Provides a compact variation of the usual syntax of function
  declaration, in order to support tidyverse-style quasiquotation of a
  function's arguments and body.
The NOIA model, as described extensively in Alvarez-Castro & Carlborg (2007), is a framework facilitating the estimation of genetic effects and genotype-to-phenotype maps. This package provides the basic tools to perform linear and multilinear regressions from real populations (provided the phenotype and the genotype of every individuals), estimating the genetic effects from different reference points, the genotypic values, and the decomposition of genetic variances in a multi-locus, 2 alleles system. This package is presented in Le Rouzic & Alvarez-Castro (2008).
Functions to calculate estimates of intrinsic and extrinsic noise from the two-reporter single-cell experiment, as in Elowitz, M. B., A. J. Levine, E. D. Siggia, and P. S. Swain (2002) Stochastic gene expression in a single cell. Science, 297, 1183-1186.  Functions implement multiple estimators developed for unbiasedness or min Mean Squared Error (MSE) in Fu, A. Q. and Pachter, L. (2016). Estimating intrinsic and extrinsic noise from single-cell gene expression measurements. Statistical Applications in Genetics and Molecular Biology, 15(6), 447-471.
An extensive implementation of state-of-the-art and classical
    algorithms to preprocess label noise in classification problems.
Cross-Entropy optimisation of unconstrained deterministic and noisy
    functions illustrated in Rubinstein and Kroese (2004, ISBN:
    978-1-4419-1940-3) through a highly flexible and customisable function which 
    allows user to define custom variable domains, sampling distributions,
    updating and smoothing rules, and stopping criteria. Several built-in
    methods and settings make the package very easy-to-use under standard
    optimisation problems.
Quantifies and removes technical noise from high-throughput 
        sequencing data. Two approaches are used, one based on the count
        matrix, and one using the alignment BAM files directly.
        Contains several options for every step of the process, as well
        as tools to quality check and assess the stability of output.
Variational Expectation-Maximization algorithm to fit the noisy stochastic block model to an observed dense graph 
    and to perform a node clustering. Moreover, a graph inference procedure to recover the underlying 
    binary graph. This procedure comes with a control of the false discovery rate. The method is described
    in the article "Powerful graph inference with false discovery rate control" by T. Rebafka, 
    E. Roquain, F. Villers (2020) <arXiv:1907.10176>.
Converts numeric vectors to character vectors of English
    number names. Provides conversion to cardinals, ordinals, numerators,
    and denominators. Supports negative and non-integer numbers.
Similarity measures for hierarchical clustering of objects characterized by
    nominal (categorical) variables. Evaluation criteria for nominal data clustering.
Analysis of a matrix of polytomous items using Nominal Logistic Biplots (NLB)
  according to Hernandez-Sanchez and Vicente-Villardon (2013). 
  The NLB procedure extends the binary logistic biplot to nominal (polytomous) data. 
  The  individuals are represented as points on a plane and the  variables are represented 
  as convex prediction regions rather than vectors as in a classical or binary biplot. 
  Using the methods from Computational Geometry, the set of prediction regions is converted to a set of points 
  in such a way that the prediction for each individual is established by its closest 
  "category point". Then interpretation is based on distances rather than on projections. 
  In this package we implement the geometry of such a representation and construct computational algorithms 
  for the estimation of parameters and the calculation of prediction regions.
Lite interface for getting data from 'OSM' service
    'Nominatim' <https://nominatim.org/release-docs/latest/>. Extract
    coordinates from addresses, find places near a set of coordinates,
    search for amenities and return spatial objects on 'sf' format.
Access UK official statistics from the 'Nomis' database. 
    'Nomis' includes data from the Census, the Labour Force Survey, DWP benefit 
    statistics and other economic and demographic data from the Office for 
    National Statistics, based around statistical geographies. See 
    <https://www.nomisweb.co.uk/api/v01/help> for full API documentation.
A tool for drawing sassy 'UML' diagrams based on a simple syntax,
  see <https://www.nomnoml.com>. Supports styling, R Markdown and exporting diagrams 
  in the PNG format.
A nomogram can not be easily applied,
    because it is difficult to calculate the points or even the survival probability.
  The package, including a function of nomogramEx(),
    is to extract the polynomial equations to calculate the points of each variable,
    and the survival probability corresponding to the total points.
A nomogram, which can be carried out in 'rms' package, provides a 
    graphical explanation of a prediction process. However, it is not very easy
    to draw straight lines, read points and probabilities accurately. Even, it 
    is hard for users to calculate total points and probabilities for all 
    subjects.
  This package provides formula_rd() and formula_lp() functions to fit the 
    formula of total points with raw data and linear predictors respectively by
    polynomial regression. Function points_cal() will help you calculate the 
    total points. prob_cal() can be used to calculate the probabilities after
    lrm(), cph() or psm() regression. 
  For more complex condition, interaction or restricted cubic spine, TotalPoints.rms() 
    can be used.
Implements the nomord_probe() function, which performs a statistical analysis on an input vector (sequence) of nominal (categorical) values.
Conduct a noncompartmental analysis with industrial strength.
             Some features are
             1) Use of CDISC SDTM terms
             2) Automatic or manual slope selection
             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>.
Function and data sets in the book entitled "Nonlinear Time Series Analysis with R Applications" B.Guris (2020). The book will be published in Turkish and the original name of this book will be "R Uygulamali Dogrusal Olmayan Zaman Serileri Analizi". It is possible to perform nonlinearity tests, nonlinear unit root tests, nonlinear cointegration tests and estimate nonlinear error correction models by using the functions written in this package. The Momentum Threshold Autoregressive (MTAR), the Smooth Threshold Autoregressive (STAR) and the Self Exciting Threshold Autoregressive (SETAR) type unit root tests can be performed using the functions written. In addition, cointegration tests using the Momentum Threshold Autoregressive (MTAR), the Smooth Threshold Autoregressive (STAR) and the Self Exciting Threshold Autoregressive (SETAR) models can be applied. It is possible to estimate nonlinear error correction models. The Granger causality test performed using nonlinear models can also be applied.
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/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>.
Estimates and plots (as a heat map) the statistical significance of rolling window correlation coefficients, which is carried out through a non-parametric computing-intensive method. This method addresses the effects due to the multiple testing (inflation of the Type I error) when the statistical significance is estimated for the rolling window correlation coefficients. The method is based on Monte Carlo simulations by permuting one of the variables (dependent) under analysis and keeping fixed the other variable (independent). We improve the computational efficiency of this method to reduce the computation time through parallel computing. The 'NonParRolCor' package also provides examples with synthetic and real-life ecological time series to exemplify its use. Methods derived from R. Telford (2013) <https://quantpalaeo.wordpress.com/2013/01/04/> and J.M. Polanco-Martinez (2020) <doi:10.1016/j.ecoinf.2020.101163>.
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. Zambom and Akritas (2014) <doi:10.5705/ss.2013.112>, Zambom and Akritas (2015) <doi:10.1016/j.jmva.2014.08.014>, Zambom and Akritas (2017) <doi:10.18637/jss.v077.i10>.
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 for use with 'ggplot2' via custom functions.
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).
Package including an interactive Shiny application for
    testing normality visually.
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.
A 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".
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.
Despite that several tests for normality in stationary processes have been proposed
   in the literature, consistent implementations of these tests in programming languages are limited. 
   Four normality test are implemented. The Lobato and Velasco's, Epps, Psaradakis and  Vavra, and the 
   random projections tests for stationary process. Some other diagnostics such as, unit root test for 
   stationarity, seasonal tests for seasonality, and arch effect test for volatility; are also performed. 
   The package also offers residual diagnostic for linear time series models developed in several packages.
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.
The aim of 'nosoi' (pronounced no.si) is to provide a flexible agent-based stochastic transmission chain/epidemic simulator (Lequime et al. Methods in Ecology and Evolution 11:1002-1007). It is named after the daimones of plague, sickness and disease that escaped Pandora's jar in the Greek mythology. 'nosoi' is able to take into account the influence of multiple variable on the transmission process (e.g. dual-host systems (such as arboviruses), within-host viral dynamics, transportation, population structure), alone or taken together, to create complex but relatively intuitive epidemiological simulations.
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.
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!)
The dataset and model used in Lai et al. (2021) 
    Decoupled responses of native and exotic tree diversities to 
    distance from old-growth forest and soil phosphorous in 
    novel secondary forests. Applied Vegetation Science, 24, e12548.
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, <https://www.nserc-crsng.gc.ca/>), the Social Sciences and Humanities Research Council of Canada (SSHRC, <https://www.sshrc-crsh.gc.ca/>), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, <https://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, described in Manrique-Vallier and Reiter (2014) <doi:10.1080/10618600.2013.844700>.
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.
Nonparametric smoothing methods for density and regression estimation involving circular data, 
				including methods described in Oliveira et al. (2014) and proposals in Alonso-Pena et al. (2021).
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. Retrospective 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 package also contains a
  test sensitive to changes in the autocopula and a combined test of
  stationarity sensitive to changes in the distribution function and
  the autocopula. The latest additions are a closed-end sequential
  test based on empirical distribution functions that can be used for
  monitoring changes in the contemporary distribution of possibly
  serially dependent univariate or multivariate observations, and an
  open-end sequential test based on the retrospective CUSUM statistic
  that can be used for monitoring changes in the mean of possibly
  serially dependent univariate observations.
We connect the multi-class Neyman-Pearson classification (NP) problem to the cost-sensitive learning (CS) problem, and propose two algorithms (NPMC-CX and NPMC-ER) to solve the multi-class NP problem through cost-sensitive learning tools. Under certain conditions, the two algorithms are shown to satisfy multi-class NP properties. More details are available in the paper "Neyman-Pearson Multi-class Classification via Cost-sensitive Learning" (Ye Tian and Yang Feng, 2021), which will be posted on arXiv soon.
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 routines to compute normalised prediction distribution errors, a metric designed to evaluate non-linear mixed effect models such as those used in pharmacokinetics and pharmacodynamics. Documentation about 'npde' is provided by a comprehensive user guide on the github repository (<https://github.com/ecomets/npde30/blob/main/userguide_npde_3.0.pdf>), and references concerning the methods include the papers by Brendel et al. (2006, <doi:10.1007/s11095-006-9067-5>; 2010, <doi:10.1007/s10928-009-9143-7>), Comets et al. (2008, <doi:10.1016/j.cmpb.2007.12.002> ; 2010, <http://journal-sfds.fr/article/view/45>), and Nguyen et al. (2012, <doi:10.1007/s10928-012-9264-2>). See 'citation("npde")' for details.
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. Method is 
             detailed in: Hejblum, Alkhassimn, Gottardo, Caron & Thiebaut (2019) <doi: 10.1214/18-AOAS1209>.
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.
An implementation of the Nonparametric Predictive Inference approach in R. It provides tools for quantifying uncertainty via lower and upper probabilities. It includes useful functions for pairwise and multiple comparisons: comparing two groups with and without terminated tails, selecting the best group, selecting the subset of best groups, selecting the subset including the best group.
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).
We develop three procedures for estimation in a two-groups model with covariates. One of them is a nonparametric maximum likelihood estimation based approach when the signal distribution is an infinite Gaussian location mixture and the signal proportion is a logistic function of the available covariates. Two other functions - marg1() and marg2() have also been implemented for inference in the above framework. All these methods can be used for inference in multiple hypotheses testing. For more information, see the paper: Deb, Saha, Guntuboyina and Sen (2019), ''Two-component Mixture Model in the Presence of Covariates'' <arXiv:1810.07897v2>.
Nonparametric maximum likelihood estimation or Gaussian
        quadrature for overdispersed generalized linear models and
        variance component models.
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.
Provides genetic tools for colony management and is a derivation 
    of the work in Amanda Vinson and Michael J Raboin (2015) 
    <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4671785/> "A Practical 
    Approach for Designing Breeding Groups to Maximize Genetic Diversity in a 
    Large Colony of Captive Rhesus Macaques ('Macaca' 'mulatto')".
    It provides a 'Shiny' application with an exposed API. 
    The application supports five groups of functions: 
    (1) Quality control of studbooks contained in text files or 'Excel' 
    workbooks and of pedigrees within 'LabKey' Electronic Health Records 
    (EHR); 
    (2) Creation of pedigrees from a list of animals using the 'LabKey' EHR 
    integration; 
    (3) Creation and display of an age by sex pyramid plot of the living 
    animals within the designated pedigree; 
    (4) Generation of genetic value analysis reports; and 
    (5) Creation of potential breeding groups with and without proscribed sex 
    ratios and defined maximum kinships.
Partial informational correlation (PIC) is used to identify the meaningful predictors to the response from a large set of potential predictors. Details of Methodoligies used in the package can be found in Sharma, A., Mehrotra, R. (2014) <doi:10.1002/2013WR013845>, Sharma, A., Mehrotra, R., Li, J., & Jha, S. (2016). <doi:10.1016/j.envsoft.2016.05.021>, and Mehrotra, R., & Sharma, A. (2006). <doi:10.1016/j.advwatres.2005.08.007>.
Multiple and generalized nonparametric regression using smoothing spline ANOVA models and generalized additive models, as described in Helwig (2020) <doi:10.4135/9781526421036885885>. 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.
Estimating the first and second derivatives of a regression function by the method of Wang and Lin (2015) <http://www.jmlr.org/papers/v16/wang15b.html>.
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, <doi:10.1080/01621459.2017.1285776>): lprobust() for local polynomial point estimation and robust bias-corrected inference, lpbwselect() for local polynomial bandwidth selection, kdrobust() for kernel density point estimation and robust bias-corrected inference, kdbwselect() for kernel density bandwidth selection, and nprobust.plot() for plotting results. The main methodological and numerical features of this package are described in Calonico, Cattaneo and Farrell (2019, <doi:10.18637/jss.v091.i08>).
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.
Nonparametric efficiency measurement and statistical inference via DEA type estimators (see Färe, Grosskopf, and Lovell (1994) <doi:10.1017/CBO9780511551710>, Kneip, Simar, and Wilson (2008) <doi:10.1017/S0266466608080651> and Badunenko and Mozharovskyi (2020) <doi:10.1080/01605682.2019.1599778>) as well as Stochastic Frontier estimators for both cross-sectional data and 1st, 2nd, and 4th generation models for panel data (see Kumbhakar and Lovell (2003) <doi:10.1017/CBO9781139174411>, Badunenko and Kumbhakar (2016) <doi:10.1016/j.ejor.2016.04.049>). The stochastic frontier estimators can handle both half-normal and truncated normal models with conditional mean and heteroskedasticity. The marginal effects of determinants can be obtained.
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.
Non-parametric survival analysis of exact and
	     interval-censored observations. The methods implemented
	     are developed by Wang (2007)
	     <doi:10.1111/j.1467-9868.2007.00583.x>, Wang (2008)
	     <doi:10.1016/j.csda.2007.10.018>, Wang and Taylor (2013)
	     <doi:10.1007/s11222-012-9341-9> and Wang and Fani (2018)
	     <doi:10.1007/s11222-017-9724-z>.
A number of statistical tests have been proposed to compare two 
  survival curves, including the difference in (or ratio of) t-year 
  survival, difference in (or ratio of) p-th percentile survival, difference in
  (or ratio of) restricted mean survival time, and the weighted log-rank test. 
  Despite the multitude of options, the convention in survival studies is to assume
  proportional hazards and to use the unweighted log-rank test for design and 
  analysis. This package provides sample size and power 
  calculation for all of the above statistical tests with allowance for 
  flexible accrual, censoring, and survival (eg. Weibull, piecewise-exponential, 
  mixture cure). It is the companion R package to the paper by Yung and Liu (2019)
  <doi:10.1111/biom.13196>. Specific to the weighted log-rank test, users may 
  specify which approximations they wish to use to estimate the large-sample mean 
  and variance. The default option has been shown to provide substantial
  improvement over the conventional sample size and power equations based on Schoenfeld 
  (1981) <doi:10.1093/biomet/68.1.316>.
Robust nonparametric bootstrap and permutation tests for location, correlation, and regression problems, as described in Helwig (2019a) <doi:10.1002/wics.1457> and Helwig (2019b) <doi:10.1016/j.neuroimage.2019.116030>. Univariate and multivariate tests are supported. For each problem, exact tests and Monte Carlo approximations are available. Five different nonparametric bootstrap confidence intervals are implemented. Parallel computing is implemented via the 'parallel' package.
Implements the procedure from G. J. Ross (2021) - "Nonparametric Detection of Multiple Location-Scale Change Points via Wild Binary Segmentation" <arxiv:2107.01742>. This uses a version of Wild Binary Segmentation to detect multiple location-scale (i.e. mean and/or variance) change points in a sequence of univariate observations, with a strict control on the probability of incorrectly detecting a change point in a sequence which does not contain any.
Implements methods in Mathur and VanderWeele (in preparation) to characterize global evidence strength across W correlated ordinary least squares (OLS) hypothesis tests. Specifically, uses resampling to estimate a null interval for the total number of rejections in, for example, 95% of samples generated with no associations (the global null), the excess hits (the difference between the observed number of rejections and the upper limit of the null interval), and a test of the global null based on the number of rejections.
Calculating the net reclassification improvement (NRI) for risk prediction models with time to event and binary data.
Executes spatial nonstationary Fay-Herriot models for small area estimation.The empirical best linear unbiased 
    predictor (EBLUP) under stationary and nonstationary Fay-Herriot models along with the mean squared error estimation
    are included. EBLUP for prediction of non-sample area is also included under both stationary and nonstationary 
    Fay-Herriot models. This extension to the Fay-Herriot model that accounts for the presence  of spatial nonstationarity
    was developed by Hukum Chandra, Nicola Salvati and Ray Chambers (2015) <doi:10.1093/jssam/smu026>. This package is dedicated
    to the memory of Dr. Hukum Chandra who passed away while the package creation was in progress.
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.
Routines for fitting and simulating data under autoregressive fractionally integrated moving average (ARFIMA) models, without the constraint of covariance stationarity. Two fitting methods are implemented, a pseudo-maximum likelihood method and a minimum distance estimator. Mayoral, L. (2007) <doi:10.1111/j.1368-423X.2007.00202.x>. Beran, J. (1995) <doi:10.1111/j.2517-6161.1995.tb02054.x>.
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. For more details see Tanaka, Saga and Nakano
 <doi:10.18637/jss.v098.i06>.
Provides tools to implement both unsupervised and supervised nonlinear dimension reduction methods. Principal Component Analysis (PCA), Sliced Inverse Regression (SIR), and Sliced Average Variance Estimation (SAVE) are useful methods to reduce the dimensionality of covariates. However, they produce linear combinations of covariates. Kernel PCA, generalized SIR, and generalized SAVE address this problem by extending the applicability of the dimension reduction problem to nonlinear settings. This package includes a comprehensive algorithm for  kernel PCA, generalized SIR, and generalized SAVE, including methods for choosing tuning parameters and some essential functions.
Collection of functions designed to calculate numerical standard error (NSE) of univariate time series as described in Ardia et al. (2018) <doi:10.1515/jtse-2017-0011> and Ardia and Bluteau (2017) <doi:10.21105/joss.00172>.
Fetch data related to stocks, index, futures & options from the 
    'NSE (National Stock Exchange, India)'. This package is community maintained 
    and is not officially supported by 'NSE'. The accuracy of data is only as 
    correct as provided on <https://www.nseindia.com>.
Download Historical Bhavcopy and get Live Market data from NSE India of Equities and Derivatives(F&O) segment. Data source  <https://www.nseindia.com/>.
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 to official data via its 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.
Mainly for maximum likelihood estimation of nonparametric
             and semiparametric mixture models, but can also be used
             for fitting finite mixtures. The algorithms are
             developed in Wang (2007)
             <doi:10.1111/j.1467-9868.2007.00583.x> and Wang (2010)
             <doi:10.1007/s11222-009-9117-z>.
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 (Ning et al 2019) <doi:10.1073/pnas.1904623116>. A modified version, MST, is a special case of NST, used in some recent or upcoming publications, e.g. Liang et al (2020) <doi:10.1016/j.soilbio.2020.108023>. 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>. Version 3 added NST based on phylogenetic beta diversity, used by Ning et al (2020) <doi:10.1038/s41467-020-18560-z>.
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.
Counts syllables in character vectors for English words.  Imputes syllables as the number of vowel sequences for words not found.
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, John Wiley & Sons, 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. 
    See Volker J. Schmid, Marion Cremer, Thomas Cremer (2017) <doi:10.1016/j.ymeth.2017.03.013>.
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 (2019) Numero: a statistical framework to define multivariable subgroups in complex population-based datasets, Int J Epidemiology, 48:369-37, <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.
    A shiny app exposing the main functionalities of the package is also provided.
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>.
EM algorithm for fitting Bayesian varying coefficient models with the nonparametric varying coefficient spike-and-slab lasso of Bai et al. (2020) <arXiv:1907.06477>. Also fits penalized frequentist varying coefficient models with the group lasso, group smoothly clipped absolute deviation, and group minimax concave penalty.
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, Araujo-Voces and Quesada (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'.
Functions for working with (grouped) multivariate normal variance mixture
  distributions (evaluation of distribution functions and densities,
  random number generation and parameter estimation), including
  Student's t distribution for non-integer degrees-of-freedom as well as the grouped t
  distribution and copula with multiple degrees-of-freedom parameters.
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.
Access the New Zealand Freshwater Fish Database from R and a few functions to clean the data once in R.
'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 20210601.1528 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>.
Bayesian reconstruction of who infected whom during past outbreaks using routinely-collected surveillance data. Inference of transmission trees using genotype, age specific social contacts, distance between cases and onset dates of the reported cases. (Robert A, Kucharski AJ, Gastanaduy PA, Paul P, Funk S. 2020 <doi:10.1098/rsif.2020.0084>).
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
  <https://www.openarchives.org/OAI/openarchivesprotocol.html>).
Offers a suite of functions for enhancing R plots.
Implements the efficient algorithm by Ortmann and Brandes (2017)
  <doi:10.1007/s41109-017-0027-2> to compute the orbit-aware frequency distribution of induced and non-induced quads, i.e. subgraphs of size four. Given an edge matrix, data frame, or a graph object (e.g., 'igraph'), the orbit-aware counts are computed respective each of the edges and nodes.
Generalise the starting point of the array index.
A set of indexes and tests for the analysis of social segregation.
An implementation of the Blinder-Oaxaca decomposition for linear regression models.
Data processing, visualisation and analysis of Limit Order Book
    event data.
It makes an objective Bayesian analysis of the spatial regression model using both the normal (NSR) and student-T (TSR) distributions. The functions provided give prior and posterior objective densities and allow default Bayesian estimation of the model regression parameters. Details can be found in Ordonez et al. (2020) <arXiv:2004.04341>.
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. For more detail: Chorus et al., 2021, Obfuscation maximization-based decision-making: Theory, methodology and first empirical evidence, Mathematical Social Sciences, 109, 28-44.
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.
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."
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, see Deldossi and Nai Ruscone (2020) <doi:10.18637/jss.v094.i02>.
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.
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'.
Facilitates the gathering of biodiversity occurrence data 
  from disparate sources. Metadata is managed throughout the process to facilitate 
  reporting and enhanced ability to repeat analyses.
The classical and extended occupancy distributions occur in cases where balls are randomly allocated 
    to bins. The PDF, CDF, quantile functions, generation of random variates, and calculating 
    the first four central moments of the distributions are implemented as described in 
    O’Neill (2019) <doi:10.1080/00031305.2019.1699445>.
Implements the algorithm in Chen, Wang and Samworth (2020) <arxiv:2003.03668> for online detection of sudden mean changes in a sequence of high-dimensional observations. It also implements methods by Mei (2010) <doi:10.1093/biomet/asq010>, Xie and Siegmund (2013) <doi:10.1214/13-AOS1094> and Chan (2017) <doi:10.1214/17-AOS1546>.
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, sea surface temperature, chlorophyll, ocean fronts & bathymetry). Recognized classes and formats include netcdf, Raster, '.nc' and '.gz' files.
Functions for transforming and viewing 2-D and 3-D (oceanographic) data and model output.
Calculate ocean wave height summary statistics and process data 
    from bottom-mounted pressure sensor data loggers. Derived primarily from 
    MATLAB functions provided by U. Neumeier at 
    <http://neumeier.perso.ch/matlab/waves.html>. Wave number 
    calculation based on the algorithm in Hunt, J. N. (1979, ISSN:0148-9895) 
    "Direct Solution of Wave Dispersion Equation", American Society of Civil 
    Engineers Journal of the Waterway, Port, Coastal, and Ocean Division, 
    Vol 105, pp 457-459.
Several Oceanographic data sets are provided for use
    by the 'oce' package and for other purposes.
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>).
Provides a function to detect and trim outliers in Gaussian mixture model-based clustering using methods described in Clark and McNicholas (2019) <arXiv:1907.01136>.
Generate and analyze Optimal Channel Networks (OCNs): 
	oriented spanning trees reproducing all scaling features characteristic 
	of real, natural river networks. As such, they can be used in a variety 
	of numerical experiments in the fields of hydrology, ecology and 
	epidemiology. See Carraro et al. (2020) <doi:10.1002/ece3.6479> 
	for a presentation of the package; Rinaldo et al. (2014) 
	<doi:10.1073/pnas.1322700111> for a theoretical overview on the OCN 
	concept; Furrer and Sain (2010) <doi:10.18637/jss.v036.i10> for the 
	construct used.
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.
Provides an Interface to Open Collaboration Services 'OCS' (<https://www.open-collaboration-services.org/>) REST API.
Provides functions to access and download data from the 'Open Case Studies' <https://www.opencasestudies.org/> 
    repositories on 'GitHub' <https://github.com/opencasestudies>. Different functions enable 
    users to grab the data they need at different sections in the case study, as well as 
    download the whole case study repository. All the user needs to do is input the name of 
    the case study being worked on. The package relies on the httr::GET() function to access
    files through the 'GitHub' API. The functions usethis::use_zip() and usethis::create_from_github() 
    are used to clone and/or download the case study repositories. To cite an individual case study,
    please see the respective 'README' file at <https://github.com/opencasestudies/>.
    <https://github.com/opencasestudies/ocs-bp-rural-and-urban-obesity> 
    <https://github.com/opencasestudies/ocs-bp-air-pollution>
    <https://github.com/opencasestudies/ocs-bp-vaping-case-study>
    <https://github.com/opencasestudies/ocs-bp-opioid-rural-urban>
    <https://github.com/opencasestudies/ocs-bp-RTC-wrangling>
    <https://github.com/opencasestudies/ocs-bp-RTC-analysis>
    <https://github.com/opencasestudies/ocs-bp-youth-disconnection>
    <https://github.com/opencasestudies/ocs-bp-youth-mental-health>
    <https://github.com/opencasestudies/ocs-bp-school-shootings-dashboard>
    <https://github.com/opencasestudies/ocs-bp-co2-emissions>
    <https://github.com/opencasestudies/ocs-bp-diet>.
The aim of 'od' is to provide tools and example datasets for working with
  origin-destination ('OD') datasets of the type used to describe aggregate
  urban mobility patterns (Carey et al. 1981) <doi:10.1287/trsc.15.1.32>.
  The package builds on functions for working with 'OD' data in the package 'stplanr',
  (Lovelace and Ellison 2018) <doi:10.32614/RJ-2018-053> with a focus on computational
  efficiency and support for  the 'sf' class system (Pebesma 2018) <doi:10.32614/RJ-2018-009>.
  With few dependencies and a simple class system based on data frames,
  the package is intended to facilitate efficient analysis of 'OD' datasets
  and to provide a place for developing new functions.
  The package enables the creation and analysis of geographic entities
  representing large scale mobility patterns,
  from daily travel between zones in cities to migration between countries.
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/>.
Make querying on 'OData' easier. It exposes an 'ODataQuery' object
    that can be manipulated and provides features such as selection, filtering
    and ordering.
Functions to create, connect, update and query 'HSQL' databases embedded in Open Document Databases 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.
Uses the outputs of a logistic regression model, from caret <https://CRAN.R-project.org/package=caret>, to build an odds plot.
    This allows for the rapid visualisation of odds plot ratios and works best with the outputs of CARET's GLM model class, by returning the final trained model.
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.
We proposes a framework that provides real time support for early detection of
    anomalous series within a large collection of streaming time series data. By definition, anomalies
    are rare in comparison to a system's typical behaviour. We define an anomaly as an observation that
    is very unlikely given the forecast distribution. The algorithm first forecasts a boundary for the
    system's typical behaviour using a representative sample of the typical behaviour of the system. An
    approach based on extreme value theory is used for this boundary prediction process. Then a sliding
    window is used to test for anomalous series within the newly arrived collection of series. Feature
    based representation of time series is used as the input to the model. To cope with concept drift,
    the forecast boundary for the system's typical behaviour is updated periodically.  More details
    regarding the algorithm can be found in Talagala, P. D., Hyndman, R. J., Smith-Miles, K., et al.
    (2019) <doi:10.1080/10618600.2019.1617160>.
Allows performing forwards prediction for the General Unified 
          Threshold model of Survival using compiled ode code. This package 
          was created to avoid dependency with the 'morse' package that requires 
          the installation of 'JAGS'. This package is based on functions from 
          the 'morse' package v3.3.1: Virgile Baudrot, Sandrine Charles, 
          Marie Laure Delignette-Muller, Wandrille Duchemin, Benoit Goussen, 
          Nils Kehrein, Guillaume Kon-Kam-King, Christelle Lopes, Philippe Ruiz, 
          Alexander Singer and Philippe Veber (2021) <https://CRAN.R-project.org/package=morse>.
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 Peña, Smucler and Yohai (2019)
	<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 produces the highest statistical power
    for experimental studies under a budget constraint,
    perform power analyses with and without accommodating cost structures of sampling, and calculate
    the relative efficiency between two sample allocations. 
    The references 
    for the proposed methods are: 
    (1) Shen, Z., & Kelcey, B. (2020). 
    Optimal sample allocation under unequal costs in 
    cluster-randomized trials.
    Journal of Educational and Behavioral Statistics, 45(4): 446-474.
        <doi:10.3102/1076998620912418>
    (2) Shen, Z., & Kelcey, B. (in press). Optimal sample 
    allocation in multisite randomized trials. The Journal of Experimental Education.
    <doi:10.1080/00220973.2020.1830361>
    (3) Shen, Z., & Kelcey, B. (in press). Optimal sampling ratios in three-level 
    multisite experiments. Journal of Research on Educational Effectiveness.
    <doi:10.1080/19345747.2021.1953200>
    (4) Champely, S. (2020). pwr: Basic functions for power analysis 
    (Version 1.3-0) [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>.
Search and extract data from the Organization for Economic 
    Cooperation and Development (OECD).
Package for estimating the parameters of a nonlinear function using iterated linearization via Taylor series.  Method is based on  Kubacek (2000) ISBN: 80-244-0093-6. The algorithm is a generalization of the procedure given in Koning, R., Wimmer, G. and Witkovsky, V. (2014) <doi:10.1088/0957-0233/25/11/115001>.
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.
Tools to access data from the data web service of the Oesterreichische Nationalbank (OeNB), <https://www.oenb.at/en/Statistics/User-Defined-Tables/webservice.html>.
Developed to help researchers who need to model the kinetics of carbon dioxide (CO2) production in alcoholic fermentation of wines, beers and other fermented products. The following models are available for modeling the CO2 production curve as a function of time: 5PL, Gompertz and 4PL. This package has different functions, which applied can: perform the modeling of the data obtained in the fermentation and return the coefficients, analyze the model fit and return different statistical metrics, and calculate the kinetic parameters: Maximum production of carbon dioxide; Maximum rate of production of CO2; Moment in which maximum fermentation rate occurs; Duration of the latency phase for CO2 production; CO2 Produced until Maximum fermentation rate occurs. In addition, a function that generates graphs with the observed and predicted data from the models, isolated and combined, is available. Gava, A., Borsato, D., & Ficagna, E. (2020). "Effect of mixture of fining agents on the fermentation kinetics of base wine for sparkling wine production: Use of methodology for modeling". <doi:10.1016/j.lwt.2020.109660>.
Allows production of 'Microsoft' corporate documents from 'R Markdown' by 
 reusing formatting defined in 'Microsoft Word' documents. You can reuse table styles, 
 list styles but also add column sections, landscape oriented pages. Table and image 
 captions as well as cross-references are transformed into 'Microsoft Word' fields, 
 allowing documents edition and merging without issue with references; the syntax 
 conforms to the 'bookdown' cross-reference definition. Objects generated by 
 the 'officer' package are also supported in the 'knitr' chunks. 
 'Microsoft PowerPoint' presentations also benefit from this as well as the 
 ability to produce editable vector graphics in 'PowerPoint' and also to 
 define placeholder where content is to be added.
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. 
  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 (2017) <doi:10.1109/TSP.2017.2711558>.
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.
Supports the modeling of ordinal random variables, 
    like the outcomes of races, via Softmax regression,
    under the Harville <doi:10.1080/01621459.1973.10482425> and
    Henery <doi:10.1111/j.2517-6161.1981.tb01153.x> models.
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. Petrowski et al. (2019) <doi:10.5334/jors.250>.
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. For details, see the the corresponding publication in the Journal of Statistical Software <doi:10.18637/jss.v092.i08>.
Crawler for 'OJS' pages and scraper for meta-data from articles. 
    You can crawl 'OJS' archives, issues, articles, galleys, and search results. 
    You can scrape articles metadata from their head tag in html, 
    or from Open Archives Initiative ('OAI') records.
    Most of these functions rely on 'OJS' routing conventions 
    (<https://docs.pkp.sfu.ca/dev/documentation/en/architecture-routes>).
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.
Optimal k Nearest Neighbours Ensemble is an ensemble of base k nearest neighbour models each constructed on a bootstrap sample with a random subset of features. k closest observations are identified for a test point "x" (say), in each base k nearest neighbour model to fit a stepwise regression to predict the output value of "x". The final predicted value of "x" is the mean of estimates given by all the models. The implemented model takes training and test datasets and trains the model on training data to predict the test data. Ali, A., Hamraz, M., Kumam, P., Khan, D.M., Khalil, U., Sulaiman, M. and Khan, Z. (2020) <DOI:10.1109/ACCESS.2020.3010099>.
'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.
Density, distribution function, quantile function and random generation for the Odd Log-Logistic Generalized Gamma proposed in Prataviera, F. et al (2017) <doi:10.1080/00949655.2016.1238088>.
The optimal linear regression olr(), runs all the possible combinations of linear regression
  equations. The olr() returns the equation which has the greatest adjusted R-squared term or the greatest R-squared term based
  on the user's discretion. Essentially, the olr() returns the best fit equation out of all the possible equations. R-squared increases 
  with the addition of an explanatory variable whether it is 'significant' or not, thus this was developed to eliminate that conundrum. 
  Adjusted R-squared is preferred to overcome this phenomenon, but each combination will still produce different results and this will
  return the best one. Complimentary functions are included which list all of the equations, all of the equations in ascending order, 
  a function to give the user a specific model's summary, and the list of adjusted R-squared terms & R-squared terms.
  A 'Python' version is available at: <https://pypi.org/project/olr/>.
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 is a computer tool to estimate the item-sum score's reliability (composite reliability, CR) in multidimensional scales with overlapping items. An item that measures more than one domain construct is called an overlapping item. 
    The estimation is based on factor models allowing unlimited cross-factor loadings such as exploratory structural equation modeling (ESEM) and Bayesian structural equation modeling (BSEM). The factor models include correlated-factor models and bi-factor models. Specifically for bi-factor models, a type of hierarchical factor model, the package estimates the CR hierarchical subscale/hierarchy and CR subscale/scale total. The CR estimator 'Omega-generic' was proposed by Mai, Srivastava, and Krull (2021) <https://whova.com/embedded/subsession/enars_202103/1450751/1452993/>. The current version can only handle continuous data. 
    Yujiao Mai contributes to the algorithms, R programming, and application example. Deo Kumar Srivastava contributes to the algorithms and the application example. Kevin R. Krull contributes to the application example. The package 'OmegaG' was sponsored by American Lebanese Syrian Associated Charities (ALSAC). However, the contents of 'OmegaG' do not necessarily represent the policy of the ALSAC.
Omics integration and detailed gene network exploration to identify expression patterns, prepare for pathway building, and find disease candidate genes; the package compliments research "Insights into therapeutic targets and biomarkers using integrated multi-'omics' approaches for dilated and ischemic cardiomyopathies"; Auste Kanapeckaite and Neringa Burokiene; 2021, <doi:10.1093/intbio/zyab007>.
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 tool for interactive exploration of the results from 'omics'
  experiments to facilitate novel discoveries from high-throughput biology. The
  software includes R functions for the 'bioinformatician' to deposit study
  metadata and the outputs from statistical analyses (e.g. differential
  expression, enrichment). These results are then exported to an interactive
  JavaScript dashboard that can be interrogated on the user’s local machine or
  deployed online to be explored by collaborators. The dashboard includes
  'sortable' tables, interactive plots including network visualization, and
  fine-grained filtering based on statistical significance.
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>.
    It also performs Sparse Group (Penalized) O2PLS, see Gu et al (2020) <doi:10.1186/s12859-021-03958-3> and cross-validation for the degree of sparsity.
In bulk epigenome/transcriptome experiments, molecular expression
    is measured in a tissue, which is a mixture of multiple types of cells.
    This package tests association of a disease/phenotype with a molecular marker
    for each cell type.
    The proportion of cell types in each sample needs to be given as input.
    The package is applicable to epigenome-wide association study (EWAS) and
    differential gene expression analysis.
    Takeuchi and Kato (submitted)
    "omicwas: cell-type-specific epigenome-wide and transcriptome association study".
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: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.
Utility functions for querying electronic health record (EHR) data in
    'OMOP' Common Data Model <https://www.ohdsi.org/data-standardization/the-common-data-model/>
    databases using a 'tidyverse' approach based on 'dbplyr' lazy queries.
    This allows efficient in-database querying and data wrangling without explicit
    writing of 'SQL' queries.
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.
Build 'SVG' components using element-based functions. With
    an 'svg' object, we can modify its graphical elements with a suite of
    transform functions.
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>.
A database containing the names 
  of the babies born in Ontario between 1917 and 2018. 
  Counts of fewer than 5 names were suppressed for privacy.
Automated reporting in Word and PowerPoint can require customization for each organizational template. This package works around this by adding standard reporting functions and an abstraction layer to facilitate automated reporting workflows that can be replicated across different organizational templates.
Allows users to discover and retrieve Ocean Networks Canada's oceanographic data in raw, text, image, audio, video or any other format available. Provides a class that wraps web service calls and business logic so that users can download data with a single line of code.
Bayesian logistic regression model with optional
  EXchangeability-NonEXchangeability parameter modelling for flexible
  borrowing from historical or concurrent data-sources. The safety model
  can guide dose-escalation decisions for adaptive oncology Phase I
  dose-escalation trials which involve an arbitrary number of
  drugs. Please refer to Neuenschwander et al. (2008)
  <doi:10.1002/sim.3230> and Neuenschwander et al. (2016)
  <doi:10.1080/19466315.2016.1174149> for details on the methodology.
Computing probabilistic tree models for oncogenesis based
        on genetic data using maximum likelihood.
Bridges in vitro drug screening with in vivo drug and biomarker discovery. Specifically, predicts in vivo or cancer patient drug response and biomarkers to enrich for response from cell line screening data. Builds model using ridge regression, and enables biomarker discovery by imputing drug response in large cancer molecular datasets. It also enables drug specific biomarker identification by correcting for general level of drug sensitivity shared among the population.
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.
Single-cell datasets are growing in size, posing challenges as well as opportunities for biology researchers. 'ondisc' (short for "on-disk single cell") enables users to easily and efficiently analyze large-scale single-cell data. 'ondisc' makes computing on large-scale single-cell data FUN: Fast, Universal, and iNtuitive.
Maximum homogeneity clustering algorithm for one-dimensional data
    described in W. D. Fisher (1958) <doi:10.1080/01621459.1958.10501479>
    via dynamic programming.
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.
An R wrapper for the 'OneMap.Sg' API <https://docs.onemap.sg/>. 
    Functions help users query data from the API and return raw JSON data
    in "tidy" formats. Support is also available for users to retrieve data from multiple API calls 
    and integrate results into single dataframes, without
    needing to clean and merge the data themselves. This package is best suited for users who would 
    like to perform analyses with Singapore's spatial data without having to perform excessive data cleaning.
Interaction with 1Password via the command-line tool <https://1password.com/downloads/command-line/> to read vault contents and download credentials.
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.
This ONEST software implements the method of assessing the pathologist agreement in reading PD-L1 assays (Reisenbichler et al. (2020 <doi:10.1038/s41379-020-0544-x>)), to determine the minimum number of evaluators needed to estimate agreement involving a large number of raters. Input to the program should be binary(1/0) pathology data, where “0” may stand for negative and “1” for positive. Additional examples were given using the data from Rimm et al. (2017 <doi:10.1001/jamaoncol.2017.0013>).
Provide principally an eponymic function that numerically computes the Le Cam one-step estimator which is asymptotically efficient (see e.g. L. Le Cam (1956) <https://projecteuclid.org/euclid.bsmsp/1200501652>) and can be computed faster than the maximum likelihood estimator for large observation samples.
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 including homoscedastic and heteroscedastic tests. These are 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 the package vignette for more details.
It implements the online Bayesian methods for change point analysis. It can
            also perform missing data imputation with methods from 'VIM'. The reference 
            is Yigiter A, Chen J, An L, Danacioglu N (2015) <doi:10.1080/02664763.2014.1001330>.
Implement a new stopping rule to detect anomaly in the covariance structure of high-dimensional online data. The detection procedure can be applied to Gaussian or non-Gaussian data with a large number of components. Moreover, it allows both spatial and temporal dependence in data. The dependence can be estimated by a data-driven procedure. The level of threshold in the stopping rule can be determined at a pre-selected average run length. More detail can be seen in Li, L. and Li, J. (2020) "Online Change-Point Detection in High-Dimensional Covariance Structure with Application to Dynamic Networks." <arXiv:1911.07762>.
A framework for fitting adaptive forecasting models. Provides a way to use forecasts as input to models, e.g. weather forecasts for energy related forecasting. The models can be fitted recursively and can easily be setup for updating parameters when new data arrives. See the included vignettes, the website <https://onlineforecasting.org> and the pre-print paper "onlineforecast: An R package for adaptive and recursive forecasting" <arXiv:2109.12915>.
Online PCA for multivariate and functional data using perturbation methods, low-rank incremental methods, and stochastic optimization methods.
Transactions occurring for a UK-based and registered, non-store online 
    retail between 01/12/2010 and 09/12/2011 (Chen et. al., 2012, <doi:10.1145/1835804.1835882>). 
    This dataset is included in this package with the donor's permission, Dr. Daqing Chen.
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.
Growing collection of helper functions for point pattern analysis. Most functions
  are designed to work with the 'spatstat' (<http://spatstat.org>) package. The focus of 
  most functions are either null models or summary functions for spatial point patterns. 
  For a detailed description of all null models and summary functions, see 
  Wiegand and Moloney (2014, ISBN:9781420082548).
Client for the 'Office of National Statistics'
    ('ONS') API <https://api.beta.ons.gov.uk/v1>.
Tools for annotating characters (character matrices) with anatomical and phenotype ontologies. Includes functions for visualising character annotations and creating simple queries using ontological relationships.
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>.
Create R plots visualising ontological terms and the relationships between them with various graphical options - Greene et al. 2017 <doi:10.1093/bioinformatics/btw763>.
Calculate similarity between ontological terms and sets of ontological terms based on term information content and assess statistical significance of similarity in the context of a collection of terms sets - Greene et al. 2017 <doi:10.1093/bioinformatics/btw763>.
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.
Calculates the Outside Option Index proposed by Caldwell and Danieli
  (2018) <https://drive.google.com/file/d/1j-uwD19S4gqgXIXeYch9jGBCaDhWZlRQ/view>. 
  This index uses the cross- sectional concentration of similar workers 
  across job types to quantify the availability of outside options as a function of 
  workers’ characteristics (e.g. commuting costs, preferences, and skills.)
Intended to create standard human-in-the-loop validity tests for typical automated content analysis such as topic modeling and dictionary-based methods. This package offers a standard workflow with functions to prepare, administer and evaluate a human-in-the-loop validity test. This package provides functions for validating topic models using word intrusion, topic intrusion (Chang et al. 2009,  <https://papers.nips.cc/paper/3700-reading-tea-leaves-how-humans-interpret-topic-models>) and word set intrusion (Ying et al. Forthcoming) tests. This package also provides functions for generating gold-standard data which are useful for validating dictionary-based methods. The default settings of all generated tests match those suggested in Chang et al. (2009) and Song et al. (2020) <doi:10.1080/10584609.2020.1723752>.
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.
Helper functions for coding object-oriented programming with
    a focus on R6. Includes functions for assertions and testing, looping,
    and re-usable design patterns including Abstract and Decorator
    classes.
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.
A comprehensive and cohesive API for the out-of-sample forecasting workflow: 
             data preparation, forecasting - including both traditional econometric time series models and 
             modern machine learning techniques - forecast combination, model and error analysis, and 
             forecast visualization.
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.
An optimized method for distribution-preserving class-proportional down-sampling of bio-medical data.
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.
Interface to make HTTP requests to 'OpenBlender' API services. Go to <https://openblender.io> for more information.
Geocode with the OpenCage API, either from place name to longitude 
    and latitude (forward geocoding) or from longitude and latitude to the name 
    and address of a location (reverse geocoding), see 
    <https://opencagedata.com/>.
This package provides an interface to OpenCL, allowing R to leverage computing power of GPUs and other HPC accelerator devices.
A system for embedded scientific computing and reproducible research with R.
    The OpenCPU server exposes a simple but powerful HTTP api for RPC and data interchange
    with R. This provides a reliable and scalable foundation for statistical services or 
    building R web applications. The OpenCPU server runs either as a single-user development
    server within the interactive R session, or as a multi-user Linux stack based on Apache2. 
    The entire system is fully open source and permissively licensed. The OpenCPU website
    has detailed documentation and example apps.
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' <https://opencv.org/> 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.
Access data from the "City of Toronto
    Open Data Portal" (<https://open.toronto.ca>) directly from R.
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 
    (<https://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.
Tools for the analysis of land use and cover (LUC) time series. It 
    includes support for loading spatiotemporal raster data and synthesized 
    spatial plotting. Several LUC change (LUCC) metrics in regular or irregular 
    time intervals can be extracted and visualized through one- and multistep 
    sankey and chord diagrams. A complete intensity analysis according to 
    Aldwaik and Pontius (2012) <doi:10.1016/j.landurbplan.2012.02.010> is 
    implemented, including tools for the generation of standardized multilevel 
    output graphics.
Provides a client for the open-source monitoring and alerting
  toolkit, 'Prometheus', that emits metrics in the 'OpenMetrics' format. Allows
  users to automatically instrument 'Plumber' and 'Shiny' applications, collect
  standard process metrics, as well as define custom counter, gauge, and
  histogram metrics of their own.
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 'openMSE' package is designed for building operating models, 
    doing simulation modelling and management strategy evaluation for fisheries.
    'openMSE' is an umbrella package for the 'MSEtool' (Management Strategy Evaluation
    toolkit), 'DLMtool' (Data-Limited Methods toolkit), and 
    SAMtool (Stock Assessment Methods toolkit) packages. By loading and installing
    'openMSE', users have access to the full functionality contained within
    these packages. Learn more about 'openMSE' at <https://openmse.com/>.
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 <https://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).
Shiny UI to identify cliques of related constructs in repertory grid data. 
    See Burr, King, & Heckmann (2020) <doi:10.1080/14780887.2020.1794088> for a description 
    of the interpretive clustering (IC) method.
Provides functionalities and data structures to retrieve, analyze and visualize aviation 
    data. It includes a client interface to the 'OpenSky' API <https://opensky-network.org>. It allows 
    retrieval of flight information, as well as aircraft state vectors.
Raman and (FT)IR spectral analysis tool for plastic particles and
    other environmental samples (Cowger et al. 2021,
    <doi:10.1021/acs.analchem.1c00123>). Supported features include reading
    spectral data files (.asp, .csv, .jdx, .spc, .spa, .0), Savitzky-Golay
    smoothing of spectral intensities with smooth_intens(), correcting
    background noise with subtr_bg() in accordance with Zhao et al. (2007)
    <doi:10.1366/000370207782597003>, and identifying spectra using an onboard
    reference library (Cowger et al. 2020, <doi:10.1177/0003702820929064>).
    Analyzed spectra can be shared with the Open Specy community. A Shiny app is
    available via run_app() or online at
    <https://wincowger.shinyapps.io/OpenSpecy/>.
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.
A free, open-source package designed for
    handling .tdf data files produced by Bruker's 'timsTOF' mass
    spectrometers.
    Fast, free, crossplatform, with no reading through
    EULAs or messing with binary .dll files involved.
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.
Setup and connect to 'OpenTripPlanner' (OTP) <http://www.opentripplanner.org/>.
    OTP is an open source platform for multi-modal and multi-agency
    journey planning written in 'Java'. The package allows you to manage a local version or 
    connect to remote OTP server to find walking, cycling, driving, or transit routes.
    This package has been peer-reviewed by rOpenSci (v. 0.2.0.0).
Implements multiple existing open-source algorithms for coding cause of death from verbal autopsies. The methods implemented include 'InterVA4' by Byass et al (2012) <doi:10.3402/gha.v5i0.19281>, 'InterVA5' by Byass at al (2019) <doi:10.1186/s12916-019-1333-6>, 'InSilicoVA' by McCormick et al (2016) <doi:10.1080/01621459.2016.1152191>, 'NBC' by Miasnikof et al (2015) <doi:10.1186/s12916-015-0521-2>, and a replication of 'Tariff' method by James et al (2011) <doi:10.1186/1478-7954-9-31> and Serina, et al. (2015) <doi:10.1186/s12916-015-0527-9>. 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. The 'NBC' method is implemented by the 'nbc4va' package that can be installed from <https://github.com/rrwen/nbc4va>. Note that this package was not developed by authors affiliated with the Institute for Health Metrics and Evaluation and thus unintentional discrepancies may exist in the implementation of the 'Tariff' method.
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. See 
             Cesa-Bianchi and Lugosi (2006) <doi:10.1017/CBO9780511546921> for an overview.
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.
Necessary functions for optimized automated evaluation of the number and parameters of Gaussian mixtures in one-dimensional data. Various methods are available for parameter estimation and for determining the number of modes in the mixture.
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.
Designed for performing impact analysis of
  opinions in a digital text document (DTD). The 
  package allows a user to assess the extent to which a theme
  or subject within a document impacts the overall opinion 
  expressed in the document. The package can be applied to a wide 
  range of opinion-based DTD, including commentaries on social media
  platforms (such as 'Facebook', 'Twitter' and 'Youtube'), 
  online products reviews, and so on. 
  The utility of 'opitools' was originally demonstrated 
  in Adepeju and Jimoh (2021) <doi:10.31235/osf.io/c32qh> in the 
  assessment of COVID-19 impacts on neighbourhood policing using 
  Twitter data. Further examples can be found in the vignette of 
  the package.
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. For more information, see
    Hanson et al. (2019) <doi:10.1111/2041-210X.13264>.
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.
Defines thresholds for breaking data into a number of
  discrete levels, minimizing the (mean) squared error within all bins.
Estimates optimal number of biomarkers for two-group
        classification based on microarray data.
Fast, optimal, and reproducible clustering algorithms for
 circular, periodic, or framed data. The algorithms introduced here
 are based on a core algorithm for optimal framed clustering the authors
 have developed (Debnath & Song 2021) <doi:10.1109/TCBB.2021.3077573>.
 The runtime of these algorithms is O(K N log^2 N), where K is the number
 of clusters and N is the number of circular data points. On a desktop
 computer using a single processor core, millions of data points can be
 grouped into a few clusters within seconds. One can apply the algorithms
 to characterize events along circular DNA molecules, circular RNA
 molecules, and circular genomes of bacteria, chloroplast, and
 mitochondria. One can also cluster climate data along any given
 longitude or latitude. Periodic data clustering can be formulated as
 circular clustering. The algorithms offer a general high-performance
 solution to circular, periodic, or framed data clustering.
Cluster analysis using statistical and biological
	validation measures for both continuous and count data.
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-, I-, and c-optimal designs. Some of the functions in this package require the 'gurobi' software and its accompanying R package. For their installation, please follow the instructions at <https://www.gurobi.com> and the file gurobi_inst.txt, respectively.
Functions for the design process of survey sampling, with specific tools for multi-wave and multi-phase designs. Perform optimum allocation using Neyman (1934) <doi:10.2307/2342192> or Wright (2012) <doi:10.1080/00031305.2012.733679> allocation, split strata based on quantiles or values of known variables, randomly select samples from strata, allocate sampling waves iteratively, and organize a complex survey design. Also includes a Shiny application for observing the effects of different strata splits.
This is a tool to find the optimal rerandomization threshold in non-sequential experiments. We offer three procedures based 
	on assumptions made on the residuals distribution: (1) normality assumed (2) excess kurtosis assumed (3) entire distribution assumed.
	Illustrations are included. Also included is a routine to unbiasedly estimate Frobenius norms of variance-covariance matrices.
	Details of the method can be found in "Optimal Rerandomization via a Criterion that Provides Insurance Against Failed Experiments"
	Adam Kapelner, Abba M. Krieger, Michael Sklar and David Azriel (2020) <arXiv:1905.03337>.
Functions to estimate the optimal threshold of diagnostic markers or treatment selection markers. The optimal threshold is the marker value that maximizes the utility of the marker based-strategy (for diagnostic or treatment selection) in a given population. The utility function depends on the type of marker (diagnostic or treatment selection), but always takes into account the preferences of the patients or the physician in the decision process. For estimating the optimal threshold, ones must specify the distributions of the marker in different groups (defined according to the type of marker, diagnostic or treatment selection) and provides data to estimate the parameters of these distributions. Ones must also provide some features of the target populations (disease prevalence or treatment efficacies) as well as the preferences of patients or physicians. The functions rely on Bayesian inference which helps producing several indicators derived from the optimal threshold. See Blangero, Y, Rabilloud, M, Ecochard, R, and Subtil, F (2019) <doi:10.1177/0962280218821394> for the original article that describes the estimation method for treatment selection markers and Subtil, F, and Rabilloud, M (2019) <doi:10.1002/bimj.200900242> for diagnostic markers.
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.
Tools for checking that the output of an optimization algorithm is indeed at a local mode of the objective function.  This is accomplished graphically by calculating all one-dimensional "projection plots" of the objective function, i.e., varying each input variable one at a time with all other elements of the potential solution being fixed.  The numerical values in these plots can be readily extracted for the purpose of automated and systematic unit-testing of optimization routines.
Provides general purpose tools for helping users to implement steepest
             gradient descent methods for function optimization; for details see
             Ruder (2016) <arXiv:1609.04747v2>. Currently, the Steepest 2-Groups
             Gradient Descent and the Adaptive Moment Estimation (Adam) are the
             methods implemented. Other methods will be implemented in the future.
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.
Robust multi-criteria land-allocation optimization that explicitly accounts for the uncertainty of the indicators in the objective function. Solves the problem of allocating scarce land to various land-use options with regard to multiple, coequal indicators. The method aims to find the land allocation that represents the indicator composition with the best possible trade-off under uncertainty. optimLanduse includes the actual optimization procedure as described by Knoke et al. (2016) <doi:10.1038/ncomms11877> and the post-hoc calculation of the portfolio performance as presented by Gosling et al. (2020) <doi:10.1007/s10457-020-00519-0>.
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.
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.
    This is the version for CRAN.
Optimal two and three stage designs monitoring
        time-to-event endpoints at a specified timepoint
Provides both parametric and non-parametric estimates of the 
  correlates of some desired
  outcome (e.g. test scores, income) using a new method proposed by 
  Danieli, Devi and Fryer (2019). This method relaxes the assumption
  that one can alter individual characteristics in any way the data 
  suggest is optimal, and so can be used anytime one wants to use observational
  data to better optimize social experiments designed to increase some desired
  outcome.
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.
Optimal scaling of a data vector, relative to a set of targets, is
    obtained through a least-squares transformation subject to appropriate measurement
    constraints. The targets are usually predicted values from a statistical
    model. If the data are nominal level, then the transformation must be
    identity-preserving. If the data are ordinal level, then the
    transformation must be monotonic. If the data are discrete, then tied data
    values must remain tied in the optimal transformation. If the data are
    continuous, then tied data values can be untied in the optimal
    transformation.
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.
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'.
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, 2020, Choosing the Level of Significance: A Decision-Theoretic Approach, Abacus.
        See also Kim, Jae H., 2020, Decision-theoretic hypothesis testing: A primer with R package OptSig, The American Statistician.
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.
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.
Automated discovery of differentially expressed genes. The method (called ORdensity) is composed of two phases: discovering potential differentially expressed genes and recognizing differentially expressed genes. It makes use of a permutation resampling procedure to build outlying and density indexes. References: a) Irigoien, I. and Arenas, C. (2018). "Identification of differentially expressed genes by means of outlier detection". <doi:10.1186/s12859-018-2318-8>. b) Martínez-Otzeta, J. M., Irigoien, I., Sierra, B., and Arenas, C. (2020). "ORdensity: user-friendly R package to identify differentially expressed genes". <doi:10.1186/s12859-020-3463-4>.
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.
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).
Order, create and store reports from R.  By defining a
    lightweight interface around the inputs and outputs of an
    analysis, a lot of the repetitive work for reproducible research
    can be automated.  We define a simple format for organising and
    describing work that facilitates collaborative reproducible
    research and acknowledges that all analyses are run multiple
    times over their lifespans.
Set of tools to generate samples of k-th order statistics and others quantities of interest from new families of distributions. 
    The main references for this package are: C. Kleiber and S. Kotz (2003) Statistical size distributions in economics and actuarial sciences; Gentle, J. (2009), Computational Statistics, Springer-Verlag; 
    Naradajah, S. and Rocha, R. (2016), <DOI:10.18637/jss.v069.i10> and Stasinopoulos, M. and Rigby, R. (2015), <DOI:10.1111/j.1467-9876.2005.00510.x>.
    The families of distributions are: Burr distributions, Feller-Pareto distributions, Generalized Pareto Distributions, The Inverse Paralogistic Distributions, 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, 
    exponentiated exponential Poisson G distributions, Poisson-inverse gaussian distribution, Skew normal type 1 distributions, Skew student t distribution, Sinh-Arcsinh distribution, Sichel distribution, Zero inflated Poisson.
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. Such models are described in Manuguerra et al (2020) <doi:10.18637/jss.v096.i08>.
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 importance in the prediction of the 
  values of the ordinal target variable.
  OF is presented in Hornung (2020).
  NOTE: Starting with package version 2.4, it is also possible to obtain class probability 
  predictions in addition to the class point predictions. Moreover, the variable importance values
  can also be based on the class probability predictions. Preliminary results indicate that
  this might lead to a better discrimination between influential and non-influential covariates.
  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. (2020) Ordinal Forests. Journal of Classification 37, 4–17. 
  <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 (2021) 
    <doi:10.18637/jss.v099.i06>.
Ordinal patterns describe the dynamics of a time series by looking at the ranks of subsequent observations. By comparing ordinal patterns of two times series, Schnurr (2014) <doi:10.1007/s00362-013-0536-8> defines a robust and non-parametric dependence measure: the ordinal pattern coefficient. Functions to calculate this and a method to detect a change in the pattern coefficient proposed in Schnurr and Dehling (2017) <doi:10.1080/01621459.2016.1164706> are provided.
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 a pre-specified correlation matrix and marginal distributions.
             The details of the method are explained in Demirtas et al. (2015) <DOI:10.1080/10543406.2014.920868>.
Selection, fusion, and/or smoothing of ordinally scaled independent variables using a group lasso, fused lasso or generalized ridge penalty, as well as non-linear principal components analysis for ordinal variables using a second-order difference/smoothing 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.
The Sequence of Physical Processes (SPP) framework is a way of interpreting the transient data derived from oscillatory rheological tests. It is designed to allow both the linear and non-linear deformation regimes to be understood within a single unified framework. This code provides a convenient way to determine the SPP framework metrics for a given sample of oscillatory data. It will produce a text file containing the SPP metrics, which the user can then plot using their software of choice. It can also produce a second text file with additional derived data (components of tangent, normal, and binormal vectors), as well as pre-plotted figures if so desired. It is the R version of the Package SPP by Simon Rogers Group for Soft Matter (Simon A. Rogers, Brian M. Erwin, Dimitris Vlassopoulos, Michel Cloitre (2011) <doi:10.1122/1.3544591>).
A connector to ORES (<https://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.
An implementation of the Ordered Forest estimator as developed 
    in Lechner & Okasa (2019) <arXiv:1907.02436>. The Ordered Forest flexibly
    estimates the conditional probabilities of models with ordered categorical
    outcomes (so-called ordered choice models). Additionally to common machine 
    learning algorithms the 'orf' package provides functions for estimating
    marginal effects as well as statistical inference thereof and thus provides
    similar output as in standard econometric models for ordered choice. The
    core forest algorithm relies on the fast C++ forest implementation from
    the 'ranger' package (Wright & Ziegler, 2017) <arXiv:1508.04409>.
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. This implementation
    was first described by Coyle and Hejazi (2018) <doi:10.21105/joss.00512>.
Automatically adding 'pkg::' to a function, i.e. mutate()
    becomes dplyr::mutate(). It is up to the user to determine which
    packages should be used explicitly, whether to include base R packages
    or use the functionality on selected text, a file, or a complete
    directory. User friendly logging is provided in the 'RStudio' Markers
    pane. Lives in the spirit of 'lintr' and 'styler'.
Functions to handle ordinal relations reflected within the feature space. Those function allow to search for ordinal relations in multi-class datasets. One can check whether proposed relations are reflected in a specific feature representation. Furthermore, it provides functions to filter, organize and further analyze those ordinal relations.
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>.
A big data version for fitting cumulative probability models using the orm() function.  See Liu et al. (2017) <DOI:10.1002/sim.7433> for details.
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" and "Park/Durand" fronts as
    'SpatialLinesDataFrame' objects. The Orsi et al. (1995) fronts are published at
    the Southern Ocean Atlas Database Page, and the Park et al. (2019) fronts are published at the 'SEANOE' 
    Altimetry-derived Antarctic Circumpolar Current fronts 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.
Connects to Google cloud vision <https://cloud.google.com/vision> to perform label detection and repurpose this feature for image classification.
A first implementation of automated parsing of user stories, when used 
  to defined functional requirements for operational research mathematical 
  models. It allows reading user stories, splitting them on the who-what-why template,
  and classifying them according to the parts of the mathematical model that 
  they represent. Also provides semantic grouping of stories, 
  for project management purposes.
Allows distance based spatial clustering of georeferenced data by implementing the City Clustering Algorithm - CCA. Multiple versions allow clustering for a 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.
A suite of functions for the design of case-control and two-phase studies, and the analysis of data that arise from them. 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. For additional detail see: Haneuse S, Saegusa T and Lumley T (2011)<doi:10.18637/jss.v043.i11>.
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>.
An interface for interacting with 'OSF' (<https://osf.io>). 'osfr' 
    enables you to access open research materials and data, or create and
    manage your own private or public projects.
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.
Aims to support all features of the system credential store,
    including non-portable ones. Supports 'Keychain' on 'macOS',
    and 'Credential Manager' on 'Windows'. See the 'keyring' package if you
    need a portable 'API'.
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 (<https://overpass-api.de/>) and processed with very fast 'C++'
    routines for return to 'R'.
Match, download, convert and import Open Street Map data extracts 
    obtained from several providers.
The utilisation of the functionality provided by the 'OSMData' and 
    'geosample' to allow users to create spatially continuous or discrete
    random samples from a pre-defined spatial border using OSM data. Reference:
    Joao Porto de Albuquerque, Godwin Yeboah, Vangelis Pitidis, Philipp Ulbrich 
    (2019) <doi:10.13140/RG.2.2.13710.20804>.
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.
A novel method to implement cancer subtyping and subtype specific drug targets identification via non-negative matrix tri-factorization.
    To improve the interpretability, we introduce orthogonal constraint to the row coefficient matrix and column coefficient matrix. To meet the prior knowledge
    that each subtype should be strongly associated with few gene sets, we introduce sparsity constraint to the association sub-matrix. The average residue was
    introduced to evaluate the row and column cluster numbers. This is part of the work "Liver Cancer Analysis via Orthogonal Sparse Non-Negative Matrix Tri-
    Factorization" which will be submitted to BBRC.
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. See <arXiv:1711.08013> for details.
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 routes, trips, isochrones and
    travel distances matrices (travel time and kilometric distance).
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.
O-statistics, or overlap statistics, measure the degree of community-level trait overlap. 
    They are estimated by fitting nonparametric kernel density functions to each species’ trait 
    distribution and calculating their areas of overlap. For instance, the median pairwise overlap 
    for a community is calculated by first determining the overlap of each species pair 
    in trait space, and then taking the median overlap of each species pair in a community. 
    This median overlap value is called the O-statistic (O for overlap).
    The Ostats() function calculates separate univariate overlap statistics for each trait, 
    while the Ostats_multivariate() function calculates a single multivariate overlap statistic for all traits. 
    O-statistics can be evaluated against null models to obtain standardized effect sizes. 
    Ostats is part of the collaborative Macrosystems Biodiversity Project "Local- to continental-scale 
    drivers of biodiversity across the National Ecological Observatory Network (NEON)." 
    For more information on this project, see the Macrosystems Biodiversity Website 
    (<https://neon-biodiversity.github.io/>). Calculation of O-statistics is described in
    Read et al. (2018) <doi:10.1111/ecog.03641>, and a teaching module for introducing the
    underlying biological concepts at an undergraduate level is described in Grady et al.
    (2018) <http://tiee.esa.org/vol/v14/issues/figure_sets/grady/abstract.html>.
'Open Tracing' <https://opentracing.io> allows developers to
    add instrumentation to their application code using interfaces that are
    vendor-agnostic. This is used to monitor services, triage
    failures and find performance bottlenecks, among other things. The 'ot'
    package has generic methods to be extended when implementing the
    specification for a specific vendor.
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 (Khan, Z., Gul, A., Perperoglou, A., Miftahuddin, M., Mahmoud, O., Adler, W., & Lausen, B. (2019). (2019) <doi:10.1007/s11634-019-00364-9>) and class membership probability estimation (Khan, Z, Gul, A, Mahmoud, O, Miftahuddin, M, Perperoglou, A, Adler, W & Lausen, B (2016) <doi:10.1007/978-3-319-25226-1_34>) 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. Three different methods of tree selection for the case of classification are given. 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.
Generating and validating One-time Password based on 
    Hash-based Message Authentication Code (HOTP) 
    and Time Based One-time Password (TOTP)
    according to RFC 4226 <https://tools.ietf.org/html/rfc4226> and
    RFC 6238 <https://tools.ietf.org/html/rfc6238>.
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.
In the context of data fusion, the package provides a set of functions dedicated to the solving of 'recoding problems' using optimal transportation theory (Gares, Guernec, Savy (2019) <doi:10.1515/ijb-2018-0106> and Gares, Omer (2020) <doi:10.1080/01621459.2020.1775615>). From two databases with no overlapping part except a subset of shared variables, the functions of the package assist users until obtaining a unique synthetic database, where the missing information is fully completed.
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) <https://jmlr.org/papers/v18/16-382.html>.
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. 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>.
Provides a random forest based implementation of
    the method described in Chapter 7.1.2 (Regression model based anomaly
    detection) of Chandola et al. (2009) <doi:10.1145/1541880.1541882>. It
    works as follows: Each numeric variable is regressed onto all other
    variables by a random forest. If the scaled absolute difference
    between observed value and out-of-bag prediction of the corresponding
    random forest is suspiciously large, then a value is considered an
    outlier. The package offers different options to replace such
    outliers, e.g. by realistic values found via predictive mean matching.
    Once the method is trained on a reference data, it can be applied to
    new data.
This package provides three algorithms to detect outlying observations for censored survival data.
Detecting outlying values such as genes, peptides or samples for multi-replicated high-throughput high-dimensional data
Ensemble functions for outlier/anomaly detection. There is a new ensemble method proposed using 
    Item Response Theory. Existing outlier ensemble methods from Schubert et al (2012) <doi:10.1137/1.9781611972825.90>, 
    Chiang et al (2017) <doi:10.1016/j.jal.2016.12.002> and Aggarwal and Sathe (2015) <doi:10.1145/2830544.2830549>
    are also included.
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'.
Outlier detection method that flags suspicious values within observations,
  constrasting them against the normal values in a user-readable format, potentially
  describing conditions within the data that make a given outlier more rare.
  Full procedure is described in Cortes (2020) <arXiv:2001.00636>.
  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.
Install and run external command-line programs in R through use of
    'Docker' <https://www.docker.com/> and online repositories.
Base package for 'outsider' <https://github.com/ropensci/outsider>.
    The 'outsider' package and its sister packages enable the installation and
    running of external, command-line software within R. This base package is a
    key dependency of the user-facing 'outsider' package as it provides the
    utilities for interfacing between 'Docker' <https://www.docker.com> and R.
    It is intended that end-users of 'outsider' do not directly work with this
    base package.
Estimates rates for continuous character evolution under Brownian motion and a new set of Ornstein-Uhlenbeck based Hansen models that allow both the strength of the pull and stochastic motion to vary across selective regimes. Beaulieu et al (2012) <doi:10.1111/j.1558-5646.2012.01619.x>.
Performs statistical inference on the models of adaptive trait evolution under approximate Bayesian computation. This can simulate traits from four models, compute trait data summary statistics. Parameters are estimated under Approximate Bayesian Computation, model selection as well as posterior parameter mean will be reported. Users need to enter a comparative dataset and a phylogenetic tree.
Detection of overdispersion in count data for multiple regression analysis.
    Log-linear count data regression is one of the most popular techniques for predictive 
    modeling where there is a non-negative discrete quantitative dependent variable. In 
    order to ensure the inferences from the use of count data models are appropriate, 
    researchers may choose between the estimation of a Poisson model and a negative binomial
    model, and the correct decision for prediction from a count data estimation is directly
    linked to the existence of overdispersion of the dependent variable, conditional to the 
    explanatory variables. Based on the studies of Cameron and Trivedi (1990)
    <doi:10.1016/0304-4076(90)90014-K> and Cameron and Trivedi (2013, ISBN:978-1107667273), 
    the overdisp() command is a contribution to researchers, providing a fast and secure 
    solution for the detection of overdispersion in count data. Another advantage is that 
    the installation of other packages is unnecessary, since the command runs in the basic 
    R language.
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>.
Makes it easy to display descriptive information on
    a data set.  Getting an easy overview of a data set by displaying and
    visualizing sample information in different tables (e.g., time and
    scope conditions).  The package also provides publishable 'LaTeX' code
    to present the sample information.
This tool was designed to assess the sensitivity of research findings to omitted variables when estimating causal effects using propensity score (PS) weighting. This tool produces graphics and summary results that will enable a researcher to quantify the impact an omitted variable would have on their results. Burgette et al. (2021) describe the methodology behind the primary function in this package, ov_sim. The method is demonstrated in Griffin et al. (2020) <doi:10.1016/j.jsat.2020.108075>.
Open the current working directory (or a given directory path) in your computer's file manager.
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.
Evaluates the Owen Q-function for an integer value of the degrees of freedom, by applying Owen's algorithm (1965) <doi:10.1093/biomet/52.3-4.437>. 
    It is useful for the calculation of the power of equivalence tests.
Scrapes data from the Our World in Data website to offer easy to use functions for searching for datasets and downloading them into R.
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.
The Oxford COVID-19 Government Response Tracker (OxCGRT) tracks and 
    compares worldwide government responses to the COVID-19 pandemic rigorously 
    and consistently. OxCGRT makes available systematic information in a 
    consistent way, aiding those who require information have access to it
    efficiently for their purposes. This package facilitates access to the 
    OxCGRT data via its API <https://covidtracker.bsg.ox.ac.uk/> and 
    includes functions to calculate the various OxCGRT indices in R.
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.
Collects a list of your third party R packages, and
    scans them with the 'OSS' Index provided by 'Sonatype', reporting back
    on any vulnerabilities that are found in the third party packages you
    use.
Functions for plotting Australia's coastline and state
  boundaries.
Maps of Australian coastline and administrative regions. Data 
 can be drawn or accessed directly as simple features objects. Includes
 simple functions for country or state maps of Australia and in-built data
 sets of administrative regions from the Australian Bureau of Statistics 
 <https://www.abs.gov.au/>. Layers include electoral divisions and local 
 government areas, simplified from the original sources but with sufficient 
 detail to allow mapping of a local municipality.
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.
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.
It provides utility functions for investigating changes within R
    packages. The pkgInfo() function extracts package information such as
    exported and non-exported functions as well as their arguments. The
    pkgDiff() function compares this information for two versions of a package
    and creates a diff file viewable in a browser.
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. If you don't want to search from the R console, use the comfortable R Studio add-in.
