See <https://arxiv.org/abs/1611.04696> for details.
Partial correlation estimation with joint sparse
        regression model
SPACECAP is a user-friendly software package for
        estimating animal densities using closed model
        capture-recapture sampling based on photographic captures using
        Bayesian spatially-explicit capture-recapture models. This
        approach offers advantage such as: substantially dealing with
        problems posed by individual heterogeneity in capture
        probabilities in conventional capture-recapture analyses. It
        also offers non-asymptotic inferences which are more
        appropriate for small samples of capture data typical of
        photo-capture studies.
undirected graph inference with missing data
This package provides an extension of conditional
        independence (CIG) and directed acyclic graph (DAG) estimation
        to the case where conditional relationships are (non-linear)
        additive models.
Classes and methods for spatio-temporal data, including space-time regular lattices, sparse lattices, irregular data, and trajectories; utility functions for plotting data as map sequences (lattice or animation) or multiple time series; methods for spatial and temporal selection and subsetting, as well as for spatial/temporal/spatio-temporal matching or aggregation, retrieving coordinates, print, summary, etc.
SPACoDi is primarily designed to characterise the
        structure and phylogenetic diversity of communities using
        abundance or presence-absence data of species among community
        plots.
Provides tools to construct and exploit spatially weighted context data. Spatial weights are derived by a Kernel function from a user-defined matrix of distances between contextual units. Spatial weights can then be applied either to precise contextual measures or to aggregate estimates based on micro-level survey data, to compute spatially weighted context data. Available aggregation functions include indicators of central tendency, dispersion, or inter-group variability, and take into account survey design weights. The package further allows combining the resulting spatially weighted context data with individual-level predictor and outcome variables, for the purposes of multilevel modelling. An ad hoc stratified bootstrap resampling procedure generates robust point estimates for multilevel regression coefficients and model fit indicators, and computes confidence intervals adjusted for measurement dependency and measurement error of aggregate estimates. As an additional feature, residual and explained spatial dependency can be estimated for the tested models.
A set of RStudio addins that are designed to be used in
             combination with user-defined RStudio keyboard shortcuts. These
             addins either:
             1) insert text at a cursor position (e.g. insert
             operators %>%, <<-, %$%, etc.),
             2) replace symbols in selected pieces of text (e.g., convert
             backslashes to forward slashes which results in stings like
             "c:\data\" converted into "c:/data/") or
             3) enclose text with special symbols (e.g., converts "bold" into
             "**bold**") which is convenient for editing R Markdown files.
Estimation of various biodiversity indices and related (dis)similarity measures based on individual-based (abundance) data or sampling-unit-based (incidence) data taken from one or multiple communities/assemblages.
Implement a variety of event-based models, with a focus on
    spatially explicit models. These include raster-based, event-based, and
    agent-based models. The core simulation components are built upon a discrete
    event simulation (DES) framework that facilitates modularity, and easily
    enables the user to include additional functionality by running user-built
    simulation modules. Included are numerous tools to visualize raster and
    other maps. The suggested package 'fastshp' can be installed with
    `install.packages("fastshp", repos = "http://rforge.net", type = "source")`.
Implements methods for anticipating the emergence and eradication
    of infectious diseases from surveillance time series. Also provides support
    for computational experiments testing the performance of such methods.
Set of functions for sparse matrix algebra.
  Differences with SparseM/Matrix are: 
  (1) we only support (essentially) one sparse matrix format, 
  (2) based on transparent and simple structure(s), 
  (3) tailored for MCMC calculations within GMRF. 
  (4) S3 and S4 like-"compatible"  ...     and it is fast.
Inference in mixed models, including GLMMs with spatial correlations and models with non-Gaussian random effects (e.g., Beta Binomial, or negative-binomial mixed models). Heteroscedasticity can further be fitted by a linear model. The algorithms are currently various Laplace approximations methods for ML or REML, in particular h-likelihood and penalized-likelihood methods.
Fit the spatial panel data models: the fixed effects, random
    effects and between models.
Carries out a search for an optimal partition in terms of a
    regular Boolean expression
This package performs robust nonparametric tests for rare variants
          association analysis using summation of partition approaches that incorporate
          gene-gene and gene-environmental interactions
We provide an efficient solver for estimating semiparametric generalized linear models.
Implements the sparse clustering methods of Witten and
        Tibshirani (2010): "A framework for feature selection in
        clustering"; published in Journal of the American Statistical
        Association 105(490): 713-726.
Decide which server to connect to,
    based on previous response times, and configuration.
A sparkline is a line chart, without axes and labels.
    Its goal is to show the general shape of changes over time, or
    another quantity. This package is an 'R' implementation
    of the original shell project: <http://zachholman.com/spark/>.
Include interactive sparkline charts
    <http://omnipotent.net/jquery.sparkline> in 
    all R contexts with the convenience of 'htmlwidgets'.
R interface to Apache Spark, a fast and general engine for big data
    processing, see <http://spark.apache.org>. This package supports connecting to
    local and remote Apache Spark clusters, provides a 'dplyr' compatible back-end,
    and provides an interface to Spark's built-in machine learning algorithms.
Read in 'SAS' Data ('.sas7bdat' Files) into 'Apache Spark' from R. 'Apache Spark' is an open source cluster computing framework available at <http://spark.apache.org>. This R package uses the 'spark-sas7bdat' 'Spark' package (<https://spark-packages.org/package/saurfang/spark-sas7bdat>) to import and process 'SAS' data in parallel using 'Spark'. Hereby allowing to execute 'dplyr' statements in parallel on top of 'SAS' data.
Create sparklines and graphical tables for documents and websites.
Generate syntax for use with the sparklines package for
        LaTeX.
Load WARC (Web ARChive) files into Apache Spark using 'sparklyr'. This
    allows to read files from the Common Crawl project <http://commoncrawl.org/>.
Use SPARQL to pose SELECT or UPDATE queries to an end-point.
Provides functions to estimate kernel-smoothed relative risk functions and perform subsequent inference.
Implements the sparse biclustering proposal of Tan and Witten (2014), Sparse biclustering of transposable data. Journal of Computational and Graphical Statistics  23(4):985-1008.
Fast methods for learning sparse Bayesian networks from high-dimensional data using sparse regularization. Designed to incorporate mixed experimental and observational data with thousands of variables with either continuous or discrete observations.
A set of tools for representing and estimating sparse Bayesian networks from continuous and discrete data.
A collection of sparse and regularized discriminant analysis
    methods intended for small-sample, high-dimensional data sets. The package
    features the High-Dimensional Regularized Discriminant Analysis classifier.
Multidimensional scaling provides a means of uncovering a latent structure underlying observed data, while estimating the number of latent dimensions.  This package presents a means for scaling binary and count data, for example the votes and word counts for legislators.  Future work will include an EM implementation and extend this work to ordinal and continuous data.
Estimation of functional linear mixed models for irregularly or
    sparsely sampled data based on functional principal component analysis.
SparseGrid is a package to create sparse grids for numerical integration, based on code from www.sparse-grids.de
Estimates Hessian of a scalar-valued function, and returns it
    in a sparse Matrix format. The sparsity pattern must be known in advance. The
    algorithm is especially efficient for hierarchical models with a large number of
    heterogeneous units.
Performs sparse linear discriminant analysis for Gaussians and mixture of Gaussian models.
Coefficient estimation and model prediction based on the LASSO sparse 
  learning algorithm and its improved versions such as Bolasso, bootstrap ranking 
  LASSO, two-stage hybrid LASSO and others. These LASSO estimation procedures are 
  applied in the fields of variable selection, graphical modeling and ensemble 
  learning. The bagging LASSO model uses a Monte Carlo cross-entropy algorithm to 
  determine the best base-level models and improve predictive performance.
Use RcppEigen to fit least trimmed squares
    regression models with an L1 penalty in order to obtain
    sparse models.
Some basic linear algebra functionality for sparse matrices is
  provided:  including Cholesky decomposition and backsolving as well as 
  standard R subsetting and Kronecker products.
Computes multivariate normal (MVN) densities, and
    samples from MVN distributions, when the covariance or
    precision matrix is sparse.
Sparsenet uses the MC+ penalty of Zhang. It computes the regularization surface over both the family parameter and the tuning parameter by coordinate descent.
Provides interface to 'sparsepp' - fast, memory efficient hash map. 
