Fit and compare nonlinear mixed-effects models in differential
    equations with flexible dosing information commonly seen in pharmacokinetics
    and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 
    <doi:10.1007/s10928-015-9409-1>). Differential equation solving is 
    by compiled C code provided in the 'RxODE' package
    (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).
Provides neutral landscape models 
    (Gardner et al. 1987 <doi:10.1007/BF02275262>,
    With 1997 <doi:10.1046/j.1523-1739.1997.96210.x>) that can easily extend in 
    existing landscape analyses. Neutral landscape models range from "hard" 
    neutral models (only random functions) to "soft" ones (with parameters) and 
    generate landscape patterns that are not grounded in ecological reasoning.
    Thus, these patterns can be used as null models in landscape ecology. 'NLMR' 
    combines a large number of algorithms from published software (Saura & 
    Martínez 2000   <doi:10.1023/A:1008107902848>, Etherington et
    al. 2015 <doi:10.1111/2041-210X.12308>) for simulating neutral landscapes and
    includes utility functions to classify and combine the landscapes. The 
    simulation results are obtained in a geospatial data format (raster* objects 
    from the 'raster' package) and can, therefore, be used 
    in any sort of raster data operation that is performed with standard 
    observation data.
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.
A few utilities for summarizing, testing, and plotting non-linear
    regression models fit with nls(), nlsList() or nlme().
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 as its Gauss-Newton method frequently stops with 'singular 
	gradient' messages. nlsr is based on the now-deprecated package nlmrt,
	and has refactored functions and R-language symbolic derivative features.
Performs univariate or multivariate computation of a single ODE or of a set of ODE (ordinary differential equations).
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 with an emphasis on nonparametric autoregression and order estimation, and tests for linearity / additivity.
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.
Performs network meta-analysis using integrated nested Laplace approximations ('INLA'). Includes methods to assess the heterogeneity and inconsistency in the network. Contains more than ten different network meta-analysis data. 'INLA' package can be obtained from <http://www.r-inla.org>. We recommend the testing version.
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.
Provides a version of the Expectation-Maximization algorithm for
    mix-models, reducing the numbers of parameters to estimate using
    Non-negative Matrix Factorization methods.
    For more explanations, see pre-print of Carel and Alquier (2017) <arXiv:1709.03346>.
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.
This shows how NONMEM(R) <http://www.iconplc.com/innovation/nonmem/> software works. NONMEM's classical estimation methods like 'First Order(FO) approximation', 'First Order Conditional Estimation(FOCE)', and 'Laplacian approximation' are explained.
'NEON' 'API' (<http://data.neonscience.org/data-api>) client.
    Includes methods for interacting with all 'API' routes.
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.
Contains functions to facilitate automatic time series modelling with neural networks. 
    Allows fully automatic, semi-manual or fully manual specification of networks. For details of the
	specification methodology see: (i) Crone and Kourentzes (2010) <doi:10.1016/j.neucom.2010.01.017>;
	and (ii) Kourentzes et al. (2014) <doi:10.1016/j.eswa.2013.12.011>.
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.
Imputation for both missing covariates and censored observations (optional) for survival data with missing covariates by the nearest neighbor based multiple imputation algorithm as described in Hsu et al. (2006) <doi:10.1002/sim.2452>, Long et al. (2012) <doi:10.5705/ss.2010.069>, Hsu et al. (2014) <doi:10.1080/10543406.2014.888444>, and Hsu and Yu (2017) <arXiv:1710.04721>. Note that the current version can only impute for a situation with one missing covariate.
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.
Allows users to explore and plot data from the
    National Oceanic and Atmospheric Administration (NOAA) 
    Storm Events database through R for United States counties. 
    Functionality includes matching storm event listings by time and 
    location to hurricane best tracks data. This work was 
    supported by grants from the Colorado Water Center, the National Institute 
    of Environmental Health Sciences (R00ES022631) and the National Science 
    Foundation (1331399).
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.
Provides a compact variation of the usual syntax of function
  declaration, in order to support tidyverse-style quasiquotation of a
  function's arguments and body.
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.
Functions to use the 'NamePrism' API <http://name-prism.com/api> or 'NameSor' API <https://api.namsor.com/namsor/faces/viewapikey.xhtml> for classifying names based on gender, 6 U.S. ethnicities, or 39 leaf nationalities.
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 are
             1) Use of CDISC SDTM terms
             2) Automatic slope selection with the same criterion of WinNonlin(R)
             3) Supporting both 'linear-up linear-down' and 'linear-up log-down' method
             4) Interval(partial) AUCs with 'linear' or 'log' interpolation method
             * Reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107).
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 Bayesian analysis of data from randomized experiments with non-compliance. The functions are based on the models described in Imbens and Rubin (1997) <doi:10.1214/aos/1034276631>. Currently only two types of outcome models are supported: binary outcomes and normally distributed outcomes. Models can be fit with and without the exclusion restriction and/or the strong access monotonicity assumption. Models are fit using the data augmentation algorithm as described in Tanner and Wong (1987) <doi:10.2307/2289457>.
Performs 'nonlinear Invariant Causal Prediction' to estimate the 
    causal parents of a given target variable from data collected in
    different experimental or environmental conditions, extending
    'Invariant Causal Prediction' from Peters, Buehlmann and Meinshausen (2016), 
    <arXiv:1501.01332>, to nonlinear settings. For more details, see C. Heinze-Deml, 
    J. Peters and N. Meinshausen: 'Invariant Causal Prediction for Nonlinear Models', 
    <arXiv:1706.08576>.
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.
Systematically creates and modifies NONMEM(R) control streams. Harvests
 NONMEM output, builds run logs, creates derivative data, generates diagnostics.
 NONMEM (ICON Development Solutions <http://www.iconplc.com/>) is software for 
 nonlinear mixed effects modeling. See 'package?nonmemica'.
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 partially based on
    work supported by the National Science Foundation under Grant Number SES-1061334.
Contains the following 5 nonparametric hypothesis tests:
    The Sign Test,
    The 2 Sample Median Test,
    Miller's Jackknife Procedure,
    Cochran's Q Test, &
    The Stuart-Maxwell Test.
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.
Plot, process, and analyze NPO files produced by
    'Nonpareil' <http://enve-omics.ce.gatech.edu/nonpareil>.
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.  Efficient random
   number generation and graphics; 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).
Metabolomics data are inevitably subject to a component of unwanted
    variation, due to factors such as batch effects, matrix effects, and confounding
    biological variation. This package is a collection of functions designed to 
    implement, assess, and choose a suitable normalization method 
    for a given metabolomics study (De Livera et al (2015) <doi:10.1021/ac502439y>).
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.
Calculate NOS (node overlap and segregation) and
    the associated metrics described in Strona and Veech (2015)
    <DOI:10.1111/2041-210X.12395> and Strona et al. (2017, In Press).
    The functions provided in the package enable assessment of
    structural patterns ranging from complete node segregation to perfect
    nestedness in a variety of network types. In addition, they provide a
    measure of network modularity.
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.
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.
Methods and tools to estimate 'forecasts' of Brazilian macroeconomic variables in the near future or the recent past, i.e. 'nowcast'. It allows: extract information in real time, creating a real time data base; estimate relationship between macroeconomic variables via dynamic factors; forecast time series in previous periods of reference; forecast time series in the current period of reference (nowcasting); recreate a data base simulating the information available in the past for evaluating forecasting models accuracy (pseudo real-time data base); access information available in a specific date in the past (real-time data base), for some variables.
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.
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, <http://www.nserc-crsng.gc.ca>), the Social Sciences and Humanities Research Council of Canada (SSHRC, <http://www.sshrc-crsh.gc.ca>), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, <http://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 CUSUM tests for detecting changes
  in possibly serially dependent univariate or multivariate
  observations. Tests sensitive to changes in the expectation, the
  variance, the covariance, the autocovariance, the distribution
  function, Spearman's rho, Kendall's tau, Gini's mean difference, and
  the copula are provided, as well as a test for detecting changes in
  the distribution of independent block maxima (with environmental
  studies in mind). The latest additions are a test sensitive to
  changes in the autocopula and a combined test of stationarity
  sensitive to changes in the distribution function and the
  autocopula.
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 preprocessing 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
Non-Parametric Module (NPMOD) is a graphical interface dedicated to the testing of non-parametric data for educational purposes.
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.
Calculates phenological cycle and anomalies using a non-parametric
    approach applied to time series of vegetation indices derived from remote sensing data 
    or field measurements. The package implements basic and high-level functions for 
    manipulating vector data (numerical series) and raster data (satellite derived products).
    Processing of very large raster files is supported.
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 statistical analysis using local polynomial regression and kernel density estimation methods as described in Calonico, Cattaneo and Farrell (2017a): lprobust() for local polynomial point estimation and robust bias-corrected inference and kdrobust() for kernel density point estimation and robust bias-corrected inference. Several optimal bandwidth selection procedures are computed by lpbwselect() and kdbwselect() for local polynomial and kernel density estimation, respectively. Finally, nprobust.plot() for density and regression plots with robust confidence interval.
In many binary classification applications, such as disease
    diagnosis and spam detection, practitioners commonly face the need to limit
    type I error (i.e., the conditional probability of misclassifying a class 0
    observation as class 1) so that it remains below a desired threshold. To address
    this need, the Neyman-Pearson (NP) classification paradigm is a natural choice;
    it minimizes type II error (i.e., the conditional probability of misclassifying
    a class 1 observation as class 0) while enforcing an upper bound, alpha, on the
    type I error. Although the NP paradigm has a century-long history in hypothesis
    testing, it has not been well recognized and implemented in classification
    schemes. Common practices that directly limit the empirical type I error to
    no more than alpha do not satisfy the type I error control objective because
    the resulting classifiers are still likely to have type I errors much larger
    than alpha. As a result, the NP paradigm has not been properly implemented
    for many classification scenarios in practice. In this work, we develop the
    first umbrella algorithm that implements the NP paradigm for all scoring-type
    classification methods, including popular methods such as logistic regression,
    support vector machines and random forests. Powered by this umbrella algorithm,
    we propose a novel graphical tool for NP classification methods: NP receiver
    operating characteristic (NP-ROC) bands, motivated by the popular receiver
    operating characteristic (ROC) curves. NP-ROC bands will help choose in a data
    adaptive way and compare different NP classifiers. The paper is available at
    <arXiv:1608.03109>.
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.
Prepares and writes files to submit to the National Student Clearinghouse's 
  StudentTracker service <http://www.studentclearinghouse.org/colleges/studenttracker/>.
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.
Contains functions for maximum likelihood estimation of
	     nonparametric and semiparametric mixture models.
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.
Tools for estimating Receiver Operating Characteristic (ROC) curves,
        building confidence bands, comparing several curves both for dependent and 
        independent data, estimating the cumulative-dynamic ROC curve in presence of
        censored data, and performing meta-analysis studies, among others.
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 executable of the model can downloaded from <https://github.com/jeroenpullens/NUCOMBog>.
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.
High-dimensional datasets that do not exhibit a clear intrinsic clustered structure pose a challenge to conventional clustering algorithms. For this reason, we developed an unsupervised framework that helps scientists to better subgroup their datasets based on visual cues [Makinen V-P et al. (2011) J Proteome Res 11:1782-1790, <doi:10.1021/pr201036j>]. The framework includes the necessary functions to import large data files, to construct a self-organizing map of the data, to evaluate the statistical significance of the observed data patterns, and to visualize the results in scalable vector graphics.
Format numbers and plots for publication; includes the removal of leading zeros,
           standardization of number of digits, addition of affixes, and a p-value formatter. These
           tools combine the functionality of several 'base' functions such as 'paste()', 'format()',
           and 'sprintf()' into specific use case functions that are named in a way that is
           consistent with usage, making their names easy to remember and easy to deploy.
A number series generator that creates number series items based on cognitive models.
To add the table of numbers at risk below the Kaplan-Meier plot.
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.
New York City's Taxi and Limousine Commission (TLC) Trip Data 
    <http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml> is a 
    collection of green and yellow taxi trip data. The data was collected 
    and provided by technology providers under the Taxicab & 
    Livery Passenger Enhancement Programs <http://www.nyc.gov/html/tlc/html/industry/shl.shtml>.
    This package helps users to download New York City Trip Data.
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.
    Opinion polls since 2002 and some convenience analytical function are also supplied.
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 (for more information, see
  <http://www.openarchives.org/OAI/openarchivesprotocol.html>).
Offers a suite of functions for enhancing R plots.
Implements the efficient algorithm by Ortmann and Brandes (2017)
  <doi:10.1007/s41109-017-0027-2> to compute the orbit-aware frequency distribution of induced and non-induced quads, i.e. subgraphs of size four. Given an edge matrix, data frame, or a graph object (e.g., 'igraph'), the orbit-aware counts are computed respective each of the edges and nodes.
Generalise the starting point of the array index
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.
Applies an objective Bayesian method to the Mb capture-recapture model to estimate the population size N.  The Mb model is a class of capture-recapture methods used to account for variations in capture probability due to animal behavior.  Under the Mb formulation,  the initial capture of an animal may effect the probability of subsequent captures due to their becoming "trap happy" or "trap shy."
An implementation for computing Optimal B-Robust Estimators (OBRE) of two
    parameters distributions. The procedure is composed by some equations
    that are evaluated alternatively until the solution is reached. Some tools
    for analyzing the estimates are included. The most relevant is OBRE
    covariance matrix computation using a closed formula.
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. G.Consonni, L. Deldossi (2014) Objective Bayesian Model Discrimination in Follow-up Experimental Designs, Test. <DOI:10.1007/s11749-015-0461-3>.
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 (Poole 2000) <doi:10.1093/oxfordjournals.pan.a029814>
		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.
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.
After develop a 'ODK' <https://opendatakit.org/> frame, we can link the frame to 'Google Sheets' <https://www.google.com/sheets/about/> and collect data through 'Android' <https://www.android.com/>. This data uploaded to a 'Google sheets'. odk2spss() function help to convert the 'odk' frame into 'SPSS' <https://www.ibm.com/analytics/us/en/technology/spss/> frame. Also able to add downloaded 'Google sheets' data or read data from 'Google sheets' by using 'ODK' frame 'submission_url'.
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.
Functions to compute the one-sided dynamic
	principal components ('odpc') introduced in Smucler, Peña and Yohai (2017)
	<arXiv:1708.04705>. 'odpc' is a novel dimension
	reduction technique for multivariate time series, that is useful
	for forecasting. These dynamic principal components are defined as
	the linear combinations of the present and past values of the series
	that minimize the reconstruction mean squared error.
Calculate the optimal sample allocation that minimizes variance of
    treatment effect in a multilevel randomized trial under fixed budget and cost structure,
    perform power analyses with and without
    accommodating costs and budget. The reference for proposed methods is:
    Shen, Z., & Kelcey, B. (under review). Optimal design of cluster
    randomized trials under condition- and unit-specific cost structures.
    2018 American Educational Research Association (AERA) annual conference.
Access The Observatory of Economic Complexity API from R to download international trade data and create and D3Plus visualizations.
Solves penalized least squares problems for big tall data
    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.
Access and manipulate 'Microsoft Word' and 'Microsoft PowerPoint' documents from R. 
  The package focuses on tabular and graphical reporting from R; it also provides two functions
  that let users get document content into data objects. A set of functions 
  lets add and remove images, tables and paragraphs of text in new or existing documents. 
  When working with 'PowerPoint' presentations, slides can be added or removed; shapes inside 
  slides can also be added or removed. When working with 'Word' documents, a cursor can be 
  used to help insert or delete content at a specific location in the document. The package 
  does not require any installation of Microsoft products to be able to write Microsoft files.
Consider a data matrix of n individuals with p variates. The objective general index (OGI)
    is a general index that combines the p variates into a univariate index in order to rank the n
    individuals. The OGI is always positively correlated with each of the variates.
    More details can be found in Sei (2016) <doi:10.1016/j.jmva.2016.02.005>.
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 (Weiss (1997)). 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.
Ing and Lai (2011) <doi:10.5705/ss.2010.081> proposed a high-dimensional model selection procedure that comprises three steps: orthogonal greedy algorithm (OGA), high-dimensional information criterion (HDIC), and Trim. The first two steps, OGA and HDIC, are used to sequentially select input variables and determine stopping rules, respectively. The third step, Trim, is used to delete irrelevant variables remaining in the second step. This package aims at fitting a high-dimensional linear regression model via OGA+HDIC+Trim.
R Client for Ohmage 2 server. Implements basic R
    functions to retrieve and process data.
Ordered homogeneity pursuit lasso (OHPL)
    algorithm for group variable selection proposed in Lin et al. (2017)
    <DOI:10.1016/j.chemolab.2017.07.004>. The OHPL method exploits the
    homogeneity structure in high-dimensional data and enjoys the
    grouping effect to select groups of important variables
    automatically. This feature makes it particularly useful for
    high-dimensional datasets with strongly correlated variables,
    such as spectroscopic 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.
Tools for building OLS regression models . Includes 
    comprehensive regression output, heteroskedasticity tests, collinearity diagnostics, 
    residual diagnostics, measures of influence, model fit assessment and variable selection procedures.
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.
Performs the O2PLS data integration method for two datasets yielding joint and data-specific parts for each dataset.
    The algorithm automatically switches to a memory-efficient approach to fit O2PLS to high dimensional data.
    It provides a rigorous and a faster alternative cross-validation method to select the number of components,
    as well as functions to report proportions of explained variation and to construct plots of the results.
    See Trygg and Wold (2003) <doi:10.1002/cem.775> and el Bouhaddani et al (2016) <doi:10.1186/s12859-015-0854-z>.
Model mixed integer linear programs in an algebraic way directly in R.
             The model is solver-independent and thus offers the possibility
             to solve a model with different solvers. It currently only supports
             linear constraints and objective functions. See the 'ompr'
             website <https://dirkschumacher.github.io/ompr> for more information, 
             documentation and examples.
A solver for 'ompr' based on the R Optimization Infrastructure ('ROI').
  The package makes all solvers in 'ROI' available to solve 'ompr' models. Please see the
  'ompr' website <https://dirkschumacher.github.io/ompr> and package docs for more information
  and examples on how to use it.
Implementation of a likelihood ratio test of differential
    onset of senescence between two groups. Given two groups with
    measures of age and of an individual trait likely to be subjected to
    senescence (e.g. body mass), 'OnAge' provides an asymptotic p-value
    for the null hypothesis that senescence starts at the same age in
    both groups. The package implements the procedure used in 
    Douhard et al. (2017) <doi:10.1111/oik.04421>.
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.
Quickly create numeric matrices for machine learning algorithms
    that require them. It converts factor columns into onehot vectors.
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) according to Wu et al. (2002)
    <doi:10.1006/tpbi.2002.1577>. All analysis are based on multipoint 
    approaches using hidden Markov models.
Implements the One Rule (OneR) Machine Learning classification algorithm (Holte, R.C. (1993) <doi:10.1023/A:1022631118932>) 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.
Performs one-way tests in independent groups designs, pairwise comparisons, graphical approaches, and assess variance homogeneity and normality of data in each group via tests and plots.
Quaternions and Octonions are four- and eight- dimensional
  extensions of the complex numbers.  They are normed division
  algebras over the real numbers and find applications in spatial
  rotations (quaternions) and string theory and relativity
  (octonions).  The quaternions are noncommutative and the octonions
  nonassociative.  See RKS Hankin 2006, Rnews Volume 6/2: 49-51, and
  the package vignette, for more details.
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.
Provides functions to calculate the out-of-bag learning curve for random forests for any measure that is available in the 'mlr' package. Supported random forest packages are 'randomForest' and 'ranger' and trained models of these packages with the train function of 'mlr'. The main function is 'OOBCurve' that calculates the out-of-bag curve depending on the number of trees. With the 'OOBCurvePars' function out-of-bag curves can also be calculated for 'mtry', 'sample.fraction' and 'min.node.size' for the 'ranger' package.
Provides the class unions that must be
  preloaded in order for the basic tools in the OOMPA (Object-Oriented 
  Microarray and Proteomics Analysis) project to be defined and loaded.
  It also includes vectorized operations for row-by-row means,
  variances, and t-tests. Finally, it provides new color schemes.
  Details on the packages in the OOMPA project can be found at
  <http://oompa.r-forge.r-project.org/>.
This is a data-only package to provide example data for
  other packages that are part of the "Object-Oriented Microrray and
  Proteomics Analysis" suite of packages. These are described in more
  detail at the package URL.
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.
A system for embedded scientific computing and reproducible research with R.
    The OpenCPU server exposes a simple but powerful HTTP api for RPC and data interchange
    with R. This provides a reliable and scalable foundation for statistical services or 
    building R web applications. The OpenCPU server runs either as a single-user development
    server within the interactive R session, or as a multi-user Linux stack based on Apache2. 
    The entire system is fully open source and permissively licensed. The OpenCPU website
    has detailed documentation and example apps.
Enables the usage of the OpenDota API from <https://www.opendota.com/>, get game lists, and download JSON's of parsed replays from
    the OpenDota API. Also has functionality to execute own code to extract the specific parts of the JSON file.
An implementation of DuMouchel's (1999) 
  <doi:10.1080/00031305.1999.10474456> Bayesian data mining method for the
  market basket problem. Calculates Empirical Bayes Geometric Mean (EBGM) and
  quantile scores from the posterior distribution using the Gamma-Poisson
  Shrinker (GPS) model to find unusually large cell counts in large, sparse
  contingency tables. Can be used to find unusually high reporting rates of
  adverse events associated with products. In general, can be used to mine any
  database where the co-occurrence of two variables or items is of interest.
  Also calculates relative and proportional reporting ratios. Builds on the work
  of the 'PhViD' package, from which much of the code is derived. Some of the
  added features include stratification to adjust for confounding variables and
  data squashing to improve computational efficiency.
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.
Supplemental functions and data for 'OpenIntro' resources, which includes open-source textbooks and resources for introductory statistics at <http://www.openintro.org>. The package contains data sets used in our open-source textbooks 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) when run in some versions of Windows operating system.
We provide an R interface to 'OpenML.org' which is an online machine learning platform where researchers can access open data, download and upload data sets, share their machine learning tasks and experiments and organize them online to work and collaborate with other researchers. 
    The R interface allows to query for data sets with specific properties, and allows the downloading and uploading of data sets, tasks, flows and runs. 
    See <https://www.openml.org/guide/api> 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. See Neale, Hunter, Pritikin, Zahery, Brick,
    Kirkpatrick, Estabrook, Bates, Maes, & Boker (2016) <doi:10.1007/s11336-014-9435-8>.
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.
A set of functions to analyze repertory grid data.
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.
An open source implementation of the 'STARS' toolbox
    (Peterson & Ver Hoef, 2014, <doi:10.18637/jss.v056.i02>) using 'R' and 'GRASS GIS'.
    It prepares the *.ssn object needed for the 'SSN' package.
    A Digital Elevation Model (DEM) is used to derive stream networks 
    (in contrast to 'STARS' that can clean an existing stream network).
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.
Provides an API client to retrieve data from 'La Haute Borne', 
    a wind farm of the ENGIE Group whose ten minutes time series have been  
    published under the Open License 2.0 of Etalab. 
    See <https://opendata-renewables.engie.com> for more information on this 
    wind farm and data availability. The main function to be used is get_owf().
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.
Provides a collection of utilities that allow programming with 
    R's operators. Routines allow classifying operators, 
    translating to and from an operator and its underlying function, and inverting 
    some operators (e.g. comparison operators), etc. All methods can be extended
    to custom infix operators.
A set of binary operators for common tasks such as regex
        manipulation.
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 October 2017 it is fully implemented on the Octopus 900 and partially on the Heidelberg Edge Perimeter, the Kowa AP 7000, the CrewT imo and the Centervue Compass. It also has a cousin: the R package 'visualFields', which has tools for analysing and manipulating visual field data.
Computes the routing distribution, the expectation of the number of broadcasts, transmissions and receptions considering an Opportunistic transport model. It provides theoretical results and also estimated values based on Monte Carlo simulations.
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.
A set of standard benchmark optimization functions for R and
    a common interface to sample them.
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.
Identify the optimal timing for new treatment initiation during multiple state disease transition, including  multistate model fitting, simulation of mean residual lifetime for a given transition state, and estimation of confidence interval. The method is referred to de Wreede, L., Fiocco, M., & Putter, H. (2011) <doi:10.18637/jss.v038.i07>.
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 optimizer with numerous input specifications for detailed parameterisation. Designed for complex loss functions with state and parameter space constraints. Visualization tools for validation and analysis of the convergence are 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. (1962) <doi:10.1080/00401706.1962.10490033>, the method of 
        Nelder and Mead (1965) <doi:10.1093/comjnl/7.4.308>, Box's algorithm for
        constrained optimization (1965) <doi:10.1093/comjnl/8.1.42>, the 
        multi-dimensional search by Torczon (1989) 
        <http://www.cs.wm.edu/~va/research/thesis.pdf>, etc...
Assessment and diagnostics for comparing competing
    clustering solutions, using predictive models. The main intended
    use is for comparing clustering/classification solutions of
    ecological data (e.g. presence/absence, counts, ordinal scores) to
    1) find an optimal partitioning solution, 2) identify
    characteristic species and 3) refine a classification by merging
    clusters that increase predictive performance. However, in a more
    general sense, this package can do the above for any set of
    clustering solutions for i observations of j variables.
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
Introduces some() and none, as well as match_with() from functional languages.
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
A framework for the optimization of breeding programs via optimum contribution selection and mate allocation. An easy to use set of function for computation of optimum contributions of selection candidates, and of the population genetic parameters to be optimized. These parameters can be estimated using pedigree or genotype information, and include kinships, kinships at native haplotype segments, and breed composition of crossbred individuals. They are suitable for managing genetic diversity, removing introgressed genetic material, and accelerating genetic gain. Additionally, functions are provided for computing genetic contributions from ancestors, inbreeding coefficients, the native effective size, the native genome equivalent, pedigree completeness, and 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.
Solver for linear, quadratic, and rational programs with linear, quadratic, and rational constraints. A unified interface to different R packages is provided. Optimization problems are transformed into equivalent formulations and solved by the respective package. For example, quadratic programming problems with linear, quadratic and rational constraints can be solved by augmented Lagrangian minimization using package 'alabama', or by sequential quadratic programming using solver 'slsqp'.  Alternatively, they can be reformulated as optimization problems with second order cone constraints and solved with package 'cccp', or transformed into semidefinite programming problems and solved using solver 'csdp'.
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.
Computes A-, MV-, D- and E-optimal or near-optimal row-column designs for two-colour cDNA microarray experiments using the linear fixed effects and mixed effects models where the interest is in a comparison of all pairwise treatment contrasts. The algorithms used in this package are based on the array exchange and treatment exchange algorithms adopted from Debusho, Gemechu and Haines (2016, unpublished) algorithms after adjusting for the row-column designs setup. The package also provides an optional method of using the graphical user interface (GUI) R package tcltk to ensure that it is user friendly.
Optimized inference in regression discontinuity designs, as proposed by Imbens and Wager (2017) <arXiv:1705.01677>.
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.
Calculates the optimal level of significance based on a decision-theoretic approach. 
	The optimal level is chosen so that the expected loss from hypothesis testing is minimized. 
	A range of statistical tests are covered, including the test for the population mean, population proportion, and a linear restriction in a multiple regression model. 
        The details are covered in Kim, Jae H. and Choi, In, Choosing the Level of Significance: A Decision-Theoretic Approach (December 18, 2017), available at SSRN: <https://ssrn.com/abstract=2652773> or <doi:10.2139/ssrn.2652773>.
        See also Kim and Ji (2015) <doi:10.1016/j.jempfin.2015.08.006>.
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 as default). 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'.
All the methods in this package generate a vector of uniform order statistics using a beta distribution and use an inverse cumulative distribution function for some distribution to give a vector of random order statistic variables for some distribution. This is much more efficient than using a loop since it is directly sampling from the order statistic distribution.
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.
Ordinal data classification, clustering and co-clustering using model-based approach with the Bos distribution for ordinal data (Christophe Biernacki and Julien Jacques (2016) <doi:10.1007/s11222-015-9585-2>).
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.
The ordinal forest (OF) method allows ordinal regression with high-dimensional
  and low-dimensional data. After having constructed an OF prediction rule using a training dataset, 
  it can be used to predict the values of the ordinal target variable for new observations.
  Moreover, by means of the (permutation-based) variable importance measure of OF, it is also
  possible to rank the covariates with respect to their importances in the prediction of the 
  values of the ordinal target variable.
  OF is presented in Hornung (2017).
  The main functions of the package are: ordfor() (construction of OF) and predict.ordfor() 
  (prediction of the target variable values of new observations).
  References:
  Hornung R. (2017) Ordinal Forests. Tech. Rep. 212, Department of Statistics, 
  University of Munich.
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 model families include cumulative probability, stopping ratio, 
    continuation ratio, and adjacent category. These families are a subset of 
    vector glm's which belong to a model class we call the elementwise link 
    multinomial-ordinal (ELMO) class. Each family in this class links a vector 
    of covariates to a vector of class probabilities. Each of these families 
    has a parallel form, which is appropriate for ordinal response data, as 
    well as a nonparallel form that is appropriate for an unordered categorical
    response, or as a more flexible model for ordinal data. The parallel model
    has a single set of coefficients, whereas the nonparallel model has a set of
    coefficients for each response category except the baseline category. It is 
    also possible to fit a model with both parallel and nonparallel terms, which 
    we call the semi-parallel model. The semi-parallel model has the flexibility 
    of the nonparallel model, but the elastic net penalty shrinks it toward the 
    parallel model. For details, refer to Wurm, Hanlon, and Rathouz (2017) 
    <arXiv:1706.05003>.
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 Onigmo 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.
Helper functions for Org files (<http://orgmode.org/>):
  a generic function 'toOrg' for transforming R objects into Org
  markup (most useful for data frames; there are also methods for
  Dates/POSIXt) and a function to read Org tables into data frames.
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.
Provides a general framework for the application of
    cross-validation schemes to particular functions. By allowing arbitrary
    lists of results, origami accommodates a range of cross-validation
    applications.
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/>).
Objects and methods to handle and solve 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. See "The Fermat-Weber location problem revisited" by Brimberg, Mathematical Programming, 1, pg. 71-76, 1995. <DOI:10.1007/BF01592245>. General global optimization algorithms are used to solve the problem, along with the adhoc Weiszfeld method, see "Sur le point pour lequel la Somme des distances de n points donnes est minimum", by Weiszfeld, Tohoku Mathematical Journal, First Series, 43, pg. 355-386, 1937.
Help and demo in Spanish of the orloca package. (Ayuda y demo en espanol del paquete orloca.) Objetos y metodos para manejar y resolver el problema de localizacion de suma minima, tambien conocido como problema de Fermat-Weber. El problema de localizacion de suma minima busca un punto tal que la suma ponderada de las distancias a los puntos de demanda se minimice. Vease "The Fermat-Weber location problem revisited" por Brimberg, Mathematical Programming, 1, pag. 71-76, 1995. <DOI: 10.1007/BF01592245>. Los algoritmos generales de optimizacion global se usan para resolver el problema, junto con el metodo adhoc Weiszfeld, ver "Sur le point pour lequel la Somme des distance de n points donnes est minimum", por Weiszfeld, Tohoku Mathematical Journal, First Series, 43, pag. . 355-386, 1937.
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.
Utilize an orthogonality constrained optimization algorithm of Wen & Yin (2013) <DOI:10.1007/s10107-012-0584-1> to solve a variety of dimension reduction problems in the semiparametric framework, such as Ma & Zhu (2013) <DOI:10.1214/12-AOS1072>, and Sun, Zhu, Wang & Zeng (2017) <arXiv:1704.05046>. It also serves as a general purpose optimization solver for problems with orthogonality constraints.
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).
Functions for implementing different versions of the OSCV method in the kernel regression and density estimation frameworks. The package mainly supports the following articles: (1) Savchuk, O.Y., Hart, J.D. (2017). Fully robust one-sided cross-validation for regression functions. Computational Statistics, <doi:10.1007/s00180-017-0713-7> and (2) Savchuk, O.Y. (2017). One-sided cross-validation for nonsmooth density functions, <arXiv:1703.05157>.
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.
Expands quoted language by recursively replacing any symbol that
    points to quoted language with the language it points to.  The recursive
    process continues until only symbols that point to non-language objects
    remain.  The resulting quoted language can then be evaluated normally.  This
    differs from the traditional 'quote'/'eval' pattern because it resolves
    intermediate language objects that would interfere with evaluation.
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).
Download and import of 'OpenStreetMap' ('OSM') data as 'sf' or 'sp'
    objects.  'OSM' data are extracted from the 'Overpass' web server and
    processed with very fast 'C++' routines for return to 'R'.
The multispecies and individual-based model (IBM) 'OSMOSE'
  (Shin and Curry (2001) <doi:10.1016/S0990-7440(01)01106-8> and Shin and Curry (2004) <doi:10.1139/f03-154>)
  focuses on fish species. This model assumes opportunistic predation based on spatial co-occurrence and size
  adequacy between a predator and its prey (size-based opportunistic predation). It
  represents fish individuals grouped into schools, which are characterized by their
  size, weight, age, taxonomy and geographical location (2D model), and which undergo
  major processes of fish life cycle (growth, explicit predation, natural and starvation
  mortalities, reproduction and migration) and fishing exploitation. The model needs
  basic biological parameters that are often available for a wide range of species, and
  which can be found in 'FishBase' for instance (see <http://www.fishbase.org/search.php>), and fish spatial distribution data. This
  package provides tools to build and run simulations using the 'OSMOSE' model.
Bespoke 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. This package allows to compute distances (travel time and 
    kilometric distance) between points and travel time matrices.
Oversampling of imbalanced univariate time series classification data 
    using integrated ESPO and ADASYN methods. Enhanced Structure Preserving Oversampling 
    (ESPO) is used to generate a large percentage of the synthetic minority samples 
    from univariate labeled time series under the modeling assumption that the predictors 
    are Gaussian. ESPO estimates the covariance structure of the minority-class samples 
    and applies a spectral filer to reduce noise. Adaptive Synthetic (ADASYN) sampling 
    approach is a nearest neighbor interpolation approach which is subsequently applied 
    to the ESPO samples. This code is ported from a 'MATLAB' implementation by Cao et al. 
    <doi:10.1109/TKDE.2013.37> and adapted for use with Recurrent Neural Networks 
    implemented in 'TensorFlow'.
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.
Sample from the limiting distributions of empirical Wasserstein
    distances under the null hypothesis and under the alternative. Perform a 
    two-sample test on multivariate data using these limiting distributions and 
    binning.
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 (2016) <doi:10.1080/01621459.2015.1100996>, and Coretto and Hennig (2017) <arXiv: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 rRNA gene amplicon sequencing, as well as example data. It contains the data and scripts used in the paper Linz, et al. (2017) "Bacterial community composition and dynamics spanning five years in freshwater bog lakes," <doi: 10.1128/mSphere.00169-17>.
Enables automated visualization of variable 
    distribution and changes over time for predictive model building.
    Computes summary statistics aggregated by time for 
    large datasets, and creates plots for variable level monitoring.
Fit and compare Ornstein-Uhlenbeck models for evolution along a phylogenetic tree.