Helper functions for package creation, building and
    maintenance. Designed to work with a build system such as 'GNU make' or
    package 'fakemake' to help you to conditionally work through the stages of
    package development (such as spell checking, linting, testing, before
    building and checking a package).
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.
Algorithms to find arrangements of non-overlapping circles.
This package comes to illustrate the book "Petit Manuel de
        Programmation Orientee Objet sous R"
A collection of functions to construct Markov model for model-based 
    cost-effectiveness analysis. This includes creating Markov model (both time
    homogenous and time dependent models), decision analysis, sensitivity 
    analysis (deterministic and probabilistic). The package allows estimation 
    of parameters for the Markov model from a given individual patient level 
    data, provided the data file follows some standard data entry rules.
Enforces good practice and provides convenience functions to make work with 'JavaScript' 
  not just easier but also scalable. It is a robust wrapper to 'NPM', 'yarn', and 'webpack' that 
  enables to compartmentalize 'JavaScript' code, leverage 'NPM' and 'yarn' packages, include
  'TypeScript', 'React', or 'Vue' in web applications, and much more.
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 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.
Supplementary utils for CRAN maintainers and R packages developers.
  Validating the library or packages.
  Exploring a complexity of a specific package like evaluating sizes in bytes of all its dependencies.
  Assessing the life duration of a specific package version.
  Checking a CRAN package check page status for any errors and warnings.
  Retrieving a DESCRIPTION or NAMESPACE file for any package version. 
  Comparing DESCRIPTION or NAMESPACE files between different package versions.
  Getting a list of all releases for a specific package.
  The Bioconductor is partly supported.
Provides a broad-view perspective on data via
    linear mapping of data onto a radial coordinate system. The package
    contains functions to visualize the residual values of linear
    regression and Cartesian data in the defined radial scheme. See the
    'pacviz' documentation page for more information:
    <https://spencerriley.me/pacviz/book/>.
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 (2020) <doi:10.1186/s12859-020-3475-0>.
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 (Baker, 1975)
    <ISBN:9780120748556>.
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>. Thong Pham et al. (2020) <doi:10.18637/jss.v092.i03>. Thong Pham et al. (2021) <doi:10.1093/comnet/cnab024>.
Provides functions to read, process and visualize pairwise sequence 
 alignments in the 'PAF' format used by 'minimap2' and other whole-genome aligners. 
 'minimap2' is described by Li H. (2018) <doi:10.1093/bioinformatics/bty191>.
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.
Quickly and easily add a mini map to your 'rmarkdown' html documents.
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.
Analyzing and interactively exploring large-scale single-cell RNA-seq datasets. 'pagoda2' primarily performs normalization and differential gene expression analysis, with an interactive application for exploring single-cell RNA-seq datasets. It performs basic tasks such as cell size normalization, gene variance normalization, and can be used to identify subpopulations and run differential expression within individual samples. 'pagoda2' was written to rapidly process modern large-scale scRNAseq datasets of approximately 1e6 cells. The companion web application allows users to explore which gene expression patterns form the different subpopulations within your data. The package also serves as the primary method for preprocessing data for conos, <https://github.com/kharchenkolab/conos>. This package interacts with data available through the 'p2data' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/pagoda2>. The size of the 'p2data' package is approximately 6 MB.
Provides an encapsulated, object-oriented class system for
  analyzing bacterial pangenomes. For a definition of this concept, see
  Tettelin, et al. (2005) <doi:10.1073/pnas.0506758102>. It uses the R6
  package as backend. It was designed in order to facilitate and speed-up
  the comparative analysis of multiple bacterial genomes, standardizing and
  optimizing routine tasks performed everyday. There are a handful of things
  done everyday when working with bacterial pangenomes: subset, summarize,
  extract, visualize and store data. So, 'pagoo' is intended to facilitate these
  tasks as much as possible. For a description of the implemented data structure
  and methods, see Ferres & Iraola (2020), <doi:10.1101/2020.07.29.226951>.
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.
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 2014 Household Listing questionnaire data for Punjab, Pakistan.
Provides data set and function for exploration of Multiple Indicator Cluster Survey 2014 Women (age 15-49 years) questionnaire data for Punjab, Pakistan.
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. 2020 <doi:10.1007/s11634-019-00375-6>). 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.
This extension of the poems pattern-oriented modeling (POM) framework
    provides a collection of modules and functions customized for paleontological
    time-scales, and optimized for single-generation transitions and large populations,
    across multiple generations.
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.
Streamlines the steps for adding colour scales and associated legends 
         when working with base R graphics, especially for interactive use. Popular
         palettes are included and pretty legends produced when mapping a large 
         variety of vector classes to a colour scale. An additional helper for 
         adding axes and grid lines complements the base::plot() work flow.
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. (2019) <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, 2019 <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.
Size measurements, clutch observations, and blood isotope ratios for adult foraging Adélie, Chinstrap, and Gentoo penguins observed on islands in the Palmer Archipelago near Palmer Station, Antarctica. Data were collected and made available by Dr. Kristen Gorman and the Palmer Station Long Term Ecological Research (LTER) Program.
R Analysis suite for viral RNA dependent RNA polymerase (RdRP). 
    Statistical and meta-data analysis of 'palmscan' output and 'palmDB'/
    'DIAMOND' alignment files. Cross reference an input RNA virus against
    145,000 RdRP identified in the Serratus project.
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. Includes helper functions to map absolute values to known palettes, and 
    capture the work of image colour mapping as raster data sets.
A comprehensive collection of color palettes, colormaps, and tools to evaluate them.
Rank aggregation aims to achieve a better ranking list given multiple observations. 'PAMA' implements Partition-Mallows model for rank aggregation where the rankers' quality are different. Both Bayesian inference and Maximum likelihood estimation (MLE) are provided. It can handle partial list as well. When covariates information is available, this package can make inference by incorporating the covariate information. More information can be found in the paper "Integrated Partition-Mallows Model and Its Inference for Rank Aggregation".
Functions for easily reading and processing binary data files created by
    'Pamguard' (<https://www.pamguard.org/>). All functions for directly reading the
    binary data files are based on 'MATLAB' code written by Michael Oswald.
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.
Data are partitioned (clustered) into k clusters "around medoids", which is
    a more robust version of K-means implemented in the function pam() in the 'cluster' package.
    The PAM algorithm is described in Kaufman and Rousseeuw (1990) <doi:10.1002/9780470316801>.
    Please refer to the pam() function documentation for more references.
    Clustered data is plotted as a split heatmap allowing visualisation of representative
    "group-clusters" (medoids) in the data as separated fractions of the graph while those
    "sub-clusters" are visualised as a traditional heatmap based on hierarchical clustering.
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" and "lmerTest" packages.
A collection of miscellaneous functions for passive acoustics. 
    Much of the content here is adapted to R from code written by other people.
    If you have any ideas of functions to add, please contact Taiki Sakai.
The Piece-wise exponential (Additive Mixed) Model
    (PAMM; Bender and others (2018) <doi: 10.1177/1471082X17748083>) is a
    powerful model class for the analysis of survival (or time-to-event) data,
    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 and others (2019)), as well as support for left-truncated, competing risks and recurrent events data.
    pammtools provides tidy workflow for survival analysis with PAMMs,
    including data simulation, transformation and other functions for data
    preprocessing and model post-processing as well as visualization.
Tools for loading and processing passive acoustic data. Read in data
    that has been processed in 'Pamguard' (<https://www.pamguard.org/>), apply a suite
    processing functions, and export data for reports or external modeling tools. Parameter 
    calculations implement methods by Oswald et al (2007) <doi:10.1121/1.2743157>,
    Griffiths et al (2020) <doi:10.1121/10.0001229> and Baumann-Pickering et al (2010)
    <doi:10.1121/1.3479549>.
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.
Provides function for performing Bayesian survival regression using 
    Horseshoe prior in the accelerated failure time model with log normal assumption 
    in order to achieve high dimensional pan-cancer variable selection as developed in
    Maity et. al. (2019) <doi:10.1111/biom.13132>.
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.
Implementation of the methodology described in <http://est.ufmg.br/covidlp/home/pt/> which can be
    also found in help(models). Implemented models are currently the Poisson distribution. The mean function can be the
    basic generalized logistic form, or the seasonal effect which has under- or over-reporting effects in up to three
    weekdays, or the two curves form. Bayesian inference is made available through the 'stan' software and its diagnostic
    functions pool can be used. Plot methods are implemented to mimic the graphics from the 'shiny' app in the URL using
    the 'plotly' library.
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.
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
Implements a set of methodological tools
	     that enable researchers to apply matching methods to
	     time-series cross-sectional data. Imai, Kim, and Wang
	     (2021) <http://web.mit.edu/insong/www/pdf/tscs.pdf> 
	     proposes a nonparametric generalization of the
	     difference-in-differences estimator, which does not rely
	     on the linearity assumption as often done in
	     practice. Researchers first select a method of matching
	     each treated observation for a given unit in a
	     particular time period with control observations from
	     other units in the same time period that have a similar
	     treatment and covariate history. These methods include
	     standard matching methods based on propensity score and
	     Mahalanobis distance, as well as weighting methods. Once 
	     matching is done, both short-term and long-term average 
	     treatment effects for the treated can be estimated with 
	     standard errors. The package also offers a visualization 
	     technique that allows researchers to assess the quality 
	     of matches by examining the resulting covariate balance.
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.
Leading/lagging a panel, creating dummy variables,
             taking panel differences, looking for panel autocorrelations,
             and more. Implemented via a 'data.table' back end.
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 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.
Sankey diagrams are a powerfull and visually attractive way
  to visualize the flow of conservative substances through a system.
  They typically consists of a network of nodes, and fluxes between them,
  where the total balance in each internal node is 0, i.e. input equals output.
  Sankey diagrams are typically used to display energy systems, material flow accounts etc.
  Unlike so-called alluvial plots, Sankey diagrams also allow for cyclic flows:
  flows originating from a single node can, either direct or indirect, contribute to the input of that same node.
  This package, named after the Greek aphorism Panta Rhei (everything flows),
  provides functions to create publication-quality diagrams, using data in tables (or spread sheets)
  and a simple syntax.
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.
Positive predictive value (PPV) defined as the conditional probability of clinical trial assay (CTA) being positive given Companion diagnostic device (CDx) being positive is a key performance parameter for evaluating the clinical validity utility of a companion diagnostic test in clinical bridging studies. When bridging study patients are enrolled based on CTA assay results, Binomial-based confidence intervals (CI) may are not appropriate for PPV CI estimation. Bootstrap CIs which are not restricted by the Binomial assumption may be used for PPV CI estimation only when PPV is not 100%. Bootstrap CI is not valid when PPV is 100% and becomes a single value of [1, 1]. We proposed a risk ratio-based method for constructing CI for PPV. By simulation we illustrated that the coverage probability of the proposed CI is close to the nominal value even when PPV is high and negative percent agreement (NPA) is close to 100%. There is a lack of R package for PPV CI calculation. we developed a publicly available R package along with this shiny app to implement the proposed approach and some other existing methods.
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.
Parallel computing, multi-core CPU is used to efficiently compute
    and process multi-dimensional soil data.This package includes the 
    parallelized Quantile Regression Forests algorithm for Digital Soil Mapping 
    and is mainly dependent on the package 'quantregForest' and 'snowfall'.
    Detailed references to the R package and the web site are described in the
    methods, as detailed in the method documentation.
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).
Utility functions that enhance the 'parallel' package and support the built-in parallel backends of the 'future' package.  For example, availableCores() gives the number of CPU cores available to your R process as given by the operating system, 'cgroups' and Linux containers, R options, and environment variables, including those set by job schedulers on high-performance compute clusters. If none is set, it will fall back to parallel::detectCores(). Another example is makeClusterPSOCK(), which is backward compatible with parallel::makePSOCKcluster() while doing a better job in setting up remote cluster workers without the need for configuring the firewall to do port-forwarding to your local computer.
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.
See Miroshnikov and Conlon (2014) <doi:10.1371/journal.pone.0108425>. 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.
Create a parallel coordinates plot, using 'htmlwidgets' package and 'd3.js'.
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.
By making use of 'set6', alongside the S3 and R6 paradigms, this package provides a fast and lightweight R6 interface for parameters and parameter sets.
Utilities for processing the parameters of various
    statistical models. Beyond computing p values, CIs, and other indices
    for a wide variety of models (see list of supported models using the
    function 'insight::supported_models()'), this package implements
    features like bootstrapping or simulating of parameters and models,
    feature reduction (feature extraction and variable selection) as well
    as functions to describe data and variable characteristics (e.g.
    skewness, kurtosis, smoothness or distribution).
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).
Parametric linkage analysis of monogenic traits in medical 
    pedigrees. Features include singlepoint analysis, multipoint analysis via 
    'MERLIN' (Abecasis et al. (2002) <doi:10.1038/ng786>), visualisation of 
    log of the odds (LOD) scores and summaries of linkage peaks. Disease models
    may be specified to  accommodate phenocopies, reduced penetrance and 
    liability classes. 'paramlink2' is part of the 'ped suite' package 
    ecosystem, presented in 'Pedigree Analysis in R' (Vigeland, 2021, 
    ISBN:9780128244302).
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.
Complex graphical representations of data are best explored using interactive elements.
  'parcats' adds interactive graphing capabilities to the 'easyalluvial' package.
  The 'plotly.js' parallel categories diagrams offer a good framework for
  creating interactive flow graphs that allow manual drag and drop sorting of dimensions
  and categories, highlighting single flows and displaying mouse over information. The
  'plotly.js' dependency is quite heavy and therefore is outsourced into a separate package.
Create interactive parallel coordinates charts with this 'htmlwidget'
    wrapper for 'd3.js' <https://github.com/BigFatDog/parcoords-es>
    {'parallel-coordinates'}.
Parallelized version of the "segment" function from Bioconductor package "DNAcopy", utilizing multi-core computation on host CPU
Utilities for the Pareto, piecewise Pareto and generalized Pareto distribution
    that are useful for reinsurance pricing. In particular, the package provides
    a non-trivial algorithm that can be used to match the expected losses of a 
    tower of reinsurance layers with a layer-independent collective risk model.
    The theoretical background of the matching algorithm and most other methods
    are described in Ulrich Riegel (2018) <doi:10.1007/s13385-018-0177-3>.
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.
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.
Parallel estimation of the mutual information based on entropy
    estimates from k-nearest neighbors distances and algorithms for the
    reconstruction of gene regulatory networks
    (Sales et al, 2011 <doi:10.1093/bioinformatics/btr274>).
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.
Enable optimization of parameters of ordinary differential equations. Therefore, using 'SUNDIALS' to solve the ODE-System (see Hindmarsh, Alan C., Peter N. Brown, Keith E. Grant, Steven L. Lee, Radu Serban, Dan E. Shumaker, and Carol S. Woodward. (2005) <doi:10.1145/1089014.1089020>). Furthermore, for optimization the particle swarm algorithm is used (see: Akman, Devin, Olcay Akman, and Elsa Schaefer. (2018) <doi:10.1155/2018/9160793> and Sengupta, Saptarshi, Sanchita Basak, and Richard Peters. (2018) <doi:10.3390/make1010010>). The ODE-System has to be passed as 'Rcpp'-function. The information for the parameter boundaries and states are conveyed using data.frames.
Reads in multi-part parquet files. Will read in parquet files that have not been previously coalesced into one file. Convenient for reading in moderately sized, but split files.
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.
An implementation of a formal grammar and parser for R Markdown documents
    using the Boost Spirit X3 library. It also includes a collection of high level
    functions for working with the resulting abstract syntax tree.
Functions to load Research Patient Data Registry ('RPDR') text queries from Partners Healthcare institutions into R.
             The package also provides helper functions to manipulate data and execute common procedures
             such as finding the closest radiological exams considering a given timepoint, or creating a DICOM header database
             from the downloaded images. 'parseRPDR' currently supports txt sources: "mrn", "con", "dem", "enc", "rdt", "lab",
             "med", "dia", "rfv", "prc", "phy", "lno", "car", "dis", "end", "hnp", "opn", "pat", "prg", "pul", "rad" and "vis".
             All functionalities are parallelized for fast and efficient analyses.
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
A toolbox to create a particle swarm optimisation (PSO), the package contains two classes: the Particle and the Particle Swarm, this two class is used to run the PSO with methods to easily print, plot and save the result.
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.
    'partition' is based on the Partition framework discussed in Millstein
    et al. (2020) <doi: 10.1093/bioinformatics/btz661>.
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 and now compositions are 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.
Partitioning the R2 of GLMMs into variation explained by each 
    predictor and combination of predictors using semi-partial (part) R2 and
    inclusive R2. Methods are based on the R2 for GLMMs described in
    Nakagawa & Schielzeth (2013) <doi:10.1111/j.2041-210x.2012.00261.x> and
    Nakagawa, Johnson & Schielzeth (2017) <doi:10.1098/rsif.2017.0213>.
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 enhancements 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) <https://jmlr.org/papers/v16/hothorn15a.html>.
Parviol combines parallel coordinates and violin plot
Parse geographic coordinates from various formats
    to decimal degree numeric values. Parse coordinates into
    their parts (degree, minutes, seconds); calculate hemisphere
    from coordinates; pull out individually degrees,
    minutes, or seconds; add and subtract degrees, minutes,
    and seconds. C++ code herein originally inspired from code
    written by Jeffrey D. Bogan, but then completely re-written.
An R package for polygenic trait analysis.
Anomaly detection method based on the paper "Truth will out: Departure-based process-level detection of stealthy attacks on control systems" from Wissam Aoudi, Mikel Iturbe, and Magnus Almgren (2018) <DOI:10.1145/3243734.3243781>. Also referred to the following implementation: <https://github.com/rahulrajpl/PyPASAD>.
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>.
Power calculations are a critical component of any research study to determine the 
	minimum sample size necessary to detect differences between multiple groups. Here we 
	present an 'R' package, 'PASSED', that performs power and sample size calculations for 
	the test of two-sample means or ratios with data following beta, 
	gamma (Chang et al. (2011), <doi:10.1007/s00180-010-0209-1>), normal, 
	Poisson (Gu et al. (2008), <doi:10.1002/bimj.200710403>), binomial, geometric, and 
	negative binomial (Zhu and Lakkis (2014), <doi:10.1002/sim.5947>) distributions.
An implementation of the unified framework for assessing partial association 
            between ordinal variables after adjusting for a set of covariates (Dungang Liu, Shaobo 
            Li, Yan Yu and Irini Moustaki (2020), accepted by the Journal of the American 
            Statistical Association). This package provides a set of tools to quantify, visualize, 
            and test partial associations between multiple ordinal variables. It can produce a number
            of $phi$ measures, partial regression plots, 3-D plots, and $p$-values for testing 
            $H_0: phi=0$ or $H_0: phi <= delta$.
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.
Simulates judgments of frequency and duration based on
    the Probability Associator Time (PASS-T) model. PASS-T is a memory
    model based on a simple competitive artificial neural network. It 
    can imitate human judgments of frequency and duration, which have
    been extensively studied in cognitive psychology
    (e.g. Hintzman (1970) <doi:10.1037/h0028865>, Betsch et al. (2010)
    <https://psycnet.apa.org/record/2010-18204-003>). The PASS-T model
    is an extension of the PASS model (Sedlmeier, 2002,
    ISBN:0198508638). The package provides an easy way to run
    simulations, which can then be compared with empirical data in
    human judgments of frequency and duration.
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.
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.
The 'ggplot2' package provides a strong API for sequentially 
    building up a plot, but does not concern itself with composition of multiple
    plots. 'patchwork' is a package that expands the API to allow for 
    arbitrarily complex composition of plots by, among others, providing 
    mathematical operators for combining multiple plots. Other packages that try 
    to address this need (but with a different approach) are 'gridExtra' and 
    'cowplot'.
Converts TXT and XML data curated by the United States Patent and
    Trademark Office (USPTO). Allows conversion of bulk data after downloading
    directly from the USPTO bulk data website, eliminating need for users to
    wrangle multiple data formats to get large patent databases in tidy,
    rectangular format. Data details can be found on the USPTO website
    <https://bulkdata.uspto.gov/>. Currently, all 3 formats: 1. TXT data
    (1976-2001); 2. XML format 1 data (2002-2004); and 3. XML format 2 data
    (2005-current) can be converted to rectangular, CSV format.
    Relevant literature that uses data from USPTO includes Wada (2020)
    <doi:10.1007/s11192-020-03674-4> and Plaza & Albert (2008)
    <doi:10.1007/s11192-007-1763-3>.
Provides functions to simplify the 'PatentsView' API
    (<https://patentsview.org/apis/purpose>) query language,
    send GET and POST requests to the API's seven endpoints, and parse the data
    that comes back.
Provides path_chain class and functions, which facilitates loading and saving 
             directory structure in YAML configuration files via 'config' package. 
             The file structure you created during exploration can be transformed 
             into legible section in the config file, and then easily loaded for further usage.
Implementation of simple functions to draw
    basic path diagrams just for visualization purposes.
Enrichment analysis enables researchers to uncover mechanisms 
    underlying a phenotype. However, conventional methods for enrichment 
    analysis do not take into account protein-protein interaction information, 
    resulting in incomplete conclusions. pathfindR is a tool for enrichment 
    analysis utilizing active subnetworks. The main function identifies active 
    subnetworks in a protein-protein interaction network using a user-provided 
    list of genes and associated p values. It then performs enrichment analyses 
    on the identified subnetworks, identifying enriched terms (i.e. pathways or, 
    more broadly, gene sets) that possibly underlie the phenotype of interest.
    pathfindR also offers functionalities to cluster the enriched terms and 
    identify representative terms in each cluster, to score the enriched terms 
    per sample and to visualize analysis results. The enrichment, clustering and 
    other methods implemented in pathfindR are described in detail in 
    Ulgen E, Ozisik O, Sezerman OU. 2019. pathfindR: An R Package for 
    Comprehensive Identification of Enriched Pathways in Omics Data Through 
    Active Subnetworks. Front. Genet. <doi:10.3389/fgene.2019.00858>.
This is a data-only package, containing data needed to run the CRAN 
    package 'pathfindR', a package for enrichment analysis utilizing active 
    subnetworks. This package contains protein-protein interaction network data, 
    data related to gene sets and example input/output data.
An OO Interface for path manipulation, emulating pythons 
             "pathlib".
This wrapper houses PathLit API endpoints for R. The usage of
    these endpoints require the use of an API key which can be obtained at
    <https://www.pathlit.io/docs/cli/>.
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>.
Functions for computing fit indices for 
    evaluating the path component of latent variable structural equation models. 
    Available fit indices include RMSEA-P and NSCI-P originally presented and evaluated 
    by Williams and O'Boyle (2011) <doi:10.1177/1094428110391472> and demonstrated by 
    O'Boyle and Williams (2011) <doi:10.1037/a0020539> and Williams, O'Boyle, & Yu (2020) 
    <doi:10.1177/1094428117736137>. Also included are fit indices described by 
    Hancock and Mueller (2011) <doi:10.1177/0013164410384856>.
In causal mediation analysis with multiple causally ordered mediators, a set of path-specific
  effects are identified under standard ignorability assumptions. This package implements an imputation
  approach to estimating these effects along with a set of bias formulas for conducting sensitivity analysis
  (Zhou and Yamamoto <doi:10.31235/osf.io/2rx6p>). It contains two main functions: paths() for estimating 
  path-specific effects and sens() for conducting sensitivity analysis. Estimation uncertainty is quantified 
  using the nonparametric bootstrap.
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.
Tools to import, clean, and visualize movement data,
    particularly from motion capture systems such as Optitrack's 
    'Motive', the Straw Lab's 'Flydra', or from other sources. We provide 
    functions to remove artifacts, standardize tunnel position and tunnel 
    axes, select a region of interest, isolate specific trajectories, fill
    gaps in trajectory data, and calculate 3D and per-axis velocity. For 
    experiments of visual guidance, we also provide functions that use 
    subject position to estimate perception of visual stimuli.
A systematic bioinformatics tool to develop a new pathway-based gene panel for tumor mutational burden (TMB) assessment (pathway-based tumor mutational burden, PTMB), using somatic mutations files in an efficient manner from either The Cancer Genome Atlas sources or any in-house studies as long as the data is in mutation annotation file (MAF) format. Besides, we develop a multiple machine learning method using the sample's PTMB profiles to identify cancer-specific dysfunction pathways, which can be a biomarker of prognostic and predictive for cancer immunotherapy.
Creation of patient profile visualizations for
  exploration, diagnostic or monitoring purposes during a clinical trial.
  These static visualizations display a patient-specific overview
  of the evolution during the trial time frame of 
  parameters of interest (as laboratory, ECG, vital signs),
  presence of adverse events, exposure to a treatment; 
  associated with metadata patient information, 
  as demography, concomitant medication.
  The visualizations can be tailored for specific domain(s) or endpoint(s) of interest.
  Visualizations are exported into patient profile report(s)
  or can be embedded in custom report(s).
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.
NOTE: This content is being migrated to the package ShapePattern to provide a comprehensive set of tools for shape and pattern analysis. All future maintenance will be in that package -- please update your links. This current package 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.  Current limitation is for binary (classes 1, 2) maps that are 64x64 cells in extent.
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, ring charts, bar charts and box plots with colors, patterns, and images.
A modeling tool dedicated to biological network modeling (Bertrand and others 2020, <doi:10.1093/bioinformatics/btaa855>). 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 (Jung and others 2014, <doi:10.1093/bioinformatics/btt705>). 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 the spectral and spatial analysis of 
    colour described in Maia, Eliason, Bitton, Doucet & Shawkey (2013) 
    <doi:10.1111/2041-210X.12069> and Maia, Grsuon, Endler & White (2019)
    <doi:10.1111/2041-210X.13174>.
This is a collection of functions to process, format and store accelerometer data.
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' services,
    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
    services, 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 services,
    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 services,
    including cost and usage reports, budgets, pricing, and more
    <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' customer engagement
    services, including 'Simple Email Service', 'Connect' contact center
    service, and more <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' database services,
    including 'Relational Database Service' ('RDS'), 'DynamoDB' 'NoSQL'
    database, and more <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' developer tools services,
    including version control, continuous integration and deployment, and
    more <https://aws.amazon.com/products/developer-tools/>.
Interface to 'Amazon Web Services' end user computing
    services, including collaborative document editing, mobile intranet,
    and more <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' machine learning services,
    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
    services, 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 services, 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 services, 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 services,
    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.
Generates a position balanced or nearly position balanced block design with given parameters.  This package can also convert a given proper and equireplicate block design into a position balanced or nearly position balanced block design.
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.
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.
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.
Make statistical inference on the probability of being in response, 
             the duration of response, and the cumulative response rate up to a given time point. 
             The method can be applied to analyze phase II randomized clinical trials with the endpoints 
             being time to treatment response and time to progression or death.
Provides a vectorized R function for calculating
    probabilities from a standard bivariate normal CDF.
Access data and metadata from 'Microsoft' 'Power BI' ('.pbix', <https://powerbi.microsoft.com>) documents with R. The 'pbixr' package enables one to extract 'Power Query M' formulas (<https://docs.microsoft.com/en-us/power-query/>) 'Data Analysis Expressions' queries ('DAX', <https://docs.microsoft.com/en-us/dax/>) and their properties, report layout and style, and data and data models.
Test in mixed effects models. Attention is on mixed
    effects models as implemented in the 'lme4' package. For linear
    mixed models, this package implements (1) a parametric bootstrap
    test, (2) a Kenward-Roger-typ modification of F-tests for linear
    mixed effects models and (3) a Satterthwaite-type modification of
    F-tests for linear mixed effects models. The package also
    implements a parametric bootstrap test for generalized linear
    mixed models.  The facilities of the package are documented in the
    paper by Halehoh and Højsgaard, (2012,
    <doi:10.18637/jss.v059.i09>).  Please see 'citation("pbkrtest")'
    for citation details.
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'.
Functions for solving systems of delay differential equations by
   interfacing with numerical routines written by Simon N. Wood, including
   contributions from 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.
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.
Calibrate p-values under a robust perspective using the methods developed
    by Sellke, Bayarri, and Berger (2001) <doi:10.1198/000313001300339950> and obtain 
    measures of the evidence provided by the data in favor of point null hypotheses 
    which are safer and more straightforward to interpret.
Implementations of several methods for principal component analysis 
        using the L1 norm.  The package depends on COIN-OR Clp version >= 
        1.17.4.  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 (2018, <doi:10.1146/annurev-statistics-031017-100307>). Covers several common testing scenarios such as z-tests, t-tests, likelihood ratio tests and the F-test.
Compute the price of different types of call using different methods. The types available are Vanilla European Calls, Vanilla American Calls and American Digital Calls. Available methods are Montecarlo Simulation, Montecarlo Simulation with Antithetic Variates, Black-Scholes and the Binary Tree.
Matches cases to controls based on genotype principal components (PC). 
      In order to produce better results, matches are based on the weighted 
      distance of PCs where the weights are equal to the % variance explained 
      by that PC. A weighted Mahalanobis distance metric (Kidd et al. (1987)
      <DOI:10.1016/0031-3203(87)90066-5>) is used to determine matches.
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>.
Contains the functions for generating patterns of segregation, association, complete spatial randomness (CSR))
    and Uniform data in one, two and three dimensional cases, for testing these patterns based on two invariants of
    various families of the proximity catch digraphs (PCDs) (see (Ceyhan (2005) ISBN:978-3-639-19063-2).
    The graph invariants used in testing spatial point data are the domination number (Ceyhan (2011)
    <doi:10.1080/03610921003597211>) and arc density (Ceyhan et al. (2006) <doi:10.1016/j.csda.2005.03.002>;
    Ceyhan et al. (2007) <doi:10.1002/cjs.5550350106>) of for two-dimensional data for visualization of PCDs for one,
    two and three dimensional data. The PCD families considered are Arc-Slice PCDs,
    Proportional-Edge PCDs and Central Similarity PCDs.
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. This
    package is described in Pritikin (2020) <doi:10.1016/j.heliyon.2020.e04821>.
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.
Piecewise constant hazard models for survival data. 
    The package allows for right-censored, left-truncated, and interval-censored data.
Bayesian network learning using the PCHC algorithm. PCHC stands for PC Hill-Climbing, a new hybrid algorithm that uses PC to construct the skeleton of the BN and then 
			 applies the Hill-Climbing greedy search. More algorithms and variants have been added, such as MMHC, FEDHC, and the Tabu search variants, PCTABU, MMTABU and FEDTABU. 
			 The relevant papers are 
			 a) Tsagris M. (2021). A new scalable Bayesian network learning algorithm with applications to economics. Computational Economics, 57(1): 341-367. <doi:10.1007/s10614-020-10065-7>. 
			 b) Tsagris M. (2021). The FEDHC Bayesian network learning algorithm. <arXiv:2012.00113>.
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.
We fit causal models using proxies. We implement two stage proximal least squares estimator. E.J. Tchetgen Tchetgen, A. Ying, Y. Cui, X. Shi, and W. Miao. (2020). An Introduction to Proximal Causal Learning. arXiv e-prints, arXiv-2009 <arXiv:2009.10982>.
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>.
Two protein complex-based group regression models (PCLasso and PCLasso2) for risk protein complex identification. PCLasso is a prognostic model that identifies risk protein complexes associated with survival. PCLasso2 is a classification model that identifies risk protein complexes associated with classes. For more information, see Wang and Liu (2021) <doi:10.1093/bib/bbab212>.
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) <doi: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 inference 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.
Provides a C++ backend for multivariate phylogenetic comparative
  models implemented in the R-package 'PCMBase'. Can be used in combination
  with 'PCMBase' to enable fast and parallel likelihood calculation. Implements
  the pruning likelihood calculation algorithm described in Mitov et al. (2018) 
  <arXiv:1809.09014>. Uses the 'SPLITT' C++ library for parallel tree traversal 
  described in Mitov and Stadler (2018) <doi:10.1111/2041-210X.13136>.
Implementation of PCMRS (Partial Credit Model with Response Styles) as proposed in by Tutz, Schauberger and Berger (2018) <doi:10.1177/0146621617748322> .  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 Bayesian arm-based network meta-analysis for datasets with binary, continuous, and count outcomes
 (Zhang et al., 2014 <doi:10.1177/1740774513498322>;
  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 (de Jong S. & Kiers H. A. L. (1992) <doi:10.1016/0169-7439(92)80100-I>). Several rotation and model selection options are provided.
Fits penalized models for both time-independent and time-dependent survival data. It fully implements elastic net and uses proximal gradient descent to solve the optimization problem. The package is an implementation of Steve Cygu and Benjamin M. Bolker. (2021) <arXiv:2102.02297>.
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>.
Extracts features from amplification curve data of quantitative 
    Polymerase Chain Reactions (qPCR) (Pabinger S. et al. (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 (Burdukiewicz M. et al. (2018) <doi:10.1016/j.bdq.2018.08.001>) 
    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.
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.
Defines functions to describe regression models using only
    pre-computed summary statistics (i.e. means, variances, and covariances)
    in place of individual participant data.
    Possible models include linear models for linear combinations, products, 
    and logical combinations of phenotypes.
    Implements methods presented in 
    Wolf et al. (2021) <doi:10.1101/2021.03.08.433979>
    Wolf et al. (2020) <doi:10.1142/9789811215636_0063> and 
    Gasdaska et al. (2019) <doi:10.1142/9789813279827_0036>.
The Prize-Collecting Steiner Tree problem asks to find a subgraph
    connecting a given set of vertices with the most expensive nodes and least
    expensive edges. Since it is proven to be NP-hard, exact and efficient algorithm
    does not exist. This package provides convenient functionality for obtaining an
    approximate solution to this problem using loopy belief propagation algorithm.
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>.
Classes and methods for modelling and simulation of
    periodically correlated (PC) and periodically integrated time
    series.  Compute theoretical periodic autocovariances and related
    properties of PC autoregressive moving average models. Some original
    methods including Boshnakov & Iqelan (2009)
    <doi:10.1111/j.1467-9892.2009.00617.x>, Boshnakov (1996)
    <doi:10.1111/j.1467-9892.1996.tb00281.x>.
A collection of privacy-preserving distributed algorithms for conducting multi-site data analyses. The regression analyses can be linear regression for continuous outcome, logistic regression for binary outcome, Cox proportional hazard regression for time-to event outcome, or Poisson regression for count outcome. The PDA algorithm runs on a lead site and only requires summary statistics from collaborating sites, with one or few iterations. For more information, please visit our software websites: <https://github.com/Penncil/pda>, and <https://pdamethods.org/>.
Permutation Distribution Clustering is a clustering method for time series. Dissimilarity of time series is formalized as the divergence between their permutation distributions. The permutation distribution was proposed as measure of the complexity of a time series.
The PDE (Pdf Data Extractor) allows the extraction of 
    information and tables optionally based on search words from 
    PDF (Portable Document Format) files and enables the visualization 
    of the results, both by providing a convenient user-interface.
Data sets for the Panel Data Econometrics with R <doi:10.1002/9781119504641> 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.
Provides an interface to 'PDFMiner' <https://github.com/pdfminer/pdfminer.six> 
    a 'Python' package for extracting information from 'PDF'-files. 
    'PDFMiner' has the goal to get all information available in a 'PDF'-file, 
    position of the characters, font type, font size and informations about lines. 
    Which makes it the perfect starting point for extracting tables from 'PDF'-files.
    More information can be found in the package 'README'-file.
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.
Oak declines are complex disease syndromes and consist of many visual indicators that include aspects of tree size, crown condition and trunk condition. This can cause difficulty in the manual classification of symptomatic and non-symptomatic trees from what is in reality a broad spectrum of oak tree health condition. Two phenotypic oak decline indexes have been developed to quantitatively describe and differentiate oak decline syndromes in Quercus robur. This package provides a toolkit to generate these decline indexes from phenotypic descriptors using the machine learning algorithm random forest. The methodology for generating these indexes is outlined in Finch et al. (2121) <doi:10.1016/j.foreco.2021.118948>.
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.
Create, transform, and summarize custom random
    variables with distribution functions (analogues of 'p*()', 'd*()',
    'q*()', and 'r*()' functions from base R). Two types of distributions
    are supported: "discrete" (random variable has finite number of output
    values) and "continuous" (infinite number of values in the form of
    continuous random variable). Functions for distribution
    transformations and summaries are available. Implemented approaches
    often emphasize approximate and numerical solutions: all distributions
    assume finite support and finite values of density function; some
    methods implemented with simulation techniques.
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.
An engaging collection of datasets from Portland Parks and Recreation. The city of Portland inventoried every tree in over 170 parks and along the streets in 96 neighborhoods.
Linear dynamic panel data modeling based on linear and
    nonlinear moment conditions as proposed by
    Holtz-Eakin, Newey, and Rosen (1988) <doi:10.2307/1913103>,
    Ahn and Schmidt (1995) <doi:10.1016/0304-4076(94)01641-C>,
    and Arellano and Bover (1995) <doi:10.1016/0304-4076(94)01642-D>.
    Estimation of the model parameters relies on the Generalized
    Method of Moments (GMM), numerical optimization (when nonlinear
    moment conditions are employed) and the computation of closed
    form solutions (when estimation is based on linear moment
    conditions). One-step, two-step and iterated estimation is
    available. of closed form solutions. For inference and specification
    testing, Windmeijer (2005) <doi:10.1016/j.jeconom.2004.02.005>
    and doubly corrected standard errors
    (Hwang, Kang, Lee, 2021 <doi:10.1016/j.jeconom.2020.09.010>)
    are available. Additionally, serial correlation tests, tests for
    overidentification, and Wald tests are provided. Functions for
    visualizing panel data structures and modeling results obtained
    from GMM estimation are also available. The plot methods include
    functions to plot unbalanced panel structure, coefficient ranges
    and coefficient paths across GMM iterations (the latter is
    implemented according to the plot shown in
    Hansen and Lee, 2021 <doi:10.3982/ECTA16274>).
These are useful tools and data sets for the study of quantitative 
    peace science. The goal for this package is to include tools and data sets
    for doing original research that mimics well what a user would have to previously
    get from a software package that may not be well-sourced or well-supported.
    Those software bundles were useful the extent to which they encourage replications 
    of long-standing analyses by starting the data-generating process from scratch. However, 
    a lot of the functionality can be done relatively quickly and more transparently
    in the R programming language.
A meta gene set analysis tool developed based on principles of Pareto dominance (William B T Mock (2011) <doi:10.1007/978-1-4020-9160-5_341>). It is designed to combine gene set analysis p-values from multiple transcriptome datasets (e.g., microarray and RNA-Seq). The novel Pareto method for p-value combination allows PEACH to properly model heterogeneity and correlation in Omics datasets.
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 region labels.
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.
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. Most of the 'pec' functionality has been moved to 'riskRegression'.
It provides functions to perform permutation conditional random one-sample and two-samples t-tests in a multivariate framework.
Reconstruct pedigrees from genotype data, by optimising the likelihood 
    over all possible pedigrees subject to given restrictions. Tailor-made plots
    facilitate evaluation of the output. This package is part of the 'ped suite'
    ecosystem for pedigree analysis. In particular, it imports 'pedprobr' for 
    calculating pedigree likelihoods and 'forrel' for estimating pairwise relatedness.
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.
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>.
Provides functions to estimate mixed probit models using, for 
    instance, pedigree data like in <doi:10.1002/sim.1603>. The models are also 
    commonly called liability threshold models. The approximation is 
    based on direct log marginal likelihood approximations like the randomized 
    Quasi-Monte Carlo suggested by <doi:10.1198/106186002394> with a similar 
    procedure to approximate the derivatives. Graph-based methods are also 
    provided that can be used to simplify pedigrees.
A collection of functions for modelling mutations in pedigrees with 
    marker data, as used e.g. in likelihood computations with microsatellite data.
    Implemented models include proportional and stepwise models, as well as random 
    models for experimental work, and custom models allowing the user to apply any 
    valid mutation matrix. Allele lumping is done following the lumpability criteria 
    of Kemeny and Snell (1976), ISBN:0387901922.
An implementation of the Elston-Stewart algorithm for calculating
    pedigree likelihoods given genetic marker data (Elston and Stewart (1971) 
    <doi:10.1159/000152448>). The standard algorithm is extended to allow inbred 
    founders. 'pedprobr' is part of the ped suite, a collection of packages for 
    pedigree analysis in R, based on 'pedtools' for handling pedigrees and 
    markers. Mutation modelling is supported by the 'pedmut' package.
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.
Simulate pedigree, genetic merits and phenotypes with random/non-random matings followed by random/non-random selection with different intensities and patterns in males and females. Genotypes can be simulated for a given pedigree.
   Bijma, P. & Rutten, M. (2002) <https://www.wur.nl/en/Research-Results/Chair-groups/Animal-Sciences/Animal-Breeding-and-Genomics-Group/Research/Software.htm>.
The 'ped suite' is a collection of packages for pedigree
    analysis, covering applications in forensic genetics, medical genetics
    and more. A detailed presentation of the 'ped suite' is given in the
    book 'Pedigree Analysis in R' (Vigeland, 2021, ISBN: 9780128244302).
A comprehensive collection of tools for creating,
    manipulating and visualising pedigrees and genetic marker data.
    Pedigrees can be read from text files or created on the fly with
    built-in functions. A range of utilities enable modifications like
    adding or removing individuals, breaking loops, and merging pedigrees.
    Pedigree plots are produced by wrapping the plotting functionality of
    the 'kinship2' package. A Shiny app for creating pedigrees, based on
    'pedtools', is available at <https://magnusdv.shinyapps.io/quickped>.
    'pedtools' is the hub of the 'ped suite', a collection of packages for
    pedigree analysis. A detailed presentation of the 'ped suite' is given
    in the book 'Pedigree Analysis in R' (Vigeland, 2021,
    ISBN:9780128244302).
Enables direct cloud access to health care decision models hosted on the PRISM server of the Peer Models Network.
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 L1 and L2 penalized conditional logistic regression with penalty 
    factors allowing for integration of multiple data sources. Implements
    stability selection for variable selection.
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.
Computes penalized regression calibration (PRC), a
    statistical method that allows to predict survival from
    high-dimensional longitudinal predictors. PRC is described in
    Signorelli et al.  (2021, arXiv preprint: <doi:10.1002/sim.9178>)).
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.
Fits single- and multiple-group penalized factor analysis models 
    via a trust-region algorithm with integrated automatic multiple tuning 
    parameter selection (Geminiani et al., 2021 <doi:10.1007/s11336-021-09751-8>). 
    Available penalties include lasso, adaptive lasso, scad, mcp, and ridge.
Currently incorporate the generalized odds-rate model (a type of linear 
    transformation model) for interval-censored data based on penalized monotonic B-Spline. 
    More methods under other semiparametric models such as cure model or additive model will
    be included in future versions. For more details see Lu, M., Liu, Y., Li, C. and Sun, J. 
    (2019) <arXiv:1912.11703>.
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).
Implementation of the semi-parametric proportional-hazards (PH) of Sy and Taylor (2000) <doi:10.1111/j.0006-341X.2000.00227.x> extended to time-varying covariates. Estimation and variable selection are based on the methodology described in Beretta and Heuchenne (2019) <doi:10.1080/02664763.2018.1554627>; confidence intervals of the parameter estimates may be computed using a bootstrap approach. Moreover, data following the PH cure model may be simulated using a method similar to Hendry (2014) <doi:10.1002/sim.5945>, where the event-times are generated on a continuous scale from a piecewise exponential distribution conditional on time-varying covariates.
A set of tools that enables efficient estimation of penalized 
    Poisson Pseudo Maximum Likelihood regressions, using lasso or ridge penalties, for models 
    that feature one or more sets of high-dimensional fixed effects. The methodology is based on 
    Breinlich, Corradi, Rocha, Ruta, Santos Silva, and Zylkin (2021) <http://hdl.handle.net/10986/35451> 
    and takes advantage of the method of alternating projections of Gaure (2013) 
    <doi:10.1016/j.csda.2013.03.024> for dealing with HDFE, as well as 
    the coordinate descent algorithm of Friedman, Hastie and Tibshirani (2010) 
    <doi:10.18637/jss.v033.i01> for fitting lasso regressions. The package is also able to carry out 
    cross-validation and to implement the plugin lasso of Belloni, Chernozhukov, Hansen and Kozbur (2016) 
    <doi:10.1080/07350015.2015.1102733>.
Robust penalized (adaptive) elastic net S and M estimators for
    linear regression. The methods are proposed in
    Cohen Freue, G. V., Kepplinger, D., Salibián-Barrera, M., and Smucler, E.
    (2019) <https://projecteuclid.org/euclid.aoas/1574910036>.
    The package implements the extensions and algorithms described in
    Kepplinger, D. (2020) <doi:10.14288/1.0392915>.
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.
Data sets for statistical inference modeling related to People Analytics.  
  Contains various data sets from the book 'Handbook of Regression Modeling in People Analytics' 
  by Keith McNulty (2020).
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. Methods used in the package are detailed in
        Porzelius Ch., Binder H. and Schumacher M. (2009) <doi:10.1093/bioinformatics/btp062>
        and were used, for instance, in
        Porzelius Ch., Schumacher M.and  Binder H. (2011) <doi:10.1007/s00180-011-0236-6>.
Fits the Piecewise Exponential distribution with random time grids using the clustering structure of the Product Partition Models. Details of the implemented model can be found in Demarqui et al. (2008) <doi:10.1007/s10985-008-9086-0>.
A PEP, or Portable Encapsulated Project, is a dataset that 
    subscribes to the PEP structure for organizing metadata. It is written using
    a simple YAML + CSV format, it is your one-stop solution to metadata 
    management across data analysis environments. This package reads this 
    standardized project configuration structure into R.
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.
Calculate (stratified) percentiles on a data.frame
    Stratification will split the data.frame into subgroups and calculate percentiles for each independently.
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) <bioRxiv:10.1101/674523>.
Several person-fit statistics 
  (PFSs; Meijer and Sijtsma, 2001, <doi:10.1177/01466210122031957>) 
  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 [see Fay and Shaw <doi:10.18637/jss.v036.i02>].
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).
Calculates multivariate analysis of variance based on permutations and some associated pictorial representations. The pictorial representation is based on the principal coordinates of the group means. There are some original results that will be published soon.
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.
An add-on to the 'party' package, with a faster implementation 
   of the partial-conditional permutation importance for random forests. The 
   standard permutation importance is implemented exactly the same as in 
   the 'party' package. The conditional permutation importance can be 
   computed faster, with an option to be backward compatible to the 'party' 
   implementation. The package is compatible with random forests fit using the 
   'party' and the 'randomForest' package. The methods are described in
   Strobl et al. (2007) <doi:10.1186/1471-2105-8-25> and 
   Debeer and Strobl (2020) <doi:10.1186/s12859-020-03622-2>.
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 a 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' or 'Anders'). 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 COLUMN format; v1.2 and olders incorporates -optimize- function to maximize evaluation of features with largest inter-class variation; v1.3 and olders includes the -size.effect- function to perform estimation statistics using the bootstrap-coupled approach implemented in the 'dabestr' (>=0.3.0) R package.
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. Mixed-effects models are supported using an implementation of
    the approach by Lee & Braun (2012) <doi:10.1111/j.1541-0420.2011.01675.x>.
Tools that allow developers to write functions for prediction
    error estimation with minimal programming effort and assist users with
    model selection in regression problems.
Examples for integrating package 'perry' for prediction error estimation into regression models.
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 and is described in detail in Huling and Yu (2021) 
    <doi:10.18637/jss.v098.i05>.
Implements the methodology of Huling, Smith, and 
    Chen (2020) <doi:10.1080/01621459.2020.1801449>, which allows for subgroup identification 
    for semi-continuous outcomes by estimating individualized treatment rules. It uses a two-part 
    modeling framework to handle semi-continuous data by separately modeling the positive part 
    of the outcome and an indicator of whether each outcome is positive, but still results in a 
    single treatment rule. High dimensional data is handled with a cooperative lasso penalty, 
    which encourages the coefficients in the two models to have the same sign.
Functions to setup a personal R package that
    attaches given libraries and exports personal helper functions.
Visualizes treatment effects using person icons, similar to Cates (NNT) charts.
An R-package-version of an open online science-based personality
    test from <https://openpsychometrics.org/tests/IPIP-BFFM/>,
    providing a better-designed interface and a more detailed report.
    The core command launch_test() opens a personality test in your browser,
    and generates a report after you click "Submit". In this report, your results
    are compared with other people's, to show what these results mean.
    Other people's data is from <https://openpsychometrics.org/_rawdata/BIG5.zip>.
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).
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.
A friendly API for sequence iteration and set comprehension.
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>.
Computes the Danish Pesticide Load Indicator as described in Kudsk et al. (2018) <doi:10.1016/j.landusepol.2017.11.010> and Moehring et al. (2019) <doi:10.1016/j.scitotenv.2018.07.287> for pesticide use data. Additionally offers the possibility to directly link pesticide use data to pesticide properties given access to the Pesticide properties database (Lewis et al., 2016) <doi:10.1080/10807039.2015.1133242>.
Set of tools to automatize extraction of data on pests from 'EPPO
    Data Services' and 'EPPO Global Database' and to put them into tables with
    human readable format. Those function use 'EPPO database API', thus you 
    first need to register on <https://data.eppo.int> (free of charge).
    Additional helpers allow to download, check and connect to
    'SQLite EPPO database'.
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.
Reproducible, programmatic retrieval of survey datasets from the
    Pew Research Center.
Load the Just Another Gibbs Sampling (JAGS) module 'pexm'. The module provides the tools to work with the Piecewise Exponential (PE) distribution in a Bayesian model with the corresponding Markov Chain Monte Carlo algorithm (Gibbs Sampling) implemented via JAGS. Details about the module implementation can be found in Mayrink et al. (2020) <arXiv:2004.12359>.
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.
Performs penalized independent component analysis for univariate functional data [<doi:10.3390/math9111243>].
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.
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, model selection and specific statistical tests for ecologists. It includes functions to interpolate regular positions of points between landmarks, to discretize polylines into regular point positions, link distant observations to points, read subsets of big rasters, compute zonal statistics or table of categories from raster  within polygons or circular buffers. The package also provides miscellaneous functions for model selection, spatial statistics and inference on diversity indexes, 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), 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.
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.
Create PostgreSQL statements/scripts from R, optionally executing the SQL statements.
    Common SQL operations are included, although not every configurable option is available at this time. 
    SQL output is intended to be compliant with PostgreSQL syntax specifications. PostgreSQL documentation is available here
    <https://www.postgresql.org/docs/current/index.html>.
Reproducible cleaning of bio-medical laboratory data using methods of visualization,error correction and transformation implemented as interactive R-notebooks.
The purpose of 'PH1XBAR' is to build a Phase I Shewhart control chart for the basic Shewhart, the variance components and the ARMA models in R for subgrouped and individual data. More details can be found: Yao and Chakraborti (2020) <doi: 10.1002/qre.2793>, and Yao and Chakraborti (2021) <doi: 10.1080/08982112.2021.1878220>.
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).
Provides functions to assist with the design of randomized
    comparative phase II oncology trials that assume their primary outcome
    variable is Bernoulli distributed. Specifically, support is provided to (a)
    perform a sample size calculation when using one of several published
    designs, (b) evaluate the operating characteristics of a given design (both
    analytically and via simulation), and (c) produce informative plots.
There are 4 possible methods: "ExhaustiveSearch"; "ExhaustivePhi";
    "ClusteringSearch"; and "ClusteringPhi". 
    "ExhaustiveSearch"--> gives you the best phage cocktail from a phage-bacteria
	infection network. It checks different phage cocktail sizes from 1 to 7 and
	only stops before if it lyses all bacteria. Other option is when users have
	decided not to obtain a phage cocktail size higher than a limit value.
    "ExhaustivePhi"--> firstly, it finds Phi out. Phi is a formula
	indicating the necessary phage cocktail size. Phi needs nestedness temperature
	and fill, which are internally calculated. This function will only look for the
	best combination (phage cocktail) with a Phi size.
    "ClusteringSearch"--> firstly, an agglomerative hierarchical clustering using
	Ward's algorithm is calculated for phages. They will be clustered according to
	bacteria lysed by them. PhageCocktail() chooses how many clusters are needed in
	order to select 1 phage per cluster. Using the phages selected during the
	clustering, it checks different phage cocktail sizes from 1 to 7 and only stops
	before if it lyses all bacteria. Other option is when users have decided not to
	obtain a phage cocktail size higher than a limit value.
    "ClusteringPhi"--> firstly, an agglomerative hierarchical clustering using Ward's
	algorithm is calculated for phages. They will be clustered according to bacteria
	lysed by them. PhageCocktail() chooses how many clusters are needed in order to
	select 1 phage per cluster. Once the function has one phage per cluster, it
	calculates Phi. If the number of clusters is less than Phi number, it will be
	changed to obtain, as minimum, this quantity of candidates (phages). Then, it
	calculates the best combination of Phi phages using those selected during the
	clustering with Ward algorithm.
Allows for estimation of phylogenetic trees and networks
    using Maximum Likelihood, Maximum Parsimony, distance methods and
    Hadamard conjugation. Offers methods for tree comparison, model
    selection and visualization of phylogenetic networks as described in
    Schliep et al. (2017) <doi:10.1111/2041-210X.12760>.
Enhanced RTF wrapper written in R for use with existing R tables
    packages such as 'Huxtable' or 'GT'. This package fills a gap where tables in
    certain packages can be written out to RTF, but cannot add certain metadata
    or features to the document that are required/expected in a report for a
    regulatory submission, such as multiple levels of titles and footnotes,
    making the document landscape, and controlling properties such as margins.
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.
Interface to the 'Pharmpy' 'pharmacometrics' library. The 'Reticulate' package is used to interface Python from R.
Compiles functions to trim, bin, visualise, and analyse activity/sleep time-series data collected from the Drosophila Activity Monitor (DAM) system (Trikinetics, USA). The following methods were used to compute periodograms - Chi-square periodogram: Sokolove and Bushell (1978) <doi:10.1016/0022-5193(78)90022-X>, Lomb-Scargle periodogram: Lomb (1976) <doi:10.1007/BF00648343>, Scargle (1982) <doi:10.1086/160554> and Ruf (1999) <doi:10.1076/brhm.30.2.178.1422>, and Autocorrelation: Eijzenbach et al. (1986) <doi:10.1111/j.1440-1681.1986.tb00943.x>. Identification of activity peaks is done after using a Savitzky-Golay filter (Savitzky and Golay (1964) <doi:10.1021/ac60214a047>) to smooth raw activity data. Three methods to estimate anticipation of activity are used based on the following papers - Slope method: Fernandez et al. (2020) <doi:10.1016/j.cub.2020.04.025>, Harrisingh method: Harrisingh et al. (2007) <doi:10.1523/JNEUROSCI.3680-07.2007>, and Stoleru method: Stoleru et al. (2004) <doi:10.1038/nature02926>. Rose plots and circular analysis are based on methods from - Batschelet (1981) <ISBN:0120810506> and Zar (2010) <ISBN:0321656865>.
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.
Simulating and conducting four phase 12 clinical trials with correlated binary bivariate outcomes described. Uses the 'Efftox' (efficacy and toxicity tradeoff, <https://biostatistics.mdanderson.org/SoftwareDownload/SingleSoftware/Index/2>) and SPSO (Semi-Parametric Stochastic Ordering) models with Utility and Desirability based objective functions for dose finding.
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 ordinary differential equation 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. For further details can be
   found in Grayling (2014) <doi:10.32614/RJ-2014-023>.
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 publication in Nature Biotechnology
  at <doi:10.1038/s41587-019-0336-3>.
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.
Implement surrogate-assisted feature extraction (SAFE) and
             common machine learning approaches to train and validate phenotyping models.
             Background and details about the methods can be found at 
             Zhang et al. (2019) <doi:10.1038/s41596-019-0227-6>,
             Yu et al. (2017) <doi:10.1093/jamia/ocw135>, and 
             Liao et al. (2015) <doi:10.1136/bmj.h1885>.
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).
Generates Weibull-parameterized estimates of phenology for any percentile of 
    a distribution using the framework established in Cooke (1979)
    <doi:10.1093/biomet/66.2.367>. Extensive testing against other 
    estimators suggest the weib_percentile() function is especially useful in 
    generating more accurate and less biased estimates of onset and offset 
    (Belitz et al. 2020 <doi.org:10.1111/2041-210X.13448>. Non-parametric 
    bootstrapping can be used to generate confidence intervals around those 
    estimates, although this is computationally expensive. Additionally, this 
    package offers an easy way to perform non-parametric bootstrapping to 
    generate confidence intervals for quantile estimates, mean estimates, 
    or any statistical function of interest.
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.
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:
    Kong, D., (2020). R package: A state-of-the-art Vegetation Phenology extraction 
    package, phenofit version 0.3.1, <doi:10.5281/zenodo.5150204>;
    Kong, D., Zhang, Y., Wang, D., Chen, J., & Gu, X. (2020). Photoperiod Explains 
    the Asynchronization Between Vegetation Carbon Phenology and Vegetation Greenness 
    Phenology. Journal of Geophysical Research: Biogeosciences, 125(8), e2020JG005636. 
    <doi:10.1029/2020JG005636>;
    Kong, D., Zhang, Y., Gu, X., & Wang, D. (2019). A robust method for reconstructing 
    global MODIS EVI time series on the Google Earth Engine. 
    ISPRS Journal of Photogrammetry and Remote Sensing, 155, 13–24;
    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.
The algorithm combines the most predictive variable, such as count of the main International Classification of Diseases (ICD) codes, and other Electronic Health Record (EHR) features (e.g. health utilization and processed clinical note data), to obtain a score for accurate risk prediction and disease classification. In particular, it normalizes the surrogate to resemble gaussian mixture and leverages the remaining features through random corruption denoising. Background and details about the method can be found at Yu et al. (2018) <doi:10.1093/jamia/ocx111>.
Large-scale phenotypic data processing is essential in research. Researchers need to eliminate outliers from the data in order to obtain true and reliable results. Best linear unbiased prediction (BLUP) is a standard method for estimating random effects of a mixed model. This method can be used to process phenotypic data under different conditions and is widely used in animal and plant breeding. The 'Phenotype' can remove outliers from phenotypic data and performs the best linear unbiased prediction (BLUP), help researchers quickly complete phenotypic data analysis. H.P.Piepho. (2008) <doi:10.1007/s10681-007-9449-8>.
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.
Using Electronic Health Record (EHR) is difficult because most of the time the true characteristic of the patient is not available. Instead we can retrieve the International Classification of Disease code related to the disease of interest or we can count the occurrence of the Unified Medical Language System. None of them is the true phenotype which needs chart review to identify. However chart review is time consuming and costly. 'PheVis' is an algorithm which is phenotyping (i.e identify a characteristic) at the visit level in an unsupervised fashion. It can be used for chronic or acute diseases. An example of how to use 'PheVis' is available in the vignette. Basically there are two functions that are to be used: `train_phevis()` which trains the algorithm and `test_phevis()` which get the predicted probabilities. The detailed method is described in preprint by Ferté et al. (2020) <doi:10.1101/2020.06.15.20131458>.
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.
Proportional hazards estimation in the presence of a partially monotone likelihood has difficulties, in that finite estimators do not exist.  These difficulties are related to those arising from logistic and multinomial regression.  References for methods are given in the separate function documents.  Supported by grant NSF DMS 1712839.
A bioinformatics method developed for analyzing the heterogeneity of single-cell populations. Phitest provides an objective and automatic method to evaluate the performance of clustering and quality of cell clusters.
Functions to extract and handle commonly occurring principal phrases
    obtained from collections of texts.
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 have to be solved during phonetic research and experiments. This includes creating a presentation that will contain all stimuli, renaming and concatenating multiple sound files recorded during a session, automatic annotation in 'Praat' TextGrids (this is one of the sound annotation standards provided by 'Praat' software, see Boersma & Weenink 2020 <https://www.fon.hum.uva.nl/praat/>), creating an html table with annotations and spectrograms, and converting multiple formats ('Praat' TextGrid, 'ELAN', 'EXMARaLDA', 'Audacity', subtitles '.srt', and 'FLEx' flextext). All of these tasks can be solved by a mixture of different tools (any programming language has programs for automatic renaming, and Praat contains scripts for concatenating and renaming files, etc.). 'phonfieldwork' provides a functionality that will make it easier to solve those tasks independently of any additional tools. You can 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.  The package is
    documented in <doi:10.18637/jss.v095.i08>.
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.
Use 'Phosphor' icons in 'shiny' applications or 'rmarkdown' documents. Icons are available in
    5 different weights and can be customized by setting color, size, orientation and more.
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: peaks,
    valleys and spikes. 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. Spectral 
    reflectance data for frequently encountered materials. 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>.
Connect R to the PhotosynQ platform (<https://photosynq.org>). It allows to login and logout,
    as well as receive project information and project data. Further it transforms the received JSON objects
    into a data frame, which can be used for the final data analysis.
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.
Provides tools for penalised maximum likelihood estimation of hidden semi-Markov models (HSMMs) with flexible state dwell-time distributions. These include functions for model fitting, model checking and state-decoding. The package considers HSMMs for univariate time series with state-dependent gamma, normal, Poisson or Bernoulli distributions. For details, see Pohle, J., Adam, T. and Beumer, L.T. (2021): Flexible estimation of the state dwell-time distribution in hidden semi-Markov models. <arXiv:2101.09197>.
Assessment of habitat selection by means of the permutation-based combination of sign tests (Fattorini et al., 2014 <DOI:10.1007/s10651-013-0250-7>). To exemplify the application of this procedure, habitat selection is assessed for a population of European Brown Hares settled in central Italy.
A collection of several pharmacovigilance signal detection methods extended to the multiple comparison setting.
Calls 'Phoenix NLME' (non-linear mixed effects), a population
    modeling and simulation software, for pharmacokinetics and pharmacodynamics
    analyses and conducts post-processing of the results. This includes creation of
    various diagnostic plots, bootstrap and visual predictive checks. See <http://
    www.certara.com/software/pkpd-modeling-and-simulation/phoenix-nlme/> for more
    information about 'Phoenix NLME'.
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.
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.
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>.
Computational infrastructure for biogeography, community ecology,
    and biodiversity conservation (Daru et al. 2020) <doi:10.1111/2041-210X.13478>.
    It is based on the methods described in Daru et al. (2020) <doi:10.1038/s41467-020-15921-6>.
    The original conceptual work is described in Daru et al. (2017) <doi:10.1016/j.tree.2017.08.013>
    on patterns and processes of biogeographical regionalization. Additionally, the package
    contains fast and efficient functions to compute more standard conservation measures
    such as phylogenetic diversity, phylogenetic endemism, evolutionary distinctiveness
    and global endangerment, as well as compositional turnover (e.g., beta diversity).
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.
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.
A collection of functions to do model-based phylogenetic analysis. 
    It includes functions to calculate community phylogenetic diversity,
    to estimate correlations among functional traits while accounting for 
    phylogenetic relationships, and to fit phylogenetic generalized linear
    mixed models. The Bayesian phylogenetic generalized linear mixed models
    are fitted with the 'INLA' package (<https://www.r-inla.org>).
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.  Version 1.1 adds preschool thresholds and corrects for possible errors in algorithm implementation.
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.      
    "deliveryPred" and "markDelivery" can classify days for ActiGraph delivery by mail;
    "deliveryPreprocess" can process accelerometry data for analysis by zeropadding incomplete 
    days and removing low activity days; "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.
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 numerous methods for visualizing, manipulating, reading or writing, and even inferring phylogenetic trees. 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 numerous 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 computing 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.
Contains functions to fit proportional hazards (PH) model to partly interval-censored (PIC) data (Pan et al. (2020) <doi:10.1177/0962280220921552>), PH model with spatial frailty to spatially dependent PIC data (Pan and Cai (2021) <doi:10.1080/03610918.2020.1839497>), and mixed effects PH model to clustered PIC data. Each random intercept/random effect can follow both a normal prior and a Dirichlet process mixture prior. It also includes the corresponding functions for general interval-censored data.
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.
Tokenizers break text into pieces that are more usable by machine 
    learning models. Many tokenizers share some preparation steps. This package
    provides those shared steps, along with a simple tokenizer.
Functions to make board game graphics.  Specializes in game diagrams, animations, and "Print & Play" layouts for the 'piecepack' <https://www.ludism.org/ppwiki> but can make graphics for other board game systems.  Includes configurations for several public domain 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.
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, 2012: <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.
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. (2019) <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'.
Publish data sets, models, and other R objects, making it
    easy to share them across projects and with your colleagues. You can
    pin objects to a variety of "boards", including local folders (to
    share on a networked drive or with 'DropBox'), 'RStudio' connect, Amazon
    S3, and more.
Provides a robust approach for omics data integration and disease subtyping. PINSPlus is fast and supports the analysis of large datasets with hundreds of thousands of samples and features. The software automatically determines the optimal number of clusters and then partitions the samples in a way such that the results are robust against noise and data perturbation (Nguyen et.al. (2019) <DOI: 10.1093/bioinformatics/bty1049>, Nguyen et.al. (2017)<DOI: 10.1101/gr.215129.116>).
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.
Functions representing some useful empirical and data-driven 
    models of heat losses, corrosion diagnostics, reliability and predictive 
    maintenance of pipeline systems. The package is an option for digital 
    transformation of technical engineering departments of heat generating and 
    heat transferring companies. Methods are described in 
    Timashev et al. (2016) <doi:10.1007/978-3-319-25307-7>, 
    A.C.Reddy (2017) <doi:10.1016/j.matpr.2017.07.081>,
    Minenergo (2008) <https://docs.cntd.ru/document/902148459>,
    Minenergo (2005) <http://www.complexdoc.ru/ntdtext/547103>.
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.
Implements multiple state-of-the-art prediction interval methodologies for random forests. 
	These include: quantile regression intervals, out-of-bag intervals, bag-of-observations intervals, 
	one-step boosted random forest intervals, bias-corrected intervals, high-density intervals, and 
	split-conformal intervals. The implementations include a combination of novel adjustments to the 
	original random forest methodology and novel prediction interval methodologies. All of these 
	methodologies can be utilized using solely this package, rather than a collection of separate 
	packages. Currently, only regression trees are supported. Also capable of handling high dimensional data. 
	Roy, Marie-Helene and Larocque, Denis (2019) <doi:10.1177/0962280219829885>.
	Ghosal, Indrayudh and Hooker, Giles (2018) <arXiv:1803.08000>.
	Zhu, Lin and Lu, Jiaxin and Chen, Yihong (2019) <arXiv:1905.10101>.
	Zhang, Haozhe and Zimmerman, Joshua and Nettleton, Dan and Nordman, Daniel J. (2019) <doi:10.1080/00031305.2019.1585288>.
	Meinshausen, Nicolai (2006) <http://www.jmlr.org/papers/volume7/meinshausen06a/meinshausen06a.pdf>.
	Romano, Yaniv and Patterson, Evan and Candes, Emmanuel (2019) <arXiv:1905.03222>.
	Tung, Nguyen Thanh and Huang, Joshua Zhexue and Nguyen, Thuy Thi and Khan, Imran (2014) <doi:10.13140/2.1.2500.8002>.
Scored responses and responses times from the Canadian subsample of the PISA 2018 assessment, accessible as the "Cognitive items total time/visits data file" by OECD (2020) <https://www.oecd.org/pisa/data/2018database/>.
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 'VMware Data Stack' running on
    'PostgreSQL' or 'Greenplum' 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', 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.
Run Queries against the API of 'Piwik Pro' <https://developers.piwik.pro/en/latest/custom_reports/http_api/http_api.html>. The result is a tibble.
Data about Disney Pixar films provided by Wikipedia. This
    package contains data about the films, the people involved, and their
    awards.
Contains functions to classify the pixels of an image file by its colour. It implements a simple form of the techniques known as Support Vector Machine adapted to this particular problem.
Puzzle game that can be played in the R console.
  Restore the pixel art by shifting rows.
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>.
Prepare pharmacokinetic/pharmacodynamic (PK/PD) data for PK/PD analyses. 
    This package provides functions to standardize infusion and bolus dose data while
    linking it to drug level or concentration data.
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.
Find recursive dependencies of 'R' packages from various
    sources. Solve the dependencies to obtain a consistent set of
    packages to install. Download packages, and install them. It
    supports packages on 'CRAN', 'Bioconductor' and other 'CRAN-like'
    repositories, 'GitHub', package 'URLs', and local package trees
    and files. It caches metadata and package files via the 'pkgcache'
    package, and performs all 'HTTP' requests, downloads, builds and
    installations in parallel. 'pkgdepends' is the workhorse of the
    'pak' package.
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.
Manage optional data for your package. The data can be hosted anywhere, and you have to give a Uniform Resource Locator (URL) for each file. File integrity checks are supported. This is useful for package authors who need to ship more than the 5 Megabyte of data currently allowed by the the Comprehensive R Archive Network (CRAN).
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. Unit test support can be added via the 'tinytest'
 package (if present), and documentation-creation support can be
 added via 'roxygen2' (if present).
A tool, grammar, and standard to represent and exchange
    R package source code as text files. Converts one or more source
    packages to a text file and restores the package structures from the file.
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.
It checks the heaviness of the packages that user's
    package depends on. For each package listed in the "Depends", "Imports" and
    "Suggests" fields in the DESCRIPTION file, it opens a new R session, loads the
    package and counts the number of namespaces that are loaded. The summary of
    the dependencies is visualized by a customized heatmap. Examples of dependency
    analysis can be found at <https://jokergoo.github.io/pkgndep/stat/>.
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.  See the pkgnet website for vignettes 
             and other supplementary information.
Read R package news files, regardless of whether or not the package
  is installed.
Search CRAN metadata about packages by keyword, popularity,
    recent activity, package name and more. Uses the 'R-hub' search server,
    see <https://r-pkg.org> and the CRAN metadata database, that
    contains information about CRAN packages. Note that this is _not_
    a CRAN project.
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.
Public Key Infrastucture functions such as verifying certificates, RSA encription and signing which can be used to build PKI infrastructure and perform cryptographic tasks.
Implementation of a KL-based test for MCAR in the context of missing data as introduced in Michel et al. (2021)  <arXiv:2109.10150>.
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 for
    typical pharmacokinetic analyses and summarize them.
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 physiologically based 
    kinetic (PK) models, especially the physiologically based 
    pharmacokinetic/toxicokinetic model with multivariate outputs. 
    The package also provides some functions to check the convergence 
    and sensitivity of model parameters. The workflow was first mentioned 
    in Hsieh et al., (2018) <doi:10.3389/fphar.2018.00588>, then further 
    refined (Hsieh et al., 2020 <doi:10.1016/j.softx.2020.100609>).
Tools to process legacy format summary redistricting data files
    produced by the United States Census Bureau pursuant to P.L. 94-171. These
    files are generally available earlier but are difficult to work with as-is.
A semi-parametric estimation method for the Cox model
    with left-truncated data using augmented information
    from the marginal of truncation times.
Assessment of the prevalence of plastic
    debris in bird nests based on bootstrap replicates. The package allows for 
    calculating bootstrapped 95% confidence intervals for the estimated prevalence 
    of debris. Combined with a Bayesian approach, the resampling simulations can 
    be also used to define appropriate sample sizes to detect prevalence of plastics. 
    The method has wide application, and can also be applied to estimate confidence 
    intervals and define sample sizes for the prevalence of plastics ingested by any 
    other organisms. The method is described in Tavares et al. (Submitted).
Clusters GPS coordinates into places (i.e., meaningful stops). Additionally, categorizes places into types (e.g., home, cafe, gym). Places are identified as home using a rules-based algorithm defining home as the stop occurring most frequently during the night. Other places (e.g., cafe, gym) are identified using the Google Maps API.
Functions to prepare rankings data and fit the Plackett-Luce model
    jointly attributed to Plackett (1975) <doi:10.2307/2346567> and Luce
    (1959, ISBN:0486441369). The standard Plackett-Luce model is generalized
    to accommodate ties of any order in the ranking. Partial rankings, in which
    only a subset of items are ranked in each ranking, are also accommodated in
    the implementation. Disconnected/weakly connected networks implied by the
    rankings may be handled by adding pseudo-rankings with a hypothetical item.
    Optionally, a multivariate normal prior may be set on the log-worth
    parameters and ranker reliabilities may be incorporated as proposed by
    Raman and Joachims (2014) <doi:10.1145/2623330.2623654>. Maximum a
    posteriori estimation is used when priors are set. Methods are provided to
    estimate standard errors or quasi-standard errors for inference as well as
    to fit Plackett-Luce trees. See the package website or vignette for further
    details.
Provides methods for plotting potentially large (raster) images
    interactively on a plain HTML canvas. In contrast to package 'mapview'
    data are plotted without background map, but data can be projected to
    any spatial coordinate reference system.
    Supports plotting of classes 'RasterLayer', 'RasterStack', 'RasterBrick'
    (from package 'raster') as well as 'png' files located on disk. 
    Interactivity includes zooming, panning, and mouse location information. 
    In case of multi-layer 'RasterStacks' or 'RasterBricks', RGB image plots 
    are created (similar to 'raster::plotRGB' - but interactive).