    It is derived from Google's excellent 'sparsehash' implementation.
    We believe 'sparsepp' provides an unparalleled combination of performance and memory usage, 
    and will outperform your compiler's unordered_map on both counts. 
    Only Google's 'dense_hash_map' is consistently faster, at the cost of much greater 
    memory usage (especially when the final size of the map is not known in advance).
Sparse modeling provides a mean selecting a small number of non-zero effects from a large possible number of candidate effects.  This package includes a suite of methods for sparse modeling: estimation via EM or MCMC, approximate confidence intervals with nominal coverage, and diagnostic and summary plots.  The method can implement sparse linear regression and sparse probit regression.  Beyond regression analyses, applications include subgroup analysis, particularly for conjoint experiments, and panel data. Future versions will include extensions to  models with truncated outcomes, propensity score, and instrumental variable analysis.
Sparse-aware maximum likelihood for structural equation models in inferring gene regulatory networks
Implements the SparseStep model for solving regression
    problems with a sparsity constraint on the parameters. The SparseStep 
    regression model was proposed in Van den Burg, Groenen, and Alfons (2017) 
    <https://arxiv.org/abs/1701.06967>. In the model, a regularization term is 
    added to the regression problem which approximates the counting norm of 
    the parameters.  By iteratively improving the approximation a sparse 
    solution to the regression problem can be obtained.  In this package both 
    the standard SparseStep algorithm is implemented as well as a path 
    algorithm which uses golden section search to determine solutions with 
    different values for the regularization parameter.
Wrapper around the 'SVDLIBC' library for (truncated) singular value decomposition of a sparse matrix.
             Currently, only sparse real matrices in Matrix package format are supported.
Fast algorithm for fitting solution paths of sparse linear SVM with lasso or elastic-net regularization generate sparse solutions.
Computes sparse vector autoregressive coefficients and precision 
 matrices for time series chain graphical models.
A wrapper for sparse VAR/VECM time series models estimation
             using penalties like ENET, SCAD and MCP.
Computer simulations are becoming a popular technique to use in attempts to further our understanding of complex systems. SPARTAN, described in our 2013 publication in PLoS Computational Biology, provides code for four techniques described in available literature which aid the analysis of simulation results, at both single and multiple timepoints in the simulation run. The first technique addresses aleatory uncertainty in the system caused through inherent stochasticity, and determines the number of replicate runs necessary to generate a representative result. The second examines how robust a simulation is to parameter perturbation, through the use of a one-at-a-time parameter analysis technique. Thirdly, a latin hypercube based sensitivity analysis technique is included which can elucidate non-linear effects between parameters and indicate implications of epistemic uncertainty with reference to the system being modelled. Finally, a further sensitivity analysis technique, the extended Fourier Amplitude Sampling Test (eFAST) has been included to partition the variance in simulation results between input parameters, to determine the parameters which have a significant effect on simulation behaviour. Version 1.3 adds support for Netlogo simulations, aiding simulation developers who use Netlogo to build their simulations perform the same analyses. We have also added user support through the group spartan-group[AT]york[DOT]ac[DOT]uk. Version 2.0 added the ability to read all simulations in from a single CSV file in addition to the prescribed folder structure in previous versions.
Sample size estimation and blinded sample size reestimation in Adaptive Study Design.
Fit spatial CAR count regression models using MCMC
Functionality for spatio-temporal modeling of large data sets is provided. A Gaussian process in space and time is defined through a stochastic partial differential equation (SPDE). The SPDE is solved in the spectral space, and after discretizing in time and space, a linear Gaussian state space model is obtained. When doing inference, the main computational difficulty consists in evaluating the likelihood and in sampling from the full conditional of the spectral coefficients, or equivalently, the latent space-time process. In comparison to the traditional approach of using a spatio-temporal covariance function, the spectral SPDE approach is computationally advantageous. This package aims at providing tools for two different modeling approaches. First, the SPDE based spatio-temporal model can be used as a component in a customized hierarchical Bayesian model (HBM). The functions of the package then provide parameterizations of the process part of the model as well as computationally efficient algorithms needed for doing inference with the HBM. Alternatively, the adaptive MCMC algorithm implemented in the package can be used as an algorithm for doing inference without any additional modeling. The MCMC algorithm supports data that follow a Gaussian or a censored distribution with point mass at zero. Covariates can be included in the model through a regression term.
Graphs (or networks) and graph component
        calculations for spatial locations in *D.
Functions for kriging and point pattern analysis.
Perform Spatial Clustering Analysis using Fuzzy Geographically Weighted Clustering. Provide optimization using Gravitational Search Algorithm.
Functions that compute the spatial covariance matrix for the matern and power classes of spatial models, for data that arise on rectangular units.  This code can also be used for the change of support problem and for spatial data that arise on irregularly shaped regions like counties or zipcodes by laying a fine grid of rectangles and aggregating the integrals in a form of Riemann integration.
Utilities to support spatial data manipulation, query, sampling
    and modelling. Functions include models for species population density, download
    utilities for climate and global deforestation spatial products, spatial
    smoothing, multivariate separability, point process model for creating pseudo-
    absences and sub-sampling, polygon and point-distance landscape metrics,
    auto-logistic model, sampling models, cluster optimization and statistical
    exploratory tools.
Methods and data for cluster detection and disease mapping.
Tools for the statistical modelling of spatial extremes using max-stable processes, copula or Bayesian hierarchical models.
Filter matrices or (three dimensional) array data using different convolution kernels.
This package fits a hierarchical spatial model for the generalized extreme value distribution with the option of model averaging over the space of covariates.
Edge-corrected kernel density estimation and binary kernel
        regression estimation for multivariate spatial point process
        data
Network based diffusion analysis (NBDA) allows inference on
             the asocial and social transmission of information.  This may involve
             the social transmission of a particular behaviour such as tool use, for example.
            For the NBDA, the key parameters estimated are the social effect and baseline rate 
            parameters.  The baseline rate parameter gives the rate at which the behaviour
            is first performed (or acquired) asocially amongst the individuals in a given population.
            The social effect parameter quantifies the effect of the social associations amongst 
            the individuals on the rate at which each individual first performs or displays
            the behaviour.  Spatial NBDA involves incorporating spatial information in the analysis.  
            This is done by incorporating social networks derived from 
            spatial point patterns (of the home bases of the individuals under study).  In addition, 
            a spatial covariate such as vegetation cover, or slope may be included in the modelling
            process.
This package contains test and estimates of location, 
             tests of independence, tests of sphericity and several 
             estimates of shape all based on spatial signs, symmetrized 
             signs, ranks and signed ranks.
This package provides tools to assess the association between two spatial processes.
    Currently, three methodologies are implemented: An adapted t-test to perform hypothesis
    testing about the independence between the processes, a suitable nonparametric correlation
    coefficient, and the codispersion coefficient. SpatialPack gives methods to complement
    methodologies that are available in geoR for one spatial process.
Computes spatial position models: Stewart potentials, Reilly
    catchment areas, Huff catchment areas.
Bayesian Estimation of Spatial Probit and Tobit Models.
Summaries for measuring segregation/mingling in multitype spatial
    point patterns with graph based neighbourhood description. Included indices:
    Mingling, Shannon, Simpson (also the non-spatial) Included functionals:
    Mingling, Shannon, Simpson, ISAR, MCI. Included neighbourhoods: Geometric, k-
    nearest neighbours, Gabriel, Delaunay. Dixon's test.
Provides functions implementing the pairwise M-estimator for
    parametric models for stable tail dependence functions described in "An
    M-estimator of spatial tail dependence"
    (Einmahl, J.H.J., Kiriliouk, A., Krajina, A. and Segers, J., 2014).
    See http://arxiv.org/abs/1403.1975.
Spatial functions meant to enhance the core functionality of the
    package "raster", including a parallel processing engine for use with
    rasters.
Tools for spatial data analysis.  Emphasis on kriging.  Provides functions for prediction and simulation.  Intended to be relatively straightforward, fast, and flexible.
Spatial forecast verification arose from verifying high-resolution forecasts, where coarser-resolution models generally are favored even when a human forecaster finds the higher-resolution model to be considerably better.  Most newly proposed methods, which largely come from image analysis, computer vision, and similar, are available, with more on the way.
Mixture model is used to achieve the clustering goal. Each component is itself a mixture model of polynomial autoregressive regressions whose the logistic weights consider the spatial and temporal information.
