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>.
It provides ensemble capabilities to supervised and unsupervised learning models predictions without using training labels. It decides the relative weights of the different models predictions by using best models predictions as response variable and rest of the mo. User can decide the best model, therefore, It provides freedom to user to ensemble models based on their design solutions.
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/>) and PSN (Perl-speaks-NONMEM, <https://uupharmacometrics.github.io/PsN/>) output files to extract parameter estimates, provide visual predictive check (VPC) and goodness of fit (GOF) plots, 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'.
Minimize a differentiable function subject to all the variables being non-negative (i.e. >= 0),
  using a Conjugate-Gradient algorithm based on a modified Polak-Ribiere-Polyak formula as described in
  (Li, Can, 2013, <https://www.hindawi.com/journals/jam/2013/986317/abs/>).
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.
Implements calculation of probability density function, cumulative distribution function, equicoordinate quantile function and survival function, and random numbers generation for the following multivariate distributions: Lomax (Pareto Type II), generalized Lomax, Mardia’s Pareto of Type I, Logistic, Burr, Cook-Johnson’s uniform, F and Inverted Beta. See Tapan Nayak (1987) <doi:10.2307/3214068>.
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.
Different inference procedures are proposed in the literature to correct for selection bias that might be introduced with non-random selection mechanisms. A class of methods to correct for selection bias is to apply a statistical model to predict the units not in the sample (super-population modeling). Other studies use calibration or Statistical Matching (statistically match nonprobability and probability samples). To date, the more relevant methods are weighting by Propensity Score Adjustment (PSA).
    The Propensity Score Adjustment method was originally developed to construct weights by estimating response probabilities and using them in Horvitz–Thompson type estimators. This method is usually  used by combining a non-probability sample with a reference sample to construct propensity models for the non-probability sample. Calibration can be used in a posterior way to adding  information of auxiliary variables.
    Propensity scores in PSA are usually estimated using logistic regression models. Machine learning classification algorithms can be used as alternatives for logistic regression as a technique to estimate propensities.
    The package 'NonProbEst' implements some of these methods and thus provides a wide options to work with data coming from a non-probabilistic sample.
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 (i.e. Gaussian) 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. Fitting to data by efficient ML (Maximum
   Likelihood) or traditional EM estimation.
Provides the Arctic Ice Studio's Nord and Group of Seven inspired colour palettes.
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.
Implements Bayesian linear regression, variable selection, normal means estimation, and multiple hypothesis testing using the normal-beta prime prior, as introduced by Bai and Ghosh (2019) <arXiv:1807.02421> and Bai and Ghosh (2019) <arXiv:1807.06539>. Normal means estimation and multiple testing for the Dirichlet-Laplace <doi:10.1080/01621459.2014.960967> and horseshoe+ priors <doi:10.1214/16-BA1028> are also available in this package.
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>).
Functions for the normal Laplace distribution. The package is
        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.
It provides a framework and a fast and simple way for researchers to evaluate methods (particularly some data-driven methods or their own methods) and then select a best one for data normalization in the gene expression analysis, based on the consistency of metrics and the consistency of datasets.
             Zhenfeng Wu, Weixiang Liu, Xiufeng Jin, Deshui Yu, Hua Wang, Gustavo Glusman, Max Robinson, Lin Liu, Jishou Ruan and Shan Gao (2018) <doi:10.1101/251140>.
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. See Philipps et al (2014) <doi:10.1159/000365637> for details.
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".
Utility functions for the lovely 'ICNDB' API, <http://www.icndb.com/api/>, which allows users to retrieve Chuck Norris jokes either randomly or by joke number.
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, see  R. Baranowski, Y. Chen and P. Fryzlewicz (2019) <doi:10.1111/rssb.12322>. 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.
When building a 'shiny' app to generate reports (pdf or 'word'), we can insert a comment box in front-end side for user to write down them notes and use this package to document those notes in reports.
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!)
It contains the tools to implement dynamic factor models to forecast economic variables. The user will be able to construct pseudo real time vintages, use information criteria for determining the number of factors and shocks, estimate the model, and visualize results among other things.
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. See Konietschke et al. (2015) <doi:10.18637/jss.v064.i09> for details.
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.
Analysis of multivariate data with two-way completely randomized
    factorial design. The analysis is based on fully nonparametric, rank-based
    methods and uses test statistics based on the Dempster's ANOVA, Wilk's Lambda,
    Lawley-Hotelling and Bartlett-Nanda-Pillai criteria. The multivariate
    response is allowed to be ordinal, quantitative, binary or a mixture of the
    different variable types. The package offers two functions performing the
    analysis, one for small and the other for large sample sizes. The underlying methodology
    is largely described in Bathke and Harrar (2016) <doi:10.1007/978-3-319-39065-9_7> and in 
    Munzel and Brunner (2000) <doi:10.1016/S0378-3758(99)00212-8>.
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, and create multiply imputed synthesis of categorical data, with or without structural zeros. Imputations and syntheses 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. The full details, justification and examples are published in Rohmer (2016) <doi:10.1016/j.spl.2016.06.026>.
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.
Performs nonparametric estimation in mixture cure models, and significance tests for the cure probability. For details, see López-Cheda et al. (2017a) <doi:10.1016/j.csda.2016.08.002> and López-Cheda et al. (2017b) <doi:10.1007/s11749-016-0515-1>.
Provides several novel exact hypothesis tests with minimal assumptions on the errors. The tests are exact, meaning that their p-values are correct for the given sample sizes (the p-values are not derived from asymptotic analysis). The test for stochastic inequality is for ordinal comparisons based on two independent samples and requires no assumptions on the errors. The other tests include tests for the mean and variance of a single sample and comparing means in independent samples. All these tests only require that the data has known bounds (such as percentages that lie in [0,100]. These bounds are part of the input.
Dirichlet process mixture of multivariate normal, skew normal or skew t-distributions
             modeling oriented towards flow-cytometry data preprocessing applications.
Piecewise constant hazard functions are used to flexibly model survival distributions with non-proportional hazards and 
	to simulate data from the specified distributions. Also, a function to calculate weighted log-rank tests for the 
	comparison of two hazard functions is included. Finally, a function to calculate a test using the maximum of a set of 
	test statistics from weighted log-rank tests is provided. This test utilizes the asymptotic multivariate normal joint distribution
	of the separate test statistics. The correlation is estimated from the data.
Provides functions and examples for histogram, kernel (classical, variable bandwidth and transformations based), discrete and semiparametric hazard rate estimators.
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.
Support the book: Wu CO and Tian X (2018). Nonparametric Models for Longitudinal Data. 
             Chapman & Hall/CRC (to appear); and provide fit for using global and local smoothing methods
             for the conditional-mean and conditional-distribution based models with longitudinal Data.
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).
To compute the non-parametric maximum likelihood estimates (NPMLEs) and penalized NPMLEs with SCAD, HARD and LASSO penalties for nested case-control or case-cohort sampling design with time matching under Cox's regression model. It also proposes the standard error formula for estimator using observed profile likelihood. For details about (penalized) NPNLEs see the original paper "Penalized Full Likelihood Approach to Variable Selection for Cox's Regression Model under Nested Case-Control Sampling" by Wang et al. (2019) <doi:10.1007/s10985-019-09475-z>.
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.
Fit multinomial logistic regression with a penalty on the nuclear
    norm of the estimated regression coefficient matrix, using proximal
    gradient descent. Reference: Powers S, Hastie T and Tibshirani R
    (2018) <doi:10.1177/1471082X18777669>.
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
Performs nonparametric tests for equality of location against ordered alternatives.
Posterior sampling in several commonly used distributions using
    normalized power prior as described in 
    Duan, Ye and Smith (2006) <doi:10.1002/env.752> and 
    Ibrahim et.al. (2015) <doi:10.1002/sim.6728>. 
    Sampling of the power parameter is achieved via 
    either independence Metropolis-Hastings or random walk Metropolis-Hastings 
    based on transformation.
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.
Multiple and generalized nonparametric regression using smoothing spline ANOVA models and generalized additive models. Includes support for Gaussian and non-Gaussian responses, smoothers for multiple types of predictors, interactions between smoothers of mixed types, and eight different methods for smoothing parameter selection.
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 (2018): 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.
Implements several nonparametric regression approaches for the inclusion of covariate information on the receiver operating characteristic (ROC) framework.
Fits sphere-sphere regression models by estimating locally weighted rotations. 
    Simulation of sphere-sphere data according to non-rigid rotation models.
    Provides methods for bias reduction applying iterative 
    procedures within a Newton-Raphson learning scheme. 
    Cross-validation is exploited to select smoothing parameters.
    See Marco Di Marzio, Agnese Panzera & Charles C. Taylor (2018)
    <doi:10.1080/01621459.2017.1421542>.
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 simultaneous inference on both spatial trend
    and variogram functions (for spatial processes).
    Nonparametric residual kriging (spatial prediction).
An R implementation of the Necessary and Probably Sufficient (NPS) test for finding valid instrumental variables, as suggested by Amit Sharma (2016, Working Paper) <http://amitsharma.in/pubs/necessary_probably_sufficient_iv_test.pdf>. The NPS test, compares the likelihood that a given set of observational data of the three variables Z, X and Y is generated by a valid instrumental variable model (Z -> X -> Y) to the likelihood that the data is generated by an invalid IV model.
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.
Robust permutation tests for location, correlation, and regression problems, as described in Helwig (2019) <doi:10.1002/wics.1457>. Univariate and multivariate tests are supported. For each problem, exact tests and Monte Carlo approximations are available. Parallel computing is implemented via the 'parallel' package.
Calculating the net reclassification improvement (NRI) for risk prediction models with time to event and binary data.
Access the NS api and download current departure times, 
    disruptions and engineering work, the station list, and
    travel recommendations from station to station. All results will be 
    returned as a 'data.frame'. 
    NS (Nederlandse Spoorwegen; Dutch Railways) is the largest train travel
    provider in the Netherlands. for more information about the API itself
    see <https://www.ns.nl/en/travel-information/ns-api>.
    To use the API, and this package, you will need to obtain a username
    and password. More information about authentication and the use of the functions
    are described in the vignette.
Two implementations of canonical correlation analysis
        (CCA) that are based on iterated regression. By choosing the
        appropriate regression algorithm for each data domain, 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. See
        <http://sigg-iten.ch/learningbits/2014/01/20/canonical-correlation-analysis-under-constraints/>
        and Sigg et al. (2007) <doi:10.1109/MLSP.2007.4414315> for more
        details.
Simulation and estimation for Neyman-Scott spatial cluster point
 process models and their extensions, based on the methodology in Tanaka, Ogata,
 and Stoyan (2008) <doi:10.1002/bimj.200610339>. To estimate 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. (2018) <doi:10.2139/ssrn.2741587> and Ardia and Bluteau (2017) <doi:10.21105/joss.00172>.
Facilities to capture, inspect, manipulate, and create
             lazy values (promises), "..." lists, and active calls.
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.
An adaptation of Non-dominated Sorting Genetic Algorithm III for multi objective 
    feature selection tasks. Non-dominated Sorting Genetic Algorithm III is a genetic algorithm
    that solves multiple optimization problems simultaneously by applying a non-dominated sorting 
    technique. It uses a reference points based selection operator to explore 
    solution space and preserve diversity. See the original paper by K. Deb and 
    H. Jain (2014) <DOI:10.1109/TEVC.2013.2281534> for a detailed description.
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.
National Statistical Office of Mongolia (NSO) is the national statistical service and an organization of Mongolian government. NSO provides open access and official data via its web site <http://www.1212.mn/> and API <http://opendata.1212.mn/en/doc>. The package NSO1212 has functions for accessing the API service. The functions are compatible with the API v2.0 and get data sets and its detailed informations from the API.
Contains functions for maximum likelihood estimation of
	     nonparametric and semiparametric mixture models.
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'. See
        <https://sigg-iten.ch/learningbits/2013/05/27/nsprcomp-is-on-cran/>
        and Sigg et al. (2008) <doi:10.1145/1390156.1390277> for more
        details.
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.
             Most of the methods are those described in the Flood Estimation Handbook 
            (Centre for Ecology & Hydrology, 1999, ISBN:9781906698003).
             Homogeneity tests from Hosking and Wallis (1993) <doi:10.1029/92WR01980> 
             and Viglione et al. (2007) <doi:10.1029/2006WR005095> are available.
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.
Allows users to access data from the National Sleep
    Research Resource ('NSRR') <https://sleepdata.org/>.
To estimate ecological stochasticity in community assembly. Understanding the community assembly mechanisms controlling biodiversity patterns is a central issue in ecology. Although it is generally accepted that both deterministic and stochastic processes play important roles in community assembly, quantifying their relative importance is challenging. The new index, normalized stochasticity ratio (NST), is to estimate ecological stochasticity, i.e. relative importance of stochastic processes, in community assembly. With functions in this package, NST can be calculated based on different similarity metrics and/or different null model algorithms, as well as some previous indexes, e.g. previous Stochasticity Ratio (ST), Standard Effect Size (SES), modified Raup-Crick metrics (RC). Functions for permutational test and bootstrapping analysis are also included. Previous ST is published by Zhou et al (2014) <doi:10.1073/pnas.1324044111>. NST is modified from ST by considering two alternative situations and normalizing the index to range from 0 to 1 which will be published soon (Ning et al 2019). A modified version, MST, is a special case of NST, used in some recent or upcoming publications, e.g. Liang et al (2019) <doi:10.1101/638908>. SES is calculated as described in Kraft et al (2011) <doi:10.1126/science.1208584>. RC is calculated as reported by Chase et al (2011) <doi:10.1890/es10-00117.1> and Stegen et al (2013) <doi:10.1038/ismej.2013.93>.
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.
Simulation, estimation, prediction procedure, and model identification methods for nonlinear time series analysis, including threshold autoregressive models, Markov-switching models, convolutional functional autoregressive models, nonlinearity tests, Kalman filters and various sequential Monte Carlo methods. More examples and details about this package can be found in the book "Nonlinear Time Series Analysis" by Ruey S. Tsay and Rong Chen, Wiley, 2018 (ISBN: 978-1-119-26407-1).
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 logarithm and
    inverses, extended GCD, Farey series and continuous fractions.
    Includes Legendre and Jacobi symbols, some divisor functions,
    Euler's Phi function, etc.
Validate, format and compare identification numbers used in Brazil.
  These numbers are used to identify individuals (CPF), vehicles (RENAVAN),
  companies (CNPJ) and etc.
  Functions to format, validate and compare these numbers have been 
  implemented in a vectorized way in order to speed up
  validations and comparisons in big datasets.
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, please see Gao S, Mutter S, Casey A, Makinen V-P (2018) Numero: a statistical framework to define multivariable subgroups in complex population-based datasets, Int J Epidemiology, dyy113, <doi:10.1093/ije/dyy113>. The framework includes the necessary functions to construct a self-organizing map of the data, to evaluate the statistical significance of the observed data patterns, and to visualize the results.
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.
Provides a tool set for food information and dietary assessment. It uses food composition data from several reference databases, including: 'USDA' (United States), 'CIQUAL' (France), and 'BEDCA' (Spain). 'NutrienTrackeR' calculates the intake levels for both macronutrient and micronutrients, and compares them with the recommended dietary allowances (RDA). It includes a number of visualization tools, such as time series plots of nutrient intake, and pie-charts showing the main foods contributing to the intake level of a given nutrient.
Statistical tool for learning the structure of direct associations among variables for 
			 continuous data, discrete data and mixed discrete-continuous data. The package is based
			 on the copula graphical model in Behrouzi and Wit (2017) <doi:10.1111/rssc.12287>.
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
The n-vector framework uses the normal vector to the Earth ellipsoid
  (called n-vector) as a non-singular position representation that turns out to
  be very convenient for practical position calculations.
  The n-vector is simple to use and gives exact answers for all global positions,
  and all distances, for both ellipsoidal and spherical Earth models.
  This package is a translation of the 'Matlab' library from FFI,
  the Norwegian Defence Research Establishment, as described in Gade (2010)
  <doi:10.1017/S0373463309990415>.
Provides an interface for the nVenn algorithm
    (Perez-Silva et al. 2018) <DOI:10.1093/bioinformatics/bty109>. This algorithm works for any number of sets, 
    and usually yields pleasing and informative Venn diagrams with proportionality information. 
    However, representing more than six sets takes a long time and is hard to interpret, unless many of the 
    regions are empty. If you cannot make sense of the result, you may want to consider 'UpSetR'
    <https://cran.r-project.org/package=UpSetR/README.html>.
Functions for working with multivariate normal variance mixture
  distributions including evaluating their distribution functions, densities
  and random number generation.
Provides coordination and parallel execution facilities,
        as well as limited cross-language data exchange, using the
        netWorkSpaces server developed by REvolution Computing
Airline on-time data for all flights departing NYC in 2013.
    Also includes useful 'metadata' on airlines, airports, weather, and planes.
Convenient access to New Zealand election
    results by voting place.  Voting places have been matched to Regional Council,
    Territorial Authority, and Area Unit, to facilitate matching with additional data.
    Opinion polls since 2002 and some convenience analytical function are also supplied.
'LaBB-CAT' is a web-based language corpus management
 system developed by the New Zealand Institute of Language, Brain
 and Behaviour (NZILBB) - see <https://labbcat.canterbury.ac.nz>.
 This package defines functions for accessing corpus data in a 'LaBB-CAT'
 instance. You must have at least version 20190425.1121 of 'LaBB-CAT'
 to use this package.
 For more information about 'LaBB-CAT', see
 Robert Fromont and Jennifer Hay (2008) <doi:10.3366/E1749503208000142>
 or 
 Robert Fromont (2017) <doi:10.1016/j.csl.2017.01.004>.
Datasets of driving offences and fines in New Zealand between 2009 and 2017.
    Originally published by the New Zealand Police at
    <http://www.police.govt.nz/about-us/publication/road-policing-driver-offence-data-january-2009-december-2017>.
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>).
Functions and classes to create and manipulate trees and nodes.
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.
Trains and makes predictions from the OASIS method, described in
    detail in the paper "OASIS is Automated Statistical Inference for Segmentation,
    with applications to multiple sclerosis lesion segmentation in MRI" 
    <doi:10.1016/j.nicl.2013.03.002>. 
    OASIS is a method for multiple sclerosis (MS)
    lesion segmentation on structural magnetic resonance image (MRI) studies. OASIS
    creates probability maps of lesion presence using the FLAIR, T2, T1, and PD
    structural MRI volumes. This packages allows for training of the OASIS model
    and prediction of OASIS probability maps from a trained model with user supplied
    studies that have a gold standard lesion segmentation masks. The package will
    also create OASIS probability maps for MRI studies using the OASIS model from
    the OASIS paper if no gold standard lesion segmentation masks are available.
A set of indexes and tests for the analysis of social segregation.
An implementation of the Blinder-Oaxaca decomposition for linear regression models.
Data processing, visualisation and analysis of Limit Order Book
    event data.
When people make decisions, they may do so using a wide variety of decision rules. The package allows users to easily create obfuscation games to test the obfuscation hypothesis. It provides an easy to use interface and multiple options designed to vary the difficulty of the game and tailor it to the user's needs.
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.
An 'RStudio' addin to assist with removing objects from the global environment. Features include removing objects according to name patterns and object type. During the course of an analysis, temporary objects are often created and this tool assists with removing them quickly. This can be useful when memory management within 'R' is important.
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.
Oblique random survival forests incorporate linear combinations of input variables into random survival forests (Ishwaran, 2008 <DOI:10.1214/08-AOAS169>). Regularized Cox proportional hazard models (Simon, 2016 <DOI:10.18637/jss.v039.i05>) are used to identify optimal linear combinations of input variables.
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.
Two-part system for first collecting then managing direct
    observation data, as described by Hibbing PR, Ellingson LD,
    Dixon PM, & Welk GJ (2018) <doi:10.1249/MSS.0000000000001486>.
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>.
Generic function for estimating positron emission tomography (PET) neuroreceptor occupancies from the total volumes of distribution of a set of regions of interest. Fittings methods include the simple 'reference region' and 'ordinary least squares' (sometimes known as occupancy plot) methods, as well as the more efficient 'restricted maximum likelihood estimation'.
Supports the analysis of Oceanographic data, including 'ADCP'
    measurements, measurements made with 'argo' floats, 'CTD' measurements,
    sectional data, sea-level time series, coastline and topographic data, etc.
    Provides specialized functions for calculating seawater properties such as
    potential temperature in either the 'UNESCO' or 'TEOS-10' equation of state.
    Produces graphical displays that conform to the conventions of the
    Oceanographic literature. This package is discussed extensively in
    Dan Kelley's book Oceanographic Analysis with R, published
    in 2018 by 'Springer-Verlag' with ISBN 978-1-4939-8842-6.
Creating maps for statistical analysis such as proportional circles, chroropleth, typology and flows. Some functions use 'shiny' or 'leaflet' technologies for dynamism and interactivity.
	The great features are :
	- Create maps in a web environment where the parameters are modifiable on the fly ('shiny' and 'leaflet' technology).
	- Create interactive maps through zoom and pop-up ('leaflet' technology).
	- Create frozen maps with the possibility to add labels.
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 data sets are provided. These
    are particularly useful to the 'oce' package, but can also be helpful in a
    general context.
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.
Implements the Bayesian online changepoint
     detection method by Adams and MacKay (2007) 
     <arXiv:0710.3742> for univariate or multivariate data.
     Gaussian and Poisson probability models are implemented.
      Provides post-processing functions with 
      alternative ways to extract changepoints.
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.
Computes odds ratios and 95% confidence intervals from a generalized linear model object. It also computes model significance with the chi-squared statistic and p-value and it computes model fit using a contingency table to determine the percent of observations for which the model correctly predicts the value of the outcome. Calculates model sensitivity and specificity.
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.
Simulates a network of ordinary differential equations of order
    two. The package provides an easy interface to construct networks. In addition
    you are able to define different external triggers to manipulate the trajectory.
    The method is described by Surmann, Ligges, and Weihs (2014) <doi:10.1109/ENERGYCON.2014.6850482>.
Performs sensitivity analysis in ordinary differential equation (ode) models.
    The package utilize the ode interface from 'deSolve' and connects it with the 
    sensitivity analysis from 'sensitivity'. Additionally we add a method to
    run the sensitivity analysis on variables with class 'ODEnetwork'. A detailed
    plotting function provides outputs on the calculations.
    The method is described by Weber, Theers, Surmann, Ligges, and Weihs (2018) <doi:10.17877/DE290R-18874>.
Generate systems of ordinary differential equations
    (ODE) and integrate them, using a domain specific language
    (DSL).  The DSL uses R's syntax, but compiles to C in order to
    efficiently solve the system.  A solver is not provided, but
    instead interfaces to the packages 'deSolve' and 'dde' are
    generated.  With these, while solving the differential equations,
    no allocations are done and the calculations remain entirely in
    compiled code.  Alternatively, a model can be transpiled to R for
    use in contexts where a C compiler is not present.  After
    compilation, models can be inspected to return information about
    parameters and outputs, or intermediate values after calculations.
    'odin' is not targeted at any particular domain and is suitable
    for any system that can be expressed primarily as mathematical
    expressions.  Additional support is provided for working with
    delays (delay differential equations, DDE), using interpolated
    functions during interpolation, and for integrating quantities
    that represent arrays.
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'.
Functions to compute the one-sided dynamic
	principal components ('odpc') introduced in Smucler, Peña and Yohai (2018)
	<DOI:10.1080/01621459.2018.1520117>. '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 the variance of
    treatment effect in multilevel randomized trials under fixed budget and cost structure,
    perform power analyses with and without accommodating costs and budget. The references 
    for proposed methods are: 
    (1) Shen, Z. (in progress). Using optimal sample allocation to
    improve statistical precision and design efficiency for multilevel randomized trials.
    (unpublished doctoral dissertation). University of Cincinnati, Cincinnati, OH.
    (2) Shen, Z., & Kelcey, B. (revise & resubmit). 
    Optimal sample allocation accounts for the full variation of sampling costs 
    in cluster-randomized trials.
    Journal of Educational and Behavioral Statistics. 
    (3) Shen, Z., & Kelcey, B. (2018, April). Optimal design of cluster
    randomized trials under condition- and unit-specific cost structures. Roundtable
    discussion presented at American Educational Research Association (AERA)
    annual conference.
    (4) Champely., S. (2018). pwr: Basic functions for power analysis 
    (Version 1.2-2) [Software]. Available from 
    <https://CRAN.R-project.org/package=pwr>.
Outcome-dependent sampling (ODS) schemes are cost-effective ways to enhance study efficiency. 
    In ODS designs, one observes the exposure/covariates with a probability that depends on the outcome 
    variable. Popular ODS designs include case-control for binary outcome, case-cohort for time-to-event 
    outcome, and continuous outcome ODS design (Zhou et al. 2002) <doi: 10.1111/j.0006-341X.2002.00413.x>. 
    Because ODS data has biased sampling nature, standard statistical analysis such as linear regression 
    will lead to biases estimates of the population parameters. This package implements four statistical 
    methods related to ODS designs: (1) An empirical likelihood method analyzing the primary continuous 
    outcome with respect to exposure variables in continuous ODS design (Zhou et al., 2002). (2) A partial 
    linear model analyzing the primary outcome in continuous ODS design (Zhou, Qin and Longnecker, 2011) 
    <doi: 10.1111/j.1541-0420.2010.01500.x>. (3) Analyze a secondary outcome in continuous ODS design 
    (Pan et al. 2018) <doi: 10.1002/sim.7672>. (4) An estimated likelihood method analyzing a secondary 
    outcome in case-cohort data (Pan et al. 2017) <doi: 10.1111/biom.12838>.
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.
Detect the number and locations of change points. The locations can be either exact or in terms of ranges, 
            depending on the available computational resource. The method is based on Jie Ding, Yu Xiang, Lu Shen, Vahid Tarokh (2016) <arXiv:1605.00346v2>.
Offers a gene-based meta-analysis test with filtering to detect gene-environment interactions (GxE) with association data, proposed by Wang et al. (2018) <doi:10.1002/gepi.22115>. It first conducts a meta-filtering test to filter out unpromising SNPs by combining all samples in the consortia data. It then runs a test of omnibus-filtering-based GxE meta-analysis (ofGEM) that combines the strengths of the fixed- and random-effects meta-analysis with meta-filtering. It can also analyze data from multiple ethnic groups.
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.
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.
Calculate's Tomoka Ohta's partitioning of linkage disequilibrium,
 deemed D-statistics, for pairs of loci. Beissinger et al. (2016) <doi:10.1038/hdy.2015.81>.
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.
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 designed to make it easier for users, particularly beginner/intermediate R users 
    to build ordinary least squares 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 the software article by el Bouhaddani et al (2018) <doi:10.1186/s12859-018-2371-3>, and Trygg and Wold (2003) <doi:10.1002/cem.775>.
Primarily devoted to implementing the Univariate Bootstrap (as well as the Traditional Bootstrap). In addition there are multiple functions for DeFries-Fulker behavioral genetics models. The univariate bootstrapping functions, DeFries-Fulker functions, regression and traditional bootstrapping functions form the original core. Additional features may come online later, however this software is a work in progress. For more information about univariate bootstrapping see: Lee and Rodgers (1998) and Beasley et al (2007) <doi.org/10.1037/1082-989X.12.4.414>.
The separate p-values of SNPs, RNA expressions and DNA
    methylations are calculated by KM regression. The correlation between
    different omics data are taken into account. This method can be applied to
    either samples with all three types of omics data or samples with two types.
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.
Facilitates the creation of intuitive figures to describe metabolomics data by utilizing Kyoto Encyclopedia of Genes and Genomes (KEGG) hierarchy data, and gathers functional orthology and gene data using the package 'KEGGREST' to access the 'KEGG' API.
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.
Measures different aspects of page content, structure and performance for SEO (Search Engine Optimization).
    Aspects covered include HTML tags used in SEO, duplicate and near-duplicate content, structured data, on-site linking structure and popularity transfer, and many other amazing things. 
    This package can be used to generate a real, full SEO audit report, which serves to detect errors or inefficiencies on a page that can be corrected in order to optimise its performance on search engines.
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.
The identity provider ['OneLogin']<http://onelogin.com> is used for 
  authentication via Single Sign On (SSO). This package provides an R interface to their API.
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; one-way analysis of variance (ANOVA), Welch's heteroscedastic F test, Welch's heteroscedastic F test with trimmed means and Winsorized variances, Brown-Forsythe test, Alexander-Govern test, James second order test, Kruskal-Wallis test, Scott-Smith test, Box F test and Johansen F test, Generalized tests equivalent to Parametric Bootstrap and Fiducial tests. The package performs pairwise comparisons and graphical approaches. Also, the package includes Student's t test, Welch's t test and Mann-Whitney U test for two samples. Moreover, it assesses variance homogeneity and normality of data in each group via tests and plots (Dag et al., 2018, <https://journal.r-project.org/archive/2018/RJ-2018-022/RJ-2018-022.pdf>).
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.
Functions for recursive online fitting of time-adaptive lasso vector auto regression. A recursive coordinate descent algorithm is used to estimate sparse vector auto regressive models and exponential forgetting is applied to allow model changes. Details can be found in Jakob W. Messner and Pierre Pinson (2018). "Online adaptive LASSO estimation in Vector Auto Regressive models for wind power forecasting in high dimension". International Journal of Forecasting, in press. <doi:10.1016/j.ijforecast.2018.02.001>.
Orthogonal Nonlinear Least-Squares Regression using Levenberg-Marquardt Minimization.
R Interface to 'ONNX' - Open Neural Network Exchange <https://onnx.ai/>. 
             'ONNX' provides an open source format for machine learning models. 
             It defines an extensible computation graph model, as well as definitions
             of built-in operators and standard data types.
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.
Data integration Web application for biobanks by 'OBiBa'. 'Opal' is
    the core database application for biobanks. Participant data, once
    collected from any data source, must be integrated and stored in a central
    data repository under a uniform model. 'Opal' is such a central repository.
    It can import, process, validate, query, analyze, report, and export data.
    'Opal' is typically used in a research center to analyze the data acquired at
    assessment centres. Its ultimate purpose is to achieve seamless
    data-sharing among biobanks. This 'Opal' client allows to interact with 'Opal'
    web services and to perform operations on the R server side. 'DataSHIELD'
    administration tools are also provided.
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.
Several function related to Experimental Design are implemented here, see
  "Optimal Experimental Design with R" by Rasch D. et. al (ISBN 9781439816974).
'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).
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.
Functions for non-spatial and spatial open-population capture-recapture analysis.
Experimenting with computer vision and machine learning in R. This 
    package exposes some of the available 'OpenCV' vision algorithms, such as edge, 
    body or face detection. These can either be applied to analyze static images,
    or to filter live video footage from a camera device.
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. Now includes an
  implementation of the EM algorithm for hyperparameter estimation loosely
  derived from the 'mederrRank' package.
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, the average_hash(), dhash() and phash() functions are based on the 'ImageHash' python library. The Gabor Feature Extraction functions are based on 'Matlab' code of the paper, "CloudID: Trustworthy cloud-based and cross-enterprise biometric identification" by M. Haghighat, S. Zonouz, M. Abdel-Mottaleb, Expert Systems with Applications, vol. 42, no. 21, pp. 7905-7916, 2015, <doi:10.1016/j.eswa.2015.06.025>. The 'SLIC' and 'SLICO' superpixel algorithms were explained in detail in (i) "SLIC Superpixels Compared to State-of-the-art Superpixel Methods", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, num. 11, p. 2274-2282, May 2012, <doi:10.1109/TPAMI.2012.120> and (ii) "SLIC Superpixels", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, EPFL Technical Report no. 149300, June 2010.
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.
Create structural equation models that can be manipulated programmatically.
    Models may be specified with matrices or paths (LISREL or RAM)
    Example models include confirmatory factor, multiple group, mixture
    distribution, categorical threshold, modern test theory, differential
    Fit functions include full information maximum likelihood, maximum likelihood, and weighted least squares.
    equations, state space, and many others.
	Support and advanced package binaries available at <http://openmx.ssri.psu.edu>.
    The software is described in 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.
Analyze repertory grids, a qualitative-quantitative 
    data collection technique devised by George A. Kelly in the 1950s. Today, grids are used across 
    various domains ranging from clinical psychology to marketing. The package contains
    functions to quantitatively analyze and visualize repertory grid data 
    (see e.g. Bell, 2005, <doi:10.1002/0470013370.ch9>; 
    Fransella, Bell, & Bannister, 2004, ISBN: 978-0-470-09080-0).
Bindings to OpenSSL libssl and libcrypto, plus custom SSH key parsers.
    Supports RSA, DSA and EC curves P-256, P-384, P-521, and curve25519. 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.
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.
A decision support tool for prioritizing conservation projects.
    Prioritizations can be developed by maximizing expected feature richness,
    expected phylogenetic diversity, the number of features that meet
    persistence targets, or identifying a set of projects that meet persistence
    targets for minimal cost. Constraints (e.g. lock in specific actions) and
    feature weights can also be specified to further customize prioritizations.
    After defining a project prioritization problem, solutions can be obtained
    using exact algorithms, heuristic algorithms, or random processes. In
    particular, it is recommended to install the 'Gurobi' optimizer  (available
    from <https://www.gurobi.com>) because it can identify optimal solutions
    very quickly. Finally, methods are provided for comparing different
    prioritizations and evaluating their benefits.
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.
Obtain and evaluate various optimal designs for the 3, 4, and 5-parameter logistic models. The optimal designs are obtained based on the numerical algorithm in Hyun, Wong, Yang (2018) <doi:10.18637/jss.v083.i05>.
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).
Provides a novel density-based cluster extraction method, OPTICS k-Xi, and a framework to compare k-Xi models using distance-based metrics to investigate datasets with unknown number of clusters.
Likelihood based optimal partitioning and indicator
  species analysis. Finding the best binary partition for each species
  based on model selection, with the possibility to take into account
  modifying/confounding variables as described
  in Kemencei et al. (2014) <doi:10.1556/ComEc.15.2014.2.6>.
  The package implements binary and multi-level response models,
  various measures of uncertainty, Lorenz-curve based thresholding,
  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.
Computes 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...
Patterns searching and binary classification in economic and financial data is a
              large field of research. There are a large part of the data
              that the target variable is binary. Nowadays, many methodologies
              are used, this package collects most popular and compare different
              configuration options for Linear Models (LM), Generalized Linear Models (GLM), 
              Linear Mixed Models (LMM), Discriminant Analysis (DA), 
              Classification And Regression Trees (CART), Neural Networks (NN) and Support Vector Machines (SVM).
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.
Calculates autoecological data (optima and tolerance ranges) of a biological species
    given an environmental matrix. The package calculates by weighted averaging, using the number of occurrences to adjust the 
    tolerance assigned to each taxon to estimate optima and tolerance range in cases where taxa 
    have unequal occurrences. See the detailed methodology by Birks et al. (1990) <doi:10.1098/rstb.1990.0062>, and a 
    case example by Potapova and Charles (2003) <doi:10.1046/j.1365-2427.2003.01080.x>.
Provides a parallel version of the L-BFGS-B method of optim(). The main function of the package is optimParallel(), which has the same usage and output as optim(). Using optimParallel() can significantly reduce the optimization time.
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...
Intended to assist in the choice of the sampling strategy to implement in a survey.
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 call to several function minimization codes in R in a single
    statement. These methods handle smooth, possibly box constrained functions 
    of several or many parameters. Note that function 'optimr()' was prepared to
    simplify the incorporation of minimization codes going forward. Also implements some
    utility codes and some extra solvers, including safeguarded Newton methods.
    Many methods previously separate are now included here.
Optimal two and three stage designs monitoring
        time-to-event endpoints at a specified timepoint
Introduces optional types with 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.
Utilizes the Black-Scholes-Merton option pricing model to 
    calculate key option analytics and perform graphical analysis of various option strategies.
    Provides functions to calculate the option premium and option
    greeks of European-style options.
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'.
The popular population genetic software 'Treemix' by
    'Pickrell and Pritchard' (2012) <DOI:10.1371/journal.pgen.1002967>
    estimates the number of migration edges on a population tree.
    However, it can be difficult to determine the number of migration
    edges to include. Previously, it was customary to stop adding migration
    edges when 99.8% of variation in the data was explained, but 'OptM'
    automates this process using an ad hoc statistic based on the
    second-order rate of change in the log likelihood.  'OptM' also
    has added functionality for various threshold modeling
    to compare with the ad hoc statistic.
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.
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>.
Use optimization to estimate weights that balance covariates for binary, multinomial, and continuous treatments in the spirit of Zubizarreta (2015) <doi:10.1080/01621459.2015.1023805>. The degree of balance can be specified for each covariate. In addition, sampling weights can be estimated that allow a sample to generalize to a population specified with given target moments of covariates.
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.
Functions for computing the Value-at-Risk in compound Poisson models. 
	The implementation comprises functions for modeling loss frequencies and loss severities with plain, mixed (Frigessi et al. (2012) <doi:10.1023/A:1024072610684>) or spliced distributions using Maximum Likelihood estimation and Bayesian approaches (Ergashev et al. (2013) <doi:10.21314/JOP.2013.131>). 
	In particular, the parametrization of tail distributions includes the fitting of Tukey-type distributions (Kuo and Headrick (2014) <doi:10.1155/2014/645823>). Furthermore, the package contains the modeling of bivariate dependencies between loss severities and frequencies, Monte Carlo simulation for total loss estimation as well as a closed-form approximation based on Degen (2010) <doi:10.21314/JOP.2010.084> to determine the value-at-risk.
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).
I tend to repeat the same code chunks over and over again. At
    first, this was fine for me and I paid little attention to such redundancies.
    A little later, when I got tired of manually replacing Linux filepaths with
    the referring Windows versions, and vice versa, I started to stuff some very
    frequently used work-steps into functions and, even later, into a proper R
    package. And that's what this package is - a hodgepodge of various R functions
    meant to simplify (my) everyday-life coding work without, at the same time,
    being devoted to a particular scope of application.
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'.
Functions to test/check/verify/investigate the ordering of vectors. 
    The 'is_[strictly_]*' family of functions test vectors for 
    'sorted', 'monotonic', 'increasing', 'decreasing' order; 'is_constant' 
    and 'is_incremental' test for the degree of ordering. `ordering` 
    provides a numeric indication of ordering -2 (strictly decreasing) to 
    2 (strictly increasing).
Set of tools to generate samples of order statistics from new families of distributions. 
    The main references for this package are: Gentle, J. (2009), Computational Statistics, Springer-Verlag 
    and Naradajah, S. and Rocha, R. (2016),<DOI:10.18637/jss.v069.i10>. 
    The families of distributions are: Marshall Olkin G distributions, exponentiated G distributions, beta G distributions, gamma G distributions, Kumaraswamy G distributions, generalized beta G distributions, 
    beta extended G distributions, gamma G distributions, gamma uniform G distributions, beta exponential G distributions, Weibull G distributions, log gamma G I distributions, log gamma G II distributions, 
    exponentiated generalized G distributions, exponentiated Kumaraswamy G distributions, geometric exponential Poisson G distributions, truncated-exponential skew-symmetric G distributions, modified beta G distributions, 
    and exponentiated exponential Poisson G distributions.
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. A variety of
    link functions are currently 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 (2019).
  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. (2019) Ordinal Forests. Journal of Classification, 
  <doi:10.1007/s00357-018-9302-x>.
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.
It implements functions for simulation and estimation of the ordinal latent block model (OLBM), as described in Corneli, Bouveyron and Latouche (2019).
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>.
Implements Bayesian data analyses of balanced repeatability and reproducibility studies with ordinal measurements. Model fitting is based on MCMC posterior sampling with 'rjags'. Function ordinalRR() directly carries out the model fitting, and this function has the flexibility to allow the user to specify key aspects of the model, e.g., fixed versus random effects. Functions for preprocessing data and for the numerical and graphical display of a fitted model are also provided. There are also functions for displaying the model at fixed (user-specified) parameters and for simulating a hypothetical data set at a fixed (user-specified) set of parameters for a random-effects rater population. For additional technical details, refer to Culp, Ryan, Chen, and Hamada (2018) and cite this Technometrics paper when referencing any aspect of this work. The demo of this package reproduces results from the Technometrics paper.
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.
A system to help you organize projects. Most analyses have three (or more) main sections: code, results, and data, each with different requirements (version control/sharing/encryption). You provide folder locations and 'org' helps you take care of the details.
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.
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 or "On the point for which the sum of the distances to n given points is minimum", by E. Weiszfeld and F. Plastria, Annals of Operations Research, 167, pg. 7-41, 2009. <DOI:10.1007/s10479-008-0352-z>.
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>.
	     Se usan algoritmos generales de optimizacion global para resolver el problema, junto con el metodo adhoc Weiszfeld, vease "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 o "On the point for which the sum of the distances to n given points is minimum", por E. Weiszfeld y F. Plastria, Annals of Operations Research, 167, pg. 7-41, 2009. <DOI:10.1007/s10479-008-0352-z>.
Odds ratio based multifactor-dimensionality reduction
        method for detecting gene-gene interactions
An extension to the Regression Modeling Strategies package that
    facilitates plotting ordinal regression  model predictions together with confidence
    intervals for each dependent variable level. 
    It also adds a functionality to plot the  model summary as a modifiable 
    object.
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 odds ratio to relative risk in cohort studies with partial data information (Wang (2013) <doi:10.18637/jss.v055.i05>).
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 (2012) <DOI:10.1080/01621459.2011.646925>, Ma & Zhu (2013) <DOI:10.1214/12-AOS1072>, Sun, Zhu, Wang & Zeng (2017) <arXiv:1704.05046> and Zhou & Zhu (2018+) <arXiv:1802.06156>. It also serves as a general purpose optimization solver for problems with orthogonality constraints. Parallel computing for approximating the gradient is enabled through `OpenMP'.
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).
The supplied code allows for the generation of discrete time series of oscillating species. General shapes can be selected by means of individual functions, which are widely customizable by means of function arguments. All code was developed in the Biological Information Processing Group at the BioQuant Center at Heidelberg University, Germany.
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.
Provides routines for finding an Optimal System of Distinct Representatives (OSDR),
 as defined by D.Gale (1968) <doi:10.1016/S0021-9800(68)80039-0>.
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.
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.
Provides bindings to the 'OSQP' solver. The 'OSQP' solver is a numerical optimization package or solving convex quadratic programs written in 'C' and based on the alternating direction method of multipliers, 'ADMM'. B.  Stellato, G. Banjac, P. Goulart, A. Bemporad, S. Boyd (2018) <arXiv:1711.08013>.
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.
Wrapper around the 'Open Source Routing Machine (OSRM)'
        API <http://project-osrm.org/>. 'osrmr' works with API versions
        4 and 5 and can handle servers that run locally as well as the
        'OSRM' webserver.
Estimates the temporal changes of isotopic values of bone and teeth data solely based on the renewal rate of different bones/teeth and given measurements. The package furthermore provides plotting and exporting functionalities.
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'.
Providing mean partition for ensemble clustering by optimal transport alignment(OTA), uncertainty measures for both partition-wise and cluster-wise assessment and multiple visualization functions to show uncertainty, for instance, membership heat map and plot of covering point set. A partition refers to an overall clustering result.
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.
A wrapper for the 'OpenTripPlanner' <http://www.opentripplanner.org/>
    REST API. Queries are submitted to the relevant 'OpenTripPlanner' API resource, the response
    is parsed and useful R objects are returned.
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.
Implements a set of online fault detectors for time-series, called: PEWMA see M. Carter
             et al. (2012) <doi:10.1109/SSP.2012.6319708>, SD-EWMA and TSSD-EWMA see H. Raza et al. 
             (2015) <doi:10.1016/j.patcog.2014.07.028>, KNN-CAD see E. Burnaev et al. (2016)
             <arXiv:1608.04585>, KNN-LDCD see V. Ishimtsev et al. (2017) <arXiv:1706.03412> and 
             CAD-OSE see M. Smirnov (2018) <https://github.com/smirmik/CAD>. The first three 
             algorithms belong to prediction-based techniques and the last three belong to 
             window-based techniques. In addition, the SD-EWMA and PEWMA algorithms are algorithms 
             designed to work in stationary environments, while the other four 
             are algorithms designed to work in non-stationary environments.
Summarizes the taxonomic composition, diversity contribution of the rare and abundant community by using OTU (operational taxonomic unit) table which was generated by analyzing pipeline of 'QIIME' or 'mothur'. The rare biosphere in this package is subset by the relative abundance threshold (for details about rare biosphere please see Lynch and Neufeld (2015) <doi:10.1038/nrmicro3400>).
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.
    It efficiently computes summary statistics aggregated by time for 
    large datasets, and create 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>. Campbell, F,
    Cori A, Ferguson N, Jombart T. 2019. <doi:10.1371/journal.pcbi.1006930>.
Empirical or simulated disease outbreak data, provided either as
    RData or as text files.
Standardized survey outcome rate functions, including the response rate, contact rate, cooperation rate, and refusal rate. These outcome rates allow survey researchers to measure the quality of survey data using definitions published by the American Association of Public Opinion Research (AAPOR). For details on these standards, see AAPOR (2016) <https://www.aapor.org/Standards-Ethics/Standard-Definitions-(1).aspx>.
This package provides three algorithms to detect outlying observations for censored survival data.
To detect outliers using different methods namely model based outlier detection (Barnett, V. 1978 <https://www.jstor.org/stable/2347159>), distance based outlier detection (Hautamaki, V., Karkkainen, I., and Franti, P. 2004 <http://cs.uef.fi/~franti/papers.html>), dispersion based outlier detection (Jin, W., Tung, A., and Han, J. 2001 <https://link.springer.com/chapter/10.1007/0-387-25465-X_7>), depth based outlier detection (Johnson, T., Kwok, I., and Ng, R.T. 1998 <http://www.aaai.org/Library/KDD/1998/kdd98-038.php>) and density based outlier detection (Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. 1996 <https://dl.acm.org/citation.cfm?id=3001507>). This package provides labelling of observations as outliers and outlierliness of each outlier. For univariate, bivariate and trivariate data, visualization is also provided.
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. O3 plots are described in Unwin(2019) <doi:10.1080/10618600.2019.1575226>. The available methods are HDoutliers() from the package 'HDoutliers', FastPCS() from the package 'FastPCS', mvBACON() from 'robustX', adjOutlyingness() from 'robustbase', DectectDeviatingCells() from 'cellWise', covMcd() from 'robustbase'.
Will try to fit decision trees that try to "predict" values for each column based on the values of each other column.
	Along the way, each time a split is evaluated, it will take the observations that fall into each branch as a homogeneous
	cluster in which it will search for outliers in the 1-d distribution of the column being predicted. Outliers are determined
	according to confidence intervals on this 1-d distribution, and need to have a large gap with respect to the next observation
	in sorted order to be flagged as outliers. Since outliers are searched for in a decision tree branch, it will know the conditions
	that make it a rare observation compared to others that meet the same conditions, and the conditions will always be correlated
	with the target variable (as it's being predicted from them). Loosely based on the 'GritBot' <https://www.rulequest.com/gritbot-info.html> software.
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. Beaulieu et al (2012) <doi:10.1111/j.1558-5646.2012.01619.x>.
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 kernel density estimations from empirical data.
Tests the observed overlapping polygon area in a collection of polygons against a null model of random rotation, as explained in De la Cruz et al. (2017) <doi:10.13140/RG.2.2.12825.72801>.
Simplifies MCMC setup by automatically looping through sampling 
    functions and saving the results.  Reduces the memory footprint of running 
    MCMC and saves samples to disk as the chain runs.  Allows samples from the 
    chain to be analyzed while the MCMC is still running.  Provides functions 
    for commonly performed operations such as calculating Metropolis acceptance 
    ratios and creating adaptive Metropolis samplers.  References: Roberts and 
    Rosenthal (2009) <doi:10.1198/jcgs.2009.06134>.
Tools for collecting municipal-level data <http://www.transparencia.gov.br/swagger-ui.html> from several Brazilian governmental social programs.
An implementation of optimal weight exchange algorithm Yang(2013) <doi:10.1080/01621459.2013.806268> for three models.
    They are Crossover model with subject dropout, crossover model with proportional
    first order residual effects and interference model. You can use it to find either
    A-opt or D-opt approximate designs. Exact designs can be automatically rounded
    from approximate designs and relative efficiency is provided as well.
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.
Provides an Interface to Web-Services defined as standards by the Open Geospatial Consortium (OGC), including Web Feature Service
 (WFS) for vector data, Catalogue Service (CSW) for ISO/OGC metadata and associated standards such as the common web-service specification (OWS) and
 OGC Filter Encoding. The long-term purpose is to add support for additional OGC service standards such as Web Coverage Service (WCS) and
 Web Processing Service (WPS).
A set of tools that enables using 'OxCal' from within R. 'OxCal' (<https://c14.arch.ox.ac.uk/oxcal.html>) is a standard archaeological tool intended to provide 14C calibration and analysis of archaeological and environmental chronological information. 'OxcAAR' allows simple calibration with 'Oxcal' and plotting of the results as well as the execution of sophisticated ('OxCal') code and the import of the results of bulk analysis and complex Bayesian sequential calibration.
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.
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. Please see Li et al. (2017) <doi:10.1371/journal.pcbi.1005875> for detail method description.
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.
Search for R packages on CRAN directly from the R console, based on the packages' titles, short and long descriptions, or other fields. Combine multiple keywords with logical operators ('and', 'or'), view detailed information on any package and keep track of the latest package contributions to CRAN.
Compute and visualize the cross-sectional and longitudinal number
    and rank percentile of package downloads from RStudio's CRAN mirror.
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...
Several functions are provided to implement a MBPLSDA : components search, optimal model components number search, optimal model validity test by permutation tests, observed values evaluation of optimal model parameters and predicted categories, bootstrap values evaluation of optimal model parameters and predicted cross-validated categories. The use of this package is described in Brandolini-Bunlon et al (2019, Multi-block PLS discriminant analysis for the joint analysis of metabolomic and epidemiological data (submitted in Metabolomics)).
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'.
An implementation of both the equality and inequality constrained lasso
    functions for the algorithm described in "Penalized and Constrained Optimization"
    by James, Paulson, and Rusmevichientong (Journal of the American Statistical Association, 2019;
    see <http://www-bcf.usc.edu/~gareth/research/PAC.pdf> for a full-text version of the paper). 
    The algorithm here is designed to allow users to define linear constraints (either equality
    or inequality constraints) and use a penalized regression approach to solve the constrained
    problem. The functions here are used specifically for constraints with the lasso formulation,
    but the method described in the PaC paper can be used for a variety of scenarios. In addition
    to the simple examples included here with the corresponding functions, complete code to 
    entirely reproduce the results of the paper is available online through the Journal of the
    American Statistical Association.
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 Constant Conditional Correlation (CCC) test and the Vectorial Independence (VI) test are provided (Kurz and Spanhel (2017) <arXiv:1706.02338>). 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 CCC 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.
Visualizes the coverage depth of a complete plastid genome as well as the equality of its inverted repeat regions in relation to the circular, quadripartite genome structure and the location of individual genes. For more information, please see Gruenstaeudl and Jenke (2019) <doi:10.1101/697821>.
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. Thong Pham et al. (2015) <doi:10.1371/journal.pone.0137796>. Thong Pham et al. (2016) <doi:10.1038/srep32558>.
Use the paged media properties in CSS and the JavaScript
  library 'paged.js' to split the content of an HTML document into discrete
  pages. Each page can have its page size, page numbers, margin boxes, and
  running headers, etc. Applications of this package include books, letters,
  reports, papers, business cards, resumes, and posters.
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.
Functions for creating color palettes, visualizing
    palettes, modifying colors, and assigning colors for plotting.
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.
Improving graphics by ameliorating order effects, using Eulerian tours and Hamiltonian decompositions of  graphs.
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.
The goal of 'pak' is to make package installation faster and
    more reliable. In particular, it performs all HTTP operations in parallel,
    so metadata resolution and package downloads are fast. Metadata and package
    files are cached on the local disk as well. 'pak' has a dependency solver,
    so it finds version conflicts before performing the installation. This
    version of 'pak' supports CRAN, 'Bioconductor' and 'GitHub' packages as well.
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>).
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of SDG monitoring, as the survey produces information on 32 global SDG indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using Probability Proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Household questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of SDG monitoring, as the survey produces information on 32 global SDG indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using Probability Proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Children Age 5-17 questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Household questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Maternal Mortality questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Men questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).
Several tests of quantitative palaeoenvironmental reconstructions 
  from microfossil assemblages, including the null model tests of the 
  statistically significant of reconstructions developed by Telford and Birks
  (2011) <doi:10.1016/j.quascirev.2011.03.002>, and tests of the effect of 
  spatial autocorrelation on transfer function model performance using methods 
  from Telford and Birks (2009) <doi:10.1016/j.quascirev.2008.12.020> and 
  Trachsel and Telford (2016) <doi:10.5194/cp-12-1215-2016>. Age-depth models with 
  generalized mixed-effect regression from Heegaard et al (2005)
  <doi:10.1191/0959683605hl836rr> are also included.
Implements sparse regression with paired covariates (Rauschenberger et al. 2019). For the optional shrinkage, install ashr (<https://github.com/stephens999/ashr>) and CorShrink (<https://github.com/kkdey/CorShrink>) from GitHub (see README).
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 date cladograms of
    fossil taxa using stratigraphic data. 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.  
    Functions are provided to fit, using maximum likelihood, simple 
    evolutionary models (including unbiased random walks, directional 
    evolution,stasis, Ornstein-Uhlenbeck, covariate-tracking) and 
    complex models (punctuation, mode shifts).
The choices of color palettes in R can be quite overwhelming with 
    palettes spread over many packages with many different API's. This packages
    aims to collect all color palettes across the R ecosystem under the same
    package with a streamlined API.
A set of palettes imported from 'Gimp' distributed 
  under GPL3 (<https://www.gimp.org/about/COPYING>), and 'Inkscape' 
  distributed under GPL2 (<https://inkscape.org/about/license/>).
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 the void processes proposed by Jones-Todd et al. (in press) <DOI:10.1002/sim.8046>. 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 (Stevenson, Borchers, and Fewster, in press <DOI:10.1111/biom.12983>). As such, some of the functions in this package are specifically for the estimation of cetacean density from two-camera aerial surveys.
This is an implementation of model-based trees with global model
  parameters (PALM trees). The PALM tree algorithm is an extension to the MOB
  algorithm (implemented in the 'partykit' package), where some parameters are
  fixed across all groups. Details about the method can be found in Seibold,
  Hothorn, Zeileis (2016) <arXiv:1612.07498>. The package offers coef(),
  logLik(), plot(), and predict() functions for PALM trees.
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).
We propose a pair of summary measures for the predictive power of a prediction
    function based on a regression model. The regression model can be linear
    or nonlinear, parametric, semi-parametric, or nonparametric, and correctly
    specified or mis-specified. The first measure, R-squared, is an extension of
    the classical R-squared statistic for a linear model, quantifying the prediction
    function's ability to capture the variability of the response. The second
    measure, L-squared, quantifies the prediction function's bias for predicting the
    mean regression function. When used together, they give a complete summary of
    the predictive power of a prediction function. Please refer to Gang Li and Xiaoyan Wang (2016) <arXiv:1611.03063> for more details.
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.
The Piece-wise exponential (Additive Mixed) Model
    (PAMM; Bender and Scheipl (2018) <doi: 10.1177/1471082X17748083>) is a
    powerful model class for survival analysis, based on Generalized Additive
    (Mixed) Models (GA(M)Ms). It offers intuitive specification and robust
    estimation of complex survival models with stratified baseline hazards,
    random effects, time-varying effects, time-dependent covariates and
    cumulative effects (Bender et. al. (2018) <doi:10.1093/biostatistics/kxy003>.
    pammtools provides tidy workflow for survival analysis with PAMMs,
    including data transformation and other pre- and post-processing
    functions as well as visualization.
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.
It provides functions and examples for maximum likelihood estimation for
              generalized linear mixed models and Gibbs sampler for multivariate linear
              mixed models with incomplete data, as described in Schafer JL (1997)
              "Imputation of missing covariates under a multivariate linear mixed model".
              Technical report 97-04, Dept. of Statistics, The Pennsylvania State University.
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
Provides an object type and associated tools for storing and 
  wrangling panel data. Implements several methods for creating regression
  models that take advantage of the unique aspects of 
  panel data. Among other capabilities, automates the "within-between" 
  (also known as "between-within" and "hybrid") panel regression specification
  that combines the desirable aspects of both fixed effects and random effects 
  econometric models and fits them as multilevel models 
  (Allison, 2009 <doi:10.4135/9781412993869.d33>; 
  Bell & Jones, 2015 <doi:10.1017/psrm.2014.7>). These models can also be 
  estimated via generalized estimating equations 
  (GEE; McNeish, 2019 <doi:10.1080/00273171.2019.1602504>) and Bayesian 
  estimation is (optionally) supported via 'Stan'. 