Bayesian reconstruction of disease outbreaks using epidemiological
    and genetic information.
Bayesian reconstruction of disease outbreaks using epidemiological
    and genetic information. Jombart T, Cori A, Didelot X, Cauchemez S, Fraser
    C and Ferguson N. 2014. <doi:10.1371/journal.pcbi.1003457>.
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.
Potential outliers are identified for all combinations of a dataset's variables. The available methods are HDoutliers() from the package 'HDoutliers', FastPCS() from the package 'FastPCS', mvBACON() from 'robustX', adjOutlyingness() from 'robustbase', DectectDeviatingCells() from 'cellWise'.
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.
Create regression tables for publication.
    Currently supports 'lm', 'glm', 'survreg', and 'ivreg' outputs.
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.
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.
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 core functionalities; aggregating data to a higher level interval
    (thicken) and imputing records where observations were absent (pad).
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).
Provides access to material from the book "Processing and Analyzing Financial Data with R" by Marcelo Perlin (2017) available at <https://sites.google.com/view/pafdr/home>.
Statistical methods for estimating preferential attachment and node fitness generative mechanisms in temporal complex networks are provided.
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.
Many datasets and a set of graphics (based on ggplot2), statistics, effect sizes and hypothesis tests are provided 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.
Pairing observations according to a chosen formula and facilitates bilateral analysis of the panel data. Paring is possible for observations, as well as for vectors of observations ordered with respect to time.
Provides data sets and functions for exploration of Pakistan Population Census 2017 (<http://www.pbscensus.gov.pk/>).
Provides data set and functions for exploration of Multiple Indicator Cluster Survey (MICS) 2014 Child questionnaire data for Punjab, Pakistan (<http://www.mics.unicef.org/surveys>).
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2014 Household questionnaire data for Punjab, Pakistan (<http://www.mics.unicef.org/surveys>).
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2014 Household Listing questionnaire data for Punjab, Pakistan (<http://www.mics.unicef.org/surveys>).
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2014 Women (age 15-49 years) questionnaire data for Punjab, Pakistan (<http://www.mics.unicef.org/surveys>).
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 to fit 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 for point process models 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' 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.
A fast parallelized alternative to R's native 'dist' function to
    calculate distance matrices for continuous, binary, and multi-dimensional
    input matrices, which supports a broad variety of 39 predefined distance
    functions from the 'stats', 'proxy' and 'dtw' R packages, as well as user-
    defined functions written in C++. For ease of use, the 'parDist' function
    extends the signature of the 'dist' function and uses the same parameter
    naming conventions as distance methods of existing R packages. The package
    is mainly implemented in C++ and leverages the 'RcppParallel' package to
    parallelize the distance computations with the help of the 'TinyThread'
    library. Furthermore, the 'Armadillo' linear algebra library is used for
    optimized matrix operations during distance calculations. The curiously
    recurring template pattern (CRTP) technique is applied to avoid virtual
    functions, which improves the Dynamic Time Warping calculations while
    the implementation stays flexible enough to support different DTW step
    patterns and normalization methods.
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.
A suite of tools for analysing pedigrees with marker data, including parametric linkage analysis, forensic computations, relatedness analysis and marker simulations. The core of the package is an implementation of the Elston-Stewart algorithm for pedigree likelihoods, extended to allow mutations as well as complex inbreeding. Features for linkage analysis include singlepoint LOD scores, power analysis, and multipoint analysis (the latter through a wrapper to the 'MERLIN' software). Forensic applications include exclusion probabilities, genotype distributions and conditional simulations. Data from the 'Familias' software can be imported and analysed in 'paramlink'. Finally, 'paramlink' offers many utility functions for creating, manipulating and plotting pedigrees with or without marker data (the actual plotting is done by the 'kinship2' package).
An interface to simplify organizing parameters used in a package,
    using external configuration files. This attempts to provide a cleaner
    alternative to options().
Run simulations or other functions while easily varying parameters
    from one iteration to the next. Some common use cases would be grid search
    for machine learning algorithms, running sets of simulations (e.g.,
    estimating statistical power for complex models), or bootstrapping under
    various conditions. See the 'paramtest' documentation for more information
    and examples.
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:
                 exponential, Weibull, inverse Weibull (Fréchet),
                 Gompertz, lognormal, log-skew-normal, 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.
Provides various tools for analysing UK political data, including creating political cartograms and retrieving data.
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 ThermoFisher 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.
Perform flexible simulation studies using one or multiple computer cores.
          The package is set up to be usable on high-performance clusters in addition
          to being run locally, see examples on <https://github.com/SachaEpskamp/parSim>.
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.
A time series is said to be partially autoregressive if it can be represented as a sum of a random walk and an autoregressive sequence without unit roots. This package fits partially autoregressive time series, where the autoregressive  component is AR(1).  This may be of use in modeling certain financial time series.
A collection of time series is partially cointegrated if a linear combination of these time series can be found so that the residual spread is partially autoregressive - meaning that it can be represented as a sum of an autoregressive series and a random walk. This concept is useful in modeling certain sets of financial time series and beyond, as it allows for the spread to contain transient and permanent components alike. Partial cointegration has been introduced by Clegg and Krauss (2016) <https://hdl.handle.net/10419/140632>, along with a large-scale empirical application to financial market data. The partialCI package comprises estimation, testing, and simulation routines for partial cointegration models in state space. Clegg et al. (2017) <https://hdl.handle.net/10419/150014> provide an in in-depth discussion of the package functionality as well as illustrating examples in the fields of finance and macroeconomics.
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
Provides several measures ((dis)similarity, distance/metric,
    correlation, entropy) for comparing two partitions of the same set of
    objects. The different measures can be assigned to three different
    classes: Pair comparison (containing the famous Jaccard and Rand
    indices), set based, and information theory based.
    Many of the implemented measures can be found in
    Albatineh AN, Niewiadomska-Bugaj M and Mihalko D (2006)
    <doi:10.1007/s00357-006-0017-z> and
    Meila M (2007) <doi:10.1016/j.jmva.2006.11.013>.
    Partitions are represented by vectors of class labels which allow a
    straightforward integration with existing clustering algorithms
    (e.g. kmeans()). The package is mostly based on the S4 object system.
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. A description of this package was published
  by Hothorn and Zeileis (2015) <http://jmlr.org/papers/v16/hothorn15a.html>.
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.
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
Smooths the process of working with country names and codes via 
    powerful parsing, standardization, and conversion utilities arranged in a 
    simple, consistent API. Country name formats include multiple sources 
    including the Unicode Common Locale Data 
    Repository (CLDR, <http://cldr.unicode.org/>) common-sense standardized 
    names in hundreds of languages.
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.
Provides functions to simplify the PatentsView API
    (<http://www.patentsview.org/api/doc.html>) query language,
    send GET and POST requests to the API's seven endpoints, and parse the data
    that comes back.
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.
Functions to compute and display the area-based deviation between spatial paths and to compute a mapping based on minimizing area and distance-based cost.  For details, see: Mueller, S. T., Perelman, B. S., & Veinott, E. S. (2016) <DOI:10.3758/s13428-015-0562-7>.
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 a set of functions to efficiently recognize and clean the continuous dorsal pattern of a female brown anole lizard (Anolis sagrei) traced from 'ImageJ', an open platform for scientific image analysis (see <https://imagej.net> for more information), and extract common features such as the pattern sinuosity indices, coefficient of variation, and max-min width.
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.
Quantification of variation in organismal color patterns as obtained from image data. 
    Patternize defines homology between pattern positions across images either through 
    fixed landmarks or image registration. Pattern identification is performed by 
    categorizing the distribution of colors using either an RGB threshold or unsupervised 
    image segmentation.
Creates aesthetically pleasing and informative pie charts, bar charts and box plots with colors, patterns, and images.
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.
Efficient algorithms for fitting weighted least squares regression with \eqn{L_{1}}{L1} regularization on both the
 coefficients and weight vectors, which is able to perform simultaneous variable selection 
 and outliers detection efficiently.
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.
  Supports several parallel processing backends.
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 birth-death model of diversification. See
    Etienne, R.S. & J. Rosindell 2012 <doi:10.1093/sysbio/syr091>;
    Lambert, A., H. Morlon & R.S. Etienne 2014, <doi:10.1007/s00285-014-0767-x>;
    Etienne, R.S., H. Morlon & A. Lambert 2014, <doi:10.1111/evo.12433>.
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.
The PBIB designs are important type of incomplete block designs having wide area of their applications for example in agricultural experiments, in plant breeding, in sample surveys etc. This package constructs various series of PBIB designs and assists in checking all the necessary conditions of PBIB designs and the association scheme on which these designs are based on. It also assists in calculating the efficiencies of PBIB designs with any number of associate classes. The package also constructs Youden-m square designs which are Row-Column designs for the two-way elimination of heterogeneity. The incomplete columns of these Youden-m square designs constitute PBIB designs. With the present functionality, the package will be of immense importance for the researchers as it will help them to construct PBIB designs, to check if their PBIB designs and association scheme satisfy various necessary conditions for the existence, to calculate the efficiencies of PBIB designs based on any association scheme and to construct Youden-m square designs for the two-way elimination of heterogeneity. R. C. Bose and K. R. Nair (1939) <http://www.jstor.org/stable/40383923>.
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).
  Parallelization (mc.core > 1) works only on *nix (Linux, Unix such as macOS) system due to
  the lack of fork() functionality, which is essential for mc*apply, on Windows.
Permutation based
    non-parametric analysis of CRISPR screen data. Details about this algorithm are published in the following paper published on BMC genomics, Jia et al. (2017) <doi:10.1186/s12864-017-3938-5>: A permutation-based non-parametric analysis of CRISPR screen data. Please cite this paper if you use this algorithm for your paper.
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, data for a global shoreline, and other data sets in the
   public domain. Under the user's R library directory '.libPaths()',
   specifically in './PBSmapping/doc', a complete user's guide is offered and
   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.
A central part of using PCA and similar methods are creating and using plots. Visual inspection of these plots is an extremely powerful way of understanding your matrix-form data. Doing this is essential when applying multivariate methods on real-life matrix data. This package provides plots, tailor made for understanding a PCA model on a matrix dataset. We have worked to design plots that supports the mathematical properties of PCA, but still easy to use and understand. The flexibility of the plotting capacity of R has been utilized to create a look and feel that supports the understanding of the PCA model. In addition to that, this package contains a prediction function, using PCA to fill "holes" in a matrix. This package does not provide you with the scores and loadings etc in numeric form. This is provided through the underlying package 'mixOmics' which is the basis of this package. In addition, applied PCA on real life data requires flexibility. The original way of calculating a PCA is by using the method SVD, used in e.g. 'stats' package. This is preferred from a precision point of view. But 'PCA4you' is based on the method NIPALS for calculating the PCA model. Hence, the following cases, which are not (or poorly) supported by SVD, are fully supported by 'PCA4you':  1) Non complete matrices with little or a lot of missing values 2) More row than columns, sometimes a huge difference 3) More columns than rows, sometimes a huge difference 4) Complete decomposition is not possible or desired. Sometimes all components can not be calculated for different reasons. Big data, system resources or time can limit the desired number of principal components.
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>.
A suite of non-parametric, visual tools for assessing differences in data structures
    for two datasets that contain different observations of the same variables. These tools are all 
    based on Principal Component Analysis (PCA) and thus effectively address differences in the structures
    of the covariance matrices of the two datasets. The PCASDC tools consist of easy-to-use, 
    intuitive plots that each focus on different aspects of the PCA decompositions. The cumulative eigenvalue
    (CE) plot describes differences in the variance components (eigenvalues) of the deconstructed covariance matrices. The
    angle plot presents the information loss when moving from the PCA decomposition of one dataset to the 
    PCA decomposition of the other. The chroma plot describes the loading patterns of the two datasets, thereby
    presenting the relative weighting and importance of the variables from the original dataset.
Implementations of several 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) <DOI: 10.1109/ICDM.2016.0054>,
        awPCA (Park and Klabjan 2016) <DOI: 10.1109/ICDM.2016.0054>,
        PCA-Lp (Kwak 2014) <DOI:10.1109/TCYB.2013.2262936>, and
        SharpEl1-PCA (Brooks and Dula, submitted).
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), the Generalized Adjustment Criterion (GAC)
  and some related functions are implemented. Functions for incorporating
  background knowledge are provided.
Implements transformations of P-values to the smallest possible Bayes factor within the specified class of alternative hypotheses, as described in Held & Ott (2017, On p-values and Bayes factors, Annual Review of Statistics and Its Application, 5, to appear). Covers several common testing scenarios such as z-tests, t-tests, likelihood ratio tests and the F-test of overall significance in the linear model.
Implements 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. The methods are described in Croux et al. (2006) <doi:10.2139/ssrn.968376>, Croux et al. (2013) <doi:10.1080/00401706.2012.727746>, Todorov and Filzmoser (2013) <doi:10.1007/978-3-642-33042-1_31>.
An implementation of the pediatric complex chronic conditions (CCC)
    classification system using R and C++.
Implements methods to automate the Auer-Gervini graphical
  Bayesian approach for determining the number of significant
  principal components. Automation uses clustering, change points, or
  simple statistical models to distinguish "long" from "short" steps
  in a graph showing the posterior number of components as a function
  of a prior parameter.
Method extends multivariate and functional dynamic principal components
    to periodically correlated multivariate time series. This package allows you to
    compute true dynamic principal components in the presence of periodicity. 
    We follow implementation guidelines as described in Kidzinski, Kokoszka and
    Jouzdani (2017), in Principal component analysis of periodically correlated
    functional time series <arXiv:1612.00040>.
Semiparametric regression analysis of panel count data under the non-homogeneous Poisson
             process model with and without Gamma frailty using monotone splines.
Functions for generating progressively Type-II censored data in
    a mixture structure and fitting models using a constrained EM algorithm. It can also
    create a progressive Type-II censored version of a given real dataset to be considered for
    model fitting.
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), that seeks
    to maximize the proportion of variance (in the response vector) being explained
    by a set of covariates.
We provide several algorithms to compute the genotype ancestry scores (such as eigenvector projections) in the case where highly correlated individuals are involved.
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.
Implementation of PCMRS (Partial Credit Model with Response Styles) as proposed in by Tutz, Schauberger and Berger (2016) <https://epub.ub.uni-muenchen.de/29373/> .  PCMRS is an extension of the regular partial credit model. PCMRS allows for an additional person parameter that characterizes the response style of the person. By taking the response style into account, the estimates of the item parameters are less biased than in partial credit models.
Performs arm-based network meta-analysis for datasets with binary, continuous, and count outcomes using the Bayesian methods of Zhang et al (2014) <doi:10.1177/1740774513498322> and Lin et al (2017) <doi:10.18637/jss.v080.i05>.
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).
Calculates the amplification efficiency and curves from real-time 
  quantitative PCR (Polymerase Chain Reaction) data. Estimates the relative 
  expression from PCR data using the double delta CT and the standard curve 
  methods Livak & Schmittgen (2001) <doi:10.1006/meth.2001.1262>. Tests for 
  statistical significance using two-group tests and linear regression 
  Yuan et al. (2006) <doi: 10.1186/1471-2105-7-85>.
Implementing the Coalescent Approach to PCR Simulation.
Extracts features from amplification curve data of quantitative Polymerase Chain Reactions (qPCR) (Pabinger, Stephan, Stefan Roediger, Albert Kriegner, Klemens Vierlinger, and Andreas Weinhauusel (2014) <doi:10.1016/j.bdq.2014.08.002>) for machine learning purposes. Helper functions prepare the amplification curve data for processing as functional data (e.g., Hausdorff distance) or enable the plotting of amplification curve classes (negative, ambiguous, positive). The hookreg() and hookregNL() functions can be used to predict amplification curves with an hook effect-like curvature. The pcrfit_single() function can be used to extract features from an amplification curve.
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.
Data sets for the Panel Data Econometrics with R book.
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, Deutsche Bundesbank, 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 supports high quality rendering of PDF documents info
    PNG, JPEG, TIFF format, or into raw bitmap vectors for further processing in R.
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.
Building patient level networks for prediction of 
        medical outcomes and draw the cluster of network.        
        This package is based on paper Personalized disease 
        networks for understanding and predicting cardiovascular 
        diseases and other complex processes 
        (See Cabrera et al. <http://circ.ahajournals.org/content/134/Suppl_1/A14957>).
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 and/or small
    ligands coordinated to a given prosthetic centre. Files have to be in the local
    file system and contain proper 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
An implementation of data analysis tools for samples of symmetric or 
  Hermitian positive definite matrices, such as collections of covariance matrices 
  or spectral density matrices. The tools in this package can be used to perform (i) 
  intrinsic manifold wavelet regression and clustering for curves (1D) or surfaces (2D) 
  of Hermitian positive definite matrices, and (ii) exploratory data analysis and inference 
  for samples of (curves of) Hermitian positive definite matrices by means of intrinsic manifold 
  data depth and manifold rank-based hypothesis tests.
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.
Chromatin immunoprecipitation DNA sequencing results in genomic
    tracks that show enriched regions or peaks where proteins are bound.
    This package implements fast C code that computes the true and false
    positives with respect to a database of annotated regions.
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.
A quadratic time dynamic programming algorithm
 can be used to compute an approximate solution to the problem of
 finding the most likely changepoints
 with respect to the Poisson likelihood, subject
 to a constraint on the number of segments, and the changes which must
 alternate: up, down, up, down, etc. For more info read
 <http://proceedings.mlr.press/v37/hocking15.html>
 "PeakSeg: constrained optimal segmentation and supervised penalty learning
 for peak detection in count data" by TD Hocking et al,
 proceedings of ICML2015.
Jointly segment several ChIP-seq samples to find the peaks 
 which are the same and different across samples. The fast approximate
 maximum Poisson likelihood algorithm is described in
 "PeakSegJoint: fast supervised peak detection via joint segmentation
 of multiple count data samples"
 <arXiv:1506.01286> by TD Hocking and G Bourque.
Computes optimal changepoint models using the
 Poisson likelihood for non-negative count data,
 subject to the PeakSeg constraint:
 the first change must be up, second change down, third change up, etc.
 For more info about the models and algorithms,
 read "A log-linear time algorithm for constrained changepoint detection"
 <arXiv:1703.03352> by TD Hocking et al.
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.
Implementations of algorithms from 
 Learning Sparse Penalties for Change-point Detection
 using Max Margin Interval Regression, by
 Hocking, Rigaill, Vert, Bach
 <http://proceedings.mlr.press/v28/hocking13.html>
 published in proceedings of ICML2013.
Flexible copula density estimation with penalized hierarchical B-Splines.
Estimating Non-Simplified Vine Copulas Using Penalized 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).
Offers routines for estimating densities and copula distribution of R-vines using penalized splines.
Robust penalized elastic net S and MM estimator for linear
    regression. The method is described in detail in
    Cohen Freue, G. V., Kepplinger, D., Salibian-Barrera, M., and Smucler, E.
    (2017) <https://gcohenfr.github.io/pdfs/PENSE_manuscript.pdf>.
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.
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.
Includes functions to calculate several physicochemical properties and indices for amino-acid sequences as well as to read and plot 'XVG' 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.
An implementation of two functions that estimate values for percentiles from an ordered categorical variable as described by Reardon (2011, isbn:978-0-87154-372-1). One function estimates percentile differences from two percentiles while the other returns the values for every percentile from 1 to 100.
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).
Implementations of permutation approach to hypothesis testing for quasi-independence of truncation time and failure time. The implemented approaches are powerful against non-monotone alternatives and thereby offer protection against erroneous assumptions of quasi-independence. The proposed tests use either a conditional or an unconditional method to evaluate the permutation p-value. The conditional method was first developed in Tsai (1980) <doi:10.2307/2336059> and Efron and Petrosian (1992) <doi:10.1086/171931>. The unconditional method provides a valid approximation to the conditional method, yet computationally simpler and does not hold fixed the size of each risk sets. Users also have an option to carry out the proposed permutation tests in a parallel computing fashion.
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.
Provides functions for fitting and validation of subgroup
    identification and personalized medicine models under the general subgroup
    identification framework of Chen et al. (2017) <doi:10.1111/biom.12676>.
    This package is intended for use for both randomized controlled trials and
    observational studies.
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.
The perturbR() function incrementally perturbs network edges 
    and compares the resulting community detection solutions 
    from the rewired networks with the solution found for the original network. 
    These comparisons aid in understanding the stability of the 
    original solution. The package requires symmetric, weighted (specifically, count) matrices/networks.
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.
Application that retrieves papers metadata from the OnePetro website.
    Thousands of papers on oil and gas live in OnePetro. By retrieving metadata 
    from the search queries, a summary of papers that match the
    query words, can be retrieved for further analysis and text mining. There 
    are some statistics and data mining provided such as word cloud plots, keywords 
    frequency, conversion to corpus document, and removal of common usage words.
    OnePetro link: <https://www.onepetro.org/>.
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.
Package contains methods for estimation of phylogenetic trees and
    networks using Maximum Likelihood, Maximum Parsimony, distance methods and
    Hadamard conjugation. Allows to compare trees, models selection and offers
    visualizations for trees and split networks.
Pareto front based statistical tool for detecting heterogeneity in gene sets and biological modules from time-course data.
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.
Implements our Bayesian phase I repeated measurement design that accounts for multidimensional toxicity endpoints from multiple treatment cycles. The package also provides a novel design to account for both multidimensional toxicity endpoints and early-stage efficacy endpoints in the phase I design. For both designs, functions are provided to recommend the next dosage selection based on the data collected in the available patient cohorts and to simulate trial characteristics given design parameters. Yin, Jun, et al. (2017) <doi:10.1002/sim.7134>.
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.
This takes in a series of multi-layer raster files and returns a phenology projection raster, following methodologies described in John (2016) <https://etda.libraries.psu.edu/catalog/13521clj5135>.
A collection of functions to process digital images, depict greenness index trajectories and extract relevant phenological stages.
Phenotype simulator allows for the flexible simulation of 
    phenotypes under different models, including fixed and background genetic 
    effects as well as correlated, fixed and background noise effects. Different 
    phenotypic effects can be combined into a final phenotype while controling 
    for the proportion of variance explained by each of the components. For each 
    component, the number of variables, their distribution and the design of 
    their effect across traits can be customised. The final simulated phenotypes
    and its components can be automatically saved into .rds or .csv files. In
    addition, for simulated genotypes, export into plink format is possible.
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.
Computes 46 optimized distance and similarity measures for comparing probability functions. These comparisons between probability functions have their foundations in a broad range of scientific disciplines from mathematics to ecology. The aim of this package is to provide a core framework for clustering, classification, statistical inference, goodness-of-fit, non-parametric statistics, information theory, and machine learning tasks that are based on comparing univariate or multivariate probability functions.
Fits proportional hazards model incorporating random effects using
    an EM algorithm using Markov Chain Monte Carlo at E-step. Vaida and Xu (2000) <doi:10.1002/1097-0258(20001230)19:24%3C3309::AID-SIM825%3E3.0.CO;2-9>.
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 in some cases writing, foreign files 
    containing spectral data from spectrometers and their associated software, 
    output from daylight simulation models in common use, and some spectral 
    data repositories. 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 (2017) <doi:10.1371/journal.pcbi.1005495>. 
    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.
Contains functions for importing and exporting 'dendrogram' 
    objects in parenthetic text format, and several 
    functions for command-line tree manipulation. 
    With an emphasis on speed and computational efficiency, 
    the package also includes a suite of tools for rapidly computing 
    distance matrices and building large trees using fast alignment-free 
    'k-mer' counting and divisive clustering techniques.
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.
A collection of tools for building RAxML supermatrix using
             PHYLIP or aligned FASTA files. These functions will be
             useful for building large phylogenies using multiple markers.
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.
A wide range of functions for phylogenetic analysis. Functionality is concentrated in phylogenetic comparative biology, but also includes a diverse array of methods for visualizing, manipulating, reading or writing, and even inferring phylogenetic trees and data. Included among the functions in phylogenetic comparative biology are various for ancestral state reconstruction, model-fitting, simulation of phylogenies and data, and multivariate analysis. There are a broad range of plotting methods for phylogenies and comparative data which include, but are not restricted to, methods for mapping trait evolution on trees, for projecting trees into phenotypic space or a geographic map, and for visualizing correlated speciation between trees. Finally, there are a number of functions for reading, writing, analyzing, inferring, simulating, and manipulating phylogenetic trees and comparative data not covered by other packages. For instance, there are functions for randomly or non-randomly attaching species or clades to a phylogeny, for estimating supertrees or consensus phylogenies from a set, for simulating trees and phylogenetic data under a range of models, and for a wide variety of other manipulations and analyses that phylogenetic biologists might find useful in their research.
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.
Phylocom integration, community analyses, null-models, traits and evolution in R
Computationally efficient tools for fitting generalized linear models and square root loss 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.
This function fits a reversible jump Bayesian piecewise exponential model that also includes the intensity of each event considered along with the rate of events.
Implements piecewise structural equation models.
Uses a generalized method to estimate the Potential Impact Fraction (PIF) and the Population Attributable Fraction (PAF) from cross-sectional data. It creates point-estimates, confidence intervals, and estimates of variance. In addition it generates plots for conducting sensitivity analysis. The estimation method corresponds to  Zepeda-Tello,  Camacho-García-Formentí, et al. 2017. 'Nonparametric Methods to Estimate the Potential Impact Fraction from Cross-sectional Data'. Unpublished manuscript. This package was developed under funding by Bloomberg Philanthropies.
Extension of the 'ARTP' package (Yu, K., et al. (2009) <doi:10.1002/gepi.20422>) for gene- and pathway-environment interaction. A permutation and a parametric bootstrap approaches have been implemented  for investigating gene- and pathway-environment interaction analysis (Truong, T., et al. (2014) <doi:10.1530/ERC-14-0121>).
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>.
Provides a 'pillar' generic designed for formatting columns
   of data using the full range of colours provided by modern terminals.
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 interface to the API by Pinnacle that allows Pinnacle customers to interact with the sports market data in R.See <https://www.pinnacle.com/en/api> for more information. The Pinnacle API can be used to place wagers, retrieve line information, retrieve account information.Please be aware that the TOC of Pinnacle apply <https://www.pinnacle.com/en/termsandconditions>. An account with Pinnacle is necessary to use the Pinnacle API.
Market odds from from Pinnacle, an online sports betting bookmaker (see <https://www.pinnacle.com> for more information). Included are datasets for the Major League Baseball (MLB) 2016 season and the USA election 2016. These datasets can be used to build models and compare statistical information with the information from prediction markets.The Major League Baseball (MLB) 2016 dataset can be used for sabermetrics analysis and also can be used in conjunction with other popular Major League Baseball (MLB) datasets such as Retrosheets or the Lahman package by merging by GameID.
A 'PNAS'-alike style for 'rmarkdown', derived from the
 'Proceedings of the National Academy of Sciences of the United States
 of America' ('PNAS', see <https://www.pnas.org>) 'LaTeX' style, and
 adapted for use with 'markdown' and 'pandoc'.
Convert Chinese characters into Pinyin (the official romanization system for Standard Chinese in mainland China, Malaysia, Singapore, and Taiwan. See <https://en.wikipedia.org/wiki/Pinyin> for details).
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" <arXiv: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.
A wrapper around the 'Parsing Expression Grammar Template Library', a C++11 library for generating
    Parsing Expression Grammars, that makes it accessible within Rcpp. With this, developers can implement
    their own grammars and easily expose them in R packages.
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.
Create regular pivot tables with just a few lines of R.  
    More complex pivot tables can also be created, e.g. pivot tables
    with irregular layouts, multiple calculations and/or derived 
    calculations based on multiple data frames.  Pivot tables are
    constructed using R only and can be written to a range of
    output formats (plain text, 'HTML', 'Latex' and 'Excel'), 
    including with styling/formatting.
Provides tools to show and draw image pixels using 'HTML' widgets 
  and 'Shiny' applications.  It can be used to visualize the 'MNIST' dataset
  for handwritten digit recognition or to create new image recognition datasets.
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.
Interactively explore various dependencies of a package(s) (on the Comprehensive R Archive Network Like repositories) and perform analysis using tidy philosophy. Most of the functions return a 'tibble' object (enhancement of 'dataframe') which can be used for further analysis. The package offers functions to produce 'network' and 'igraph' dependency graphs. The 'plot' method produces a static plot based on 'ggnetwork' and 'plotd3' function produces an interactive D3 plot based on 'networkD3'.
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.
A toolbox for data management common to pharmacokinetic and
  pharmacokinetic modeling and simulation, such as resampling,
  area-under-the-curve calculation, data chunking, custom csv
  output, and project scaffolding.
Provides functions to evaluate common
        pharmacokinetic/pharmacodynamic models and their gradients.
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 are
             1) CDISC SDTM terms
             2) Automatic slope selection with the same criterion of WinNonlin(R)
             3) Supporting both 'linear-up linear-down' and 'linear-up log-down' method
             4) Interval(partial) AUCs with 'linear' or 'log' interpolation method
             * Reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107).
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.
A tool for inferring kinase activity changes from 
    phosphoproteomics data. 'pKSEA' uses kinase-substrate 
    prediction scores to weight observed changes in 
    phosphopeptide abundance to calculate a phosphopeptide-level 
    contribution score, then sums up these contribution scores by 
    kinase to obtain a phosphoproteome-level kinase activity 
    change score (KAC score). 'pKSEA' then assesses the 
    significance of changes in predicted substrate abundances for 
    each kinase using permutation testing. This results in a 
    permutation score (pKSEA significance score) reflecting the 
    likelihood of a similarly high or low KAC from random chance, 
    which can then be interpreted in an analogous manner to an 
    empirically calculated p-value. 'pKSEA' contains default 
    databases of kinase-substrate predictions from 'NetworKIN' 
    (NetworKINPred_db) <http://networkin.info> 
    Horn, et. al (2014) <doi:10.1038/nmeth.2968>
    and of known kinase-substrate links from 'PhosphoSitePlus' 
    (KSEAdb) <https://www.phosphosite.org/>
    Hornbeck PV, et. al (2015) <doi:10.1093/nar/gku1267>.
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>).
Functions to prepare rankings data and fit the Plackett-Luce model
    jointly attributed to Plackett (1975) <doi:10.2307/2346567> and Luce 
    (1959, ISBN:0486441369). The standard Plackett-Luce model is generalized 
    to accommodate ties of any order in the ranking. Partial rankings, in which 
    only a subset of items are ranked in each ranking, are also accommodated in
    the implementation. Disconnected/weakly connected networks implied by the 
    rankings are handled by adding pseudo-rankings with a hypothetical item. 
    Methods are provided to estimate standard errors or quasi-standard errors 
    for inference as well as to fit Plackett-Luce trees. See the package website
    or vignette for full details.
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. 
    See Duursma (2015) <doi:10.1371/journal.pone.0143346>.
Estimation, prediction, thresholding, transformation, and plotting for partially linear additive quantile regression.  Intuitive functions for fitting and plotting partially linear additive quantile regression models.  Uses and works with functions from the 'quantreg' package.
Aims at detecting single nucleotide variation
    (SNV) and insertion/deletion (INDEL) in circulating tumor DNA (ctDNA), used
    as a surrogate marker for tumor, at each base position of an Next Generation
    Sequencing (NGS) analysis. Mutations are assessed by comparing the minor-allele
    frequency at each position to the measured PER in control samples.
Contains functions developed to combine the results of querying a plasmid database using
    short-read sequence typing with the results of a blast analysis against the query results.
Creates realistic simulated datasets for causal inference based on a user-supplied example study, Franklin JM, Schneeweiss S, Polinski JM, and Rassen JA (2014) <doi:10.1016/j.csda.2013.10.018>. 
              It samples units from the data with replacement, and then simulates the exposure, the outcome, or both, based on the observed covariate values in the real data.
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' (<https://pleiades.stoa.org/>) 'API', including 
    getting status data, places data, and creating a 'GeoJSON' 
    based map on 'GitHub' 'gists'.
Perform tests for pleiotropy of multiple traits of various variable types on genotypes for a genetic marker.
Functions for estimating probabilistic latent feature models with a disjunctive, conjunctive or additive mapping rule on (aggregated) binary three-way data.
Provides a graphical user interface for viewing and designing various types of graphs of the data. The graphs can be saved in different formats of an image.
Sequential Monte Carlo 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
Item response theory based methods are used to compute
        linking constants and conduct chain linking of unidimensional
        or multidimensional tests for multiple groups under a common
        item design.  The unidimensional methods include the Mean/Mean,
        Mean/Sigma, Haebara, and Stocking-Lord methods for dichotomous
        (1PL, 2PL and 3PL) and/or polytomous (graded response, partial
        credit/generalized partial credit, nominal, and multiple-choice
        model) items.  The multidimensional methods include the least
        squares method and extensions of the Haebara and Stocking-Lord
        method using single or multiple dilation parameters for
        multidimensional extensions of all the unidimensional
        dichotomous and polytomous item response models.  The package
        also includes functions for importing item and/or ability
        parameters from common IRT software, conducting IRT true score
        and observed score equating, and plotting item response
        curves/surfaces, vector plots, information plots, and comparison 
        plots for examining parameter drift.
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 econometrics.
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.
Utilizes the 'lme4' package and the optim() function from 'stats'
    to estimate (generalized) linear mixed models (GLMM) with factor
    structures using a profile likelihood approach, as outlined in
    Jeon and Rabe-Hesketh (2012) <doi:10.3102/1076998611417628>.
    Factor analysis and item response models can be extended to allow
    for an arbitrary number of nested and crossed random effects,
    making it useful for multilevel and cross-classified models.
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 with nested data easier.
Contains a graphical user interface to generate the diagnostic
    plots proposed by Bauer (2005; <doi:10.1207/s15328007sem1204_1>), 
    Pek & Chalmers (2015; <doi:10.1080/10705511.2014.937790>), and
    Pek, Chalmers, R. Kok, & Losardo (2015; <doi:10.3102/1076998615589129>) to investigate
    nonlinear bivariate relationships in latent regression models using structural
    equation mixture models (SEMMs).
Small self-contained plots for use in larger plots or to
    delegate plotting in other functions. Also contains a number of
    alternative color palettes and HSL color space based tools to modify
    colors or palettes.
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: logit-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.
Calculates and depicts probabilistic long-term effects
    in binary models with temporal dependence variables. The package performs
    two tasks. First, it calculates the change in the probability of the event
    occurring given a change in a theoretical variable. Second, it calculates
    the rolling difference in the future probability of the event for two
    scenarios: one where the event occurred at a given time and one where the
    event does not occur. The package is consistent with the recent movement to
    depict meaningful and easy-to-interpret quantities of interest with the
    requisite measures of uncertainty. It is the first to make it easy for
    researchers to interpret short- and long-term effects of explanatory
    variables in binary autoregressive models, which can have important
    implications for the correct interpretation of these models.
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.
Provides tools for download, read, and analyse the PNADc
	household survey from Brazilian Institute of Geography and Statistics.
	The data must be downloaded from the official website <https://www.ibge.gov.br/>. 
	Further analyses must be made using package 'survey'.
This package provides an easy and simple way to read, write and display bitmap images stored in the PNG format. It can read and write both files and in-memory raw vectors.