Utilities that estimate, predict and cross-validate the
    spatio-temporal model developed for MESA Air.
Provide regularized maximum covariance analysis incorporating smoothness, sparseness and orthogonality of couple patterns by using the alternating direction method of multipliers algorithm. The method can be applied to either regularly or irregularly spaced data.
Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigenfunctions by using the alternating direction method of multipliers algorithm. The method can be applied to either regularly or irregularly spaced data.
Analysis of field trial experiments by modelling spatial trends using two-dimensional Penalised spline (P-spline) models.
This is an experimental package containing a subset of 'spatstat'.
Bayesian inference for parametric proportional hazards spatial
    survival models; flexible spatial survival models.
Tools to convert from specific formats to more general forms of 
    spatial data. Using tables to store the actual entities present in spatial
    data provides far greater flexibility, and the functions here deliberately 
    minimize the level of interpretation applied, leaving that for specific 
    applications. Includes round-trip for 'Spatial' classes and long-form 
    tables, analogous to 'ggplot2::fortify'.
Fits univariate and multivariate spatio-temporal
        models with Markov chain Monte Carlo (MCMC).
Provides several Bayesian survival models for spatial/non-spatial survival data: marginal Bayesian Nonparametric models, marginal Bayesian proportional hazards models, generalized accelerated failure time frailty models, and standard semiparametric frailty models within the context of proportional hazards, proportional odds and accelerated failure time.
Evaluation of control charts by means of
        the zero-state, steady-state ARL (Average Run Length) and RL quantiles.
        Setting up control charts for given in-control ARL. The control charts
        under consideration are one- and two-sided EWMA, CUSUM, and
        Shiryaev-Roberts schemes for monitoring the mean of normally
        distributed independent data. ARL calculation
        of the same set of schemes under drift are added. 
        Other charts and parameters are in preparation.
        Further SPC areas will be covered as well
        (sampling plans, capability indices ...).
Calibration of thresholds of control charts such as
    CUSUM charts based on past data, taking estimation error into account.
A new reduced-rank LDA method which works for high dimensional multi-class data.
Spatial coverage sampling and random sampling from compact
        geographical strata created by k-means.
Provides a covariance estimator for multivariate normal
        data that is sparse and positive definite.  Implements the
        majorize-minimize algorithm described in Bien, J., and
        Tibshirani, R. (2011), "Sparse Estimation of a Covariance
        Matrix," Biometrika. 98(4). 807--820.
The sparse principal component regression is computed. The regularization parameters are optimized by cross-validation.
The Semi Parametric Piecewise Distribution blends the Generalized Pareto Distribution for the tails with a kernel based interior.
A collection of functions to create spatial weights matrix
  objects from polygon contiguities, from point patterns by distance and
  tessellations, for summarizing these objects, and for permitting their
  use in spatial data analysis, including regional aggregation by minimum
  spanning tree; a collection of tests for spatial autocorrelation,
  including global Moran's I, APLE, Geary's C, Hubert/Mantel general cross
  product statistic, Empirical Bayes estimates and Assunção/Reis Index,
  Getis/Ord G and multicoloured join count statistics, local Moran's I
  and Getis/Ord G, saddlepoint approximations  and exact tests for global
  and local Moran's I; and functions for estimating spatial simultaneous
  autoregressive (SAR) lag and error models, impact measures for lag
  models, weighted and unweighted SAR and CAR spatial regression models,
  semi-parametric and Moran eigenvector spatial filtering, GM SAR error
  models, and generalized spatial two stage least squares models.
Spatial downscaling of climate data (Global Circulation Models/Regional Climate Models) using quantile-quantile bias correction technique.
Methods for 'dplyr' verbs for 'sp' 'Spatial' classes. The basic 
    verbs that modify data attributes, remove or re-arrange rows are supported
    and provide complete 'Spatial' analogues of the input data. The group by
    and summarize workflow returns a non-topological spatial union. There is 
    limited support for joins, with left and inner to copy attributes from 
    another table.
Functions for estimating split-duration regression models and
    various associated generic function methods.
A spatio-dynamic modelling package that focuses on three
    characteristic wetland plant communities in a semiarid Mediterranean
    wetland in response to hydrological pressures from the catchment. The
    package includes the data on watershed hydrological pressure and the
    initial raster maps of plant communities but also allows for random initial
    distribution of plant communities. Ongoing developments of the package
    focus on offering easy to use tools for creating other spatio-dynamic
    models.
Implements stochastic proximity embedding as described by
 Agrafiotis et al. in PNAS, 2002, 99, pg 15869 and J. Comput. Chem., 2003,24, pg 1215
We introduce a novel suite of informatics tools for the quantitative analysis of NMR metabolomic profile data. The core of the processing cascade is a novel peak alignment algorithm, called hierarchical Cluster-based Peak Alignment (CluPA). The algorithm aligns a target spectrum to the reference spectrum in a top-down fashion by building a hierarchical cluster tree from peak lists of reference and target spectra and then dividing the spectra into smaller segments based on the most distant clusters of the tree. To reduce the computational time to estimate the spectral misalignment, the method makes use of Fast Fourier Transformation (FFT) cross-correlation. Since the method returns a high-quality alignment, we can propose a simple methodology to study the variability of the NMR spectra. For each aligned NMR data point the ratio of the between-group and within-group sum of squares (BW-ratio) is calculated to quantify the difference in variability between and within predefined groups of NMR spectra. This differential analysis is related to the calculation of the F-statistic or a one-way ANOVA, but without distributional assumptions. Statistical inference based on the BW-ratio is achieved by bootstrapping the null distribution from the experimental data. See Vu et al. (2011)<DOI:10.1186/1471-2105-12-405> for further information.
Tools for feature based analysis of Nuclear Magnetic Resonance (NMR) spectra. Wavelets are
    used to quantify the peaks which are grouped into features with hierarchical
    clustering. The results can easily be incorporated in large-scale studies that include LC-MS analysis (as this is also peak/feature based).
Alternative to the kernlab::specc function. Includes a spectral clustering implementation, a locally adapted kernel function akin to what is already proposed in kernlab, and an optional procedure that automatically estimates the optimal number of clusters. Several sample data sets are also included.
Utility functions for spectroscopy. 1. Functions to simulate
    spectra for use in teaching or testing. 2. Functions to process files created by
    'LoggerPro' and 'SpectraSuite' software.
SPECIES is an R package for estimation of species richness
        or diversity.
Preparation of species occurrences and distribution data for the use in phylogenetic analyses. SpeciesgeocodeR is built for data cleaning, data exploration and data analysis and especially suited for biogeographical and ecological questions on large datasets. The package includes the easy creation of summary-tables and -graphs and geographical maps, the automatic cleaning of geographic occurrence data, the calculating of coexistence matrices and species ranges (EOO) as well as mapping diversity in geographic areas.
Fitting Mixtures to Species distributions using BFGS and analytical derivatives.
The package helps to test the specificity of personality trait-outcome (or trait-trait) associations by comparing the observed associations to those obtained using randomly created personality scales.
Provides a set of methods for metabolomics 
	data analysis, including data loading in different formats, 
	pre-processing, metabolite identification, univariate and multivariate 
	data analysis, machine learning and feature selection. Case studies 
	can be found on the website: http://darwin.di.uminho.pt/metabolomics .
A collection of new forecast verification routines developed for the SPECS
    FP7 project. The emphasis is on comparative verification of ensemble forecasts.
Fourier and Hilbert transforms are utilized to perform several types
             of spectral analysis on the supplied data. Also fragmented and
             irregularly spaced data can be processed. A user friendly interface
             helps to interpret the results.
Routines for creating, manipulating, and performing 
 Bayesian inference about Gaussian processes in 
 one and two dimensions using the Fourier basis approximation: 
 simulation and plotting of processes, calculation of 
 coefficient variances, calculation of process density, 
 coefficient proposals (for use in MCMC).  It uses R environments to
 store GP objects as references/pointers.
Implements the diffusion map method of dimensionality reduction and spectral method of combining multiple diffusion maps, including creation of the spectra and visualization of maps.
Contains some implementations of Singular Spectrum Analysis (SSA) for the gapfilling and spectral decomposition of time series. It contains the code used by Buttlar et. al. (2014), Nonlinear Processes in Geophysics. In addition, the iterative SSA gapfilling method of Kondrashov and Ghil (2006) is implemented. All SSA calculations are done via the truncated and fast SSA algorithm of Korobeynikov (2010) (package 'Rssa').