Supports the creation of 'burndown' charts and 'gantt' diagrams.
Solves the electromagnetic problem of reflection and transmission at a planar multilayer interface. Also computed are the decay rates and emission profile for a dipolar emitter.
An extensive set of plane geometry routines. Provides R6 classes representing triangles, circles, circular arcs, ellipses, elliptical arcs and lines, and their plot methods. Also provides R6 classes representing transformations: rotations, reflections, homotheties, scalings, general affine transformations, inversions, Möbius transformations.
Calculates an acceptance sampling plan, (sample size and acceptance number) based in MIL STD 105E, Dodge  Romig and MIL STD 414 tables and procedures. The arguments for each function are related to lot size, inspection level and quality level. The specific plan operating curve (OC), is calculated by the binomial distribution.
It includes functions to download and process the 'Planet NICFI' (Norway's International Climate and Forest Initiative) Satellite Imagery utilizing the Planet Mosaics API <https://developers.planet.com/docs/basemaps/reference/#tag/Basemaps-and-Mosaics>. 'GDAL' (library for raster and vector geospatial data formats) and 'aria2c' (paralleled download utility) must be installed and configured in the user's Operating System.
The goal of 'planets' is to provide of very simple and accessible data containing basic information from all known planets.
Coupled leaf gas exchange model, A-Ci curve simulation and
    fitting, Ball-Berry stomatal conductance models, 
    leaf energy balance using Penman-Monteith, Cowan-Farquhar
    optimization, humidity unit conversions. 
    See Duursma (2015) <doi:10.1371/journal.pone.0143346>.
Provides wrapping functions to add to capabilities to 'plantecophys' 
    (Duursma, 2015, <doi:10.1371/journal.pone.0143346>). Key added capabilities 
    include temperature responses of mesophyll conductance (gm, gmeso), apparent 
    Michaelis-Menten constant for rubisco carboxylation in air (Km, Kcair),and
    photorespiratory CO2 compensation point (GammaStar) for fitting A-Ci or A-Cc
    curves for C3 plants (for temperature responses of gm, Km, & GammaStar,  see 
    Bernacchi et al., 2002, <doi:10.1104/pp.008250>; for theory on fitting A-Ci 
    or A-Cc curves, see Farquhar et al., 1980; <doi:10.1007/BF00386231>, von 
    Caemmerer, 2000, ISBN:064306379X; Ethier & Livingston, 2004 
    <doi:10.1111/j.1365-3040.2004.01140.x>; and Gu et al., 2010, 
    <doi:10.1111/j.1365-3040.2010.02192.x>). Includes the ability to fit the 
    Arrhenius and modified Arrhenius temperature response functions (see Medlyn 
    et al., 2002, <doi:10.1046/j.1365-3040.2002.00891.x>) for maximum rubisco 
    carboxylation rates (Vcmax) and maximum electron transport rates (Jmax) (see
    Farquhar et al., 1980; <doi:10.1007/BF00386231>).
Estimation, prediction, thresholding, transformation, and plotting for partially linear additive quantile regression.  Intuitive functions for fitting and plotting partially linear additive quantile regression models.  Uses and works with functions from the 'quantreg' package.
Aims at detecting single nucleotide variation
    (SNV) and insertion/deletion (INDEL) in circulating tumor DNA (ctDNA), used
    as a surrogate marker for tumor, at each base position of an Next Generation
    Sequencing (NGS) analysis. Mutations are assessed by comparing the minor-allele
    frequency at each position to the measured PER in control samples.
Contains functions developed to combine the results of querying a plasmid database using
    short-read sequence typing with the results of a blast analysis against the query results.
Tools for interacting with data from experiments done in microtiter
    plates. Easily read in plate-shaped data and convert it to tidy format, 
    combine plate-shaped data with tidy data, and view tidy data in plate shape.
Collection of functions for working with multi-well microtitre
    plates, mainly 96, 384 and 1536 well plates.
Data and analysis from an experiment with improving touch 
    typing speed, using the tDCS PlatoWork headset produced by PlatoScience.
We present 'Platypus', an open-source software platform providing a user-friendly interface to investigate B-cell receptor and T-cell receptor repertoires from scSeq experiments. 'Platypus' provides a framework to automate and ease the analysis of single-cell immune repertoires while also incorporating transcriptional information involving unsupervised clustering, gene expression and gene ontology (Yermanos A, et al (2021) <doi:10.1093/nargab/lqab023>).
Implements schemes for estimating player or 
  team skill based on dynamic updating. Implemented methods include 
  Elo, Glicko, Glicko-2 and Stephenson. Contains pdf documentation 
  of a reproducible analysis using approximately two million chess 
  matches. Also contains an Elo based method for multi-player games
  where the result is a placing or a score. This includes zero-sum
  games such as poker and mahjong.
We present a penalized log-density estimation method using Legendre polynomials with lasso penalty to adjust estimate's smoothness. Re-expressing the logarithm of the density estimator via a linear combination of Legendre polynomials, we can estimate parameters by maximizing the penalized log-likelihood function. Besides, we proposed an implementation strategy that builds on the coordinate decent algorithm, together with the Bayesian information criterion (BIC).
Perform tests for pleiotropy of multiple traits of various variable types on genotypes for a genetic marker.
It performs a fast multi-trait genome-wide association analysis based on seemingly unrelated regressions. It tests for pleiotropic effects based on a series of Intersection-Union Wald tests. The package can handle large and unbalanced data and plot results.
Log-multiplicative association models (LMA) are
    models for cross-classifications of categorical variables
    where interactions are represented by products of category
    scale values and an association parameter. Maximum
    likelihood estimation (MLE) fails for moderate to large
    numbers of categorical variables. The 'pleLMA' package
    overcomes this limitation of MLE by using pseudo-likelihood
    estimation to fit the models to small or large
    cross-classifications dichotomous or multi-category variables.
    Originally proposed by Besag (1974,
    <doi:10.1111/j.2517-6161.1974.tb00999.x>), pseudo-likelihood
    estimation takes large complex models and breaks it down
    into smaller ones. Rather than maximizing the likelihood
    of the joint distribution of all the variables, a
    pseudo-likelihood function, which is the product likelihoods
    from conditional distributions, is maximized. LMA models can
    be derived from a number of different frameworks including
    (but not limited to) graphical models and uni-dimensional
    and multi-dimensional item response theory models. More
    details about the models and estimation can be found in
    the vignette.
The portmanteau local feature discriminant approach first identifies 
    the local discriminant features and their differential structures, 
    then constructs the discriminant rule by pooling the identified local
    features together. This method is applicable to high-dimensional 
    matrix-variate data.
Functions for estimating probabilistic latent feature models with a disjunctive, conjunctive or additive mapping rule on (aggregated) binary three-way data.
Provides a graphical user interface for viewing and designing various types of graphs of the data. The graphs can be saved in different formats of an image.
Sequential Monte Carlo 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
Provides tools for image manipulation that will help you to
    quantify plant leaf area, disease severity, number of disease lesions,
    and obtain statistics of image objects such as grains, pods, pollen,
    leaves, and more. Tools for segment images and create binary images
    using the method of automatic threshold selection method proposed by
    Otsu (1979) <doi:10.1109/tsmc.1979.4310076> are also provided.
Item response theory based methods are used to compute
        linking constants and conduct chain linking of unidimensional
        or multidimensional tests for multiple groups under a common
        item design.  The unidimensional methods include the Mean/Mean,
        Mean/Sigma, Haebara, and Stocking-Lord methods for dichotomous
        (1PL, 2PL and 3PL) and/or polytomous (graded response, partial
        credit/generalized partial credit, nominal, and multiple-choice
        model) items.  The multidimensional methods include the least
        squares method and extensions of the Haebara and Stocking-Lord
        method using single or multiple dilation parameters for
        multidimensional extensions of all the unidimensional
        dichotomous and polytomous item response models.  The package
        also includes functions for importing item and/or ability
        parameters from common IRT software, conducting IRT true score
        and observed score equating, and plotting item response
        curves/surfaces, vector plots, information plots, and comparison 
        plots for examining parameter drift.
Provide function that reads binary genotype produced by 'PLINK' <https://www.cog-genomics.org/plink/1.9/input#bed> into a R matrix, or scan the genotype one variant at a time like apply(), it also provides functions that reads and writes genotype relatedness/kinship matrices created by 'PLINK' <https://www.cog-genomics.org/plink/1.9/distance#make_rel> or 'GCTA' <https://cnsgenomics.com/software/gcta/#MakingaGRM>. Currently it does not support writing back into 'PLINK' binary, it is best used for bringing data produced by 'PLINK' and 'GCTA' into R environment.
Genotyping arrays enable the direct measurement of an individuals
    genotype at thousands of markers. 'plinkQC' facilitates genotype quality
    control for genetic association studies as described by Anderson and
    colleagues (2010) <doi:10.1038/nprot.2010.116>. It makes 'PLINK' basic
    statistics (e.g. missing genotyping rates per individual, allele frequencies
    per genetic marker) and relationship functions accessible from 'R' and
    generates a per-individual and per-marker quality control report.
    Individuals and markers that fail the quality control can subsequently be
    removed to generate a new, clean dataset. Removal of individuals based on
    relationship status is optimised to retain as many individuals as possible
    in the study.
PLIS is a multiple testing procedure for testing several
        groups of hypotheses. Linear dependency is expected from the
        hypotheses within the same group and is modeled by hidden
        Markov Models. It is noted that, for PLIS, a smaller p value
        does not necessarily imply more significance because of
        dependency among the hypotheses. A typical application of PLIS
        is to analyze genome wide association studies datasets, where
        SNPs from the same chromosome are treated as a group and
        exhibit strong linear genomic dependency.
A set of estimators and tests for panel data
             econometrics, as described in Baltagi (2021), Econometric Analysis of Panel Data, ISBN-13:978-3-030-53952-8,
             Hsiao (2014), Analysis of Panel Data <doi:10.1017/CBO9781139839327>, and
             Croissant and Millo (2018), Panel Data Econometrics with R, ISBN-13:978-1-118-94918-4.
Fit finite mixtures of Plackett-Luce models for partial top rankings/orderings within the Bayesian framework. It provides MAP point estimates via EM algorithm and posterior MCMC simulations via Gibbs Sampling. It also fits MLE as a special case of the noninformative Bayesian analysis with vague priors. In addition to inferential techniques, the package assists other fundamental phases of a model-based analysis for partial rankings/orderings, by including functions for data manipulation, simulation, descriptive summary, model selection and goodness-of-fit evaluation. Main references on the methods are Mollica and Tardella (2017) <doi.org/10.1007/s11336-016-9530-0> and Mollica and Tardella (2014) <doi/10.1002/sim.6224>.
Utilizes the 'lme4' and 'optimx' packages (previously the optim()
    function from 'stats') to estimate (generalized) linear mixed models (GLMM)
    with factor structures using a profile likelihood approach, as outlined in
    Jeon and Rabe-Hesketh (2012) <doi:10.3102/1076998611417628> and
    Rockwood and Jeon (2019) <doi:10.1080/00273171.2018.1516541>.
    Factor analysis and item response models can be extended to allow
    for an arbitrary number of nested and crossed random effects,
    making it useful for multilevel and cross-classified models.
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 (Chiquet, Mariadassou and Robin, 
    2020 <doi:10.1101/2020.10.07.329383>) can be used for 
    a variety of multivariate problems when count data are at play, including 
    principal component analysis for count data, discriminant analysis, model-based clustering and 
    network inference. Implements variational algorithms to fit such models accompanied with a set of 
    functions for visualization and diagnostic.
A simple header-only logging library for C++.
  Add 'LinkingTo: plogr' to 'DESCRIPTION', and '#include <plogr.h>' in your C++ modules to use it.
Multivariate ordered probit model, i.e. the extension of the scalar ordered probit model where the observed variables have dimension greater than one. Estimation of the parameters is done via maximization of the pairwise likelihood, a special case of the composite likelihood obtained as product of bivariate marginal distributions. The package uses the Fortran 77 subroutine SADMVN by Alan Genz, with minor adaptations made by Adelchi Azzalini in his "mvnormt" package for evaluating the two-dimensional Gaussian integrals involved in the pairwise log-likelihood. Optimization of the latter objective function  is performed via quasi-Newton box-constrained optimization algorithm, as implemented in nlminb.
Visualizes a matrix object plainly as heatmap. It provides S3 functions to plot simple matrices and loading matrices.
Functions for viewing 2-D and 3-D data, including perspective plots, slice plots, surface plots, scatter plots, etc. Includes data sets from oceanography.
The 'rgl' implementation of plot3D functions.
An implementation of the ternary plot for interpreting regression
    coefficients of trinomial regression models, as proposed in Santi, Dickson
    and Espa (2019) <doi:10.1080/00031305.2018.1442368>. Ternary plots can be
    drawn using either 'ggtern' package (based on 'ggplot2') or 'Ternary'
    package (based on standard graphics).
Proof of concept for implementing grammar of graphics using base plot. The bbplot() function initializes a 'bbplot' object to store input data, aesthetic mapping, a list of layers and theme elements. The object will be rendered as a graphic using base plot command if it is printed.
This function plots a contour line with a user-defined probability and tightness of fit.
Easily visualize and animate 'tabledap' and 'griddap' objects obtained via the 'rerddap' package in a simple one-line command,  using either base graphics or 'ggplot2' graphics. 'plotdap' handles extracting and reshaping the data,  map projections and continental outlines.  Optionally the data can be animated through time using the 'gganmiate' package.
Provides a ggplot2 front end to plot summary statistics on danish provinces,
    regions, municipalities, and zipcodes. The needed geoms of each of the four levels are 
    inherent in the package, thus making these types of plots easy for the user. This is essentially 
    an updated port of the previously available 'mapDK' package by Sebastian Barfort.
When analyzing data, plots are a helpful tool for visualizing data and interpreting statistical models. This package provides a set of simple tools for building plots incrementally, starting with an empty plot region, and adding bars, data points, regression lines, error bars, gradient legends, density distributions in the margins, and even pictures. The package builds further on R graphics by simply combining functions and settings in order to reduce the amount of code to produce for the user. As a result, the package does not use formula input or special syntax, but can be used in combination with default R plot functions. Note: Most of the functions were part of the package 'itsadug', which is now split in two packages: 1. the package 'itsadug', which contains the core functions for visualizing and evaluating nonlinear regression models, and 2. the package 'plotfunctions', which contains more general plot functions.
The main function, plot_GMM, is used for plotting output from Gaussian mixture models (GMMs), 
    including both densities and overlaying mixture weight component curves from the fit GMM. The package
    also include the function, plot_cut_point, which plots the cutpoint (mu) from the GMM over a histogram
    of the distribution with several color options. Finally, the package includes the function, plot_mix_comps, 
    which is used in the plot_GMM function, and can be used to create a custom plot for overlaying mixture 
    component curves from GMMs. For the plot_mix_comps function, usage most often will be specifying 
    the "fun" argument within "stat_function" in a ggplot2 object.
An extension to 'ggplot2' and 'magick'. It contains 
    three groups of functions: Functions in the first group draw 'ggplot2' - based plots: geom_shading_bar() draws barplot 
	with shading colors in each bar. geom_rect_cm(), geom_circle_cm() and geom_ellipse_cm() draw rectangles, circles 
	and ellipses with centimeter as their unit. Thus their sizes do not change when the coordinate system or the aspect ratio 
	changes. annotation_transparent_text() draws labels with transparent texts. annotation_shading_polygon() draws irregular 
	polygons with shading colors. Functions in the second group generate coordinates for regular shapes and make linear transformations. Functions in the third group are 'magick' - based functions facilitating image processing.
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.
The main function, plot_mm(), is used for plotting output from mixture models, 
    including both densities and overlaying mixture weight component curves from the fit models. In line with the
    tidyverse, the package also includes the plot_cut_point() function to visualize the cutpoint (mu) from the model
    over a histogram of the data density with several color options. Finally, the package includes the plot_mix_comps() 
    helper function, which is used for both added customization as well as in the plot_mm() function. 
    Supported model objects include: 'mixtools', 'EMCluster', and 'flexmix', with more from each forthcoming. 
    Supported mixture model specifications include mixtures of univariate Gaussians, multivariate Gaussians, Gammas, 
    logistic regressions, linear regressions, and Poisson regressions.
Plot model surfaces for a wide variety of models
        using partial dependence plots and other techniques.
        Also plot model residuals and other information on the model.
Plot principal component histograms around a bivariate
        scatter plot.
Useful set of tools for plotting network diagrams in any kind of project.
Creates an envelope around a set of plotted points. The
        envelope is compact with a boundary that is continuous, smooth
        and convex. Each point is represented as a circle and the
        circles and connecting lines are the solution to the multiple
        pulley problem. This method can be used to highlight regions in
        a two-dimensional space.
Lots of plots, various labeling, axis and color scaling functions.
Most ROC curve plots obscure the cutoff values and inhibit
    interpretation and comparison of multiple curves. This attempts to address
    those shortcomings by providing plotting and interactive tools. Functions
    are provided to generate an interactive ROC curve plot for web use, and
    print versions. A Shiny application implementing the functions is also
    included.
Functions for making visual exploratory data analysis with nested data easier.
Figures rendered on graphics devices are usually 
 rescaled to fit pre-determined device dimensions. 'plotscale' 
 implements the reverse: desired plot dimensions are specified 
 and device dimensions are calculated to accommodate marginal
 material, giving consistent proportions for plot elements.
 Default methods support grid graphics such as lattice and ggplot.
 See "example('devsize')" and "vignette('plotscale')".
Contains a graphical user interface to generate the diagnostic
    plots proposed by Bauer (2005; <doi:10.1207/s15328007sem1204_1>), 
    Pek & Chalmers (2015; <doi:10.1080/10705511.2014.937790>), and
    Pek, Chalmers, R. Kok, & Losardo (2015; <doi:10.3102/1076998615589129>) to investigate
    nonlinear bivariate relationships in latent regression models using structural
    equation mixture models (SEMMs).
Small self-contained plots for use in larger plots or to
    delegate plotting in other functions. Also contains a number of
    alternative color palettes and HSL color space based tools to modify
    colors or palettes.
Prediction limits for the Poisson distribution
  are produced from both frequentist and Bayesian viewpoints. Limiting results
  are provided in a Bayesian setting with uniform, Jeffreys and gamma as prior
  distributions. More details on the methodology are discussed in Bejleri and
  Nandram (2018) <doi:10.1080/03610926.2017.1373814>.
Fit Log Linear by Linear Association models and Rasch family models by pseudolikelihood estimation
This package provides statistical inference tools applied to Partial Linear Regression (PLR)
 models. Specifically, point estimation, confidence intervals estimation, bandwidth selection, goodness-of-fit tests and analysis of
 covariance are considered. 
 Kernel-based methods, combined with ordinary least squares estimation, are used and time series 
 errors are allowed. In addition, these techniques are also implemented for both parametric (linear) 
 and nonparametric regression models.
Multivariate regression methods
	Partial Least Squares Regression (PLSR), Principal Component
	Regression (PCR) and Canonical Powered Partial Least Squares (CPPLS).
plsdepot contains different methods for PLS analysis of
        one or two data tables such as Tucker's Inter-Battery, NIPALS,
        SIMPLS, SIMPLS-CA, PLS Regression, and PLS Canonical Analysis.
        The main reference for this software is the awesome book (in
        French) 'La Regression PLS: Theorie et Pratique' by Michel
        Tenenhaus.
The plsdof package provides Degrees of Freedom estimates
        for Partial Least Squares (PLS) Regression. Model selection for
        PLS is based on various information criteria (aic, bic, gmdl)
        or on cross-validation. Estimates for the mean and covariance
        of the PLS regression coefficients are available. They allow
        the construction of approximate confidence intervals and the
        application of test procedures (Kramer and Sugiyama 
        2012 <doi:10.1198/jasa.2011.tm10107>).
        Further, cross-validation procedures for Ridge Regression and 
        Principal Components Regression are available.
Routines for PLS-based genomic analyses,
        implementing PLS methods for classification with
        microarray data and prediction of transcription factor
        activities from combined ChIP-chip analysis. The >=1.2-1
        versions include two new classification methods for microarray
        data: GSIM and Ridge PLS. The >=1.3 versions includes a
        new classification method combining variable selection and
        compression in logistic regression context: logit-SPLS; and
        an adaptive version of the sparse PLS.
Bindings for additional regression models for use
    with the 'parsnip' package, including ordinary and spare partial least 
    squares models for regression and classification (Rohart et al (2017) 
    <doi:10.1371/journal.pcbi.1005752>).
Fit a model with potentially many linear and smooth predictors. Interaction
    effects can also be quantified. Variable selection is done using penalisation. For l1-type penalties
    we use iterative steps alternating between using linear predictors (lasso) and smooth predictors
    (generalised additive model).
Provides partial least squares analysis for the analysis of the relation
    between two high-dimensional data sets. Includes permutation testing and
    bootstrapping for resulting latent variables (following McIntosh & Lobaugh (2004) <doi:10.1016/j.neuroimage.2004.07.020>)
    and several visualization functions.
Provides Partial least squares Regression for (weighted) beta regression models (Bertrand 2013,  <http://journal-sfds.fr/article/view/215>) and k-fold cross-validation of such models using various criteria. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.
Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models in high dimensional settings <doi:10.1093/bioinformatics/btu660>, Bastien, P., Bertrand, F., Meyer N., Maumy-Bertrand, M. (2015), Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data, Bioinformatics, 31(3):397-404. Cross validation criteria were studied in <arXiv:1810.02962>, Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data.
Provides (weighted) Partial least squares Regression for generalized linear models and repeated k-fold cross-validation of such models using various criteria <arXiv:1810.01005>. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.
Interfaces and methods for variable selection in Partial Least
    Squares. The methods include filter methods, wrapper methods and embedded
    methods. Both regression and classification is supported.
Calculates and depicts probabilistic long-term effects
    in binary models with temporal dependence variables. The package performs
    two tasks. First, it calculates the change in the probability of the event
    occurring given a change in a theoretical variable. Second, it calculates
    the rolling difference in the future probability of the event for two
    scenarios: one where the event occurred at a given time and one where the
    event does not occur. The package is consistent with the recent movement to
    depict meaningful and easy-to-interpret quantities of interest with the
    requisite measures of uncertainty. It is the first to make it easy for
    researchers to interpret short- and long-term effects of explanatory
    variables in binary autoregressive models, which can have important
    implications for the correct interpretation of these models.
Converts English phrases to singular or plural form based on
    the length of an associated vector.  Contains helper functions to
    create natural language lists from vectors and to include the length
    of a vector in natural language.
Kernel density estimation with global bandwidth selection
	     via "plug-in".
Gives the ability to automatically generate and serve an HTTP API
    from R functions using the annotations in the R documentation around your
    functions.
Gives the ability to automatically deploy a plumber API
    from R functions on 'DigitalOcean' and other cloud-based servers.
Build 'Plumber' APIs that can be used in 'Tableau' workbooks.
    Annotations in R comments allow APIs to conform to the 'Tableau Analytics
    Extension' specification, so that R code can be used to power 'Tableau'
    workbooks.
The base R data.frame, like any vector, is
    copied upon modification. This behavior is at odds with
    that of GUIs and interactive graphics. To rectify this,
    plumbr provides a mutable, dynamic tabular data model.
    Models may be chained together to form the complex
    plumbing necessary for sophisticated graphical
    interfaces. Also included is a
    general framework for linking datasets; an typical
    use case would be a linked brush.
Tools are provided to create plural, singular and regular forms of 
    English words along with tools to augment the built-in rules to fit 
    specialized needs. Core functionality is based on a JavaScript library, 
    <https://github.com/blakeembrey/pluralize>.
Efficient procedures for fitting an entire regression
        sequences with different model types.
Retrieves a 'pluscode' by inputting latitude and longitude. Includes additional functions to retrieve neighbouring 'pluscodes'.
plusser provides an API interface to Google+ so that posts,
    profiles and pages can be automatically retrieved.
A set of tools that solves a common set of
    problems: you need to break a big problem down into manageable pieces,
    operate on each piece and then put all the pieces back together.  For
    example, you might want to fit a model to each spatial location or
    time point in your study, summarise data by panels or collapse
    high-dimensional arrays to simpler summary statistics. The development
    of 'plyr' has been generously supported by 'Becton Dickinson'.
Performs Penalized Multivariate Analysis: a penalized
        matrix decomposition, sparse principal components analysis,
        and sparse canonical correlation analysis, described in
        Witten, Tibshirani and Hastie (2009)
        <doi:10.1093/biostatistics/kxp008> and Witten and Tibshirani
        (2009) Extensions of sparse canonical correlation analysis,
        with applications to genomic data
        <doi:10.2202/1544-6115.1470>.
Monte Carlo based model choice for applied phylogenetics of
    continuous traits. Method described in  Carl Boettiger, Graham Coop,
    Peter Ralph (2012) Is your phylogeny informative? Measuring
    the power of comparative methods, Evolution 66 (7)
    2240-51. <doi:10.1111/j.1558-5646.2011.01574.x>.
Parsimonious Mixtures of Contaminated Gaussian Distributions
Aims to utilize model-based clustering (unsupervised)
        for high dimensional and ultra large data, especially in a distributed
        manner. The code employs 'pbdMPI' to perform a
        expectation-gathering-maximization algorithm
        for finite mixture Gaussian
        models. The unstructured dispersion matrices are assumed in the
        Gaussian models. The implementation is default in the single program
        multiple data programming model. The code can be executed
        through 'pbdMPI' and MPI' implementations such as 'OpenMPI'
        and 'MPICH'.
        See the High Performance Statistical Computing website
	<https://snoweye.github.io/hpsc/>
	for more information, documents and examples.
Note, that the 'PMCMR' package is superset
	     by the novel 'PMCMRplus' package. The 'PMCMRplus'
	     package contains all functions from 
	     'PMCMR' and many more parametric and non-parametric
	     multiple comparison procedures, one-factorial trend
	     tests, as well as improved method functions, 
	     such as print, summary and plot. 
	     The 'PMCMR' package is no longer maintained,
	     but kept for compatibility of reverse depending
	     packages for some time.
For one-way layout experiments the one-way ANOVA can
	     be performed as an omnibus test. All-pairs multiple comparisons 
	     tests (Tukey-Kramer test, Scheffe test, LSD-test) 
	     and many-to-one tests (Dunnett test) for normally distributed 
	     residuals and equal within variance are available. Furthermore,
	     all-pairs tests (Games-Howell test, Tamhane's T2 test, 
	     Dunnett T3 test, Ury-Wiggins-Hochberg test) and many-to-one
	     (Tamhane-Dunnett Test) for normally distributed residuals 
	     and heterogeneous variances are provided. Van der Waerden's normal
	     scores test for omnibus, all-pairs and many-to-one tests is
	     provided for non-normally distributed residuals and homogeneous
	     variances. The Kruskal-Wallis, BWS and Anderson-Darling
	     omnibus test and all-pairs tests
	     (Nemenyi test, Dunn test, Conover test, Dwass-Steele-Critchlow-
	     Fligner test) as well as many-to-one (Nemenyi test, Dunn test,
	     U-test) are given for the analysis of variance by ranks. 
             Non-parametric trend tests (Jonckheere test, Cuzick test,
	     Johnson-Mehrotra test, Spearman test) are included. 
	     In addition, a Friedman-test for one-way ANOVA with repeated 
	     measures on ranks (CRBD) and Skillings-Mack test for unbalanced 
	     CRBD is provided with consequent all-pairs tests (Nemenyi test, 
	     Siegel test, Miller test, Conover test, Exact test)
	     and many-to-one tests (Nemenyi test, Demsar test, Exact test). 
	     A trend can be tested with Pages's test. Durbin's test 
	     for a two-way balanced incomplete block design (BIBD) is given 
	     in this package as well as Gore's test for CRBD with multiple
	     observations per cell is given.  Outlier tests, Mandel's k- and
	     h statistic as well as functions for Type I error and Power 
	     analysis as well as generic summary, print and plot methods 
             are provided.
Paired mass distance (PMD) analysis proposed in Yu, Olkowicz and Pawliszyn (2018) <doi:10.1016/j.aca.2018.10.062> for gas/liquid chromatography–mass spectrometry (GC/LC-MS) based non-targeted analysis. PMD analysis including GlobalStd algorithm and structure/reaction directed analysis. GlobalStd algorithm could found independent peaks in m/z-retention time profiles based on retention time hierarchical cluster analysis and frequency analysis of paired mass distances within retention time groups. Structure directed analysis could be used to find potential relationship among those independent peaks in different retention time groups based on frequency of paired mass distances. Reactomics analysis could also be performed to build PMD network, assign sources and make biomarker reaction discovery. GUIs for PMD analysis is also included as 'shiny' applications.
Allows to download current and historical METAR weather reports
  extract and parse basic parameters and present main weather information. 
  Current reports are downloaded from Aviation Weather Center 
  <https://www.aviationweather.gov/metar> and historical reports from
  Iowa Environmental Mesonet web page of Iowa State University
  ASOS-AWOS-METAR <http://mesonet.agron.iastate.edu/AWOS/>.
Routines for state estimate in a linear
    Gaussian state space model and a simple stochastic volatility model using
    particle filtering. Parameter inference is also carried out in these models
    using the particle Metropolis-Hastings algorithm that includes the particle
    filter to provided an unbiased estimator of the likelihood. This package is
    a collection of minimal working examples of these algorithms and is only
    meant for educational use and as a start for learning to them on your own.
Check available classification and regression data sets from the PMLB repository and download them.
    The PMLB repository (<https://github.com/EpistasisLab/pmlbr>) contains a curated collection of data sets for evaluating and comparing machine learning algorithms.
    These data sets cover a range of applications, and include binary/multi-class classification problems and 
    regression problems, as well as combinations of categorical, ordinal, and continuous features.
    There are currently over 150 datasets included in the PMLB repository.
Given a sample with additive measurement error, the package estimates the deconvolution density - that is, the density of the underlying distribution of the sample without measurement error. The method maximises the log-likelihood of the estimated density, plus a quadratic smoothness penalty. The distribution of the measurement error can be either a known family, or can be estimated from a "pure error" sample. For known error distributions, the package supports Normal, Laplace or Beta distributed error. For unknown error distribution, a pure error sample independent from the data is used.
The Predictive Model Markup Language (PMML) is an XML-based language which provides a way for applications to define machine learning, statistical and data mining models and to share models between PMML compliant applications. More information about the PMML industry standard and the Data Mining Group can be found at <http://dmg.org/>. The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products, 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>.
Provides an R implementation of the Particle Metropolis within
    Gibbs sampler for model parameter, covariance matrix and random effect
    estimation. A more general implementation of the sampler based on the paper
    by Gunawan, D., Hawkins, G. E., Tran, M. N., Kohn, R., & Brown, S. D.
    (2020) <doi:10.1016/j.jmp.2020.102368>. An HTML tutorial document describing
    the package is available at <https://newcastlecl.github.io/samplerDoc/> and
    includes several detailed examples, some background and troubleshooting
    steps.
Functions and examples for 'Portfolio
  Management with R': backtesting investment and
  trading strategies, computing profit/loss and
  returns, analysing trades, handling lists of
  transactions, reporting, and more.
Pharmacometric tools for common data analytical tasks; closed-form solutions for calculating concentrations at given 
    times after dosing based on compartmental PK models (1-compartment, 2-compartment and 3-compartment, covering infusions, zero- 
    and first-order absorption, and lag times, after single doses and at steady state, per Bertrand & Mentre (2008) 
    <http://lixoft.com/wp-content/uploads/2016/03/PKPDlibrary.pdf>); parametric simulation from NONMEM-generated parameter estimates 
    and other output; and parsing, tabulating and plotting results generated by Perl-speaks-NONMEM (PsN).
Provides tools for downloading, reading and analysing the PNADC,
	a household survey from Brazilian Institute of Geography and Statistics - IBGE.
	The data must be downloaded from the official website <https://www.ibge.gov.br/>. 
	Further analysis must be made using package 'survey'.
This package provides an easy and simple way to read, write and display bitmap images stored in the PNG format. It can read and write both files and in-memory raw vectors.
An R package for Probit-Normal Marginalized Transition
        Random Effects Models
The program pnn implements the algorithm proposed by
        Specht (1990).  It is written in the R statistical language. It
        solves a common problem in automatic learning. Knowing a set of
        observations described by a vector of quantitative variables,
        we classify them in a given number of groups. Then, the
        algorithm is trained with this datasets and should guess
        afterwards the group of any new observation. This neural
        network has the main advantage to begin generalization
        instantaneously even with a small set of known observations. It
        is delivered with four functions (learn, smooth, perf and
        guess) and a dataset. The functions are documented with
        examples and provided with unit tests.
Provides tools for downloading, reading and analysing the PNS,
	a household survey from Brazilian Institute of Geography and Statistics - IBGE.
	The data must be downloaded from the official website <https://www.ibge.gov.br/>. 
	Further analysis must be made using package 'survey'.
PNW-Inspired Palettes for 'R' data visualizations. Palettes are variable 
	in length and checked for colorblind accessibility from hue, saturation, 
	and lightness value scaling using the 'Chroma.js  Color Palette Helper' 
	<https://gka.github.io/palettes/>.
Presence-only model with Elastic Net penalty is a regularized generalized linear model training on the presence-absence response. This package provides functions for tuning and fitting the presence-only model. The presence-only model can be used to predict regulatory effects of genetic variants at sequence-level resolution by integrating a large number of epigenetic features and massively parallel reporter assays (MPRAs).
Computes the optimal flow, Nash flow and the Price of Anarchy for any routing game defined within the game theoretical framework. The input is a routing game in the form of it’s cost and flow functions. Then transforms this into an optimisation problem, allowing both Nash and Optimal flows to be solved by nonlinear optimisation. See <https://en.wikipedia.org/wiki/Congestion_game> and Knight and Harper (2013) <doi:10.1016/j.ejor.2013.04.003> for more information.
Functions that interface with the 'Pocket' API (<https://getpocket.com/developer/>). Allows the user to get, add, and modify items in their own 'Pocket' account.
Penalized orthogonal-components regression (POCRE) is a supervised dimension reduction method for high-dimensional data. It sequentially constructs orthogonal components (with selected features) which are maximally correlated to the response residuals. POCRE can also construct common components for multiple responses and thus build up latent-variable models.
Provides functions to implement and simulate the partial order continual reassessment method (PO-CRM) of Wages, Conaway and O'Quigley (2011) <doi:10.1177/1740774511408748> for use in Phase I trials of combinations of agents. Provides a function for generating a set of initial guesses (skeleton) for the toxicity probabilities at each combination that correspond to the set of possible orderings of the toxicity probabilities specified by the user.
This tool computes the probability of detection (POD) curve and the limit of detection (LOD), i.e. the number of copies of the target DNA sequence required to ensure a 95 % probability of detection (LOD95). Other quantiles of the LOD can be specified.
    This is a reimplementation of the mathematical-statistical modelling of the validation of qualitative polymerase chain reaction (PCR) methods within a single laboratory as provided by the commercial tool 'PROLab' <http://quodata.de/>. The modelling itself has been described by Uhlig et al. (2015) <doi:10.1007/s00769-015-1112-9>.
Set of functions that implement the PoDBAY method, described in the publication 'A method to estimate probability of disease and vaccine efficacy from clinical trial immunogenicity data' by Julie Dudasova, Regina Laube, Chandni Valiathan, Matthew C. Wiener, Ferdous Gheyas, Pavel Fiser, Justina Ivanauskaite, Frank Liu and Jeffrey R. Sachs (NPJ Vaccines, 2021), <doi:10.1038/s41541-021-00377-6>.
Attempts to clean optical character recognition (OCR) errors in 
  legacy Scottish Post Office Directories. Further attempts to match records from 
  trades and general directories.
The poems package provides a framework of interoperable R6 classes for 
    building ensembles of viable models via the pattern-oriented modeling (POM) 
    approach. The package includes classes for encapsulating and generating model 
    parameters, and managing the POM workflow. The workflow includes: model setup; 
    generating model parameters via Latin hyper-cube sampling; running multiple sampled
    model simulations; collating summary results; and validating and selecting an 
    ensemble of models that best match known patterns. By default, model validation and 
    selection utilizes an approximate Bayesian computation (ABC) approach, although 
    alternative user-defined functionality could be employed. The package includes 
    a spatially explicit demographic population model simulation engine, which 
    incorporates default functionality for density dependence, correlated environmental 
    stochasticity, stage-based transitions, and distance-based dispersal. The user may 
    customize the simulator by defining functionality for translocations, harvesting, 
    mortality, and other processes, as well as defining the sequence order for the 
    simulator processes. The framework could also be adapted for use with other model
    simulators by utilizing its extendable (inheritable) base classes.
Estimate large covariance matrices in approximate factor
        models by thresholding principal orthogonal complements.
Provides tools for downloading, reading and analysing the POF,
	a household survey from Brazilian Institute of Geography and Statistics - IBGE.
	The data must be downloaded from the official website <https://www.ibge.gov.br/>. 
	Further analysis must be made using package 'survey'.
Bayesian variable selection for regression models of under-reported
    count data as well as for (overdispersed) Poisson, negative binomal and
    binomial logit regression models using spike and slab priors.
The data sets used in the online course ,,PogromcyDanych''. You can process data in many ways. The course Data Crunchers will introduce you to this variety. For this reason we will work on datasets of different size (from several to several hundred thousand rows), with various level of complexity (from two to two thousand columns) and prepared in different formats (text data, quantitative data and qualitative data). All of these data sets were gathered in a single big package called PogromcyDanych to facilitate access to them. It contains all sorts of data sets such as data about offer prices of cars, results of opinion polls, information about changes in stock market indices, data about names given to newborn babies, ski jumping results or information about outcomes of breast cancer patients treatment.
Implementation of both the exact and approximation methods for computing the cdf of the Poisson binomial distribution as described in Hong (2013) <doi: 10.1016/j.csda.2012.10.006>. It also provides the pmf, quantile function, and random number generation for the Poisson binomial distribution. The C code for fast Fourier transformation (FFT) is written by R Core Team (2019)<https://www.R-project.org/>, which implements the FFT algorithm in Singleton (1969) <doi: 10.1109/TAU.1969.1162042>.
Implements the methods described in the paper, Witten (2011) Classification and Clustering of Sequencing Data using a Poisson Model, Annals of Applied Statistics 5(4) 2493-2518.
Functions for obtaining the density, random deviates 
             and maximum likelihood estimates of the Poisson lognormal 
             distribution and the bivariate Poisson lognormal distribution.
Provides an implementation of a rare variant association test that utilizes protein tertiary structure to increase signal and to identify likely causal variants. Performs structure-guided collapsing, which leads to local tests that borrow information from neighboring variants on a protein and that provide association information on a variant-specific level. For details of the implemented method see West, R. M.,  Lu, W., Rotroff, D. M., Kuenemann, M., Chang, S-M., Wagner M. J., Buse, J. B., Motsinger-Reif, A., Fourches, D., and Tzeng, J-Y. (2019) <doi:10.1371/journal.pcbi.1006722>.
Validate data in data frames, 'tibble' objects, 'Spark'
    'DataFrames', and database tables. Validation pipelines can be made using
    easily-readable, consecutive validation steps. Upon execution of the
    validation plan, several reporting options are available. User-defined
    thresholds for failure rates allow for the determination of appropriate
    reporting actions. Many other workflows are available including an
    information management workflow, where the aim is to record, collect, and
    generate useful information on data tables.
The function pointdensity returns a density count and the temporal average for
    every point in the original list. The dataframe returned includes four
    columns: lat, lon, count, and date_avg. The "lat" column is the original
    latitude data; the "lon" column is the original longitude data; the "count"
    is the density count of the number of points within a radius of
    radius*grid_size (the neighborhood); and the date_avg column includes the
    average date of each point in the neighborhood.
Estimate specification models for the state-dependent level of an optimal quantile/expectile forecast.
  Wald Tests and the test of overidentifying restrictions are implemented. Plotting of the estimated specification model is possible.
  The package contains two data sets with forecasts and realizations: the daily accumulated precipitation at London, UK from the high-resolution model of the
  European Centre for Medium-Range Weather Forecasts (ECMWF, <https://www.ecmwf.int/>) and GDP growth Greenbook data by the US Federal Reserve.
  See Schmidt, Katzfuss and Gneiting (2015) <arXiv:1506.01917> for more details on the identification and estimation of a directive behind a point forecast.
R has no built-in pointer functionality. The 'pointr' package fills this gap and lets you create pointers to R objects, including subsets of dataframes. This makes your R code more readable and maintainable.
Functions to calculate and plot event and pointer years as well as resilience indices. Designed for dendroecological applications, but also suitable to analyze patterns in other ecological time series.
Read and write PO and POT files, for package translations.
Generation of multiple count, binary and continuous variables simultaneously 
             given the marginal characteristics and association structure. Throughout the package,
             the word 'Poisson' is used to imply count data under the assumption of Poisson distribution. 
             The details of the method are explained in Amatya et al. (2015) <DOI:10.1080/00949655.2014.953534>.
Provides the probability, distribution, and quantile functions and random number generator for the Poisson-Binomial distribution.  This package relies on FFTW to implement the discrete Fourier transform, so that it is much faster than the existing implementation of the same algorithm in R.
Generation of multiple count, binary and ordinal variables simultaneously 
             given the marginal characteristics and association structure. Throughout the package,
             the word 'Poisson' is used to imply count data under the assumption of Poisson distribution. The details of the method are explained in Amatya, A. and Demirtas, H. (2015) <DOI:10.1080/00949655.2014.953534>.
Generation of a chosen number of count, binary, ordinal, and continuous random variables, with specified correlations and marginal properties. The details of the method are explained in Demirtas (2012) <DOI:10.1002/sim.5362>.
Generation of multiple count, binary, ordinal and normal variables simultaneously given the marginal characteristics and association structure.
             The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>.
Functions to create confidence intervals for ratios of Poisson
    rates under misclassification using double sampling.
Computation of robust standard errors of Poisson fixed effects 
    models, following Wooldridge (1999).
Creates a 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, (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 gradient-based methods instead of variational inference.
Generation of count (assuming Poisson distribution) and continuous data (using Fleishman polynomials) simultaneously. The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>.
Generates multivariate data with count and continuous variables with a pre-specified correlation matrix. The count and continuous variables are assumed to have Poisson and normal marginals, respectively. The data generation mechanism is a combination of the normal to anything principle and a connection between Poisson and normal correlations in the mixture.
             The details of the method are explained in Yahav et al. (2012) <DOI:10.1002/asmb.901>.
Contains functions and classes for simulating, plotting and analysing homogenous and non-homogenous Poisson processes.
High dimensional mixtures of Poisson Generalized Linear models with three different parameterizations of Poisson means are considered. Moreover, partitioning the response variables into a set of blocks is possible. The package estimates parameters via EM algorithm. For an efficient initialization, a random splitting small-EM is introduced.
Efficient implementations of multiple exact and approximate methods as described in Hong (2013) <doi:10.1016/j.csda.2012.10.006>, Biscarri, Zhao & Brunner (2018) <doi:10.1016/j.csda.2018.01.007> and Zhang, Hong & Balakrishnan (2018) <doi:10.1080/00949655.2018.1440294> for computing the probability mass, cumulative distribution and quantile functions, as well as generating random numbers for both the ordinary and generalized Poisson binomial distribution.
R functions for the computation of Least Square based on transformation (L2T) and robust M-estimators based on transformations (MT-estimators) for Poisson regression models.
For a multivariate dataset with independent Poisson measurement error, calculates principal components of transformed latent Poisson means. T. Kenney, T. Huang, H. Gu (2019) <arXiv:1904.11745>.
Bindings for Poisson regression models for use with the
    'parsnip' package. Models include simple generalized linear models,
    Bayesian models, and zero-inflated Poisson models (Zeileis, Kleiber,
    and Jackman (2008) <doi:10.18637/jss.v027.i08>).
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.
Create hexagonal shape sticker image.
  'polaroid' can be used in user's web browser.
  'polaroid' can be used in 'shinyapps.io'.
  In both way, user can download created 'hexSticker' as 'PNG' image.
  'polaroid' is built based on 'argonDash', 'colourpicker' and 'hexSticker' R package.
Latent class analysis and latent class regression models 
    for polytomous outcome variables.  Also known as latent structure analysis.
Tools for simplifying the creation and management of data structures
    suitable for dealing with policy portfolios, that is, two-dimensional spaces 
    of policy instruments and policy targets. The package also allows to generate measures of
    portfolio characteristics and facilitates their visualization.
Learn optimal policies via doubly robust empirical
 welfare maximization over trees. This package implements the multi-action doubly
 robust approach of Zhou, Athey and Wager (2018) <arXiv:1810.04778> in the case where
 we want to learn policies that belong to the class of depth k decision trees.
Bundles the datasets and functions used in the textbook by Philip
    Pollock and Barry Edwards, an R Companion to Essentials of Political Analysis,
    Second Edition.
Easily add modern authentication and user administration to your 'shiny' apps. 
  Customize user sign in and registration pages to match your brand.  Control who can
  access one or more of your 'shiny' apps.
Be responsible when scraping data from websites by following polite principles: introduce yourself, ask for permission, take slowly and never ask twice.
Detecting markers of politeness in English natural language. This package allows researchers to easily visualize and quantify politeness between groups of documents. This package combines prior research on the linguistic markers of politeness. We thank the Spencer Foundation, the Hewlett Foundation, and Harvard's Institute for Quantitative Social Science for support.
Supports analysis of aerobiological data. 
    Available features include determination of pollen season limits, 
    replacement of outliers (Kasprzyk and Walanus (2014) <doi:10.1007/s10453-014-9332-8>),
    and calculation of growing degree days (Baskerville and Emin (1969) <doi:10.2307/1933912>).
Tools to estimate pollinator body size and co-varying traits. This package contains novel Bayesian predictive models of pollinator body size (for bees and hoverflies) as well as preexisting predictive models for pollinator body size (currently implemented for ants, bees, butterflies, flies, moths and wasps) as well as bee tongue length and foraging distance, total field nectar loads and wing loading. An additional GitHub repository <https://github.com/liamkendall/pollimetrydata> provides model objects to use the bodysize function internally. All models are described in Kendall et al (2018) <doi:10.1101/397604>.
Calculate common types of tables for weighted survey data.
    Options include topline and (2-way and 3-way) crosstab tables of
    categorical or ordinal data as well as summary tables of weighted
    numeric variables. Optionally, include the margin of error at
    selected confidence intervals including the design effect. The
    design effect is calculated as described by 
    Kish (1965) <doi:10.1002/bimj.19680100122> beginning
    on page 257. Output takes the form of tibbles (simple data frames).
    This package conveniently handles labelled data, such as that
    commonly used by 'Stata' and 'SPSS.' Complex survey design is 
    not supported at this time.
Package for corpus analysis using the Corpus Workbench 
    ('CWB', <http://cwb.sourceforge.net/>) as an efficient back end for indexing
    and querying large corpora. The package offers functionality to flexibly create
    subcorpora and to carry out basic statistical operations (count, co-occurrences
    etc.). The original full text of documents can be reconstructed and inspected at
    any time. Beyond that, the package is intended to serve as an interface to 
    packages implementing advanced statistical procedures. Respective data structures
    (document-term matrices, term-co-occurrence matrices etc.) can be created based 
    on the indexed corpora.
Routines for the polynomial spline fitting routines
  hazard regression, hazard estimation with flexible tails, logspline,
  lspec, polyclass, and polymars, by C. Kooperberg and co-authors.
Functions for evaluating the mass density, cumulative distribution function, quantile function and random variate generation for the Polya-Aeppli distribution, also known as the geometric compound Poisson distribution.
Simulate via Markov chain Monte Carlo (hit-and-run algorithm)
    a Dirichlet distribution conditioned to satisfy a finite set of linear
    equality and inequality constraints (hence to lie in a convex polytope
    that is a subset of the unit simplex).
Tools for creating, viewing, and assessing qualitative
 palettes with many (20-30 or more) colors. See Coombes and colleagues
 (2019) <doi:10.18637/jss.v090.c01>.
R port of Angus Johnson's open source library Clipper. Performs polygon clipping operations (intersection, union, set minus, set difference) for polygonal regions of arbitrary complexity, including holes. Computes offset polygons (spatial buffer zones, morphological dilations, Minkowski dilations) for polygonal regions and polygonal lines. Computes Minkowski Sum of general polygons. There is a function for removing self-intersections from polygon data.
Computes polychoric and polyserial correlations by quick "two-step" methods or ML, 
  optionally with standard errors; tetrachoric and biserial correlations are special cases.
Numerical integration of continuously differentiable
    functions f(x,y) over simple closed polygonal domains.
    The following cubature methods are implemented:
    product Gauss cubature (Sommariva and Vianello, 2007,
    <doi:10.1007/s10543-007-0131-2>),
    the simple two-dimensional midpoint rule
    (wrapping 'spatstat.geom' functions),
    adaptive cubature for radially symmetric functions via line
    integrate() along the polygon boundary (Meyer and Held, 2014,
    <doi:10.1214/14-AOAS743>, Supplement B),
    and integration of the bivariate Gaussian density based on
    polygon triangulation.
    For simple integration along the axes, the 'cubature' package
    is more appropriate.
Implements a Gibbs sampling algorithm to perform Bayesian inference
    on biallelic SNP frequencies, genotypes and heterozygosity (observed and
    expected) in a population of autopolyploids. See the published paper in
    Molecular Ecology Resources: Blischak et al. (2016) <doi:10.1111/1755-0998.12493>.
Infer the genetic composition of individuals
    in terms of haplotype dosages for a haploblock, based
    on bi-allelic marker dosages, for any ploidy level.
    Reference: Voorrips and Tumino: PolyHaplotyper: haplotyping in polyploids based on bi-allelic marker dosage data.
    Submitted to BMC Bioinformatics (2021).
A wrapper around the C++ library 'polylabel' from 'Mapbox', 
    providing an efficient routine for finding the approximate pole of 
    inaccessibility of a polygon, which usually serves as an excellent candidate
    for labeling of a polygon.
Creation of linkage maps in polyploid species from marker dosage
    scores of an F1 cross from two heterozygous parents. Currently works for outcrossing diploid, autotriploid, autotetraploid and autohexaploid species, 
    as well as segmental allotetraploids. Methods are described in a manuscript of Bourke et al. (2018) <doi:10.1093/bioinformatics/bty371>. Since version 1.1.0,
    both discrete and probabilistic genotypes are acceptable input; for more details on the latter see Liao et al. (submitted, 2020).
Implementation of class "polyMatrix" for storing a matrix of polynomials and implements 
  basic matrix operations; including a determinant and characteristic polynomial.
  It is based on the package 'polynom' and uses a lot of its methods to implement matrix operations.
  This package includes 3 methods of triangularization of polynomial matrices:
  Extended Euclidean algorithm which is most classical but numerically unstable;
  Sylvester algorithm based on LQ decomposition;
  Interpolation algorithm is based on LQ decomposition and Newton interpolation.
  Both methods are described in 
  D. Henrion & M. Sebek, Reliable numerical methods for polynomial matrix triangularization,
  IEEE Transactions on Automatic Control (Volume 44, Issue 3, Mar 1999, Pages 497-508) <doi:10.1109/9.751344>
  and in 
  Salah Labhalla, Henri Lombardi & Roger Marlin, 
  Algorithmes de calcule de la reduction de Hermite d'une matrice a coefficients polynomeaux,
  Theoretical Computer Science (Volume 161, Issue 1-2, July 1996, Pages 69-92) <doi:10.1016/0304-3975(95)00090-9>.
A collection of functions to implement a class for univariate
  polynomial manipulations.
Implements univariate polynomial operations in R, including
  polynomial arithmetic, finding zeros, plotting, and some operations on
  lists of polynomials.
Functions to perform paternity exclusion via allele
    matching, in autopolyploid species having ploidy 4, 6, or 8. The
    marker data used can be genotype data (copy numbers known) or
    'allelic phenotype data' (copy numbers not known).
Analyse prescription drug deliveries to calculate several indicators of polypharmacy corresponding to the various definitions found in the literature.
  Bjerrum, L., Rosholm, J. U., Hallas, J., & Kragstrup, J. (1997) <doi:10.1007/s002280050329>.
  Chan, D.-C., Hao, Y.-T., & Wu, S.-C. (2009a) <doi:10.1002/pds.1712>.
  Fincke, B. G., Snyder, K., Cantillon, C., Gaehde, S., Standring, P., Fiore, L., ... Gagnon, D.R. (2005) <doi:10.1002/pds.966>.
  Hovstadius, B., Astrand, B., & Petersson, G. (2009) <doi:10.1186/1472-6904-9-11>.
  Hovstadius, B., Astrand, B., & Petersson, G. (2010) <doi:10.1002/pds.1921>.
  Kennerfalk, A., Ruigómez, A., Wallander, M.-A., Wilhelmsen, L., & Johansson, S. (2002) <doi:10.1345/aph.1A226>.
  Masnoon, N., Shakib, S., Kalisch-Ellett, L., & Caughey, G. E. (2017) <doi:10.1186/s12877-017-0621-2>.
  Narayan, S. W., & Nishtala, P. S. (2015) <doi:10.1007/s40801-015-0020-y>.
  Nishtala, P. S., & Salahudeen, M. S. (2015) <doi:10.1159/000368191>.
  Park, H. Y., Ryu, H. N., Shim, M. K., Sohn, H. S., & Kwon, J. W. (2016) <doi:10.5414/cp202484>.
  Veehof, L., Stewart, R., Haaijer-Ruskamp, F., & Jong, B. M. (2000) <doi:10.1093/fampra/17.3.261>.
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.
Quantitative trait loci (QTL) analysis and exploration of meiotic patterns in 
    autopolyploid bi-parental F1 populations. 
    For all ploidy levels, identity-by-descent (IBD) probabilities can be estimated.
    Significance thresholds, exploring QTL allele effects and visualising results are provided. 
    For background, see the 2018 dissertation of P.M. Bourke <doi:10.18174/444415>.
Read depth data from genotyping-by-sequencing (GBS) or restriction 
  site-associated DNA sequencing (RAD-seq) are imported and used to make Bayesian
  probability estimates of genotypes in polyploids or diploids.  The genotype 
  probabilities, posterior mean genotypes, or most probable genotypes can then
  be exported for downstream analysis.  'polyRAD' is described by Clark et al.
  (2019) <doi:10.1534/g3.118.200913>.  A variant calling pipeline for highly
  duplicated genomes is also included and is described by Clark et al. (2020)
  <doi:10.1101/2020.01.11.902890>.
Automate formation and evaluation of polynomial regression models. The motivation for this package is described in 'Polynomial Regression As an Alternative to Neural Nets' by Xi Cheng, Bohdan Khomtchouk, Norman Matloff, and Pete Mohanty (<arXiv:1806.06850>).
A collection of tools to handle microsatellite data of
 any ploidy (and samples of mixed ploidy) where allele copy number is not
 known in partially heterozygous genotypes.  It can import and export data in
 ABI 'GeneMapper', 'Structure', 'ATetra', 'Tetrasat'/'Tetra', 'GenoDive', 'SPAGeDi',
 'POPDIST', 'STRand', and binary presence/absence formats.  It can calculate
 pairwise distances between individuals using a stepwise mutation model or
 infinite alleles model, with or without taking ploidies and allele frequencies
 into account.  These distances can be used for the calculation of clonal
 diversity statistics or used for further analysis in R.  Allelic diversity
 statistics and Polymorphic Information Content are also available.  polysat can 
 assist the user in estimating the ploidy of samples, and it can estimate allele 
 frequencies in populations, calculate pairwise or global differentiation statistics 
 based on those frequencies, and export allele frequencies to 'SPAGeDi' and 'adegenet'.  
 Functions are also included for assigning alleles to isoloci in cases where one pair 
 of microsatellite primers amplifies alleles from two or more independently
 segregating isoloci.  polysat is described by Clark and Jasieniuk (2011)
 <doi:10.1111/j.1755-0998.2011.02985.x> and Clark and Schreier (2017)
 <doi:10.1111/1755-0998.12639>.
Perform classic chi-squared tests and Ripol et al(1999)
             binomial confidence interval approach for autopolyploid
             dominant markers. Also, dominant markers may be generated
             for families of offspring where either one or both of the
             parents possess the marker. Missing values and
             misclassified markers may be generated at random.
Fits Bayesian mixture models to estimate marker dosage for dominant markers in autopolyploids using JAGS (1.0 or greater) as outlined in Baker et al "Bayesian estimation of marker dosage in sugarcane and other autopolyploids" (2010, <doi:10.1007/s00122-010-1283-z>). May be used in conjunction with polySegratio for simulation studies and comparison with standard methods.
This algorithm classifies the trends into linear, quadratic, cubic, concealed and no-trend types. The "concealed trends" are those trends that possess quadratic or cubic forms, but the net change from the start of the time period to the end of the time period hasn't been significant. The "no-trend" category includes simple linear trends with statistically in-significant slope coefficient.
Routines for flexible functional form estimation via basis
    regression, with model selection via the adaptive LASSO or SCAD to prevent
    overfitting.
This package fits a patch occupancy model
POM-aSPU test evaluates an association between an ordinal response and multiple phenotypes, for details see Kim and Pan (2017) <DOI:10.1002/gepi.22033>.
Implements the method described at the UCLA Statistical
    Consulting site <https://stats.idre.ucla.edu/r/dae/ordinal-logistic-regression/>
    for checking if the proportional odds assumption holds for a 
    cumulative logit model.
Provides the infrastructure to define and analyze the solutions of Partially Observable Markov Decision Processes (POMDP) models. The package includes pomdp-solve to solve POMDPs using a variety of exact and approximate value iteration algorithms. Smallwood and Sondik (1973) <doi:10.1287/opre.21.5.1071>.
Calculations of an information criterion are proposed to check the quality of simulations results of Agent-based models (ABM/IBM) or other non-linear rule-based models. The POMDEV measure (Pattern Oriented Modelling DEViance) is based on the Kullback-Leibler divergence and likelihood theory. It basically indicates the deviance of simulation results from field observations. Once POMDEV scores and metropolis-hasting sampling on different model versions are effectuated, POMIC scores (Pattern Oriented Modelling Information Criterion) can be calculated. This method could be further developed to incorporate multiple patterns assessment. Piou C, U Berger and V Grimm (2009) <doi:10.1016/j.ecolmodel.2009.05.003>.
Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models).  The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods.  It is also a versatile platform for implementation of inference methods for general POMP models.
An implementation of a hybrid method of person-oriented method and perturbation on the model. Pompom is the initials of the two methods. The hybrid method will provide a multivariate intraindividual variability metric (iRAM). The person-oriented method used in this package refers to uSEM (unified structural equation modeling, see Kim et al., 2007, Gates et al., 2010 and Gates et al., 2012 for details). Perturbation on the model was conducted according to impulse response analysis introduced in Lutkepohl (2007). 
    Kim, J., Zhu, W., Chang, L., Bentler, P. M., & Ernst, T. (2007) <doi:10.1002/hbm.20259>. 
    Gates, K. M., Molenaar, P. C. M., Hillary, F. G., Ram, N., & Rovine, M. J. (2010) <doi:10.1016/j.neuroimage.2009.12.117>. 
    Gates, K. M., & Molenaar, P. C. M. (2012) <doi:10.1016/j.neuroimage.2012.06.026>. 
    Lutkepohl, H. (2007, ISBN:3540262393).
Finds equivalence classes corresponding to a symmetric relation
    or undirected graph.  Finds total order consistent with partial order
    or directed graph (so-called topological sort).
Enables the creation of object pools, which make it
    less computationally expensive to fetch a new object. Currently the
    only supported pooled objects are 'DBI' connections.
The 2017 American College of Cardiology and American Heart
  Association blood pressure guideline recommends using 10-year predicted 
  atherosclerotic cardiovascular disease risk to guide the decision to 
  initiate or intensify antihypertensive medication. The guideline recommends 
  using the Pooled Cohort risk prediction equations to predict 10-year 
  atherosclerotic cardiovascular disease risk. This package implements the 
  original Pooled Cohort risk prediction equations and also incorporates 
  updated versions based on more contemporary data and statistical methods.
  References:
  Goff DC, Lloyd-Jones DM, Bennett G, Coady S, D’Agostino RB, Gibbons R, 
  Greenland P, Lackland DT, Levy D, O’Donnell CJ, and Robinson JG (2014) 
  <doi:10.1016/j.jacc.2014.03.006>
  Yadlowsky S, Hayward RA, Sussman JB, McClelland RL, Min YI, and Basu S (2018)
  <doi:10.7326/m17-3011>.
Calculates the pooled mean group (PMG) estimator for dynamic panel data models, as described by Pesaran, Shin and Smith (1999) <doi:10.1080/01621459.1999.10474156>.
Functions for the computation of f- and D-statistics (estimation of 'Fst', Patterson's 'F2', 'F3', 'F3*', 'F4' and D parameters) in population genomics studies from allele count or Pool-Seq read count data and for the fitting, building and visualization of admixture graphs. The package also includes several utilities to manipulate Pool-Seq data stored in standard format (e.g., such as 'vcf' files or 'rsync' files generated by the the 'PoPoolation' software) and perform conversion to alternative format (as used in the 'BayPass' and 'SelEstim' software). As of version 2.0, the package also includes utilities to manipulate standard allele count data (e.g., stored in 'TreeMix', 'BayPass' or 'SelEstim' format).
Functions for calculating power and fitting regression models in studies where a biomarker is measured in "pooled" samples rather than for each individual. Approaches for handling measurement error follow the framework of Schisterman et al. (2010) <doi:10.1002/sim.3823>.
Functions for pooling/combining the results (i.e., p-values) from (dependent) hypothesis tests. Included are Fisher's method, Stouffer's method, the inverse chi-square method, the Bonferroni method, Tippett's method, and the binomial test. Each method can be adjusted based on an estimate of the effective number of tests or using empirically derived null distribution using pseudo replicates. For Fisher's, Stouffer's, and the inverse chi-square method, direct generalizations based on multivariate theory are also available (leading to Brown's method, Strube's method, and the generalized inverse chi-square method).
An easy-to-use tool for working with presence/absence tests on 'pooled'
    or 'grouped' samples. The primary application is for estimating prevalence of 
    a marker in a population based on the results of tests on pooled specimens.
    This sampling method is often employed in surveillance of rare conditions in
    humans or animals (e.g. molecular xenomonitoring). The package was initially
    conceived as an R-based alternative to the molecular xenomonitoring software,
    'PoolScreen' <https://sites.uab.edu/statgenetics/software/>. However, it goes
    further, allowing for estimates of prevalence to be adjusted for hierarchical
    sampling frames, and perform flexible mixed-effect regression analyses
    (McLure et al. pre-print <arXiv:2012.05405>). The package is currently in
    early stages, however more features are planned or in the works: e.g.
    adjustments for imperfect test specificity/sensitivity, functions for helping
    with optimal experimental design, and functions for spatial modelling.
Gene-based association tests using the actual impurity reduction (AIR) variable importance. The function aggregates AIR importance measures from a group of SNPs or probes and outputs a p-value for each gene. The procedures builds upon the method described in <doi:10.1093/Bioinformatics/Bty373> and will be published soon.
A replication of key functionality from 'dplyr' and the wider 'tidyverse' using only 'base'.
Population dynamic models underpin a range of analyses and applications in ecology and epidemiology. The various approaches for analysing population dynamics models (MPMs, IPMs, ODEs, POMPs, PVA) each require the model to be defined in a different way. This makes it difficult to combine different modelling approaches and data types to solve a given problem. 'pop' aims to provide a flexible and easy to use common interface for constructing population dynamic models and enabling to them to be fitted and analysed in lots of different ways.
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.
Optimal experimental designs for both population and individual
    studies based on nonlinear mixed-effect models. Often this is based on a
    computation of the Fisher Information Matrix. This package was developed
    for pharmacometric problems, and examples and predefined models are available
    for these types of systems. The methods are described in Nyberg et al. 
    (2012) <doi:10.1016/j.cmpb.2012.05.005>, and Foracchia et al. (2004) 
    <doi:10.1016/S0169-2607(03)00073-7>.
Enables computation of epidemiological statistics, including those 
    where counts or mortality rates of the reference population are used. 
    Currently supported: excess hazard models (Dickman, Sloggett, Hills, and 
    Hakulinen (2012) <doi:10.1002/sim.1597>), rates, mean survival times, 
    relative/net survival (in particular the Ederer II (Ederer and Heise (1959))
    and Pohar Perme (Pohar Perme, Stare, and Esteve 
    (2012) <doi:10.1111/j.1541-0420.2011.01640.x>) estimators), 
    and standardized incidence and mortality ratios, 
    all of which can be easily adjusted for by covariates such as 
    age. Fast splitting and aggregation of 'Lexis' objects (from package 'Epi') 
    and other computations achieved using 'data.table'.
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 several data sets and functions to accompany the book "Population Genetics with R: An Introduction for Life Scientists" (2021, ISBN:9780198829546).
Provides beginner friendly framework to analyse population genetic
    data. Based on 'adegenet' objects it uses 'knitr' to create comprehensive reports on spatial genetic data. 
    For detailed information how to use the package refer to the comprehensive
    tutorials or visit <http://www.popgenreport.org/>.
Provides functions to estimate the kinship matrix of individuals from a large set of biallelic SNPs, and extract inbreeding coefficients and the generalized FST (Wright's fixation index).  Method described in Ochoa and Storey (2021) <doi:10.1371/journal.pgen.1009241>.
Provides a suite of tools for various methods of estimating
    confidence intervals for the mean of selected populations.
Estimates DNA target concentration by classifying digital PCR (polymerase chain reaction) droplets as positive, negative, or rain, using Expectation-Maximization Clustering. The fitting is accomplished using the 'EMMIXskew' R package (v. 1.0.3) by Kui Wang, Angus Ng, and Geoff McLachlan (2018) as based on their paper "Multivariate Skew t Mixture Models: Applications to Fluorescence-Activated Cell Sorting Data" <doi:10.1109/DICTA.2009.88>.
Population genetic analyses for hierarchical analysis of partially
    clonal populations built upon the architecture of the 'adegenet' package. 
    Originally described in Kamvar, Tabima, and Grünwald (2014) 
    <doi:10.7717/peerj.281> with version 2.0 described in Kamvar, Brooks, and 
    Grünwald (2015) <doi:10.3389/fgene.2015.00208>.
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.
Disaggregating census-based areal population counts to finer 
    gridded population surfaces using Random Forest algorithm to determine 
    the target area weights 
    (see _Stevens, et al._ (2015) <doi:10.1371/journal.pone.0107042>).
Kohonen's self-organizing maps with a number of distinguishing features:
    (1) A very efficient, single threaded, stochastic training algorithm based on ideas from tensor algebra.  Up to 60x faster than traditional single-threaded training algorithms. No special accelerator hardware required.
    (2) Automatic centroid detection and visualization using starbursts.
    (3) Two models of the data: (a) a self-organizing map model, (b) a centroid based clustering model.
    (4) A number of easily accessible quality metrics for the self-organizing map and the centroid based cluster model.
Functions to estimate and plot smooth or linear population trends, or population indices, 
    from animal or plant count survey data.
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()'.
Population down-scaling from a set of source zone geometrical features to another set of target zone 
    geometries gem units using Areametric and Volumetric approaches. The aforementioned methods were adopted by 
    Lwin K. K. and Murayama Y. work <doi:10.1111/j.1467-9671.2009.01171.x>.
The main attribute of 'PopVar' is the prediction of genetic variance in bi-parental populations, 
    from which the package derives its name. 'PopVar' contains a set of functions that use phenotypic and genotypic
    data from a set of candidate parents to 1) predict the mean, genetic variance, and superior progeny value of all, 
    or a defined set of pairwise bi-parental crosses, and 2) perform cross-validation to estimate genome-wide prediction 
    accuracy of multiple statistical models. More details are available in Mohammadi, Tiede, and Smith (2015, <doi:10.2135/cropsci2015.01.0030>). 
    A dataset 'think_barley.rda' is included for reference and examples.
The name of the package is derived from the French, 'pour' ridge, and provides functionality for ridge-type estimation of a potpourri of models. Currently, this estimation concerns that of various Gaussian graphical models from different study designs. Among others it considers the regular Gaussian graphical model and a mixture of such models. The porridge-package implements the estimation of the former either from i) data with replicated observations by penalized loglikelihood maximization using the regular ridge penalty on the parameters (van Wieringen, Chen, 2019) or ii) from non-replicated data by means of either a ridge estimator with multiple shrinkage targets (as presented in van Wieringen et al. 2020, <doi:10.1016/j.jmva.2020.104621>) or the generalized ridge estimator that allows for both the inclusion of quantitative and qualitative prior information on the precision matrix via element-wise penalization and shrinkage (van Wieringen, 2019, <doi:10.1080/10618600.2019.1604374>). Additionally, the porridge-package facilitates the ridge penalized estimation of a mixture of Gaussian graphical models (Aflakparast et al., 2018, <doi:10.1002/bimj.201700102>).
Obtener listado de datos, acceder y extender series del Portal de 
    Datos de Hacienda.Las proyecciones se realizan con 'forecast', 
    Hyndman RJ, Khandakar Y (2008) <doi:10.18637/jss.v027.i03>. 
    Search, download and forecast time-series from the Ministry of Economy 
    of Argentina. Forecasts are built with the 'forecast' package, 
    Hyndman RJ, Khandakar Y (2008) <doi:10.18637/jss.v027.i03>.
Download and generate summaries for the rodent,
    plant, ant, and weather data from the Portal Project. Portal is a
    long-term (and ongoing) experimental monitoring site in the Chihuahua
    desert. The raw data files can be found at
    <https://github.com/weecology/portaldata>.
Contains common univariate and multivariate portmanteau test statistics in time series based on the asymptotic distributions and the Monte Carlo significance tests. Simulate univariate and multivariate data from seasonal and nonseasonal time series models. See Mahdi and McLeod (2012) <doi:10.1111/j.1467-9892.2011.00752.x> and
  Mahdi and McLeod (2020) <arXiv:2005.00931>.
Classes for analysing and implementing equity portfolios,
    including routines for generating tradelists and calculating
    exposures to user-specified risk factors.
Simplify your portfolio optimization process by applying a contemporary modeling way to model and solve your portfolio problems. While most approaches and packages are rather complicated this one tries to simplify things and is agnostic regarding risk measures as well as optimization solvers. Some of the methods implemented are described by Konno and Yamazaki (1991) <doi:10.1287/mnsc.37.5.519>, Rockafellar and Uryasev (2001) <doi:10.21314/JOR.2000.038> and Markowitz (1952) <doi:10.1111/j.1540-6261.1952.tb01525.x>.
Portfolio optimization and analysis routines and graphics.
Automated backtesting of multiple portfolios over multiple 
    datasets of stock prices in a rolling-window fashion. Intended for 
    researchers and practitioners to backtest a set of different portfolios, 
    as well as by a course instructor to assess the students in their portfolio 
    design in a fully automated and convenient manner, with results conveniently 
    formatted in tables and plots. Each portfolio design is easily defined as a
    function that takes as input a window of the stock prices and outputs the 
    portfolio weights. Multiple portfolios can be easily specified as a list 
    of functions or as files in a folder. Multiple datasets can be conveniently 
    extracted randomly from different markets, different time periods, and 
    different subsets of the stock universe. The results can be later assessed 
    and ranked with tables based on a number of performance criteria (e.g., 
    expected return, volatility, Sharpe ratio, drawdown, turnover rate, return 
    on investment, computational time, etc.), as well as plotted in a number of 
    ways with nice barplots and boxplots.
R interface to PortfolioEffect cloud service for estimating
    high frequency price variance, quarticity, microstructure noise variance, 
    and other metrics in both aggregate and rolling window flavors. 
    Constructed estimators could use client-side market data or access 
    HF intraday price history for all major US Equities.
    See <https://www.portfolioeffect.com/> for more information on the 
    PortfolioEffect high frequency portfolio analytics platform.
R interface to PortfolioEffect cloud service for backtesting
    high frequency trading (HFT) strategies, intraday portfolio analysis
    and optimization. Includes auto-calibrating model pipeline for market
    microstructure noise, risk factors, price jumps/outliers, tail risk
    (high-order moments) and price fractality (long memory). Constructed
    portfolios could use client-side market data or access HF intraday price
    history for all major US Equities. See <https://www.portfolioeffect.com/>
    for more information on the PortfolioEffect high frequency portfolio
    analytics platform.
Two functions for financial portfolio optimization by linear programming are provided. One function implements Benders decomposition algorithm and can be used for very large data sets. The other, applicable for moderate sample sizes, finds optimal portfolio which has the smallest distance to a given benchmark portfolio.
Risk Attribution of a portfolio with Volatility Risk Analysis.
Designed to aid both academic researchers and asset managers in conducting factor based portfolio sorts.  
    Provides functionality to sort assets into portfolios for up to three factors via a conditional or unconditional sorting procedure.