An R package for Probit-Normal Marginalized Transition
        Random Effects Models
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.
Validate data in local data frames,
    local 'tibble' objects, in 'CSV' and 'TSV' files,
    and in database tables ('PostgreSQL' and 'MySQL').
    Validation pipelines can be made using
    easily-readable, consecutive validation steps
    and such pipelines allow for switching of the
    data table context. Upon execution of the
    validation plan, several reporting options are
    available. User-defined thresholds for failure
    rates allow for the determination of appropriate
    reporting actions (e.g., sending email
    notifications).
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.
Provides the probability, distribution, and quantile functions and random number generator for the Poisson-Binomial distribution.  This package relies on FFTW to implement the discrete Fourier transform, so that it is much faster than the existing implementation of the same algorithm in R.
Generation of multiple count, binary and ordinal variables simultaneously 
             given the marginal characteristics and association structure. Throughout the package,
             the word 'Poisson' is used to imply count data under the assumption of Poisson distribution.
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.
Type testRoundOfPoker() to demonstrate the game of Texas Hold ‘Em poker.  Rotate the dealer button, deal cards, rank each hand, compare ranks, break ties (if necessary), determine the winner, output a textual summary, and output a graphical user interface.
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.
Detecting markers of politeness in English natural language. This package allows researchers to easily visualize and quantify politeness between groups of documents. This package combines prior research on the linguistic markers of politeness (Brown & Levinson, 1987 <http://psycnet.apa.org/record/1987-97641-000>; Danescu-Niculescu-Mizil et al., 2013 <arXiv:1306.6078>; Voigt et al., 2017 <doi:10.1073/pnas.1702413114>). We thank the Spencer Foundation, the Hewlett Foundation, and Harvard's Institute for Quantitative Social Science for support.
Supports analysis of aerobiological data. Available features include determination of pollen season limits, replacement of outliers (Kasprzyk and Walanus (2014) <doi:10.1007/s10453-014-9332-8>), and calculation of growing degree days.
Client for the HuffPost Pollster API, which provides
    access to U.S. polls on elections and political opinion.
Library for corpus analysis using the Corpus Workbench as an
    efficient back end for indexing and querying large corpora. The package offers
    functionality to flexibly create partitions and to carry out basic statistical
    operations (count, co-occurrences etc.). The original full text of documents
    can be reconstructed and inspected at any time. Beyond that, the package is
    intended to serve as an interface to packages implementing advanced statistical
    procedures. Respective data structures (document term matrices, term co-
    occurrence matrices etc.) can be created based on the indexed corpora.
Provides a user-friendly R wrapper on top of the Poloniex (Cryptocurrency Trading) REST API (see <https://poloniex.com/support/api/> for more information).
    Results are converted into R data structures and are returned to the users in an intuitive manner.
    The package provides the users with two separate S4 classes:
    - 'PoloniexPublicAPI': Consists of wrapper methods on top of the Poloniex Public REST API.
    - 'PoloniexTradingAPI': Consists of wrapper methods on top of the Poloniex Trading REST API.
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.
Tools for creating, viewing, and assessing qualitative
 palettes with many (20-30 or more) colors.
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>.
Creation of linkage maps in polyploid species from marker dosage
    scores of an F1 cross from two heterozygous parents. Currently works for autotriploid, autotetraploid and autohexaploid species, 
    as well as segmental allotetraploids. Methods are described in a preprint manuscript of Bourke et al. (2017) <doi:10.1101/228817>.
A collection of functions to implement a class for univariate
  polynomial manipulations.
Implements univariate polynomial operations in R, including
  polynomial arithmetic, finding zeros, plotting, and some operations on
  lists of polynomials.
Functions to perform paternity exclusion via allele
    matching, in autopolyploid species having ploidy 4, 6, or 8. The
    marker data used can be genotype data (copy numbers known) or
    'allelic phenotype data' (copy numbers not known).
Poly-PK strategy is a new strategy of pharmacokinetic analysis of multi-component drugs (Guoxiang Xie, Tianlu Chen, Wei Jia, et al. (2012)<doi:10.1021/pr300318m>; Ke Lan, Guoxiang Xie and Wei Jia. (2013)<doi:10.1155/2013/819147>). This package is the first implementation of the Poly-PK strategy with 10 easy-to-use functions.
Tools for reshaping, plotting, and manipulating matrices of orthogonal polynomials.
A collection of tools to handle microsatellite data of
 any ploidy (and samples of mixed ploidy) where allele copy number is not
 known in partially heterozygous genotypes.  It can import and export data in
 ABI 'GeneMapper', 'Structure', 'ATetra', 'Tetrasat'/'Tetra', 'GenoDive', 'SPAGeDi',
 'POPDIST', 'STRand', and binary presence/absence formats.  It can calculate
 pairwise distances between individuals using a stepwise mutation model or
 infinite alleles model, with or without taking ploidies and allele frequencies
 into account.  These distances can be used for the calculation of clonal
 diversity statistics or used for further analysis in R.  Allelic diversity
 statistics and Polymorphic Information Content are also available.  polysat can 
 assist the user in estimating the ploidy of samples, and it can estimate allele 
 frequencies in populations, calculate pairwise or global differentiation statistics 
 based on those frequencies, and export allele frequencies to 'SPAGeDi' and 'adegenet'.  
 Functions are also included for assigning alleles to isoloci in cases where one pair 
 of microsatellite primers amplifies alleles from two or more independently
 segregating isoloci.  polysat is described by Clark and Jasieniuk (2011)
 <doi:10.1111/j.1755-0998.2011.02985.x> and Clark and Schreier (2016)
 <doi:10.1111/1755-0998.12639>.
Perform classic chi-squared tests and Ripol et al(1999)
             binomial confidence interval approach for autopolyploid
             dominant markers. Also, dominant markers may be generated
             for families of offspring where either one or both of the
             parents possess the marker. Missing values and
             misclassified markers may be generated at random.
Fits Bayesian mixture models to estimate marker dosage for dominant markers 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
POM-aSPU test evaluates an association between an ordinal response and multiple phenotypes, for details see Kim and Pan (2017) <DOI:10.1002/gepi.22033>.
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).
Enables the creation of object pools, which make it less
    computationally expensive to fetch a new object. Currently the
    only supported pooled objects are 'DBI' connections.
Calculates the pooled mean group (PMG) estimator for dynamic panel data models, as described by Pesaran, Shin and Smith (1999) <doi:10.1080/01621459.1999.10474156>.
Population dynamic models underpin a range of analyses and applications in ecology and epidemiology. The various approaches for analysing population dynamics models (MPMs, IPMs, ODEs, POMPs, PVA) each require the model to be defined in a different way. This makes it difficult to combine different modelling approaches and data types to solve a given problem. 'pop' aims to provide a flexible and easy to use common interface for constructing population dynamic models and enabling to them to be fitted and analysed in lots of different ways.
Simulate the dynamic of wolf populations using a specific Individual-Based Model (IBM) compiled in C.
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.
Plot demographic graphs for single/multiple populations from coalescent simulation program input. Currently, this package can support the 'ms', 'msHot', 'MaCS', 'msprime', 'SCRM', and 'Cosi2' simulation programs. It does not check the simulation program input for correctness, but assumes the simulation program input has been validated by the simulation program. More features will be added to this package in the future, please check the 'GitHub' page for the latest updates: <https://github.com/YingZhou001/POPdemog>.
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/>.
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.
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 a univariate/multivariate data from seasonal and nonseasonal time series models. It implements the well-known univariate and multivariate portmanteau test statistics based on the asymptotic distributions and the Monte-Carlo significance tests.
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.
Two functions for financial portfolio optimization by linear programming are provided. One function implements Benders decomposition algorithm and can be used for very large data sets. The other, applicable for moderate sample sizes, finds optimal portfolio which has the smallest distance to a given benchmark portfolio.
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'.
This is a wrapper for the Mercury Parser API. The Mercury Parser is 
    a single API endpoint that takes a URL and gives you back the content reliably 
    and easily. 
    With just one API request, Mercury takes any web article and returns 
    only the relevant content — headline, author, body text, relevant images and 
    more — free from any clutter. It’s reliable, easy-to-use and free.
    See the webpage here: <https://mercury.postlight.com/>.
Some functions useful to perform a Peak Over Threshold
        analysis in univariate and bivariate cases. A user's guide is
        available.
Do Markov chain Monte Carlo (MCMC) simulation of Potts models
   (Potts, 1952, <https://doi.org/10.1017/S0305004100027419>),
   which are the multi-color generalization of Ising models
   (so, as as special case, also simulates Ising models).
   Use the Swendsen-Wang algorithm (Swendsen and Wang, 1987,
   <https://doi.org/10.1103/PhysRevLett.58.86>) so MCMC is fast.
   Do maximum composite likelihood estimation of parameters
   (Besag, 1975, <https://doi.org/10.2307/2987782>,
   Lindsay, 1988, <https://doi.org/10.1090/conm/080>).
A package including several functions related to the Potts models.
The Phylogenetic Ornstein-Uhlenbeck Mixed Model (POUMM) allows to 
    estimate the phylogenetic heritability of continuous traits, to test 
    hypotheses of neutral evolution versus stabilizing selection, to quantify 
    the strength of stabilizing selection, to estimate measurement error and to
    make predictions about the evolution of a phenotype and phenotypic variation 
    in a population. The package implements combined maximum likelihood and 
    Bayesian inference of the univariate Phylogenetic Ornstein-Uhlenbeck Mixed 
    Model, fast parallel likelihood calculation, maximum likelihood 
    inference of the genotypic values at the tips, functions for summarizing and
    plotting traces and posterior samples, functions for simulation of a univariate 
    continuous trait evolution along a phylogenetic tree. A quick example on
    using the POUMM package can be found in the README. More elaborate
    examples and use-cases are provided in the vignette 
    "A User Guide to The POUMM R-package".
Density, distribution function, quantile function and random generation for the family of power and reversal power distributions.
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.
A power analysis tool for jointly testing the cause-1 cause-specific hazard and the any-cause hazard with competing risks data.
Power and sample size calculation for 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.
Calculate power for two- and three-level multilevel longitudinal
    studies with missing data. Both the third-level factor (e.g. therapists,
    schools, or physicians), and the second-level factor (e.g. subjects),
    can be assigned random slopes. Studies with partially nested designs,
    unequal cluster sizes, unequal allocation to treatment arms, and different
    dropout patterns per treatment are supported. For all designs power can be
    calculated both analytically and via simulations. The analytical
    calculations extends the method described in Galbraith et al. (2002)
    <doi:10.1016/S0197-2456(02)00205-2>, to three-level models.
    Additionally, the simulation tools provides flexible ways to investigate
    bias, type I errors and the consequences of model misspecification.
Functions to 
        calculate power and sample size for testing
        (1) mediation effects; 
        (2) the slope in a simple linear regression; 
        (3) odds ratio in a simple logistic regression;
        (4) mean change for longitudinal study with 2 time points;
        (5) interaction effect in 2-way ANOVA; and
        (6) the slope in a simple Poisson regression.
Miscellaneous functions for a descriptive analysis and initial Bayesian and classical 
             inference for the power parameter of the the Power Normal (PN) distribution. This 
             miscellaneous will be extend for more distributions into the power family and the 
             three-parameter model.
(1) To estimate the power of testing for linkage using an
        affected sib pair design, as a function of the recurrence risk
        ratios. We will use analytical power formulae as implemented in
        R. These are based on a Mathematica notebook created by Martin
        Farrall. (2) To examine how the power of the transmission
        disequilibrium test (TDT) depends on the disease allele
        frequency, the marker allele frequency, the strength of the
        linkage disequilibrium, and the magnitude of the genetic
        effect. We will use an R program that implements the power
        formulae of Abel and Muller-Myhsok (1998). These formulae allow
        one to quickly compute power of the TDT approach under a
        variety of different conditions. This R program was modeled on
        Martin Farrall's Mathematica notebook.
Computation of matrix and scalar exponentiation.
Functions to calculate power and
                sample size for testing main effect or interaction effect in
                the survival analysis of epidemiological studies
                (non-randomized studies), taking into account the 
                correlation between the covariate of the
                interest and other covariates. Some calculations also take 
                into account the competing risks and stratified analysis. 
                This package also includes
                a set of functions to calculate power and sample size
                for testing main effect in the survival analysis of 
                randomized clinical trials.
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 the Probability Plot Correlation Coefficient (PPCC) 
             between a continuous variable X and a specified distribution. The corresponding 
	     composite hypothesis test can be done to test whether the sample 
	     X is element of either the Normal, log-Normal, Exponential,
	     Uniform, Cauchy, Logistic, Generalized Logistic, Gumbel (GEVI), Weibull,
	     Generalized Extreme Value, Pearson III (Gamma 2), Mielke's Kappa, Rayleigh
	     or Generalized Logistic Distribution. The PPCC test is performed with
	     a fast Monte-Carlo simulation.
Implements recently developed projection 
    pursuit algorithms for finding optimal linear cluster
    separators. The clustering algorithms use optimal
    hyperplane separators based on minimum density, Pavlidis et. al (2016) <https://jmlr.csail.mit.edu/papers/volume17/15-307/15-307.pdf>;
    minimum normalised cut, Hofmeyr (2017) <doi:10.1109/TPAMI.2016.2609929>;
    and maximum variance ratio clusterability, Hofmeyr and Pavlidis (2015) <doi:10.1109/SSCI.2015.116>.
Partitioning clustering divides the objects in a data set into non-overlapping subsets or clusters by using the prototype-based probabilistic and possibilistic clustering algorithms. This package covers a set of the functions for Fuzzy C-Means (Bezdek, 1974) <doi:10.1080/01969727308546047>, Possibilistic C-Means (Krishnapuram & Keller, 1993) <doi:10.1109/91.227387>, Possibilistic Fuzzy C-Means (Pal et al, 2005) <doi:10.1109/TFUZZ.2004.840099>, Possibilistic Clustering Algorithm (Yang et al, 2006) <doi:10.1016/j.patcog.2005.07.005>, Possibilistic C-Means with Repulsion (Wachs et al, 2006) <doi:10.1007/3-540-31662-0_6> and the other variants of hard and soft clustering algorithms. The cluster prototypes and membership matrices required by these partitioning algorithms are initialized with different initialization techniques that are available in the package 'inaparc'. As the distance metrics, not only the Euclidean distance but also a set of the commonly used distance metrics are available to use with some of the algorithms in the package.
Calculates partial and semi-partial
        (part) correlations along with p-value.
Implements projection pursuit forest algorithm for supervised classification.
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 data sets 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
A collection of feature selection filters performing greedy optimisation of mutual information-based usefulness criteria, inspired by the overview by Brown, Pocock, Zhao and Lujan (2012) <http://www.jmlr.org/papers/v13/brown12a.html>.
 Implements, among other, minimum redundancy maximal relevancy ('mRMR') method by Peng, Long and Ding (2005) <doi:10.1109/TPAMI.2005.159>; joint mutual information ('JMI') method by Yang and Moody (1999) <http://papers.nips.cc/paper/1779-data-visualization-and-feature-selection-new-algorithms-for-nongaussian-data>; double input symmetrical relevance ('DISR') method by Meyer and Bontempi  (2006) <doi:10.1007/11732242_9> as well as joint mutual information maximisation ('JMIM') method by Bennasar, Hicks and Setchi (2015) <doi:10.1016/j.eswa.2015.07.007>.
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.
Provides an easy-to-use yet adaptable set of tools to conduct person-center analysis using a two-step clustering procedure. As described in Bergman and El-Khouri (1999) <DOI:10.1002/(SICI)1521-4036(199910)41:6%3C753::AID-BIMJ753%3E3.0.CO;2-K>, hierarchical clustering is performed to determine the initial partition for the subsequent k-means clustering procedure.
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 prediction rule ensembles consisting of rules 
    and/or linear terms for binary, count and continuous responses. Function 
    gpe() derives generalized prediction ensembles, consisting of rules, hinge
    and linear functions of the predictor variables.
It contains functions to analyze the precipitation
    intensity, concentration and anomaly.
Most of the time floating point arithmetic does
  approximately the right thing.  When adding sums or having products
  of numbers that greatly differ in magnitude, the floating point
  arithmetic may be incorrect.  This package implements the Kahan
  (1965) sum <doi:10.1145/363707.363723>, Neumaier (1974) sum
  <doi:10.1002/zamm.19740540106>, pairwise-sum (adapted from 'NumPy',
  See Castaldo (2008) <doi:10.1137/070679946> for a discussion of
  accuracy), and arbitrary precision sum (adapted from the fsum in
  'Python' ; Shewchuk (1997)
  <http://www.cs.berkeley.edu/~jrs/papers/robustr.pdf>).  In addition,
  products are changed to long double precision for accuracy, or
  changed into a log-sum for accuracy.
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 a web scraping script. 
   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. The package currently supports common model types (e.g., "lm", "glm") from the 'stats' package, as well as numerous other model classes from other add-on packages. See the README or main package documentation page for a complete listing.
A common problem faced by journal reviewers and authors is the question of
     whether the results of a replication study are consistent with the original
     published study. One solution to this problem is to examine the effect size
     from the original study and generate the range of effect sizes that could
     reasonably be obtained (due to random sampling) in a replication attempt
     (i.e., calculate a prediction interval). This package has functions that calculate
     the prediction interval for the correlation (i.e., r),
     standardized mean difference (i.e., d-value), and mean.
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.
Implements the predictive k-means method for clustering observations, using a mixture of experts model to allow covariates to influence cluster centers. Motivated by air pollution epidemiology settings, where cluster membership needs to be predicted across space. Includes functions for predicting cluster membership using spatial splines and principal component analysis (PCA) scores using either multinomial logistic regression or support vector machines (SVMs). For method details see Keller et al. (2017) <doi:10.1214/16-AOAS992>.
"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.
Makes the time series prediction easier by automatizing this process
  using four main functions: prep(), modl(), pred() and postp(). Features different
  preprocessing methods to homogenize variance and to remove trend and seasonality.
  Also has the potential to bring together different predictive models to make comparatives.
  Features ARIMA and Data Mining Regression models (using caret).
Allows users to derive multi-objective weights from pairwise comparisons, which
    research shows is more repeatable, transparent, and intuitive other techniques. These weights
    can be rank existing alternatives or to define a multi-objective utility function for optimization.
Design and analyze two-stage randomized trials with a continuous
    outcome measure. The package contains functions to compute the required 
    sample size needed to detect a given preference, treatment, and selection 
    effect; alternatively, the package contains functions that can report the 
    study power given a fixed sample size. Finally, analysis functions are 
    provided to test each effect using either summary data (i.e. means, 
    variances) or raw study data.
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.
Provides classes to pre-process microarray gene
  expression data as part of the OOMPA collection of packages
  described at <http://oompa.r-forge.r-project.org/>.
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.
Provides a collection of templates to author preregistration documents for scientific studies in PDF format.
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.
The relation between the number of species and the number of individuals in a random sample is a classic problem back to Fisher (1943) <doi:10.2307/1411>. We generalize this problem to estimate the number of species represented at least r times in a random sample. In particular when r=1, it becomes the classic problem. We use a mixture of Poisson processes to model sampling procedures and apply a nonparametric empirical Bayes approach to obtain an estimator. For more information on 'preseqR', see Deng C, Daley T and Smith AD (2015) <doi:10.1007/s40484-015-0049-7> and Deng C and Smith AD (2016) <arXiv:1607.02804v2>.
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/.
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.
R interface to PRIMME, a C library for computing a few
    eigenvalues and their corresponding eigenvectors of a real symmetric or complex
    Hermitian matrix.  It can also compute singular values and vectors of a square
    or rectangular matrix.  It can find largest, smallest, or interior
    singular/eigenvalues and can use preconditioning to accelerate convergence.
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.
Extends the S3 generic function knit_print() in 'knitr'
    to automatically print some objects using an appropriate format such as
    Markdown or LaTeX. For example, data frames are automatically printed as
    tables, and the help() pages can also be rendered in 'knitr' documents.
Conservation prioritization using integer
    programming techniques. To solve large-scale problems, users
    should install the 'gurobi' optimizer
    (available from <http://www.gurobi.com/>).
Conservation planning data sets and tutorials for learning how to use the 'prioritizr' package <https://CRAN.R-project.org/package=prioritizr>.
Fits successive Lasso models for several blocks of (omics) data with different priorities and takes the predicted values as an offset for the next block.
Allows users to access the Oregon State Prism climate data. 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.
Loads and processes huge text
    corpora processed with the sally toolbox (<http://www.mlsec.org/sally/>).
    sally acts as a very fast preprocessor which splits the text files into
    tokens or n-grams. These output files can then be read with the PRISMA
    package which applies testing-based token selection and has some
    replicate-aware, highly tuned non-negative matrix factorization and
    principal component analysis implementation which allows the processing of
    very big data sets even on desktop machines.
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.
Download, parses and tidies information from the World Prison Brief project <http://www.prisonstudies.org/>.
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.
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.
Estimates unsupervised outlier probabilities for multivariate numeric data with many observations from a nonparametric outlier statistic.
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.
Visualize of process maps based on
    event logs, in the form of directed graphs. Part of the 'bupaR' framework.
Functions for constructing dashboards for business process monitoring. Building on the event log objects class from package 'bupaR'. Allows the use to assemble custom shiny dashboards based on process data.
Portable tools to run system processes in the background.
    It can check if a background process is running; wait on a background
    process to finish; get the exit status of finished processes; kill
    background processes and their children; restart processes. It can read
    the standard output and error of the processes, using non-blocking
    connections. 'processx' can poll a process for standard output or
    error, with a timeout. It can also poll several processes at once.
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
Implements the methods proposed by Olley, G.S. and Pakes, A. (1996) <doi:10.2307/2171831>, Levinsohn, J. and Petrin, A. (2003) <doi:10.1111/1467-937X.00246>, Ackerberg, D.A. and Caves, K. and Frazer, G. (2015) <doi:10.3982/ECTA13408> and Wooldridge, J.M. (2009) <doi:10.1016/j.econlet.2009.04.026> for structural productivity estimation .
Create a project directory structure, along with typical files
    for that project.  This allows projects to be quickly and easily created,
    as well as for them to be standardized. Designed specifically with scientists
    in mind (mainly bio-medical researchers, but likely applies to other fields).
Fast and user friendly implementation of nonparametric estimators
    for censored event history (survival) analysis. Kaplan-Meier and
    Aalen-Johansen method.
Levels and changes of productivity and profitability are measured with various indices.
  In addition to the classic Malmquist productivity index, the 'productivity' package contains 
  also the multiplicatively complete Färe-Primont, Fisher, Laspeyres, Lowe, and Paasche indices. 
  Färe-Primont and Lowe indices verify the transitivity property and can therefore be used for 
  multilateral or multitemporal comparison.
  Fisher, Laspeyres, Malmquist, and Paasche indices are not transitive and are only to be used for 
  binary comparison. 
  All indices can also be decomposed into different components, providing insightful information 
  on the sources of productivity and profitability improvements. 
  In the use of Malmquist productivity index, the technological change index can be further 
  decomposed into bias technological change components. 
  In the case of the Fisher, Laspeyres, Paasche and the transitive Färe-Primont and Lowe indices, 
  it is furthermore possible to rule out technological change. Deflated shadow prices, used in 
  those indices' computations, are also returned.
  The package also allows to prohibit negative technological change. 
  All computations are carried out with the nonparametric Data Envelopment Analysis (DEA), 
  and several assumptions regarding returns to scale are available.
  The package allows parallel computing as an option, 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.
An alternative data structure for the profiling information
    generated by Rprof().
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) <http://conservancy.umn.edu/handle/11299/155592>, 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).
Core package containing all the tools for simple and advanced source extraction. This is used to create inputs for 'ProFit', or for source detection, extraction and photometry in its own right.
profr provides an alternative data structure
    and visual rendering for the profiling information
    generated by Rprof.
Tools for examining Rprof profile output.
Interactive visualizations for profiling R code.
Implementing the Progeny Clustering algorithm, the 'progenyClust' package assesses the clustering stability and identifies the optimal clustering number for a given data matrix. It uses k-means clustering as a default, provides a tailored hierarchical clustering function, and can be customized to work with other clustering algorithms and different parameter settings. The package includes a main function progenyClust(), plot and summary methods for 'progenyClust' object, a function hclust.progenyClust() for hierarchical clustering, and two example datasets (test and cell) for testing.
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.
A clustering approach for every projection method based on the generalized  U*-matrix visualization of a topographic map is made available here [Thrun/Ultsch,2017] <DOI:10.13140/RG.2.2.13124.53124>. The number of clusters and the cluster structure can be estimated by counting the valleys in a topographic map. If the number of clusters and the clustering method are chosen correctly, then the clusters will be well separated by mountains in the visualization. Most projection methods are wrappers for already available methods in R. However, the neighbor retrieval visualizer (NeRV) is based on C++ source code of the 'dredviz' software package and the Curvilinear Component Analysis (CCA) is translated from 'MATLAB' ('SOM Toolbox' 2.0) to R.
Provides functions and graphics for projecting daily incidence based on past incidence, and estimates of the serial interval and reproduction number. Projections are based on a branching process using a Poisson-distributed number of new cases per day, similar to the model used for estimating R0 in 'EpiEstim' or in 'earlyR', and described by Nouvellet et al. (2017) <doi:10.1016/j.epidem.2017.02.012>.
Display dense vector representation of texts on a 2D plan to better
  understand embeddings by observing the neighbors of a selected text.
  It also includes an interactive application to change dynamically the pivot text.
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.
Calculates the critical path for a series of tasks, 
	creates Gantt charts and generates network diagrams in order to 
	provide similar functionality to the basic tools offered by 'MS Project'.
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>.
Deploy, maintain, and invoke predictive models using the 'Alteryx
    Promote' REST API.  'Alteryx Promote' is available at the URL:
    <https://www.alteryx.com/products/alteryx-promote>.
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.
Carrying out inferences about any linear combination of proportions and the ratio of two proportions.
Propagation of uncertainty using higher-order Taylor expansion and Monte Carlo simulation.
Computes two-sample confidence intervals for single, paired and independent proportions
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 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.
Abundant statistical literature has revealed the importance of constructing and evaluating various methods for constructing confidence intervals (CI) for single binomial proportion (p). We comprehensively provide procedures in frequentist (approximate with or without adding pseudo counts or continuity correction or exact) and in Bayesian cultures. Evaluation procedures for CI warrant active computational attention and required summaries pertaining to  four criterion (coverage probability, expected length, p-confidence, p-bias, and error) are implemented.
A package for selecting the most relevant features (genes) in the high-dimensional binary classification problems. The discriminative features are identified using analyzing the overlap between the expression values across both classes. The package includes functions for measuring the proportional overlapping score for each gene avoiding the outliers effect. The used measure for the overlap is the one defined in the "Proportional Overlapping Score (POS)" technique for feature selection. A gene mask which represents a gene's classification power can also be produced for each gene (feature). The set size of the selected genes might be set by the user. The minimum set of genes that correctly classify the maximum number of the given tissue samples (observations) can be also produced.
The bioinformatic evaluation of gene co-expression often begins with
    correlation-based analyses. However, this approach lacks statistical validity
    when applied to relative data. This includes, for example, biological count data
    generated by high-throughput RNA-sequencing, chromatin immunoprecipitation (ChIP),
    ChIP-sequencing, Methyl-Capture sequencing, and other techniques. This package
    implements several metrics for proportionality, including
    phi [Lovell et al (2015) <DOI:10.1371/journal.pcbi.1004075>] and
    rho [Erb and Notredame (2016) <DOI:10.1007/s12064-015-0220-8>]. This package also
    implements several metrics for differential proportionality. Unlike correlation,
    these measures give the same result for both relative and absolute data.
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.
An API wrapper around the 'ProPublica' API <https://projects.propublica.org/api-docs/congress-api/> for U.S. Congressional Bills. Users can include their API key, U.S. Congress, branch, and offset ranges, to return a dataframe of all results within those parameters. This package is different from the 'RPublica' package because it is for the 'ProPublica' U.S. Congress data API, and the 'RPublica' package is for the Nonprofit Explorer, Forensics, and Free the Files data APIs.
Offers a variety of tools, such as specific plots and regression model approaches, for analyzing different patient reported questionnaires. Specially, mixed-effects models based on the beta-binomial distribution are implemented to deal with binomial data with over-dispersion (see Najera-Zuloaga J., Lee D.-J. and Arostegui I. (2017) <doi:10.1177/0962280217690413>).
An extensible repository of accurate, up-to-date functions to score 
    commonly used patient-reported outcome (PRO), quality of life (QOL), and 
    other psychometric and psychological measures.  'PROscorer', together with 
    the 'PROscorerTools' package, is a system to facilitate the incorporation of 
    PRO measures into research studies and clinical settings in a scientifically 
    rigorous and reproducible manner.  These packages and their vignettes are 
    intended to help establish and promote "best practices" to improve the 
    planning, scoring, and reporting of PRO-like measures in research.  
    The 'PROscorer' "Instrument Descriptions" vignette contains descriptions of 
    each instrument scored by 'PROscorer', complete with references.  These 
    instrument descriptions are suitable for inclusion in formal study protocol 
    documents, grant proposals, and manuscript Method sections.  Each 
    'PROscorer' function is composed of helper functions from the 
    'PROscorerTools' package, and users are encouraged to contribute new 
    functions to 'PROscorer'.  More scoring functions are currently in 
    development and will be added in future updates.
Provides a reliable and flexible toolbox to score 
    patient-reported outcome (PRO), Quality of Life (QOL), and other 
    psychometric measures. The guiding philosophy is that scoring errors can 
    be eliminated by using a limited number of well-tested, well-behaved 
    functions to score PRO-like measures. The workhorse of the package is 
    the 'scoreScale' function, which can be used to score most single-scale 
    measures. It can reverse code items that need to be reversed before 
    scoring and pro-rate scores for missing item data. Currently, three 
    different types of scores can be output: summed item scores, mean item 
    scores, and scores scaled to range from 0 to 100. The 'PROscorerTools' 
    functions can be used to write new functions that score more complex 
    measures. In fact, 'PROscorerTools' functions are the building blocks of 
    the scoring functions in the 'PROscorer' package (which is a repository 
    of functions that score specific commonly-used instruments). Users are 
    encouraged to use 'PROscorerTools' to write scoring functions for their 
    favorite PRO-like instruments, and to submit these functions for 
    inclusion in 'PROscorer' (a tutorial vignette will be added soon). The 
    long-term vision for the 'PROscorerTools' and 'PROscorer' packages is to 
    provide an easy-to-use system to facilitate the incorporation of PRO 
    measures into research studies in a scientifically rigorous and 
    reproducible manner. These packages and their vignettes are intended to 
    help establish and promote "best practices" for scoring and describing 
    PRO-like measures in research.
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.
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.
Tools for Natural Language Processing in French and texts from Marcel Proust's collection 
  "A La Recherche Du Temps Perdu". The novels contained in this collection are 
  "Du cote de chez Swann ", "A l'ombre des jeunes filles en fleurs","Le Cote de Guermantes", 
  "Sodome et Gomorrhe I et II", "La Prisonniere", "Albertine disparue", and "Le Temps retrouve".
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, Kuiper or Sircombe-Hazelton L2 distances. 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. Utility functions for creating libraries with decoys.
    Planned is peptide FDR estimation for search results.
Computes the areas under the precision-recall (PR) and ROC curve for weighted (e.g., soft-labeled) and unweighted data. In contrast to other implementations, the interpolation between points of the PR curve is done by a non-linear piecewise function. In addition to the areas under the curves, the curves themselves can also be computed and plotted by a specific S3-method. References: Davis and Goadrich (2006) <doi:10.1145/1143844.1143874>; Keilwagen et al. (2014) <doi:10.1371/journal.pone.0092209>; Grau et al. (2015) <doi:10.1093/bioinformatics/btv153>.
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.
An implementation of symbolic polygonal data analysis. The package presents the estimation of main descriptive statistical measures, e.g, mean, covariance, variance, correlation and coefficient of variation. 
            In addition, transformation of the data in polygons. Empirical probability distribution function based on 
            polygonal histogram and regression models are presented.
This R package includes functions for gathering commonly used and
    regularly maintained data set in political science. It also includes
    functions for combining components from these data sets into variables that
    have been suggested in the literature, but are not regularly maintained.
Functions for creating Latin Hypercubes with
    prescribed correlations and performing parameter space exploration.
    Also implements the PLUE method.
    Based on the package sensitivity, by Gilles Pujol,
    Bertrand Iooss & Alexandre Janon.
Various functions for computing pseudo-observations for censored data regression. Computes pseudo-observations for modeling: competing risks based on the cumulative incidence function, survival function based on the restricted mean,  survival function based on the Kaplan-Meier estimator see Klein et al. (2008) <doi:10.1016/j.cmpb.2007.11.017>.
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.
Generalization of the SIMEX algorithm from Cook & Stefanski (1994) <doi:10.2307/2290994> for the calculation of inbreeding depression or heritability on pedigree structures affected by missing or misassigned paternities. It simulates errors and tracks the behavior of the estimate as a function of the error proportion. It extrapolates back a true value corresponding to the null error rate.
Data and statistics of Pakistan Social and Living Standards Measurement (PSLM) survey 2014-15 from Pakistan Bureau of Statistics (<http://www.pbs.gov.pk/>).
Functions for fitting linear and non-linear mixed-effects models using
        stochastic differential equations (SDEs). The provided pipeline relies on the 
        coupling of the FOCE algorithm and Kalman filtering as outlined by 
        Klim et al (2009) <doi:10.1016/j.cmpb.2009.02.001> and has been validated
        against the proprietary software NONMEM (TornÃ¸e et al, 2005, 
        <doi:10.1007/s11095-005-5269-5>). Further functions are provided 
        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, covariates, 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.
Calculating Pst values to assess differentiation among populations from a set of quantitative traits is the primary purpose of such a package. The bootstrap method provides confidence intervals and distribution histograms of Pst. Variations of Pst in function of the parameter c/h^2 are studied as well. Finally, the package proposes different transformations especially to eliminate any variation resulting from allometric growth (calculation of residuals from linear regressions, Reist standardizations or Aitchison transformation).
The propensity score is one of the most widely used tools in studying the causal effect
    of a treatment, intervention, or policy. Given that the propensity score is usually unknown,
    it has to be estimated, implying that the reliability of many treatment effect estimators depends
    on the correct specification of the (parametric) propensity score. This package provides
    data-driven nonparametric diagnostic tools for detecting propensity score misspecification.
Provides the Panel Smooth Transition Regression (PSTR) modelling.
    The modelling procedure consists of three stages: Specification, Estimation and Evaluation.
    The package offers sharp tools helping the package user(s) to conduct model specification tests,
    to do PSTR model estimation, and to do model evaluation.
    The tests implemented in the package allow for cluster-dependency and are heteroskedasticity-consistent.
    The wild bootstrap and wild cluster bootstrap tests are also implemented.
    Parallel computation (as an option) is implemented in some functions for bootstrap tests.
    The package suits tasks running many cores on super-computation servers.
Functions to simulate Poisson or Normally distributed responses relative to 
  a baseline and compute achieved significance level and powers for tests on the
  simulated responses.