  Supports estimation of asymmetric effects models via first differences
  (Allison, 2019 <doi:10.1177/2378023119826441>) as well as a generalized
  linear model extension thereof using GEE.
We extend two general methods of moment estimators to panel vector 
    autoregression models (PVAR) with p lags of endogenous variables, predetermined 
    and strictly exogenous variables. This general PVAR model contains the first 
    difference GMM estimator by Holtz-Eakin et al. (1988) <doi:10.2307/1913103>, 
    Arellano and Bond (1991) <doi:10.2307/2297968> and the system GMM estimator 
    by Blundell and Bond (1998) <doi:10.1016/S0304-4076(98)00009-8>. We also 
    provide specification tests (Hansen overidentification test, lag selection 
    criterion and stability test of the PVAR polynomial) and classical structural 
    analysis for PVAR models such as orthogonal and generalized impulse response 
    functions, bootstrapped confidence intervals for impulse response analysis and 
    forecast error variance decompositions.
Visualizes panel data. It has two main functionalities: (1) it visualizes the treatment and missing-value statuses of each observation in a panel/time-series-cross-sectional (TSCS) dataset; and (2) it plots the outcome variable (either continuous or discrete) in a time-series fashion.
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.
Tool for producing Pen's parade graphs, useful for visualizing inequalities in income, wages or other variables, as proposed by Pen (1971, ISBN: 978-0140212594). Income or another economic variable is captured by the vertical axis, while the population is arranged in ascending order of income along the horizontal axis. Pen's income parades provide an easy-to-interpret visualization of economic inequalities.
Define parameter spaces, constraints and
    dependencies for arbitrary algorithms, to program on such spaces. Also
    includes statistical designs and random samplers. Objects are implemented
    as 'R6' classes.
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 41 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.
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.
Support for parallel computation with progress bar, and option to stop or proceed on errors. Also provides logging to console and disk, 
  and the logging persists in the parallel threads. Additional functions support function call automation with delayed execution (e.g. for executing functions in
  parallel).
Unified parallelization framework for multiple
    back-end, designed for internal package and interactive usage.  The
    main operation is parallel mapping 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.
Performs statistical tests to compare coefficients and residual 
    variance across multiple models. Also provides graphical methods for 
    assessing heterogeneity in coefficients and residuals. Currently supports 
    linear and generalized linear models, as well as their multi-level and 
    complex survey-weighted variations from the 'lme4' and 'survey' packages, 
    respectively. Reference: Li (2015) <https://scholarscompass.vcu.edu/etd/3985/>.
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.
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.
Fast, flexible framework for implementing Bayesian optimization of model 
	hyperparameters according to the methods described in Snoek et al. <arXiv:1206.2944>.
	The package allows the user to run scoring function in parallel, save intermediary 
	results, and tweak other aspects of the process to fully utilize the computing resources
	available to the user.
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.
Create interactive parallel coordinates charts with this 'htmlwidget'
    wrapper for 'd3.js' <https://github.com/BigFatDog/parcoords-es>
    {'parallel-coordinates'}.
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 a parallel estimation method for generalized 
  linear models without compiling with a multithreaded LAPACK or BLAS.
Provides various tools for analysing UK political data, including 
    election result datasets, hexagonal cartograms and functions to 
    retrieve council member 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.
Executes simple parametric models for right-censored 
    survival data.  Functionality emulates capabilities in 'Minitab', including
    fitting right-censored data, assessing fit, plotting survival functions,
    and summary statistics and probabilities.
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 tools for the analysis of partially ordered data, with a particular focus on the evaluation of multidimensional systems of indicators and on the analysis of poverty. References, Fattore M. (2016) <doi:10.1007/s11205-015-1059-6> Fattore M., Arcagni A. (2016) <doi:10.1007/s11205-016-1501-4> Arcagni A. (2017) <doi:10.1007/978-3-319-45421-4_19>.
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.
Tools and utilities for dealing with parse data. 
    Parse data represents the parse tree as data with location and type
    information.  This package provides functions for navigating the  
    parse tree as a data frame.
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 common interface is provided to allow users to specify a model without having to remember the different argument names across different functions or computational engines (e.g. 'R', 'Spark', 'Stan', etc).
It estimates the parameters of a partially linear regression censored model via maximum penalized likelihood through of ECME algorithm. The model belong to the semiparametric class, that including a parametric and nonparametric component. The error term considered belongs to the scale-mixture of normal (SMN) distribution, that includes well-known heavy tails distributions as the Student-t distribution, among others. To examine the performance of the fitted model, case-deletion and local influence techniques are provided to show its robust aspect against outlying and influential observations. This work is based in Ferreira, C. S., & Paula, G. A. (2017) <doi:10.1080/02664763.2016.1267124> but considering the SMN family.
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 (2017) <doi:10.1080/14697688.2017.1370122>, 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.
Tests for a comparison of two partially overlapping samples.
  A comparison of means using the partially overlapping samples t-test: 
  See Derrick, Russ, Toher and White (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). 
  A comparison of proportions using the partially overlapping samples z-test:
  See Derrick, Dobson-Mckittrick, Toher and White (2015), Test statistics
  for comparing two proportions with partially overlapping samples.
  Journal of Applied Quantitative Methods, 10(3).
Computes Odds Ratio adjusted for a vector of possibly
        continuous covariates
Simulating particle movement in 2D space has many application. The
    'particles' package implements a particle simulator based on the ideas 
    behind the 'd3-force' 'JavaScript' library. 'particles' implements all 
    forces defined in 'd3-force' as well as others such as vector fields, traps, 
    and attractors.
A fast and flexible framework for agglomerative partitioning. 'partition' uses an approach 
  called Direct-Measure-Reduce to create new variables that maintain the 
  user-specified minimum level of information. Each reduced variable is also interpretable:
  the original variables map to one and only one variable in the reduced data set. 'partition'
  is flexible, as well: how variables are selected to reduce, how information loss is measured, 
  and the way data is reduced can all be customized.
A collection of functions that can be used to estimate selection and complementarity effects,
	sensu Loreau & Hector (2001) <doi:10.1038/35083573>, even in cases where data are only available for
	a random subset of species (i.e. incomplete sample-level data). A full derivation and explanation of the 
	statistical corrections used here is available in Clark et al. (2019) <doi:10.1111/2041-210X.13285>.
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.
Power and sample size calculation for testing fixed effect coefficients in multilevel linear mixed effect models with one or more than one independent populations. Laird, Nan M. and Ware, James H. (1982) <doi:10.2307/2529876>.
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.
Regularisation, decomposition and analysis of space-time series.
  The pastecs R package is a PNEC-Art4 and IFREMER (Benoit Beliaeff
  <Benoit.Beliaeff@ifremer.fr>) initiative to bring PASSTEC 2000 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.
Implementation of simple functions to draw
    basic path diagrams just for visualization purposes.
Pathway enrichment analysis enables researchers to uncover mechanisms 
    underlying the phenotype. pathfindR is a tool for pathway enrichment analysis 
    utilizing active subnetworks. It identifies active subnetworks in a 
    protein-protein interaction network using user-provided a list of genes. 
    It performs pathway enrichment analyses on the identified subnetworks. 
    pathfindR also offers functionalities to cluster enriched pathways and identify 
    representative pathways and to score the pathways per sample. The method is
    described in detail in Ulgen E, Ozisik O, Sezerman OU. 2018. pathfindR: An R
    Package for Pathway Enrichment Analysis Utilizing Active Subnetworks. bioRxiv. 
    <doi:10.1101/272450>.
An OO Interface for path manipulation, emulating pythons 
             "pathlib".
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.
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).
This is an extension of the 'testthat' package that lets you add
  parameters to your unit tests. Parameterized unit tests are often easier to
  read and more reliable, since they follow the DNRY (do not repeat yourself)
  rule.
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 RGB
    thresholds or image segmentation.
Creates aesthetically pleasing and informative pie charts, bar charts and box plots with colors, patterns, and images.
A modeling tool dedicated to biological network modeling. It allows for single or joint modeling of, for instance, genes and proteins. It starts with the selection of the actors that will be the used in the reverse engineering upcoming step. An actor can be included in that selection based on its differential measurement (for instance gene expression or protein abundance) or on its time course profile. Wrappers for actors clustering functions and cluster analysis are provided. It also allows reverse engineering of biological networks taking into account the observed time course patterns of the actors. Many inference functions are provided and dedicated to get specific features for the inferred network such as sparsity, robust links, high confidence links or stable through resampling links. Some simulation and prediction tools are also available for cascade networks. Example of use with microarray or RNA-Seq data are provided.
A collection of utilities that are useful for a broad range of
    tasks that are common in physical activity research, including the
    following: creation of Bland-Altman plots, formatted descriptive
    statistics, metabolic calculations (e.g. basal metabolic rate predictions)
    and conversions, demographic calculations (age and age-for-body-mass-index
    percentile), bout analysis of moderate-to-vigorous intensity physical
    activity, and analysis of bout detection algorithm performance.
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 colour
    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.
Interface to Amazon Web Services
    <https://aws.amazon.com>, including storage, database, and compute
    services, such as 'Simple Storage Service' ('S3'), 'DynamoDB' 'NoSQL'
    database, and 'Lambda' functions-as-a-service.
Interface to Amazon Web Services analytics APIs,
    including 'Elastic MapReduce' 'Hadoop' and 'Spark' big data service,
    'Elasticsearch' search engine, and more <https://aws.amazon.com/>.
Interface to Amazon Web Services application
    integration APIs, including 'Simple Queue Service' ('SQS') message
    queue, 'Simple Notification Service' ('SNS') publish/subscribe
    messaging, and more <https://aws.amazon.com/>.
Functions for making low-level API requests to Amazon Web Services
    <https://aws.amazon.com>. The functions handle building, signing, and
    sending requests, and receiving responses. They are designed to help build 
    higher-level interfaces to individual services, such as Simple Storage
    Service (S3).
Interface to Amazon Web Services compute APIs,
    including 'Elastic Compute Cloud' ('EC2'), 'Lambda'
    functions-as-a-service, containers, batch processing, and more
    <https://aws.amazon.com/>.
Interface to Amazon Web Services cost management
    APIs, including cost and usage reports, budgets, pricing, and more
    <https://aws.amazon.com/>.
Interface to Amazon Web Services customer
    engagement APIs, including 'Simple Email Service', 'Connect' contact
    center service, and more <https://aws.amazon.com/>.
Interface to Amazon Web Services database APIs,
    including 'Relational Database Service' ('RDS'), 'DynamoDB' 'NoSQL'
    database, and more <https://aws.amazon.com/>.
Interface to Amazon Web Services machine learning
    APIs, including 'SageMaker' managed machine learning service, natural
    language processing, speech recognition, translation, and more
    <https://aws.amazon.com/machine-learning/>.
Interface to Amazon Web Services management and
    governance APIs, including 'CloudWatch' application and infrastructure
    monitoring, 'Auto Scaling' for automatically scaling resources, and
    more <https://aws.amazon.com/>.
Interface to Amazon Web Services networking and
    content delivery APIs, including 'Route 53' Domain Name System
    service, 'CloudFront' content delivery, load balancing, and more
    <https://aws.amazon.com/>.
Interface to Amazon Web Services security,
    identity, and compliance APIs, including the 'Identity & Access
    Management' ('IAM') service for managing access to services and
    resources, and more <https://aws.amazon.com/>.
Interface to Amazon Web Services storage APIs,
    including 'Simple Storage Service' ('S3') and more
    <https://aws.amazon.com/>.
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 R package provides power calculations via internal simulation methods. The package also provides a frontend to the now abandoned PBAT program (developed by Christoph Lange), and reads in the corresponding output and displays results and figures when appropriate. The license of this R package itself is GPL. However, to have the program interact with the PBAT program for some functionality of the R package, users must additionally obtain the PBAT program from Christoph Lange, and accept his license. 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 (4.2.2)
    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.
Binding models which are useful when analysing protein-ligand interactions by techniques such as Biolayer Interferometry (BLI) or Surface Plasmon Resonance (SPR). Naman B. Shah, Thomas M. Duncan (2014) <doi:10.3791/51383>. Hoang H. Nguyen et al. (2015) <doi:10.3390/s150510481>. After initial binding parameters are known, binding curves can be simulated and parameters can be varied. The models within this package may also be used to fit a curve to measured binding data using non-linear regression.
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.
Provides three ways to estimate the probability of being in response function (PBRF) 
             The estimates are presented in Tsai, Luo and Crowley (2017) <doi: 10.1007/978-981-10-0126-0_10>.
A collection of software provides R support for 'ADMB'
             (Automatic Differentiation Model Builder) and a 'GUI'
             interface facilitates the conversion of 'ADMB' template
             code to 'C code' followed by compilation to a binary executable.
             Stand-alone functions can also be run by users
             not interested in clicking a 'GUI'.
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.
Computes probabilities of the bivariate normal distribution
    in a vectorized R function (Drezner & Wesolowsky, 1990, 
    <doi:10.1080/00949659008811236>).
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.
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. See <doi:10.1101/237883>.
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.
Provides convenience functions and pre-programmed Stan models
    related to the paired comparison factor model. Its purpose is to make fitting
    paired comparison data using Stan easy.
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.
Implements the pcgen algorithm, which is a modified version of the standard pc-algorithm,
             with specific conditional independence tests and modified orientation rules. pcgen extends 
			 the approach of Valente et al. (2010) <doi:10.1534/genetics.109.112979> with reconstruction of 
			 direct genetic effects.
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.
Provides 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) with conditional maximum likelihood estimation.
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.
A method for fitting the entire regularization path 
    of the principal components lasso for linear and
    logistic regression models. The algorithm uses cyclic coordinate descent
    in a path-wise fashion. See URL below for more information on the algorithm.
    See Tay, K., Friedman, J. ,Tibshirani, R., (2014) 'Principal component-guided sparse regression'
    <arXiv:1810.04651>.
Fits by ABC, the parameters of a stochastic process modelling the phylogeny and evolution of a suite of traits following the tree. The user may define an arbitrary Markov process for the trait and phylogeny. Importantly, trait-dependent speciation models are handled and fitted to data. See K. Bartoszek, P. Lio' (2019) <10.5506/APhysPolBSupp.12.25>.
Phylogenetic comparative methods represent models of continuous trait 
  data associated with the tips of a phylogenetic tree. Examples of such models 
  are Gaussian continuous time branching stochastic processes such as Brownian 
  motion (BM) and Ornstein-Uhlenbeck (OU) processes, which regard the data at the 
  tips of the tree as an observed (final) state of a Markov process starting from 
  an initial state at the root and evolving along the branches of the tree. The 
  PCMBase R package provides a general framework for manipulating such models. 
  This framework consists of an application programming interface for specifying 
  data and model parameters, and efficient algorithms for simulating trait evolution 
  under a model and calculating the likelihood of model parameters for an assumed
  model and trait data. The package implements a growing collection of models, 
  which currently includes BM, OU, BM/OU with jumps, two-speed OU as well as mixed 
  Gaussian models, in which different types of the above models can be associated 
  with different branches of the tree. The PCMBase package is limited to 
  trait-simulation and likelihood calculation of (mixed) Gaussian phylogenetic 
  models. The PCMFit package provides functionality for ML and Bayesian fit of 
  these models to tree and trait data. The package web-site 
  <https://venelin.github.io/PCMBase/>
  provides access to the documentation and other resources.
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.
An implementation of the parameter cascade method Ramsay, J. O., Hooker,G., Campbell, D., and Cao, J. (2007) <doi:10.1111/j.1467-9868.2007.00610.x> for estimating ordinary differential equation models with missing or complete observations. It combines smoothing method and profile estimation to estimate any non-linear dynamic system. The package also offers variance estimates for parameters of interest based on either bootstrap or Delta method.
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.
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.
Functions and example data to teach and
  increase the reproducibility of the methods and code underlying 
  the Propensity to Cycle Tool (PCT), a research project and web application 
  hosted at <https://www.pct.bike/>. 
  For an academic paper on the methods,
  see Lovelace et al (2017) <doi:10.5198/jtlu.2016.862>.
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.
Cluster analysis via nonparametric density 
   estimation is performed. 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.
Farmer, J., D. Jacobs (2108) <DOI:10.1371/journal.pone.0196937>. A nonparametric density estimator based on the maximum-entropy method.  Accurately predicts a probability density function (PDF) for random data using a novel iterative scoring function to determine the best fit without overfitting to the sample.
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 into
    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.
Fits 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>).
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>.
Reads Planetary Data Systems v3 (PDS3 - <https://pds.jpl.nasa.gov/>)
  Object Description Language (ODL) formatted files, typically used in NASA
  missions for storing metadata about observations.
A package to compute and tune some positive definite and
        sparse covariance estimators
Interactive shiny application for working with Probability Distributions. Calculations and  Graphs are provided.
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 wavelet transforms for curves (1D) or surfaces (2D) of Hermitian positive 
  definite matrices with applications to dimension reduction, denoising and clustering in the 
  space of Hermitian positive definite matrices; and (ii) exploratory data analysis and inference 
  for samples of positive definite matrices by means of intrinsic data depth functions and 
  rank-based hypothesis tests in the space of Hermitian positive definite matrices.
The original definition of the two and three dimensional Kolmogorov-Smirnov two-sample
             test statistics given by Peacock (1983) is implemented. Two R-functions: peacock2 and peacock3, 
             are provided to compute the test statistics in two and three dimensional spaces, respectively. 
             Note the Peacock test is different from the Fasano and Franceschini test (1987). The latter is 
             a variant of the Peacock test.
Calculates the periodogram of a time series, maximum-likelihood fits an Ornstein-Uhlenbeck state space (OUSS) null model and evaluates the statistical significance of periodogram peaks against the OUSS null hypothesis. The OUSS is a parsimonious model for stochastically fluctuating variables with linear stabilizing forces, subject to uncorrelated measurement errors. Contrary to the classical white noise null model for detecting cyclicity, the OUSS model can account for temporal correlations typically occurring in ecological and geological time series.
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.
Disk-based implementation of
 Functional Pruning Optimal Partitioning with up-down constraints
 <arXiv:1810.00117> for single-sample peak calling
 (independently for each sample and genomic problem),
 can handle huge data sets (10^7 or more).
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.
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 (2013, <doi: 10.1002/gepi.21727>).
Tools to sort, edit and prune pedigrees and to extract the inbreeding coefficients
    and the relationship matrix (includes code for pedigrees from self-pollinated species). 
    The use of pedigree data is central to genetics research within the animal and plant breeding communities to predict 
    breeding values. The relationship matrix between the individuals can be derived from pedigree structure following
    the algorithms described for example in Vazquez et al., 2010 <doi:10.2527/jas.2009-1952>.
Functions to employ many of the tools and techniques used in the 
    field of pedometrics.
Provides an interface to access public economic and financial data for 
    economic research and quantitative analysis. The data sources including 
    NBS, FRED, Yahoo Finance, 163 Finance and etc.
Provides functions to perform the peer performance
    analysis of funds' returns as described in Ardia and Boudt (2018) <doi:10.1016/j.jbankfin.2017.10.014>.
Functions for reading, writing, plotting, analysing, and manipulating allelic and haplotypic data, including from VCF files, and for the analysis of population nucleotide sequences and micro-satellites including coalescent analyses, linkage disequilibrium, population structure (Fst, Amova) and equilibrium (HWE), haplotype networks, minimum spanning tree and network, and median-joining networks.
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.
An R-Shiny application implementing a method of sexing the human os coxae based on logistic regressions and Bruzek's nonmetric traits <doi:10.1002/ajpa.23855>.
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".
Support Vector Machine (SVM) classification with simultaneous feature selection using penalty
        functions is implemented. The smoothly clipped absolute deviation (SCAD),
        'L1-norm', 'Elastic Net' ('L1-norm' and 'L2-norm') and 'Elastic
        SCAD' (SCAD and 'L2-norm') penalties are available. The tuning
        parameters can be found 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).
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.
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.
Reconstructs perfect phylogeny at a user-given focal point and to depict and test association in a genomic region based on the reconstructed partitions. Charith B Karunarathna and Jinko Graham (2019) <bioarXiv:10.1101/674523>.
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.
Utilities for computing measures to assess model quality,
    which are not directly provided by R's 'base' or 'stats' packages. These 
    include e.g. measures like r-squared, intraclass correlation coefficient
    (Nakagawa, Johnson & Schielzeth (2017) <doi:10.1098/rsif.2017.0213>), 
    root mean squared error or functions to check models for overdispersion, 
    singularity or zero-inflation and more. Functions apply to a large variety of
    regression models, including generalized linear models, mixed effects models
    and Bayesian models.
Collection of econometric functions for performance and risk 
    analysis. In addition to standard risk and performance metrics, 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.
Functions for generating variants of curves:
    restricted cubic spline, periodic restricted cubic spline,
    periodic cubic spline. Periodic splines can be used to model data
    that has periodic nature / seasonality.
Provides a dataset containing properties for chemical elements.
    Helper functions are also provided to access some atomic properties.
An enterprise-targeted scalable and UI-standardized 'shiny' framework 
    including a variety of developer convenience functions with the goal of both 
    streamlining robust application development while assisting with creating a 
    consistent user experience regardless of application or developer.
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).
The permubiome R package was created to perform a permutation-based non-parametric analysis on microbiome data for biomarker discovery aims. This test executes thousands of comparisons in a pairwise manner, after a random shuffling of data into the different groups of study with prior selection of the microbiome features with the largest variation among groups. Previous to the permutation test itself, data can be normalized according to different methods proposed to handle microbiome data ('proportions', 'anders', and 'paulson'). The median-based differences between groups resulting from the multiple simulations are fitted to a normal distribution with the aim to calculate their significance. A multiple testing correction based on Benjamini-Hochberg method (fdr) is finally applied to extract the differentially presented features between groups of your dataset. LATEST UPDATES: v1.1 and olders incorporates function to parse BIOM-like format; v1.2 and olders incorporates "optimize" function to maximize evaluation of features with largest inter-class variation.
Functions to compute p-values based on permutation tests. Regression, ANOVA and ANCOVA, omnibus F-tests, marginal unilateral and bilateral t-tests are available. Several methods to handle nuisance variables are implemented (Kherad-Pajouh, S., & Renaud, O. (2010) <doi:10.1016/j.csda.2010.02.015> ; Kherad-Pajouh, S., & Renaud, O. (2014) <doi:10.1007/s00362-014-0617-3> ; Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014) <doi:10.1016/j.neuroimage.2014.01.060>). An extension for the comparison of signals issued from experimental conditions (e.g. EEG/ERP signals) is provided. Several corrections for multiple testing are possible, including the cluster-mass statistic (Maris, E., & Oostenveld, R. (2007) <doi:10.1016/j.jneumeth.2007.03.024>) and the threshold-free cluster enhancement (Smith, S. M., & Nichols, T. E. (2009) <doi:10.1016/j.neuroimage.2008.03.061>).
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.
Helps you determine the analysis window to use when analyzing densely-sampled
    time-series data, such as EEG data, using permutation testing (Maris & Oostenveld 2007)
    <doi:10.1016/j.jneumeth.2007.03.024>. These permutation tests can help identify the timepoints
    where significance of an effect begins and ends, and the results can be plotted in various
    types of heatmap for reporting.
Tools that allow developers to write functions for
        prediction error estimation with minimal programming effort and
        assist users with model selection in regression problems.
Enables the interoperability between the Perseus platform for omics data analysis (Tyanova et al. 2016) <doi:10.1038/nmeth.3901> and R. It provides the foundation for developing and running Perseus plugins implemented in R by providing all required input and output handling, including data and parameter parsing as described in Rudolph and Cox 2018 <doi:10.1101/447268>.
Translate words from English to Persian (Over 67,000
        words)
Allows users to stem Persian texts for text analysis.
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 models for subgroup
    identification and personalized medicine / precision medicine 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.
Interface to the 'Perspective' API, which can be found at the following URL: <https://github.com/conversationai/perspectiveapi#perspective-comment-analyzer-api>. 
    The 'Perspective' API uses machine learning models to score the perceived impact a comment might have on a conversation (i.e. TOXICITY, INFLAMMATORY, etc.).     
    'peRspective' provides access to the API and returns tidy data frames with results of the specified machine learning model(s).
Use the perturb() function to evaluates collinearity by adding 
    random noise to selected variables (Hendrickx & Pelzer 2004). The colldiag 
    function() calculates condition numbers and variance decomposition proportions 
    to test for collinearity and uncover its sources (Belsley 1980).
The perturbR() function incrementally perturbs network edges (using the   rewireR function)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.
Automatic estimation of number of principal components in PCA
    with PEnalized SEmi-integrated Likelihood (PESEL). See Piotr Sobczyk, Malgorzata Bogdan, Julie Josse
    'Bayesian dimensionality reduction with PCA using penalized semi-integrated likelihood' 
    (2017) <doi:10.1080/10618600.2017.1340302>.
Implementation of different analytic/direct and 
 iterative reconstruction methods of radon transformed data
 such as PET data. It also offer the possibility to simulate PET data.
Wrapper of the 'Petfinder API' <https://www.petfinder.com/developers/v2/docs/> that implements 
  methods for interacting with and extracting data from the 'Petfinder' database. The 'Petfinder 
  REST API' allows access to the 'Petfinder' database, one of the largest online databases of 
  adoptable animals and animal welfare organizations across North America.
Calculates the relative growth rate (RGR) of a series of individuals by building a life table and solving the Lotka-Birch equation. (See Birch, L. C. 1948. The intrinsic rate of natural increase of an insect population. - Journal of Animal Ecology 17: 15-26) <doi:10.2307/1605>.
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.
The Particle Gibbs sampler and Gibbs/Metropolis-Hastings sampler were implemented
             to fit Bayesian additive regression tree model. Construction of the model (training) and prediction
             for a new data set (testing) can be separated. Our reference papers are: 
             Lakshminarayanan B, Roy D, Teh Y W. Particle Gibbs for Bayesian additive regression trees[C],
             Artificial Intelligence and Statistics. 2015: 553-561, 
             <http://proceedings.mlr.press/v38/lakshminarayanan15.pdf>
             and Chipman, H., George, E., and McCulloch R. (2010) Bayesian Additive Regression Trees. The Annals of Applied Statistics, 4,1, 266-298, <doi:10.1214/09-aoas285>.
Generates random samples from the Polya-Gamma distribution using an implementation of the algorithm described in J. Windle's PhD thesis (2013) <https://repositories.lib.utexas.edu/bitstream/handle/2152/21842/WINDLE-DISSERTATION-2013.pdf>. The underlying implementation is in C.
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).
Set of tools for reading, writing and transforming spatial and seasonal data in ecology, model selection and specific statistical tests. It includes functions to discretize polylines into regular point intervals, link observations to those points, compute geographical coordinates at regular intervals between waypoints, read subsets of big rasters, compute zonal statistics or table of categories within polygons or circular buffers from raster. The package also provides miscellaneous functions for model selection, spatial statistics, geometries, writing data.frame with Chinese characters, and some other functions for field ecologists.
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. McNicholas and Murphy (2008) <doi:10.1007/s11222-008-9056-0>, McNicholas (2010) <doi:10.1016/j.jspi.2009.11.006>, McNicholas and Murphy (2010) <doi:10.1093/bioinformatics/btq498>, McNicholas et al. (2010) <doi:10.1016/j.csda.2009.02.011>.
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.
Computes pseudo-realizations from the posterior distribution of a Gaussian Process (GP) with the method described in Azzimonti et al. (2016) <doi:10.1137/141000749>. The realizations are obtained from simulations of the field at few well chosen points that minimize the expected distance in measure between the true excursion set of the field and the approximate one. Also implements a R interface for (the main function of) Distance Transform of sampled Functions (<http://cs.brown.edu/people/pfelzens/dt/index.html>).
Implements a general framework for creating dependency graphs using projection as introduced in Fan, Feng and Xia (2019)<arXiv:1501.01617>. Both lasso and sparse additive model projections are implemented. Both Pearson correlation and distance covariance options are available 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.
Computes the generalized synthetic control estimator described in
    Powell (2017) <doi:10.7249/WR1142>.  Provides both point estimates, and hypothesis testing.
An implementation of Bayesian single-arm phase II design methods for binary outcome based on posterior
  probability (Thall and Simon (1994) <doi:10.2307/2533377>) and predictive probability (Lee and Liu (2008) <doi:10.1177/1740774508089279>).
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).
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. Details about this method are described in Gu, J., et al. (2017) <doi: 10.1093/bioinformatics/btx348>: Phantom: investigating heterogeneous gene sets in 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.
Contains three simulation functions for implementing the entire Phase 123 trial and the separate Eff-Tox and Phase 3 portions of the trial, which may be beneficial for use on clusters. The functions AssignEffTox() and RandomizeEffTox() assign doses to patient cohorts during phase 12 and Reoptimize() determines the optimal dose to continue with during Phase 3. The functions ReturnMeansAgent() and ReturnMeanControl() gives the true mean survival for the agent doses and control and ReturnOCS() gives the operating characteristics of the design.
Implements Bayesian phase I repeated measurement design that 
    accounts for multidimensional toxicity endpoints and longitudinal efficacy 
    measure from multiple treatment cycles. The package provides flags to 
    fit a variety of model-based phase I design, including 1 stage models with
    or without individualized dose modification, 3-stage models with or without
    individualized dose modification, etc. Functions are provided to recommend 
    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>.
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>.
Performs a 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, programs are additionally available to plot 
   nullclines and stable/unstable manifolds of saddle points. 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.
PHATE is a tool for visualizing high dimensional single-cell data
  with natural progressions or trajectories. PHATE uses a novel conceptual framework
  for learning and visualizing the manifold inherent to biological systems in which
  smooth transitions mark the progressions of cells from one state to another.
  To see how PHATE can be applied to single-cell RNA-seq datasets from hematopoietic
  stem cells, human embryonic stem cells, and bone marrow samples, check out our preprint on bioRxiv
  at <http://biorxiv.org/content/early/2017/03/24/120378>.
Provides permutation methods for testing in high-dimensional linear models.
    The tests are often robust against heteroscedasticity and non-normality 
    and usually perform well under anti-sparsity.
    See Hemerik and Goeman (2018) <doi:10.1007/s11749-017-0571-1>.
Implementation of heatmaps that offers more control
    over dimensions and appearance.
Functions to calculate commonly used public health statistics and 
    their confidence intervals using methods approved for use in the production  
    of Public Health England indicators such as those presented via Fingertips 
    (<http://fingertips.phe.org.uk/>). It provides functions for the generation 
    of proportions, crude rates, means, directly standardised rates, indirectly 
    standardised rates, standardised mortality ratios, slope and relative index
    of inequality and life expectancy. 
    Statistical methods are referenced in the following publications. 
    Breslow NE, Day NE (1987) <doi:10.1002/sim.4780080614>.
    Dobson et al (1991) <doi:10.1002/sim.4780100317>. 
    Armitage P, Berry G (2002) <doi:10.1002/9780470773666>.
    Wilson EB. (1927) <doi:10.1080/01621459.1927.10502953>.
    Altman DG et al (2000, ISBN: 978-0-727-91375-3).
    Chiang CL. (1968, ISBN: 978-0-882-75200-6).
    Newell C. (1994, ISBN: 978-0-898-62451-9).
    Eayres DP, Williams ES (2004) <doi:10.1136/jech.2003.009654>.
    Silcocks PBS et al (2001) <doi:10.1136/jech.55.1.38>.
    Low and Low (2004) <doi:10.1093/pubmed/fdh175>.
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.
Generates predicted stage change days for an insect, based on daily temperatures and development rate parameters, as developed by Pollard (2014) <http://mural.maynoothuniversity.ie/view/ethesisauthor/Pollard=3ACiaran_P=2E=3A=3A.html>. A few example datasets are included and implemented for P. vulgatissima, the blue willow beetle, but the approach can be readily applied to other species that display similar behaviour.
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.
A bundle to facilitate working with PhenoCam timeseries and data.   The user would be able to obtain phenological time-series and site metadata from the PhenoCam network <https://phenocam.sr.unh.edu/webcam/>.
Programmatic interface to the 'PhenoCam' web services (<http://phenocam.sr.unh.edu>).
    Allows for easy downloading of 'PhenoCam' data directly to your R workspace
    or your computer and provides post-processing routines for consistent and easy
    timeseries outlier detection, smoothing and estimation of phenological transition dates.
    Methods for this package are described in detail in Hufkens et. al (2018) <doi:10.1111/2041-210X.12970>.
Using the Bayesian state-space approach, we developed a continuous development model to quantify dynamic incremental changes in the response variable. While the model was originally developed for daily changes in forest green-up, the model can be used to predict any similar process. The CDM can capture both timing and rate of nonlinear processes. Unlike statics methods, which aggregate variations into a single metric, our dynamic model tracks the changing impacts over time. The CDM accommodates nonlinear responses to variation in predictors, which changes throughout development.
The merits of 'TIMESAT' and 'phenopix' are adopted. Besides, a simple and 
    growing season dividing method and a practical snow elimination method 
    based on Whittaker were proposed. 7 curve fitting methods and 4 phenology 
    extraction methods were provided. Parameters boundary are considered for 
    every curve fitting methods according to their ecological meaning. 
    And 'optimx' is used to select best optimization method for different 
    curve fitting methods.
    Reference:
    Dongdong Kong, R package: A state-of-the-art Vegetation Phenology extraction package, 
    phenofit version 0.2.3, <https://github.com/kongdd/phenofit>;
    Zhang, Q., Kong, D., Shi, P., Singh, V.P., Sun, P., 2018. Vegetation phenology 
    on the Qinghai-Tibetan Plateau and its response to climate change (1982–2013). 
    Agric. For. Meteorol. 248, 408–417. <doi:10.1016/j.agrformet.2017.10.026>.
Functions used to fit and test the phenology of species based on counts.
This tool projects annual phenology metrics and long-term phenology trends, 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.
Simulation is a critical part of method development and assessment
    in quantitative genetics. 'PhenotypeSimulator' allows for the flexible 
    simulation of phenotypes under different models, including genetic variant 
    and  infinitesimal genetic effects (reflecting population structure) as well 
    as non-genetic covariate effects, observational noise and additional 
    correlation effects. The different phenotype components are combined into a 
    final phenotype while controlling for the proportion of variance explained 
    by each of the components. For each effect component, the number of 
    variables, their distribution and the design of their effect across traits 
    can be customised. For the simulation of the genetic effects, external 
    genotype data from a number of standard software ('plink', 'hapgen2'/
    'impute2', 'genome', 'bimbam', simple text files) can be imported. The final 
    simulated phenotypes and its components can be automatically saved into .rds 
    or .csv files. In addition, they can be saved in formats compatible with 
    commonly used genetic association software ('gemma', 'bimbam', 'plink', 
    'snptest', 'LiMMBo').
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.
Analysis of features by phi delta diagrams. In particular, functions for reading data and calculating phi and delta as well as the functionality to plot it. Moreover it is possible to do further analysis on the data by generating rankings. For more information on phi delta diagrams, see also Giuliano Armano (2015) <doi:10.1016/j.ins.2015.07.028>.
Computes 46 optimized distance and similarity measures for comparing probability functions (Drost (2018) <doi:10.21105/joss.00765>). 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").
There are a lot of different typical tasks that should be solved during phonetic research and experiments, such as creation of presentation that will contain all stimuli, rename and concatenate multiple sound files recorded during phonetic session, automatically annotate 'Praat' TextGrids (this is one of the sound annotation standards provided by 'Praat' software, see Paul Boersma, David Weenink 2018 <http://www.fon.hum.uva.nl/praat/>), create an html that includes annotation and spectrogram tables, convert multiple formats ('Praat' TextGrid, 'EXMARaLDA' and 'ELAN') into each other. All those tasks could be solved by mixture of different tools (some programs in any programming language for automatic renaming, some Praat scripts for the file concatenation and renaming, etc). This package provides functionality that will make it easier to solve those tasks independently of any of tools. You could also compare the functionality with other packages: 'rPraat' <https://CRAN.R-project.org/package=rPraat>, 'textgRid' <https://CRAN.R-project.org/package=textgRid>.
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. This package is part of the
    'r4photobiology' suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Spectral 'transmittance' data for frequently used filters and 
    similar materials. Plastic sheets and films; photography filters; 
    theatrical gels; machine-vision filters; various types of window glass;
    optical glass and some laboratory plastics and glassware. Part of the
    'r4photobiology' suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
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. Part of the 'r4photobiology' suite, 
    Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Spectral emission data for some frequently used lamps excluding 
    led emitting diodes (LEDs) available as electronic components. Original 
    spectral irradiance data for incandescent-, LED- and discharge lamps are 
    included. They are complemented by data on the effect of temperature on
    the emission by fluorescent tubes. Part of the 'r4photobiology' suite, 
    Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
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. Part of the 'r4photobiology' 
    suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Spectral response data for broadband ultraviolet and visible
    radiation sensors. Different data sources were used: author-supplied data
    from scientific research papers, sensor-manufacturer supplied data, and
    published sensor specifications. Part of the 'r4photobiology' suite
    Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Data for the extraterrestrial solar spectral irradiance and ground 
    level solar spectral irradiance and irradiance. In addition data for 
    shade light under vegetation and irradiance time series.  Part of the 
    'r4photobiology' suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
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.  Part of the
    'r4photobiology' suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Simulate C3 photosynthesis using the Farquhar, von Caemmerer, Berry (1980) <doi:10.1007/BF00386231> model as described in Buckley and Diaz-Espejo (2015) <doi:10.1111/pce.12459>. It uses units to ensure that parameters are properly specified and transformed before calculations. Temperature response functions get automatically "baked" into all parameters based on leaf temperature following Bernacchi et al. (2002) <doi:10.1104/pp.008250>. The package includes boundary layer, cuticular, stomatal, and mesophyll conductances to CO2, which each can vary on the upper and lower portions of the leaf. Use straightforward functions to simulate photosynthesis over environmental gradients such as Photosynthetic Photon Flux Density (PPFD) and leaf temperature, or over trait gradients such as CO2 conductance or photochemistry.
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.
Make it easy to review, download and execute scripts stored in Github
  'phuse-scripts' repository <https://github.com/phuse-org/phuse-scripts>. Some examples
  included show the web application framework using the script metadata. The 'PhUSE'
  is Pharmaceutical Users Software Exchange <http://www.phuse.eu>.
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.
Implements some methods in phyloclimatic modeling: 
  estimation of ancestral climatic niches, age-range-correlation, 
  niche equivalency test and background-similarity test.
Interface to 'Phylocom' (<http://phylodiversity.net/phylocom/>),
    a library for analysis of 'phylogenetic' community structure and
    character evolution. Includes low level methods for interacting with
    the three executables, as well as higher level interfaces for methods
    like 'aot', 'ecovolve', 'bladj', 'phylomatic', and more.
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. See Bastide et al. (2017) <doi:10.1111/rssb.12206> and
    Bastide et al. (2018) <doi:10.1093/sysbio/syy005>.
Manipulation and analysis of phylogenetically simulated
        data sets and phylogenetically based analyses using GLS.
Contains functions for developing phylogenetic trees as
    deeply-nested lists ("dendrogram" objects).
    Enables bi-directional conversion between dendrogram and
    "phylo" objects
    (see Paradis et al (2004) <doi:10.1093/bioinformatics/btg412>),
    and features several tools for command-line tree
    manipulation and import/export via Newick parenthetic text.
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>.
Provides functions for graph-based multiple-sample
    testing and visualization of microbiome data, in particular data
    stored in 'phyloseq' objects. The tests are based on those
    described in Friedman and Rafsky (1979)
    <http://www.jstor.org/stable/2958919>, and the tests are described
    in more detail in Callahan et al. (2016)
    <doi:10.12688/f1000research.8986.1>.
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. Citation: Botond Sipos, Tim Massingham, Gregory E Jordan and Nick Goldman (2011) PhyloSim - Monte Carlo simulation of sequence evolution in the R statistical computing environment <doi:10.1186/1471-2105-12-104>.
A pipeline for the identification, within taxonomic groups, of
    orthologous sequence clusters from
    'GenBank' <https://www.ncbi.nlm.nih.gov/genbank/> as the first step in a
    phylogenetic analysis. The pipeline depends on a local alignment search tool
    and is, therefore, not dependent on differences in gene naming conventions
    and naming errors.
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.
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 method is, however, based on the nowadays rather uncommon situation in which uncertainty about a phylogeny is well represented by adopting a single polytomous tree. 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.
It provides a function "wearingMarking" for classification of monitor
    wear and nonwear time intervals in accelerometer data collected to assess
    physical activity. The package also contains functions for making plot for 
    accelerometer data and obtaining the summary of various information including 
    daily monitor wear time and the mean monitor wear time during valid days.      
    The revised package version 0.2-1 improved the functions in the previous version
    regarding speed and robustness. In addition, several functions were added: 
    "markDelivery" can classify days for ActiGraph delivery by mail; 
    "markPAI" can categorize physical activity intensity level based on user-defined 
    cut-points of accelerometer counts. It also supports importing ActiGraph AGD files 
    with "readActigraph" and "queryActigraph" functions. The package also better supports
    time zones and daylight saving.
A variety of formulae are provided for estimation
    of physiologic characteristics of infants, children, and adults.
    Calculations include: body surface area, ideal weight, airway
    dead-space, the alveolar gas equation, and GFR.  Each formula is
    referenced to the original publication. 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.
Functions for phylocom integration, community analyses, null-models, traits and evolution. Implements numerous ecophylogenetic approaches including measures of community phylogenetic and trait diversity, phylogenetic signal, estimation of trait values for unobserved taxa, null models for community and phylogeny randomizations, and utility functions for data input/output and phylogeny plotting. A full description of package functionality and methods are provided by Kembel et al. (2010) <doi:10.1093/bioinformatics/btq166>.
Computationally efficient tools for fitting generalized linear model with convex or non-convex penalty. Users can enjoy the superior statistical property of non-convex penalty such as SCAD and MCP which has significantly less estimation error and overfitting compared to convex penalty such as lasso and ridge. Computation is handled by multi-stage convex relaxation and the PathwIse CAlibrated Sparse Shooting algOrithm (PICASSO) which exploits warm start initialization, active set updating, and strong rule for coordinate preselection to boost computation, and attains a linear convergence to a unique sparse local optimum with optimal statistical properties. The computation is memory-optimized using the sparse matrix output.
A collection of scripts and data files for the statistics text: 
 "Process Improvement using Data" <https://learnche.org/pid> and the online 
 course "Experimentation for Improvement" found on Coursera. 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.
Functions to make board game graphics.  By default makes game diagrams, animations, and "Print & Play" layouts for the 'piecepack' <http://www.ludism.org/ppwiki> but can be configured to make graphics for other board game systems.
Implements piecewise structural equation modeling from a single
    list of structural equations, with new methods for non-linear, latent, and
    composite variables, standardized coefficients, query-based prediction and
    indirect effects. See <http://jslefche.github.io/piecewiseSEM/> for more.
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>).
Because larger (> 50 MB) data files cannot easily be committed to git,
  a different approach is required to manage data associated with an analysis in a 
  GitHub repository.  This package provides a simple work-around by allowing larger
  (up to 2 GB) data files to piggyback on a repository as assets attached to individual
  GitHub releases.  These files are not handled by git in any way, but instead are
  uploaded, downloaded, or edited directly by calls through the GitHub API. These
  data files can be versioned manually by creating different releases.  This approach
  works equally well with public or private repositories.  Data can be uploaded
  and downloaded programmatically from scripts. No authentication is required to
  download data from public repositories.
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 'pillar' and 'colonnade' generics 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').
An implementation of prediction intervals for random-effects meta-analysis:
  Higgins et al. (2009) <doi:10.1111/j.1467-985X.2008.00552.x>, Partlett and Riley (2017)
  <doi:10.1002/sim.7140>, and Nagashima et al. (2018) <doi:10.1177/0962280218773520>,
  <arXiv:1804.01054>.
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.
To assist you with troubleshooting internet connection issues and
    assist in isolating packet loss on your network. It does this by allowing you to 
    retrieve the top trace route destinations your internet provider uses, and recursively 
    ping each server in series while capturing the results and writing them to a log file. 
    Each iteration it queries the destinations again, before shuffling the sequence of 
    destinations to ensure the analysis is unbiased and consistent across each trace route.
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.
Packages data about the victims of the Pinochet regime as compiled by the Chilean National Commission for Truth and Reconciliation Report (1991, ISBN:9780268016463).
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'.
Provides a robust approach for omics data integration and disease subtyping. 'PINSPlus' supports both single and multiple data types. The software automatically determines the optimal number of clusters and then partitions the samples in a way such that the results are robust to noise and data perturbation. 'PINSPlus' is fast and it supports parallel computing on Windows, Linux, and Mac OS.
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), Sijiao (four or five numerical digits per character. See <https://en.wikipedia.org/wiki/Four-Corner_Method>.), Wubi (an input method with five strokes. See <https://en.wikipedia.org/wiki/Wubi_method>) or user-defined codes.
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>).
Calculate and optimize dynamic performance ratings of association football 
  teams competing in matches, in accordance with the method used in 
  the research paper "Determining the level of ability of football teams by 
  dynamic ratings based on the relative discrepancies in scores between adversaries", 
  by Constantinou and Fenton (2013) 
  <doi:10.1515/jqas-2012-0036>    
  This dynamic rating system has proven to provide superior 
  results for predicting association football outcomes.
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.
Collection of pivotal algorithms 
             for: relabelling the MCMC chains in order to undo the label 
             switching problem in Bayesian mixture models,
             as proposed in Egidi, Pappadà, Pauli and Torelli (2018a)<doi:10.1007/s11222-017-9774-2>;
             initializing the centers of the classical k-means algorithm 
             in order to obtain a better clustering solution. For further details see
             Egidi, Pappadà, Pauli and Torelli (2018b)<ISBN:9788891910233>.
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.
'Pivotal Tracker' <https://www.pivotaltracker.com> is a project
    management software-as-a-service that provides a REST API. This package
    provides an R interface to that API, allowing you to query it and work with
    its responses.
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.
Pharmacokinetics is the study of drug absorption, distribution, 
   metabolism, and excretion. The pharmacokinetics model explains that how the 
   drug concentration change as the drug moves through the different compartments
   of the body. For pharmacokinetic modeling and analysis, it is essential
   to understand the basic pharmacokinetic parameters. All parameters are 
   considered, but only some of parameters are used in the model. Therefore, 
   we need to convert the estimated parameters to the other parameters after 
   fitting the specific pharmacokinetic model. This package is developed to help
   this converting work. For more detailed explanation of pharmacokinetic 
   parameters, see "Gabrielsson and Weiner" (2007), "ISBN-10: 9197651001";
   "Benet and Zia-Amirhosseini" (1995) <DOI: 10.1177/019262339502300203>; 
   "Mould and Upton" (2012) <doi:10.1038/psp.2012.4>;
   "Mould and Upton" (2013) <doi:10.1038/psp.2013.14>.
Provides functions used to build R packages. Locates compilers
  needed to build R packages on various platforms and ensures the PATH is
  configured appropriately so R can use them.
Metadata and package cache for CRAN-like repositories.
     This is a utility package to be used by package management tools
     that want to take advantage of caching.
This provides utilities for creating classed error and warning
  conditions based on where the error originated.
Set configuration options on a per-package basis.
    Options set by a given package only apply to that package,
    other packages are unaffected.
Generate an attractive and useful website from a source package.
    'pkgdown' converts your documentation, vignettes, 'README', and more to 
    'HTML' making it easy to share information about your package online.
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.
Simulates the process of installing a package
    and then attaching it. This is a key part of the 'devtools' package as it
    allows you to rapidly iterate while developing a 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.
Tools from the domain of graph theory can be used to quantify the complexity
             and vulnerability to failure of a software package. That is the guiding philosophy
             of this package. 'pkgnet' provides tools to analyze the dependencies between functions
             in an R package and between its imported packages.
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.
Search CRAN R packages. Uses the 'METACRAN' search server,
  see <https://r-pkg.org>.
Build your own universe of packages similar to the 'tidyverse'
    package <https://tidyverse.org/> with this meta-package creator. Create a
    package-verse, or meta package, by supplying a custom name for the
    collection of packages and the vector of desired package names to include–
    and optionally supply a destination directory, an indicator of whether to
    keep the created package directory, and/or a vector of verbs implement via
    the 'usethis' <http://usethis.r-lib.org/> package.
PKI functions such as verifying certificates, RSA encription and signing which can be used to build PKI infrastructure and perform cryptographic tasks.
We implement two least-squares estimators under k-monotony constraint using a method based on the Support Reduction Algorithm from Groeneboom et al (2008) <DOI:10.1111/j.1467-9469.2007.00588.x>. The first one is a projection estimator on the set of k-monotone discrete functions. The second one is a projection on the set of k-monotone discrete probabilities. This package provides functions to generate samples from the spline basis from Lefevre and Loisel (2013) <DOI:10.1239/jap/1378401239>, and from mixtures of splines.
Compute standard Non-Compartmental Analysis (NCA)
    parameters and summarize them.  In addition to this core work, it
    also provides standardized plotting routines, basic assessments
    for biocomparison or drug interaction, and model-based estimation
    routines for calculating doses to reach specific values of AUC or
    Cmax.
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
  (Heller & Wickelmaier, 2013 <doi:10.1016/j.endm.2013.05.145>).
A tool for inferring kinase activity changes from 
    phosphoproteomics data. 'pKSEA' uses kinase-substrate 
    prediction scores to weight observed changes in 
    phosphopeptide abundance to calculate a phosphopeptide-level 
    contribution score, then sums up these contribution scores by 
    kinase to obtain a phosphoproteome-level kinase activity 
    change score (KAC score). 'pKSEA' then assesses the 
    significance of changes in predicted substrate abundances for 
    each kinase using permutation testing. This results in a 
    permutation score (pKSEA significance score) reflecting the 
    likelihood of a similarly high or low KAC from random chance, 
    which can then be interpreted in an analogous manner to an 
    empirically calculated p-value. 'pKSEA' contains default 
    databases of kinase-substrate predictions from 'NetworKIN' 
    (NetworKINPred_db) <http://networkin.info> 
    Horn, et. al (2014) <doi:10.1038/nmeth.2968>
    and of known kinase-substrate links from 'PhosphoSitePlus' 
    (KSEAdb) <https://www.phosphosite.org/>
    Hornbeck PV, et. al (2015) <doi:10.1093/nar/gku1267>.
Applying the global sensitivity analysis workflow to investigate the parameter uncertainty and sensitivity in pharmacokinetic (PK) models, especially the physiologically-based pharmacokinetic (PBPK) model with multivariate outputs. The package also provide some functions to check the sensitivity measures and its convergence of model parameters.
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.
Functions to prepare rankings data and fit the Plackett-Luce model
    jointly attributed to Plackett (1975) <doi:10.2307/2346567> and Luce
    (1959, ISBN:0486441369). The standard Plackett-Luce model is generalized
    to accommodate ties of any order in the ranking. Partial rankings, in which
    only a subset of items are ranked in each ranking, are also accommodated in
    the implementation. Disconnected/weakly connected networks implied by the
    rankings may be handled by adding pseudo-rankings with a hypothetical item.
    Optionally, a multivariate normal prior may be set on the log-worth
    parameters and ranker reliabilities may be incorporated as proposed by
    Raman and Joachims (2014) <doi:10.1145/2623330.2623654>. Maximum a
    posteriori estimation is used when priors are set. Methods are provided to
    estimate standard errors or quasi-standard errors for inference as well as
    to fit Plackett-Luce trees. See the package website or vignette for further
    details.
Provides methods for plotting potentially large (raster) images
    interactively on a plain HTML canvas. In contrast to package 'mapview'
    data are plotted without background map, but data can be projected to
    any spatial coordinate reference system.
    Supports plotting of classes 'RasterLayer', 'RasterStack', 'RasterBrick'
    (from package 'raster') as well as 'png' files located on disk. 
    Interactivity includes zooming, panning, and mouse location information. 
    In case of multi-layer 'RasterStacks' or 'RasterBricks', RGB image plots 
    are created (similar to 'raster::plotRGB' - but interactive).
Supports the creation of 'burndown' charts and 'gantt' diagrams.
Solves the electromagnetic problem of reflection and transmission at a planar multilayer interface. Also computed are the decay rates and emission profile for a dipolar emitter.
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.
The goal of 'planets' is to provide of very simple and accessible data containing basic information from all known planets.
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, Glicko-2 and Stephenson. Contains pdf documentation 
  of a reproducible analysis using approximately two million chess 
  matches.
We present a penalized log-density estimation method using Legendre polynomials with lasso penalty to adjust estimate's smoothness. Re-expressing the logarithm of the density estimator via a linear combination of Legendre polynomials, we can estimate parameters by maximizing the penalized log-likelihood function. Besides, we proposed an implementation strategy that builds on the coordinate decent algorithm, together with the Bayesian information criterion (BIC).
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, data and user interfaces for performing Physiologically Based Pharmacokinetic('PBPK') Modeling, In-vitro to In-vivo Extrapolation ('IVIVE') and exposure estimation. Also contains user interfaces to run models from the 'httk' package. Taken together these provide an easy to use and powerful modeling tool that can be used for all steps along the source-to-outcome continuum.All the analysis tools in the package can only be run as shiny apps. Check vignettes and package help for more information. More information on PBPK modeling can be found in the book 'Physiologically Based Pharmacokinetic Modeling: Science and Applications' by Reddy et al <doi:10.1002/0471478768>.
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
Fits a pliable lasso model.  For details see Tibshirani and Friedman (2018) <arXiv:1712.00484>.
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.
Genotyping arrays enable the direct measurement of an individuals
    genotype at thousands of markers. 'plinkQC' facilitates genotype quality
    control for genetic association studies as described by Anderson and
    colleagues (2010) <doi:10.1038/nprot.2010.116>. It makes 'PLINK' basic
    statistics (e.g. missing genotyping rates per individual, allele frequencies
    per genetic marker) and relationship functions accessible from 'R' and
    generates a per-individual and per-marker quality control report.
    Individuals and markers that fail the quality control can subsequently be
    removed to generate a new, clean dataset. Removal of individuals based on
    relationship status is optimised to retain as many individuals as possible
    in the study.
PLIS is a multiple testing procedure for testing several
        groups of hypotheses. Linear dependency is expected from the
        hypotheses within the same group and is modeled by hidden
        Markov Models. It is noted that, for PLIS, a smaller p value
        does not necessarily imply more significance because of
        dependency among the hypotheses. A typical application of PLIS
        is to analyze genome wide association studies datasets, where
        SNPs from the same chromosome are treated as a group and
        exhibit strong linear genomic dependency.
A set of estimators and tests for panel data
             econometrics, as described in Baltagi (2013) Econometric Analysis of Panel Data, ISBN-13:978-1-118-67232-7,
	     Hsiao (2014) Analysis of Panel Data  <doi:10.1017/CBO9781139839327> and
	     Croissant and Millo (2018), Panel Data Econometrics with R, ISBN:978-1-118-94918-4.
Fit finite mixtures of Plackett-Luce models for partial top rankings/orderings within the Bayesian framework. It provides MAP point estimates via EM algorithm and posterior MCMC simulations via Gibbs Sampling. It also fits MLE as a special case of the noninformative Bayesian analysis with vague priors. In addition to inferential techniques, the package assists other fundamental phases of a model-based analysis for partial rankings/orderings, by including functions for data manipulation, simulation, descriptive summary, model selection and goodness-of-fit evaluation. Main references on the methods are Mollica and Tardella (2017) <doi.org/10.1007/s11336-016-9530-0> and Mollica and Tardella (2014) <doi/10.1002/sim.6224>.
Utilizes the 'lme4' and 'optimx' packages (previously the optim()
    function from 'stats') to estimate (generalized) linear mixed models (GLMM)
    with factor structures using a profile likelihood approach, as outlined in
    Jeon and Rabe-Hesketh (2012) <doi:10.3102/1076998611417628> and
    Rockwood and Jeon (2019) <doi:10.1080/00273171.2018.1516541>.
    Factor analysis and item response models can be extended to allow
    for an arbitrary number of nested and crossed random effects,
    making it useful for multilevel and cross-classified models.
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.
The Poisson-lognormal model and variants can be used for 
    a variety of multivariate problems when count data are at play, including 
    principal component analysis for count data (Chiquet, Mariadassou and Robin, 
    2018 <doi:10.1214/18-AOAS1177>), discriminant analysis and 
    network inference (Chiquet, Mariadassou and Robin, 2018 <arXiv:1806.03120>). 
    Implements variational algorithms to fit such models accompanied with a set of 
    functions for visualization and diagnostic.
A simple header-only logging library for C++.
  Add 'LinkingTo: plogr' to 'DESCRIPTION', and '#include <plogr.h>' in your C++ modules to use it.
Multivariate ordered probit model, i.e. the extension of the scalar ordered probit model where the observed variables have dimension greater than one. Estimation of the parameters is done via maximization of the pairwise likelihood, a special case of the composite likelihood obtained as product of bivariate marginal distributions. The package uses the Fortran 77 subroutine SADMVN by Alan Genz, with minor adaptations made by Adelchi Azzalini in his "mvnormt" package for evaluating the two-dimensional Gaussian integrals involved in the pairwise log-likelihood. Optimization of the latter objective function  is performed via quasi-Newton box-constrained optimization algorithm, as implemented in nlminb.