Provides a set of basic tools for generating, analyzing, summarizing and visualizing finite partially ordered sets. In particular, it implements flexible and very efficient algorithms for the extraction of linear extensions and for the computation of mutual ranking probabilities and other user-defined functionals, over them. The package is meant as a computationally efficient "engine", for the implementation of data analysis procedures, on systems of multidimensional ordinal indicators and partially ordered data, in the spirit of Fattore, M. (2016) "Partially ordered sets and the measurement of multidimensional ordinal deprivation", Social Indicators Research <DOI:10.1007/s11205-015-1059-6>, and Fattore M. and Arcagni, A. (2018) "A reduced posetic approach to the measurement of multidimensional ordinal deprivation", Social Indicators Research <DOI:10.1007/s11205-016-1501-4>.
In linear LS regression, calculate for a given design matrix
  the multiplier K of coefficient standard errors such that the
  confidence intervals [b - K*SE(b), b + K*SE(b)] have a
  guaranteed coverage probability for all coefficient estimates
  b in any submodels after performing arbitrary model selection.
Computes the exact probability density function of X/Y conditioned on positive quadrant for series of  bivariate distributions,for more details see Nadarajah,Song and Si (2019) <DOI:10.1080/03610926.2019.1576893>.
A collection of R Markdown templates for creating simple and easy 
  to personalize single page websites.
Free UK geocoding using data from Office for National Statistics.
    It is using several functions to get information about post codes, outward
    codes, reverse geocoding, nearest post codes/outward codes, validation, or
    randomly generate a post code. API wrapper around <https://postcodes.io>.
Use 'rmarkdown' and 'pagedown' to generate
    HTML and PDF conference posters.
Provides useful tools for both users and developers of packages 
  for fitting Bayesian models or working with output from Bayesian models. 
  The primary goals of the package are to: 
  (a) Efficiently convert between many different useful formats of
  draws (samples) from posterior or prior distributions.
  (b) Provide consistent methods for operations commonly performed on draws, 
  for example, subsetting, binding, or mutating draws.
  (c) Provide various summaries of draws in convenient formats.
  (d) Provide lightweight implementations of state of the art posterior 
  inference diagnostics. References: Vehtari et al. (2021) 
  <doi:10.1214/20-BA1221>.
An implementation of a non-parametric statistical model using a
    parallelised Monte Carlo sampling scheme. The method implemented in this
    package allows non-parametric inference to be regularized for small sample
    sizes, while also being more accurate than approximations such as
    variational Bayes. The concentration parameter is an effective sample size
    parameter, determining the faith we have in the model versus the data. When
    the concentration is low, the samples are close to the exact Bayesian
    logistic regression method; when the concentration is high, the samples are
    close to the simplified variational Bayes logistic regression. The method is
    described in full in the paper Lyddon, Walker, and Holmes (2018),
    "Nonparametric learning from Bayesian models with randomized objective
    functions" <arXiv:1806.11544>.
Generate all necessary R/Rmd/shell files for data processing after running 'GGIR' (v2.4.0) for accelerometer data. In part 1, all csv files in the GGIR output directory were read, transformed and then merged. In part 2, the GGIR output files were checked and summarized in one excel sheet. In part 3, the merged data was cleaned according to the number of valid hours on each night and the number of valid days for each subject. In part 4, the cleaned activity data was imputed by the average Euclidean norm minus one (ENMO) over all the valid days for each subject. Finally, a comprehensive report of data processing was created using Rmarkdown, and the report includes few exploratory plots and multiple commonly used features extracted from minute level actigraphy data.
Functions to convert geometry and 'hstore' data types from
    'PostgreSQL' into standard R objects, as well as to simplify
    the import of R data frames (including spatial data frames) into 'PostgreSQL'.
    Note: This package is deprecated. For new projects, we recommend 
    using the 'sf' package to interface with geodatabases.
Implements a range of facilities for post-hoc analysis and
             summarizing linear models, generalized linear models and 
             generalized linear mixed models, including grouping and clustering 
             via pairwise comparisons using graph representations and efficient
             algorithms for finding maximal cliques of a graph. 
             Includes also non-parametric toos for post-hoc analysis.
             It has S3 methods for printing summarizing, and producing plots, 
             line and barplots suitable for post-hoc analyses.
Functions to estimate the incubation period distribution of post-infectious syndrome which is defined as the time between the symptom onset of the antecedent infection and that of the post-infectious syndrome.
This is a wrapper for the Mercury Parser API. The Mercury Parser is 
    a single API endpoint that takes a URL and gives you back the content reliably 
    and easily. 
    With just one API request, Mercury takes any web article and returns 
    only the relevant content — headline, author, body text, relevant images and 
    more — free from any clutter. It’s reliable, easy-to-use and free.
    See the webpage here: <https://mercury.postlight.com/>.
Provides adds postfix and infix logic operators for
    if, then, unless, and otherwise.
Implements the Phylogeny-Guided Microbiome OTU-Specific Association 
    Test method, which boosts the testing power by adaptively borrowing 
    information from phylogenetically close OTUs (operational taxonomic units)
    of the target OTU. This method
    is built on a kernel machine regression framework and allows for flexible 
    modeling of complex microbiome effects, adjustments for covariates, and 
    can accommodate both continuous and binary outcomes.
The aim of 'postpack' is to provide the infrastructure for a standardized workflow for 'mcmc.list' objects.
    These objects can be used to store output from models fitted with Bayesian inference using
    'JAGS', 'WinBUGS', 'OpenBUGS', 'NIMBLE', 'Stan', or even custom MCMC algorithms. Although the 'coda' R package provides
    some methods for these objects, it is somewhat limited in easily performing post-processing tasks for
    specific nodes. Models are ever increasing in their complexity and the number of tracked nodes, and oftentimes
    a user may wish to summarize/diagnose sampling behavior for only a small subset of nodes at a time
    for a particular question or figure. Thus, many 'postpack' functions support performing tasks on a
    subset of nodes, where the subset is specified with regular expressions. The functions in 'postpack'
    streamline the extraction, summarization, and diagnostics of specific monitored nodes after model fitting.
    Further, because there is rarely only ever one model under consideration, 'postpack' scales efficiently 
    to perform the same tasks on output from multiple models simultaneously, facilitating rapid assessment 
    of model sensitivity to changes in assumptions.
Some functions useful to perform a Peak Over Threshold
        analysis in univariate and bivariate cases, see Beirlant et al. (2004)
        <doi:10.1002/0470012382>. A user's guide is available.
Provides functions to compute the potential model as defined by
    Stewart (1941) <doi:10.1126/science.93.2404.89>. Several options are available
    to customize the model, such as the possibility to fine-tune the distance
    friction functions or to use custom distance matrices. Some computations are
    parallelized to improve their efficiency.
Translating messages in R packages is managed using the po top-level directory and the 'gettext' program. This package provides some helper functions for building this support in R packages, e.g. common validation & I/O tasks.
Do Markov chain Monte Carlo (MCMC) simulation of Potts models
   (Potts, 1952, <doi:10.1017/S0305004100027419>),
   which are the multi-color generalization of Ising models
   (so, as as special case, also simulates Ising models).
   Use the Swendsen-Wang algorithm (Swendsen and Wang, 1987,
   <doi:10.1103/PhysRevLett.58.86>) so MCMC is fast.
   Do maximum composite likelihood estimation of parameters
   (Besag, 1975, <doi:10.2307/2987782>,
   Lindsay, 1988, <doi:10.1090/conm/080>).
There are three sets of functions. The first produces
             basic properties of a graph and generates samples from
             multinomial distributions to facilitate the simulation
	     functions (they maybe used for other purposes as well).
	     The second provides various simulation functions for a
	     Potts model in Potts, R. B. (1952)
	     <doi:10.1017/S0305004100027419>.
	     The third currently includes only one function which
	     computes the normalizing constant of a Potts model
	     based on simulation results.
Computes the D', Wn, and conditional asymmetric linkage disequilibrium (ALD) measures for pairs of genetic loci. Performs these linkage disequilibrium (LD) calculations on phased genotype data recorded using Genotype List (GL) String or columnar formats. Alternatively, generates expectation-maximization (EM) estimated haplotypes from phased data, or performs LD calculations on EM estimated haplotypes. Performs sign tests comparing LD values for phased and unphased datasets, and generates heat-maps for each LD measure. Described by Osoegawa et al. (2019a) <doi:10.1016/j.humimm.2019.01.010>, and Osoegawa et. al. (2019b) <doi:10.1016/j.humimm.2019.05.018>.
The Phylogenetic Ornstein-Uhlenbeck Mixed Model (POUMM) allows to 
    estimate the phylogenetic heritability of continuous traits, to test 
    hypotheses of neutral evolution versus stabilizing selection, to quantify 
    the strength of stabilizing selection, to estimate measurement error and to
    make predictions about the evolution of a phenotype and phenotypic variation 
    in a population. The package implements combined maximum likelihood and 
    Bayesian inference of the univariate Phylogenetic Ornstein-Uhlenbeck Mixed 
    Model, fast parallel likelihood calculation, maximum likelihood 
    inference of the genotypic values at the tips, functions for summarizing and
    plotting traces and posterior samples, functions for simulation of a univariate 
    continuous trait evolution model along a phylogenetic tree. So far, the 
    package has been used for estimating the heritability of quantitative traits
    in macroevolutionary and epidemiological studies, see e.g. 
    Bertels et al. (2017) <doi:10.1093/molbev/msx246> and 
    Mitov and Stadler (2018) <doi:10.1093/molbev/msx328>. The algorithm for 
    parallel POUMM likelihood calculation has been published in 
    Mitov and Stadler (2019) <doi:10.1111/2041-210X.13136>.
An implementation of the Partition Of variation (POV) method as
    developed by Dr. Thomas A Little <https://thomasalittleconsulting.com> in
    1993 for the analysis of semiconductor data for hard drive manufacturing.
    POV is based on sequential sum of squares and is an exact method that
    explains all observed variation. It quantitates both the between and within
    factor variation effects and can quantitate the influence of both continuous
    and categorical factors.
Provides an interface to compute poverty and inequality indicators for more than 160 countries and regions from the World Bank's database of household surveys. It has the same functionality as the 'Povcalnet' website (<http://iresearch.worldbank.org/PovcalNet/>). 'Povcalnet' is a computational tool that allows users to estimate poverty rates for regions, sets of countries or individual countries, over time and at any poverty line. 'Povcalnet' is managed jointly by the data and research group in the World Bank's development economics division. It draws heavily upon a strong collaboration with the poverty and equity global practice, which is responsible for the gathering and harmonization of the underlying survey data.
Density, distribution function, quantile function and random generation for the family of power and reversal power distributions.
Full pattern summation of X-ray powder diffraction data as
  described in Chipera and Bish (2002) <doi:10.1107/S0021889802017405> and
  Butler and Hillier (2021) <doi:10.1016/j.cageo.2020.104662>.
  Derives quantitative estimates of crystalline and amorphous phase
  concentrations in complex mixtures.
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 in Two-Stage Designs (TSD) via simulations.
Basic functions for power analysis and effect size calculation.
Functions for bootstrapping the power of ANOVA designs
    based on estimated means and standard deviations of the conditions.
    Please refer to the documentation of the boot.power.anova() function
    for further details.
A power analysis tool for jointly testing the cause-1 cause-specific hazard and the any-cause hazard with competing risks data.
Power and sample size calculation for bulk tissue and single-cell eQTL analysis
             based on ANOVA, simple linear regression, or linear mixed effects model. It can also calculate power/sample size 
             for testing the association of a SNP to a continuous type phenotype.
             Please see the reference: Dong X, Li X, Chang T-W, Scherzer CR, Weiss ST, Qiu W. (2021) <doi:10.1093/bioinformatics/btab385>.
Analytical power calculations for GxE and GxG interactions for case-control studies of candidate genes and genome-wide association studies (GWAS). This includes power calculation for four two-step screening and testing procedures.  It can also calculate power for GxE and GxG without any screening.
An implementation of the generalized power analysis for the local average treatment effect (LATE), proposed by Bansak (2020) <doi:10.1214/19-STS732>. Power analysis is in the context of estimating the LATE (also known as the complier average causal effect, or CACE), with calculations based on a test of the null hypothesis that the LATE equals 0 with a two-sided alternative. The method uses standardized effect sizes to place a conservative bound on the power under minimal assumptions. Package allows users to recover power, sample size requirements, or minimum detectable effect sizes. Package also allows users to work with absolute effects rather than effect sizes, to specify an additional assumption to narrow the bounds, and to incorporate covariate adjustment.
An implementation of maximum likelihood estimators
    for a variety of heavy tailed distributions, including both the
    discrete and continuous power law distributions. Additionally, a
    goodness-of-fit based approach is used to estimate the lower cut-off
    for the scaling region.
An implementation of the sample size computation method for network
    models proposed by Constantin et al. (2021) <doi:10.31234/osf.io/j5v7u>
    The implementation takes the form of a three-step recursive algorithm
    designed to find an optimal sample size given a model specification and a
    performance measure of interest. It starts with a Monte Carlo simulation
    step for computing the performance measure and a statistic at various sample
    sizes selected from an initial sample size range. It continues with a
    monotone curve-fitting step for interpolating the statistic across the entire
    sample size range. The final step employs stratified bootstrapping to quantify
    the uncertainty around the fitted curve.
Functions to 
        calculate power and sample size for testing
        (1) mediation effects; 
        (2) the slope in a simple linear regression; 
        (3) odds ratio in a simple logistic regression;
        (4) mean change for longitudinal study with 2 time points;
        (5) interaction effect in 2-way ANOVA; and
        (6) the slope in a simple Poisson regression.
Miscellaneous functions for a descriptive analysis and initial Bayesian and classical 
             inference for the power parameter of the the Power Normal (PN) distribution. This 
             miscellaneous will be extend for more distributions into the power family and the 
             three-parameter model.
(1) To estimate the power of testing for linkage using an
        affected sib pair design, as a function of the recurrence risk
        ratios. We will use analytical power formulae as implemented in
        R. These are based on a Mathematica notebook created by Martin
        Farrall. (2) To examine how the power of the transmission
        disequilibrium test (TDT) depends on the disease allele
        frequency, the marker allele frequency, the strength of the
        linkage disequilibrium, and the magnitude of the genetic
        effect. We will use an R program that implements the power
        formulae of Abel and Muller-Myhsok (1998). These formulae allow
        one to quickly compute power of the TDT approach under a
        variety of different conditions. This R program was modeled on
        Martin Farrall's Mathematica notebook.
Computation of matrix and scalar exponentiation.
Functions to calculate power and
                sample size for testing main effect or interaction effect in
                the survival analysis of epidemiological studies
                (non-randomized studies), taking into account the 
                correlation between the covariate of the
                interest and other covariates. Some calculations also take 
                into account the competing risks and stratified analysis. 
                This package also includes
                a set of functions to calculate power and sample size
                for testing main effect in the survival analysis of 
                randomized clinical trials and conditional logistic regression for nested case-control study.
Contains functions to calculate power and sample size for
    various study designs used in bioequivalence studies. Use known.designs() to
    see the designs supported. Power and sample size can be obtained based on
    different methods, amongst them prominently the TOST procedure (two one-sided t-tests).
    See README and NEWS for further information.
Includes tools to calculate statistical power, minimum detectable effect size (MDES), MDES difference (MDESD), and minimum required sample size for various multilevel randomized experiments (MRE) with continuous outcomes.
 Accomodates 14 types of MRE designs to detect main treatment effect, seven types of MRE designs to detect moderated treatment effect (2-1-1, 2-1-2, 2-2-1, 2-2-2, 3-3-1, 3-3-2, and 3-3-3 designs; <total.lev> - <trt.lev> - <mod.lev>),
 five types of MRE designs to detect mediated treatment effects (2-1-1, 2-2-1, 3-1-1, 3-2-1, and 3-3-1 designs; <trt.lev> - <med.lev> - <out.lev>), four types of partially nested (PN) design to detect main treatment effect,
 and three types of PN designs to detect mediated treatment effects (2/1, 3/1, 3/2; <trt.arm.lev> / <ctrl.arm.lev>). 
 See 'PowerUp!' Excel series at <https://www.causalevaluation.org/>.
The PP package includes estimation of (MLE, WLE, MAP, EAP, ROBUST)
    person parameters for the 1,2,3,4-PL model and the GPCM (generalized
    partial credit model). The parameters are estimated under the assumption
    that the item parameters are known and fixed. The package is useful e.g. in
    the case that items from an item pool / item bank with known item parameters
    are administered to a new population of test-takers and an ability
    estimation for every test-taker is needed.
Exploratory projection pursuit is a method to discovers
	structure in multivariate data. At heart this package uses
	a projection index to evaluate how interesting a specific
	three-dimensional projection of multivariate data (with more
	than three dimensions) is. Typically, the main structure
	finding algorithm starts at a random projection and then
	iteratively changes the projection direction to move to
	a more interesting one. In other words, the projection index
	is maximised over the projection direction to find the most
	interesting projection. This maximum is, though, a local
	maximum. So, this code has the ability to restart the
	algorithm from many different starting positions automatically.
	Routines exist to plot a density estimate of projection indices 
	over the runs, this enables the user to obtain an idea of
	the distribution of the projection indices,
	and, hence,  which ones might be interesting. Individual
	projection solutions, including those identified as interesting,
	can be extracted and plotted individually. The package
	can make use of the mclapply() function to execute multiple runs in
	parallel to speed up index discovery. Projection pursuit is
	similar to independent component analysis. This package
	uses a projection index that maximises an entropy measure to
	look for projections that exhibit non-normality, and operates
	on sphered data. Hence, information from this package is
	different from that obtained from principal components analysis,
	but the rationale behind both methods is similar. 
        Nason, G. P. (1995) <doi:10.2307/2986135>.
Calculates the Probability Plot Correlation Coefficient (PPCC) 
             between a continuous variable X and a specified distribution. The corresponding 
	     composite hypothesis test that was first introduced by 
	     Filliben (1975) <doi: 10.1080/00401706.1975.10489279> 
	     can be performed to test whether the sample 
	     X is element of either the Normal, log-Normal, Exponential,
	     Uniform, Cauchy, Logistic, Generalized Logistic, Gumbel (GEVI), Weibull,
	     Generalized Extreme Value, Pearson III (Gamma 2), Mielke's Kappa, Rayleigh
	     or Generalized Logistic Distribution. The PPCC test is performed with
	     a fast Monte-Carlo simulation.
Implements recently developed projection 
    pursuit algorithms for finding optimal linear cluster
    separators. The clustering algorithms use optimal
    hyperplane separators based on minimum density, Pavlidis et. al (2016) <http://jmlr.org/papers/volume17/15-307/15-307.pdf>;
    minimum normalised cut, Hofmeyr (2017) <doi:10.1109/TPAMI.2016.2609929>;
    and maximum variance ratio clusterability, Hofmeyr and Pavlidis (2015) <doi:10.1109/SSCI.2015.116>.
Partitioning clustering divides the objects in a data set into non-overlapping subsets or clusters by using the prototype-based probabilistic and possibilistic clustering algorithms. This package covers a set of the functions for Fuzzy C-Means (Bezdek, 1974) <doi:10.1080/01969727308546047>, Possibilistic C-Means (Krishnapuram & Keller, 1993) <doi:10.1109/91.227387>, Possibilistic Fuzzy C-Means (Pal et al, 2005) <doi:10.1109/TFUZZ.2004.840099>, Possibilistic Clustering Algorithm (Yang et al, 2006) <doi:10.1016/j.patcog.2005.07.005>, Possibilistic C-Means with Repulsion (Wachs et al, 2006) <doi:10.1007/3-540-31662-0_6> and the other variants of hard and soft clustering algorithms. The cluster prototypes and membership matrices required by these partitioning algorithms are initialized with different initialization techniques that are available in the package 'inaparc'. As the distance metrics, not only the Euclidean distance but also a set of the commonly used distance metrics are available to use with some of the algorithms in the package.
A simple interface for interacting with ProPublica's 'Congress' API,
    which provides data about current and former members of both chambers of the
    U.S. Congress and can be found at the following URL: <https://projects.propublica.org/api-docs/congress-api/>.
Calculates partial and semi-partial
        (part) correlations along with p-value.
Spatial Analysis for exploration of Pakistan Population Census 2017 (<http://www.pbscensus.gov.pk/>). It uses data from R package 'PakPC2017'.
A suite of diagnostic tools for univariate point processes.
    This includes tools for simulating and fitting both common and more
    complex temporal point processes. We also include functions to 
    visualise these point processes and collect existing diagnostic
    tools of Brown et al. (2002) <doi:10.1162/08997660252741149> and
    Wu et al. (2021) <doi:10.1002/9781119821588.ch7>,
    which can be used to assess the fit of a chosen point process
    model.
Implements projection pursuit forest algorithm for supervised classification.
Methods for fitting point processes with parameters of generalised additive 
      model (GAM) form are provided. For an introduction to point processes see Cox, D.R & 
      Isham, V. (Point Processes, 1980, CRC Press), GAMs see Wood, S.N. (2017) 
      <doi:10.1201/9781315370279>, and the fitting approach see Wood, S.N., Pya, N. & 
      Safken, B. (2016) <doi:10.1080/01621459.2016.1180986>.
Projection Pursuit (PP) algorithm for dimension reduction based on Gaussian Mixture Models (GMMs) for density estimation using Genetic Algorithms (GAs) to maximise an approximated negentropy index. For more details see Scrucca and Serafini (2019) <doi:10.1080/10618600.2019.1598871>.
The Poverty Probability Index (PPI) is a poverty measurement tool 
    for organizations and businesses with a mission to serve the poor. The PPI 
    is statistically-sound, yet simple to use: the answers to 10 questions about 
    a household’s characteristics and asset ownership are scored to compute the 
    likelihood that the household is living below the poverty line – or above by 
    only a narrow margin. This package contains country-specific lookup data tables
    used as reference to determine the poverty likelihood of a household based
    on their score from the country-specific PPI questionnaire. These lookup 
    tables have been extracted from documentation of the PPI found at 
    <https://www.povertyindex.org> and managed by Innovations for Poverty Action 
    <https://www.poverty-action.org>.
An implementation of the one-step privacy-protecting method for estimating the overall and site-specific hazard ratios using inverse probability weighted Cox models in distributed data network studies, as proposed by Shu, Yoshida, Fireman, and Toh (2019) <doi: 10.1177/0962280219869742>. This method only requires sharing of summary-level riskset tables instead of individual-level data. Both the conventional inverse probability weights and the stabilized weights are implemented.
Efficient statistical inference of two-sample MR (Mendelian Randomization) analysis. 
    It can account for the correlated instruments and the horizontal pleiotropy, 
    and can provide the accurate estimates of both causal effect and horizontal 
    pleiotropy effect as well as the two corresponding p-values. There are two main 
    functions in the 'PPMR' package. One is PMR_individual() for individual level data, 
    the other is PMR_summary() for summary data.
Provides functions that fit hierarchical Gaussian and probit ordinal models.  
                A (covariate dependent) product partition model is used as a prior.
                If a covariate dependent product partition model  is selected,  
                then all the options detailed in Page, G.L.; Quintana, F.A.; (2018)
                <doi:10.1007/s11222-017-9777-z> are available.  If covariate values
                are missing, then the approach detailed in Page, G.L.; Quintana, F.A.; 
                Mueller, P (2020) <arXiv:1912.13119> is employed.  Also included in the
                package is a function that fits a Gaussian likelihood spatial product 
                partition model that is detailed in Page, G.L.; Quintana, F.A.; (2016) 
                <doi:10.1214/15-BA971>.
Assessment for statistically-based PPQ sampling plan, including calculating the passing probability, optimizing the baseline and high performance cutoff points, visualizing the PPQ plan and power dynamically. The analytical idea is based on the simulation methods from the textbook Burdick, R. K., LeBlond, D. J., Pfahler, L. B., Quiroz, J., Sidor, L., Vukovinsky, K., & Zhang, L. (2017). Statistical Methods for CMC Applications. In Statistical Applications for Chemistry, Manufacturing and Controls (CMC) in the Pharmaceutical Industry (pp. 227-250). Springer, Cham.
A toolbox for deterministic, probabilistic and privacy-preserving record linkage techniques. Combines the functionality of the 'Merge ToolBox' (<http://record-linkage.de>) with current privacy-preserving techniques.
Functions to select samples using PPS (probability proportional to size) sampling. The package also includes a function for stratified simple random sampling, a function to compute joint inclusion probabilities for Sampford's method of PPS sampling, and a few utility functions. The user's guide pps-ug.pdf is included in the .../pps/doc directory. The methods are described in standard survey sampling theory books such as Cochran's "Sampling Techniques"; see the user's guide for references.
Stochastic block model used for dynamic graphs represented by Poisson processes.
    To model recurrent interaction events in continuous time, an extension of the stochastic block model is proposed where every individual belongs to a latent group and interactions between two individuals follow a conditional inhomogeneous Poisson process with intensity driven by the individuals’ latent groups. The model is shown to be identifiable and its estimation is based on a semiparametric variational expectation-maximization algorithm. Two versions of the method are developed, using either a nonparametric histogram approach (with an adaptive choice of the partition size) or kernel intensity estimators. The number of latent groups can be selected by an integrated classification likelihood criterion.
    Y. Baraud and L. Birgé (2009). <doi:10.1007/s00440-007-0126-6>.
    C. Biernacki, G. Celeux and G. Govaert (2000). <doi:10.1109/34.865189>.
    M. Corneli, P. Latouche and F. Rossi (2016). <doi:10.1016/j.neucom.2016.02.031>.
    J.-J. Daudin, F. Picard and S. Robin (2008). <doi:10.1007/s11222-007-9046-7>.
    A. P. Dempster, N. M. Laird and D. B. Rubin (1977). <http://www.jstor.org/stable/2984875>.
    G. Grégoire (1993). <http://www.jstor.org/stable/4616289>.
    L. Hubert and P. Arabie (1985). <doi:10.1007/BF01908075>.
    M. Jordan, Z. Ghahramani, T. Jaakkola and L. Saul (1999). <doi:10.1023/A:1007665907178>.
    C. Matias, T. Rebafka and F. Villers (2018). <doi:10.1093/biomet/asy016>.
    C. Matias and S. Robin (2014). <doi:10.1051/proc/201447004>.
    H. Ramlau-Hansen (1983). <doi:10.1214/aos/1176346152>.
    P. Reynaud-Bouret (2006). <doi:10.3150/bj/1155735930>.
Functions are available to calibrate designs over a range of posterior and predictive thresholds, to plot the various design options, and to obtain the operating characteristics of optimal accuracy and optimal efficiency designs.
The PPS is an asymmetric, data-type-agnostic score that can detect 
    linear or non-linear relationships between two columns. The score ranges 
    from 0 (no predictive power) to 1 (perfect predictive power). It can be useful
    for data exploration purposes, in the same way correlation analysis is.
    For more information on PPS, see Wetschoreck (2020) <https://towardsdatascience.com/rip-correlation-introducing-the-predictive-power-score-3d90808b9598> 
    or github <https://github.com/paulvanderlaken/ppsr>.
Provides functionality for the prior and posterior projected Polya tree for the analysis of circular data (Nieto-Barajas and Nunez-Antonio (2019) <arXiv:1902.06020>).
Tools for exploring projection pursuit classification tree using
    various projection pursuit indexes.
This package allows users to calculate the number of
    under-five child deaths caused by consumption of poor quality
    antimalarials across 39 sub-Saharan nations. The package supports one
    function, that starts an interactive web tool created using
    the shiny R package. The web tool runs locally on the user's machine.
    The web tool allows users to set input parameters (prevalence of poor
    quality antimalarials, case fatality rate of children who take poor
    quality antimalarials, and sample size) which are then used to perform
    an uncertainty analysis following the Latin hypercube
    sampling scheme. Users can download the output figures as PDFs, and the
    output data as CSVs. Users can also download their input parameters
    for reference. This package was designed to accompany the analysis
    presented in:
    J. Patrick Renschler, Kelsey Walters, Paul Newton, Ramanan Laxminarayan
    "Estimated under-five deaths associated with poor-quality
    antimalarials in sub-Saharan Africa", 2014. Paper submitted.
An efficient tool designed for differential analysis of large-scale RNA sequencing (RNAseq) data and Bisulfite sequencing (BSseq) data in the presence of individual relatedness and population structure. 'PQLseq' first fits a Generalized Linear Mixed Model (GLMM) with adjusted covariates, predictor of interest and random effects to account for population structure and individual relatedness, and then performs Wald tests for each gene in RNAseq or site in BSseq.
Distance-based parametric bootstrap tests for clustering with 
  spatial neighborhood information. Some distance measures, 
  Clustering of presence-absence, abundance and multilocus genetic data 
  for species delimitation, nearest neighbor 
  based noise detection. Genetic distances between communities.
  Tests whether various distance-based regressions
  are equal. Try package?prabclus for on overview.
Provides a large number of functions from numerical analysis and
    linear algebra, numerical optimization, differential equations,
    time series, plus some well-known special mathematical functions.
    Uses 'MATLAB' function names where appropriate to simplify porting.
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 toolbox of fast, native and parallel implementations of various information-based importance criteria estimators and feature selection filters based on them, inspired by the overview by Brown, Pocock, Zhao and Lujan (2012) <https://www.jmlr.org/papers/v13/brown12a.html>.
 Contains, among other, minimum redundancy maximal relevancy ('mRMR') method by Peng, Long and Ding (2005) <doi:10.1109/TPAMI.2005.159>; joint mutual information ('JMI') method by Yang and Moody (1999) <https://papers.nips.cc/paper/1779-data-visualization-and-feature-selection-new-algorithms-for-nongaussian-data>; double input symmetrical relevance ('DISR') method by Meyer and Bontempi  (2006) <doi:10.1007/11732242_9> as well as joint mutual information maximisation ('JMIM') method by Bennasar, Hicks and Setchi (2015) <doi:10.1016/j.eswa.2015.07.007>.
A testing workbench 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.
An implementation of the "Design Analysis" proposed by 
    Gelman and Carlin (2014) <doi:10.1177/1745691614551642>. It combines 
    the evaluation of Power-Analysis with other inferential-risks as 
    Type-M error (i.e. Magnitude) and Type-S error (i.e. Sign). See also
    Altoè et al. (2020) <doi:10.3389/fpsyg.2019.02893> and 
    Bertoldo et al. (2020) <doi:10.31234/osf.io/q9f86>.
Derives prediction rule ensembles (PREs). Largely follows the
    procedure for deriving PREs as described in Friedman & Popescu (2008; 
    <DOI:10.1214/07-AOAS148>), with adjustments and improvements. The 
    main function pre() derives prediction rule ensembles consisting of 
    rules and/or linear terms for continuous, binary, count, multinomial, 
    and multivariate continuous responses. Function gpe() derives 
    generalized prediction ensembles, consisting of rules, hinge and linear 
    functions of the predictor variables.
It contains functions to analyze the precipitation
    intensity, concentration and anomaly.
An open-access tool/framework to download, validate, visualize, and analyze multi-source precipitation data across various spatio-temporal scales. Ultimately providing the hydrology science community with the tools for consistent and reproducible analysis regarding precipitation.
Estimate sample size based on precision rather than power.
    'precisely' is a study planning tool to calculate sample size based on
    precision. Power calculations are focused on whether or not an
    estimate will be statistically significant; calculations of precision
    are based on the same principles as power calculation but turn the
    focus to the width of the confidence interval. 'precisely' is based on
    the work of 'Rothman and Greenland' (2018).
Provides a selection of tools that make it easier to place elements onto a (base R) plot exactly where you want them. It allows users to identify points and distances on a plot in terms of inches, pixels, margin lines, data units, and proportions of the plotting space, all in a manner more simple than manipulating par().
Most of the time floating point arithmetic does
  approximately the right thing.  When adding sums or having products
  of numbers that greatly differ in magnitude, the floating point
  arithmetic may be incorrect.  This package implements the Kahan
  (1965) sum <doi:10.1145/363707.363723>, Neumaier (1974) sum
  <doi:10.1002/zamm.19740540106>, pairwise-sum (adapted from 'NumPy',
  See Castaldo (2008) <doi:10.1137/070679946> for a discussion of
  accuracy), and arbitrary precision sum (adapted from the fsum in
  'Python' ; Shewchuk (1997)
  <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.
Useful git hooks for R building on top of the
    multi-language framework 'pre-commit' for hook management. This
    package provides git hooks for common tasks like formatting files with
    'styler' or spell checking as well as wrapper functions to access the
    'pre-commit' executable.
Accurate calculations and visualization of precision-recall and ROC (Receiver Operator Characteristics)
    curves. Saito and Rehmsmeier (2015) <doi:10.1371/journal.pone.0118432>.
A computational model for predicting proteins encoded by circadian genes. The support vector machine has been employed with Laplace kernel for prediction of circadian proteins, where compositional, transitional and physico-chemical features were utilized as numeric features. User can predict for the test dataset using the proposed computational model. Besides, the user can also build their own training model using their training dataset, followed by prediction for the test set.
Based on different statistical definitions of discrimination, several methods have been proposed to detect and mitigate social inequality in machine learning models. 
  This package aims to provide an alternative to fairness treatment in predictive models. The ROC method implemented in this package
  is described by Kamiran, Karim and Zhang (2012) <https://ieeexplore.ieee.org/document/6413831/>.
Performs genomic prediction of hybrid performance using eight GS methods including GBLUP, BayesB, RKHS, PLS, LASSO, Elastic net, Random forest and XGBoost. It also provides fast cross-validation and mating design scheme for training population (Xu S et al (2016) <doi:10.1111/tpj.13242>; Xu S (2017) <doi:10.1534/g3.116.038059>).
Draw 2 dimensional and three dimensional plot for multiple regression models using package 'ggplot2' and 'rgl'.
   Supports linear models (lm), generalized linear models (glm) and local polynomial regression fittings (loess).
We included functions to assess the performance of
 risk models. The package contains functions for the various measures that are
 used in empirical studies, including univariate and multivariate odds ratios
 (OR) of the predictors, the c-statistic (or area under the receiver operating
 characteristic (ROC) curve (AUC)), Hosmer-Lemeshow goodness of fit test,
 reclassification table, net reclassification improvement (NRI) and
 integrated discrimination improvement (IDI). Also included are functions
 to create plots, such as risk distributions, ROC curves, calibration plot,
 discrimination box plot and predictiveness curves. In addition to functions
 to assess the performance of risk models, the package includes functions to
 obtain weighted and unweighted risk scores as well as predicted risks using
 logistic regression analysis. These logistic regression functions are
 specifically written for models that include genetic variables, but they
 can also be applied to models that are based on non-genetic risk factors only.
 Finally, the package includes function to construct a simulated dataset with 
 genotypes, genetic risks, and disease status for a hypothetical population, which 
 is used for the evaluation of genetic risk models.
A one-function package containing 'prediction()', a type-safe alternative to 'predict()' that always returns a data frame. The 'summary()' method provides a data frame with average predictions, possibly over counterfactual versions of the data (a la the 'margins' command in 'Stata'). Marginal effect estimation is provided by the related package, 'margins' <https://cran.r-project.org/package=margins>. The package currently supports common model types (e.g., "lm", "glm") from the 'stats' package, as well as numerous other model classes from other add-on packages. See the README or main package documentation page for a complete listing.
A common problem faced by journal reviewers and authors is the question of
     whether the results of a replication study are consistent with the original
     published study. One solution to this problem is to examine the effect size
     from the original study and generate the range of effect sizes that could
     reasonably be obtained (due to random sampling) in a replication attempt
     (i.e., calculate a prediction interval). This package has functions that calculate
     the prediction interval for the correlation (i.e., r),
     standardized mean difference (i.e., d-value), and mean.
Functions to get prediction intervals and prediction points of future observations from any continuous distribution.
Three prediction algorithms described in the paper
        "On-line predictive linear regression" Annals of Statistics 37,
        1566 - 1590 (2009)
Providing functions to diagnose 
  and make inferences from various linear models, such as those obtained from 'aov', 
  'lm', 'glm', 'gls', 'lme', and 'lmer'. Inferences include predicted 
  means and standard errors, contrasts, multiple comparisons, permutation tests and graphs.
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 and based on U.S. Census
    data, and the most common first named based on U.S. Social Security Administration data.
An implementation of prediction intervals for overdispersed count data,
            for overdispersed binomial data and for linear random effects models.
Implements the predictive k-means method for clustering observations, using a mixture of experts model to allow covariates to influence cluster centers. Motivated by air pollution epidemiology settings, where cluster membership needs to be predicted across space. Includes functions for predicting cluster membership using spatial splines and principal component analysis (PCA) scores using either multinomial logistic regression or support vector machines (SVMs). For method details see Keller et al. (2017) <doi:10.1214/16-AOAS992>.
"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.
An implementation of reliability estimation methods described in the paper (Bosnic, Z., & Kononenko, I. (2008) <doi:10.1007/s10489-007-0084-9>), which allows you to test the reliability of a single predicted instance made
             by your model and prediction function. It also allows you to make a correlation test to estimate which reliability estimate is the most 
             accurate for your model.
Provides additional functions for evaluating predictive models, including plotting calibration curves and model-based Receiver Operating Characteristic (mROC) based on Sadatsafavi et al (2021) <arXiv:2003.00316>.
Makes the time series prediction easier by automatizing this process
  using four main functions: prep(), modl(), pred() and postp(). Features different
  preprocessing methods to homogenize variance and to remove trend and seasonality.
  Also has the potential to bring together different predictive models to make comparatives.
  Features ARIMA and Data Mining Regression models (using caret).
Allows users to derive multi-objective weights from pairwise comparisons, which
    research shows is more repeatable, transparent, and intuitive other techniques. These weights
    can be rank existing alternatives or to define a multi-objective utility function for optimization.
This is an accessible template for 'pkgdown'. It uses two
    bootstrap themes, Flatly and Darkly and utilizes the
    'prefers-color-scheme' CSS variable to automatically serve either of
    the two based on user’s operating system setting, or allowing them to
    manually toggle between them.
Design and analyze two-stage randomized trials with a continuous
    outcome measure. The package contains functions to compute the required 
    sample size needed to detect a given preference, treatment, and selection 
    effect; alternatively, the package contains functions that can report the 
    study power given a fixed sample size. Finally, analysis functions are 
    provided to test each effect using either summary data (i.e. means, 
    variances) or raw study data <doi:10.18637/jss.v094.c02>.
Generates design matrix for analysing real paired comparisons and derived paired comparison data (Likert type items/ratings or rankings) using a loglinear approach. Fits loglinear Bradley-Terry model (LLBT) exploiting an eliminate feature. Computes pattern models for paired comparisons, rankings, and ratings. Some treatment of missing values (MCAR and MNAR). Fits latent class (mixture) models for paired comparison, rating and ranking patterns using a non-parametric ML approach.
Dynamize headers or R code within 'Rmd' files to prevent proliferation of 'Rmd' files for similar reports. Add in external HTML document within 'rmarkdown' rendered HTML doc.
Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically linking a response vector to covariate data through cluster membership. The package allows Bernoulli, Binomial, Poisson, Normal, survival and categorical response, as well as Normal and discrete covariates. It also allows for fixed effects in the response model, where a spatial CAR (conditional autoregressive) term can be also included. Additionally, predictions may be made for the response, and missing values for the covariates are handled. Several samplers and label switching moves are implemented along with diagnostic tools to assess convergence. A number of R functions for post-processing of the output are also provided. In addition to fitting mixtures, it may additionally be of interest to determine which covariates actively drive the mixture components. This is implemented in the package as variable selection. The main reference for the package is Liverani, Hastie, Azizi, Papathomas and Richardson (2015) <doi:10.18637/jss.v064.i07>.
A database containing the names 
  of the babies born in Quebec between 1980 and 2020.
Prepares data for statistical analysis (e.g., analysis of variance
  ;ANOVA) by enabling the user to easily and quickly merge (using the
  file_merge() function) raw data files into one merged table and then
  aggregate the merged table (using the prep() function) into a finalized
  table while keeping track and summarizing every step of the preparation.
  The finalized table contains several possibilities for dependent measures of
  the dependent variable. Most suitable when measuring variables in an
  interval or ratio scale (e.g., reaction-times) and/or discrete values such
  as accuracy. Main functions included are file_merge() and prep(). The
  file_merge() function vertically merges individual data files (in a long
  format) in which each line is a single observation to one single dataset.
  The prep() function aggregates the single dataset according to any
  combination of grouping variables (i.e., between-subjects and
  within-subjects independent variables, respectively), and returns a data
  frame with a number of dependent measures for further analysis for each cell
  according to the combination of provided grouping variables. Dependent
  measures for each cell include among others means before and after rejecting
  all values according to a flexible standard deviation criteria, number of
  rejected values according to the flexible standard deviation criteria,
  proportions of rejected values according to the flexible standard deviation
  criteria, number of values before rejection, means after rejecting values
  according to procedures described in Van Selst & Jolicoeur (1994; suitable
  when measuring reaction-times), standard deviations, medians, means according
  to any percentile (e.g., 0.05, 0.25, 0.75, 0.95) and harmonic means. The data
  frame prep() returns can also be exported as a txt file to be used for
  statistical analysis in other statistical programs.
A figure region is prepared, creating a plot region with suitable background color, grid lines or shadings, and providing axes and labeling if not suppressed. Subsequently, information carrying graphics elements can be added (points, lines, barplot with add=TRUE and so forth).
Provides classes to pre-process microarray gene
  expression data as part of the OOMPA collection of packages
  described at <http://oompa.r-forge.r-project.org/>.
Preprocess numeric data matrices into desired transformed representations.
    Standardization, Unitization, Cubitization and adaptive intervals are offered.
An interactive document for preprocessing the dataset using  'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://analyticmodels.shinyapps.io/PREPShiny/>.
Miscellaneous small utilities are provided to mitigate issues with messy, inconsistent or high dimensional data and help for preprocessing and preparing analyses.
Provides a collection of templates to author preregistration documents for scientific studies in PDF format.
Preregistrations, or more generally, registrations, enable
    explicit timestamped and (often but not necessarily publicly) frozen
    documentation of plans and expectations as well as decisions and
    justifications. In research, preregistrations are commonly used to
    clearly document plans and facilitate justifications of deviations from
    those plans, as well as decreasing the effects of publication bias by
    enabling identification of research that was conducted but not published.
    Like reporting guidelines, (pre)registration forms often have specific
    structures that facilitate systematic reporting of important items. The
    'preregr' package facilitates specifying (pre)registrations in R and
    exporting them to a human-readable format (using R Markdown partials or
    exporting to an 'HTML' file) as well as human-readable embedded data
    (using 'JSON'), as well as importing such exported (pre)registration
    specifications from such embedded 'JSON'.
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.
Consists of custom wrapper functions using packages
    'openxlsx', 'flextable', and 'officer' to create highly formatted MS office friendly output of your data frames.
    These viewer friendly outputs are intended to match expectations of professional looking presentations
    in business and consulting scenarios. The functions are opinionated in the sense that they expect the input data
    frame to have certain properties in order to take advantage of the automated formatting.
Compilation and digitalization of the official registry of victims of state terrorism in Argentina during the last
             military coup. The original data comes from RUVTE-ILID (2019) <https://www.argentina.gob.ar/sitiosdememoria/ruvte/informe> and <http://basededatos.parquedelamemoria.org.ar/registros/>. The title, presentes, comes from present in spanish.
Originally as an R version of Preseq <doi:10.1038/nmeth.2375>, the package has extended its functionality to predict the r-species accumulation curve (r-SAC), which is the number of species represented at least r times as a function of the sampling effort. When r = 1, the curve is known as the species accumulation curve, or the library complexity curve in high-throughput genomic sequencing. The package includes both parametric and nonparametric methods, as described by Deng C, et al. (2018) <arXiv:1607.02804v3>.
Computes probability-scale residuals and residual correlations
    for continuous, ordinal, binary, count, and time-to-event data <doi:10.18637/jss.v094.i12>.
Bland (2009) <doi:10.1136/bmj.b3985> recommended to
    base study sizes on the width of the confidence interval rather the power of 
    a statistical test. The goal of 'presize' is to provide functions for such 
    precision based sample size calculations. For a given sample size, the 
    functions will return the precision (width of the confidence interval), and 
    vice versa.
Provides a function for estimating the transition probabilities in an illness-death model. 
            The transition probabilities can be estimated from the unsmoothed landmark estimators developed 
            by de Una-Alvarez and Meira-Machado (2015) <doi:10.1111/biom.12288>.
            Presmoothed estimates can also be obtained through the use of a parametric family of binary
            regression curves, such as logit, probit or cauchit. The additive logistic regression model 
            and nonparametric regression are also alternatives which have been implemented.
            The idea behind the presmoothed landmark estimators is to use the presmoothing techniques 
            developed by  Cao et al. (2005) <doi:10.1007/s00180-007-0076-6> in the 
            landmark estimation of the transition probabilities.
Create a web app that makes it easier to test web clients
  without using the internet. It includes a web app framework with path
  matching and parameters and templates. Can parse various 'HTTP' request
  bodies. Can send 'JSON' data or files from the disk. Includes a web app
  that implements the <https://httpbin.org> web service.
This is a computational package designed to identify the most sensitive interactions within a network which must be estimated most accurately in order to produce qualitatively robust predictions to a press perturbation. This is accomplished by enumerating the number of sign switches (and their magnitude) in the net effects matrix when an edge experiences uncertainty. The package produces data and visualizations when uncertainty is associated to one or more edges in the network and according to a variety of distributions. The software requires the network to be described by a system of differential equations but only requires as input a numerical Jacobian matrix evaluated at an equilibrium point. This package is based on Koslicki, D., & Novak, M. (2017) <doi:10.1007/s00285-017-1163-0>.
Provides 'RStudio' addins to prettify 'HTML', 'CSS', 'SCSS', 'JavaScript', 'JSX', 'Markdown', 'C(++)', 'LaTeX', 'Python', 'Julia', 'XML', 'Java', 'JSON', 'Ruby', and to reindent 'C(++)', 'Fortran', 'Java', 'Julia', 'Python', 'SAS', 'Scala', 'Shell' and 'SQL'. Two kinds of addins are provided: 'Prettify' and 'Indent'. The 'Indent' addins only reindent the code, while the 'Prettify' addins also modify the code, e.g. trailing semi-colons are added to 'JavaScript' code when they are missing.
Drop-in replacements for standard base graphics
    functions. The replacements are prettier versions of the originals.
Replace the standard print method for functions with one that
    performs syntax highlighting, using ANSI colors, if the terminal
    supports them.
Creating tiny yet beautiful documents and vignettes from R
    Markdown. The package provides the 'html_pretty' output format as an
    alternative to the 'html_document' and 'html_vignette' engines that
    convert R Markdown into HTML pages. Various themes and syntax highlight
    styles are supported.
One of the main advantages of using Generalised
    Linear Models is their interpretability.  The goal of 'prettyglm' is to
    provide a set of functions which easily create beautiful coefficient
    summaries which can readily be shared and explained.
Simple and crisp publication-quality graphics for the ExPosition family of packages.
  See An ExPosition of the Singular Value Decomposition in R (Beaton et al 2014) <doi:10.1016/j.csda.2013.11.006>.
Automates the process of creating a scale bar and north arrow in
    any package that uses base graphics to plot in R. Bounding box tools help find
    and manipulate extents. Finally, there is a function to automate the process
    of setting margins, plotting the map, scale bar, and north arrow, and resetting
    graphic parameters upon completion.
Functions for conventionally formatting descriptive stats,
 reshaping data frames and formatting R output as HTML.
Pretty, human readable formatting of quantities.
    Time intervals: '1337000' -> '15d 11h 23m 20s'.
    Vague time intervals: '2674000' -> 'about a month ago'.
    Bytes: '1337' -> '1.34 kB'.
The prevalence package provides Frequentist and Bayesian methods for prevalence assessment studies. IMPORTANT: the truePrev functions in the prevalence package call on JAGS (Just Another Gibbs Sampler), which therefore has to be available on the user's system. JAGS can be downloaded from http://mcmc-jags.sourceforge.net/.
Easy and efficient access to the API provided by 'Prevedere', 
  an industry insights and predictive analytics company. Query and 
  download indicators, models and workbenches built with 'Prevedere' for further 
  analysis and reporting <https://www.prevedere.com/>.
For working with the 'Prevision.io' AI model management platform's API <https://prevision.io/>.
Provides functions for both likelihood-based
    and Bayesian analysis of spatially referenced prevalence data. For a tutorial on the use of the R package, see Giorgi and Diggle (2017) <doi:10.18637/jss.v078.i08>.
Spatial estimation of a prevalence surface
    or a relative risks surface, using data from a Demographic and Health
    Survey (DHS) or an analog survey, see Larmarange et al. (2011)
    <doi:10.4000/cybergeo.24606>.
Functions to simulate point prevalence studies (PPSs) of healthcare-associated infections (HAIs) and to convert prevalence to incidence in steady state setups. 
    Companion package to the preprint Willrich et al., From prevalence to incidence - a new approach in the hospital setting;
    <doi:10.1101/554725> , where methods are explained in detail.
Standardize the pre-processing of genomic variants before 
    performing a bacterial genome-wide association study (bGWAS). 'prewas'
    creates a variant matrix (where each row is a variant, each column is a 
    sample, and the entries are presence - 1 - or absence - 0 - of the variant) 
    that can be used as input for bGWAS tools. When creating the binary variant
    matrix, 'prewas' can perform 3 pre-processing steps including: dealing with 
    multiallelic SNPs, (optional) dealing with SNPs in overlapping genes, and 
    choosing a reference allele. 'prewas' can output matrices for use with both 
    SNP-based bGWAS and gene-based bGWAS. This method is described in Saund et 
    al. (2020) <doi:10.1099/mgen.0.000368>. 'prewas' can also provide 
    gene matrices for variants with specific annotations from the 'SnpEff' 
    software (Cingolani et al. 2012).
Estimate False Discovery Rates (FDRs) for importance metrics from
    random forest runs.
Preparing a scanner data set for price dynamics calculations (data selecting, data classification, data matching, data filtering). Computing bilateral and multilateral indexes. For details on these methods see: de Haan and Krsinich (2017) <doi:10.1111/roiw.12304> and Diewert and Fox (2020) 
    <doi:10.1080/07350015.2020.1816176>.
Functions to aid in micro and macro economic analysis and handling of price and
    currency data. Includes extraction of relevant inflation and exchange rate data from World Bank
    API, data cleaning/parsing, and standardisation. Inflation adjustment
    calculations as found in Principles of Macroeconomics by Gregory Mankiw et al (2014). Current
    and historical end of day exchange rates for 171 currencies from the European Central Bank
    Statistical Data Warehouse (2020) <https://sdw.ecb.europa.eu/curConverter.do>.
An implementation of the van Westendorp Price
    Sensitivity Meter in R, which is a survey-based approach
	to analyze consumer price preferences and sensitivity
    (van Westendorp 1976, isbn:9789283100386).
Implementation of an oligonucleotide primer and probe design algorithm using a linearly scaling approximation of set coverage. A detailed description available at Smolander and Tamminen, 2021; <doi:10.1101/2021.09.06.459073>.
Patient Rule Induction Method (PRIM) for bump hunting in high-dimensional data.
Implements a unified framework of parametric simplex method for a variety of sparse learning problems (e.g., Dantzig selector (for linear regression), sparse quantile regression, sparse support vector machines, and compressive sensing) combined with efficient hyper-parameter selection strategies. The core algorithm is implemented in C++ with Eigen3 support for portable high performance linear algebra. For more details about parametric simplex method, see Haotian Pang (2017) <https://papers.nips.cc/paper/6623-parametric-simplex-method-for-sparse-learning.pdf>.
Use Prime Factorization for simplifying computations,
    for instance for ratios of large factorials.
Implements the primePCA algorithm, developed and analysed in Zhu, Z., Wang, T. and Samworth, R. J. (2019) High-dimensional principal component analysis with heterogeneous missingness. <arXiv:1906.12125>.
Functions are primarily functions for systems of ordinary differential equations, difference equations, and eigenanalysis and projection of demographic matrices; data are for examples.
Identifies potential target sequences for a given set of primers
    and generates phylogenetic trees annotated with the taxonomies of the predicted
    amplification products.
Fast functions for dealing with prime numbers, such as testing
    whether a number is prime and generating a sequence prime numbers.
    Additional functions include finding prime factors and Ruth-Aaron pairs,
    finding next and previous prime numbers in the series, finding or estimating
    the nth prime, estimating the number of primes less than or equal to an
    arbitrary number, computing primorials, prime k-tuples (e.g., twin primes),
    finding the greatest common divisor and smallest (least) common multiple,
    testing whether two numbers are coprime, and computing Euler's totient
    function. Most functions are vectorized for speed and convenience.
R interface to 'PRIMME' <http://www.cs.wm.edu/~andreas/software/>, a C library for computing a few
    eigenvalues and their corresponding eigenvectors of a real symmetric or complex
    Hermitian matrix, or generalized Hermitian eigenproblem.  It can also compute
    singular values and vectors of a square or rectangular matrix. 'PRIMME' finds
    largest, smallest, or interior singular/eigenvalues and can use preconditioning
    to accelerate convergence. General description of the methods are provided in the papers
    Stathopoulos (2010, <doi:10.1145/1731022.1731031>) and Wu (2017, <doi:10.1137/16M1082214>).
    See 'citation("PRIMME")' for details.
Fitting a principal curve to a data matrix in arbitrary dimensions. 
  Hastie and Stuetzle (1989) <doi:10.2307/2289936>.
Provides capabilities beyond principal components
    analysis to focus on finding structure in low variability
    subspaces. Constructs and plots simple basis vectors for
    pre-defined and user-defined measures of simplicity.
Extends the S3 generic function knit_print() in 'knitr'
    to automatically print some objects using an appropriate format such as
    Markdown or LaTeX. For example, data frames are automatically printed as
    tables, and the help() pages can also be rendered in 'knitr' documents.
In gene sequencing methods, the topological features of protein-protein interaction (PPI) networks are 
    often used, such as ToppNet <https://toppgene.cchmc.org>. In this study, a candidate gene prioritization method
    was proposed for non-communicable diseases considering disease risks transferred between genes in weighted disease 
    PPI networks with weights for nodes and edges based on functional information.
Prioritize candidate cancer drugs for drug repositioning based on the random walk with restart algorithm in a drug-drug functional similarity network. 1) We firstly constructed a drug-drug functional similarity network by integrating pathway activity and drug activity derived from the NCI-60 cancer cell lines. 2) Secondly, we calculated drug repurposing score according to a set of approved therapeutic drugs of interested cancer based on the random walk with restart algorithm in the drug-drug functional similarity network. 3) Finally, the permutation test was used to calculate the statistical significance level for the drug repurposing score.
Translates beliefs into prior information in the form of Beta and Gamma distributions. It can be mainly used for the generation of priors on the prevalence of disease and the sensitivity/specificity of diagnostic tests.
Systematic conservation prioritization using mixed integer linear
    programming (MILP). It provides a flexible interface for building and
    solving conservation planning problems. Once built, conservation planning
    problems can be solved using a variety of commercial and open-source exact
    algorithm solvers. By using exact algorithm solvers, solutions can be
    generated that are guaranteed to be optimal (or within a pre-specified
    optimality gap). Furthermore, conservation problems can be constructed to
    optimize the spatial allocation of different management actions or zones,
    meaning that conservation practitioners can identify solutions that benefit
    multiple stakeholders. To solve large-scale or complex conservation
    planning problems, users should install the Gurobi optimization software
    (available from <https://www.gurobi.com/>) and the 'gurobi' R package (see
    Gurobi Installation Guide vignette for details). Additionally, the 'rcbc' R
    package (available at <https://github.com/dirkschumacher/rcbc>) can be used
    to generate solutions using the CBC optimization software
    (<https://projects.coin-or.org/Cbc>).
Conservation planning datasets for learning how to use the
    'prioritizr' package <https://CRAN.R-project.org/package=prioritizr>.
Fits successive Lasso models for several blocks of (omics) data with different priorities and takes the predicted values as an offset for the next block.
Allows users to access the Oregon State Prism climate data
    (<https://prism.nacse.org/>). Using the web service API data
    can easily downloaded in bulk and loaded into R for spatial analysis.
    Some user friendly visualizations are also provided.
Implements Penalized Regression with Inferred Seasonality Module (PRISM) to generate forecast estimation of weekly unemployment initial claims using 'Google Trends' data. It includes required data and tools for backtesting the performance in 2007-2020.
Loads and processes huge text
    corpora processed with the sally toolbox (<http://www.mlsec.org/sally/>).
    sally acts as a very fast preprocessor which splits the text files into
    tokens or n-grams. These output files can then be read with the PRISMA
    package which applies testing-based token selection and has some
    replicate-aware, highly tuned non-negative matrix factorization and
    principal component analysis implementation which allows the processing of
    very big data sets even on desktop machines.
Systematic reviews should be described in a high degree of 
    methodological detail. The 'PRISMA' Statement calls for a high level of 
    reporting detail in systematic reviews and meta-analyses. An integral part 
    of the methodological description of a review is a flow diagram. 
    This package produces an interactive flow diagram that conforms to the 
    'PRISMA2020' preprint. When made interactive, the reader/user can click 
    on each box and be directed to another website or file online (e.g. a 
    detailed description of the screening methods, or a list of excluded full 
    texts), with a mouse-over tool tip that describes the information linked 
    to in more detail. Interactive versions can be saved as HTML files, 
    whilst static versions for inclusion in manuscripts can be saved as 
    HTML, PDF, PNG, SVG, PS or WEBP files.
Creates 'PRISMA' <http://prisma-statement.org/> diagram from a minimal dataset of included and excluded studies and allows for more custom diagrams. 'PRISMA' diagrams are used to track the identification, screening, eligibility, and inclusion of studies in a systematic review.
Plot a PRISMA <http://prisma-statement.org/> flow
    chart describing the identification, screening, eligibility and
    inclusion or studies in systematic reviews. The PRISMA statement
    defines an evidence-based, minimal set of items for reporting in
    systematic reviews and meta-analyses. PRISMA should be used for the
    reporting of studies evaluating randomized clinical trials (RCT), and
    is also for reporting on systematic reviews of other types of
    research. There is also a function to generate flow charts describing
    exclusions and inclusions for any kind of study.
Manipulate and visualize colors in a intuitive,
    low-dependency and functional way.
Download, parses and tidies information from the World Prison Brief project <http://www.prisonstudies.org/>.
Enables user to perform the following:
    1. Roll 'n' number of die/dice (roll()).
    2. Toss 'n' number of coin(s) (toss()).
    3. Play the game of Rock, Paper, Scissors.
    4. Choose 'n' number of card(s) from a pack of 52 playing cards (Joker optional).
Implements two differentially private algorithms for 
  estimating L2-regularized logistic regression coefficients. A randomized
  algorithm F is epsilon-differentially private (C. Dwork, Differential
  Privacy, ICALP 2006 <DOI:10.1007/11681878_14>), if 
     |log(P(F(D) in S)) - log(P(F(D') in S))| <= epsilon
  for any pair D, D' of datasets that differ in exactly one record, any
  measurable set S, and the randomness is taken over the choices F makes.
Estimation of prevalence ratios using logistic models and confidence intervals with delta and bootstrap methods.
Survey sampling using permanent random numbers (PRN's). A solution to the
    problem of unknown overlap between survey samples, which leads to a low
    precision in estimates when the survey is repeated or combined with other
    surveys. The PRN solution is to supply the U(0, 1) random numbers to the 
    sampling procedure, instead of having the sampling procedure generate them.
    In Lindblom (2014) <doi:10.2478/jos-2014-0047>, and therein cited articles,
    it is shown how this is carried out and how it improves the estimates. This
    package supports two common fixed-size sampling procedures (simple random 
    sampling and probability-proportional-to-size sampling) and includes a 
    function for transforming the PRN's in order to control the sample overlap.
Optogenetics is a new tool to study neuronal circuits that have been genetically modified to allow stimulation by flashes of light.  This package implements the methodological framework, Point-process Response model for Optogenetics (PRO), for analyzing data from these experiments.  This method provides explicit nonlinear transformations to link the flash point-process with the spiking point-process.  Such response functions can be used to provide important and interpretable scientific insights into the properties of the biophysical process that governs neural spiking in response to optogenetic stimulation.
A collection of tools to facilitate standardized analysis 
    and graphical procedures when using the National Cancer Institute’s 
    Patient-Reported Outcomes version of the Common Terminology Criteria for 
    Adverse Events (PRO-CTCAE).
A framework for performing elementary probability
 calculations on finite sample spaces, which may be represented by data frames
 or lists.  Functionality includes setting up sample spaces, counting tools,
 defining probability spaces, performing set algebra, calculating probability
 and conditional probability, tools for simulation and checking the law of
 large numbers, adding random variables, and finding marginal distributions.
 Characteristic functions for all base R distributions are included.
Models can be improved by post-processing class probabilities, by: recalibration, conversion to hard probabilities, assessment of equivocal zones, and other activities. 'probably' contains tools for conducting these operations.
Functions and datasets  to accompany J. Albert and J. Hu, "Probability and Bayesian Modeling", CRC Press, (2019, ISBN: 1138492566).
This tool can develop hybridization probes for target sequences based on melting temperature value calculated by R package 'TmCalculator' <https://CRAN.R-project.org/package=TmCalculator> and methods extended from Beliveau, B. J.,(2018) <doi:10.1073/pnas.1714530115>, and those hybridization probes can be used to capture specific target regions in fluorescence in situ hybridization and next generation sequence experiments.
Contains functions that are useful for probing moderation effects (or interactions) including techniques such as pick-a-point (also known as spotlight analysis) and Johnson-Neyman (also known as floodlight analysis). Plot function is also provided to facilitate visualization of results from each of these techniques.
Probabilistic Fisher discriminant analysis (pFDA) is a probabilistic version of the popular and powerful Fisher linear discriminant analysis for dimensionality reduction and classification.
Fast estimation of binomial spatial probit regression models with spatial autocorrelation for big datasets.
Computes predicted probabilities and marginal effects for 
  binary & ordinal logit and probit, (partial) generalized ordinal & 
  multinomial logit models estimated with the glm(), clm() (in the 
  'ordinal' package), and vglm() (in the 'VGAM' package) functions.
Estimates unsupervised outlier probabilities for multivariate numeric data with many observations from a nonparametric outlier statistic.
Training of reconciliation weights for probabilistic forecasts to optimise total energy (or variogram) score using Stochastic Gradient Descent with automatically differentiated gradients. See Panagiotelis, Gamakumara, Athanasopoulos and Hyndman, (2020) <https://www.monash.edu/business/ebs/research/publications/ebs/wp26-2020.pdf> for a description of the methods.
It allows the user to determine sample sizes, select probabilistic samples,
 make estimates of different parameters for the total finite population and in studio domains,
 using the main design drawings.
An interactive document on  the topic of basic probability using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://analyticmodels.shinyapps.io/BayesShiny/>.
Confidence intervals and point estimation for R under various parametric model assumptions; likelihood inference based on classical first-order approximations and higher-order asymptotic procedures.
Tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). (Partial) area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.
Package for processing downloaded MODIS Calibrated radiances
    Product HDF files. Specifically, MOD02 calibrated radiance product files, and
    the associated MOD03 geolocation files (for MODIS-TERRA). The package will be
    most effective if the user installs MRTSwath (MODIS Reprojection Tool for swath
    products; <https://lpdaac.usgs.gov/tools/modis_reprojection_tool_swath>, and
    adds the directory with the MRTSwath executable to the default R PATH by editing
    ~/.Rprofile.
Provides tools for exploratory process data analysis. Process data refers to the data describing
    participants' problem-solving processes in computer-based assessments. It is often recorded in computer
    log files. This package provides functions to read, process, and write process data. It also implements
    two feature extraction methods to compress the information stored in process data into standard 
    numerical vectors. This package also provides recurrent neural network based models that relate response processes 
    with other binary or scale variables of interest. The functions that involve training and evaluating neural networks 
    are wrappers of functions in 'keras'.
A small, dependency-free way to generate random names. Methods 
    provided include the adjective-surname approach of Docker containers 
    ('<https://github.com/moby/moby/blob/master/pkg/namesgenerator/names-generator.go'),
    and combinations of common English or Spanish words.
Provides animated process maps based on the 'procesmapR' package.
  Cases stored in event logs created with with 'bupaR' S3 class eventlog() are
  rendered as tokens (SVG shapes) and animated according to their occurrence 
  times on top of the process map. For rendering SVG animations ('SMIL') and the
  'htmlwidget' package are used.
Check compliance of event-data from (business) processes with respect to specified rules. Rules supported are of three types: frequency (activities that should (not) happen x number of times), order (succession between activities) and exclusiveness (and and exclusive choice between activities).
Visualize event logs using directed graphs, i.e. process maps. Part of the 'bupaR' framework.
Functions for constructing dashboards for business process monitoring. Building on the event log objects class from package 'bupaR'. Allows the use to assemble custom shiny dashboards based on process data.
Perform moderation, mediation, moderated mediation and moderated moderation. 
   Inspired from famous 'PROCESS' macro for 'SPSS' and 'SAS' created by Andrew Hayes.
Tools to run system processes in the background.
    It can check if a background process is running; wait on a background
    process to finish; get the exit status of finished processes; kill
    background processes. It can read the standard output and error of
    the processes, using non-blocking connections. 'processx' can poll
    a process for standard output or error, with a timeout. It can also
    poll several processes at once.
Portable '/proc/self/maps' as a data frame.
    Determine which library or other region is mapped to a specific
    address of a process. --
    R packages can contain native code, compiled to shared libraries at build or
    installation time.
    When loaded, each shared library occupies a portion of the address space of
    the main process.
    When only a machine instruction pointer is available (e.g. from a backtrace
    during error inspection or profiling), the address space map determines
    which library this instruction pointer corresponds to.
Estimates corrected Procrustean correlation between matrices for removing overfitting effect. Coissac Eric and Gonindard-Melodelima Christelle (2019) <doi:10.1101/842070>.
A direct and flexible method for estimating an ICA model.
        This approach estimates the densities for each component
        directly via a tilted gaussian. The tilt functions are
        estimated via a GAM poisson model. Details can be found in
        "Elements of Statistical Learning (2nd Edition)" Section 14.7.4
Implements the methods proposed by Olley, G.S. and Pakes, A. (1996) <doi:10.2307/2171831>, Levinsohn, J. and Petrin, A. (2003) <doi:10.1111/1467-937X.00246>, Ackerberg, D.A. and Caves, K. and Frazer, G. (2015) <doi:10.3982/ECTA13408> and Wooldridge, J.M. (2009) <doi:10.1016/j.econlet.2009.04.026> for structural productivity estimation .
Create a project directory structure, along with typical files
    for that project.  This allows projects to be quickly and easily created,
    as well as for them to be standardized. Designed specifically with scientists
    in mind (mainly bio-medical researchers, but likely applies to other fields).
Fast and user friendly implementation of nonparametric estimators
    for censored event history (survival) analysis. Kaplan-Meier and
    Aalen-Johansen method.
Levels and changes of productivity and profitability are measured with various indices.
  The package contains the multiplicatively complete Färe-Primont, Fisher, Hicks-Moorsteen, 
  Laspeyres, Lowe, and Paasche indices, as well as the classic Malmquist productivity index.
  Färe-Primont and Lowe indices verify the transitivity property and can therefore be used for 
  multilateral or multitemporal comparison.
  Fisher, Hicks-Moorsteen, Laspeyres, Malmquist, and Paasche indices are not transitive and are 
  only to be used for binary comparison.
  All indices can also be decomposed into different components, providing insightful information 
  on the sources of productivity and profitability changes.
  In the use of Malmquist productivity index, the technological change index can be further 
  decomposed into bias technological change components.
  The package also allows to prohibit technological regression (negative technological change). In 
  the case of the Fisher, Hicks-Moorsteen, Laspeyres, Paasche and the transitive Färe-Primont 
  and Lowe indices, it is furthermore possible to rule out technological change. 
  Deflated shadow prices can also be obtained. Besides, the package allows parallel computing as 
  an option, depending on the user's computer configuration. 
  All computations are carried out with the nonparametric Data Envelopment Analysis (DEA), and 
  several assumptions regarding returns to scale are available.
  All DEA linear programs are implemented using 'lp_solve'.
Framework for visualising tables of counts, proportions
  and probabilities. The framework is called product plots, alluding to
  the computation of area as a product of height and width, and the
  statistical concept of generating a joint distribution from the
  product of conditional and marginal distributions. The framework,
  with extensions, is sufficient to encompass over 20 visualisations
  previously described in fields of statistical graphics and 'infovis',
  including bar charts, mosaic plots, 'treemaps', equal area plots and
  fluctuation diagrams.
An alternative data structure for the profiling information
    generated by Rprof().
Programs to determine student grades and create
  examinations from Question banks.  Programs will create numerous
  multiple choice exams, randomly shuffled, for different versions of same question list.
Computes profile extrema functions for arbitrary functions. If the function is expensive-to-evaluate it computes profile extrema by emulating the function with a Gaussian process (using package 'DiceKriging'). In this case uncertainty quantification on the profile extrema can also be computed. The different plotting functions for profile extrema give the user a tool to better locate excursion sets.
Like similar profiling tools,
  the 'proffer' package automatically detects
  sources of slowness in R code.
  The distinguishing feature of 'proffer' is its utilization of
  'pprof', which supplies interactive visualizations
  that are efficient and easy to interpret.
  Behind the scenes, the 'profile' package converts
  native Rprof() data to a protocol buffer
  that 'pprof' understands.
  For the documentation of 'proffer',
  visit <https://r-prof.github.io/proffer/>.
  To learn about the implementations and methodologies of
  'pprof', 'profile', and protocol buffers,
  visit <https://github.com/google/pprof>.
  <https://developers.google.com/protocol-buffers>,
  and <https://github.com/r-prof/profile>, respectively.
Defines a data structure for profiler data, and methods to read and
    write from the 'Rprof' and 'pprof' file formats.
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).
Combine probabilistic forecasts using CRPS learning algorithms proposed in Berrisch, Ziel (2021) <arXiv:2102.00968>. The package implements multiple online learning algorithms like Bernstein online aggregation; see Wintenberger (2014) <arXiv:1404.1356>. Quantile regression is also implemented for comparison purposes. Model parameters can be tuned automatically with respect to the loss of the forecast combination. Methods like predict(), update(), plot() and print() are available for convenience. This package utilizes the optim C++ library for numeric optimization <https://github.com/kthohr/optim>.
Core package containing all the tools for simple and advanced source extraction. This is used to create inputs for 'ProFit', or for source detection, extraction and photometry in its own right.
Provides an R interface for the PROFOUND database <doi:10.5880/PIK.2019.008>. The 
    PROFOUND database contains a wide range of data  to evaluate vegetation models and simulate climate 
    impacts at the forest stand scale. It includes 9 forest sites across Europe, and provides for them a site 
    description as well as soil, climate, CO2, Nitrogen deposition, tree-level, forest stand-level 
    and remote sensing data. Moreover, for a subset of 5 sites, also time series of carbon fluxes, 
    energy balances and soil water are available.
An alternative data structure and visual rendering
    for the profiling information generated by Rprof.
Tools for examining Rprof profile output.
Interactive visualizations for profiling R code.
Implementing the Progeny Clustering algorithm, the 'progenyClust' package assesses the clustering stability and identifies the optimal clustering number for a given data matrix. It uses k-means clustering as a default, provides a tailored hierarchical clustering function, and can be customized to work with other clustering algorithms and different parameter settings. The package includes a main function progenyClust(), plot and summary methods for 'progenyClust' object, a function hclust.progenyClust() for hierarchical clustering, and two example datasets (test and cell) for testing.
Prognostic ROC curve is an alternative graphical approach to represent the discriminative capacity of the marker: a receiver operating characteristic (ROC) curve by plotting 1 minus the survival in the high-risk group against 1 minus the survival in the low-risk group. This package contains functions to assess prognostic ROC curve. The user can enter the survival according to a model previously estimated or the user can also enter individual survival data for estimating the prognostic ROC curve by using Kaplan-Meier estimator. The area under the curve (AUC) corresponds to the probability that a patient in the low-risk group has a longer lifetime than a patient in the high-risk group. The prognostic ROC curve provides complementary information compared to survival curves. The AUC is assessed by using the trapezoidal rules. When survival curves do not reach 0, the prognostic ROC curve is incomplete and the extrapolations of the AUC are performed by assuming pessimist, optimist and non-informative situations.
Configurable Progress bars, they may include percentage,
    elapsed time, and/or the estimated completion time. They work in
    terminals, in 'Emacs' 'ESS', 'RStudio', 'Windows' 'Rgui' and the
    'macOS' 'R.app'. The package also provides a 'C++' 'API', that works
    with or without 'Rcpp'.
A minimal, unifying API for scripts and packages to report progress updates from anywhere including when using parallel processing.  The package is designed such that the developer can to focus on what progress should be reported on without having to worry about how to present it.  The end user has full control of how, where, and when to render these progress updates, e.g. in the terminal using utils::txtProgressBar() or progress::progress_bar(), in a graphical user interface using utils::winProgressBar(), tcltk::tkProgressBar() or shiny::withProgress(), via the speakers using beep::beepr(), or on a file system via the size of a file. Anyone can add additional, customized, progression handlers. The 'progressr' package uses R's condition framework for signaling progress updated. Because of this, progress can be reported from almost anywhere in R, e.g. from classical for and while loops, from map-reduce API:s like the lapply() family of functions, 'purrr', 'plyr', and 'foreach'. It will also work with parallel processing via the 'future' framework, e.g. future.apply::future_lapply(), furrr::future_map(), and 'foreach' with 'doFuture'. The package is compatible with Shiny applications.