Provides propensity score weighting methods to control for confounding in causal inference with dichotomous treatments and continuous/binary outcomes. It includes the following functional modules: (1) visualization of the propensity score distribution in both treatment groups with mirror histogram, (2) covariate balance diagnosis, (3) propensity score model specification test, (4) weighted estimation of treatment effect, and (5) doubly robust estimation of treatment effect. The weighting methods include the inverse probability weight (IPW) for estimating the average treatment effect (ATE), the IPW for average treatment effect of the treated (ATT), the IPW for the average treatment effect of the controls (ATC), the matching weight (MW), the overlap weight (OVERLAP), and the trapezoidal weight (TRAPEZOIDAL). Sandwich variance estimation is provided to adjust for the sampling variability of the estimated propensity score. These methods are discussed by Hirano et al (2003) <DOI:10.1111/1468-0262.00442>, Li and Greene (2013) <DOI:10.1515/ijb-2012-0030>, and Li et al (2016) <DOI:10.1080/01621459.2016.1260466>.
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 <http://personality-project.org/r> web page.
Tools for computing bare-bones and psychometric meta-analyses and for generating psychometric data for use in meta-analysis simulations. Supports bare-bones, individual-correction, and artifact-distribution methods for meta-analyzing correlations and d values. Includes tools for converting effect sizes, computing sporadic artifact corrections, reshaping meta-analytic databases, computing multivariate corrections for range variation, and more.
Toolbox for psychologists, neuropsychologists and neuroscientists. 
    It implements methods to create report-ready outputs for statistical models, 
    as well as various convenient statistical functions used in psychological science, 
    such as correlation matrices and normalization.
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),
  Bradley-Terry mixture models, and MPT mixture models are implemented. These mixture models can be estimated
  with or without concomitant variables. See vignette('raschmix', package = 'psychomix') for details on the
  Rasch mixture models.
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.
Person fit statistics based on Quality Control measures are provided for questionnaires and tests given a specified IRT model. Statistics based on Cumulative Sum (CUSUM) charts are provided. Options are given for banks with polytomous and dichotomous data.
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.
We provide inference for personalized medicine models. Namely, we answer the questions: (1) how much better does a purported personalized recommendation engine for treatments do over a business-as-usual approach and (2) is that difference statistically significant?
Implements p-value computations using an approximation to the cumulative distribution function for a variety of tests for periodicity. These tests include harmonic regression tests with normal and double exponential errors as well as modifications of Fisher's g test. An accompanying vignette illustrates the application of these tests.
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.
A bunch of convenience functions that transform the results of some basic statistical analyses
       into table format nearly ready for publication. This includes descriptive tables, tables of
       logistic regression and Cox regression results as well as forest plots.
Text mining of PubMed Abstracts (text and XML) from <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.
Efficient algorithm for solving PU (Positive and Unlabelled) problem in low or high dimensional setting with lasso or group lasso penalty. The algorithm uses Maximization-Minorization and (block) coordinate descent. Sparse calculation and parallel computing via 'OpenMP' are supported for the computational speed-up. See Hyebin Song, Garvesh Raskutti (2017) <arXiv:1711.08129>.
R Interface to Pullword Service for natural language processing
    in Chinese. It enables users to extract valuable words from text by deep learning models. 
    For more details please visit the official site (in Chinese) http://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).
Computes p-values for the interaction term in a very large number of linear regression models.
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.
A complete and consistent functional programming toolkit for R.
Some functions at the intersection of 'dplyr' and 'purrr' that 
  formerly lived in 'purrr'.
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.
The pvsR package facilitates data retrieval from Project
    Vote Smart's rich online data base on US politics via the Project Vote
    Smart application programming interface (PVS API). The functions in this
    package cover most PVS API classes and methods and return the
    requested data in a data frame.
Contains functions which allow the user to perform time series regression quickly using the Power Weighted Densities (PWD) approach. alphahat_LR_one_Rcpp() is the main workhorse function within this package.
Calculates various functions needed for design and monitoring survival trials
    accounting for complex situations such as delayed treatment effect, treatment crossover, non-uniform accrual,
    and different censoring distributions between groups. The event time distribution is assumed to be
    piecewise exponential (PWE) distribution and the entry time is assumed to be piecewise uniform distribution.
    As compared with Version 1.1.0, this version includes the calculation of the restricted mean survival time.
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.
Utilities for working with air quality monitoring data
    with a focus on small particulates (PM2.5) generated by wildfire
    smoke. Functions are provided for downloading available data from
    the United States Environmental Protection Agency (US EPA) and
    it's AirNow air quality site. Additional sources of PM2.5 data
    made accessible by the package include: AIRSIS (password protected),
    the Western Regional Climate Center (WRCC) and the open source site OpenAQ.
Power analysis functions along the lines of Cohen (1988).
User friendly functions for power and sample size analysis at one-way and two-way ANOVA settings take either effect size or delta and sigma as arguments. They are designed for both one-way and two-way ANOVA settings. In addition, a function for plotting power curves is available for power comparison, which can be easily visualized by statisticians and clinical researchers.
Power analysis for AB testing. The calculations are based on the Welch's unequal variances t-test,
  which is generally preferred over the Student's t-test when sample sizes and variances of the two groups are
  unequal, which is frequently the case in AB testing. In such situations, the Student's t-test will give 
  biased results due to using the pooled standard deviation, unlike the Welch's t-test.
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.
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'), multi-value ('mvQCA')
             and fuzzy sets ('fsQCA'), using a GUI - graphical user interface.
             'QCA' is a methodology that bridges the qualitative and quantitative divide
             in social science research. It uses a Boolean algorithm that results in a
             minimal causal combination which explains a given phenomenon.
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.
CBA postprocessing algorithm that creates smaller models for datasets containing quantitative (numerical) attributes. Article describing QCBA is published in Tomas Kliegr (2017) <arXiv:1711.10166>.
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.
Smooth additive quantile regression models, fitted using the methods of Fasiolo et al. (2017) <arXiv:1707.03307>. Differently from 'quantreg', the smoothing parameters are estimated automatically by marginal loss minimization, while the regression coefficients are estimated using either PIRLS or Newton algorithm. The learning rate is determined so that the Bayesian credible intervals of the estimated effects have approximately the correct coverage. The main function is qgam() which is similar to gam() in 'mgcv', but fits non-parametric quantile regression models.
Density, distribution function, quantile function and 
  random generation for the q-gaussian distribution with parameters mu and sig.
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 Peng,B and Wang,L. (2015) 
              <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.
Functions for making run charts, Shewhart control charts and
    Pareto charts for continuous quality improvement. Included control charts are:
    I, MR, Xbar, S, T, C, U, U', P, P', and G charts. Non-random variation in the
    form of minor to moderate persistent shifts in data over time is identified by
    the Anhoej rules for unusually long runs and unusually few crossing 
    [Anhoej, Olesen (2014) <doi:10.1371/journal.pone.0113825>].
    Non-random variation in the form of larger, possibly transient, shifts is
    identified by Shewhart's 3-sigma rule [Mohammed, Worthington, Woodall (2008)
    <doi:10.1136/qshc.2004.012047>].
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.
A simulation-based quasi-likelihood method (Baaske, M., Ballani, F., v.d. Boogaart, K.G. (2014) <doi:10.5566/ias.v33.p107-119>) for parameter estimation of parametric statistical models for which closed-form representations of distributional characteristics are unavailable and can only be obtained by computationally intensive simulations of the model.
Functions to implement Q-learning for estimating optimal
        dynamic treatment regimes from two stage sequentially
        randomized trials, and to perform inference via m-out-of-n
        bootstrap for parameters indexing the optimal regime.
Implements Q-Learning, a model-free form of reinforcement
        learning, described in work by Strehl, Li, Wiewiora, Langford &
        Littman (2006) <doi:10.1145/1143844.1143955>.
Empirical adjustment of the distribution of variables originating from (regional) climate model simulations using quantile mapping.
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 distributions 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 <http://www.eortc.be/qol/files/SCManualQLQ-C30.pdf> and supplementary modules and to determine the time to health-related quality of life score deterioration in longitudinal analysis.
Provides functionality for model validation by computing a
    parametric bootstrap and comparing the Quantile Spectral Densities.
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.
Create Q-Q and manhattan plots for GWAS data from PLINK results.
Provides users the necessary utility functions to generate permutation-based QQ plots and also estimate inflation factor based on the empirical NULL distribution. While it has general utility, it is particularly helpful when the skewness of the Fisher's Exact test in sparse data situations with imbalanced case-control sample sizes renders the reliance on the uniform chi-square expected distribution inappropriate.
Extensions of 'ggplot2' Q-Q plot functionalities.
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.
Nonlinear and Penalized parametric modeling of quantile regression coefficient functions. Frumento P and Bottai M (2016) <doi:10.1111/biom.12410>.
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.
Implements the robust algorithm for fitting finite mixture models based on quantile regression proposed by Emir et al., 2017 (unpublished).
Functions and data sets for reproducing selected results from
  the book "Quantitative Risk Management: Concepts, Techniques and Tools".
  Furthermore, new developments and auxiliary functions for Quantitative
  Risk Management practice.
Functionality for generating (randomized) quasi-random numbers in
  high dimensions.
Quantile regression (QR) for Nonlinear
             Mixed-Effects Models via the asymmetric Laplace distribution (ALD). 
             It uses the Stochastic Approximation of the EM (SAEM) algorithm for 
             deriving exact maximum likelihood estimates and full inference results 
             for the fixed-effects and variance components.
             It also provides graphical summaries for assessing the algorithm
             convergence and fitting results.
Fit quantile regression neural network models with optional
    left censoring, partial monotonicity constraints, generalized additive
    model constraints, and the ability to fit multiple non-crossing quantile
    functions following Cannon (2011) <doi:10.1016/j.cageo.2010.07.005>
    and Cannon (2017) <doi:10.17605/OSF.IO/WG7SN>.
Quantile Regression (QR) using Support Vector Machines under the Pinball-Loss. Estimation is based on "Nonparametric Quantile Regression" by I. Takeuchi,  Q.V.Le , T. Sears, A.J.Smola (2004). Implementation relies on 'quadprog' package, package 'kernlab' Kernelfunctions and package 'Matrix' nearPD to find next Positive definite Kernelmatrix. Package estimates quantiles individually but an Implementation of non crossing constraints coming soon. Function multqrsvm() now supports parallel backend for faster fitting.
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.
Extends the quadprog package to solve quadratic programs with
    absolute value constraints and absolute values in the objective function.
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. This package is 
    community-maintained and is not officially supported 
    by Qualtrics.
Qualitative methods for the validation of dynamic models.
    It contains (i) an orthogonal set of deviance measures for absolute,
    relative and ordinal scale and (ii) approaches accounting for time
    shifts. The first approach transforms time to take time delays and speed
    differences into account. The second divides the time series into
    interval units according to their main features and finds the longest
    common subsequence (LCS) using a dynamic programming algorithm.
Implements indices of qualitative variation proposed by Wilcox (1973).
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, and comprehensive framework for 
    quantitative text analysis in R.  Provides functionality for corpus management,
    creating and manipulating tokens and ngrams, exploring keywords in context, 
    forming and manipulating sparse matrices
    of documents by features and feature co-occurrences, analyzing keywords, computing feature similarities and
    distances, applying content dictionaries, applying supervised and unsupervised machine learning, 
    visually representing text and text analyses, and more.
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.
Estimation methods for optimal treatment regimes under three different criteria, namely marginal quantile, marginal mean, and mean absolute difference. For the first two criteria, both one-stage and two-stage estimation method are implemented. A doubly robust estimator for estimating the quantile-optimal treatment regime is also included.
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.
Implements the nonparametric quantile regression method developed by Belloni, Chernozhukov, and Fernandez-Val (2011) to partially linear quantile models. Provides point estimates of the conditional quantile function and its derivatives based on series approximations to the nonparametric part of the model. Provides pointwise and uniform confidence intervals using analytic and resampling methods.
Quantile Regression Forests is a tree-based ensemble
        method for estimation of conditional quantiles. It is
        particularly well suited for high-dimensional data. Predictor
        variables of mixed classes can be handled. The package is
        dependent on the package 'randomForest', written by Andy Liaw.
Fits non-crossing regression quantiles as a function of linear covariates and a smooth terms via B-splines with difference penalties.
This is the implementation of quantile regression forests for the fast random forest package 'ranger'.
Methods to determine, smooth and plot quantile periodograms for
    univariate and multivariate time series.
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.
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.
Provides functions for assigning treatments in randomized experiments using
    near-optimal threshold blocking. The package is made with large data sets in
    mind and derives blocks more than an order of magnitude quicker than other
    methods.
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.
Provides functions for constructing near-optimal generalized full matching.
    Generalized full matching is an extension of the original full matching method
    to situations with more intricate study designs. The package is made with
    large data sets in mind and derives matches more than an order of magnitude
    quicker than other methods.
A high-level plotting system, built using 'grid' graphics, that is
    optimized for speed and modularity. This has great utility for quick
    visualizations when testing code, with the key benefit that visualizations
    are updated independently of one another.
Quickly fits and plots psychometric functions (normal, logistic,
    Weibull or any or any function defined by the user) for multiple groups.
A set of functions to extract results from regression models and
 plot the effect size using 'ggplot2' seamlessly. While 'broom' is useful to
 convert statistical analysis objects into tidy data frames, 'coefplot' is adept at showing
 multivariate regression results. With specific outcome, this package could build regression models
 automatically, extract results into a data frame and provide a quicker way to summarize
 models' statistical findings using 'ggplot2'.
Helps to perform linear regression analysis by reducing manual effort. Reduces the independent variables based on specified p-value and Variance Inflation Factor (VIF) level.
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.
Diagnostics methods for quantile regression models for detecting influential observations:
  robust distance methods for general quantile regression models; generalized Cook's distance and 
  Q-function distance method for quantile regression models using aymmetric Laplace distribution. Reference
  of this method can be found in Luis E. Benites, Víctor H. Lachos, Filidor E. Vilca (2015) <arXiv:1509.05099v1>; 
  mean posterior probability and Kullback–Leibler divergence methods for Bayes quantile regression model.
  Reference of this method is Bruno Santos, Heleno Bolfarine (2016) <arXiv:1601.07344v1>.
A facility for writing functions that quote their arguments,
 may sometimes evaluate them in the environment where they were quoted,
 and may pass them as quoted to other functions.
Selection of a threshold parameter based on the Quantile Universal Threshold (QUT) 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.)
Memoization can be used to speed up repetitive and computational expensive function calls.  The first time a function that implements memoization is called the results are stored in a cache memory.  The next time the function is called with the same set of parameters, the results are momentarily retrieved from the cache avoiding repeating the calculations.  With this package, any R object can be cached in a key-value storage where the key can be an arbitrary set of R objects.  The cache memory is persistent (on the file system).
Functions for creating plots and image files in a unified way
    regardless of output format (EPS, PDF, PNG, SVG, TIFF, WMF, etc.). Default
    device options as well as scales and aspect ratios are controlled in a uniform
    way across all device types. Switching output format requires minimal changes
    in code. This package is ideal for large-scale batch processing, because it
    will never leave open graphics devices or incomplete image files behind, even on
    errors or user interrupts.
A file set refers to a set of files located in one or more directories on the file system.  This package provides classes and methods to locate, setup, subset, navigate and iterate such sets.  The API is designed such that these classes can be extended via inheritance to provide a richer API for special file formats.  Moreover, a specific name format is defined such that filenames and directories can be considered to have full names which consists of a name followed by comma-separated tags.  This adds additional flexibility to identify file sets and individual files.  NOTE: This package's API should be considered to be in an beta stage.  Its main purpose is currently to support the aroma.* packages, where it is one of the main core components; if you decide to build on top of this package, please contact the author first.
DEPRECATED. Do not start building new projects based on this package. Cross-platform alternatives are the following packages: bigmemory (CRAN), ff (CRAN), BufferedMatrix (Bioconductor).  The main usage of it was inside the aroma.affymetrix package. (The package currently provides a class representing a matrix where the actual data is stored in a binary format on the local file system.  This way the size limit of the data is set by the file system and not the memory.)
Performs the JIVE decomposition on a list of data sets when the data share a dimension, returning low-rank matrices that capture the joint and individual structure of the data [O'Connell, MJ and Lock, EF (2016) <doi:10.1093/bioinformatics/btw324>]. It provides two methods of rank selection when the rank is unknown, a permutation test and a BIC selection algorithm. Also included in the package are three plotting functions for visualizing the variance attributed to each data source: a bar plot that shows the percentages of the variability attributable to joint and individual structure, a heatmap that shows the structure of the variability, and principal component plots.
Methods readMat() and writeMat() for reading and writing MAT files.  For user with MATLAB v6 or newer installed (either locally or on a remote host), the package also provides methods for controlling MATLAB (trademark) via R and sending and retrieving data between R and MATLAB.
Methods that simplify the setup of S3 generic functions and S3 methods.  Major effort has been made in making definition of methods as simple as possible with a minimum of maintenance for package developers.  For example, generic functions are created automatically, if missing, and naming conflict are automatically solved, if possible.  The method setMethodS3() is a good start for those who in the future may want to migrate to S4.  This is a cross-platform package implemented in pure R that generates standard S3 methods.
Methods and classes for object-oriented programming in R with or without references.  Large effort has been made on making definition of methods as simple as possible with a minimum of maintenance for package developers.  The package has been developed since 2001 and is now considered very stable.  This is a cross-platform package implemented in pure R that defines standard S3 classes without any tricks.
The 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.
Utility functions useful when programming and developing R packages.
Estimation of reproduction numbers for disease outbreak, based on
    incidence data. The R0 package implements several documented methods. It is
    therefore possible to compare estimations according to the methods used.
    Depending on the methods requested by user, basic reproduction number
    (commonly denoted as R0) or real-time reproduction number (referred to as
    R(t)) is computed, along with a 95% Confidence Interval. Plotting outputs
    will give different graphs depending on the methods requested : basic
    reproductive number estimations will only show the epidemic curve
    (collected data) and an adjusted model, whereas real-time methods will also
    show the R(t) variations throughout the outbreak time period. Sensitivity
    analysis tools are also provided, and allow for investigating effects of
    varying Generation Time distribution or time window on estimates.
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.
Provides functions for aligning 2D gas chromatography mass spectrometry derived metabolite peaks obtained from primary processing and generates an alignment table that
        allows for a comparison of common peaks across samples and metabolite identification. Publication describing the package in detail is available at the following citation: Ryne C. Ramaker, Emily Gordon, Sara J. Cooper (2017) <doi:10.1101/179168>.
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. First, The
    Kenward-Roger approach. Due to some inconsistency between the 'pbkrtest'
    package and the 'glmmPQL' function, the Kenward-Roger approach in the
    'r2glmm' package is limited to the LMM. Second, The method introduced
    by Nakagawa and Schielzeth (2013) <DOI:10.1111/j.2041-210x.2012.00261.x>
    and later extended by Johnson (2014) <DOI:10.1111/2041-210X.12225>.
    The 'r2glmm' package only computes marginal R squared for the LMM and does
    not generalize the statistic to the GLMM; however, confidence limits and
    semi-partial R squared for fixed effects are useful additions. Lastly, an
    approach using standardized generalized variance (SGV) can be used for
    covariance model selection. Package installation instructions can be found
    in the readme file.
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'.
Performs goodness-of-fit tests for capture-recapture models. Also contains several functions to process capture-recapture data.
This package uses either the statconnDCOM server (via the
        rcom package) or the RDCOMClient to communicate with MS-Word
        via the COM interface.
Invoke a 'BUGS' model in 'OpenBUGS' or 'WinBUGS', a class "bugs" for 'BUGS' 
  results and functions to work with that class.
  Function write.model() allows a 'BUGS' model file to be written.  
  The class and auxiliary functions could be used with other MCMC programs, including 'JAGS'.
This is a collection of useful functions and data for
        Stats 330
Provides a collection of functions for basic
    database and document management operations such as add, get, list access
    or delete. Every cdbFunction() gets and returns a list() containing the
    connection setup. Such a list can be generated by cdbIni().
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.
Facilitates the design and generation of optimal color (or symbol) codes that can be used to mark and identify individual animals. These codes are made such that the IDs are robust to partial erasure: even if sections of the code are lost, the entire identity of the animal can be reconstructed. Thus, animal subjects are not confused and no ambiguity is introduced.
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.
The Radiant Data menu includes interfaces for loading, saving,
    viewing, visualizing, summarizing, transforming, and combining data. It also
    contains functionality to generate reproducible reports of the analyses
    conducted in the application.
The Radiant Design menu includes interfaces for design of
    experiments, sampling, and sample size calculation. The application extends
    the functionality in radiant.data.
The Radiant Model menu includes interfaces for linear and logistic
    regression, 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.
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>.
Implementation of ADMIXTURE for individual ancestry inference in R. Specifically, ADMIXTURE is a software tool for maximum likelihood estimation of individual ancestries from multilocus SNP genotype datasets, see <https://www.genetics.ucla.edu/software/admixture/>. Users can use 'radmixture' to calculate ancestry components with different public datasets. It is very convenient and fast for personal genotype data. For more details, see <https://github.com/wegene-llc/radmixture/blob/master/README.md>.
Designed for the import, analysis, and visualization of dosimetric and volumetric data in Radiation Oncology, the tools herein enable import of dose-volume histogram information from multiple treatment planning system platforms and 3D structural representations and dosimetric information from 'DICOM-RT' files.  These tools also enable subsequent visualization and statistical analysis of these data.
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 (see <https://developers.google.com/adwords/> for more information). 
    Therefore the package implements three main features. First, the package
    provides an authentication process for R with the Google Adwords API (see 
    <https://developers.google.com/adwords/api/> for more information) via OAUTH2.
    Second, the package offers an interface to apply the Adwords query language in
    R and query the Adwords API with ad-hoc reports. Third, the received data are
    transformed into suitable data formats for further data processing and data
    analysis.
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.
The ragt2ridges-package provides ridge maximum likelihood estimation of vector auto-regressive processes: the VAR(1), VAR(2) and VARX(1) model (more to be added). Prior knowledge may be incorporated in the estimation through a) specification of the edges believed to be absent in the time series chain graph, and b) a shrinkage target towards which the parameter estimate is shrunken for large penalty parameter values. Estimation functionality is accompanied by methodology for penalty parameter selection. In addition, the package offers supporting functionality for the exploitation of estimated models. Among others, i) a procedure to infer the support of the non-sparse ridge estimate (and thereby of the time series chain graph) is implemented, ii) a table of node-wise network summary statistics, iii) mutual information analysis, and iv) impulse response analysis. Cf. Miok et al. (2017) <DOI:10.1002/bimj.201500269> and submitted follow-up work for details on the implemented methods.
Algorithms to price American and European
    equity options, convertible bonds and a
    variety of other financial derivatives. It uses an
    extension of the usual Black-Scholes model in which
    jump to default may occur at a probability specified
    by a power-law link between stock price and hazard
    rate as found in the paper by Takahashi, Kobayashi,
    and Nakagawa (2001) <doi:10.3905/jfi.2001.319302>.  We
    use ideas and techniques from Andersen and
    Buffum (2002) <doi:10.2139/ssrn.355308> and
    Linetsky (2006) <doi:10.1111/j.1467-9965.2006.00271.x>.
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.
Rail trail data from the excellent 'TrailLink' website, sponsored by the 
    Rails-to-Trails Conservancy <https://www.traillink.com/>. Includes information 
    (such as name, length, surface, and reviews) about more than 11,000 trails in every
    state in the United States. Data can be used to better recreational trail use in 
    the United States.
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.
Provides a programmatic interface to the citation information and alternate metrics provided by 'Altmetric'. Data from Altmetric allows researchers to immediately track the impact of their published work, without having to wait for citations. This allows for faster engagement with the audience interested in your work. For more information, visit <https://www.altmetric.com/>.
Characterizing environmental microbiota diversity using amplicon-based next generation sequencing (NGS) data. Functions are developed to manipulate operational taxonomic unit (OTU) table, perform descriptive and inferential statistics, and generate publication-quality plots.
Parser generator for R using combinatory parsers. It
    is inspired by combinatory parsers developed in Haskell.
Estimate the parameters, the number of classes and cluster vertices of a random network into groups with homogeneous connection profiles. The clustering is performed for directed graphs with typed edges (edges are assumed to be drawn from multinomial distributions) for which a partition of the vertices is available.
Provides an R interface for using 'AmCharts' Library. Based on 'htmlwidgets', it
    provides a global architecture to generate 'JavaScript' source code for charts.
    Most of classes in the library have their equivalent in R with S4 classes;
    for those classes, not all properties have been referenced but can easily be
    added in the constructors. Complex properties (e.g. 'JavaScript' object) can
    be passed as named list. See examples at 
    <http://datastorm-open.github.io/introduction_ramcharts/>
    and <http://www.amcharts.com/> for more information
    about the library. The package includes the free version of 'AmCharts'
    Library. Its only limitation is a small link to the web site displayed on
    your charts. If you enjoy this library, do not hesitate to refer to this
    page <http://www.amcharts.com/online-store/> to purchase a licence, and thus
    support its creators and get a period of Priority Support. See also 
    <http://www.amcharts.com/about/> for more information about 'AmCharts' company.
It is widely documented in psychology, economics and other disciplines that socio-economic agents do not pay full attention to all available choices, rendering standard revealed preference theory invalid. This package implements the estimation and inference procedures documented in Cattaneo, Ma, Masatlioglu and Suleymanov (2017) <http://www-personal.umich.edu/~cattaneo/papers/Cattaneo-Ma-Masatlioglu-Suleymanov_2017_RAM.pdf>, which utilizes standard choice data to partially identify and estimate decision maker's preference. For inference, different simulation-based critical values are provided.
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.
A novel numerical algorithm that provides functionality for estimating the exact 95% confidence interval of the location parameter in the random effects model, and is much faster than the naive method. Works best when the number of studies is between 6-20.
Generates random names with additional information including fake
    SSNs, gender, location, zip, age, address, and nationality.
Model selection and parameter tuning procedures for a class of random network models. The model selection can be done by a general cross-validation framework called ECV from Li et. al. (2016) <arXiv:1612.04717> . Several other model-based and task-specific methods are also included, such as NCV from Chen and Lei (2016) <arXiv:1411.1715>, likelihood ratio method from Wang and Bickel (2015) <arXiv:1502.02069>, spectral methods from Le and Levina (2015) <arXiv:1507.00827>. Many network analysis methods are also implemented, such as the regularized spectral clustering (Amini et. al. 2013 <doi:10.1214/13-AOS1138>) and its degree corrected version and graphon neighborhood smoothing (Zhang et. al. 2015 <arXiv:1509.08588>).
The true random number service provided by the RANDOM.ORG
 website created by Mads Haahr samples atmospheric noise via radio tuned to
 an unused broadcasting frequency together with a skew correction algorithm
 due to John von Neumann.  More background is available in the included
 vignette based on an essay by Mads Haahr.  In its current form, the package
 offers functions to retrieve random integers, randomized sequences and
 random strings.
The Function performs a parallel analysis using simulated polychoric correlation matrices. The nth-percentile of the eigenvalues distribution obtained from both the randomly generated and the real data polychoric correlation matrices is returned. A plot comparing the two types of eigenvalues (real and simulated) will help determine the number of real eigenvalues that outperform random data. The function is based on the idea that if real data are non-normal and the polychoric correlation matrix is needed to perform a Factor Analysis, then the Parallel Analysis method used to choose a non-random number of factors should also be based on randomly generated polychoric correlation matrices and not on Pearson correlation matrices. Random data sets are simulated assuming or a uniform or a multinomial distribution or via the bootstrap method of resampling (i.e., random permutations of cases). Also Multigroup Parallel analysis is made available for random (uniform and multinomial distribution and with or without difficulty factor) and bootstrap methods. An option to choose between default or full output is also available as well as a parameter to print Fit Statistics (Chi-squared, TLI, RMSEA, RMR and BIC) for the factor solutions indicated by the Parallel Analysis.
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 set of tools to help explain which variables are most important in a random forests. Various variable importance measures are calculated and visualized in different settings in order to get an idea on how their importance changes depending on our criteria (Hemant Ishwaran and Udaya B. Kogalur and Eiran Z. Gorodeski and Andy J. Minn and Michael S. Lauer (2010) <doi:10.1198/jasa.2009.tm08622>, Leo Breiman (2001) <doi:10.1023/A:1010933404324>).
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.  Now extended to include multivariate and unsupervised forests.  Also includes quantile regression forests for univariate and multivariate training/testing settings.  The package runs in both serial and parallel (OpenMP) modes.
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 and 
	    random samples for common sampling designs.
Fits standard and random effects latent class models. The single level random effects model is described in Qu et al <doi:10.2307/2533043> and the two level random effects model in Beath and Heller <doi:10.1177/1471082X0800900302>. Examples are given for their use in diagnostic testing.
Function for generating random gender and ethnicity correct first and/or last names. Names are chosen proportionally based upon their probability of appearing in a large scale data base of real names.
Ensemble model, for classification, regression
	and unsupervised learning, based on a forest of unpruned 
	and randomized binary decision trees. Each tree is grown 
	by sampling, with replacement, a set of variables at each node. 
	Each cut-point is generated randomly, according to the continuous 
	Uniform distribution. For each tree, data are either bootstrapped 
	or subsampled. The unsupervised mode introduces clustering, dimension reduction
	and variable importance, using a three-layer engine. Random Uniform Forests are mainly 
	aimed to lower correlation between trees (or trees residuals), to provide a deep analysis 
	of variable importance and to allow native distributed and incremental learning.
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 distributions.
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.
Performs aggregation of ordered lists based
        on the ranks using several different algorithms:
        Cross-Entropy Monte Carlo algorithm, Genetic algorithm, and a
        brute force algorithm (for small problems).
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.
Functions to generate plots and tables for comparing independently-
    sampled populations. Companion package to "A Primer on Visualizations
    for Comparing Populations, Including the Issue of Overlapping Confidence
    Intervals" by Wright, Klein, and Wieczorek (2017, in press).
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. The distance is computed using the L2
    (Euclidean) metric. Please see package 'RANN.L1' for the same
    functionality using the L1 (Manhattan, taxicab) metric.
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.
Access to the C-level R date and datetime code is provided for
 C-level API use by other packages via registration of native functions.
 Client packages simply include a single header 'RApiDatetime.h' provided
 by this package, and also 'import' it.  The R Core group is the original
 author of the code made available with slight modifications by this package.
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.
Performs wood cell anatomical data analyses on spatially explicit xylem (tracheids) datasets 
                  derived from thin sections of woody tissue. The package includes functions for visualisation, 
                  detection and alignment of continuous tracheid radial file (defined as rows) and individual tracheid position 
                  within an annual ring of coniferous species. This package is designed to be used with elaborate cell output, 
                  e.g. as provided with ROXAS (von Arx & Carrer, 2014 <doi:10.1016/j.dendro.2013.12.001>). The package has been validated for Picea abies, 
                  Larix Siberica, Pinus cembra and Pinus sylvestris.
Biodiversity is in crisis. The overarching aim of conservation
    is to preserve biodiversity patterns and processes. To this end, protected
    areas are established to buffer species and preserve biodiversity processes.
    But resources are limited and so protected areas must be cost-effective.
    This package contains tools to generate plans for protected areas
    (prioritizations), using spatially explicit targets for biodiversity
    patterns and processes. To obtain solutions in a feasible amount  of time,
    this package uses the commercial 'Gurobi' software package (obtained from
    <http://www.gurobi.com/>). Additionally, the 'rgurobi' package can also be
    installed to provide extra functionality (obtained from
    <http://github.com/jeffreyhanson/rgurobi>). For more information on using
    this package, see Hanson et al. (2017) <doi:10.1111/2041-210X.12862>.
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
Fit Gaussian hidden Markov (or semi-Markov) models with / without autoregressive coefficients and with / without regularization. The fitting algorithm for the hidden Markov model is illustrated by Rabiner (1989) <doi:10.1109/5.18626>. The shrinkage estimation on the covariance matrices is based on the method by Ledoit et al. (2004) <doi:10.1016/S0047-259X(03)00096-4>. The shrinkage estimation on the autoregressive coefficients uses the elastic net shrinkage detailed in Zou et al. (2005) <doi:10.1111/j.1467-9868.2005.00503.x>.
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.
Implements the Range Ancestral State Estimation for phylogeography described in Quintero, I., Keil, P., Jetz, W., & Crawford, F. W. (2015) <DOI:10.1093/sysbio/syv057>. 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.
A S4 class has been created such that complex operations can be
    executed on each cells of a raster map. The raster of objects contains the
    traditional raster map with the addition of a list of generic objects: one
    object for each raster cells. It allows to write few lines of R code for complex
    map algebra. Two environmental applications about frequency analysis of raster
    map of precipitation and creation of a raster map of soil water retention curves
    have been presented.
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 rate ratio (or relative risk, "RR") for 
    binomial proportions or Poisson rates, or for odds ratio 
    ("OR", binomial only). Also confidence intervals for a single 
    binomial or Poisson rate, and intervals for matched pairs. 
    Includes asymptotic score methods including skewness corrections, 
    which have been developed in Laud (2017, in press)
    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,
    and generalised for incorporating prior information. 
    Also methods for stratified calculations (e.g. meta-analysis),
    either assuming fixed effects or incorporating stratum
    heterogeneity.
A collection of randomization tests, data sets and examples. The current version focuses on the description and implementation of a permutation test for testing the continuity assumption of the baseline covariates in the sharp regression discontinuity design (RDD) as in Canay and Kamat (2017) <https://goo.gl/UZFqt7>. More specifically, it allows the user to select a set of covariates and test the aforementioned hypothesis using a permutation test based on the Cramer-von Miss test statistic. Graphical inspection of the empirical CDF and histograms for the variables of interest is also supported in the package.
Describes a new procedure of reducing items in a rating scale called Rating Scale Reduction (RSR). The new stop criterion in RSR procedure is added (stop global max).
This package provides several functions to
    manipulate rational functions, including basic
    arithmetic operators, derivatives and integrals with
    EXPLICIT forms.
Calculation of ratios between two data sets containing environmental data like
    element concentrations by different methods. Additionally plant element 
    concentrations can be corrected for adhering particles (soil, airborne dust).
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 science. 
  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.
Contains the datasets used as default examples
  by the rattle package. The datasets themselves can be used
  independently of the rattle package to illustrate analytics,
  data mining, and data science tasks.
Implementation of the R-Average method for parameter estimation of averaging models of the Anderson's Information Integration Theory by Vidotto, G., Massidda, D., & Noventa, S. (2010) <https://www.uv.es/psicologica/articulos3FM.10/3Vidotto.pdf>.
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.
Bacon is an approach to age-depth modelling that uses Bayesian statistics to reconstruct accumulation histories for deposits, through combining radiocarbon and other dates with prior information. See Blaauw & Christen (2011) <doi:10.1214/11-BA618>.
Provides an interface to functions of the 'SAMtools' C-Library by Heng Li.
A Pure R implementation of Bayesian Global Optimization with Gaussian Processes.
The Brazilian Central Bank API delivers many datasets which regard economic
  activity, regional economy, international economy, public finances, credit
  indicators and many more. For more information please see <http://dadosabertos.bcb.gov.br/>.