Visualizes a matrix object plainly as heatmap. It provides S3 functions to plot simple matrices and loading matrices.
Functions for viewing 2-D and 3-D data, including perspective plots, slice plots, surface plots, scatter plots, etc. Includes data sets from oceanography.
The 'rgl' implementation of plot3D functions.
An implementation of the ternary plot for interpreting regression
    coefficients of trinomial regression models, as proposed in Santi, Dickson
    and Espa (2018) <doi:10.1080/00031305.2018.1442368>. Ternary plots can be
    drawn using either 'ggtern' package (based on 'ggplot2') or 'Ternary'
    package (based on standard graphics).
This function plots a contour line with a user-defined probability and tightness of fit.
Easily visualize and animate 'tabledap' and 'griddap' objects obtained via the 'rerddap' package in a simple one-line command,  using either base graphics or 'ggplot2' graphics. 'plotdap' handles extracting and reshaping the data,  map projections and continental outlines.  Optionally the data can be animated through time using the 'gganmiate' package.
When analyzing data, plots are a helpful tool for visualizing data and interpreting statistical models. This package provides a set of simple tools for building plots incrementally, starting with an empty plot region, and adding bars, data points, regression lines, error bars, gradient legends, density distributions in the margins, and even pictures. The package builds further on R graphics by simply combining functions and settings in order to reduce the amount of code to produce for the user. As a result, the package does not use formula input or special syntax, but can be used in combination with default R plot functions. Note: Most of the functions were part of the package 'itsadug', which is now split in two packages: 1. the package 'itsadug', which contains the core functions for visualizing and evaluating nonlinear regression models, and 2. the package 'plotfunctions', which contains more general plot functions.
The main function, plot_GMM, is used for plotting output from Gaussian mixture models (GMMs), 
    including both densities and overlaying mixture weight component curves from the fit GMM. The package
    also include the function, plot_cut_point, which plots the cutpoint (mu) from the GMM over a histogram
    of the distribution with several color options. Finally, the package includes the function, plot_mix_comps, 
    which is used in the plot_GMM function, and can be used to create a custom plot for overlaying mixture 
    component curves from GMMs. For the plot_mix_comps function, usage most often will be specifying 
    the "fun" argument within "stat_function" in a ggplot2 object.
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.
An extension to 'ggplot2' with miscellaneous functions. It contains 
    two groups of functions: Functions in the first group draw 'ggplot2' - based plots: gg_shading_bar() draws barplot 
	with shading colors in each bar. geom_rect_cm(), geom_circle_cm() and geom_ellipse_cm() draw rectangles, circles 
	and ellipses with centimeter as their unit. Thus their sizes do not change when the coordinate system or the aspect ratio 
	changes. annotation_transparent_text() draws labels with transparent texts. annotation_shading_polygon() draws irregular 
	polygons with shading colors. Functions in the second group generate coordinates for regular shapes and make linear transformations.
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'.
Create interactive web graphics from 'ggplot2' graphs and/or a custom interface to the (MIT-licensed) JavaScript library 'plotly.js' inspired by the grammar of graphics.
Includes 'JavaScript' files that allow 'plotly' maps to render without an internet connection.
Markov chain Monte Carlo diagnostic plots. The purpose of the
  package is to combine existing tools from the 'coda' and 'lattice' packages,
  and make it easy to adjust graphical details.
Plot marginal effects for interactions estimated
    from linear models.
Plot model surfaces for a wide variety of models
        using partial dependence plots and other techniques.
        Also plot model residuals and other information on the model.
Plot principal component histograms around a bivariate
        scatter plot.
Useful set of tools for plotting network diagrams in any kind of project.
Creates an envelope around a set of plotted points. The
        envelope is compact with a boundary that is continuous, smooth
        and convex. Each point is represented as a circle and the
        circles and connecting lines are the solution to the multiple
        pulley problem. This method can be used to highlight regions in
        a two-dimensional space.
Lots of plots, various labeling, axis and color scaling functions.
Most ROC curve plots obscure the cutoff values and inhibit
    interpretation and comparison of multiple curves. This attempts to address
    those shortcomings by providing plotting and interactive tools. Functions
    are provided to generate an interactive ROC curve plot for web use, and
    print versions. A Shiny application implementing the functions is also
    included.
Functions for making visual exploratory data analysis with nested data easier.
Figures rendered on graphics devices are usually 
 rescaled to fit pre-determined device dimensions. 'plotscale' 
 implements the reverse: desired plot dimensions are specified 
 and device dimensions are calculated to accommodate marginal
 material, giving consistent proportions for plot elements.
 Default methods support grid graphics such as lattice and ggplot.
 See "example('devsize')" and "vignette('plotscale')".
Contains a graphical user interface to generate the diagnostic
    plots proposed by Bauer (2005; <doi:10.1207/s15328007sem1204_1>), 
    Pek & Chalmers (2015; <doi:10.1080/10705511.2014.937790>), and
    Pek, Chalmers, R. Kok, & Losardo (2015; <doi:10.3102/1076998615589129>) to investigate
    nonlinear bivariate relationships in latent regression models using structural
    equation mixture models (SEMMs).
Small self-contained plots for use in larger plots or to
    delegate plotting in other functions. Also contains a number of
    alternative color palettes and HSL color space based tools to modify
    colors or palettes.
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.
Fit a model with potentially many linear and smooth predictors. Interaction
    effects can also be quantified. Variable selection is done using penalisation. For l1-type penalties
    we use iterative steps alternating between using linear predictors (lasso) and smooth predictors
    (generalised additive model).
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.
Provides partial least squares analysis for the analysis of the relation
    between two high-dimensional data sets. Includes permutation testing and
    bootstrapping for resulting latent variables (following McIntosh & Lobaugh (2004) <doi:10.1016/j.neuroimage.2004.07.020>)
    and several visualization functions.
Provides Partial least squares Regression for (weighted) beta regression models (Bertrand 2013,  <http://journal-sfds.fr/article/view/215>) and k-fold cross-validation of such models using various criteria. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.
Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models in high dimensional settings <doi:10.1093/bioinformatics/btu660>, Bastien, P., Bertrand, F., Meyer N., Maumy-Bertrand, M. (2015), Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data, Bioinformatics, 31(3):397-404. Cross validation criteria were studied in <arXiv:1810.02962>, Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data.
Provides (weighted) Partial least squares Regression for generalized linear models and repeated k-fold cross-validation of such models using various criteria. 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.
Efficient procedures for fitting an entire regression
        sequences with different model types.
Retrieves a 'pluscode' by inputting latitude and longitude. Includes additional functions to retrieve neighbouring 'pluscodes'.
plusser provides an API interface to Google+ so that posts,
    profiles and pages can be automatically retrieved.
A set of tools that solves a common set of problems: you
    need to break a big problem down into manageable pieces, operate on each
    piece and then put all the pieces back together.  For example, you might
    want to fit a model to each spatial location or time point in your study,
    summarise data by panels or collapse high-dimensional arrays to simpler
    summary statistics. The development of 'plyr' has been generously supported
    by 'Becton Dickinson'.
Interface to 'PM4py' <http://pm4py.org>, a process mining library 
    in 'Python'. This package uses the 'reticulate' package to act as a bridge 
    between 'PM4Py' and the 'R' package 'bupaR'. It provides several process 
    discovery algorithms, evaluation measures, and alignments.
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 MPI' implementations such as 'OpenMPI'
        and 'MPICH'.
        See the High Performance Statistical Computing website
	<https://snoweye.github.io/hpsc/>
	for more information, documents and examples.
Note, that the 'PMCMR' package is superseded
	     by the novel 'PMCMRplus' package. The 'PMCMRplus'
	     package contains all functions from 
	     'PMCMR' and many more parametric and non-parametric
	     multiple comparison procedures, one-factorial trend
	     tests, as well as improved method functions, 
	     such as print, summary and plot. 
	     The 'PMCMR' package is no longer maintained,
	     but kept for compatibility of reverse depending
	     packages for some time.
For one-way layout experiments the one-way ANOVA can
	     be performed as an omnibus test. All-pairs multiple comparisons 
	     tests (Tukey-Kramer test, Scheffe test, LSD-test) 
	     and many-to-one tests (Dunnett test) for normally distributed 
	     residuals and equal within variance are available. Furthermore,
	     all-pairs tests (Games-Howell test, Tamhane's T2 test, 
	     Dunnett T3 test, Ury-Wiggins-Hochberg test) and many-to-one
	     (Tamhane-Dunnett Test) for normally distributed residuals 
	     and heterogeneous variances are provided. Van der Waerden's normal
	     scores test for omnibus, all-pairs and many-to-one tests is
	     provided for non-normally distributed residuals and homogeneous
	     variances. The Kruskal-Wallis, BWS and Anderson-Darling
	     omnibus test and all-pairs tests
	     (Nemenyi test, Dunn test, Conover test, Dwass-Steele-Critchlow-
	     Fligner test) as well as many-to-one (Nemenyi test, Dunn test,
	     U-test) are given for the analysis of variance by ranks. 
             Non-parametric trend tests (Jonckheere test, Cuzick test,
	     Johnson-Mehrotra test, Spearman test) are included. 
	     In addition, a Friedman-test for one-way ANOVA with repeated 
	     measures on ranks (CRBD) and Skillings-Mack test for unbalanced 
	     CRBD is provided with consequent all-pairs tests (Nemenyi test, 
	     Siegel test, Miller test, Conover test, Exact test)
	     and many-to-one tests (Nemenyi test, Demsar test, Exact test). 
	     A trend can be tested with Pages's test. Durbin's test 
	     for a two-way balanced incomplete block design (BIBD) is given 
	     in this package as well as Gore's test for CRBD with multiple
	     observations per cell is given.  Outlier tests, Mandel's k- and
	     h statistic as well as functions for Type I error and Power 
	     analysis as well as generic summary, print and plot methods 
             are provided.
Paired mass distance (PMD) analysis proposed in Yu, Olkowicz and Pawliszyn (2018) <doi:10.1016/j.aca.2018.10.062> for gas/liquid chromatography–mass spectrometry (GC/LC-MS) based non-targeted analysis. PMD analysis including GlobalStd algorithm and structure/reaction directed analysis. GlobalStd algorithm could found independent peaks in m/z-retention time profiles based on retention time hierarchical cluster analysis and frequency analysis of paired mass distances within retention time groups. Structure directed analysis could be used to find potential relationship among those independent peaks in different retention time groups based on frequency of paired mass distances. A GUI for PMD analysis is also included as a 'shiny' application.
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.
Penalized Multi-Band Learning algorithm can be effectively implemented for circadian rhythm analysis and daily activity pattern characterization using actigraphy (continuously measured objective physical activity data). Functions for interactive visualization of actigraph data are also included. Method reference: Li, X., Kane, M., Zhang, Y., Sun, W., Song, Y., Dong, S., Lin, Q., Zhu, Q., Jiang, F., Zhao, H. (2019) A Novel Penalized Multi-band Learning Approach Characterizes the Consolidation of Sleep-Wake Circadian Rhythms During Early Childhood Development.
Miscellaneous functions for data analysis, graphics, data manipulation, statistical investigation, including descriptive statistics, creating leading and lagging variables, portfolio return analysis, time series difference and percentage change calculation, stacking data for higher efficient analysis.
The Predictive Model Markup Language (PMML) is an XML-based language which provides a way for applications to define machine learning, statistical and data mining models and to share models between PMML compliant applications. More information about the PMML industry standard and the Data Mining Group can be found at <http://www.dmg.org>. The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products, which integrate with web services, relational database systems and deploy natively on Hadoop in conjunction with Hive, Spark or Storm, as well as allow predictive analytics to be executed for IBM z Systems mainframe applications and real-time, streaming analytics platforms. The package isofor (used for anomaly detection) can be installed with devtools::install_github("Zelazny7/isofor").
Allows for data to be transformed before using it to construct models. Builds structures to allow functions in the PMML package to
    output transformation details in addition to the model in the resulting PMML file. The Predictive Model Markup Language (PMML) is an XML-based language which provides a way for applications to define machine learning, statistical and data mining models and to share models between PMML compliant applications. More information about the PMML industry standard and the Data Mining Group can be found at <http://www.dmg.org>. The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products, which integrate with web services, relational database systems and deploy natively on Hadoop in conjunction with Hive, Spark or Storm, as well as allow predictive analytics to be executed for IBM z Systems mainframe applications and real-time, streaming analytics platforms.
Dynamic panel modelling framework based on an empirical-Bayes approach.
             Contains tools for computing point forecasts and bootstrapping prediction intervals.
             Reference: Liu et al. (2016) <doi:10.2139/ssrn.2889000>.
Descriptive statistics (mean rank, pairwise frequencies, and marginal matrix), Analytic Hierarchy Process models (with Saaty's and Koczkodaj's inconsistencies), probability models (Luce models, distance-based models, and rank-ordered logit models) and visualization with multidimensional preference analysis for ranking data are provided. Current, only complete rankings are supported by this package.
Computes the minimum sample size required for the development of a new multivariable prediction model using the criteria proposed by Riley et al. (2018) <doi: 10.1002/sim.7992>. pmsampsize can be used to calculate the minimum sample size for the development of models with continuous, binary or survival (time-to-event) outcomes. Riley et al. (2018) <doi: 10.1002/sim.7992> lay out a series of criteria the sample size should meet. These aim to minimise the overfitting and to ensure precise estimation of key parameters in the prediction model.
Implements multinomial CDF (P(N1<=n1, ..., Nk<=nk)) and tail probabilities (P(N1>n1, ..., Nk>nk)), as well as probabilities with both constraints (P(l1<N1<=u1, ..., lk<Nk<=uk)). Uses a method suggested by Bruce Levin (1981) <doi:10.1214/aos/1176345593>.
Functions and examples for 'Portfolio
  Management with R': backtesting investment and
  trading strategies, computing profit/loss and
  returns, analysing trades, handling lists of
  transactions, reporting, and more.
Pharmacometric tools for common data analytical tasks; closed-form solutions for calculating concentrations at given 
    times after dosing based on compartmental PK models (1-compartment, 2-compartment and 3-compartment, covering infusions, zero- 
    and first-order absorption, and lag times, after single doses and at steady state, per Bertrand & Mentre (2008) 
    <http://lixoft.com/wp-content/uploads/2016/03/PKPDlibrary.pdf>); parametric simulation from NONMEM-generated parameter estimates 
    and other output; and parsing, tabulating and plotting results generated by Perl-speaks-NONMEM (PsN).
Provides tools for 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.
Computes the optimal flow, Nash flow and the Price of Anarchy for any routing game defined within the game theoretical framework. The input is a routing game in the form of it’s cost and flow functions. Then transforms this into an optimisation problem, allowing both Nash and Optimal flows to be solved by nonlinear optimisation. See <https://en.wikipedia.org/wiki/Congestion_game> and Knight and Harper (2013) <doi:10.1016/j.ejor.2013.04.003> for more information.
Penalized orthogonal-components regression (POCRE) is a supervised dimension reduction method for high-dimensional data. It sequentially constructs orthogonal components (with selected features) which are maximally correlated to the response residuals. POCRE can also construct common components for multiple responses and thus build up latent-variable models.
Provides functions to implement and simulate the partial order continual reassessment method (PO-CRM) of Wages, Conaway and O'Quigley (2011) <doi:10.1177/1740774511408748> for use in Phase I trials of combinations of agents. Provides a function for generating a set of initial guesses (skeleton) for the toxicity probabilities at each combination that correspond to the set of possible orderings of the toxicity probabilities specified by the user.
This tool computes the probability of detection (POD) curve and the limit of detection (LOD), i.e. the number of copies of the target DNA sequence required to ensure a 95 % probability of detection (LOD95). Other quantiles of the LOD can be specified.
    This is a reimplementation of the mathematical-statistical modelling of the validation of qualitative polymerase chain reaction (PCR) methods within a single laboratory as provided by the commercial tool 'PROLab' <http://quodata.de/>. The modelling itself has been described by Uhlig et al. (2015) <doi:10.1007/s00769-015-1112-9>.
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.
Implementation of 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.
Provides an implementation of a rare variant association test that utilizes protein tertiary structure to increase signal and to identify likely causal variants. Performs structure-guided collapsing, which leads to local tests that borrow information from neighboring variants on a protein and that provide association information on a variant-specific level. For details of the implemented method see West, R. M.,  Lu, W., Rotroff, D. M., Kuenemann, M., Chang, S-M., Wagner M. J., Buse, J. B., Motsinger-Reif, A., Fourches, D., and Tzeng, J-Y. (2019) <doi:10.1371/journal.pcbi.1006722>.
Validate data in data frames,
    'tibble' objects, 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.
The function pointdensity returns a density count and the temporal average for
    every point in the original list. The dataframe returned includes four
    columns: lat, lon, count, and date_avg. The "lat" column is the original
    latitude data; the "lon" column is the original longitude data; the "count"
    is the density count of the number of points within a radius of
    radius*grid_size (the neighborhood); and the date_avg column includes the
    average date of each point in the neighborhood.
Labels longitudinal and latitudinal coordinates located inside a polygon.
    For a singular polygon, the label is a logical vector of TRUE and FALSE values. For multiple polygons, the label is a character vector based on the names of each polygon. 
    The package is designed to work with both sf and SpatialPolygonsDataFrame objects.
Estimate specification models for the state-dependent level of an optimal quantile/expectile forecast.
  Wald Tests and the test of overidentifying restrictions are implemented. Plotting of the estimated specification model is possible.
  The package contains two data sets with forecasts and realizations: the daily accumulated precipitation at London, UK from the high-resolution model of the
  European Centre for Medium-Range Weather Forecasts (ECMWF, <https://www.ecmwf.int/>) and GDP growth Greenbook data by the US Federal Reserve.
  See Schmidt, Katzfuss and Gneiting (2015) <arXiv:1506.01917> for more details on the identification and estimation of a directive behind a point forecast.
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 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.
Computation of robust standard errors of Poisson fixed effects 
    models, following Wooldridge (1999).
Creates a low-rank factorization of a sparse counts matrix by maximizing Poisson likelihood with l1/l2 regularization
	with all non-negative latent factors (e.g. for recommender systems or topic modeling) (Cortes, David, 2018, <arXiv:1811.01908>).
	Similar to hierarchical Poisson factorization, but follows an optimization-based approach with regularization instead of a hierarchical
	structure, and is fit through either proximal gradient or conjugate gradient instead of variational inference.
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.
R functions for the computation of Least Square based on transformation (L2T) and robust M-estimators based on transformations (MT-estimators) for Poisson regression models.
For a multivariate dataset with independent Poisson measurement error, calculates principal components of transformed latent Poisson means. T. Kenney, T. Huang, H. Gu (2019) <arXiv:1904.11745>.
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 textbook 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.
Provides useful functions for obtaining commonly-used data
    in political analysis and political science, including from sources
    such as the Comparative Agendas Project <https://www.comparativeagendas.net>,
    which provides data on politics and policy from 20+ countries, the
    MIT Election and Data Science Lab <https://www.electionlab.mit.edu>,
    and FiveThirtyEight <https://www.FiveThirtyEight.com>.
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.
Tools to estimate pollinator body size and co-varying traits. This package contains novel Bayesian predictive models of pollinator body size (for bees and hoverflies) as well as preexisting predictive models for pollinator body size (currently implemented for ants, bees, butterflies, flies, moths and wasps) as well as bee tongue length and foraging distance, total field nectar loads and wing loading. An additional GitHub repository <https://github.com/liamkendall/pollimetrydata> provides model objects to use the bodysize function internally. All models are described in Kendall et al (2018) <doi:10.1101/397604>.
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.
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).
Tools for creating, viewing, and assessing qualitative
 palettes with many (20-30 or more) colors. See Coombes and colleagues
 (2019) <doi:10.18637/jss.v090.c01>.
R port of Angus Johnson's open source library Clipper. Performs polygon clipping operations (intersection, union, set minus, set difference) for polygonal regions of arbitrary complexity, including holes. Computes offset polygons (spatial buffer zones, morphological dilations, Minkowski dilations) for polygonal regions and polygonal lines. Computes Minkowski Sum of general polygons. There is a function for removing self-intersections from polygon data.
Computes polychoric and polyserial correlations by quick "two-step" methods or ML, 
  optionally with standard errors; tetrachoric and biserial correlations are special cases.
Numerical integration of continuously differentiable
    functions f(x,y) over simple closed polygonal domains.
    The following cubature methods are implemented:
    product Gauss cubature (Sommariva and Vianello, 2007,
    <doi:10.1007/s10543-007-0131-2>),
    the simple two-dimensional midpoint rule
    (wrapping 'spatstat' functions),
    adaptive cubature for radially symmetric functions via line
    integrate() along the polygon boundary (Meyer and Held, 2014,
    <doi:10.1214/14-AOAS743>, Supplement B),
    and integration of the bivariate Gaussian density based on
    polygon triangulation.
    For simple integration along the axes, the 'cubature' package
    is more appropriate.
Implements a Gibbs sampling algorithm to perform Bayesian inference
    on biallelic SNP frequencies, genotypes and heterozygosity (observed and
    expected) in a population of autopolyploids. See the published paper in
    Molecular Ecology Resources: Blischak et al. (2016) <doi:10.1111/1755-0998.12493>.
Use the R console as an interactive learning environment to memorize any two columns dataset.
A wrapper around the C++ library 
    'polylabel' from 'Mapbox', providing an efficient routine for
    finding the approximate pole of inaccessibility of a polygon, which
    usually serves as an excellent candidate for labeling of a polygon.
Creation of linkage maps in polyploid species from marker dosage
    scores of an F1 cross from two heterozygous parents. Currently works for outcrossing diploid, autotriploid, autotetraploid and autohexaploid species, 
    as well as segmental allotetraploids. Methods are described in a manuscript of Bourke et al. (2018) <doi:10.1093/bioinformatics/bty371>.
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.
Read depth data from genotyping-by-sequencing (GBS) or restriction 
  site-associated DNA sequencing (RAD-seq) are imported and used to make Bayesian
  probability estimates of genotypes in polyploids or diploids.  The genotype 
  probabilities, or genotypes sampled from those probabilties, can then be exported
  for downstream analysis.  'polyRAD' is described by Clark et al. (2019)
  <doi:10.1534/g3.118.200913>.
Automate formation and evaluation of polynomial regression models. Provides support for cross-validating categorical variables. The motivation for this package is described in 'Polynomial Regression As an Alternative to Neural Nets' by Xi Cheng, Bohdan Khomtchouk, Norman Matloff, and Pete Mohanty (<arXiv:1806.06850>).
A collection of tools to handle microsatellite data of
 any ploidy (and samples of mixed ploidy) where allele copy number is not
 known in partially heterozygous genotypes.  It can import and export data in
 ABI 'GeneMapper', 'Structure', 'ATetra', 'Tetrasat'/'Tetra', 'GenoDive', 'SPAGeDi',
 'POPDIST', 'STRand', and binary presence/absence formats.  It can calculate
 pairwise distances between individuals using a stepwise mutation model or
 infinite alleles model, with or without taking ploidies and allele frequencies
 into account.  These distances can be used for the calculation of clonal
 diversity statistics or used for further analysis in R.  Allelic diversity
 statistics and Polymorphic Information Content are also available.  polysat can 
 assist the user in estimating the ploidy of samples, and it can estimate allele 
 frequencies in populations, calculate pairwise or global differentiation statistics 
 based on those frequencies, and export allele frequencies to 'SPAGeDi' and 'adegenet'.  
 Functions are also included for assigning alleles to isoloci in cases where one pair 
 of microsatellite primers amplifies alleles from two or more independently
 segregating isoloci.  polysat is described by Clark and Jasieniuk (2011)
 <doi:10.1111/j.1755-0998.2011.02985.x> and Clark and Schreier (2017)
 <doi:10.1111/1755-0998.12639>.
Perform classic chi-squared tests and Ripol et al(1999)
             binomial confidence interval approach for autopolyploid
             dominant markers. Also, dominant markers may be generated
             for families of offspring where either one or both of the
             parents possess the marker. Missing values and
             misclassified markers may be generated at random.
Fits Bayesian mixture models to estimate marker dosage for dominant markers in autopolyploids using JAGS (1.0 or greater) as outlined in Baker et al "Bayesian estimation of marker dosage in sugarcane and other autopolyploids" (2010, <doi:10.1007/s00122-010-1283-z>). May be used in conjunction with polySegratio for simulation studies and comparison with standard methods.
This algorithm classifies the trends into linear, quadratic, cubic, concealed and no-trend types. The "concealed trends" are those trends that possess quadratic or cubic forms, but the net change from the start of the time period to the end of the time period hasn't been significant. The "no-trend" category includes simple linear trends with statistically in-significant slope coefficient.
Routines for flexible functional form estimation via basis
    regression, with model selection via the adaptive LASSO or SCAD to prevent
    overfitting.
This package fits a patch occupancy model
POM-aSPU test evaluates an association between an ordinal response and multiple phenotypes, for details see Kim and Pan (2017) <DOI:10.1002/gepi.22033>.
Provides an interface to pomdp-solve, a solver for Partially Observable Markov Decision Processes (POMDP). The package enables the user to simply define all components of a POMDP model and solve the problem using several methods. The package also contains functions to analyze and visualize the POMDP solutions (e.g., the optimal policy).
Calculations of an information criterion are proposed to check the quality of simulations results of Agent-based models (ABM/IBM) or other non-linear rule-based models. The POMDEV measure (Pattern Oriented Modelling DEViance) is based on the Kullback-Leibler divergence and likelihood theory. It basically indicates the deviance of simulation results from field observations. Once POMDEV scores and metropolis-hasting sampling on different model versions are effectuated, POMIC scores (Pattern Oriented Modelling Information Criterion) can be calculated. This method could be further developed to incorporate multiple patterns assessment. Piou C, U Berger and V Grimm (2009) <doi:10.1016/j.ecolmodel.2009.05.003>.
Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models).  The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods.  It is also a versatile platform for implementation of inference methods for general POMP models.
An implementation of a hybrid method of person-oriented method and perturbation on the model. Pompom is the initials of the two methods. The hybrid method will provide a multivariate intraindividual variability metric (iRAM). The person-oriented method used in this package refers to uSEM (unified structural equation modeling, see Kim et al., 2007, Gates et al., 2010 and Gates et al., 2012 for details). Perturbation on the model was conducted according to impulse response analysis introduced in Lutkepohl (2007). 
    Kim, J., Zhu, W., Chang, L., Bentler, P. M., & Ernst, T. (2007) <doi:10.1002/hbm.20259>. 
    Gates, K. M., Molenaar, P. C. M., Hillary, F. G., Ram, N., & Rovine, M. J. (2010) <doi:10.1016/j.neuroimage.2009.12.117>. 
    Gates, K. M., & Molenaar, P. C. M. (2012) <doi:10.1016/j.neuroimage.2012.06.026>. 
    Lutkepohl, H. (2007, ISBN:3540262393).
Finds equivalence classes corresponding to a symmetric relation
    or undirected graph.  Finds total order consistent with partial order
    or directed graph (so-called topological sort).
Enables the creation of object pools, which make it less
    computationally expensive to fetch a new object. Currently the
    only supported pooled objects are 'DBI' connections.
Calculates the pooled mean group (PMG) estimator for dynamic panel data models, as described by Pesaran, Shin and Smith (1999) <doi:10.1080/01621459.1999.10474156>.
Functions for the computation of F-statistics from Pool-Seq data in population genomics studies. The package also includes several utilities to manipulate Pool-Seq data stored in standard format ('vcf' or 'rsync' files generated by the the 'PoPoolation' software) and perform conversion to alternative format (as used in the 'BayPass' and 'SelEstim' software).
Functions for calculating power and fitting regression models in studies where a biomarker is measured in "pooled" samples rather than for each individual. Approaches for handling measurement error follow the framework of Schisterman et al. (2010) <doi:10.1002/sim.3823>.
Gene-based association tests using the actual impurity reduction (AIR) variable importance. The function aggregates AIR importance measures from a group of SNPs or probes and outputs a p-value for each gene. The procedures builds upon the method described in <doi:10.1093/Bioinformatics/Bty373> and will be published soon.
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,
 with deterministic and stochastic model implementations. Includes population projection,
 indices of short- and long-term population size and growth, perturbation analysis,
 convergence to stability or stationarity, and diagnostic and manipulation tools.
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. The methods are described in Nyberg et al. 
    (2012) <doi:10.1016/j.cmpb.2012.05.005>, and Foracchia et al. (2004) 
    <doi:10.1016/S0169-2607(03)00073-7>.
Enables computation of epidemiological statistics, including those 
    where counts or mortality rates of the reference population are used. 
    Currently supported: excess hazard models, rates, mean survival times, 
    relative survival, and standardized incidence and mortality ratios 
    (SIRs/SMRs), all of which can be easily adjusted for by covariates such as 
    age. Fast splitting and aggregation of 'Lexis' objects (from package 'Epi') 
    and other computations achieved using 'data.table'.
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 <DOI:10.1093/molbev/msu136> not only 
	implements a wide range of population genetics statistics, but also facilitates the easy 
	implementation of new algorithms by other researchers. PopGenome is optimized for speed via 
	the seamless integration of C code.
Provides beginner friendly framework to analyse population genetic
    data. Based on 'adegenet' objects it uses 'knitr' to create comprehensive reports on spatial genetic data. 
    For detailed information how to use the package refer to the comprehensive
    tutorials or visit <http://www.popgenreport.org/>.
Provides functions to estimate the kinship matrix of individuals from a large set of biallelic SNPs, and extract inbreeding coefficients and the generalized FST (Wright's fixation index).  Method described in Ochoa and Storey (2016) <doi:10.1101/083923>.
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. 
    Originally described in Kamvar, Tabima, and Grünwald (2014) 
    <doi:10.7717/peerj.281> with version 2.0 described in Kamvar, Brooks, and 
    Grünwald (2015) <doi:10.3389/fgene.2015.00208>.
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: <doi:10.1080/01621459.2012.737729>) for simultaneously estimating age-specific population counts, fertility rates, mortality rates and net international migration flows, at the national level.
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.
Run simulations to assess the impact of various designs features
    and the underlying biological behaviour on the outcome of a Patient Derived
    Xenograft (PDX) population study. This project can either be deployed to a
    server as a 'shiny' app or installed locally as a package and run the app
    using the command 'populationPDXdesignApp()'.
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.
Download and generate summaries for the rodent, plant, ant, and 
    weather data from the Portal Project. Portal is a long-term (and ongoing) 
    experimental monitoring site in the Chihuahua desert. The raw data files 
    can be found at <https://github.com/weecology/portaldata>.
Simulate a univariate and multivariate data from seasonal and nonseasonal time series models. It implements the popular univariate and multivariate portmanteau test statistics based on the asymptotic distributions and the Monte Carlo significance tests.
Classes for analysing and implementing equity portfolios.
Simplify your portfolio optimization process by applying a contemporary modeling way to model and solve your portfolio problems. While most approaches and packages are rather complicated this one tries to simplify things and is agnostic regarding risk measures as well as optimization solvers. Some of the methods implemented are described by Konno and Yamazaki (1991) <doi:10.1287/mnsc.37.5.519>, Rockafellar and Uryasev (2001) <doi:10.21314/JOR.2000.038> and Markowitz (1952) <doi:10.1111/j.1540-6261.1952.tb01525.x>.
Portfolio optimization and analysis routines and graphics.
Automated backtesting of multiple portfolios over multiple 
    datasets of stock prices in a rolling-window fashion. Intended for 
    researchers and practitioners to backtest a set of different portfolios, 
    as well as by a course instructor to assess the students in their portfolio 
    design in a fully automated and convenient manner, with results conveniently 
    formatted in tables and plots. Each portfolio design is easily defined as a
    function that takes as input a window of the stock prices and outputs the 
    portfolio weights. Multiple portfolios can be easily specified as a list 
    of functions or as files in a folder. Multiple datasets can be conveniently 
    extracted randomly from different markets, different time periods, and 
    different subsets of the stock universe. The results can be later assessed 
    and ranked with tables based on a number of performance criteria (e.g., 
    expected return, volatility, Sharpe ratio, drawdown, turnover rate, return 
    on investment, computational time, etc.), as well as plotted in a number of 
    ways with nice barplots and boxplots.
R interface to PortfolioEffect cloud service for estimating
    high frequency price variance, quarticity, microstructure noise variance, 
    and other metrics in both aggregate and rolling window flavors. 
    Constructed estimators could use client-side market data or access 
    HF intraday price history for all major US Equities.
    See <https://www.portfolioeffect.com/> for more information on the 
    PortfolioEffect high frequency portfolio analytics platform.
R interface to PortfolioEffect cloud service for backtesting
    high frequency trading (HFT) strategies, intraday portfolio analysis
    and optimization. Includes auto-calibrating model pipeline for market
    microstructure noise, risk factors, price jumps/outliers, tail risk
    (high-order moments) and price fractality (long memory). Constructed
    portfolios could use client-side market data or access HF intraday price
    history for all major US Equities. See <https://www.portfolioeffect.com/>
    for more information on the PortfolioEffect high frequency portfolio
    analytics platform.
Two functions for financial portfolio optimization by linear programming are provided. One function implements Benders decomposition algorithm and can be used for very large data sets. The other, applicable for moderate sample sizes, finds optimal portfolio which has the smallest distance to a given benchmark portfolio.
Classes that serve as a framework for designing equity
        portfolio simulations.
Risk Attribution of a portfolio with Volatility Risk Analysis.
Designed to aid both academic researchers and asset managers in conducting factor based portfolio sorts.  
    Provides functionality to sort assets into portfolios for up to three factors via a conditional or unconditional sorting procedure.
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.
Free UK geocoding using data from Office for National Statistics.
    It is using several functions to get information about post codes, outward
    codes, reverse geocoding, nearest post codes/outward codes, validation, or
    randomly generate a post code. API wrapper around <https://postcodes.io>.
Functions to convert geometry and 'hstore' data types from
    'PostgreSQL' into standard R objects, as well as to simplify
    the import of R data frames (including spatial data frames) into 'PostgreSQL'.
    Note: This package is deprecated. For new projects, we recommend 
    using the 'sf' package to interface with geodatabases.
Functions to estimate the incubation period distribution of post-infectious syndrome which is defined as the time between the symptom onset of the antecedent infection and that of the post-infectious syndrome.
This is a wrapper for the Mercury Parser API. The Mercury Parser is 
    a single API endpoint that takes a URL and gives you back the content reliably 
    and easily. 
    With just one API request, Mercury takes any web article and returns 
    only the relevant content — headline, author, body text, relevant images and 
    more — free from any clutter. It’s reliable, easy-to-use and free.
    See the webpage here: <https://mercury.postlight.com/>.
Provides adds postfix and infix logic operators for
    if, then, unless, and otherwise.
Some functions useful to perform a Peak Over Threshold
        analysis in univariate and bivariate cases, see Beirlant et al. (2004)
        <doi:10.1002/0470012382>. A user's guide is available.
Do Markov chain Monte Carlo (MCMC) simulation of Potts models
   (Potts, 1952, <doi:10.1017/S0305004100027419>),
   which are the multi-color generalization of Ising models
   (so, as as special case, also simulates Ising models).
   Use the Swendsen-Wang algorithm (Swendsen and Wang, 1987,
   <doi:10.1103/PhysRevLett.58.86>) so MCMC is fast.
   Do maximum composite likelihood estimation of parameters
   (Besag, 1975, <doi:10.2307/2987782>,
   Lindsay, 1988, <doi:10.1090/conm/080>).
There are three sets of functions. The first produces
             basic properties of a graph and generates samples from
             multinomial distributions to facilitate the simulation
	     functions (they maybe used for other purposes as well).
	     The second provides various simulation functions for a
	     Potts model in Potts, R. B. (1952)
	     <doi:10.1017/S0305004100027419>.
	     The third currently includes only one function which
	     computes the normalizing constant of a Potts model
	     based on simulation results.
The Phylogenetic Ornstein-Uhlenbeck Mixed Model (POUMM) allows to 
    estimate the phylogenetic heritability of continuous traits, to test 
    hypotheses of neutral evolution versus stabilizing selection, to quantify 
    the strength of stabilizing selection, to estimate measurement error and to
    make predictions about the evolution of a phenotype and phenotypic variation 
    in a population. The package implements combined maximum likelihood and 
    Bayesian inference of the univariate Phylogenetic Ornstein-Uhlenbeck Mixed 
    Model, fast parallel likelihood calculation, maximum likelihood 
    inference of the genotypic values at the tips, functions for summarizing and
    plotting traces and posterior samples, functions for simulation of a univariate 
    continuous trait evolution model along a phylogenetic tree. So far, the 
    package has been used for estimating the heritability of quantitative traits
    in macroevolutionary and epidemiological studies, see e.g. 
    Bertels et al. (2017) <doi:10.1093/molbev/msx246> and 
    Mitov and Stadler (2018) <doi:10.1093/molbev/msx328>. The algorithm for 
    parallel POUMM likelihood calculation has been published in 
    Mitov and Stadler (2019) <doi:10.1111/2041-210X.13136>.
Density, distribution function, quantile function and random generation for the family of power and reversal power distributions.
Full pattern summation of X-ray powder diffraction data as
  described in Chipera and Bish (2002) <doi:10.1107/S0021889802017405>.
  Derives quantitative estimates of crystalline and amorphous phase
  concentrations in complex mixtures.
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.
An implementation for using powerful algorithm to compute the
    maximal eigenpair of Hermitizable tridiagonal matrices in R. It 
    provides two algorithms to find the maximal and the next to maximal 
    eigenpairs under the tridiagonal matrix. Besides, it also provides 
    two auxiliary algorithms to generate tridiagonal matrix and solve 
    the linear equation by Thomas algorithm. Several examples are included 
    in the vignettes to illustrate the usage of the functions.
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 the 'time x treatment' effect
    in 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.
Includes tools to calculate statistical power, minimum detectable effect size (MDES), MDES difference (MDESD), and minimum required sample size for various multilevel randomized experiments with continuous outcomes. Some of the functions can assist with planning two- and three-level cluster-randomized trials (CRTs) sensitive to multilevel moderation and mediation (2-1-1, 2-2-1, and 3-2-1). See 'PowerUp!' Excel series at <https://www.causalevaluation.org/>.
The PP package includes estimation of (MLE, WLE, MAP, EAP, ROBUST)
    person parameters for the 1,2,3,4-PL model and the GPCM (generalized
    partial credit model). The parameters are estimated under the assumption
    that the item parameters are known and fixed. The package is useful e.g. in
    the case that items from an item pool / item bank with known item parameters
    are administered to a new population of test-takers and an ability
    estimation for every test-taker is needed.
Exploratory projection pursuit is a method to discovers
	structure in multivariate data. At heart this package uses
	a projection index to evaluate how interesting a specific
	three-dimensional projection of multivariate data (with more
	than three dimensions) is. Typically, the main structure
	finding algorithm starts at a random projection and then
	iteratively changes the projection direction to move to
	a more interesting one. In other words, the projection index
	is maximised over the projection direction to find the most
	interesting projection. This maximum is, though, a local
	maximum. So, this code has the ability to restart the
	algorithm from many different starting positions automatically.
	Routines exist to plot a density estimate of projection indices 
	over the runs, this enables the user to obtain an idea of
	the distribution of the projection indices,
	and, hence,  which ones might be interesting. Individual
	projection solutions, including those identified as interesting,
	can be extracted and plotted individually. The package
	can make use of the mclapply() function to execute multiple runs in
	parallel to speed up index discovery. Projection pursuit is
	similar to independent component analysis. This package
	uses a projection index that maximises an entropy measure to
	look for projections that exhibit non-normality, and operates
	on sphered data. Hence, information from this package is
	different from that obtained from principal components analysis,
	but the rationale behind both methods is similar. 
        Nason, G. P. (1995) <doi:10.2307/2986135>.
Calculates the Probability Plot Correlation Coefficient (PPCC) 
             between a continuous variable X and a specified distribution. The corresponding 
	     composite hypothesis test that was first introduced by 
	     Filliben (1975) <doi: 10.1080/00401706.1975.10489279> 
	     can be performed to test whether the sample 
	     X is element of either the Normal, log-Normal, Exponential,
	     Uniform, Cauchy, Logistic, Generalized Logistic, Gumbel (GEVI), Weibull,
	     Generalized Extreme Value, Pearson III (Gamma 2), Mielke's Kappa, Rayleigh
	     or Generalized Logistic Distribution. The PPCC test is performed with
	     a fast Monte-Carlo simulation.
Implements recently developed projection 
    pursuit algorithms for finding optimal linear cluster
    separators. The clustering algorithms use optimal
    hyperplane separators based on minimum density, Pavlidis et. al (2016) <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.
Spatial Analysis for exploration of Pakistan Population Census 2017 (<http://www.pbscensus.gov.pk/>). It uses data from R package 'PakPC2017'.
Implements projection pursuit forest algorithm for supervised classification.
Projection Pursuit (PP) algorithm for dimension reduction based on Gaussian Mixture Models (GMMs) for density estimation using Genetic Algorithms (GAs) to maximise an approximated negentropy index. For more details see Scrucca and Serafini (2019) <doi:10.1080/10618600.2019.1598871>.
The Poverty Probability Index (PPI) is a poverty measurement tool 
    for organizations and businesses with a mission to serve the poor. The PPI 
    is statistically-sound, yet simple to use: the answers to 10 questions about 
    a household’s characteristics and asset ownership are scored to compute the 
    likelihood that the household is living below the poverty line – or above by 
    only a narrow margin. This package contains country-specific lookup data tables
    used as reference to determine the poverty likelihood of a household based
    on their score from the country-specific PPI questionnaire. These lookup 
    tables have been extracted from documentation of the PPI found at 
    <https://www.povertyindex.org> and managed by Innovations for Poverty Action 
    <https://www.poverty-action.org>.
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.
Efficient statistical inference of two-sample MR (Mendelian Randomization) analysis. 
    It can account for the correlated instruments and the horizontal pleiotropy, 
    and can provide the accurate estimates of both causal effect and horizontal 
    pleiotropy effect as well as the two corresponding p-values. There are two main 
    functions in the 'PPMR' package. One is PMR_individual() for individual level data, 
    the other is PMR_summary() for summary data.
Assessment for statistically-based PPQ sampling plan, including calculating the passing probability, optimizing the baseline and high performance cutoff points, visualizing the PPQ plan and power dynamically. The analytical idea is based on the simulation methods from the textbook "Burdick, R. K., LeBlond, D. J., Pfahler, L. B., Quiroz, J., Sidor, L., Vukovinsky, K., & Zhang, L. (2017). Statistical Methods for CMC Applications. In Statistical Applications for Chemistry, Manufacturing and Controls (CMC) in the Pharmaceutical Industry (pp. 227-250). Springer, Cham."
A toolbox for deterministic, probabilistic and privacy-preserving record linkage techniques. Combines the functionality of the 'Merge ToolBox' (<http://record-linkage.de>) with current privacy-preserving techniques.
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.
Stochastic block model used for dynamic graphs represented by Poisson processes.
    To model recurrent interaction events in continuous time, an extension of the stochastic block model is proposed where every individual belongs to a latent group and interactions between two individuals follow a conditional inhomogeneous Poisson process with intensity driven by the individuals’ latent groups. The model is shown to be identifiable and its estimation is based on a semiparametric variational expectation-maximization algorithm. Two versions of the method are developed, using either a nonparametric histogram approach (with an adaptive choice of the partition size) or kernel intensity estimators. The number of latent groups can be selected by an integrated classification likelihood criterion.
    Y. Baraud and L. Birgé (2009). <doi:10.1007/s00440-007-0126-6>.
    C. Biernacki, G. Celeux and G. Govaert (2000). <doi:10.1109/34.865189>.
    M. Corneli, P. Latouche and F. Rossi (2016). <doi:10.1016/j.neucom.2016.02.031>.
    J.-J. Daudin, F. Picard and S. Robin (2008). <doi:10.1007/s11222-007-9046-7>.
    A. P. Dempster, N. M. Laird and D. B. Rubin (1977). <http://www.jstor.org/stable/2984875>.
    G. Grégoire (1993). <http://www.jstor.org/stable/4616289>.
    L. Hubert and P. Arabie (1985). <doi:10.1007/BF01908075>.
    M. Jordan, Z. Ghahramani, T. Jaakkola and L. Saul (1999). <doi:10.1023/A:1007665907178>.
    C. Matias, T. Rebafka and F. Villers (2018). <doi:10.1093/biomet/asy016>.
    C. Matias and S. Robin (2014). <doi:10.1051/proc/201447004>.
    H. Ramlau-Hansen (1983). <doi:10.1214/aos/1176346152>.
    P. Reynaud-Bouret (2006). <doi:10.3150/bj/1155735930>.
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.
An efficient tool designed for differential analysis of large-scale RNA sequencing (RNAseq) data and Bisulfite sequencing (BSseq) data in the presence of individual relatedness and population structure. 'PQLseq' first fits a Generalized Linear Mixed Model (GLMM) with adjusted covariates, predictor of interest and random effects to account for population structure and individual relatedness, and then performs Wald tests for each gene in RNAseq or site in BSseq.
Distance-based parametric bootstrap tests for clustering with 
  spatial neighborhood information. Some distance measures, 
  Clustering of presence-absence, abundance and multilocus genetical data 
  for species delimitation, nearest neighbor 
  based noise detection. Genetic distances between communities.
  Tests whether various distance-based regressions
  are equal. Try package?prabclus for on overview.
Provides a large number of functions from numerical analysis and
    linear algebra, numerical optimization, differential equations,
    time series, plus some well-known special mathematical functions.
    Uses 'MATLAB' function names where appropriate to simplify porting.
Functions and datasets to support Valliant, Dever, and Kreuter, ``Practical Tools for Designing and Weighting Survey Samples'' (2nd edition, 2018). Contains functions for sample size calculation for survey samples using stratified or clustered one-, two-, and three-stage sample designs. Other functions compute variance components for multistage designs and sample sizes in two-phase designs. A number of example data sets are included.
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 estimator (Prais & Winsten, 1954) takes into account AR(1) serial correlation of the errors in a linear regression model. The procedure recursively estimates the coefficients and the error autocorrelation of the specified model until sufficient convergence of the AR(1) coefficient is attained.
Build friendly R packages that
    praise their users if they have done something
    good, or they just need it to feel better.
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 (Fong and Yu 2015 <DOI:10.1080/19466315.2015.1093019>) using paired response curve, which is a model of the nonlinear functional relationship between experimental outcomes measured at two different dilutions.
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 adjustments and improvements. The 
    main function pre() derives prediction rule ensembles consisting of 
    rules and/or linear terms for continuous, binary, count, multinomial, 
    and multivariate continuous responses. Function gpe() derives 
    generalized prediction ensembles, consisting of rules, hinge and linear 
    functions of the predictor variables.
It contains functions to analyze the precipitation
    intensity, concentration and anomaly.
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.
Draw 2 dimensional and three dimensional plot for multiple regression models using package 'ggplot2' and 'rgl'.
   Supports linear models (lm), generalized linear models (glm) and local polynomial regression fittings (loess).
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 'summary()' method provides a data frame with average predictions, possibly over counterfactual versions of the data (a la the 'margins' command in 'Stata'). Marginal effect estimation is provided by the related package, 'margins' <https://cran.r-project.org/package=margins>. The package currently supports common model types (e.g., "lm", "glm") from the 'stats' package, as well as numerous other model classes from other add-on packages. See the README or main package documentation page for a complete listing.
A common problem faced by journal reviewers and authors is the question of
     whether the results of a replication study are consistent with the original
     published study. One solution to this problem is to examine the effect size
     from the original study and generate the range of effect sizes that could
     reasonably be obtained (due to random sampling) in a replication attempt
     (i.e., calculate a prediction interval). This package has functions that calculate
     the prediction interval for the correlation (i.e., r),
     standardized mean difference (i.e., d-value), and mean.
Functions to get prediction intervals and prediction points of future observations from any continuous distribution.
Three prediction algorithms described in the paper
        "On-line predictive linear regression" Annals of Statistics 37,
        1566 - 1590 (2009)
Providing functions to diagnose 
  and make inferences from various linear models, such as those obtained from 'aov', 
  'lm', 'glm', 'gls', 'lme', and 'lmer'. Inferences include predicted 
  means and standard errors, contrasts, multiple comparisons, permutation tests and graphs.
Perform a supervised data analysis on a database through a 'shiny' graphical interface. It includes methods such as K-Nearest Neighbors, Decision Trees, ADA Boosting, Extreme Gradient Boosting, Random Forest, Neural Networks, Deep Learning, Support Vector Machines and Bayesian Methods.
Predicts the most common race of a surname based on U.S. Census data.
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. The main reference for the package is Liverani, Hastie, Azizi, Papathomas and Richardson (2015) <doi:10.18637/jss.v064.i07>.
Prepares data for statistical analysis (e.g., analysis of variance
  ;ANOVA) by enabling the user to easily and quickly merge (using the
  file_merge() function) raw data files into one merged table and then
  aggregate the merged table (using the prep() function) into a finalized
  table while keeping track and summarizing every step of the preparation.
  The finalized table contains several possibilities for dependent measures of
  the dependent variable. Most suitable when measuring variables in an
  interval or ratio scale (e.g., reaction-times) and/or discrete values such
  as accuracy. Main functions included are file_merge() and prep(). The
  file_merge() function vertically merges individual data files (in a long
  format) in which each line is a single observation to one single dataset.
  The prep() function aggregates the single dataset according to any
  combination of grouping variables (i.e., between-subjects and
  within-subjects independent variables, respectively), and returns a data
  frame with a number of dependent measures for further analysis for each cell
  according to the combination of provided grouping variables. Dependent
  measures for each cell include among others means before and after rejecting
  all values according to a flexible standard deviation criteria, number of
  rejected values according to the flexible standard deviation criteria,
  proportions of rejected values according to the flexible standard deviation
  criteria, number of values before rejection, means after rejecting values
  according to procedures described in Van Selst & Jolicoeur (1994; suitable
  when measuring reaction-times), standard deviations, medians, means according
  to any percentile (e.g., 0.05, 0.25, 0.75, 0.95) and harmonic means. The data
  frame prep() returns can also be exported as a txt file to be used for
  statistical analysis in other statistical programs.
A figure region is prepared, creating a plot region with suitable background color, grid lines or shadings, and providing axes and labeling if not suppressed. Subsequently, information carrying graphics elements can be added (points, lines, barplot with add=TRUE and so forth).
Provides classes to pre-process microarray gene
  expression data as part of the OOMPA collection of packages
  described at <http://oompa.r-forge.r-project.org/>.
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.
An interactive document for preprocessing the dataset using  'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://analyticmodels.shinyapps.io/PREPShiny/>.
Miscellaneous small utilities are provided to mitigate issues with messy, inconsistent or high dimensional data and help for preprocessing and preparing analyses.
Provides a collection of templates to author preregistration documents for scientific studies in PDF format.
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.
Originally as an R version of Preseq <doi:10.1038/nmeth.2375>, the package has extended its functionality to predict the r-species accumulation curve (r-SAC), which is the number of species represented at least r times as a function of the sampling effort. When r = 1, the curve is known as the species accumulation curve, or the library complexity curve in high-throughput genomic sequencing. The package includes both parametric and nonparametric methods, as described by Deng C, et al. (2018) <arXiv:1607.02804v3>.
Computes probability-scale residuals and residual correlations
    for continuous, ordinal, binary, count, and time-to-event data.
Functions to assess the effects of different text preprocessing decisions on the inferences drawn from the resulting document-term matrices they generate.
Drop-in replacements for standard base graphics functions. The 
    replacements are prettier versions of the originals.
Replace the standard print method for functions with one that
    performs syntax highlighting, using ANSI colors, if the terminal
    supports them.
Creating tiny yet beautiful documents and vignettes from R
    Markdown. The package provides the 'html_pretty' output format as an
    alternative to the 'html_document' and 'html_vignette' engines that
    convert R Markdown into HTML pages. Various themes and syntax highlight
    styles are supported.
Simple and crisp publication-quality graphics for the ExPosition family of packages.
  See An ExPosition of the Singular Value Decomposition in R (Beaton et al 2014) <doi:10.1016/j.csda.2013.11.006>.
Automates the process of creating a scale bar and north arrow in
    any package that uses base graphics to plot in R. Bounding box tools help find
    and manipulate extents. Finally, there is a function to automate the process
    of setting margins, plotting the map, scale bar, and north arrow, and resetting
    graphic parameters upon completion.
Functions for conventionally formatting descriptive stats,
 reshaping data frames and formatting R output as HTML.
Pretty, human readable formatting of quantities.
    Time intervals: 1337000 -> 15d 11h 23m 20s.
    Vague time intervals: 2674000 -> about a month ago.
    Bytes: 1337 -> 1.34 kB.
The prevalence package provides Frequentist and Bayesian methods for prevalence assessment studies. IMPORTANT: the truePrev functions in the prevalence package call on JAGS (Just Another Gibbs Sampler), which therefore has to be available on the user's system. JAGS can be downloaded from http://mcmc-jags.sourceforge.net/.
Easy and efficient access to the API provided by 'Prevedere', 
  an industry insights and predictive analytics company. Query and 
  download indicators, models and workbenches built with 'Prevedere' for further 
  analysis and reporting <https://www.prevedere.com/>.
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, see Larmarange et al. (2011)
    <doi:10.4000/cybergeo.24606>.
Functions to simulate point prevalence studies (PPSs) of healthcare-associated infections (HAIs) and to convert prevalence to incidence in steady state setups. 
    Companion package to the preprint Willrich et al., From prevalence to incidence - a new approach in the hospital setting;
    <doi:10.1101/554725> , where methods are explained in detail.
Estimate False Discovery Rates (FDRs) for importance metrics from
    random forest runs.
Functions to aid in the analysis of price and currency data by expediting data preprocessing. This includes extraction of relevant data (e.g. from text fields), conversion into numeric class, cleaning, and standardisation as appropriate.
An implementation of the van Westendorp Price
    Sensitivity Meter in R, which is a survey-based approach
	to analyze consumer price preferences and sensitivity
    (van Westendorp 1976, isbn:9789283100386).
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.
Implements the primePCA algorithm, developed and analysed in Zhu, Z., Wang, T. and Samworth, R. J. (2019) High-dimensional principal component analysis with heterogeneous missingness. <arXiv:1906.12125>.
Functions are primarily functions for systems of ordinary
        differential equations, difference equations, and eigenanalysis
        and projection of demographic matrices; data are for examples.
Identifies potential target sequences for a given set of primers
  and generates phylogenetic trees annotated with the taxonomies of the predicted
  amplification products.
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.
Fitting a principal curve to a data matrix in arbitrary dimensions. 
  Hastie and Stuetzle (1989) <doi:10.2307/2289936>.
Provides capabilities beyond principal components
    analysis to focus on finding structure in low variability
    subspaces. Constructs and plots simple basis vectors for
    pre-defined and user-defined measures of simplicity.
Extends the S3 generic function knit_print() in 'knitr'
    to automatically print some objects using an appropriate format such as
    Markdown or LaTeX. For example, data frames are automatically printed as
    tables, and the help() pages can also be rendered in 'knitr' documents.
Prioritize candidate cancer drugs for drug repositioning based on the random walk with restart algorithm in a drug-drug functional similarity network. 1) We firstly constructed a drug-drug functional similarity network by integrating pathway activity and drug activity derived from the NCI-60 cancer cell lines. 2) Secondly, we calculated drug repurposing score according to a set of approved therapeutic drugs of interested cancer based on the random walk with restart algorithm in the drug-drug functional similarity network. 3) Finally, the permutation test was used to calculate the statistical significance level for the drug repurposing score.
Translates beliefs into prior information in the form of Beta and Gamma distributions. It can be mainly used for the generation of priors on the prevalence of disease and the sensitivity/specificity of diagnostic tests.
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 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.
Implements Penalized Regression with Inferred Seasonality Module (PRISM) to generate forecast estimation of weekly unemployment initial claims using 'Google Trends' data. It includes required data and tools for backtesting the performance in 2007-2016.
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. The PRISMA statement
    defines an evidence-based, minimal set of items for reporting in
    systematic reviews and meta-analyses. PRISMA should be used for the
    reporting of studies evaluating randomized clinical trials (RCT), and
    is also for reporting on systematic reviews of other types of
    research. There is also a function to generate flow charts describing
    exclusions and inclusions for any kind of study.
Download, parses and tidies information from the World Prison Brief project <http://www.prisonstudies.org/>.