Spectra visualization, organizer and data preparation
        from within R or stand-alone. Binary (application) part is 
        installed separately by running spnInstallApp() 
        immediately after installing the package.
From output files obtained from the software 'ModestR', the relative contribution of factors to explain species distribution is depicted using several plots. A global geographic raster file for each environmental variable may be also obtained with the mean relative contribution, considering all species present in each raster cell, of the factor to explain species distribution. Finally, for each variable it is also possible to compare the frequencies of any variable obtained in the cells where the species is present with the frequencies of the same variable in the cells of the extent.
Fitting linear models and generalized linear models to large data sets by updating algorithms.
Functions for fitting semiparametric regression models for
        panel count survival data.
The package performs estimation and testing of the
        treatment effect in a 2-group randomized clinical trial with a
        quantitative, dichotomous, or right-censored time-to-event
        endpoint. The method improves efficiency by leveraging baseline
        predictors of the endpoint. The inverse probability weighting
        technique of Robins, Rotnitzky, and Zhao (JASA, 1994) is used
        to provide unbiased estimation when the endpoint is missing at
        random.
A set of functions for computing potential evapotranspiration and several widely used drought indices including the Standardized Precipitation-Evapotranspiration Index (SPEI).
Corrects the spelling of a given word in English 
    using a modification of Peter Norvig's spell correct 
    algorithm (see <http://norvig.com/spell-correct.html>) 
    which handles up to three edits. The algorithm tries to 
    find the spelling with maximum probability of intended
    correction out of all possible candidate corrections from
    the original word.
Provides some easy-to-use functions to interpolate species range based on species occurrences and to estimate centers of biodiversity.
This package implements spatial error estimation and
        permutation-based variable importance measures for predictive
        models using spatial cross-validation and spatial block
        bootstrap.
Functions to produce a fully fledged 'Spatial' object extent as a
    'SpatialPolygonsDataFrame'.
A set of tools for estimation of various spatial specifications of
    stochastic frontier models.
Functions to manipulate, pre-process and analyze spectra in the mid-infrared region. The pre-processing of the mid-infrared spectra is a transcendental step in the spectral analysis. Preprocessing of the spectra includes smoothing, offset, baseline correction, and normalization,  is performed before the analysis of the spectra and is essential to obtain conclusive results in subsequent quantitative or qualitative analysis. This package was supported by FONDECYT 3150630, and CIPA Conicyt-Regional R08C1002 is gratefully acknowledged.
Interpreted interface between GRASS 6+ geographical 
    information system and R, based on starting R from within the GRASS 
    environment, or running free-standing R in a temporary GRASS location;
    the package provides facilities for using all GRASS commands from the 
    R command line. This package may not be used for GRASS 7, for which
    rgrass7 should be used.
A collection of statistical hypothesis tests and other 
	techniques for identifying certain spatial relationships/phenomena in 
	DNA sequences. In particular, it provides tests and graphical methods for determining 
	whether or not DNA sequences comply with Chargaff's second parity rule 
	or exhibit purine-pyrimidine parity. In addition, there are functions for 
	efficiently simulating discrete state space Markov chains and testing 
	arbitrary symbolic sequences of symbols for the presence of first-order 
	Markovianness.
	Also, it has functions for counting words/k-mers (and cylinder patterns) in 
	arbitrary symbolic sequences. Functions which take a DNA sequence as input 
	can handle sequences stored as SeqFastadna objects from the 'seqinr' package.
Functions for computing geographically weighted
  regressions are provided, based on work by Chris
  Brunsdon, Martin Charlton and Stewart Fotheringham.
Various functions for creating spherical coordinate system plots via extensions to rgl.
Provides several methods to integrate functions over the unit
 sphere and ball in n-dimensional Euclidean space.  Routines for converting to/from
 multivariate polar/spherical coordinates are also provided.
Spherical K-function for point-pattern analysis on the sphere.
This package carries out spherical wavelet transform
        developed by Li (1999) and Oh (1999), and implements wavelet
        thresholding approaches proposed by Oh and Li (2004).
Generalized Method of Moment estimation of Cliff-Ord-type
        spatial autoregressive models with and without heteroskedastic
        innovations
The SNP Panel Identification Assay (SPIA) is a package that enables an accurate determination of cell line identity from the genotype of single nucleotide polymorphisms (SNPs). The SPIA test allows to discern when two cell lines are close enough to be called similar and when they are not. Details about the method are available at "Demichelis et al. (2008) SNP panel identification assay (SPIA): a genetic-based assay for the identification of cell lines. Nucleic Acids Res., 3, 2446-2456".
A package for the analysis of species limits and DNA barcoding data
Fits and simulates data from our predator preferences model, <DOI:10.1007/s10651-016-0341-3>.
Calculate the Standardized Precipitation Index (SPI) for monitoring drought, using Artificial Intelligence techniques (SPIGA) and traditional numerical technique Maximum Likelihood (SPIML).  For more information see: http://drought.unl.edu/monitoringtools/downloadablespiprogram.aspx.
Applies re-sampled kernel density method to detect vote fraud. It estimates the proportion of coarse vote-shares in the observed data relative to the null hypothesis of no fraud.
Spike and slab for prediction and variable selection in
        linear regression models. Uses a generalized elastic net for
        variable selection.
Bayesian variable selection, model choice, and regularized
    estimation for (spatial) generalized additive mixed regression models
    via stochastic search variable selection with spike-and-slab priors.
An optimal weighting strategy to compute
        simulation-efficient shortest probability intervals (spins).
Implements a generative model that uses a
    spike-and-slab like prior distribution obtained by multiplying a
    deterministic binary vector. Such a model allows an EM algorithm,
    optimizing a type-II log-likelihood.
The Splancs package was written as an enhancement to S-Plus for display and analysis of spatial point pattern data; it has been ported to R and is in "maintenance mode".
A complementary package on splines providing functions
    constructing B-splines, integral of B-splines, monotone splines
    (M-splines) and its integral (I-splines), convex splines (C-splines),
    and their derivatives of given order. Piecewise constant basis is
    allowed for B-spline and M-spline basis.
Some R functions, such as optim(), require a function
  its gradient passed as separate arguments. When these are
  expensive to calculate it may be much faster to calculate
  the function (fn) and gradient (gr) together since they often share
  many calculations (chain rule). This package allows the user
  to pass in a single function that returns both the function
  and gradient, then splits (hence 'splitfngr') them
  so the results can be accessed
  separately. The functions provided allow this to be done with
  any number of functions/values, not just for functions and gradients.
Online data collection tools like Google Forms often export
    multiple-response questions with data concatenated in cells. The
    concat.split (cSplit) family of functions splits such data into 
    separate cells. The package also includes functions to stack groups 
    of columns and to reshape wide data, even when the data are 
    "unbalanced"---something which reshape (from base R) does not handle, 
    and which melt and dcast from reshape2 do not easily handle.
ML and GM estimation and diagnostic testing of econometric models for spatial panel data.
This package provides functions for fitting a Sparse
        Partial Least Squares Regression and Classification
Currently there are many functions in S-PLUS that are
        missing in R. To facilitate the conversion of S-PLUS packages
        to R packages, this package provides some missing S-PLUS
        functionality in R.
A collection of classes and methods for working with
  times and dates. The code was originally available in S-PLUS.
A collection of classes and methods for working with time series.
  The code was originally available in S-PLUS.
Installs 'SPM12' to the R library directory and has associated
    functions for 'fMRI' and general imaging utilities, called through 'MATLAB'.
A set of functions is provided for 1) the stratum lengths analysis along a chosen direction, 2) fast estimation of continuous lag spatial Markov chains model parameters and probability computing (also for large data sets), 3) transition probability maps and transiograms drawing, 4) simulation methods for categorical random fields.
Implements the method of general semiparametric maximum likelihood estimation for logistic models in case-mother control-mother designs.
Facilitates the rendering of networks for which nodes have a specific position on a map (cities, participants in a political debate, etc.). Map data and network data are stored together in a single object which handles the match between network nodes and their respective position on the map. The plot method renders both the map and the network data. Several networks can be plot simultaneously. The graphic is highly customisable and the legend is automatically printed. Map data have to be supplied as 'SpatialPolygons' objects (from the 'sp' package) and network data as 'named matrix'.
