A set of estimators for models and (robust) covariance matrices, and tests for panel data
             econometrics, including within/fixed effects, random effects, between, first-difference, 
             nested random effects as well as instrumental-variable (IV) and Hausman-Taylor-style models,
             panel generalized method of moments (GMM) and general FGLS models,
             mean groups (MG), demeaned MG, and common correlated effects (CCEMG) and pooled (CCEP) estimators
             with common factors, variable coefficients and limited dependent variables models.
             Test functions include model specification, serial correlation, cross-sectional dependence,
             panel unit root and panel Granger (non-)causality. Typical references are general econometrics 
             text books such as Baltagi (2021), Econometric Analysis of Panel Data (<doi:10.1007/978-3-030-53953-5>),
             Hsiao (2014), Analysis of Panel Data (<doi:10.1017/CBO9781139839327>), and Croissant and Millo (2018), 
             Panel Data Econometrics with R (<doi:10.1002/9781119504641>).
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: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.
Fits penalized linear mixed models that correct for
    unobserved confounding factors. 'plmmr' infers and corrects for the
    presence of unobserved confounding effects such as population
    stratification and environmental heterogeneity. It then fits a linear
    model via penalized maximum likelihood. Originally designed for the
    multivariate analysis of single nucleotide polymorphisms (SNPs)
    measured in a genome-wide association study (GWAS), 'plmmr' eliminates
    the need for subpopulation-specific analyses and post-analysis p-value
    adjustments.  Functions for the appropriate processing of 'PLINK'
    files are also supplied. For examples, see the package homepage.
    <https://pbreheny.github.io/plmmr/>.
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, 2021 <doi:10.3389/fevo.2021.588292>) 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 system to plan analyses within the mental model
    where you have one (or more) datasets and want to run either A) the same 
    function multiple times with different arguments, or B) multiple functions. 
    This is appropriate when you have multiple strata (e.g. locations, age groups)
    that you want to apply the same function to, or you have multiple variables 
    (e.g. exposures) that you want to apply the same statistical method to, or
    when you are creating the output for a report and you need multiple different
    tables or graphs.
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.
A method for the quantitative prediction with much predictors. 
    This package provides functions to construct the quantitative prediction 
    model with less overfitting and robust to noise.
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.
Explore the world of R graphics with fun and interesting plot functions! 
    Use make_LED() to create dynamic LED screens, draw interconnected rings with Olympic_rings(), 
    and make festive Chinese couplets with chunlian(). 
    Unleash your creativity and turn data into exciting visuals!
Interactively annotate 'base R graphics' plots with freehand drawing, symbols (points, lines, arrows, rectangles, circles, ellipses), and text. This is useful for teaching, for example to visually explain certain plot elements, and creating quick sketches.
Functions to assist in diagnostics and plotting during the causal inference modeling process. 
  Supplements the 'bartCause' package.
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.
Create the density contour plot for bivariate inverse Gaussian distribution for given non negative random variables.
The 'plotcli' package provides terminal-based plotting in R.
             It supports colored scatter plots, line plots, bar plots, and box plots. The package allows users 
             to customize plot appearance, add titles, labels, ticks, and legends, and output the plot as a text-based visualization.
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.
The goal of 'PlotFTIR' is to easily and quickly kick-start the production of journal-quality Fourier Transform Infra-Red (FTIR) spectral plots in R using 'ggplot2'. The produced plots can be published directly or further modified by 'ggplot2' functions.
    L'objectif de 'PlotFTIR' est de démarrer facilement et rapidement la production des tracés spectraux de spectroscopie infrarouge à transformée de Fourier (IRTF) de qualité journal dans R à l'aide de 'ggplot2'. Les tracés produits peuvent être publiés directement ou modifiés davantage par les fonctions 'ggplot2'.
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.
Hidden Markov Models are useful for modeling
 sequential data. This package provides several functions
 implemented in C++ for explaining the algorithms used for
 Hidden Markov Models (forward, backward, decoding,
 learning).
Provides publication‑quality and interactive plots for exploring 
             the posterior output of Latent Space Item Response Models, including 
             Posterior Interaction Profiles, radar charts, 2‑D latent maps, and 
             item‑similarity heat maps. The methods implemented in this package 
             are based on work by Jeon, M., Jin, I. H., Schweinberger, M., Baugh, S. (2021) <doi:10.1007/s11336-021-09762-5>.
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 (gg)plotting output from mixture models, 
    including both densities and overlaying mixture weight component curves from the fit models in line with the
    tidy principles. The package includes several additional functions for added plot customization. 
    Supported model objects include: 'mixtools', 'EMCluster', and 'flexmix', with more from each in active dev. 
    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.
Graphical methods testing multivariate normality assumption. Methods including assessing score function, and moment generating functions,independent transformations and linear transformations. For more details see Tran (2024),"Contributions to Multivariate Data Science: Assessment and Identification of Multivariate Distributions and Supervised Learning for Groups of Objects." , PhD thesis,
  <https://our.oakland.edu/items/c8942577-2562-4d2f-8677-cb8ec0bf6234>.
Produces an Odds Ratio (OR) Plot to visualise the result of a 
    logistic regression analysis.
    Provide it with a binomial regression model produced by 'glm()' and it will 
    convert the estimates to odds ratios with a 95% confidence interval and 
    plot the results using 'ggplot2'.
Plot principal component histograms around a bivariate
        scatter plot.
Useful set of tools for plotting network diagrams in any kind of project.
Lots of plots, various labeling, axis and color scaling functions. The author/maintainer died in September 2023.
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.
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')".
A framework for creating interactive figures for data exploration. 
    All plots are automatically linked and support several kinds of interactive features,
    including selection, zooming, panning, and parameter manipulation. The figures
    can be interacted with either manually, using a mouse and a keyboard, 
    or by running code from inside an active R session.
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).
Provides high-level API and a wide range of options to create stunning, publication-quality plots effortlessly.
    It is built upon 'ggplot2' and other plotting packages, and is designed to be easy to use and to work seamlessly with 'ggplot2' objects.
    It is particularly useful for creating complex plots with multiple layers, facets, and annotations.
    It also provides a set of functions to create plots for specific types of data, such as Venn diagrams, alluvial diagrams, and phylogenetic trees.
    The package is designed to be flexible and customizable, and to work well with the 'ggplot2' ecosystem.
    The API can be found at <https://pwwang.github.io/plotthis/reference/index.html>.
Annotate plots with legends for continuous variables and colour
  spectra using the base graphics plotting tools; and manipulate irregular
  polygons.
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> and Bejleri, Sartore and
  Nandram (2021) <doi:10.1007/s42952-021-00157-x>.
Power logit regression models for bounded
  continuous data, in which the density generator may be normal, Student-t, 
  power exponential, slash, hyperbolic, sinh-normal, or type II logistic. 
  Diagnostic tools associated with the fitted model, such as the residuals, 
  local influence measures, leverage measures, and goodness-of-fit statistics,
  are implemented. The estimation process follows the maximum likelihood approach
  and, currently, the package supports two types of estimators: the usual maximum 
  likelihood estimator and the penalized maximum likelihood estimator. More details
  about power logit regression models are described in 
  Queiroz and Ferrari (2022) <arXiv:2202.01697>.
Contains 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).
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.
Estimation, hypothesis tests, and variable selection in partially linear single-index models. Please see H. (2010) at <doi:10.1214/10-AOS835> for more details.
Implements a partial linear semiparametric mixed-effects model (PLSMM) featuring a random intercept and applies a lasso penalty to both the fixed effects and the coefficients associated with the nonlinear function. 
    The model also accommodates interactions between the nonlinear function and a grouping variable, allowing for the capture of group-specific nonlinearities. Nonlinear functions are modeled using a set of bases functions. Estimation is conducted using a penalized Expectation-Maximization algorithm, and the package offers flexibility in choosing between various information criteria for model selection. 
    Post-selection inference is carried out using a debiasing method, while inference on the nonlinear functions employs a bootstrap approach.
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).
Partial Least Squares Path Modeling (PLS-PM), Tenenhaus, Esposito Vinzi, Chatelin, Lauro (2005) <doi:10.1016/j.csda.2004.03.005>,
    analysis for both metric and non-metric data, as well as REBUS analysis, Esposito Vinzi, Trinchera, Squillacciotti, and Tenenhaus (2008) <doi:10.1002/asmb.728>.
Provides Partial least squares Regression for (weighted) beta regression models (Bertrand 2013,  <https://ojs-test.apps.ocp.math.cnrs.fr/index.php/J-SFdS/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 <doi:10.48550/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 <doi:10.48550/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.
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.
Provides a secure and user-friendly interface to interact with the 'Plug' <https://plugbytpf.com.br> 'API'. It enables developers to store and manage tokens securely using the 'keyring' package, retrieve data from 'API' endpoints with the 'httr2' package, and handle large datasets with chunked data fetching. Designed for simplicity and security, the package facilitates seamless integration with 'Plug' ecosystem.
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.
Automatically create a web server from annotated 'R' files or by
    building it up programmatically. Provides automatic 'OpenAPI' documentation,
    input handling, asynchronous evaluation, and plugin support.
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.
Handles and formats author information in scientific writing
    in 'R Markdown' and 'Quarto'. 'plume' provides easy-to-use and
    flexible tools for inserting author data in 'YAML' as well as
    generating author and contribution lists (among others) as strings
    from tabular data.
Retrieves a 'pluscode' by inputting latitude and longitude. Includes additional functions to retrieve neighbouring 'pluscodes'.
Generates 'Plus Code' of geometric objects or data frames that contain them, giving the possibility to specify the precision of the area. The main feature of the package comes from the open-source code developed by 'Google Inc.' present in the repository <https://github.com/google/open-location-code/blob/main/java/src/main/java/com/google/openlocationcode/OpenLocationCode.java>. For details about 'Plus Code', visit <https://maps.google.com/pluscodes/> or <https://github.com/google/open-location-code>.
In ancient Roman mythology, 'Pluto' was the ruler of the underworld
  and presides over the afterlife. 'Pluto' was frequently conflated with 
  'Plutus', the god of wealth, because mineral wealth was found underground. 
  When plotting with R, you try once, twice, practice again and again, and finally 
  you get a pretty figure you want. It's a 'plot tour', a tour about repetition 
  and reward. Hope 'plutor' helps you on the tour!
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'.
You can use this program for 3 sets of categorical data for propensity score matching.
            Assume that the data has 3 different categorical variables. You can use it to perform propensity matching of baseline indicator groupings. 
            The matching will make the differences in the baseline data smaller.
            This method was described by Alvaro Fuentes (2022) <doi:10.1080/00273171.2021.1925521>.
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>.
We innovatively defined a pathway mutation accumulate perturbation score (PMAPscore) to reflect the position and the cumulative effect of the genetic mutations at the pathway level. Based on the PMAPscore of pathways, identified prognosis-related pathways altered by somatic mutation and predict immunotherapy efficacy by constructing a multiple-pathway-based risk model (Tarca, Adi Laurentiu et al (2008) <doi:10.1093/bioinformatics/btn577>).
Provides functionality for quality control processing and statistical analysis of mass spectrometry (MS) omics data, in particular proteomic (either at the peptide or the protein level), lipidomic, and metabolomic data, as well as RNA-seq based count data and nuclear magnetic resonance (NMR) data. This includes data transformation, specification of groups that are to be compared against each other, filtering of features and/or samples, data normalization, data summarization (correlation, PCA), and statistical comparisons between defined groups.  Implements methods described in:  Webb-Robertson et al. (2014) <doi:10.1074/mcp.M113.030932>.  Webb-Robertson et al. (2011) <doi:10.1002/pmic.201100078>.  Matzke et al. (2011) <doi:10.1093/bioinformatics/btr479>.  Matzke et al. (2013) <doi:10.1002/pmic.201200269>.  Polpitiya et al. (2008) <doi:10.1093/bioinformatics/btn217>.  Webb-Robertson et al. (2010) <doi:10.1021/pr1005247>.