Currently non-operational, a harmless wrapper to allow package 'reproj' to install and 
  function while relying on the 'proj4' package.
A simple interface to lat/long projection and datum
	     transformation of the PROJ.4 cartographic projections library. It
	     allows transformation of geographic coordinates from one projection
	     and/or datum to another.
A clustering approach applicable to every projection method is proposed here. The two-dimensional scatter plot of any projection method can construct a topographic map which displays unapparent data structures by using distance and density information of the data. The generalized U*-matrix renders this visualization in the form of a topographic map, which can be used to automatically define the clusters of high-dimensional data. The whole system is based on Thrun and Ultsch, "Using Projection based Clustering to Find Distance and Density based Clusters in High-Dimensional Data" <DOI:10.1007/s00357-020-09373-2>. Selecting the correct projection method will result in a visualization in which mountains surround each cluster. The number of clusters can be determined by counting valleys on the topographic map. Most projection methods are wrappers for already available methods in R. By contrast, the neighbor retrieval visualizer (NeRV) is based on C++ source code of the 'dredviz' software package, and the Curvilinear Component Analysis (CCA) is translated from 'MATLAB' ('SOM Toolbox' 2.0) to R.
Provides functions and graphics for projecting daily incidence based on past incidence, and estimates of the serial interval and reproduction number. Projections are based on a branching process using a Poisson-distributed number of new cases per day, similar to the model used for estimating R in 'EpiEstim' or in 'earlyR', and described by Nouvellet et al. (2017) <doi:10.1016/j.epidem.2017.02.012>. The package provides the S3 class 'projections' which extends 'matrix', with accessors and additional helpers for handling, subsetting, merging, or adding these objects, as well as dedicated printing and plotting methods.
Management problems of deterministic and stochastic projects. It obtains the duration of a project and the appropriate slack for each activity in a deterministic context. In addition it obtains a schedule of activities' time (Castro, Gómez & Tejada (2007) <doi:10.1016/j.orl.2007.01.003>). It also allows the management of resources. When the project is done, and the actual duration for each activity is known, then it can know how long the project is delayed and make a fair delivery of the delay between each activity (Bergantiños, Valencia-Toledo & Vidal-Puga (2018) <doi:10.1016/j.dam.2017.08.012>). In a stochastic context it can estimate the average duration of the project and plot the density of this duration, as well as, the density of the early and last times of the chosen activities. As in the deterministic case, it can make a distribution of the delay generated by observing the project already carried out.
Provides a project infrastructure with a focus on
    manuscript creation. Creates a project folder with a single command,
    containing subdirectories for specific components, templates for
    manuscripts, and so on.
Provides functions to
    automatically build a directory structure for a new R
    project. Using this structure, 'ProjectTemplate'
    automates data loading, preprocessing, library
    importing and unit testing.
Provides programmatic access to 'GitHub' API with a
    focus on project management.  Key functionality includes
    setting up issues and milestones from R objects or 'YAML' configurations,
    querying outstanding or completed tasks, and generating progress updates
    in tables, charts, and RMarkdown reports. Useful for those using 'GitHub' in personal,
    professional, or academic settings with an emphasis on streamlining
    the workflow of data analysis projects.
Performs projection predictive feature selection for generalized linear
    models and generalized linear and additive multilevel models
    (see, Piironen, Paasiniemi and Vehtari, 2020, <https://projecteuclid.org/euclid.ejs/1589335310>,
    Catalina, Bürkner and Vehtari, 2020, <arXiv:2010.06994>).
    The package is compatible with the 'rstanarm' and 'brms' packages, but other
    reference models can also be used. See the 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).
The PROMETHEE method is a multi-criteria decision-making method addressing with outranking problems.
            The method establishes a preference structure between the alternatives, having a preference function for
            each criterion. IN this context, three variants of the method is carried out: PROMETHEE I (Partial Outranking),
            PROMETHEE II (Total Outranking), and PROMETHEE III (Outranking by Intervals).
Provides fundamental abstractions for doing asynchronous programming
    in R using promises. Asynchronous programming is useful for allowing a single
    R process to orchestrate multiple tasks in the background while also attending
    to something else. Semantics are similar to 'JavaScript' promises, but with a
    syntax that is idiomatic R.
Deploy, maintain, and invoke predictive models using the 'Alteryx
    Promote' REST API.  'Alteryx Promote' is available at the URL:
    <https://www.alteryx.com/products/alteryx-promote>.
Analysis and measurement of promotion effectiveness on a given target variable (e.g. daily sales). After converting promotion schedule into dummy or smoothed predictor variables, the package estimates the effects of these variables controlled for trend/periodicity/structural change using prophet by Taylor and Letham (2017) <doi:10.7287/peerj.preprints.3190v2> and some prespecified variables (e.g. start of a month).
Set the 'R' prompt dynamically, from a function. The package
    contains some examples to include various useful dynamic information
    in the prompt: the status of the last command (success or failure);
    the amount of memory allocated by the current 'R' process; the name of
    the R package(s) loaded by 'pkgload' and/or 'devtools'; various 'git'
    information: the name of the active branch, whether it is dirty,
    if it needs pushes pulls. You can also create your own prompt if you
    don't like the predefined examples.
In 'Shiny' apps, it is sometimes useful to store information
    on a particular item in a tooltip. 'Prompter' allows you to easily 
    create such tooltips, using 'Hint.css'.
Carrying out inferences about any linear combination of proportions and the ratio of two proportions.
Propagation of uncertainty using higher-order Taylor expansion and Monte Carlo simulation.
Computes two-sample confidence intervals for single, paired and independent proportions.
Implementation of propensity clustering and
        decomposition as described in Ranola et al. (2013) <doi:10.1186/1752-0509-7-21>. 
        Propensity decomposition can be viewed on the
        one hand as a generalization of the eigenvector-based
        approximation of correlation networks, and on the other hand as
        a generalization of random multigraph models and
        conformity-based decompositions.
Estimate treatment effect in strata when subjects have missing strata labels, 
  via inverse probability weighting or propensity score matching.
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.
Perform scale linking to establish relationships between instruments
    that measure similar constructs according to the PROsetta Stone methodology, as in Choi, Schalet, Cook, & Cella (2014) <doi:10.1037/a0035768>.
Implementation of penalized regression with second-generation p-values for variable
    selection. The algorithm can handle linear regression, GLM, and Cox regression. S3 methods print(), summary(), coef(), predict(), and plot() are available for the algorithm. Technical details
    can be found at Zuo et al. (2021) <doi:10.1080/00031305.2021.1946150>.
Functions to preprocess spectroscopic data 
    and conduct (representative) sample selection/calibration sampling.
An environment to simulate the development of annual plant populations with regard to population dynamics and genetics, especially herbicide resistance. It combines genetics on the individual level (Renton et al. 2011) with a stochastic development on the population level (Daedlow, 2015).
	Renton, M, Diggle, A, Manalil, S and Powles, S (2011) <doi:10.1016/j.jtbi.2011.05.010> 
	Daedlow, Daniel (2015, doctoral dissertation: University of Rostock, Faculty of Agriculture and Environmental Sciences.)
In a typical protein labelling procedure, proteins are chemically tagged with a functional group, usually at specific sites, then digested into peptides, which are then analyzed using matrix-assisted laser desorption ionization - time of flight mass spectrometry (MALDI-TOF MS) to generate peptide fingerprint. Relative to the control, peptides that are heavier by the mass of the labelling group are informative for sequence determination. Searching for peptides with such mass shifts, however, can be difficult. This package, designed to tackle this inconvenience, takes as input the mass list of two or multiple MALDI-TOF MS mass lists, and makes pairwise comparisons between the labeled groups vs. control, and restores centroid mass spectra with highlighted peaks of interest for easier visual examination. Particularly, peaks differentiated by the mass of the labelling group are defined as a “pair”, those with equal masses as a “match”, and all the other peaks as a “mismatch”.For more bioanalytical background information, refer to following publications: Jingjing Deng (2015) <doi:10.1007/978-1-4939-2550-6_19>; Elizabeth Chang (2016) <doi:10.7171/jbt.16-2702-002>.
Read Protein Data Bank (PDB) files, performs its analysis, and 
    presents the result using different visualization types including 3D. The 
    package also has additional capability for handling Virus Report data from 
    the National Center for Biotechnology Information (NCBI) database.
    Nature Structural Biology 10, 980 (2003) <doi:10.1038/nsb1203-980>.
    US National Library of Medicine (2021) <https://www.ncbi.nlm.nih.gov/datasets/docs/reference-docs/data-reports/virus/>.
This package provides functions to identify the optimal
        solution that maximizes numbers of detectable differentiated
        proteins from a multi-stage clinical proteomic study.
Provides methods for making inference in isobaric labelled
    LC-MS/MS experiments, i.e. iTRAQ experiments. It provides a function that
    reasonably parses a CSV-export from Proteome Discoverer(TM) into a data
    frame that can be easily handled in R. Functions and methods are provided
    for quality control, filtering, norming, and the calculation of response
    variables for further analysis. The merging of multiple iTRAQ experiments
    with respect to a reference is also covered.
Seq2seq time-feature analysis based on variational model, with a wide range of distributions available for the latent variable.
An object oriented system using object-based, also
	called prototype-based, rather than class-based object oriented ideas.
Greedy algorithm described in Bien and Tibshirani (2011)
        Prototype Selection for Interpretable Classification. Annals of
        Applied Statistics. 5(4). 2403-2424
Performs minimax linkage hierarchical clustering.  Every cluster
    has an associated prototype element that represents that cluster as
    described in Bien, J., and Tibshirani, R. (2011), "Hierarchical Clustering 
    with Prototypes via Minimax Linkage," The Journal of the American 
    Statistical Association, 106(495), 1075-1084.
PROTOLIDAR package contains functions for analyze the
        LIDAR scan of plants (grapevine) and make 3D maps in GRASS GIS.
Pure C++ implementations for reading and writing several common data 
    formats based on Google protocol-buffers. Currently supports 'rexp.proto' for 
    serialized R objects, 'geobuf.proto' for binary geojson, and 'mvt.proto' for 
    vector tiles. This package uses the auto-generated C++ code by protobuf-compiler, 
    hence the entire serialization is optimized at compile time. The 'RProtoBuf' 
    package on the other hand uses the protobuf runtime library to provide a general-
    purpose toolkit for reading and writing arbitrary protocol-buffer data in R.
'The Proton Game' is a console-based data-crunching game for younger and older data scientists.
  Act as a data-hacker and find Slawomir Pietraszko's credentials to the Proton server.
  You have to solve four data-based puzzles to find the login and password.
  There are many ways to solve these puzzles. You may use loops, data filtering, ordering, aggregation or other tools.
  Only basics knowledge of R is required to play the game, yet the more functions you know, the more approaches you can try.
  The knowledge of dplyr is not required but may be very helpful.
  This game is linked with the ,,Pietraszko's Cave'' story available at http://biecek.pl/BetaBit/Warsaw.
  It's a part of Beta and Bit series.
  You will find more about the Beta and Bit series at http://biecek.pl/BetaBit.
Procedures for testing for group-wide signal in clusters of variables. Tests can be performed for single groups in isolation (univariate) or multiple groups together (multivariate). Specific tests include the exact and approximate (un)selective likelihood ratio tests described in Reid et al (2015), the selective F test and marginal screening prototype test of Reid and Tibshirani (2015). User may pre-specify columns to be included in prototype formation, or allow the function to select them itself. A mixture of these two is also possible. Any variable selection is accounted for using the selective inference framework. Options for non-sampling and hit-and-run null reference distributions.
Comprehensive toolkit for generating various numerical
    features of protein sequences described in Xiao et al. (2015)
    <DOI:10.1093/bioinformatics/btv042>. For full functionality,
    the software 'ncbi-blast+' is needed, see
    <https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastDocs&DOC_TYPE=Download>
    for more information.
'ProTracker' is a popular music tracker to sequence
    music on a Commodore Amiga machine. This package offers the
    opportunity to import, export, manipulate and play 'ProTracker'
    module files. Even though the file format could be considered
    archaic, it still remains popular to this date. This package
    intends to contribute to this popularity and therewith
    keeping the legacy of 'ProTracker' and the Commodore Amiga
    alive.
Useful functions and workflows for proteomics quality control and data analysis of both limited proteolysis-coupled mass spectrometry (LiP-MS) (Feng et. al. (2014) <doi:10.1038/nbt.2999>) and regular bottom-up proteomics experiments. Data generated with search tools such as 'Spectronaut', 'MaxQuant' and 'Proteome Discover' can be easily used due to flexibility of functions.
Helps with quality checks, visualizations 
    and analysis of mass spectrometry data, coming from proteomics 
    experiments. The package is developed, tested and used at the Functional 
    Genomics Center Zurich <https://fgcz.ch>. We use this package
    mainly for prototyping, teaching, and having fun with proteomics data.
    But it can also be used to do data analysis for small scale data sets.
Tools for Natural Language Processing in French and texts from Marcel Proust's collection 
  "A La Recherche Du Temps Perdu". The novels contained in this collection are 
  "Du cote de chez Swann ", "A l'ombre des jeunes filles en fleurs","Le Cote de Guermantes", 
  "Sodome et Gomorrhe I et II", "La Prisonniere", "Albertine disparue", and "Le Temps retrouve".
Uses provenance post-execution to help the user understand
    and debug their script by providing functions to look at intermediate steps and
    data values, their forwards and backwards lineage, and to understand the
    steps leading up to warning and error messages. 'provDebugR' uses
    provenance produced by 'rdtLite' (available on CRAN), stored in PROV-JSON format.
Bundles a number of established statistical methods to facilitate the visual interpretation of large datasets in sedimentary geology. Includes functionality for adaptive kernel density estimation, principal component analysis, correspondence analysis, multidimensional scaling, generalised procrustes analysis and individual differences scaling using a variety of dissimilarity measures. Univariate provenance proxies, such as single-grain ages or (isotopic) compositions are compared with the Kolmogorov-Smirnov, Kuiper or Sircombe-Hazelton L2 distances. Categorical provenance proxies such as chemical compositions are compared with the Aitchison and Bray-Curtis distances, and point-counting data with the chi-square distance. Also included are tools to plot compositional and point-counting data on ternary diagrams and point-counting data on radial plots, to calculate the sample size required for specified levels of statistical precision, and to assess the effects of hydraulic sorting on detrital compositions. Includes an intuitive query-based user interface for users who are not proficient in R.
Inspects provenance collected by the 'rdt' or 'rdtLite' packages, 
    or other tools providing compatible PROV JSON output created by 
    the execution of a script, and find differences between two provenance 
    collections. Factors under examination included the hardware and 
    software used to execute the script, versions of attached libraries, 
    use of global variables, modified inputs and outputs, and changes 
    in main and sourced scripts. Based on detected changes, 'provExplainR' 
    can be used to study how these factors affect the behavior of 
    the script and generate a promising diagnosis of the causes of different 
    script results. More information about 'rdtLite' and associated tools is available 
    at <https://github.com/End-to-end-provenance/> and Barbara Lerner, 
    Emery Boose, and Luis Perez (2018), Using Introspection to Collect 
    Provenance in R, Informatics, <doi:10.3390/informatics5010012>.
Creates and manages a provenance graph corresponding to the 
    provenance created by the 'rdtLite' package, which
    collects provenance from R scripts.  'rdtLite' is available on CRAN.
    The provenance format is an extension of the 
    W3C PROV JSON format (<https://www.w3.org/Submission/2013/SUBM-prov-json-20130424/>).
    The extended JSON provenance format is described 
    in <https://github.com/End-to-end-provenance/ExtendedProvJson>.
R functions to access provenance information collected by 'rdt' or 
    'rdtLite'.  The information is stored inside a 'ProvInfo' object and can be 
    accessed through a collection of functions that will return the requested 
    data. The exact format of the JSON created by 'rdt' and 'rdtLite' is described 
    in <https://github.com/End-to-end-provenance/ExtendedProvJson>.
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>.
Uses provenance collected by 'rdtLite' package or comparable tool
    to display information about input files, output files, and exchanged files
    for a single R script or a series of R scripts.
Displays provenance graphically for provenance collected by the 'rdt' or
  'rdtLite' packages, or other tools providing compatible PROV JSON output. The exact 
  format of the JSON created by 'rdt' and 'rdtLite' is described in 
  <https://github.com/End-to-end-provenance/ExtendedProvJson>.  More information about
  rdtLite and associated tools is available at <https://github.com/End-to-end-provenance/> 
  and Barbara Lerner, Emery Boose, and Luis Perez (2018), Using Introspection to Collect 
  Provenance in R, Informatics, <doi: 10.3390/informatics5010012>.
Functions to measure Alpha and Beta Proximity to Irreplaceability.
  The methods for Alpha and Beta irreplaceability were first described in:
    Baisero D., Schuster R. & Plumptre A.J.
    Redefining and Mapping Global Irreplaceability.
    Conservation Biology 2021;1-11.
    <doi:10.1111/cobi.13806>.
Provides an extensible framework for the efficient calculation of auto- and cross-proximities, along with implementations of the most popular ones.
Computes proximity between rows or columns of large matrices efficiently in C++.
    Functions are optimised for large sparse matrices using the Armadillo and Intel TBB libraries.
    Among several built-in similarity/distance measures, computation of correlation,
    cosine similarity and Euclidean distance is particularly fast.
Determine minimal protein set explaining
    peptide spectrum matches. Utility functions for creating fasta amino acid databases with decoys and contaminants.
    Peptide false discovery rate estimation for target decoy search results on psm, precursor, peptide and protein
    level.
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 or the phases of the wavelet transform. The function prsim() is applicable 
  to single site simulation and uses the Fourier transform. 
  The function prsim.wave() extends the approach to multiple sites 
  and is based on the complex wavelet 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 for single sites 
  and an application example can be found in <https://hess.copernicus.org/articles/23/3175/2019/>.
  A detailed description and evaluation of the wavelet-based multi-site approach
  can be found in <https://hess.copernicus.org/articles/24/3967/2020/>.
Tests periodicity in short time series using response surface regression.
Intended for larger-than-memory tabular data, 'prt' objects provide an interface to read row and/or column subsets into memory as data.table objects. Data queries, constructed as 'R' expressions, are evaluated using the non-standard evaluation framework provided by 'rlang' and file-backing is powered by the fast and efficient 'fst' package.
Compute bending energies, principal warps, partial warp scores, and the non-affine component of shape variation for 2D landmark configurations, as well as Mardia-Dryden distributions and self-similar distributions of landmarks, as described in Mitteroecker et al. (2020) <doi:10.1093/sysbio/syaa007>. Working examples to decompose shape variation into small-scale and large-scale components, and to decompose the total shape variation into outline and residual shape components are provided. Two landmark datasets are provided, that quantify skull morphology in humans and papionin primates, respectively from Mitteroecker et al. (2020) <doi:10.5061/dryad.j6q573n8s> and Grunstra et al. (2020) <doi:10.5061/dryad.zkh189373>.
Useful tools to pry back the covers of R and understand the
    language at a deeper level.
Data sets and functions used in the polish book 
    "Przewodnik po pakiecie R" (The Hitchhiker's Guide to the R). 
    See more at <http://biecek.pl/R>. Among others you will find here 
    data about housing prices, cancer patients, running times and many others.
List, query and manipulate all system processes, on 'Windows',
    'Linux' and 'macOS'.
A collection of functions that primarily produce graphics
        to aid in a Propensity Score Analysis (PSA).  Functions
        include: cat.psa and box.psa to test balance within strata of
        categorical and quantitative covariates, circ.psa for a
        representation of the estimated effect size by stratum,
        loess.psa that provides a graphic and loess based effect size
        estimate, and various balance functions that provide measures
        of the balance achieved via a PSA in a categorical covariate.
Algorithms to implement various Bayesian penalized survival regression models including: semiparametric proportional hazards models with lasso priors (Lee et al., Int J Biostat, 2011 <doi:10.2202/1557-4679.1301>) and three  other shrinkage and group priors (Lee et al., Stat Anal Data Min, 2015 <doi:10.1002/sam.11266>); parametric accelerated failure time models with group/ordinary lasso prior (Lee et al. Comput Stat Data Anal, 2017 <doi:10.1016/j.csda.2017.02.014>).
A tool which aims to help evaluate the effect of external borrowing using an integrated approach described in Lewis et al., (2019) <doi:10.1080/19466315.2018.1497533> that combines propensity score and Bayesian dynamic borrowing methods.
Segmentation of allele-specific DNA copy number data and detection of regions with abnormal copy number within each parental chromosome.  Both tumor-normal paired and tumor-only analyses are supported.
Bayesian analysis of item-response theory (IRT) models,
	     roll call analysis; computing highest density regions; maximum
	     likelihood estimation of zero-inflated and hurdle models for count
	     data; goodness-of-fit measures for GLMs; data sets used
	     in writing	and teaching at the Political Science
	     Computational Laboratory; seats-votes curves.
Provides a number of functions to
  simplify and automate the scoring, comparison, and evaluation of
  different ways of creating composites of data.  It is particularly
  aimed at facilitating the creation of physiological composites of
  metabolic syndrome symptom score (MSSS) and allostatic load (AL).
Produces power spectral density estimates through iterative
    refinement of the optimal number of sine-tapers at each frequency. This
    optimization procedure is based on the method of Riedel and Sidorenko
    (1995), which minimizes the Mean Square Error (sum of variance and bias)
    at each frequency, but modified for computational stability. The same
    procedure can now be used to calculate the cross spectrum.
A toolbox in symbolic data framework as a statistical learning and data mining solution for symbolic polygonal data analysis. This study is a new approach in data analysis and it was proposed by 
            Silva et al. (2019) <doi:10.1016/j.knosys.2018.08.009>. The package presents the estimation of main descriptive statistical measures, e.g, mean, covariance, variance, correlation and coefficient of variation. 
            In addition, a method to obtain polygonal data from classical data is presented. Empirical probability distribution function based on symbolic polygonal histogram and a regression model with its main measures are presented.
This R package includes functions for gathering commonly used and
    regularly maintained data set in political science. It also includes
    functions for combining components from these data sets into variables that
    have been suggested in the literature, but are not regularly maintained.
Functions that allow you to generate and compare power spectral density (PSD) 
	plots given time series data. Fast Fourier Transform (FFT) is used to take a time series 
	data, analyze the oscillations, and then output the frequencies of these oscillations 
	in the time series in the form of a PSD plot.Thus given a time series, the dominant 
	frequencies in the time series can be identified. Additional functions in this package 
	allow the dominant frequencies of multiple groups of time series to be compared with each other. 
	To see example usage with the main functions of this package, please visit
	this site: <https://yhhc2.github.io/psdr/articles/Introduction.html>. 
	The mathematical operations used to generate the PSDs are described in these sites:
	<https://www.mathworks.com/help/matlab/ref/fft.html>.
	<https://www.mathworks.com/help/signal/ug/power-spectral-density-estimates-using-fft.html>.
Functions for creating Latin Hypercubes with
    prescribed correlations and performing parameter space exploration.
    Also implements the PLUE method.
    Based on the package sensitivity, by Gilles Pujol,
    Bertrand Iooss & Alexandre Janon.
Various functions for computing pseudo-observations for censored data regression. Computes pseudo-observations for modeling: competing risks based on the cumulative incidence function, survival function based on the restricted mean,  survival function based on the Kaplan-Meier estimator see Klein et al. (2008) <doi:10.1016/j.cmpb.2007.11.017>.
Efficient calculation of pseudo-ranks and (pseudo)-rank based test statistics. In case of equal sample sizes, pseudo-ranks and mid-ranks are equal. When used for inference mid-ranks may lead to paradoxical results. Pseudo-ranks are in general not affected by such a problem <doi:10.18637/jss.v095.c01>.
Contains the core methods for the evaluation of principal
    surrogates in a single clinical trial. Provides a flexible interface for
    defining models for the risk given treatment and the surrogate, the models
    for integration over the missing counterfactual surrogate responses, and the
    estimation methods. Estimated maximum likelihood and pseudo-score can be used
    for estimation, and the bootstrap for inference. A variety of post-estimation
    summary methods are provided, including print, summary, plot, and testing.
Pattern Sequence Based Forecasting (PSF) takes univariate
    time series data as input and assist to forecast its future values.
    This algorithm forecasts the behavior of time series
    based on similarity of pattern sequences. Initially, clustering is done with the
    labeling of samples from database. The labels associated with samples are then
    used for forecasting the future behaviour of time series data. The further
    technical details and references regarding PSF are discussed in Vignette.
Pooling, backward and forward selection of linear, logistic and Cox regression models in 
	multiply imputed datasets. Backward and forward selection can be done 
	from the pooled model using Rubin's Rules (RR), the D1, D2, D3, D4 and 
	the median p-values method. This is also possible for Mixed models. 
	The models can contain continuous, dichotomous, categorical and restricted 
	cubic spline predictors and interaction terms between	all these type of predictors. 
	The stability of the models	can be evaluated using bootstrapping and cluster 
	bootstrapping. The package further contains functions to pool the model performance 
	as ROC/AUC, R-squares, scaled Brier score, H&L test and calibration	plots for logistic 
	regression models. Internal validation can be	done with cross-validation or bootstrapping. 
	The adjusted intercept after shrinkage of pooled regression coefficients can be obtained. 
	Backward and forward selection as part of internal validation is possible. 
	A function to externally validate logistic prediction models in multiple imputed 
	datasets is available and a function to compare models. 
	Eekhout (2017) <doi:10.1186/s12874-017-0404-7>.
	Wiel (2009) <doi:10.1093/biostatistics/kxp011>.
	Marshall (2009) <doi:10.1186/1471-2288-9-57>.
Implements projected sparse Gaussian process Kriging (Ingram 'et. al.', 2008, <doi:10.1007/s00477-007-0163-9>) as an additional method for the 'intamap' package. More details on implementation (Barillec 'et. al.', 2010, <doi:10.1016/j.cageo.2010.05.008>).
In the situation when multiple alternative treatments or
    interventions available, different population groups may respond differently
    to different treatments. This package implements a method that discovers
    the population subgroups in which a certain treatment has a better effect
    than the other alternative treatments. This is done by first estimating the
    treatment effect for a given treatment and its uncertainty by computing random
    forests, and the resulting model is summarized by a decision tree in which the
    probabilities that the given treatment is best for a given subgroup is shown in
    the corresponding terminal node of the tree.
Makes it easy to build panel data in wide format from Panel Survey
    of Income Dynamics ('PSID') delivered raw data. Downloads data directly from
    the PSID server using the 'SAScii' package. 'psidR' takes care of merging
    data from each wave onto a cross-period index file, so that individuals can be
    followed over time. The user must specify which years they are interested in,
    and the 'PSID' variable names (e.g. ER21003) for each year (they differ in each
    year). The package offers helper functions to retrieve variable names from different
    waves. There are different panel data designs and sample subsetting criteria
    implemented ("SRC", "SEO", "immigrant" and "latino" samples).
Generalization of the SIMEX algorithm from Cook & Stefanski (1994) <doi:10.2307/2290994> for the calculation of inbreeding depression or heritability on pedigree structures affected by missing or misassigned paternities. It simulates errors and tracks the behavior of the estimate as a function of the error proportion. It extrapolates back a true value corresponding to the null error rate.
Data and statistics of Pakistan Social and Living Standards Measurement (PSLM) survey 2014-15 from Pakistan Bureau of Statistics (<http://www.pbs.gov.pk/>).
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.
Implements an n-dimensional parameter space partitioning algorithm for evaluating the global behaviour of formal computational models as described by Pitt, Kim, Navarro and Myung (2006) <doi:10.1037/0033-295X.113.1.57>.
Spearman's rank correlation test with precomputed exact
        null distribution for n <= 22.
Smoothing splines with penalties on order m derivatives.
Inference of infectious disease outcomes using generalized additive
    (mixed) models with penalized basis splines (P-Splines). See 
    <https://medrxiv.org/cgi/content/short/2020.07.14.20138180v1>.
Performs demographic, bifurcation and evolutionary analysis of physiologically structured population models, which is a class of models that consistently translates continuous-time models of individual life history to the population level.
    A model of individual life history has to be implemented specifying the individual-level functions that determine the life history, such as development and mortality rates and fecundity. 
    M.A. Kirkilionis, O. Diekmann, B. Lisser, M. Nool, B. Sommeijer & A.M. de Roos (2001) <doi:10.1142/S0218202501001264>.
    O.Diekmann, M.Gyllenberg & J.A.J.Metz (2003) <doi:10.1016/S0040-5809(02)00058-8>.
    A.M. de Roos (2008) <doi:10.1111/j.1461-0248.2007.01121.x>.
Provides quasi-Newton methods to minimize partially separable
    functions. The methods are largely described by  
    Nocedal and Wright (2006) <doi:10.1007/978-0-387-40065-5>.
Contains functions to compute statistics relevant to performance scientists, coaches, and athletes. The functions within the package that report group-wide statistics have to do with measurement instrument reliability and individual change of athletes. Many of them come from the work of Hopkins (2000) <doi:10.2165/00007256-200030010-00001>. There are also functions that apply a particular statistical concept to each measurement in the data.
High-quality real-world data can be transformed into scientific
    real-world evidence (RWE) for regulatory and healthcare decision-making
    using proven analytical methods and techniques. For example, propensity
    score (PS) methodology can be applied to pre-select a subset of real-world
    data containing patients that are similar to those in the current clinical
    study in terms of covariates, and to stratify the selected patients together
    with those in the current study into more homogeneous strata. Then, methods
    such as power prior approach or composite likelihood approach can be applied
    in each stratum to draw inference for the parameters of interest. This
    package provides functions that implement the PS-integrated RWE analysis
    methods proposed in Wang et al. (2019) <doi:10.1080/10543406.2019.1657133>,
    Wang et al. (2020) <doi:10.1080/10543406.2019.1684309> and Chen et al.
    (2020) <doi:10.1080/10543406.2020.1730877>.
Power and Sample Size for Health Researchers is a Shiny application that brings together a 
    series of functions  related to sample size and power calculations for common analysis in the healthcare 
    field. There are functionalities to calculate the power, sample size to estimate or test hypotheses for means and 
    proportions (including test for correlated groups, equivalence, non-inferiority and superiority), association, correlations coefficients, 
    regression coefficients (linear, logistic and Cox), linear mixed model, 
    Cronbach's alpha, interobserver agreement, intraclass correlation coefficients,
    limit of agreement on Bland-Altman plots,
    area under the curve, sensitivity and specificity incorporating the prevalence of disease. 
    You can also use the online version at <https://hcpa-unidade-bioestatistica.shinyapps.io/PSS_Health/>.
Test-based Image structural similarity measure and test of independence. 
    This package implements the key functions of two tasks: (1) computing
    image structural similarity measure PSSIM of Wang, Maldonado and
    Silwal (2011) <DOI:10.1016/j.csda.2011.04.021>;
    and (2) test of independence between a response and 
    a covariate in presence of heteroscedastic treatment effects 
    proposed by Wang, Tolos, and Wang (2010) <DOI:10.1002/cjs.10068>.
Estimates parameters of a piecewise exponential model for time to progression and time from progression to death with interval censoring of the time to progression and covariates for each distribution using proportional hazards.
Returns almost all features that has been extracted from Position Specific 
             Scoring Matrix (PSSM) so far, which is a matrix of L rows (L is protein length) 
             and 20 columns produced by 'PSI-BLAST' which is a program to produce
             PSSM Matrix from multiple sequence alignment of proteins
             see <https://www.ncbi.nlm.nih.gov/books/NBK2590/> for mor details. some 
             of these features are described in Zahiri, J., et al.(2013)
             <DOI:10.1016/j.ygeno.2013.05.006>,
             Saini, H., et al.(2016)
             <DOI:10.17706/jsw.11.8.756-767>,
             Ding, S., et al.(2014)
             <DOI:10.1016/j.biochi.2013.09.013>,
             Cheng, C.W., et al.(2008)
             <DOI:10.1186/1471-2105-9-S12-S6>,
             Juan, E.Y., et al.(2009)
             <DOI:10.1109/CISIS.2009.194>.
Implements estimation and testing procedures for evaluating an intermediate biomarker response as a principal surrogate of a clinical response to treatment (i.e., principal stratification effect modification analysis), as described in Juraska M, Huang Y, and Gilbert PB (2020), Inference on treatment effect modification by biomarker response in a three-phase sampling design, Biostatistics, 21(3): 545-560 <doi:10.1093/biostatistics/kxy074>. The methods avoid the restrictive 'placebo structural risk' modeling assumption common to past methods and further improve robustness by the use of nonparametric kernel smoothing for biomarker density estimation. A randomized controlled two-group clinical efficacy trial is assumed with an ordered categorical or continuous univariate biomarker response measured at a fixed timepoint post-randomization and with a univariate baseline surrogate measure allowed to be observed in only a subset of trial participants with an observed biomarker response (see the flexible three-phase sampling design in the paper for details). Bootstrap-based procedures are available for pointwise and simultaneous confidence intervals and testing of four relevant hypotheses. Summary and plotting functions are provided for estimation results.
A network-based systems biology tool for flexible identification of phenotype-specific subpathways in the cancer gene expression data 
  with multiple categories (such as multiple subtype or developmental stages of cancer). Subtype Set Enrichment Analysis (SubSEA) and Dynamic Changed 
  Subpathway Analysis (DCSA) are developed to flexible identify subtype specific and dynamic changed subpathways respectively. The operation modes 
  include extraction of subpathways from biological pathways, inference of subpathway activities in the context of gene expression data, identification 
  of subtype specific subpathways with SubSEA, identification of dynamic changed subpathways associated with the cancer developmental stage with DCSA, 
  and visualization of the activities of resulting subpathways by using box plots and heat maps. Its capabilities render the tool could find the specific
  abnormal subpathways in the cancer dataset with multi-phenotype samples.
Provides a framework for analysing state sequences with probabilistic suffix trees (PST), the construction that stores variable length Markov chains (VLMC). Besides functions for learning and optimizing VLMC models, the PST library includes many additional tools to analyse sequence data with these models: visualization tools, functions for sequence prediction and artificial sequences generation, as well as for context and pattern mining. The package is specifically adapted to the field of social sciences by allowing to learn VLMC models from sets of individual sequences possibly containing missing values, and by accounting for case weights. The  library also allows to compute probabilistic divergence between two models, and to fit segmented VLMC, where sub-models fitted to distinct strata of the learning sample are stored in a single PST. This software results from research work executed within the framework of the Swiss National Centre of Competence in Research LIVES, which is financed by the Swiss National Science Foundation. The authors are grateful to the Swiss National Science Foundation for its financial support.
Calculating Pst values to assess differentiation among populations from a set of quantitative traits is the primary purpose of such a package. The bootstrap method provides confidence intervals and distribution histograms of Pst. Variations of Pst in function of the parameter c/h^2 are studied as well. Finally, the package proposes different transformations especially to eliminate any variation resulting from allometric growth (calculation of residuals from linear regressions, Reist standardizations or Aitchison transformation).
The propensity score is one of the most widely used tools in
    studying the causal effect of a treatment, intervention, or policy. Given that
    the propensity score is usually unknown, it has to be estimated, implying that
    the reliability of many treatment effect estimators depends on the correct
    specification of the (parametric) propensity score. This package implements the
    data-driven nonparametric diagnostic tools for detecting propensity score
    misspecification proposed by Sant'Anna and Song (2019) <doi:10.1016/j.jeconom.2019.02.002>.
Functions to simulate Poisson or Normally distributed responses relative to 
  a baseline and compute achieved significance level and powers for tests on the
  simulated responses.
Provides propensity score weighting methods to control for confounding in causal inference with dichotomous treatments and continuous/binary outcomes. It includes the following functional modules: (1) visualization of the propensity score distribution in both treatment groups with mirror histogram, (2) covariate balance diagnosis, (3) propensity score model specification test, (4) weighted estimation of treatment effect, and (5) augmented estimation of treatment effect with outcome regression. The weighting methods include the inverse probability weight (IPW) for estimating the average treatment effect (ATE), the IPW for average treatment effect of the treated (ATT), the IPW for the average treatment effect of the controls (ATC), the matching weight (MW), the overlap weight (OVERLAP), and the trapezoidal weight (TRAPEZOIDAL). Sandwich variance estimation is provided to adjust for the sampling variability of the estimated propensity score. These methods are discussed by Hirano et al (2003) <DOI:10.1111/1468-0262.00442>, Lunceford and Davidian (2004) <DOI:10.1002/sim.1903>, Li and Greene (2013) <DOI:10.1515/ijb-2012-0030>, and Li et al (2016) <DOI:10.1080/01621459.2016.1260466>.
Supports propensity score weighting analysis of observational studies and randomized trials. Enables the estimation and inference of average causal effects with binary and multiple treatments using overlap weights (ATO), inverse probability of treatment weights (ATE), average treatment effect among the treated weights (ATT), matching weights (ATM) and entropy weights (ATEN), with and without propensity score trimming. These weights are members of the family of balancing weights introduced in Li, Morgan and Zaslavsky (2018) <doi:10.1080/01621459.2016.1260466> and Li and Li (2019) <doi:10.1214/19-AOAS1282>.
Kappa, ICC, 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 <doi:10.31234/osf.io/8ha93>. Allows for confirmatory testing and fit as well as exploratory model search.
Infrastructure for psychometric modeling such as data classes (for
  item response data and paired comparisons), basic model fitting functions (for
  Bradley-Terry, Rasch, parametric logistic IRT, generalized partial credit,
  rating scale, multinomial processing tree models), extractor functions for
  different types of parameters (item, person, threshold, discrimination,
  guessing, upper asymptotes), unified inference and visualizations, and various
  datasets for illustration.  Intended as a common lightweight and efficient
  toolbox for psychometric modeling and a common building block for fitting
  psychometric mixture models in package "psychomix" and trees based on
  psychometric models in package "psychotree".
Recursive partitioning based on psychometric models,
  employing the general MOB algorithm (from package partykit) to obtain
  Bradley-Terry trees, Rasch trees, rating scale and partial credit trees, and
  MPT trees.
Helper functions for producing reports in Psychology (Reproducible Research). Provides required formatted strings (APA style) for use in 'Knitr'/'Latex' integration within *.Rnw files.
Implementation of 'PsychroLib'
    <https://github.com/psychrometrics/psychrolib> library which contains
    functions to enable the calculation properties of moist and dry air in both
    metric (SI) and imperial (IP) systems of units. References: Meyer, D. and
    Thevenard, D (2019) <doi:10.21105/joss.01137>.
Provides text mining methods for social science research. The
    package implements estimation, inference, summarization, and goodness-of-fit
    methods for topic models including Latent Dirichlet Allocation (LDA),
    supervised LDA, and supervised LDA with covariates using Bayesian Markov Chain
    Monte Carlo. A description of the key models and estimation methods is available
    in Wilcox, Jacobucci, Zhang, & Ammerman (2021). <doi:10.31234/osf.io/62tc3>.
Support functions,  data sets, and vignettes for the 'psych' package. Contains several of the biggest data sets for the 'psych' package as well as one vignette. A few helper functions for file manipulation are included as well. For more information, see the <https://personality-project.org/r/> web page.
A beginner-friendly R package for modeling in psychology or
    related field. It allows fitting models, plotting, checking goodness
    of fit, and model assumption violations all in one place. It also
    produces beautiful and easy-to-read output.
Person fit statistics based on Quality Control measures are provided for questionnaires and tests given a specified IRT model. Statistics based on Cumulative Sum (CUSUM) charts are provided. Options are given for banks with polytomous and dichotomous data.
Data and examples from meta-analyses in psychology research.
Apply the popular real-time monitoring strategy
    proposed by Phillips, Shi and Yu (2015a,b;PSY) <doi:10.1111/iere.12132>, <doi:10.1111/iere.12131>, along with a new
    bootstrap procedure designed to mitigate the potential impact of
    heteroskedasticity and to effect family-wise size control in recursive
    testing algorithms (Phillips and Shi, forthcoming).
Provides functions and data-sets that are helpful 
  for teaching statistics and data analysis. It was originally designed for use
  when teaching students in the Psychology Department at Nottingham Trent University.
An assortment of functions that could be useful in analyzing data from psychophysical experiments. It includes functions for calculating d' from several different experimental designs, links for m-alternative forced-choice (mafc) data to be used with the binomial family in glm (and possibly other contexts) and self-Start functions for estimating gamma values for CRT screen calibrations.
The constructs used to study the human psychology have
    many definitions and corresponding instructions for eliciting
    and coding qualitative data pertaining to constructs' content and
    for measuring the constructs. This plethora of definitions and
    instructions necessitates unequivocal reference to specific
    definitions and instructions in empirical and secondary research.
    This package implements a human- and machine-readable standard for
    specifying construct definitions and instructions for measurement
    and qualitative research based on 'YAML'. This standard facilitates
    systematic unequivocal reference to specific construct definitions
    and corresponding instructions in a decentralized manner (i.e.
    without requiring central curation; Peters (2020)
    <doi:10.31234/osf.io/xebhn>).
A multiway method to decompose a tensor (array) of any order, as a generalisation of SVD also supporting non-identity metrics and penalisations. 2-way SVD with these extensions is also available. The package includes also some other multiway methods: PCAn (Tucker-n) and PARAFAC/CANDECOMP with these extensions.
We provide inference for personalized medicine models. Namely, we answer the questions: (1) how much better does a purported personalized recommendation engine for treatments do over a business-as-usual approach and (2) is that difference statistically significant?
Implements p-value computations using an approximation to the cumulative distribution function for a variety of tests for periodicity. These tests include harmonic regression tests with normal and double exponential errors as well as modifications of Fisher's g test. An accompanying vignette illustrates the application of these tests.
Efficient algorithms to implement Probit Tensor Factorization (PTF) model for statistical relational learning, which not only inherits the computation efficiency from the classic tensor factorization model but also accounts for the binary nature of relational data. The methodology is based on Ye Liu (2021) <https://repository.lib.ncsu.edu/bitstream/handle/1840.20/37507/etd.pdf?sequence=1> "Computational Methods for Complex Models with Latent Structure".
Function pip3d() tests whether a point in 3D space is
  within, exactly on, or outside an enclosed surface defined by a triangular mesh.
  Function pip2d() tests whether a point in 2D space is within, exactly on, or outside a polygon.
  For a reference, see: Liu et al., A new point containment test algorithm based on preprocessing
  and determining triangles, Computer-Aided Design 42(12):1143-1150.
Contains utilities for the analysis of post-translational modifications (PTMs) in proteins, 
    with particular emphasis on the sulfoxidation of methionine residues. Features include the ability to download, 
    filter and analyze data from the sulfoxidation database 'MetOSite', and integrate data from other main PTMs 
    (other databases). Utilities to search and characterize S-aromatic motifs in proteins are also provided. 
    In addition, functions to analyze sequence environments around modifiable residues in proteins can be found. 
    For instance, 'ptm' allows to search for amino acids either overrepresented or avoided around the modifiable 
    residues from the proteins of interest. Functions tailored to test statistical hypothesis related to
    these differential sequence environments are also implemented. A number of utilities to assess the effect
    of the modification/mutation of a given residue on the protein stability, have also been included in this package.
    Further and detailed information regarding the methods in this package can be found in (Aledo (2020) <https://metositeptm.com>).
Fits the Poisson-Tweedie generalized linear mixed
    model described in Signorelli et al. (2020,
    <doi:10.1177/1471082X20936017>).  Likelihood approximation based on
    adaptive Gauss Hermite quadrature rule.
Fits and analyses time dependent marked point process models with an emphasis on earthquake modelling. For a more detailed introduction to the package, see the topic "PtProcess". A list of recent changes can be found in the topic "Change Log".
Various applications in invasive species biology,
    conservation biology, epidemiology and elsewhere involve 
    sampling of sets of 2D points from a posterior distribution.
    The number of such point sets may be large, say 1000 or 10000. 
    This package facilitates visualisation of such output by 
    constructing seven nested polygons representing the location and 
    variability of the point sets. This can be used, for example, to 
    visualise the range boundary of a species, and uncertainty in 
    the location of that boundary.
Utility functions produced specifically for (but not limited to) 
    working with 'ProjectTemplate' data pipelines. This package helps to quickly
    create and manage sequentially numbered scripts, quickly set up logging with
    'log4r' and functions to help debug and monitor procedures.
Various estimation methods for the shape parameter of Pareto
    distributed data. This package contains functions for various estimation 
    methods such as maximum likelihood 
    (Newman, 2005)<doi:10.1016/j.cities.2012.03.001>, 
    Hill's estimator (Hill, 1975)<doi:10.1214/aos/1176343247>, 
    least squares (Zaher et al., 2014)<doi:10.9734/BJMCS/2014/10890>, 
    method of moments (Rytgaard, 1990)<doi:10.2143/AST.20.2.2005443>, 
    percentiles (Bhatti et al., 2018)<doi:10.1371/journal.pone.0196456>,
    and weighted least squares (Nair et al., 2019) to estimate the shape 
    parameter of Pareto distributed data. It also provides both a heuristic 
    method (Hubert et al., 2013)<doi:10.1016/j.csda.2012.07.011> and a 
    goodness of fit test 
    (Gulati and Shapiro, 2008)<doi:10.1007/978-0-8176-4619-6> for testing for 
    Pareto data as well as a method for generating Pareto distributed data.
Access the 'Public Transport Victoria' Timetable API 
    <https://www.ptv.vic.gov.au/footer/data-and-reporting/datasets/ptv-timetable-api/>,
    with results returned as familiar R data structures. Retrieve information on
    stops, routes, disruptions, departures, and more.
Parametric Time Warping aligns patterns, i.e. it aims to
        put corresponding features at the same locations. The algorithm
        searches for an optimal polynomial describing the warping. It
        is possible to align one sample to a reference, several samples
        to the same reference, or several samples to several
        references. One can choose between calculating individual
        warpings, or one global warping for a set of samples and one
        reference. Two optimization criteria are implemented: RMS (Root
        Mean Square error) and WCC (Weighted Cross Correlation). Both
	warping of peak profiles and of peak lists are supported. A
	vignette for the latter is contained in the inst/doc directory
	of the source package - the vignette source can be found on
	the package github site.
Contains a dataset of words used in 15.000 randomly extracted pages
    from the Portuguese Wikipedia (<https://pt.wikipedia.org/>).
Package to Percentile estimation of fetal weight for twins by chorionicity (dichorionic-diamniotic or monochorionic-diamniotic).
Generates Proteomics (PTX) quality control (QC) reports for shotgun LC-MS data analyzed with the 
             MaxQuant software suite (from .txt files) or mzTab files (ideally from OpenMS 'QualityControl' tool).
             Reports are customizable (target thresholds, subsetting) and available in HTML or PDF format.
             Published in J. Proteome Res., Proteomics Quality Control: Quality Control Software for MaxQuant Results (2015)
             <doi:10.1021/acs.jproteome.5b00780>.
Bayesian variable selection for linear regression models using hierarchical
  priors. There is a prior that combines information across responses and one
  that combines information across covariates, as well as a standard spike and
  slab prior for comparison. An MCMC samples from the marginal posterior
  distribution for the 0-1 variables indicating if each covariate belongs to the
  model for each response.
Get chunks of XML scholarly articles without
    having to know how to work with XML. Custom mappers
    for each publisher and for each article section pull
    out the information you want. Works with outputs from
    package 'fulltext', 'xml2' package documents, and file
    paths to XML documents.
A toolbox for making R functions and capabilities more
    accessible to students and professionals from Epidemiology and
    Public Health related disciplines. Includes a function to report 
    coefficients and confidence intervals from models using robust
    standard errors (when available), functions that expand 'ggplot2'
    plots and functions relevant for introductory papers in Epidemiology 
    or Public Health. Please note that use of the 
    provided data sets is for educational purposes only.
Performs sensitivity analysis for publication bias in meta-analyses (per Mathur & VanderWeele, 2020 [<https://osf.io/s9dp6>]).
    These analyses enable statements such as: "For publication bias to shift the observed point estimate to the null,
    'significant' results would need to be at least 30-fold more likely to be published than negative or 'nonsignificant'
    results." Comparable statements can be made regarding shifting to a chosen non-null value or shifting the confidence
    interval. Provides a worst-case meta-analytic point estimate under maximal publication bias obtained simply by conducting
    a standard meta-analysis of only the negative and "nonsignificant" studies.
Tools for Bayesian estimation of meta-analysis models that account 
    for publications bias or p-hacking. For publication bias, this package 
    implements a variant of the p-value based selection model of Hedges (1992)
    <doi:10.1214/ss/1177011364> with discrete selection probabilities. It also 
    implements the mixture of truncated normals model for p-hacking described 
    in Moss and De Bin (2019) <arXiv:1911.12445>.
A bunch of convenience functions that transform the results of some basic statistical analyses
       into table format nearly ready for publication. This includes descriptive tables, tables of
       logistic regression and Cox regression results as well as forest plots.
Text mining of PubMed Abstracts (text and XML) from <https://pubmed.ncbi.nlm.nih.gov/>.
Easy function for text-mining the 'PubMed' repository based on defined sets of terms.
    The relationship between fix-terms (related to your research topic) and pub-terms (terms which pivot around your research focus)
    is calculated using the pointwise mutual information algorithm ('PMI'). Church, Kenneth Ward and Hanks, Patrick (1990) <https://www.aclweb.org/anthology/J90-1003/>
    A text file is generated with the 'PMI'-scores for each fix-term. Then for each collocation pairs (a fix-term + a pub-term),
    a text file is generated with related article titles and publishing years. Additional Author section will follow in the next version updates.
A set of tools to extract bibliographic content from 'PubMed' database using 'NCBI' REST API <https://www.ncbi.nlm.nih.gov/home/develop/api/>.
Create a word cloud using the abstract of publications from 'Pubmed'.
'PubTator' <https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator/> is a National Center for Biotechnology Information (NCBI) tool that enhances the annotation of articles on PubMed <https://www.ncbi.nlm.nih.gov/pubmed/>. It makes it possible to rapidly identify potential relationships between genes or proteins using text mining techniques. In contrast, manually searching for and reading the annotated articles would be very time consuming. 'PubTator' offers both an online interface and a RESTful API, however, neither of these approaches are well suited for frequent, high-throughput analyses. The package 'pubtatordb' provides a set of functions that make it easy for the average R user to download 'PubTator' annotations, create, and then query a local version of the database.
Efficient algorithm for solving PU (Positive and Unlabeled) problem in low or high dimensional setting with lasso or group lasso penalty. The algorithm uses Maximization-Minorization and (block) coordinate descent. Sparse calculation and parallel computing are supported for the computational speed-up. See Hyebin Song, Garvesh Raskutti (2018) <arXiv:1711.08129>.
R Interface to Pullword Service for natural language processing
    in Chinese. It enables users to extract valuable words from text by deep learning models. 
    For more details please visit the official site (in Chinese) <http://www.pullword.com/>.
A method of clustering functional data using
    subregion information of the curves. It is intended to supplement the
    'fda' and 'fda.usc' packages in functional data object clustering. It
    also facilitates the printing and plotting of the results in a tree
    format and limits the partitioning candidates into a specific set of
    subregions.
Model selection for penalized graphical models using the Stability Approach to Regularization Selection ('StARS'), with options for speed-ups including Bounded StARS (B-StARS), batch computing, and other stability metrics (e.g., graphlet stability G-StARS). Christian L. Müller, Richard Bonneau, Zachary Kurtz (2016) <arXiv:1605.07072>.
A tool for analyzing the transcription, processing and degradation rates of genes by 4sU-seq (the Metabolic Label 4-thiouridine) data and RNA-seq (RNA sequencing) data. It can not only recognize the transcriptional dynamic rates at the measurement time points, but also obtain continuous changes in transcriptional dynamics. More importantly, it is able to predict the trend of mRNA (Mature RNA) transcription and expression changes in the future.
R package to query and get data out of a Pumilio sound archive system (http://ljvillanueva.github.io/pumilio/).
Adsorption isotherm equations are linearized plots of different solid-liquid phase equilibria used in calculating different parameters related to the adsorption process. Isotherm equations deals with physical adsorption of gases and  vapor and gives the most important characteristics  of industrial adsorbents that include pore volume, pore size or energy distribution. PUPAIM has 28 documented adsorption isotherm models listed by Dabrowski (2001) <doi:10.1016/S0001-8686(00)00082-8> and Ayawei et al.(2017) <doi:10.1155/2017/3039817>. These models could be easily  fitted in R using adsorption data (Ce and Qe) obtained from experiments.
Provides a unified pipeline to clean, prepare, plot,
    and run basic analyses on pupillometry experiments.
Pupillometric data collected using SR Research Eyelink eye trackers 
    requires significant preprocessing. This package contains functions for 
    preparing pupil dilation data for visualization and statistical analysis. 
    Specifically, it provides a pipeline of functions which aid in data validation,
    the removal of blinks/artifacts, downsampling, and baselining, among others. 
    Additionally, plotting functions for creating grand average and conditional 
    average plots are provided. See the vignette for samples of the functionality.
    The package is designed for handling data collected with SR Research 
    Eyelink eye trackers using Sample Reports created in SR Research Data 
    Viewer.
PurBayes is an MCMC-based algorithm that uses
        next-generation sequencing data to estimate tumor purity and
        clonality for paired tumor-normal data.
Proteins reside in either the cell plasma or in the
    cell membrane. A membrane protein goes through the 
    membrane at least once. Given the amino acid sequence of a
    membrane protein, the tool
    'PureseqTM' (<https://github.com/PureseqTM/pureseqTM_package>,
    as described in "Efficient And Accurate Prediction Of Transmembrane 
    Topology From Amino acid sequence only.", Wang, Qing, et al (2019), 
    <doi:10.1101/627307>),
    can predict the topology of a membrane protein. This package
    allows one to use 'PureseqTM' from R.
Enables the removal of training data from fitted R models while
    retaining predict functionality. The purged models are more portable as their
    memory footprints do not scale with the training sample size.
Inbreeding-purging analysis of pedigreed populations, including the computation of the inbreeding coefficient, partial, ancestral and purged inbreeding coefficients, and measures of the opportunity of purging related to the individual reduction of inbreeding load.
    In addition, functions to calculate the effective population size and other parameters relevant to population genetics are included.
    See López-Cortegano E. (2021) <doi:10.1093/bioinformatics/btab599>.
Simple method of purging independent variables of mediating effects. First, regress the direct variable on the indirect variable. Then, used the stored residuals as the new purged (direct) variable in the updated specification. This purging process allows for use of a new direct variable uncorrelated with the indirect variable. Please cite the method and/or package using Waggoner, Philip D. (2018) <doi:10.1177/1532673X18759644>.
A complete and consistent functional programming
    toolkit for R.
Some functions at the intersection of 'dplyr' and
    'purrr' that formerly lived in 'purrr'.
Package for projection pursuit (PP) with 17 methods and grand tour with 3 methods. Being that projection pursuit searches for low-dimensional linear projections in high-dimensional data structures, while grand tour is a technique used to explore multivariate statistical data through animation.
Create sliders from left, right, top and bottom which may include any html or 'Shiny' input or output.
Send push notifications to mobile devices or the desktop using Pushover. These notifications can display job status, results, scraped web data, or any other text or numeric data.
To Simplify the time consuming and error prone task of assembling complex data sets for non-linear mixed effects modeling. Users are able to select from different absorption processes such as zero and first order, or a combination of both. Furthermore, data sets containing data from several entities, responses, and covariates can be simultaneously assembled.
Likelihood based population viability analysis in the
  presence of observation error and missing data.
  The package can be used to fit, compare, predict,
  and forecast various growth model types using data cloning.
Contains functions to compute and plot confidence distributions, confidence densities, p-value functions and s-value (surprisal) functions for several commonly used estimates. Instead of just calculating one p-value and one confidence interval, p-value functions display p-values and confidence intervals for many levels thereby allowing to gauge the compatibility of several parameter values with the data. These methods are discussed by Infanger D, Schmidt-Trucksäss A. (2019) <doi:10.1002/sim.8293>; Poole C. (1987) <doi:10.2105/AJPH.77.2.195>; Schweder T, Hjort NL. (2002) <doi:10.1111/1467-9469.00285>; Bender R, Berg G, Zeeb H. (2005) <doi:10.1002/bimj.200410104> ; Singh K, Xie M, Strawderman WE. (2007) <doi:10.1214/074921707000000102>; Rothman KJ, Greenland S, Lash TL. (2008, ISBN:9781451190052); Amrhein V, Trafimow D, Greenland S. (2019) <doi:10.1080/00031305.2018.1543137>; Greenland S. (2019) <doi:10.1080/00031305.2018.1529625> and Rafi Z, Greenland S. (2020) <doi:10.1186/s12874-020-01105-9>.
The calculation of p-variation of the finite sample data. 
	This package is a realisation of the procedure described in Butkus, V. & Norvaisa, R. Lith Math J (2018). <doi: 10.1007/s10986-018-9414-3>
	The formal definitions and reference into literature are given in vignette.
Computes nonparametric p-values for the potential class
        memberships of new observations as well as cross-validated
        p-values for the training data. The p-values are based on
        permutation tests applied to an estimated Bayesian likelihood
        ratio, using a plug-in statistic for the Gaussian model, 'k
        nearest neighbors', 'weighted nearest neighbors' or
        'penalized logistic regression'.
        Additionally, it provides graphical displays and quantitative
        analyses of the p-values.
An implementation of multiscale bootstrap resampling for
             assessing the uncertainty in hierarchical cluster analysis.
             It provides SI (selective inference) p-value, AU (approximately unbiased)
             p-value and BP (bootstrap probability) value for each cluster in a dendrogram.
Enables the manufacturing, analysis and display of pressure volume curves. From the progression of the curves, turgor loss point, osmotic potential and apoplastic fraction can be derived. Methods adapted from Bartlett, Scoffoni and Sack (2012) <doi:10.1111/j.1461-0248.2012.01751.x>.
Simplifies the manufacturing, analysis and display of pressure volume and leaf drying curves. From the progression of the curves turgor loss point, osmotic potential, apoplastic fraction as well as minimum conductance and stomatal closure can be derived. Methods adapted from Bartlett, Scoffoni, Sack (2012) <doi:10.1111/j.1461-0248.2012.01751.x> and Sack, Scoffoni, PrometheusWikiContributors (2011) <http://prometheuswiki.org/tiki-index.php?page=Minimum+epidermal+conductance+%28gmin%2C+a.k.a.+cuticular+conductance%29>.
The pipeline contained in this package provides tools used in the
    Solar Durability and Lifetime Extension Center (SDLE) for the analysis of
    Performance Loss Rates (PLR) in real world photovoltaic systems. Functions
    included allow for data cleaning, feature correction, power predictive modeling, 
    PLR determination, and uncertainty bootstrapping through various methods 
    <doi:10.1109/PVSC40753.2019.8980928>.
    The vignette "Pipeline Walkthrough" gives
    an explicit run through of typical package usage. 
    This material is based upon work supported by the U.S Department
    of Energy's Office of Energy Efficiency and Renewable Energy (EERE) under
    Solar Energy Technologies Office (SETO) Agreement Number DE-EE-0008172. 
    This work made use of the High Performance Computing Resource in the 
    Core Facility for Advanced Research Computing at
    Case Western Reserve University.
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 two differing notions of power, or deriving sample
 sizes for specified requisite power in multiple testing experiments under a variety of methods for
 control of the distribution of the False Discovery Proportion (FDP). More specifically, one can
 choose to control the FDP distribution according to control of its (i) mean, e.g. the usual BH-FDR
 procedure, or via the probability that it exceeds a given value, delta, via (ii) the Romano
 procedure, or via (iii) my procedure based upon asymptotic approximation. Likewise, we can think of
 the power in multiple testing experiments in terms of a summary of the distribution of the
 True Positive Proportion (TPP). The package will compute power, sample size or any other missing
 parameter required for power based upon (i) the mean of the TPP which is the average power
 (ii) the probability that the TPP exceeds a given value, lambda, via my asymptotic approximation
 procedure. The theoretical results are described in Izmirlian, G. (2020) Stat. and Prob. letters,
 <doi:10.1016/j.spl.2020.108713>, and an applied  paper describing the methodology with a
 simulation study is in preparation.
Tools for the evaluation of interim analysis plans for sequentially
 monitored trials on a survival endpoint; tools to construct efficacy and 
 futility boundaries, for deriving power of a sequential design at a specified
 alternative, template for evaluating the performance of candidate plans at a 
 set of time varying alternatives. See Izmirlian, G. (2014) <doi:10.4310/SII.2014.v7.n1.a4>.
Statistical power simulation for testing the Rasch Model based on a three-way analysis of variance design with mixed classification.
The Penn World Table provides purchasing power parity and
	national income accounts converted to international prices for
	189 countries for some or all of the years 1950-2010.
The Penn World Table 10.x (<http://www.ggdc.net/pwt/>) provides information
	on relative levels of income, output, input, and productivity
	for 183 countries between 1950 and 2019.
The Penn World Table 8.x provides information on relative levels of
	income, output, inputs, and productivity for 167 countries
	between 1950 and 2011.
The Penn World Table 9.x (<http://www.ggdc.net/pwt/>) provides information
	on relative levels of income, output, inputs, and productivity
	for 182 countries between 1950 and 2017.
Provides a set of functions for reading and writing PC-Axis files, used by different statistical organizations around the globe for data dissemination.
Generic interface for the PX-Web/PC-Axis API. The PX-Web/PC-Axis
    API is used by organizations such as Statistics Sweden and Statistics
    Finland to disseminate data. The R package can interact with all
    PX-Web/PC-Axis APIs to fetch information about the data hierarchy, extract
    metadata and extract and parse statistics to R data.frame format. PX-Web is
    a solution to disseminate PC-Axis data files in dynamic tables on the web.
    Since 2013 PX-Web contains an API to disseminate PC-Axis files.
Function to read PX-Web data into R via API. The example code reads data from the three national statistical institutes, Statistics Norway, Statistics Sweden and Statistics Finland.
Given a SpatialPolygonsDataFrame and a set of populations for each polygon,
 compute a population density estimate based on Tobler's pycnophylactic interpolation
 algorithm. The result is a SpatialGridDataFrame.
Deterministic Pena-Yohai initial estimator for robust S estimators
    of regression. The procedure is described in detail in
    Pena, D., & Yohai, V. (1999) <doi:10.2307/2670164>.
Allow to run 'pylint' on Python files with a R command or a 'RStudio' addin. The report appears in the RStudio viewer pane as a formatted HTML file.
Drawing population pyramid using (1) data.frame or (2) vectors.
	 The former is named as pyramid() and the latter pyramids(), as wrapper
	 function of pyramid().  pyramidf() is the function to draw population
	 pyramid within the specified frame.
Using the R package 'reticulate', this package creates an interface to the 'pysd' toolset.
    The package provides an R interface to a number of 'pysd' functions, and can read files in
    'Vensim' 'mdl' format, and 'xmile' format. The resulting simulations are returned as a 'tibble', and from 
    that the results can be processed using 'dplyr' and 'ggplot2'. The package has been tested  using 'python3'.
Read and write 'GraphPad Prism' '.pzfx' files in R.
Mortality Rates are usually published following an abridged description, 
              i.e., by age groups 0, [1, 5[, [5, 10[, [10, 15[ and so on. For some applications, 
              a detailed (single) ages description is required. Despite the huge number of the 
              proposed methods in the literature, there is a limited number of methods ensuring a
              high performance at lower and higher ages in the same time. For example, the 6-terms 
              'Lagrange' interpolation function is well adapted to mortality  interpolation at lower
              ages (with unequal intervals) but is not adapted to higher ages. On the other hand, 
              the 'Karup-King' method allows a good performance at higher ages but not adapted to lower ages.
              Interested readers can refer to the book of Shryock, Siegel and Associates (1993) for a detailed
              overview of the two cited methods.The package Q2q allows combining both the two methods to allow
              interpolating mortality rates at all ages. First, it starts by implementing each method separately,
              then the resulted curves are joined based on a 5-age averaged error between the two curves.
Construct message-passing style objects with types and features. Q7 types uses composition instead of inheritance in creating derived types, allowing defining any code segment as feature and associating any feature to any object. Compared to R6, Q7 is simpler and more flexible, and is more friendly in syntax.
A copula-based measure for quantifying asymmetry in dependence and associations. Documentation and theory about 'qad' is provided
    by the paper by Junker, Griessenberger & Trutschnig (2021, <doi:10.1016/j.csda.2020.107058>), and the paper by Trutschnig (2011, <doi:10.1016/j.jmaa.2011.06.013>).
A tool for automatic generation of sibling items from a parent item model defined by
           the user. It is an implementation of the process automatic item generation (AIG) focused
           on generating quantitative multiple-choice type of items (see Embretson, Kingston
           (2018) <doi:10.1111/jedm.12166>).
Implements heuristics for the Quadratic Assignment Problem (QAP). Currently only a simulated annealing heuristic is available.
Estimates QAPE using bootstrap procedures. The residual, parametric and double bootstrap is used. The test of normality using Cholesky decomposition is added.
Calculates the right-tail probability of quadratic forms of Gaussian variables using the skewness-kurtosis ratio matching method, modified Liu-Tang-Zhang method and Satterthwaite-Welch method. The technical details can be found in Hong Zhang, Judong Shen and Zheyang Wu (2020) <arXiv:2005.00905>.
Functions for a scientific quality assurance of meteorological data.
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.
Query the Breeding Management System 'BMS' database (using 'BrAPI' calls) to help breeders as targeted end-users retrieving data directly into their analyzing pipelines.
An extensive set of functions to perform Qualitative Comparative Analysis:
             crisp sets ('csQCA'), temporal ('tQCA'), multi-value ('mvQCA')
             and fuzzy sets ('fsQCA'), using a GUI - graphical user interface.
             'QCA' is a methodology that bridges the qualitative and quantitative divide
             in social science research. It uses a Boolean algorithm, resulting in a
             minimal causal configuration that explains a given phenomenon.
Clustered set-relational data in Qualitative Comparative Analysis
    (QCA) can have a hierarchical structure, a panel structure or repeated cross
    sections. 'QCAcluster' allows QCA researchers to supplement the analysis
    of pooled the data with a disaggregated perspective focusing on selected 
    partitions of the data. The pooled data can be partitioned along the 
    dimensions of the clustered data (individual cross sections or time series) 
    to perform partition-specific truth table minimizations. Empirical 
    researchers can further calculate the weight that each partition has on the 
    parameters of the pooled solution and the diversity of the cases under 
    analysis within and across partitions 
    (see <https://ingorohlfing.github.io/QCAcluster/>).
Implements tests for Type I error in Qualitative Comparative Analysis (QCA) that take into account the multiple hypothesis tests inherent in the procedure. Tests can be carried out on three variants of QCA: crisp-set QCA (csQCA), multi-value QCA (mvQCA) and fuzzy-set QCA (fsQCA). For fsQCA, the fsQCApermTest() command implements a permutation test that provides 95% confidence intervals for the number of counterexamples and degree of consistency, respectively. The distributions of permuted values can be plotted against the observed values. For csQCA and mvQCA, simple binomial tests are implemented in csQCAbinTest() and mvQCAbinTest(), respectively.
Researchers working with Qualitative Comparative Analysis (QCA)
    can use the package to estimate power of a sufficient term
    using permutation tests. A term can be anything: A condition, conjunction or 
    disjunction of any combination of these. The package further allows users to plot 
    the estimation results and to estimate the number of cases required to achieve a
    certain level of power, given a prespecified null and alternative hypothesis.
    Reference for the article introducing power estimation for QCA is: 
    Rohlfing, Ingo (2018) <doi:10.1017/pan.2017.30> 
    (ungated version: <doi:10.17605/OSF.IO/PC4DF>).
Provides advanced functionality for performing configurational comparative research with Qualitative Comparative Analysis (QCA), including crisp-set, multi-value, and fuzzy-set QCA. It also offers advanced tools for sensitivity diagnostics and methodological evaluations of QCA.
Helper functions for Qualitative Comparative Analysis: evaluate and
    plot Boolean formulae on fuzzy set score data, apply Boolean operations, compute
    consistency and coverage measures.
CBA postprocessing algorithm that creates smaller models for datasets containing quantitative (numerical) attributes. Article describing QCBA is published in Tomas Kliegr (2017) <arXiv:1711.10166>. The package can also postprocess results of the SBRL package, which is no longer in CRAN, but can be obtained from <https://github.com/cran/sbrl>.
Shewhart quality control charts for continuous, attribute and count data. Cusum and EWMA charts. Operating characteristic curves. Process capability analysis. Pareto chart and cause-and-effect chart. Multivariate control charts.
Functions to calculate Average Sample Numbers (ASN), Average Run Length (ARL1) and value of k, k1 and k2 for quality control charts under repetitive sampling as given in Aslam et al. (2014) (<DOI:10.7232/iems.2014.13.1.101>).
Tools for (automated and manual) quality control of
  the results of Epigenome-Wide Association Studies.
Tools for (automated and manual) quality control of
  the results of Genome Wide Association Studies
Provides functions for the joint analysis of K sets
    of p-values obtained for a same list of items. This joint analysis is
    performed by querying a composed hypothesis, i.e. an arbitrary complex
    combination of simple hypotheses, as described in Mary-Huard et al.
    (2021) <arXiv:2104.14601>. The null distribution corresponding to the
    composed hypothesis of interest is obtained by fitting non-parametric
    mixtures models (one for each of the simple hypothesis of the complex
    combination). Type I error rate control is achieved through Bayesian
    False Discovery Rate control.  The 3 main functions of the package
    GetHinfo(), qch.fit() and qch.test() correspond to the 3 steps for
    querying a composed hypothesis (composed H0/H1 formulation, inferring
    the null distribution and testing the null hypothesis).
Quality control of chromatin immunoprecipitation libraries (ChIP-seq) by quantitative polymerase chain reaction (qPCR). This function calculates Enrichment value with respect to reference for each histone modification (specific to 'Vii7' software <http://www.thermofisher.com/ca/en/home/life-science/pcr/real-time-pcr/real-time-pcr-instruments/viia-7-real-time-pcr-system/viia-7-software.html>). This function is applicable to full panel of histone modifications described by International Human Epigenomic Consortium (IHEC).
Univariate and multivariate SQC tools that completes and increases
    the SQC techniques available in R. Apart from integrating different R packages devoted to SQC
    ('qcc','MSQC'), provides nonparametric tools that are highly useful when Gaussian assumption is not
    met. This package computes standard univariate control charts for individual measurements, X-bar,
    S, R, p, np, c, u, EWMA and CUSUM. In addition, it includes functions to perform multivariate
    control charts such as Hotelling T2, MEWMA and MCUSUM. As representative feature, multivariate
    nonparametric alternatives based on data depth are implemented in this package: r, Q and S control
    charts. In addition, Phase I and II control charts for functional data are included. This package also allows 
    the estimation of the most complete set of capability indices from first to fourth generation, covering the
    nonparametric alternatives, and performing the corresponding capability analysis graphical outputs,
    including the process capability plots.
Simulates a 5 qubit Quantum Computer and evaluates quantum circuits with 1,2
             qubit quantum gates.
Quantile correlation-sure independence screening (QC-SIS) and composite quantile correlation-sure independence screening (CQC-SIS) for ultrahigh-dimensional data.
Primarily, the 'qcv' package computes key indices related to the Quantifying Construct
    Validity procedure (QCV; Westen & Rosenthal, 2003 <doi:10.1037/0022-3514.84.3.608>; 
    see also Furr & Heuckeroth, in press). The qcv() function is the heart of the 'qcv' package, 
    but additional functions in the package provide useful ancillary information related to the QCV procedure.
Automates many of the tasks associated with quantitative discourse analysis of transcripts containing discourse
              including frequency counts of sentence types, words, sentences, turns of talk, syllables and other assorted
              analysis tasks. The package provides parsing tools for preparing transcript data. Many functions enable the user
              to aggregate data by any number of grouping variables, providing analysis and seamless integration with other R
              packages that undertake higher level analysis and visualization of text. This affords the user a more efficient
              and targeted analysis. 'qdap' is designed for transcript analysis, however, many functions are applicable to other
              areas of Text Mining/ Natural Language Processing.
A collection of text analysis dictionaries and word lists for use with
        the 'qdap' package.
A collection of regular expression tools associated with
        the 'qdap' package that may be useful outside of the context of
        discourse analysis. Tools include
        removal/extraction/replacement of abbreviations, dates, dollar
        amounts, email addresses, hash tags, numbers, percentages,
        citations, person tags, phone numbers, times, and zip codes.
A collection of tools associated with the 'qdap' package that may be useful outside of the
            context of text analysis.
Allows practitioners to determine (i) if two univariate distributions (which can be continuous, discrete, or even mixed) are equal, (ii) how two distributions differ (shape differences, e.g., location, scale, etc.), and (iii) where two distributions differ (at which quantiles), all using nonparametric LP statistics. The primary reference is Jungreis, D. (2019, Technical Report).