A programmatic interface to many species occurrence data sources,
    including Global Biodiversity Information Facility ('GBIF'), 'USGSs'
    Biodiversity Information Serving Our Nation ('BISON'), 'iNaturalist',
    Berkeley 'Ecoinformatics' Engine, 'eBird', 'AntWeb', Integrated Digitized
    'Biocollections' ('iDigBio'), 'VertNet', Ocean 'Biogeographic' Information
    System ('OBIS'), and Atlas of Living Australia ('ALA'). Includes
    functionality for retrieving species occurrence data, and combining
    those data.
SPODT is a spatial partitioning method based on oblique decision trees, in order to classify study area into zones of different risks, determining their boundaries
Transform a Movie into a Synthetic Picture. A frame every 10 seconds is summarized into one colour, then every generated colors are stacked together.
R implementation of the methods described in "A rank-based
        empirical likelihood approach to two-sample proportional odds
        model and its goodness-of-fit" by Zhong Guan and Cheng Peng,
        Journal of Nonparametric Statistics, to appear.
The aim of this package is to provide infrastructure for
        sports analysis. Anyway, currently it is a selection of data
        sets, functions to fetch sports data, examples, and demos --
        with the ambition to develop bit by bit a set of classes to
        represent general concepts of sports analysis.
The Sequential Parameter Optimization Toolbox provides a set of
    tools for Parameter-tuning, based on modelling techniques, DoE and statistical
    methods.
We propose a novel two-step procedure to combine epidemiological
    data obtained from diverse sources with the aim to quantify risk factors
    affecting the probability that an individual develops certain disease such as
    cancer. See  Hui Huang, Xiaomei Ma, Rasmus Waagepetersen, Theodore R. Holford,
    Rong Wang, Harvey Risch, Lloyd Mueller & Yongtao Guan (2014) A New Estimation Approach
    for Combining Epidemiological Data From Multiple Sources, Journal of the American Statistical
    Association, 109:505, 11-23,  <doi:10.1080/01621459.2013.870904>.
Sparse arrays interpreted as multivariate polynomials.
The Statistical Package for REliability Data Analysis (SPREDA) implements recently-developed statistical methods for the analysis of reliability data. Modern technological developments, such as sensors and smart chips, allow us to dynamically track product/system usage as well as other environmental variables, such as temperature and humidity. We refer to these variables as dynamic covariates. The package contains functions for the analysis of time-to-event data with dynamic covariates and degradation data with dynamic covariates. The package also contains functions that can be used for analyzing time-to-event data with right censoring, and with left truncation and right censoring. Financial support from NSF and DuPont are acknowledged.
The recent advancement of high-throughput technologies has led to frequent utilization of gene expression and other "omics" data for toxicological, diagnostic or prognostic studies in and clinical applications. Unlike in classical predictions where the number of samples is greater than the number of variables (n>p), the challenge faced with prediction using "omics" data is that the number of parameters greatly exceeds the number of samples (p>>n). To solve this curse of dimensionality problem, several predictive functions have been proposed for direct and probabilistic classification and survival predictions. Nevertheless, these predictive functions have been shown to perform differently across datasets. Comparing predictive functions and choosing the best is computationally intensive and leads to selection bias. Thus, the question which function should one choose for a given dataset is to be ascertained. This package implements the approach proposed by Jong et al., (2016) to address this question.
Calculate species richness functions for rarefaction and
    extrapolation.
SPRINT (Simple Parallel R INTerface) is a parallel
        framework for R. It provides a High Performance Computing (HPC)
        harness which allows R scripts to run on HPC clusters. SPRINT
        contains a library of selected R functions that have been
        parallelized. Functions are named after the original R function
        with the added prefix 'p', i.e. the parallel version of cor()
        in SPRINT is called pcor(). Call to the parallel R functions
        are included directly in standard R scripts.
		SPRINT contains functions for correlation (pcor), partitioning around medoids (ppam), 
		apply (papply), permutation testing (pmaxT), bootstrapping (pboot), random forest (prandomForest), 
		rank product (pRP) and hamming distance (pstringdistmatrix).
The main function of this package builds prognostic models that consider interactions by combining available statistical components. Furthermore, it provides a function for evaluating the relevance of the selected interactions by resampling techniques.
Robust dimension reduction methods for regression and discriminant analysis are implemented that yield estimates with a partial least squares alike interpretability. Partial robust M regression (PRM) is robust to both vertical outliers and leverage points. Sparse partial robust M regression (SPRM) is a related robust method with sparse coefficient estimate, and therefore with intrinsic variable selection. For binary classification related discriminant methods are PRM-DA and SPRM-DA.
R functions to mine sparse models from data.
Perform Wald's Sequential Probability Ratio Test on variables with a Normal, Bernoulli, Exponential and Poisson distribution. Plot acceptance and continuation regions, or create your own with the help of closures.
Methods to optimize spatial sample configurations using spatial simulated annealing. Multiple 
    objective functions are implemented for various purposes, such as variogram estimation, spatial trend 
    estimation, and spatial interpolation. A general purpose spatial simulated annealing function enables the 
    user to define his/her own objective function.
Fits spatial scale (SS) forward stepwise regression, SS incremental forward stagewise regression, SS least angle regression (LARS), and SS lasso models.  All area-level covariates are considered at all available scales to enter a model, but the SS algorithms are constrained to select each area-level covariate at a single spatial scale.
Program uses method of polynomial of variable degrees to interpolate gridded data preserving monotonicity and/or convexity or none. Method is implemented for univariate and bivariate cases. If values of derivatives are provided, spline will fix them,if not program will estimate them numerically. Package written purely in R.
SPSL package provides functionality for labeling of
        percolation clusters on 2D & 3D square lattice with various
        lattice size, relative fraction of accessible sites (occupation
        probability), iso- & anisotropy, von Neumann & Moore
        (1,d)-neighborhood
Produces Slepian smoother basis objects for use in Generalized Additive Models, as estimated by package:mgcv.
This group of functions implements algorithms for design and
    analysis of probability surveys.  The functions are tailored for Generalized
    Random Tessellation Stratified survey designs.
This package collects algorithms related to Sierpinski pedal triangle (SPT).
Fits, spatially predicts, and temporally forecasts space-time data using Gaussian Process (GP): (1) spatially varying coefficient process models and (2) spatio-temporal dynamic linear models.
Implements nonparametric hypothesis tests to check isotropy and
    symmetry properties for two-dimensional spatial data.
spThin is a set of functions that can be used to spatially thin
    species occurrence data. The resulting thinned data can be used in ecological
    modeling, such as ecological niche modeling.
Fits, spatially predicts and temporally forecasts large amounts of space-time data using  [1] Bayesian Gaussian Process (GP) Models, [2] Bayesian Auto-Regressive (AR) Models, and [3] Bayesian Gaussian Predictive Processes (GPP) based AR Models for spatio-temporal big-n problems.
Implements semiparametric transformation model two-phase estimation using calibration weights.
This package provides functions and datasets from the book "Introduction to Scientific Programming and Simulation Using R".
Sparse Quadratic Discriminant Analysis (SQDA) can be performed. In SQDA, the covariance matrix are assumed to be block-diagonal.And, for each block, sparsity assumption is imposed on the covariance matrix. It is useful in high-dimensional setting.
Description: Manipulate R data frames using SQL.
sqliter helps users, mainly data munging practioneers, to organize
    their sql calls in a clean structure. It simplifies the process of
    extracting and transforming data into useful formats.
A rendering tool for parameterized SQL that also translates into
    different SQL dialects.  These dialects include Sql Server, Oracle, 
    PostgreSql, Amazon RedShift, and Microsoft PDW.
Provides utilities for generating SQL queries (particularly CREATE
    TABLE statements) from R model objects. The most important use case is
    generating SQL to score a generalized linear model or related model
    represented as an R object, in which case the package handles parsing
    formula operators and including the model's response function.
This package provides utilities for working with a library of SQL
    files.
Normalization based a subset of negative control probes as
        described in 'Subset quantile normalization using negative
        control features'. Wu Z, Aryee MJ, J Comput Biol. 2010
        Oct;17(10):1385-95 [PMID 20976876].