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>.
Fit calibrations curves for clinical prediction models and calculate several associated 
  metrics (Eavg, E50, E90, Emax). Ideally predicted probabilities from a prediction model 
  should align with observed probabilities. Calibration curves relate predicted probabilities 
  (or a transformation thereof) to observed outcomes via a flexible non-linear smoothing function. 
  'pmcalibration' allows users to choose between several smoothers (regression splines, generalized 
  additive models/GAMs, lowess, loess). Both binary and time-to-event outcomes are supported. 
  See Van Calster et al. (2016) <doi:10.1016/j.jclinepi.2015.12.005>; 
  Austin and Steyerberg (2019) <doi:10.1002/sim.8281>; 
  Austin et al. (2020) <doi:10.1002/sim.8570>.
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> and PMD based reactomics analysis proposed in Yu and Petrick (2020) <doi:10.1038/s42004-020-00403-z> 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.
Calculation of Predictive Moran's eigenvector maps (pMEM), as
  defined by Guénard and Legendre (In Press) "Spatially-explicit predictions
  using spatial eigenvector maps" <doi:10.5281/zenodo.13356457>. Methods in
  Ecology and Evolution. This method enables scientists to predict the values of
  spatially-structured environmental variables. Multiple types of pMEM are
  defined, each one implemented on the basis of spatial weighting function
  taking a range parameter, and sometimes also a shape parameter. The code's
  modular nature enables programers to implement new pMEM by defining new
  spatial weighting functions.
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://aviationweather.gov/data/metar/> and historical reports from
  Iowa Environmental Mesonet web page of Iowa State University
  ASOS-AWOS-METAR <http://mesonet.agron.iastate.edu/AWOS/>.
Given a project schedule and associated costs, this package calculates the earned value to date. It is an implementation of Project Management Body of Knowledge (PMBOK) methodologies (reference Project Management Institute. (2021). A guide to the Project Management Body of Knowledge (PMBOK guide) (7th ed.). Project Management Institute, Newtown Square, PA, ISBN 9781628256673 (pdf)).
The Food and Agriculture Organization-56 Penman-Monteith is one of the important method for estimating evapotranspiration from vegetated land areas. This package helps to calculate reference evapotranspiration using the weather variables collected from weather station. Evapotranspiration is the process of water transfer from the land surface to the atmosphere through evaporation from soil and other surfaces and transpiration from plants. The package aims to support agricultural, hydrological, and environmental research by offering accurate and accessible reference evapotranspiration calculation. This package has been developed using concept of Córdova et al. (2015)<doi:10.1016/j.apm.2022.09.004> and Debnath et al. (2015) <doi:10.1007/s40710-015-0107-1>.
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.
Conduct internal validation of a clinical prediction model for a binary outcome.
  Produce bias corrected performance metrics (c-statistic, Brier score, calibration intercept/slope)
  via bootstrap (simple bootstrap, bootstrap optimism, .632 optimism) and cross-validation (CV optimism,
  CV average). Also includes functions to assess model stability via bootstrap resampling.
  See Steyerberg et al. (2001) <doi:10.1016/s0895-4356(01)00341-9>;
  Harrell (2015) <doi:10.1007/978-3-319-19425-7>;
  Riley and Collins (2023) <doi:10.1002/bimj.202200302>.
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. The package isofor (used for anomaly detection) can be installed with devtools::install_github("gravesee/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.
Provides a simple interface for extracting various elements from
  the publicly available PubMed XML files, incorporating PubMed's regular
  updates, and combining the data with the NIH Open Citation Collection. See
  Schoenbachler and Hughey (2021) <doi:10.7717/peerj.11071>.
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.
Provides analytic and simulation tools to estimate the minimum
    sample size required for achieving a target prediction mean-squared error
    (PMSE) or a specified proportional PMSE reduction (pPMSEr) in linear
    regression models.  Functions implement the criteria of Ma (2023) 
    <https://digital.wpi.edu/downloads/0g354j58c>, support covariance-matrix 
    handling, and include helpers for root-finding and diagnostic plotting.
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>.
Computes the minimum sample size required for the external validation of an existing multivariable prediction model using the criteria proposed by Archer (2020) <doi:10.1002/sim.8766> and Riley (2021) <doi:10.1002/sim.9025>.
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://university-of-newcastle-research.github.io/samplerDoc/> and
    includes several detailed examples, some background and troubleshooting
    steps.
Tools for the practical management of financial
  portfolios: backtesting investment and trading strategies,
  computing profit/loss and returns, analysing trades,
  handling lists of transactions, reporting, and more.  The
  package provides a small set of reliable, efficient and
  convenient tools for processing and analysing
  trade/portfolio data.  The manual provides all the details;
  it is available from
  <https://enricoschumann.net/R/packages/PMwR/manual/PMwR.html>.
  Examples and descriptions of new features are provided at
  <https://enricoschumann.net/notes/PMwR/>.
Provides a user interface to create or modify pharmacometric
    models for various modeling and simulation software platforms.
Estimate coefficient of variation percent (CV%) for any arbitrary distribution, including some built-in estimates for commonly-used transformations in pharmacometrics. Methods are described in various sources, but applied here as summarized in: Prybylski, (2024) <doi:10.1007/s40262-023-01343-2>.
Generate nicely formatted HTML tables to display estimation results for
  pharmacometric models.
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) 
    <https://www.facm.ucl.ac.be/cooperation/Vietnam/WBI-Vietnam-October-2011/Modelling/Monolix32_PKPD_library.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 analyzing the Continuous National
  Household Sample Survey - 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'.
Quasi likelihood-based methods for estimating linear and log-linear Poisson Network Autoregression models with p lags and covariates. Tools for testing the linearity versus several non-linear alternatives. Tools for simulation of multivariate count distributions, from linear and non-linear PNAR models, by using a specific copula construction. References include: Armillotta, M. and K. Fokianos (2023). "Nonlinear network autoregression". Annals of Statistics, 51(6): 2526--2552. <doi:10.1214/23-AOS2345>. Armillotta, M. and K. Fokianos (2024). "Count network autoregression". Journal of Time Series Analysis, 45(4): 584--612. <doi:10.1111/jtsa.12728>. Armillotta, M., Tsagris, M. and Fokianos, K. (2024). "Inference for Network Count Time Series with the R Package PNAR". The R Journal, 15/4: 255--269. <doi:10.32614/RJ-2023-094>.
Numerical derivatives through finite-difference approximations
    can be calculated using the 'pnd' package with parallel capabilities and
    optimal step-size selection to improve accuracy. These functions facilitate
    efficient computation of derivatives, gradients, Jacobians, and Hessians,
    allowing for more evaluations to reduce the mathematical and machine errors.
    Designed for compatibility with the 'numDeriv' package,
    which has not received updates in several years, it introduces advanced features
    such as computing derivatives of arbitrary order, improving
    the accuracy of Hessian approximations by avoiding repeated differencing,
    and parallelising slow functions on Windows, Mac, and Linux.
Implements the methods for assessing heterogeneous cluster-specific treatment effects in partially nested designs as described in Liu (2024) <doi:10.1037/met0000723>. The estimation uses the multiply robust method, allowing for the use of machine learning methods in model estimation (e.g., random forest, neural network, and the super learner ensemble).  Partially nested designs (also known as partially clustered designs) are designs where individuals in the treatment arm are assigned to clusters (e.g., teachers, tutoring groups, therapists), whereas individuals in the control arm have no such clustering.
Provides tools for downloading, reading and analyzing the National
  Survey of Demographic and Health - PNDS, 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.
Provides tools for downloading, reading and analyzing the National
  Survey of Health - 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'.
This program contains a function to find the peaks and troughs of 
    a data set. It filters the set of peaks to remove noise based on the expected
    height and expected slope of a peak. Peaks that are too short (caused by random 
    noise), or too shallow (part of the background data) are filtered out.
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/>.
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.
Village potential statistics (PODES) collects various information on village potential and challenges faced by villages in Indonesia. Information related to village potential includes economy, security, health, employment, communication and information, sports, entertainment, development, community empowerment, education, socio-culture, transportation in the village. Information related to challenges includes natural disasters, public health, environmental pollution, social problems and security disturbances that occur in the village.
A framework of interoperable R6 classes (Chang, 2020, <https://CRAN.R-project.org/package=R6>) for building ensembles of viable models via the pattern-oriented modeling (POM) approach (Grimm et al.,2005, <doi:10.1126/science.1116681>). 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 (Iman & Conover, 1980, <doi:10.1080/03610928008827996>); 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 (Beaumont et al., 2002, <doi:10.1093/genetics/162.4.2025>), 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.
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.
Integrated species distribution modeling is a rising field in quantitative ecology thanks to significant rises in the quantity of data available, increases in computational speed and the proven benefits of using such models. 
  Despite this, the general software to help ecologists construct such models in an easy-to-use framework is lacking. 
  We therefore introduce the R package 'PointedSDMs': which provides the tools to help ecologists set up integrated models and perform inference on them.
  There are also functions within the package to help run spatial cross-validation for model selection, as well as generic plotting and predicting functions.
  An introduction to these methods is discussed in Issac, Jarzyna, Keil, Dambly, Boersch-Supan, Browning, Freeman, Golding, Guillera-Arroita, Henrys, Jarvis, Lahoz-Monfort, Pagel, Pescott, Schmucki, Simmonds and O’Hara (2020) <doi:10.1016/j.tree.2019.08.006>.
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.
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. Implementations of the 
    methods described in Kahle, D., P. Young, B. Greer, and D. Young (2016). 
    "Confidence Intervals for the Ratio of Two Poisson Rates Under One-Way 
    Differential Misclassification Using Double Sampling." Computational 
    Statistics & Data Analysis, 95:122–132.
Computation of robust standard errors of Poisson fixed effects 
    models, following Wooldridge (1999).
Creates a non-negative low-rank approximate factorization of a sparse counts matrix by maximizing Poisson
    likelihood with L1/L2 regularization (e.g. for implicit-feedback recommender systems or bag-of-words-based topic modeling)
    (Cortes, (2018) <arXiv:1811.01908>), which usually leads to very sparse user and item factors (over 90% zero-valued).
    Similar to hierarchical Poisson factorization (HPF), but follows an optimization-based approach with regularization
    instead of a hierarchical prior, 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>.
Mixtures of Poisson Generalized Linear Models for high dimensional count data clustering. The (multivariate) responses can be partitioned into set of blocks. Three different parameterizations of the linear predictor are considered. The models are estimated according to the EM algorithm with an efficient initialization scheme <doi:10.1016/j.csda.2014.07.005>.
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.
Poisson disk sampling is a method of generating blue noise 
    sample patterns where all samples are at least a specified distance 
    apart. Poisson samples may be generated in two or three dimensions 
    with this package. The algorithm used is an 
    implementation of  Bridson's "Fast Poisson disk sampling in 
    arbitrary dimensions" <doi:10.1145%2F1278780.1278807>.
Implementation of the exact, normal approximation, and simulation-based methods for computing the probability mass function (pmf) and cumulative distribution function (cdf) of the Poisson-Multinomial distribution, together with a random number generator for the distribution. The exact method is based on multi-dimensional fast Fourier transformation (FFT) of the characteristic function of the Poisson-Multinomial distribution. The normal approximation method uses a multivariate normal distribution to approximate the pmf of the distribution based on central limit theorem. The simulation method is based on the law of large numbers. Details about the methods are available in Lin, Wang, and Hong (2022) <DOI:10.1007/s00180-022-01299-0>.
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>).
Provides a dataset of Pokemon information in 
  both English and Brazilian Portuguese. The dataset contains 949 rows and 
  22 columns, including information such as the Pokemon's name, ID, height, 
  weight, stats, type, and more.