  These datasets can be accessed through 'rbcb' functions and can be obtained in
  different data structures common to R ('tibble', 'data.frame', 'xts', ...).
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."
Tool-set to support Bayesian evidence synthesis.
    This includes meta-analysis, (robust) prior derivation
    from historical data, operating characteristics
    and analysis (1 and 2 sample cases).
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.
Functions for connecting to BioUML server, querying BioUML repository and launching BioUML analyses.
Interface to the 'USGS' 'BISON' (<https://bison.usgs.gov/>)
    'API', a 'database' for species occurrence data. Data comes from
    species in the United States from participating data providers. You can get
    data via 'taxonomic' and location based queries. A simple function
    is provided to help visualize data.
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.
Tu & Zhou (1999) <doi:10.1002/(SICI)1097-0258(19991030)18:20%3C2749::AID-SIM195%3E3.0.CO;2-C> showed that comparing the means of populations whose data-generating distributions are non-negative with excess zero observations is a problem of great importance in the analysis of medical cost data. In the same study, Tu & Zhou discuss that it can be difficult to control type-I error rates of general-purpose statistical tests for comparing the means of these particular data sets. This package allows users to perform a modified bootstrap-based t-test that aims to better control type-I error rates in these situations.
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)).
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.
Enables the calibration and analysis of radiocarbon dates, often but not exclusively for the purposes of archaeological research. It includes functions not only for basic calibration, uncalibration, and plotting of one or more dates, but also a statistical framework for building demographic and related longitudinal inferences from aggregate radiocarbon date lists, including: Monte-Carlo simulation test (Timpson et al 2014 <doi:10.1016/j.jas.2014.08.011>), random mark permutation test (Crema et al 2016 <doi:10.1371/journal.pone.0154809>) and spatial permutation tests (Crema, Bevan, and Shennan 2017 <doi:10.1016/j.jas.2017.09.007>).
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.
Provides color schemes for maps and other graphics
        designed by 'CARTO' as described at <https://carto.com/carto-colors/>.
        It includes four types of palettes: aggregation, diverging, qualitative, 
        and quantitative.
This packages provides a direct interface (without the
	     use of Java) to the most basic functionality of Apache
	     Cassanda such as login, updates and queries.
A set of tools 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.
Functions to implement the parametric and non-parametric bootstrap 
    confidence interval methods described in Morrison and Simon (2017) <arXiv:1702.06986>.
Identify, convert and handle dates as used within the Swedish cancer register and associated cancer quality registers in Sweden. Especially the cancer register sometimes use nonstandard date variables where day and/or month can be "00" or were the date format is a mixture of"%Y-%m-%d", "%Y%m%d" and "%y%V" (two digit year and week number according to ISO 8601,which is not completely supported by R).These dates must be approximated to valid dates before being used in for example survival analysis. The package also includes some convenient functions for calculating "lead times" (relying on 'difftime') and introduce a "year" class with relevant S3-methods to handle yearly cohort.
Functions either required by other Swedish Regional Cancer Center packages or standalone functions outside the scope of other packages.
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>.
An implementation of a stochastic heuristic method for performing multidimensional function optimization. The method is inspired in the Cross-Entropy Method. It does not relies on derivatives, neither imposes particularly strong requirements into the function to be optimized. Additionally, it takes profit from multi-core processing to enable optimization of time-consuming functions.
Example dataset for 'Rcell' package. Contains images and cell data object.
To facilitate using 'cereal' with Rcpp.
    'cereal' is a header-only C++11 serialization library.
    'cereal' takes arbitrary data types and reversibly turns them into
    different representations, such as compact binary encodings, XML,
    or JSON. 'cereal' was designed to be fast, light-weight, and easy
    to extend - it has no external dependencies and can be easily
    bundled with other code or used standalone. Please see
    <http://uscilab.github.io/cereal> for more information.
Set of analytical procedures based on advanced data analysis in support of Brazil's public sector external control activity.
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.
A set of functions to generate, access and analyze standard data products from archival tagging data.
An implementation of simulated maximum likelihood method for the
    estimation of Binary (Probit and Logit), Ordered (Probit and Logit) and
    Poisson models with random parameters for cross-sectional and longitudinal
    data.
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.
'Yandex Clickhouse' (<https://clickhouse.yandex/>) is a high-performance relational column-store database to enable
    big data exploration and 'analytics' scaling to petabytes of data. Methods are
    provided that enable working with 'Yandex Clickhouse' databases via
    'DBI' 'methods' and using 'dplyr'/'dbplyr' idioms.
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
Leverages the functionality of 'clipboard.js', a JavaScript library
    for HMTL5-based copy to clipboard from web pages (see <https://clipboardjs.com>
    for more information), and provides a reactive copy-to-clipboard UI button 
    component, called 'rclipButton', for 'shiny' R applications.
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.
R Commander plug-in for repeated-measures and mixed-design
    ('split-plot') ANOVA.
    It adds a new menu entry for repeated measures that allows
    to deal with up to three within-subject factors and optionally with
    one or several between-subject factors.
    It also provides supplementary options to oneWayAnova() and
    multiWayAnova() functions, such as choice of ANOVA type, display of
    effect sizes and post hoc analysis for multiWayAnova().
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. This report has been cited in more than 1,000 scientific articles.
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
R Commander plug-in to demonstrate various actuarial and financial risks. It includes valuation of bonds and stocks, portfolio optimization, classical ruin theory and demography.
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.
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.
The 'sutteForecastR' is a package of Alpha-Sutte indicator. To make the 'sutteForecastR' user friendly, so we develop an 'Rcmdr' plug-in based on the Alpha-Sutte indicator function.
Provides an Rcmdr "plug-in" based on the TeachingDemos package, and is primarily for illustrative purposes.
An 'R Commander' plug-in providing an integrated solution to perform
    a series of text mining tasks such as importing and cleaning a corpus, and
    analyses like terms and documents counts, vocabulary tables, terms
    co-occurrences and documents similarity measures, time series analysis,
    correspondence analysis and hierarchical clustering. Corpora can be imported
    from spreadsheet-like files, directories of raw text files, 'Twitter' queries,
    as well as from 'Dow Jones Factiva', 'LexisNexis', 'Europresse' and 'Alceste' files.
Some extensions to Rcmdr (R Commander), randomness test, 
 variance test for one normal sample and predictions using active model, 
 made by R-UCA project and used in teaching statistics at University of Cadiz (UCA).
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.
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 Program Evaluation in R" and "An R
             Companion for the Handbook of Biological Statistics". 
             Vignettes are available at <http://rcompanion.org>.
Provides propositions, votings, deputies and parties data from 
    Brazilian Chamber of Deputies <https://dadosabertos.camara.leg.br> in data frames.
Solve some conic related problems (intersection of conics with lines and conics, arc length of an ellipse, polar lines, etc.).
Client for the CORE API (<https://core.ac.uk/docs/>).
    CORE (<https://core.ac.uk>) aggregates open access research
    outputs from repositories and journals worldwide and make them
    available to the public.
A collection of small text corpora of interesting data.
    It contains all data sets from 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, <doi:10.18637/jss.v040.i08>), the book by Eddelbuettel (2013,
 <doi:10.1007/978-1-4614-6868-4>) and the paper by Eddelbuettel and Balamuta (2017,
 <doi:10.7287/peerj.preprints.3188v1>); see 'citation("Rcpp")' for details.
Rcpp11 includes a header only C++11 library that facilitates 
  integration between R and modern C++.
Provides optimized functions implemented in C++ with 'Rcpp'
    for solving problems in combinatorics and computational mathematics.
    There is a combination function and a permutation function that are
    both flexible as well as efficient with respect to speed and memory.
    There are optional contraint parameters that when utilized, generate
    all combinations/permutations of a vector meeting a specific criteria
    (E.g. finding all combinations such that the sum is less than a
    bound). Both functions are capable of handling mulitsets as well.
    Additionally, there are various sieving functions that quickly
    generate essential components for problems common in computational
    mathematics (E.g. number of comprime elements, divisors, prime
    factorizations, and complete factorizations for many numbers as
    well as primes numbers in a range).
'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'. From release 7.800.0 on, 'Armadillo' is
 licensed under Apache License 2; previous releases were under licensed as
 MPL 2.0 from version 3.800.0 onwards and LGPL-3 prior to that;
 'RcppArmadillo' (the 'Rcpp' bindings/bridge to Armadillo) is licensed under
 the GNU GPL version 2 or later, as is the rest of 'Rcpp'. 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 are disabled 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 
    'Blaze' has committed to 'C++14' commit to 'C++14' which does not used by most R users
    from version 3.0, 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 3.3.3). 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. There
 are over 1000 functions in total with an extensive test suite. The 'RcppGSL'
 package provides an easy-to-use interface between 'GSL' data structures and
 R using concepts from 'Rcpp' which is itself a package that eases the
 interfaces between R and C++. This package also serves as a prime example of
 how to build a package that uses 'Rcpp' to connect to another third-party
 library. The 'autoconf' script, 'inline' plugin and example package can all
 be used as a stanza to  write a similar package against another library.
Collection of functions to evaluate sequences, decode hidden states and estimate parameters from a single or multiple sequences of a discrete time Hidden Markov Model. The observed values can be modeled by a multinomial distribution for categorical/labeled emissions, a mixture of Gaussians for continuous data and also a mixture of Poissons for discrete values. It includes functions for random initialization, simulation, backward or forward sequence evaluation, Viterbi or forward-backward decoding and parameter estimation using an Expectation-Maximization approach.
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.
'MessagePack' is an efficient binary serialization format.
 It lets you exchange data among multiple languages like 'JSON'. But it is
 faster and smaller. Small integers are encoded into a single byte, and
 typical short strings require only one extra byte in addition to the strings
 themselves. This package provides headers from the 'msgpack-c'
 implementation for C and C++(11) for use by R, particularly 'Rcpp'. The
 included 'msgpack-c' headers are licensed under the Boost Software License
 (Version 1.0); the code added by this package as well the R integration are
 licensed under the GPL (>= 2). See the files 'COPYRIGHTS' and 'AUTHORS' for
 a full list of  copyright holders and contributors to 'msgpack-c'.
A collection of open source libraries for numerical computing
    (numerical integration, optimization, etc.) and their integration with
    'Rcpp'.
High level functions for parallel programming with 'Rcpp'.
    For example, the 'parallelFor()' function can be used to convert the work of
    a standard serial "for" loop into a parallel one and the 'parallelReduce()'
    function can be used for accumulating aggregate or other values.
Allows to display a progress bar in the R
    console for long running computations taking place in c++ code,
    and support for interrupting those computations even in multithreaded
    code, typically using OpenMP.
'QuantLib' bindings are provided for R using 'Rcpp' and the
 header-only 'Quantuccia' variant (put together by Peter Caspers) offering
 an essential subset of 'QuantLib'. See the included file 'AUTHORS' for a full
 list of contributors to both 'QuantLib' and 'Quantuccia'.
Connection to the 'Redis' key/value store using the
  C-language client library 'hiredis'. '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.
R access to the Sequential Monte Carlo Template Classes
 by Johansen (Journal of Statistical Software, 2009, v30, i6) is provided.
 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.
Provides a C++11-style thread class and thread pool that can safely
    be interrupted from R.
R-level and C++-level functionality
    to generate random deviates from and calculate moments of a
    Truncated Normal distribution using the algorithm of Robert (1995) <DOI:10.1007/BF00143942>.
    In addition to RNG, functions for calculating moments, densities,
    and entropies are provided at both levels.
The configuration format defined by 'TOML' (which expands to
 "Tom's Obvious Markup Language") specifies an excellent format (described at
 <https://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.
Provides the means to compile user-supplied C++ functions with 
  'Rcpp' and retrieve an 'XPtr' that can be passed to other C++ components.
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.
Implements Corpus Query Protocol functions based on the
        CWB software. Rely on CWB (GPL v2), PCRE (BSD licence), glib2
        (LGPL).
Performs parallel web crawling and web scraping. It is designed to crawl, parse and store web pages to produce data that can be directly used for analysis application. For details see Khalil and Fakir (2017) <DOI:10.1016/j.softx.2017.04.004>.
Tools to write a list of credentials to an encrypted file and later read from that file into R. The goal is to have a useful alternative to including username/passwords as part of a script or even stored in the clear in a separate text file. Additional tools provided which are specific for connecting to a database.
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/).
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.
Provides a means to style plots through cascading style sheets.
    This separates the aesthetics from the data crunching in plots and charts.
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.
A collection of ranked choice voting data and functions to 
    manipulate, run elections with, and visualize this data and others. 
    It can bring in raw data, transform it into a ballot you can read, 
    and return election results for an RCV contest.
The native R functionalities only allow PDF exports of reference manuals. This shall be extended by converting the package documentation files into markdown files and combining them into a markdown version of the package reference manual.
Functions to convert Rd to 'roxygen' documentation. It can parse an
    Rd file to a list, create the 'roxygen' documentation and update the original
    R script (e.g. the one containing the definition of the function)
    accordingly. This package also provides utilities that can help developers
    build packages using 'roxygen' more easily. The 'formatR' package can be used
    to reformat the R code in the examples sections so that the code will be
    more readable.
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 an R interface to the Data Retriever
    <http://data-retriever.org/> via the Data Retriever's
    command line interface. The Data Retriever automates the
    tasks of finding, downloading, and cleaning public datasets,
    and then stores them in a local database.
Provides the tools to undertake estimation in
    Regression Discontinuity Designs. Both sharp and fuzzy designs are
    supported. Estimation is accomplished using local linear regression.
    A provided function will utilize Imbens-Kalyanaraman optimal
    bandwidth calculation. A function is also included to test the
    assumption of no-sorting effects.
Estimation of both single- and multiple-assignment Regression Discontinuity Designs 
    (RDDs). Provides both parametric (global) and non-parametric (local) estimation choices for
    both sharp and fuzzy designs, along with power analysis and assumption checks. 
    Introductions to the underlying logic and analysis of RDDs are in 
    Thistlethwaite, D. L., Campbell, D. T. (1960) <doi:10.1037/h0044319> and
    Lee, D. S., Lemieux, T. (2010) <doi:10.1257/jel.48.2.281>.
Density discontinuity test (a.k.a. manipulation test) is commonly employed in regression discontinuity designs and other treatment effect settings to detect whether there is evidence suggesting perfect self-selection (manipulation) around a cutoff where a treatment/policy assignment changes. This package provides tools for conducting the aforementioned statistical test: rddensity() to construct local polynomial based density discontinuity test given a prespecified cutoff, rdbwdensity() to perform bandwidth selection, and rdplotdensity() to construct density plot near the cutoff.
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 <https://uk-air.defra.gov.uk/>. It basically scrapes 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.
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.
We provide a rich collection of linear and nonlinear
    dimension reduction techniques implemented using 'RcppArmadillo'.
    The question on what we should use as the target dimension is
    addressed by intrinsic dimension estimation methods introduced as well.
    For more details on dimensionality techniques, see the paper by
    Ma and Zhu (2013) <doi:10.1111/j.1751-5823.2012.00182.x> if you are interested in
    statistical approach, or Engel, Huttenberger, and Hamann (2012)
    <doi:10.4230/OASIcs.VLUDS.2011.135> for a broader cross-disciplinary overview.
A common framework for calculating distance matrices.
Analysis of distance sampling data collected on line transect surveys.  Estimates distance-based detection functions and abundances.
Provides a framework for the measurement and partitioning of
    the (similarity-sensitive) biodiversity of a metacommunity and its
    constituent subcommunities. Richard Reeve, et al. (2015) 
    <arXiv:1404.6520v3>.
The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. Under the local randomization approach, RD designs can be interpreted as randomized experiments inside a window around the cutoff. This package provides tools to perform randomization inference for RD designs under local randomization: rdrandinf() to perform hypothesis testing using randomization inference, rdwinselect() to select a window around the cutoff in which randomization is likely to hold, rdsensitivity() to assess the sensitivity of the results to different window lengths and null hypotheses and rdrbounds() to construct Rosenbaum bounds for sensitivity to unobserved confounders.
Imports real-time thermo cycler (qPCR) data from Real-time PCR
    Data Markup Language (RDML) and transforms to the appropriate formats of
    the 'qpcR' and 'chipPCR' packages. Contains a dendrogram visualization 
    for the structure of RDML object and GUI for RDML editing.
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.
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/>.
Low-level interface to '.NET' virtual machine along the lines of the R C .call interface.  Can create '.NET' object, call methods, get or set properties, call static functions, etc.
Create doxygen documentation for source code in R packages. 
  Includes a RStudio Addin, that allows to trigger the doxygenize process.
Functions for manipulation of Rd objects, including function reprompt()
    for updating existing Rd documentation for functions, methods and classes,
    function rebib() for import of references from 'bibtex' files, a macro for
    importing 'bibtex' references which can be used in Rd files and 'roxygen2'
    comments and other convenience functions for references.
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).
An interface for using the popular Java based Drools, which is a Business Rule Management System (See <https://www.drools.org> for more information). This package allows you to run a set of rules written in DRL format on the data using the Drools engine. Credits to Mu Sigma for their continued support throughout the development of the package.
External jars required for package 'Rdrools'.
Provides full programmatic access to the 'Dropbox' file hosting platform <https://dropbox.com>, including support for all standard file operations.
Interface to the Dryad "Solr" API, their "OAI-PMH" service, and
    fetch datasets. Dryad (<http://datadryad.org/>) is a curated host of
    data underlying scientific publications.
Provides functionality for carrying out estimation
    with data collected using Respondent-Driven Sampling. This includes
    Heckathorn's RDS-I and RDS-II estimators as well as Gile's Sequential
    Sampling estimator. The package is part of the "RDS Analyst" suite of
    packages for the analysis of respondent-driven sampling data.
    See Gile and Handcock (2010) <doi:10.1111/j.1467-9531.2010.01223.x> and
    Gile and Handcock (2015) <doi:10.1111/rssa.12091>.
R interface to DSDP semidefinite programming library. The DSDP software is a free open source implementation of an interior-point method for semidefinite programming. It provides primal and dual solutions, exploits low-rank structure and sparsity in the data, and has relatively low memory requirements for an interior-point method.
Provides a threads-type programming environment for R.
   The package gives the R programmer the clearer, more concise
   shared memory world view, and in some cases gives superior
   performance as well.  In addition, it enables parallel processing on
   very large, out-of-core matrices.
This package provides an R interface to Pete Warden's Data
        Science Toolkit.  See www.datasciencetoolkit.org for more
        information.  The source code for this package can be found at
        github.com/rtelmore/RDSTK Happy hacking!
A tree bootstrap method for estimating uncertainty in respondent-driven samples (RDS). Quantiles are estimated by multilevel resampling in such a way that preserves the dependencies of and accounts for the high variability of the RDS process.
Implementation of density tracking by quadrature (DTQ) algorithms for stochastic differential equations (SDEs).  DTQ algorithms numerically compute the density function of the solution of an SDE with user-specified drift and diffusion functions.  The calculation does not require generation of sample paths, but instead proceeds in a deterministic fashion by repeatedly applying quadrature to the Chapman-Kolmogorov equation associated with a discrete-time approximation of the SDE.  The DTQ algorithm is provably convergent.  For several practical problems of interest, we have found the DTQ algorithm to be fast, accurate, and easy to use.
Functions for connecting to and interfacing with an 'Arduino' or similar device. Functionality includes uploading of sketches, setting and reading digital and analog pins, and rudimentary servo control. This project is not affiliated with the 'Arduino' company, <https://www.arduino.cc/>.
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()'.
RE2 <https://github.com/google/re2> is a primarily deterministic finite automaton based regular expression engine from Google that is very fast
    at matching large amounts of text.
Make it easy to use 'React' in R with helper
              dependency functions, embedded 'Babel' transpiler,
              and examples.
Routines for developing models that describe reaction and advective-diffusive transport in one, two or three dimensions.
  Includes transport routines in porous media, in estuaries, and in bodies with variable shape.
Functions for reading and decompressing the DBC (compressed DBF) files. Please note that this is the file format used by the Brazilian Ministry of Health (DATASUS) to publish healthcare datasets. It is not related to the FoxPro or CANdb DBC file formats.
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/>.
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.
Wraps 'tiny_obj_loader' C++ library for reading the 'Wavefront' OBJ
    3D file format including both mesh objects and materials files. The
    resultant R objects are either structured to match the 'tiny_obj_loader'
    internal data representation or in a form directly compatible with the 'rgl'
    package.
Import ODS (OpenDocument Spreadsheet) into R as a data frame. Also support writing data frame into ODS file.
Reads in text from 'unstructured' modern Microsoft Office files (XML based files) such as Word and PowerPoint.
    This does not read in structured data (from Excel or Access) as there are many other great packages to that do so already.
The goal of 'readr' is to provide a fast and friendly way to read
    rectangular data (like 'csv', 'tsv', and 'fwf'). It is designed to flexibly
    parse many types of data found in the wild, while still cleanly failing when
    data unexpectedly changes.
Function to read and write the 'Stata' file format.
Functions for importing and handling text files and formatted text
    files with additional meta-data, such including '.csv', '.tab', '.json', '.xml',
    '.pdf', '.doc', '.docx', '.xls', '.xlsx', and others.
Import excel files into R. Supports '.xls' via the embedded
    'libxls' C library <https://sourceforge.net/projects/libxls/> and '.xlsx' via
    the embedded 'RapidXML' C++ library <https://rapidxml.sourceforge.net>. Works on
    Windows, Mac and Linux without external dependencies.
Provides quarterly information on Housing Market Statistics. This 
    includes average square meter prices and the number of free trades for parcel 
    and terraced houses, condominiums and holiday homes in Denmark since 1992.
    Visit <http://finansdanmark.dk/toerre-tal/boligstatistik/boligmarkedsstatistikken/> 
    and <http://rkr.statistikbank.dk/> for more information.
Fits a multivariate value-added model (VAM), see Broatch and Lohr (2012) <doi:10.3102/1076998610396900>, with normally distributed test scores and a binary outcome indicator. A pseudo-likelihood approach, Wolfinger (1993) <doi:10.1080/00949659308811554>, is used for the estimation of this joint generalized linear mixed model.  The inner loop of the pseudo-likelihood routine (estimation of a linear mixed model) occurs in the framework of the EM algorithm presented by Karl, Yang, and Lohr (2013) <DOI:10.1016/j.csda.2012.10.004>. This material is based upon work supported by the National Science Foundation under grants DRL-1336027 and DRL-1336265.
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.  See Koenker and Gu (2017) REBayes: An R Package for Empirical
    Bayes Mixture Methods, Journal of Statistical Software, 82, 1--26, 
    <DOI:10.18637/jss.v082.i08>.
A programmatic client for the eBird database, 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, Dirac or circular von Mises parametric families.
Build regular expressions piece by piece using human readable code.
    This package is designed for interactive use.  For package development, use
    the rebus.* dependencies.
Build regular expressions piece by piece using human readable code.
    This package contains core functionality, and is primarily intended to be
    used by package developers.
Build regular expressions piece by piece using human readable code.
    This package contains date and time functionality, and is primarily intended
    to be used by package developers.
Build regular expressions piece by piece using human readable code.
    This package contains number-related functionality, and is primarily intended
    to be used by package developers.
Build regular expressions piece by piece using human readable code.
    This package contains Unicode functionality, and is primarily intended to be
    used by package developers.
Relevant Component Analysis (RCA) tries to find a linear
    transformation of the feature space such that the effect of irrelevant
    variability is reduced in the transformed space.
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.
An extensible framework to create and preprocess 
    design matrices. Recipes consist of one or more data manipulation 
    and analysis "steps". Statistical parameters for the steps can 
    be estimated from an initial data set and then applied to 
    other data sets. The resulting design matrices can then be used 
    as inputs into statistical or machine learning models.
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.
Turns nested lists into data.frames in an orderly manner.
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.
Computes revisitation metrics for trajectory data, such as the number of revisitations for each location as well as the time spent for that visit and the time since the previous visit. Also includes functions to plot data.
Includes algorithms to facilitate the assessment of extinction
    risk of species according to the IUCN (International Union for Conservation of
    Nature, see <http://www.iucn.org> for more information) red list criteria.
Functions for (1) simulating survival and recurrent event data from
    stochastic process point of view, (2) exploring and modeling recurrent event
    data through the mean cumulative function (MCF) or also called the
    Nelson-Aalen estimator of the cumulative hazard rate function, and gamma
    frailty model with spline rate function, and (3) comparing two-sample
    recurrent event responses with the pseudo-score tests.
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 toolbox created by members of the International Union for
    Conservation of Nature (IUCN) Red List of Ecosystems Committee for
    Scientific Standards. Primarily, it is a set of tools suitable
    for calculating the metrics required for making assessments of species and
    ecosystems against the IUCN Red List of Threatened Species and the IUCN Red
    List of Ecosystems categories and criteria. See the IUCN website for
    detailed guidelines, the criteria, publications and other information.
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.
Regularization by Denoising uses a denoising engine to solve
  many image reconstruction ill-posed inverse problems. This is a R
  implementation of the algorithm developed by Romano et.al. (2016) <arXiv:1611.02862>. Currently,
  only the gradient descent optimization framework is implemented. Also,
  only the median filter is implemented as a denoiser engine. However,
  (almost) any denoiser engine can be plugged in. There are currently available
  3 reconstruction tasks: denoise, deblur and super-resolution. And again,
  any other task can be easily plugged into the main function 'RED'.
A 'hiredis' wrapper that includes support for
    transactions, pipelining, blocking subscription, serialisation of
    all keys and values, 'Redis' error handling with R errors.
    Includes an automatically generated 'R6' interface to the full
    'hiredis' 'API'.  Generated functions are faithful to the
    'hiredis' documentation while attempting to match R's argument
    semantics.  Serialisation must be explicitly done by the user, but
    both binary and text-mode serialisation is supported.
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,<doi:10.1093/bioinformatics/btu029>) 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, <doi:10.1186/s12859-016-1140-4>) 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.
'shiny' extension that adds regular expression filtering capabilities to 
  the choice vector of the select list.
A set of functions used to automate commonly used methods in
    regression analysis. This includes plotting interactions, calculating simple
    slopes, calculating standardized coefficients, etc. See the reghelper
    documentation for more information, documentation, and examples.
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.
Network-based regularization has achieved success in variable selection for 
    high-dimensional biological data due to its ability to incorporate correlations 
    between genomic features. This package provides procedures for fitting network-based 
    regularization, minimax concave penalty (MCP) and lasso penalty for generalized linear 
    models. This current version, regnet0.2.0, focuses on binary outcomes. Functions for 
    continuous, survival outcomes and other regularization methods will be included in the 
    forthcoming upgraded versions.
A function to plot a regression nomogram of coxph, lm and glm regression 
    objects. Covariate distributions are superimposed on nomogram scales and the plot
	is animated to allow on the fly changes to distribution representation and to 
	enable outcome calculation.
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 structural
    equation models.
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.
It performs regularized simultaneous component based data
    integration for multiblock data.
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.
Performs model-free reinforcement learning in R. This implementation enables the learning
    of an optimal policy based on sample sequences consisting of states, actions and rewards. In 
    addition, it supplies multiple predefined reinforcement learning algorithms, such as experience 
    replay.
Functions from the book "Reinsurance: Actuarial and Statistical Aspects" (2017) by Hansjoerg Albrecher, Jan Beirlant and Jef Teugels <http://www.wiley.com/WileyCDA/WileyTitle/productCd-0470772689.html>.
Search R files for not installed packages and run install.packages.
Derives point estimates with confidence intervals for Bennett et als S, Cohen's kappa, Conger's kappa, Fleiss' kappa, Gwet's AC, intraclass correlation coefficients, Krippendorff's alpha, Scott's pi, the standard error of measurement, 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, Laporte, F., Charcosset, A. and Mary-Huard, T. (2017) <doi:10.1111/biom.12634>.
Data structures and algorithms for k-ary relations with
  arbitrary domains, featuring relational algebra, predicate functions,
  and fitters for consensus relations.
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.
Performs Principal Components Analysis (also known as PCA) dimensionality reduction in the context of a linear regression. In most cases, PCA dimensionality reduction is performed independent of the response variable for a regression. This captures the majority of the variance of the model's predictors, but may not actually be the optimal dimensionality reduction solution for a regression against the response variable. An alternative method, optimized for a regression against the response variable, is to use both PCA and a relative importance measure. This package applies PCA to a given data frame of predictors, and then calculates the relative importance of each PCA factor against the response variable. It outputs ordered factors that are optimized for model fit. By performing dimensionality reduction with this method, an individual can achieve a the same r-squared value as performing just PCA, but with fewer PCA factors. References: Yuri Balasanov (2017) <https://ilykei.com>.
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.
Wrappers on 'regexpr' and 'gregexpr' to return the match
    results in tidy data frames.
Insert/extract text "reminders" into/from function source code 
    comments or as the "comment" attribute of any object.  
    The former can be handy in development as reminders of e.g. argument
    requirements, expected objects in the calling environment, required options
    settings, etc. The latter can be used to provide information of the object and 
    as simple manual "tooltips" for users, among other things.
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'.
ENA (Shaffer, D. W. (2017) Quantitative Ethnography. ISBN: 0578191687) is a method used to identify meaningful and quantifiable patterns in discourse or reasoning. ENA moves beyond the traditional frequency-based assessments by examining the structure of the co-occurrence, or connections in coded data. Moreover, compared to other methodological approaches, ENA has the novelty of (1) modeling whole networks of connections and (2) affording both quantitative and qualitative comparisons between different network models.   Shaffer, D.W., Collier, W., & Ruis, A.R. (2016) <doi:10.18608/jla.2016.33.3>.
Fits linear models with endogenous regressor using latent
    instrumental variable approaches. The methods included in the package
    are Lewbel's (1997) <doi:10.2307/2171884> higher moments approach as well as Lewbel's (2012) <doi:10.1080/07350015.2012.643126>
    heteroskedasticity approach, Park and Gupta's (2012) <doi:10.1287/mksc.1120.0718>joint estimation method
    that uses Gaussian copula and Kim and Frees's (2007) <doi:10.1007/s11336-007-9008-1> multilevel generalized
    method of moment approach that deals with endogeneity in a multilevel setting.
    These are statistical techniques to address the endogeneity problem where no
    external instrumental variables are needed.
    This version: 
       - includes an omitted variable test in the multilevel estimation. It is reported in the summary() function of the 
         multilevelIV() function.
       - resolves the error "Error in listIDs[, 1] : incorrect number of dimensions" when using the multilevelIV() function.
       - a new simulated dataset is provided, dataMultilevelIV, on which to exemplify the multilevelIV() function.
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 (Heller & Yekutieli, 2014 <doi:10.1214/13-AOAS697> ;
      Heller at al., 2015 <doi: 10.1093/bioinformatics/btu434>).
Supplies classes and routines to convert data to and from EpiJSON files. This package provides conversion functions for data.frame, sp and obkData.
For a multisite replication project, computes metrics and confidence intervals representing: (1) the probability that the original study would observe an estimated effect size as extreme or more extreme than it actually did, if in fact the original study is statistically consistent with the replications; (2) the probability of a true effect of scientifically meaningful size in the same direction as the estimate the original study; and (3) the probability of a true effect of meaningful size in the direction opposite the original study's estimate. Additionally computes older metrics used in replication projects (namely expected agreement in "statistical significance" between an original study and replication studies as well as prediction intervals for the replication estimates). See Mathur and VanderWeele (2017; <https://osf.io/apnjk/>) for details.
Test for effects of  both individual factors and their interaction on  replicated spatial patterns in a two factorial design, as explained in Ramon et al. (2016) <doi:10.1111/ecog.01848>.
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 use 'dplyr' remote data sources ('SQL' databases,
   'Spark' 2.0.0 and above) in a reliable "generic" fashion (generic meaning
   user code works similarly on all such sources, without needing per-source
   adaption).
   Adds convenience functions to make such tasks more like
   working with an in-memory R 'data.frame'.
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 rich annotations,
    including corresponding code chunks, and easily searched and
    retrieved.
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".
Provides an easy way to report the results of regression analysis, including:
    1. Proportional hazards regression model from function 'coxph' of package 'survival';
    2. Ordered logistic regression from function 'polr' of package 'MASS';
    3. Binary logistic regression from function 'glm' of package 'stats';
    4. Linear regression from function 'lm' of packages 'stats'.
Provides an easy way to report the results of ROC analysis, including:
    1. an ROC curve. 2. the value of Cutoff, SEN (sensitivity), SPE (specificity),
    AUC (Area Under Curve), AUC.SE (the standard error of AUC), 
    PLR (positive likelihood ratio), NLR (negative likelihood ratio), 
    PPV (positive predictive value), NPV (negative predictive value).
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.
Built on top of 'git2r' and 'archivist', this package aims at
    making high-level, robust, machine and OS independent tools for making
    deeply reproducible and reusable content in R. This extends beyond the
    package management utilities of 'packrat' and 'checkpoint' by including
    tools for caching, and accessing GitHub repositories.
Predicting regulatory DNA elements based on epigenomic signatures. This package is more of a set of building blocks than a direct solution. REPTILE regulatory prediction pipeline is built on this R package. See <https://github.com/yupenghe/REPTILE> for more information.
Recursive lists in the form of R objects, 'JSON', and 'XML', for use in
    teaching and examples. Examples include color palettes, Game of Thrones
    characters, 'GitHub' users and repositories, and entities from the Star Wars
    universe. Data from the 'gapminder' package is also included, as a simple data
    frame and in nested and split forms.
In order to facilitate parsing of http requests and creating 
    appropriate responses this package provides two classes to handle a lot of
    the housekeeping involved in working with http exchanges. The infrastructure
    builds upon the 'rook' specification and is thus well suited to be combined
    with 'httpuv' based web servers.
This package contains the function run.eqs() which calls
        an EQS script file, executes the EQS estimation, and, finally,
        imports the results as R objects. These two steps can be
        performed separately: call.eqs() calls and executes EQS,
        whereas read.eqs() imports existing EQS outputs as objects into
        R. It requires EQS 6.2 (build 98 or higher).
High level and easy 'HTTP' client for 'R'. Provides functions for
    building 'HTTP' queries, including query parameters, body requests, headers,
    authentication, and more.
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: 
    <https://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.
Random Forester (RerF) is an algorithm developed by Tomita (2016) <arXiv:1506.03410v2> which is similar to Random Forest - Random Combination (Forest-RC) developed by Breiman (2001) <doi:10.1023/A:1010933404324>.  Random Forests create axis-parallel, or orthogonal trees. That is, the feature space is recursively split along directions parallel to the axes of the feature space. Thus, in cases in which the classes seem inseparable along any single dimension, Random Forests may be suboptimal.  To address this, Breiman also proposed and characterized Forest-RC, which uses linear combinations of coordinates rather than individual coordinates, to split along.  This package, 'rerf', implements RerF which is similar to Forest-RC.  The difference between the two algorithms is where the random linear combinations occur: Forest-RC combines features at the per tree level whereas RerF takes linear combinations of coordinates at every node in the tree.
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.
A fast Gibbs sampler for multivariate normal with affine constraints.
Allow for easy-to-use testing of linear equality and inequality 
  restrictions about parameters and effects in (generalized) linear statistical models.
Using a database, LaTeX and R easily build attractive resumes.