Enables user to perform the following:
    1. Roll 'n' number of die/dice (roll()).
    2. Toss 'n' number of coin(s) (toss()).
    3. Play the game of Rock, Paper, Scissors.
    4. Choose 'n' number of card(s) from a pack of 52 playing cards (Joker optional).
Implements two differentially private algorithms for 
  estimating L2-regularized logistic regression coefficients. A randomized
  algorithm F is epsilon-differentially private (C. Dwork, Differential
  Privacy, ICALP 2006 <DOI:10.1007/11681878_14>), if 
     |log(P(F(D) in S)) - log(P(F(D') in S))| <= epsilon
  for any pair D, D' of datasets that differ in exactly one record, any
  measurable set S, and the randomness is taken over the choices F makes.
Estimation of prevalence ratios using logistic models and confidence intervals with delta and bootstrap methods.
Survey sampling using permanent random numbers (PRN's). A solution to the
    problem of unknown overlap between survey samples, which leads to a low
    precision in estimates when the survey is repeated or combined with other
    surveys. The PRN solution is to supply the U(0, 1) random numbers to the 
    sampling procedure, instead of having the sampling procedure generate them.
    In Lindblom (2014) <doi:10.2478/jos-2014-0047>, and therein cited articles,
    it is shown how this is carried out and how it improves the estimates. This
    package supports two common fixed-size sampling procedures (simple random 
    sampling and probability-proportional-to-size sampling) and includes a 
    function for transforming the PRN's in order to control the sample overlap.
Optogenetics is a new tool to study neuronal circuits that have been genetically modified to allow stimulation by flashes of light.  This package implements the methodological framework, Point-process Response model for Optogenetics (PRO), for analyzing data from these experiments.  This method provides explicit nonlinear transformations to link the flash point-process with the spiking point-process.  Such response functions can be used to provide important and interpretable scientific insights into the properties of the biophysical process that governs neural spiking in response to optogenetic stimulation.
A framework for performing elementary probability
 calculations on finite sample spaces, which may be represented by data frames
 or lists.  Functionality includes setting up sample spaces, counting tools,
 defining probability spaces, performing set algebra, calculating probability
 and conditional probability, tools for simulation and checking the law of
 large numbers, adding random variables, and finding marginal distributions.
 Characteristic functions for all base R distributions are included.
Models can be improved by post-processing class probabilities, by: recalibration, conversion to hard probabilities, assessment of equivocal zones, and other activities. 'probably' contains tools for conducting these operations.
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).
Computes nonparametric probability distributions (probability density functions, cumulative distribution functions and quantile functions) using kernel smoothing. Supports univariate, multivariate and conditional distributions, and weighted data (possibly useful mixed with fuzzy clustering or frequency data). Also, supports empirical continuous cumulative distribution functions and their inverses, and random number generation.
Binomial Spatial Probit models for big data.
Estimates unsupervised outlier probabilities for multivariate numeric data with many observations from a nonparametric outlier statistic.
An interactive document on  the topic of basic probability using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://analyticmodels.shinyapps.io/BayesShiny/>.
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.
Provides animated process maps based on the 'procesmapR' package.
  Cases stored in event logs created with with 'bupaR' S3 class eventlog() are
  rendered as tokens (SVG shapes) and animated according to their occurrence 
  times on top of the process map. For rendering SVG animations ('SMIL') and the
  'htmlwidget' package are used.
Check compliance of event data from (business) processes with respect to specified rules. Rules supported are of three types: frequency (activities that should (not) happen x number of times), order (succession between activities) and exclusiveness (and and exclusive choice between activities).
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.
Perform moderation, mediation, moderated mediation and moderated moderation. 
   Inspired from famous 'PROCESS' macro for 'SPSS' and 'SAS' created by Andrew Hayes.
Tools to run system processes in the background.
    It can check if a background process is running; wait on a background
    process to finish; get the exit status of finished processes; kill
    background processes. It can read the standard output and error of
    the processes, using non-blocking connections. 'processx' can poll
    a process for standard output or error, with a timeout. It can also
    poll several processes at once.
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.
  The package contains the multiplicatively complete Färe-Primont, Fisher, Hicks-Moorsteen, 
  Laspeyres, Lowe, and Paasche indices, as well as the classic Malmquist productivity index.
  Färe-Primont and Lowe indices verify the transitivity property and can therefore be used for 
  multilateral or multitemporal comparison.
  Fisher, Hicks-Moorsteen, Laspeyres, Malmquist, and Paasche indices are not transitive and are 
  only to be used for binary comparison.
  All indices can also be decomposed into different components, providing insightful information 
  on the sources of productivity and profitability changes.
  In the use of Malmquist productivity index, the technological change index can be further 
  decomposed into bias technological change components.
  The package also allows to prohibit technological regression (negative technological change). In 
  the case of the Fisher, Hicks-Moorsteen, Laspeyres, Paasche and the transitive Färe-Primont 
  and Lowe indices, it is furthermore possible to rule out technological change. 
  Deflated shadow prices can also be obtained. Besides, the package allows parallel computing as 
  an option, depending on the user's computer configuration. 
  All computations are carried out with the nonparametric Data Envelopment Analysis (DEA), and 
  several assumptions regarding returns to scale are available.
  All DEA linear programs are implemented using 'lp_solve'.
Framework for visualising tables of counts, proportions
  and probabilities. The framework is called product plots, alluding to
  the computation of area as a product of height and width, and the
  statistical concept of generating a joint distribution from the
  product of conditional and marginal distributions. The framework,
  with extensions, is sufficient to encompass over 20 visualisations
  previously described in fields of statistical graphics and 'infovis',
  including bar charts, mosaic plots, 'treemaps', equal area plots and
  fluctuation diagrams.
An alternative data structure for the profiling information
    generated by Rprof().
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.
Computes profile extrema functions for arbitrary functions. If the function is expensive-to-evaluate it computes profile extrema by emulating the function with a Gaussian process (using package 'DiceKriging'). In this case uncertainty quantification on the profile extrema can also be computed. The different plotting functions for profile extrema give the user a tool to better locate excursion sets.
Defines a data structure for profiler data, and methods to read and
    write from the 'Rprof' and 'pprof' file formats.
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.
Provides tools that can be used to calculate, evaluate, plot and use for inference the profiles of *arbitrary* inference functions for *arbitrary* 'glm'-like fitted models with linear predictors. More information on the methods that are implemented can be found in Kosmidis (2008) <https://www.r-project.org/doc/Rnews/Rnews_2008-2.pdf>.
A suite of multivariate methods and data visualization 
      tools to implement profile analysis and cross-validation techniques described in 
      Davison & Davenport (2002) <DOI: 10.1037/1082-989X.7.4.468>, Bulut (2013), and other published and unpublished resources.
      The package includes routines to perform criterion-related profile analysis, profile analysis 
      via multidimensional scaling, moderated profile analysis, profile analysis by group, and a 
      within-person factor model to derive score profiles.
Get data / Define model / ??? / Profit! 'ProFit' is a Bayesian galaxy fitting tool that uses a fast 'C++' image generation library and a flexible interface to a large number of likelihood samplers.
A simple and light-weight API for memory profiling of R expressions.  The profiling is built on top of R's built-in memory profiler ('utils::Rprofmem()'), which records every memory allocation done by R (also native code).
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.
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 applicable to every projection method is proposed here [Thrun/Ultsch,2017] <DOI:10.13140/RG.2.2.13124.53124>. The two-dimensional scatter plot of any projection method can construct a topographic map which displays unapparent data structures by using distance and density information of the data. The generalized U*-matrix renders this visualization in the form of a topographic map, which can be used to automatically define the clusters of high-dimensional data. The whole system is based on the book "Projection-Based Clustering through Self-Organization and Swarm Intelligence" <DOI:10.1007/978-3-658-20540-9>. Selecting the correct projection method will result in a visualization in which mountains surround each cluster. The number of clusters can be determined by counting valleys on the topographic map. Most projection methods are wrappers for already available methods in R. By contrast, the neighbor retrieval visualizer (NeRV) is based on C++ source code of the 'dredviz' software package, and the Curvilinear Component Analysis (CCA) is translated from 'MATLAB' ('SOM Toolbox' 2.0) to R.
Provides functions and graphics for projecting daily incidence based on past incidence, and estimates of the serial interval and reproduction number. Projections are based on a branching process using a Poisson-distributed number of new cases per day, similar to the model used for estimating R0 in 'EpiEstim' or in 'earlyR', and described by Nouvellet et al. (2017) <doi:10.1016/j.epidem.2017.02.012>.
Management problems of deterministic and stochastic projects. It obtains the duration of a project and the appropriate slack for each activity in a deterministic context. In addition it obtains a schedule of activities' time (Castro, Gómez & Tejada (2007) <doi:10.1016/j.orl.2007.01.003>). It also allows the management of resources. When the project is done, and the actual duration for each activity is known, then it can know how long the project is delayed and make a fair delivery of the delay between each activity (Bergantiños, Valencia-Toledo & Vidal-Puga (2018) <doi:10.1016/j.dam.2017.08.012>). In a stochastic context it can estimate the average duration of the project and plot the density of this duration, as well as, the density of the early and last times of the chosen activities. As in the deterministic case, it can make a distribution of the delay generated by observing the project already carried out.
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 a project infrastructure with a focus on
    manuscript creation. Creates a project folder with a single command,
    containing subdirectories for specific components, templates for
    manuscripts, and so on.
Provides functions to
    automatically build a directory structure for a new R
    project. Using this structure, 'ProjectTemplate'
    automates data loading, preprocessing, library
    importing and unit testing.
Provides programmatic access to 'GitHub' API with a
    focus on project management.  Key functionality includes
    setting up issues and milestones from R objects or 'YAML' configurations,
    querying outstanding or completed tasks, and generating progress updates
    in tables, charts, and RMarkdown reports. Useful for those using 'GitHub' in personal,
    professional, or academic settings with an emphasis on streamlining
    the workflow of data analysis projects.
Performs projection predictive feature selection for generalized linear models
    (see, Piironen, Paasiniemi and Vehtari, 2018, <arXiv:1810.02406>).
    The package is compatible with the 'rstanarm' and 'brms' packages, but other 
    reference models can also be used. See the package vignette for more 
    information and examples.
Provides functions for calculating and analyzing the proliferative 
    index (PI) from an RNA-seq dataset. As described in Ramaker & Lasseigne, 
    et al. bioRxiv, 2016 <doi:10.1101/063057>.
Functions which can be used to support the Multicriteria Decision Analysis (MCDA) process 
    involving multiple criteria, by PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations).
Provides fundamental abstractions for doing asynchronous programming
    in R using promises. Asynchronous programming is useful for allowing a single
    R process to orchestrate multiple tasks in the background while also attending
    to something else. Semantics are similar to 'JavaScript' promises, but with a
    syntax that is idiomatic R.
Deploy, maintain, and invoke predictive models using the 'Alteryx
    Promote' REST API.  'Alteryx Promote' is available at the URL:
    <https://www.alteryx.com/products/alteryx-promote>.
Analysis and measurement of promotion effectiveness on a given target variable (e.g. daily sales). After converting promotion schedule into dummy or smoothed predictor variables, the package estimates the effects of these variables controlled for trend/periodicity/structural change using prophet by Taylor and Letham (2017) <doi:10.7287/peerj.preprints.3190v2> and some prespecified variables (e.g. start of a month).
Carrying out inferences about any linear combination of proportions and the ratio of two proportions.
Propagation of uncertainty using higher-order Taylor expansion and Monte Carlo simulation.
Computes two-sample confidence intervals for single, paired and independent proportions.
Implementation of propensity clustering and
        decomposition as described in Ranola et al. (2013) <doi:10.1186/1752-0509-7-21>. 
        Propensity decomposition can be viewed on the
        one hand as a generalization of the eigenvector-based
        approximation of correlation networks, and on the other hand as
        a generalization of random multigraph models and
        conformity-based decompositions.
Allows to parse 'Java' properties files in the context of
  'R Service Bus' applications.
Implements a procedure for forecasting time series data based on
    an additive model where non-linear trends are fit with yearly, weekly, and
    daily seasonality, plus holiday effects. It works best with time series
    that have strong seasonal effects and several seasons of historical data.
    Prophet is robust to missing data and shifts in the trend, and typically
    handles outliers well.
Abundant statistical literature has revealed the importance of constructing and evaluating various methods for constructing confidence intervals (CI) for single binomial proportion (p). We comprehensively provide procedures in frequentist (approximate with or without adding pseudo counts or continuity correction or exact) and in Bayesian cultures. Evaluation procedures for CI warrant active computational attention and required summaries pertaining to  four criterion (coverage probability, expected length, p-confidence, p-bias, and error) are implemented.
A package for selecting the most relevant features (genes) in the high-dimensional binary classification problems. The discriminative features are identified using analyzing the overlap between the expression values across both classes. The package includes functions for measuring the proportional overlapping score for each gene avoiding the outliers effect. The used measure for the overlap is the one defined in the "Proportional Overlapping Score (POS)" technique for feature selection. A gene mask which represents a gene's classification power can also be produced for each gene (feature). The set size of the selected genes might be set by the user. The minimum set of genes that correctly classify the maximum number of the given tissue samples (observations) can be also produced.
The bioinformatic evaluation of gene co-expression often begins with
    correlation-based analyses. However, correlation lacks validity when
    applied to relative data, including count data generated by next-generation
    sequencing. This package implements several metrics for proportionality, including
    phi [Lovell et al (2015) <DOI:10.1371/journal.pcbi.1004075>] and
    rho [Erb and Notredame (2016) <DOI:10.1007/s12064-015-0220-8>]. This package also
    implements several metrics for differential proportionality. Unlike correlation,
    these measures give the same result for both relative and absolute data.
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.
Provides wrapper functions to access the ProPublica's Congress and Campaign Finance APIs.
    The Congress API provides near real-time access to legislative data from the House of 
    Representatives, the Senate and the Library of Congress.
    The Campaign Finance API provides data from United States Federal Election Commission 
    filings and other sources. The API covers summary information for candidates and 
    committees, as well as certain types of itemized data.
    For more information about these APIs go to: <https://www.propublica.org/datastore/apis>.
Offers a variety of tools, such as specific plots and regression model approaches, for analyzing different patient reported questionnaires. Specially, mixed-effects models based on the beta-binomial distribution are implemented to deal with binomial data with over-dispersion (see Najera-Zuloaga J., Lee D.-J. and Arostegui I. (2017) <doi:10.1177/0962280217690413>).
An extensible repository of accurate, up-to-date functions to score 
    commonly used patient-reported outcome (PRO), quality of life (QOL), and 
    other psychometric and psychological measures.  'PROscorer', together with 
    the 'PROscorerTools' package, is a system to facilitate the incorporation of 
    PRO measures into research studies and clinical settings in a scientifically 
    rigorous and reproducible manner.  These packages and their vignettes are 
    intended to help establish and promote "best practices" to improve the 
    planning, scoring, and reporting of PRO-like measures in research.  
    The 'PROscorer' "Instrument Descriptions" vignette contains descriptions of 
    each instrument scored by 'PROscorer', complete with references.  These 
    instrument descriptions are suitable for inclusion in formal study protocol 
    documents, grant proposals, and manuscript Method sections.  Each 
    'PROscorer' function is composed of helper functions from the 
    'PROscorerTools' package, and users are encouraged to contribute new 
    functions to 'PROscorer'.  More scoring functions are currently in 
    development and will be added in future updates.
Provides a reliable and flexible toolbox to score 
    patient-reported outcome (PRO), Quality of Life (QOL), and other 
    psychometric measures. The guiding philosophy is that scoring errors can 
    be eliminated by using a limited number of well-tested, well-behaved 
    functions to score PRO-like measures. The workhorse of the package is 
    the 'scoreScale' function, which can be used to score most single-scale 
    measures. It can reverse code items that need to be reversed before 
    scoring and pro-rate scores for missing item data. Currently, three 
    different types of scores can be output: summed item scores, mean item 
    scores, and scores scaled to range from 0 to 100. The 'PROscorerTools' 
    functions can be used to write new functions that score more complex 
    measures. In fact, 'PROscorerTools' functions are the building blocks of 
    the scoring functions in the 'PROscorer' package (which is a repository 
    of functions that score specific commonly-used instruments). Users are 
    encouraged to use 'PROscorerTools' to write scoring functions for their 
    favorite PRO-like instruments, and to submit these functions for 
    inclusion in 'PROscorer' (a tutorial vignette will be added soon). The 
    long-term vision for the 'PROscorerTools' and 'PROscorer' packages is to 
    provide an easy-to-use system to facilitate the incorporation of PRO 
    measures into research studies in a scientifically rigorous and 
    reproducible manner. These packages and their vignettes are intended to 
    help establish and promote "best practices" for scoring and describing 
    PRO-like measures in research.
The package provides functions for pretreatment and sample
    selection of visible and near infrared diffuse reflectance spectra
An environment to simulate the development of annual plant populations with regard to population dynamics and genetics, especially herbicide resistance. It combines genetics on the individual level (Renton et al. 2011) with a stochastic development on the population level (Daedlow, 2015).
	Renton, M, Diggle, A, Manalil, S and Powles, S (2011) <doi:10.1016/j.jtbi.2011.05.010> 
	Daedlow, Daniel (2015, doctoral dissertation: University of Rostock, Faculty of Agriculture and Environmental Sciences.)
In a typical protein labelling procedure, proteins are chemically tagged with a functional group, usually at specific sites, then digested into peptides, which are then analyzed using matrix-assisted laser desorption ionization - time of flight mass spectrometry (MALDI-TOF MS) to generate peptide fingerprint. Relative to the control, peptides that are heavier by the mass of the labelling group are informative for sequence determination. Searching for peptides with such mass shifts, however, can be difficult. This package, designed to tackle this inconvenience, takes as input the mass list of two or multiple MALDI-TOF MS mass lists, and makes pairwise comparisons between the labeled groups vs. control, and restores centroid mass spectra with highlighted peaks of interest for easier visual examination. Particularly, peaks differentiated by the mass of the labelling group are defined as a “pair”, those with equal masses as a “match”, and all the other peaks as a “mismatch”.For more bioanalytical background information, refer to following publications: Jingjing Deng (2015) <doi:10.1007/978-1-4939-2550-6_19>; Elizabeth Chang (2016) <doi:10.7171/jbt.16-2702-002>.
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," The Journal of the American 
    Statistical Association, 106(495), 1075-1084.
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, principal component analysis, correspondence analysis, multidimensional scaling, generalised procrustes analysis and individual differences scaling using a variety of dissimilarity measures. Univariate provenance proxies, such as single-grain ages or (isotopic) compositions are compared with the Kolmogorov-Smirnov, Kuiper or Sircombe-Hazelton L2 distances. Categorical provenance proxies such as chemical compositions are compared with the Aitchison and Bray-Curtis distances, and point-counting data with the chi-square distance. Also included are tools to plot compositional and point-counting data on ternary diagrams and point-counting data on radial plots, to calculate the sample size required for specified levels of statistical precision, and to assess the effects of hydraulic sorting on detrital compositions. Includes an intuitive query-based user interface for users who are not proficient in R.
R functions to access provenance information collected by 'rdt' or 
    'rdtLite'.  The information is stored inside a 'ProvInfo' object and can be 
    accessed through a collection of functions that will return the requested 
    data. The exact format of the JSON created by 'rdt' and 'rdtLite' is described 
    in <https://github.com/End-to-end-provenance/ExtendedProvJson>.
Reads the provenance collected by the 'rdt' or
    'rdtLite' packages, or other tools providing compatible PROV JSON output
    created by the execution of a script, and provides a human-readable 
    summary identifying the input and output files, the script used 
    (if any), errors and warnings produced, and the environment in 
    which it was executed.  It can also optionally package all the 
    files into a zip file.  The exact format of the JSON created by 
    'rdt' and 'rdtLite' is described in 
    <https://github.com/End-to-end-provenance/ExtendedProvJson>.  
    More information about 'rdtLite' and associated tools is available 
    at <https://github.com/End-to-end-provenance/> and Barbara Lerner, 
    Emery Boose, and Luis Perez (2018), Using Introspection to Collect 
    Provenance in R, Informatics, <doi: 10.3390/informatics5010012>.
Displays provenance graphically for provenance collected by the 'rdt' or
  'rdtLite' packages, or other tools providing compatible PROV JSON output. The exact 
  format of the JSON created by 'rdt' and 'rdtLite' is described in 
  <https://github.com/End-to-end-provenance/ExtendedProvJson>.  More information about
  rdtLite and associated tools is available at <https://github.com/End-to-end-provenance/> 
  and Barbara Lerner, Emery Boose, and Luis Perez (2018), Using Introspection to Collect 
  Provenance in R, Informatics, <doi: 10.3390/informatics5010012>.
Provides an extensible framework for the efficient calculation of auto- and cross-proximities, along with implementations of the most popular ones.
Computes proximity between rows or columns of large matrices efficiently in C++.
    Functions are optimised for large sparse matrices using the Armadillo and Intel TBB libraries.
    Among several built-in similarity/distance measures, computation of correlation,
    cosine similarity and Euclidean distance is particularly fast.
Determine minimal protein set explaining
    peptide spectrum matches. Utility functions for creating fasta amino acid databases with decoys and contaminants.
    Peptide false discovery rate estimation for target decoy search results on psm, precursor, peptide and protein
    level.
Reverse depends for a given package are queued such that multiple
 workers can run the tests in parallel.
Computes the areas under the precision-recall (PR) and ROC curve for weighted (e.g., soft-labeled) and unweighted data. In contrast to other implementations, the interpolation between points of the PR curve is done by a non-linear piecewise function. In addition to the areas under the curves, the curves themselves can also be computed and plotted by a specific S3-method. References: Davis and Goadrich (2006) <doi:10.1145/1143844.1143874>; Keilwagen et al. (2014) <doi:10.1371/journal.pone.0092209>; Grau et al. (2015) <doi:10.1093/bioinformatics/btv153>.
Provides a simulation framework to simulate streamflow time series with similar
  main characteristics as observed data. These characteristics include the distribution of daily 
  streamflow values and their temporal correlation as expressed by short- and long-range 
  dependence. The approach is based on the randomization of the phases of the Fourier 
  transform. We further use the flexible four-parameter Kappa distribution, which allows 
  for the extrapolation to yet unobserved low and high flows. Alternatively, the empirical or any other distribution can be used. A detailed description of 
  the simulation approach and an application example can be found 
  in <https://www.hydrol-earth-syst-sci-discuss.net/hess-2019-142/>.
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.
List, query and manipulate all system processes, on 'Windows',
    'Linux' and 'macOS'.
A collection of functions that primarily produce graphics
        to aid in a Propensity Score Analysis (PSA).  Functions
        include: cat.psa and box.psa to test balance within strata of
        categorical and quantitative covariates, circ.psa for a
        representation of the estimated effect size by stratum,
        loess.psa that provides a graphic and loess based effect size
        estimate, and various balance functions that provide measures
        of the balance achieved via a PSA in a categorical covariate.
Algorithms 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.
A toolbox in symbolic data framework as a statistical learning and data mining solution for symbolic polygonal data analysis. This study is a new approach in data analysis and it was proposed by 
            Silva et al. (2019) <doi:10.1016/j.knosys.2018.08.009>. The package presents the estimation of main descriptive statistical measures, e.g, mean, covariance, variance, correlation and coefficient of variation. 
            In addition, a method to obtain polygonal data from classical data is presented. Empirical probability distribution function based on symbolic polygonal histogram and a regression model with its main measures are presented.
This R package includes functions for gathering commonly used and
    regularly maintained data set in political science. It also includes
    functions for combining components from these data sets into variables that
    have been suggested in the literature, but are not regularly maintained.
Functions for creating Latin Hypercubes with
    prescribed correlations and performing parameter space exploration.
    Also implements the PLUE method.
    Based on the package sensitivity, by Gilles Pujol,
    Bertrand Iooss & Alexandre Janon.
Various functions for computing pseudo-observations for censored data regression. Computes pseudo-observations for modeling: competing risks based on the cumulative incidence function, survival function based on the restricted mean,  survival function based on the Kaplan-Meier estimator see Klein et al. (2008) <doi:10.1016/j.cmpb.2007.11.017>.
Efficient calculation of pseudo-ranks and (pseudo)-rank based test statistics. In case of equal sample sizes, pseudo-ranks and mid-ranks are equal. When used for inference mid-ranks may lead to paradoxical results. Pseudo-ranks are in general not affected by such a problem. For details, see Brunner, E., Bathke A. C. and Konietschke, F: Rank- and Pseudo-Rank Procedures in Factorial Designs - Using R and SAS, Springer Verlag, to appear.
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.
Provides functions to apply pooling or backward selection 
	for logistic or Cox regression prediction models in multiple imputed
	datasets. Backward selection can be done from the pooled model using 
	Rubin's Rules (RR), the total covariance matrix (D1 method), pooling 
	chi-square values (D2 method), pooling likelihood ratio statistics	
	(D3) or pooling the median p-values. The model can contain 
	continuous, dichotomous, categorical predictors and interaction terms 
	between all	type of these predictors. Continuous predictors	can also 
	be introduced as restricted cubic spline coefficients. It is also possible 
	to force (spline) predictors or interaction terms in the model during predictor 
	selection. The package also contains functions to generate apparent model performance 
	measures over imputed datasets as ROC/AUC, R-squares, fit test values and 
	calibration	plots. A wrapper function	over Frank Harrell's validate function 
	is used for that. Bootstrap internal validation is performed in each imputed dataset 
	and results are pooled. Backward selection as part of internal validation	
	is optional and recommended. Also a function to externally validate logistic
	prediction models in multiple imputed datasets is available.
	Eekhout (2017) <doi:10.1186/s12874-017-0404-7>.
	Wiel (2009) <doi:10.1093/biostatistics/kxp011>.
	Marshall (2009) <doi:10.1186/1471-2288-9-57>.
Implements projected sparse Gaussian process Kriging (Ingram 'et. al.', 2008, <doi:10.1007/s00477-007-0163-9>) as an additional method for the 'intamap' package. More details on implementation (Barillec 'et. al.', 2010, <doi:10.1016/j.cageo.2010.05.008>).
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/
In the situation when multiple alternative treatments or
    interventions available, different population groups may respond differently
    to different treatments. This package implements a method that discovers
    the population subgroups in which a certain treatment has a better effect
    than the other alternative treatments. This is done by first estimating the
    treatment effect for a given treatment and its uncertainty by computing random
    forests, and the resulting model is summarized by a decision tree in which the
    probabilities that the given treatment is best for a given subgroup is shown in
    the corresponding terminal node of the tree.
Makes it easy to build panel data in wide format from Panel Survey
    of Income Dynamics ('PSID') delivered raw data. Downloads data directly from
    the PSID server using the 'SAScii' package. 'psidR' takes care of merging
    data from each wave onto a cross-period index file, so that individuals can be
    followed over time. The user must specify which years they are interested in,
    and the 'PSID' variable names (e.g. ER21003) for each year (they differ in each
    year). The package offers helper functions to retrieve variable names from different
    waves. There are different panel data designs and sample subsetting criteria
    implemented ("SRC", "SEO", "immigrant" and "latino" samples).
Generalization of the SIMEX algorithm from Cook & Stefanski (1994) <doi:10.2307/2290994> for the calculation of inbreeding depression or heritability on pedigree structures affected by missing or misassigned paternities. It simulates errors and tracks the behavior of the estimate as a function of the error proportion. It extrapolates back a true value corresponding to the null error rate.
Data and statistics of Pakistan Social and Living Standards Measurement (PSLM) survey 2014-15 from Pakistan Bureau of Statistics (<http://www.pbs.gov.pk/>).
Functions for fitting linear and non-linear mixed-effects models using
        stochastic differential equations (SDEs). 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 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' (Tornoe 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 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.
Implements estimation and testing procedures for evaluating an intermediate biomarker response as a principal surrogate of a clinical response to treatment (i.e., principal stratification effect modification analysis), as described in Juraska M, Huang Y, and Gilbert PB (2018), Inference on treatment effect modification by biomarker response in a three-phase sampling design, Biostatistics, kxy074 <doi:10.1093/biostatistics/kxy074>. The methods avoid the restrictive 'placebo structural risk' modeling assumption common to past methods and further improve robustness by the use of nonparametric kernel smoothing for biomarker density estimation. A randomized controlled two-group clinical efficacy trial is assumed with an ordered categorical or continuous univariate biomarker response measured at a fixed timepoint post-randomization and with a univariate baseline surrogate measure allowed to be observed in only a subset of trial participants with an observed biomarker response (see the flexible three-phase sampling design in the paper for details). Bootstrap-based procedures are available for pointwise and simultaneous confidence intervals and testing of four relevant hypotheses. Summary and plotting functions are provided for estimation results.
A network-based systems biology tool for flexible identification of phenotype-specific subpathways in the cancer gene expression data 
  with multiple categories (such as multiple subtype or developmental stages of cancer). Subtype Set Enrichment Analysis (SubSEA) and Dynamic Changed 
  Subpathway Analysis (DCSA) are developed to flexible identify subtype specific and dynamic changed subpathways respectively. The operation modes 
  include extraction of subpathways from biological pathways, inference of subpathway activities in the context of gene expression data, identification 
  of subtype specific subpathways with SubSEA, identification of dynamic changed subpathways associated with the cancer developmental stage with DCSA, 
  and visualization of the activities of resulting subpathways by using box plots and heat maps. Its capabilities render the tool could find the specific
  abnormal subpathways in the cancer dataset with multi-phenotype samples.
Provides a framework for analysing state sequences with probabilistic suffix trees (PST), the construction that stores variable length Markov chains (VLMC). Besides functions for learning and optimizing VLMC models, the PST library includes many additional tools to analyse sequence data with these models: visualization tools, functions for sequence prediction and artificial sequences generation, as well as for context and pattern mining. The package is specifically adapted to the field of social sciences by allowing to learn VLMC models from sets of individual sequences possibly containing missing values, and by accounting for case weights. The  library also allows to compute probabilistic divergence between two models, and to fit segmented VLMC, where sub-models fitted to distinct strata of the learning sample are stored in a single PST. This software results from research work executed within the framework of the Swiss National Centre of Competence in Research LIVES, which is financed by the Swiss National Science Foundation. The authors are grateful to the Swiss National Science Foundation for its financial support.
Calculating Pst values to assess differentiation among populations from a set of quantitative traits is the primary purpose of such a package. The bootstrap method provides confidence intervals and distribution histograms of Pst. Variations of Pst in function of the parameter c/h^2 are studied as well. Finally, the package proposes different transformations especially to eliminate any variation resulting from allometric growth (calculation of residuals from linear regressions, Reist standardizations or Aitchison transformation).
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, especially the 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) augmented estimation of treatment effect with outcome regression. The weighting methods include the inverse probability weight (IPW) for estimating the average treatment effect (ATE), the IPW for average treatment effect of the treated (ATT), the IPW for the average treatment effect of the controls (ATC), the matching weight (MW), the overlap weight (OVERLAP), and the trapezoidal weight (TRAPEZOIDAL). Sandwich variance estimation is provided to adjust for the sampling variability of the estimated propensity score. These methods are discussed by Hirano et al (2003) <DOI:10.1111/1468-0262.00442>, Lunceford and Davidian (2004) <DOI:10.1002/sim.1903>, Li and Greene (2013) <DOI:10.1515/ijb-2012-0030>, and Li et al (2016) <DOI:10.1080/01621459.2016.1260466>.
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 <https://personality-project.org/r> web page.
Tools for computing bare-bones and psychometric meta-analyses and for generating psychometric data for use in meta-analysis simulations. Supports bare-bones, individual-correction, and artifact-distribution methods for meta-analyzing correlations and d values. Includes tools for converting effect sizes, computing sporadic artifact corrections, reshaping meta-analytic databases, computing multivariate corrections for range variation, and more. Bugs can be reported to <https://github.com/psychmeta/psychmeta/issues> or <issues@psychmeta.com>.
The main goal of the psycho package is to provide tools for psychologists, neuropsychologists and neuroscientists, 
   to facilitate and speed up the time spent on data analysis. It aims at supporting best practices and tools to format the output 
   of statistical methods to directly paste them into a manuscript, ensuring statistical reporting standardization and conformity.
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.
Multi-group (dynamical) structural equation models in combination with confirmatory network models from cross-sectional, time-series and panel data. Allows for confirmatory testing and fit as well as exploratory model search.
Infrastructure for psychometric modeling such as data classes (for
  item response data and paired comparisons), basic model fitting functions (for
  Bradley-Terry, Rasch, parametric logistic IRT, generalized partial credit,
  rating scale, multinomial processing tree models), extractor functions for
  different types of parameters (item, person, threshold, discrimination,
  guessing, upper asymptotes), unified inference and visualizations, and various
  datasets for illustration.  Intended as a common lightweight and efficient
  toolbox for psychometric modeling and a common building block for fitting
  psychometric mixture models in package "psychomix" and trees based on
  psychometric models in package "psychotree".
Recursive partitioning based on psychometric models,
  employing the general MOB algorithm (from package partykit) to obtain
  Bradley-Terry trees, Rasch trees, rating scale and partial credit trees, and
  MPT trees.
Helper functions for producing reports in Psychology (Reproducible Research). Provides required formatted strings (APA style) for use in 'Sweave'/'Knitr' 'Latex' integration within *.Rnw files.
Support functions,  data sets, and vignettes for the 'psych' package. Contains several of the biggest data sets for the 'psych' package as well as one vignette. A few helper functions for file manipulation are included as well. For more information, see the <https://personality-project.org/r> web page.
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.
Apply the popular real-time monitoring strategy
    proposed by Phillips, Shi and Yu (2015a,b;PSY) <doi:10.1111/iere.12132>, <doi:10.1111/iere.12131>, along with a new
    bootstrap procedure designed to mitigate the potential impact of
    heteroskedasticity and to effect family-wise size control in recursive
    testing algorithms (Phillips and Shi, forthcoming).
Analyse location data such as latitude,
    longitude, and elevation. Based on spherical trigonometry, variables such as
    speed, bearing, and distances can be calculated from moment to moment, 
    depending on the sampling frequency of the equipment used, and independent 
    of scale. Additionally, the package can plot tracks, coordinates, and shapes
    on maps, and sub-tracks can be selected with point-in-polygon or other 
    techniques. The package is optimized to support behavioural science 
    experiments with multiple tracks. It can detect and clean up errors in the 
    data, and resulting data can be exported to be analysed in statistical 
    software or geographic information systems (GIS).
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.
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?
Plot the summary graphic called the persistence terrace for topological inference. It also provides the aid tool called the terrace area plot for determining the number of significant topological features. Moon, C., Giansiracusa, N., and Lazar, N. A. (2018) <doi:10.1080/10618600.2017.1422432>.
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 Poisson-Tweedie generalized linear mixed model. 
    Likelihood approximation based on adaptive Gauss Hermite quadrature rule.
Fits and analyses time dependent marked point process models with an emphasis on earthquake modelling. For a more detailed introduction to the package, see the topic "PtProcess". A list of recent changes can be found in the topic "Change Log".
Various applications in invasive species biology,
    conservation biology, epidemiology and elsewhere involve 
    sampling of sets of 2D points from a posterior distribution.
    The number of such point sets may be large, say 1000 or 10000. 
    This package facilitates visualisation of such output by 
    constructing seven nested polygons representing the location and 
    variability of the point sets. This can be used, for example, to 
    visualise the range boundary of a species, and uncertainty in 
    the location of that boundary.
Wraps a collection of stemming algorithms for the Portuguese
    Language.
Various estimation methods for the shape parameter of Pareto
    distributed data. This package contains functions for various estimation 
    methods such as maximum likelihood 
    (Newman, 2005)<doi:10.1016/j.cities.2012.03.001>, 
    Hill's estimator (Hill, 1975)<doi:10.1214/aos/1176343247>, 
    least squares (Zaher et al., 2014)<doi:10.9734/BJMCS/2014/10890>, 
    method of moments (Rytgaard, 1990)<doi:10.2143/AST.20.2.2005443>, 
    percentiles (Bhatti et al., 2018)<doi:10.1371/journal.pone.0196456>,
    and weighted least squares (Nair et al., 2019) to estimate the shape 
    parameter of Pareto distributed data. It also provides both a heuristic 
    method (Hubert et al., 2013)<doi:10.1016/j.csda.2012.07.011> and a 
    goodness of fit test 
    (Gulati and Shapiro, 2008)<doi:10.1007/978-0-8176-4619-6> for testing for 
    Pareto data as well as a method for generating Pareto distributed data.
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.
             Reports are customizable (target thresholds, subsetting) and available in HTML or PDF format.
             Published in J. Proteome Res., Proteomics Quality Control: Quality Control Software for MaxQuant Results (2015) 'doi:10.1021/acs.jproteome.5b00780'.
Bayesian variable selection for linear regression models using hierarchical
  priors. There is a prior that combines information across responses and one
  that combines information across covariates, as well as a standard spike and
  slab prior for comparison. An MCMC samples from the marginal posterior
  distribution for the 0-1 variables indicating if each covariate belongs to the
  model for each response.
Get chunks of XML scholarly articles without 
    having to know how to work with XML. Custom mappers
    for each publisher and for each article section pull 
    out the information you want. Works with outputs from 
    package 'fulltext', 'xml2' package documents, and file paths to 
    XML documents.
A toolbox for making R functions and capabilities more
    accessible to students and professionals from Epidemiology and
    Public Health related disciplines. Includes a function to report 
    coefficients and confidence intervals from models using robust
    standard errors (when available), functions that expand lattice
    plots and functions relevant for introductory papers in Epidemiology 
    or Public Health. Please note that use of the 
    provided data sets is for educational purposes only.
Performs sensitivity analysis for publication bias in meta-analyses (per Mathur & VanderWeele, 2019 [<https://osf.io/s9dp6>]).
    These analyses enable statements such as: "For publication bias to shift the observed point estimate to the null,
    'significant' results would need to be at least 30-fold more likely to be published than negative or 'nonsignificant'
    results." Comparable statements can be made regarding shifting to a chosen non-null value or shifting the confidence
    interval. Provides a worst-case meta-analytic point estimate under maximal publication bias obtained simply by conducting
    a standard meta-analysis of only the negative and "nonsignificant" studies.
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'.
'PubTator' <https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator/> is a National Center for Biotechnology Information (NCBI) tool that enhances the annotation of articles on PubMed <https://www.ncbi.nlm.nih.gov/pubmed/>. It makes it possible to rapidly identify potential relationships between genes or proteins using text mining techniques. In contrast, manually searching for and reading the annotated articles would be very time consuming. 'PubTator' offers both an online interface and a RESTful API, however, neither of these approaches are well suited for frequent, high-throughput analyses. The package 'pubtatordb' provides a set of functions that make it easy for the average R user to download 'PubTator' annotations, create, and then query a local version of the database.
Efficient algorithm for solving PU (Positive and Unlabeled) problem in low or high dimensional setting with lasso or group lasso penalty. The algorithm uses Maximization-Minorization and (block) coordinate descent. Sparse calculation and parallel computing are supported for the computational speed-up. See Hyebin Song, Garvesh Raskutti (2018) <arXiv:1711.08129>.
R Interface to Pullword Service for natural language processing
    in Chinese. It enables users to extract valuable words from text by deep learning models. 
    For more details please visit the official site (in Chinese) http://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). Christian L. Müller, Richard Bonneau, Zachary Kurtz (2016) <arXiv:1605.07072>.
A tool for analyzing the transcription, processing and degradation rates of genes by 4sU-seq (the Metabolic Label 4-thiouridine) data and RNA-seq (RNA sequencing) data. It can not only recognize the transcriptional dynamic rates at the measurement time points, but also obtain continuous changes in transcriptional dynamics. More importantly, it is able to predict the trend of mRNA (Mature RNA) transcription and expression changes in the future.
R package to query and get data out of a Pumilio sound archive system (http://ljvillanueva.github.io/pumilio/).
Provides meta-analysis methods that correct for
    publication bias. Four methods are currently included in the package.
    The p-uniform method as described in van Assen, van Aert, and Wicherts (2015) 
    <doi:10.1037/met0000025> can be used for estimating the average effect size,
    testing the null hypothesis of no effect, and testing for publication bias 
    using only the statistically significant effect sizes of primary studies. The 
    second method in the package is the p-uniform* method as described in Chapter 
    5 of van Aert (2018) <doi:10.31222/osf.io/eqhjd>. This method is an extension 
    of the p-uniform method that allows for estimation of the average effect size 
    and the between-study variance in a meta-analysis, and uses both the statistically 
    significant and nonsignificant effect sizes. The third method in the package 
    is the hybrid method as described in van Aert and van Assen (2017) 
    <doi:10.3758/s13428-017-0967-6>. The hybrid method is a meta-analysis method 
    for combining an original study and replication and while taking into account 
    statistical significance of the  original study. The p-uniform and hybrid method 
    are based on the statistical theory that the distribution of p-values is 
    uniform conditional on the population effect size. The fourth method in the 
    package is the Snapshot Bayesian Hybrid Meta-Analysis Method as described in 
    van Aert and van Assen (2017) <doi:10.1371/journal.pone.0175302>. This method 
    computes posterior probabilities for four true effect sizes (no, small, medium, 
    and large) based on an original study and replication while taking into account 
    publication bias in the original study. The method can also be used for computing 
    the required sample size of the replication akin to power analysis in null 
    hypothesis significance testing.
Adsorption isotherm equations are linearized plots of different solid-liquid phase equilibria used in calculating different parameters related to the adsorption process. Isotherm equations deals with physical adsorption of gases and  vapor and gives the most important characteristics  of industrial adsorbents that include pore volume, pore size or energy distribution. PUPAIM has 28 documented adsorption isotherm models listed by Dabrowski (2001) <doi:10.1016/S0001-8686(00)00082-8> and Ayawei et al.(2017) <doi:10.1155/2017/3039817>. These models could be easily  fitted in R using adsorption data (Ce and Qe) obtained from experiments.
Pupillometric data collected using SR Research Eyelink eye trackers 
    requires significant preprocessing. This package contains functions for 
    preparing pupil dilation data for visualization and statistical analysis. 
    Specifically, it provides a pipeline of functions which aid in data validation,
    the removal of blinks/artifacts, downsampling, and baselining, among others. 
    Additionally, plotting functions for creating grand average and conditional 
    average plots are provided. See the vignette for samples of the functionality.
    The package is designed for handling data collected with SR Research 
    Eyelink eye trackers using Sample Reports created in SR Research Data 
    Viewer.
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.
Simple method of purging independent variables of mediating effects. First, regress the direct variable on the indirect variable. Then, used the stored residuals as the new purged (direct) variable in the updated specification. This purging process allows for use of a new direct variable uncorrelated with the indirect variable. Please cite the method and/or package using Waggoner, Philip D. (2018) <doi:10.1177/1532673X18759644>.
A complete and consistent functional programming toolkit for R.
Some functions at the intersection of 'dplyr' and 'purrr' that 
  formerly lived in 'purrr'.
Provides functions to easily add progress bars to 
    apply calls.
Create sliders from left, right, top and bottom which may include any html or 'Shiny' input or output.
Send push notifications to mobile devices or the desktop using Pushover. 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.
Contains functions to compute and plot confidence distributions, confidence densities, p-value functions and s-value (surprisal) functions for several commonly used estimates. Instead of just calculating one p-value and one confidence interval, p-value functions display p-values and confidence intervals for many levels thereby allowing to gauge the compatibility of several parameter values with the data. These methods are discussed by Infanger D, Schmidt-Trucksäss A. (2019) <doi:10.1002/sim.8293>; Poole C. (1987) <doi:10.2105/AJPH.77.2.195>; Schweder T, Hjort NL. (2002) <doi:10.1111/1467-9469.00285>; Bender R, Berg G, Zeeb H. (2005) <doi:10.1002/bimj.200410104> ; Singh K, Xie M, Strawderman WE. (2007) <doi:10.1214/074921707000000102>; Rothman KJ, Greenland S, Lash TL. (2008, ISBN:9781451190052); Amrhein V, Trafimow D, Greenland S. (2019) <doi:10.1080/00031305.2018.1543137>; and Greenland S. (2019) <doi:10.1080/00031305.2018.1529625>.
The calculation of p-variation of the finite sample data. 
	This package is a realisation of the procedure described in Butkus, V. & Norvaisa, R. Lith Math J (2018). <doi: 10.1007/s10986-018-9414-3>
	The formal definitions and reference into literature are given in vignette.
Computes nonparametric p-values for the potential class
        memberships of new observations as well as cross-validated
        p-values for the training data. The p-values are based on
        permutation tests applied to an estimated Bayesian likelihood
        ratio, using a plug-in statistic for the Gaussian model, 'k
        nearest neighbors', 'weighted nearest neighbors' or
        'penalized logistic regression'.
        Additionally, it provides graphical displays and quantitative
        analyses of the p-values.
An implementation of multiscale bootstrap resampling for
             assessing the uncertainty in hierarchical cluster analysis.
             It provides AU (approximately unbiased) p-value as well as
             BP (bootstrap probability) value for each cluster in a dendrogram.
Estimates (and controls for) phylogenetic signal through phylogenetic eigenvectors regression (PVR) and phylogenetic signal-representation (PSR) curve, along with some plot utilities.
The pvsR package facilitates data retrieval from Project
    Vote Smart's rich online data base on US politics via the Project Vote
    Smart application programming interface (PVS API). The functions in this
    package cover most PVS API classes and methods and return the
    requested data in a data frame.
Contains functions which allow the user to perform time series regression quickly using the Power Weighted Densities (PWD) approach. alphahat_LR_one_Rcpp() is the main workhorse function within this package.
Calculates various functions needed for design and monitoring survival trials
    accounting for complex situations such as delayed treatment effect, treatment crossover, non-uniform accrual,
    and different censoring distributions between groups. The event time distribution is assumed to be
    piecewise exponential (PWE) distribution and the entry time is assumed to be piecewise uniform distribution.
    As compared with Version 1.2.1, two more types of hybrid crossover are added. 
    A bug is corrected in the function "pwecx" that calculates the crossover-adjusted survival, distribution, 
    density, hazard and cumulative hazard functions. 
    Also, to generate the crossover-adjusted event time random variable,  a more efficient 
    algorithm is used and the output includes crossover indicators.
Utilities for working with air quality monitoring data
    with a focus on small particulates (PM2.5) generated by wildfire
    smoke. Functions are provided for downloading available data from
    the United States 'EPA' <https://www.epa.gov/outdoor-air-quality-data> and
    it's 'AirNow' air quality site <https://www.airnow.gov>.
    Additional sources of PM2.5 data made accessible by the package include:
    'AIRSIS' (password protected) <https://www.oceaneering.com/data-management/>
    and 'WRCC' <https://wrcc.dri.edu/cgi-bin/smoke.pl>.
    Data compilations are provided by 'PWFSL'
    <https://www.fs.fed.us/pnw/pwfsl/>.
Power analysis functions along the lines of Cohen (1988).
User friendly functions for power and sample size analysis at one-way and two-way ANOVA settings take either effect size or delta and sigma as arguments. They are designed for both one-way and two-way ANOVA settings. In addition, a function for plotting power curves is available for power comparison, which can be easily visualized by statisticians and clinical researchers.
Statistical power analysis for designs including t-tests, correlations, 
    multiple regression, ANOVA, mediation, and logistic regression. Functions accompany 
    Aberson (2019) <doi:10.4324/9781315171500>.
Power analysis for AB testing. The calculations are based on the Welch's unequal variances t-test,
  which is generally preferred over the Student's t-test when sample sizes and variances of the two groups are
  unequal, which is frequently the case in AB testing. In such situations, the Student's t-test will give 
  biased results due to using the pooled standard deviation, unlike the Welch's t-test.
This is a package for calculating power and sample size 
 in multiple testing situations using the Benjamini-Hochberg (BH)
 false discovery rate (FDR) procedure. The package computes power 
 and sample size in one of either two ways, using the average 
 power or the lambda-power. See Izmirlian, G. (2018) <arXiv:1801.03989>.
Tools for the evaluation of interim analysis plans for sequentially
 monitored trials on a survival endpoint; tools to construct efficacy and 
 futility boundaries, for deriving power of a sequential design at a specified
 alternative, template for evaluating the performance of candidate plans at a 
 set of time varying alternatives. See Izmirlian, G. (2014) <doi:10.4310/SII.2014.v7.n1.a4>.
Statistical power simulation for testing the Rasch Model based on a three-way analysis of variance design with mixed classification.
The Penn World Table provides purchasing power parity and
	national income accounts converted to international prices for
	189 countries for some or all of the years 1950-2010.
The Penn World Table 8.x provides information on relative levels of
	income, output, inputs, and productivity for 167 countries
	between 1950 and 2011.
The Penn World Table 9.x (<http://www.ggdc.net/pwt/>) provides information
	on relative levels of income, output, inputs, and productivity
	for 182 countries between 1950 and 2017.
Provides a set of functions for reading and writing PC-Axis files, used by different statistical organizations around the globe for data dissemination.
Generic interface for the PX-Web/PC-Axis API. The PX-Web/PC-Axis
    API is used by organizations such as Statistics Sweden and Statistics
    Finland to disseminate data. The R package can interact with all
    PX-Web/PC-Axis APIs to fetch information about the data hierarchy, extract
    metadata and extract and parse statistics to R data.frame format. PX-Web is
    a solution to disseminate PC-Axis data files in dynamic tables on the web.
    Since 2013 PX-Web contains an API to disseminate PC-Axis files.
Function to read PX-Web data into R via API. The example code reads data from the three national statistical institutes, Statistics Norway, Statistics Sweden and Statistics Finland.
Given a SpatialPolygonsDataFrame and a set of populations for each polygon,
 compute a population density estimate based on Tobler's pycnophylactic interpolation
 algorithm. The result is a SpatialGridDataFrame.
Deterministic Pena-Yohai initial estimator for robust S estimators
    of regression. The procedure is described in detail in
    Pena, D., & Yohai, V. (1999) <doi:10.2307/2670164>.
Drawing population pyramid using (1) data.frame or (2) vectors.
	 The former is named as pyramid() and the latter pyramids(), as wrapper
	 function of pyramid().  pyramidf() is the function to draw population
	 pyramid within the specified frame.
Using the R package 'reticulate', this package creates an interface to the 'pysd' toolset.
    The package provides an R interface to a number of 'pysd' functions, and can read files in
    'Vensim' 'mdl' format, and 'xmile' format. The resulting simulations are returned as a 'tibble', and from 
    that the results can be processed using 'dplyr' and 'ggplot2'. The package has been tested  using 'python3'.
Interact with 'Python' <https://www.python.org/> from within 'R'.
Read and write 'GraphPad Prism' '.pzfx' files in R.
A copula-based measure for quantifying asymmetry in dependence and associations.
A tool for automatic generation of sibling items from a parent item model defined by
           the user. It is an implementation of the process automatic item generation (AIG) focused
           on generating quantitative multiple-choice type of items (see Embretson, Kingston
           (2018) <doi:10.1111/jedm.12166>).
Implements heuristics for the Quadratic Assignment Problem (QAP). Currently only a simulated annealing heuristic is available.
Functions for a scientific quality assurance of meteorological data.
Provides the local polynomial maximum likelihood estimates for the location and scale functions as well as the semiparametric quantile estimates in the generalized quantile-based asymmetric distributional setting. These functions are useful for any member of the generalized quantile-based asymmetric family of distributions.
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 that 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.
Provides advanced functionality for performing configurational comparative research with Qualitative Comparative Analysis (QCA), including crisp-set, multi-value, and fuzzy-set QCA. It also offers advanced tools for sensitivity diagnostics and methodological evaluations of QCA.
Helper functions for Qualitative Comparative Analysis: evaluate and
    plot Boolean formulae on fuzzy set score data, apply Boolean operations, compute
    consistency and coverage measures.
CBA postprocessing algorithm that creates smaller models for datasets containing quantitative (numerical) attributes. Article describing QCBA is published in Tomas Kliegr (2017) <arXiv:1711.10166>.
Shewhart quality control charts for continuous, attribute and count data. Cusum and EWMA charts. Operating characteristic curves. Process capability analysis. Pareto chart and cause-and-effect chart. Multivariate control charts.
Functions to calculate Average Sample Numbers (ASN), Average Run Length (ARL1) and value of k, k1 and k2 for quality control charts under repetitive sampling as given in Aslam et al. (2014) (<DOI:10.7232/iems.2014.13.1.101>).
Tools for (automated and manual) quality control of
  the results of Epigenome-Wide Association Studies.
Tools for (automated and manual) quality control of
  the results of Genome Wide Association Studies
Quality control of chromatin immunoprecipitation libraries (ChIP-seq) by quantitative polymerase chain reaction (qPCR). This function calculates Enrichment value with respect to reference for each histone modification (specific to 'Vii7' software <http://www.thermofisher.com/ca/en/home/life-science/pcr/real-time-pcr/real-time-pcr-instruments/viia-7-real-time-pcr-system/viia-7-software.html>). This function is applicable to full panel of histone modifications described by International Human Epigenomic Consortium (IHEC).
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.
Primarily, the 'qcv' package computes key indices related to the Quantifying Construct
    Validity procedure (QCV; Westen & Rosenthal, 2003 <doi:10.1037/0022-3514.84.3.608>; 
    see also Furr & Heuckeroth, in press). The qcv() function is the heart of the 'qcv' package, 
    but additional functions in the package provide useful ancillary information related to the QCV procedure.
Automates many of the tasks associated with quantitative discourse analysis of transcripts containing discourse
              including frequency counts of sentence types, words, sentences, turns of talk, syllables and other assorted
              analysis tasks. The package provides parsing tools for preparing transcript data. Many functions enable the user
              to aggregate data by any number of grouping variables, providing analysis and seamless integration with other R
              packages that undertake higher level analysis and visualization of text. This affords the user a more efficient
              and targeted analysis. 'qdap' is designed for transcript analysis, however, many functions are applicable to other
              areas of Text Mining/ Natural Language Processing.
A collection of text analysis dictionaries and word lists for use with
        the 'qdap' package.
A collection of regular expression tools associated with
        the 'qdap' package that may be useful outside of the context of
        discourse analysis. Tools include
        removal/extraction/replacement of abbreviations, dates, dollar
        amounts, email addresses, hash tags, numbers, percentages,
        citations, person tags, phone numbers, times, and zip codes.
A collection of tools associated with the 'qdap' package
        that may be useful outside of the context of text analysis.
Allows practitioners to determine (i) if two univariate distributions (which can be continuous, discrete, or even mixed) are equal, (ii) how two distributions differ (shape differences, e.g., location, scale, etc.), and (iii) where two distributions differ (at which quantiles), all using nonparametric LP statistics. The primary reference is Jungreis, D. (2019, Technical Report).
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.
G-computation for a set of time-fixed exposures
    with quantile-based basis functions, possibly under linearity and
    homogeneity assumptions. This approach estimates a regression line
    corresponding to the expected change in the outcome (on the link
    basis) given a simultaneous increase in the quantile-based category
    for all exposures.  Reference: Alexander P. Keil, Jessie P. Buckley,
    Katie M. OBrien, Kelly K. Ferguson, Shanshan Zhao Alexandra J. White
    (2019) A quantile-based g-computation approach to addressing the
    effects of exposure mixtures; <arXiv:1902.04200> [stat.ME].
Compute various quantitative genetics parameters from a Generalised Linear Mixed Model (GLMM) estimates. Especially, it yields the observed phenotypic mean, phenotypic variance and additive genetic variance.
Weighted network visualization and analysis, as well as Gaussian graphical model computation. See Epskamp et al. (2012) <doi:10.18637/jss.v048.i04>.
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.
This function produces both the numerical and graphical summaries of the QTL hotspot detection in the genomes that are available on the worldwide web including the flanking markers of QTLs.
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>].
Developed to perform the estimation and inference for regression 
    coefficient parameters in longitudinal marginal models using the method of 
    quadratic inference functions. Like generalized estimating equations, this 
    method is also a quasi-likelihood inference method. It has been showed that 
    the method gives consistent estimators of the regression coefficients even if 
    the correlation structure is misspecified, and it is more efficient than GEE 
    when the correlation structure is misspecified. Based on Qu, A., Lindsay, 
    B.G. and Li, B. (2000) <doi:10.1093/biomet/87.4.823>.
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.
Nonlinear machine learning tool for classification, clustering
        and dimensionality reduction. It integrates 12 q-kernel functions and
        15 conditional negative definite kernel functions and includes the 
        q-kernel and conditional negative definite kernel version of
        density-based spatial clustering of applications with noise,
        spectral clustering, generalized discriminant analysis, principal
        component analysis, multidimensional scaling, locally linear embedding,
        sammon's mapping and t-Distributed stochastic neighbor embedding.
This is a collection of functions to read, recode, and transcode data for QLC.
Extension of the functionality of the Matrix package for using sparse matrices. Some of the functions are very general, while other are highly specific for special data format as used for quantitative language comparison (QLC).
Functions to implement Q-learning for estimating optimal
        dynamic treatment regimes from two stage sequentially
        randomized trials, and to perform inference via m-out-of-n
        bootstrap for parameters indexing the optimal regime.
Implements Q-Learning, a model-free form of reinforcement
        learning, described in work by Strehl, Li, Wiewiora, Langford &
        Littman (2006) <doi:10.1145/1143844.1143955>.
Empirical adjustment of the distribution of variables originating from (regional) climate model simulations using quantile mapping.