Distributes data from the Polarization in Comparative Attitudes Project. Helper
    functions enable data retrieval in wide and tidy formats for user-defined
    countries and years. Provides support for case-insensitive country names in many
    languages. Mehlhaff (2022) <https://imehlhaff.net/files/Polarization%20and%20Democracy.pdf>.
A 'shiny' application for visualizing high-dimensional data
    using non-linear dimensionality reduction (NLDR) techniques such as
    t-SNE and UMAP. It provides an interactive platform to explore
    high-dimensional datasets, diagnose the quality of the embeddings
    using the 'quollr' package, and compare different NLDR methods.
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.
In each odd dimension is a convex body - the polar zonoid - whose generating functions are trigonometric polynomials.  The polar zonoid is a straightforward generalization of the polar zonohedron in dimension 3, as defined by Chilton and Coxeter (1963) <doi:10.2307/2313051>. The package has some applications of the polar zonoid, including the properties of configuration spaces of arcs on the circle and 3x3 rotation matrices.  There is also a root solver for trigonometric polynomials.
Latent class analysis and latent class regression models 
    for polytomous outcome variables.  Also known as latent structure analysis.
Wrangle and annotate different types of political texts.
    It also introduces Urgency Analysis,
    a new method for the analysis of urgency in political texts.
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. Given doubly robust reward estimates, this package
 finds a rule-based treatment prescription policy, where the policy takes the form of
 a shallow decision tree that is globally (or close to) optimal.
This is a collection of data and functions for common metrics in political science research. Data measuring ideology, and functions calculating geographical diffusion and ideological diffusion - geog.diffuse() and ideo.dist(), respectively. Functions derived from methods developed in: Soule and King (2006) <doi:10.1086/499908>, Berry et al. (1998) <doi:10.2307/2991759>, Cruz-Aceves and Mallinson (2019) <doi:10.1177/0160323X20902818>, and Grossback et al. (2004) <doi:10.1177/1532673X04263801>.
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.
Authentication, user administration, hosting, and additional infrastructure for 'shiny' apps. See
  <https://polished.tech> for additional documentation and examples.
A very small package for more convenient use of 'NaileR'. You provide a data set containing a latent variable you want to understand. It generates a description and an interpretation of this latent variable using a Large Language Model. For perceptual data, it describes the stimuli used in the experiment.
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.
A toolbox to facilitate the calculation of political system indicators for researchers. 
    This package offers a variety of basic indicators related to electoral systems, party systems, elections,
    and parliamentary studies, as well as others. Main references are:
    Loosemore and Hanby (1971) <doi:10.1017/S000712340000925X>;
    Gallagher (1991) <doi:10.1016/0261-3794(91)90004-C>;
    Laakso and Taagepera (1979) <doi:10.1177/001041407901200101>;
    Rae (1968) <doi:10.1177/001041406800100305>;
    Hirschmaņ (1945) <ISBN:0-520-04082-1>;
    Kesselman (1966) <doi:10.2307/1953769>;
    Jones and Mainwaring (2003) <doi:10.1177/13540688030092002>;
    Rice (1925) <doi:10.2307/2142407>;
    Pedersen (1979) <doi:10.1111/j.1475-6765.1979.tb01267.x>;
    SANTOS (2002) <ISBN:85-225-0395-8>.
Package for evaluating user-specified finite stage policies and learning optimal treatment policies via doubly robust loss functions. Policy learning methods include doubly robust learning of the blip/conditional average treatment effect and sequential policy tree learning. The package also include methods for optimal subgroup analysis. See Nordland and Holst (2022) <doi:10.48550/arXiv.2212.02335> for documentation and references.
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>),
    calculation of growing degree days (Baskerville and Emin (1969) <doi:10.2307/1933912>), 
    and determination of the base temperature for growing degree days
    (Yang et al. (1995) <doi:10.1016/0168-1923(94)02185-M).
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', <https://cwb.sourceforge.io>) 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.
A 'shiny' app that allows to access and use the 'INVEKOS' API for field polygons in Austria. API documentation is available at <https://gis.lfrz.gv.at/api/geodata/i009501/ogc/features/v1/>.
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.  More information on the implementation can be found at Conrad J. Burden (2014) <arXiv:1406.2780>.
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.
A polycross is the pollination by natural hybridization of a group of genotypes, generally selected, grown in isolation from other compatible genotypes in such a way to promote random open pollination. A particular practical application of the polycross method occurs in the production of a synthetic variety resulting from cross-pollinated plants. Laying out these experiments in appropriate designs, known as polycross designs, would not only save experimental resources but also gather more information from the experiment. Different experimental situations may arise in polycross nurseries which may be requiring different polycross designs (Varghese et. al. (2015) <doi:10.1080/02664763.2015.1043860>. " Experimental designs for open pollination in polycross trials"). This package contains a function named PD() which generates nine types of polycross designs suitable for various experimental situations.
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), and
    adaptive cubature for radially symmetric functions via line
    integrate() along the polygon boundary (Meyer and Held, 2014,
    <doi:10.1214/14-AOAS743>, Supplement B).
    For simple integration along the axes, the 'cubature' package
    is more appropriate.
Provide easy methods to translate pieces of text.
  Functions send requests to translation services online.
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).
Kernel density estimation on the polysphere, (hyper)sphere, and
    circle. Includes functions for density estimation, regression estimation,
    ridge estimation, bandwidth selection, kernels, samplers, and homogeneity
    tests. Companion package to García-Portugués and Meilán-Vila (2025)
    <doi:10.1080/01621459.2025.2521898> and García-Portugués and Meilán-Vila
    (2023) <doi:10.1007/978-3-031-32729-2_4>.
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. (2021) <doi:10.1007/s00122-021-03834-x>.
Includes functions implementing the conditionally optimal matching algorithm, 
    which can be used to generate matched samples in designs with multiple groups. The algorithm
    is described in Nattino, Song and Lu (2022) <doi:10.1016/j.csda.2021.107364>.
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>.
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 more background and to reference the package see <doi:10.1093/bioinformatics/btab574>.
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>, and the Hind/He statistic for marker
  filtering is described by Clark et al. (2022) <doi:10.1186/s12859-022-04635-9>.
  A variant calling pipeline for highly duplicated genomes is also included and
  is described by Clark et al. (2020, Version 1) <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.
Given a data matrix with rows representing data vectors and columns representing variables, produces a directed polytree for the underlying causal structure. Based on the algorithm developed in Chatterjee and Vidyasagar (2022) <arxiv:2209.07028>. The method is fully nonparametric, making no use of linearity assumptions, and especially useful when the number of variables is large.
Routines for flexible functional form estimation via basis
    regression, with model selection via the adaptive LASSO or SCAD to prevent
    overfitting.
Provides functions to compute and plot power levels, minimum detectable effect sizes, and minimum required sample sizes for the test of the overall average effect size in meta-analysis of dependent effect sizes.
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 Process (POMDP) models. Interfaces for various exact and approximate solution algorithms are available including value iteration, point-based value iteration and SARSOP. Hahsler and Cassandra <doi:10.32614/RJ-2024-021>.
Installs an updated version of 'pomdp-solve', a program to solve Partially Observable Markov Decision Processes (POMDPs) using a variety of exact and approximate value iteration algorithms. A convenient R infrastructure is provided in the separate package pomdp. Kaelbling, Littman and Cassandra (1998) <doi:10.1016/S0004-3702(98)00023-X>.
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>.
Runs generalized and multinominal logistic (GLM and MLM) models, as well as random forest (RF), Bagging (BAG), and Boosting (BOOST). This package prints out to predictive outcomes easy for the selected data and data splits.
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).
Inspired by Moreira and Gamerman (2022) <doi:10.1214/21-AOAS1569>,
    this methodology expands the idea by including Marks in the point process.
    Using efficient 'C++' code, the estimation is possible and made faster with
    'OpenMP' <https://www.openmp.org/> enabled computers. This package was
    developed under the project PTDC/MAT-STA/28243/2017, supported by
    Portuguese funds through the Portuguese Foundation for Science and
    Technology (FCT).
Code to identify functional enrichments across diverse taxa
    in phylogenetic tree, particularly where these taxa differ in
    abundance across samples in a non-random pattern. The motivation for
    this approach is to identify microbial functions encoded by diverse
    taxa that are at higher abundance in certain samples compared to
    others, which could indicate that such functions are broadly adaptive
    under certain conditions. See 'GitHub' repository for tutorial and
    examples: <https://github.com/gavinmdouglas/POMS/wiki>. Citation: Gavin M. Douglas, Molly G. Hayes, Morgan G. I. Langille, Elhanan Borenstein (2022) <doi:10.1093/bioinformatics/btac655>.
Homogeneity tests of the coefficients in panel data. Currently,
             only the Hsiao test for determining coefficient homogeneity between
             the panel data individuals is implemented, as described in Hsiao
             (2022), "Analysis of Panel Data" (<doi:10.1017/9781009057745>).
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.
Provides functions to simulate Pool-seq data under models of
    demographic formation and to import Pool-seq data from real populations.
    Implements two ABC algorithms for performing parameter estimation and 
    model selection using Pool-seq data. Cross-validation can also be 
    performed to assess the accuracy of ABC estimates and model choice.
    Carvalho et al., (2022) <doi:10.1111/1755-0998.13834>.
When using pooled p-values to adjust for multiple testing, there is an inherent balance that must be struck between rejection based on weak evidence spread among many tests and strong evidence in a few, explored in Salahub and Olford (2023) <arXiv:2310.16600>. This package provides functionality to compute marginal and central rejection levels and the centrality quotient for p-value pooling functions and provides implementations of the chi-squared quantile pooled p-value (described in Salahub and Oldford (2023)) and a proposal from Heard and Rubin-Delanchy (2018) <doi:10.1093/biomet/asx076> to control the quotient's value.
Pool dilution is a isotope tracer technique wherein a 
    biogeochemical pool is artifically enriched with its heavy isotopologue 
    and the gross productive and consumptive fluxes of that pool are 
    quantified by the change in pool size and isotopic composition over time. 
    This package calculates gross production and consumption rates from
    closed-system isotopic pool dilution time series data. Pool size 
    concentrations and heavy isotope (e.g., 15N) content are measured over time 
    and the model optimizes production rate (P) and the first order rate 
    constant (k) by minimizing error in the model-predicted total pool size, 
    as well as the isotopic signature. The model optimizes rates by weighting 
    information against the signal:noise ratio of concentration and heavy-
    isotope signatures using measurement precision as well as the magnitude of 
    change over time. The calculations used here are based on von Fischer and 
    Hedin (2002) <doi:10.1029/2001GB001448> with some modifications.
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.
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>.
Analyzing genetic data obtained from pooled samples. 
    This package can read in Fragment Analysis output files, process the data, 
    and score peaks, as well as facilitate various analyses, including cluster 
    analysis, calculation of genetic distances and diversity indices, 
    as well as bootstrap resampling for statistical inference. Specifically 
    tailored to handle genetic data efficiently, researchers can explore 
    population structure, genetic differentiation, and genetic relatedness 
    among samples. We updated some functions from Covarrubias-Pazaran et al. 
    (2016) <doi:10.1186/s12863-016-0365-6> to allow for the use of new file formats 
    and referenced the following to write our genetic analysis functions: 
    Long et al. (2022) <doi:10.1038/s41598-022-04776-0>, Jost (2008) 
    <doi:10.1111/j.1365-294x.2008.03887.x>, Nei (1973) 
    <doi:10.1073/pnas.70.12.3321>, Foulley et al. (2006) 
    <doi:10.1016/j.livprodsci.2005.10.021>, Chao et al. (2008) 
    <doi:10.1111/j.1541-0420.2008.01010.x>.
Functions for the computation of F-, f- and D-statistics (e.g., Fst, hierarchical F-statistics, 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 and SelEstim format).
Simulates pooled sequencing data under a variety of conditions. 
    Also allows for the evaluation of the average absolute difference between allele frequencies 
    computed from genotypes and those computed from pooled data. 