Algorithms for accelerating the convergence of slow,
        monotone sequences from smooth, contraction mapping such as the
        EM algorithm. It can be used to accelerate any smooth, linearly
        convergent acceleration scheme.  A tutorial style introduction
        to this package is available in a vignette on the CRAN download
        page or, when the package is loaded in an R session, with
        vignette("SQUAREM").
Functions for color-based visualization of multivariate data, i.e. colorgrams or heatmaps.  Lower-level functions  map numeric values to colors, display a matrix as an array of colors, and draw color keys.  Higher-level plotting functions generate a bivariate histogram, a dendrogram aligned with a color-coded matrix, a triangular distance matrix, and more.
A simulation-based tool made to help researchers to become familiar with
    multilevel variations, and to build up sampling designs for their study. 
    This tool has two main objectives: First, it provides an educational tool useful for students, 
    teachers and researchers who want to learn to use mixed-effects models. 
    Users can experience how the mixed-effects model framework can be used to understand 
    distinct biological phenomena by interactively exploring simulated multilevel data. 
    Second, it offers research opportunities to those who are already familiar with 
    mixed-effects models, as it enables the generation of data sets that users may download 
    and use for a range of simulation-based statistical analyses such as power 
    and sensitivity analysis of multilevel and multivariate data.
This package (sra) provides a set of tools to analyse artificial-selection response datasets. The data typically feature for several generations the average value of a trait in a population, the variance of the trait, the population size and the average value of the parents that were chosen to breed. Sra implements two families of models aiming at describing the dynamics of the genetic architecture of the trait during the selection response. The first family relies on purely descriptive (phenomenological) models, based on an autoregressive framework. The second family provides different mechanistic models, accounting e.g. for inbreeding, mutations, genetic and environmental canalization, or epistasis. The parameters underlying the dynamics of the time series are estimated by maximum likelihood. The sra package thus provides (i) a wrapper for the R functions mle() and optim() aiming at fitting in a convenient way a predetermined set of models, and (ii) some functions to plot and analyze the output of the models.
Implementation of the SRCS method for a color-based visualization of the
    results of multiple pairwise tests on a large number of problem configurations, proposed in: 
    I.G. del Amo, D.A. Pelta. SRCS: a technique for comparing multiple algorithms under several
    factors in dynamic optimization problems. In: E. Alba, A. Nakib, P. Siarry
    (Eds.), Metaheuristics for Dynamic Optimization. Series: Studies in
    Computational Intelligence 433, Springer, Berlin/Heidelberg, 2012.
Draws scaled rectangle diagrams to represent a 2^k contingency
    table, for k<=6
This package contains a collection of functions to perform
        nonparametric estimation of receiver operating characteristic
        (ROC) curves for continuous data.
This package implements the SRRS method introduced in Phoa (2013) into a graphical user interface (GUI) R program.
Use piping, verbs like 'group_by' and 'summarize', and other
    'dplyr' inspired syntactic style when calculating summary statistics on survey
    data using functions from the 'survey' package.
Develops a framework for fisheries stock assessment simulation
    testing with Stock Synthesis 3 (SS3).
Procedures for analyzing simultaneous signals, e.g., features that are simultaneously significant in two different studies. Includes methods for detecting simultaneous signals, for identifying them under false discovery rate control, and for leveraging them to improve prediction.
A set of functions to calculate sample size for two-sample difference in means tests. Does adjustments for either nonadherence or variability that comes from using data to estimate parameters.
Small functions used by other packages from Statistics Norway are gathered. Both general data manipulation functions and some more special functions for statistical disclosure control are included. One reason for a separate package is possible reuse of the functions within a Renjin environment.
Provides a collection of self-labeled techniques for semi-supervised classification. In semi-supervised classification, both labeled and unlabeled data are used to train a classifier. This learning paradigm has obtained promising results, specifically in the presence of a reduced set of labeled examples. This package implements a collection of self-labeled techniques to construct a distance-based classification model. This family of techniques enlarges the original labeled set using the most confident predictions to classify unlabeled data. The techniques implemented can be applied to classification problems in several domains by the specification of a suitable base classifier and distance measure. At low ratios of labeled data, it can be shown to perform better than classical supervised classifiers.
Provides the spatial sign correlation and the two-stage spatial sign correlation as well as a one-sample test for the correlation coefficient.
ssd calculates the sample size needed to detect the differences between two sets of unordered categorical data.
Functions to visually and statistically analyze single system data.
Allows to map species richness and endemism based on stacked
    species distribution models (SSDM). Individuals SDMs can be created using a
    single or multiple algorithms (ensemble SDMs). For each species, an SDM can
    yield a habitat suitability map, a binary map, a between-algorithm variance
    map, and can assess variable importance, algorithm accuracy, and between-
    algorithm correlation. Methods to stack individual SDMs include summing
    individual probabilities and thresholding then summing. Thresholding can be
    based on a specific evaluation metric or by drawing repeatedly from a Bernoulli
    distribution. The SSDM package also provides a user-friendly interface.
Performs structured OLS (sOLS) and structured SIR (sSIR).
Spatial Stochastic Frontier Analysis (SSFA) is an original method for controlling the spatial heterogeneity in Stochastic Frontier Analysis (SFA) models, for cross-sectional data, by splitting the inefficiency term into three terms: the first one related to spatial peculiarities of the territory in which each single unit operates, the second one related to the specific production features and the third one representing the error term.
Fits complex parametric models using the method proposed by Cox and Kartsonaki (2012) without likelihoods.
This package provides utility functions for system command
    execution, both locally and remotely using ssh/scp. The command
    output is captured and provided to the caller. This functionality is
    intended to streamline calling shell commands from R, retrieving and
    using their output, while instrumenting the calls with appropriate
    error handling. NOTE: this first version is limited to unix with local
    and remote systems running bash as the default shell.
This package contains a set of functions that calculates 
 appropriate sample sizes for one-sample t-tests, two-sample t-tests, 
 and F-tests for microarray experiments based on desired power while 
 controlling for false discovery rates. For all tests, the standard
 deviations (variances) among genes can be assumed fixed or random. 
 This is also true for effect sizes among genes in one-sample and two
 sample experiments. Functions also output a chart of power versus sample
 size, a table of power at different sample sizes, and a table of critical
 test values at different sample sizes.
We propose a procedure for sample size calculation while
    controlling false discovery rate for RNA-seq experimental design. Our
    procedure depends on the Voom method proposed for RNA-seq data analysis
    by Law et al. (2014) <DOI:10.1186/gb-2014-15-2-r29> and the sample size 
    calculation method proposed for microarray experiments by Liu and Hwang 
    (2007) <DOI:10.1093/bioinformatics/btl664>. We develop a set of functions
    that calculates appropriate sample sizes for two-sample t-test for RNA-seq
    experiments with fixed or varied set of parameters. The outputs also contain a
    plot of power versus sample size, a table of power at different sample sizes,
    and a table of critical test values at different sample sizes. 
    To install this package, please use 
    'source("http://bioconductor.org/biocLite.R"); biocLite("ssizeRNA")'.
Semi-supervised learning has attracted the attention of machine learning community because of its high accuracy with less annotating effort compared with supervised learning.The question that semi-supervised learning wants to address is: given a relatively small labeled dataset and a large unlabeled dataset, how to design classification algorithms learning from both ? This package is a collection of some classical semi-supervised learning algorithms in the last few decades.
Performs the EM algorithm for regression models using Skew Scale Mixtures of Normal Distributions.
Package provides a set of tools for robust estimation and inference for models with sample selectivity.
It provides the density and random number generator for the Scale-Shape Mixtures of Skew-Normal Distributions proposed by Jamalizadeh and Lin (2016) <doi:10.1007/s00180-016-0691-1>.
Geostatistical modeling for data on stream networks, including models based on in-stream distance.  Models are created using moving average constructions. Spatial linear models, including covariates, can be fit with ML or REML.  Mapping and other graphical functions are included.
R package for computing the spherical smoothing splines
An integrated set of tools to estimate the size of a networked population based on respondent-driven sampling data. The package is part of the "RDS Analyst" suite of packages for the analysis of respondent-driven sampling data.
Takeya Semantic Structure Analysis (TSSA) and Sakai Sequential Relation Analysis (SSRA)
             for polytomous items for examining whether each pair of items has a sequential or equal
             relation. Package includes functions for generating a sequential relation table and a
             treegram to visualize sequential or equal relations between pairs of items.