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.
Implementation of methods for estimation of quantitative maps
    from Multi-Parameter Mapping (MPM) acquisitions (Weiskopf et al. (2013)
    <doi:10.3389/fnins.2013.00095>) including adaptive
    smoothing methods in the framework of the ESTATICS model
    (Estimating the apparent transverse relaxation time (R2*) from images with
    different contrasts, Weiskopf et al. (2014) <doi:10.3389/fnins.2014.00278>).
    The smoothing method is described in Mohammadi et al. (2017).
    <doi:10.20347/WIAS.PREPRINT.2432>.
Basic functions for building parsers, with an application to PC-AXIS format files.
Transport data and commands across the 'FAME' <https://fame.sungard.com/support.html> / 'R' border.
    A set of utilities for: reading 'FAME' databases into 'R'; writing
    'R' data into 'FAME' databases; executing 'FAME' commands in 'R' environment; and, executing
    'R' commands from the 'FAME' environment.
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).
Content-preserving transformations transformations of PDF files such 
    as split, combine, and compress. This package interfaces directly to the 'qpdf' 
    C++ API and does not require any command line utilities. Note that 'qpdf' does
    not read actual content from PDF files: to extract text and data you need the
    'pdftools' package.
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.
The string "bird.carp.7TsBWtwqtKAeCTNk8f" is a "QQID":
             a representation of a 128-bit number, constructed
             from two "cues" of short, common, English words, and
             Base64 encoded characters.  The primary intended use of
             QQIDs is as random unique identifiers, e.g. database
             keys like the "UUIDs" defined in the RFC 4122 Internet
             standard.  QQIDs can be identically interconverted with
             UUIDs, IPv6 addresses, MD5 hashes etc., and are suitable
             for a host of applications in which identifiers are read
             by humans.  They are compact, can safely be transmitted
             in binary and text form, can be used as components of
             URLs, and it can be established at a glance whether two
             QQIDs are different or potentially identical.  The qqid
             package contains functions to retrieve true, quantum-random
             QQIDs, to generate pseudo- random QQIDs, to validate
             them, and to interconvert them with other 128-bit number
             representations.
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.
The ANU Quantum Random Number Generator provided by the Australian National University
 generates true random numbers in real-time by measuring the quantum fluctuations of the vacuum. This package offers an interface using their API.
 The electromagnetic field of the vacuum exhibits random fluctuations in phase and amplitude at all frequencies.
 By carefully measuring these fluctuations, one is able to generate ultra-high bandwidth random numbers.
 The quantum Random Number Generator is based on the papers by Symul et al., (2011) <doi:10.1063/1.3597793>
 and Haw, et al. (2015) <doi:10.1103/PhysRevApplied.3.054004>.
 The package offers functions to retrieve a sequence of random integers or hexadecimals and true random samples from a normal or uniform distribution.
A Quantile Rank-score based test for the identification of expression quantitative trait loci.
Parametric modeling of quantile regression coefficient functions.
    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.
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 (2018) <doi:10.1007/s00477-018-1573-6>.
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.
Provides functions for quickly writing and reading any R object to and from disk.
Molecular descriptors and outcomes for several public domain data sets
Computes scores from Q-sort data, using criteria sorts and
  derived scales from subsets of items.
  The 'qsort' package includes descriptions and scoring procedures
  for four different Q-sets commonly used in developmental psychology research:
  Attachment Q-set (version 3.0) (Waters, 1995, <doi:10.1111/j.1540-5834.1995.tb00214.x>);
  California Child Q-set (Block and Block, 1969, <doi:10.1037/0012-1649.21.3.508>);
  Maternal Behaviour Q-set (version 3.1)
  (Pederson et al., 1999, <https://ir.lib.uwo.ca/cgi/viewcontent.cgi?article=1000&context=psychologypub>);
  Preschool Q-set (Baumrind, 1968 revised by Wanda Bronson, <doi:10.1111/j.1540-5834.1995.tb00214.x>).
Run lapply() calls in parallel by submitting them to 
    'gridengine' clusters using the 'qsub' command.
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 (QTT). The main cases covered are (i) Treatment is randomly assigned, (ii) Treatment is as good as randomly assigned after conditioning on some covariates (also called conditional independence or selection on observables) using the methods developed in Firpo (2007) <doi:10.1111/j.1468-0262.2007.00738.x>, (iii) Identification is based on a Difference in Differences assumption (several varieties are available in the package e.g. Athey and Imbens (2006) <doi:10.1111/j.1468-0262.2006.00668.x> Callaway and Li (2019) <https://ssrn.com/abstract=3013341>, Callaway, Li, and Oka (2018) <doi:10.1016/j.jeconom.2018.06.008>).
Analysis of experimental crosses to identify genes
  (called quantitative trait loci, QTLs) contributing to variation in
  quantitative traits.
Conduct multiple quantitative trait loci (QTL) mapping under the framework of random-QTL-effect mixed linear model. First, each position on the genome is detected in order to construct a negative logarithm P-value curve against genome position. Then, all the peaks on each effect (additive or dominant) curve are viewed as potential QTL, all the effects of the potential QTL are included in a multi-QTL model, their effects are estimated by empirical Bayes in doubled haploid or by adaptive lasso in F2, and true QTL are identified by likelihood radio test. Wang S-B, Wen Y-J, Ren W-L, Ni Y-L, Zhang J, Feng J-Y, Zhang Y-M (2016) <doi:10.1038/srep29951>.
Datasets for the book, A Guide to QTL Mapping with R/qtl.
    Broman and Sen (2009) <doi:10.1007/978-0-387-92125-9>.
Densitometric evaluation of the photo-archived quantitative thin-layer chromatography (TLC) plates.
Web-based interactive charts (using D3.js) for the analysis of
    experimental crosses to identify genetic loci (quantitative trait
    loci, QTL) contributing to variation in quantitative traits.
Functions to infer co-mapping trait hotspots and causal models.
  Chaibub Neto E, Keller MP, Broman AF, Attie AD, Jansen RC, Broman KW, Yandell BS (2012) 
  Quantile-based permutation thresholds for QTL hotspots. Genetics 191 : 1355-1365. 
  <doi:10.1534/genetics.112.139451>.
  Chaibub Neto E, Broman AT, Keller MP, Attie AD, Zhang B, Zhu J, Yandell BS (2013)
  Modeling causality for pairs of phenotypes in system genetics. Genetics 193 : 1003-1013.
  <doi:10.1534/genetics.112.147124>.
Provides tools for joint analysis of multiple traits in a backcross (BC) or recombinant inbred lines (RIL) population. It can be used to select an optimal subset of traits for multiple-trait mapping, analyze multiple traits via the SURE model, which can associate different QTL with different traits, and perform multiple-trait composite multiple-interval mapping.
Functions to Simultaneously Infer Causal Graphs and Genetic Architecture.
  Includes acyclic and cyclic graphs for data from an experimental cross with a modest number (<10) of phenotypes driven by
  a few genetic loci (QTL).
  Chaibub Neto E, Keller MP, Attie AD, Yandell BS (2010)
  Causal Graphical Models in Systems Genetics: a unified framework for joint inference of causal network and genetic architecture for correlated phenotypes.
  Annals of Applied Statistics 4: 320-339.
  <doi:10.1214/09-AOAS288>.
This software provides tools for quantitative trait mapping in populations such as advanced intercross lines where relatedness among individuals should not be ignored. It can estimate background genetic variance components, impute missing genotypes, simulate genotypes, perform a genome scan for putative quantitative trait loci (QTL), and plot mapping results. It also has functions to calculate identity coefficients from pedigrees, especially suitable for pedigrees that consist of a large number of generations, or estimate identity coefficients from genotypic data in certain circumstances.
Provides methods for estimation of mean- and quantile-optimal treatment regimes from censored data. 
    Specifically, we have developed distinct functions for three types of right censoring for static treatment using quantile criterion: (1) independent/random censoring, (2) treatment-dependent random censoring, and (3) covariates-dependent random censoring. It also includes a function to estimate quantile-optimal dynamic treatment regimes for independent censored data. Finally, this package also includes a simulation data generative model of a dynamic treatment experiment proposed in literature.
Functions for unconditional and conditional quantiles. These
    include methods for transformation-based quantile regression,
    quantile-based measures of location, scale and shape, methods for quantiles
    of discrete variables, quantile-based multiple imputation, and restricted
    quantile regression. A vignette is given in Geraci (2016, The R Journal)
	<doi:10.32614/RJ-2016-037> and included in the package.
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.
Given inputs A,B and C, this package solves the matrix equation A*X^2 - B*X - C = 0.
Create surface forms from matrix or 'raster' data for flexible plotting and
 conversion to other mesh types. The functions 'quadmesh' or 'triangmesh'
 produce a continuous surface as a 'mesh3d' object as used by the 'rgl'
 package. This is used for plotting raster data in 3D (optionally with
 texture), and allows the application of a map projection without data loss and 
 many processing applications that are restricted by inflexible regular grid rasters.
 There are discrete forms of these continuous surfaces available with
 'dquadmesh' and 'dtriangmesh' functions.
This package contains routines and documentation for
        solving quadratic programming problems.
Extends the quadprog package to solve quadratic programs with
    absolute value constraints and absolute values in the objective function.
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.
Provides a set of functions for taking qualitative GIS data, hand drawn on a map, and 
   converting it to a simple features object. These tools are focused on data that are drawn on a map
   that contains some type of polygon features. For each area identified on the map, the id numbers
   of these polygons can be entered as vectors and transformed using qualmap.
Automatic generation of distinct qualitative color palettes,
    optionally adapted to color deficiency. It takes a subspace of the HSL color
    space as input and projects it to the DIN99d color space where it selects
    and return colors that are maximally distinct.
Provides functions to access survey results directly into R using 
    the 'Qualtrics' API. 'Qualtrics' <https://www.qualtrics.com/about/> is an 
    online survey and data collection software platform. See 
    <https://api.qualtrics.com/> for more information about the 'Qualtrics' API. 
    This package is community-maintained and is not officially supported by 
    'Qualtrics'.
Qualitative methods for the validation of dynamic models.
    It contains (i) an orthogonal set of deviance measures for absolute,
    relative and ordinal scale and (ii) approaches accounting for time
    shifts. The first approach transforms time to take time delays and speed
    differences into account. The second divides the time series into
    interval units according to their main features and finds the longest
    common subsequence (LCS) using a dynamic programming algorithm.
Implements indices of qualitative variation proposed by Wilcox
    (1973).
These functions use data augmentation and Bayesian techniques for the assessment of single-member and incomplete ensembles of climate projections. It provides unbiased estimates of climate change responses of all simulation chains and of all uncertainty variables. It additionally propagates uncertainty due to missing information in the estimates.
  - Evin, G., B. Hingray, J. Blanchet, N. Eckert, S. Morin, and D. Verfaillie. (2019) <doi:10.1175/JCLI-D-18-0606.1>.
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.
    Includes methods for visualization (e.g. Heatmaps, Volcanos, pairwise QQ, Bland-Altman plot),
    summary statistics (e.g. CV), data normalization methods (e.g. robustscale). Read function for Progenesis.
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.
Based on Alan D. Hutson (1999) <doi:10.1080/02664769922458>, "Calculating nonparametric confidence intervals for quantiles using fractional order statistics", Journal of Applied Statistics, 26:3, 343-353.
Integration of the 'units' and 'errors' packages for a complete quantity
    calculus system for R vectors, matrices and arrays, with automatic propagation,
    conversion, derivation and simplification of magnitudes and uncertainties.
Specify, build, trade, and analyse quantitative financial trading strategies.
Modifies the distance matrix obtained from data with batch effects, so as to improve the performance of sample pattern detection, such as clustering, dimension reduction, and construction of networks between subjects. The method has been published in Bioinformatics (Fei et al, 2018, <doi:10.1093/bioinformatics/bty117>). Also available on 'GitHub' <https://github.com/tengfei-emory/QuantNorm>.
Estimation methods for optimal treatment regimes under three different criteria, namely marginal quantile, marginal mean, and mean absolute difference. For the first two criteria, both one-stage and two-stage estimation method are implemented. A doubly robust estimator for estimating the quantile-optimal treatment regime is also included.
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 multiple smooth terms via B-splines with L1-norm difference penalties. Monotonicity constraints on the fitted curves are allowed. See Muggeo, Sciandra, Tomasello and Calvo (2013) <doi:10.1007/s10651-012-0232-1> and <doi:10.13140/RG.2.2.12924.85122> for some code example.
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.
Contains basic structures and operations used frequently in quantum computing. Intended to be a convenient tool to help in practicing the linear algebra involved in quantum operations. Has functionality for the creation of arbitrarily sized kets, bras, matrices and implements quantum gates, inner products, and tensor products. Contains all commonly used quantum gates, creates arbitrarily controlled versions of all gates, and can simulate complete or partial measurements of kets. Implements modular arithmetic commonly found in quantum algorithms and can convert functions into equivalent quantum gates. It can also simulate larger applications, including Steane error correction <DOI:10.1103/physrevlett.77.793>, the Quantum Fourier Transform developed by Shor (1999), Shor's algorithm which factors numbers up to 21, Grover's algorithm (1996), the Quantum Approximation Optimization Algorithm (QAOA) (Farhi, Goldstone, and Gutmann 2014) <arXiv:1411.4028>, and the training of a quantum neural network developed by Schuld (2018) <arXiv:1804.00633> which can classify the MNIST dataset.
An implementation of two interaction indices between extractive
    activity and groundwater resources based on hazard and vulnerability
    parameters used in the assessment of natural hazards. One index is based
    on a discrete choice model and the other is relying on an artificial
    neural network.
Calculates the number of four-taxon subtrees consistent with a pair
  of cladograms, calculating the symmetric quartet distance of Bandelt & Dress (1986),
  Reconstructing the shape of a tree from observed dissimilarity data,
  Advances in Applied Mathematics, 7, 309-343 <doi:10.1016/0196-8858(86)90038-2>, 
  and using the tqDist algorithm of Sand et al. (2014), tqDist: a library for
  computing the quartet and triplet distances between binary or general trees,
  Bioinformatics, 30, 2079–2080 <doi:10.1093/bioinformatics/btu157>
  for pairs of bifurcating trees.
Identify differentially expressed genes in RNA-seq count data using quasi-Poisson or quasi-negative binomial models with 'QL', 'QLShrink' and 'QLSpline' methods described by Lund, Nettleton, McCarthy, and Smyth (2012) <DOI:10.1515/1544-6115.1826>. Report bias-reduced estimates of log fold changes.
Various quantile-based clustering algorithms: algorithm CU (Common theta and Unscaled variables), algorithm CS (Common theta and Scaled variables through lambda_j), algorithm VU (Variable-wise theta_j and Unscaled variables) and algorithm VW (Variable-wise theta_j and Scaled variables through lambda_j). Hennig, Viroli, Anderlucci (2018) <arXiv:1806.10403v1>.
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.
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', 'sf' (via coercion to 'sp'), and 'raster' objects, allows for basic 
    zooming, panning, identifying,labeling, selecting, and measuring spatial 
    objects.  Importantly, it does not require that the data be in geographic 
    coordinates.
Provides functions for constructing near-optimal generalized full matching.
    Generalized full matching is an extension of the original full matching method
    to situations with more intricate study designs. The package is made with
    large data sets in mind and derives matches more than an order of magnitude
    quicker than other methods.
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.
Provides tools for an automated identification of diagnostic molecular characters, i.e. such columns in a given nucleotide or amino acid alignment that allow to distinguish taxa from each other. These characters can then be used to complement the formal descriptions of the taxa, which are often based on morphological and anatomical features. Especially for morphologically cryptic species, this will be helpful. QUIDDICH distinguishes between four different types of diagnostic characters. For more information, see "Kuehn, A.L., Haase, M. 2019. QUIDDICH: QUick IDentification of DIagnostic CHaracters."
Simplifies output suppression logic in R packages, as it's common
    to develop some form of it in R. 'quietR' intends to simplify that problem 
    and allow a set of simple toggle functions to be used to suppress console 
    output.
Grows a qualitative interaction tree. Quint is a tool for subgroup analysis, suitable for data from a two-arm randomized controlled trial.
Diagnostics methods for quantile regression models for detecting influential observations:
  robust distance methods for general quantile regression models; generalized Cook's distance and 
  Q-function distance method for quantile regression models using aymmetric Laplace distribution. Reference
  of this method can be found in Luis E. Benites, Víctor H. Lachos, Filidor E. Vilca (2015) <arXiv:1509.05099v1>; 
  mean posterior probability and Kullback–Leibler divergence methods for Bayes quantile regression model.
  Reference of this method is Bruno Santos, Heleno Bolfarine (2016) <arXiv:1601.07344v1>.
A facility for writing functions that quote their arguments,
 may sometimes evaluate them in the environment where they were quoted,
 and may pass them as quoted to other functions.
Full text, in data frames containing one row per verse, of the 
    Qur'an in Arabic (with and without vowels) and in English (the Yusuf Ali 
    and Saheeh International translations), formatted to be convenient for 
    text analysis.
Thresholding based tests for null hypothesis of the form A beta =c, and the Quantile Universal Threshold (QUT) for 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 eigenvectors
        use QZ decomposition (generalized Schur decomposition).
        The decomposition needs an N-by-N non-symmetric
        matrix A or paired matrices (A,B) with eigenvalues reordering
        mechanism. The decomposition functions are mainly based Fortran
        subroutines in complex*16 and double precision of LAPACK
        library (version 3.4.2. or later).
Allows the user to learn Bayesian networks from datasets containing thousands of variables. It focuses on score-based learning, mainly the 'BIC' and the 'BDeu' score functions. It provides state-of-the-art algorithms for the following tasks: (1) parent set identification - Mauro Scanagatta (2015) <http://papers.nips.cc/paper/5803-learning-bayesian-networks-with-thousands-of-variables>; (2) general structure optimization - Mauro Scanagatta (2018) <doi:10.1007/s10994-018-5701-9>, Mauro Scanagatta (2018) <http://proceedings.mlr.press/v73/scanagatta17a.html>; (3) bounded treewidth structure optimization - Mauro Scanagatta (2016) <http://papers.nips.cc/paper/6232-learning-treewidth-bounded-bayesian-networks-with-thousands-of-variables>; (4) structure learning on incomplete data sets - Mauro Scanagatta (2018) <doi:10.1016/j.ijar.2018.02.004>. Distributed under the LGPL-3 by IDSIA.
Memoization can be used to speed up repetitive and computational expensive function calls.  The first time a function that implements memoization is called the results are stored in a cache memory.  The next time the function is called with the same set of parameters, the results are momentarily retrieved from the cache avoiding repeating the calculations.  With this package, any R object can be cached in a key-value storage where the key can be an arbitrary set of R objects.  The cache memory is persistent (on the file system).
Functions for creating plots and image files in a unified way
    regardless of output format (EPS, PDF, PNG, SVG, TIFF, WMF, etc.). Default
    device options as well as scales and aspect ratios are controlled in a uniform
    way across all device types. Switching output format requires minimal changes
    in code. This package is ideal for large-scale batch processing, because it
    will never leave open graphics devices or incomplete image files behind, even on
    errors or user interrupts.
A file set refers to a set of files located in one or more directories on the file system.  This package provides classes and methods to locate, setup, subset, navigate and iterate such sets.  The API is designed such that these classes can be extended via inheritance to provide a richer API for special file formats.  Moreover, a specific name format is defined such that filenames and directories can be considered to have full names which consists of a name followed by comma-separated tags.  This adds additional flexibility to identify file sets and individual files.  NOTE: This package's API should be considered to be in an beta stage.  Its main purpose is currently to support the aroma.* packages, where it is one of the main core components; if you decide to build on top of this package, please contact the author first.
DEPRECATED. Do not start building new projects based on this package. Cross-platform alternatives are the following packages: bigmemory (CRAN), ff (CRAN), BufferedMatrix (Bioconductor).  The main usage of it was inside the aroma.affymetrix package. (The package currently provides a class representing a matrix where the actual data is stored in a binary format on the local file system.  This way the size limit of the data is set by the file system and not the memory.)
Performs the 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.
Processing pipeline for 'SamBada' from pre- to post-processing. 
	'SamBada' is a landscape genomic software designed to run univariate or multivariate logistic regression between the presence of a genotype and one or several environmental variables. See Stucki (2017) <doi:10.1111/1755-0998.12629> and <https://github.com/Sylvie/sambada>. 
	The package provides functions that can be classified into four categories: 
	1) Install 'SamBada' 
	2) Preprocessing (prepare genomic file into standards compatible with 'SamBada' and apply quality-control; retrieve environmental conditions at sampling location; prepare environmental file including removal of correlated variables and computation of population structure) 
	3) Processing (run 'SamBada' on multiple cores using 'Supervision')
	4) Post-processing (calculate p-values and q-values, produce interactive Manhattan plots and query 'Ensembl' database, produce maps).
An integrated solution to perform
    a series of text mining tasks such as importing and cleaning a corpus, and
    analyses like terms and documents counts, lexical summary, terms
    co-occurrences and documents similarity measures, graphs of terms,
    correspondence analysis and hierarchical clustering. Corpora can be imported
    from spreadsheet-like files, directories of raw text files,
    as well as from 'Dow Jones Factiva', 'LexisNexis', 'Europresse' and 'Alceste' files.
Utility functions useful when programming and developing R packages.
Estimation of reproduction numbers for disease outbreak, based on
    incidence data. The R0 package implements several documented methods. It is
    therefore possible to compare estimations according to the methods used.
    Depending on the methods requested by user, basic reproduction number
    (commonly denoted as R0) or real-time reproduction number (referred to as
    R(t)) is computed, along with a 95% Confidence Interval. Plotting outputs
    will give different graphs depending on the methods requested : basic
    reproductive number estimations will only show the epidemic curve
    (collected data) and an adjusted model, whereas real-time methods will also
    show the R(t) variations throughout the outbreak time period. Sensitivity
    analysis tools are also provided, and allow for investigating effects of
    varying Generation Time distribution or time window on estimates.
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'.
Multivariate optimal allocation for different domains in one and two stages stratified sample design. R2BEAT extends the Neyman (1934) <doi:10.2307/2342192> – Tschuprow (1923) allocation method to the case of several variables, adopting a generalization of the Bethel’s proposal (1989). R2BEAT develops this methodology but, moreover, it allows to determine the sample allocation in the multivariate and multi-domains case of estimates for two-stage stratified samples.
This package provides generic functions to analyze the distribution
  of two continuous variables: 'conf2d' to calculate a smooth empirical
  confidence region, and 'freq2d' to calculate a frequency distribution.
Suite of tools for using 'D3', a library for producing dynamic, interactive data
  visualizations. Supports translating objects into 'D3' friendly data structures, rendering
  'D3' scripts, publishing 'D3' visualizations, incorporating 'D3' in R Markdown, creating
  interactive 'D3' applications with Shiny, and distributing 'D3' based 'htmlwidgets' in R
  packages.
Provides functions for aligning 2D gas chromatography mass spectrometry derived metabolite peaks obtained from primary processing and generates an alignment table that
        allows for a comparison of common peaks across samples and metabolite identification. Publication describing the package in detail is available at the following citation: Ryne C. Ramaker, Emily Gordon, Sara J. Cooper (2017) <doi:10.1101/179168>.
2dRUE is a methodology to make a diagnostic of land
        condition in a large territory during a given time period. The
        following projects have funded this package: DeSurvey IP (EC
        FP6 Integrated Project contract No. 003950), DesertWatch (ESA
        DUE contract No. 18487/04/I-LG) and MesoTopos (Junta de
        Andalucia PE ref. RNM-4023).
Some heavily used base R functions are reconstructed to also be compliant to data.table objects. Also, some general helper functions that could be of interest for working with data.table objects are included.
The model R squared and semi-partial R squared for the linear and
    generalized linear mixed model (LMM and GLMM) are computed with confidence
    limits. The R squared measure from Edwards et.al (2008) <DOI:10.1002/sim.3429>
    is extended to the GLMM using penalized quasi-likelihood (PQL) estimation
    (see Jaeger et al. 2016 <DOI:10.1080/02664763.2016.1193725>). Three methods
    of computation are provided and described as follows. First, The
    Kenward-Roger approach. Due to some inconsistency between the 'pbkrtest'
    package and the 'glmmPQL' function, the Kenward-Roger approach in the
    'r2glmm' package is limited to the LMM. Second, The method introduced
    by Nakagawa and Schielzeth (2013) <DOI:10.1111/j.2041-210x.2012.00261.x>
    and later extended by Johnson (2014) <DOI:10.1111/2041-210X.12225>.
    The 'r2glmm' package only computes marginal R squared for the LMM and does
    not generalize the statistic to the GLMM; however, confidence limits and
    semi-partial R squared for fixed effects are useful additions. Lastly, an
    approach using standardized generalized variance (SGV) can be used for
    covariance model selection. Package installation instructions can be found
    in the readme file.
Wrapper functions for GUESS, a GPU-accelerated
    sparse Bayesian variable selection method for linear regression based
    analysis of 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.
An R command interface to the 'MLwiN' multilevel
    modelling software package.
Using this package,
 it is possible to call a BUGS model, summarize inferences and
 convergence in a table and graph, and save the simulations in arrays for easy access
 in R.
R wrapper for the JPMML-R library <https://github.com/jpmml/jpmml-r>,
    which converts R models to Predictive Model Markup Language (PMML).
R2PPT provides a simple set of wrappers to easily use rcom
        or RDCOMClient for generating Microsoft PowerPoint
        presentations.
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
Create and combine HTML and PDF reports from within R.
    Possibility to design tables and listings for reporting and also include R plots.
Provides a collection of functions for basic
    database and document management operations such as add, get, list access
    or delete. Every cdbFunction() gets and returns a list() containing the
    connection setup. Such a list can be generated by cdbIni().
This package provides an R interface with 2-part 4dfp MRI images
  (.4dfp.ifh and .4dfp.img files.)
Since the early 1970s eyewitness testimony researchers have recognised the 
    importance of estimating properties such as lineup bias (is the lineup biased against 
    the suspect, leading to a rate of choosing higher than one would expect by chance?), 
    and lineup size (how many reasonable choices are in fact available to the witness? 
    A lineup is supposed to consist of a suspect and a number of additional members, 
    or foils, whom a poor-quality witness might mistake for the perpetrator). Lineup 
    measures are descriptive, in the first instance, but since the earliest articles in 
    the literature researchers have recognised the importance of reasoning inferentially 
    about them. This package contains functions to compute various properties of 
    laboratory or police lineups, and is intended for use by researchers in forensic 
    psychology and/or eyewitness testimony research. Among others, the r4lineups package 
    includes functions for calculating lineup proportion, functional size, various 
    estimates of effective size, diagnosticity ratio, homogeneity of the diagnosticity 
    ratio, ROC curves for confidence x accuracy data and the degree of similarity of 
    faces in a lineup.
A collection of R functions for use with Stock Synthesis, a
    fisheries stock assessment modeling platform written in ADMB by Dr. Richard
    D.  Methot at the NOAA Northwest Fisheries Science Center. The functions
    include tools for summarizing and plotting results, manipulating files,
    visualizing model parameterizations, and various other common stock
    assessment tasks.
For the 9 counties in the San Francisco Bay area 
    the Metropolitan Transportation Commission
    maintains an Application Programming Interface
    with information on more than 20 transit operators. 
    This package allows users to retrieve
    information about transit schedules in the General Transit Feed Specification
    as published by the Metropolitan Transportation Commission at the following url: 
    <https://511.org/developers/list/apis/>.
Creates classes with reference semantics, similar to R's built-in
    reference classes. Compared to reference classes, R6 classes are simpler
    and lighter-weight, and they are not built on S4 classes so they do not
    require the methods package. These classes allow public and private
    members, and they support inheritance, even when the classes are defined in
    different packages.
After defining an R6 class, R62S3 is used to automatically generate optional S3/S4 generics and methods for dispatch. Also allows piping for R6 objects.
Provides reference classes implementing some useful data structures.
    The package implements these data structures by using the reference class R6.
    Therefore, the classes of the data structures are also reference classes which means that their instances are passed by reference.
    The implemented data structures include stack, queue, double-ended queue, doubly linked list, set, dictionary and binary search tree.
    See for example <https://en.wikipedia.org/wiki/Data_structure> for more information about the data structures.
Useful methods and data fields to extend the
             bare bones 'R6' class provided by the 'R6' package - ls-method, 
             hashes, warning- and message-method, general get-method and a 
             debug-method that assigns self and private to the global environment.
Infers V-D-J haplotypes and gene deletions from AIRR-seq data, 
    based on IGHJ, IGHD or IGHV as anchor, by adapting a Bayesian framework.
    It also calculates a Bayes factor, a number that indicates the certainty level of the inference, for each haplotyped gene.
    Citation:
    Gidoni, et al (2019) <doi:10.1038/s41467-019-08489-3>.
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 individual bioenergetic balance for different aquaculture sea fish (Sea Bream and Sea Bass; Brigolin et al., 2014 <doi:10.3354/aei00093>) and shellfish (Mussel and Clam; Brigolin et al., 2009 <doi:10.1016/j.ecss.2009.01.029>; Solidoro et al., 2000 <doi:10.3354/meps199137>). Allows for spatialized model runs and population simulations.
Application of 'RaceID' allows inference of cell types and prediction of lineage trees by he StemID2 algorithm. Herman, J.S., Sagar, Grün D. (2018) <DOI:10.1038/nmeth.4662>.
Fit a variety of models to Rank Abundance Data
It has tools for normalization of rank abundance
     distributions (RAD) to a desired number of ranks using MaxRank
     Normalization method.
     RADs are commonly used in biology/ecology and mathematically equivalent
     to complementary cumulative distributions (CCDFs) which are used in
     physics, linguistics and sociology and more generally in data science.
Fundamental formulas for Radar, for attenuation, range, velocity,
    effectiveness, power, scatter, doppler, geometry, radar equations, etc.
    Based on Nick Guy's Python package PyRadarMet
Creates the radar-boxplot, a plot that was created by the 
    author during his doctoring in forest resources. 
    The radar-boxplot is a visualization feature suited for  
    multivariate classification/clustering. It provides an intuitive 
    deep understanding of the data.
Create interactive radar charts using the 'Chart.js' 'JavaScript' library
    and the 'htmlwidgets' package. 'Chart.js' <http://www.chartjs.org/> is a 
    lightweight library that supports several types of simple chart using the 'HTML5' 
    canvas element. This package provides an R interface specifically to the 
    radar chart, sometimes called a spider chart, for visualising multivariate data.
Nuclear Decay Data for Dosimetric Calculations from the 
    International Commission on Radiological Protection from ICRP 
    Publication 107. Ann. ICRP 38 (3). Eckerman, Keith and Endo, Akira 2008 
    <doi:10.1016/j.icrp.2008.10.004> 
    <http://www.icrp.org/publication.asp?id=ICRP%20Publication%20107>. 
    This is a database of the physical data needed in calculations of 
    radionuclide-specific protection and operational quantities. The 
    data is prescribed by the ICRP, the international authority on 
    radiation dose standards, for estimating dose from the intake of or 
    exposure to radionuclides in the workplace and the environment. 
    The database contains information on the half-lives, decay chains, 
    and yields and energies of radiations emitted in nuclear transformations 
    of 1252 radionuclides of 97 elements.
A platform-independent browser-based interface for business
    analytics in R, based on the shiny package. The application combines the
    functionality of radiant.data, radiant.design, radiant.basics,
    radiant.model, and radiant.multivariate.
The Radiant Basics menu includes interfaces for probability 
    calculation, central limit theorem simulation, comparing means and proportions, 
    goodness-of-fit testing, cross-tabs, and correlation. The application extends 
    the functionality in radiant.data.
The Radiant Data menu includes interfaces for loading, saving,
    viewing, visualizing, summarizing, transforming, and combining data. It also
    contains functionality to generate reproducible reports of the analyses
    conducted in the application.
The Radiant Design menu includes interfaces for design of
    experiments, sampling, and sample size calculation. The application extends
    the functionality in radiant.data.
The Radiant Model menu includes interfaces for linear and logistic
    regression, naive Bayes, neural networks, classification and regression trees,
    model evaluation, collaborative filtering, decision analysis, and simulation. 
    The application extends the functionality in radiant.data.
The Radiant Multivariate menu includes interfaces for perceptual
    mapping, factor analysis, cluster analysis, and conjoint analysis. The
    application extends the functionality in radiant.data.
Computational tool box for radio-genomic analysis which integrates
    radio-response data, radio-biological modelling and comprehensive cell line
    annotations for hundreds of cancer cell lines. The 'RadioSet' class enables
    creation and manipulation of standardized datasets including information
    about cancer cells lines, radio-response assays and dose-response indicators. 
    Included methods allow fitting and plotting dose-response data using 
    established radio-biological models along with quality control to validate
    results. Additional functions related to fitting and plotting dose response 
    curves, quantifying statistical correlation and calculating area under the
    curve (AUC) or survival fraction (SF) are included. For more details please 
    see the included documentation, references, as well as:
    Manem, V. et al (2018) <doi:10.1101/449793>.
Functions to extract first and second order statistics from
    images.
RadioSonde is a collection of programs for reading and
        plotting SKEW-T,log p diagrams and wind profiles for data
        collected by radiosondes (the typical weather balloon-borne
        instrument), which we will call "flights", "sondes", or
        "profiles" throughout the associated documentation.  The raw
        data files are in a common format that has a header followed by
        specific variables.  Use "help(ExampleSonde)" for the full
        explanation of the data files.
Radioactive doses estimation using individual chromosomal aberrations information. See Higueras M, Puig P, Ainsbury E, Rothkamm K. (2015) <doi:10.1088/0952-4746/35/3/557>.
Scientific and technical article format for the web. 'Radix' articles 
    feature attractive, reader-friendly typography, flexible layout options
    for visualizations, and full support for footnotes and citations.
Calculates adjusted p-values for the null hypothesis of no replicability
 across studies for two study designs: (i) a primary and follow-up study, where the features in the follow-up study
 are selected from the primary study, as described in Bogomolov and Heller (2013) <doi:10.1080/01621459.2013.829002> and Heller, Bogomolov and Benjamini (2014) <doi:10.1073/pnas.1314814111>;
 (ii) two independent studies, where the features for replicability are first
 selected in each study separately, as described in Bogomolov and Heller (2018) <doi:10.1093/biomet/asy029>. 
 The latter design is the one encountered in a typical meta-analysis of
 two studies, but the inference is for replicability rather than for identifying the features that are non-null
 in at least one study.
Make your phrase or sentence into something funny! Pass a string with the keywords in, 
    and get out a bit of humor.
Implementation of ADMIXTURE for individual ancestry inference in R. Specifically, ADMIXTURE is a software tool for maximum likelihood estimation of individual ancestries from multilocus SNP genotype datasets, see <https://www.genetics.ucla.edu/software/admixture/>. Users can use 'radmixture' to calculate ancestry components with different public datasets. It is very convenient and fast for personal genotype data. For more details, see <https://github.com/wegene-llc/radmixture/blob/master/README.md>.
Designed for the import, analysis, and visualization of dosimetric and volumetric data in Radiation Oncology, the tools herein enable import of dose-volume histogram information from multiple treatment planning system platforms and 3D structural representations and dosimetric information from 'DICOM-RT' files.  These tools also enable subsequent visualization and statistical analysis of these data.
Provides functions for radiation safety, also known as "radiation protection" and "radiological control". The science of radiation protection is called "health physics" and its engineering functions are called "radiological engineering". Functions in this package cover many of the computations needed by radiation safety professionals. Examples include: obtaining updated calibration and source check values for radiation monitors to account for radioactive decay in a reference source, simulating instrument readings to better understand measurement uncertainty, correcting instrument readings for geometry and ambient atmospheric conditions. Many of these functions are described in Johnson and Kirby (2011, ISBN-13:  978-1609134198). Utilities are also included for developing inputs and processing outputs with radiation transport codes, such as MCNP, a general-purpose Monte Carlo N-Particle code that can be used for neutron, photon, electron, or coupled neutron/photon/electron transport (Werner et. al. (2018) <doi:10.2172/1419730>).
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.
Anti-Grain Geometry (AGG) is a high-quality and high-performance
    2D drawing library. The 'ragg' package provides a set of graphic devices 
    based on AGG to use as alternative to the raster devices provided through
    the 'grDevices' package.
Data objects in 'R' can be rendered as 'HTML' tables using the
    'JavaScript' library 'ag-grid' (typically via 'R Markdown' or 'Shiny'). The
    'ag-grid' library has been included in this 'R' package. The package name
    'RagGrid' is an abbreviation of 'R agGrid'.
Proper L2-penalized 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 Miok et al. (2018) <DOI:10.1002/bimj.201700195> for details on the implemented methods.
Algorithms to price American and European
    equity options, convertible bonds and a
    variety of other financial derivatives. It uses an
    extension of the usual Black-Scholes model in which
    jump to default may occur at a probability specified
    by a power-law link between stock price and hazard
    rate as found in the paper by Takahashi, Kobayashi,
    and Nakagawa (2001) <doi:10.3905/jfi.2001.319302>.  We
    use ideas and techniques from Andersen and
    Buffum (2002) <doi:10.2139/ssrn.355308> and
    Linetsky (2006) <doi:10.1111/j.1467-9965.2006.00271.x>.
Enables downloading detailed reports from <https://ahrefs.com>
    about backlinks from pointing to website, provides authentication with an 
    API key as well as ordering, grouping and filtering functionalities.
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.
A modified implementation of stepwise regression that greedily searches 
    the space of interactions among features in order to build polynomial regression models.
    Furthermore, the hypothesis tests conducted are valid-post model selection
    due to the use of a revisiting procedure that implements an alpha-investing
    rule. As a result, the set of rejected sequential hypotheses is proven to 
    control the marginal false discover rate. When not searching for polynomials,
    the package provides a statistically valid algorithm
    to run and terminate stepwise regression. For more information, see 
    Johnson, Stine, and Foster (2019) <arXiv:1510.06322>.
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, reviews, and trailhead latitude and longitude) on
    3,846 trails and 24,413 reviews in every state in the United States. Data can be 
    used to better understand recreational trail use in the United States and for
    examples / teaching, particularly examples and teaching involving hierarchical
    or repeated measures data.
Visualizing functional data and identifying functional outliers.
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.
Determination of rainfall-runoff erosivity factor.
An implementation of the RainFARM (Rainfall Filtered Autoregressive Model) stochastic precipitation downscaling method (Rebora et al. (2006) <doi:10.1175/JHM517.1>). Adapted for climate downscaling according to D'Onofrio et al. (2018) <doi:10.1175/JHM-D-13-096.1> and for complex topography as in Terzago et al. (2018) <doi:10.5194/nhess-18-2825-2018>. The RainFARM method is based on the extrapolation to small scales of the Fourier spectrum of a large-scale precipitation field, using a fixed logarithmic slope and random phases at small scales, followed by a nonlinear transformation of the resulting linearly correlated stochastic field. RainFARM allows to generate ensembles of spatially downscaled precipitation fields which conserve precipitation at large scales and whose statistical properties are consistent with the small-scale statistics of observed precipitation, based only on knowledge of the large-scale precipitation field.
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 agent may not pay full attention to all available alternatives, rendering standard revealed preference theory invalid. This package implements the estimation and inference procedures of Cattaneo, Ma, Masatlioglu and Suleymanov (2019) <arXiv:1712.03448>, which utilizes standard choice data to partially identify and estimate a decision maker's preference. For inference, several 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.
Additional matrix functionality for R including: (1) wrappers for 
    the base matrix function that allow matrices to be created from character
    strings and lists (the former is especially useful for creating block
    matrices), (2) better printing of large matrices via the generic "pretty" 
    print function, and (3) a number of convenience functions for users more
    familiar with other scientific languages like 'Julia', 'Matlab'/'Octave', or
    'Python'+'NumPy'.
Contains functions to download, cache and read in 'Excel' version of the
    RAM Legacy Stock Assessment Data Base, an online compilation of stock assessment
    results for commercially exploited marine populations from around the world. 
    The database is named after Dr. Ransom A. Myers whose original stock-recruitment database,
    is no longer being updated. More information about the database can be found at
    <https://ramlegacy.org/>. Ricard, D., Minto, C., Jensen, O.P. and Baum, J.K. (2012) <doi:10.1111/j.1467-2979.2011.00435.x>.
Provides an efficient procedure for fitting the entire solution
    path for high-dimensional regularized quadratic generalized linear models with
    interactions effects under the strong or weak heredity constraint.
We rewrite of RAMpath software developed by John McArdle and Steven Boker as an R package. In addition to performing regular SEM analysis through the R package lavaan, RAMpath has unique features.  First, it can generate path diagrams according to a given model. Second, it can display path tracing rules through path diagrams and decompose total effects into their respective direct and indirect effects as well as decompose variance and covariance into individual bridges. Furthermore, RAMpath can fit dynamic system models automatically based on latent change scores and generate vector field plots based upon results obtained from a bivariate dynamic system. Starting version 0.4, RAMpath can conduct power analysis for both univariate and bivariate latent change score models.
Bayesian geostatistical modeling of Gaussian processes using a reparameterized and marginalized posterior sampling (RAMPS) algorithm designed to lower autocorrelation in MCMC samples.  Package performance is tuned for large spatial datasets.
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"
Implements the algorithm by Pourahmadi and Wang (2015) <doi:10.1016/j.spl.2015.06.015> for generating a random p x p correlation matrix. Briefly, the idea is to represent the correlation matrix using Cholesky factorization and p(p-1)/2 hyperspherical coordinates (i.e., angles), sample the angles from a particular distribution and then convert to the standard correlation matrix form. The angles are sampled from a distribution with pdf proportional to sin^k(theta) (0 < theta < pi, k >= 1) using the efficient sampling algorithm described in Enes Makalic and Daniel F. Schmidt (2018) <arXiv:1809.05212>.
Generate random positions (latitude/longitude), 
    Well-known text ('WKT') points or polygons, or 'GeoJSON' points or 
    polygons.
A novel numerical algorithm that provides functionality for estimating the exact 95% confidence interval of the location parameter in the random effects model, and is much faster than the naive method. Works best when the number of studies is between 6-20.
Generates random names with additional information including fake
    SSNs, gender, location, zip, age, address, and nationality.
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.
We implement adaptive estimation of the joint density linear model where the coefficients - intercept and slopes - are random and independent from regressors which support is a proper subset. The estimator proposed in Gaillac and Gautier (2019) <arXiv:1905.06584> is based on Prolate Spheroidal Wave Functions which are computed  efficiently in 'RandomCoefficients'. This package also provides a parallel implementation of the estimator.
Simple methods to generate attractive random colors. The random
  colors are from a wrapper of 'randomColor.js'
  <https://github.com/davidmerfield/randomColor>. In addition, it also generates
  optimally distinct colors based on k-means (inspired by 'IWantHue'
  <https://github.com/medialab/iwanthue>).
Methods for the inference on and the simulation of Gaussian fields are provided, 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, based on Breiman (2001) <DOI:10.1023/A:1010933404324>.
A set of tools to help explain which variables are most important in a random forests. Various variable importance measures are calculated and visualized in different settings in order to get an idea on how their importance changes depending on our criteria (Hemant Ishwaran and Udaya B. Kogalur and Eiran Z. Gorodeski and Andy J. Minn and Michael S. Lauer (2010) <doi:10.1198/jasa.2009.tm08622>, Leo Breiman (2001) <doi:10.1023/A:1010933404324>).
Fast OpenMP parallel computing of Breiman's random forests for survival, competing risks, regression and classification based on Ishwaran and Kogalur's popular random survival forests (RSF) package.  Handles missing data and now includes multivariate, unsupervised forests, quantile regression and solutions for class imbalanced data.  New fast interface using subsampling and confidence regions for variable importance.
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.
Simple Random Search function for the 'smoof' and 'ParamHelpers' ecosystem with termination criteria and parallelization.
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 dimension reduction technique, where the data in the high dimensional space is projected into the low dimensional space using JL transform. The original high dimensional data matrix is multiplied with the low dimensional projection matrix which results in reduced matrix. The projection matrix can be generated using the projection function that is independent to the original data. Then finally apply the classification task on the projected data.
Connects to the site <http://quotesondesign.com/> 
            that uses the 'WordPress' JSON REST API 
            to provide a way for you to grab quotes.
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
    generator tests - the gap test, the serial test, the poker test.
    See e.g. Gentle (2003) <doi:10.1007/b97336>. The package can be provided
    without the rngWELL dependency on demand.
    Take a look at the Distribution task view of types and tests of random number generators.
    Version in Memoriam of Diethelm and Barbara Wuertz.
Implements random variables by means of S4 classes and methods.
Provides tools for filtering occurrence records, generating alpha-hull-derived range polygons and mapping species distributions.
Tools for easy generation of (life-history) traits maps based on
    species range (extent-of-occurrence) maps.
Range Modeling Metadata Standards (RMMS) address three challenges: they (i) are designed for convenience to encourage use, (ii) accommodate a wide variety of applications, and (iii) are extensible to allow the community of range modelers to steer it as needed. RMMS are based on a data dictionary that specifies a hierarchical structure to catalog different aspects of the range modeling process. The dictionary balances a constrained, minimalist vocabulary to improve standardization with flexibility for users to provide their own values. Associated manuscript in review.
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') and 'dgCMatrix' (R package 'Matrix') 
          can be directly analyzed.
Displays palette of 5 colors based on photos depicting the unique 
             and vibrant culture of Punjab in Northern India. Since Punjab translates 
             to ``Land of 5 Rivers'' there are 5 colors per palette. If users need 
             more than 5 colors, they can merge 2 to 3 palettes to create their own 
             color-combination, or they can cherry-pick their own custom colors. 
             Users can view up to 3 palettes together. Users can also list all the 
             palette choices. And last but not least, users can see the photo that 
             inspired a particular palette.
Performs aggregation of ordered lists based
        on the ranks using several different algorithms:
        Cross-Entropy Monte Carlo algorithm, Genetic algorithm, and a
        brute force algorithm (for small problems).
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.
Provide common functions and resources for registry specific
    R-packages at Rapporteket
    <https://rapporteket.github.io/rapporteket/articles/short_introduction.html>.
    This package is relevant for developers of packages/registries at
    Rapporteket.
Access services specified in OpenAPI (formerly Swagger) format.
  It is not a code generator. Client is generated dynamically as a list of R 
  functions.
Access to the C-level R date and datetime code is provided for
 C-level API use by other packages via registration of native functions.
 Client packages simply include a single header 'RApiDatetime.h' provided
 by this package, and also 'import' it.  The R Core group is the original
 author of the code made available with slight modifications by this package.
Provides JSON parsing capability through the 'Rapidjson' 'C++' header-only library.
Facilitates efficient polygon search using kd trees.
    Coordinate level spatial data can be aggregated to higher geographical
    identities like census blocks, ZIP codes or police district boundaries.
    This process requires mapping each point in the given data set to a
    particular identity of the desired geographical hierarchy. Unless efficient
    data structures are used, this can be a daunting task. The operation
    point.in.polygon() from the package sp is computationally expensive.
    Here, we exploit kd-trees as efficient nearest neighbor search algorithm
    to dramatically reduce the effective number of polygons being searched.
Package to perform non-invasive fetal testing for aneuploidies
    using sequencing count data from cell-free DNA
A 'Java' implementation of the RAKE algorithm (Rose, S., Engel, D., 
  Cramer, N. and Cowley, W. (2010) <doi:10.1002/9780470689646.ch1>), which can 
  be used to extract keywords from documents without any training data.
Provides XML parsing capability through the 'Rapidxml' 'C++' header-only library.
This package provides other packages with access to the internal 
 R serialization code. Access to this code is provided at the C function
 level by using the registration of native function mechanism. Client
 packages simply include a single header file RApiSerializeAPI.h provided by
 this package.

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

    The R Core group is the original author of the serialization code made
 available by this package.
Kernel method that performs online outlier detection through random low-dimensional projections in a kernel space. Controls the probability of false alarm error. See Kellner J., "Gaussian models and kernel methods" (2016), PhD thesis for reference <https://ori-nuxeo.univ-lille1.fr/nuxeo/site/esupversions/789d119a-6763-4205-972f-b318cd4fdb27>.
Bindings to kernel methods for enforcing security restrictions. 
    AppArmor can apply mandatory access control (MAC) policies on a given task 
    (process) via security profiles with detailed ACL definitions. In addition
    this package implements bindings for setting process resource limits (rlimit),
    uid, gid, affinity and priority. The high level R function 'eval.secure' 
    builds on these methods to perform dynamic sandboxing: it evaluates a single
    R expression within a temporary fork which acts as a sandbox by enforcing 
    fine grained restrictions without affecting the main R process. A portable 
    version of this function is now available in the 'unix' package.
An easy way to determine which directories on the users computer
    you should use to save data, caches and logs. A port of Python's 'Appdirs'
    (\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.
Calculate sketches of a data set reducing the number of observations using random projections. These can be used for Bayesian or frequentist linear regression on large data sets as described in Geppert et. al (2017) <doi:10.1007/s11222-015-9608-z>.
Performs wood cell anatomical data analyses on spatially explicit xylem (tracheids) datasets 
                  derived from thin sections of woody tissue. The package includes functions for visualisation, 
                  detection and alignment of continuous tracheid radial file (defined as rows) and individual tracheid position 
                  within an annual ring of coniferous species. This package is designed to be used with elaborate cell output, 
                  e.g. as provided with ROXAS (von Arx & Carrer, 2014 <doi:10.1016/j.dendro.2013.12.001>). The package has been validated for Picea abies, 
                  Larix Siberica, Pinus cembra and Pinus sylvestris.
Biodiversity is in crisis. The overarching aim of conservation
    is to preserve biodiversity patterns and processes. To this end, protected
    areas are established to buffer species and preserve biodiversity processes.
    But resources are limited and so protected areas must be cost-effective.
    This package contains tools to generate plans for protected areas
    (prioritizations), using spatially explicit targets for biodiversity
    patterns and processes. To obtain solutions in a feasible amount  of time,
    this package uses the commercial 'Gurobi' software package (obtained from
    <http://www.gurobi.com/>). For more information on using
    this package, see Hanson et al. (2018) <doi:10.1111/2041-210X.12862>.
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.
Implementation of an alternating direction method of multipliers
    algorithm for fitting a linear model with tree-based lasso regularization, 
    which is proposed in Algorithm 1 of Yan and Bien (2018) <arXiv:1803.06675>. 
    The package allows efficient model fitting on the entire 2-dimensional 
    regularization path for large datasets. The complete set of functions 
    also makes the entire process of tuning regularization parameters and 
    visualizing results hassle-free.
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.
Inspired by Karl Broman`s reader on using 'knitr'
    with 'asciidoc'
    (<http://kbroman.org/knitr_knutshell/pages/asciidoc.html>), this is
    merely a wrapper to 'knitr' and 'asciidoc'.
Software to perform supervised and pixel based raster image classification. It has been designed to facilitate land-cover analysis. Five classification algorithms can be used: Maximum Likelihood Classification, Multinomial Logistic Regression, Neural Networks, Random Forests and Support Vector Machines. The output includes the classified raster and standard classification accuracy assessment such as the accuracy matrix, the overall accuracy and the kappa coefficient. An option for in-sample verification is available.
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. There is a also support for vector data operations such as intersections. See the manual and tutorials on <https://rspatial.org/> to get started.
This is a wrapper function for image(), which makes reasonable
    raster plots with nice axis and other useful features.
Provides R functions to selectively rasterize components
             of 'grid' output.
Performs kernel based estimates on in-memory raster images 
  from the raster package.  These kernel estimates include local means
  variances, modes, and quantiles.  All results are in the form of 
  raster images, preserving original resolution and projection attributes.
A S4 class has been created such that complex operations can be
    executed on each cells of a raster map. The raster of objects contains the
    traditional raster map with the addition of a list of generic objects: one
    object for each raster cells. It allows to write few lines of R code for complex
    map algebra. Two environmental applications about frequency analysis of raster
    map of precipitation and creation of a raster map of soil water retention curves
    have been presented.
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.
Estimates the evolutionary rate matrix (R) using Markov chain Monte Carlo (MCMC) as described in Caetano and Harmon (2017) <doi:10.1111/2041-210X.12826>. The package has functions to run MCMC chains, plot results, evaluate convergence, and summarize posterior distributions.
A function which performs exact rate ratio tests and returns an object of class htest.
The kappa statistic implemented by Fleiss is a very popular index for assessing the reliability of agreement among multiple observers. It is used both in the psychological and in the psychiatric field. Other fields of application are typically medicine, biology and engineering. Unfortunately,the kappa statistic may behave inconsistently in case of strong agreement between raters, since this index assumes lower values than it would have been expected. We propose a modification kappa implemented by Fleiss in case of nominal and ordinal variables. Monte Carlo simulations are used both to testing statistical hypotheses and to calculating percentile bootstrap confidence intervals based on proposed statistic in case of nominal and ordinal data.
Computes confidence intervals for the rate (or risk)
    difference ('RD') or rate ratio (or relative risk, 'RR') for 
    binomial proportions or Poisson rates, or for odds ratio 
    ('OR', binomial only). Also confidence intervals for a single 
    binomial or Poisson rate, and intervals for matched pairs. 
    Includes skewness-corrected asymptotic score ('SCAS') methods, 
    which have been developed in Laud (2017) <doi:10.1002/pst.1813>
    from Miettinen & Nurminen (1985) <doi:10.1002/sim.4780040211> and 
    Gart & Nam (1988) <doi:10.2307/2531848>. Also includes MOVER methods
    (Method Of Variance Estimates Recovery) for all contrasts, derived 
    from the Newcombe method but using equal-tailed Jeffreys intervals,
    and generalised for Bayesian applications incorporating prior 
    information. So-called 'exact' methods for strictly conservative 
    coverage are approximated using continuity corrections.
    Also includes methods for stratified calculations (e.g. meta-analysis),
    either assuming fixed effects or incorporating stratum
    heterogeneity.
A collection of randomization tests, data sets and examples. The current version focuses on three testing problems and their implementation in empirical work. First, it facilitates the empirical researcher to test for particular hypotheses, such as comparisons of means, medians, and variances from k populations using robust permutation tests, which asymptotic validity holds under very weak assumptions, while retaining the exact rejection probability in finite samples when the underlying distributions are identical. Second, the description and implementation of a permutation test for testing the continuity assumption of the baseline covariates in the sharp regression discontinuity design (RDD) as in Canay and Kamat (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. Third, it provides the practitioner with an effortless implementation of a permutation test based on the martingale decomposition of the empirical process for the goodness-of-fit testing problem with an estimated nuisance parameter. An application of this testing problem is the one of testing for heterogeneous treatment effects in a randomized control trial.
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). Data sets are added.
We implement a test of the rational expectations hypothesis based on the marginal distributions of realizations and subjective beliefs from D'Haultfoeuille, Gaillac, and Maurel (2018) <doi:10.3386/w25274>. This test can be used in cases where realizations and subjective beliefs are observed in two different datasets that cannot be matched, or when they are observed in the same dataset. The package also computes the estimator of the minimal deviations from rational expectations than can be rationalized by the data.
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).
The R Analytic Tool To Learn Easily (Rattle) provides a 
  collection of utilities functions for the data scientist. A
  Gnome (RGtk2) based graphical interface is included with 
  the aim to provide a simple and intuitive introduction to R 
  for data science, allowing a user to quickly load data from a CSV file 
  (or via ODBC), transform and explore the data, 
  build and evaluate models, and export models as PMML (predictive
  modelling markup language) or as scores. A key aspect of the GUI
  is that all R commands are logged and commented through the log tab.
  This can be saved as a standalone R script file and as
  an aid for the user to 
  learn R or to copy-and-paste directly into R itself.
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.
Functions for downloading Australian economic statistics
  from the Australian Bureau of Statistics (ABS) (see <https://www.abs.gov.au/>) and
  Reserve Bank of Australia (RBA) (see <https://www.rba.gov.au/>) websites.
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.
This is an R wrapper from the AWS Command Line Interface that 
  provides methods to manage the user configuration on Amazon Web Service. You 
  can create as many profiles as you want, manage them, and delete them. The 
  profiles created with this tool work with all AWS products such as S3, 
  Glacier, and EC2. It also provides a function to automatically install 
  AWS CLI, but you can download it and install it manually if you prefer.
Uses a combination of raytracing, spherical texture mapping, lambertian reflectance, and ambient occlusion to produce 2D and 3D data visualizations and maps. Includes water detection and layering functions, programmable color palette generation, several built-in textures for hill shading, 2D and 3D plotting options, and the ability to export 3D visualizations to a 3D printable format.
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>.
A model of the form Y = f(x) + s(x) Z is fit where functions f and s are modeled with ensembles of trees and Z is standard normal.  
    This model is developed in the paper 'Heteroscedastic BART Via Multiplicative Regression Trees' 
    (Pratola, Chipman, George, and McCulloch, 2019, <arXiv:1709.07542v2>).
    BART refers to Bayesian Additive Regression Trees. See the R-package 'BART'.