    Carvalho et al., (2022) <doi:10.1101/2023.01.20.524733>.
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 introduction can be found in Cinar and Viechtbauer (2022) <doi:10.18637/jss.v101.i01>.
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. Environmental Modelling and
    Software.  <DOI:10.1016/j.envsoft.2021.105158>). 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.
Simulate the dynamic of lion populations using a specific Individual-Based Model (IBM) compiled in C.
Simulate the dynamic of wolf populations using a specific Individual-Based Model (IBM) compiled in C, see Chapron et al. (2016) <doi:10.1016/j.ecolmodel.2016.08.012>.
Infers the trends of one or several animal populations over time 
    from series of counts. It does so by accounting for count precision 
    (provided or inferred based on expert knowledge, e.g. guesstimates), 
    smoothing the population rate of increase over time, and accounting for the
    maximum demographic potential of species. Inference is carried out in a 
    Bayesian framework. This work is part of the FRB-CESAB working group
    AfroBioDrivers 
    <https://www.fondationbiodiversite.fr/en/the-frb-in-action/programs-and-projects/le-cesab/afrobiodrivers/>.
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).
Facilitates population-level analysis of ligand-receptor (LR) interactions 
    using large-scale single-cell transcriptomic data. Identifies significant LR pairs 
    and quantifies their interactions through correlation-based filtering and projection score 
    computations. Designed for large-sample single-cell studies, the package employs 
    statistical modeling, including linear regression, to investigate LR relationships 
    between cell types. It provides a systematic framework for understanding cell-cell 
    communication, uncovering regulatory interactions and signaling mechanisms. 
    Offers tools for LR pair-level, sample-level, and differential interaction analyses, 
    with comprehensive visualization support to aid biological interpretation. 
    The methodology is described in a manuscript currently under review and will be 
    referenced here once published or publicly available.
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.
The primary goal of phase I clinical trials is to find the maximum tolerated dose (MTD). To reach this objective, we introduce a new design for phase I clinical trials, the posterior predictive (PoP) design. The PoP design is an innovative model-assisted design that is as simply as the conventional algorithmic designs as its decision rules can be pre-tabulated prior to the onset of trial, but is of more flexibility of selecting diverse target toxicity rates and cohort sizes. The PoP design has desirable properties, such as coherence and consistency. Moreover, the PoP design provides better empirical performance than the BOIN and Keyboard design with respect to high average probabilities of choosing the MTD and slightly lower risk of treating patients at subtherapeutic or overly toxic doses.
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'.
Estimate commonly used population genomic statistics and generate publication quality figures. 'PopGenHelpR' uses vcf, 'geno' (012), and csv files to generate output.
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/>.
Implementation of assumption-lean and data-adaptive post-prediction inference (POPInf), for valid and efficient statistical inference based on data predicted by machine learning. See Miao, Miao, Wu, Zhao, and Lu (2023) <arXiv:2311.14220>.
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>.
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>.
Functions that facilitate the elaboration of population pyramids.
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.
Methods for building self-organizing maps (SOMs) with a number of distinguishing features such automatic centroid detection and cluster visualization using starbursts.  For more details see the paper "Improved Interpretability of the Unified Distance Matrix with Connected Components" by Hamel and Brown (2011) in <ISBN:1-60132-168-6>.  The package provides user-friendly access to two models we construct: (a) a SOM model and (b) a centroid based clustering model. The package also exposes a number of quality metrics for the quantitative evaluation of the map, Hamel (2016) <doi:10.1007/978-3-319-28518-4_4>.  Finally, we reintroduced our fast, vectorized training algorithm for SOM with substantial improvements. It is about an order of magnitude faster than the canonical, stochastic C implementation <doi:10.1007/978-3-030-01057-7_60>.
The use of overparameterization is proposed with combinatorial analysis to test a broader spectrum of possible ARIMA models.
    In the selection of ARIMA models, the most traditional methods such as correlograms or others, do not usually cover many alternatives to define the number of coefficients to be estimated in the model, which represents an estimation method that is not the best.
    The popstudy package contains several tools for statistical analysis in demography and time series based in Shryock research (Shryock et. al. (1980) <https://books.google.co.cr/books?id=8Oo6AQAAMAAJ>).
Functions to estimate and plot smooth or linear population trends, or population indices, 
    from animal or plant count survey data.
Tools for constructing detailed synthetic human populations
     from frequency tables. Add ages based on age groups and sex, create households, add 
     students to education facilities, create employers, add employers to employees, and 
     create interpersonal networks.
Run population simulations using an Individual-Based Model (IBM) compiled in C.
Fit linear splines to species time series to detect population growth scenarios based on Hyndman, R J and Mesgaran, M B and Cousens, R D (2015) <doi:10.1007/s10530-015-0962-8>.
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()'.
Given a 
    set of source zone polygons such as
    census tracts or city blocks alongside with population counts and a 
    target zone of incogruent yet superimposed polygon features (such as
    individual buildings) populR transforms population counts from the 
    former to the latter using Areal Interpolation methods.
Perform tasks commonly encountered when
    preparing and analysing demographic data.
    Some functions are intended for end users, and
    others for developers. Includes functions for
    working with life tables.
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, 2021) 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). On another note, the package also includes functionality for ridge-type estimation of the generalized linear model (as presented in van Wieringen, Binder, 2022, <doi:10.1080/10618600.2022.2035231>).
An R implementation of the cross-platform, language-independent "port4me" algorithm (<https://github.com/HenrikBengtsson/port4me>), which (1) finds a free Transmission Control Protocol ('TCP') port in [1024,65535] that the user can open, (2) is designed to work in multi-user environments, (3), gives different users, different ports, (4) gives the user the same port over time with high probability, (5) gives different ports for different software tools, and (6) requires no configuration.
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 Chihuahuan
    desert. The raw data files can be found at
    <https://github.com/weecology/portaldata>.
Contains common univariate and multivariate portmanteau test statistics for time series models. These tests are based on using asymptotic distributions such as chi-square distribution and based on using the Monte Carlo significance tests. Also, it can be used to simulate from univariate and multivariate seasonal time series models.
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.
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.
Design, backtest, and analyze portfolio strategies using simple,
    English-like function chains. Includes technical indicators, flexible stock
    selection, portfolio construction methods (equal weighting, signal weighting,
    inverse volatility, hierarchical risk parity), and a compact backtesting
    engine for portfolio returns, drawdowns, and summary metrics.
Provides simple methods to extract data portions from various objects. 
    The relative portion size and the way the portion is selected can be chosen.
The functions are designed to find the efficient mean-variance frontier or 
 portfolio weights for static portfolio (called  Markowitz portfolio) analysis in resource 
 economics or nature conservation. Using the nonlinear programming solver ('Rsolnp'), 
 this package deals with the quadratic minimization of the variance-covariances without 
 shorting (i.e., non-negative portfolio weights) studied in Ando and Mallory (2012) 
 <doi:10.1073/pnas.1114653109>. See the examples, testing versions, and more details from: 
 <https://github.com/ysd2004/portn>.
Following Sommer (2022) <https://mediatum.ub.tum.de/1658240>
    portfolio level risk estimates (e.g. Value at Risk, Expected
    Shortfall) are estimated by modeling each asset univariately by an
    ARMA-GARCH model and then their cross dependence via a Vine Copula
    model in a rolling window fashion. One can even condition on
    variables/time series at certain quantile levels to stress test the
    risk measure estimates.
Win ratio approach to partially ordered data, such as multivariate ordinal 
  responses under product (consensus) or prioritized order. Two-sample tests and multiplicative
  regression models are implemented (Mao, 2024, under revision).
Build and manipulate partially ordered sets (posets), to perform some data analysis on them and to implement multi-criteria decision making procedures. Several efficient ways for generating linear extensions are implemented, together with functions for building mutual ranking probabilities, incomparability, dominance and separation scores (Fattore, M., De Capitani, L., Avellone, A., Suardi, A. (2024). A fuzzy posetic toolbox for multi-criteria evaluation on ordinal data systems. ANNALS OF OPERATIONS RESEARCH <doi:10.1007/s10479-024-06352-3>).
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.
Conduct post-selection inference for regression coefficients in linear models after they have been selected by adjusted R squared. The p-values and confidence intervals are valid after model selection with the same data. This allows the user to use all data for both model selection and inference without losing control over the type I error rate. The provided tests are more powerful than data splitting, which bases inference on less data since it discards all information used for selection.
Post-selection inference in linear regression models,
    constructing simultaneous confidence intervals across a user-specified universe of models.
    Implements the methodology described in Kuchibhotla, Kolassa, and Kuffner (2022) "Post-Selection Inference"
    <doi:10.1146/annurev-statistics-100421-044639> to ensure valid inference after model
    selection, with applications in high-dimensional settings like Lasso selection.
Collection of tutorials for working with 'Positron'. Covers scripts, 
    'Quarto' documents, 'Git', 'GitHub', and 'Quarto' websites. Makes extensive use of 
    the tools in the 'tutorial.helpers' package.
Personalize drug regimens using individual pharmacokinetic (PK) and 
    pharmacokinetic-pharmacodynamic (PK-PD) profiles. By combining therapeutic 
    drug monitoring (TDM) data with a population model, 'posologyr' offers 
    accurate posterior estimates and helps compute optimal individualized dosing
    regimens. The empirical Bayes estimates are computed following the method 
    described by Kang et al. (2012) <doi:10.4196/kjpp.2012.16.2.97>.
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>.
Calculates, via simulation, power and appropriate stopping
  alpha boundaries (and/or futility bounds) for sequential analyses (i.e.,
  group sequential design) as well as for multiple hypotheses (multiple tests
  included in an analysis), given any specified global error rate. This enables
  the sequential use of practically any significance test, as long as the
  underlying data can be simulated in advance to a reasonable approximation.
  Lukács (2022) <doi:10.21105/joss.04643>.
Conduct power analyses and inference of marginal effects. 
    Uses plug-in estimation and influence functions to perform robust inference,
    optionally leveraging historical data to increase precision with
    prognostic covariate adjustment. The methods are described in
    Højbjerre-Frandsen et al. (2025) <doi:10.48550/arXiv.2503.22284>.
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>.
Generates simple and beautiful one-page HTML reference manuals 
    with package documentation. Math rendering and syntax highlighting are done 
    server-side in R such that no JavaScript libraries are needed in the
    browser, which makes the documentation portable and fast to load.
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.
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 guide is available in the vignette.
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.
Calculate POTH for treatment hierarchies from frequentist and Bayesian network meta-analysis. POTH quantifies the certainty in a treatment hierarchy. Subset POTH, POTH residuals, and best k treatments POTH can also be calculated to improve interpretation of treatment hierarchies.
Store and retrieve data from options() using syntax derived from
    the 'here' package. 'potions' makes it straightforward to update and 
    retrieve options, either in the workspace or during package development, 
    without overwriting global options.
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.
The R package 'povmap' supports small area estimation of means and 
  poverty headcount rates. It adds several new features to the 'emdi' package
  (see "The R Package emdi for Estimating and Mapping Regionally Disaggregated 
  Indicators" by Kreutzmann et al. (2019) <doi:10.18637/jss.v091.i07>). 
  These include new options for incorporating survey weights, ex-post 
  benchmarking of estimates, two additional transformations, several new 
  convenient functions to assist with reporting results, and a wrapper function 
  to facilitate access from 'Stata'.
Fast exponentiation when the exponent is an integer.
Density, distribution function, quantile function and random generation for the family of power and reversal power distributions.
Computes power and level tables for goodness-of-fit tests for the normal, Laplace, and uniform distributions. Generates output in 'LaTeX' format to facilitate reporting and reproducibility. Explanatory graphs help visualize the statistical power of test statistics under various alternatives. For more details, see Lafaye De Micheaux and Tran (2016) <doi:10.18637/jss.v069.i03>.