SSRAT is a computer program for two-dimensional sociometric status
    determination with rating scales. For each person assessed, SSRAT computes
    probability distributions of the total scores for `sympathy' (S),
    `antipathy' (A), `social preference' (P) and `social impact' (I), and
    applies the criteria for sociometric status categorization.
Calculates the power and sample size based on the difference in Restricted Mean Survival Times.
Tools to import survey files
    in the .sss (triple-s) format. The package provides the function
    read.sss() that reads the .asc and .sss files of a
    triple-s survey data file.  The package is experimental
    - feedback, issues and bug reports are welcome.
Fast iterative thresholding sparse SVD, together with an initialization algorithm
Set of tools to fit a semi-parametric regression model suitable for analysis of data sets in which the response variable is continuous, strictly positive, asymmetric and possibly, censored. Under this setup, both the median and the skewness of the response variable distribution are explicitly modeled by using semi-parametric functions, whose non-parametric components may be approximated by natural cubic splines or P-splines. Supported distributions for the model error include log-normal, log-Student-t, log-power-exponential, log-hyperbolic, log-contaminated-normal, log-slash, Birnbaum-Saunders and Birnbaum-Saunders-t distributions.
Implements the "shrinkage t" statistic 
   introduced in Opgen-Rhein and Strimmer (2007) and a shrinkage estimate
   of the "correlation-adjusted t-score" (CAT score) described in
   Zuber and Strimmer (2009).  It also offers a convenient interface 
   to a number of other regularized t-statistics commonly 
   employed in high-dimensional case-control studies.
Density, distribution, quantile and hazard functions of a
    stable variate; generalized regression models for the parameters
    of a stable distribution. See the README for how to make equivalent calls
    to those of 'stabledist'.
Density, Probability and Quantile functions, and random number
  generation for (skew) stable distributions, using the parametrizations of
  Nolan.
Estimate the four parameters of stable laws using maximum
             likelihood method, generalised method of moments with
             finite and continuum number of points, iterative
             Koutrouvelis regression and Kogon-McCulloch method.  The
             asymptotic properties of the estimators (covariance
             matrix, confidence intervals) are also provided.
An exploratory and heuristic approach for specification search
    in Structural Equation Modeling. The basic idea is to subsample the original
    data and then search for optimal models on each subset. Optimality is defined
    through two objectives: model fit and parsimony. As these objectives are
    conflicting, we apply a multi-objective optimization methods, specifically
    NSGA-II, to obtain optimal models for the whole range of model complexities.
    From these optimal models, we consider only the relevant model specifications
    (structures), i.e., those that are both stable (occur frequently) and
    parsimonious and use those to infer a causal model.
Resampling procedures to assess the stability of selected variables
    with additional finite sample error control for high-dimensional variable
    selection procedures such as Lasso or boosting. Both, standard stability
    selection (Meinshausen & Buhlmann, 2010, <doi:10.1111/j.1467-9868.2010.00740.x>) 
    and complementary pairs stability selection with improved error bounds 
    (Shah & Samworth, 2013, <doi:10.1111/j.1467-9868.2011.01034.x>) are
    implemented. The package can be combined with arbitrary user specified
    variable selection approaches.
Stacks rectangular datasets on top of each other, possibly
    performing several type coercions along the way. For large datasets,
    depends on the ff package. Provides an aggressive version of
    ffbase::compact for data that may appear be real-typed but is in fact
    int/short/byte. For many purposes plyr::rbind.fill may be more appropriate,
    but for some kinds of survey data, the rules here work better.
Consists of helper functions collected from StackOverflow.com, a 
  question and answer site for professional and enthusiast programmers.
Analyzing a mixture of two-stage logistic regressions with fixed candidate knots. See Bruch, E., F. Feinberg, K. Lee (in press)<DOI:10.1073/pnas.1522494113>.
Provides facilities to implement and run population models of
    stage-structured species...
stam is an evolving package that target on the various
        methods to conduct Spatio-Temporal Analysis and
        Modelling,including Exploratory Spatio-Temporal Analysis and
        Inferred Spatio-Temporal Modelling.
Allows users to calculate pairwise Nei's Genetic Distances (Nei 1972), pairwise Fixation
 Indexes (Fst) (Weir & Cockerham 1984) and also Genomic Relationship matrixes following Yang et al. (2010) in mixed and single
 ploidy populations. Bootstrapping across loci is implemented during Fst calculation to generate confidence intervals and p-values
 around pairwise Fst values. StAMPP utilises SNP genotype data of any ploidy level (with the ability to handle missing data) and is coded to  
 utilise multithreading where available to allow efficient analysis of large datasets. StAMPP is able to handle genotype data from genlight objects 
 allowing integration with other packages such adegenet.
 Please refer to LW Pembleton, NOI Cogan & JW Forster, 2013, Molecular Ecology Resources, 13(5), 946-952. doi:10.1111/1755-0998.12129 for the appropriate citation and user manual. Thank you in advance.
Perform spatial temporal analysis of moving polygons; a longstanding analysis problem in Geographic Information Systems. Facilitates directional analysis, shape analysis, and some other simple functionality for examining spatial-temporal patterns of moving polygons.
Provides functions for the analysis of
        occupational and environmental data with non-detects. Maximum
        likelihood (ML) methods for censored log-normal data and
        non-parametric methods based on the product limit estimate (PLE)
        for left censored data are used to calculate all of the
        statistics recommended by the American Industrial Hygiene
        Association (AIHA) for the complete data case. Functions for
        the analysis of complete samples using exact methods are also
        provided for the lognormal model. Revised from 2007-11-05
        'survfit~1'.
Standardizes text according to a template; particularly
        useful for country names.
The C++ header files of the Stan project are provided by this package, but it contains no R code, vignettes, or function documentation. There is a shared object containing part of the 'CVODES' library, but it is not accessible from R. 'StanHeaders' is only useful for developers who want to utilize the 'LinkingTo' directive of their package's DESCRIPTION file to build on the Stan library without incurring unnecessary dependencies. The Stan project develops a probabilistic programming language that implements full or approximate Bayesian statistical inference via Markov Chain Monte Carlo or 'variational' methods and implements (optionally penalized) maximum likelihood estimation via optimization. The Stan library includes an advanced automatic differentiation scheme, 'templated' statistical and linear algebra functions that can handle the automatically 'differentiable' scalar types (and doubles, 'ints', etc.), and a parser for the Stan language. The 'rstan' package provides user-facing R functions to parse, compile, test, estimate, and analyze Stan models.
Functions to analyze neuronal spike trains from a single
        neuron or from several neurons recorded simultaneously.
Produces LaTeX code, HTML/CSS code and ASCII text for well-formatted tables that hold 
    regression analysis results from several models side-by-side, as well as summary
    statistics.
Statistical functions to identify, estimate and diagnose a Space-Time AutoRegressive Moving Average (STARMA) model.
Data structures and methods for  manipulating output of genetic population structure clustering algorithms.
  'starmie' can parse output from 'STRUCTURE' (see <https://pritchardlab.stanford.edu/structure.html> for details) or
  'ADMIXTURE' (see <https://www.genetics.ucla.edu/software/admixture/> for details). 'starmie' performs model selection via
  information criterion, and provides functions for MCMC diagnostics, correcting label switching and visualisation of admixture coefficients.
Adds support for R startup configuration via '.Renviron.d' and '.Rprofile.d' directories in addition to '.Renviron' and '.Rprofile' files.  This makes it possible to keep private / secret environment variables separate from other environment variables.  It also makes it easier to share specific startup settings by simply copying a file to a directory.
Provides utilities to create or suppress start-up messages.
Datasets for Stat2 textbook (by Cannon, et. al., published
        by WH Freeman)
A set of tools inspired by 'Stata' to explore data.frames ('summarize',
    'tabulate', 'xtile', 'pctile', 'binscatter', elapsed quarters/month, lead/lag).
Extract statistics from articles and recompute p values.
An implementation of local and global statistical complexity measures (aka Information Theory Quantifiers, ITQ) for time series analysis based on ordinal statistics (Bandt and Pompe (2002) <DOI:10.1103/PhysRevLett.88.174102>). Several distance measures that operate on ordinal pattern distributions, auxiliary functions for ordinal pattern analysis, and generating functions for stochastic and deterministic-chaotic processes for ITQ testing are provided.
This package offers different possibilities to make statistical analysis for Environmental Data.
Support for reading and writing files in StatDataML---an XML-based data exchange format.