    The predictor vector x may be high dimensional.
    A Markov Chain Monte Carlo (MCMC) algorithm provides Bayesian posterior uncertainty for both f and s.
    The MCMC uses the recent innovations in 
    Efficient Metropolis--Hastings proposal mechanisms for Bayesian regression tree models 
    (Pratola, 2015, Bayesian Analysis, <doi:10.1214/16-BA999>).
A Pure R implementation of Bayesian Global Optimization with Gaussian Processes.
Interpretation of time series data is affected by model choices. Different models can give different or even contradicting estimates of patterns, trends, and mechanisms for the same data--a limitation alleviated by the Bayesian estimator of abrupt change,seasonality, and trend (BEAST) of this package. BEAST seeks to improve time series decomposition by forgoing the "single-best-model" concept and embracing all competing models into the inference via a Bayesian model averaging scheme. It is a flexible tool to uncover abrupt changes (i.e., change-points), cyclic variations (e.g., seasonality), and nonlinear trends in time-series observations. BEAST not just tells when changes occur but also quantifies how likely the detected changes are true. It detects not just piecewise linear trends but also arbitrary nonlinear trends. BEAST is applicable to real-valued time series data of all kinds, be it for remote sensing, economics, climate sciences, ecology, and hydrology. Example applications include its use to identify regime shifts in ecological data, map forest disturbance and land degradation from satellite imagery, detect market trends in economic data, pinpoint anomaly and extreme events in climate data, and unravel system dynamics in biological data. Details on BEAST are reported in Zhao et al. (2019).
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). Please refer to Neuenschwander et al. (2010)
    <doi:10.1177/1740774509356002> and Schmidli et al. (2014)
    <doi:10.1111/biom.12242> for details on the methodology.
The package contains functions to generate random numbers
        from the beta distribution and random vectors from the
        Dirichlet distribution.
A robust backfitting algorithm for additive models based on (robust) local polynomial 
             kernel smoothers. It includes both bounded and re-descending (kernel) M-estimators, and
             it computes predictions for points outside the training set if desired. See
             Boente, Martinez and Salibian-Barrera (2017) <doi:10.1080/10485252.2017.1369077> for
             details.
Facilities for working with Atlantis box-geometry model (BGM) 
 files. Atlantis is a deterministic, biogeochemical, whole-of-ecosystem model. 
 Functions are provided to read from BGM files directly, preserving their 
 internal topology, as well as helper functions to generate 'Spatial' 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.
Methods that allow video reading and loading in R. Also provides nine different methods for  background subtraction.
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> and <doi:10.18637/jss.v067.i10> for more information). This includes functions for manipulating LibBi models, for reading and writing LibBi input/output files, for converting LibBi output to provide traces for use with the coda package, and for running LibBi from R.
Contains a collection of helper functions to use with 'RBi', the R interface to 'LibBi', described in Murray et al. (2015) <doi:10.18637/jss.v067.i10>. It contains functions to adapt the proposal distribution and number of particles in particle Markov-Chain Monte Carlo, as well as calculating the Deviance Information Criterion (DIC) and converting between times in 'LibBi' results and R time/dates.
Manually bin data using weight of evidence and information value. Includes other binning 
    methods such as equal length, quantile and winsorized. Options for combining levels of categorical
    data are also available. Dummy variables can be generated based on the bins created using any of 
    the available binning methods. References: Siddiqi, N. (2006) <doi:10.1002/9781119201731.biblio>.
Functions for connecting to BioUML server, querying BioUML repository and launching BioUML analyses.
Interface to the 'USGS' 'BISON' (<https://bison.usgs.gov/>)
    API, a 'database' for species occurrence data. Data comes from
    species in the United States from participating data providers. You can get
    data via 'taxonomic' and location based queries. A simple function
    is provided to help visualize data.
A simple implementation of Binary Indexed Tree by R. The BinaryIndexedTree class supports construction of Binary Indexed Tree from a vector, update of a value in the vector and query for the sum of a interval of the vector.
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 wrapper to the overly complicated 'Bitmoji' API allowing users 
  to access and plot various comic strips using their personalized 'Bitstrips'
  avatar (<https://www.bitmoji.com/>).
R interface to access prices and market data with the 
    'Bloomberg Data License' service from 
    <https://www.bloomberg.com/professional/product/data-license/>. 
    As a prerequisite, a valid Data License from 'Bloomberg' is needed 
    together with the corresponding SFTP credentials and whitelisting 
    of the IP from which accessing the service. 
    This software and its author are in no way affiliated, 
    endorsed, or approved by 'Bloomberg' or any of its affiliates.
    'Bloomberg' is a registered trademark.
An R Interface to 'Bloomberg' is provided via the 'Blp API'.
An R-shiny application to plot datalogger time series at a microsecond precision as Acceleration, Temperature, 
  Pressure, Light intensity from CATS, AXY-TREK LUL and WACU bio-loggers. It is possible to link behavioral labels extracted
  from 'BORIS' software <http://www.boris.unito.it> or manually written in a csv file.
  CATS bio-logger are manufactured by <http://www.cats.is>, AXY-TREK are manufactured by <http://www.technosmart.eu> and 
  LUL and WACU are manufactured by <http://www.iphc.cnrs.fr/-MIBE-.html>.
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 implementation of classification and regression forests, as described by Breiman (2001), <DOI:10.1023/A:1010933404324>.
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.
Interface to the 'Libraries.io' API (<https://libraries.io/api>).
    'Libraries.io' indexes data from 36 different package managers for 
    programming languages.
Implementation of the RPC-JSON API for Bitcoin and utility functions for address creation and content analysis of the blockchain.
The regression-based (RB) approach is a method to test the missing data mechanism.
			This package contains two functions that test the type of missing data (Missing Completely 
			At Random vs Missing At Random) on the basis of the RB approach. The first function applies 
			the RB approach independently on each variable with missing data, using the completely 
			observed variables only. The second function tests the missing data mechanism globally 
			(on all variables with missing data) with the use of all available information. The 
			algorithm is adapted both to continuous and categorical data.
Tu & Zhou (1999) <doi:10.1002/(SICI)1097-0258(19991030)18:20%3C2749::AID-SIM195%3E3.0.CO;2-C> showed that comparing the means of populations whose data-generating distributions are non-negative with excess zero observations is a problem of great importance in the analysis of medical cost data. In the same study, Tu & Zhou discuss that it can be difficult to control type-I error rates of general-purpose statistical tests for comparing the means of these particular data sets. This package allows users to perform a modified bootstrap-based t-test that aims to better control type-I error rates in these situations.
Rbundler manages a project-specific library for dependency
    package installation. By specifying dependencies in a DESCRIPTION file
    in a project's root directory, one may install and use dependencies
    in a repeatable fashion without requiring manual maintenance.
    rbundler creates a project-specific R library in
    `PROJECT_ROOT/.Rbundle` (by default) and a project-specific
    `R_LIBS_USER` value, set in `PROJECT_ROOT/.Renviron`. It supports
    dependency management for R standard "Depends", "Imports",
    "Suggests", and "LinkingTo" package dependencies. rbundler also
    attempts to validate and install versioned dependencies, such
    as ">=", "==", "<=". Note that, due to the way R manages package
    installation, differing nested versioned dependencies are not
    allowed. For example, if your project depends on packages A (== 1),
    and B (== 2), but package A depends on B (== 1), then a nested
    dependency violation will cause rbundler to error out.
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)).
Regularized calibrated estimation for causal inference and missing-data problems with high-dimensional data, based on Tan (2017) <arXiv:1710.08074> and Tan (2018) <arXiv:1801.09817>.
Calculate the flow of particles between polygons by two integration methods:
  integration by a cubature method and integration on a grid of points.
  Annie Bouvier, Kien Kieu, Kasia Adamczyk and Herve Monod (2009)
  <doi:10.1016/j.envsoft.2008.11.006>.
Tools for basic and advance cancer statistics and graphics.
	Groups individual data, merges registry data and population data, calculates age-specific rate, age-standardized rate, estimated annual percentage rate with standards error. Creates graphics across variable and
    time, such as age-specific trends, bar chart and period-cohort trends.
There are different numeric optimizations which are used in order to estimate coefficients in models such as linear regression and neural networks. This package covers parameter estimation in linear regression using different methods such as batch gradient descent, stochastic gradient descent, minibatch gradient descent and coordinate descent. Kiwiel, Krzysztof C (2001) <doi:10.1007/PL00011414> Yu Nesterov (2004) <ISBN:1-4020-7553-7> Ferguson, Thomas S (1982) <doi:10.1080/01621459.1982.10477894> Zeiler, Matthew D (2012) <arXiv:1212.5701> Wright, Stephen J (2015) <arXiv:1502.04759>.
Provides an interface to the National Topographic System (NTS),
    which is the way in which a number of freely available Canadian datasets are
    organized. CanVec and CanVec+ datasets, which include all data used to create
    Canadian topographic maps, are two such datasets that are useful in creating
    vector-based maps for locations across Canada. This packages searches CanVec
    data by location, plots it using pretty defaults, and exports it to human-
    readable shapefiles for use in another GIS.
Estimation of abundance and other of demographic parameters for closed 
             populations, open populations and the robust design in capture-recapture experiments 
             using loglinear models.
Translation of the 'MATLAB' program 'Carb' (Nathan and Mauz 2008 <DOI:10.1016/j.radmeas.2007.12.012>; Mauz and Hoffmann 2014) for dose rate modelling for carbonate-rich samples in the context of trapped charged dating (e.g., luminescence dating) applications.
Enables the calibration and analysis of radiocarbon dates, often but not exclusively for the purposes of archaeological research. It includes functions not only for basic calibration, uncalibration, and plotting of one or more dates, but also a statistical framework for building demographic and related longitudinal inferences from aggregate radiocarbon date lists, including: Monte-Carlo simulation test (Timpson et al 2014 <doi:10.1016/j.jas.2014.08.011>), random mark permutation test (Crema et al 2016 <doi:10.1371/journal.pone.0154809>) and spatial permutation tests (Crema, Bevan, and Shennan 2017 <doi:10.1016/j.jas.2017.09.007>).
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 a classifier based on the
    "Classification Based on Associations" (CBA). It can be used for building
    classification models from association rules. Rules are pruned in the order of
    precedence given by the sort criteria and a default rule is added. The final
    classifier labels provided instances. CBA was originally proposed by Liu,
    B. Hsu, W. and Ma, Y. Integrating Classification and Association Rule
    Mining. Proceedings KDD-98, New York, 27-31 August. AAAI. pp80-86 (1998, ISBN:1-57735-070-7).
Tools for large, sparse optimal matching of treated units
	and control units in observational studies.  Provisions are
	made for refined covariate balance constraints, which include
	fine and near-fine balance as special cases.  Matches are 
	optimal in the sense that they are computed as solutions to
	network optimization problems rather than greedy algorithms.
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.
Provides a dataset of functions in all base packages of R versions 1.0.1 onwards.
R package for chess validations, pieces movements and check
    detection. Also integrates functions to plot chess boards given a
    Forsyth Edwards and Portable Game notations.
Parses the 'ArchieML' format from the New York Times <http://archieml.org>.
  Also provides utilities for retrieving Google Drive documents for parsing.
A set of functions to generate, access and analyze standard data products from archival tagging data.
An implementation of simulated maximum likelihood method for the
    estimation of Binary (Probit and Logit), Ordered (Probit and Logit) and
    Poisson models with random parameters for cross-sectional and longitudinal
    data.
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.
A programmatic interface to the Species+ <https://speciesplus.net/> database via the Species+/CITES Checklist API <https://api.speciesplus.net/>.
Client for 'Citoid' (<https://www.mediawiki.org/wiki/Citoid>),
    an API for getting citations for various scholarly work identifiers
    found on 'Wikipedia'.
To create clearer, more concise code provides this
	     toolbox helps coders to isolate the essential parts of a script that
	     produces a chosen result, such as an object, tables and figures
	     written to disk and even warnings and errors. This work
	     was funded by US National Science Foundation grant
	     SSI-1450277 for applications of End-to-End Data Provenance.
'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).
Tool for providing access to the Java version 'CMAEvolutionStrategy' of
    Nikolaus Hansen. 'CMA-ES' is the Covariance Matrix Adaptation Evolution Strategy,
    see https://www.lri.fr/~hansen/cmaes_inmatlab.html#java.
Run 'R CMD check' from 'R' and capture the
    results of the individual checks. Supports running checks in the
    background, timeouts, pretty printing and comparing check results.
A platform-independent basic-statistics GUI (graphical user interface) for R, based on the tcltk package.
Various statistical, graphics, and data-management functions used by the Rcmdr package in the R Commander GUI for R.
R Commander plug-in for repeated-measures and mixed-design
    ('split-plot') ANOVA.
    It adds a new menu entry for repeated measures that allows
    to deal with up to three within-subject factors and optionally with
    one or several between-subject factors.
    It also provides supplementary options to oneWayAnova() and
    multiWayAnova() functions, such as choice of ANOVA type, display of
    effect sizes and post hoc analysis for multiWayAnova().
A plug-in for R Commander ('Rcmdr'). The package is a Graphical
    User Interface (GUI) in which several biclustering methods can be executed,
    followed by diagnostics and plots of the results. Further, the GUI also has
    the possibility to connect the methods to more general diagnostic packages for
    biclustering. Biclustering methods from 'biclust', 'fabia', 's4vd', 'iBBiG',
    'isa2', 'BiBitR', 'rqubic' and 'BicARE' are implemented. Additionally, 'superbiclust' and
    'BcDiag' are also implemented to be able to further investigate results. The
    GUI also provides a couple of extra utilities to export, save, search through
    and plot the results. 'RcmdrPlugin.BiclustGUI' also provides a very specific
    framework for biclustering in which new methods, diagnostics and plots can be
    added. Scripts were prepared so that R-package developers can freely design
    their own dialogs in the GUI which can then be added by the maintainer of
    'RcmdrPlugin.BiclustGUI'. These scripts do not required any knowledge of 'tcltk'
    and 'Rcmdr' and are easy to fill in.
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.
Este paquete proporciona una interfaz grafica de usuario (GUI) para algunos de los procedimientos estadisticos detallados en un curso de 'Estadistica aplicada a las Ciencias Sociales mediante el programa informatico R' (EACSPIR). LA GUI se ha desarrollado como un Plugin del programa R-Commander.
Rcmdr plug-in GUI extension for Evidence Based Medicine medical indicators calculations (Sensitivity, specificity, absolute risk reduction, relative risk, ...).
A Rcmdr "plug-in" for the EcoVirtual package, designed primarily for teaching ecological models using simulations.
Export Rcmdr output to LaTeX or HTML code. The
        plug-in was originally intended to facilitate exporting Rcmdr
        output to formats other than ASCII text and to provide R
        novices with an easy-to-use, easy-to-access reference on
        exporting R objects to formats suited for printed output. The
        package documentation contains several pointers on creating
        reports, either by using conventional word processors or
        LaTeX/LyX.
EZR (Easy R) adds a variety of statistical functions, including survival analyses, ROC analyses, metaanalyses, sample size calculation, and so on, to the R commander. EZR enables point-and-click easy access to statistical functions, especially for medical statistics. EZR is platform-independent and runs on Windows, Mac OS X, and UNIX. Its complete manual is available only in Japanese (Chugai Igakusha, ISBN: 978-4-498-10901-8 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 2,000 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.
Provides an R Commander plugin that accompanies IPSUR, an Introduction to Probability and Statistics Using R.
A GUI front-end for 'ggplot2' supports Kaplan-Meier plot, histogram,
    Q-Q plot, box plot, errorbar plot, scatter plot, line chart, pie chart,
    bar chart, contour plot, and distribution plot.
Provides an Rcmdr "plug-in" based on the 'lfstat' package for low flow analysis.
Easy to use interface for conducting meta-analysis in R. This
    package is an Rcmdr-plugin, which allows the user to conduct analyses in a
    menu-driven, graphical user interface environment (e.g., CMA, SPSS). It
    uses recommended procedures as described in The Handbook of Research
    Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009).
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.
An R Commander "plug-in" providing an interface to OptimClassifier functions.
A GUI for the orloca package is provided as a Rcmdr plug-in. The package deals with planar location problems.
The R commander plug-in for robust principal component analysis. The Graphical User Interface for Principal Component Analysis (PCA) with Hubert Algorithm method.
Rcmdr menu support for some of the graphics by group in
        the lattice package
This package provides a Rcmdr "plug-in" to analyze questionnaire data.
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 as described in Bulte and Onghena (2013) <doi:10.22237/jmasm/1383280020>. The package is written as an Rcmdr plugin.
This package provides a GUI for the SLC package, it is
        written as an Rcmdr plug-in.
Rcmdr interface to the 'sos' package. The plug-in renders
  the 'sos' searching functionality easily accessible via the Rcmdr
  menus. It also simplifies the task of performing multiple searches and 
  subsequently obtaining the union or the intersection of the results.
This package provides a GUI for the steepness package, it
        is written as an Rcmdr plug-in.
An R Commander plug-in for the survival
  package, with dialogs for Cox models, parametric survival regression models,
  estimation of survival curves, and testing for differences in survival
  curves, along with data-management facilities and a variety of tests, 
  diagnostics and graphs.
The 'sutteForecastR' is a package of Alpha-Sutte indicator. To make the 'sutteForecastR' user friendly, so we develop an 'Rcmdr' plug-in based on the Alpha-Sutte indicator function.
Provides an Rcmdr "plug-in" based on the TeachingDemos package, and is primarily for illustrative purposes.
RCommander plugin for teaching statistical methods. 
    It adds a new menu for making easier the teaching of the main concepts about the main statistical methods.
An 'R Commander' plug-in providing an integrated solution to perform
    a series of text mining tasks such as importing and cleaning a corpus, and
    analyses like terms and documents counts, vocabulary tables, terms
    co-occurrences and documents similarity measures, time series analysis,
    correspondence analysis and hierarchical clustering. Corpora can be imported
    from spreadsheet-like files, directories of raw text files, 'Twitter' queries,
    as well as from 'Dow Jones Factiva', 'LexisNexis', 'Europresse' and 'Alceste' files.
Some extensions to Rcmdr (R Commander), randomness test, 
 variance test for one normal sample and predictions using active model, 
 made by R-UCA project and used in teaching statistics at University of Cadiz (UCA).
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.
Get your data (forms, structures, answers) from Coletum 
    <https://coletum.com> to handle and analyse.
Provides programmatic access to Colombos, a web based
    interface for exploring and analyzing comprehensive organism-specific
    cross-platform expression compendia of bacterial organisms.
Provides color schemes for maps (and other graphics)
        designed by Cynthia Brewer as described at http://colorbrewer2.org
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>.
Solve some conic related problems (intersection of conics with lines and conics, arc length of an ellipse, polar lines, etc.).
Client for the CORE API (<https://core.ac.uk/docs/>).
    CORE (<https://core.ac.uk>) aggregates open access research
    outputs from repositories and journals worldwide and make them
    available to the public.
A collection of small text corpora of interesting data.
    It contains all data sets from 'dariusk/corpora'. Some examples:
    names of animals: birds, dinosaurs, dogs; foods: beer categories,
    pizza toppings; geography: English towns, rivers, oceans;
    humans: authors, US presidents, occupations; science: elements,
    planets; words: adjectives, verbs, proverbs, US president quotes.
Handling and Analysing Spherical, 
      HEALPix and Cosmic Microwave Background data on a HEALPix grid.
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 (2018,
 <doi:10.1080/00031305.2017.1375990>); see 'citation("Rcpp")' for details.
Rcpp11 includes a header only C++11 library that facilitates 
  integration between R and modern C++.
Provides optimized functions implemented in C++ with 'Rcpp'
    for solving problems in combinatorics and computational mathematics.
    Utilizes parallel programming via 'RcppThread' for maximal performance.
    Also makes use of the RMatrix class from the 'RcppParallel' library.
    There are combination/permutation functions with constraint parameters
    that allow for generation of all combinations/permutations of a vector
    meeting specific criteria (e.g. finding all combinations such
    that the sum is between two bounds). Capable of generating specific
    combinations/permutations (e.g. retrieve only the nth lexicographical
    result) which sets up nicely for parallelization as well as random
    sampling. Gmp support permits exploration where the total number of
    results is large (e.g. comboSample(10000, 500, n = 4)). Additionally,
    there are several high performance number theoretic functions that
    are useful for problems common in computational mathematics. Some of
    these functions make use of the fast integer division library
    'libdivide' by <http://ridiculousfish.com>. The primeSieve function
    is based on the segmented sieve of Eratosthenes implementation by
    Kim Walisch. It is also efficient for large numbers by using the
    cache friendly improvements originally developed by Tomás Oliveira.
    Finally, there is a prime counting function that implements Legendre's
    formula based on the algorithm by Kim Walisch.
'Annoy' is a small C++ library for Approximate Nearest Neighbors 
 written for efficient memory usage as well an ability to load from / save to
 disk. This package provides an R interface by relying on the 'Rcpp' package,
 exposing the same interface as the original Python wrapper to 'Annoy'. See
 <https://github.com/spotify/annoy> for more on 'Annoy'. 'Annoy' is released
 under Version 2.0 of the Apache License. Also included is a small Windows
 port of 'mmap' which is released under the MIT license.
The 'APT Package Management System' provides Debian and
 Debian-derived Linux systems with a powerful system to resolve package
 dependencies. This package offers access directly from R.  This can
 only work on a system with a suitable 'libapt-pkg-dev' installation
 so functionality is curtailed if such a library is not found.
'Armadillo' is a templated C++ linear algebra library (by Conrad
 Sanderson) that aims towards a good balance between speed and ease of use. Integer,
 floating point and complex numbers are supported, as well as a subset of
 trigonometric and statistics functions. Various matrix decompositions are
 provided through optional integration with LAPACK and ATLAS libraries.
 The 'RcppArmadillo' package includes the header files from the templated
 'Armadillo' library. Thus users do not need to install 'Armadillo' itself in
 order to use 'RcppArmadillo'. From release 7.800.0 on, 'Armadillo' is
 licensed under Apache License 2; previous releases were under licensed as
 MPL 2.0 from version 3.800.0 onwards and LGPL-3 prior to that;
 'RcppArmadillo' (the 'Rcpp' bindings/bridge to Armadillo) is licensed under
 the GNU GPL version 2 or later, as is the rest of 'Rcpp'. 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.
'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 compatibility in the package
 'RcppClassic').  This package 'RcppClassicExamples' provides usage examples for
 the older, deprecated API. There is also a corresponding package
 'RcppExamples' with examples for the newer, current API which we
 strongly recommend as the basis for all new development.
The 'cnpy' library written by Carl Rogers provides read and write
 facilities for files created with (or for) the 'NumPy' extension for 'Python'.
 Vectors and matrices of numeric types can be read or written to and from
 files as well as compressed files. Support for integer files is available if
 the package has been built with -std=c++11 which should be the default on
 all platforms since the release of R 3.3.0.
'Rcpp' Bindings for the C code of the 'Corpus Workbench' ('CWB'), an indexing and query 
  engine to efficiently analyze large corpora (<http://cwb.sourceforge.net>). 'RcppCWB' is licensed
  under the GNU GPL-3, in line with the GPL-3 license of the 'CWB' (<https://www.r-project.org/
  Licenses/GPL-3>). The 'CWB' relies on 'pcre' (BSD license, see <https://www.pcre.org/
  licence.txt>) and 'GLib' (LGPL license, see <https://www.gnu.org/licenses/lgpl-3.0.en.
  html>). See the file LICENSE.note for further information. The package includes modified code of the
  'rcqp' package (GPL-2, see <https://cran.r-project.org/package=rcqp>). The original work of the authors
  of the 'rcqp' package is acknowledged with great respect, and they are listed as authors of this
  package. To achieve cross-platform portability (including Windows), using 'Rcpp' for wrapper code
  is the approach used by 'RcppCWB'.
An efficient C++ based implementation of the 'DEoptim'
 function which performs global optimization by differential evolution.  
 Its creation was motivated by trying to see if the old approximation "easier,
 shorter, faster: pick any two" could in fact be extended to achieving all
 three goals while moving the code from plain old C to modern C++.  The
 initial version did in fact do so, but a good part of the gain was due to 
 an implicit code review which eliminated a few inefficiencies which have
 since been eliminated in 'DEoptim'.
The 'Rcpp' package provides a C++ library to make it easier
    to use C++ with R. R and 'Rcpp' provide functions for a variety of
    statistical distributions. Several R packages make functions
    available to R for additional statistical distributions. However,
    to access these functions from C++ code, a costly call to the R
    functions must be made. 'RcppDist' provides a header-only C++ library
    with functions for additional statistical distributions that can be
    called from C++ when writing code using 'Rcpp' or 'RcppArmadillo'.
    Functions are available that return a 'NumericVector' as well as
    doubles, and for multivariate or matrix distributions, 'Armadillo'
    vectors and matrices. 'RcppDist' provides functions for the following
    distributions: the four parameter beta distribution; the location-
    scale t distribution; the truncated normal distribution; the
    truncated t distribution; a truncated location-scale t distribution;
    the triangle distribution; the multivariate normal distribution*;
    the multivariate t distribution*; the Wishart distribution*; and
    the inverse Wishart distribution*. Distributions marked with an
    asterisk rely on 'RcppArmadillo'.
This package is based on the C++ code from Yusuke Sugomori,
             which implements basic machine learning methods with 
             many layers (deep learning), including dA (Denoising Autoencoder), 
             SdA (Stacked Denoising Autoencoder), RBM (Restricted Boltzmann machine) and 
             DBN (Deep Belief Nets).
Dynamic Programming implemented in 'Rcpp'.  Includes example partition and out of sample fitting applications.  Also supplies additional custom coders for the 'vtreat' package.
R and 'Eigen' integration using 'Rcpp'.
 'Eigen' is a C++ template library for linear algebra: matrices, vectors,
 numerical solvers and related algorithms.  It supports dense and sparse
 matrices on integer, floating point and complex numbers, decompositions of
 such matrices, and solutions of linear systems. Its performance on many
 algorithms is comparable with some of the best implementations based on
 'Lapack' and level-3 'BLAS'. The 'RcppEigen' package includes the header
 files from the 'Eigen' C++ template library (currently version 3.3.4). Thus
 users do not need to install 'Eigen' itself in order to use 'RcppEigen'.
 Since version 3.1.1, 'Eigen' is licensed under the Mozilla Public License
 (version 2); earlier version were licensed under the GNU LGPL version 3 or
 later. 'RcppEigen' (the 'Rcpp' bindings/bridge to 'Eigen') is licensed under
 the GNU GPL version 2 or later, as is the rest of 'Rcpp'.
Compiles 'C++' code using 'Rcpp', 'Eigen' and 'CppAD' to produce first and second order partial derivatives. Also provides an implementation of Faa' di Bruno's formula to combine the partial derivatives of composed functions, (see Hardy, M (2006) <arXiv:math/0601149v1>).
'Ensmallen' is a templated C++ mathematical optimization library 
 (by the 'MLPACK' team) that provides a simple set of abstractions for writing an
 objective function to optimize. Provided within are various standard and
 cutting-edge optimizers that include full-batch gradient descent techniques, 
 small-batch techniques, gradient-free optimizers, and constrained optimization.
 The 'RcppEnsmallen' package includes the header files from the 'Ensmallen' library
 and pairs the appropriate header files from 'armadillo' through the 
 'RcppArmadillo' package. Therefore, users do not need to install 'Ensmallen' nor
 'Armadillo' to use 'RcppEnsmallen'. Note that 'Ensmallen' is licensed under 
 3-Clause BSD, 'Armadillo' starting from 7.800.0 is licensed under Apache License 2,
 'RcppArmadillo' (the 'Rcpp' bindings/bridge to 'Armadillo') is licensed under 
 the GNU GPL version 2 or later. Thus, 'RcppEnsmallen' is also licensed under
 similar terms. Note that 'Ensmallen' requires a compiler that supports 
 'C++11' and 'Armadillo' 6.500 or later.
Examples for Seamless R and C++ integration
 The 'Rcpp' package contains a C++ library that facilitates the integration of
 R and C++ in various ways. This package provides some usage examples.
 Note that the documentation in this package currently does not cover all the
 features in the package. The site <http://gallery.rcpp.org> regroups a large
 number of examples for 'Rcpp'.
Access to a family of Gauss error functions for arbitrary complex arguments is provided via the 'Faddeeva' package by Steven G. Johnson (see <http://ab-initio.mit.edu/wiki/index.php/Faddeeva_Package> for more information).
The 'getconf' command-line tool provided by 'libc' allows
 querying of a large number of system variables. This package provides
 similar functionality.
A fast implementation of the greedy algorithm for the set cover problem using 'Rcpp'.
'Rcpp' integration for 'GNU GSL' vectors and matrices
 The 'GNU Scientific Library' (or 'GSL') is a collection of numerical routines for
 scientific computing. It is particularly useful for C and C++ programs as it
 provides a standard C interface to a wide range of mathematical routines. There
 are over 1000 functions in total with an extensive test suite. The 'RcppGSL'
 package provides an easy-to-use interface between 'GSL' data structures and
 R using concepts from 'Rcpp' which is itself a package that eases the
 interfaces between R and C++. This package also serves as a prime example of
 how to build a package that uses 'Rcpp' to connect to another third-party
 library. The 'autoconf' script, 'inline' plugin and example package can all
 be used as a stanza to  write a similar package against another library.
Collection of functions to evaluate sequences, decode hidden states and estimate parameters from a single or multiple sequences of a discrete time Hidden Markov Model. The observed values can be modeled by a multinomial distribution for categorical/labeled emissions, a mixture of Gaussians for continuous data and also a mixture of Poissons for discrete values. It includes functions for random initialization, simulation, backward or forward sequence evaluation, Viterbi or forward-backward decoding and parameter estimation using an Expectation-Maximization approach.
'Hnswlib' is a C++ library for Approximate Nearest Neighbors. This 
 package provides a minimal R interface by relying on the 'Rcpp' package. See 
 <https://github.com/nmslib/hnswlib> for more on 'hnswlib'. 'hnswlib' is 
 released under Version 2.0 of the Apache License.
Header library and R functions to solve minimum cost bipartite matching problem 
 using Huhn-Munkres algorithm (Hungarian algorithm; <https://en.wikipedia.org/wiki/Hungarian_algorithm>;
 Kuhn (1955) doi:10.1002/nav.3800020109). 
 This is a repackaging of code written by Cong Ma in the GitHub repo <https://github.com/mcximing/hungarian-algorithm-cpp>.
R package based on 'Rcpp' for 'MeCab': Yet Another Part-of-Speech and Morphological Analyzer. 
	The purpose of this package is providing a seamless developing and analyzing environment for CJK texts.
	This package utilizes parallel programming for providing highly efficient text preprocessing 'posParallel()' function.
	For installation, please refer to README.md file.
'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.
'MsgPack' header files are provided for use by R packages, along 
 with the ability to access, create and alter 'MsgPack' objects directly from R.
 'MsgPack' is an efficient binary serialization format. It lets you exchange
 data among multiple languages like 'JSON' but it is faster and smaller.
 Small integers are encoded into a single byte, and typical short strings
 require only one extra byte in addition to the strings themselves. This
 package provides headers from the 'msgpack-c' implementation for C and
 C++(11) for use by R, particularly 'Rcpp'. The included 'msgpack-c' headers
 are licensed under the Boost Software License (Version 1.0); the code added
 by this package as well the R integration are licensed under the GPL (>= 2).
 See the files 'COPYRIGHTS' and 'AUTHORS' for a full list of  copyright holders
 and contributors to 'msgpack-c'.
An example package which shows use of 'NLopt' functionality from
 C++ via 'Rcpp' without requiring linking, and relying just on 'nloptr' thanks
 to the exporting API added there by Jelmer Ypma. This package is a fully
 functioning, updated, and expanded version of the initial example by
 Julien Chiquet at <https://github.com/jchiquet/RcppArmadilloNLoptExample>
 also containing a large earlier pull request of mine.
A collection of open source libraries for numerical computing
    (numerical integration, optimization, etc.) and their integration with
    'Rcpp'.
High level functions for parallel programming with 'Rcpp'.
    For example, the 'parallelFor()' function can be used to convert the work of
    a standard serial "for" loop into a parallel one and the 'parallelReduce()'
    function can be used for accumulating aggregate or other values.
Allows to display a progress bar in the R
    console for long running computations taking place in c++ code,
    and support for interrupting those computations even in multithreaded
    code, typically using OpenMP.
'QuantLib' bindings are provided for R using 'Rcpp' 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' (included as a fallback) with
 'MsgPack' encoding provided via 'RcppMsgPack' headers.
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.
R access to the Sequential Monte Carlo Template Classes
 by Johansen <doi:10.18637/jss.v030.i06> is provided. At present, four
 additional examples have been added, and the first example from the JSS
 paper has been extended. Further integration and extensions are planned.
The 'Streamulus' (template, header-only) library by
 Irit Katriel (at <https://github.com/iritkatriel/streamulus>)
 provides a very powerful yet convenient framework for stream
 processing. This package connects 'Streamulus' to R by providing 
 both the header files and all examples.
Provides a C++11-style thread class and thread pool that can safely
    be interrupted from R; see, Nagler (2018) <arXiv:1811.00450>.
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 header-only library provides modern, portable C++ wrappers for SIMD
    intrinsics and parallelized, optimized math implementations (SSE, AVX, NEON, AVX512).
    By placing this library in this package, we offer an efficient distribution system for
    Xsimd <https://github.com/QuantStack/xsimd> for R packages using CRAN.
This package provides access to some of the C level
        functions of the xts package.

  In its current state, the package is mostly a proof-of-concept to
        support adding useful functions, and does not yet add any of
        its own.
The Ziggurat generator for normally distributed random
 numbers, originally proposed by Marsaglia and Tsang (2000, 
 <doi:10.18637/jss.v005.i08>) has been improved upon a few times
 starting with Leong et al (2005, <doi:10.18637/jss.v012.i07>).
 This package provides an aggregation in order to compare different
 implementations in order to provide an 'faster but good enough'
 alternative for use with R and C++ code.
Performs parallel web crawling and web scraping. It is designed to crawl, parse and store web pages to produce data that can be directly used for analysis application. For details see Khalil and Fakir (2017) <DOI:10.1016/j.softx.2017.04.004>.
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.
Goldwin-Pierre correlogram. Research of critical periods in the past. Integrates a time series in a given window.
Fit a 2-parameter continual reassessment method (CRM) model (O'Quigley and Shen (1996), <doi: 10.2307/2532905>) regularized with L2 norm (Friedman et al. (2010), <doi: 10.18637/jss.v033.i01>) adjusted by the distance with the target dose limiting toxicity (DLT) rate.
'NanoString nCounter' is a medium-throughput platform that measures gene or microRNA expression levels. Here is a publication that introduces this platform: Malkov (2009) <doi:10.1186/1756-0500-2-80>. Here is the webpage of 'NanoString nCounter' where you can find detailed information about this platform <https://www.nanostring.com/scientific-content/technology-overview/ncounter-technology>. It has great clinical application, such as diagnosis and prognosis of cancer. Implements integrated system of random-coefficient hierarchical regression model to normalize data from 'NanoString nCounter' platform so that noise from various sources can be removed.
Client for various 'CrossRef' 'APIs', including 'metadata' search
    with their old and newer search 'APIs', get 'citations' in various formats
    (including 'bibtex', 'citeproc-json', 'rdf-xml', etc.), convert 'DOIs'
    to 'PMIDs', and 'vice versa', get citations for 'DOIs', and get links to
    full text of articles when available.
Contains methods for criterion-referenced test analyses as 
    described in Brown & Hudson (2002). This includes cut-score item discrimination 
    analyses and measures of dependability.
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/).
Pulls current pricing and market cap info from the <https://coinmarketcap.com/api/> API and presents it in a dataframe, see function help documentation for headerdetails.
R interface to the CSDP semidefinite programming library. Installs version 6.1.1 of CSDP from the COIN-OR website if required. An existing installation of CSDP may be used by passing the proper configure arguments to the installation command. See the INSTALL file for further details.
Cloth Simulation Filter (CSF) is an airborne LiDAR (Light Detection and Ranging) ground 
    points filtering algorithm  which is based on cloth simulation. It tries to simulate the interactions
    between the cloth nodes and the corresponding LiDAR points, the locations of the cloth nodes can be
    determined to generate an approximation of the ground surface.
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.
Provides simplified methods for managing classic Rubik's cubes and many other modifications of it (such as NxNxN size cubes, void cubes and 8-coloured cubes - so called octa cubes). Includes functions of handling special syntax for managing such cubes; and different approach to plotting 3D cubes without using external libraries (for example 'OpenGL').
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.
Administrative regions and other spatial objects of the Czech Republic.
Creation and manipulation of R documentation (Rd) as 
    R objects.  Also contains functions for checking consistency 
    of Rd and S4 generics and methods for converting  
    objects to Rd formatted output.
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.
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.
R access to hundreds of millions data series from DBnomics API
    (<https://db.nomics.world/>).
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 program evaluation settings to detect whether there is evidence of 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.
Allows caching of raw data directly in R code. This allows R
  scripts and R Notebooks to be shared and re-run on a machine without access
  to the original data. Cached data is encoded into an ASCII string that can
  be pasted into R code. When the code is run, the data is automatically
  loaded from the cached version if the original data file is unavailable.
  Works best for small datasets (a few hundred observations).
Data Envelopment Analysis for R, estimating robust DEA scores without and with environmental variables and doing returns-to-scale tests.
The package provides functions for estimating the relevant
        dimension of a data set in feature spaces, applications to
        model selection, graphical illustrations and prediction.
The Resource Description Framework, or 'RDF' is a widely used
             data representation model that forms the cornerstone of the 
             Semantic Web. 'RDF' represents data as a graph rather than 
             the familiar data table or rectangle of relational databases.
             The 'rdflib' package provides a friendly and concise user interface
             for performing common tasks on 'RDF' data, such as reading, writing
             and converting between the various serializations of 'RDF' data,
             including 'rdfxml', 'turtle', 'nquads', 'ntriples', and 'json-ld';
             creating new 'RDF' graphs, and performing graph queries using 'SPARQL'.
             This package wraps the low level 'redland' R package which
             provides direct bindings to the 'redland' C library.  Additionally,
             the package supports the newer and more developer friendly
             'JSON-LD' format through the 'jsonld' package. The package
             interface takes inspiration from the Python 'rdflib' library.
Functions to interact with the 'Google DoubleClick for Publishers (DFP)' API 
    <https://developers.google.com/ad-manager/api/start> (recently renamed to 
    'Google Ad Manager'). This package is automatically compiled from the API WSDL 
    (Web Service Description Language) files to dictate how the API is structured. 
    Theoretically, all API actions are possible using this package; however, care 
    must be taken to format the inputs correctly and parse the outputs correctly. 
    Please see the 'Google Ad Manager' API reference <https://developers.google.com/ad-manager/api/rel_notes> 
    and this package's website <https://stevenmmortimer.github.io/rdfp/> for more 
    information, documentation, and examples.
Different Tensor Factorization techniques suitable for RDF Tensors.
            RDF Tensors are three-mode-tensors, binary tensors and usually very sparse.
            Currently implemented methods are 
              'RESCAL' Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel (2012) <doi:10.1145/2187836.2187874>, 
              'NMU' Daniel D. Lee and H. Sebastian Seung (1999) <doi:10.1038/44565>,
              'ALS', Alternating Least Squares
              'parCube' Papalexakis, Evangelos, C. Faloutsos, and N. Sidiropoulos (2012) <doi:10.1007/978-3-642-33460-3_39>,  
              'CP_APR' C. Chi and T. G. Kolda (2012) <doi:10.1137/110859063>.
            The code is mostly converted from MATLAB and Python implementations of these methods.
            The package also contains functions to get Boolean (Binary) transformation of the real-number-decompositions.
            These methods also are for general tensors, so with few modifications they can be applied for other types of tensor.
Provides a client for (1) querying the DHS API for survey indicators
  and metadata (<https://api.dhsprogram.com/#/index.html>), (2) identifying surveys
  and datasets for analysis, (3) downloading survey datasets from the DHS website,
  (4) loading datasets and associate metadata into R, and (5) extracting variables
  and combining datasets for pooled analysis.
Methods for calculation and visualization of the Repertoire
    Dissimilarity Index. Citation: Bolen and Rubelt, et al (2017) 
    <doi:10.1186/s12859-017-1556-5>.
A client library for 'The Guardian' (https://www.guardian.com/)
    and their API, this package allows users to search for Guardian articles and
    retrieve both the content and metadata.
A collection of functions to simulate
  dice rolls and the like. In particular, experiments and exercises can
  be performed looking at combinations and permutations of values in dice
  rolls and coin flips, together with the corresponding frequencies of
  occurrences. When applying each function, the user has to input the
  number of times (rolls, flips) to toss the dice. Needless to say, the more
  the tosses, the more the frequencies approximate the actual probabilities.
  Moreover, the package provides functions to generate non-transitive sets
  of dice (like Efron's) and to check whether a given set of dice is non-transitive
  with given probability.
The package has many function that helps to perform
        various quality check on the data.It basically provides many
        function that helps in performing Extrapolative data analysis.
The 'RDieHarder' package provides an R interface to 
 the 'DieHarder' suite of random number generators and tests that 
 was developed by Robert G. Brown and David Bauer, extending 
 earlier work by George Marsaglia and others. The 'DieHarder'
 library is included, but if a version is already installed
 it will be used instead.
We provide linear and nonlinear dimension reduction techniques.
	Intrinsic dimension estimation methods for exploratory analysis are also provided.
    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 multi-disciplinary overview.
A common framework for calculating distance matrices.
Distance-sampling is a popular method for estimating density and 
  abundance of organisms in ecology. Rdistance contains routines that  
  assist with analysis of
  distance-sampling data collected on point or line transects. 
  Distance models are specified using regression-like formula (similar 
  to lm, glm, etc.). Abundance routines
  perform automated bootstrapping and automated detection-function 
  selection. Overall (study area) and site-level (transect or point) 
  abundance estimates are available. A large suite of classical, 
  parametric detection functions are 
  included along with some uncommon parametric 
  functions (e.g., Gamma, negative exponential) and non-parametric
  smoothed distance functions. Custom (user-defined) detection functions
  are easily implemented (see vignette). 
  The help files and vignettes have been 
  vetted by multiple authors and tested in workshop 
  settings.
Provides a framework for the measurement and partitioning of
    the (similarity-sensitive) biodiversity of a metacommunity and its
    constituent subcommunities. Richard Reeve, et al. (2016) 
    <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. See Cattaneo, Titiunik and Vazquez-Bare (2016) <https://sites.google.com/site/rdpackages/rdlocrand/Cattaneo-Titiunik-VazquezBare_2016_Stata.pdf> for further methodological details.
Imports real-time thermo cycler (qPCR) data from Real-time PCR
    Data Markup Language (RDML) and transforms to the appropriate formats of
    the 'qpcR' and 'chipPCR' packages. Contains a dendrogram visualization 
    for the structure of RDML object and GUI for RDML editing.
The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. The 'rdmulti' package provides tools to analyze RD designs with multiple cutoffs or scores: rdmc() estimates pooled and cutoff specific effects for multi-cutoff designs, rdmcplot() draws RD plots for multi-cutoff designs and rdms() estimates effects in cumulative cutoffs or multi-score designs. See Cattaneo, Titiunik and Vazquez-Bare (2018) <https://sites.google.com/site/rdpackages/rdmulti/Cattaneo-Titiunik-VazquezBare_2018_rdmulti.pdf> for further methodological details.
Comprehensive toolkit for generating various numerical                                 representation schemes of DNA sequence. The descriptors and similarity
    scores included are extensively used in bioinformatics and chemogenomics.
A wrapper for the 'Deutsche Nationalbibliothek (German National
    Library) API', available at <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.
Extends tools::Rd2txt() by adding customisable text and colour formatting to R documentation 
    contents. If used from a terminal, output will be displayed via file.show() otherwise contents
    will be printed in sections. Also provides stand-in replacements for ?() and help().
Wraps around the default help functionality in R. Instead of plain documentation files, documentation will show up as it does on <https://www.rdocumentation.org>, a platform that shows R documentation from 'CRAN', 'GitHub' and 'Bioconductor', together with informative stats to assess the package quality.
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 R documentation objects,
    including functions reprompt() and ereprompt() for updating 'Rd'
    documentation for functions, methods and classes; 'Rd' macros for
    citations and import of references from 'bibtex' files for use in
    'Rd' files and 'roxygen2' comments; 'Rd' macros for evaluating and
    inserting snippets of 'R' code and the results of its evaluation or
    creating graphics on the fly; and many functions for manipulation of
    references and Rd files.
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.
The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. The 'rdpower' package provides tools to perform power and sample size calculations in RD designs: rdpower() calculates the power of an RD design and rdsampsi() calculates the required sample size to achieve a desired power. See Cattaneo, Titiunik and Vazquez-Bare (2018) <https://sites.google.com/site/rdpackages/rdpower/Cattaneo-Titiunik-VazquezBare_2018_Stata.pdf> for further methodological details.
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 provides data scientists an intuitive interface to execute business rules on datasets for the purpose of analysis or designing intelligent systems, while leveraging the Drools rule engine. Rules written in DRL format accepted natively by Drools can also be executed through an R interface. 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.
Defines functions that can be used to collect provenance as
  an R script executes or during a console session. The output is a text 
  file in PROV-JSON format.
Implementation of density tracking by quadrature (DTQ) algorithms for stochastic differential equations (SDEs).  DTQ algorithms numerically compute the density function of the solution of an SDE with user-specified drift and diffusion functions.  The calculation does not require generation of sample paths, but instead proceeds in a deterministic fashion by repeatedly applying quadrature to the Chapman-Kolmogorov equation associated with a discrete-time approximation of the SDE.  The DTQ algorithm is provably convergent.  For several practical problems of interest, we have found the DTQ algorithm to be fast, accurate, and easy to use.
Functions for connecting to and interfacing with an 'Arduino' or similar device. Functionality includes uploading of sketches, setting and reading digital and analog pins, and rudimentary servo control. This project is not affiliated with the 'Arduino' company, <https://www.arduino.cc/>.
Handle climate data from the 'DWD' ('Deutscher Wetterdienst', see 
             <https://www.dwd.de/EN/climate_environment/cdc/cdc.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.
Building interactive web applications with R is incredibly easy
  with 'shiny'. Behind the scenes, 'shiny' builds a reactive graph that can
  quickly become intertwined and difficult to debug. 'reactlog' 
  (Schloerke 2019) <doi:10.5281/zenodo.2591517> provides a visual insight into
  that black box of 'shiny' reactivity by constructing a directed dependency
  graph of the application's reactive state at any time point in a reactive
  recording.
Make it easy to use 'React' in R with 'htmlwidget' scaffolds,
              helper dependency functions, an embedded 'Babel' 'transpiler',
              and examples.
Routines for developing models that describe reaction and advective-diffusive transport in one, two or three dimensions.
  Includes transport routines in porous media, in estuaries, and in bodies with variable shape.
Functions for reading and decompressing the DBC (compressed DBF) files. Please note that this is the file format used by the Brazilian Ministry of Health (DATASUS) to publish healthcare datasets. It is not related to the FoxPro or CANdb DBC file formats.
Opens complete record(s) with .gb extension from the NCBI/GenBank Nucleotide database and returns a list containing shaped record(s). These kind of files contains detailed records of DNA samples (locus, organism, type of sequence, source of the sequence...). An example of record can be found at <https://www.ncbi.nlm.nih.gov/nuccore/HE799070>.
Loads Axon Binary Files (both 'ABF' and 'ABF2') created by Axon Instruments/Molecular Devices software such as 'pClamp'.
Downloads, imports, and tidies time series data from the 
    Australian Bureau of Statistics <https://www.abs.gov.au/>.
Identifies and reads Windows BMP, JPEG, PNG, and TIFF format bitmap
    images. Identification defaults to the use of the magic number embedded in
    the file rather than the file extension. Reading of JPEG and PNG image
    depends on libjpg and libpng libraries. See file INSTALL for details if
    necessary.
Reads data files acquired by Bruker Daltonics' matrix-assisted
    laser desorption/ionization-time-of-flight mass spectrometer of the *flex
    series.
Combine multiple data files from a common directory.
    The data files will be read into R and bound together, creating a
    single large data.frame. A general function is provided along with
    a specific function for data that was collected using the open-source
    experiment builder 'OpenSesame' <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.
Providing just one primary function, 'readit' uses a set of reasonable
    heuristics to apply the appropriate reader function to the given file path.
    As long as the data file has an extension, and the data is (or can be
    coerced to be) rectangular, readit() can probably read it.
Import data written in the JCAMP-DX format. This is an instrument-independent format used in the field of spectroscopy. Examples include IR, NMR, and Raman spectroscopy. See the vignette for background and supported formats.  The official JCAMP-DX site is <http://www.jcamp-dx.org/>.
Functions for reading data sets in different formats
  for testing machine learning tools are provided. This allows to run
  a loop over several data sets in their original form, for example
  if they are downloaded from UCI Machine Learning Repository.
  The data are not part of the package and have to be downloaded
  separately.
You can use the function Read.mnist() to read data and arrange them properly from MNIST dataset (the open handwriting digit database <http://yann.lecun.com/exdb/mnist/>). With this package, you can conveniently get all of necessary informations and then immediately start to check whether your machine learning algorithm works well. It can automatically recognize the type of dataset and returns the informations in corresponding structure.
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.
A convenient way to read fixed-width ASCII polling datasets from providers like the Roper Center <https://ropercenter.cornell.edu>.
Read Statistical Data and Metadata Exchange (SDMX) XML data. 
    This the main transmission format used in official statistics. Data can be imported from
    local SDMX-ML files or a SDMX web-service and will be read in 'as is' into a dataframe object.
    The 'RapidXML' C++ library <http://rapidxml.sourceforge.net> is used to parse the XML data.
Function to read and write the 'Stata' file format.
Functions for importing and handling text files and formatted text
    files with additional meta-data, such including '.csv', '.tab', '.json', '.xml',
    '.html', '.pdf', '.doc', '.docx', '.rtf', '.xls', '.xlsx', and others.
Reads and writes data frames to 'SQLite' databases while 
  preserving time zones (for POSIXct columns), 
  projections (for 'sfc' columns), units (for 'units' columns), 
  levels (for factors and ordered factors) and classes for 
  logical, Date and 'hms' columns.
  It also logs changes to tables and provides more informative error messages.
Import excel files into R. Supports '.xls' via the
    embedded 'libxls' C library <https://github.com/libxls/libxls> and
    '.xlsx' via the embedded 'RapidXML' C++ library
    <http://rapidxml.sourceforge.net>.  Works on Windows, Mac and Linux
    without external dependencies.
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, Green, and Karl (2018) <doi:10.32614/RJ-2018-033> and Broatch and Lohr (2012) <doi:10.3102/1076998610396900>, with normally distributed test scores and a binary outcome indicator. A pseudo-likelihood approach, Wolfinger (1993) <doi:10.1080/00949659308811554>, is used for the estimation of this joint generalized linear mixed model.  The inner loop of the pseudo-likelihood routine (estimation of a linear mixed model) occurs in the framework of the EM algorithm presented by Karl, Yang, and Lohr (2013) <DOI:10.1016/j.csda.2012.10.004>. This material is based upon work supported by the National Science Foundation under grants DRL-1336027 and DRL-1336265.
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, latent class analysis 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.
Functions to assist in performing probabilistic record linkage and
    deduplication: generating pairs, comparing records, em-algorithm for
    estimating m- and u-probabilities, forcing one-to-one matching. Can also be
    used for pre- and post-processing for machine learning methods for record
    linkage.
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 (Panse C. (2018) <doi:10.18637/jss.v086.c01>). This algorithm
  draws maps according to a given statistical value (e.g., election results,
  population or epidemiological data). The basic idea of the RecMap algorithm is
  that each map region (e.g., different countries) is represented by a
  rectangle. The area of each rectangle represents the statistical value given
  as input (maintain zero cartographic error). Documentation about the usage
  of the recmap algorithm is provided by a vignette included in this package.
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.
Implements solutions to canonical models of Economics such as Monopoly Profit Maximization, Cournot's Duopoly, Solow (1956, <doi:10.2307/1884513>) growth model and Mankiw, Romer and Weil (1992, <doi:10.2307/2118477>) growth model.
Functions to reconstruct sessions from web log or other user trace data
             and calculate various metrics around them, producing tabular,
             output that is compatible with 'dplyr' or 'data.table' centered processes.
A lightweight toolkit to validate new observations when computing
    their predictions with a predictive model. The validation process 
    consists of two steps: (1) record relevant statistics and meta data of the
    variables in the original training data for the predictive model and
    (2) use these data to run a set of basic validation tests on the new set of 
    observations.
Provides functions for linking and 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;
    <https://projectredcap.org>) is
    a web application for building and managing online surveys and databases
    developed at Vanderbilt University.  The API allows users to access data
    and project meta data (such as the data dictionary) from the web
    programmatically.  The 'redcapAPI' package facilitates the process of
    accessing data with options to prepare an analysis-ready data set
    consistent with the definitions in a database's data dictionary.
Encapsulates functions to streamline calls from R to the REDCap
    API.  REDCap (Research Electronic Data CAPture) is a web application for
    building and managing online surveys and databases developed at Vanderbilt
    University.  The Application Programming Interface (API) offers an avenue
    to access and modify data programmatically, improving the capacity for
    literate and reproducible programming.
A collection of tools for extracting structured data from <https://www.reddit.com/>.
Computes quality control for daily precipitation datasets, 
    reconstructs the original series by estimating precipitation in missing values, 
    creates new series in a specified pair of coordinates and creates grids.
Standard and extensible Eddy-Covariance data post-processing 
  (Wutzler et al. (2018) <doi:10.5194/bg-15-5015-2018>)
  includes  
  uStar-filtering, gap-filling, and flux-partitioning.
  The Eddy-Covariance (EC)  micrometeorological technique quantifies continuous 
  exchange fluxes of gases, energy, and momentum between an ecosystem and the atmosphere.
  It is important for understanding ecosystem dynamics and upscaling exchange fluxes.
  (Aubinet et al. (2012) <doi:10.1007/978-94-007-2351-1>).
  This package inputs pre-processed (half-)hourly data and supports further processing. 
  First, a quality-check and filtering is performed based on the relationship between 
  measured flux and friction
  velocity (uStar) to discard biased data 
  (Papale et al. (2006) <doi:10.5194/bg-3-571-2006>).
  Second, gaps in the data are filled based on information from environmental conditions
  (Reichstein et al. (2005) <doi:10.1111/j.1365-2486.2005.001002.x>).
  Third, the net flux of carbon dioxide is partitioned
  into its gross fluxes in and out of the ecosystem by night-time 
  based and day-time based approaches
  (Lasslop et al. (2010) <doi:10.1111/j.1365-2486.2009.02041.x>).
Extension to 'REddyProc' that allows reading data from netCDF files.
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
    <https://imai.fas.harvard.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'.
Efficiently upload data into an Amazon Redshift database using the approach recommended by Amazon <https://aws.amazon.com/es/redshift/>.
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.
Implements an objective Bayes intrinsic conditional autoregressive 
    prior. This model provides an objective Bayesian approach 
    for modeling spatially correlated areal data using an intrinsic conditional autoregressive prior on a vector of 
    spatial random effects.
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 functionalities for importing and managing downloaded genome annotation data from 'Ensembl' genome browser (European Bioinformatics Institute, <https://www.ensembl.org>) and from 'UCSC' genome browser (University of California, Santa Cruz, <https://genome.ucsc.edu/>) 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>.
These functions take a character vector as input, identify and 
  cluster similar values, and then merge clusters together so their values 
  become identical. The functions are an implementation of the key collision 
  and ngram fingerprint algorithms from the open source tool Open Refine 
  <http://openrefine.org/>. More info on key collision and ngram fingerprint 
  can be found here <https://github.com/OpenRefine/OpenRefine/wiki/Clustering-In-Depth>.
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.
Access the 'Refuge' API, a web-application for locating trans and 
    intersex-friendly restrooms, including unisex and accessible restrooms. 
    Includes data on the location of restrooms, along with directions, 
    comments, user ratings and amenities. Coverage is global, but data is 
    most comprehensive in the United States.
    See <https://www.refugerestrooms.org/api/docs/> for full API documentation.
Methods for regression for functional
    data, including function-on-scalar, scalar-on-function, and
    function-on-function regression. Some of the functions are applicable to
    image data.
Interactive plotting for functional data analyses.
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.
'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 among 
    genomic features. This package provides procedures of network-based variable selection 
    for generalized linear models (Ren et al. (2017) <doi:10.1186/s12863-017-0495-5> and 
    Ren et al. (2019) <doi:10.1002/gepi.22194>). Two recent additions are the robust network 
    regularization for the survival response and the network regularization for continuous 
    response. Functions for other regularization methods will be included in the forthcoming 
    upgraded versions.
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.