Location- and scale-invariant Box-Cox and Yeo-Johnson power transformations
    allow for transforming variables with distributions distant from 0 to
    normality. Transformers are implemented as S4 objects. These allow for
    transforming new instances to normality after optimising fitting parameters
    on other data. A test for central normality allows for rejecting 
    transformations that fail to produce a suitably normal distribution, 
    independent of sample number.
Contains functions to obtain the operational characteristics of 
    bioequivalence studies in Two-Stage Designs (TSD) via simulations.
Power analysis and sample size determination
  for moderation, mediation, and moderated mediation in models
  fitted by structural equation modelling using the 'lavaan'
  package by Rosseel (2012) <doi:10.18637/jss.v048.i02> or
  by multiple regression. The package 'manymome' by
  Cheung and Cheung (2024) <doi:10.3758/s13428-023-02224-z>
  is used to specify the indirect paths or conditional
  indirect paths to be tested.
The first goal of this package is to provide a multitude of tree models,
    i.e., functions that generate rooted binary trees with a given number of leaves.
    Second, the package allows for an easy evaluation and comparison of tree shape
    statistics by estimating their power to differentiate between different tree models.
    Please note that this R package was developed alongside the manuscript
    "Tree balance in phylogenetic models" by
    S. J. Kersting, K. Wicke, and M. Fischer (2024) <doi:10.48550/arXiv.2406.05185>,
    which provides further background and the respective mathematical definitions.
    This project was supported by the project ArtIGROW, which is a part of the
    WIR!-Alliance ArtIFARM –  Artificial Intelligence in Farming funded by the
    German Federal Ministry of Education and Research (No. 03WIR4805).
Makes it easy to push data to 'Power BI' using R and the 'Power BI 
    REST APIs' (see <https://docs.microsoft.com/en-us/rest/api/power-bi/>). 
    A set of functions for turning data frames into 'Power BI' datasets and 
    refreshing these datasets are provided. Administrative tasks such as 
    monitoring refresh statuses and pulling metadata about workspaces and users
    are also supported.
Provides tools for Bayesian power analysis and assurance calculations using the statistical frameworks of 'brms' and 'INLA'. Includes simulation-based approaches, support for multiple decision rules (direction, threshold, ROPE), sequential designs, and visualisation helpers. Methods are based on Kruschke (2014, ISBN:9780124058880) "Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan", O'Hagan & Stevens (2001) <doi:10.1177/0272989X0102100307> "Bayesian Assessment of Sample Size for Clinical Trials of Cost-Effectiveness", Kruschke (2018) <doi:10.1177/2515245918771304> "Rejecting or Accepting Parameter Values in Bayesian Estimation", Rue et al. (2009) <doi:10.1111/j.1467-9868.2008.00700.x> "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations", and Bürkner (2017) <doi:10.18637/jss.v080.i01> "brms: An R Package for Bayesian Multilevel Models using Stan".
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>.
Power estimation and sample size calculation for 10X Visium Spatial Transcriptomics data to detect differential expressed genes between two conditions based on bootstrap resampling. See Shui et al. (2025) <doi:10.1371/journal.pcbi.1013293> for method details.
Evaluate a function across a grid of parameters. The function may be evaluated once, or many times for simulation. Parallel computing is facilitated. Utilities aim at performing analyses of power and sample size, allowing for easy search of minimum n (or min/max of any other parameter) to achieve a desired minimal level of power (or maximum of any other objective). Plotting functions are included that present the dependency of n and power in relation to further assumptions.
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.
Calculate sample size or power for hierarchical endpoints. 
  The package can handle any type of outcomes (binary, continuous, count, ordinal, 
  time-to-event) and any number of such endpoints. It allows users to calculate 
  sample size with a given power or to calculate power with a given sample size 
  for hypothesis testing based on win ratios, win odds, net benefit, or DOOR 
  (desirability of outcome ranking) as treatment effect between two groups for 
  hierarchical endpoints. The methods of this package are described further in 
  the paper by Barnhart, H. X. et al. (2024, <doi:10.1080/19466315.2024.2365629>).
This R package allows the determination of some distributions of the voters' power when passing laws in weighted voting situations.
We extend 'dplyr' and 'fuzzyjoin' join functions with
    features to preprocess the data, apply various data checks, and deal with
    conflicting columns.
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. (2023) <doi:10.1037/met0000555>.
    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.
Model-implied simulation-based power estimation (MSPE) for
    nonlinear (and linear) SEM, path analysis and regression analysis. A
    theoretical framework is used to approximate the relation between
    power and sample size for given type I error rates and effect sizes.
    The package offers an adaptive search algorithm to find the optimal N for
    given effect sizes and type I error rates. Plots can be used to visualize
    the power relation to N for different parameters of interest (POI). 
    Theoretical justifications are given in Irmer et al. 
    (2024a) <doi:10.31219/osf.io/pe5bj> and detailed description
    are given in Irmer et al. (2024b) <doi:10.3758/s13428-024-02476-3>.
There are two main functions: (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.
It estimates power and sample size for Partial Least Squares-based methods described in Andreella, et al., (2024), <doi:10.48550/arXiv.2403.10289>.
A set of functions designed to calculate the standardised
    precipitation and standardised precipitation evapotranspiration indices
    using NASA POWER data as described in Blain et al. (2023)
    <doi:10.2139/ssrn.4442843>.  These indices are calculated using a reference
    data source.  The functions verify if the indices' estimates meet the
    assumption of normality and how well NASA POWER estimates represent
    real-world data.  Indices are calculated in a routine mode.  Potential
    evapotranspiration amounts and the difference between rainfall and potential
    evapotranspiration are also calculated.  The functions adopt a basic time
    scale that splits each month into four periods.  Days 1 to 7, days 8 to 14,
    days 15 to 21, and days 22 to 28, 29, 30, or 31, where 'TS=4' corresponds to
    a 1-month length moving window (calculated 4 times per month) and 'TS=48'
    corresponds to a 12-month length moving window (calculated 4 times per
    month).
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.
Power and sample size calculations for a variety of study designs and 
    outcomes. Methods include t tests, ANOVA (including tests for interactions, 
    simple effects and contrasts), proportions, categorical data (chi-square tests 
    and proportional odds), linear, logistic and Poisson regression, alternative 
    and coprimary endpoints, power for confidence intervals, correlation 
    coefficient tests, cluster randomized trials, individually randomized group 
    treatment trials, multisite trials, treatment-by-covariate interaction effects 
    and nonparametric tests of location. Utilities are provided for 
    computing various effect sizes. Companion package to the book 
    "Power and Sample Size in R", Crespi (2025, ISBN:9781138591622). 
    Further resources available at <https://powerandsamplesize.org/>.
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/>.
Perform user-friendly power analyses for the random
    intercept cross-lagged panel model (RI-CLPM) and the bivariate stable trait 
    autoregressive trait state (STARTS) model. The strategy as proposed by 
    Mulder (2023) <doi:10.1080/10705511.2022.2122467> is implemented. Extensions 
    include the use of parameter constraints over time, bounded estimation,
    generation of data with skewness and kurtosis, and the option to setup the power 
    analysis for Mplus.
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.
Perform 1-dim/2-dim projection pursuit, grand tour and guided tour for big data based on data nuggets. 
    Reference papers: [1] Beavers et al., (2024) <doi:10.1080/10618600.2024.2341896>. 
    [2] Duan, Y., Cabrera, J., & Emir, B. (2023). "A New Projection Pursuit Index for Big Data." <doi:10.48550/arXiv.2312.06465>.
Performs partial principal component analysis of a large sparse matrix. 
  The matrix may be stored as a list of matrices to be concatenated (implicitly) horizontally.
  Useful application includes cases where the number of total nonzero entries exceed the capacity of 
  32 bit integers (e.g., with large Single Nucleotide Polymorphism data).
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.
In the era of big data, data redundancy and distributed characteristics present novel challenges to data analysis. This package introduces a method for estimating optimal subsets of redundant distributed data, based on PPCDT (Conjunction of Power and P-value in Distributed Settings). Leveraging PPC technology, this approach can efficiently extract valuable information from redundant distributed data and determine the optimal subset. Experimental results demonstrate that this method not only enhances data quality and utilization efficiency but also assesses its performance effectively. The philosophy of the package is described in Guo G. (2020) <doi:10.1007/s00180-020-00974-4>.
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.
Calculates partial and semi-partial
        (part) correlations along with p-value.
Spatial Analysis for exploration of Pakistan Population Census 2017 (<https://www.pbs.gov.pk/content/population-census>). 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.
Introducing a novel and updated database showcasing Peru's endemic plants. This meticulously compiled and revised botanical collection encompasses a remarkable assemblage of over 7,898 distinct species. The data for this resource was sourced from the work of Govaerts, R., Nic Lughadha, E., Black, N. et al., titled 'The World Checklist of Vascular Plants: A continuously updated resource for exploring global plant diversity', published in Sci Data 8, 215 (2021) <doi:10.1038/s41597-021-00997-6>.
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>.
Reconstruction of paleoclimate niches using phylogenetic comparative 
    methods and projection reconstructed niches onto paleoclimate maps.
    The user can specify various models of trait evolution or estimate the best fit
    model, include fossils, use one or multiple phylogenies for inference, and make
    animations of shifting suitable habitat through time. This model was first used
    in Lawing and Polly (2011), and further implemented in Lawing et al (2016) and 
    Rivera et al (2020).
    Lawing and Polly (2011) <doi:10.1371/journal.pone.0028554> "Pleistocene climate,
    phylogeny and climate envelope models: An integrative approach to better 
    understand species' response to climate change"
    Lawing et al (2016) <doi:10.1086/687202> "Including fossils in phylogenetic 
    climate reconstructions: A deep time perspective on the climatic niche evolution
    and diversification of spiny lizards (Sceloporus)"
    Rivera et al (2020) <doi:10.1111/jbi.13915> "Reconstructing historical shifts 
    in suitable habitat of Sceloporus lineages using phylogenetic niche modelling.".
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://poverty-action.org/>.
Permutation based Kolmogorov-Smirnov test for paired samples. The test was proposed by Wang W.S., Amsler C. and Schmidt, P. (2025) <doi:10.1007/s00181-025-02779-0>.
We provide new tools for the identification of prognostic and predictive biomarkers. For further details we refer the reader to the paper: Zhu et al. Identification of prognostic and predictive biomarkers in high-dimensional data with PPLasso. BMC Bioinformatics. 2023 Jan 23;24(1):25.
Generates chronological and ordered p-plots for data vectors or vectors of p-values. The p-plot visualizes the evolution of the p-value of a significance test across the sampled data. It allows for assessing the consistency of the observed effects, for detecting the presence of potential moderator variables, and for estimating the influence of outlier values on the observed results. For non-significant findings, it can diagnose patterns indicative of underpowered study designs. The p-plot can thus either back the binary accept-vs-reject decision of common null-hypothesis significance tests, or it can qualify this decision and stimulate additional empirical work to arrive at more robust and replicable statistical inferences.
Linear and nonlinear regression
        methods based on Partial Least Squares and Penalization
        Techniques. Model parameters are selected via cross-validation,
        and confidence intervals ans tests for the regression
        coefficients can be conducted via jackknifing. 
        The method is described and applied to simulated and experimental 
        data in Kraemer et al. (2008) <doi:10.1016/j.chemolab.2008.06.009>.
Implements data processing described in <doi:10.1126/sciadv.abk3283>
    to align modern differentially private data with formatting of older US Census
    data releases. The primary goal is to read in Census Privacy Protected Microdata
    Files data in a reproducible way. This includes tools for aggregating to relevant
    levels of geography by creating geographic identifiers which match the US Census
    Bureau's numbering. Additionally, there are tools for grouping race numeric
    identifiers into categories, consistent with OMB (Office of Management and Budget)
    classifications. Functions exist for downloading and linking to existing
    sources of privacy protected microdata.
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.