Calculate the risk of developing type 2 diabetes using risk prediction algorithms derived by 'ClinRisk'.
This package provides different specifications of a Quadrilateral
    Dissimilarity Model which can be used to fit same-different judgments
    in order to get a predicted matrix that satisfies regular minimality
    [Colonius & Dzhafarov, 2006, Measurement and representations of
    sensations, Erlbaum]. From such a matrix, Fechnerian distances can be
    computed.
The computation of quadratic form (QF) distributions is often not trivial and it requires numerical routines. The package contains functions aimed at evaluating the exact distribution of quadratic forms (QFs) and ratios of QFs. In particular, we propose to evaluate density, quantile and distribution functions of positive definite QFs and ratio of independent positive QFs by means of an algorithm based on the numerical inversion of Mellin transforms.
Accurate estimates of the diets of predators are required
    in many areas of ecology, but for many species current methods are
    imprecise, limited to the last meal, and often biased. The diversity
    of fatty acids and their patterns in organisms, coupled with the
    narrow limitations on their biosynthesis, properties of digestion in
    monogastric animals, and the prevalence of large storage reservoirs of
    lipid in many predators, led to the development of quantitative
    fatty acid signature analysis (QFASA) to study predator diets.
An implementation of Quantitative Fatty Acid Signature
    Analysis (QFASA) in R.  QFASA is a method of estimating the diet
    composition of predators.  The fundamental unit of information in
    QFASA is a fatty acid signature (signature), which is a vector of
    proportions describing the composition of fatty acids within lipids.
    Signature data from at least one predator and from samples of all
    potential prey types are required.  Calibration coefficients, which
    adjust for the differential metabolism of individual fatty acids by
    predators, are also required. Given those data inputs, a predator
    signature is modeled as a mixture of prey signatures and its diet
    estimate is obtained as the mixture that minimizes a measure of
    distance between the observed and modeled signatures.  A variety of
    estimation options and simulation capabilities are implemented.
    Please refer to the vignette for additional details and references.
Option pricing (financial derivatives) techniques mainly following textbook 'Options, Futures and Other Derivatives', 9ed by John C.Hull, 2014. Prentice Hall. Implementations are via binomial tree option model (BOPM), Black-Scholes model, Monte Carlo simulations, etc. 
  This package is a result of Quantitative Financial Risk Management course (STAT 449 and STAT 649) at Rice University, Houston, TX, USA, taught by Oleg Melnikov, statistics PhD student, as of Spring 2015.
Smooth additive quantile regression models, fitted using
    the methods of Fasiolo et al. (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.
General purpose toolbox for simulating quantum versions of game theoretic models (Flitney and Abbott 2002) <arXiv:quant-ph/0208069>. Quantum (Nielsen and Chuang 2010, ISBN:978-1-107-00217-3) versions of models that have been handled are: Penny Flip Game (David A. Meyer 1998) <arXiv:quant-ph/9804010>, Prisoner's Dilemma (J. Orlin Grabbe 2005) <arXiv:quant-ph/0506219>, Two Person Duel (Flitney and Abbott 2004) <arXiv:quant-ph/0305058>, Battle of the Sexes (Nawaz and Toor 2004) <arXiv:quant-ph/0110096>, Hawk and Dove Game (Nawaz and Toor 2010) <arXiv:quant-ph/0108075>, Newcomb's Paradox (Piotrowski and Sladkowski 2002) <arXiv:quant-ph/0202074> and Monty Hall Problem (Flitney and Abbott 2002) <arXiv:quant-ph/0109035>.
Density, distribution function, quantile function and 
  random generation for the q-gaussian distribution with parameters mu and sig.
G-computation for a set of time-fixed exposures
    with quantile-based basis functions, possibly under linearity and
    homogeneity assumptions. This approach estimates a regression line
    corresponding to the expected change in the outcome (on the link
    basis) given a simultaneous increase in the quantile-based category
    for all exposures. Works with continuous, binary, and right-censored
    time-to-event outcomes.  Reference: Alexander P. Keil, Jessie P.
    Buckley, Katie M. OBrien, Kelly K. Ferguson, Shanshan Zhao, and
    Alexandra J. White (2019) A quantile-based g-computation approach to
    addressing the effects of exposure mixtures; <doi:10.1289/EHP5838>.
G-computation for a set of time-fixed exposures
    with quantile-based basis functions, possibly under linearity and
    homogeneity assumptions. Effect measure modification in this method is a way
    to assess how the effect of the mixture varies by a binary, categorical or continuous variable.  
    Reference: Alexander P. Keil, Jessie P.
    Buckley, Katie M. OBrien, Kelly K. Ferguson, Shanshan Zhao, and
    Alexandra J. White (2019) A quantile-based g-computation approach to
    addressing the effects of exposure mixtures; <doi:10.1289/EHP5838>.
Provides an infrastructure for efficient processing of large-scale genetic and phenotypic data including core functions for: 1) fitting linear mixed models, 2) constructing marker-based genomic relationship matrices, 3) estimating genetic parameters (heritability and correlation), 4) performing genomic prediction and genetic risk profiling, and 5) single or multi-marker association analyses.
    Rohde et al. (2019) <doi:10.1101/503631>.
Compute various quantitative genetics parameters from a Generalised Linear Mixed Model (GLMM) estimates. Especially, it yields the observed phenotypic mean, phenotypic variance and additive genetic variance.
Weighted network visualization and analysis, as well as Gaussian graphical model computation. See Epskamp et al. (2012) <doi:10.18637/jss.v048.i04>.
A 'shiny' application for teaching introductory quantitative genetics
    and plant breeding through interactive simulations. The application relies on 
    established plant breeding and quantitative genetic theory found in 
    Falconer and Mackay (1996, ISBN:0582243025) and Bernardo (2010, ISBN:978-0972072427).
Two linear mixed model approaches: REML(restricted maximum likelihood) and MINQUE (minimum norm quadratic unbiased estimation) approaches and several resampling techniques are integrated for various quantitative genetics analyses. With these two types of approaches, various unbalanced data structures, missing data, and any irregular genetic  mating designs can be analyzed and statistically tested. This package also offers fast computations for many large data sets.
Multivariate description of the state changes of a qualitative variable by  
             Correspondence Analysis and Clustering. See:
             Deville, J.C., & Saporta, G. (1983). 
                 Correspondence analysis, with an extension towards nominal time series. 
                 Journal of econometrics, 22(1-2), 169-189.
             Corrales, M.L., & Pardo, C.E. (2015) <doi:10.15332/s2027-3355.2015.0001.01>. 
                 Analisis de datos longitudinales cualitativos con analisis de correspondencias y clasificacion. 
                 Comunicaciones en Estadistica, 8(1), 11-32.
This function produces both the numerical and graphical summaries of the QTL hotspot detection in the genomes that are available on the worldwide web including the flanking markers of QTLs.
Functions for making run charts 
    [Anhoej, Olesen (2014) <doi:10.1371/journal.pone.0113825>] and basic
    Shewhart control charts [Mohammed, Worthington, Woodall (2008)
    <doi:10.1136/qshc.2004.012047>] for measure and count data.
    The main function, qic(), creates run and control charts and has a
    simple interface with a rich set of options to control data analysis
    and plotting, including options for automatic data aggregation by
    subgroups, easy analysis of before-and-after data, exclusion of one
    or more data points from analysis, and splitting charts into
    sequential time periods.
    Missing values and empty subgroups are handled gracefully.
Functions for making run charts, Shewhart control charts and
    Pareto charts for continuous quality improvement. Included control charts
    are: I, MR, Xbar, S, T, C, U, U', P, P', and G charts. Non-random variation
    in the form of minor to moderate persistent shifts in data over time is
    identified by the Anhoej rules for unusually long runs and unusually few
    crossing  [Anhoej, Olesen (2014) <doi:10.1371/journal.pone.0113825>].
    Non-random variation in the form of larger, possibly transient, shifts is
    identified by Shewhart's 3-sigma rule [Mohammed, Worthington, Woodall (2008)
    <doi:10.1136/qshc.2004.012047>].
Developed to perform the estimation and inference for regression 
    coefficient parameters in longitudinal marginal models using the method of 
    quadratic inference functions. Like generalized estimating equations, this 
    method is also a quasi-likelihood inference method. It has been showed that 
    the method gives consistent estimators of the regression coefficients even if 
    the correlation structure is misspecified, and it is more efficient than GEE 
    when the correlation structure is misspecified. Based on Qu, A., Lindsay, 
    B.G. and Li, B. (2000) <doi:10.1093/biomet/87.4.823>.
Qiita is a technical knowledge sharing and collaboration platform for programmers.
  See <https://qiita.com/api/v2/docs> for more information.
Nonlinear machine learning tool for classification, clustering
        and dimensionality reduction. It integrates 12 q-kernel functions and
        15 conditional negative definite kernel functions and includes the 
        q-kernel and conditional negative definite kernel version of
        density-based spatial clustering of applications with noise,
        spectral clustering, generalized discriminant analysis, principal
        component analysis, multidimensional scaling, locally linear embedding,
        sammon's mapping and t-Distributed stochastic neighbor embedding.
Extension of the functionality of the Matrix package for using sparse matrices. Some of the functions are very general, while other are highly specific for special data format as used for quantitative language comparison (QLC).
Functions to implement Q-learning for estimating optimal
        dynamic treatment regimes from two stage sequentially
        randomized trials, and to perform inference via m-out-of-n
        bootstrap for parameters indexing the optimal regime.
Implements Q-Learning, a model-free form of reinforcement
        learning, described in work by Strehl, Li, Wiewiora, Langford &
        Littman (2006) <doi:10.1145/1143844.1143955>.
Empirical adjustment of the distribution of variables originating from (regional) climate model simulations using quantile mapping.
A multivariate copula-based dependence measure. For more information, see Griessenberger, Junker, Trutschnig (2021), On a multivariate copula-based dependence measure and its estimation, <arXiv:2109.12883>.
Analysis of Q methodology, used to identify distinct perspectives existing within a group.
  This methodology is used across social, health and environmental sciences to understand diversity of attitudes, discourses, or decision-making styles (for more information, see <https://qmethod.org/>).
  A single function runs the full analysis. Each step can be run separately using the corresponding functions: for automatic flagging of Q-sorts (manual flagging is optional), for statement scores, for distinguishing and consensus statements, and for general characteristics of the factors.
  The package allows to choose either principal components or centroid factor extraction, manual or automatic flagging, a number of mathematical methods for rotation (or none), and a number of correlation coefficients for the initial correlation matrix, among many other options.
  Additional functions are available to import and export data (from raw *.CSV, 'HTMLQ' and 'FlashQ' *.CSV, 'PQMethod' *.DAT and 'easy-htmlq' *.JSON files), to print and plot, to import raw data from individual *.CSV files, and to make printable cards.
  The package also offers functions to print Q cards and to generate Q distributions for study administration.
  See further details in the package documentation, and in the web pages below, which include a cookbook, guidelines for more advanced analysis (how to perform manual flagging or change the sign of factors), data management, and a beta graphical user interface for online and offline use.
Estimates finite quantile mixture models using Markov chain Monte Carlo methods. The finite quantile mixture models include both fixed- and random-quantile specifications that are applicable to both continuous and binary dependent variables. Tools are available to assess convergence and summarize the estimation results.
Implementation of methods for estimation of quantitative maps
    from Multi-Parameter Mapping (MPM) acquisitions (Weiskopf et al. (2013)
    <doi:10.3389/fnins.2013.00095>) including adaptive
    smoothing methods in the framework of the ESTATICS model
    (Estimating the apparent transverse relaxation time (R2*) from images with
    different contrasts, Weiskopf et al. (2014) <doi:10.3389/fnins.2014.00278>).
    The smoothing method is described in Mohammadi et al. (2017).
    <doi:10.20347/WIAS.PREPRINT.2432>. Usage of the package is also described in
    Polzehl and Tabelow (2019),
    Magnetic Resonance Brain Imaging, Chapter 6, Springer, Use R! Series.
    <doi:10.1007/978-3-030-29184-6_6>.
Basic functions for building parsers, with an application to PC-AXIS format files.
Model fitting, optimal model selection and calculation of various features that are essential in the analysis of quantitative real-time polymerase chain reaction (qPCR).
Content-preserving transformations transformations of PDF files such 
    as split, combine, and compress. This package interfaces directly to the 'qpdf' 
    C++ API and does not require any command line utilities. Note that 'qpdf' does
    not read actual content from PDF files: to extract text and data you need the
    'pdftools' package.
Efficiently solve quadratic problems with linear inequality, equality and box constraints. The method used is outlined in D. Goldfarb, and A. Idnani (1983) <doi:10.1007/BF02591962>.
Active set method solver for the solution of indefinite quadratic programs, subject to lower bounds on linear functions of the variables and simple bounds on the variables themselves. The function QPmin() implements an algorithm similar to the one described in Gould (1991) <doi:10.1093/imanum/11.3.299>  with the exception that an efficient sparse internal representation of the basis matrix is maintained thus allowing the solution of somewhat large problems.
Computes noncompartmental pharmacokinetic parameters
 for drug concentration profiles.  For each profile, data
 imputations and adjustments are made as necessary and
 basic parameters are estimated. Supports single dose, multi-dose,
 and multi-subject data.  Supports steady-state calculations
 and various routes of drug administration. See ?qpNCA and vignettes.
 Methodology follows Rowland and Tozer (2011, ISBN:978-0-683-07404-8), 
 Gabrielsson and Weiner (1997, ISBN:978-91-9765-100-4), and
 Gibaldi and Perrier (1982, ISBN:978-0824710422).
A system to implement the Q-Q boxplot.  It is implemented as an
    extension to 'ggplot2'.  The Q-Q boxplot is an amalgam of the boxplot and the
    Q-Q plot and allows the user to rapidly examine summary statistics and tail
    behavior for multiple distributions in the same pane.  As an extension of
    the 'ggplot2' implementation of the boxplot, possible modifications to the 
    boxplot extend to the Q-Q boxplot.
Provides functionality for creating Quantile-Quantile (QQ) and Probability-Probability (PP) plots with simultaneous 
    testing bands to asses significance of sample deviation from a reference distribution.
Create Q-Q and manhattan plots for GWAS data from PLINK results.
Provides users the necessary utility functions to generate permutation-based QQ plots and also estimate inflation factor based on the empirical NULL distribution. While it has general utility, it is particularly helpful when the skewness of the Fisher's Exact test in sparse data situations with imbalanced case-control sample sizes renders the reliance on the uniform chi-square expected distribution inappropriate.
Extensions of 'ggplot2' Q-Q plot functionalities.
Get data about the Brazilian soccer championship since 2014. Official data can 
  be found at <https://www.cbf.com.br/futebol-brasileiro/competicoes/campeonato-brasileiro-serie-a/>.
Provides the function qqtest which incorporates uncertainty in its
    qqplot display(s) so that the user might have a better sense of the
    evidence against the specified distributional hypothesis.  qqtest draws a
    quantile quantile plot for visually assessing whether the data come from a
    test distribution that has been defined in one of many ways.  The vertical
    axis plots the data quantiles, the horizontal those of a test distribution.
    The default behaviour generates 1000 samples from the test distribution and
    overlays the plot with shaded pointwise interval estimates for the ordered
    quantiles from the test distribution.  A small number of independently
    generated exemplar quantile plots can also be overlaid.  Both the interval
    estimates and the exemplars provide different comparative information to
    assess the evidence provided by the qqplot for or against the hypothesis
    that the data come from the test distribution (default is normal or
    gaussian).  Finally, a visual test of significance (a lineup plot) can also
    be displayed to test the null hypothesis that the data come from the test
    distribution.
Presents an explanatory animation of normal quantile-quantile plots based on a water-filling analogy.  The animation presents a normal QQ plot as the parametric plot of the water levels in vases defined by two distributions.  The distributions decorate the axes in the normal QQ plot and are optionally shown as vases adjacent to the plot.  The package draws QQ plots for several distributions, either as samples or continuous functions.
Functions are provided that implement the 
    use of the Fieller's formula methodology, for 
    calculating a confidence interval for a ratio of
    (commonly, correlated) means.  See Fieller (1954)
    <doi:10.1111/j.2517-6161.1954.tb00159.x>.  Here,
    the application of primary interest is to studies
    of insect mortality response to increasing doses
    of a fumigant, or, e.g., to time in coolstorage.
    The formula is used to calculate a confidence
    interval for the dose or time required to achieve
    a specified mortality proportion, commonly 0.5 
    or 0.99.  Vignettes demonstrate link functions
    that may be considered, checks on fitted models,
    and alternative choices of error family.  Note
    in particular the betabinomial error family.
    See also Maindonald, Waddell, and Petry (2001) 
    <doi:10.1016/S0925-5214(01)00082-5>.
Upload raster data and easily create interactive quantitative risk analysis 'QRA' visualizations. Select
    from numerous color palettes, base-maps, and different configurations.
Tools that create D3 JavaScript force directed graph from R. D3 JavaScript was created by Michael Bostock. See http://d3js.org/ and, more specifically for Force Directed Graph https://github.com/mbostock/d3/wiki/Force-Layout.
The ANU Quantum Random Number Generator provided by the Australian National University
 generates true random numbers in real-time by measuring the quantum fluctuations of the vacuum. This package offers an interface using their API.
 The electromagnetic field of the vacuum exhibits random fluctuations in phase and amplitude at all frequencies.
 By carefully measuring these fluctuations, one is able to generate ultra-high bandwidth random numbers.
 The quantum Random Number Generator is based on the papers by Symul et al., (2011) <doi:10.1063/1.3597793>
 and Haw, et al. (2015) <doi:10.1103/PhysRevApplied.3.054004>.
 The package offers functions to retrieve a sequence of random integers or hexadecimals and true random samples from a normal or uniform distribution.
A Quantile Rank-score based test for the identification of expression quantitative trait loci.
Parametric modeling of quantile regression coefficient functions.
Nonlinear and Penalized parametric modeling of quantile regression coefficient functions. Sottile G, Frumento P, Chiodi M and Bottai M (2020) <doi:10.1177/1471082X19825523>.
Quantile regression in varying-coefficient models (VCM) using one particular nonparametric technique called P-splines. The functions can be applied on three types of VCM; (1) Homoscedastic VCM, (2) Simple heteroscedastic VCM, and (3) General heteroscedastic VCM.
Quick Response codes (QR codes) are a type of matrix bar code and can be
    used to authenticate transactions, provide access to multi-factor authentication
    services and enable general data transfer in an image. QR codes use four standardized 
    encoding modes (numeric, alphanumeric, byte/binary, and kanji) to efficiently store 
    data. Matrix barcode generation is performed efficiently in C via the included
    'libqrencoder' library created by Kentaro Fukuchi.
The QRI_func() function performs quantile regression analysis using age and sex as predictors to calculate the Quantile Regression Index (QRI) score for each individual’s regional brain imaging metrics and then averages across the regional scores to generate an average tissue specific score for each subject. The QRI_plot() is used to plot QRI and generate the normative curves for individual measurements.
This function aims to calculate risk of developing cardiovascular disease of individual patients in next 10 years. The package was based on published risk prediction algorithm <https://qrisk.org/three/src.php>.
Joint estimation of quantile specific intercept and slope parameters in a linear regression setting.
Quantile regression (QR) for Linear 
             Mixed-Effects Models via the asymmetric Laplace distribution (ALD). 
             It uses the Stochastic Approximation of the EM (SAEM) algorithm for 
             deriving exact maximum likelihood estimates and full inference results 
             for the fixed-effects and variance components. 
             It also provides graphical summaries for assessing the algorithm 
             convergence and fitting results.
Provides functions/methods to accompany the book
 Quantitative Risk Management: Concepts, Techniques and Tools by
 Alexander J. McNeil, Ruediger Frey, and Paul Embrechts.
Various data sets (stocks, stock indices, constituent data, FX,
 zero-coupon bond yield curves, volatility, commodities) for Quantitative
 Risk Management practice.
Implements the robust algorithm for fitting finite mixture models based on quantile regression proposed by Emir et al., 2017 (unpublished).
Functions and data sets for reproducing selected results from
  the book "Quantitative Risk Management: Concepts, Techniques and Tools".
  Furthermore, new developments and auxiliary functions for Quantitative
  Risk Management practice.
Functionality for generating (randomized) quasi-random numbers in
  high dimensions.
Quantile regression (QR) for Nonlinear
             Mixed-Effects Models via the asymmetric Laplace distribution (ALD). 
             It uses the Stochastic Approximation of the EM (SAEM) algorithm for 
             deriving exact maximum likelihood estimates and full inference results 
             for the fixed-effects and variance components.
             It also provides prediction and graphical summaries for assessing the algorithm
             convergence and fitting results.
Fit quantile regression neural network models with optional
    left censoring, partial monotonicity constraints, generalized additive
    model constraints, and the ability to fit multiple non-crossing quantile
    functions following Cannon (2011) <doi:10.1016/j.cageo.2010.07.005>
    and Cannon (2018) <doi:10.1007/s00477-018-1573-6>.
Provides functions for quickly writing and reading any R object to and from disk.
Molecular descriptors and outcomes for several public domain data sets
A quantum computer simulator framework with up to 24 qubits. It allows to
    define general single qubit gates and general controlled single
    qubit gates. For convenience, it currently provides the
    most common gates (X, Y, Z, H, Z, S, T, Rx, Ry, Rz, CNOT, SWAP, Toffoli or
    CCNOT, Fredkin or CSWAP). 'qsimulatR' supports plotting of circuits and is able to
    export circuits to 'Qiskit' <https://qiskit.org/>, a python package
    which can be used to run on IBM's hardware <https://quantum-computing.ibm.com/>.
Computes scores from Q-sort data, using criteria sorts and
  derived scales from subsets of items.
  The 'qsort' package includes descriptions and scoring procedures
  for four different Q-sets commonly used in developmental psychology research:
  Attachment Q-set (version 3.0) (Waters, 1995, <doi:10.1111/j.1540-5834.1995.tb00214.x>);
  California Child Q-set (Block and Block, 1969, <doi:10.1037/0012-1649.21.3.508>);
  Maternal Behaviour Q-set (version 3.1)
  (Pederson et al., 1999, <https://ir.lib.uwo.ca/cgi/viewcontent.cgi?article=1000&context=psychologypub>);
  Preschool Q-set (Baumrind, 1968 revised by Wanda Bronson, <doi:10.1111/j.1540-5834.1995.tb00214.x>).
Provides functions for quickly writing (and 
  reading back) a data.frame to file in 'SQLite' format. The name 
  stands for *Store Tables using 'SQLite'*, or alternatively for *Quick 
  Store Tables* (either way, it could be pronounced as *Quest*). 
  For data.frames containing the supported data 
  types it is intended to work as a drop-in replacement for the 
  'write_*()' and 'read_*()' functions provided by similar packages.
Run lapply() calls in parallel by submitting them to 
    'gridengine' clusters using the 'qsub' command.
Provides several methods for computing the Quantile Treatment Effect (QTE) and Quantile Treatment Effect on the Treated (QTT). The main cases covered are (i) Treatment is randomly assigned, (ii) Treatment is as good as randomly assigned after conditioning on some covariates (also called conditional independence or selection on observables) using the methods developed in Firpo (2007) <doi:10.1111/j.1468-0262.2007.00738.x>, (iii) Identification is based on a Difference in Differences assumption (several varieties are available in the package e.g. Athey and Imbens (2006) <doi:10.1111/j.1468-0262.2006.00668.x> Callaway and Li (2019) <https://ssrn.com/abstract=3013341>, Callaway, Li, and Oka (2018) <doi:10.1016/j.jeconom.2018.06.008>).
Analysis of experimental crosses to identify genes
  (called quantitative trait loci, QTLs) contributing to variation in
  quantitative traits.
  Broman et al. (2003) <doi:10.1093/bioinformatics/btg112>.
Conduct multiple quantitative trait loci (QTL) mapping under the framework of random-QTL-effect linear mixed model. First, each position on the genome is detected in order to obtain a negative logarithm P-value curve against genome position. Then, all the peaks on each effect (additive or dominant) curve are viewed as potential QTL, all the effects of the potential QTL are included in a multi-QTL model, their effects are estimated by empirical Bayes in doubled haploid population or by adaptive lasso in F2 population, and true QTL are identified by likelihood radio test. See Wen et al. (2018) <doi:10.1093/bib/bby058>.
Provides a set of tools to perform quantitative
    trait locus (QTL) analysis in experimental crosses. It is a
    reimplementation of the 'R/qtl' package to better handle
    high-dimensional data and complex cross designs.
    Broman et al. (2018) <doi:10.1534/genetics.118.301595>.
Functions to convert data structures among the 'qtl2', 'qtl', and 'DOQTL' packages for mapping quantitative trait loci (QTL).
Uses the 'fst' package to store genotype probabilities on disk for the 'qtl2' package. These genotype probabilities are a central data object for mapping quantitative trait loci (QTL), but they can be quite large. The facilities in this package enable the genotype probabilities to be stored on disk, leading to reduced memory usage with only a modest increase in computation time.
Functions to plot QTL (quantitative trait loci) analysis
    results and related diagnostics.
    Part of 'qtl2', an upgrade of the 'qtl'
    package to better handle high-dimensional data and complex cross
    designs.
Routines in 'qtl2' to study allele patterns in quantitative trait loci (QTL)
    mapping over a chromosome.
    Useful in crosses with more than two alleles to identify how sets of alleles,
    genetically different strands at the same locus, have different response levels.
    Plots show profiles over a chromosome.
    Can handle multiple traits together.
    See <https://github.com/byandell/qtl2pattern>.
We implement an
    adaptation of Jiang & Zeng's (1995) <https://www.genetics.org/content/140/3/1111> likelihood ratio test for testing
    the null hypothesis of pleiotropy against the alternative hypothesis,
    two separate quantitative trait loci. The test differs from that in Jiang & Zeng (1995) <https://www.genetics.org/content/140/3/1111> 
    and that in Tian et al. (2016) <doi:10.1534/genetics.115.183624> in
    that our test accommodates multiparental populations.
Datasets for the book, A Guide to QTL Mapping with R/qtl.
    Broman and Sen (2009) <doi:10.1007/978-0-387-92125-9>.
Densitometric evaluation of the photo-archived quantitative thin-layer chromatography (TLC) plates.
Web-based interactive charts (using D3.js) for the analysis of
    experimental crosses to identify genetic loci (quantitative trait
    loci, QTL) contributing to variation in quantitative traits.
    Broman (2015) <doi:10.1534/genetics.114.172742>.
Functions to infer co-mapping trait hotspots and causal models.
  Chaibub Neto E, Keller MP, Broman AF, Attie AD, Jansen RC, Broman KW, Yandell BS (2012) 
  Quantile-based permutation thresholds for QTL hotspots. Genetics 191 : 1355-1365. 
  <doi:10.1534/genetics.112.139451>.
  Chaibub Neto E, Broman AT, Keller MP, Attie AD, Zhang B, Zhu J, Yandell BS (2013)
  Modeling causality for pairs of phenotypes in system genetics. Genetics 193 : 1003-1013.
  <doi:10.1534/genetics.112.147124>.
Provides tools for joint analysis of multiple traits in a backcross (BC) or recombinant inbred lines (RIL) population. It can be used to select an optimal subset of traits for multiple-trait mapping, analyze multiple traits via the SURE model, which can associate different QTL with different traits, and perform multiple-trait composite multiple-interval mapping.
Functions to Simultaneously Infer Causal Graphs and Genetic Architecture.
  Includes acyclic and cyclic graphs for data from an experimental cross with a modest number (<10) of phenotypes driven by
  a few genetic loci (QTL).
  Chaibub Neto E, Keller MP, Attie AD, Yandell BS (2010)
  Causal Graphical Models in Systems Genetics: a unified framework for joint inference of causal network and genetic architecture for correlated phenotypes.
  Annals of Applied Statistics 4: 320-339.
  <doi:10.1214/09-AOAS288>.
This software provides tools for quantitative trait mapping in populations such as advanced intercross lines where relatedness among individuals should not be ignored. It can estimate background genetic variance components, impute missing genotypes, simulate genotypes, perform a genome scan for putative quantitative trait loci (QTL), and plot mapping results. It also has functions to calculate identity coefficients from pedigrees, especially suitable for pedigrees that consist of a large number of generations, or estimate identity coefficients from genotypic data in certain circumstances.
Provides methods for estimation of mean- and quantile-optimal treatment regimes from censored data. 
    Specifically, we have developed distinct functions for three types of right censoring for static treatment using quantile criterion: (1) independent/random censoring, (2) treatment-dependent random censoring, and (3) covariates-dependent random censoring. It also includes a function to estimate quantile-optimal dynamic treatment regimes for independent censored data. Finally, this package also includes a simulation data generative model of a dynamic treatment experiment proposed in literature.
Functions for unconditional and conditional quantiles. These
    include methods for transformation-based quantile regression,
    quantile-based measures of location, scale and shape, methods for quantiles
    of discrete variables, quantile-based multiple imputation, restricted
    quantile regression, and directional quantile classification. A vignette
	is given in Geraci (2016, The R Journal) <doi:10.32614/RJ-2016-037> and
	included in the package.
Quantitative Analysis of Complex Networks. This package offers a set of topological network measures to analyze complex Networks structurally.
This package gives you the exact first four permutation moments for the most commonly used quadratic form statistics, which need not be positive definite. The extension of this work to quadratic forms greatly expands the utility of density approximations for these problems, including for high-dimensional applications, where the statistics must be extreme in order to exceed stringent testing thresholds. Approximate p-values are obtained by matching the exact moments to the Pearson family of distributions using the PearsonDS package.
Given inputs A,B and C, this package solves the matrix equation A*X^2 - B*X - C = 0.
Create surface forms from matrix or 'raster' data for flexible plotting and
 conversion to other mesh types. The functions 'quadmesh' or 'triangmesh'
 produce a continuous surface as a 'mesh3d' object as used by the 'rgl'
 package. This is used for plotting raster data in 3D (optionally with
 texture), and allows the application of a map projection without data loss and 
 many processing applications that are restricted by inflexible regular grid rasters.
 There are discrete forms of these continuous surfaces available with
 'dquadmesh' and 'dtriangmesh' functions.
This package contains routines and documentation for
        solving quadratic programming problems.
Extends the quadprog package to solve quadratic programs with
    absolute value constraints and absolute values in the objective function.
It will assist the user to find simple quadratic roots from any quadratic equation.
Fits classical sparse regression models with
    efficient active set algorithms by solving quadratic problems as described by 
    Grandvalet, Chiquet and Ambroise (2017) <arXiv:1210.2077>. Also provides a few 
    methods for model selection purpose (cross-validation, stability selection).
Provides functionality for working with raster-like quadtrees
    (also called “region quadtrees”), which allow for variable-sized
    cells. The package allows for flexibility in the quadtree creation
    process.  Several functions defining how to split and aggregate cells
    are provided, and custom functions can be written for both of these
    processes. In addition, quadtrees can be created using other quadtrees
    as “templates”, so that the new quadtree's structure is identical to
    the template quadtree. The package also includes functionality for
    modifying quadtrees, querying values, saving quadtrees to a file, and
    calculating least-cost paths using the quadtree as a resistance
    surface.
Exact one-sided p-values and confidence intervals for an outcome variable defined on an interval measurement scale with only qualitative and ordinal information available.
Used for testing for qualitative interactions between
    treatment effects and patient subgroups. Here the term treatment effect
    means a comparison result of two treatments within each patient subgroup.
    Models included in this package are Gaussian, binomial and Cox models.
    Methods included here are interval based graphical approach and Gail Simon
    LRT.
Provides a set of functions for taking qualitative GIS data, hand drawn on a map, and 
   converting it to a simple features object. These tools are focused on data that are drawn on a map
   that contains some type of polygon features. For each area identified on the map, the id numbers
   of these polygons can be entered as vectors and transformed using qualmap.
Automatic generation of distinct qualitative color palettes,
    optionally adapted to color deficiency. It takes a subspace of the HSL color
    space as input and projects it to the DIN99d color space where it selects
    and return colors that are maximally distinct.
Provides functions to access survey results directly into R
    using the 'Qualtrics' API. 'Qualtrics'
    <https://www.qualtrics.com/about/> is an online survey and data
    collection software platform. See <https://api.qualtrics.com/> for
    more information about the 'Qualtrics' API.  This package is
    community-maintained and is not officially supported by 'Qualtrics'.
Qualitative methods for the validation of dynamic models.
    It contains (i) an orthogonal set of deviance measures for absolute,
    relative and ordinal scale and (ii) approaches accounting for time
    shifts. The first approach transforms time to take time delays and speed
    differences into account. The second divides the time series into
    interval units according to their main features and finds the longest
    common subsequence (LCS) using a dynamic programming algorithm.
Implements indices of qualitative variation proposed by Wilcox
    (1973).
These functions use data augmentation and Bayesian techniques for the assessment of single-member and incomplete ensembles of climate projections. It provides unbiased estimates of climate change responses of all simulation chains and of all uncertainty variables. It additionally propagates uncertainty due to missing information in the estimates.
  - Evin, G., B. Hingray, J. Blanchet, N. Eckert, S. Morin, and D. Verfaillie. (2019) <doi:10.1175/JCLI-D-18-0606.1>.
These functions use smoothing-splines, data augmentation and Bayesian techniques for the assessment of single-member and incomplete ensembles of climate projections.
  - Cheng, C.-I. and P. L. Speckman (2012) <doi:10.1016/j.csda.2012.05.020>.
  - Evin, G., B. Hingray, J. Blanchet, N. Eckert, S. Morin, and D. Verfaillie. (2019) <doi:10.1175/JCLI-D-18-0606.1>.
Functions for interacting directly with the Quandl API to offer data in a number of formats usable in R, downloading a zip with all data from a Quandl database, and the ability to search. This R package uses the Quandl API. For more information go to <https://docs.quandl.com>. For more help on the package itself go to <https://www.quandl.com/tools/r>.
Methods which streamline the descriptive analysis of quantitative
    matrices. Matrix columns are samples while rows are features i.e. proteins, genes.
    Includes methods for visualization (e.g. Heatmaps, Volcanos, pairwise QQ, Bland-Altman plot),
    summary statistics (e.g. CV), data normalization methods (e.g. robustscale). Read function for Progenesis.
Functions to manipulate dates and count days for quantitative finance analysis. The 'quantdates' package considers leap, holidays and business days for relevant calendars in a financial context to simplify quantitative finance calculations, consistent with International Swaps and Derivatives Association (ISDA) (2006) <https://www.isda.org/book/2006-isda-definitions/> regulations.
An implementation of dimension reduction techniques
    for conditional quantiles. Nonparametric estimation of 
    conditional quantiles is also available.
A fast, flexible, and comprehensive framework for 
    quantitative text analysis in R.  Provides functionality for corpus management,
    creating and manipulating tokens and ngrams, exploring keywords in context, 
    forming and manipulating sparse matrices
    of documents by features and feature co-occurrences, analyzing keywords, computing feature similarities and
    distances, applying content dictionaries, applying supervised and unsupervised machine learning, 
    visually representing text and text analyses, and more.
Scaling models and classifiers for sparse matrix objects representing 
    textual data in the form of a document-feature matrix.  Includes original 
    implementations of 'Laver', 'Benoit', and Garry's (2003) <doi:10.1017/S0003055403000698>,
    'Wordscores' model, Perry and 'Benoit's' (2017) <arXiv:1710.08963> class affinity scaling model, 
    and 'Slapin' and 'Proksch's' (2008) <doi:10.1111/j.1540-5907.2008.00338.x> 'wordfish'
    model, as well as methods for correspondence analysis, latent semantic analysis,
    and fast Naive Bayes and linear 'SVMs' specially designed for sparse textual data.
Plotting functions for visualising textual data.  Extends 'quanteda' and 
   related packages with plot methods designed specifically for text data, textual statistics, 
   and models fit to textual data. Plot types include word clouds, lexical dispersion plots, 
   scaling plots, network visualisations, and word 'keyness' plots.
Textual statistics functions formerly in the 'quanteda' package.
    Textual statistics for characterizing and comparing textual data. Includes 
    functions for measuring term and document frequency, the co-occurrence of 
    words, similarity and distance between features and documents, feature entropy, 
    keyword occurrence, readability, and lexical diversity.  These functions 
    extend the 'quanteda' package and are specially designed for sparse textual data.
Provides different functions for quantifying qualitative survey data. It supports the Carlson-Parkin method, the regression approach, the balance approach and the conditional expectations method.
Code for centroid, median and quantile classifiers.
Based on Alan D. Hutson (1999) <doi:10.1080/02664769922458>, "Calculating nonparametric confidence intervals for quantiles using fractional order statistics", Journal of Applied Statistics, 26:3, 343-353.
Estimation and inference methods for the
    cross-quantilogram.  The cross-quantilogram is a measure of nonlinear
    dependence between two variables, based on either unconditional or
    conditional quantile functions.  The cross-quantilogram can be
    considered as an extension of the correlogram, which is a correlation
    function over multiple lag periods and mainly focuses on linear
    dependency.  One can use the cross-quantilogram to detect the presence
    of directional predictability from one time series to another.  This
    package provides a statistical inference method based on the
    stationary bootstrap.  See Linton and Whang (2007)
    <doi:10.1016/j.jeconom.2007.01.004> for univariate time series
    analysis and Han, Linton, Oka and Whang (2016)
    <doi:10.1016/j.jeconom.2016.03.001> for multivariate time series
    analysis.
Integration of the 'units' and 'errors' packages for a complete
    quantity calculus system for R vectors, matrices and arrays, with automatic
    propagation, conversion, derivation and simplification of magnitudes and
    uncertainties. Documentation about 'units' and 'errors' is provided in the
    papers by Pebesma, Mailund & Hiebert (2016, <doi:10.32614/RJ-2016-061>) and
    by Ucar, Pebesma & Azcorra (2018, <doi:10.32614/RJ-2018-075>), included in
    those packages as vignettes; see 'citation("quantities")' for details.
A re-implementation of quantile kriging. Quantile kriging was described by Plumlee and Tuo (2014) <doi:10.1080/00401706.2013.860919>.  With computational savings when dealing with replication from the recent paper by Binois, Gramacy, and Ludovski (2018) <doi:10.1080/10618600.2018.1458625> it is now possible to apply quantile kriging to a wider class of problems.  In addition to fitting the model, other useful tools are provided such as the ability to automatically perform leave-one-out cross validation.
Specify, build, trade, and analyse quantitative financial trading strategies.
Modifies the distance matrix obtained from data with batch effects, so as to improve the performance of sample pattern detection, such as clustering, dimension reduction, and construction of networks between subjects. The method has been published in Bioinformatics (Fei et al, 2018, <doi:10.1093/bioinformatics/bty117>). Also available on 'GitHub' <https://github.com/tengfei-emory/QuantNorm>.
Estimation methods for optimal treatment regimes under three different criteria, namely marginal quantile, marginal mean, and mean absolute difference. For the first two criteria, both one-stage and two-stage estimation method are implemented. A doubly robust estimator for estimating the quantile-optimal treatment regime is also included.
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 now included. See Koenker (2006) <doi:10.1017/CBO9780511754098> and
  Koenker et al. (2017) <doi:10.1201/9781315120256>.
Implements the nonparametric quantile regression method developed by Belloni, Chernozhukov, and Fernandez-Val (2011) to partially linear quantile models. Provides point estimates of the conditional quantile function and its derivatives based on series approximations to the nonparametric part of the model. Provides pointwise and uniform confidence intervals using analytic and resampling methods.
Quantile Regression Forests is a tree-based ensemble
        method for estimation of conditional quantiles. It is
        particularly well suited for high-dimensional data. Predictor
        variables of mixed classes can be handled. The package is
        dependent on the package 'randomForest', written by Andy Liaw.
Fits non-crossing regression quantiles as a function of linear covariates and multiple smooth terms, including varying coefficients, via B-splines with L1-norm difference penalties. 
    The smoothing parameters are estimated as part of the model fitting, see Muggeo and others (2021) <doi:10.1177/1471082X20929802>. Monotonicity and concavity 
    constraints on the fitted curves are allowed, see Muggeo and others (2013) <doi:10.1007/s10651-012-0232-1> and also <doi:10.13140/RG.2.2.12924.85122> for some code examples.
This is the implementation of quantile regression forests for the fast random forest package 'ranger'.
Methods to determine, smooth and plot quantile periodograms for
    univariate and multivariate time series. See Kley (2016) <doi:10.18637/jss.v070.i03>
    for a description and tutorial.
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.
Contains basic structures and operations used frequently in quantum computing. Intended to be a convenient tool to help learn quantum mechanics and algorithms. Can create arbitrarily sized kets and bras and implements quantum gates, inner products, and tensor products. Creates arbitrarily controlled versions of all gates and can simulate complete or partial measurements of kets. Has functionality to convert functions into equivalent quantum gates and model quantum noise. Includes larger applications, such as Steane error correction <DOI:10.1103/physrevlett.77.793>, Quantum Fourier Transform and Shor's algorithm (Shor 1999), Grover's algorithm (1996), Quantum Approximation Optimization Algorithm (QAOA) (Farhi, Goldstone, and Gutmann 2014) <arXiv:1411.4028>, and a variational quantum classifier (Schuld 2018) <arXiv:1804.00633>. Can be used with the gridsynth algorithm <arXiv:1212.6253> to perform decomposition into the Clifford+T set.
Enables the user to calculate Value at Risk (VaR)
    and Expected Shortfall (ES) by means of various types of historical
    simulation. Currently plain-, age-, volatility-weighted- and filtered
    historical simulation are implemented in this package. Volatility weighting
    can be carried out via an exponentially weighted moving average model
    (EWMA) or other GARCH-type models. The methods of the package are described in 
    Gurrola-Perez, P. and Murphy, D. (2015)
    <https://EconPapers.repec.org/RePEc:boe:boeewp:0525>.
An implementation of two interaction indices between extractive
    activity and groundwater resources based on hazard and vulnerability
    parameters used in the assessment of natural hazards. One index is based
    on a discrete choice model and the other is relying on an artificial
    neural network.
Calculates the number of four-taxon subtrees consistent with a pair
  of cladograms, calculating the symmetric quartet distance of Bandelt & Dress (1986),
  Reconstructing the shape of a tree from observed dissimilarity data,
  Advances in Applied Mathematics, 7, 309-343 <doi:10.1016/0196-8858(86)90038-2>, 
  and using the tqDist algorithm of Sand et al. (2014), tqDist: a library for
  computing the quartet and triplet distances between binary or general trees,
  Bioinformatics, 30, 2079–2080 <doi:10.1093/bioinformatics/btu157>
  for pairs of binary trees.
Convert R Markdown documents and 'Jupyter' notebooks to a variety of
  output formats using 'Quarto'.
Identify differentially expressed genes in RNA-seq count data using quasi-Poisson or quasi-negative binomial models with 'QL', 'QLShrink' and 'QLSpline' methods described by Lund, Nettleton, McCarthy, and Smyth (2012) <DOI:10.1515/1544-6115.1826>. Report bias-reduced estimates of log fold changes.
Various quantile-based clustering algorithms: algorithm CU (Common theta and Unscaled variables), algorithm CS (Common theta and Scaled variables through lambda_j), algorithm VU (Variable-wise theta_j and Unscaled variables) and algorithm VW (Variable-wise theta_j and Scaled variables through lambda_j). Hennig, Viroli, Anderlucci (2018) <arXiv:1806.10403v1>.
Translate 'SQL' 'SELECT' statements into lists of 'R' expressions.
Offers a suite of functions to prepare questionnaire data for analysis (perhaps other types of data as well). By data preparation, I mean data analytic tasks to get your raw data ready for statistical modeling (e.g., regression). There are functions to investigate missing data, reshape data, validate responses, recode variables, score questionnaires, center variables, aggregate by groups, shift scores (i.e., leads or lags), etc. It provides functions for both single level and multilevel (i.e., grouped) data. With a few exceptions (e.g., ncases()), functions without an "s" at the end of their primary word (e.g., center_by()) act on atomic vectors, while functions with an "s" at the end of their primary word (e.g., centers_by()) act on multiple columns of a data.frame.
Set of functions to make the processing and analysis of
    surveys easier : interactive shiny apps and addins for data recoding,
    contingency tables, dataset metadata handling, and several convenience
    functions.
Implementation of a computationally efficient method for
    simulating queues with arbitrary arrival and service times. 
    Please see Ebert, Wu, Mengersen & Ruggeri (2020, <doi:10.18637/jss.v095.i05>) 
    for further details.
It provides versatile tools for analysis of birth and death based Markovian Queueing Models
    and Single and Multiclass Product-Form Queueing Networks.
    It implements M/M/1, M/M/c, M/M/Infinite, M/M/1/K, M/M/c/K, M/M/c/c, M/M/1/K/K, M/M/c/K/K, M/M/c/K/m, M/M/Infinite/K/K,
    Multiple Channel Open Jackson Networks, Multiple Channel Closed Jackson Networks,
    Single Channel Multiple Class Open Networks, Single Channel Multiple Class Closed Networks
    and Single Channel Multiple Class Mixed Networks.
    Also it provides a B-Erlang, C-Erlang and Engset calculators.
    This work is dedicated to the memory of D. Sixto Rios Insua.
Calculates the Quandle, Rack and Degenerate Homology groups of
    Racks and Biracks (as well as Quandles and Biquandles). In addition, a test is
    provided to ascertain if a given set with one or two given functions is indeed a
    biquandle or not.
Provides functions for assigning treatments in randomized experiments using
    near-optimal threshold blocking. The package is made with large data sets in
    mind and derives blocks more than an order of magnitude quicker than other
    methods.
Provides several convenience functions for searching and pulling data from
    the 'USDA NASS Quick Stats API' <https://quickstats.nass.usda.gov/api>. 
    Users can easily search for specific data items, and then download county-level or 
    state-level Census of Agricultural data from a specified year.
While analyzing geospatial data, easy visualization is often
    needed that allows for quick plotting, and simple, but easy interactivity.
    Additionally, visualizing geospatial data in projected coordinates is also
    desirable. The 'quickmapr' package provides a simple method to visualize 
    'sp', 'sf' (via coercion to 'sp'), and 'raster' objects, allows for basic 
    zooming, panning, identifying,labeling, selecting, and measuring spatial 
    objects.  Importantly, it does not require that the data be in geographic 
    coordinates.
Provides functions for constructing near-optimal generalized full matching.
    Generalized full matching is an extension of the original full matching method
    to situations with more intricate study designs. The package is made with
    large data sets in mind and derives matches more than an order of magnitude
    quicker than other methods.
For fitting asymptotic N-mixture models. Asymptotic N-mixture models extend the work of Dail and Madsen (2011) <doi:10.1111/j.1541-0420.2010.01465.x>, to consider asymptotic solutions to the open population N-mixture models. The asymptotic models are derived and described in: "Parker, M.R.P., Elliott, L., Cowen, L.L.E., Cao, J. (2021). Asymptotic solutions for N-mixture models with count data [Manuscript in preparation]. Department of Statistics and Actuarial Sciences, Simon Fraser University.".
A high-level plotting system, built using 'grid' graphics, that is
    optimized for speed and modularity. This has great utility for quick
    visualizations when testing code, with the key benefit that visualizations
    are updated independently of one another.
Quickly fits and plots psychometric functions (normal, logistic,
    Weibull or any or any function defined by the user) for multiple groups.
A set of functions to extract results from regression models and
 plot the effect size using 'ggplot2' seamlessly. While 'broom' is useful to
 convert statistical analysis objects into tidy data frames, 'coefplot' is adept at showing
 multivariate regression results. With specific outcome, this package could build regression models
 automatically, extract results into a data frame and provide a quicker way to summarize
 models' statistical findings using 'ggplot2'.
Helps to perform linear regression analysis by reducing manual effort. Reduces the independent variables based on specified p-value and Variance Inflation Factor (VIF) level.
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.
Programmatic access to the 'PGS' Catalog.
    This package provides easy access to 'PGS' Catalog data by
    accessing the REST API <https://www.pgscatalog.org/rest/>.
Grows a qualitative interaction tree. Quint is a tool for subgroup analysis, suitable for data from a two-arm randomized controlled trial.
Diagnostics methods for quantile regression models for detecting influential observations:
  robust distance methods for general quantile regression models; generalized Cook's distance and 
  Q-function distance method for quantile regression models using aymmetric Laplace distribution. Reference
  of this method can be found in Luis E. Benites, Víctor H. Lachos, Filidor E. Vilca (2015) <arXiv:1509.05099v1>; 
  mean posterior probability and Kullback–Leibler divergence methods for Bayes quantile regression model.
  Reference of this method is Bruno Santos, Heleno Bolfarine (2016) <arXiv:1601.07344v1>.
A facility for writing functions that quote their arguments,
 may sometimes evaluate them in the environment where they were quoted,
 and may pass them as quoted to other functions.
Full text, in data frames containing one row per verse, of the 
    Qur'an in Arabic (with and without vowels) and in English (the Yusuf Ali 
    and Saheeh International translations), formatted to be convenient for 
    text analysis.
Thresholding based tests for null hypothesis of the form A beta =c, and the Quantile Universal Threshold (QUT) for lasso regularization of Generalized Linear Models (GLM) and square-root lasso to obtain a sparse model with a good compromise between high true positive rate and low false discovery rate. Giacobino et al. (2017) <doi:10.1214/17-EJS1366>. Sardy et al. (2017) <arXiv:1708.02908>.
Functions to compute quasi variances and associated measures of approximation error.
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 Joint and Individual Variation Explained (JIVE) decomposition on a list of data sets when the data share a dimension, returning low-rank matrices that capture the joint and individual structure of the data [O'Connell, MJ and Lock, EF (2016) <doi:10.1093/bioinformatics/btw324>]. It provides two methods of rank selection when the rank is unknown, a permutation test and a Bayesian Information Criterion (BIC) selection algorithm. Also included in the package are three plotting functions for visualizing the variance attributed to each data source: a bar plot that shows the percentages of the variability attributable to joint and individual structure, a heatmap that shows the structure of the variability, and principal component plots.
Methods readMat() and writeMat() for reading and writing MAT files.  For user with MATLAB v6 or newer installed (either locally or on a remote host), the package also provides methods for controlling MATLAB (trademark) via R and sending and retrieving data between R and MATLAB.
Methods that simplify the setup of S3 generic functions and S3 methods.  Major effort has been made in making definition of methods as simple as possible with a minimum of maintenance for package developers.  For example, generic functions are created automatically, if missing, and naming conflict are automatically solved, if possible.  The method setMethodS3() is a good start for those who in the future may want to migrate to S4.  This is a cross-platform package implemented in pure R that generates standard S3 methods.
Methods and classes for object-oriented programming in R with or without references.  Large effort has been made on making definition of methods as simple as possible with a minimum of maintenance for package developers.  The package has been developed since 2001 and is now considered very stable.  This is a cross-platform package implemented in pure R that defines standard S3 classes without any tricks.
The RSP markup language makes any text-based document come alive.  RSP provides a powerful markup for controlling the content and output of LaTeX, HTML, Markdown, AsciiDoc, Sweave and knitr documents (and more), e.g. 'Today's date is <%=Sys.Date()%>'.  Contrary to many other literate programming languages, with RSP it is straightforward to loop over mixtures of code and text sections, e.g. in month-by-month summaries.  RSP has also several preprocessing directives for incorporating static and dynamic contents of external files (local or online) among other things.  Functions rstring() and rcat() make it easy to process RSP strings, rsource() sources an RSP file as it was an R script, while rfile() compiles it (even online) into its final output format, e.g. rfile('report.tex.rsp') generates 'report.pdf' and rfile('report.md.rsp') generates 'report.html'.  RSP is ideal for self-contained scientific reports and R package vignettes.  It's easy to use - if you know how to write an R script, you'll be up and running within minutes.
Processing pipeline for 'SamBada' from pre- to post-processing. 
	'SamBada' is a landscape genomic software designed to run univariate or multivariate logistic regression between the presence of a genotype and one or several environmental variables. See Stucki (2017) <doi:10.1111/1755-0998.12629> and <https://github.com/Sylvie/sambada>. 
	The package provides functions that can be classified into four categories: 
	1) Install 'SamBada' 
	2) Preprocessing (prepare genomic file into standards compatible with 'SamBada' and apply quality-control; retrieve environmental conditions at sampling location; prepare environmental file including removal of correlated variables and computation of population structure) 
	3) Processing (run 'SamBada' on multiple cores using 'Supervision')
	4) Post-processing (calculate p-values and q-values, produce interactive Manhattan plots and query 'Ensembl' database, produce maps).
An integrated solution to perform
    a series of text mining tasks such as importing and cleaning a corpus, and
    analyses like terms and documents counts, lexical summary, terms
    co-occurrences and documents similarity measures, graphs of terms,
    correspondence analysis and hierarchical clustering. Corpora can be imported
    from spreadsheet-like files, directories of raw text files,
    as well as from 'Dow Jones Factiva', 'LexisNexis', 'Europresse' and 'Alceste' files.
Utility functions useful when programming and developing R packages.
Estimation of reproduction numbers for disease outbreak, based on
    incidence data. The R0 package implements several documented methods. It is
    therefore possible to compare estimations according to the methods used.
    Depending on the methods requested by user, basic reproduction number
    (commonly denoted as R0) or real-time reproduction number (referred to as
    R(t)) is computed, along with a 95% Confidence Interval. Plotting outputs
    will give different graphs depending on the methods requested : basic
    reproductive number estimations will only show the epidemic curve
    (collected data) and an adjusted model, whereas real-time methods will also
    show the R(t) variations throughout the outbreak time period. Sensitivity
    analysis tools are also provided, and allow for investigating effects of
    varying Generation Time distribution or time window on estimates.
This is a companion package of the book "R Programming: Zero to Pro"  <https://r02pro.github.io/>. It contains the datasets used in the book and provides interactive exercises corresponding to the book. It covers a wide range of topics including visualization, data transformation, tidying data, data input and output.
Utilities for sparse signal recovery suitable for compressed sensing. L1, L2 and TV penalties, DFT basis matrix, simple sparse signal generator, mutual cumulative coherence between two matrices and examples, Lp complex norm, scaling back regression coefficients.
Since December 2019, Wuhan City, Hubei Province has continued to carry out surveillance of influenza and related diseases, and found multiple cases of viral pneumonia, all of which were diagnosed with viral pneumonia / pulmonary infection. On January 12, 2020, the World Health Organization officially named the new coronavirus causing the pneumonia epidemic in Wuhan as "2019 New Coronavirus (2019-nCoV)". The current epidemic situation is very serious, here we developed an R package for 2019-nCoV analysis(Real-time monitoring and Visualization) by querying real-time statistics of 2019-nCoV virus cases from <https://view.inews.qq.com/g2/getOnsInfo?name=disease_h5&callback=1580373566110> and performing follow-up analysis.
R^2 measure of explained variation under the semiparametric additive hazards model is estimated. The measure can be used as a measure of predictive capability and therefore it can be adopted in model selection process. Rava, D. and Xu, R. (2020) <arXiv:2003.09460>.
A series of functions to call 'AD Model Builder' (i.e.,
    compile and run models) from within R, read the results back
    into R as 'admb' objects, and provide standard accessors (i.e.
    coef(), vcov(), etc.)
An R interface to estimate structured additive regression (STAR) models with 'BayesX'.
Multivariate optimal allocation for different domains in one and two stages stratified sample design. R2BEAT extends the Neyman (1934) – Tschuprow (1923) allocation method to the case of several variables, adopting a generalization of the Bethel’s proposal (1989).R2BEAT develops this methodology but, moreover, it allows to determine the sample allocation in the multivariate and multi-domains case of estimates for two-stage stratified samples. It also allows to perform both Primary Stage Units and Secondary Stage Units selection. This package requires the availability of ReGenesees, that can be installed from <https://github.com/DiegoZardetto/ReGenesees>.
This package provides generic functions to analyze the distribution
  of two continuous variables: 'conf2d' to calculate a smooth empirical
  confidence region, and 'freq2d' to calculate a frequency distribution.
Suite of tools for using 'D3', a library for producing dynamic, interactive data
  visualizations. Supports translating objects into 'D3' friendly data structures, rendering
  'D3' scripts, publishing 'D3' visualizations, incorporating 'D3' in R Markdown, creating
  interactive 'D3' applications with Shiny, and distributing 'D3' based 'htmlwidgets' in R
  packages.
Provides functions for aligning 2D gas chromatography mass spectrometry derived metabolite peaks obtained from primary processing and generates an alignment table that
        allows for a comparison of common peaks across samples and metabolite identification. Publication describing the package in detail is available at the following citation: Ryne C. Ramaker, Emily Gordon, Sara J. Cooper (2017) <doi:10.1101/179168>.
Despite the predominant use of 'R' for data manipulation and various robust statistical calculations, in recent years, more people from various disciplines are beginning to use 'R' for other purposes. A critical milestone that has enabled large influx of users to the 'R' community is the development of the 'Tidyverse' family of packages and 'Rmarkdown'. With the latter, one can write all kinds of documents and produce output in formats such 'HTML' and 'PDF' very easily. In doing this seemlessly, further tools are needed for such users to easily and freely write in 'R' for all kinds of purposes. The 'r2dictionary' introduces a means for users to directly search for definitions of terms from within the 'R' environment. The source dictionary is an original work of 'The Online Plain Text English Dictionary (OPTED)'.
These tools help you to assess if a corporate lending
    portfolio aligns with climate goals. They summarize key climate
    indicators attributed to the portfolio (e.g. production, emission
    factors), and calculate alignment targets based on climate scenarios.
    They implement in R the last step of the free software 'PACTA' (Paris
    Agreement Capital Transition Assessment;
    <https://2degrees-investing.org/>). Financial institutions use 'PACTA'
    to study how their capital allocation decisions align with climate
    change mitigation goals.
These datasets support the implementation in R of the
    software 'PACTA' (Paris Agreement Capital Transition Assessment),
    which is a free tool that calculates the alignment between corporate
    lending portfolios and climate scenarios
    (<https://2degrees-investing.org/>). Financial institutions use
    'PACTA' to study how their capital allocation decisions align with
    climate change mitigation goals. Because both financial institutions
    and market data providers keep their data private, this package
    provides fake, public data to enable the development and use of
    'PACTA' in R.
These tools implement in R a fundamental part of the software
    'PACTA' (Paris Agreement Capital Transition Assessment), which is a
    free tool that calculates the alignment between financial portfolios
    and climate scenarios (<https://2degrees-investing.org/>). Financial
    institutions use 'PACTA' to study how their capital allocation
    decisions align with climate change mitigation goals. This package
    matches data from corporate lending portfolios to asset level data
    from market-intelligence databases (e.g. power plant capacities,
    emission factors, etc.). This is the first step to assess if a
    financial portfolio aligns with climate goals.
Create plots to visualize the alignment of a corporate
    lending financial portfolio to climate change scenarios based on
    climate indicators (production and emission intensities) across key
    climate relevant sectors of the 'PACTA' methodology (Paris Agreement
    Capital Transition Assessment; <https://2degrees-investing.org/>).
    Financial institutions use 'PACTA' to study how their capital
    allocation decisions align with climate change mitigation goals.
2dRUE is a methodology to make a diagnostic of land
        condition in a large territory during a given time period. The
        following projects have funded this package: DeSurvey IP (EC
        FP6 Integrated Project contract No. 003950), DesertWatch (ESA
        DUE contract No. 18487/04/I-LG) and MesoTopos (Junta de
        Andalucia PE ref. RNM-4023).
Some heavily used base R functions are reconstructed to also be compliant to data.table objects. Also, some general helper functions that could be of interest for working with data.table objects are included.
The model R squared and semi-partial R squared for the linear and
    generalized linear mixed model (LMM and GLMM) are computed with confidence
    limits. The R squared measure from Edwards et.al (2008) <DOI:10.1002/sim.3429>
    is extended to the GLMM using penalized quasi-likelihood (PQL) estimation
    (see Jaeger et al. 2016 <DOI:10.1080/02664763.2016.1193725>). Three methods
    of computation are provided and described as follows. First, The
    Kenward-Roger approach. Due to some inconsistency between the 'pbkrtest'
    package and the 'glmmPQL' function, the Kenward-Roger approach in the
    'r2glmm' package is limited to the LMM. Second, The method introduced
    by Nakagawa and Schielzeth (2013) <DOI:10.1111/j.2041-210x.2012.00261.x>
    and later extended by Johnson (2014) <DOI:10.1111/2041-210X.12225>.
    The 'r2glmm' package only computes marginal R squared for the LMM and does
    not generalize the statistic to the GLMM; however, confidence limits and
    semi-partial R squared for fixed effects are useful additions. Lastly, an
    approach using standardized generalized variance (SGV) can be used for
    covariance model selection. Package installation instructions can be found
    in the readme file.
Includes HTML function and methods to write in an HTML
        file. Thus, making HTML reports is easy. Includes a function
        that allows redirection on the fly, which appears to be very
        useful for teaching purpose, as the student can keep a copy of
        the produced output to keep all that he did during the course.
        Package comes with a vignette describing how to write HTML
        reports for statistical analysis. Finally, a driver for 'Sweave'
        allows to parse HTML flat files containing R code and to
        automatically write the corresponding outputs (tables and
        graphs).
Providing wrapper functions to implement Bayesian analysis in JAGS.  Some major features include monitoring convergence of a MCMC model using Rubin and Gelman Rhat statistics, automatically running a MCMC model till it converges, and implementing parallel processing of a MCMC model for multiple chains.
Generates both total- and level-specific R-squared measures from
    Rights and Sterba’s (2019) <doi:10.1037/met0000184> framework of R-squared measures for multilevel
    models with random intercepts and/or slopes, which is based on a complete
    decomposition of variance. Additionally generates graphical 
    representations of these R-squared measures to allow visualizing and 
    interpreting all measures in the framework together as an integrated set.
    This framework subsumes 10 previously-developed R-squared measures for 
    multilevel models as special cases of 5 measures from the framework, and it
    also includes several newly-developed measures. Measures in the framework 
    can be used to compute R-squared differences when comparing multilevel 
    models (following procedures in Rights & Sterba (2020) <doi:10.1080/00273171.2019.1660605>).
An R command interface to the 'MLwiN' multilevel
    modelling software package.
Using this package,
 it is possible to call a BUGS model, summarize inferences and
 convergence in a table and graph, and save the simulations in arrays for easy access
 in R.
R wrapper for the JPMML-R library <https://github.com/jpmml/jpmml-r>,
    which converts R models to Predictive Model Markup Language (PMML).
R2PPT provides a simple set of wrappers to easily use rcom
        or RDCOMClient for generating Microsoft PowerPoint
        presentations.
Provides a friendly, object oriented API for creating PowerPoint
    slide decks in R.
Implementation of hash tables (hash sets and hash maps) in R, 
	featuring arbitrary R objects as keys, 
	arbitrary hash and key-comparison functions, 
	and customizable behaviour upon queries of missing keys.
Create production-ready Rich Text Format (RTF) table and figure with flexible format.
When creating a package, authors may sometimes struggle with coming up with easy and straightforward function names, and at the same time hoping that other packages do not already have the same function names. In trying to meet this goal, sometimes, function names are not descriptive enough and may confuse the potential users. The purpose of this package is to serve as a package function short form generator and also provide shorthand names for other functions. Having this package will entice authors to create long function names without the fear of users not wanting to use their packages because of the long names. In a way, everyone wins - the authors can use long descriptive function names, and the users can use this package to make short functions names while still using the package in question.
Wrapper for widely used 'SUNDIALS' software (SUite of Nonlinear and DIfferential/ALgebraic Equation Solvers) and more precisely to its 'CVODES' solver. It is aiming to solve ordinary differential equations (ODE) and optionally pending forward sensitivity problem. The wrapper is made 'R' friendly by allowing to pass custom parameters to user's callback functions. Such functions can be both written in 'R' and in 'C++' ('RcppArmadillo' flavor). In case of 'C++', performance is greatly improved so this option is highly advisable when performance matters. If provided, Jacobian matrix can be calculated either in dense or sparse format. In the latter case 'rmumps' package is used to solve corresponding linear systems. Root finding and pending event management are optional and can be specified as 'R' or 'C++' functions too. This makes them a very flexible tool for controlling the ODE system during the time course simulation. 'SUNDIALS' library was published in Hindmarsh et al. (2005) <doi:10.1145/1089014.1089020>.
Using the 'Ming' library
    <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'.
Direct insertion of symbols (e.g. currencies, letters, arrows, mathematical symbols and so on) into 'Rmarkdown' documents and 'Shiny' applications by incorporating 'HTML' hex codes.
Performs goodness-of-fit tests for capture-recapture models. Also contains several functions to process capture-recapture data.
This package uses either the statconnDCOM server (via the
        rcom package) or the RDCOMClient to communicate with MS-Word
        via the COM interface.
Invoke a 'BUGS' model in 'OpenBUGS' or 'WinBUGS', a class "bugs" for 'BUGS' 
  results and functions to work with that class.
  Function write.model() allows a 'BUGS' model file to be written.  
  The class and auxiliary functions could be used with other MCMC programs, including 'JAGS'.
This is a collection of useful functions and data for
        Stats 330
Create rich and fully interactive 3D visualizations of molecular data.
    Visualizations can be included in Shiny apps and R markdown documents, or viewed
    from the R console and 'RStudio' Viewer. 'r3dmol' includes an extensive API
    to manipulate the visualization after creation, and supports getting data out of
    the visualization into R. Based on the '3dmol.js' and the 'htmlwidgets' R package.
Provides a flexible and easy-to-use interface for the Physiological Processes Predicting Growth (3-PG) model written in Fortran. The r3PG serves as a flexible and easy-to-use interface for the 3-PGpjs (monospecific, evenaged and evergreen forests) described in Landsberg & Waring (1997) <doi:10.1016/S0378-1127(97)00026-1> and the 3-PGmix (deciduous, uneven-aged or mixed-species forests) described in Forrester & Tang (2016) <doi:10.1016/j.ecolmodel.2015.07.010>.
Create and combine HTML and PDF reports from within R.
    Possibility to design tables and listings for reporting and also include R plots.
Provides a collection of functions for basic
    database and document management operations such as add, get, list access
    or delete. Every cdbFunction() gets and returns a list() containing the
    connection setup. Such a list can be generated by cdbIni().
Since the early 1970s eyewitness testimony researchers have recognised the 
    importance of estimating properties such as lineup bias (is the lineup biased against 
    the suspect, leading to a rate of choosing higher than one would expect by chance?), 
    and lineup size (how many reasonable choices are in fact available to the witness? 
    A lineup is supposed to consist of a suspect and a number of additional members, 
    or foils, whom a poor-quality witness might mistake for the perpetrator). Lineup 
    measures are descriptive, in the first instance, but since the earliest articles in 
    the literature researchers have recognised the importance of reasoning inferentially 
    about them. This package contains functions to compute various properties of 
    laboratory or police lineups, and is intended for use by researchers in forensic 
    psychology and/or eyewitness testimony research. Among others, the r4lineups package 
    includes functions for calculating lineup proportion, functional size, various 
    estimates of effective size, diagnosticity ratio, homogeneity of the diagnosticity 
    ratio, ROC curves for confidence x accuracy data and the degree of similarity of 
    faces in a lineup.
A collection of R functions for use with Stock Synthesis, a
    fisheries stock assessment modeling platform written in ADMB by Dr. Richard
    D.  Methot at the NOAA Northwest Fisheries Science Center. The functions
    include tools for summarizing and plotting results, manipulating files,
    visualizing model parameterizations, and various other common stock
    assessment tasks.
For the 9 counties in the San Francisco Bay area 
    the Metropolitan Transportation Commission
    maintains an Application Programming Interface
    with information on more than 20 transit operators. 
    This package allows users to retrieve
    information about transit schedules in the General Transit Feed Specification
    as published by the Metropolitan Transportation Commission at the following url: 
    <https://511.org/developers/list/apis/>.
Rapid realistic routing on multimodal transport networks (walk, 
             bike, public transport and car) using 'R5', the Rapid Realistic 
             Routing on Real-world and Reimagined networks engine 
             <https://github.com/conveyal/r5>. The package allows users to 
             generate detailed routing analysis or calculate travel time matrices
             using seamless parallel computing on top of the R5 Java machine.
             While R5 is developed by Conveyal, the package r5r is independently 
             developed by a team at the Institute for Applied Economic Research
             (Ipea) with contributions from collaborators. Apart from the 
             documentation in this package, users will find additional information
             on R5 documentation at <https://docs.conveyal.com/>. Although we try
             to keep new releases of r5r in synchrony with R5, the development of R5
             follows Conveyal's independent update process. Hence, users should
             confirm the R5 version implied by the Conveyal user manual (see
             <https://docs.conveyal.com/changelog>) corresponds with the R5 version
             that r5r depends on.
Creates classes with reference semantics, similar to R's built-in
    reference classes. Compared to reference classes, R6 classes are simpler
    and lighter-weight, and they are not built on S4 classes so they do not
    require the methods package. These classes allow public and private
    members, and they support inheritance, even when the classes are defined in
    different packages.
After defining an R6 class, R62S3 is used to automatically generate optional S3/S4 generics and methods for dispatch. Also allows piping for R6 objects.
The implemented R6 class 'SCM' aims to simplify working with structural causal models. The missing data mechanism can be defined as a part of the structural model. The class contains methods for 1) defining a structural causal model via functions, text or conditional probability tables, 2) printing basic information on the model, 3) plotting the graph for the model using packages 'igraph' or 'qgraph', 4) simulating data from the model, 5) applying an intervention, 6) checking the identifiability of a query using the R packages 'causaleffect' and 'dosearch', 7) defining the missing data mechanism, 8) simulating incomplete data from the model according to the specified missing data mechanism and 9) checking the identifiability in a missing data problem using the R package 'dosearch'. In addition, there are functions for running experiments and doing counterfactual inference using simulation.
Provides reference classes implementing some useful data structures.
    The package implements these data structures by using the reference class R6.
    Therefore, the classes of the data structures are also reference classes which means that their instances are passed by reference.
    The implemented data structures include stack, queue, double-ended queue, doubly linked list, set, dictionary and binary search tree.
    See for example <https://en.wikipedia.org/wiki/Data_structure> for more information about the data structures.
Useful methods and data fields to extend the
             bare bones 'R6' class provided by the 'R6' package - ls-method, 
             hashes, warning- and message-method, general get-method and a 
             debug-method that assigns self and private to the global environment.
Generate boilerplate code for R6 classes. Given R6 class create getters 
    and/or setters for selected class fields or use RStudio addins to insert methods 
    straight into class definition.
Build robust and maintainable software with object-oriented design 
    patterns in R. Design patterns abstract and present in neat, well-defined 
    components and interfaces the experience of many software designers and 
    architects over many years of solving similar problems. These are solutions 
    that have withstood the test of time with respect to re-usability,
    flexibility, and maintainability. 'R6P' provides abstract base classes with 
    examples for a few known design patterns. The patterns were selected by 
    their applicability to analytic projects in R. Using these patterns in R 
    projects have proven effective in dealing with the complexity that 
    data-driven applications possess.
Functionality for performing a principled reference analysis in the Bayesian normal-normal hierarchical model used for Bayesian meta-analysis, as described in Ott, Plummer and Roos (2021, "How vague is vague? How informative is informative? Reference analysis for Bayesian meta-analysis", under minor revision for Statistics in Medicine). Computes a reference posterior, induced by a minimally informative improper reference prior for the between-study (heterogeneity) standard deviation. Determines additional proper anti-conservative (and conservative) prior benchmarks. Includes functions for reference analyses at both the posterior and the prior level, which, given the data, quantify the informativeness of a heterogeneity prior of interest relative to the minimally informative reference prior and the proper prior benchmarks. The functions operate on data sets which are compatible with the 'bayesmeta' package.
Facilitates the design and generation of optimal color (or symbol) codes that can be used to mark and identify individual animals. These codes are made such that the IDs are robust to partial erasure: even if sections of the code are lost, the entire identity of the animal can be reconstructed. Thus, animal subjects are not confused and no ambiguity is introduced.
Conduct simulations of the Response Adaptive Block Randomization (RABR) design to evaluate its type I error rate, power and operating characteristics for binary and continuous endpoints. For more details of the proposed method, please refer to Zhan et al. (2020) <arXiv:2004.07356>.
Solves the individual bioenergetic balance for different aquaculture sea fish (Sea Bream and Sea Bass; Brigolin et al., 2014 <doi:10.3354/aei00093>) and shellfish (Mussel and Clam; Brigolin et al., 2009 <doi:10.1016/j.ecss.2009.01.029>; Solidoro et al., 2000 <doi:10.3354/meps199137>). Allows for spatialized model runs and population simulations.
Application of 'RaceID' allows inference of cell types and prediction of lineage trees by he StemID2 algorithm. Herman, J.S., Sagar, Grün D. (2018) <DOI:10.1038/nmeth.4662>.
Implements a computational framework for a pattern-based, 
    zoneless analysis, and visualization of (ethno)racial topography 
    (Dmowska, Stepinski, and Nowosad (2020) <doi:10.1016/j.apgeog.2020.102239>).