Perform a supervised data analysis on a database through a 'shiny' graphical interface. It includes methods such as linear regression, penalized regression, k-nearest neighbors, decision trees, ada boosting, extreme gradient boosting, random forest, neural networks, deep learning and support vector machines.
Compiling regression results into a publishable format, conducting post-hoc hypothesis testing, and plotting moderating effects (the effect of X on Y becomes stronger/weaker as Z increases).
Performs Random Subspace Method (RSM) for high-dimensional linear regression to obtain variable importance measures. The final model is chosen based on validation set or Generalized Information Criterion.
Implementation of the methods described in the paper with the above title: Langsrud, Ø. (2019) <doi:10.1007/s11222-018-9848-9>. Open view-only version at <https://rdcu.be/bfeWQ>. The package can be used to generate synthetic or hybrid continuous microdata, and the relationship to the original data can be controlled in several ways. A function for replacing suppressed tabular cell frequencies with decimal numbers is included.
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. Also contains a function to perform exploratory mediation (XMed).
Computes linear Bayesian spectral estimates from multirate
	data for second-order stationary time series. Provides credible
	intervals and methods for plotting various spectral estimates.
For a sequence of event occurence times, we are interested in
  finding subsequences in it that are too "regular". We define regular as being
  significantly different from a homogeneous Poisson process. The departure from
  the Poisson process is measured using a L1 distance. See Di and Perlman 2007
  for more details.
Functions for unary and binary regression tests
Tools for linear, nonlinear and nonparametric regression
             and classification.  Novel graphical methods for assessment 
             of parametric models using nonparametric methods. One 
             vs. All and All vs. All multiclass classification, optional
             class probabilities adjustment.  Nonparametric regression 
             (k-NN) for general dimension, local-linear option.  Nonlinear 
             regression with Eickert-White method for dealing with 
             heteroscedasticity.  Utilities for converting time series
             to rectangular form.  Utilities for conversion between
             factors and indicator variables.  Some code related to
             "Statistical Regression and Classification: from Linear
             Models to Machine Learning", N. Matloff, 2017, CRC,
             ISBN 9781498710916.
It performs regularized simultaneous component based data
    integration for multiblock data.
Population genetic data such as 'Single Nucleotide
        Polymorphisms' (SNPs) is often used to identify genomic regions
        that have been under recent natural or artificial selection
        and might provide clues about the molecular mechanisms of adaptation. 
        One approach, the concept of an 'Extended Haplotype Homozygosity' (EHH), 
        introduced by (Sabeti 2002) <doi:10.1038/nature01140>, has given rise to 
        several statistics designed for whole genome scans. 
        The package provides functions to compute three of these,
        namely: 'iHS' (Voight 2006) <doi:10.1371/journal.pbio.0040072> for
        detecting positive or 'Darwinian' selection within a single population as well as
        'Rsb' (Tang 2007) <doi:10.1371/journal.pbio.0050171> and 
        'XP-EHH' (Sabeti 2007) <doi:10.1038/nature06250>, targeted
        at differential selection between two populations. 
        Various plotting functions are also included to facilitate
        visualization and interpretation of these statistics. 
        Due to changes in the API, albeit mostly minor, 
        this version is not compatible with versions 2.0.X.
Contains example data for the 'rehh' package.
Facilitates replication of Twitter-based research by handling
    common programming tasks needed when downloading tweets.  Specifically, it ensures a 
    user does not exceed Twitter’s rate limits, and it saves tweets in moderately sized files.  
    While a user could perform these tasks in their own code, doing so may be beyond the 
    capabilities of many users.
Contains the REIDS
  model presented in Van Moerbeke et al
  (2017) <doi:10.1186/s12859-017-1687-8> for the detection of alternative splicing.  The method is extended by incorporating junction information for the
  assessment of alternative splicing. The vignette introduces the model and shows an example work flow.
Traditional risk prediction only utilizes baseline factors known 
    to be associated with the disease. Given that longitudinal information are
    routinely measured and documented for patients, it is worthwhile to make
    full use of these data. The available longitudinal biomarker data will 
    likely improve prediction. However, repeated biomarker collection could be 
    costly and inconvenient, and risk prediction for patients at a later time 
    could delay necessary medical decisions. Thus, there is a trade-off between
    high quality prediction and cost. This package implements a cost-effective 
    statistical procedure that recursively incorporates comprehensive 
    longitudinal information into the risk prediction model, taking into 
    account the cost of delaying the decision to a follow-up time when more 
    information is available. The statistical methods are described in the 
    following paper: Pan, Y., Laber, E., Smith, M., Zhao, Y. (2018). Reinforced 
    risk prediction with budget constraint: application to electronic health 
    records data. Manuscript submitted for publication.
Implements reinforcement learning environments and algorithms as described in Sutton & Barto (1998, ISBN:0262193981).
    The Q-Learning algorithm can be used with function approximation, 
    eligibility traces (Singh & Sutton (1996) <doi:10.1007/BF00114726>) 
    and experience replay (Mnih et al. (2013) <arXiv:1312.5602>).
Performs model-free reinforcement learning in R. This implementation enables the learning
    of an optimal policy based on sample sequences consisting of states, actions and rewards. In 
    addition, it supplies multiple predefined reinforcement learning algorithms, such as experience 
    replay. Methodological details can be found in Sutton and Barto (1998) <ISBN:0262039249>.
Functions from the book "Reinsurance: Actuarial and Statistical Aspects" (2017) by Hansjoerg Albrecher, Jan Beirlant and Jef Teugels <http://www.wiley.com/WileyCDA/WileyTitle/productCd-0470772689.html>.
Search R files for not installed packages and run install.packages.
Application of reinsurance treaties to claims portfolios. 
            The package creates a class Claims whose objective is to 
            store claims and premiums, on which different treaties can be applied.
            A statistical analysis can then be applied to measure the impact of
            reinsurance, producing a table or graphical output. This package can
            be used for estimating the impact of reinsurance on several portfolios
            or for pricing treaties through statistical analysis. Documentation
            for the implemented methods can be found in "Reinsurance: Actuarial
            and Statistical Aspects" by Hansjöerg Albrecher, Jan Beirlant,
            Jozef L. Teugels (2017, ISBN: 978-0-470-77268-3) and 
            "REINSURANCE: A Basic Guide to Facultative and Treaty Reinsurance"
            by Munich Re (2010) <https://www.munichre.com/site/mram/get/documents_E96160999/mram/assetpool.mr_america/PDFs/3_Publications/reinsurance_basic_guide.pdf>.
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.
Provides several metrics for assessing relative importance in linear models. These can be printed, plotted and bootstrapped. The recommended metric is lmg, which provides a decomposition of the model explained variance into non-negative contributions. There is a version of this package available that additionally provides a new and also recommended metric called pmvd. If you are a non-US user, you can download this extended version from Ulrike Groempings web site.
Functions to safely map from a vector of keys to a vector of values, determine properties of a given relation, or ensure a relation conforms to a given type, such as many-to-many, one-to-many, injective, surjective, or bijective. Permits default return values for use similar to a vectorised switch statement, as well as safely handling large vectors, NAs, and duplicate mappings.
Inference of relatedness coefficients from a bi-allelic genotype matrix using a Maximum Likelihood estimation, Laporte, F., Charcosset, A. and Mary-Huard, T. (2017) <doi:10.1111/biom.12634>.
Data structures and algorithms for k-ary relations with
  arbitrary domains, featuring relational algebra, predicate functions,
  and fitters for consensus relations.
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.
The purpose of 'Relexon' is to allow R users to pull data from the UK's Balancing Mechanism Reporting Service, otherwise known as 'Elexon', using the 'BMRS' API <https://www.bmreports.com/bmrs/?q=help/about-us>. This can range from different types of electricity generation, how much electricity is used in a 5-minute period, imports, temperatures and forecasts. Users will require an 'Elexon' key, which are freely available by registering at <https://www.elexonportal.co.uk>.
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.
Contains functions for analysing relative survival data, including nonparametric estimators of net (marginal relative) survival, relative survival ratio, crude mortality, methods for fitting  and checking additive and multiplicative regression models, transformation approach, methods for dealing with population mortality tables.
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.
An 'RStudio' addin providing shortcuts for writing in 'Markdown'. This package provides a series of 
             functions that allow the user to be more efficient when using 'Markdown'. For example, you can select 
             a word, and put it in bold or in italics, or change the alignment of elements inside you Rmd. The idea
             is to map all the functionalities from 'remedy' on keyboard shortcuts, so that it provides an interface 
             close to what you can find in any other text editor.
Insert/extract text "reminders" into/from function source code 
    comments or as the "comment" attribute of any object.  
    The former can be handy in development as reminders of e.g. argument
    requirements, expected objects in the calling environment, required options
    settings, etc. The latter can be used to provide information of the object and 
    as simple manual "tooltips" for users, among other things.
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 client/server computing with R, controlling
    a remote R session (the server) from a local one (the client).  Simply set
    up a server (see package vignette for more details) and connect to it from
    your local R session ('RStudio', terminal, etc).  The client/server
    framework is a custom 'REPL' and runs entirely in your R session without the
    need for installing a custom environment on your system.  Network
    communication is handled by the 'ZeroMQ' library by way of the 'pbdZMQ'
    package.
Download and install R packages stored in 'GitHub',
    'BitBucket', or plain 'subversion' or 'git' repositories. This package
    provides 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> heteroscedasticity approach, Park and Gupta's (2012) <doi:10.1287/mksc.1120.0718> joint estimation method 
    that uses Gaussian copula and Kim and Frees's (2007) <doi:10.1007/s11336-007-9008-1> multilevel generalized
    method of moment approach that deals with endogeneity in a multilevel setting.
    These are statistical techniques to address the endogeneity problem where no external instrumental variables are needed.
    Note that with version 2.0.0 sweeping changes were introduced which greatly improve functionality and usability but break backwards compatibility.
Peaks Over Threshold (POT) or 'methode du renouvellement'. The distribution for the exceedances can be chosen, and heterogeneous data (including historical data or block data) can be used in a Maximum-Likelihood framework.
Supports calculations and visualization for renewable power systems and the environment. Analysis and graphical tools for DC and AC circuits and their use in electric power systems. Analysis and graphical tools for thermodynamic cycles and heat engines, supporting efficiency calculations in coal-fired power plants, gas-fired power plants. Calculations of carbon emissions and atmospheric CO2 dynamics. Analysis of power flow and demand for the grid, as well as power models for microgrids and off-grid systems. Provides resource and power generation for hydro power, wind power, and solar power.
Provides an R interface to the NCBI's 'EUtils' API, 
    allowing users to search databases like 'GenBank' 
    <https://www.ncbi.nlm.nih.gov/genbank/> and 'PubMed' 
    <https://www.ncbi.nlm.nih.gov/pubmed/>, process the 
    results of those searches and pull data into their R sessions.
Provides a general routine, envMU, which allows  estimation of the M envelope of span(U) given root n consistent estimators of M and U. The routine envMU does not presume a model.  This package implements response envelopes,  partial response envelopes,  envelopes in the predictor space,  heteroscedastic envelopes,  simultaneous envelopes,  scaled response envelopes,  scaled envelopes in the predictor space,  groupwise envelopes, weighted envelopes,  envelopes in logistic regression and envelopes in Poisson regression. For each of these model-based routines the package provides inference tools including bootstrap, cross validation, estimation and prediction, hypothesis testing on coefficients are included except for weighted envelopes. Tools for selection of dimension include AIC, BIC and likelihood ratio testing.   Background is available at Cook, R. D., Forzani, L. and Su, Z. (2016) <doi:10.1016/j.jmva.2016.05.006>. Optimization is based on a clockwise coordinate descent algorithm.
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.
Various functions to fit models for non-normal repeated
    measurements, such as Binary Random Effects Models with Two Levels of Nesting,
    Bivariate Beta-binomial Regression Models, Marginal Bivariate Binomial Regression Models,
    Cormack capture-recapture models, Continuous-time Hidden Markov Chain Models, 
    Discrete-time Hidden Markov Chain Models,
    Changepoint Location Models using a Continuous-time Two-state Hidden Markov Chain,
    generalized nonlinear autoregression models, multivariate Gaussian copula models,
    generalized non-linear mixed models with one random effect,  
    generalized non-linear mixed models using h-likelihood for one random effect, 
    Repeated Measurements Models for Counts with Frailty or Serial Dependence,
    Repeated Measurements Models for Continuous Variables with Frailty or Serial Dependence,
    Ordinal Random Effects Models with Dropouts, marginal homogeneity models for square
    contingency tables, correlated negative binomial models with Kalman update.
    References include Lindey's text books, 
    JK Lindsey (2001) <isbn-10:0198508123> and JK Lindsey (1999) <isbn-10:0198505590>.
Global tests for expression data of high-dimensional sets of
    molecular features.
Utilities for accessing RePEc (Research Papers in Economics)
    through a RESTful API. You can request a code and get detailed
    information at the following page: <https://ideas.repec.org/api.html>.
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>).
Create presentations and display them inside the R 'REPL'
    (Read-Eval-Print loop), aka the R console. Presentations can be written in
    'RMarkdown' or any other text format. A set of convenient navigation options
    as well as code evaluation during a presentation is provided. It is great
    for tech talks with live coding examples and tutorials. While this is not a
    replacement for standard presentation formats, it's old-school looks might
    just be what sets it apart. This project has been inspired by the
    'REPLesent' project for presentations in the 'Scala' 'REPL'.
For a multisite replication project, computes 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.
Performs comparative bioavailability-calculations for the EMA's Average Bioequivalence
  with Expanding Limits (ABEL). Implemented are 'Method A' and 'Method B', detection of outliers.
  If the design allows, assessment of the empiric Type I Error and iteratively adjusting alpha to
  control the consumer risk.
  Average Bioequivalence (ABE) - optionally with tighter (EMA: NTIDs) or wider limits (GCC: Cmax) -
  is implemented as well.
Test for effects of  both individual factors and their interaction on  replicated spatial patterns in a two factorial design, as explained in Ramon et al. (2016) <doi:10.1111/ecog.01848>.
Allows for the computation of a prior predictive p-value to test replication of relevant features of original studies. Relevant features are captured in informative hypotheses. The package also allows for the computation of power. The statistical underpinnings are described in Zondervan-Zwijnenburg (2019) <doi:10.31234/osf.io/uvh5s>.
A common problem faced by journal reviewers and authors is the question of
     whether the results of a replication study are consistent with the original
     published study. One solution to this problem is to examine the effect size
     from the original study and generate the range of effect sizes that could
     reasonably be obtained (due to random sampling) in a replication attempt
     (i.e., calculate a replication interval). If a replication effect size falls
     outside the replication interval, then that effect likely did not occur
     due to the effects of sampling error alone. Alternatively, if a replication
     effect size falls within the replication interval, then the replication
     effect could have reasonably occurred due to the effects of sampling error
     alone. This package has functions that calculate the replication interval for 
     the correlation (i.e., r), standardized mean difference (i.e., d-value), and mean. 
     The calculations used in version 2.0.0 and onward differ from past calculations 
     due to feedback during the journal review process. The new calculations allow 
     for a more precise interpretation of the replication interval.
Create, Plot and Compare Replication Timing Profiles. The method is described in Muller et al., (2014) <doi: 10.1093/nar/gkt878>.
Patches to use 'dplyr' on 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).  Due to the fluctuating nature of 'dplyr'/'dbplyr'/'rlang' 'APIs' this package
   is going into maintenance mode.  Most of the 'replyr' functions are already 
   done better by one of the non-monolithic replacement packages: 'wrapr', 'seplyr', 'rquery',
   or 'cdata'.
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.
Generates a project and repo for easy initialization of a GitHub repo for R workshops. The repo includes a README with instructions to ensure that all users have the needed packages, an 'RStudio' project with the right directories and the proper data. The repo can then be used for hosting code taught during the workshop.
Fits linear models to repeated ordinal scores using GEE methodology.
Provides a system for reporting messages, which provides certain useful features over the standard R system, such as the incorporation of output consolidation, message filtering, assertions, expression substitution, automatic generation of stack traces for debugging, and conditional reporting based on the current "output level".
Provides an easy way to report the results of regression analysis, including:
    1. Proportional hazards regression from function 'coxph' of package 'survival';
    2. Conditional logistic regression from function 'clogit' of package 'survival';
    3. Ordered logistic regression from function 'polr' of package 'MASS';
    4. Binary logistic regression from function 'glm' of package 'stats';
    5. Linear regression from function 'lm' of package 'stats';
    6. Risk regression model for survival analysis with competing risks from function 'FGR' of package 'riskRegression';
    7. Multilevel model from function 'lme' of package 'nlme'.
Provides an easy way to report the results of ROC analysis, including:
  1. an ROC curve. 2. the value of Cutoff, AUC (Area Under Curve), ACC (accuracy),
  SEN (sensitivity), SPE (specificity),
  PLR (positive likelihood ratio), NLR (negative likelihood ratio),
  PPV (positive predictive value), NPV (negative predictive value).
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 <https://stackoverflow.com> and
  <https://github.com>, or in email. The user's clipboard is the default source
  of input code and the default target for rendered output. 'reprex' also
  extracts clean, runnable R code from various common formats, such as
  copy/paste from an R session.
Includes data analysis functions (e.g., to calculate effect sizes and 95% Confidence Intervals (CI) on Standardised Effect Sizes (d) for 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.
Collection of high-level, robust, machine- and OS-independent tools
    for making deeply reproducible and reusable content in R.
    This includes light weight package management (similar to 'packrat' and
    'checkpoint', but more flexible, lightweight, simpler and less tested than both), 
    tools for caching, downloading and verifying or writing checksums, post-processing 
    of common spatial datasets, and accessing GitHub repositories. 
    Some features are still under active development.
Transform coordinates via 'PROJ' using the library directly, by 
 wrapping the 'proj4' package. The 'reproj' function handles the need for
 radian units for either source or target and allows removing an explicit 
 source definition in methods that extend the generic. The 'PROJ' library is available 
 at <https://proj4.org/>.
Predicting regulatory DNA elements based on epigenomic signatures. This package is more of a set of building blocks than a direct solution. REPTILE regulatory prediction pipeline is built on this R package. See <https://github.com/yupenghe/REPTILE> for more information.
Recursive lists in the form of R objects, 'JSON',
    and 'XML', for use in teaching and examples. Examples include color
    palettes, Game of Thrones characters, 'GitHub' users and repositories,
    music collections, and entities from the Star Wars universe. Data from
    the 'gapminder' package is also included, as a simple data frame and
    in nested and split forms.
In order to facilitate parsing of http requests and creating 
    appropriate responses this package provides two classes to handle a lot of
    the housekeeping involved in working with http exchanges. The infrastructure
    builds upon the 'rook' specification and is thus well suited to be combined
    with 'httpuv' based web servers.
This package contains the function run.eqs() which calls
        an EQS script file, executes the EQS estimation, and, finally,
        imports the results as R objects. These two steps can be
        performed separately: call.eqs() calls and executes EQS,
        whereas read.eqs() imports existing EQS outputs as objects into
        R. It requires EQS 6.2 (build 98 or higher).
High level and easy 'HTTP' client for 'R'. Provides functions for
    building 'HTTP' queries, including query parameters, body requests, headers,
    authentication, and more.
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>.
Contains three functions that access
    environmental data from any 'ERDDAP' data web service. The rxtracto() function extracts
    data along a trajectory for a given "radius" around the point. The
    rxtracto_3D() function extracts data in a box. The rxtractogon() function
    extracts data in a polygon. All of those three function use the 'rerddap' package
    to extract the data, and should work with any 'ERDDAP' server.
    There are also two functions, plotBBox() and plotTrack() that use the 'plotdap'
    package to simplify the creation of maps of the data.
Executes a post-rotation algorithm that REorders and/or REflects FACTors (REREFACT) for each replication of a simulation study with exploratory factor analysis.
R-RerF (aka Randomer Forest (RerF) or Random Projection
  Forests) 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.
Fits a linear excess relative risk model by maximum likelihood, possibly including several variables and allowing for lagged exposures. Allow time dependent covariates.
Bootstrap, permutation tests, and other resampling functions,
	featuring easy-to-use syntax.
Package of data sets from "Mathematical Statistics
    with Resampling in R" (1st Ed. 2011, 2nd Ed. 2018) by Laura Chihara and Tim Hesterberg.
This package is the R implementation of the Autoencoder-based Residual Deep Network that is based on this paper (https://arxiv.org/abs/1812.11262).
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 using matrix methods.
Resource Selection (Probability) Functions
  for use-availability wildlife data
  based on weighted distributions as described in
  Lele and Keim (2006) <doi:10.1890/0012-9658(2006)87%5B3021:WDAEOR%5D2.0.CO;2>,
  Lele (2009) <doi:10.2193/2007-535>,
  and Solymos & Lele (2016) <doi:10.1111/2041-210X.12432>.
Provides tools to enable the researcher to more precisely conduct
    respirometry experiments. Strong emphasis is on aquatic respirometry. Tools
    focus on helping the researcher setup and conduct experiments. Functions for
    analysis of resulting respirometry data are also provided. This package
    provides tools for intermittent, flow-through, and closed respirometry
    techniques.
Contains three functions that query AuriQ Systems' Essentia Database and return the results in R. 'essQuery' takes a single Essentia command and captures the output in R, where you can save the output to a dataframe or stream it directly into additional analysis. 'read.essentia' takes an Essentia script and captures the output csv data into R, where you can save the output to a dataframe or stream it directly into additional analysis. 'capture.essentia' takes a file containing any number of Essentia commands and captures the output of the specified statements into R dataframes. Essentia can be downloaded for free at http://www.auriq.com/documentation/source/install/index.html.
Contains easy scripts which can be used to quickly create GUI windows for 'Rcmdr' Plugins. No knowledge about Tcl/Tk is required to make use of these scripts (These scripts are a generalisation of the template scripts in the 'RcmdrPlugin.BiclustGUI' package).
Eurostat is the statistical office of the European Union and provides high quality statistics for Europe.
             Large set of the data is disseminated through the Eurostat database (<https://ec.europa.eu/eurostat/data/database>). 
             The tools are using the REST API with the Statistical Data and Metadata eXchange (SDMX <https://sdmx.org>) Web Services 
             (<https://ec.europa.eu/eurostat/web/sdmx-web-services/about-this-service>) to search and download data from 
             the Eurostat database using the SDMX standard.
Models a RESTful service as if it were a nested R list.
Provides the user with functions to develop their trading strategy,
    uncover actionable trading ideas, and monitor consensus shifts with
    crowdsourced earnings and economic estimate data directly from
    <www.estimize.com>. Further information regarding the web services this
    package invokes can be found at <www.estimize.com/api>.
The restlos package provides algorithms for robust estimation of location (mean and mode) and scatter based on minimum spanning trees (pMST), self-organizing maps (Flood Algorithm), Delaunay triangulations (RDELA), and nested minimum volume convex sets (MVCH). The functions are also suitable for outlier detection.
Debugging with restore points instead of break points. A restore
    point stores all local variables when called inside a function. The stored
    values can later be retrieved and evaluated in a modified R console that
    replicates the function's environment. To debug step by step, one can simply
    copy & paste the function body from the R script. Particularly convenient
    in combination with "RStudio". See the "Github" page inst/vignettes for a
    tutorial.
A fast Gibbs sampler for multivariate normal with affine constraints.
Allow for easy-to-use testing or evaluating of linear equality and inequality 
 restrictions about parameters and effects in (generalized) linear statistical models.
Using a database, LaTeX and R easily build attractive resumes.
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.
Using 'Digital Object Identifiers', check for retracted (or otherwise updated) articles using 'Open Retractions' <http://openretractions.com>.
Provides tools for working with Type S (Sign) and
    Type M (Magnitude) errors, as proposed in Gelman and Tuerlinckx (2000)
    <doi.org/10.1007/s001800000040> and
    Gelman & Carlin (2014) <doi.org/10.1177/1745691614551642>.
    In addition to simply calculating the probability of
    Type S/M error, the package includes functions for calculating these errors
    across a variety of effect sizes for comparison, and recommended sample size
    given "tolerances" for Type S/M errors. To improve the speed of these
    calculations, closed forms solutions for the probability of a Type S/M error
    from Lu, Qiu, and Deng (2018) <doi.org/10.1111/bmsp.12132>
    are implemented. As of 1.0.0, this includes
    support only for simple research designs. See the
    package vignette for a fuller exposition on how Type S/M errors arise in
    research, and how to analyze them using the type of design analysis proposed
    in the above papers.
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>.
    Also provided are d,p,q,r functions for the Generalised Extreme Value 
    ('GEV') and Generalised Pareto ('GP') distributions that deal 
    appropriately with cases where the shape parameter is very close to zero.
Computation of (direct and indirect) revealed preferences, fast non-parametric tests of rationality axioms (WARP, SARP, GARP), simulation of axiom-consistent data, and detection of axiom-consistent subpopulations. Rationality tests follow Varian (1982) <doi:10.2307/1912771>, axiom-consistent subpopulations follow Crawford and Pendakur (2012) <doi:10.1111/j.1468-0297.2012.02545.x>.
R Markdown format for 'reveal.js' presentations, a framework 
  for easily creating beautiful presentations using HTML.
An implementation of the reverse ecology framework. Reverse ecology
    refers to the use of genomics to study ecology with no a priori assumptions
    about the organism(s) under consideration, linking organisms to their
    environment. It allows researchers to reconstruct the metabolic networks and
    study the ecology of poorly characterized microbial species from their
    genomic information, and has substantial potentials for microbial community
    ecological analysis.
Decoupled (e.g. separate averages) and censored (e.g. > 100 species) variables are continually reported by many well-established organizations (e.g. World Health Organization (WHO), Centers for Disease Control and Prevention (CDC), World Bank, and various national censuses).  The challenge therefore is to infer what the original data could have been given summarized information.  We present an R package that reverse engineers decoupled and/or censored count data with two main functions.  The cnbinom.pars function estimates the average and dispersion parameter of a censored univariate frequency table.  The rec function reverse engineers summarized data into an uncensored bivariate table of probabilities.
Tests linear regressions for significance reversal through leave-one(multiple)-out and shifting/addition of response values. The paradigm of the package is loosely based on the somewhat forgotten "dfstat" criterion (Belsley, Kuh & Welsch 1980 <doi:10.1002/0471725153.ch2>), which tests influential values in linear models from their effect on statistical inference, i.e. changes in p-value.
Function revgeo() allows you to use the Photon geocoder for OpenStreetMap <http://photon.komoot.de>, Google Maps <http://maps.google.com>, and Bing <https://www.bingmapsportal.com> to reverse geocode coordinate pairs with minimal hassle.
Researchers commonly need to summarize scientific information, a process known as 'evidence synthesis'. The first stage of a synthesis process (such as a systematic review or meta-analysis) is to download a list of references from academic search engines such as 'Web of Knowledge' or 'Scopus'. The traditional approach to systematic review is then to sort these data manually, first by locating and removing duplicated entries, and then screening to remove irrelevant content by viewing titles and abstracts (in that order). 'revtools' provides interfaces for each of these tasks. An alternative approach, however, is to draw on tools from machine learning to visualise patterns in the corpus. In this case, you can use 'revtools' to render ordinations of text drawn from article titles, keywords and abstracts, and interactively select or exclude individual references, words or topics.
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.
Load data by campaigns, ads, ad sets and insights, ad account and business manager 
    from Facebook Marketing API into R. For more details see official documents by Facebook 
	Marketing API <https://developers.facebook.com/docs/marketing-apis/>.
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. Reference: Tsagris M, Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <doi:10.7287/peerj.preprints.26605v1>.
A collection of fast statistical and utility functions for data analysis. Functions for regression, maximum likelihood, column-wise statistics and many more have been included. C++ has been utilized to speed up the functions.
Downloads and parses data from official site on Department
    of Federal Revenue in Brazil (Receita Federal do Brasil, RFB). Data are 
    collected from RFB site (<http://idg.receita.fazenda.gov.br>). Latest update 
    was in December 15, 2017. There are more than 9 million companies IDs and 
    17 million partner information.
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).
Provides the random ferns classifier by Ozuysal, Calonder, Lepetit and Fua (2009) <doi:10.1109/TPAMI.2009.23>, modified for generic and multi-label classification and featuring OOB error approximation and importance measure as introduced in Kursa (2014) <doi:10.18637/jss.v061.i10>.
RFGLS  uses a generalized least-squares method to perform single-marker association analysis, 
  in datasets of nuclear families containing parents, twins, and/or adoptees
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
An integrated package for constructing random forest prediction intervals using a fast implementation package 'ranger'. This package can apply the following three methods described in Haozhe Zhang, Joshua Zimmerman, Dan Nettleton, and Daniel J. Nordman (2019) <doi:10.1080/00031305.2019.1585288>: the out-of-bag prediction interval, the split conformal method, and the quantile regression forest.
A programmatic interface to <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.'
Helps fisheries scientists collect measurements from calcified
    structures and back-calculate estimated lengths at previous ages using
    standard procedures and models. This is intended to replace much of the
    functionality provided by the now out-dated 'fishBC' software
    (<https://fisheries.org/bookstore/all-titles/software/70317/>).
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.
Functions for the detection of spatial clusters using the flexible
    spatial scan statistic developed by Tango and Takahashi (2005) <doi:10.1186/1476-072X-4-11>.
Query plane tickets, from several airlines, using the 'Kiwi' API
    (similar to 'Google Flights').
    The API is documented at <https://docs.kiwi.com/>.
RFLPtools provides functions to analyse DNA fragment samples 
  (i.e. derived from RFLP-analysis) and standalone BLAST report files 
  (i.e. DNA sequence analysis).
Tools for RFM (recency, frequency and monetary value) analysis. 
    Generate RFM score from both transaction and customer level data. Visualize the
    relationship between recency, frequency and monetary value using heatmap, 
    histograms, bar charts and scatter plots. Includes a 'shiny' app for 
    interactive segmentation. References:
    i. Blattberg R.C., Kim BD., Neslin S.A (2008) <doi:10.1007/978-0-387-72579-6_12>.
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.
Graphics for statistics on a sphere, as applied to geological fault data, crystallography, earthquake focal mechanisms, radiation patterns, ternary plots and geographical/geological maps.  Non-double couple plotting of focal spheres and source type maps are included for statistical analysis of moment tensors.
The basic algorithm to perform the folding test of unimodality. 
  Given a dataset X (d dimensional, n samples), the test checks whether the 
  distribution of the data are rather unimodal or rather multimodal. This 
  package stems from the following research publication: 
  Siffer Alban, Pierre-Alain Fouque, Alexandre Termier, and Christine Largouët. 
  "Are your data gathered?" In Proceedings of the 24th ACM SIGKDD International 
  Conference on Knowledge Discovery Data Mining, pp. 2210-2218. ACM, 2018.
  <doi:10.1145/3219819.3219994>.
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" (2nd
    edition) by Andrie de Vries and Joris Meys. You can view the table of 
    contents as well as the sample code for each chapter.
A modified version (with great help from Ian J. Wilson) of Ian J. Wilson's program BATWING for calculating forensic trace-suspect match probabilities.
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.
Methods and tools for implementing functional singular spectrum analysis for functional time series
    as described in Haghbin H., Najibi, S.M., Mahmoudvand R., Maadooliat M. (2019). Functional singular spectrum Analysis. Manuscript submitted for publication.
Utilities for Random Forest model selection, class balance
    correction, significance test, cross validation and partial dependency
    plots.
Computes a novel variable importance for random forests: Impurity reduction importance scores for out-of-bag (OOB) data complementing the existing inbag Gini importance, see also Strobl et al (2007) <doi:10.1186/1471-2105-8-25>, Strobl et al (2007) <doi:10.1016/j.csda.2006.12.030> and Breiman (2001) <DOI:10.1023/A:1010933404324>. 
    The Gini impurities for inbag and OOB data are combined in three different ways, after which the information gain is computed at each split.
    This gain is aggregated for each split variable in a tree and averaged across trees.
An interactive data visualization and exploration toolkit
    that implements Breiman and Cutler's original random forest Java based        
    visualization tools in R, for supervised and unsupervised classification and 
    regression within the algorithm random forest.
Provides functions for accessing and retrieving data from the
    Google Analytics APIs (https://developers.google.com/analytics/). Supports
    OAuth 2.0 authorization. Package provides access to the Management, Core
    Reporting, Multi-Channel Funnels Reporting, Real Time Reporting and
    Metadata APIs. Access to all the Google Analytics accounts which the user
    has access to. Auto-pagination to return more than 10,000 rows of the
    results by combining multiple data requests. Also package provides
    shiny app to explore the core reporting API dimensions and metrics.
An Interface to the GA4GH API that allows users to easily GET responses and POST requests to
  GA4GH Servers. See <http://ga4gh.org> for more information about the GA4GH project.
This package was created to analyze multi-level one-way
        experimental designs. It is designed to handle vectorized
        observation and factor data where there are unequal sample
        sizes and population variance homogeneity can not be assumed.
        To conduct the Gabriel test, create two vectors: one for your 
        observations and one for the factor level of each observation. 
        The function, rgabriel, conduct the test and save the output as
        a vector to input into the gabriel.plot function, which produces 
        a confidence interval plot for Multiple Comparison.
This package implements a Gamma convolution model for
        background correction.
Classes and methods to efficiently handle (slice, annotate, draw ...) genomic features (such as genes or transcripts), and an interactive interface to browse them.
A programmatic interface to the Web Service methods
    provided by the Global Biodiversity Information Facility ('GBIF';
    <https://www.gbif.org/developer/summary>). 'GBIF' is a database
    of species occurrence records from sources all over the globe.
    'rgbif' includes functions for searching for taxonomic names,
    retrieving information on data providers, getting species occurrence
    records, getting counts of occurrence records, and using the 'GBIF'
    tile map service to make 'rasters' summarizing huge amounts of data.
Provides an implementation of Regularized LS-TreeBoost & LAD-TreeBoost algorithm for Regulatory Network inference from any type of expression data (Microarray/RNA-seq etc). See Mall et al (2017) <doi:10.1101/132670>.
We utilize  approximate Bayesian machinery to fit two-level conjugate hierarchical models on overdispersed Gaussian, Poisson, and Binomial data and evaluates whether the resulting approximate Bayesian interval estimates for random effects meet the nominal confidence levels via frequency coverage evaluation. The data that Rgbp assumes comprise observed sufficient statistic for each random effect, such as an average or a proportion of each group, without population-level data.  The approximate Bayesian tool equipped with the adjustment for density maximization produces approximate point and interval estimates for model parameters including second-level variance component, regression coefficients, and random effect. For the Binomial data, the package provides an option to produce  posterior samples of all the model parameters via the acceptance-rejection method. The package provides a quick way to evaluate coverage rates of the resultant Bayesian interval estimates for random effects via a parametric bootstrapping, which we call frequency method checking.
Multiblock data analysis concerns the analysis of several sets of variables (blocks) observed on the same group of individuals. The main aims of the RGCCA package are: (i) to study the relationships between blocks and (ii) to identify subsets of variables of each block which are active in their relationships with the other blocks.
Implementation of chemometrics analysis for bidimensional gas 
    chromatography data. This package can handle data for common scientific data
    format (netCDF) and fold it to 2D chromatogram. Then, it can perform
    preprocessing and multivariate analysis. In the preprocessing algorithms,
    baseline correction, smoothing, and peak alignment are available.
    While in multivariate analysis, multiway principal component analysis is
    incorporated.
Provides bindings to the 'Geospatial' Data Abstraction Library ('GDAL') (>= 1.11.4) 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. From 'rgdal' 1.4.1, provision is made for 'PROJ6' accommodation, with 'PROJ6' functionality to follow; from 1.4.1 'rgdal' will build and function when 'PROJ' >= 6. 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'.
Allow access to both public and private end points to Coinbase Pro (erstwhile GDAX) 
    cryptocurrency exchange. For authenticated flow, users must have valid api, secret and passphrase to be able to connect.
Provides popular sampling distributions C++ routines based in 
    armadillo through a header file approach.
The GenDataSample() and GenDataPopulation() functions create, 
    respectively, a sample or population of multivariate nonnormal data 
    using methods described in Ruscio and Kaczetow (2008). Both of these 
    functions call a FactorAnalysis() function to reproduce a correlation 
    matrix. The EFACompData() function allows users to determine how many 
    factors to retain in an exploratory factor analysis of an empirical data
    set using a method described in Ruscio and Roche (2012). The latter 
    function uses populations of comparison data created by calling the 
    GenDataPopulation() function.
    <DOI: 10.1080/00273170802285693>.
    <DOI: 10.1037/a0025697>.
A method 'generate()' is implemented in this package for the random
    generation of vector time series according to models obtained by 'RMAWGEN',
    'vars' or other packages.  This package was created to generalize the
    algorithms of the 'RMAWGEN' package for the analysis and generation of any
    environmental vector time series.
The method 'generate()' is extended for spatial multi-site
    stochastic generation of daily precipitation. It generates precipitation
    occurrence in several sites using logit regression (Generalized Linear
    Models) and D.S. Wilks' approach (Journal of Hydrology, 1998).
A genetic algorithm plus derivative optimizer.
Provides the hybrid Bayesian method Geometric Density Estimation. On the one hand, it scales the dimension of our data, on the other it performs inference. The method is fully described in the paper "Scalable Geometric Density Estimation" by Y. Wang, A. Canale, D. Dunson (2016) <http://proceedings.mlr.press/v51/wang16e.pdf>.
Connectors to online and offline sources for taking IP addresses
    and geolocating them to country, city, timezone and other geographic ranges. For
    individual connectors, see the package index.
Supports analysis of spatial data processed with the 'GeoPAT' 2
    software <http://sil.uc.edu/cms/index.php?id=geopat2>. 
    Available features include creation of a grid based on the 'GeoPAT' 2
    grid header file and reading a 'GeoPAT' 2 text outputs.
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'. ('rgeos' >= 0.5-1): Up to and including 'GEOS' 3.7.1, topological operations succeeded with some invalid geometries for which the same operations fail from and including 'GEOS' 3.7.2. The 'checkValidity=' argument defaults and structure have been changed, from default FALSE to integer default '0L' for 'GEOS' < 3.7.2 (no check), '1L' 'GEOS' >= 3.7.2 (check and warn). A value of '2L' is also provided that may be used, assigned globally using 'set_RGEOS_CheckValidity(2L)', or locally using the 'checkValidity=2L' argument, to attempt zero-width buffer repair if invalid geometries are found. The previous default (FALSE, now '0L') is fastest and used for 'GEOS' < 3.7.2, but will not warn users of possible problems before the failure of topological operations that previously succeeded.
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.
Regularized Greedy Forest wrapper of the 'Regularized Greedy Forest' <https://github.com/RGF-team/rgf/tree/master/python-package> 'python' package, which also includes a Multi-core implementation (FastRGF) <https://github.com/RGF-team/rgf/tree/master/FastRGF>.
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.
Previously, when one is working with in the Google Ecosystem (Using Google Drive etc), there is hardly any good workflow of getting the values calculated from R and getting that into Google Slides. The normal and easy way out would be to just copy your work over but when you have a number of analysis to present with a lot of changes between each environment, it just becomes quite cumbersome.
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.
Fully 'DBI'-compliant interface to 'Greenplum' <https://greenplum.org/>,
    an open-source parallel database. This is an extension of the 'RPostgres'
    package <https://github.com/r-dbi/RPostgres>.
Integrates the Groovy scripting language with the R Project for Statistical Computing.
Functions to calculate Sample Number and Average Sample Number for Repetitive Group Sampling Plan Based on Cpk as given in Aslam et al. (2013) (<DOI:10.1080/00949655.2012.663374>).
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.
Implements the hierarchical Bayesian analysis of populations structure (hierBAPS) 
  algorithm of Cheng et al. (2013) <doi:10.1093/molbev/mst028> for clustering DNA sequences 
  from multiple sequence alignments in FASTA format. 
  The implementation includes improved defaults and plotting capabilities 
  and unlike the original 'MATLAB' version removes singleton SNPs by default.
Complete access from 'R' to the FIS 'MarketMap 
    C-Toolkit' ('FAME C-HLI'). 'FAME' is a fully integrated software and database 
    management system from FIS that provides the following capabilities: 
    Time series and cross-sectional data management;
    Financial calculation, data analysis, econometrics, and forecasting;
    Table generation and detailed multicolor, presentation-quality report writing;
    Multicolor, presentation-quality graphics;
    "What-if" analysis;
    Application development and structured programming;
    Data transfer to and from other applications;
    Tools for building customized graphical user interfaces.
Implements simple Hamiltonian Monte Carlo routines in R for
             sampling from any desired target distribution which is continuous
             and smooth. See Neal (2017) <arXiv:1701.02434> for further details
             on Hamiltonian Monte Carlo. Automatic parameter selection is not
             supported.
'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', 'OpenBLAS', 'ACML', 'BLIS' and 'MKL').
  And possible to control the number of threads in 'OpenMP'.
  Get a number of logical cores and physical cores if feasible.
rHpcc is an R package providing an Interface between R and
        HPCC.Familiarity with ECL (Enterprise Control Language) is a
        must to use this package.HPCC is a massive parallel-processing
        computing platform that solves Big Data problems.ECL is the
        Enterprise Control Language designed specifically for huge data
        projects using the HPCC platform.Its extreme scalability comes
        from a design that allows you to leverage every query you
        create for re-use in subsequent queries as needed. To do this,
        ECL takes a dictionary approach to building queries wherein
        each ECL definition defines an Attribute. Each previously
        defined Attribute can then be used in succeeding ECL Attribute
        definitions as the language extends itself as you use it.
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' (<https://builder.r-hub.io/>)
    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.
Randomization inference procedures for simple and complex randomized designs, including multi-armed trials, as described in Gerber and Green (2012, ISBN: 978-0393979954). Users formally describe their randomization procedure and test statistic. The randomization distribution of the test statistic under some null hypothesis is efficiently simulated.
Computes attributable effects based confidence interval, permutation
    test confidence interval, or asymptotic confidence interval for the
    the average treatment effect on a binary outcome.
Radiomics image analysis toolbox for 2D and 3D radiological images. RIA supports DICOM, NIfTI and
             nrrd file formats. RIA calculates first-order, gray level co-occurrence matrix,
             gray level run length matrix and geometry-based statistics. Almost all calculations are done
             using vectorized formulas to optimize run speeds. Calculation of several thousands of parameters
             only takes minutes on a single core of a conventional PC.
People's Republic of China resident identity card contains a lot of personal information. 
    It can extract the location of the household's residence in the identity card, the date of birth, the sex, 
    the location of the registered police station, and can identify the authenticity of the ID card through the check code. 
    'Ricetl' provides graphical user interface to process the function of extracting the above effective private information in the ID card number,
    and provides a public data set related to the ID number.
The Diceware method can be used to generate strong passphrases.
  In short, you roll a 6-faced dice 5 times in a row, the number obtained is
  matched against a dictionary of easily remembered words. By combining together 7
  words thus generated, you obtain a password that is relatively easy to remember,
  but would take several millions years (on average) for a powerful computer to guess.
Computes rarefaction curves, cumulated and mean species
        richness. Compares these estimates by means of randomization
        tests.
It provides a list of genes associated to diseases (g2d$clean and g2d$complete) based on the following  4 publications (GS2D, Fontaine (2016) <doi:10.18547/gcb.2016.vol2.iss1.e33>, DisGeNET, Pinero (2016) <doi:10.1093/nar/gkw943> Berto2016, Berto (2016) <doi:10.3389/fgene.2016.00031> and  PsyGeNET, Sacristan (2015) <doi:10.1093/bioinformatics/btv301>). Those lists were combined and manually curated to have matching disease names.  When provided a list of gene names, it calculates the disease enrichment of the gene set. The enrichment is calculated using proportion test and Fisher's exact test. Adjusted fdr p-values are returned alongside with p-values combined using the Fisher's method.
For whole-genome analysis, idiograms are virtually the most intuitive and effective way to map and visualize the genome-wide information. RIdeogram was developed to visualize and map whole-genome data in idiograms with no restriction of species.
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.
Functions to compute ridit scores of vectors, compute mean ridits
  and their standard errors for vectors compared to a reference vector,
  as described in Fleiss (1981, ISBN:0-471-06428-9), and compute
  means/SEs for multiple groups in matrices. Data can be either
  counts or proportions. Emphasis is on ridit analysis of ordered categorical
  data such as Likert items and pain-rating scales.
Allows to get weather data from Automated Surface Observing System (ASOS) stations (airports) in the
    whole world thanks to the Iowa Environment Mesonet website.
We provide a number of algorithms to estimate fundamental statistics including Fréchet mean and geometric median for manifold-valued data. Also, C++ header files are contained that implement elementary operations on manifolds such as Sphere, Grassmann, and others. See Bhattacharya and Bhattacharya (2012) <doi:10.1017/CBO9781139094764> if you are interested in statistics on manifolds, and Absil et al (2007) <isbn:978-0-691-13298-3> on computational aspects of optimization on matrix manifolds.
Retrieves efficiently and reliably Investors Exchange ('IEX') stock and market data using 'IEX Cloud API'. The platform is offered by Investors Exchange Group (IEX Group).
    Main goal is to leverage 'R' capabilities including existing packages to effectively provide financial and statistical analysis as well as visualization in support of fact-based decisions.
    In addition, continuously improve and enhance 'Riex' by applying best practices and being in tune with users' feedback and requirements.
    Please, make sure to review and acknowledge Investors Exchange Group (IEX Group) terms and conditions before using 'Riex' (<https://iexcloud.io/terms/>).
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.
Implements the algorithms for solving sparse generalized eigenvalue problem by Tan, et. al. (2018). Sparse Generalized Eigenvalue Problem: Optimal Statistical Rates via Truncated Rayleigh Flow.  To appear in Journal of the Royal Statistical Society: Series B. <arXiv:1604.08697>.
RIFS package provides functionality for generating &
        plotting prefractals in R^n with various protofractal sets and
        partition coefficient for iterative segments
Functionality to download stock prices, cryptocurrency data, and
   more from the 'Tiingo' API <https://api.tiingo.com/>.
Create colour palettes based on famous paintings. Using the 
    function rijksPalette(), you can search for any painting in the collection
    of the Dutch Rijksmuseum and generate a colour palette from it. This package 
    was developed using the fantastic Rijksmuseum API 
    <https://www.rijksmuseum.nl/api>.
Tools to download data from the ilostat database
    <https://ilostat.ilo.org> 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!
The metrics() function calculates measures of scholarly impact. 
    These include conventional measures, such as the number of publications
    and the total citations to all publications, as well as modern and 
    robust metrics based on the vector of citations associated with each 
    publication, such as the h index and many of its variants or rivals.
    These methods are described in Ruscio et al. (2012) 
    <DOI: 10.1080/15366367.2012.711147>.
A programmatic interface to the API provided by the iNaturalist website <http://inaturalist.org> to download species occurrence data submitted by citizen scientists.
Circular / ring buffers in R and C.  There are a couple
    of different buffers here with different implementations that
    represent different trade-offs.
Installs shiny apps packaged as stand-alone Electron apps using Inno Setup, an open source software that builds installers for Windows programs <http://www.jrsoftware.org/ishelp/>.
C++ classes to embed R in C++ applications
 A C++ class providing the R interpreter is offered by this package
 making it easier to have "R inside" your C++ application. As R itself
 is embedded into your application, a shared library build of R is
 required. This works on Linux, OS X and even on Windows provided you
 use the same tools used to build R itself. d 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 examples use 'GNUmakefile(s)'
 with GNU extensions, so a GNU make is required (and will use the
 'GNUmakefile' automatically). 'Doxygen'-generated documentation of
 the C++ classes is available at the 'RInside' website as well.
Functions to calculate several ecological indices of individual and population niche width
   (Araujo's E, clustering and pairwise similarity among individuals, IS, Petraitis' W, and Roughgarden's
   WIC/TNW) to assess individual specialization based on data of resource use. Resource use can be
   quantified by counts of categories, measures of mass/lenght or proportions. Monte Carlo resampling
   procedures are available for hypothesis testing against multinomial null models.
Provides functions to use the 'Instagram' API to get feed and user 
    information, but also performs basic in-app functionality such as liking, 
    commenting, following, and blocking. Use of this package means that you will 
    not use it to spam, harass, or perform other nefarious acts. For more details 
    on how to use the API please see this package's website 
    <https://eric88tchong.github.io/Rinstapkg/> for more information, documentation, 
    and examples.
This tool can be used to build binary interval trees using real number inputs. 
    The tree supports queries of intervals overlapping a single number or an interval (start, end). 
    Intervals with same bounds but different names are treated as distinct intervals. 
    Insertion of intervals is also allowed. Deletion of intervals is not implemented at this point.
    See  Mark de Berg, Otfried Cheong, Marc van Kreveld, Mark Overmars (2008). Computational Geometry: Algorithms and Applications, for a reference.
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.
Constrained clustering, transfer functions, and other methods for analysing Quaternary science data.
Takes an array of IPs and the keys for the services the user wishes to use (IP Hub, IP Intel, and Proxycheck), 
    and passes these to all respective APIs. Returns a dataframe with the IP addresses (used for merging), 
    country, ISP, labels for non-US IP Addresses, VPS use, and recommendations for blocking. The package 
    also provides optional visualization tools for checking the distributions. Additional
    functions are provided to call each discrete API endpoint. The package and methods are detailed in the recent paper 
    Waggoner, Kennedy, and Clifford (2019) <doi:10.21105/joss.01285>.
Utility functions and S3 classes for IPv4 and IPv6 addresses, including 
             conversion to and from binary representation.
Parameter estimation, computation of probability, information, and 
    (log-)likelihood, and visualization of item/test characteristic curves and
    item/test information functions for three uni-dimensional item response theory
    models: the 3-parameter-logistic model, generalized partial credit model, 
    and graded response model. The full documentation and tutorials are at 
    <https://github.com/xluo11/Rirt>.
We propose numerous functions for cohort-based analyses, either for prediction or causal inference. For causal inference, it includes Inverse Probability Weighting and G-computation for marginal estimation of an exposure effect when confounders are expected. We deal with binary outcomes, times-to-events (Le Borgne, 2016, <doi:10.1002/sim.6777>), competing events (Trebern-Launay, 2018, <doi: 10.1007/s10654-017-0322-3>), and multi-state data (Gillaizeau, 2018, <doi: 10.1002/sim.7550>). For multistate data, semi-Markov model with interval censoring (Foucher, 2008, <doi: 10.1177/0962280208093889>) may be considered and we propose the possibility to consider the excess of mortality related to the disease compared to reference lifetime tables (Gillaizeau, 2017, <doi: 10.1177/0962280215586456>). For predictive studies, we propose a set of functions to estimate time-dependent receiver operating characteristic (ROC) curves with the possible consideration of right-censoring times-to-events or the presence of confounders (Le Borgne, 2018,  <doi: 10.1177/0962280217702416>). Finally, several functions are available to assess time-dependant ROC curves (Combescure, 2017, <doi: 10.1177/0962280212464542>) or survival curves (Combescure, 2014, <doi: 10.1002/sim.6111>) from aggregated data.
Implements techniques for educational resource inspection, selection, and evaluation (RISE) described in Bodily, Nyland, and Wiley (2017) 
    <doi:10.19173/irrodl.v18i2.2952>.	Automates the process of identifying learning materials that are not effectively supporting student learning in 
    technology-mediated courses by synthesizing information about access to course content and performance on assessments.
Fits an Ising model to a binary dataset using L1 regularized
    logistic regression and extended BIC. Also includes a fast lasso logistic
    regression function for high-dimensional problems. Uses the 'libLBFGS'
    optimization library by Naoaki Okazaki.
Computes 26 financial risk measures for any continuous distribution.  The 26 financial risk measures  include value at risk, expected shortfall due to Artzner et al. (1999) <DOI:10.1007/s10957-011-9968-2>, tail conditional median due to Kou et al. (2013) <DOI:10.1287/moor.1120.0577>, expectiles due to Newey and Powell (1987) <DOI:10.2307/1911031>, beyond value at risk due to Longin (2001) <DOI:10.3905/jod.2001.319161>, expected proportional shortfall due to Belzunce et al. (2012) <DOI:10.1016/j.insmatheco.2012.05.003>, elementary risk measure due to Ahmadi-Javid (2012) <DOI:10.1007/s10957-011-9968-2>, omega due to Shadwick and Keating (2002), sortino ratio due to Rollinger and Hoffman (2013), kappa  due to Kaplan and Knowles  (2004), Wang (1998)'s <DOI:10.1080/10920277.1998.10595708> risk measures, Stone (1973)'s <DOI:10.2307/2978638> risk measures, Luce (1980)'s <DOI:10.1007/BF00135033> risk measures, Sarin (1987)'s <DOI:10.1007/BF00126387> risk measures, Bronshtein and Kurelenkova (2009)'s risk measures.
A collection of functions related to the study of etiologic heterogeneity both across disease subtypes and across individual tumor markers. The included functions allow one to quantify the extent of etiologic heterogeneity in the context of a case-control study, and provide p-values to test for etiologic heterogeneity across individual risk factors.
Fast design of risk parity portfolios for financial investment.
    The goal of the risk parity portfolio formulation is to equalize or distribute
    the risk contributions of the different assets, which is missing if we simply
    consider the overall volatility of the portfolio as in the mean-variance
    Markowitz portfolio. In addition to the vanilla formulation, where the risk
    contributions are perfectly equalized subject to no shortselling and budget
    constraints, many other formulations are considered that allow for box
    constraints and shortselling, as well as the inclusion of additional
    objectives like the expected return and overall variance. See vignette for
    a detailed documentation and comparison, with several illustrative examples.
    The package is based on the papers:
    Y. Feng, and D. P. Palomar (2015). SCRIP: Successive Convex Optimization Methods
    for Risk Parity Portfolio Design. IEEE Trans. on Signal Processing, vol. 63,
    no. 19, pp. 5285-5300. <doi:10.1109/TSP.2015.2452219>.
    F. Spinu (2013), An Algorithm for Computing Risk Parity Weights.
    <doi:10.2139/ssrn.2297383>.
    T. Griveau-Billion, J. Richard, and T. Roncalli (2013). A fast algorithm for computing 
    High-dimensional risk parity portfolios. <arXiv:1311.4057>.
Collection of functions designed to compute risk-based portfolios as described 
    in Ardia et al. (2017) <doi:10.1007/s10479-017-2474-7> and Ardia et al. (2017) <doi:10.21105/joss.00171>.
Assessing and comparing risk prediction rules for clustered data. The method is based on the paper: Rosner B, Qiu W, and Lee MLT.(2013) <doi: 10.1007/s10985-012-9240-6>.
Computes risk measures from data, as well as performs risk management procedures.
Implementation of the following methods for event history analysis.
    Risk regression models for survival endpoints also in the presence of competing
    risks are fitted using binomial regression based on a time sequence of binary
    event status variables. A formula interface for the Fine-Gray regression model
    and an interface for the combination of cause-specific Cox regression models.
    A toolbox for assessing and comparing performance of risk predictions (risk
    markers and risk prediction models). Prediction performance is measured by the
    Brier score and the area under the ROC curve for binary possibly time-dependent
    outcome. Inverse probability of censoring weighting and pseudo values are used
    to deal with right censored data. Lists of risk markers and lists of risk models
    are assessed simultaneously. Cross-validation repeatedly splits the data, trains
    the risk prediction models on one part of each split and then summarizes and
    compares the performance across splits.
Compute time-dependent Incident/dynamic accuracy measures
        (ROC curve, AUC, integrated AUC )from censored survival data
        under proportional or non-proportional hazard assumption of
        Heagerty & Zheng (Biometrics, Vol 61 No 1, 2005, PP 92-105).
Implements efficient simulation procedures to estimate tail loss probabilities and conditional excess for a stock portfolio. The log-returns are assumed to follow a t-copula model with generalized hyperbolic or t marginals.
Risk-related information (like the prevalence of conditions and the sensitivity and specificity of diagnostic tests or treatment decisions) can be expressed in terms of probabilities or frequencies. By providing a toolbox of methods and metrics, 'riskyr' computes, translates, and visualizes risk-related information in a variety of ways. Offering multiple complementary perspectives on the interplay between key parameters renders teaching and training of risk literacy more transparent.
A set of tools to extract bibliographic content from the National
    Center for Biotechnology Information (NCBI) databases, including PubMed. The
    name RISmed is a portmanteau of RIS (for Research Information Systems, a common
    tag format for bibliographic data) and PubMed.
Implements the simulation and regression of
        integrated Isothermal Titration Calorimetry (ITC) data using
        the most commonly used one-to-one binding reaction model.
An interface to the Integrated Taxonomic Information System ('ITIS')
    (<https://www.itis.gov>). Includes functions to work with the 'ITIS' REST
    API methods (<https://www.itis.gov/ws_description.html>), as well as the
    'Solr' web service (<https://www.itis.gov/solr_documentation.html>).
Tools for randomization-based inference. Current focus is on the d^2 omnibus test of differences of means following Hansen and Bowers (2008) useful for assessing balance in matched observational studies or for analysis of outcomes in block-randomized experiments.