Implements the copula-based estimator for univariate long-range dependent processes, introduced in Pumi et al. (2023) <doi:10.1007/s00362-023-01418-z>. Notably, this estimator is capable of handling missing data and has been shown to perform exceptionally well, even when up to 70% of data is missing (as reported in <arXiv:2303.04754>) and has been found to outperform several other commonly applied estimators.
Toolkit for fitting point process models with sequences of LASSO penalties ("regularisation paths"), as described in Renner, I.W. and Warton, D.I. (2013) <doi:10.1111/j.1541-0420.2012.01824.x>. Regularisation paths of Poisson point process models or area-interaction models can be fitted with LASSO, adaptive LASSO or elastic net penalties. A number of criteria are available to judge the bias-variance tradeoff.
Efficient statistical inference of two-sample MR (Mendelian Randomization) analysis. 
    It can account for the correlated instruments and the horizontal pleiotropy, 
    and can provide the accurate estimates of both causal effect and horizontal 
    pleiotropy effect as well as the two corresponding p-values. There are two main 
    functions in the 'PPMR' package. One is PMR_individual() for individual level data, 
    the other is PMR_summary() for summary data.
Provides a suite of functions that fit models that use PPM type priors for partitions.
                Models include hierarchical Gaussian and probit ordinal models with  a (covariate 
                dependent) PPM.  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) 
                <doi:10.1080/10618600.2021.1999824> 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>, and 
                multivariate PPM change point models that are detailed in Quinlan, J.J.; Page, G.L.; 
                Castro, L.M. (2023) <doi:10.1214/22-BA1344>. In addition, a function that fits a 
                univariate or bivariate functional data model that employs a PPM or a PPMx to 
                cluster curves based on B-spline coefficients is provided.
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.
Provides functionality for Bayesian analysis of replication studies using power prior approaches (Pawel et al., 2023) <doi:10.1007/s11749-023-00888-5>.
A toolbox for deterministic, probabilistic and privacy-preserving record linkage techniques. Combines the functionality of the 'Merge ToolBox' (<https://www.record-linkage.de>) with current privacy-preserving techniques.
Implements linear and generalized linear models for provider profiling,
    incorporating both fixed and random effects. For large-scale providers, the linear 
    profiled-based method and the SerBIN method for binary data 
    reduce the computational burden. Provides post-modeling features, such as 
    indirect and direct standardization measures, hypothesis testing, 
    confidence intervals, and post-estimation visualization. 
    For more information, see Wu et al. (2022) <doi:10.1002/sim.9387>.
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.
This is an implementation of the partial profile score feature 
    selection (PPSFS) approach to generalized linear (interaction) models. 
    The PPSFS is highly scalable even for ultra-high-dimensional feature space. 
    See the paper by Xu, Luo and Chen (2022, <doi:10.4310/21-SII706>).
The Predictive Power Score (PPS) is an asymmetric, data-type-agnostic score 
    that can detect linear or non-linear relationships between two variables. 
    The score ranges from 0 (no predictive power) to 1 (perfect predictive power). 
    PPS can be useful for data exploration purposes, in the same way correlation analysis is.
    For more information on PPS, see <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.
Weighted Deming regression, also known as "errors-in-variable" 
    regression, is applied with suitable weights. Weights are modeled via a 
    precision profile; functions are provided for implementing it in both known 
    and unknown precision profile situations. The package provides tools for 
    precision profile weighted Deming (PWD) regression. 
    It covers two settings – one where the precision profiles are known either 
    from external studies or from adequate replication of the X and Y readings, 
    and one in which there is a plausible functional form for the precision 
    profiles but the exact function must be estimated from the (generally 
    singlicate) readings. 
    The function set includes tools for: estimated standard errors (via 
    jackknifing); standardized-residual analysis function with regression 
    diagnostic tools for normality, linearity and constant variance; and an 
    outlier analysis identifying significant outliers for closer investigation.
    Further information on mathematical derivations and applications can be 
    found on arXiv: Hawkins and Kraker (2025) <doi:10.48550/arXiv.2508.02888>.
In the big data setting, working data sets are often distributed on multiple machines. However, classical statistical methods are often developed to solve the problems of single estimation or inference. We employ a novel parallel quasi-likelihood method in generalized linear models, to make the variances between different sub-estimators relatively similar. Estimates are obtained from projection subsets of data and later combined by suitably-chosen unknown weights. The philosophy of the package is described in Guo G. (2020) <doi:10.1007/s00180-020-00974-4>.
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.
Bayesian regularized quantile regression utilizing sparse priors to 
    promote exact sparsity leads to efficient Bayesian shrinkage estimation, variable 
    selection and statistical inference. In this package, we have implemented robust
    Bayesian variable selection with spike-and-slab priors under high-dimensional
    linear regression models (Fan et al. (2024) <doi:10.3390/e26090794> and  
    Ren et al. (2023) <doi:10.1111/biom.13670>), and regularized quantile varying
    coefficient models (Zhou et al.(2023) <doi:10.1016/j.csda.2023.107808>). In particular, 
    valid robust Bayesian inferences under both models in the presence of heavy-tailed errors
    can be validated on finite samples. Additional models with spike-and-slab priors include 
    robust Bayesian group LASSO and robust binary Bayesian LASSO (Fan and Wu (2025) 
    <doi:10.1002/sta4.70078>). The Markov Chain Monte Carlo (MCMC) algorithms
    of the proposed and alternative models are implemented in C++.
Quantile regression with fixed effects is a general model for longitudinal data. Here we proposed to solve it by several methods. The estimation methods include three loss functions as check, asymmetric least square and asymmetric Huber functions; and three structures as simple regression, fixed effects and fixed effects with penalized intercepts by LASSO.
It aggregates protein panel data and metadata for protein quantitative
 trait locus (pQTL) analysis using 'pQTLtools' (<https://jinghuazhao.github.io/pQTLtools/>).
 The package includes data from affinity-based panels such as 'Olink' (<https://olink.com/>) 
 and 'SomaScan' (<https://somalogic.com/>), as well as mass spectrometry-based panels from
 'CellCarta' (<https://cellcarta.com/>) and 'Seer' (<https://seer.bio/>). The metadata
 encompasses updated annotations and publication details.
Data analysis for Project Risk Management via the Second Moment Method, 
  Monte Carlo Simulation, Contingency Analysis, Sensitivity Analysis, Earned Value Management, 
  Learning Curves, Design Structure Matrices, and more.
Quickly and easily generate plots of acoustic data aligned with transcriptions similar to those made in 'Praat' using either derived signals generated directly in R with 'wrassp' or imported derived signals from 'Praat'. Provides easy and fast out-of-the-box solutions but also a high extent of  flexibility. Also provides options for embedding audio in figures and animating figures.
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.
Streamline the creation of 'Docker' images with 'R' packages and dependencies embedded. The 'pracpac' package provides a 'usethis'-like interface to creating Dockerfiles with dependencies managed by 'renv'. The 'pracpac' functionality is described in Nagraj and Turner (2023) <doi:10.48550/arXiv.2303.07876>.
Sample size calculations for practical equivalence trial design with a time to event endpoint.
Consider a possibly nonlinear nonparametric regression
   with p regressors. We provide evaluations by 13 methods to rank
   regressors by their practical significance or importance using 
   various methods, including machine learning tools. Comprehensive
   methods are as follows. 
   m6=Generalized partial correlation coefficient or
   GPCC by Vinod (2021)<doi:10.1007/s10614-021-10190-x> and
   Vinod (2022)<https://www.mdpi.com/1911-8074/15/1/32>.
   m7= a generalization of psychologists' effect size incorporating 
   nonlinearity and many variables.
   m8= local linear partial (dy/dxi) using the 'np' package for kernel 
   regressions.
   m9= partial (dy/dxi) using the 'NNS' package.
   m10= importance measure using the 'NNS' boost function.
   m11= Shapley Value measure of importance (cooperative game theory).
   m12 and m13= two versions of the random forest algorithm.
   Taraldsen's exact density for sampling distribution of correlations added.
Functions and datasets to support Valliant, Dever, and Kreuter (2018), <doi:10.1007/978-3-319-93632-1>, "Practical Tools for Designing and Weighting Survey Samples". Contains functions for sample size calculation for survey samples using stratified or clustered one-, two-, and three-stage sample designs, and single-stage audit sample designs. Functions are included that will group geographic units accounting for distances apart and measures of size. Other functions compute variance components for multistage designs, sample sizes in two-phase designs, and a stopping rule for ending data collection. A number of example data sets are included.
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.
A novel pseudo-value regression approach for the differential co-expression network analysis in expression data, which can incorporate additional clinical variables in the model. This is a direct regression modeling for the differential network analysis, and it is therefore computationally amenable for the most users. The full methodological details can be found in Ahn S et al (2023) <doi:10.1186/s12859-022-05123-w>.
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 to evaluate tools that calculate precision-recall curves.
    Saito and Rehmsmeier (2015) <doi:10.1371/journal.pone.0118432>.
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 described in 
    Fokkema (2020; <DOI:10.18637/jss.v092.i12>) and Fokkema & Strobl 
    (2020; <DOI:10.1037/met0000256>). The main function pre() derives 
    prediction rule ensembles consisting of rules and/or linear terms for 
    continuous, binary, count, multinomial, survival and multivariate 
    continuous responses. Function gpe() derives generalized prediction 
    ensembles, consisting of rules, hinge and linear functions of the 
    predictor variables.
An efficient data integration method is provided for multiple spatial transcriptomics data with non-cluster-relevant effects such as the complex batch effects. It unifies spatial factor analysis simultaneously with spatial clustering and embedding alignment, requiring only partially shared cell/domain clusters across datasets. More details can be referred to Wei Liu, et al. (2023) <doi:10.1038/s41467-023-35947-w>.
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. More information and an example of
  implementation can be found in Vargas Godoy and Markonis (2023,
  <doi:10.1016/j.envsoft.2023.105711>).
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)
  <https://people.eecs.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.
A doubly robust precision medicine approach to fit, cross-validate and visualize prediction models for the conditional average treatment effect (CATE). It implements doubly robust estimation and semiparametric modeling approach of treatment-covariate interactions as proposed by Yadlowsky et al. (2020) <doi:10.1080/01621459.2020.1772080>.
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.
Implements fast, safe, and customizable assertions routines, which 
    can be used in place of base::stopifnot().
Accurate calculations and visualization of precision-recall and ROC (Receiver Operator Characteristics)
    curves. Saito and Rehmsmeier (2015) <doi:10.1371/journal.pone.0118432>.
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 statistical methods including GBLUP, BayesB, RKHS, PLS, LASSO, EN, LightGBM and XGBoost along with additive and additive-dominance models. Users are able to incorporate parental phenotypic information in all methods based on their specific needs. (Xu S et al(2017) <doi:10.1534/g3.116.038059>; Xu Y et al (2021) <doi: 10.1111/pbi.13458>).
Performs genomic prediction of hybrid performance using eight GS methods including GBLUP, BayesB, RKHS, PLS, LASSO, Elastic net, XGBoost and LightGBM.
             GBLUP: genomic best liner unbiased prediction, RKHS: reproducing kernel Hilbert space, PLS: partial least squares regression, LASSO: least absolute shrinkage and selection operator, XGBoost: extreme gradient boosting, LightGBM: light gradient boosting machine.
             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 (à 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 file 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.
Enables researchers to visualize the prediction performance of any algorithm on the individual level (or close to it), given that the predicted outcome is either binary or continuous. Visual results are instantly comprehensible.
Providing functions to diagnose and make inferences from various linear models, 
    such as those obtained from 'aov', 'lm', 'glm', 'gls', 'lme', 'lmer', 'glmmTMB' and 'semireg'. 
	Inferences include predicted means and standard errors, contrasts, multiple comparisons, 
	permutation tests, adjusted R-square and graphs.