    It is a reimagined
    approach for analyzing residential segregation and racial diversity based on 
    the concept of 'landscape’ used in the domain of landscape ecology.
Contains functions useful for reading in Licor 6800 files,
    correcting and analyzing rapid A/Ci response (RACiR) data. Requires some
    user interaction to adjust the calibration (empty chamber) data file to 
    a useable range. Calibration uses a 1st to 5th order polynomial as 
    suggested in Stinziano et al. (2017) <doi:10.1111/pce.12911>.
    Data can be processed individually or batch processed for all files paired
    with a given calibration file. RACiR is a trademark of LI-COR Biosciences,
    and used with permission.
Interface for loading data from 'ActiveCampaign API v3' 
    <https://developers.activecampaign.com/reference>. Provide functions
    for getting data by deals, contacts, accounts, campaigns and messages.
Fit a variety of models to Rank Abundance Data
Systematically transform immunoassay data, evaluate if the data is normally distributed, and pick the right method for cut point determination based on that evaluation. This package can also produce plots that are needed for reports, so data analysis and visualization can be done easily.
It has tools for normalization of rank abundance
     distributions (RAD) to a desired number of ranks using MaxRank
     Normalization method.
     RADs are commonly used in biology/ecology and mathematically equivalent
     to complementary cumulative distributions (CCDFs) which are used in
     physics, linguistics and sociology and more generally in data science.
Fundamental formulas for Radar, for attenuation, range, velocity,
    effectiveness, power, scatter, doppler, geometry, radar equations, etc.
    Based on Nick Guy's Python package PyRadarMet
Creates the radar-boxplot, a plot that was created by the 
    author during his Ph.D. in forest resources. 
    The radar-boxplot is a visualization feature suited for  
    multivariate classification/clustering. It provides an intuitive 
    deep understanding of the data.
Create interactive radar charts using the 'Chart.js' 'JavaScript' library
    and the 'htmlwidgets' package. 'Chart.js' <http://www.chartjs.org/> is a 
    lightweight library that supports several types of simple chart using the 'HTML5' 
    canvas element. This package provides an R interface specifically to the 
    radar chart, sometimes called a spider chart, for visualising multivariate data.
Nuclear Decay Data for Dosimetric Calculations from the 
    International Commission on Radiological Protection from ICRP 
    Publication 107. Ann. ICRP 38 (3). Eckerman, Keith and Endo, Akira 2008 
    <doi:10.1016/j.icrp.2008.10.004> 
    <https://www.icrp.org/publication.asp?id=ICRP%20Publication%20107>. 
    This is a database of the physical data needed in calculations of 
    radionuclide-specific protection and operational quantities. The 
    data is prescribed by the ICRP, the international authority on 
    radiation dose standards, for estimating dose from the intake of or 
    exposure to radionuclides in the workplace and the environment. 
    The database contains information on the half-lives, decay chains, 
    and yields and energies of radiations emitted in nuclear transformations 
    of 1252 radionuclides of 97 elements.
Shiny-based interactive gadgets of radial visualization methods and extensions thereof.
A platform-independent browser-based interface for business
    analytics in R, based on the shiny package. The application combines the
    functionality of 'radiant.data', 'radiant.design', 'radiant.basics',
    'radiant.model', and 'radiant.multivariate'.
The Radiant Basics menu includes interfaces for probability 
    calculation, central limit theorem simulation, comparing means and proportions, 
    goodness-of-fit testing, cross-tabs, and correlation. The application extends 
    the functionality in 'radiant.data'.
The Radiant Data menu includes interfaces for loading, saving,
    viewing, visualizing, summarizing, transforming, and combining data. It also
    contains functionality to generate reproducible reports of the analyses
    conducted in the application.
The Radiant Design menu includes interfaces for design of
    experiments, sampling, and sample size calculation. The application extends
    the functionality in radiant.data.
The Radiant Model menu includes interfaces for linear and logistic
    regression, naive Bayes, neural networks, classification and regression trees,
    model evaluation, collaborative filtering, decision analysis, and simulation. 
    The application extends the functionality in 'radiant.data'.
The Radiant Multivariate menu includes interfaces for perceptual
    mapping, factor analysis, cluster analysis, and conjoint analysis. The
    application extends the functionality in radiant.data.
RadioSonde is a collection of programs for reading and
        plotting SKEW-T,log p diagrams and wind profiles for data
        collected by radiosondes (the typical weather balloon-borne
        instrument), which we will call "flights", "sondes", or
        "profiles" throughout the associated documentation.  The raw
        data files are in a common format that has a header followed by
        specific variables.  Use "help(ExampleSonde)" for the full
        explanation of the data files.
Radioactive doses estimation using individual chromosomal aberrations information. See Higueras M, Puig P, Ainsbury E, Rothkamm K. (2015) <doi:10.1088/0952-4746/35/3/557>.
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.
Make your phrase or sentence into something funny! Pass a string with the keywords in, 
    and get out a bit of humor.
Implementation of ADMIXTURE for individual ancestry inference in R. Specifically, ADMIXTURE is a software tool for maximum likelihood estimation of individual ancestries from multilocus SNP genotype datasets, see <https://www.genetics.ucla.edu/software/admixture/>. Users can use 'radmixture' to calculate ancestry components with different public datasets. It is very convenient and fast for personal genotype data. For more details, see <https://github.com/wegene-llc/radmixture/blob/master/README.md>.
Designed for the import, analysis, and visualization of dosimetric and volumetric data in Radiation Oncology, the tools herein enable import of dose-volume histogram information from multiple treatment planning system platforms and 3D structural representations and dosimetric information from 'DICOM-RT' files.  These tools also enable subsequent visualization and statistical analysis of these data.
Generate random user data from the Random User Generator API.
    For more information, see <https://randomuser.me/>.
Provides functions for radiation safety, also known as
    "radiation protection" and "radiological control". The science of 
    radiation protection is called "health physics" and its engineering 
    functions are called "radiological engineering". Functions in this 
    package cover many of the computations needed by radiation safety 
    professionals. Examples include: obtaining updated calibration and
    source check values for radiation monitors to account for radioactive 
    decay in a reference source, simulating instrument readings to better
    understand measurement uncertainty, correcting instrument readings 
    for geometry and ambient atmospheric conditions. Many of these 
    functions are described in Johnson and Kirby (2011, ISBN-13:  
    978-1609134198). Utilities are also included for developing inputs 
    and processing outputs with radiation transport codes, such as MCNP, 
    a general-purpose Monte Carlo N-Particle code that can be used for 
    neutron, photon, electron, or coupled neutron/photon/electron transport
    (Werner et. al. (2018) <doi:10.2172/1419730>).
Offers a handful of useful wrapper functions which streamline
    the reading, analyzing, and visualizing of variant call format (vcf) files in R.
    This package was designed to facilitate an explicit pipeline for optimizing
    Stacks (Rochette et al., 2019) (<doi:10.1111/mec.15253>)
    parameters during de novo (without a reference genome) assembly and variant
    calling of restriction-enzyme associated DNA sequence (RADseq) data. The
    pipeline implemented here is based on the 2017 paper "Lost in Parameter Space"
    (Paris et al., 2017) (<doi:10.1111/2041-210X.12775>) which
    establishes clear recommendations for optimizing the parameters
    'm', 'M', and 'n', during the process of assembling loci.
Contains 4 different functions for radon and soil gas transport in a porous medium.
An implementation of the radviz projection in R. It enables the visualization of
    multidimensional data while maintaining the relation to the original dimensions.
    This package provides functions to create and plot radviz projections, and a number of summary
    plots that enable comparison and analysis. For reference see Ankerst *et al.* (1996) 
    (<http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.68.1811>) for original implementation, 
    see Di Caro *et al* (2012) (<http://link.springer.com/chapter/10.1007/978-3-642-13672-6_13>) 
    for the original method for dimensional anchor arrangements, see Demsar *et al.* (2007) 
    (<doi:10.1016/j.jbi.2007.03.010>) for the original Freeviz implementation.
Aims at loading Google Adwords data into R. Adwords is an online
    advertising service that enables advertisers to display advertising copy to web
    users (see <https://developers.google.com/adwords/> for more information). 
    Therefore the package implements three main features. First, the package
    provides an authentication process for R with the Google Adwords API (see 
    <https://developers.google.com/adwords/api/> for more information) via OAUTH2.
    Second, the package offers an interface to apply the Adwords query language in
    R and query the Adwords API with ad-hoc reports. Third, the received data are
    transformed into suitable data formats for further data processing and data
    analysis.
The Proportional Subdistribution Hazard (PSH) model has been popular for estimating the effects of the covariates on the cause of interest in Competing Risks analysis. The fast accumulation of large scale datasets has posed a challenge to classical statistical methods. Current penalized variable selection methods show unsatisfactory performance in ultra-high dimensional data. We propose a novel method, the Random Approximate Elastic Net (RAEN), with a robust and generalized solution to the variable selection problem for the PSH model. Our method shows improved sensitivity for variable selection compared with current methods.
A series of shortcuts for routine tasks originally developed by Rafael A. Irizarry to facilitate data exploration.
Functions for calculating life history metrics using matrix population models ('MPMs'). Described in Jones et al. (2021) <doi:10.1101/2021.04.26.441330>.
Anti-Grain Geometry (AGG) is a high-quality and high-performance
    2D drawing library. The 'ragg' package provides a set of graphic devices 
    based on AGG to use as alternative to the raster devices provided through
    the 'grDevices' package.
Data objects in 'R' can be rendered as 'HTML' tables using the
    'JavaScript' library 'ag-grid' (typically via 'R Markdown' or 'Shiny'). The
    'ag-grid' library has been included in this 'R' package. The package name
    'RagGrid' is an abbreviation of 'R agGrid'.
Proper L2-penalized maximum likelihood estimators for precision 
  matrices and supporting functions to employ these estimators in a graphical 
  modeling setting. For details see van Wieringen & Peeters (2016) 
  <doi:10.1016/j.csda.2016.05.012> and associated publications.
The ragt2ridges-package provides ridge maximum likelihood estimation of vector auto-regressive processes: the VAR(1), VAR(2) and VARX(1) model (more to be added). Prior knowledge may be incorporated in the estimation through a) specification of the edges believed to be absent in the time series chain graph, and b) a shrinkage target towards which the parameter estimate is shrunken for large penalty parameter values. Estimation functionality is accompanied by methodology for penalty parameter selection. In addition, the package offers supporting functionality for the exploitation of estimated models. Among others, i) a procedure to infer the support of the non-sparse ridge estimate (and thereby of the time series chain graph) is implemented, ii) a table of node-wise network summary statistics, iii) mutual information analysis, and iv) impulse response analysis. Cf. Miok et al. (2017) <DOI:10.1002/bimj.201500269> and Miok et al. (2019) <DOI:10.1002/bimj.201700195> for details on the implemented methods.
Algorithms to price American and European
    equity options, convertible bonds and a
    variety of other financial derivatives. It uses an
    extension of the usual Black-Scholes model in which
    jump to default may occur at a probability specified
    by a power-law link between stock price and hazard
    rate as found in the paper by Takahashi, Kobayashi,
    and Nakagawa (2001) <doi:10.3905/jfi.2001.319302>.  We
    use ideas and techniques from Andersen and
    Buffum (2002) <doi:10.2139/ssrn.355308> and
    Linetsky (2006) <doi:10.1111/j.1467-9965.2006.00271.x>.
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.
A modified implementation of stepwise regression that greedily searches 
    the space of interactions among features in order to build polynomial regression models.
    Furthermore, the hypothesis tests conducted are valid-post model selection
    due to the use of a revisiting procedure that implements an alpha-investing
    rule. As a result, the set of rejected sequential hypotheses is proven to 
    control the marginal false discover rate. When not searching for polynomials,
    the package provides a statistically valid algorithm
    to run and terminate stepwise regression. For more information, see 
    Johnson, Stine, and Foster (2019) <arXiv:1510.06322>.
Visualizing functional data and identifying functional outliers.
By using 'RAINBOWR' (Reliable Association INference By Optimizing Weights with R), users can test multiple SNPs (Single Nucleotide Polymorphisms) simultaneously by kernel-based (SNP-set) methods. This package can also be applied to haplotype-based GWAS (Genome-Wide Association Study). Users can test not only additive effects but also dominance and epistatic effects. In detail, please check our paper on PLOS Computational Biology: Kosuke Hamazaki and Hiroyoshi Iwata (2020) <doi:10.1371/journal.pcbi.1007663>.
Various statistical and mathematical ranking and rating methods with incomplete information are included. This package is initially designed for the scoring system in a high school project showcase to rank student research projects, where each judge can only evaluate a set of projects in a limited time period. See Langville, A. N. and Meyer, C. D. (2012), Who is Number 1: The Science of Rating and Ranking, Princeton University Press <doi:10.1515/9781400841677>, and Gou, J. and Wu, S. (2020), A Judging System for Project Showcase: Rating and Ranking with Incomplete Information, Technical Report.
An R implementation of the Reinert text clustering method. For more 
    details about the algorithm see the included vignettes or Reinert (1990) 
    <doi:10.1177/075910639002600103>.
Determination of rainfall-runoff erosivity factor.
An implementation of the RainFARM (Rainfall Filtered Autoregressive Model) stochastic precipitation downscaling method (Rebora et al. (2006) <doi:10.1175/JHM517.1>). Adapted for climate downscaling according to D'Onofrio et al. (2018) <doi:10.1175/JHM-D-13-096.1> and for complex topography as in Terzago et al. (2018) <doi:10.5194/nhess-18-2825-2018>. The RainFARM method is based on the extrapolation to small scales of the Fourier spectrum of a large-scale precipitation field, using a fixed logarithmic slope and random phases at small scales, followed by a nonlinear transformation of the resulting linearly correlated stochastic field. RainFARM allows to generate ensembles of spatially downscaled precipitation fields which conserve precipitation at large scales and whose statistical properties are consistent with the small-scale statistics of observed precipitation, based only on knowledge of the large-scale precipitation field.
A robust alternative to the aJIVE (angle based Joint and Individual Variation Explained) method (Feng et al 2018: <doi:10.1016/j.jmva.2018.03.008>) for the estimation of joint and individual components in the presence of outliers in multi-source data. It decomposes the multi-source data into joint, individual and residual (noise) contributions. The decomposition is robust to outliers and noise in the data. The method is illustrated in Ponzi et al (2021) <arXiv:2101.09110>.
Functions for performing spatial microsimulation ('raking')
    in R.
The goal of 'ralger' is to facilitate web scraping in R.
Prepare and analyze data from large-scale assessments and surveys with
    complex sampling and assessment design (see 'Rutkowski', 2010
    <doi:10.3102/0013189X10363170>). Such studies are, for example,
    international assessments like 'TIMSS', 'PIRLS' and 'PISA'. A graphical
    interface is available for the non-technical user.The package includes
    functions to covert the original data from 'SPSS' into 'R' data sets
    keeping the user-defined missing values, merge data from different
    respondents and/or countries, generate variable dictionaries, modify
    data, produce descriptive statistics (percentages, means, percentiles,
    benchmarks) and multivariate statistics (correlations, linear
    regression, binary logistic regression). The number of supported
    studies and analysis types will increase in future. For a general
    presentation of the package, see 'Mirazchiyski', 2021a
    (<doi:10.1186/s40536-021-00114-4>). For detailed technical aspects of the
    package, see 'Mirazchiyski', 2021b (<doi:10.3390/psych3020018>).
Provides a programmatic interface to the citation information and alternate metrics provided by 'Altmetric'. Data from Altmetric allows researchers to immediately track the impact of their published work, without having to wait for citations. This allows for faster engagement with the audience interested in your work. For more information, visit <https://www.altmetric.com/>.
Characterizing environmental microbiota diversity using amplicon-based next generation sequencing (NGS) data. Functions are developed to manipulate operational taxonomic unit (OTU) table, perform descriptive and inferential statistics, and generate publication-quality plots.
Pre-processing and polymer identification of Raman spectra of plastics. Pre-processing includes normalisation functions, peak identification based on local maxima, smoothing process and removal of spectral region of no interest. Polymer identification can be performed using Pearson correlation coefficient or Euclidean distance (Renner et al. (2019), <doi:10.1016/j.trac.2018.12.004>), and the comparison can be done with a user-defined database or with the database already implemented in the package, which currently includes 356 spectra, with several spectra of plastic colorants.
Parser generator for R using combinatory parsers. It
    is inspired by combinatory parsers developed in Haskell.
Estimate the parameters, the number of classes and cluster vertices of a random network into groups with homogeneous connection profiles. The clustering is performed for directed graphs with typed edges (edges are assumed to be drawn from multinomial distributions) for which a partition of the vertices is available.
Provides an R interface for using 'AmCharts' Library. Based on
    'htmlwidgets', it provides a global architecture to generate 'JavaScript' source
    code for charts. Most of classes in the library have their equivalent in R
    with S4 classes; for those classes, not all properties have been referenced but
    can easily be added in the constructors. Complex properties (e.g. 'JavaScript'
    object) can be passed as named list. See examples at 
    <http://datastorm-open.github.io/introduction_ramcharts/> 
    and <http://www.amcharts.com/> for
    more information about the library. The package includes the free version
    of 'AmCharts' Library. Its only limitation is a small link to the web site
    displayed on your charts. If you enjoy this library, do not hesitate to refer
    to this page <http://www.amcharts.com/online-store/> to purchase a licence,
    and thus support its creators and get a period of Priority Support. See also
    <http://www.amcharts.com/about/> for more information about 'AmCharts' company.
Creates JavaScript charts. The charts can be included in 'Shiny' apps and R markdown documents, or viewed from the R console and 'RStudio' viewer. Based on the JavaScript library 'amCharts 4' and the R packages 'htmlwidgets' and 'reactR'. Currently available types of chart are: vertical and horizontal bar chart, radial bar chart, stacked bar chart, vertical and horizontal Dumbbell chart, line chart, scatter chart, range area chart, gauge chart, boxplot chart, pie chart, and 100% stacked bar chart.
It is widely documented in psychology, economics and other disciplines that socio-economic agent may not pay full attention to all available alternatives, rendering standard revealed preference theory invalid. This package implements the estimation and inference procedures of Cattaneo, Ma, Masatlioglu and Suleymanov (2020) <arXiv:1712.03448> and Cattaneo, Cheung, Ma, and Masatlioglu (2021) <arXiv:2110.10650>, which utilizes standard choice data to partially identify and estimate a decision maker's preference and attention. For inference, several simulation-based critical values are provided.
A feature clustering algorithm for non-targeted mass spectrometric metabolomics data. This method is compatible with gas and liquid chromatography coupled mass spectrometry, including indiscriminant tandem mass spectrometry <DOI: 10.1021/ac501530d> data.
Function for adapting the shape of the random walk Metropolis proposal
    as specified by robust adaptive Metropolis algorithm by Vihola (2012) <doi:10.1007/s11222-011-9269-5>. 
    The package also includes fast functions for rank-one Cholesky update and downdate.
    These functions can be used directly from R or the corresponding C++ header files 
    can be easily linked to other R packages.
Use R to interface with the 'TD Ameritrade' API <https://developer.tdameritrade.com/>.
    Functions include authentication, trading, price requests, account information, and option 
    chains. A user will need a TD brokerage account and TD Ameritrade developer app. See README 
    for authentication process and examples.
Additional matrix functionality for R including: (1) wrappers for 
    the base matrix function that allow matrices to be created from character
    strings and lists (the former is especially useful for creating block
    matrices), (2) better printing of large matrices via the generic "pretty" 
    print function, and (3) a number of convenience functions for users more
    familiar with other scientific languages like 'Julia', 'Matlab'/'Octave', or
    'Python'+'NumPy'.
Contains functions to download, cache and read in 'Excel' version of the
    RAM Legacy Stock Assessment Data Base, an online compilation of stock assessment
    results for commercially exploited marine populations from around the world. 
    The database is named after Dr. Ransom A. Myers whose original stock-recruitment database,
    is no longer being updated. More information about the database can be found at
    <https://ramlegacy.org/>. Ricard, D., Minto, C., Jensen, O.P. and Baum, J.K. (2012) <doi:10.1111/j.1467-2979.2011.00435.x>.
Provides an efficient procedure for fitting the entire solution
    path for high-dimensional regularized quadratic generalized linear models with
    interactions effects under the strong or weak heredity constraint.
We rewrite of RAMpath software developed by John McArdle and Steven Boker as an R package. In addition to performing regular SEM analysis through the R package lavaan, RAMpath has unique features.  First, it can generate path diagrams according to a given model. Second, it can display path tracing rules through path diagrams and decompose total effects into their respective direct and indirect effects as well as decompose variance and covariance into individual bridges. Furthermore, RAMpath can fit dynamic system models automatically based on latent change scores and generate vector field plots based upon results obtained from a bivariate dynamic system. Starting version 0.4, RAMpath can conduct power analysis for both univariate and bivariate latent change score models.
Bayesian geostatistical modeling of Gaussian processes using a
    reparameterized and marginalized posterior sampling (RAMPS) algorithm
    designed to lower autocorrelation in MCMC samples.  Package performance is
    tuned for large spatial datasets.
R-based access to mass-spectrometry (MS) data. While many packages 
  exist to process MS data, many of these make it difficult to 
  access the underlying mass-to-charge ratio (m/z), intensity, and 
  retention time of the files 
  themselves. This package is designed to format MS data in a tidy fashion and 
  allows the user perform the plotting and analysis.
Provides a solution path for Reinforced Angle-based Multicategory Support Vector Machines, with linear learning, polynomial learning, and Gaussian kernel learning. C. Zhang, Y. Liu, J. Wang and H. Zhu. (2016) <doi:10.1080/10618600.2015.1043010>.
The deterministic part of the Fortuna cryptographic
        pseudorandom number generator, described by Schneier & Ferguson
        "Practical Cryptography"
Provides randomization tests and graphical diagnostics for assessing randomized assignment and covariate balance for a binary treatment variable. See Branson (2021) <arXiv:1804.08760> for details.
Implements the algorithm by Pourahmadi and Wang (2015) <doi:10.1016/j.spl.2015.06.015> for generating a random p x p correlation matrix. Briefly, the idea is to represent the correlation matrix using Cholesky factorization and p(p-1)/2 hyperspherical coordinates (i.e., angles), sample the angles from a particular distribution and then convert to the standard correlation matrix form. The angles are sampled from a distribution with pdf proportional to sin^k(theta) (0 < theta < pi, k >= 1) using the efficient sampling algorithm described in Enes Makalic and Daniel F. Schmidt (2018) <arXiv:1809.05212>.
Generate random positions (latitude/longitude), 
    Well-known text ('WKT') points or polygons, or 'GeoJSON' points or 
    polygons.
A novel numerical algorithm that provides functionality for estimating the exact 95% confidence interval of the location parameter in the random effects model, and is much faster than the naive method. Works best when the number of studies is between 6-20.
Generates random names with additional information including fake
    SSNs, gender, location, zip, age, address, and nationality.
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>). It also includes the consensus clustering of Gao et. al. (2014) <arXiv:1410.5837>, the method of moments estimation of nomination SBM of Li et. al. (2020) <arxiv:2008.03652>, and the network mixing method of Li and Le (2021) <arxiv:2106.02803>. The work to build and improve this package is partially supported by the NSF grants DMS-2015298 and DMS-2015134.
Provides random number generating functions that
    are much more context aware than the built-in functions. The
    functions are also much safer, as they check for incompatible 
    values, and more reproducible.
The true random number service provided by the RANDOM.ORG
 website created by Mads Haahr samples atmospheric noise via radio tuned to
 an unused broadcasting frequency together with a skew correction algorithm
 due to John von Neumann.  More background is available in the included
 vignette based on an essay by Mads Haahr.  In its current form, the package
 offers functions to retrieve random integers, randomized sequences and
 random strings.
The Function performs a parallel analysis using simulated polychoric correlation matrices. The nth-percentile of the eigenvalues distribution obtained from both the randomly generated and the real data polychoric correlation matrices is returned. A plot comparing the two types of eigenvalues (real and simulated) will help determine the number of real eigenvalues that outperform random data. The function is based on the idea that if real data are non-normal and the polychoric correlation matrix is needed to perform a Factor Analysis, then the Parallel Analysis method used to choose a non-random number of factors should also be based on randomly generated polychoric correlation matrices and not on Pearson correlation matrices. Random data sets are simulated assuming or a uniform or a multinomial distribution or via the bootstrap method of resampling (i.e., random permutations of cases). Also Multigroup Parallel analysis is made available for random (uniform and multinomial distribution and with or without difficulty factor) and bootstrap methods. An option to choose between default or full output is also available as well as a parameter to print Fit Statistics (Chi-squared, TLI, RMSEA, RMR and BIC) for the factor solutions indicated by the Parallel Analysis. Also weighted correlation matrices may be considered for PA.
We implement adaptive estimation of the joint density linear model where the coefficients - intercept and slopes - are random and independent from regressors which support is a proper subset. The estimator proposed in Gaillac and Gautier (2019) <arXiv:1905.06584> is based on Prolate Spheroidal Wave Functions which are computed  efficiently in 'RandomCoefficients'. This package also provides a parallel implementation of the estimator.
Simple methods to generate attractive random colors. The random
  colors are from a wrapper of 'randomColor.js'
  <https://github.com/davidmerfield/randomColor>. In addition, it also generates
  optimally distinct colors based on k-means (inspired by 'IWantHue'
  <https://github.com/medialab/iwanthue>).
Methods for the inference on and the simulation of Gaussian fields are provided. Furthermore, methods for the simulation of extreme value random fields are provided. Main geostatistical parts are based on the books by Christian Lantuejoul <doi:10.1007/978-3-662-04808-5>,  Jean-Paul Chiles and Pierre Delfiner <doi:10.1002/9781118136188> and Noel A. Cressie <doi:10.1002/9781119115151>.
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>).
Fits non-linear regression models on dependant data with Generalised Least Square (GLS) based Random Forest (RF-GLS) detailed in Saha, Basu and Datta (2020) <arXiv:2007.15421>.
Fast OpenMP parallel computing of Breiman's random forests for univariate, multivariate, unsupervised, survival, competing risks, class imbalanced classification and quantile regression. New Mahalanobis splitting for correlated outcomes.  Extreme random forests and randomized splitting.  Suite of imputation methods for missing data.  Fast random forests using subsampling. Confidence regions and standard errors for variable importance. New improved holdout importance. Case-specific importance.  Minimal depth variable importance. Visualize trees on your Safari or Google Chrome browser. Anonymous random forests for data privacy.
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.
Based on data of real user-agent strings, we can set filtering conditions
    and randomly sample user-agent strings from the user-agent string pool.
Performs random projection using Johnson-Lindenstrauss (JL) Lemma (see William B.Johnson and Joram Lindenstrauss (1984) <doi:10.1090/conm/026/737400>). Random Projection is a dimension reduction technique, where the data in the high dimensional space is projected into the low dimensional space using JL transform. The original high dimensional data matrix is multiplied with the low dimensional projection matrix which results in reduced matrix. The projection matrix can be generated using the projection function that is independent to the original data. Then finally apply the classification task on the projected data.
Connects to the site <http://quotesondesign.com/> 
            that uses the 'WordPress' built-in REST API 
            to provide a way for you to grab quotes.
Several non parametric randomness tests for numeric sequences
Provides (1) pseudo random generators - general linear congruential generators, 
    multiple recursive generators and generalized feedback shift register (SF-Mersenne Twister
    algorithm and WELL generators); (2) quasi random generators - the Torus algorithm, the
    Sobol sequence, the Halton sequence (including the Van der Corput sequence) and (3) some
    generator tests - the gap test, the serial test, the poker test.
    See e.g. Gentle (2003) <doi:10.1007/b97336>. The package can be provided
    without the rngWELL dependency on demand.
    Take a look at the Distribution task view of types and tests of random number generators.
    Version in Memoriam of Diethelm and Barbara Wuertz.
Implements random variables by means of S4 classes and methods.
Provides tools for filtering occurrence records, generating alpha-hull-derived range polygons and mapping species distributions.
A collection of tools to create species range maps based on 
    occurrence data, statistics, and spatial objects. Other tools in this 
    collection can be used to analyze the environmental characteristics of 
    the species ranges. Plotting options to represent results in various 
    manners are also available. Results obtained using these tools can be 
    used to explore the distribution of species and define areas of occupancy 
    and extent of occurrence of species. Other packages help to explore species 
    distributions using distinct methods, but options presented in this set of 
    tools (e.g., using trend surface analysis and concave hull polygons) are 
    exclusive. Description of methods, approaches, and comments for some of the 
    tools implemented here can be found in: 
    IUCN (2001) <https://portals.iucn.org/library/node/10315>,
    Peterson et al. (2011) <https://www.degruyter.com/princetonup/view/title/506966>, 
    and Graham and Hijmans (2006) <doi:10.1111/j.1466-8238.2006.00257.x>.
Tools for generation of (life-history) traits and diversity maps on hexagonal or square grids. Valcu et al.(2012) <doi:10.1111/j.1466-8238.2011.00739.x>.
Range Modeling Metadata Standards (RMMS) address three challenges: 
  they (i) are designed for convenience to encourage use, (ii) accommodate a wide 
  variety of applications, and (iii) are extensible to allow the community of range 
  modelers to steer it as needed. RMMS are based on a data dictionary that specifies 
  a hierarchical structure to catalog different aspects of the range modeling process. 
  The dictionary balances a constrained, minimalist vocabulary to improve 
  standardization with flexibility for users to provide their own values. 
  Merow et al. (2019) <DOI:10.1111/geb.12993> describe the standards in 
  more detail. Note that users who prefer to use the R package 'ecospat' can obtain it from
  <https://github.com/ecospat/ecospat>.
A fast implementation of Random Forests, particularly suited for high
          dimensional data. Ensembles of classification, regression, survival and
          probability prediction trees are supported. Data from genome-wide association
          studies can be analyzed efficiently. In addition to data frames, datasets of
          class 'gwaa.data' (R package 'GenABEL') and 'dgCMatrix' (R package 'Matrix') 
          can be directly analyzed.
Displays palette of 5 colors based on photos depicting the unique 
             and vibrant culture of Punjab in Northern India. Since Punjab translates 
             to ``Land of 5 Rivers'' there are 5 colors per palette. If users need 
             more than 5 colors, they can merge 2 to 3 palettes to create their own 
             color-combination, or they can cherry-pick their own custom colors. 
             Users can view up to 3 palettes together. Users can also list all the 
             palette choices. And last but not least, users can see the photo that 
             inspired a particular palette.
Performs aggregation of ordered lists based
        on the ranks using several different algorithms:
        Cross-Entropy Monte Carlo algorithm, Genetic algorithm, and a
        brute force algorithm (for small problems).
Easily compute an aggregate ranking (also called a median ranking or a
             consensus ranking) according to the axiomatic approach presented
             by Cook et al. (2007). This approach minimises the number of violations
             between all candidate consensus rankings and all input (partial) rankings,
             and draws on a branch and bound algorithm and a heuristic algorithm to
             drastically improve speed. The package also provides an option to bootstrap
             a consensus ranking based on resampling input rankings (with 
             replacement). Input rankings can be either incomplete (partial) or complete.
             Reference: Cook, W.D., Golany, B., Penn, M. and Raviv, T. (2007) <doi:10.1016/j.cor.2005.05.030>.
Implementation of a model-based clustering algorithm for
    ranking data (C. Biernacki, J. Jacques (2013) <doi:10.1016/j.csda.2012.08.008>). 
    Multivariate rankings as well as partial rankings are taken
    into account. This algorithm is based on an extension of the Insertion
    Sorting Rank (ISR) model for ranking data, which is a meaningful and
    effective model parametrized by a position parameter (the modal ranking,
    quoted by mu) and a dispersion parameter (quoted by pi). The heterogeneity
    of the rank population is modelled by a mixture of ISR, whereas conditional
    independence assumption is considered for multivariate rankings.
Implements distance based probability models for ranking data. 
    The supported distance metrics include Kendall distance, Spearman distance, Footrule distance, Hamming distance,
    Weighted-tau distance and Weighted Kendall distance.
    Phi-component model and mixture models are also supported.
The rankFD() function calculates the Wald-type statistic (WTS) and the ANOVA-type
	     statistic (ATS) for nonparametric factorial designs, e.g., for count, ordinal or score data
	     in a crossed design with an arbitrary number of factors.
	     Brunner, E., Bathke, A. and Konietschke, F. (2018) <doi:10.1007/978-3-030-02914-2>.
Rank-hazard plots Karvanen and Harrell (2009) <DOI:10.1002/sim.3591> visualize the relative importance of covariates in a proportional hazards model. The key idea is to rank the covariate values and plot the relative hazard as a function of ranks scaled to interval [0,1]. The relative hazard is plotted in respect to the reference hazard, which can bee.g. the hazard related to the median of the covariate.
Functions to generate plots and tables for comparing independently-
    sampled populations. Companion package to "A Primer on Visualizations
    for Comparing Populations, Including the Issue of Overlapping Confidence
    Intervals" by Wright, Klein, and Wieczorek (2019)
    <DOI:10.1080/00031305.2017.1392359> and "A Joint Confidence Region for an
    Overall Ranking of Populations" by Klein, Wright, and Wieczorek (2020)
    <DOI:10.1111/rssc.12402>.
Methods for ranking responses of a single response question or a multiple response question
Implementation of Kernelized score functions and other semi-supervised learning algorithms for node label ranking in biomolecular networks. RANKS can be easily applied to a large set of different relevant problems in computational biology, ranging from automatic protein function prediction, to gene disease prioritization and drug repositioning, and more in general to any bioinformatics problem that can be formalized as a node label ranking problem in a graph. The modular nature of the implementation allows to experiment with different score functions and kernels and to easily compare the results with baseline network-based methods such as label propagation and random walk algorithms, as well as to enlarge the algorithmic scheme by adding novel user-defined score functions and kernels.
Provides methods for measuring and describing uncertainty in
             rankings.  See Rising (2021) <arXiv:2107.03459> for background.
Random vectors with arbitrary Lipschitz density are generated using acceptance/ rejection. The method is based on G. Beliakov (2005) <doi:10.1016/j.cpc.2005.03.105>.
Finds the k nearest neighbours for every point in a given dataset
    in O(N log N) time using Arya and Mount's ANN library (v1.1.3). There is
    support for approximate as well as exact searches, fixed radius searches
    and 'bd' as well as 'kd' trees. The distance is computed using the L2
    (Euclidean) metric. Please see package 'RANN.L1' for the same
    functionality using the L1 (Manhattan, taxicab) metric.
To find the reversal association between variables.
Provide common functions and resources for registry specific
    R-packages at Rapporteket
    <https://rapporteket.github.io/rapporteket/articles/short_introduction.html>.
    This package is relevant for developers of packages/registries at
    Rapporteket.
Access services specified in OpenAPI (formerly Swagger) format.
  It is not a code generator. Client is generated dynamically as a list of R 
  functions.
Access to the C-level R date and datetime code is provided for
 C-level API use by other packages via registration of native functions.
 Client packages simply include a single header 'RApiDatetime.h' provided
 by this package, and also 'import' it.  The R Core group is the original
 author of the code made available with slight modifications by this package.
Provides JSON parsing capability through the 'Rapidjson' 'C++' header-only library.
A collection of 'HTML', 'JavaScript', 'CSS' and fonts
  assets that generate 'RapiDoc' documentation from an 'OpenAPI' Specification:
   <https://mrin9.github.io/RapiDoc/>.
Quickly computes polygenic scores from GWAS summary statistics of either case-control or quantitative traits, without LD matrix computation or parameter tuning. Reales,G., Vigorito, E., Kelemen,M., Wallace,C. (2021) <doi:10.1101/2020.07.24.220392> "RápidoPGS: A rapid polygenic score calculator for summary GWAS data without a test dataset".
Facilitates efficient polygon search using kd trees.
    Coordinate level spatial data can be aggregated to higher geographical
    identities like census blocks, ZIP codes or police district boundaries.
    This process requires mapping each point in the given data set to a
    particular identity of the desired geographical hierarchy. Unless efficient
    data structures are used, this can be a daunting task. The operation
    point.in.polygon() from the package sp is computationally expensive.
    Here, we exploit kd-trees as efficient nearest neighbor search algorithm
    to dramatically reduce the effective number of polygons being searched.
Package to perform non-invasive fetal testing for aneuploidies
    using sequencing count data from cell-free DNA
A 'Java' implementation of the RAKE algorithm ('Rose', S., 'Engel', D., 
  'Cramer', N. and 'Cowley', W. (2010) <doi:10.1002/9780470689646.ch1>), which can 
  be used to extract keywords from documents without any training data.
Provides XML parsing capability through the 'Rapidxml' 'C++' header-only library.
This package provides other packages with access to the internal 
 R serialization code. Access to this code is provided at the C function
 level by using the registration of native function mechanism. Client
 packages simply include a single header file RApiSerializeAPI.h provided by
 this package.

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

    The R Core group is the original author of the serialization code made
 available by this package.
Bindings to kernel methods for enforcing security restrictions. 
    AppArmor can apply mandatory access control (MAC) policies on a given task 
    (process) via security profiles with detailed ACL definitions. In addition
    this package implements bindings for setting process resource limits (rlimit),
    uid, gid, affinity and priority. The high level R function 'eval.secure' 
    builds on these methods to perform dynamic sandboxing: it evaluates a single
    R expression within a temporary fork which acts as a sandbox by enforcing 
    fine grained restrictions without affecting the main R process. A portable 
    version of this function is now available in the 'unix' package.
An easy way to determine which directories on the
    users computer you should use to save data, caches and logs. A port of
    Python's 'Appdirs' (<https://github.com/ActiveState/appdirs>) to
    R.
Facilitating the creation of reproducible statistical
    report templates. Once created, rapport templates can be exported to
    various external formats (HTML, LaTeX, PDF, ODT etc.) with pandoc as the
    converter backend.
Helper functions that act as wrappers to more advanced statistical
    methods with the advantage of having sane defaults for quick reporting.
Loading data from AppsFlyer Pull API
    <https://support.appsflyer.com/hc/en-us/articles/207034346-Using-Pull-API-aggregate-data>.
Calculate sketches of a data set reducing the number of observations using random projections. These can be used for Bayesian or frequentist linear regression on large data sets as described in Geppert et. al (2017) <doi:10.1007/s11222-015-9608-z>.
The Agricultural Production Systems sIMulator ('APSIM') is a widely
    used to simulate the agricultural systems for multiple crops. This package 
    is designed to create, modify and run 'apsimx' files in the 'APSIM' Next 
    Generation <https://www.apsim.info/>.
Performs wood cell anatomical data analyses on spatially explicit xylem (tracheids) datasets 
                  derived from thin sections of woody tissue. The package includes functions for visualisation, 
                  detection and alignment of continuous tracheid radial file (defined as rows) and individual tracheid position 
                  within an annual ring of coniferous species. This package is designed to be used with elaborate cell output, 
                  e.g. as provided with ROXAS (von Arx & Carrer, 2014 <doi:10.1016/j.dendro.2013.12.001>). The package has been validated for Picea abies, 
                  Larix Siberica, Pinus cembra and Pinus sylvestris.
Biodiversity is in crisis. The overarching aim of conservation
    is to preserve biodiversity patterns and processes. To this end, protected
    areas are established to buffer species and preserve biodiversity processes.
    But resources are limited and so protected areas must be cost-effective.
    This package contains tools to generate plans for protected areas
    (prioritizations), using spatially explicit targets for biodiversity
    patterns and processes. To obtain solutions in a feasible amount  of time,
    this package uses the commercial 'Gurobi' software package (obtained from
    <http://www.gurobi.com/>). For more information on using
    this package, see Hanson et al. (2018) <doi:10.1111/2041-210X.12862>.
Retrieve air monitoring data and associated metadata from the US
  Environmental Protection Agency's Air Quality System service using functions.
  See <https://aqs.epa.gov/aqsweb/documents/data_api.html> for details about
  the US EPA Data Mart API.
Generate a table of cumulative water influx into hydrocarbon reservoirs over time using un-steady and pseudo-steady state models. Van Everdingen, A. F. and Hurst, W. (1949) <doi:10.2118/949305-G>. Fetkovich, M. J. (1971) <doi:10.2118/2603-PA>. Yildiz, T. and Khosravi, A. (2007) <doi:10.2118/103283-PA>.
Implementation of an alternating direction method of multipliers
    algorithm for fitting a linear model with tree-based lasso regularization, 
    which is proposed in Algorithm 1 of Yan and Bien (2018) <arXiv:1803.06675>. 
    The package allows efficient model fitting on the entire 2-dimensional 
    regularization path for large datasets. The complete set of functions 
    also makes the entire process of tuning regularization parameters and 
    visualizing results hassle-free.
A custom implementation of the apriori algorithm and binomial tests to identify combinations of features (genes, variants etc) significantly enriched for simultaneous mutations/events from sparse Boolean input, see Vijay Kumar Pounraja, Santhosh Girirajan (2021). <doi:10.1101/2021.10.01.462832>.
Includes functions for the calculation of spatially and non-spatially explicit rarefaction curves using different indices of taxonomic, functional and phylogenetic diversity. The user can also rarefy any biodiversity metric as provided by a self-written function (or an already existent one) that gives as output a vector with the values of a certain index of biodiversity calculated per plot (Ricotta, C., Acosta, A.,  Bacaro, G., Carboni, M., Chiarucci, A.,  Rocchini, D., Pavoine, S. (2019) <doi:10.1016/j.ecolind.2019.105606>; Bacaro, G., Altobelli, A., Cameletti, M., Ciccarelli, D., Martellos, S., Palmer, M. W., … Chiarucci, A. (2016) <doi:10.1016/j.ecolind.2016.04.026>; Bacaro, G., Rocchini, D., Ghisla, A., Marcantonio, M., Neteler, M., & Chiarucci, A. (2012) <doi:10.1016/j.ecocom.2012.05.007>).
Tests gene-environment interaction for rare genetic variants using Sequence Kernel Association Test (SKAT) type gene-based tests. Includes two tests for the interaction term only, and one joint test for genetic main effects and gene-environment interaction.
Randomization tests for the statistical comparison of \emph{i} = two or more individual-based, sample-based or coverage-based rarefaction curves. The ecological null hypothesis is that the \emph{i} samples were all drawn randomly from a single assemblage, with (necessarily) a single underlying species abundance distribution. The biogeographic null hypothesis is that the \emph{i} samples were all drawn from different assemblages that, nonetheless, share similar species richness and species abundance distributions
Allows calculation of rarity weights for species and indices of rarity for assemblages of species according to different methods (Leroy et al. 2012, Insect. Conserv. Divers. 5:159-168 <doi:10.1111/j.1752-4598.2011.00148.x>; Leroy et al. 2013, Divers. Distrib. 19:794-803 <doi:10.1111/ddi.12040>).
Interface to easily access data via the United States Department of Agriculture (USDA)'s Agricultural Resource Management Survey (ARMS) 
  Data API <https://www.ers.usda.gov/developer/data-apis/arms-data-api/>. The downloaded data can be saved for later off-line use. 
  Also provide relevant information and metadata for each of the input variables needed for sending the data inquery.
Previously an R wrapper of the 'ARPACK' library
    <http://www.caam.rice.edu/software/ARPACK/>, and now a shell of the
    R package 'RSpectra', an R interface to the 'Spectra' library
    <http://yixuan.cos.name/spectra/> for solving large scale
    eigenvalue/vector problems. The current version of 'rARPACK'
    simply imports and exports the functions provided by 'RSpectra'.
    New users of 'rARPACK' are advised to switch to the 'RSpectra' package.
MCMC based sampling of binary matrices with fixed margins as used in exact Rasch model tests.
Inspired by Karl Broman`s reader on using 'knitr'
    with 'asciidoc'
    (<https://kbroman.org/knitr_knutshell/pages/asciidoc.html>), this is
    merely a wrapper to 'knitr' and 'asciidoc'.
Software to perform supervised and pixel based raster image classification. It has been designed to facilitate land-cover analysis. Five classification algorithms can be used: Maximum Likelihood Classification, Multinomial Logistic Regression, Neural Networks, Random Forests and Support Vector Machines. The output includes the classified raster and standard classification accuracy assessment such as the accuracy matrix, the overall accuracy and the kappa coefficient. An option for in-sample verification is available.
We propose a general ensemble classification framework, RaSE algorithm, for the sparse classification problem. In RaSE algorithm, for each weak learner, some random subspaces are generated and the optimal one is chosen to train the model on the basis of some criterion. To be adapted to the problem, a novel criterion, ratio information criterion (RIC) is put up with based on Kullback-Leibler divergence. Besides minimizing RIC, multiple criteria can be applied, for instance, minimizing extended Bayesian information criterion (eBIC), minimizing training error, minimizing the validation error, minimizing the cross-validation error, minimizing leave-one-out error. There are various choices of base classifier, for instance, linear discriminant analysis, quadratic discriminant analysis, k-nearest neighbour, logistic regression, decision trees, random forest, support vector machines. RaSE algorithm can also be applied to do feature ranking, providing us the importance of each feature based on the selected percentage in multiple subspaces. RaSE framework can be extended to the general prediction framework, including both classification and regression. We can use the selected percentages of variables for variable screening. The latest version added the variable screening function for both regression and classification problems.
Algorithms for the spatial stratification of landscapes, sampling and
    modeling of spatially-varying phenomena. These algorithms offer a simple framework
    for the stratification of geographic space based on raster layers representing
    landscape factors and/or factor scales. The stratification process follows a 
    hierarchical approach, which is based on first level units (i.e., classification 
    units) and second-level units (i.e., stratification units). Nonparametric techniques
    allow to measure the correspondence between the geographic space and the landscape
    configuration represented by the units. These correspondence metrics are useful to
    define sampling schemes and to model the spatial variability of environmental
    phenomena.
Reading, writing, manipulating, analyzing and modeling of spatial data. The package implements basic and high-level functions for raster data and for vector data operations such as intersections. See the manual and tutorials on <https://rspatial.org/> to get started.
Providing functions to calculate indices of diversity on numerical matrices based on information theory. The rationale behind the package is described in Rocchini, Marcantonio and Ricotta (2017) <doi:10.1016/j.ecolind.2016.07.039> and Rocchini, Marcantonio,..., Ricotta (2021) <doi:10.1101/2021.01.23.427872>.
Fast alternatives to several relatively slow 'raster' package
  functions. For large rasters, the functions run from 5 to
  approximately 100 times faster than the 'raster' package functions
  they replace. The 'fasterize' package, on which one function in this
  package depends, includes an implementation of the scan line
  algorithm attributed to Wylie et al. (1967)
  <doi:10.1145/1465611.1465619>.
This is a wrapper function for image(), which makes reasonable
    raster plots with nice axis and other useful features.
Provides R functions to selectively rasterize components
             of 'grid' output.
Performs kernel based estimates on in-memory raster images 
  from the raster package.  These kernel estimates include local means
  variances, modes, and quantiles.  All results are in the form of 
  raster images, preserving original resolution and projection attributes.
A S4 class has been created such that complex operations can be
    executed on each cells of a raster map. The raster of objects contains the
    traditional raster map with the addition of a list of generic objects: one
    object for each raster cells. It allows to write few lines of R code for complex
    map algebra. Two environmental applications about frequency analysis of raster
    map of precipitation and creation of a raster map of soil water retention curves
    have been presented.
It aims to easily and rapidly generate raster data in R, even for very large datasets, with an aesthetics-based mapping syntax that should be familiar to users of the 'ggplot2' package. While 'rasterly' does not attempt to reproduce the full functionality of the 'Datashader' graphics pipeline system for Python, the 'rasterly' API has several core elements in common with that software package.
The ability to plot raster graphics in PDF files can be useful
    when one needs multi-page documents, but the plots contain so many
    individual elements that (the usual) use of vector graphics results in
    inconveniently large file sizes. Internally, the package plots each
    individual page as a PNG, and then combines them in one PDF file.
Methods for enhanced visualization and interaction with raster data. It implements visualization methods for quantitative data and categorical data, both for univariate and multivariate rasters. It also provides methods to display spatiotemporal rasters, and vector fields. See the website for examples.
Automated test assembly of linear and adaptive tests using the 
    mixed-integer programming. The full documentation and tutorials are at 
    <https://github.com/xluo11/Rata>.
Allows to limit the rate at which one or more functions can be called.
The Evolutionary Rate Matrix is a variance-covariance matrix which describes both the rates of trait evolution and the evolutionary correlation among multiple traits. This package has functions to estimate these parameters using Bayesian MCMC. It is possible to test if the pattern of evolutionary correlations among traits has changed between predictive regimes painted along the branches of the phylogenetic tree. Regimes can be created a priori or estimated as part of the MCMC under a joint estimation approach. The package has functions to run MCMC chains, plot results, evaluate convergence, and summarize posterior distributions.
Fit statistical models based on the Dawid-Skene model - Dawid
    and Skene (1979) <doi:10.2307/2346806> - to repeated categorical
    rating data.  Full Bayesian inference for these models is supported
    through the Stan modelling language. 'rater' also allows the user to
    extract and plot key parameters of these models.
A function which performs exact rate ratio tests and returns an object of class htest.
The kappa statistic implemented by Fleiss is a very popular index for assessing the reliability of agreement among multiple observers. It is used both in the psychological and in the psychiatric field. Other fields of application are typically medicine, biology and engineering. Unfortunately,the kappa statistic may behave inconsistently in case of strong agreement between raters, since this index assumes lower values than it would have been expected. We propose a modification kappa implemented by Fleiss in case of nominal and ordinal variables. Monte Carlo simulations are used both to testing statistical hypotheses and to calculating percentile bootstrap confidence intervals based on proposed statistic in case of nominal and ordinal data.
Computes confidence intervals for the rate (or risk)
    difference ('RD') or rate ratio (or relative risk, 'RR') for 
    binomial proportions or Poisson rates, or for odds ratio 
    ('OR', binomial only). Also confidence intervals for a single 
    binomial or Poisson rate, and intervals for matched pairs. 
    Includes skewness-corrected asymptotic score ('SCAS') methods, 
    which have been developed in Laud (2017) <doi:10.1002/pst.1813>
    from Miettinen & Nurminen (1985) <doi:10.1002/sim.4780040211> and 
    Gart & Nam (1988) <doi:10.2307/2531848>. 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 five testing problems and their implementation in empirical work. First, it facilitates the empirical researcher to test for particular hypotheses, such as comparisons of means, medians, and variances from k populations using robust permutation tests, which asymptotic validity holds under very weak assumptions, while retaining the exact rejection probability in finite samples when the underlying distributions are identical. Second, the description and implementation of a permutation test for testing the continuity assumption of the baseline covariates in the sharp regression discontinuity design (RDD) as in Canay and Kamat (2018) <https://goo.gl/UZFqt7>. More specifically, it allows the user to select a set of covariates and test the aforementioned hypothesis using a permutation test based on the Cramer-von Misses test statistic. Graphical inspection of the empirical CDF and histograms for the variables of interest is also supported in the package. Third, it provides the practitioner with an effortless implementation of a permutation test based on the martingale decomposition of the empirical process for testing for heterogeneous treatment effects in the presence of an estimated nuisance parameter as in Chung and Olivares (2021) <doi:10.1016/j.jeconom.2020.09.015>. Fourth, this version considers the two-sample goodness-of-fit testing problem under covariate adaptive randomization and implements a permutation test based on a prepivoted Kolmogorov-Smirnov test statistic. Lastly, it implements an asymptotically valid permutation test based on the quantile process for the hypothesis of constant quantile treatment effects in the presence of an estimated nuisance parameter.
Designed to be compatible with the R package 'DBI' (Database Interface)
    when connecting to Amazon Web Service ('AWS') Athena <https://aws.amazon.com/athena/>.
    To do this 'Python' 'Boto3' Software Development Kit ('SDK')
    <https://boto3.amazonaws.com/v1/documentation/api/latest/index.html> is used as a driver.
Describes a new procedure of reducing items in a rating scale called Rating Scale Reduction (RSR). The new stop criterion in RSR procedure is added (stop global max). The function order is replaced by sort.list.
We implement a test of the rational expectations hypothesis based on the marginal distributions of realizations and subjective beliefs from D'Haultfoeuille, Gaillac, and Maurel (2018) <doi:10.3386/w25274>. This test can be used in cases where realizations and subjective beliefs are observed in two different datasets that cannot be matched, or when they are observed in the same dataset. The package also computes the estimator of the minimal deviations from rational expectations than can be rationalized by the data.
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).
An R interface to United States Environmental Protection Agency (EPA)  
    Assessment, Total Maximum Daily Load (TMDL) Tracking and Implementation System 
    ('ATTAINS') data. 'ATTAINS' is the EPA database used to track information 
    provided by states about water quality assessments conducted under federal 
    Clean Water Act requirements. ATTAINS information and API information is available at <https://www.epa.gov/waterdata/attains>.
The R Analytic Tool To Learn Easily (Rattle) provides a 
  collection of utilities functions for the data scientist. A
  Gnome (RGtk2) based graphical interface is included with 
  the aim to provide a simple and intuitive introduction to R 
  for data science, allowing a user to quickly load data from a CSV file 
  (or via ODBC), transform and explore the data, 
  build and evaluate models, and export models as PMML (predictive
  modelling markup language) or as scores. A key aspect of the GUI
  is that all R commands are logged and commented through the log tab.
  This can be saved as a standalone R script file and as
  an aid for the user to 
  learn R or to copy-and-paste directly into R itself.
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.
Rare variant association tests: burden tests (Bocher et al. 2019 <doi:10.1002/gepi.22210>) and the Sequence Kernel Association Test (Bocher et al. 2021 <doi:10.1038/s41431-020-00792-8>) in the whole genome; and genetic simulations.
Includes multiple cross-platform read/write interfaces for
    'RAVE' project. 'RAVE' stands for "R analysis and visualization of human 
    intracranial electroencephalography data". The whole project aims at 
    providing powerful free-source package that analyze brain recordings from 
    patients with electrodes placed on the cortical surface or inserted into 
    the brain. 'raveio' as part of this project provides tools to read/write 
    neurophysiology data from/to 'RAVE' file structure, as well as several 
    popular formats including  'EDF(+)', 'Matlab', 'BIDS-iEEG', and 'HDF5', 
    etc. Documentation and examples about 'RAVE' project are provided at 
    <https://openwetware.org/wiki/RAVE>, and the paper by John F. Magnotti, 
    Zhengjia Wang, Michael S. Beauchamp (2020) 
    <doi:10.1016/j.neuroimage.2020.117341>; see 'citation("raveio")' for 
    details.
Provides access to the 'Ravelry' API <https://www.ravelry.com/groups/ravelry-api>. An R wrapper for pulling data from 'Ravelry.com', an organizational tool for crocheters, knitters, spinners, and weavers. You can retrieve pattern, yarn, author, and shop information by search or by a given id.
Provides an I/O interface between R data.frames and
    Raven DataFrames. Defines functions to both read and write DataFrame
    files, as well as serialize/deserialize data.frames/DataFrames.
Utilities for processing input and output files associated with the Raven Hydrological Modelling Framework. Includes various plotting functions, model diagnostics, reading output files into extensible time series format, and support for writing Raven input files. 
    The RavenR package is also archived at Chlumsky et al. (2020) <doi:10.5281/zenodo.4248183>.
    The Raven Hydrologic Modelling Framework method can be referenced with Craig et al. (2020) <doi:10.1016/j.envsoft.2020.104728>.
Implementation of the R-Average method for parameter estimation of averaging models of the Anderson's Information Integration Theory by Vidotto, G., Massidda, D., & Noventa, S. (2010) <https://www.uv.es/psicologica/articulos3FM.10/3Vidotto.pdf>.
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.
Assess LC–MS system performance by visualizing instrument log files and monitoring raw quality control samples within a project.
The Kolmogorov-Smirnov (K-S) statistic is a standard method to measure the model strength for 
    credit risk scoring models. This package calculates the K–S statistic and 
    plots the true-positive rate and false-positive rate to measure the model strength. 
    This package was written with the credit marketer, who uses risk models in 
    conjunction with his campaigns. The users could read more details from 
    Thrasher (1992) <doi:10.1002/dir.4000060408> and 'pyks' <https://pypi.org/project/pyks/>.
This is an R wrapper from the AWS Command Line Interface that 
  provides methods to manage the user configuration on Amazon Web Service. You 
  can create as many profiles as you want, manage them, and delete them. The 
  profiles created with this tool work with all AWS products such as S3, 
  Glacier, and EC2. It also provides a function to automatically install 
  AWS CLI, but you can download it and install it manually if you prefer.
Uses convolution-based techniques to generate simulated camera bokeh, depth of field, and other camera effects, using an image and an optional depth map. Accepts both filename inputs and in-memory array representations of images and matrices. Includes functions to perform 2D convolutions, reorient and resize images/matrices, add image overlays, generate camera vignette effects, and add titles to images.
Render scenes using pathtracing. Build 3D scenes out of spheres, cubes, planes, disks, triangles, cones, curves, line segments, cylinders, ellipsoids, and 3D models in the 'Wavefront' OBJ file format or the PLY Polygon File Format. Supports several material types, textures, multicore rendering, and tone-mapping. Based on the "Ray Tracing in One Weekend" book series. Peter Shirley (2018) <https://raytracing.github.io>.
Uses a combination of raytracing and multiple hill shading methods to produce 2D and 3D data visualizations and maps. Includes water detection and layering functions, programmable color palette generation, several built-in textures for hill shading, 2D and 3D plotting options, a built-in path tracer, 'Wavefront' OBJ file export, and the ability to save 3D visualizations to a 3D printable format.
Rossby wave ray paths are traced from 
             a determined source, specified wavenumber, and direction
             of propagation. "raytracing" also works with a set of 
             experiments changing these parameters, making possible the
             identification of Rossby wave sources automatically. 
             The theory used here is based on classical studies, 
             such as Hoskins and Karoly (1981) <doi:10.1175/1520-0469(1981)038%3C1179:TSLROA%3E2.0.CO;2>,
             Karoly (1983) <doi:10.1016/0377-0265(83)90013-1>, 
             Hoskins and Ambrizzi (1993) <doi:10.1175/1520-0469(1993)050%3C1661:RWPOAR%3E2.0.CO;2>,
             and Yang and Hoskins (1996) <doi:10.1175/1520-0469(1996)053%3C2365:PORWON%3E2.0.CO;2>.
Rasterize images using a 3D software renderer. 3D scenes are created either by importing external files, building scenes out of the included objects, or by constructing meshes manually. Supports point and directional lights, anti-aliased lines, shadow mapping, transparent objects, translucent objects, multiple materials types, reflection, refraction, environment maps, multicore rendering, bloom, tone-mapping, and screen-space ambient occlusion.
Interface to the API for the 'Bielefeld' Academic Search
    Engine ('BASE') (<https://www.base-search.net/>). 'BASE' is a
    search engine for more than 150 million scholarly documents from more
    than 7000 sources. Methods are provided for searching for documents,
    as well as getting information on higher level groupings of documents:
    collections and repositories within collections. Search includes
    faceting, so you can get a high level overview of number of documents
    across a given variable (e.g., year). 'BASE' asks users to respect a
    rate limit, but does not enforce it themselves; we enforce that
    rate limit.
An approach to age-depth modelling that uses Bayesian statistics to reconstruct accumulation histories for deposits, through combining radiocarbon and other dates with prior information. See Blaauw & Christen (2011).
A model of the form Y = f(x) + s(x) Z is fit where functions f and s are modeled with ensembles of trees and Z is standard normal.  
    This model is developed in the paper 'Heteroscedastic BART Via Multiplicative Regression Trees' 
    (Pratola, Chipman, George, and McCulloch, 2019, <arXiv:1709.07542v2>).
    BART refers to Bayesian Additive Regression Trees. See the R-package 'BART'.
    The predictor vector x may be high dimensional.
    A Markov Chain Monte Carlo (MCMC) algorithm provides Bayesian posterior uncertainty for both f and s.
    The MCMC uses the recent innovations in 
    Efficient Metropolis--Hastings proposal mechanisms for Bayesian regression tree models 
    (Pratola, 2015, Bayesian Analysis, <doi:10.1214/16-BA999>).
'BaseX' <http://basex.org> is a XML database engine and a compliant 'XQuery 3.1' processor with full support of 'W3C Update Facility'. This package is a full client-implementation of the client/server protocol for 'BaseX' and provides functionalities to create, manipulate and query on XML-data.
A Pure R implementation of Bayesian Global Optimization with Gaussian Processes.
Implementing the BDAT tree taper Fortran routines, which were
  developed for the German National Forest Inventory (NFI), to calculate 
  diameters, volume, assortments, double bark thickness and biomass for
  different tree species based on tree characteristics and sorting information.
  See Kublin (2003) <doi:10.1046/j.1439-0337.2003.00183.x> for details.
Interpretation of time series data is affected by model choices. Different models can give different or even contradicting estimates of patterns, trends, and mechanisms for the same data--a limitation alleviated by the Bayesian estimator of abrupt change,seasonality, and trend (BEAST) of this package. BEAST seeks to improve time series decomposition by forgoing the "single-best-model" concept and embracing all competing models into the inference via a Bayesian model averaging scheme. It is a flexible tool to uncover abrupt changes (i.e., change-points), cyclic variations (e.g., seasonality), and nonlinear trends in time-series observations. BEAST not just tells when changes occur but also quantifies how likely the detected changes are true. It detects not just piecewise linear trends but also arbitrary nonlinear trends. BEAST is applicable to real-valued time series data of all kinds, be it for remote sensing, economics, climate sciences, ecology, and hydrology. Example applications include its use to identify regime shifts in ecological data, map forest disturbance and land degradation from satellite imagery, detect market trends in economic data, pinpoint anomaly and extreme events in climate data, and unravel system dynamics in biological data. Details on BEAST are reported in Zhao et al. (2019) <doi:10.1016/j.rse.2019.04.034>.
Implements an interface to Minecraft (Bedrock Edition) worlds. Supports the analysis and management of these worlds and game saves.
Basic R package to access data structures offered by any
    BEFdata portal instance.
rbenchmark is inspired by the Perl module Benchmark, and
        is intended to facilitate benchmarking of arbitrary R code. The
        library consists of just one function, benchmark, which is a
        simple wrapper around system.time.  Given a specification of
        the benchmarking process (counts of replications, evaluation
        environment) and an arbitrary number of expressions, benchmark
        evaluates each of the expressions in the specified environment,
        replicating the evaluation as many times as specified, and
        returning the results conveniently wrapped into a data frame.
An implementation of robust bent line regression. It can fit the bent line regression and test the existence of change point,
    for the paper, "Feipeng Zhang and Qunhua Li (2016). Robust bent line regression, submitted."
Provides S3 class objects and methods for built environment data to ease the use of working with these data and facilitate other packages that make use of this data structure.
Tool-set to support Bayesian evidence synthesis.  This
    includes meta-analysis, (robust) prior derivation from historical
    data, operating characteristics and analysis (1 and 2 sample
    cases). Please refer to 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> and 
             Martinez and Salibian-Barrera (2021) <doi:10.21105/joss.02992> for details.
Facilities for working with Atlantis box-geometry model (BGM) 
 files. Atlantis is a deterministic, biogeochemical, whole-of-ecosystem model. 
 Functions are provided to read from BGM files directly, preserving their 
 internal topology, as well as helper functions to generate spatial data from these
 mesh forms. This functionality aims to simplify the creation and modification of box 
 and geometry as well as the ability to integrate with other data sources.
Methods that allow video reading and loading in R. Also provides nine different methods for  background subtraction.
Interface to 'Biodiversity' 'Heritage' Library ('BHL')
    (<https://www.biodiversitylibrary.org/>) API
    (<https://www.biodiversitylibrary.org/docs/api3.html>). 'BHL' is a
    repository of 'digitized' literature on 'biodiversity'
    studies, including 'floras', research papers, and more.
Provides a complete interface to 'LibBi', a library for Bayesian inference (see <http://libbi.org> and <doi:10.18637/jss.v067.i10> for more information). This includes functions for manipulating 'LibBi' models, for reading and writing 'LibBi' input/output files, for converting 'LibBi' output to provide traces for use with the coda package, and for running 'LibBi' to conduct inference.
Contains a collection of helper functions to use with 'RBi', the R interface to 'LibBi', described in Murray et al. (2015) <doi:10.18637/jss.v067.i10>. It contains functions to adapt the proposal distribution and number of particles in particle Markov-Chain Monte Carlo, as well as calculating the Deviance Information Criterion (DIC) and converting between times in 'LibBi' results and R time/dates.
Implementation of the algorithms (with minor modifications)
    to correct bias in quantitative DNA methylation analyses as described
    by Moskalev et al. (2011) <doi:10.1093/nar/gkr213>. Publication:
    Kapsner et al. (2021) <doi:10.1002/ijc.33681>.
Read and write 'Bibtex' files. Convert between bibliography
    formats, including 'Bibtex', 'Biblatex', 'PubMed', 'Endnote', and
    'Bibentry'.  Includes a port of the 'bibutils' utilities by Chris
    Putnam <https://sourceforge.net/projects/bibutils/>. Supports all
    bibliography formats and character encodings implemented in
    'bibutils'.
Manually bin data using weight of evidence and information value. Includes other binning 
    methods such as equal length, quantile and winsorized. Options for combining levels of categorical
    data are also available. Dummy variables can be generated based on the bins created using any of 
    the available binning methods. References: Siddiqi, N. (2006) <doi:10.1002/9781119201731.biblio>.
The MOSAICbioacc application is a turn-key package providing bioaccumulation
    factors (BCF/BMF/BSAF) from a toxicokinetic (TK) model fitted to
    accumulation-depuration data. It is designed to fulfil the requirements
    of regulators when examining applications for market authorization of active
    substances. See Ratier et al. (2021) <doi:10.1101/2021.09.08.459421>.
Currently fully supports Enrichr, JASPAR, miEAA, PANTHER,
    Reactome, STRING, and UniProt! The goal of rbioapi is to provide a
    user-friendly and consistent interface to biological databases and
    services: In a way that insulates the user from technicalities of
    using web services API and creates a unified and easy-to-use interface
    to biological and medical web services. This an ongoing project; New
    databases and services will be added periodically. Feel free to
    suggest any databases or services you often use.
A toolkit for working with Biological Observation Matrix ('BIOM') files.
    Features include reading/writing all 'BIOM' formats, rarefaction, alpha
    diversity, beta diversity (including 'UniFrac'), summarizing counts by 
    taxonomic level, and sample subsetting. Standalone functions for 
    reading, writing, and subsetting phylogenetic trees are also provided. 
    All CPU intensive operations are encoded in C with multi-thread support.
Functions for connecting to BioUML server, querying BioUML repository and launching BioUML analyses.
Interface to the 'USGS' 'BISON' (<https://bison.usgs.gov/>)
    API, a 'database' for species occurrence data. Data comes from
    species in the United States from participating data providers. You can get
    data via 'taxonomic' and location based queries. A simple function
    is provided to help visualize data.
A simple implementation of Binary Indexed Tree by R. The BinaryIndexedTree class supports construction of Binary Indexed Tree from a vector, update of a value in the vector and query for the sum of a interval of the vector.
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>.
Provides four boolean matrix factorization (BMF) methods.
             BMF has many applications like data mining and categorical data 
             analysis. BMF is also known as boolean matrix decomposition (BMD) 
             and was found to be an NP-hard (non-deterministic polynomial-time)
             problem. Currently implemented methods are 
             'Asso' Miettinen, Pauli and others (2008) 
                    <doi:10.1109/TKDE.2008.53>,
             'GreConD' R. Belohlavek, V. Vychodil (2010) 
                      <doi:10.1016/j.jcss.2009.05.002> ,
             'GreConDPlus' R. Belohlavek, V. Vychodil (2010) 
                      <doi:10.1016/j.jcss.2009.05.002> ,
             'topFiberM' A. Desouki, M. Roeder, A. Ngonga (2019) 
                       <arXiv:1903.10326>.
Creation, manipulation, simulation of linear Gaussian Bayesian
             networks from text files and more...
The Biological Magnetic Resonance Data Bank (BMRB,<http://
    www.bmrb.io/>) collects, annotates, archives, and disseminates (worldwide
    in the public domain) the important spectral and quantitative data derived
    from NMR(Nuclear Magnetic Resonance) spectroscopic investigations of biological
    macromolecules and metabolites. This package provides an interface to BMRB
    database for easy data access and includes a minimal set of data visualization
    functions. Users are encouraged to make their own data visualizations using BMRB
    data.
Provides a convenient way of accessing data published by the Reserve Bank of New Zealand (RBNZ) on their website, <https://www.rbnz.govt.nz/statistics>. A range of financial and economic data is provided in spreadsheet format including exchange and interest rates, commercial lending statistics, Reserve Bank market operations, financial institution statistics, household financial data, New Zealand debt security information, and economic indicators. This package provides a method to download those spreadsheets and read them directly into R.
A native R plotting library that provides a flexible declarative interface for creating interactive web-based graphics, backed by the Bokeh visualization library <https://bokeh.pydata.org/>.
This is a simple package which provides a function
      that boosts pre-ready or custom-made classifiers. Package
      uses Discrete AdaBoost (<doi:10.1006/jcss.1997.1504>) and Real AdaBoost
      (<doi:10.1214/aos/1016218223>) for two class,
      SAMME (<doi:10.4310/SII.2009.v2.n3.a8>) and
      SAMME.R (<doi:10.4310/SII.2009.v2.n3.a8>)
      for multiclass classification.
Scalable implementation of classification and regression forests, as described by Breiman (2001), <DOI:10.1023/A:1010933404324>.
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.
Bridge Regression estimation with linear restrictions defined in Yuzbasi et al. (2019) <arXiv:1910.03660>. Special cases of this approach fit the restricted LASSO, restricted RIDGE and restricted Elastic Net estimators.
Provides a fast calculation of the Blyth-Still-Casella confidence interval. The implementation follows the 'StatXact' 9 manual (Cytel 2010) and "Refining Binomial Confidence Intervals" by George Casella (1986) <doi:10.2307/3314658>.
Implementation of the RPC-JSON API for Bitcoin and utility functions for address creation and content analysis of the blockchain.
The regression-based (RB) approach is a method to test the missing data mechanism.
			This package contains two functions that test the type of missing data (Missing Completely 
			At Random vs Missing At Random) on the basis of the RB approach. The first function applies 
			the RB approach independently on each variable with missing data, using the completely 
			observed variables only. The second function tests the missing data mechanism globally 
			(on all variables with missing data) with the use of all available information. The 
			algorithm is adapted both to continuous and categorical data.
Tu & Zhou (1999) <doi:10.1002/(SICI)1097-0258(19991030)18:20%3C2749::AID-SIM195%3E3.0.CO;2-C> showed that comparing the means of populations whose data-generating distributions are non-negative with excess zero observations is a problem of great importance in the analysis of medical cost data. In the same study, Tu & Zhou discuss that it can be difficult to control type-I error rates of general-purpose statistical tests for comparing the means of these particular data sets. This package allows users to perform a modified bootstrap-based t-test that aims to better control type-I error rates in these situations.
Rbundler manages a project-specific library for dependency
    package installation. By specifying dependencies in a DESCRIPTION file
    in a project's root directory, one may install and use dependencies
    in a repeatable fashion without requiring manual maintenance.
    rbundler creates a project-specific R library in
    `PROJECT_ROOT/.Rbundle` (by default) and a project-specific
    `R_LIBS_USER` value, set in `PROJECT_ROOT/.Renviron`. It supports
    dependency management for R standard "Depends", "Imports",
    "Suggests", and "LinkingTo" package dependencies. rbundler also
    attempts to validate and install versioned dependencies, such
    as ">=", "==", "<=". Note that, due to the way R manages package
    installation, differing nested versioned dependencies are not
    allowed. For example, if your project depends on packages A (== 1),
    and B (== 2), but package A depends on B (== 1), then a nested
    dependency violation will cause rbundler to error out.
Residual balancing is a robust method of constructing weights for
  marginal structural models, which can be used to estimate (a) the average treatment effect in 
  a cross-sectional observational study, (b) controlled direct/mediator effects in causal mediation
  analysis, and (c) the effects of time-varying treatments in panel data (Zhou and Wodtke 2020
  <doi:10.1017/pan.2020.2>). This package provides three functions, rbwATE(), rbwMed(), and rbwPanel(),
  that produce residual balancing weights for estimating (a), (b), (c), respectively.
Relational Class Analysis (RCA) is a method for detecting
        heterogeneity in attitudinal data (as described in Goldberg
        A., 2011, Am. J. Soc, 116(5)).
Regionalization of multiscale spatial processes based on a criterion for spatial aggregation error. The multiscale representation is a truncated Karhunen-Loaeve expansion using Obled-Creutin eigenfunctions. The method is incorporated within a Bayesian framework using an MCMC implementation of a latent spatial model. Bradley, J. R., Wikle, C. K., and Holan, S. H. (2017) <doi:10.1111/rssb.12179>.
Regularized calibrated estimation for causal inference and missing-data problems with high-dimensional data, based on Tan (2020a) <doi:10.1093/biomet/asz059>, Tan (2020b) <doi:10.1214/19-AOS1824> and Sun and Tan (2020) <arXiv:2009.09286>.