R interface to Python modules, classes, and functions. When calling
    into Python R data types are automatically converted to their equivalent Python
    types. When values are returned from Python to R they are converted back to R
    types. Compatible with all versions of Python >= 2.7.
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 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.  Also provided
    are functions for making inferences about the extremal index, using 
    the K-gaps model of Suveges and Davison (2010) <doi:10.1214/09-AOAS292>.
    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.
A wealth of open source information is available that points to building usage including floor area of building size and likely occupancy. In the case of residential structures, census data provides a number of attributes including two values important to interior density estimation: household size (hhs) and area. If a national census revealed the raw data or provided a full uncensored contingency table (hhs x area), computing interior density as people/area would be straightforward. However, agencies rarely report this contingency table. Rather hhs and area are often decoupled and reported as separate univariate frequency tables, average values, or a combination of the two. In addition, the decoupled or contingency tables provided are typically left (<, <=), right (>, >=), and interval (-) censored. This type of information becomes problematic in estimating interior residential occupancy for numerous reasons. How can the people/area ratio be calculated when no affiliation between the variables exist? If a census reports a hhs average of 5.3, then how many houses are there with 1 person, 2 people,..., 10 people? If a census reports that there are 100 houses in an area of 26-50 square meters, then how many houses are in 26, 27,..., 50 square meters? The challenge therefore is to infer the people/area ratio when given decoupled and summarized data. The statistical package 'revengc' was designed to reverse engineer censored, decoupled census data into a likely hhs x area uncensored contingency table for estimating interior residential occupancy.
Function revgeo() allows you to use the Photon geocoder for OpenStreetMap <http://photon.komoot.de>, Google Maps <http://maps.google.com>, and Bing <https://www.bingmapsportal.com> to reverse geocode coordinate pairs with minimal hassle.
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.
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), 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, Calonder, Lepetit and Fua (2009) <doi:10.1109/TPAMI.2009.23>, modified for generic and multi-label classification and featuring OOB error approximation and importance measure as introduced in Kursa (2014) <doi:10.18637/jss.v061.i10>.
RFGLS  uses a generalized least-squares method to perform single-marker association analysis, 
  in datasets of nuclear families containing parents, twins, and/or adoptees
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.
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.
R access to the 'FOAAS' (F... Off As A Service) web service is provided.
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.
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/.
Assists in statistical model building to find optimal and semi-optimal higher order interactions
    and best subsets. Uses the lm(), glm(), and other R functions to fit models generated from a feasible 
    solution algorithm. Discussed in Subset Selection in Regression, A Miller (2002). Applied and explained
    for least median of squares in Hawkins (1993) <doi:10.1016/0167-9473(93)90246-P>. The feasible solution 
    algorithm comes up with model forms of a specific type that can have fixed variables, higher order 
    interactions and their lower order terms.
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.
Classes and methods to efficiently handle (slice, annotate, draw ...) genomic features (such as genes or transcripts), and an interactive interface to browse them.
A programmatic interface to the Web Service methods
    provided by the Global Biodiversity Information Facility ('GBIF';
    <https://www.gbif.org/developer/summary>). 'GBIF' is a database
    of species occurrence records from sources all over the globe.
    'rgbif' includes functions for searching for taxonomic names,
    retrieving information on data providers, getting species occurrence
    records, and getting counts of occurrence records.
Provides an implementation of Regularized LS-TreeBoost & LAD-TreeBoost algorithm for Regulatory Network inference from any type of expression data (Microarray/RNA-seq etc). See Mall et al (2017) <doi:10.1101/132670>.
We utilize  approximate Bayesian machinery to fit two-level conjugate hierarchical models on overdispersed Gaussian, Poisson, and Binomial data and evaluates whether the resulting approximate Bayesian interval estimates for random effects meet the nominal confidence levels via frequency coverage evaluation. The data that Rgbp assumes comprise observed sufficient statistic for each random effect, such as an average or a proportion of each group, without population-level data.  The approximate Bayesian tool equipped with the adjustment for density maximization produces approximate point and interval estimates for model parameters including second-level variance component, regression coefficients, and random effect. For the Binomial data, the package provides an option to produce  posterior samples of all the model parameters via the acceptance-rejection method. The package provides a quick way to evaluate coverage rates of the resultant Bayesian interval estimates for random effects via a parametric bootstrapping, which we call frequency method checking.
Multiblock data analysis concerns the analysis of several sets of variables (blocks) observed on the same group of individuals. The main aims of the RGCCA package are: (i) to study the relationships between blocks and (ii) to identify subsets of variables of each block which are active in their relationships with the other blocks.
Thin plate spline fitting and prediction
Provides bindings to the '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'.
Provides popular sampling distributions C++ routines based in 
    armadillo through a header file approach.
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".
Provides the hybrid Bayesian method Geometric Density Estimation. On the one hand, it scales the dimension of our data, on the other it performs inference. The method is fully described in the paper "Scalable Geometric Density Estimation" by Y. Wang, A. Canale, D. Dunson (2016) <http://proceedings.mlr.press/v51/wang16e.pdf>.
Connectors to online and offline sources for taking IP addresses
    and geolocating them to country, city, timezone and other geographic ranges. For
    individual connectors, see the package index.
Supports analysis of spatial data processed with the 'geoPAT' 2.0 
    software <http://sil.uc.edu/cms/index.php?id=geopat2>. 
    Available features include creation of a grid based on the 'geoPAT' 2.0
    grid header file.
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'.
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.
Export raw reports from 'GoodData' business intelligence platform 
    (see <http://www.gooddata.com> for more information).
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.
An interface to 'GNU gretl': running 'gretl' scripts from, estimating econometric
             models with backward passing of model results, opening 'gretl' data files (.gdt).
             'gretl' can be downloaded from <http://gretl.sourceforge.net>. This package could make
             life on introductory/intermediate econometrics courses much easier:   full battery of the
             required regression diagnostics,   including White's heteroskedasticity test, restricted ols
             estimation,   advanced weak instrument test after iv estimation, very convenient dealing
             with lagged variables in models,   standard case treatment in unit root tests,   vector auto-
             regressions, and vector error correction  models.   Datasets for   8 popular econometrics 
             textbooks can be installed into 'gretl' from its server. All datasets can be easily imported
             using this package.
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.
Simulate a renewal Hawkes (RHawkes) self-exciting process,
    with a given immigrant hazard rate function and offspring density function.
    Calculate the likelihood of a RHawkes process with given hazard rate function
    and offspring density function for an (increasing) sequence of event times.
    Calculate the Rosenblatt residuals of the event times. Predict
    future event times based on observed event times up to a given time.
    For details see Chen and Stindl (2017) <doi:10.1080/10618600.2017.1341324>.
An R interface for the HealthData.gov data API.  For each data resource, you can filter results (server-side) to select subsets of data.
'HMMER' is a profile hidden Markov model tool used primarily for sequence
    analysis in bioinformatics (<http://hmmer.org/>). 'rhmmer' provides
    utilities for parsing the 'HMMER' output into tidy data frames.
Hydrologic modelling system is an object oriented tool which enables R users to simulate and analyze hydrologic events. The package proposes functions and methods for construction, simulation, visualization, and calibration of hydrologic systems.
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.
Microbenchmarks for determining the run time
  performance of aspects of the R programming environment and packages
  relevant to high-performance computation.  The benchmarks are divided into
  three categories: dense matrix linear algebra kernels, sparse matrix linear
  algebra kernels, and machine learning functionality.
Control the number of threads on 'BLAS' (Aka 'GotoBLAS', '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.
Run 'R CMD check' on any of the 'R-hub' architectures, from
    the command line. The current architectures include 'Windows', 'macOS',
    'Solaris' and various 'Linux' distributions.
Wrapper for 'Datamuse' API to find rhyming and other associated words.
    This includes words of similar meaning, spelling, or other related words. Learn
    more about the 'Datamuse' API here <http://www.datamuse.com/api/>.
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.
Radiomics image analysis toolbox for grayscale 2D and 3D images. RIA calculates first-order,
             gray level co-occurrence matrix, gray level run length matrix and geometry-based statistics.
             Almost all calculations are done using vectorized formulas to optimize run speeds. Calculation
             of several thousands of parameters only takes minutes on a single core of a conventional PC.
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.
Linear and logistic ridge regression functions. Additionally includes special functions for 
            genome-wide single-nucleotide polymorphism (SNP) data.
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.
Client for 'Neuroscience' Information Framework ('NIF') 'APIs'
    (<https://neuinfo.org/>; <https://neuinfo.org/about/webservices>).
    Package includes functions for each 'API' route, and gives back data
    in tidy data.frame format.
RIFS package provides functionality for generating &
        plotting prefractals in R^n with various protofractal sets and
        partition coefficient for iterative segments
Tools to download data from the ilostat database
    <http://www.ilo.org/ilostat> together with search and
    manipulation utilities.
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 <http://inaturalist.org> to download species occurrence data submitted by citizen scientists.
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.
Circular / ring buffers in R and C.  There are a couple
    of different buffers here with different implementations that
    represent different trade-offs.
Installs shiny apps using Inno Setup, an open source software that builds installers for Windows programs <http://www.jrsoftware.org/ishelp/>.
C++ classes to embed R in C++ 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.
Computes 26 financial risk measures for any continuous distribution.  The 26 financial risk measures  include value at risk, expected shortfall due to Artzner et al. (1999) <DOI:10.1007/s10957-011-9968-2>, tail conditional median due to Kou et al. (2013) <DOI:10.1287/moor.1120.0577>, expectiles due to Newey and Powell (1987) <DOI:10.2307/1911031>, beyond value at risk due to Longin (2001) <DOI:10.3905/jod.2001.319161>, expected proportional shortfall due to Belzunce et al. (2012) <DOI:10.1016/j.insmatheco.2012.05.003>, elementary risk measure due to Ahmadi-Javid (2012) <DOI:10.1007/s10957-011-9968-2>, omega due to Shadwick and Keating (2002), sortino ratio due to Rollinger and Hoffman (2013), kappa  due to Kaplan and Knowles  (2004), Wang (1998)'s <DOI:10.1080/10920277.1998.10595708> risk measures, Stone (1973)'s <DOI:10.2307/2978638> risk measures, Luce (1980)'s <DOI:10.1007/BF00135033> risk measures, Sarin (1987)'s <DOI:10.1007/BF00126387> risk measures, Bronshtein and Kurelenkova (2009)'s risk measures.
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.
Implementation of the following methods for event history analysis.
    Risk regression models for survival endpoints also in the presence of competing
    risks are fitted using binomial regression based on a time sequence of binary
    event status variables. A formula interface for the Fine-Gray regression model
    and an interface for the combination of cause-specific Cox regression models.
    A toolbox for assessing and comparing performance of risk predictions (risk
    markers and risk prediction models). Prediction performance is measured by the
    Brier score and the area under the ROC curve for binary possibly time-dependent
    outcome. Inverse probability of censoring weighting and pseudo values are used
    to deal with right censored data. Lists of risk markers and lists of risk models
    are assessed simultaneously. Cross-validation repeatedly splits the data, trains
    the risk prediction models on one part of each split and then summarizes and
    compares the performance across splits.
Compute time-dependent Incident/dynamic accuracy measures
        (ROC curve, AUC, integrated AUC )from censored survival data
        under proportional or non-proportional hazard assumption of
        Heagerty & Zheng (Biometrics, Vol 61 No 1, 2005, PP 92-105).
Implements efficient simulation procedures to estimate tail loss probabilities and conditional excess for a stock portfolio. The log-returns are assumed to follow a t-copula model with generalized hyperbolic or t marginals.
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 reversible-jump Markov chain Monte Carlo (Green, 1995)
    <doi:10.2307/2337340>, specifically the restriction introduced by 
    Barker & Link (2013) <doi:10.1080/00031305.2013.791644>. By utilising 
    a 'universal parameter' space, RJMCMC is treated as a Gibbs sampling 
    problem. Previously-calculated posterior distributions are used to 
    quickly estimate posterior model probabilities. Jacobian matrices are 
    found using automatic differentiation.
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.
Creates interactive graphs with 'R'. It joins the data analysis power of R and the visualization libraries of JavaScript in one package.
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'.
The 'rkafkajars' package collects all the external jars required for the 'rkafka' package.
An R interface to KEA (Version 5.0).
  KEA (for Keyphrase Extraction Algorithm) allows for extracting
  keyphrases from text documents. It can be either used for free
  indexing or for indexing with a controlled vocabulary. For more
  information see <http://www.nzdl.org/Kea/>.
External jars required for package RKEA.
KEEL is a popular Java software for a large number of different knowledge data discovery tasks.
    This package takes the advantages of KEEL and R, allowing to use KEEL algorithms in simple R code.
    The implemented R code layer between R and KEEL makes easy both using KEEL algorithms in R as implementing new algorithms for 'RKEEL' in a very simple way.
    It includes more than 100 algorithms for classification, regression, preprocess, association rules and imbalance learning, which allows a more complete experimentation process.
    For more information about KEEL, see <http://www.keel.es/>.
KEEL is a popular Java software for a large number of different knowledge data discovery tasks. Furthermore, RKEEL is a package with a R code layer between R and KEEL, for using KEEL in R code. This package includes the datasets from KEEL in .dat format for its use in RKEEL package. For more information about KEEL, see <http://www.keel.es/>.
KEEL is a popular Java software for a large number of different knowledge data discovery tasks. Furthermore, 'RKEEL' is a package with a R code layer between R and KEEL, for using KEEL in R code. This package downloads and install the .jar files necessary for 'RKEEL' algorithms execution. For more information about KEEL, see <http://www.keel.es/>.
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.
Hybrid Mortality Modelling (HMM) provides a framework in which mortality around "the accident hump" and at very old ages can be modelled under a single model. The graphics' codes necessary for visualization of the models' output are included here. Specifically, the graphics are based on the assumption that, the mortality rates can be expressed as a function of the area under the curve between the crude mortality rates plots and the tangential transform of the force of mortality.
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.
A toolbox for working with base types, core R features
  like the condition system, and core 'Tidyverse' features like tidy
  evaluation.
Read and write 'las' and 'laz' binary file formats. The LAS file format is a public file format for the interchange of 3-dimensional point cloud data between data users. The LAS specifications are approved by the American Society for Photogrammetry and Remote Sensing. 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.
Estimates the Bayesian LDA model for mixed-membership clustering based on different types of data (i.e., Multinomial, Bernoulli, and Binomial entries).
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.
Leaf angle distribution is described by a number of functions
    (e.g. ellipsoidal, Beta and rotated ellipsoidal). The parameters of leaf angle
    distributions functions are estimated through different empirical relationship.
    This package includes estimations of parameters of different leaf angle
    distribution function, plots and evaluates leaf angle distribution functions,
    calculates extinction coefficients given leaf angle distribution.
    Reference: Wang(2007)<doi:10.1016/j.agrformet.2006.12.003>.
Display spatial data with interactive maps powered by the 
  open-source JavaScript library 'Leaflet' (see <http://leafletjs.com/>). Maps can be rendered in a web browser or
  displayed in the HTML viewer pane of 'RStudio'. This package is designed to be easy to use and
  can create complex maps with vector and raster data, web served map tiles and interface elements.
Provides an interface to the C implementation of the
        random number generator with multiple independent streams
        developed by L'Ecuyer et al (2002). The main purpose of this
        package is to enable the use of this random number generator in
        parallel R applications.
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/>.
Solving a system of linear equations is one of the most fundamental
    computational problems for many fields of mathematical studies, such as
    regression problems from statistics or numerical partial differential equations.
    We provide basic stationary iterative solvers such as Jacobi, Gauss-Seidel,
    Successive Over-Relaxation and SSOR methods. Nonstationary, also known as
	Krylov subspace methods are also provided. Sparse matrix computation is also supported
    in that solving large and sparse linear systems can be manageable using 'Matrix' package
    along with 'RcppArmadillo'. For a more detailed description, see a book by Saad (2003)
    <doi:10.1137/1.9780898718003>.
Provides a set of functions for data manipulation with
    list objects, including mapping, filtering, grouping, sorting,
    updating, searching, and other useful functions. Most functions
    are designed to be pipeline friendly so that data processing with
    lists can be chained.
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().
Random forest with a variety of additional features for regression, classification and survival analysis. The features include: parallel computing with OpenMP, embedded model for selecting the splitting variable (based on Zhu, Zeng & Kosorok, 2015), subject weight, variable weight, tracking subjects used in each tree, etc.
R interface to the 'LTP'-Cloud service for Natural Language Processing
    in Chinese (http://www.ltp-cloud.com/).
A collection of functions to simulate luminescence signals in quartz and Al2O3 based on published models.
A collection of 'shiny' applications for the R package
    'Luminescence'. These mainly, but not exclusively, include applications for
    plotting chronometric data from e.g. luminescence or radiocarbon dating. It
    further provides access to bootstraps tooltip and popover functionality and
    contains the 'jscolor.js' library with a custom 'shiny' output binding.
R implementation of the common parsing tools 'lex' and 'yacc'.
Rasch model and extensions for survey data, using Conditional Maximum likelihood (CML).
RM2 is a simple package that implements functions used in
        revenue management and pricing environments.
Compute an exact CI for the population mean under a random effects model. The routines implement the algorithm described in Michael, Thronton, Xie, and Tian (2017) <https://haben-michael.github.io/research/Exact_Inference_Meta.pdf>.
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', 'Spatial', and 'sf' objects.
    This is wrapper around the 'mapshaper' 'JavaScript' library by Matthew Bloch 
    <https://github.com/mbloch/mapshaper/> to perform topologically-aware
    polygon simplification, as well as other operations such as clipping,
    erasing, dissolving, and converting 'multi-part' to 'single-part' geometries.
    It relies on the 'geojsonio' package for working with 'geojson' objects, the 'sf' 
    package for working with 'sf' objects, and the 'sp' and 'rgdal' packages for 
    working with 'Spatial' objects.
Provides an interface to the 'Mapzen' API for geographic search 
    and geocoding, isochrone calculation, and vector data to draw map tiles. 
    See <https://mapzen.com/documentation/> for more information.
Implements a 'DBI'-compliant interface to 'MariaDB' (<https://mariadb.org/>) and 'MySQL' (<https://www.mysql.com/>) databases.
Given a time series of daily temperatures, the package provides tools
    to detect extreme thermal events, including marine heat waves, and to
    calculate the exceedances above or below specified threshold values.
    It outputs the properties of all detected events and exceedances.
An interface to the software package MARK that constructs input
    files for MARK and extracts the output. MARK was developed by Gary White
    and is freely available at <http://www.phidot.org/software/mark/downloads/>
    but is not open source.
Convert R Markdown documents into a variety of formats.
Read and write 'Matlab' MAT files from R. The 'rmatio'
    package supports reading MAT version 4, MAT version 5 and MAT
    compressed version 5. The 'rmatio' package can write version 5 MAT
    files and version 5 files with variable compression.
S3 and S4 functions are implemented for spatial multi-site
    stochastic generation of daily time series of temperature and
    precipitation. These tools make use of Vector AutoRegressive models (VARs).
    The weather generator model is then saved as an object and is calibrated by
    daily instrumental "Gaussianized" time series through the 'vars' package
    tools. Once obtained this model, it can it can be used for weather
    generations and be adapted to work with several climatic monthly time
    series.
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. M.Draminski, A.Rada-Iglesias, S.Enroth, C.Wadelius, J. Koronacki, J.Komorowski (2008) <doi:10.1093/bioinformatics/btm486>.
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.
Provides tools to evaluate the value of using a risk prediction instrument to decide treatment or intervention (versus no treatment or intervention).  Given one or more risk prediction instruments (risk models) that estimate the probability of a binary outcome, rmda provides functions to estimate and display decision curves and other figures that help assess the population impact of using a risk model for clinical decision making.   Here, "population" refers to the relevant patient population. Decision curves display estimates of the (standardized) net benefit over a range of probability thresholds used to categorize observations as 'high risk'. The curves help evaluate a treatment policy that recommends treatment for patients who are estimated to be 'high risk' by comparing the population impact of a risk-based policy to "treat all" and "treat none" intervention policies.  Curves can be estimated using data from a prospective cohort.  In addition, rmda can estimate decision curves using data from a case-control study if an estimate of the population outcome prevalence is available.  Version 1.4 of the package provides an alternative framing of the decision problem for situations where treatment is the standard-of-care and a risk model might be used to recommend that low-risk patients (i.e., patients below some risk threshold) opt out of treatment. Confidence intervals calculated using the bootstrap can be computed and displayed. A wrapper function to calculate cross-validated curves using k-fold cross-validation is also provided.
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>.
An 'Rcpp' interface for Eunjeon project <http://eunjeon.blogspot.com/>.
    The 'mecab-ko' and 'mecab-ko-dic' is based on a C++ library,
    and part-of-speech tagging with them is useful when the spacing of source Korean text is not correct.
    This package provides part-of-speech tagging and tokenization function for Korean text.
We provide functions to compute confidence
    intervals (CIs) for a well-defined nonlinear function of the model
    parameters (e.g., product of k coefficients) in single--level and
    multilevel structural equation models.
Functions for simple fixed and random effects
        meta-analysis for two-sample comparisons and cumulative
        meta-analyses. Draws standard summary plots, funnel plots, and
        computes summaries and tests for association and heterogeneity
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.
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)
A monadic solution to pipeline analysis. All operations -- and the errors,
    warnings and messages they emit -- are merged into a directed graph. Infix
    binary operators mediate when values are stored, how exceptions are
    handled, and where pipelines branch and merge. The resulting structure may
    be queried for debugging or report generation. 'rmonad' complements, rather
    than competes with, non-monadic pipeline packages like 'magrittr' or
    'pipeR'. This work is funded by the NSF (award number 1546858).
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.
Provides wrapper functions to the Java Robot class to automate user input, like mouse movements, clicks and keyboard input.
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.
We implement causal mediation analysis using the methods proposed by Hong (2010) and Hong, Deutsch & Hill (2015) <doi:10.3102/1076998615583902>. It allows the estimation and hypothesis testing of causal mediation effects through ratio of mediator probability weights (RMPW). This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. We also implement a sensitivity analysis by extending the RMPW method to assess potential bias in the presence of omitted pretreatment or posttreatment covariates. The sensitivity strategy was proposed by Hong, Qin, and Yang (2018) <doi:10.3102/1076998615583902>.
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.
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.
Display a randomly selected quote about Richard M. Stallman
 based on the collection in the 'GNU Octave' function 'fact()' which was
 aggregated by Jordi Gutiérrez Hermoso based on the (now defunct) site
 stallmanfacts.com (which is accessible only via <http://archive.org>).
Contains several useful navigation helper functions, including easily building
    folder paths, quick viewing dataframes in 'Excel', creating date vectors and changing the
    console prompt to reflect time.
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 class whose methods can be used
	for sequentially solving a sparse linear system (symmetric or not)
	with one or many right hand sides (dense or sparse).
	There is a possibility to do separately symbolic analysis,
	LU (or LDL^t) factorization and system solving.
         Third part ordering libraries are included and can be used: PORD, METIS, SCOTCH.
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>).
A 'DBI' interface to 'MySQL' / 'MariaDB'. The 'RMySQL' package contains an 
    old implementation based on legacy code from S-PLUS which being phased out. A modern
    'MySQL' client based on 'Rcpp' is available from the 'RMariaDB' package on 'Github':
    <https://github.com/rstats-db/RMariaDB>.
Infer log-linear Poisson Graphical Model with an auxiliary data
    set. Hot-deck multiple imputation method is used to improve the reliability
    of the inference with an auxiliary dataset. Standard log-linear Poisson 
    graphical model can also be used for the inference and the Stability 
    Approach for Regularization Selection (StARS) is implemented to drive the 
    selection of the regularization parameter.
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/
Provides function for RNA secondary structure plotting, comparison and module mining. Given a RNA secondary structure, you can obtain stem regions, hairpin loops, internal loops, bulge loops and multibranch loops of this RNA structure using this program. They are the basic modules of RNA secondary structure. For each module you get, you can use this program to label the RNA structure with a specific color. You can also use this program to compare two RNA secondary structures to get a score that represents similarity. Reference: Reuter JS, Mathews DH (2010) <doi:10.1186/1471-2105-11-129>.
Functions in this program is designed for RNA secondary structure plotting, comparison and module mining. Given a RNA secondary structure, you can obtain stem regions, hairpin loops, internal loops, bulge loops and multibranch loops of this RNA structure using this program. They are the basic modules of RNA secondary structure. For each module you get, you can use this program to label the RNA structure with a specific color. You can also use this program to compare two RNA secondary structures to get a score that represents similarity.
Facilitates mapping by making natural earth map data from <http://www.naturalearthdata.com/> more easily available to R users.
Vector map data from <http://www.naturalearthdata.com/>. Access functions are provided in the accompanying package 'rnaturalearth'.
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.
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>.
Novel methods are needed to analyze the large amounts of antimicrobial resistance (AMR) data generated by AMR surveillance programs. This package is used to estimate resistance relationship networks, or 'Rnets', from empirical antimicrobial susceptibility data. These networks can be used to study relationships between antimicrobial resistances (typically measured using MICs) and genes in populations. The 'GitHub' for this package is available at <https://GitHub.com/EpidemiologyDVM/Rnets>. Bug reports and features requests should be directed to the same 'GitHub' site. The methods used in 'Rnets' are available in the following publications: An overview of the method in WJ Love, et al., "Markov Networks of Collateral Resistance: National Antimicrobial Resistance Monitoring System Surveillance Results from Escherichia coli Isolates, 2004-2012" (2016) <doi:10.1371/journal.pcbi.1005160>; The graphical LASSO for sparsity in J Friedman, T Hastie, R Tibshirani "Sparse inverse covariance estimation with the graphical lasso" (2007) <doi:10.1093/biostatistics/kxm045>; L1 penalty selection in H Liu, K Roeder, L Wasserman "Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models" (2010) <arXiv:1006.3316>; Modularity for graphs with negative edge weights in S Gomez, P Jensen, A Arenas. "Analysis of community structure in networks of correlated data" (2009) <doi:10.1103/PhysRevE.80.016114>.
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://www.nitrc.org/docman/view.php/26/64/nifti1.h>, 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.
The Rnightlights package extracts raster and zonal statistics
    from satellite nightlight rasters downloaded from the United States 
    National Oceanic and Atmospheric Administration (<http://www.noaa.gov>) 
    free data repositories. Both the DMSP-OLS annual and SNPP-VIIRS monthly 
    nightlight raster data are supported. Satellite nightlight raster tiles are 
    downloaded and cropped to the country boundaries using shapefiles from the GADM
    database of Global Administrative Areas (<http://gadm.org>). Zonal statistics
    are then calculated at the lowest administrative boundary for the selected
    country and cached locally for future retrieval. Finally, a simple data
    explorer/browser is included that allows one to visualize the cached data e.g.
    graphing, mapping and clustering regional data.
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 over 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.
Implementation of genetic association tests that utilize the rank based inverse normal transformation (INT). The primary contribution is an omnibus test, which synthesizes two INT-based approaches, termed direct INT (DINT) and partially indirect INT (PIINT). In genome scale simulations, the omnibus test routinely provided valid inference against continuous phenotypes with skewed and heavy tailed residual distributions. Association testing against these same phenotypes via standard linear regression led to an excess of false positive associations. In addition to controlling the type I error, the omnibus approach provided power comparable to the more powerful of the component methods.
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.
Downloads Natural Resources Conservation Service (NRCS) data for sites in the Soil Climate Analysis Network (SCAN) <https://www.wcc.nrcs.usda.gov/scan/>, and Snow Telemetry (SNOTEL and SNOLITE) <https://www.wcc.nrcs.usda.gov/snow/> networks. Metadata can be returned for all sites in the NRCS' Air and Water Data Base (AWDB) <https://www.wcc.nrcs.usda.gov/report_generator/AWDB_Network_Codes.pdf>.
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.
Routines to interact with the Numerai Machine Learning Tournament
  API <https://numer.ai>. The functionality includes the ability to automatically download the
  current tournament data, submit predictions, and to get information for your
  user. General 'GraphQL' queries can also be executed.
This web client interfaces oaDOI <https://oadoi.org>, a service finding 
    free full-texts of academic papers by linking DOIs with open access journals and
    repositories. It provides unified access to various data sources for open access
    full-text links including Crossref and  the Directory of Open Access 
    Journals (DOAJ). API usage is free and no registration is required.
A collection of methods for the robust analysis of univariate and
    multivariate functional data, possibly in high-dimensional cases, and hence
    with attention to computational efficiency and simplicity of use.
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.
Client for the Ocean Biogeographic Information System (<http://iobis.org>). Includes functions for each of the API methods, including searching for occurrences, creating a checklist, searching for datasets and for visualizing the results.
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.
Generation of dynamic coappearance matrices for elements
             within a text along with utilities to aid in the generation
	     of Gephi dynamic networks.
Provides functions to download and parse 'robots.txt' files.
        Ultimately the package makes it easy to check if bots
        (spiders, crawler, scrapers, ...) are allowed to access specific
        resources on a domain.
Calculates periodograms based on (robustly) fitting periodic functions to light curves (irregularly observed time series, possibly with measurement accuracies, occurring in astroparticle physics). Three main functions are included: RobPer() calculates the periodogram. Outlying periodogram bars (indicating a period) can be detected with betaCvMfit(). Artificial light curves can be generated using the function tsgen(). For more details see the corresponding article: Thieler, Fried and Rathjens (2016), Journal of Statistical Software 69(9), 1-36, <doi:10.18637/jss.v069.i09>.
Three-step regression and inference for cellwise and casewise contamination.
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.
Methods for robust statistics, a state of the art in the early
  2000s, notably for robust regression and robust multivariate analysis.
R functions for the computation of the truncated maximum
	     likelihood and the robust accelerated failure time regression 
	     for gaussian and log-Weibull case.
Functions for fitting a linear regression model with ARIMA
  errors using a filtered tau-estimate.
"Essential" Robust Statistics.
 Tools allowing to analyze data with robust methods.  This includes
 regression methodology including model selections and multivariate
 statistics where we strive to cover the book "Robust Statistics,
 Theory and Methods" by 'Maronna, Martin and Yohai'; Wiley 2006.
Bayesian robust fitting of linear mixed effects models through weighted likelihood equations and approximate Bayesian computation as proposed by Ruli et al. (2017) <arXiv:1706.01752>.
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.
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. Framework was built to handle genetic data, but works for any data.
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.
Show physics, math and engineering students how an ODE solver
    is made and how effective R classes can be for the construction of
    the equations that describe natural phenomena. Inspiration for this work 
    comes from the book on "Computer Simulations in Physics" 
    by Harvey Gould, Jan Tobochnik, and Wolfgang Christian. 
    Book link: <http://www.compadre.org/osp/items/detail.cfm?ID=7375>.
Provides an R6 class and several utility methods to
    facilitate the implementation of models based on ordinary
    differential equations. The heart of the package is a code generator
    that creates compiled 'Fortran' (or 'R') code which can be passed to
    a numerical solver. There is direct support for solvers contained
    in packages 'deSolve' and 'rootSolve'.
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 non-linear optimization problems with box bounds
             transformed into 'ROI' optimization problems.
             This package provides a wrapper around the 'globalOptTests'
             which provides a collection of global optimization problems.
             More information can be found in the 'README' file.
The mixed integer programming library 'MIPLIB' (see <http://miplib.zib.de/>) 
	is commonly used to compare the performance of mixed integer optimization solvers.
	This package provides functions to access 'MIPLIB' from the 
	'R' Optimization Infrastructure ('ROI'). More information about 'MIPLIB'
	can be found in the paper by Koch et al. available at
	<http://mpc.zib.de/index.php/MPC/article/viewFile/56/28>.
	The 'README.md' file illustrates how to use this package.
A collection of 'ROI' optimization problems based on the 'NETLIB-LP' collection.
  'Netlib' is a software repository, which amongst many other software for scientific computing contains a collection of linear programming problems. The purpose of this package is to make 
  this problems easily accessible from 'R' as 'ROI' optimization problems.
Enhances the R Optimization Infrastructure ('ROI') package
        with the 'alabama' solver for solving nonlinear optimization problems.
Enhances the R Optimization Infrastructure (ROI) package by registering
	     the COIN-OR Clp open-source solver from the COIN-OR suite <https://projects.coin-or.org/>.
	     It allows for solving linear programming with continuous objective variables 
	     keeping sparse constraints definition.
Enhances the R Optimization Infrastructure (ROI) package by registering
	     the 'CPLEX' commercial solver. It allows for solving mixed integer quadratically
	     constrained programming (MIQPQC) problems as well as all
	     variants/combinations of LP, QP, QCP, IP.
Enhances the R Optimization Infrastructure ('ROI') package
        	 with the 'DEoptim' and 'DEoptimR' package. 'DEoptim' is
        	 used for unconstrained optimization and 'DEoptimR' for
        	 constrained optimization.
Enhances the 'R' Optimization Infrastructure ('ROI') package
	     with the Embedded Conic Solver ('ECOS') for solving conic optimization problems.
Enhances the 'R' Optimization Infrastructure ('ROI') package by registering
	     the free 'GLPK' solver. It allows for solving mixed integer linear programming ('MILP')
	     problems as well as all variants/combinations of 'LP', 'IP'.
Enhances the R Optimization Infrastructure ('ROI') package 
	     by registering the ipop solver from package 'kernlab'.
Enhances the 'R' Optimization Infrastructure ('ROI') package
             with the 'lp_solve' solver.
Enhances the 'R' Optimization Infrastructure ('ROI') package
             with the possibility to obtain multiple solutions for linear 
             problems with binary variables. The main function is copied 
             (with small modifications) from the relations package.
Enhances the R Optimization Infrastructure ('ROI') package
        with the 'NLopt' solver for solving nonlinear optimization problems.
Enhances the R Optimization Infrastructure ('ROI') package
        with the 'optimx' package.
Enhances the R Optimization Infrastructure ('ROI') package
	     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>.
Functions to perform propensity score matching on rolling entry interventions for which a suitable "entry" date is not observed for nonparticipants. For more details, please reference Witman, Beadles, Hoerger, Liu, Kafali, Gandhi, Amico, and Larsen (2016) <https://academyhealth.confex.com/academyhealth/2016arm/meetingapp.cgi/Paper/9375>.
Apply a function in a moving window, then
    combine the results in a data frame.
Provides fast procedures for exploring all pairs of
    cutpoints of a single covariate with respect to survival and determining
    optimal cutpoints using a hierarchical method and various ordered logrank tests.
Using enrichment of genome-wide association summary statistics to
  identify trait-relevant cellular functional annotations.
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.
R on FHIR is an easy to use wrapper around the 'HL7 FHIR' REST API (STU 3). It provides tools to easily read and search resources on a FHIR server and brings the results into the R environment. R on FHIR is based on the FhirClient of the official 'HL7 FHIR .NET API', also made by Furore Health Informatics.
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.
A set of tools to reconstruct ordered ontogenic trajectories from
    single cell RNAseq data.
Implementation of the Taylor Regression Estimator method which is 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.
 Optional use of parallel computing under request.
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).
Provides a client for the API of OpenDota. OpenDota is a web service which is provide DOTA2 real time data. Data is collected through the Steam WebAPI. With ROpenDota you can easily grab the latest DOTA2 statistics in R programming such as latest match on official international competition, analyzing your or enemy performance to learn their strategies,etc. Please see <https://github.com/rosdyana/ROpenDota> for more information.