Estimates when and where a model-guided treatment strategy may 
    outperform a treat-all or treat-none approach by Monte Carlo simulation and 
    evaluation of the Net Monetary Benefit. Details can be viewed in 
    Parsons et al. (2023) <doi:10.21105/joss.05328>.
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.
Consider a linear predictive regression setting with a potentially large set of candidate predictors. This work is concerned with detecting the presence of out of sample predictability based on out of sample mean squared error comparisons given in Gonzalo and Pitarakis (2023) <doi:10.1016/j.ijforecast.2023.10.005>.
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.
Methods for spatial predictive modeling, especially for spatial distribution models. This includes algorithms for model fitting and prediction, as well as methods for model evaluation.
Fetches the 'PREDICTS' database and relevant metadata from the Data
    Portal at the Natural History Museum, London <https://data.nhm.ac.uk>. Data
    were collated from over 400 existing spatial comparisons of local-scale
    biodiversity exposed to different intensities and types of anthropogenic
    pressures, from sites around the world. These data are described in Hudson
    et al. (2013) <doi:10.1002/ece3.2579>.
Methods for assessing the performance of a prediction model with respect to identifying patient-level treatment benefit. All methods are applicable for continuous and binary outcomes, and for any type of statistical or machine-learning prediction model as long as it uses baseline covariates to predict outcomes under treatment and control.
An implementation of prediction intervals for overdispersed count data,
            for overdispersed binomial data and for linear random effects models.
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.
Evaluate the predictive performance of an existing (i.e. previously 
    developed) prediction/ prognostic model given relevant information about the 
    existing prediction model (e.g. coefficients) and a new dataset. Provides a 
    range of model updating methods that help tailor the existing model to the 
    new dataset; see Su et al. (2018) <doi:10.1177/0962280215626466>. Techniques 
    to aggregate multiple existing prediction models on the new data are also 
    provided; see Debray et al. (2014) <doi:10.1002/sim.6080> and Martin et al. 
    (2018) <doi:10.1002/sim.7586>).
Global hypothesis tests combine information across multiple endpoints to test a single hypothesis. The prediction test is a recently proposed global hypothesis test with good performance for small sample sizes and many endpoints of interest. The test is also flexible in the types and combinations of expected results across the individual endpoints. This package provides functions for data processing and calculation of the prediction test.
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).
Implements the Single Transferable Vote (STV) electoral system, with
    clear explanatory graphics. The core function stv() uses Meek's method, the purest
    expression of the simple principles of STV, but which requires electronic
    counting. It can handle votes expressing equal preferences for subsets of
    the candidates. A function stv.wig() implementing the Weighted Inclusive
    Gregory method, as used in Scottish council elections, is also provided,
    and with the same options, as described in the manual. The required vote
    data format is as an R list: a function pref.data() is provided to transform
    some commonly used data formats into this format.
    References for methodology:
    Hill, Wichmann and Woodall (1987) <doi:10.1093/comjnl/30.3.277>,
    Hill, David (2006) <https://www.votingmatters.org.uk/ISSUE22/I22P2.pdf>,
    Mollison, Denis (2023) <arXiv:2303.15310>,
    (see also the package manual pref_pkg_manual.pdf).
Allows users to derive multi-objective weights from pairwise comparisons, which
    research shows is more repeatable, transparent, and intuitive other techniques. These weights
    can be rank existing alternatives or to define a multi-objective utility function for optimization.
Design and analyze two-stage randomized trials with a continuous
    outcome measure. The package contains functions to compute the required 
    sample size needed to detect a given preference, treatment, and selection 
    effect; alternatively, the package contains functions that can report the 
    study power given a fixed sample size. Finally, analysis functions are 
    provided to test each effect using either summary data (i.e. means, 
    variances) or raw study data <doi:10.18637/jss.v094.c02>.
Convenient structures for creating, sourcing, reading, writing
  and manipulating ordinal preference data. Methods for writing to/from PrefLib
  formats. See Nicholas Mattei and Toby Walsh "PrefLib: A Library of Preference
  Data" (2013) <doi:10.1007/978-3-642-41575-3_20>.
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.
Provides functionality for calculating pregnancy-related dates and
    tracking medications during pregnancy and fertility treatment. Calculates
    due dates from various starting points including last menstrual period and
    IVF (In Vitro Fertilisation) transfer dates, determines pregnancy progress
    on any given date, and identifies when specific pregnancy weeks are
    reached. Includes medication tracking capabilities for individuals
    undergoing fertility treatment or during pregnancy, allowing users to
    monitor remaining doses and quantities needed over specified time periods.
    Designed for those tracking their own pregnancies or supporting partners
    through the process, making use of options to personalise output messages.
    For details on due date calculations, see
    <https://www.acog.org/clinical/clinical-guidance/committee-opinion/articles/2017/05/methods-for-estimating-the-due-date>.
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.
Early generation breeding trials are to be conducted in multiple 
             environments where it may not be possible to replicate all the 
             lines in each environment due to scarcity of resources. For such 
             situations, partially replicated (p-Rep) designs have wide 
             application potential as only a proportion of the test lines are 
             replicated at each environment. A collection of several utility 
             functions related to p-Rep designs have been developed. Here, the 
             package contains six functions for a complete stepwise analytical 
             study of these designs. Five functions pRep1(), pRep2(), pRep3(), 
             pRep4() and pRep5(), are used to generate five new series of p-Rep 
             designs and also compute average variance factors and canonical 
             efficiency factors of generated designs. A fourth function NCEV() 
             is used to generate incidence matrix (N), information matrix (C), 
             canonical efficiency factor (E) and average variance factor (V). 
             This function is general in nature and can be used for studying the 
             characterization properties of any block design. A construction 
             procedure for p-Rep designs was given by Williams et al.(2011) 
             <doi:10.1002/bimj.201000102> which was tedious and time consuming. 
             Here, in this package, five different methods have been given to 
             generate p-Rep designs easily.
A set of tools to implement the non-parametric bounds and Bayesian methods for assessing post-treatment bias developed in Blackwell, Brown, Hill, Imai, and Yamamoto (2025) <doi:10.1017/pan.2025.3>.
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.
In this record linkage package, data preprocessing has been meticulously executed
  to cover a wide range of datasets, ensuring that variable names are standardized using synonyms. 
  This approach facilitates seamless data integration and analysis across various datasets. While users 
  have the flexibility to modify variable names, the system intelligently ensures that 
  changes are only permitted when they do not compromise data consistency or essential variable essence.
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'.
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>.
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.
Multi-state models are essential tools in longitudinal data analysis. 
             One primary goal of these models is the estimation of transition 
             probabilities, a critical metric for predicting clinical prognosis 
             across various stages of diseases or medical conditions. Traditionally, 
             inference in multi-state models relies on the Aalen-Johansen (AJ) 
             estimator which is consistent under the Markov assumption. However, 
             in many practical applications, the Markovian nature of the process
             is often not guaranteed, limiting the applicability of the AJ
             estimator in more complex scenarios. This package extends the landmark Aalen-Johansen
             estimator (Putter, H, Spitoni, C (2018) <doi:10.1177/0962280216674497>)
             incorporating presmoothing techniques described by Soutinho, 
             Meira-Machado and Oliveira (2020) <doi:10.1080/03610918.2020.1762895>, 
             offering a robust alternative for estimating transition probabilities
             in non-Markovian multi-state models with multiple states and potential
             reversible transitions.
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.
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>.
Allows biomechanical pressure data from a range of systems to be imported and processed in a reproducible manner. Automatic and manual tools are included to let the user define regions (masks) to be analyzed. Also includes functions for visualizing and animating pressure data. Example methods are described in Shi et al., (2022) <doi:10.1038/s41598-022-19814-0>, Lee et al., (2014) <doi:10.1186/1757-1146-7-18>, van der Zward et al., (2014) <doi:10.1186/1757-1146-7-20>, Najafi et al., (2010) <doi:10.1016/j.gaitpost.2009.09.003>, Cavanagh and Rodgers (1987) <doi:10.1016/0021-9290(87)90255-7>.
This repository contains the codes for using the predictive accuracy comparison tests developed in Pitarakis, J. (2023) <doi:10.1017/S0266466623000154>.
An application to calculate a patient's pretest probability
    (PTP) for obstructive Coronary Artery Disease (CAD) from a collection
    of guidelines or studies. Guidelines usually comes from the American
    Heart Association (AHA), American College of Cardiology (ACC) or
    European Society of Cardiology (ESC). Examples of PTP scores that
    comes from studies are the 2020 Winther et al. basic, Risk
    Factor-weighted Clinical Likelihood (RF-CL) and Coronary Artery
    Calcium Score-weighted Clinical Likelihood (CACS-CL) models
    <doi:10.1016/j.jacc.2020.09.585>, 2019 Reeh et al. basic and clinical
    models <doi:10.1093/eurheartj/ehy806> and 2017 Fordyce et al. PROMISE
    Minimal-Risk Tool <doi:10.1001/jamacardio.2016.5501>.  As diagnosis of
    CAD involves a costly and invasive coronary angiography procedure for
    patients, having a reliable PTP for CAD helps doctors to make better
    decisions during patient management.  This ensures high risk patients
    can be diagnosed and treated early for CAD while avoiding unnecessary
    testing for low risk patients.
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.
Defines aesthetically pleasing colour palettes.
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. 'prettyglm' helps users
    create coefficient summaries which include categorical base levels,
    variable importance and type III p.values. 'prettyglm' also creates
    beautiful relativity plots for categorical, continuous and splined
    coefficients.
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'.
    Rounding: '99' with 3 significant digits -> '99.0'
    p-values: '0.00001' -> '<0.0001'.
    Colors: '#FF0000' -> 'red'.
    Quantities: '1239437' -> '1.24 M'.
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 <https://mcmc-jags.sourceforge.io/>.
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/>.
Implements the American Heart Association Predicting
    Risk of cardiovascular disease EVENTs (PREVENT) equations from Khan
    SS, Matsushita K, Sang Y, and colleagues (2023)
    <doi:10.1161/CIRCULATIONAHA.123.067626>, with optional comparison 
    with their de facto predecessor, the Pooled Cohort Equations from the 
	  American Heart Association and American College of Cardiology (2013)
	  <doi:10.1161/01.cir.0000437741.48606.98> and the revision to the Pooled 
	  Cohort Equations from Yadlowsky and colleagues (2018) 
	  <doi:10.7326/M17-3011>.
For working with the 'Prevision.io' AI model management platform's API <https://prevision.io/>.
Spatial estimation of a prevalence surface
    or a relative risks surface, using data from a Demographic and Health
    Survey (DHS) or an analog survey, see Larmarange et al. (2011)
    <doi:10.4000/cybergeo.24606>.
Functions to simulate point prevalence studies (PPSs) of healthcare-associated infections (HAIs) and to convert prevalence to incidence in steady state setups. 
    Companion package to the preprint Willrich et al., From prevalence to incidence - a new approach in the hospital setting;
    <doi:10.1101/554725> , where methods are explained in detail.
Estimate False Discovery Rates (FDRs) for importance metrics from
    random forest runs.
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: Diewert and Fox (2020) 
    <doi:10.1080/07350015.2020.1816176>, Białek (2019) <doi:10.2478/jos-2019-0014> or Białek (2020) <doi:10.2478/jos-2020-0037>.
Price comparisons within or between countries provide an overall measure of the relative difference in prices, often denoted as price levels. This package provides index number methods for such price comparisons (e.g., The World Bank, 2011, <doi:10.1596/978-0-8213-9728-2>). Moreover, it contains functions for sampling and characterizing price data.
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).
Implements the 'PRIDIT' (Principal Component Analysis applied to 
    'RIDITs') scoring system described in Brockett et al. (2002) 
    <doi:10.1111/1539-6975.00027>. Provides functions for ridit scoring 
    originally developed by Bross (1958) <doi:10.2307/2527727>, calculating 
    'PRIDIT' weights, and computing final 'PRIDIT' scores for multivariate 
    analysis of ordinal data.
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>.