Calculate the flow of particles between polygons by two integration methods:
  integration by a cubature method and integration on a grid of points.
  Annie Bouvier, Kien Kieu, Kasia Adamczyk and Herve Monod (2009)
  <doi:10.1016/j.envsoft.2008.11.006>.
Tools for basic and advance cancer statistics and graphics.
	Groups individual data, merges registry data and population data, calculates age-specific rate, age-standardized rate, cumulative risk, estimated annual percentage rate with standards error. Creates graphics across variable and
    time, such as age-specific trends, bar chart and period-cohort trends.
There are different numeric optimizations which are used in order to estimate coefficients in models such as linear regression and neural networks. This package covers parameter estimation in linear regression using different methods such as batch gradient descent, stochastic gradient descent, minibatch gradient descent and coordinate descent. Kiwiel, Krzysztof C (2001) <doi:10.1007/PL00011414> Yu Nesterov (2004) <ISBN:1-4020-7553-7> Ferguson, Thomas S (1982) <doi:10.1080/01621459.1982.10477894> Zeiler, Matthew D (2012) <arXiv:1212.5701> Wright, Stephen J (2015) <arXiv:1502.04759>.
Provides an interface to the National Topographic System (NTS),
    which is the way in which a number of freely available Canadian datasets are
    organized. CanVec and CanVec+ datasets, which include all data used to create
    Canadian topographic maps, are two such datasets that are useful in creating
    vector-based maps for locations across Canada. This packages searches CanVec
    data by location, plots it using pretty defaults, and exports it to human-
    readable shapefiles for use in another GIS.
Estimation of abundance and other demographic parameters for closed 
             populations, open populations and the robust design in capture-recapture  
             experiments using loglinear models.
Translation of the 'MATLAB' program 'Carb' (Nathan and Mauz 2008 <DOI:10.1016/j.radmeas.2007.12.012>; Mauz and Hoffmann 2014) for dose rate modelling for carbonate-rich samples in the context of trapped charged dating (e.g., luminescence dating) applications.
Enables the calibration and analysis of radiocarbon dates, often but not exclusively for the purposes of archaeological research. It includes functions not only for basic calibration, uncalibration, and plotting of one or more dates, but also a statistical framework for building demographic and related longitudinal inferences from aggregate radiocarbon date lists, including: Monte-Carlo simulation test (Timpson et al 2014 <doi:10.1016/j.jas.2014.08.011>), random mark permutation test (Crema et al 2016 <doi:10.1371/journal.pone.0154809>) and spatial permutation tests (Crema, Bevan, and Shennan 2017 <doi:10.1016/j.jas.2017.09.007>).
Provides color schemes for maps and other graphics
    designed by 'CARTO' as described at <https://carto.com/carto-colors/>.
    It includes four types of palettes: aggregation, diverging, qualitative, 
    and quantitative.
This packages provides a direct interface (without the
	     use of Java) to the most basic functionality of Apache
	     Cassanda such as login, updates and queries.
A set of tools and functions to help with species conservation assessments (Red List threat assessments). Includes Extent of occurrence, Area of Occupancy, Minimum Enclosing Rectangle, a geographic Projection Wizard and Species batch processing.
Calculate 22 summary statistics coded in C on time-series vectors to enable 
    pattern detection, classification, and regression applications in the 
    feature space as proposed by Lubba et al. (2019) <doi:10.1007/s10618-019-00647-x>.
Provides implementations of a classifier based on the
    "Classification Based on Associations" (CBA). It can be used for building
    classification models from association rules. Rules are pruned in the order of
    precedence given by the sort criteria and a default rule is added. The final
    classifier labels provided instances. CBA was originally proposed by Liu,
    B. Hsu, W. and Ma, Y. Integrating Classification and Association Rule
    Mining. Proceedings KDD-98, New York, 27-31 August. AAAI. pp80-86 (1998, ISBN:1-57735-070-7).
Tools for large, sparse optimal matching of treated units
	and control units in observational studies.  Provisions are
	made for refined covariate balance constraints, which include
	fine and near-fine balance as special cases.  Matches are 
	optimal in the sense that they are computed as solutions to
	network optimization problems rather than greedy algorithms.
A collection of functions to estimate Rogers-Castro migration age schedules using 'Stan'. This model which describes the fundamental relationship between migration and age in the form of a flexible multi-exponential migration model was most notably proposed in Rogers and Castro (1978) <doi:10.1068/a100475>.
Nonparametric maximum likelihood estimation methods
    for random coefficient binary response  models and some related
    functionality for sequential processing of hyperplane arrangements.
    See J. Gu and R. Koenker (2020) <DOI:10.1080/01621459.2020.1802284>.
Tools for optimal subset matching of treated units
	and control units in observational studies, with support
	for refined covariate balance constraints, (including
	fine and near-fine balance as special cases). A close 
	relative is the 'rcbalance' package.
Functions to implement the parametric and non-parametric bootstrap 
    confidence interval methods described in Morrison and Simon (2017) <arXiv:1702.06986>.
Identify, convert and handle dates as used within the Swedish cancer register and associated cancer quality registers in Sweden. Especially the cancer register sometimes use nonstandard date variables where day and/or month can be "00" or were the date format is a mixture of"%Y-%m-%d", "%Y%m%d" and "%y%V" (two digit year and week number according to ISO 8601,which is not completely supported by R).These dates must be approximated to valid dates before being used in for example survival analysis. The package also includes some convenient functions for calculating "lead times" (relying on 'difftime') and introduce a "year" class with relevant S3-methods to handle yearly cohort.
Functions either required by other Swedish Regional Cancer Center packages or standalone functions outside the scope of other packages.
The RCC_PCA criterion is a tool to determine the optimal number of components to retain in PCA;See Alshammri (2021).
R interface to (some of) cddlib
    (<https://github.com/cddlib/cddlib>).
    Converts back and forth between two representations of a convex polytope:
    as solution of a set of linear equalities and inequalities and as
    convex hull of set of points and rays.
    Also does linear programming and redundant generator elimination
    (for example, convex hull in n dimensions).  All functions can use exact
    infinite-precision rational arithmetic.
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.
Download and open manifest files provided by the Copernicus Global Land Service data <https://land.copernicus.eu/global/>. The manifest files are available at: <https://land.copernicus.vgt.vito.be/manifest/>. Also see: <https://land.copernicus.eu/global/access/>. Before you can download the data, you will first need to register to create a username and password.
Conjugate gradient minimization of nonlinear functions
        with box constraints incorporating the Dai/Yuan update. This
        implementation should be used in place of the "CG" algorithm
        of the optim() function.
A simple data science challenge system using R Markdown and 'Dropbox' <https://www.dropbox.com/>.
    It requires no network configuration, does not depend on external platforms
    like e.g. 'Kaggle' <https://www.kaggle.com/> and can be easily installed on a personal computer.
Provides a dataset of functions in all base packages of R versions 1.0.1 onwards.
R package for chess validations, pieces movements and check
    detection. Also integrates functions to plot chess boards given a
    Forsyth Edwards and Portable Game notations.
Provides algorithms to locate multiple
    distributional change-points in piecewise stationary time series. The
    algorithms are provably consistent, even in the presence of long-range
    dependencies. Knowledge of the number of change-points is not
    required. The code is written in Go and interfaced with R.
Parses the 'ArchieML' format from the New York Times <http://archieml.org>.
  Also provides utilities for retrieving Google Drive documents for parsing.
A set of functions to generate, access and analyze standard data products from archival tagging data.
An implementation of simulated maximum likelihood method for the estimation of Binary (Probit and Logit), Ordered (Probit and Logit) and Poisson models with random parameters for cross-sectional and longitudinal data as presented in Sarrias (2016) <doi:10.18637/jss.v074.i10>.
Collection of portable choice dialog widgets
Provides a list of functions for the statistical analysis and the post-processing of the Markov Chains simulated by ChronoModel (see <http://www.chronomodel.fr> for more information).  ChronoModel is a friendly software to construct a chronological model in a Bayesian framework.  Its output is  a sampled Markov chain from the posterior distribution of dates component  the chronology. The functions can also be applied  to the  analyse of mcmc output generated by Oxcal software.
A simple and flexible way to generate Circos 2D track plot images for genomic data visualization is implemented in this package. The types of plots include: heatmap, histogram, lines, scatterplot, tiles and plot items for further decorations include connector, link (lines and ribbons), and text (gene) label. All functions require only R graphics package that comes with R base installation.
A programmatic interface to the Species+ <https://speciesplus.net/> database via the Species+/CITES Checklist API <https://api.speciesplus.net/>.
Client for 'Citoid' (<https://www.mediawiki.org/wiki/Citoid>),
    an API for getting citations for various scholarly work identifiers
    found on 'Wikipedia'.
'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.
'Climacell' is a weather platform that provides hyper-local forecasts and weather 
    data. This package enables the user to query the core layers of the 
    time line interface of the 'Climacell' v4 API <https://www.climacell.co/weather-api/>. 
    This package requires a valid API key. See vignettes for instructions on use.
R tools for integrating with the www.clinicalcodes.org web
    repository
Leverages the functionality of 'clipboard.js', a JavaScript library
    for HMTL5-based copy to clipboard from web pages (see <https://clipboardjs.com>
    for more information), and provides a reactive copy-to-clipboard UI button 
    component, called 'rclipButton', 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, <https://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).
Graphical toolbox for clustering and classification of data frames.
    It proposes a graphical interface to process clustering and classification methods on features
    data-frames, and to view initial data as well as resulted cluster or classes. According to the
    level of available labels, different approaches are proposed: unsupervised clustering,
    semi-supervised clustering and supervised classification. 
    To assess the processed clusters or classes, the toolbox can import and show some supplementary
    data formats: either profile/time series, or images. 
    These added information can help the expert to label clusters (clustering), or to constrain data
    frame rows (semi-supervised clustering), using Constrained spectral embedding algorithm by 
    Wacquet et al. (2013) <doi:10.1016/j.patrec.2013.02.003> and the methodology provided by 
    Wacquet et al. (2013) <doi:10.1007/978-3-642-35638-4_21>.
Tool for providing access to the Java version 'CMAEvolutionStrategy' of
    Nikolaus Hansen. 'CMA-ES' is the Covariance Matrix Adaptation Evolution Strategy,
    see https://www.lri.fr/~hansen/cmaes_inmatlab.html#java.
Run 'R CMD check' from 'R' and capture the results
    of the individual checks. Supports running checks in the background,
    timeouts, pretty printing and comparing check results.
A platform-independent basic-statistics GUI (graphical user interface) for R, based on the tcltk package.
Various statistical, graphics, and data-management functions used by the Rcmdr package in the R Commander GUI for R.
R Commander plug-in for repeated-measures and mixed-design
    ('split-plot') ANOVA.
    It adds a new menu entry for repeated measures that allows
    to deal with up to three within-subject factors and optionally with
    one or several between-subject factors.
    It also provides supplementary options to oneWayAnova() and
    multiWayAnova() functions, such as choice of ANOVA type, display of
    effect sizes and post hoc analysis for multiWayAnova().
A plug-in for R Commander ('Rcmdr'). The package is a Graphical
    User Interface (GUI) in which several biclustering methods can be executed,
    followed by diagnostics and plots of the results. Further, the GUI also has
    the possibility to connect the methods to more general diagnostic packages for
    biclustering. Biclustering methods from 'biclust', 'fabia', 's4vd', 'iBBiG',
    'isa2', 'BiBitR', 'rqubic' and 'BicARE' are implemented. Additionally, 'superbiclust' and
    'BcDiag' are also implemented to be able to further investigate results. The
    GUI also provides a couple of extra utilities to export, save, search through
    and plot the results. 'RcmdrPlugin.BiclustGUI' also provides a very specific
    framework for biclustering in which new methods, diagnostics and plots can be
    added. Scripts were prepared so that R-package developers can freely design
    their own dialogs in the GUI which can then be added by the maintainer of
    'RcmdrPlugin.BiclustGUI'. These scripts do not required any knowledge of 'tcltk'
    and 'Rcmdr' and are easy to fill in. (Note: rqubic currently requires manual installation through BiocManager::install('rqubic').)
Adds menu items to the R Commander for implementing case 1 (object case) best-worst scaling (BWS1) from designing choice sets to measuring preferences for items. BWS1 is a question-based survey method that constructs various combinations of items (choice sets) using the experimental designs, asks respondents to select the best and worst items in each choice set, and then measures preferences for the items by analyzing the responses. For details on BWS1, refer to Louviere et al. (2015) <doi:10.1017/CBO9781107337855>.
This package provides a Rcmdr "plug-in" based on coin (Conditional Inference Procedures in a Permutation Test Framework).
Adds menu items to the R Commander for parametric analysis of dichotomous choice contingent valuation (DCCV) data. CV is a question-based survey method to elicit individuals' preferences for goods and services. This package depends on functions regarding parametric DCCV analysis in the package DCchoice. See Carson and Hanemann (2005) <doi:10.1016/S1574-0099(05)02017-6> for DCCV.
Adds menu items for discrete choice experiments (DCEs) to the R Commander. DCE is a question-based survey method that designs various combinations (profiles) of attribute levels using the experimental designs, asks respondents to select the most preferred profile in each choice set, and then measures preferences for the attribute levels by analyzing the responses. For details on DCEs, refer to Louviere et al. (2000) <doi:10.1017/CBO9780511753831>.
We provide an Rcmdr plug-in based on the depthTools 
        package, which implements different robust statistical tools 
        for the description and analysis of gene expression data based 
        on the Modified Band Depth, namely, the scale curves for 
        visualizing the dispersion of one or various groups of samples
        (e.g. types of tumors), a rank test to decide whether two
        groups of samples come from a single distribution and two 
        methods of supervised classification techniques, the DS and 
        TAD methods.
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: 978-4-498-10918-6, Nankodo, ISBN: 978-4-524-26158-1, Ohmsha, ISBN: 978-4-274-22632-8), but an report that introduced the investigation of EZR was published in Bone Marrow Transplantation (Nature Publishing Group) as an Open article. This report can be used as a simple manual. It can be freely downloaded from the journal website as shown below. This report has been cited in more than 3,000 scientific articles.
Rcmdr Plugin for the 'FactoMineR' package.
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).
Extends R Commander with a unified menu of new and pre-existing 
        statistical functions related to public management and policy analysis 
        statistics. Functions and menus have been renamed according to the 
        usage in PMGT 630 in the Master of Public Administration program at
        Brigham Young University.
An R Commander "plug-in" extending functionality of linear models
    and providing an interface to Partial Least Squares Regression and Linear and
    Quadratic Discriminant analysis. Several statistical summaries are extended,
    predictions are offered for additional types of analyses, and extra plots, tests
    and mixed models are available.
A GUI for the orloca package is provided as a Rcmdr plug-in. The package deals with continuos planar location problems.
The R commander plug-in for robust principal component analysis. The Graphical User Interface for Principal Component Analysis (PCA) with Hubert Algorithm method.
Rcmdr menu support for some of the graphics by group in
        the lattice package
This package provides a Rcmdr "plug-in" to analyze questionnaire data.
R Commander plug-in to demonstrate various actuarial and financial risks. It includes valuation of bonds and stocks, portfolio optimization, classical ruin theory, demography and epidemic.
Mixed Treatment Comparison is a methodology to compare directly and/or indirectly health strategies (drugs, treatments, devices). This package provides an 'Rcmdr' plugin to perform Mixed Treatment Comparison for binary outcome using BUGS code from Bristol University (Lu and Ades).
Rcmdr GUI extension plug-in for Receiver Operator Characteristic tools from pROC and ROCR packages. Also it ads a Rcmdr GUI extension for Hosmer and Lemeshow GOF test from the package ResourceSelection.
This package includes tools for calculating sample sizes and 
 selecting samples using various sampling designs. This package is an extension
 of RcmdrPlugin.EHESsampling which was developed as part of the EHES pilot project.
 The EHES Pilot project has received funding from the European Commission and 
 DG Sanco. The views expressed here are those of the authors and they do not represent 
 Commission's official position.
Provides a GUI for the SCVA, SCRT and SCMA packages as described in Bulte and Onghena (2013) <doi:10.22237/jmasm/1383280020>. The package is written as an Rcmdr plugin.
This package provides a GUI for the SLC package, it is
        written as an Rcmdr plug-in.
Rcmdr interface to the 'sos' package. The plug-in renders
  the 'sos' searching functionality easily accessible via the Rcmdr
  menus. It also simplifies the task of performing multiple searches and 
  subsequently obtaining the union or the intersection of the results.
This package provides a GUI for the steepness package, it
        is written as an Rcmdr plug-in.
An R Commander plug-in for the survival
  package, with dialogs for Cox models, parametric survival regression models,
  estimation of survival curves, and testing for differences in survival
  curves, along with data-management facilities and a variety of tests, 
  diagnostics and graphs.
The 'sutteForecastR' is a package of Alpha-Sutte indicator. To make the 'sutteForecastR' user friendly, so we develop an 'Rcmdr' plug-in based on the Alpha-Sutte indicator function.
Provides an Rcmdr "plug-in" based on the TeachingDemos package, and is primarily for illustrative purposes.
R Commander plugin for teaching statistical methods. 
    It adds a new menu for making easier the teaching of the main concepts about the main statistical methods.
An 'R Commander' plug-in providing an integrated solution to perform
    a series of text mining tasks such as importing and cleaning a corpus, and
    analyses like terms and documents counts, vocabulary tables, terms
    co-occurrences and documents similarity measures, time series analysis,
    correspondence analysis and hierarchical clustering. Corpora can be imported
    from spreadsheet-like files, directories of raw text files, 'Twitter' queries,
    as well as from 'Dow Jones Factiva', 'LexisNexis', 'Europresse' and 'Alceste' files.
Some extensions to Rcmdr (R Commander), randomness test, 
 variance test for one normal sample and predictions using active model, 
 made by R-UCA project and used in teaching statistics at University of Cadiz (UCA).
An R Commander plug-in for the 'WorldFlora' package. It was mainly developed to show workflows and scripts for first-time users.
Functions and data sets for simulating and fitting minification and random coefficient minification time series models. The nth term in the model X[n] is a nonnegative random variable defined as A[n]*min(X[n-1], Y[n]), where the A's and Y's are sequences of independent random variables.  Example data sets are provided where this kind of model can be justified on physical grounds and fits well.  Functions for simulating minification time series and for maximum  likelihood estimation are included. More information can be found in Han, Braun and Loeppky (2018) <doi:10.1007/s00362-018-1000-6>.
Automatically apply different strategies to optimize R code. 
    'rco' functions take R code as input, and returns R code as output.
Client for the Catalogue of Life ('CoL')
    (<https://www.catalogueoflife.org/>); based on the new
    'CoL' service, not the old one. Catalogue of Life is a database of
    taxonomic names. Includes functions for each
    of the API methods, including searching for names, and more.
Get your data (forms, structures, answers) from Coletum 
    <https://coletum.com> to handle and analyse.
Provides programmatic access to Colombos, a web based
    interface for exploring and analyzing comprehensive organism-specific
    cross-platform expression compendia of bacterial organisms.
Provides color schemes for maps (and other graphics)
        designed by Cynthia Brewer as described at http://colorbrewer2.org
'NCL' (NCAR Command Language) is one of the most popular spatial 
    data mapping tools in meteorology studies, due to its beautiful output 
    figures with plenty of color palettes designed by experts 
    <https://www.ncl.ucar.edu/index.shtml>. 
    Here we translate all 'NCL' color palettes into R hexadecimal RGB colors and 
    provide color selection function, which will help users make a beautiful figure.
Utility functions for interacting with the 'COMPADRE' and 'COMADRE' databases of matrix population models. Described in Jones et al. (2021) <doi:10.1101/2021.04.26.441330>.
Functions and datasets to support "Summary and Analysis of
             Extension Program Evaluation in R" and "An R
             Companion for the Handbook of Biological Statistics". 
             Vignettes are available at <http://rcompanion.org>.
Makes easier the creation of R package or research compendium 
    (i.e. a predefined files/folders structure) so that users can focus on the 
    code/analysis instead of wasting time organizing files. A full 
    ready-to-work structure is set up with some additional features: version 
    control, remote repository creation, CI/CD configuration (check package 
    integrity under several OS, test code with 'testthat', and build and deploy 
    website using 'pkgdown'). This package heavily relies on the R packages 
    'devtools' and 'usethis' and follows recommendations made by Wickham H. 
    (2015) <ISBN:9781491910597> and Marwick B. et al. (2018) 
    <doi:10.7287/peerj.preprints.3192v2>.
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 <https://gallery.rcpp.org>, the paper by Eddelbuettel and
 Francois (2011, <doi:10.18637/jss.v040.i08>), the book by Eddelbuettel (2013,
 <doi:10.1007/978-1-4614-6868-4>) and the paper by Eddelbuettel and Balamuta (2018,
 <doi:10.1080/00031305.2017.1375990>); see 'citation("Rcpp")' for details.
Rcpp11 includes a header only C++11 library that facilitates 
  integration between R and modern C++.
Provides optimized functions and flexible combinatorial iterators
    implemented in C++ 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 results of a vector
    meeting specific criteria (e.g. generating integer partitions/compositions
    or finding all combinations such that the sum is between two bounds).
    Capable of generating specific combinations/permutations (e.g. retrieve
    only the nth lexicographical result) which sets up nicely for
    parallelization as well as random sampling. Gmp support permits exploration
    where the total number of results is large (e.g. comboSample(10000, 500,
    n = 4)). Additionally, there are several high performance number theoretic
    functions that are useful for problems common in computational mathematics.
    Some of these functions make use of the fast integer division library
    'libdivide'. The primeSieve function is based on the segmented sieve of
    Eratosthenes implementation by Kim Walisch. It is also efficient for large
    numbers by using the cache friendly improvements originally developed by
    Tomás Oliveira. Finally, there is a prime counting function that implements
    Legendre's formula based on the work of Kim Walisch.
'Annoy' is a small C++ library for Approximate Nearest Neighbors 
 written for efficient memory usage as well an ability to load from / save to
 disk. This package provides an R interface by relying on the 'Rcpp' package,
 exposing the same interface as the original Python wrapper to 'Annoy'. See
 <https://github.com/spotify/annoy> for more on 'Annoy'. 'Annoy' is released
 under Version 2.0 of the Apache License. Also included is a small Windows
 port of 'mmap' which is released under the MIT license.
The 'APT Package Management System' provides Debian and
 Debian-derived Linux systems with a powerful system to resolve package
 dependencies. This package offers access directly from R.  This can
 only work on a system with a suitable 'libapt-pkg-dev' installation
 so functionality is curtailed if such a library is not found.
'Armadillo' is a templated C++ linear algebra library (by Conrad
 Sanderson) that aims towards a good balance between speed and ease of
 use. Integer, floating point and complex numbers are supported, as
 well as a subset of trigonometric and statistics functions. Various
 matrix decompositions are provided through optional integration with
 LAPACK and ATLAS libraries.  The 'RcppArmadillo' package includes the
 header files from the templated 'Armadillo' library. Thus users do
 not need to install 'Armadillo' itself in order to use
 'RcppArmadillo'. From release 7.800.0 on, 'Armadillo' is licensed
 under Apache License 2; previous releases were under licensed as MPL
 2.0 from version 3.800.0 onwards and LGPL-3 prior to that;
 'RcppArmadillo' (the 'Rcpp' bindings/bridge to Armadillo) is licensed
 under the GNU GPL version 2 or later, as is the rest of 'Rcpp'.
 Armadillo requires a C++11 compiler.
Access to Boost Date_Time functionality for dates,
 durations (both for days and date time objects), time zones, and 
 posix time ('ptime') is provided by using 'Rcpp modules'. The 
 posix time implementation can support high-resolution of up to 
 nano-second  precision by using 96 bits (instead of R's 64)
 to present a 'ptime' object (but this needs recompilation with
 a #define set).
Features the multiple polynomial quadratic sieve (MPQS) algorithm
    for factoring large integers and a vectorized factoring function that
    returns the complete factorization of an integer. The MPQS is based off of
    the seminal work of Carl Pomerance (1984) <doi:10.1007/3-540-39757-4_17>
    along with the modification of multiple polynomials introduced by Peter
    Montgomery and J. Davis as outlined by Robert D. Silverman (1987)
    <doi:10.1090/S0025-5718-1987-0866119-8>. Utilizes the C library
    GMP (GNU Multiple Precision Arithmetic) and 'RcppThread' for factoring
    integers in parallel. For smaller integers, a simple Elliptic
    Curve algorithm is attempted followed by a constrained version of 
    Pollard's rho algorithm. The Pollard's rho algorithm is the same algorithm
    used by the factorize function in the 'gmp' package.
'Rcpp' Access to the 'CCTZ' timezone library is provided. 'CCTZ' is
 a C++ library for translating between absolute and civil times using the rules
 of a time zone. The 'CCTZ' source code, released under the Apache 2.0 License,
 is included in this package. See <https://github.com/google/cctz> for more
 details.
It provides functions to estimate the parameters in spatial models with censored/missing responses via the Expectation-Maximization (EM) algorithm (see Dempster, Laird, and Rubin (1977)<https://www.jstor.org/stable/2984875>), the Stochastic Approximation EM (SAEM) algorithm (see Delyon, Lavielle, and Moulines (1999)<https://www.jstor.org/stable/120120>), and the Monte Carlo EM (MCEM) algorithm (see Wei and Tanner (1990)<doi:10.1080/01621459.1990.10474930>). These algorithms are widely used to compute the maximum likelihood (ML) estimates in incomplete data problems. The EM algorithm computes the ML estimates when a closed expression for the conditional expectation of the complete-data log-likelihood function is available. In the MCEM algorithm, the conditional expectation is substituted by a Monte Carlo approximation based on many independent simulations of the missing data, while the SAEM algorithm splits the E-step into a simulation step and an integration step. The SAEM algorithm was developed as an alternative to the computationally intensive MCEM algorithm. 
  This package also approximates the standard error of the estimates using the method developed by Louis (1982)<https://www.jstor.org/stable/2345828>. It also has a function that performs spatial prediction in a set of new locations. Besides the functions to estimate parameters, this package allows computing the covariance matrix and the distance matrix.
The 'RcppClassic' package provides a deprecated C++ library which
 facilitates the integration of R and C++. New projects should use the new 'Rcpp'
 'API' in the 'Rcpp' package.
The 'Rcpp' package contains a C++ library that facilitates the
 integration of R and C++ in various ways via a rich API. This API was
 preceded by an earlier version which has been deprecated since 2010 (but is
 still supported to provide backwards compatibility in the package
 'RcppClassic').  This package 'RcppClassicExamples' provides usage examples for
 the older, deprecated API. There is also a corresponding package
 'RcppExamples' with examples for the newer, current API which we
 strongly recommend as the basis for all new development.
Time the execution of overlapping or unique 'Rcpp' code chunks using convenient methods, seamlessly write timing results to an 'RcppClock' object in the R global environment, and summarize and/or plot the results in R.
The 'cnpy' library written by Carl Rogers provides read and write
 facilities for files created with (or for) the 'NumPy' extension for 'Python'.
 Vectors and matrices of numeric types can be read or written to and from
 files as well as compressed files. Support for integer files is available if
 the package has been built with -std=c++11 which should be the default on
 all platforms since the release of R 3.3.0.
'Rcpp' Bindings for the C code of the 'Corpus Workbench' ('CWB'), an indexing and query 
  engine to efficiently analyze large corpora (<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 <http://www.pcre.org/licence.txt>)
  and 'GLib' (LGPL license, see <https://www.gnu.org/licenses/lgpl-3.0.en.html>).
  See the file LICENSE.note for further information. The package includes modified code of the
  'rcqp' package (GPL-2, see <https://cran.r-project.org/package=rcqp>). The original work of the authors
  of the 'rcqp' package is acknowledged with great respect, and they are listed as authors of this
  package. To achieve cross-platform portability (including Windows), using 'Rcpp' for wrapper code
  is the approach used by 'RcppCWB'.
A header-only C++ library is provided with support
 for dates, time zones, ISO weeks, Julian dates, and Islamic dates.
 'date' offers extensive date and time functionality for the C++11,
 C++14 and C++17 standards and was written by Howard Hinnant and released 
 under the MIT license. A slightly modified version has been accepted
 (along with 'tz.h') as part of C++20. This package regroups all
 header files from the upstream repository by Howard Hinnant so that
 other R packages can use them in their C++ code. At present, few of
 the types have explicit 'Rcpp' wrappers though these may be added as
 needed.
An efficient C++ based implementation of the 'DEoptim'
 function which performs global optimization by differential evolution.  
 Its creation was motivated by trying to see if the old approximation "easier,
 shorter, faster: pick any two" could in fact be extended to achieving all
 three goals while moving the code from plain old C to modern C++.  The
 initial version did in fact do so, but a good part of the gain was due to 
 an implicit code review which eliminated a few inefficiencies which have
 since been eliminated in 'DEoptim'.
The 'Rcpp' package provides a C++ library to make it easier
    to use C++ with R. R and 'Rcpp' provide functions for a variety of
    statistical distributions. Several R packages make functions
    available to R for additional statistical distributions. However,
    to access these functions from C++ code, a costly call to the R
    functions must be made. 'RcppDist' provides a header-only C++ library
    with functions for additional statistical distributions that can be
    called from C++ when writing code using 'Rcpp' or 'RcppArmadillo'.
    Functions are available that return a 'NumericVector' as well as
    doubles, and for multivariate or matrix distributions, 'Armadillo'
    vectors and matrices. 'RcppDist' provides functions for the following
    distributions: the four parameter beta distribution; the location-
    scale t distribution; the truncated normal distribution; the
    truncated t distribution; a truncated location-scale t distribution;
    the triangle distribution; the multivariate normal distribution*;
    the multivariate t distribution*; the Wishart distribution*; and
    the inverse Wishart distribution*. Distributions marked with an
    asterisk rely on 'RcppArmadillo'.
This package is based on the C++ code from Yusuke Sugomori,
             which implements basic machine learning methods with 
             many layers (deep learning), including dA (Denoising Autoencoder), 
             SdA (Stacked Denoising Autoencoder), RBM (Restricted Boltzmann machine) and 
             DBN (Deep Belief Nets).
Dynamic Programming implemented in 'Rcpp'.  Includes example partition and out of sample fitting applications.  Also supplies additional custom coders for the 'vtreat' package.
R and 'Eigen' integration using 'Rcpp'.
 'Eigen' is a C++ template library for linear algebra: matrices, vectors,
 numerical solvers and related algorithms.  It supports dense and sparse
 matrices on integer, floating point and complex numbers, decompositions of
 such matrices, and solutions of linear systems. Its performance on many
 algorithms is comparable with some of the best implementations based on
 'Lapack' and level-3 'BLAS'. The 'RcppEigen' package includes the header
 files from the 'Eigen' C++ template library (currently version 3.3.4). Thus
 users do not need to install 'Eigen' itself in order to use 'RcppEigen'.
 Since version 3.1.1, 'Eigen' is licensed under the Mozilla Public License
 (version 2); earlier version were licensed under the GNU LGPL version 3 or
 later. 'RcppEigen' (the 'Rcpp' bindings/bridge to 'Eigen') is licensed under
 the GNU GPL version 2 or later, as is the rest of 'Rcpp'.
Compiles 'C++' code using 'Rcpp', 'Eigen' and 'CppAD' to produce first and second order partial derivatives. Also provides an implementation of Faa' di Bruno's formula to combine the partial derivatives of composed functions, (see Hardy, M (2006) <arXiv:math/0601149v1>).
'Ensmallen' is a templated C++ mathematical optimization library 
 (by the 'MLPACK' team) that provides a simple set of abstractions for writing an
 objective function to optimize. Provided within are various standard and
 cutting-edge optimizers that include full-batch gradient descent techniques, 
 small-batch techniques, gradient-free optimizers, and constrained optimization.
 The 'RcppEnsmallen' package includes the header files from the 'Ensmallen' library
 and pairs the appropriate header files from 'armadillo' through the 
 'RcppArmadillo' package. Therefore, users do not need to install 'Ensmallen' nor
 'Armadillo' to use 'RcppEnsmallen'. Note that 'Ensmallen' is licensed under 
 3-Clause BSD, 'Armadillo' starting from 7.800.0 is licensed under Apache License 2,
 'RcppArmadillo' (the 'Rcpp' bindings/bridge to 'Armadillo') is licensed under 
 the GNU GPL version 2 or later. Thus, 'RcppEnsmallen' is also licensed under
 similar terms. Note that 'Ensmallen' requires a compiler that supports 
 'C++11' and 'Armadillo' 9.800 or later.
Examples for Seamless R and C++ integration
 The 'Rcpp' package contains a C++ library that facilitates the integration of
 R and C++ in various ways. This package provides some usage examples.
 Note that the documentation in this package currently does not cover all the
 features in the package. The site <http://gallery.rcpp.org> regroups a large
 number of examples for 'Rcpp'.
Access to a family of Gauss error functions for arbitrary complex arguments is provided via the 'Faddeeva' package by Steven G. Johnson (see <http://ab-initio.mit.edu/wiki/index.php/Faddeeva_Package> for more information).
The Google 'FarmHash' family of hash functions is used by
 the Google 'BigQuery' data warehouse via the 'FARM_FINGERPRINT'
 function. This package permits to calculate these hash digest
 fingerprints directly from R, and uses the included 'FarmHash'
 files written by G. Pike and copyrighted by Google, Inc.
Converting ascii text into (floating-point) numeric values is a
 very common problem. The 'fast_float' header-only C++ library by Daniel Lemire
 does it very well and very fast at up to or over to 1 gigabyte per second as
 described in more detail in <arXiv:2101.11408>. 'fast_float' is licensed under
 the Apache 2.0 license and provided here for use by other R packages via a simple
 'LinkingTo:' statement.
The 'getconf' command-line tool provided by 'libc' allows
 querying of a large number of system variables. This package provides
 similar functionality.
A fast implementation of the greedy algorithm for the set cover problem using 'Rcpp'.
'Rcpp' integration for 'GNU GSL' vectors and matrices
 The 'GNU Scientific Library' (or 'GSL') is a collection of numerical routines for
 scientific computing. It is particularly useful for C and C++ programs as it
 provides a standard C interface to a wide range of mathematical routines. There
 are over 1000 functions in total with an extensive test suite. The 'RcppGSL'
 package provides an easy-to-use interface between 'GSL' data structures and
 R using concepts from 'Rcpp' which is itself a package that eases the
 interfaces between R and C++. This package also serves as a prime example of
 how to build a package that uses 'Rcpp' to connect to another third-party
 library. The 'autoconf' script, 'inline' plugin and example package can all
 be used as a stanza to  write a similar package against another library.
Collection of functions to evaluate sequences, decode hidden states and estimate parameters from a single or multiple sequences of a discrete time Hidden Markov Model. The observed values can be modeled by a multinomial distribution for categorical/labeled emissions, a mixture of Gaussians for continuous data and also a mixture of Poissons for discrete values. It includes functions for random initialization, simulation, backward or forward sequence evaluation, Viterbi or forward-backward decoding and parameter estimation using an Expectation-Maximization approach.
'Hnswlib' is a C++ library for Approximate Nearest Neighbors. This 
 package provides a minimal R interface by relying on the 'Rcpp' package. See 
 <https://github.com/nmslib/hnswlib> for more on 'hnswlib'. 'hnswlib' is 
 released under Version 2.0 of the Apache License.
Header library and R functions to solve minimum cost bipartite matching problem 
 using Huhn-Munkres algorithm (Hungarian algorithm; <https://en.wikipedia.org/wiki/Hungarian_algorithm>;
 Kuhn (1955) doi:10.1002/nav.3800020109). 
 This is a repackaging of code written by Cong Ma in the GitHub repo <https://github.com/mcximing/hungarian-algorithm-cpp>.
R package based on 'Rcpp' for 'MeCab': Yet Another Part-of-Speech and Morphological Analyzer. 
	The purpose of this package is providing a seamless developing and analyzing environment for CJK texts.
	This package utilizes parallel programming for providing highly efficient text preprocessing 'posParallel()' function.
	For installation, please refer to README.md file.
Fast machine learning algorithms including matrix factorization 
    and divisive clustering for large sparse and dense matrices.
'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' via an updated
 variant of the header-only 'Quantuccia' project (put together initially by Peter
 Caspers) offering an essential subset of 'QuantLib' (and now maintained separately
 for the calendaring subset). See the included file 'AUTHORS' for a full list of
 contributors to both 'QuantLib' and 'Quantuccia'.
Connection to the 'Redis' key/value store using the
 C-language client library 'hiredis' (included as a fallback) with
 'MsgPack' encoding provided via 'RcppMsgPack' headers.
Provides fast and efficient routines for
    common rolling / windowed operations. Routines for the
    efficient computation of windowed mean, median,
    sum, product, minimum, maximum, standard deviation
    and variance are provided.
The 'JSON' format is ubiquitous for data interchange, and the
 'simdjson' library written by Daniel Lemire (and many contributors) provides 
 a high-performance parser for these files which by relying on parallel 'SIMD'
 instruction manages to parse these files as faster than disk speed. See the
 <arXiv:1902.08318> paper for more details about 'simdjson'.  This package 
 parses 'JSON' from string, file, or remote URLs under a variety of settings.
R access to the Sequential Monte Carlo Template Classes
 by Johansen <doi:10.18637/jss.v030.i06> is provided. At present, four
 additional examples have been added, and the first example from the JSS
 paper has been extended. Further integration and extensions are planned.
The mature and widely-used C++ logging library 'spdlog' by Gabi Melman provides
 many desirable features. This package bundles these header files for easy use by R packages
 via a simple 'LinkingTo:' inclusion.
The 'Streamulus' (template, header-only) library by
 Irit Katriel (at <https://github.com/iritkatriel/streamulus>)
 provides a very powerful yet convenient framework for stream
 processing. This package connects 'Streamulus' to R by providing 
 both the header files and all examples.
Provides a C++11-style thread class and thread pool that can safely
    be interrupted from R. See Nagler (2021) <doi:10.18637/jss.v097.c01>.
R-level and C++-level functionality
    to generate random deviates from and calculate moments of a
    Truncated Normal distribution using the algorithm of Robert (1995) <DOI:10.1007/BF00143942>.
    In addition to RNG, functions for calculating moments, densities,
    and entropies are provided at both levels.
The configuration format defined by 'TOML' (which expands to
 "Tom's Obvious Markup Language") specifies an excellent format
 (described at <https://toml.io/en/>) suitable for both human editing
 as well as the common uses of a machine-readable format. This package
 uses 'Rcpp' to connect the 'cpptoml' parser written by Chase Geigle
 (in C++11) to R.
Provides functions to generating a vector of Universally Unique Identifiers (UUID).
  Used implementation from the Boost C++ library. Supported random (version 4) and name (version 5)
  UUIDs.
Provides the means to compile user-supplied C++ functions with
  'Rcpp' and retrieve an 'XPtr' that can be passed to other C++ components.
This header-only library provides modern, portable C++ wrappers for SIMD
    intrinsics and parallelized, optimized math implementations (SSE, AVX, NEON, AVX512).
    By placing this library in this package, we offer an efficient distribution system for
    Xsimd <https://github.com/xtensor-stack/xsimd> for R packages using CRAN.
This package provides access to some of the C level
        functions of the xts package.

  In its current state, the package is mostly a proof-of-concept to
        support adding useful functions, and does not yet add any of
        its own.
The Ziggurat generator for normally distributed random
 numbers, originally proposed by Marsaglia and Tsang (2000, 
 <doi:10.18637/jss.v005.i08>) has been improved upon a few times
 starting with Leong et al (2005, <doi:10.18637/jss.v012.i07>).
 This package provides an aggregation in order to compare different
 implementations in order to provide an 'faster but good enough'
 alternative for use with R and C++ code.
Performs parallel web crawling and web scraping. It is designed to crawl, parse and store web pages to produce data that can be directly used for analysis application. For details see Khalil and Fakir (2017) <DOI:10.1016/j.softx.2017.04.004>.
An implementation of functions for the analysis of crime incident or records
  management system data. The package implements analysis algorithms scaled for city
  or regional crime analysis units. The package provides functions for kernel density
  estimation for crime heat maps, geocoding using the 'Google Maps' API, identification 
  of repeat crime incidents, spatio-temporal map comparison across time intervals, 
  time series analysis (forecasting and decomposition), detection of optimal parameters 
  for the identification of near repeat incidents, and near repeat analysis with crime 
  network linkage.
Aims at loading Criteo online advertising campaign data into R.
    Criteo <http://www.criteo.com/> is an online advertising service that enables
    advertisers to display commercial ads to web users. The package provides
    an authentication process for R with the Criteo API <http://kb.criteo.com/
    advertising/content/5/27/en/api.html>. Moreover, the package features an
    interface to query campaign data from the Criteo API. The data can be downloaded
    and will be transformed into a R data frame.
Goldwin-Pierre correlogram. Research of critical periods in the past. Integrates a time series in a given window.
Fit a 2-parameter continual reassessment method (CRM) model (O'Quigley and Shen (1996), <doi: 10.2307/2532905>) regularized with L2 norm (Friedman et al. (2010), <doi: 10.18637/jss.v033.i01>) adjusted by the distance with the target dose limiting toxicity (DLT) rate.
'NanoString nCounter' is a medium-throughput platform that measures gene or microRNA expression levels. Here is a publication that introduces this platform: Malkov (2009) <doi:10.1186/1756-0500-2-80>. Here is the webpage of 'NanoString nCounter' where you can find detailed information about this platform <https://www.nanostring.com/scientific-content/technology-overview/ncounter-technology>. It has great clinical application, such as diagnosis and prognosis of cancer. Implements integrated system of random-coefficient hierarchical regression model to normalize data from 'NanoString nCounter' platform so that noise from various sources can be removed.
Client for various 'CrossRef' 'APIs', including 'metadata' search
    with their old and newer search 'APIs', get 'citations' in various formats
    (including 'bibtex', 'citeproc-json', 'rdf-xml', etc.), convert 'DOIs'
    to 'PMIDs', and 'vice versa', get citations for 'DOIs', and get links to
    full text of articles when available.
Provides easy symmetric file encryption using GPG with
    cryptographically strong defaults. Only symmetric encryption is 
    supported. GPG is pre-installed with most Linux distributions. 
    Windows users will need to install 'Gpg4win' (http://www.gpg4win.org/). 
    OS X users will need to install 'GPGTools' (https://gpgtools.org/).
R interface to the CSDP semidefinite programming library. Installs version 6.1.1 of CSDP from the COIN-OR website if required. An existing installation of CSDP may be used by passing the proper configure arguments to the installation command. See the INSTALL file for further details.
Cloth Simulation Filter (CSF) is an airborne LiDAR (Light Detection and Ranging) ground 
    points filtering algorithm  which is based on cloth simulation. It tries to simulate the interactions
    between the cloth nodes and the corresponding LiDAR points, the locations of the cloth nodes can be
    determined to generate an approximation of the ground surface <https://www.mdpi.com/2072-4292/8/6/501/htm>.
A novel clustering algorithm and toolkit to accurately identify various cell types using single cell RNA sequencing data from a complex tissue. This algorithm considers both local similarity and global similarity among the cells to discern the subtle differences among cells of the same type as well as larger differences among cells of different types. This algorithm uses Spearman’s rank correlations of a cell’s expression vector with those of other cells to measure its global similarity, and learns neighbour representation of a cell as its local similarity. The overall similarity of a cell to other cells is a linear combination of its global similarity and local similarity. See Mei et. al. (2021) <DOI:10.1101/2021.04.12.439254> for more details.
Provides a means to style plots through cascading style sheets.
    This separates the aesthetics from the data crunching in plots and charts.
Assists in the whole process of designing and evaluating Randomized Control Trials.
    Robust treatment assignment by strata/blocks, that handles misfits; 
    Power calculations of the minimum detectable treatment effect or minimum populations;
    Balance tables of T-test of covariates; 
    Balance Regression: (treatment ~ all x variables) with F-test of null model; 
    Impact_evaluation: Impact evaluation regressions. This function
    gives you the option to include control_vars, fixed effect variables,
    cluster variables (for robust SE), multiple endogenous variables and
    multiple heterogeneous variables (to test treatment effect heterogeneity)
    summary_statistics: Function that creates a summary statistics table with statistics 
    rank observations in n groups: Creates a factor variable with n groups. Each group has 
    a min and max label attach to each category.
    Athey, Susan, and Guido W. Imbens (2017) <arXiv:1607.00698>.
Predict fish year-class strength by calibration
  regression analysis of multiple recruitment index series.
Provides simplified methods for managing classic Rubik's cubes and many other modifications of it (such as NxNxN size cubes, void cubes and 8-coloured cubes - so called octa cubes). Includes functions of handling special syntax for managing such cubes; and different approach to plotting 3D cubes without using external libraries (for example 'OpenGL').
A wrapper for 'libcurl' <https://curl.se/libcurl/>
	Provides functions to allow one to compose general HTTP requests
        and provides convenient functions to fetch URIs, get & post
        forms, etc. and process the results returned by the Web server.
        This provides a great deal of control over the HTTP/FTP/...
        connection and the form of the request while providing a
        higher-level interface than is available just using R socket
        connections.  Additionally, the underlying implementation is
        robust and extensive, supporting FTP/FTPS/TFTP (uploads and
        downloads), SSL/HTTPS, telnet, dict, ldap, and also supports
        cookies, redirects, authentication, etc.
Provide an R interface for processing concentration-response datasets using Curvep, a response noise filtering algorithm. The algorithm was described in the publications (Sedykh A et al. (2011) <doi:10.1289/ehp.1002476> and Sedykh A (2016) <doi:10.1007/978-1-4939-6346-1_14>). Other parametric fitting approaches (e.g., Hill equation) are also adopted for ease of comparison. Also, methods for calculating the confidence interval around the activity metrics are also provided. The methods are based on the bootstrap approach to simulate the datasets (Hsieh J-H et al. <doi:10.1093/toxsci/kfy258>). The simulated datasets can be used to derive the baseline noise threshold in an assay endpoint. This threshold is critical in the toxicological studies to derive the point-of-departure (POD).
Defines classes and methods to process text-based
  cytogenetics using the CytoGPS web site, then import the results
  into R for further analysis and graphing.
Administrative regions and other spatial objects of the Czech Republic.
The native R functionalities only allow PDF exports of reference manuals. This shall be extended by converting the package documentation files into markdown files and combining them into a markdown version of the package reference manual.
Functions to convert Rd to 'roxygen' documentation. It can parse an
    Rd file to a list, create the 'roxygen' documentation and update the original
    R script (e.g. the one containing the definition of the function)
    accordingly. This package also provides utilities that can help developers
    build packages using 'roxygen' more easily. The 'formatR' package can be used
    to reformat the R code in the examples sections so that the code will be
    more readable.
Creates interactive analytic graphs with 'R'. It joins the data analysis power of R and the visualization libraries of JavaScript in one package. The package provides interactive networks, timelines, barplots, image galleries and evolving networks. Graphs are represented as 'D3.js' graphs embedded in a web page ready for its interactive analysis and exploration.
This function conducts variation partitioning and hierarchical partitioning to calculate the unique, shared (referred as to "common") and individual contributions of each predictor (or matrix) towards explained variation (R-square and adjusted R-square) on canonical analysis (RDA,CCA and db-RDA), applying the algorithm of Chevan, A. and Sutherland, M. 1991 Hierarchical Partitioning.The American Statistician, 90-96 <doi:10.1080/00031305.1991.10475776>.
Provides basic functionalities for writing a module
    for http://datacanvas.io. The http://datacanvas.io is a big data
    analytics platform that helps data scientists to build, manage
    and share data pipelines.
Client for the web service methods provided
    by 'DataCite' (<https://www.datacite.org/>), including functions to interface with
    their 'RESTful' search API. The API is backed by 'Elasticsearch', allowing
    expressive queries, including faceting.
Provides an R interface to the Data Retriever
    <https://retriever.readthedocs.io/en/latest/> 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/>).
Arps Decline Curve Analysis (DCA) models for production rate, cumulative production, nominal decline rate, the derivative of loss-ratio, and estimated ultimate recovery (EUR) predictions for oil and gas wells. Arps, J. J. (1945) <doi:10.2118/945228-G>. Robertson, S. (1988) <https://www.onepetro.org/general/SPE-18731-MS>.
Provides the tools to undertake estimation in
    Regression Discontinuity Designs. Both sharp and fuzzy designs are
    supported. Estimation is accomplished using local linear regression.
    A provided function will utilize Imbens-Kalyanaraman optimal
    bandwidth calculation. A function is also included to test the
    assumption of no-sorting effects.
Density discontinuity testing (a.k.a. manipulation testing) is commonly employed in regression discontinuity designs and other program evaluation settings to detect perfect self-selection (manipulation) around a cutoff where treatment/policy assignment changes. This package implements manipulation testing procedures using the local polynomial density estimators: rddensity() to construct test statistics and p-values given a prespecified cutoff, rdbwdensity() to perform data-driven bandwidth selection, and rdplotdensity() to construct density plots.
Set of functions for Regression Discontinuity Design ('RDD'), for
    data visualisation, estimation and testing.
Allows caching of raw data directly in R code. This allows R
  scripts and R Notebooks to be shared and re-run on a machine without access
  to the original data. Cached data is encoded into an ASCII string that can
  be pasted into R code. When the code is run, the data is automatically
  loaded from the cached version if the original data file is unavailable.
  Works best for small datasets (a few hundred observations).
Data Envelopment Analysis for R, estimating robust DEA scores without and with environmental variables and doing returns-to-scale tests.
Classes and functions for modelling health care interventions 
  using decision trees and semi-Markov models. Mechanisms are provided for 
  associating an uncertainty distribution with each source variable and for
  ensuring transparency of the mathematical relationships between variables.
  The package terminology follows Briggs "Decision Modelling for Health
  Economic Evaluation" (2006, ISBN:978-0-19-852662-9).
Algorithms for solving a self-calibrated l1-regularized quadratic programming problem without parameter tuning. The algorithm, called DECODE, can handle high-dimensional data without cross-validation. It is found useful in high dimensional portfolio selection (see Pun (2018) <https://ssrn.com/abstract=3179569>) and large precision matrix estimation and sparse linear discriminant analysis (see Pun and Hadimaja (2019) <https://ssrn.com/abstract=3422590>).
The package provides functions for estimating the relevant
        dimension of a data set in feature spaces, applications to
        model selection, graphical illustrations and prediction.
The Resource Description Framework, or 'RDF' is a widely used
             data representation model that forms the cornerstone of the 
             Semantic Web. 'RDF' represents data as a graph rather than 
             the familiar data table or rectangle of relational databases.
             The 'rdflib' package provides a friendly and concise user interface
             for performing common tasks on 'RDF' data, such as reading, writing
             and converting between the various serializations of 'RDF' data,
             including 'rdfxml', 'turtle', 'nquads', 'ntriples', and 'json-ld';
             creating new 'RDF' graphs, and performing graph queries using 'SPARQL'.
             This package wraps the low level 'redland' R package which
             provides direct bindings to the 'redland' C library.  Additionally,
             the package supports the newer and more developer friendly
             'JSON-LD' format through the 'jsonld' package. The package
             interface takes inspiration from the Python 'rdflib' library.
Functions to interact with the 'Google DoubleClick for Publishers (DFP)' API 
    <https://developers.google.com/ad-manager/api/start> (recently renamed to 
    'Google Ad Manager'). This package is automatically compiled from the API WSDL 
    (Web Service Description Language) files to dictate how the API is structured. 
    Theoretically, all API actions are possible using this package; however, care 
    must be taken to format the inputs correctly and parse the outputs correctly. 
    Please see the 'Google Ad Manager' API reference <https://developers.google.com/ad-manager/api/rel_notes> 
    and this package's website <https://stevenmmortimer.github.io/rdfp/> for more 
    information, documentation, and examples.
Provides a client for (1) querying the DHS API for survey indicators
  and metadata (<https://api.dhsprogram.com/#/index.html>), (2) identifying surveys
  and datasets for analysis, (3) downloading survey datasets from the DHS website,
  (4) loading datasets and associate metadata into R, and (5) extracting variables
  and combining datasets for pooled analysis.
Methods for calculation and visualization of the Repertoire
    Dissimilarity Index. Citation: Bolen and Rubelt, et al (2017) 
    <doi:10.1186/s12859-017-1556-5>.
Functions and methods for manipulating SNOMED CT concepts.
    The package contains functions for loading the SNOMED CT release into
    a convenient R environment, selecting SNOMED CT concepts using regular
    expressions, and navigating the SNOMED CT ontology. It provides the
    'SNOMEDconcept' S3 class for a vector of SNOMED CT concepts (stored
    as 64-bit integers) and the 'SNOMEDcodelist' S3 class for a table
    of concepts IDs with descriptions. For more information about SNOMED CT
    visit <https://www.snomed.org/>.
A client library for 'The Guardian' (https://www.guardian.com/)
    and their API, this package allows users to search for Guardian articles and
    retrieve both the content and metadata.
A collection of functions to simulate
  dice rolls and the like. In particular, experiments and exercises can
  be performed looking at combinations and permutations of values in dice
  rolls and coin flips, together with the corresponding frequencies of
  occurrences. When applying each function, the user has to input the
  number of times (rolls, flips) to toss the dice. Needless to say, the more
  the tosses, the more the frequencies approximate the actual probabilities.
  Moreover, the package provides functions to generate non-transitive sets
  of dice (like Efron's) and to check whether a given set of dice is non-transitive
  with given probability.
The package has many function that helps to perform
        various quality check on the data.It basically provides many
        function that helps in performing Extrapolative data analysis.
The 'RDieHarder' package provides an R interface to 
 the 'DieHarder' suite of random number generators and tests that 
 was developed by Robert G. Brown and David Bauer, extending 
 earlier work by George Marsaglia and others. The 'DieHarder'
 library code is included.
We provide linear and nonlinear dimension reduction techniques.
	Intrinsic dimension estimation methods for exploratory analysis are also provided.
	For more details on the package, see the paper by You (2020) <arXiv:2005.11107>.
A common framework for calculating distance matrices.
Distance-sampling is a popular method for estimating density and 
  abundance of organisms in ecology. Rdistance contains routines that  
  assist with analysis of
  distance-sampling data collected on point or line transects. 
  Distance models are specified using regression-like formula (similar 
  to lm, glm, etc.). Abundance routines
  perform automated bootstrapping and automated detection-function 
  selection. Overall (study area) and site-level (transect or point) 
  abundance estimates are available. A large suite of classical, 
  parametric detection functions are 
  included along with some uncommon parametric 
  functions (e.g., Gamma, negative exponential) and non-parametric
  smoothed distance functions. Custom (user-defined) detection functions
  are easily implemented (see vignette). 
  The help files and vignettes have been 
  vetted by multiple authors and tested in workshop 
  settings.
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://rdpackages.github.io/references/Cattaneo-Titiunik-VazquezBare_2016_Stata.pdf> for further methodological details.
Imports real-time thermo cycler (qPCR) data from Real-time PCR
    Data Markup Language (RDML) and transforms to the appropriate formats of
    the 'qpcR' and 'chipPCR' packages. Contains a dendrogram visualization 
    for the structure of RDML object and GUI for RDML editing.
The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. The 'rdmulti' package provides tools to analyze RD designs with multiple cutoffs or scores: rdmc() estimates pooled and cutoff specific effects for multi-cutoff designs, rdmcplot() draws RD plots for multi-cutoff designs and rdms() estimates effects in cumulative cutoffs or multi-score designs. See Cattaneo, Titiunik and Vazquez-Bare (2020) <https://rdpackages.github.io/references/Cattaneo-Titiunik-VazquezBare_2020_Stata.pdf> for further methodological details.
Comprehensive toolkit for generating various numerical                                 representation schemes of DNA sequence. The descriptors and similarity
    scores included are extensively used in bioinformatics and chemogenomics.
A wrapper for the 'Deutsche Nationalbibliothek (German National
    Library) API', available at <https://www.dnb.de>. The German National Library is
    the German central archival library, collecting, archiving, bibliographically
    classifying all German and German-language publications, foreign
    publications about Germany, translations of German works, and the works of
    German-speaking emigrants published abroad between 1933 and 1945.
Test Statistics for Independence in High-Dimensional Datasets. This package consists of two functions to perform the complete independence test based on test statistics proposed by Bulut (unpublished yet) and suggested by Najarzadeh (2021) <doi: 10.1080/03610926.2019.1702699>. The Bulut's statistic is not sensitive to outliers in high-dimensional data, unlike one of Najarzadeh (2021) <doi: 10.1080/03610926.2019.1702699>. So, the Bulut's statistic can be performed robustly by using RDnp function.
Extends tools::Rd2txt() by adding customisable text and colour formatting to R documentation 
    contents. If used from a terminal, output will be displayed via file.show() otherwise contents
    will be printed in sections. Also provides stand-in replacements for ?() and help().
Wraps around the default help functionality in R. Instead of plain documentation files, documentation will show up as it does on <https://www.rdocumentation.org>, a platform that shows R documentation from 'CRAN', 'GitHub' and 'Bioconductor', together with informative stats to assess the package quality.
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.
Pretty fast implementation of the Ramer-Douglas-Peucker algorithm for reducing the number of points on a 2D curve.
    Urs Ramer (1972), "An iterative procedure for the polygonal approximation of plane curves" <doi:10.1016/S0146-664X(72)80017-0>.
    David H. Douglas and Thomas K. Peucker (1973), "Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or its Caricature" <doi:10.3138/FM57-6770-U75U-7727>.
Functions for manipulation of R documentation objects,
    including functions reprompt() and ereprompt() for updating 'Rd'
    documentation for functions, methods and classes; 'Rd' macros for
    citations and import of references from 'bibtex' files for use in
    'Rd' files and 'roxygen2' comments; 'Rd' macros for evaluating and
    inserting snippets of 'R' code and the results of its evaluation or
    creating graphics on the fly; and many functions for manipulation of
    references and Rd files.
The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. The 'rdpower' package provides tools to perform power, sample size and MDE calculations in RD designs: rdpower() calculates the power of an RD design, rdsampsi() calculates the required sample size to achieve a desired power and rdmde() calculates minimum detectable effects. See Cattaneo, Titiunik and Vazquez-Bare (2019) <https://rdpackages.github.io/references/Cattaneo-Titiunik-VazquezBare_2019_Stata.pdf> for further methodological details.
Given item set, item representation vector, and item ratings, 
    find a subset with better relevance-diversity trade-off. Also provide 
    machine learning algorithm to learn item representations maximizing 
    log likelihood under DPP assumption. 
    References: [1]Laming Chen, Guoxin Zhang, and Hanning Zhou(2017)<https://lsrs2017.files.wordpress.com/2017/08/lsrs_2017_lamingchen.pdf> 
    [2]Laming Chen, Guoxin Zhang, and Hanning Zhou(2018)<https://papers.nips.cc/paper/2018/file/dbbf603ff0e99629dda5d75b6f75f966-Paper.pdf> 
    [3]Wilhelm, Mark & Ramanathan, Ajith & Bonomo, Alexander & Jain, Sagar & Chi, Ed & Gillenwater, Jennifer(2018)<doi:10.1145/3269206.3272018>.
Regression-discontinuity (RD) designs are quasi-experimental research designs popular in social, behavioral and natural sciences. The RD design is usually employed to study the (local) causal effect of a treatment, intervention or policy. This package provides tools for data-driven graphical and analytical statistical inference in RD	designs: rdrobust() to construct local-polynomial point estimators and robust confidence intervals for average treatment effects at the 	cutoff in Sharp, Fuzzy and Kink RD settings, rdbwselect() to perform bandwidth selection for the different procedures implemented, and rdplot() to conduct exploratory data analysis (RD plots).
External jars required for package 'Rdrools'.
Provides full programmatic access to the 'Dropbox' file hosting platform <https://dropbox.com>, including support for all standard file operations.
We provide a toolbox to fit and simulate a univariate or multivariate damped random walk process that is also known as an Ornstein-Uhlenbeck process or a continuous-time autoregressive model of the first order, i.e., CAR(1) or CARMA(1, 0). This process is suitable for analyzing univariate or multivariate time series data with irregularly-spaced observation times and heteroscedastic measurement errors. When it comes to the multivariate case, the number of data points (measurements/observations) available at each observation time does not need to be the same, and the length of each time series can vary. The number of time series data sets that can be modeled simultaneously is limited to ten in this version of the package. We use Kalman-filtering to evaluate the resulting likelihood function, which leads to a scalable and efficient computation in finding maximum likelihood estimates of the model parameters or in drawing their posterior samples. Please pay attention to loading the data if this package is used for astronomical data analyses; see the details in the manual. Also see Hu and Tak (2020) <arXiv:2005.08049>.
Interface to the Dryad "Solr" API, their "OAI-PMH" service, and
    fetch datasets. Dryad (<https://datadryad.org/>) is a curated host of
    data underlying scientific publications.
Provides functionality for carrying out estimation
    with data collected using Respondent-Driven Sampling. This includes
    Heckathorn's RDS-I and RDS-II estimators as well as Gile's Sequential
    Sampling estimator. The package is part of the "RDS Analyst" suite of
    packages for the analysis of respondent-driven sampling data.
    See Gile and Handcock (2010) <doi:10.1111/j.1467-9531.2010.01223.x> and
    Gile and Handcock (2015) <doi:10.1111/rssa.12091>.
R interface to DSDP semidefinite programming library. The DSDP software is a free open source implementation of an interior-point method for semidefinite programming. It provides primal and dual solutions, exploits low-rank structure and sparsity in the data, and has relatively low memory requirements for an interior-point method.
Provides a threads-type programming environment for R.
   The package gives the R programmer the clearer, more concise
   shared memory world view, and in some cases gives superior
   performance as well.  In addition, it enables parallel processing on
   very large, out-of-core matrices.
This package provides an R interface to Pete Warden's Data
        Science Toolkit.  See www.datasciencetoolkit.org for more
        information.  The source code for this package can be found at
        github.com/rtelmore/RDSTK Happy hacking!
A tree bootstrap method for estimating uncertainty in respondent-driven samples (RDS). Quantiles are estimated by multilevel resampling in such a way that preserves the dependencies of and accounts for the high variability of the RDS process.
We provide a toolbox to fit univariate and multivariate linear mixed models via data transforming augmentation. Users can also fit these models via typical data augmentation for a comparison. It returns either maximum likelihood estimates of unknown model parameters (hyper-parameters) via an EM algorithm or posterior samples of those parameters via a Markov chain Monte Carlo method. Also see Tak, You, Ghosh, Su, and Kelly (2019+) <doi:10.1080/10618600.2019.1704295> <arXiv:1911.02748>.
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_node_en.html> for more information).
             Choose observational time series from meteorological stations with 'selectDWD()'.
             Find raster data from radar and interpolation according to <https://bookdown.org/brry/rdwd/raster-data.html>.
             Download (multiple) data sets with progress bars and no re-downloads through 'dataDWD()'.
             Read both tabular observational data and binary gridded datasets with 'readDWD()'.
Compute spatially explicit land-use metrics for stream survey sites in GRASS GIS and R as an open-source implementation of IDW-PLUS (Inverse Distance Weighted Percent Land Use for Streams). The package includes functions for preprocessing digital elevation and streams data, and one function to compute all the spatially explicit land use metrics described in Peterson et al. (2011) <doi:10.1111/j.1365-2427.2010.02507.x> and previously implemented by Peterson and Pearse (2017) <doi:10.1111/1752-1688.12558> in ArcGIS-Python as IDW-PLUS.
Pattern matching, extraction, replacement and other string
  processing operations using Google's RE2 <https://github.com/google/re2>
  regular-expression engine. Consistent interface (similar to 'stringr').
  RE2 uses finite-automata based techniques, and offers a
  fast and safe alternative to backtracking regular-expression engines
  like those used in 'stringr', 'stringi' and other PCRE implementations.
Interactive data tables for R, based on the 'React Table'
    JavaScript library. Provides an HTML widget that can be used in 'R Markdown'
    documents and 'Shiny' applications, or viewed from an R console.
Enhance the styling of interactive reactable tables with easy-to-use
    and highly-customizable functions. Apply conditional formatting to cells with
    data bars, color scales, and icon sets. Utilize custom table themes inspired by
    popular websites and bootstrap themes. Increase the portability and reproducibility
    of reactable tables by embedding images from the web directly into cells.
    Save the final table output as a static image or interactive file
    (note this feature requires the 'webshot2' package which can 
    be downloaded from <https://github.com/rstudio/webshot2>).
Building interactive web applications with R is incredibly easy
  with 'shiny'. Behind the scenes, 'shiny' builds a reactive graph that can
  quickly become intertwined and difficult to debug. 'reactlog'
  (Schloerke 2019) <doi:10.5281/zenodo.2591517> provides a visual insight into
  that black box of 'shiny' reactivity by constructing a directed dependency
  graph of the application's reactive state at any time point in a reactive
  recording.
Make it easy to use 'React' in R with 'htmlwidget' scaffolds,
              helper dependency functions, an embedded 'Babel' 'transpiler',
              and examples.
Routines for developing models that describe reaction and advective-diffusive transport in one, two or three dimensions.
  Includes transport routines in porous media, in estuaries, and in bodies with variable shape.
Functions for reading and decompressing the DBC (compressed DBF) files. Please note that this is the file format used by the Brazilian Ministry of Health (DATASUS) to publish healthcare datasets. It is not related to the FoxPro or CANdb DBC file formats.
Opens complete record(s) with .gb extension from the NCBI/GenBank Nucleotide database and returns a list containing shaped record(s). These kind of files contains detailed records of DNA samples (locus, organism, type of sequence, source of the sequence...). An example of record can be found at <https://www.ncbi.nlm.nih.gov/nuccore/HE799070>.
Implements a high performance C++ parser 
    for 'ActiGraph' 'GT3X'/'GT3X+' data format (with extension '.gt3x') 
    for 'accelerometer' samples. Activity samples can be easily read into a
    matrix or data.frame.  This allows for storing the raw 'accelerometer' 
    samples in the original binary format to reserve space.
Loads Axon Binary Files (both 'ABF' and 'ABF2') created by Axon Instruments/Molecular Devices software such as 'pClamp'.
Downloads, imports, and tidies time series data from the 
    Australian Bureau of Statistics <https://www.abs.gov.au/>.
Identifies and reads Windows BMP, JPEG, PNG, and TIFF format bitmap
    images. Identification defaults to the use of the magic number embedded in
    the file rather than the file extension. Reading of JPEG and PNG image
    depends on libjpg and libpng libraries. See file INSTALL for details if
    necessary.
Reads data files acquired by Bruker Daltonics' matrix-assisted
    laser desorption/ionization-time-of-flight mass spectrometer of the *flex
    series.
Combine multiple data files from a common directory.
    The data files will be read into R and bound together, creating a
    single large data.frame. A general function is provided along with
    a specific function for data that was collected using the open-source
    experiment builder 'OpenSesame' <https://osdoc.cogsci.nl/>.
It helps you to read (.dim) images with CRS directly into R programming. One can import both Sentinel 1 and 2 images or any processed data with this software.
A set of functions to simplify reading data from files. The main function, reader(), should read most common R datafile types without needing any parameters except the filename. Other functions provide simple ways of handling file paths and extensions, and automatically detecting file format and structure.
Providing just one primary function, 'readit' uses a set of reasonable
    heuristics to apply the appropriate reader function to the given file path.
    As long as the data file has an extension, and the data is (or can be
    coerced to be) rectangular, readit() can probably read it.
Import data written in the JCAMP-DX format. This is an instrument-independent format used in the field of spectroscopy. Examples include IR, NMR, and Raman spectroscopy. See the vignette for background and supported formats.  The official JCAMP-DX site is <http://www.jcamp-dx.org/>.
Functions for reading data sets in different formats
  for testing machine learning tools are provided. This allows to run
  a loop over several data sets in their original form, for example
  if they are downloaded from UCI Machine Learning Repository.
  The data are not part of the package and have to be downloaded
  separately.
You can use the function Read.mnist() to read data and arrange them properly from MNIST dataset (the open handwriting digit database <http://yann.lecun.com/exdb/mnist/>). With this package, you can conveniently get all of necessary informations and then immediately start to check whether your machine learning algorithm works well. It can automatically recognize the type of dataset and returns the informations in corresponding structure.
Tools to read various file types into one list of data structures, usually, but not limited to, data frames.
  Excel files are read sheet-wise, i.e., all or a selection of sheets can be read. Field delimiters and decimal
  separators are determined automatically.
Functions for reading mass spectrometry data in mzXML format.
Wraps 'tiny_obj_loader' C++ library for reading the 'Wavefront' OBJ
    3D file format including both mesh objects and materials files. The
    resultant R objects are either structured to match the 'tiny_obj_loader'
    internal data representation or in a form directly compatible with the 'rgl'
    package.
Import ODS (OpenDocument Spreadsheet) into R as a data frame. Also support writing data frame into ODS file.
Reads in text from 'unstructured' modern Microsoft Office files (XML based files) such as Word and PowerPoint.
    This does not read in structured data (from Excel or Access) as there are many other great packages to that do so already.
The goal of 'readr' is to provide a fast and
    friendly way to read rectangular data (like 'csv', 'tsv', and 'fwf').
    It is designed to flexibly parse many types of data found in the wild,
    while still cleanly failing when data unexpectedly changes.
Download up-to-date data from the Reserve Bank of Australia 
    in a tidy data frame. Package includes functions to download current and 
    historical statistical tables 
    (<https://www.rba.gov.au/statistics/tables/>) and forecasts 
    (<https://www.rba.gov.au/publications/smp/forecasts-archive.html>). Data
    includes a broad range of Australian macroeconomic and financial time
    series.
A convenient way to read fixed-width ASCII polling datasets from providers like the Roper Center <https://ropercenter.cornell.edu>.
Read Statistical Data and Metadata Exchange (SDMX) XML data. 
    This the main transmission format used in official statistics. Data can be imported from
    local SDMX-ML files or a SDMX web-service and will be read in 'as is' into a dataframe object.
    The 'RapidXML' C++ library <http://rapidxml.sourceforge.net> is used to parse the XML data.
The goal of 'readsdr' is to bridge the design capabilities from
    specialised System Dynamics software with the powerful numerical tools 
    offered by 'R' libraries. The package accomplishes this goal by parsing 
    'XMILE' files ('Vensim' and 'Stella') models into 'R' objects to construct 
    networks (graph theory); 'ODE' functions for 'Stan'; and inputs to simulate
    via 'deSolve' as described in Duggan (2016) <doi:10.1007/978-3-319-34043-2>.
Read and write labelled sparse matrices in text format as used by
    software such as 'SVMLight', 'LibSVM', 'ThunderSVM', 'LibFM', 'xLearn', 'XGBoost', 'LightGBM',
    and others. Supports labelled data for regression, classification (binary, multi-class, multi-label),
    and ranking (with 'qid' field), and can handle header metadata and comments in files.
Function to read and write the 'Stata' file format.
Functions for importing and handling text files and formatted text
    files with additional meta-data, such including '.csv', '.tab', '.json', '.xml',
    '.html', '.pdf', '.doc', '.docx', '.rtf', '.xls', '.xlsx', and others.
'Praat' is a widely used tool for manipulating, annotating and 
    analyzing speech and acoustic data. It stores annotation data in a 
    format called a 'TextGrid'. This package provides a way to read these 
    files into R.
Quickly read contents from files or URLs. Functions provide a simple, consistent, and
    performant interface for reading (all at once or line-by-line) the contents of files or web pages.
Reads and writes data frames to 'SQLite'
    databases while preserving time zones (for POSIXct columns),
    projections (for 'sfc' columns), units (for 'units' columns), levels
    (for factors and ordered factors) and classes for logical, Date and
    'hms' columns.  It also logs changes to tables and provides more
    informative error messages.
Import excel files into R. Supports '.xls' via the
    embedded 'libxls' C library <https://github.com/libxls/libxls> and
    '.xlsx' via the embedded 'RapidXML' C++ library
    <http://rapidxml.sourceforge.net>.  Works on Windows, Mac and Linux
    without external dependencies.
Import data from 'Excel' binary (.xlsb) workbooks into R.
A framework for unit testing for realistic minimalists,
    where we distinguish between expected, acceptable, current, fallback,
    ideal, or regressive behaviour. It can also be used for monitoring
    third-party software projects for changes.
Fits a multivariate value-added model (VAM), see Broatch, Green, and Karl (2018) <doi:10.32614/RJ-2018-033> and Broatch and Lohr (2012) <doi:10.3102/1076998610396900>, with normally distributed test scores and a binary outcome indicator. A pseudo-likelihood approach, Wolfinger (1993) <doi:10.1080/00949659308811554>, is used for the estimation of this joint generalized linear mixed model.  The inner loop of the pseudo-likelihood routine (estimation of a linear mixed model) occurs in the framework of the EM algorithm presented by Karl, Yang, and Lohr (2013) <DOI:10.1016/j.csda.2012.10.004>. This material is based upon work supported by the National Science Foundation under grants DRL-1336027 and DRL-1336265.