Provide a simple interface to Bloomberg's OpenFIGI API. Please
    see <https://openfigi.com> for API details and registration. You may be
    eligible to have an API key to accelerate your loading process.
An R package for the OpenSecrets.org web services API.
OpenWeatherMap (OWM) <http://openweathermap.org/api> is a service providing weather related data.
           This package can be used to access current weather data for one location or several locations.
           It can also be used to forecast weather for 5 days with data for every 3 hours.
Reproducible, programmatic retrieval of datasets from the
    Roper Center data archive.  The Roper Center for Public Opinion
    Research <https://ropercenter.cornell.edu> maintains the largest 
    archive of public opinion data in existence, but researchers using
    these datasets are caught in a bind.  The Center's terms and conditions
    bar redistribution of downloaded datasets, but to ensure that one's 
    work can be reproduced, assessed, and built upon by others, one must
    provide access to the raw data one employed.  The `ropercenter`
    package cuts this knot by providing registered users with programmatic,
    reproducible access to Roper Center datasets from within R.
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
Matrix reconstruction, also known as matrix completion, is
    the task of inferring missing entries of a partially observed matrix.
    This package provides a method called OptSpace,
    which was proposed by Keshavan, R.H., Oh, S., and Montanari, A. (2009)
    <doi:10.1109/ISIT.2009.5205567> for a case under low-rank assumption.
Oracle Database interface (DBI) driver for R.
   This is a DBI-compliant Oracle driver based on the OCI.
Client for the 'Orcid.org' 'API' (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/>,
    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 <doi:10.1109/TPAMI.2006.211>") for binary classification.
    Rotation forest is an ensemble method where each base classifier (tree) is
    fit on the principal components of the variables of random partitions of
    the feature set.
Tools for working with rotational data, including
    simulation from the most commonly used distributions on SO(3),
    methods for different Bayes, mean and median type estimators for
    the central orientation of a sample, confidence/credible
    regions for the central orientation based on those estimators and
    a novel visualization technique for rotation data.  Most recently,
    functions to identify potentially discordant (outlying) values
    have been added.
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.
In order to make sure that web request ends up in the correct 
    handler function a router is often used. 'routr' is a package implementing a
    simple but powerful routing functionality for R based servers. It is a fully
    functional 'fiery' plugin, but can also be used with other 'httpuv' based
    servers.
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>, Drury
    et al. (2016) <DOI:10.1093/sysbio/syw020>, Manceau et al. (2016) <DOI:10.1093/sysbio/syw115>,
    and Clavel & Morlon (2017) <DOI:10.1073/pnas.1606868114>.
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.
Plot 'rpart' models. Extends plot.rpart() and text.rpart()
             in the 'rpart' package.
This package contains additional tools for working with rpart
    objects.  Most importantly, it includes methods for converting rpart rules
    into a series of structured tables sufficient for executing the decision
    tree completely in SQL.
Provides algorithims for randomly sampling a feasible set defined
    by a given total and number of elements using integer partitioning.
This package contains functions that allow to build
        classification trees for ordinal responses within the CART
        framework. The trees are grown using the Generalized Gini
        impurity function, where the misclassification costs are given
        by the absolute or squared differences in scores assigned to
        the categories of the response. Pruning is based on the total
        misclassification rate or on the total misclassification cost.
Generating visual charting patterns and noise,
    smoothing to find a signal in noisy time series and enabling
    users to apply their findings to real life data.
Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Candes, E. J., Li, X., Ma, Y., & Wright, J. (2011). Robust principal component analysis?. Journal of the ACM (JACM), 58(3), 11. prove that we can recover each component individually under some suitable assumptions. It is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the L1 norm. This package implements this decomposition algorithm resulting with Robust PCA approach.
Allows users to identify relevant clinical codes and
    automate the construction of clinical code lists for primary care database
    studies. This package is analogous to the Stata command pcdsearch.
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 February 2017.
    Includes download_pdo() to scrape 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. (2017) Random-projection ensemble classification, J. Roy. Stat. Soc., Ser. B. (with discussion), 79, 959--1035". The random projection ensemble classifier is a general method for classification of high-dimensional data, based on careful combination of the results of applying an arbitrary base classifier to random projections of the feature vectors into a lower-dimensional space. The random projections are divided into non-overlapping blocks, and within each block the projection yielding the smallest estimate of the test error is selected. The random projection ensemble classifier then aggregates the results of applying the base classifier on the selected projections, with a data-driven voting threshold to determine the final assignment.
This reduced piecewise exponential survival software implements the likelihood ratio test and backward elimination procedure in Han, Schell, and Kim (2012 <doi:10.1080/19466315.2012.698945>, 2014 <doi:10.1002/sim.5915>), and Han et al. (2016 <doi:10.1111/biom.12590>). Inputs to the program can be either times when events/censoring occur or the vectors of total time on test and the number of events. Outputs of the programs are times and the corresponding p-values in the backward elimination. Details about the model and implementation are given in Han et al. 2014. This program can run in R version 3.2.2 and above.
The purpose of this package is to factor out logic and math common
    to Item Factor Analysis fitting, diagnostics, and analysis. It is
    envisioned as core support code suitable for more specialized IRT packages
    to build upon. Complete access to optimized C functions are made available
    with R_RegisterCCallable().
Allows ad hoc queries and reading and
    writing data frames to and from a database.
Fast simulation of some random variables than the usual native functions, including rnorm() and rexp(), using Ziggurat method, reference: MARSAGLIA, George, TSANG, Wai Wan, and al. (2000) <doi:10.18637/jss.v005.i08>, and fast simulation of stochastic differential equations / Poisson processes.
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.
Bindings for the 'PicoSAT' solver to solve Boolean satisfiability problems (SAT).
             The boolean satisfiability problem asks the question if a given boolean formula can be TRUE; 
             i.e. does there exist an assignment of TRUE/FALSE for each variable such that the whole formula is TRUE?
             The package bundles 'PicoSAT' solver release 965 <http://www.fmv.jku.at/picosat/>.
Get information (boards, pins and
    users) from the Pinterest <http://www.pinterest.com>
    API.
Build powerful pivot tables (aka Pivot Grid, Pivot Chart, Cross-Tab) 
    and dynamically slice & dice / drag 'n' drop your data. 'rpivotTable' is a
    wrapper of 'pivottable', a powerful open-source Pivot Table library implemented
    in 'JavaScript' by Nicolas Kruchten. Aligned to 'pivottable' v2.11.0.
A programmatic interface to the 'SOLR' based
    search 'API' (<http://api.plos.org/>) provided by the Public
    Library of Science journals to search their articles.
    Functions are included for searching for articles, retrieving
    articles, making plots, doing 'faceted' searches,
    'highlight' searches, and viewing results of 'highlighted'
    searches in a browser.
Generate basic charts either by custom applications, or from a small script launched from the system console, or within the R console. Two ASCII text files are necessary:
  (1) The graph parameters file, which name is passed to the function 'rplotengine()'.
    The user can specify the titles, choose the type of the graph, graph output formats
    (e.g. png, eps), proportion of the X-axis and Y-axis, position of the legend,
    whether to show or not a grid at the background, etc.
  (2) The data to be plotted, which name is specified as a parameter ('data_filename')
    in the previous file. This data file has a tabulated format, with a single character
    (e.g. tab) between each column, 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.
A 142 polyhedra database scraped from PHD files <http://paulbourke.net/dataformats/phd/> as R6 objects and 'rgl' visualizing capabilities. The PHD format was created to describe the geometric polyhedra definitions derived mathematically <http://www.netlib.org/polyhedra/> by Andrew Hume and by the Kaleido program of Zvi Har'El.
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.
Fully 'DBI'-compliant 'Rcpp'-backed interface to 'PostgreSQL' <https://www.postgresql.org/>,
    an open-source relational database.
Database interface and 'PostgreSQL' driver for 'R'.
 This package provides a Database Interface 'DBI' compliant 
 driver for 'R' to access 'PostgreSQL' database systems.  
 In order to build and install this package from source, 'PostgreSQL' 
 itself must be present your system to provide 'PostgreSQL' functionality 
 via its libraries and header files. These files are provided as
 'postgresql-devel' package under some Linux distributions.
 On 'macOS' and 'Microsoft Windows' system the attached 'libpq' library source will be used.
The significance of mean difference tests in clinical trials is established if at least r null hypotheses are rejected among m that are simultaneously tested. This package enables one to compute necessary sample sizes for single-step (Bonferroni) and step-wise procedures (Holm and Hochberg). These three procedures control the q-generalized family-wise error rate (probability of making at least q false rejections). Sample size is computed (for these single-step and step-wise procedures) in a such a way that the r-power (probability of rejecting at least r false null hypotheses, i.e. at least r significant endpoints among m) is above some given threshold, in the context of tests of difference of means for two groups of continuous endpoints (variables). Various types of structure of correlation are considered. It is also possible to analyse data (i.e., actually test difference in means) when these are available. The case r equals 1 is treated in separate functions that were used in Lafaye de Micheaux et al. (2014) <doi:10.1080/10543406.2013.860156>.
Using some association schemes to obtain a new series of resolvable partially pairwise balanced designs (RPPBD) and space-filling designs.
Reads in sample description and slide description files and
        annotates the expression values taken from GenePix results files
	(text file format used by many microarray scanner and software providers). 
	After normalization data can be visualized as boxplot, heatmap or dotplot.
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.
Read, write and manipulate 'Praat' <http://www.fon.hum.uva.nl/praat/> TextGrid <http://www.fon.hum.uva.nl/praat/manual/TextGrid.html>, PitchTier <http://www.fon.hum.uva.nl/praat/manual/PitchTier.html> and Pitch <http://www.fon.hum.uva.nl/praat/manual/Pitch.html> files.
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 'JSS'
 paper (2016, <doi:10.18637/jss.v071.i02>. 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.
An implementation of Recursive Partitioning Survival Trees via a node-splitting rule that builds decision tree models that reflected within-node and within-treatment responses. The algorithm aims to find the maximal difference in survival time among different treatments.
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.
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>.
Imitating the 'Ramas' (c) software, analyzes and predicts from matrix population models,  (Caswell 2006) <doi:10.1002/9781118445112.stat07481>.
An implementation of the Real-time Adaptive Penalization (RAP) algorithm through which to iteratively update a regularization parameter in a streaming context.
Replication Rate (RR) is the probability of replicating a statistically significant association in genome-wide association studies. This R-package provide the estimation method for replication rate which makes use of the summary statistics from the primary study. We can use the estimated RR to determine the sample size of the replication study, and to check the consistency between the results of the primary study and those of the replication study.
A tool to exchange data between R and 'Raven' sound analysis software <http://www.birds.cornell.edu/brp/raven/RavenOverview.html> (Cornell Lab of Ornithology). Functions work on data formats compatible with the R package 'warbleR'.
Software for genomic prediction with the RR-BLUP mixed model. 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.
Provides methods for multiway data analysis by means of Parafac
    and Tucker 3 models. Robust versions (Engelen and Hubert (2011) <doi:10.1016/j.aca.2011.04.043>) and versions
    for compositional data are also provided (Gallo (2015) <doi:10.1080/03610926.2013.798664>, Di Palma et al. (in press)).
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".
Processes standard recommendation datasets (e.g., a user-item rating matrix) as input and generates rating predictions and lists of recommended items. Standard algorithm implementations which are included in this package are the following: Global/Item/User-Average baselines, Weighted Slope One, Item-Based KNN, User-Based KNN, FunkSVD, BPR and weighted ALS. They can be assessed according to the standard offline evaluation methodology (Shani, et al. (2011) <doi:10.1007/978-0-387-85820-3_8>) for recommender systems using measures such as MAE, RMSE, Precision, Recall, F1, AUC, NDCG, RankScore and coverage measures. The package (Coba, et al.(2017) <doi: 10.1007/978-3-319-60042-0_36>) is intended for rapid prototyping of recommendation algorithms and education purposes.
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.
Assists for presentation and visualization of data from the Norwegian Health Quality Registries following the standardization based on the requirement specified by the National Service for Health Quality Registries. This requirement can be accessed from (<https://www.kvalitetsregistre.no/resultater-til-publisering-pa-nett>). Unfortunately the website is only available in Norwegian.
An R and Repast integration tool for running individual-based
    (IbM) simulation models developed using 'Repast Simphony' Agent-Based framework
    directly from R code. 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.
Computation of the International Roughness Index (IRI) given a
  longitudinal road profile. The IRI can be calculated for a single road segment
  or for a sequence of segments with a fixed length (e. g. 100m).
  For the latter, an overlap of the segments can be selected.
  The IRI and likewise the algorithms for its determination are defined in
  Sayers, Michael W; Gillespie, Thomas D; Queiroz, Cesar A.V. 1986.
  The International Road Roughness Experiment (IRRE) : establishing correlation
  and a calibration standard for measurements. World Bank technical paper;
  no. WTP 45. Washington, DC : The World Bank. (ISBN 0-8213-0589-1) available
  from <http://documents.worldbank.org/curated/en/326081468740204115>.
Multivariate regression methodologies including reduced-rank
    regression (RRR), reduced-rank ridge regression (RRS), robust reduced-rank
    regression (R4), generalized/mixed-response reduced-rank regression (mRRR),
    row-sparse reduced-rank regression (SRRR), reduced-rank regression with a
    sparse singular value decomposition (RSSVD), and sparse and orthogonal
    factor regression (SOFAR).
Reduced-rank regression, diagnostics and graphics.
Univariate and multivariate methods to analyze randomized response (RR) survey designs (e.g., Warner, S. L. (1965). Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association, 60, 63–69). <doi:10.2307/2283137> Besides univariate estimates of true proportions, RR variables can be used for correlations, as dependent variable in a logistic regression (with or without random effects), 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.
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.
Classes and functions to create and summarize different types of resampling objects (e.g. bootstrap, cross-validation).
Sample ancestral trees conditional on phased or unphased SNP genotype data. The actual tree sampling is done using a C++ program that is launched within R. The package also contains functions for specifying the tree-sampling settings (pre-processing) and for storing and manipulating the sampled trees (post-processing). More information about 'sampletrees' can be found at <http://stat.sfu.ca/statgen/research/sampletrees.html>.
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, built to be used with either a GUI or by a
	command line. The RSCABS method is detailed in "Statistical analysis of 
	histopathological endpoints" by John Green et. al. (2014) <doi:10.1002/etc.2530>.
The 'Scala' <http://www.scala-lang.org/> 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 2.11.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, Plumber APIs, 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.
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).
Tools for stream editing:  manipulating text files with insertions, replacements, deletions, substitutions, and commenting.
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. 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.
Statistical modelling for longitudinal network data
	using stochastic actor-oriented models.
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.
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 submitting R scripts to a Slurm 
    workload manager, in part by automating the division of embarrassingly
    parallel calculations across cluster nodes.
Provides functions to generate response-surface designs, 
    fit first- and second-order response-surface models, 
    make surface plots, obtain the path of steepest ascent, 
    and do canonical analysis. A good reference on these methods 
    is Chapter 10 of Wu, C-F J and Hamada, M (2009) 
    "Experiments: Planning, Analysis, and Parameter Design Optimization"
    ISBN 978-0-471-69946-0.
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.
Tools to analyze animal tracking data with remote sensing.
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.
Downloads Southern Oscillation Index, Oceanic Nino Index and North Pacific Gyre Oscillation data. Data sources are described in the README file.
General Non-linear Optimization Using Augmented Lagrange Multiplier Method.
A comprehensive R API for querying Apache Solr databases.
             A Solr core is represented as a data frame or list that
             supports Solr-side filtering, sorting,
             transformation and aggregation, all through the familiar
             base R API. Queries are processed
             lazily, i.e., a query is only sent to the database when
             the data are required.
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 .
Minimally adjust the values of numerical records in a data.frame, such
    that each record satisfies a predefined set of equality and/or inequality
    constraints. The constraints can be defined using the 'validate' package. 
    The core algorithms have recently been moved to the 'lintools' package,
    refer to 'lintools' for a more basic interface and access to a sparse version
    of the algorithm.
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.
Calculate generalized R-squared, partial R-squared, and partial correlation coefficients for generalized linear models (including quasi models with well defined variance functions).
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.
Utilises The 'jTDS' project's 'JDBC' 3.0 'SQL Server'
  driver to extend 'DBI' classes and methods. The package also
  implements a 'SQL' backend to the 'dplyr' package.
Conducts statistical downscaling of daily CMIP5 (Coupled Model Intercomparison Project 5) climate change scenario data at a station level using empirical quantile mapping method by Jaepil Cho et al. (2016) <doi:10.1002/ird.2035>.
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 previously compiled regression models using the 'rstan'
    package, which provides the R interface to the Stan C++ library for Bayesian
    estimation. Users specify models via the customary R syntax with a formula and
    data.frame plus some additional arguments for priors.
Provides a set of functions to facilitate and ease the
    running of simulation studies of Bayesian models using 'stan'. 
    Provides functionality to simulate data, fit models, and manage
    simulation results.
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.
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 with natural splines, this re-implements Stata's 'stpm2' function, which are flexible parametric survival models developed by Royston and colleagues. We have extended the parametric models to include any smooth parametric smoothers for time. We have also extended the model to include any smooth penalized smoothers from the 'mgcv' package, using penalized likelihood. These models include left truncation, right censoring, interval censoring, gamma frailties and normal random effects. This also includes a smooth implementation of accelerated failure time models.
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.
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'.
Produces tables with the level of replication (number of replicates) and the
    experimental uncoded values of the quantitative factors to be used for rotatable Central
    Composite Design (CCD) experimentation and a 2-D contour plot of the corresponding
    variance of the predicted response according to
    Mead et al. (2012) <doi:10.1017/CBO9781139020879> design_ccd(), and analyzes
    CCD data with response surface methodology ccd_analysis(). A rotatable CCD
    provides values of the variance of the predicted response that are concentrically
    distributed around the average treatment combination used in the experimentation, which
    with uniform precision (implied by the use of several replicates at the average
    treatment combination) improves greatly the search and finding of an optimum response.
    These properties of a rotatable CCD represent undeniable advantages over the classical
    factorial design, as discussed by Panneton et al. (1999) <doi:10.13031/2013.13267> and
    Mead et al. (2012) <doi:10.1017/CBO9781139020879.018> among others.
The software suite, 'Freesurfer', is a open-source software suite involving the segmentation of brain MRIs (see <http://freesurfer.net/> for more information). This package provides functionality to import the data generated by 'Freesurfer'; functions to easily manipulate the data; and provides brain specific normalisation commonly used when studying structural brain MRIs. This package has been designed using an installation of and data generated from 'Freesurfer' version 5.3.
Provides functions to estimate the proportion of treatment effect on the primary outcome that is explained by the treatment effect on the surrogate marker.
A range of functions for the design and
    analysis of disease surveillance activities. These functions were
    originally developed for animal health surveillance activities but can be
    equally applied to aquatic animal, wildlife, plant and human health
    surveillance activities. Utilities are included for sample size calculation
    and analysis of representative surveys for disease freedom, risk-based
    studies for disease freedom and for prevalence estimation.
A geographic information system (GIS) graphical user interface (GUI) that
  provides data viewing, management, and analysis tools.
Low-rank matrix decompositions are fundamental tools and widely used for data
  analysis, dimension reduction, and data compression. Classically, highly accurate 
  deterministic matrix algorithms are used for this task. However, the emergence of 
  large-scale data has severely challenged our computational ability to analyze big data. 
  The concept of randomness has been demonstrated as an effective strategy to quickly produce
  approximate answers to familiar problems such as the singular value decomposition (SVD). 
  The rsvd package provides several randomized matrix algorithms such as the randomized 
  singular value decomposition (rsvd), randomized principal component analysis (rpca), 
  randomized robust principal component analysis (rrpca), randomized interpolative 
  decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot 
  functions are provided.
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.
Allows to link data to Swiss administrative divisions (municipalities,
    districts, cantons) and to plot and save customised maps thereof.  Furthermore, the
    package allows to generate tailored templates for data collection.  The used geodata
    is publicly available on the Swiss Federal Statistical Office website
    <https://www.bfs.admin.ch/bfs/de/home/dienstleistungen/geostat/geodaten-bundesstatistik.html>.
An R interface to the SYMPHONY solver for mixed-integer linear programs.
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.
We provide functions to perform taxometric analyses. This package contains 44 functions, but only 5 should be called directly by users. CheckData() should be run prior to any taxometric analysis to ensure that the data are appropriate for taxometric analysis. RunTaxometrics() performs taxometric analyses for a sample of data. RunCCFIProfile() performs a series of taxometric analyses to generate a CCFI profile. CreateData() generates a sample of categorical or dimensional data. ClassifyCases() assigns cases to groups using the base-rate classification method.
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 (Ratcliff & McKoon,
        2008, <doi:10.1162/neco.2008.12-06-420>) based on C code by
        Andreas and Jochen Voss and (b) linear ballistic accumulator
        (LBA; Brown & Heathcote, 2008,
        <doi:10.1016/j.cogpsych.2007.12.002>) with different
        distributions underlying the drift rate.
Exploit controlled vocabularies organized on tematres servers.
A 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 <ftp://ftp.flaterco.com/xtide/harmonics-dwf-20151227-free.tar.bz2>,
    which is 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 (<http://developer.nytimes.com/>)
    and 'ProPublica' (<https://www.propublica.org/datastore/apis>). This client 
    covers a subset of the New York Times and 'ProPublica' 'APIs'.
Supports the input and reproducible analysis of RTI MicroPEM output files.
R tools to measure and compare inequality, welfare and poverty using the EU statistics on income and living conditions surveys.
Rarefy data, calculate diversity and plot the results.
'STK++' <http://www.stkpp.org> is a collection of
    C++ classes for statistics, clustering, linear algebra, arrays (with an
    'Eigen'-like API), regression, dimension reduction, etc. The integration of
    the library to 'R' is using 'Rcpp'.

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

       'STK++' is licensed under the GNU LGPL version 2 or later. 'rtkore'
    (the 'stkpp' integration into 'R') is licensed under the
    GNU GPL version 2 or later. See file LICENSE.note for details.
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.
A set of functions used to automate commonly used methods in credit risk. This includes multiple methods for bootstrapping default rates and forecasting/stress testing credit exposures migrations, via Econometrics and Machine Learning algorithms.
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  (see <https://github.com/lvdmaaten/bhtsne/> for more information on the original implementation).
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 Shiny apps for Plotting and Exploratory Analysis. 
    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 collect and
    organize Twitter data via Twitter's REST and stream Application
    Program Interfaces (API), which can be found at the following URL:
    <https://developer.twitter.com/en/docs>.
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.
Tools for creating data validation pipelines and tidy reports. This
    package offers a framework for exploring and validating data frame like
    objects using 'dplyr' grammar of data manipulation.
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 generalized 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.
    From version 1.2.0 the 'Rcpp' package 
    <https://cran.r-project.org/package=Rcpp> can be used to improve efficiency.
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.
Implements simulation-based random variable class and a suite of methods.
Contains miscellaneous functions useful in biostatistics, mostly univariate and multivariate testing procedures with a special emphasis on permutation tests. Many functions intend to simplify user's life by shortening existing procedures or by implementing plotting functions that can be used with as much methods from different packages as possible.
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
    <https://github.com/cnr-isti-vclab/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', 'Bioconductor' or 'Github').
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. Functions extending package 'officer' are provided to 
  embed 'DrawingML' graphics into 'Microsoft PowerPoint' presentations and 
  'Microsoft Excel' workbooks.
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.
Provides an interface to 'vinecopulib', a C++ library for vine 
 copula modeling based on 'Boost' and 'Eigen'. The 'rvinecopulib' 
 package implements the core features of the popular 'VineCopula' package, in 
 particular inference algorithms for both vine copula and bivariate copula 
 models. Advantages over 'VineCopula' are a sleeker and more modern API, 
 improved performances, especially in high dimensions, nonparametric and 
 multi-parameter families. The 'rvinecopulib' package includes 'vinecopulib' as
 header-only C++ library (currently version 0.2.3). Thus 
 users do not need to install 'vinecopulib' itself in order to use 
 'rvinecopulib'. Since their initial releases, 'vinecopulib' is licensed under 
 the MIT License, and 'rvinecopulib' is licensed under the GNU GPL version 3.
Variable metric nonlinear function minimization with bounds constraints.
Designed to calculate the distance between each voter in a voter file -- given lat/long coordinates -- and many potential (early) polling or vote by mail drop box locations, then return the minimum distance.
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. Note that this package contains an
 embedded older version of 'Vowpal Wabbit'. The package 'rvw' at the GitHub
 repo <https://github.com/eddelbuettel/rvw> can provide an alternative using 
 an external 'Vowpal Wabbit' library installation.
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.
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 API to Melbourne pedestrian data in tidy data form.
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.
Code to facilitate simulation and inference when connectivity is defined by underlying random walks. Methods for spatially-correlated pairwise distance data are especially considered. This provides core code to conduct analyses similar to that in Hanks and Hooten (2013) <doi:10.1080/01621459.2012.724647>.
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.
Provides Wiener process distribution functions,
  namely the Wiener first passage time density, CDF, quantile and random
  functions. Additionally supplies a modelling function (wdm) and further
  methods for the resulting object.
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 download and manage surface wind data from the Global Forecasting System <https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/global-forcast-system-gfs> and to compute wind connectivity between locations.
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.
An expansion of R's 'stats' random wishart matrix generation. 
  This package allows the user to generate singular, Uhlig and Harald (1994) 
  <doi:10.1214/aos/1176325375>, and pseudo wishart, Diaz-Garcia, et al.(1997) 
  <doi:10.1006/jmva.1997.1689>, matrices. In addition the user can generate 
  wishart matrices with fractional degrees of freedom, Adhikari (2008) 
  <doi:10.1061/(ASCE)0733-9399(2008)134:12(1029)>, commonly used in volatility 
  modeling. Users can also use this package to create random covariance matrices.
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 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 <https://xkcd.com> 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. Also the code is mostly released under GPL.  The
    VODE and LSODA are in the public domain.  The information is available
    in the inst/COPYRIGHTS.
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.
Provides access to the 'xylib' C library for to import xy 
  data from powder diffraction, spectroscopy and other experimental methods.
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'.
Implements the Zig-Zag algorithm with subsampling and control variates (ZZ-CV) of (Bierkens, Fearnhead, Roberts, 2016) <arXiv:1607.03188> as applied to Bayesian logistic regression, as well as basic Zig-Zag for a Gaussian target distribution.
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.
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.
Computes the probability of a set of species abundances of a single or multiple samples of individuals under a mainland-island model. One must specify the mainland (metacommunity) model and the island (local) community model. It assumes that species fluctuate independently. See Haegeman, B. & R.S. Etienne (2017). A general sampling formula for community structure data. Methods in Ecology & Evolution. In press.
Provides the density, distribution, quantile and generation
    functions of some obscure probability distributions, including the doubly non-
    central t, F, Beta, and Eta distributions; the 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 variables; the
    product of independent normals.
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.
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.
A consistent interface to encrypt and decrypt strings, R objects and files using symmetric and asymmetric key encryption.
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).
In a clinical trial with repeated measures designs, outcomes are often taken from subjects at fixed time-points.  The focus of the trial may be to compare the mean outcome in two or more groups at some pre-specified time after enrollment. In the presence of missing data auxiliary assumptions are necessary to perform such comparisons.  One commonly employed assumption is the missing at random assumption (MAR).   The 'samon' package allows the user to perform a (parameterized) sensitivity analysis of this assumption.  In particular it can be used to examine the sensitivity of tests in the difference in outcomes to violations of the MAR assumption.  The sensitivity analysis can be performed under two scenarios, a) where the data exhibit a monotone missing data pattern (see the samon() function), and, b) where in addition to a monotone missing data pattern the data exhibit intermittent missing values (see the samonIM() function).
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.
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
   and maximum likelihood estimation
   of Heckman-type sample selection models:
   standard sample selection models (Tobit-2),
   endogenous switching regression models (Tobit-5),
   sample selection models with binary dependent outcome variable,
   interval regression with sample selection (only ML estimation),
   and endogenous treatment effects models.
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.
The design of phase 3 clinical trials can be classified into 4 types: (1) Testing for equality;(2) Superiority trial;(3) Non-inferiority trial; and (4) Equivalence trial according to the goals. Given that none of the available packages combines these designs in a single package, this package has made it possible for researchers to calculate sample size when comparing means or proportions in phase 3 clinical trials with different designs. The ssc function can calculate the sample size with pre-specified type 1 error rate,statistical power and effect size according to the hypothesis testing framework. Furthermore, effect size is comprised of true treatment difference and non-inferiority or equivalence margins which can be set in ssc function. (Reference: Yin, G. (2012). Clinical Trial Design: Bayesian and Frequentist Adaptive Methods. John Wiley & Sons.)
Computes the required sample size for estimation of totals, means
    and proportions under complex sampling designs.
Calculates the power and sample size for Cochran-Mantel-Haenszel tests. 
    There are also several helper functions for working with probability,
    odds, relative risk, and odds ratio values.
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. Almost every function is written in C (compiled) for faster results.
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, clustered, panel, and longitudinal data.
Downloads and tidies the San Francisco Public Utilities Commission Beach Water Quality Monitoring Program data. Data sets can be downloaded per beach, or the raw data can be downloaded. See <https://sfwater.org/cfapps/lims/beachmain1.cfm>.
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.
Plots that 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.
A static code analysis tool for 'SAS' scripts. It is designed to load, count, extract, remove, and summarise components of 'SAS' code.
Settings and functions to extend the 'knitr' 'SAS' engine.
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.
Herein, we provide a broad variety of functions which are useful
    for handling, manipulating, and visualizing satellite-based remote sensing 
    data. These operations range from mere data import and layer handling (eg 
    subsetting), over Raster* typical data wrangling (eg crop, extend), to more 
    sophisticated (pre-)processing tasks typically applied to satellite imagery 
    (eg atmospheric and topographic correction). This functionality is 
    complemented by a full access to the satellite layers' metadata at any 
    stage and the documentation of performed actions in a separate log file. 
    Currently available sensors include Landsat 4-5 (TM), 7 (ETM+), and 8 
    (OLI/TIRS Combined), and additional compatibility is ensured for the Landsat 
    Global Land Survey data set. Moreover, support for Terra and Aqua-MODIS as 
    well as PROBA-V is expected to arrive shortly.
Supports the generation of maps based on the results from 
     'SaTScan' (TM) cluster analysis.
     The package handles mapping of Spatial and Spatial-Time analysis using
     the discrete Poisson, Bernoulli, and exponential models of case data generating
     cluster and location ('GIS') records containing observed, expected and observed/expected
     ratio for U. S. states (and DC), counties or census tracts of individual 
     states based on the U. S. 'FIPS' codes for state, county and census tracts 
     (locations) using 2000 or 2010 Census areas, 'FIPS' codes, and boundary data.
     'satscanMapper' uses the 'SeerMapper' package for the boundary data and 
     mapping of locations.  Not all of the 'SaTScan' (TM) analysis and models generate
     the observed, expected and observed/expected ratio values for the clusters and 
     locations.
     The user can map the observed/expected ratios for locations 
     (states, counties, or census tracts) for each cluster with a p-value less than 0.05 
     or a user specified p-value.  
     The locations are categorized and colored based on either the cluster's Observed/Expected 
     ratio or the locations' Observed/Expected ratio. 
     The place names are provided for each census tract using data from 'NCI', the 'HUD' crossover 
     tables (Tract to Zip code) as of December, 2013, the USPS Zip code 5 database for 1999, 
     and manual look ups on the USPS.gov web site.
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.
Implements the sequential BART (Bayesian Additive Regression Trees) approach to impute the missing covariates. The
     algorithm applies a Bayesian nonparametric approach on factored sets of sequential conditionals of the joint 
     distribution of the covariates and the missingness and applying the Bayesian additive regression trees to model 
     each of these univariate conditionals. Each conditional distribution is then sampled using MCMC algorithm. The published 
     journal can be found at <https://doi.org/10.1093/biostatistics/kxw009>
     Package provides a function, seqBART(), which computes and returns the imputed values.
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).
Calculating approximately unbiased (AU) p-values
	from multiscale bootstrap probabilities. See
	Shimodaira (2004) <doi:10.1214/009053604000000823> and
	Shimodaira (2008) <doi:10.1016/j.jspi.2007.04.001>.
Graphical scales map data to aesthetics, and provide
    methods for automatically determining breaks and labels
    for axes and legends.
Identifies the locations of neurons, and estimates their calcium concentrations over time using the SCALPEL method proposed in Petersen, A., Simon, N., and Witten, D. SCALPEL: Extracting Neurons from Calcium Imaging Data <https://ajpetecom.files.wordpress.com/2017/01/petersenscalpel.pdf>.
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 Monte Carlo simulation.
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.
Conditional association test based on summary data from genome-wide association study (GWAS) adjusting for heterogeneity in SNP coverage.
Allows you to make clean, good-looking scatter plots with the option to 
    easily add marginal density or box plots on the axes. It is also available as a module for 'jamovi'
    (see <https://www.jamovi.org> for more information). 'Scatr' is based on the 
    'cowplot' package by Claus O. Wilke and the 'ggplot2' package by Hadley Wickham.
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 wrappers for 'scclust', a C library for computationally efficient
    size-constrained clustering with near-optimal performance.
    See <https://github.com/fsavje/scclust> for more information.
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.
Users may specify what fundamental qualities of a new study have 
             or have not changed in an attempt to reproduce or replicate an original study. A comparison of 
			 the differences is visualized. Visualization approach follows Patil, Peng, and Leek (2016) <doi:10.1101/066803>.
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.
Perform meta-analysis of single-case experiments, including calculating various effect size measures (SMD, PND, PEM and NAP) and probability combining (additive and multiplicative method).
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.
Makes the development of credit risk scorecard easily and efficiently by providing functions such as information value, variable filter, optimal woe binning, scorecard scaling and performance evaluation etc. The references including: 
  1. Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: Development and Implementation Using SAS. 
  2. Siddiqi, N. (2006, ISBN: 9780471754510). Credit risk scorecards. Developing and Implementing Intelligent Credit Scoring.
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.
An accurate and easy tool for performing trajectory inference on
  single cells using single-cell RNA sequencing data. In addition, SCORPIUS
  provides functions for discovering the most important genes with respect to
  the reconstructed trajectory, as well as nice visualisation tools.
  Cannoodt et al. (2016) <doi:10.1101/079509>.
Division of an ANOVA experiment treatment means into
        homogeneous distinct groups using the clustering method of
        Scott & Knott
The Scott-Knott Effect Size Difference (ESD) test is a mean comparison approach that leverages a hierarchical clustering to partition the set of treatment means (e.g., means of variable importance scores, means of model performance) into statistically distinct groups with non-negligible difference [Tantithamthavorn et al., (2017) <doi:10.1109/TSE.2016.2584050>].
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.
Group of functions for spatial smoothing using cubic splines and variogram maximum likelihood estimation. Also allow the inclusion of linear parametric terms and change-points for segmented smoothing splines models.
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'.
A utility to webscrape the in-house careers of members of the European parliament, from its website <http://www.europarl.europa.eu>.