Finds a robust instrumental variables estimator using a
             high breakdown point S-estimator of multivariate location
             and scatter matrix.
Generates graphs, CSV files, and coordinates related to river valleys when calling the riverbuilder() function.
Reads river network shape files and computes network distances.
    Also included are a variety of computation and graphical tools designed 
    for fisheries telemetry research, such as minimum home range, kernel density 
    estimation, and clustering analysis using empirical k-functions with 
    a bootstrap envelope.  Tools are also provided for editing the river 
    networks, meaning there is no reliance on external software.
Implements several of the most popular load estimation procedures, including averaging methods, ratio estimators and regression methods. The package provides an easy-to-use tool to rapidly calculate the load for various compounds and to compare different methods. The package also supplies additional functions to easily organize and analyze the data.
Functions for reading, analysing and plotting river networks.
  For this package, river networks consist of sections and nodes with associated attributes, 
  e.g. to characterise their morphological, chemical and biological state.
  The package provides functions to read this data from text files, to analyse the network
  structure and network paths and regions consisting of sections and nodes that fulfill
  prescribed criteria, and to plot the river network and associated properties.
Sankey plots are a type of diagram that is convenient to
    illustrate how flow of information, resources etc. separates and joins,
    much like observing how rivers split and merge. For example, they can be
    used to compare different clusterings.
This R package is a flexible and efficient tool to 
    visualise both quantitative and qualitative data from river surveys. 
    It can be used to produce diagrams with the topological structure of 
    the river network.
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.
This is the official 'Jazz' client. 'Jazz' is a lightweight modular data processing
    framework, including a web server. It provides data persistence and computation capabilities
    accessible from 'R' and 'Python' and also through a REST API. <https://github.com/bbvadata/Jazz>
    See ?rjazz::rjazz to get a 'Jazz' server.
The RJDBC package is an implementation of R's DBI interface using JDBC as a back-end. This allows R to connect to any DBMS that has a JDBC driver.
Interface around 'JDemetra+' (<https://github.com/jdemetra/jdemetra-app>), the seasonal adjustment software officially
    recommended to the members of the European Statistical System (ESS) and the European System of Central Banks.
    It offers full access to all options and outputs of 'JDemetra+', including the two leading seasonal adjustment methods
    TRAMO/SEATS+ and X-12ARIMA/X-13ARIMA-SEATS.
Add-in to the 'RJDemetra' package on seasonal adjustments.
    It allows to produce quality assessments outputs (dashboards, quality report matrix, etc.).
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. For a detailed description of
    the package, see Gelling, Schofield & Barker (2019)
    <doi:10.1111/anzs.12263>.
R-interface to gene drop programs from Alun Thomas' Java Programs for Statistical Genetics and Computational Statistics (JPSGCS).
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.
Estimates the Joint Sentiment Topic model and its reversed variety, 
    as described by Lin and He, 2009 <DOI:10.1145/1645953.1646003> and Lin, He, Everson and Ruger 
    (2012) <DOI:10.1109/TKDE.2011.48>.
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'.
'JDemetra+' (<https://github.com/jdemetra/jdemetra-app>) is the seasonal adjustment software officially recommended
  to the members of the European Statistical System and the European System of Central Banks. Seasonal adjustment models performed
  with 'JDemetra+' can be stored into workspaces. 'JWSACruncher' (<https://github.com/jdemetra/jwsacruncher/releases>) is a console tool that 
  re-estimates all the multi-processing defined in a workspace and to export the result. 'rjwsacruncher' allows to launch easily the 'JWSACruncher'.
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/>.
Estimates the Hoeffding decomposition of an unknown function by solving ridge group sparse optimization problem based on reproducing kernel Hilbert spaces, and approximates its sensitivity indices (see Kamari, H., Huet, S. and Taupin, M.-L. (2019) <arXiv:1905.13695>).
Applies methods used to estimate animal homerange, but
    instead of geospatial coordinates, we use isotopic coordinates. The estimation
    methods include: 1) 2-dimensional bivariate normal kernel utilization density
    estimator, 2) bivariate normal ellipse estimator, and 3) minimum convex polygon
    estimator, all applied to stable isotope data. Additionally, functions to
    determine niche area, polygon overlap between groups and levels (confidence
    contours) and plotting capabilities.
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.
Robust  kernel center matrix, robust  kernel cross-covariance operator for kernel unsupervised methods, kernel canonical correlation analysis, 
 influence function of identifying significant outliers or atypical objects from multimodal datasets. Alam, M. A,  Fukumizu, K., Wang  Y.-P. (2018) <doi:10.1016/j.neucom.2018.04.008>.
   Alam, M. A,  Calhoun, C. D.,  Wang  Y.-P. (2018) <doi:10.1016/j.csda.2018.03.013>.
This package provides functionality to read files
    containing observations which consist of arbitrary key/value
    pairs.
Functions and Datasets Required for ST370 class
The 'LabKey' client library for R makes it easy for R users to
    load live data from a 'LabKey' Server, <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.
Easily acquire Landsat <https://landsat.usgs.gov>, a remote sensing satellite, data from R. Search for Landsat scenes and download the Landsat data using this library which uses API built by Development Seed <https://api.developmentseed.org/satellites> and U.S. Geological Survey <https://espa.cr.usgs.gov/api>.
A toolbox for working with base types, core R features
  like the condition system, and core 'Tidyverse' features like tidy
  evaluation.
Read and write 'las' and 'laz' binary file formats. The LAS file format is a public file format for the interchange of 3-dimensional point cloud data between data users. The LAS specifications are approved by the American Society for Photogrammetry and Remote Sensing <https://www.asprs.org/committee-general/laser-las-file-format-exchange-activities.html>. The LAZ file format is an open and lossless compression scheme for binary LAS format versions 1.0 to 1.3 <https://www.laszip.org/>.
Analyzes data from repeated low-dose challenge experiments and provide vaccine efficacy estimates. In addition, this package can provide guidance to design repeated low-dose challenge studies.
Estimates the Bayesian LDA model for mixed-membership clustering based on different types of data
    (i.e., Multinomial, Bernoulli, and Binomial entries). Albuquerque, Valle and Li (2019) <doi:10.1016/j.knosys.2018.10.024>.
Linguistic Descriptions of Complex Phenomena (LDCP) is an architecture and methodology that allows us to model complex phenomena, interpreting input data, and generating automatic text reports customized to the user needs (see <doi:10.1016/j.ins.2016.11.002> and <doi:10.1007/s00500-016-2430-5>). The proposed package contains a set of methods that facilitates the development of LDCP systems. It main goal is increasing the visibility and practical use of this research line.
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.
Converts LESS to CSS. 
    It uses V8 engine, where LESS parser is run. Functions for
    LESS text, file or folder conversion are provided.
    This work was supported by a junior grant research project by 
    Czech Science Foundation 'GACR' no. 'GJ18-04150Y'.
Contains functions for simulating linear fractional stable motions, according to techniques developed by Stoev and Taqqu (2004) <doi:10.1142/S0218348X04002379>, as well as functions for computing important statistics used with these processes introduced by Mazur, Otryakhin and Podolskij (2018) <arXiv:1802.06373>, and also different quantities related to those statistics.
An implementation of a number of Global Trend models for time series forecasting 
    that are Bayesian generalizations and extensions of some Exponential Smoothing models. 
    The main differences/additions include 1) nonlinear global trend, 2) Student-t error 
    distribution, and 3) a function for the error size, so heteroscedasticity. The methods 
    are particularly useful for short time series. When tested on the well-known M3 dataset,
    they are able to outperform all classical time series algorithms. The models are fitted 
    with MCMC using the 'rstan' package.
An R interface for libeemd (Luukko, Helske, Räsänen, 2016) <doi:10.1007/s00180-015-0603-9>, 
    a C library of highly efficient parallelizable functions  for performing the ensemble empirical mode decomposition (EEMD), 
    its complete variant (CEEMDAN), the regular empirical mode decomposition (EMD), and bivariate EMD (BEMD). 
    Due to the possible portability issues CRAN version no longer supports OpenMP, you can install OpenMP-supported version 
    from GitHub: <https://github.com/helske/Rlibeemd/>.
Set of tools for reading, processing and visualizing small set of
    LiDAR (Light Detection and Ranging) data for forest inventory applications.
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.
Data driven approach for robust regression estimation in homoscedastic and heteroscedastic context. See Wang et al. (2007), <doi:10.1198/106186007X180156> regarding homoscedastic framework.
Estimates robust rank-based fixed effects and predicts robust
    random effects in two- and three- level random effects nested models.
    The methodology is described in Bilgic & Susmann (2013) <https://journal.r-project.org/archive/2013/RJ-2013-027/>.
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). Carlo Cafiero, Sara Viviani, Mark Nord (2018) <doi:10.1016/j.measurement.2017.10.065>.
RM2 is a simple package that implements functions used in
        revenue management and pricing environments.
Estimation the conditional covariance matrix using the RiskMetrics 2006 methodology of Zumbach (2007) <doi:10.2139/ssrn.1420185>.
Compute an exact CI for the population mean under a random effects model. The routines implement the algorithm described in Michael, Thronton, Xie, and Tian (2017) <https://haben-michael.github.io/research/Exact_Inference_Meta.pdf>.
Allows for production of Czekanowski's Diagrams. See A. Vasterlund (2019) Master thesis, Linkoping University.
Uses refined moving average filter based on the optimal and data-driven moving average lag q or smoothing spline to estimate trend and seasonal components, as well as irregularity (residuals) for univariate time series or data.
MAGIC (Markov affinity-based graph imputation of cells) is a method for addressing technical noise in single-cell data, including under-sampling of mRNA molecules, often termed "dropout" which can severely obscure important gene-gene relationships. MAGIC shares information across similar cells, via data diffusion, to denoise the cell count matrix and fill in missing transcripts. Read more: van Dijk et al. (2018) <DOI:10.1016/j.cell.2018.05.061>.
Creates and maintains a build process for complex analytic tasks in R.
  Package allows to easily generate Makefile for the (GNU) 'make' tool, which drives the build process
  by (in parallel) executing build commands in order to update results accordingly to given dependencies
  on changed data or updated source files.
An EM algorithm to fit Mallows' Models to full or partial
        rankings, with or without ties.
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 'Mapzen'-based APIs (including 
    geocode.earth, Nextzen, and NYC GeoSearch) for geographic search 
    and geocoding, isochrone calculation, and vector data to draw map tiles. 
    See <https://mapzen.com/documentation/> for more information. The original 
    Mapzen has gone out of business, but 'rmapzen' can be set up to work with 
    any provider who implements the Mapzen API.
Three robust marginal integration procedures for additive models based on local 
             polynomial kernel smoothers. As a preliminary estimator of the multivariate 
             function for the marginal integration procedure, a first approach uses local 
             constant M-estimators, a second one uses local polynomials of order 1 over all the
             components of covariates, and the third one uses M-estimators based on local 
             polynomials but only in the direction of interest. For this last approach, 
             estimators of the derivatives of the additive functions can be obtained. All three
             procedures can compute predictions for points outside the training set if desired. 
             See Boente and Martinez (2017) <doi:10.1007/s11749-016-0508-0> for details.
Implements a 'DBI'-compliant interface to 'MariaDB' (<https://mariadb.org/>) and 'MySQL' (<https://www.mysql.com/>) databases.
Given a time series of daily temperatures, the package provides tools
    to detect extreme thermal events, including marine heat waves, and to
    calculate the exceedances above or below specified threshold values.
    It outputs the properties of all detected events and exceedances.
An interface to the software package MARK that constructs input
    files for MARK and extracts the output. MARK was developed by Gary White
    and is freely available at <http://www.phidot.org/software/mark/downloads/>
    but is not open source.
Convert R Markdown documents into a variety of formats.
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.
MCFS-ID (Monte Carlo Feature Selection and Interdependency Discovery) is a Monte Carlo method-based tool for feature selection. It also allows for the discovery of interdependencies between the relevant features. MCFS-ID is particularly suitable for the analysis of high-dimensional, 'small n large p' transactional and biological data. M. Draminski, J. Koronacki (2018) <doi:10.18637/jss.v085.i12>.
Compute the repeated measures correlation, a statistical technique
    for determining the overall within-individual relationship among paired measures
    assessed on two or more occasions, first introduced by Bland and Altman (1995).
    Includes functions for diagnostics, p-value, effect size with confidence
    interval including optional bootstrapping, as well as graphing. Also includes
    several example datasets.
The 'rmd' package manages multiple R markdown packages. These R markdown packages include currently 'rmarkdown', 'knitr', 'bookdown', 'bookdownplus', 'blogdown', 'rticles',  'tinytex', 'xaringan', 'citr',  and 'mindr'. They can be installed and loaded in a single step with the 'rmd' package. The conflicts between these packages are evaluated as well.
Provides tools to evaluate the value of using a risk prediction instrument to decide treatment or intervention (versus no treatment or intervention).  Given one or more risk prediction instruments (risk models) that estimate the probability of a binary outcome, rmda provides functions to estimate and display decision curves and other figures that help assess the population impact of using a risk model for clinical decision making.   Here, "population" refers to the relevant patient population. Decision curves display estimates of the (standardized) net benefit over a range of probability thresholds used to categorize observations as 'high risk'. The curves help evaluate a treatment policy that recommends treatment for patients who are estimated to be 'high risk' by comparing the population impact of a risk-based policy to "treat all" and "treat none" intervention policies.  Curves can be estimated using data from a prospective cohort.  In addition, rmda can estimate decision curves using data from a case-control study if an estimate of the population outcome prevalence is available.  Version 1.4 of the package provides an alternative framing of the decision problem for situations where treatment is the standard-of-care and a risk model might be used to recommend that low-risk patients (i.e., patients below some risk threshold) opt out of treatment. Confidence intervals calculated using the bootstrap can be computed and displayed. A wrapper function to calculate cross-validated curves using k-fold cross-validation is also provided.
Estimates different multiple discrete-continuous extreme value
	(MDCEV) demand model specifications with observed and unobserved individual heterogeneity (Bhat (2008) <doi:10.1016/j.trb.2007.06.002>). Fixed parameter,
	latent class, and random parameter models can be estimated. These models are estimated using
	maximum likelihood or Bayesian estimation techniques and are implemented in 'Stan', which is a
	C++ package for performing full Bayesian inference (see Stan Development Team (2018) <http://mc-stan.org>). 
	The 'rmdcev' package also includes functions for demand simulation (Pinjari and Bhat (2011) <https://repositories.lib.utexas.edu/handle/2152/23880>) 
	and welfare simulation (Lloyd-Smith (2018) <doi:10.1016/j.jocm.2017.12.002>).
A collection of 'Lua' filters that extend the functionality
  of R Markdown templates (e.g., count words or post-process 'pandoc-citeproc'-
  citations).
HTML formats and templates for 'rmarkdown' documents, with some extra
    features such as automatic table of contents, lightboxed figures, dynamic
    crosstab helper.
A series of functions to aid in repeated tasks for Rmd documents. All details are to my personal preference, though I am happy to add flexibility if there are use cases I am missing. I will continue updating with new functions as I add utility functions for myself.
Formats for R Markdown that undo modifications by
    'pandoc' and 'rmarkdown' to original 'latex' templates, such as
    smaller margins, paragraph spacing, and compact titles. In addition, 
    enhancements such as author blocks with affiliations and
    headers and footers are introduced. All of this functionality is built 
    around plugins that modify the default 'pandoc' template without relying on 
    custom templates.
'R' 'Markdown' format for 'shower' presentations, see
   <https://github.com/shower/shower>.
A suite of tools useful to read, visualize and export bivariate motion energy time-series. Lagged synchrony between subjects can be analyzed through windowed cross-correlation. Surrogate data generation allows an estimation of pseudosynchrony that helps to estimate the effect size of the observed synchronization. Ramseyer & Tschacher (2011) <doi:10.1037/a0023419>.
An 'Rcpp' interface for Eunjeon project <http://eunjeon.blogspot.com/>.
    The 'mecab-ko' and 'mecab-ko-dic' is based on a C++ library,
    and part-of-speech tagging with them is useful when the spacing of source Korean text is not correct.
    This package provides part-of-speech tagging and tokenization function for Korean text.
We provide functions to compute confidence
    intervals (CIs) for a well-defined nonlinear function of the model
    parameters (e.g., product of k coefficients) in single--level and
    multilevel structural equation models.
Functions for simple fixed and random effects
        meta-analysis for two-sample comparisons and cumulative
        meta-analyses. Draws standard summary plots, funnel plots, and
        computes summaries and tests for association and heterogeneity.
Implementation of the metalog distribution in R.
    The metalog distribution is a modern, highly flexible, data-driven distribution. 
    Metalogs are developed by Keelin (2016) <doi:10.1287/deca.2016.0338>.
    This package provides functions to build these distributions from raw data. 
    Resulting metalog objects are then useful for exploratory and probabilistic analysis.
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.
Provides mutual information estimators based on k-nearest neighbor estimators by A. Kraskov, et al. (2004) <doi:10.1103/PhysRevE.69.066138>, S. Gao, et al. (2015) <http://proceedings.mlr.press/v38/gao15.pdf> and local density estimators by W. Gao, et al. (2017) <doi:10.1109/ISIT.2017.8006749>.
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.
Provides header files of 'mio', a cross-platform C++11 header-only 
    library for memory mapped file IO <https://github.com/mandreyel/mio>.
The Rmisc library contains many functions useful for data analysis
    and utility operations.
Interface of 'MIXMOD' software for supervised, unsupervised and
    semi-supervised classification with mixture modelling.
The Rmixmod package provides model-based clustering by fitting a mixture model (e.g. Gaussian components for quantitative continuous data) to the data and identifying each cluster with one of its components. The number of components can be determined from the data, typically using the BIC criterion. In practice, however, individual clusters can be poorly fitted by Gaussian distributions, and in that case model-based clustering tends to represent one non-Gaussian cluster by a mixture of two or more Gaussian components. If the number of mixture components is interpreted as the number of clusters, this can lead to overestimation of the number of clusters. This is because BIC selects the number of mixture components needed to provide a good approximation to the density. This package, RmixmodCombi, according to \emph{Combining Mixture Components for Clustering} by J.P. Baudry, A.E. Raftery, G. Celeux, K. Lo, R. Gottardo, combines the components of the EM/BIC solution (provided by Rmixmod) hierarchically according to an entropy criterion. This yields a clustering for each number of clusters less than or equal to K. These clusterings can be compared on substantive grounds, and we also provide a way of selecting the number of clusters via a piecewise linear regression fit to the (possibly rescaled) entropy plot.
Provides an interface to many endpoints of Mixpanel's Data Export, Engage and JQL API. The R functions allow for event and profile data export as well as for segmentation, retention, funnel and addiction analysis. Results are always parsed into convenient R objects. Furthermore it is possible to load and update profiles.
Mixture Composer <https://github.com/modal-inria/MixtComp> is a project to build mixture models with
    heterogeneous data sets and partially missing data management. 
    It includes models for real, categorical, counting, functional and ranking data.
    This package contains the minimal R interface of the C++ 'MixtComp' library.
Sundry discrete probability distributions and helper functions.
Provides R and C++ function that enable the user to conduct multiple 
 kernel learning (MKL) and cross validation for support vector machine (SVM) models. 
 Cross validation can be used to identify kernel shapes and hyperparameter 
 combinations that can be used as candidate kernels for MKL. There are three 
 implementations provided in this package, namely SimpleMKL Alain Rakotomamonjy et. al
 (2008), Simple and Efficient MKL Xu et. al (2010), and Dual augmented Lagrangian MKL
 Suzuki and Tomioka (2011) <doi:10.1007/s10994-011-5252-9>. These methods identify the convex combination of 
 candidate kernels to construct an optimal hyperplane.
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.
    Also streaming recommendation models are made available.
External jars required for package RMOA. RMOA is a framework to
    build data stream models on top of MOA (Massive Online Analysis -
    <http://moa.cms.waikato.ac.nz>). The jar files are put in this R package, the modelling logic can be found in the RMOA package.
A monadic solution to pipeline analysis. All operations -- and the errors,
    warnings and messages they emit -- are merged into a directed graph. Infix
    binary operators mediate when values are stored, how exceptions are
    handled, and where pipelines branch and merge. The resulting structure may
    be queried for debugging or report generation. 'rmonad' complements, rather
    than competes with, non-monadic pipeline packages like 'magrittr' or
    'pipeR'. This work is funded by the NSF (award number 1546858).
This is a meta-package designed to support the
        installation of Rmosek (>= 6.0) and bring the optimization
        facilities of MOSEK (>= 6.0) to the R-language. The interface
        supports large-scale optimization of many kinds: Mixed-integer
        and continuous linear, second-order cone, exponential cone and
        power cone optimization, as well as continuous semidefinite
        optimization. Rmosek and the R-language are open-source
        projects. MOSEK is a proprietary product, but unrestricted
        trial and academic licenses are available.
Provides wrapper functions to the Java Robot class to automate user input, like mouse movements, clicks and keyboard input.
Arithmetic (via S4 classes and methods) for
  arbitrary precision floating point numbers, including transcendental
  ("special") functions.  To this end, the package interfaces to
  the 'LGPL' licensed 'MPFR' (Multiple Precision Floating-Point Reliable) Library
  which itself is based on the 'GMP' (GNU Multiple Precision) Library.
An interface (wrapper) to MPI. It also 
	     provides interactive R manager and worker environment.
We implement causal mediation analysis using the methods proposed by Hong (2010) and Hong, Deutsch & Hill (2015) <doi:10.3102/1076998615583902>. It allows the estimation and hypothesis testing of causal mediation effects through ratio of mediator probability weights (RMPW). This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. We also implement a sensitivity analysis by extending the RMPW method to assess potential bias in the presence of omitted pretreatment or posttreatment covariates. The sensitivity analysis strategy was proposed by Hong, Qin, and Yang (2018) <doi:10.3102/1076998617749561>.
Analysis of oxygen consumption data generated by Loligo (R) Systems respirometry equipment. The package includes a function for loading data output by Loligo's 'AutoResp' software (get.witrox.data()), functions for calculating metabolic rates over user-specified time intervals, extracting critical points from data using broken stick regressions based on Yeager and Ultsch (<DOI:10.1086/physzool.62.4.30157935>), and easy functions for converting between different units of barometric pressure.
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.
Efficient solvers for 10 regularized multi-task learning algorithms applicable for regression, classification, joint feature selection, task clustering, low-rank learning, sparse learning and network incorporation. Based on the accelerated gradient descent method, the algorithms feature a state-of-art computational complexity O(1/k^2). Sparse model structure is induced by the solving the proximal operator. The detail of the package is described in the paper of Han Cao and Emanuel Schwarz (2018) <doi:10.1093/bioinformatics/bty831>.
Functions for working with the Tracy-Widom laws and other distributions 
    related to the eigenvalues of large Wishart matrices.
    The tables for computing the Tracy-Widom densities and distribution
    functions were computed by 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.
         MUMPS method was first described in Amestoy et al. (2001) <doi:10.1137/S0895479899358194>
         and Amestoy et al. (2006) <doi:10.1016/j.parco.2005.07.004>.
A toolkit of functions for nonlinear regression and repeated
    measurements not to be used by itself but called by other Lindsey packages such
    as 'gnlm', 'stable', 'growth', 'repeated', and 'event' 
    (available at <http://www.commanster.eu/rcode.html>).
An integrated set of tools to allow data users to conduct 
    meteorological normalisation on air quality data. This meteorological 
    normalisation technique uses predictive random forest models to remove 
    variation of pollutant concentrations so trends and interventions can be 
    explored in a robust way. For examples, see Grange et al. (2018) 
    <doi:10.5194/acp-18-6223-2018> and Grange and Carslaw (2019) 
    <doi:10.1016/j.scitotenv.2018.10.344>.
Legacy 'DBI' interface to 'MySQL' / 'MariaDB' based on old code
    ported from S-PLUS. A modern 'MySQL' client based on 'Rcpp' is available 
    from the 'RMariaDB' package.
Allows work with 'MyTarget API v2' 
	<https://target.my.com/doc/apiv2/ru/detailed.html> and
	load data by ads, campaigns, agency clients and statistic from
	your ads account.
Infer log-linear Poisson Graphical Model with an auxiliary data
    set. Hot-deck multiple imputation method is used to improve the reliability
    of the inference with an auxiliary dataset. Standard log-linear Poisson 
    graphical model can also be used for the inference and the Stability 
    Approach for Regularization Selection (StARS) is implemented to drive the 
    selection of the regularization parameter. The method is fully described in
    <doi:10.1093/bioinformatics/btx819>.
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>.
Interface to access data via the United States Department of 
  Agriculture's National Agricultural Statistical Service (NASS) 'Quick Stats' 
  web API <https://quickstats.nass.usda.gov/api>. Convenience functions 
  facilitate building queries based on available parameters and valid parameter 
  values.
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'.
Image data used as examples in the loon R package.
Provides a set of functions to access a data visualization web service. For more information and a tutorial on how to use it, see https://navicell.curie.fr/pages/nav_web_service.html and https://github.com/sysbio-curie/RNaviCell.
Use the <http://api.nbp.pl/> API through R. Retrieve
    currency exchange rates and gold prices data published by the
    National Bank of Poland in form of convenient R objects.
Provides the libraries of the EUtils operations for the
        RNCBI package.
Contains functions to retrieve, organize, and visualize weather data from the NCEP/NCAR Reanalysis (http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html) and NCEP/DOE Reanalysis II (http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis2.html) datasets.  Data are queried via the Internet and may be obtained for a specified spatial and temporal extent or interpolated to a point in space and time.  We also provide functions to visualize these weather data on a map.  There are also functions to simulate flight trajectories according to specified behavior using either NCEP wind data or data specified by the user.
An interface to the Nexus Class Library which allows parsing
    of NEXUS, Newick and other phylogenetic tree file formats. It provides
    elements of the file that can be used to build phylogenetic objects
    such as ape's 'phylo' or phylobase's 'phylo4(d)'. This functionality
    is demonstrated with 'read_newick_phylo()' and 'read_nexus_phylo()'.
Extract the implied risk neutral density from options using various methods.
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 are also available.
An implementation of a method based on information theory devised 
    for the identification of genes showing a significant variation of expression
    across multiple conditions. Given expression estimates from any number of 
    RNA-Seq samples and conditions it identifies genes or transcripts with a 
    significant variation of expression across all the conditions studied, 
    together with the samples in which they are over- or under-expressed. 
    Zambelli et al. (2018) <doi:10.1093/nar/gky055>.
Within this package the XML-RPC API to NEOS <https://neos-server.org/neos/> is implemented. This enables the user to pass optimization problems to NEOS and retrieve results within R.
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 text messages and tracing the flow of messages over
    time and across media.
Provides access to phyloinformatic data in 'NeXML' format.  The
    package should add new functionality to R such as the possibility to
    manipulate 'NeXML' objects in more various and refined way and compatibility
    with 'ape' objects.
Generation of univariate and multivariate data that follow the generalized Poisson distribution. The details of the univariate part are explained in Demirtas (2017), and the multivariate part is an extension of the correlated Poisson data generation routine that was introduced in Yahav and Shmueli (2012).
A function setVectorSeed() is provided. Its argument
        is a numeric vector of an arbitrary nonzero length, whose
        components have integer values from [0, 2^32-1]. The input
        vector is transformed using AES (Advanced Encryption Standard)
        algorithm into an initial state of Mersenne-Twister random
        number generator. The function provides a better alternative
        to the R base function set.seed(), if the input vector is
        a single integer. Initializing a stream of random numbers
        with a vector is a convenient way to obtain several streams,
        each of which is identified by several integer indices.
Provides a set of functions for working with
    Random Number Generators (RNGs). In particular, a generic
    S4 framework is defined for getting/setting the current RNG, or RNG data
    that are embedded into objects for reproducibility.
    Notably, convenient default methods greatly facilitate the way current
    RNG settings can be changed.
It is a dedicated package to WELL pseudo random generators, which were introduced in Panneton et al. (2006), ``Improved Long-Period Generators Based on Linear Recurrences Modulo 2'', ACM Transactions on Mathematical Software. But this package is not intended to be used directly, you are strongly __encouraged__ to use the 'randtoolbox' package, which depends on this package.
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 read and write access to images stored in the
    NIfTI-1 and ANALYZE-7.5 formats, with seamless synchronisation between
    compiled C and interpreted R code. Also provides a C/C++ API that can be
    used by other packages. Not to be confused with 'RNiftyReg', which performs
    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.
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.
Perform the complete processing of a set of proton nuclear magnetic resonance spectra from the free induction decay (raw data) and based on a processing sequence (macro-command file). An additional file specifies all the spectra to be considered by associating their sample code as well as the levels of experimental factors to which they belong. More detail can be found in Jacob et al. (2017) <doi:10.1007/s11306-017-1178-y>.
Implementation of a Recurrent Neural Network architectures in native R, including Long Short-Term Memory (Hochreiter and Schmidhuber, <doi:10.1162/neco.1997.9.8.1735>), Gated Recurrent Unit (Chung et al., <arXiv:1412.3555>) and vanilla RNN.
Client for many 'NOAA' data sources including the 'NCDC' climate
    'API' at <https://www.ncdc.noaa.gov/cdo-web/webservices/v2>, with functions for
    each of the 'API' 'endpoints': data, data categories, data sets, data types,
    locations, location categories, and stations. In addition, we have an interface
    for 'NOAA' sea ice data, the 'NOAA' severe weather inventory, 'NOAA' Historical
    Observing 'Metadata' Repository ('HOMR') data, 'NOAA' storm data via 'IBTrACS',
    tornado data via the 'NOAA' storm prediction center, and more.
An interface to the National Oceanic and Atmospheric Administration's Operational Model Archive and Distribution System (NOMADS, see <http://nomads.ncep.noaa.gov/> for more information) that allows R users to quickly and efficiently download global and regional weather model data for processing.  rNOMADS currently supports a variety of models ranging from global weather data to an altitude of over 40 km, to high resolution regional weather models, to wave and sea ice models. It can also retrieve 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.
Genetic association tests that use the rank-based inverse normal transformation (INT). These tests are recommend for continuous traits with non-normally distributed residuals. INT-based tests robustly control the type I error in settings where standard linear regression does not. Moreover, INT-based tests dominate standard linear regression in terms of power. INT-based tests may be classified into two types: tests that directly transform the phenotype (D-INT) and tests that transform phenotypic residuals (I-INT). Our omnibus test (O-INT) adaptively combines D-INT and I-INT into a single robust and statistically powerful approach.
Programmatic interface to the
    Web Service methods provided by the National 'Phenology' Network
    (<https://usanpn.org/>), which includes data on various life history
    events that occur at specific times.
Apply sensitivity analysis for offline policy evaluation, as
    implemented in Jung et al. (2017) <arXiv:1702.04690> based
    on Rosenbaum and Rubin (1983) <http://www.jstor.org/stable/2345524>.
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 Unpaywall <https://unpaywall.org/products/api>, formerly
    oaDOI, a service finding free full-texts of academic papers by linking DOIs with 
    open access journals and repositories. It provides unified access to various data sources 
    for open access full-text links including Crossref and the Directory of Open Access 
    Journals (DOAJ). API usage is free and no registration is required.
A collection of methods for the robust analysis of univariate and
    multivariate functional data, possibly in high-dimensional cases, and hence
    with attention to computational efficiency and simplicity of use.
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.
Conditionally unbiased bounded influence estimates as described in 
  Kuensch et al. (1989) <doi:10.1080/01621459.1989.10478791> in three special cases of 
  the generalized linear model: Bernoulli, Binomial, and Poisson distributed responses.
Methods for analysis of compositional data including robust
    methods, 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.
Provides robust methods to detect change-points in uni- or multivariate time series. They can cope with corrupted data and heavy tails. One can detect changes in location, scale and dependence structure of a possibly multivariate time series. Procedures are based on Huberized versions of CUSUM tests proposed in Duerre and Fried (2019) <arXiv:1905.06201>.
Locations problems, M-estimates of coefficients and scale
        in linear regression, Weights for bounded influence regression,
        Covariance matrix of the coefficient estimates, Asymptotic
        relative efficiency of regression M-estimates, Robust testing
        in linear models, High breakdown point regression, M-estimates
        of covariance matrices, M-estimates for discrete generalized
        linear models.
We provide an outlier robust alternative of the function ets() in the 'forecast' package of Hyndman and Khandakar (2008) <DOI:10.18637/jss.v027.i03>. For each method of a class of exponential smoothing variants we made a robust alternative. The class includes methods with a damped trend and/or seasonal components. The robust method is developed by robustifying every aspect of the original exponential smoothing variant. We provide robust forecasting equations, robust initial values, robust smoothing parameter estimation and a robust information criterion. The method is described in more detail in Crevits and Croux (2016) <DOI:10.13140/RG.2.2.11791.18080>.
Optimally robust estimation for extreme value distributions using S4 classes and
            methods (based on packages 'distr', 'distrEx', 'distrMod', 'RobAStBase', and
            'ROptEst').
A set of functions to filter time series based on concepts
        from robust statistics.
Contains a single function named robFitConGraph() which includes two algorithms for robust estimation of scatter matrices subject to zero-constraints in its inverse. The methodology is described in Vogel & Tyler (2014) <doi:10.1093/biomet/asu041>. See robFitConGraph() function documentation for further details.
Execute API calls to the RobinHood <https://robinhood.com> investing platform. Functionality includes accessing account data and current holdings, retrieving investment statistics and quotes, placing and canceling orders, getting market trading hours, searching investments by popular tag, and interacting with watch lists.
Client for the Ocean Biogeographic Information System (<https://obis.org>).
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 (fast and robust) bootstrap test.
Robust generalized linear models (GLM) using a mixture method, as described in Beath (2018) <doi:10.1080/02664763.2017.1414164>. This assumes that the data are a mixture of standard observations, being a generalised linear model, and outlier observations from an overdispersed generalized linear model. The overdispersed linear model is obtained by including a normally distributed random effect in the linear predictor of the generalized linear model.
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.
Companion package for the book: "Robust Statistics: Theory and Methods, second edition", <http://www.wiley.com/go/maronna/robust>.  This package contains code that implements the robust estimators discussed in the recent second edition of the book above, as well as the scripts reproducing all the examples in the book.
Multiple functions to compute robust survey statistics. The package 
    supports the computations of robust means, totals, and ratios. Available 
    methods are Huber M-estimators, trimming, and winsorization. The package 
    'robsurvey' complements the 'survey' package. The package additionally 
    includes a weighted version of the resistant line function of base R (line()), 
    as well as two median based simple regression estimators. The methods are 
    described in Hulliger (1995) 
    <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X199500114407/>.
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>.
Implements full Bayesian analysis for calibrating mathematical models with new methodology for modeling the discrepancy function. It allows for emulation, calibration and prediction using complex mathematical model outputs and experimental data. See the reference: Mengyang Gu and Long Wang (2017) <arXiv:1707.08215>.
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. It allows for robust parameter estimation and prediction using Gaussian stochastic process emulator. It also implements the parallel partial Gaussian stochastic process emulator for computer model with massive outputs See the reference: Mengyang Gu and Jim Berger, 2016, Annals of Applied Statistics; Mengyang Gu, Xiaojing Wang and Jim Berger, 2018, Annals of Statistics.
Robust methods for high-dimensional data, in particular linear
    model selection techniques based on least angle regression and sparse
    regression.
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 two-sample tests for paired data with missing values (Fong, Huang, Lemos and McElrath 2018, Biostatics, <doi:10.1093/biostatistics/kxx039>) and modified Wilcoxon-Mann-Whitney two sample location test, also known as the Fligner-Policello test.
Methods for aggregating ranked lists, especially lists of
        genes. It implements the Robust Rank Aggregation (Kolde et. al
        in preparation) and some other simple algorithms for the task.
        RRA method uses a probabilistic model for aggregation that is
        robust to noise and also facilitates the calculation of
        significance probabilities for all the elements in the final
        ranking.
A collection of functions to compute the Rao-Stirling diversity index
	(Porter and Rafols, 2009) <DOI:10.1007/s11192-008-2197-2> and its extension to
	acknowledge missing data (i.e.,	uncategorized references) by calculating its
	interval of uncertainty using	mathematical optimization as proposed in Calatrava
	et al. (2016) <DOI:10.1007/s11192-016-1842-4>.
	The Rao-Stirling diversity index is a well-established bibliometric indicator
	to measure the interdisciplinarity of scientific publications. Apart from the
	obligatory dataset of publications with their respective references and	a
	taxonomy of disciplines that categorizes references as well as a measure of
	similarity between the disciplines, the Rao-Stirling diversity index requires
	a complete categorization of all references of a publication into disciplines.
	Thus, it fails for a incomplete categorization; in this case, the robust
	extension has to be used, which encodes the uncertainty caused by missing
	bibliographic data as an uncertainty interval.
	Classification / ACM - 2012: Information systems ~ Similarity measures,
	Theory of computation ~ Quadratic	programming, Applied computing ~ Digital
	libraries and archives.
Linear regression functions using Huber and bisquare psi functions. Optimal weights are calculated using IRLS algorithm.
Functions for Robust Bayesian Small Area Estimation.
Robust single cell clustering and comparison of population compositions across tissues and 
  experimental models via similarity analysis from Magen 2019 <doi:10.1101/543199>.
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.
Sensitivity (or recall or true positive rate), false positive rate, specificity, precision (or positive predictive value), negative predictive value, misclassification rate, accuracy, F-score- these are popular metrics for assessing performance of binary classifier for certain threshold. These metrics are calculated at certain threshold values. Receiver operating characteristic (ROC) curve is a common tool for assessing overall diagnostic ability of the binary classifier. Unlike depending on a certain threshold, area under ROC curve (also known as AUC), is a summary statistic about how well a binary classifier performs overall for the classification task. ROCit package provides flexibility to easily evaluate threshold-bound metrics. Also, ROC curve, along with AUC, can be obtained using different methods, such as empirical, binormal and non-parametric. ROCit encompasses a wide variety of methods for constructing confidence interval of ROC curve and AUC. ROCit also features the option of constructing empirical gains table, which is a handy tool for direct marketing. The package offers options for commonly used visualization, such as, ROC curve, KS plot, lift plot. Along with in-built default graphics setting, there are rooms for manual tweak by providing the necessary values as function arguments. ROCit is a powerful tool offering a range of things, yet it is very easy to use.
The Reproducible Open Coding Kit ('ROCK', and this package, 'rock')
  was developed to facilitate reproducible and open coding, specifically
  geared towards qualitative research methods. Although it is a
  general-purpose toolkit, three specific applications have been
  implemented, specifically an interface to the 'rENA' package that
  implements Epistemic Network Analysis ('ENA'), means to process notes
  from Cognitive Interviews ('CIs'), and means to work with a decentralized
  construct taxonomy ('DCT').
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.
Provides the robust gamma rank correlation coefficient as introduced by
	     Bodenhofer, Krone, and Klawonn (2013) <DOI:10.1016/j.ins.2012.11.026>
	     along with a permutation-based rank correlation test.
	     The rank correlation coefficient and the test are explicitly
	     designed for dealing with noisy numerical data.
ROC graphs, sensitivity/specificity curves, lift charts,
  and precision/recall plots are popular examples of trade-off
  visualizations for specific pairs of performance measures. ROCR is a
  flexible tool for creating cutoff-parameterized 2D performance curves
  by freely combining two from over 25 performance measures (new
  performance measures can be added using a standard interface).
  Curves from different cross-validation or bootstrapping runs can be
  averaged by different methods, and standard deviations, standard
  errors or box plots can be used to visualize the variability across
  the runs. The parameterization can be visualized by printing cutoff
  values at the corresponding curve positions, or by coloring the
  curve according to cutoff. All components of a performance plot can
  be quickly adjusted using a flexible parameter dispatching
  mechanism. Despite its flexibility, ROCR is easy to use, with only
  three commands and reasonable default values for all optional
  parameters.
Plots the Receiver Operating Characteristics Surface for high-throughput class-skewed data, calculates the Volume under the Surface (VUS) and the FDR-Controlled Area Under the Curve (FCAUC), and conducts tests to compare two ROC surfaces. Computes eROC curve and the corresponding AUC for imperfect reference standard.
We develop the entire solution paths for ROC-SVM presented by Rakotomamonjy. The ROC-SVM solution path algorithm greatly facilitates the tuning procedure for regularization parameter, lambda in ROC-SVM by avoiding grid search algorithm which may be computationally too intensive. For more information on the ROC-SVM, see the report in the ROC Analysis in AI workshop(ROCAI-2004) : Hernàndez-Orallo, José, et al. (2004) <doi:10.1145/1046456.1046489>.
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>.
Receiver Operating Characteristic (ROC)-guided survival trees and forests algorithms are implemented, providing a unified framework for tree-structured analysis with censored survival outcomes. A time-invariant partition scheme on the survivor population was considered to incorporate time-dependent covariates. Motivated by ideas of randomized tests, generalized time-dependent ROC curves were used to evaluate the performance of survival trees and establish the optimality of the target hazard function. The optimality of the target hazard function motivates us to use a weighted average of the time-dependent area under the curve (AUC) on a set of time points to evaluate the prediction performance of survival trees and to guide splitting and pruning. A detailed description of the implemented methods can be found in Sun et al. (2019) <arXiv:1809.05627>.
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.
	More information can be found on the 'ROI' homepage <http://roi.r-forge.r-project.org/>.
A collection of non-linear optimization problems with box bounds
             transformed into 'ROI' optimization problems.
             This package provides a wrapper around the 'globalOptTests'
             which provides a collection of global optimization problems.
             More information can be found in the 'README' file.
The mixed integer programming library 'MIPLIB' (see <http://miplib.zib.de/>) 
	is commonly used to compare the performance of mixed integer optimization solvers.
	This package provides functions to access 'MIPLIB' from the 
	'R' Optimization Infrastructure ('ROI'). More information about 'MIPLIB'
	can be found in the paper by Koch et al. available at
	<http://mpc.zib.de/index.php/MPC/article/viewFile/56/28>.
	The 'README.md' file illustrates how to use this package.
A collection of 'ROI' optimization problems based on the 'NETLIB-LP' collection.
  'Netlib' is a software repository, which amongst many other software for scientific computing contains a collection of linear programming problems. The purpose of this package is to make 
  this problems easily accessible from 'R' as 'ROI' optimization problems.
Enhances the R Optimization Infrastructure ('ROI') package
        with the 'alabama' solver for solving nonlinear optimization problems.
Enhances the R Optimization Infrastructure (ROI) package by registering
	     the 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 a connection to the 'neos' server. 'ROI' optimization
             problems can be directly be sent to the 'neos' server
             and solution obtained in the typical 'ROI' style.
Enhances the R Optimization Infrastructure ('ROI') package
        with the 'NLopt' solver for solving nonlinear optimization problems.
Enhances the R Optimization Infrastructure ('ROI') package
        with the 'optimx' package.
Enhances the 'R' Optimization Infrastructure ('ROI') package
         with the quadratic solver 'qpOASES'. More information about
         'qpOASES' can be found at <https://projects.coin-or.org/qpOASES/>.
Enhances the R Optimization Infrastructure ('ROI') package
	     by registering the 'quadprog' solver. It allows for solving quadratic programming (QP)
	     problems.
Enhances the 'R' Optimization Infrastructure ('ROI') package
         with the 'SCS' solver for solving convex cone problems.
Enhances the R Optimization Infrastructure ('ROI') package by registering
	     the 'SYMPHONY' open-source solver from the COIN-OR suite. It allows for
	     solving mixed integer linear programming (MILP) problems as well as all
	     variants/combinations of LP, IP.
Fast and efficient computation of rolling statistics for time-series data.
Reports errors and messages to Rollbar, the error tracking platform <https://rollbar.com>.
R Markdown output formats based on JavaScript libraries such as
    'Scrollama' (<https://github.com/russellgoldenberg/scrollama>) for storytelling.
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, Liu, Larsen, Kafali, Gandhi, Amico, and Hoerger (2018) <https://onlinelibrary.wiley.com/doi/abs/10.1111/1475-6773.13086>.
Methods for fast rolling and expanding linear regression models. That is, series of linear regression models estimated on either an expanding window of data or a moving window of data. The methods use rank-one updates and downdates of the upper triangular matrix from a QR decomposition (see Dongarra, Moler, Bunch, and Stewart (1979) <doi:10.1137/1.9781611971811>).
Functions to convert an R colour specification to a colour name. The user can select and create different lists of colour names and different colour metrics for the conversion.
A colour list and colour metric based on the ISCC-NBS System of Color Designation for use with the 'roloc' package for converting colour specifications to colour names.
Implements the rank-ordered logit (RO-logit) model for stratified 
    analysis of continuous outcomes introduced by 
    Tan et al. (2017) <doi:10.1177/0962280217747309>. Model diagnostics based on 
    the heuristic residuals and estimates in linear scales are available from 
    the package, and outcomes with ties are supported.
Provides fast procedures for exploring all pairs of
    cutpoints of a single covariate with respect to survival and determining
    optimal cutpoints using a hierarchical method and various ordered logrank tests.
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 Firely.
This package contains the Rook specification and
 convenience software for building and running Rook applications. To
 get started, be sure and read the 'Rook' help file first.
A set of tools to reconstruct ordered ontogenic trajectories from
    single cell RNAseq data.
Implementation of Taylor Regression Estimator (TRE), 
   Tulip Extreme Finding Estimator (TEFE), Bell Extreme Finding Estimator (BEFE),
   Integration Extreme Finding Estimator (IEFE) and 
   Integration Root Finding Estimator (IRFE) for roots, extrema and inflections of a curve .     
   Christopoulos, DT (2019) <doi:10.13140/RG.2.2.17158.32324> .
   Christopoulos, DT (2016) <doi:10.2139/ssrn.3043076> .
   Christopoulos, DT (2016) <https://veltech.edu.in/wp-content/uploads/2016/04/Paper-04-2016.pdf> .
   Christopoulos, DT (2014) <arXiv:1206.5478v2 [math.NA]> .
Routines to find the root of nonlinear functions, and to perform steady-state and equilibrium analysis of ordinary differential equations (ODE). 
  Includes routines that: (1) generate gradient and jacobian matrices (full and banded),
  (2) find roots of non-linear equations by the 'Newton-Raphson' method, 
  (3) estimate steady-state conditions of a system of (differential) equations in full, banded or sparse form, using the 'Newton-Raphson' method, or by dynamically running,
  (4) solve the steady-state conditions for uni-and multicomponent 1-D, 2-D, and 3-D partial differential equations, that have been converted to ordinary differential equations
    by numerical differencing (using the method-of-lines approach).
  Includes fortran code.
Functions for hypothesis testing in single and double Wishart
    settings, based on Roy's largest root. This test statistic is especially
    useful in multivariate analysis. The computations are based on results by
    Chiani (2014) <DOI:10.1016/j.jmva.2014.04.002> and Chiani (2016)
    <DOI:10.1016/j.jmva.2015.10.007>. They use the fact that the CDF is related
    to the Pfaffian of a matrix that can be computed in a finite number of
    iterations. This package takes advantage of the Boost and Eigen C++ libraries
    to perform multi-precision linear algebra.
Trees Classification and Regression using multivariate nodes calculated by an exhaustive numerical approach. We propose a new concept of decision tree, including multivariate knots and non hierarchical pathway. This package's model uses a multivariate nodes tree that calculates directly a risk score for each observation for the state Y observed. Nguyen JM, Gaultier A, Antonioli D (2015) <doi:10.1016/j.respe.2018.03.088> Castillo JM, Knol AC, Nguyen JM, Khammari A, Saint Jean M, Dreno B (2016) <doi:10.1684/ejd.2016.2826> Vildy S, Nguyen JM, Gaultier A, Khammari A, Dreno B (2017) <doi:10.1684/ejd.2016.2955> Nguyen JM, Gaultier A, Antonioli D (2018) <doi:10.1016/j.respe.2018.03.088>.
Selects one model with variable selection FDR controlled at a
    specified level. A q-value for each potential variable is also returned. The
    input, variable selection counts over many bootstraps for several levels of
    penalization, is modeled as coming from a beta-binomial mixture
    distribution.
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). This package has been peer-reviewed by rOpenSci (v. 0.1.0).
Installs 'OpenCV' for use by other packages. 'OpenCV' <https://opencv.org/> 
    is library of programming functions mainly aimed at real-time computer 
    vision. This 'Lite' version contains the stable base version of 'OpenCV' and 
    does not contain any of its externally contributed modules.
'Rapid7' collects 'cybersecurity' data and makes it available via
    their 'Open Data' <http://opendata.rapid7.com> portal which has an API. Tools are
    provided to assist in querying for available data sets and downloading any
    data set authorized to a free, registered account.
Provides a client for the API of OpenDota. OpenDota is a web service which is provide DOTA2 real time data. Data is collected through the Steam WebAPI. With ROpenDota you can easily grab the latest DOTA2 statistics in R programming such as latest match on official international competition, analyzing your or enemy performance to learn their strategies,etc. Please see <https://github.com/rosdyana/ROpenDota> for more information.
Provide a simple interface to Bloomberg's OpenFIGI API. Please
    see <https://openfigi.com> for API details and registration. You may be
    eligible to have an API key to accelerate your loading process.
An R package for the OpenSecrets.org web services API.
OpenWeatherMap (OWM) <http://openweathermap.org/api> is a service providing weather related data.
           This package can be used to access current weather data for one location or several locations.
           It can also be used to forecast weather for 5 days with data for every 3 hours.
Provides string arithmetic, reassignment operators, logical operators that handle missing values, and extra logical operators such as floating point equality and all or nothing. The intent is to allow R users to write code that is easier to read, write, and maintain while providing a friendlier experience to new R users from other language backgrounds (such as 'Python') who are used to concepts such as x += 1 and 'foo' + 'bar'.
Reproducible, programmatic retrieval of datasets from the
    Roper Center data archive.  The Roper Center for Public Opinion
    Research <https://ropercenter.cornell.edu> maintains the largest 
    archive of public opinion data in existence, but researchers using
    these datasets are caught in a bind.  The Center's terms and conditions
    bar redistribution of downloaded datasets, but to ensure that one's 
    work can be reproduced, assessed, and built upon by others, one must
    provide access to the raw data one employed.  The `ropercenter`
    package cuts this knot by providing registered users with programmatic,
    reproducible access to Roper Center datasets from within R.
Read the data from Origin(R) project files ('*.opj')
	<https://www.originlab.com/doc/User-Guide/Origin-File-Types>.
	No write support is planned. More object types may be available
	to be imported later.
A suite of classes and methods which provide low-level support for modeling provenance between in-memory R objects. This is an infrastructure package and is not intended to be used directly by end-users.
Optimally robust estimation in general smoothly parameterized models using S4
            classes and methods.
Optimally robust estimation using S4 classes and methods. Old version still
            needed for current versions of ROptRegTS and RobRex.
Perform general purpose optimization in R using C++. A unified 
    wrapper interface is provided to call C functions of the five optimization 
    algorithms ('Nelder-Mead', 'BFGS', 'CG', 'L-BFGS-B' and 'SANN') underlying 
    optim().
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 (<https://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.
When teaching statistics, it can often be desirable to
  uncouple the content from specific software packages. To ease such
  efforts, the Rosetta Stats website (<https://rosettastats.com>) allows
  comparing analyses in different packages. This package is the companion
  to the Rosetta Stats website, aiming to provide functions that produce
  output that is similar to output from other statistical packages, thereby
  facilitating 'software-agnostic' teaching of statistics.
'Rosette' is an API for multilingual text analysis and information
    extraction. More information can be found at <https://developer.rosette.com>.
Download and plot Open Street Map <http://www.openstreetmap.org/>,
    Bing Maps <http://www.bing.com/maps> and other tiled map sources. Use to create 
    basemaps quickly and add hillshade to vector-based maps.
Implementation of robust sparse PCA using the ROSPCA algorithm 
             of Hubert et al. (2016) <DOI:10.1080/00401706.2015.1093962>.
Provides bindings to the 'OSQP' solver, which can solve sparse convex quadratic programming problems with optional equality and inequality constraints.
Creates reproducible academic projects with integrated academic elements, including datasets, references, codes, images, manuscripts, dissertations, slides and so on. These elements are well connected so that they can be easily synchronized and updated.
Implementation of the tests for rotational symmetry on the
     hypersphere proposed in García-Portugués, Paindaveine and Verdebout
     (2019) <arXiv:1706.05030>. The package also implements the proposed
     distributions on the hypersphere, based on the tangent-normal
     decomposition, and allows for the replication of the data
     application considered in the paper.
Fit and deploy rotation forest models ("Rodriguez, J.J., Kuncheva,
    L.I., 2006. Rotation forest: A new classifier ensemble method. IEEE Trans.
    Pattern Anal. Mach. Intell. 28, 1619-1630 <doi:10.1109/TPAMI.2006.211>") for binary classification.
    Rotation forest is an ensemble method where each base classifier (tree) is
    fit on the principal components of the variables of random partitions of
    the feature set.
Tools for working with rotational data, including
    simulation from the most commonly used distributions on SO(3),
    methods for different Bayes, mean and median type estimators for
    the central orientation of a sample, confidence/credible
    regions for the central orientation based on those estimators and
    a novel visualization technique for rotation data.  Most recently,
    functions to identify potentially discordant (outlying) values
    have been added.
R build of Rothermel's (1972) model for surface fire rate of spread with some additional utilities (uncertainty propagation, standard fuel model selection, fuel model optimization by genetic algorithm) and sample datasets.
An interface to the 'Open Tree of Life' API to retrieve
    phylogenetic trees, information about studies used to assemble the synthetic
    tree, and utilities to match taxonomic names to 'Open Tree identifiers'. The
    'Open Tree of Life' aims at assembling a comprehensive phylogenetic tree for all
    named species.
Conditionally rotate or back-up files based on
    their size or the date of the last backup; inspired by the 'Linux'
    utility 'logrotate'.
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.
Visualize the objects in orbits in 2D and 3D. The packages is under developing to plot the orbits of objects in polar coordinate system. See the examples in demo.
An R wrapper to the 'Internet Chuck Norris database' ('ICNDb') API for 
  generating random Chuck Norris facts.
Detecting outliers using robust methods, 
 	i.e. the Median Absolute Deviation (MAD) for univariate outliers; Leys, Ley, Klein, Bernard, & Licata (2013) <doi:10.1016/j.jesp.2013.03.013>
        and the Mahalanobis-Minimum Covariance Determinant (MMCD) for multivariate outliers; Leys, C., Klein, O., Dominicy, Y. & Ley, C. (2018) <doi:10.1016/j.jesp.2017.09.011>.
        There is also the more known but less robust Mahalanobis distance method, only for comparison purposes.
In order to make sure that web request ends up in the correct 
    handler function a router is often used. 'routr' is a package implementing a
    simple but powerful routing functionality for R based servers. It is a fully
    functional 'fiery' plugin, but can also be used with other 'httpuv' based
    servers.
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++.
Converts elements of 'roxygen' documentation to
    'markdown'.
Performs a multivariate normality test based on Royston's H test
Analysis of corneal data obtained from a Placido disk corneal 
    topographer with calculation of irregularity indices. A corneal topographer is 
    an ophthalmic clinical device that obtains measurements in the cornea (the anterior 
    part of the eye). A Placido disk corneal topographer makes use of the Placido disk 
    (Rowsey et al. (1981), <doi:10.1001/archopht.1981.03930011093022>, Rand et al.
    (1997), <doi:10.1016/S0886-3350(99)00355-7>), which produce a circular pattern
    of measurement nodes. The raw information measured by such a topographer is 
    used by practitioners to analyze curvatures, to study optical aberrations, or
    to diagnose specific conditions of the eye. The rPACI package allows the 
    calculation of the corneal irregularity indices described in Ramos-Lopez et al.
    (2013), <doi:10.1097/OPX.0b013e3182843f2a>, and that were firstly introduced in
    Ramos-Lopez et al. (2011), <doi:10.1097/OPX.0b013e3182843f2a>. It provides a 
    simple interface to read corneal topography data files as exported by a typical
    Placido disk topographer, to compute the irregularity indices mentioned before,
    and to display summary plots that are easy to interpret for a clinician.
Packed bar charts are a variation of treemaps for visualizing skewed data.  The concept was introduced by Xan Gregg at 'JMP'.
Design and analysis of confirmatory adaptive clinical trials with continuous, binary, and survival endpoints according to the methods described in the monograph by Wassmer and Brannath (2016) <doi:10.1007/978-3-319-32562-0>. This includes classical group sequential as well as multi-stage adaptive hypotheses tests that are based on the combination testing principle.
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 present.
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.
Computes the influence functions time series of the returns for the risk and 
             performance measures as mentioned in Zhang and Martin (2017) 
             <https://ssrn.com/abstract=2747179> as well as Chen and Martin (2018) 
             <https://ssrn.com/abstract=3085672>. Also evaluates estimators influence
             functions at a set of parameter values and plots them to display the shapes of 
             the influence functions.
Implements the methodology of "Cannings, T. I. and Samworth, R. J. (2017) Random-projection ensemble classification, J. Roy. 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.