Cartogram heatmaps are an alternative to choropleth maps for USA States
    and are based on work by the Washington Post graphics department in their report
    on "The states most threatened by trade". "State bins" preserve as much of the
    geographic placement of the states as possible but has the look and feel of a
    traditional heatmap. Functions are provided that allow for use of a binned,
    discrete scale, a continuous scale or manually specified colors depending on
    what is needed for the underlying data.
Selectively acquire hourly meteorological data from stations located all over the world.
A collection of miscellaneous statistical functions for 
    probability distributions: dbern(), pbern(), qbern(), rbern() for 
    the Bernoulli distribution, and distr2name(), name2distr() for 
    distribution names; 
    probability density estimation: densityfun(); 
    most frequent value estimation: mfv(), mfv1(); 
    calculation of the Hellinger distance: hellinger(); 
    use of classical kernels: kernelfun(), kernel_properties().
Provides graphics and other functions that evaluate and display models across many different kinds of model architecture. For instance, you can evaluate the effect size of a model input in the same way, regardless of architecture, interaction terms, etc.
Integration of two data sources referred to the same target population which share a number of common variables (aka data fusion). Some functions can also be used to impute missing values in data sets through hot deck imputation methods. Methods to perform statistical matching when dealing  with data from complex sample surveys are available too.
Offers useful functions to perform day-to-day data manipulation 
    operations, data quality checks and post modelling statistical checks.
    One can effortlessly change class of a number of variables to factor, 
    remove duplicate observations from the data, create deciles of a 
    variable, perform data quality checks for continuous (integer or numeric), 
    categorical (factor) and date variables, and compute goodness of fit 
    measures such as auc for statistical models. The functions are consistent 
    for objects of class 'data.frame' and 'data.table', which is an enhanced 
    'data.frame' implemented in the package 'data.table'.
Functions and datasets to support Mayer Alvo and Philip L.H. Yu (2014). "Statistical Methods for Ranking Data", Springer. In this packages, smaller ranks stand for more preferred items.
A collection of algorithms and functions to aid statistical modeling. Includes growth curve comparisons, limiting dilution analysis (aka ELDA), mixed linear models, heteroscedastic regression, inverse-Gaussian probability calculations, Gauss quadrature and a secure convergence algorithm for nonlinear models.  Includes advanced generalized linear model functions that implement secure convergence, dispersion modeling and Tweedie power-law families.
An integrated set of tools for the representation, visualization, analysis, and simulation of network data. For an introduction, type help(package='statnet').
Non-statistical utilities used by the software developed by the Statnet Project. They may also be of use to others.
A graphical user interface for network modeling with the 'statnet'
    software.
A collection of statistical tools for social network analysis, with strong emphasis on the analysis of discrete powerlaw distributions and statistical hypothesis tests.
A small collection of data on graduate statistics programs from the United States.
Generates a random quotation from a data base of quotes on topics
    in statistics, data visualization and science.
A set of methods to implement Generalized Method of Moments and Maximal
    Likelihood methods for Random Utility Models. These methods are meant to
    provide inference on rank comparison data. These methods accept full,
    partial, and pairwise rankings, and provides methods to break down full or
    partial rankings into their pairwise components. Please see Generalized
    Method-of-Moments for Rank Aggregation from NIPS 2013 for a description of
    some of our methods.
Provides an implementation of simultaneous tolerance bounds (STB), useful for checking whether a numeric vector fits to a hypothetical null-distribution or not.
             Furthermore, there are functions for computing STB (bands, intervals) for random variates of linear mixed models fitted with package 'VCA'. All kinds of, possibly transformed 
             (studentized, standardized, Pearson-type transformed) random variates (residuals, random effects), can be assessed employing STB-methodology.
Estimates a covariance matrix using Stein's isotonized covariance
    estimator, or a related estimator suggested by Haff.
Contains three main functions including
  stddiff.numeric(), stddiff.binary() and stddiff.category().
  These are used to calculate the standardized difference between two groups.
  It is especially used to evaluate the balance between two groups
  before and after propensity score matching.
Contains functionality for regression standardization. Three general classes of models are allowed; generalized linear models, Cox proportional hazards models and shared gamma-Weibull frailty models.
Allows the creation and manipulation of C++ std::vector's in R.
Functions related to multivariate measures of independence and ICA:
 -estimate independent components by minimizing distance covariance;
 -conduct a test of mutual independence based on distance covariance; 
 -estimate independent components via infomax (a popular method but generally performs poorer than mdcovica, ProDenICA, and/or fastICA, but is useful for comparisons);
 -order indepedent components by skewness;
 -match independent components from multiple estimates;
 -other functions useful in ICA.
steepness is a package that computes steepness as a
        property of dominance hierarchies. Steepness is defined as the
        absolute slope of the straight line fitted to the normalized
        David's scores. The normalized David's scores can be obtained
        on the basis of dyadic dominance indices corrected for chance
        or by means of proportions of wins. Given an observed
        sociomatrix, it computes hierarchy's steepness and estimates
        statistical significance by means of a randomization test.
Routines for computing different types of linear estimators, based on instrumental variables (IVs), including the semi-parametric Stein-like (SPS) estimator, originally introduced by Judge and Mittelhammer (2004)  <DOI:10.1198/016214504000000430>.
A package to manage and display stellar tracks and
        isochrones from Pisa low-mass database. Includes tools for
        isochrones construction and tracks interpolation.
Estimation of the parameters of a spatio-temporal model
        using the EM algorithm, estimation of the parameter standard
        errors using a spatio-temporal parametric bootstrap, spatial
        mapping.
Collection of model estimation, and model plotting functions 
             related to the STEPCAM family of community assembly models. 
             STEPCAM is a STEPwise Community Assembly Model that infers 
             the relative contribution of Dispersal Assembly, Habitat Filtering 
             and Limiting Similarity from a dataset consisting of the 
             combination of trait and abundance data. See also <http://onlinelibrary.wiley.com/wol1/doi/10.1890/14-0454.1/abstract> for more information.
A method to explore the treatment-covariate interactions in survival or generalized 
	linear model (GLM) for continuous, binomial and count data arising from two treatment 
	arms of a clinical trial. A permutation distribution approach to inference is implemented, 
	based on permuting the covariate values within each treatment group.
Model Selection Based on Combined Penalties. This package implements a stepwise forward variable selection algorithm based on a penalized likelihood criterion that combines the L0 with L2 or L1 norms.
L2 penalized logistic regression for both continuous and
        discrete predictors, with forward stagewise/forward stepwise
        variable selection procedure.
Allows to fit step-functions to univariate serial data where neither the number of jumps nor their positions is known.
A stepwise approach to identifying recombination
        breakpoints in a sequence alignment.
Collection of stepwise procedures to conduct multiple hypotheses testing. The details of the stepwise algorithm can be found in Romano and Wolf (2007) <DOI:10.1214/009053606000001622> and Hsu, Kuan, and Yen (2014) <DOI:10.1093/jjfinec/nbu014>.
Functions for the collection of 3D points and curves using a stereo camera setup.
Functions implementing the procedure of entropy comparison between two data samples after the renormalization of respective probability distributions with the algorithm designed by Klimontovich (Zeitschrift fur Physik B Condensed Matter. 1987, Volume 66, Issue 1, pp 125-127) and extended by Anishchenko (Proc. SPIE 2098, Computer Simulation in Nonlinear Optics. 1994, pp.130-136).  The package also includes data preparation tools which can also be used separately for various applications.
A set of functions for computing the Standardized Temperature Index (STI).
In base R, object attributes are lost when objects are modified by
    common data operations such as subset, filter, slice, append, extract
    etc. This packages allows objects to be marked as 'sticky' and have
    attributes persisted during these operations or when inserted
    into or extracted from recursive (i.e. list- or table-like) objects.
Functions to build and use an interpolator ("emulator") for time series or 1D regularly spaced data in multidimensional space. The standard usage is for interpolating time-resolved computer model output between model parameter settings. It can also be used for interpolating multivariate data (e.g., oceanographic time-series data, etc.) in space.  There are functions to test the emulator using cross-validation, and to produce contour plots over 2D slices in model input parameter (or physical) space.
This package enables you to estimate a regression trunk model, which is an integration of a regression tree and a multiple regression model.
A consistently well behaved method of interpolation based
        on piecewise rational functions using Stineman's algorithm