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.
Run complex native scripts with a single command, similar to system commands.
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.
Design single-case phase, alternation and multiple-baseline experiments, and conduct 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.
Make graphical representations of single case data and 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.
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 as described for example 
    in Hundepol et al. (2012) <doi:10.1002/9781118348239> 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 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. For more information, please check the following paper:
    Naimi, B., Araujo, M.B. (2016) <doi:10.1111/ecog.01881>.
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.
Terrestrial and marine predictors for species distribution modelling
    from multiple sources, including WorldClim <http://www.worldclim.org/>,,
    ENVIREM <http://envirem.github.io/>, Bio-ORACLE <http://bio-oracle.org/>
    and MARSPEC <http://www.marspec.org/>.
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.
Summaries of distributions on clusterings and feature allocations are provided. Specifically, point estimates are obtained by the sequentially-allocated latent structure optimization (SALSO) algorithm to minimize squared error loss, absolute error loss, Binder loss, or the lower bound of the variation of information loss. Clustering uncertainty can be assessed with the confidence calculations and the associated plot.
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>.
Solves the general Semi-Definite Linear Programming formulation using an R implementation of SDPT3 (K.C. Toh, M.J. Todd, and R.H. Tutuncu (1999) <doi:10.1080/10556789908805762>). This includes problems such as the nearest correlation matrix problem (Higham (2002) <doi:10.1093/imanum/22.3.329>), D-optimal experimental design (Smith (1918) <doi:10.2307/2331929>), Distance Weighted Discrimination (Marron and Todd (2012) <doi:10.1198/016214507000001120>), as well as graph theory problems including the maximum cut problem. Technical details surrounding SDPT3 can be found in R.H Tutuncu, K.C. Toh, and M.J. Todd (2003) <doi:10.1007/s10107-002-0347-5>.
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.
Access Sea Around Us (<http://www.seaaroundus.org/>) fish 
    catch data, including tools for visualizing the data.
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 for designing spatially explicit capture-recapture studies of animal populations. This is primarily a simulation manager for package 'secr'. Extensions in version 2.5.0 include costing and evaluation of detector spacing.
Allow sharing sensitive information, for example passwords,
    'API' keys, etc., in R packages, using public key cryptography.
Tools for spatially explicit capture-recapture analysis of animal populations in linear habitats, extending package 'secr'.
Sequential factor extraction via co-sparse unit-rank estimation (SeCURE).
When security risks in web services are discovered by independent
   security researchers who understand the severity of the risk, they
   often lack the channels to properly disclose them. As a result,
   security issues may be left unreported. The 'security.txt' 'Web Security Policies'
   specification defines an 'IETF' draft standard <https://tools.ietf.org/html/draft-foudil-securitytxt-00>
   to help organizations define the process for security researchers to securely 
   disclose security vulnerabilities. Tools are provided to help identify and 
   parse 'security.txt' files to enable analysis of the usage and adoption of these policies.
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.
Functions for dimension reduction through the seeded canonical correlation analysis are provided. A classical canonical correlation analysis (CCA) is one of useful statistical methods in multivariate data analysis, but it is limited in use due to the matrix inversion for large p small n data. To overcome this, a seeded CCA has been proposed in Im, Gang and Yoo (2015) <DOI:10.1002/cem.2691>. The seeded CCA is a two-step procedure. The sets of variables are initially reduced by successively projecting cov(X,Y) or cov(Y,X) onto cov(X) and cov(Y), respectively, without loss of information on canonical correlation analysis, following Cook, Li and Chiaromonte (2007) <DOI:10.1093/biomet/asm038> and Lee and Yoo (2014) <DOI:10.1111/anzs.12057>. Then, the canonical correlation is finalized with the initially-reduced two sets of variables.
Suite of functions for the simulation, visualisation and analysis of bacterial evolution within- and between-host.
Creates SEER (Surveillance, Epidemiology and End Results) and A-bomb data binaries 
             from ASCII sources and provides tools for estimating SEER second cancer risks.
Provides an easy way to map seer registry area rate data on a U. S, map.  
   The U. S. data may be mapped at the state, U. S. NCI Seer Register, state/county 
   or census tract level. The function can categorize the data into "n" quantiles, where "n" is 3 to 11 or
   the caller can specify a cut point list for the categorizes.  
   The caller can also provide the data and the comparison operation to request
   hatching over any areas.  The default operation and value are > 0.05 (p-values).
   The location id provided in the data determines the geographic level of the mapping.
   If states, state/counties or census tracts are being mapped, the location ids 
   used must be the U.S. FIPS codes for states (2 digits), state/counties (5 digits)
   or state/county/census tracts (11 digits). If the location id references the U.S. Seer Registry 
   areas, the Seer Registry area identifier used to link the data to the geographical 
   areas, then the location id is the Seer Registry name or abbreviation.
   Additional parameters are used to provide control over the drawing of the boundaries
   at the data's boundary level and higher levels.
   The package uses modified boundary data from the 2000 and 2010 U. S. Census to reduce the 
   storage requirements and improve drawing speed.  
   The 'SeerMapper' package contains the U. S. Census 2000 and 2010 boundary data
   for the regional, state, Seer Registry, and county levels.  Six supplement packages 
   contain the census tract boundary data (see manual for more details.)
Provides supplemental 2010 census tract boundary package for 23 states
   without Seer Registries that are east of the Mississippi river 
   for use with the 'SeerMapper' package.  
   The data contained in this 
   package is derived from U. S. Census data and is in public domain.
Provides  supplemental 2010 census tract boundaries of the 15 states 
   containing Seer Registries for use with the 'SeerMapper' package.
   The data contained in this 
   package is derived from U. S. 2010 Census data and is in public domain.
Provides supplemental 2010 census tract boundaries for the 14 states
   without Seer Registries that are west of the Mississippi river 
   for use with the 'SeerMapper' package.
   The data contained in this 
   package is derived from U. S. 2010 Census data and is in public domain.
Provides supplemental 2000 census tract boundaries for the 23 states
   without Seer Registries that are east of the Mississippi river 
   for use with the 'SeerMapper' package.  
   The data contained in this 
   package is derived from U. S. Census data and is in the public domain.
Provides supplemental 2000 census tract boundaries for the 15 states
   containing Seer Registries for use with the 'SeerMapper' package.  
   The data contained in this 
   package is derived from U. S. Census data and is in the public domain.
Provides supplemental 2000 census tract boundaries for the 14 states
   without Seer Registries that are west of the Mississippi river
   for use with the 'SeerMapper' package.  
   The data contained in this 
   package is derived from U. S. Census data and is in the public domain.
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., piece-wise 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.
Provides indices such as Manly's alpha, foraging ratio, and Ivlev's selectivity to allow for analysis of dietary selectivity and preference. Can accommodate multiple experimental designs such as constant prey number of prey depletion.
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.
Provides the SELF criteria to learn causal structure. Please cite "Ruichu Cai, Jie Qiao, Zhenjie Zhang, Zhifeng Hao. SELF: Structural Equational Embedded Likelihood Framework for Causal Discovery. AAAI. 2018."
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.
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 generators:
  i) a RBF network based generator using rbfDDA() from package 'RSNNS',
  ii) a Random Forest based generator for both classification and regression problems
  iii) a density forest based generator for unsupervised data
 Data evaluation support tools include:
  a) single attribute based statistical evaluation: mean, median, standard deviation, skewness, kurtosis, medcouple, L/RMC, KS test, Hellinger distance
  b) evaluation based on clustering using Adjusted Rand Index (ARI) and FM
  c) evaluation based on classification performance with various learning models, e.g., 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; <doi:10.1111/rssc.12078>, Lee et al. 2017) and cluster-correlated data (Lee et al., 2016; <doi:10.1111/biom.12696> <doi:10.1080/01621459.2016.1164052>).
Provides routines to check identifiability or non-identifiability
    of linear structural equation models as described in Drton, Foygel, and
    Sullivant (2011) <DOI:10.1214/10-AOS859>, Foygel, Draisma, and Drton (2012) 
    <DOI:10.1214/12-AOS1012>, and other works. 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.
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 for phylogenetic comparative
 methods. The package is an umbrella of statistical and graphical methods that 
 estimate and report different types of uncertainty in PCM:
 (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.  Function senfm() performs tests and function senfmCI() creates confidence intervals.  The method uses Huber's M-statistics, including least squares, and is described in Rosenbaum (2007, Biometrics) <DOI:10.1111/j.1541-0420.2006.00717.x>.
Sensitivity analysis for multiple outcomes in observational studies.  For instance, all linear combinations of several outcomes may be explored using Scheffe projections in the comparison() function; see Rosenbaum (2016, Annals of Applied Statistics) <doi:10.1214/16-AOAS942>.  Alternatively, attention may focus on a few principal components in the principal() function.  The package includes parallel methods for individual outcomes, including tests in the senm() function and confidence intervals in the senmCI() function.
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.
External preference mapping technique is commonly used in sensory analysis
    field to explain consumer preferences in function of sensory attributes of products
    Danzart(1998) (<doi:10.1006/fstl.1998.0373>). The package provides map visualization
    with options in dimension reduction methods and prediction models from linear and
    non linear regressions. A smoothed version of the map performed using LOESS algorithm
    is available. A comparison approach of maps stability from different features before
    and after smoothing is provided. A 'shiny' application is included. It presents an
    easy GUI for the implemented functions as well as a comparative tool of fit models
    using several criteria. Basic analysis such as characterization of products,
    panelists and sessions likewise consumer segmentation are available.
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.
Statistical Methods to Analyse Sensory Data. SensoMineR: A package for sensory data analysis. S. Le and F. Husson (2008) <DOI:10.1111/j.1745-459X.2007.00137.x>.
Test sampling designs by several flexible cost functions, usually based on the simulations, and optimise sampling designs using different optimisation algorithms; load plume simulations (on lattice or points) even if they do not fit into memory.
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.
Sensitivity analysis in unmatched observational studies, with or without strata.  The main functions are sen2sample() and senstrat().  See Rosenbaum, P. R. and Krieger, A. M. (1990), JASA, 85, 493-498, <doi:10.1080/01621459.1990.10476226> and Gastwirth, Krieger and Rosenbaum (2000), JRSS-B, 62, 545–555 <doi:10.1111/1467-9868.00249> .
Provides access and analytic functions for Sensus data.
Performs a sentiment analysis of textual contents in R. This implementation
    utilizes various existing dictionaries, such as Harvard IV, or finance-specific 
    dictionaries. Furthermore, it can also create customized dictionaries. The latter 
    uses LASSO regularization as a statistical approach to select relevant terms based on 
    an exogenous response variable.
Calculate text polarity sentiment at the sentence level and optionally aggregate
          by rows or grouping variable(s).
Time series analysis based on textual sentiment, accounting for the intrinsic challenge that sentiment can be computed and pooled across texts and time in many ways. As described in Ardia et al. (2017) <https://ssrn.com/abstract=3067734>, the package provides a means to model the impact of sentiment in texts on a target variable, by first computing a wide range of textual sentiment measures and then selecting those that are most informative.
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.
The 'seplyr' (standard evaluation 'dplyr') package supplies improved
    standard evaluation adapter methods for important common data manipulation tasks.
    In addition the 'seplyr' package supplies several new "key operations
    bound together" methods.  These include 'group_summarize()' (which
    combines grouping, arranging and calculation in an atomic unit),
    'add_group_summaries()' (which joins grouped summaries into a 'data.frame'
    in a well documented manner), 'add_group_indices()' (which adds
    per-group identifiers to a 'data.frame' without depending on row-order),
    'partition_mutate_qt()' (which optimizes mutate sequences), and 'if_else_device()'
    (which simulates per-row if-else blocks in expression sequences).
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.
Contains an implementation of invariant causal prediction for sequential data. The main function in the package is 'seqICP', which performs linear sequential invariant causal prediction and has guaranteed type I error control. For non-linear dependencies the package also contains a non-linear method 'seqICPnl', which allows to input any regression procedure and performs tests based on a permutation approach that is only approximately correct. In order to test whether an individual set S is invariant the package contains the subroutines 'seqICP.s' and 'seqICPnl.s' corresponding to the respective main methods.
Exploratory data analysis and data visualization
  for biological sequence (DNA and protein) data. Seqinr includes 
  utilities for sequence data management under the ACNUC system
  described in Gouy, M. et al. (1984) Nucleic Acids Res.
  12:121-127 <doi:10.1093/nar/12.1Part1.121>.
Kataegis is a localized hypermutation occurring when a region is enriched in somatic SNVs. Kataegis can result from multiple cytosine deaminations catalyzed by the AID/APOBEC family of proteins. This package contains functions to detect kataegis from SNVs in BED format. SeqKat reports two scores per kataegic event, a hypermutation score and an APOBEC mediated kataegic score. Upon publication of the paper describing the methods used in this package, a reference will be added here.
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. This package can help you (1) read VCF/BCF files by chromosomal ranges (e.g. 1:100-200); (2) read RareMETAL summary statistics files; (3) read tables from a tabix-indexed files; (4) annotate VCF/BCF files; (5) create customized workflow based on Makefile.
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.
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.
Functions to be used in conjunction with the 'Sequential' package that allows for planning of observational database studies that will be analyzed with exact sequential analysis. This package supports Poisson- and binomial-based data. The primary function, seq_wrapper(...), accepts parameters for simulation of a simple exposure pattern and for the 'Sequential' package setup and analysis functions. The exposure matrix is used to simulate the true and false positive and negative populations (Green (1983) <doi:10.1093/oxfordjournals.aje.a113521>, Brenner (1993) <doi:10.1093/oxfordjournals.aje.a116805>). Functions are then run from the 'Sequential' package on these populations, which allows for the exploration of outcome misclassification in data.
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 citation('sequoia') for more information.
'Apache Drill' is a low-latency distributed query engine designed to enable 
    data exploration and 'analytics' on both relational and non-relational 'datastores', 
    scaling to petabytes of data. Methods are provided that enable working with 'Apache' 
    'Drill' instances via the 'REST' 'API', 'JDBC' interface (optional), 'DBI' 'methods'
    and using 'dplyr'/'dbplyr' idioms.
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.
Query and print information about the current R session.
    It is similar to 'utils::sessionInfo()', but includes more information
    about packages, and where they were installed from.
Functions for performing set-theoretic multi-method research, QCA for 		clustered data, theory evaluation, Enhanced Standard Analysis, indirect 		calibration, radar visualisations. Additionally it includes data to replicate the 	examples in the book by C. Q. Schneider and C. Wagemann "Set Theoretic Methods for 	the Social Sciences", Cambridge University Press 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'.
A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>, Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>, and Butler A and Satija R (2017) <doi:10.1101/164889> for more details.
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 vector data. Binds 
    to GDAL for reading and writing data, to GEOS for geometrical operations, and to Proj.4 for projection
	conversions and datum transformations.
Stochastic Frontier Analysis
  introduced by Aigner, Lovell and Schmidt (1976)
  and Battese and Coelli (1992, 1995).
Stochastic frontier analysis with advanced methods.
 In particular, it applies the approach proposed by Latruffe et al. (2017) 
 <DOI:10.1093/ajae/aaw077> to estimate a stochastic frontier with technical 
 inefficiency effects when one input is endogenous.
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".
Build a constrained 'Delaunay' triangulation from simple features
    objects, applying constraints based on input line segments, and triangle
    properties including maximum area, minimum internal angle.
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>.
An implementation of the Similarity-First Search algorithm (SFS), a combinatorial algorithm which can be used to solve the seriation problem and to recognize some structured weighted graphs. The SFS algorithm represents a generalization to weighted graphs of the graph search algorithm Lexicographic Breadth-First Search (Lex-BFS), a variant of Breadth-First Search. The SFS algorithm reduces to Lex-BFS when applied to binary matrices (or, equivalently, unweighted graphs). Hence this library can be also considered for Lex-BFS applications such as recognition of graph classes like chordal or unit interval graphs. In fact, the SFS seriation algorithm implemented in this package is a multisweep algorithm, which consists in repeating a finite number of SFS iterations (at most \eqn{n} sweeps for a matrix of size \eqn{n}). If the data matrix has a Robinsonian structure, then the ranking returned by the multistep SFS algorithm is a Robinson ordering of the input matrix. Otherwise the algorithm can be used as a heuristic to return a ranking partially satisfying the Robinson property.
Useful utilities ['goodies'] from Seminar fuer Statistik ETH
  Zurich, quite a few related to graphics; some were ported from S-plus.
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.  Houpt, Blaha, McIntire, Havig, and Townsend (2013) <doi:10.3758/s13428-013-0377-3> provide a basic introduction to Systems Factorial Technology along with examples using the sft R package.
Contains space filling based tools for
    machine learning and data mining. Some functions offer
    several computational techniques and deal with the out of
    memory for large big data by using the ff package.
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, bi-level, and interaction 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.
Set of tools to fit a linear multiple or semi-parametric regression
    models and non-informative right-censoring may be considered. Under this setup, the localization parameter of the response variable distribution is modeled by using linear multiple regression
    or semi-parametric functions, whose non-parametric components may be approximated
    by natural cubic spline or P-splines. The supported distribution for the model error is a generalized log-gamma distribution which includes
    the generalized extreme value distribution as an important special case.
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
Provides functions that performs popular stochastic gradient Markov chain Monte Carlo (SGMCMC) methods on user specified models. The required gradients are automatically calculated using 'TensorFlow' <https://www.tensorflow.org/>, an efficient library for numerical computation. This means only the log likelihood and log prior functions need to be specified. The methods implemented include stochastic gradient Langevin dynamics (SGLD), stochastic gradient Hamiltonian Monte Carlo (SGHMC), stochastic gradient Nose-Hoover thermostat (SGNHT) and their respective control variate versions for increased efficiency. References: M. Welling, Y. W. Teh (2011) <http://www.icml-2011.org/papers/398_icmlpaper.pdf>; T. Chen, E. B. Fox, C. E. Guestrin (2014) <arXiv:1402.4102>; N. Ding, Y. Fang, R. Babbush, C. Chen, R. D. Skeel, H. Neven (2014) <https://papers.nips.cc/paper/5592-bayesian-sampling-using-stochastic-gradient-thermostats>; J. Baker, P. Fearnhead, E. B. Fox, C. Nemeth (2017) <arXiv:1706.05439>.
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.
Regularized version of partial least square approaches providing sparse, group, and sparse group versions of partial least square regression models (Liquet, B., Lafaye de Micheaux, P., Hejblum B., Thiebaut, R. (2016) <doi:10.1093/bioinformatics/btv535>). Version of PLS Discriminant analysis is also provided.
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) shadow heights; (2) shadow footprint on ground polygons; and (3) Sky View Factor values. Inputs include a polygonal layer of obstacle outlines along with their heights, sun azimuth and sun elevation. The package also provides functions for related preliminary calculations: breaking polygons into 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.
Implement shadowtextGrob() for 'grid' and geom_shadowtext() layer for 'ggplot2'.
             These functions create/draw text grob with background shadow.
Implementations are provided for the models described in the paper D. B. Dahl, R. Day, J. Tsai (2017) <DOI:10.1080/01621459.2016.1165103>. The Ewens, Ewens-Pitman, Ewens attraction, Ewens-Pitman attraction, and ddCRP distributions are available for prior and posterior simulation. Posterior simulation is based on a user-supplied likelihood. Supporting functions for partition estimation and plotting are also provided.
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), John Wiley and Sons.
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>).
An algorithm for identifying high-resolution driver elements for datasets from a high-definition reporter assay (HiDRA) library. Xinchen Wang, Liang He, Sarah Goggin, Alham Saadat, Li Wang, Melina Claussnitzer, Manolis Kellis (2017) <doi:10.1101/193136>.
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.
Contains all the formulae of the growth and trace element uptake model described in the equally-named
    Geoscientific Model Development paper (de Winter, 2017, <doi:10.5194/gmd-2017-137>). The model takes as input a file with X- and Y-coordinates of digitized
    growth increments recognized on a longitudinal cross section through the bivalve shell, as well as a BMP file of an elemental map of the
    cross section surface with chemically distinct phases separated by phase analysis. It proceeds by a step-by-step process described in
    the paper, by which digitized growth increments are used to calculate changes in shell height, shell thickness, shell volume, shell mass
    and shell growth rate through the bivalve's life time. Then, results of this growth modelling are combined with the trace element mapping
    results to trace the incorporation of trace elements into the bivalve shell. Results of various modelling parameters can be exported in
    the form of XLSX files.
Makes it incredibly easy to build interactive web
    applications with R. Automatic "reactive" binding between inputs and
    outputs and extensive prebuilt widgets make it possible to build
    beautiful, responsive, and powerful applications with minimal effort.
Creating a great user interface for your Shiny apps
    can be a hassle, especially if you want to work purely in R
    and don't want to use, for instance HTML templates. This
    package adds support for a powerful UI library Semantic UI -
    <http://semantic-ui.com/>. It also supports universal UI input 
    binding that works with various DOM elements.
Ace editor bindings to enable a rich text editing environment
    within Shiny.
Make R data available in Web-based virtual reality experiences
  for immersive, cross-platform data visualizations. Includes the 'gg-aframe'
  JavaScript package for a Grammar of Graphics declarative HTML syntax to create
  3-dimensional data visualizations with 'Mozilla A-Frame' <https://aframe.io>.
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 a lightweight Shiny wrapper for the css-loaders created by Luke Hass <https://github.com/lukehaas/css-loaders>. Wrapping a Shiny output will automatically show a loader when the output is (re)calculating.
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.
Access functionality of the 'heatmaply' package through 'Shiny UI'.
Standard imaging operations, e.g. crop and contrast
  adjustment, but with ability to go back and forth through sequence of
  changes, with records being persistent.  Optional Shiny interface.
  Useful to help with the research reproducibility problem, and as a
  teaching tool.
Interactive shiny application for analysis of educational tests and
    their items.
An extension to shiny that brings interactions and animation effects from
    'jQuery UI' library.
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.
An interactive Shiny application to perform fast parameter inference on 
    dynamical systems (described by ordinary differential equations) using gradient matching. 
    Please see the project page for more details.
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.
Allows shiny developers to incorporate UI elements based on Google's Material design. See <https://material.io/guidelines/> for more information.
Shiny wrappers for the RGL package. This package exposes RGL's
    ability to export WebGL visualization in a shiny-friendly format.
Provides function shinyShortcut() that, 
    when given the base directory of a shiny application, will produce an
    executable file that runs the shiny app directly in the user's
    default browser. Tested on both windows and unix machines. Inspired
    by and borrowing from 
    <http://www.mango-solutions.com/wp/2017/03/shiny-based-tablet-or-desktop-app/>.
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 the 'Shiny' web
    application framework from 'RStudio' 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.
Some custom inputs widgets to use in Shiny applications, like a toggle switch to replace checkboxes. And other components to pimp your apps.
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.
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)
Performs automatic creation of short forms of scales with an 
    ant colony optimization algorithm. As implemented in the package, 
    the algorithm randomly selects items to build a model of a specified length, 
    then updates the probability of item selection according to the fit of 
    the best model within each set of searches. The algorithm continues until 
    the same items are selected by multiple ants a given number of times in a row. 
    See Leite, Huang, & Marcoulides (2008) <doi:10.1080/00273170802285743> for
    an applied example.
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 can be used by the 'showtext' package.
Functions for converting network data from a
        SpatialLinesDataFrame object to an '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.
Provides models to identify bimodally expressed genes from
 RNAseq data based on the Bimodality Index. SIBERG models the  RNAseq data in
 the finite mixture modeling framework and incorporates mechanisms  for
 dealing with RNAseq normalization. Three types of mixture models are
 implemented,  namely, the mixture of log normal, negative binomial, or 
 generalized poisson distribution. See Tong et al. (2013)
 <doi:10.1093/bioinformatics/bts713>.
Aims to quantify time intensity data by using sigmoidal and double sigmoidal curves. It fits straight lines, sigmoidal, and double sigmoidal curves on to time vs intensity data. Then all the fits are used to make decision on which model (sigmoidal, double sigmoidal, no signal or ambiguous) best describes the data. No signal means the intensity does not reach a high enough point or does not change at all over time. Sigmoidal means intensity starts from a small number than climbs to a maximum. Double sigmoidal means intensity starts from a small number, climbs to a maximum then starts to decay. After the decision between those four options, the algorithm gives the sigmoidal (or double sigmoidal) associated parameter values that quantifies the time intensity curve. The origin of the package name came from "SIngle CEll Growth Analysis in R".
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.
Allows the user to connect with IBGE's (Instituto Brasileiro de 
    Geografia e Estatistica, see <http://www.ibge.gov.br/> for more information)
    SIDRA API in a flexible way. SIDRA is the acronym to "Sistema IBGE de 
    Recuperacao Automatica" and is the system where IBGE turns available 
    aggregate data from their researches.
Methods for regression with high-dimensional predictors and  univariate or maltivariate response variables. It considers the decomposition of the coefficient matrix that leads to the best approximation to the signal part in the response given any rank, and estimates the decomposition by solving a penalized generalized eigenvalue problem followed by a least squares procedure. Ruiyan Luo and Xin Qi (2017) <doi:10.1016/j.jmva.2016.09.005>.
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.
Interfaces with the 'SigOpt' API. More info at <https://sigopt.com>.
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.
Provides gene signature quality control metrics. Namely, enables the visualization of properties such as expression, variability, correlation, and comparison of methods of standardisation and scoring metrics.
Succinctly and correctly format statistical summaries of
    various models and tests (F-test, Chi-Sq-test, Fisher-test, T-test, and rank-significance).  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).
Method to estimate the spatial influence scales of landscape variables on a response variable. The method is based on Chandler and Hepinstall-Cymerman (2016) Estimating the spatial scales of landscape effects on abundance, Landscape ecology, 31: 1383-1394, <doi:10.1007/s10980-016-0380-z>.
Provides a general framework to perform statistical inference of each gene pair 
        and global inference of whole-scale gene pairs in gene networks using the well known 
        Gaussian graphical model (GGM) in a time-efficient manner. We focus on the high-dimensional 
        settings where p (the number of genes) is allowed to be far larger than n (the number of subjects). 
        Four main approaches are supported in this package: (1) the bivariate nodewise scaled Lasso 
        (Ren et al (2015) <doi:10.1214/14-AOS1286>) (2) the de-sparsified nodewise scaled Lasso 
        (Jankova and van de Geer (2017) <doi:10.1007/s11749-016-0503-5>) (3) the de-sparsified 
        graphical Lasso (Jankova and van de Geer (2015) <doi:10.1214/15-EJS1031>) (4) the GGM 
        estimation with false discovery rate control (FDR) using scaled Lasso or Lasso 
        (Liu (2013) <doi:10.1214/13-AOS1169>). Windows users should install 'Rtools' before the 
        installation of this package.
A package for symbolic and numerical computations on scalar and multivariate systems of stochastic differential equations. It provides users with a wide range of tools to simulate, estimate, analyze, and visualize the dynamics of these systems in both forms Itô and Stratonovich. Statistical analysis with Parallel Monte-Carlo and moment equations methods 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, e.g., modeling and simulate of first passage time problem in shallow water using the attractive center (Boukhetala K, 1996).
The definition of fuzzy random variable and the methods of simulation from fuzzy random variables are two challenging statistical problems in three recent decades. This package is organized based on a special definition of fuzzy random variable and simulate fuzzy random variable by Piecewise Linear Fuzzy Numbers (PLFNs); see Coroianua et al. (2013) <doi:10.1016/j.fss.2013.02.005> for details about PLFNs. Some important statistical functions are considered for obtaining the membership function of main statistics, such as mean, variance, summation, standard deviation and coefficient of variance. Some of applied advantages of 'Sim.PLFN' package are:  (1) Easily generating / simulation a random sample of PLFN, (2) drawing the membership functions of the simulated PLFNs or the membership function of the statistical result, and (3) Considering the simulated PLFNs for arithmetic operation or importing into some statistical computation. Finally, it must be mentioned that 'Sim.PLFN' package works on the basis of 'FuzzyNumbers' package.
Generates realistic simulated genetic data in families or unrelated individuals.
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.
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).
Provides a framework to generating random variates from
             arbitrary multivariate copulae, while concentrating on
             (bivariate) extreme value copulae.  Particularly useful if
             the multivariate copulae are not available in closed
             form.
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.
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.
Contains various functions to be used for simulation education, 
    including simple Monte Carlo simulation functions, queueing simulation
    functions, variate generation functions capable of producing independent
    streams and antithetic variates, functions for illustrating random variate
    generation for various discrete and continuous distributions, and functions
    to compute time-persistent statistics.  Also contains two queueing data sets
    (one fabricated, one real-world) to facilitate input modeling.
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.
Easily simulates regression models,
    including both simple regression and generalized linear mixed
    models with up to three level of nesting. Power simulations that are
    flexible allowing the specification of missing data, unbalanced designs,
    and different random error distributions are built into the package.
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. It is designed as a generic yet powerful framework. The
    architecture encloses a robust and fast simulation core written in C++ with
    automatic monitoring capabilities. It provides a rich and flexible R API that
    revolves around 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.
Simulates somatic single base substitutions carried in cancer genomes. By only providing a human reference genome, substitutions that result from mutational processes operative in every cancer genome can be generated.
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.
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.
Provides intuitive functions for caching R objects, encouraging
    reproducible, restartable, and distributed R analysis. The user selects a
    location to store caches, and then provides  nothing more than a cache name
    and instructions (R code) for how to produce the R object. Also
    provides some advanced options like environment assignments, recreating or
    reloading caches, and cluster compute bindings (using the 'batchtools'
    package) making it flexible enough for use in large-scale data analysis
    projects.
Simple classic graph algorithms for simple graph classes.
    Graphs may possess vertex and edge attributes. 'simplegraph' has
    so dependencies and it is written entirely in R, so it is easy to
    install.
Trains neural networks (multilayer perceptrons with one hidden layer) for bi- or multi-class classification.
Simple result caching in R based on R.cache. The global environment is not 
    considered when caching results simplifying moving files between multiple instances 
    of R. Relies on more base functions than R.cache (e.g. cached results are saved using 
    saveRDS() and readRDS()).
When working across multiple machines and, similarly for
    reproducible research, it can be time consuming to ensure that you have
    all of the needed packages installed and loaded and that the correct working
    directory is set. 'simpleSetup' provides simple functions for making these
    tasks more straightforward.
SimpleTable provides a series of methods to conduct
        Bayesian inference and sensitivity analysis for causal effects
        from 2 x 2 and 2 x 2 x K tables when unmeasured confounding is
        present or suspected.
Simplex density, distribution, quantile functions as well as random variable
   	generation of the simplex distribution are given. Regression analysis of proportional data
   	using various kinds of simplex models is available. In addition, GEE method can be applied 
	to longitudinal data to model the correlation. Residual analysis is also involved. Some 
	subroutines are written in C with GNU Scientific Library (GSL) so as to facilitate the 
	computation.
Provides methods to integrate functions over m-dimensional simplices
 in n-dimensional Euclidean space.  There are exact methods for polynomials and
 adaptive methods for integrating an arbitrary function.  Dirichlet probabilities
 are calculated in certain cases.
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.
    Described in Green and MacLeod, 2016 <doi:10.1111/2041-210X.12504>.
Provides a number of functions to simulate restriction enzyme digestion, library construction and fragments size selection to predict the number of loci expected from most of the Restriction site Associated DNA (RAD) and Genotyping By Sequencing (GBS) approaches. SimRAD estimates the number of loci expected from a particular genome depending on the protocol type and parameters allowing to assess feasibility, multiplexing capacity and the amount of sequencing required.
Functions for performing Bayesian similarity regression,
    and evaluating the probability of association between sets of ontological terms
    and binary response vector. A random model is compared with one in which
    the log odds of a true response is linked to the semantic similarity
    between terms and a latent characteristic ontological profile.
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 spatially-explicit individual-based multistate movements in rivers,
  heterogeneous and homogeneous spaces. This is done by incorporating landscape bias on local behaviour, based on
  resistance rasters. Although originally conceived and designed to simulate trajectories of species constrained to
  linear habitats/dendritic ecological networks (e.g. river networks), the simulation algorithm is built to be
  highly flexible and can be applied to any (aquatic, semi-aquatic or terrestrial) organism, independently on the
  landscape in which it moves. Thus, the user will be able to use the package to simulate movements either in
  homogeneous landscapes, heterogeneous landscapes (e.g. semi-aquatic animal moving mainly along rivers but also using
  the matrix), or even in highly contrasted landscapes (e.g. fish in a river network). The algorithm and its input
  parameters are the same for all cases, so that results are comparable. Simulated trajectories can then be used as
  mechanistic null models (Potts & Lewis 2014, <DOI:10.1098/rspb.2014.0231>) to test a variety of 'Movement Ecology'
  hypotheses (Nathan et al. 2008, <DOI:10.1073/pnas.0800375105>), including landscape effects (e.g. resources, 
  infrastructures) on animal movement and species site fidelity, or for predictive purposes (e.g. road mortality risk,
  dispersal/connectivity). The package should be relevant to explore a broad spectrum of ecological phenomena, such as
  those at the interface of animal behaviour, management, landscape and movement ecology, disease and invasive species
  spread, and population dynamics.
Routines to simulate and manipulate pedigrees ascertained to
    contain multiple family members affected by a rare disease. 
    Christina Nieuwoudt, Samantha J Jones, Angela Brooks-Wilson, 
    and Jinko Graham (2017) <doi:10.1101/234153>.
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.
Simulate survival times from standard parametric survival 
    distributions (exponential, Weibull, Gompertz), 2-component mixture 
    distributions, or a user-defined hazard or log hazard function. Baseline 
    covariates can be included under a proportional hazards assumption. Time 
    dependent effects (i.e. non-proportional hazards) can be included by 
    interacting covariates with linear time or some transformation of time.
    The 2-component mixture distributions can allow for a variety of flexible
    baseline hazard functions. If the user wishes to provide a user-defined 
    hazard or log hazard function then this is also possible, and the resulting
    cumulative hazard function does not need to have a closed-form solution. 
    Note that this package is modelled on the 'survsim' package available in 
    the 'Stata' software (see Crowther and Lambert (2012) 
    <http://www.stata-journal.com/sjpdf.html?articlenum=st0275> or 
    Crowther and Lambert (2013) <doi:10.1002/sim.5823>).
Handles datetimes as integers for the usage inside
        Discrete-Event Simulations (DES). The conversion is made using
        the internally generic function as.numeric() of the base
        package. DES is described in Simulation Modeling and Analysis
        by Averill Law and David Kelton (1999) <doi:10.2307/2288169>.
Flexibly simulates a dataset with time-varying covariates with user-specified exchangeable correlation structures across and within clusters. Covariates can be normal or binary and can be static within a cluster or time-varying. Time-varying normal variables can optionally have linear trajectories within each cluster. See ?make_one_dataset for the main wrapper function. See Montez-Rath et al. <arXiv:1709.10074> for methodological details.
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 is an R implementation of a constrained l1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models (SIMULE). The SIMULE algorithm can be used to estimate multiple related precision matrices. For instance, it can identify context-specific gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogenous data sets, this tool can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(simuleDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Ritambhara Singh, Yanjun Qi (2017) <DOI:10.1007/s10994-017-5635-7>.
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.