Provides functions for working with primary
    event censored distributions and 'Stan' implementations for use in Bayesian
    modeling. Primary event censored distributions are useful for modeling
    delayed reporting scenarios in epidemiology and other fields (Charniga et
    al. (2024) <doi:10.48550/arXiv.2405.08841>). It also provides support for
    arbitrary delay distributions, a range of common primary distributions, and
    allows for truncation and secondary event censoring to be accounted for
    (Park et al. (2024) <doi:10.1101/2024.01.12.24301247>). A subset of
    common distributions also have analytical solutions implemented, allowing
    for faster computation. In addition, it provides multiple methods for
    fitting primary event censored distributions to data via optional
    dependencies.
Data from All the World's Primates relational SQL database and other tabular datasets are made available via drivers and connection functions. Additionally we provide several functions and examples to facilitate the merging and aggregation of these tabular inputs.
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>.
Perform sample size, power calculation and subsequent analysis for Immuno-oncology (IO) trials composed of responders and non-responders.
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' <https://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>.
Construct a principal surface that are two-dimensional surfaces that pass through the middle of a p-dimensional data set. They minimise the distance from the data points, and provide a nonlinear summary of data. The surfaces are nonparametric and their shape is suggested by the data. The formation of a surface is found using an iterative procedure which starts with a linear summary, typically with a principal component plane. Each successive iteration is a local average of the p-dimensional points, where an average is based on a projection of a point onto the nonlinear surface of the previous iteration. For more information on principal surfaces, see Ganey, R. (2019, "https://open.uct.ac.za/items/4e655d7d-d10c-481b-9ccc-801903aebfc8").
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.
Provides methods for reducing the number of features within a data set. See Bauer JO (2021) <doi:10.1145/3475827.3475832> and Bauer JO, Drabant B (2021) <doi:10.1016/j.jmva.2021.104754> for more information on principal loading analysis.
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.
Three-dimensional systematic conservation planning, conducting
             nested prioritization analyses across multiple depth levels and
             ensuring efficient resource allocation throughout the water column.
             It provides a structured workflow designed to address biodiversity
             conservation and management challenges in the 3 dimensions, while
             facilitating users’ choices and parameterization (Doxa et al. 2025
             <doi:10.1016/j.ecolmodel.2024.110919>).
An innovative tool-set that incorporates graph community detection
             methods into systematic conservation planning. It is designed to
             enhance spatial prioritization by focusing on the protection of
             areas with high ecological connectivity. Unlike traditional
             approaches that prioritize individual planning units, 'priorCON'
             focuses on clusters of features that exhibit strong ecological
             linkages. The 'priorCON' package is built upon the 'prioritizr'
             package <doi:10.32614/CRAN.package.prioritizr>, using commercial
             and open-source exact algorithm solvers that ensure optimal
             solutions to prioritization problems.
Translates beliefs into prior information in the form of Beta and Gamma distributions. 
	It can be used for the generation of priors on the prevalence of disease and 
	the sensitivity/specificity of diagnostic tests and any other binomial experiment.
This uses a mixed integer mathematical programming (MIP)
        approach for building and solving multi-action planning problems, 
        where the goal is to find an optimal combination of management actions that
        abate threats, in an efficient way while accounting for spatial aspects. 
        Thus, optimizing the connectivity and conservation effectiveness of 
        the prioritized units and of the deployed actions. The package is capable of 
        handling different commercial (gurobi, CPLEX) and non-commercial (symphony, CBC) MIP solvers. 
        Gurobi optimization solver can be installed using comprehensive instructions in 
        the 'gurobi' installation vignette of the prioritizr package (available in 
        <https://prioritizr.net/articles/gurobi_installation_guide.html>). Instead, 'CPLEX'
        optimization solver can be obtain from IBM CPLEX web page (available here 
        <https://www.ibm.com/es-es/products/ilog-cplex-optimization-studio>). Additionally, 
        the 'rcbc' R package (available at
    <https://github.com/dirkschumacher/rcbc>) can be used to obtain solutions
    using the CBC optimization software (<https://github.com/coin-or/Cbc>). Methods used in the 
        package refers to Salgado-Rojas et al. (2020) <doi:10.1016/j.ecolmodel.2019.108901>,
        Beyer et al. (2016) <doi:10.1016/j.ecolmodel.2016.02.005>, Cattarino et al. (2015)
        <doi:10.1371/journal.pone.0128027> and Watts et al. (2009) <doi:10.1016/j.envsoft.2009.06.005>. 
        See the prioriactions website for more information, documentations and examples.
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). Users can also install the
    IBM CPLEX software (<https://www.ibm.com/products/ilog-cplex-optimization-studio/cplex-optimizer>) and
    the 'cplexAPI' R package (available at <https://github.com/cran/cplexAPI>).
    Additionally, the 'rcbc' R package (available at
    <https://github.com/dirkschumacher/rcbc>) can be used to generate solutions
    using the CBC optimization software (<https://github.com/coin-or/Cbc>). For further details, see Hanson et al. (2024) <doi:10.1111/cobi.14376>.
Conservation planning datasets for learning how to use the
    'prioritizr' package <https://CRAN.R-project.org/package=prioritizr>.
Priority-ElasticNet extends the Priority-LASSO method (Klau et al. (2018) <doi:10.1186/s12859-018-2344-6>) by incorporating the ElasticNet penalty, allowing for both L1 and L2 regularization. 
    This approach fits successive ElasticNet models for several blocks of (omics) data with different priorities, using the predicted values from each block as an offset for the subsequent block. 
    It also offers robust options to handle block-wise missingness in multi-omics data, improving the flexibility and applicability of the model in the presence of incomplete datasets.
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. Also offers options to deal with block-wise missingness in multi-omics data.
Provides functions for prior and likelihood sensitivity analysis in Bayesian models. Currently it implements methods to determine the sensitivity of the posterior to power-scaling perturbations of the prior and likelihood.
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.
Manipulate and visualize colors in a intuitive,
    low-dependency and functional way.
Prism <https://prismjs.com/> is a lightweight, extensible syntax 
    highlighter, built with modern web standards in mind. This package provides 
    server-side rendering in R using 'V8' such that no JavaScript library is
    required in the resulting HTML documents. Over 400 languages are supported.
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.
Calculates seat allocation using the D-Hondt method, Sainte-Lague method, and Modified Sainte-Lague method, all commonly used in proportional representation electoral systems. For more information on these methods, see Michael Gallagher (1991)<doi:10.1016/0261-3794(91)90004-C>.
Miscellaneous printing of numeric or statistical results in R Markdown or Quarto documents according to guidelines of the "Publication Manual" of the American Psychological Association (2020, ISBN: 978-1-4338-3215-4). These guidelines are usually referred to as APA style (<https://apastyle.apa.org/>) and include specific rules on the formatting of numbers and statistical test results. APA style has to be implemented when submitting scientific reports in a wide range of research fields, especially in the social sciences. The default output of numbers in the R console or R Markdown and Quarto documents does not meet the APA style requirements, and reformatting results manually can be cumbersome and error-prone. This package covers the automatic conversion of R objects to textual representations that meet the APA style requirements, which can be included in R Markdown or Quarto documents. It covers some basic statistical tests (t-test, ANOVA, correlation, chi-squared test, Wilcoxon test) as well as some basic number printing manipulations (formatting p-values, removing leading zeros for numbers that cannot be greater than one, and others). Other packages exist for formatting numbers and tests according to the APA style guidelines, such as 'papaja' (<https://cran.r-project.org/package=papaja>) and 'apa' (<https://cran.r-project.org/package=apa>), but they do not offer all convenience functionality included in 'prmisc'. The vignette has an overview of most of the functions included in the package.
Provides functions for generating pseudo-random numbers that follow a uniform distribution [0,1]. Randomness tests were conducted using the National Institute of Standards and Technology test suite<https://csrc.nist.gov/pubs/sp/800/22/r1/upd1/final>, along with additional tests. The sequence generated depends on the initial values and parameters. The package includes a linear congruence map as the decision map and three chaotic maps to generate the pseudo-random sequence, which follow a uniform distribution. Other distributions can be generated from the uniform distribution using the Inversion Principle Method and BOX-Muller transformation. Small perturbations in seed values result in entirely different sequences of numbers due to the sensitive nature of the maps being used. The chaotic nature of the maps helps achieve randomness in the generator. Additionally, the generator is capable of producing random bits.
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
    papers, 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.
Automate the detection of gaps and elevations in mapped sequencing 
  read coverage using a 2D pattern-matching algorithm. 'ProActive' detects, characterizes 
  and visualizes read coverage patterns in both genomes and metagenomes. Optionally, 
  users may provide gene annotations associated with their genome or metagenome 
  in the form of a .gff file. In this case, 'ProActive' will generate an additional 
  output table containing the gene annotations found within the detected regions of gapped 
  and elevated read coverage. Additionally, users can search for gene 
  annotations of interest in the output read coverage plots.
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) and other PRO measurements.
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 as well as calibration
    tools and conformal inference techniques for regression models.
Functions and datasets  to accompany J. Albert and J. Hu, "Probability and Bayesian Modeling", CRC Press, (2019, ISBN: 1138492566).
Use probability theory under the Bayesian framework for calculating the risk of selecting candidates in a multi-environment context. Contained are functions used to fit a Bayesian multi-environment model (based on the available presets), extract posterior values and maximum posterior values, compute the variance components, check the model’s convergence, and calculate the probabilities. For both across and within-environments scopes, the package computes the probability of superior performance and the pairwise probability of superior performance. Furthermore, the probability of superior stability and the pairwise probability of superior stability across environments is estimated. A joint probability of superior performance and stability is also provided.
Implements an efficient and powerful Bayesian approach for sparse high-dimensional linear regression. It uses minimal prior assumptions on the parameters through plug-in empirical Bayes estimates of hyperparameters. An efficient Parameter-Expanded Expectation-Conditional-Maximization (PX-ECM) algorithm estimates maximum a posteriori (MAP) values of regression parameters and variable selection probabilities. The PX-ECM results in a robust computationally efficient coordinate-wise optimization, which adjusts for the impact of other predictor variables. The E-step is motivated by the popular two-group approach to multiple testing. The result is a PaRtitiOned empirical Bayes Ecm (PROBE) algorithm applied to sparse high-dimensional linear regression, implemented using one-at-a-time or all-at-once type optimization. More information can be found in McLain, Zgodic, and Bondell (2022) <arXiv:2209.08139>.
Hybridization probes for target sequences can be made 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.
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.
Performs elementary probability calculations on finite sample spaces, which may be represented by data frames  or lists.
    This package is meant to rescue some widely used functions from the archived 'prob' package (see <https://cran.r-project.org/src/contrib/Archive/prob/>).
    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.
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/>.
Utilities for multiple hypothesis testing, companion datasets from "Probability and Statistics for Economics and Business: An Introduction Using R" by Jason Abrevaya (MIT Press, under contract).
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.
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.
Means to predict process flow, such as process outcome, next activity, next time, remaining time, and remaining trace. Off-the-shelf predictive models based on the concept of Transformers are provided, as well as multiple way to customize the models. This package is partly based on work described in Zaharah A. Bukhsh, Aaqib Saeed, & Remco M. Dijkman. (2021). "ProcessTransformer: Predictive Business Process Monitoring with Transformer Network" <doi:10.48550/arXiv.2104.00721>.
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.
Provides functions for simulating from and fitting the latent hidden
    Markov models for response process data (Tang, 2024) <doi:10.1007/s11336-023-09938-1>. 
    It also includes functions for simulating from and fitting ordinary hidden Markov models.
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>.
Contains functions to simulate the most commonly used 
    'SAS®' procedures.  Specifically, the package aims to 
    simulate the functionality of 'proc freq', 'proc means', 'proc ttest',
    'proc reg', 'proc transpose', 'proc sort', and 'proc print'. 
    The simulation will include recreating 
    all statistics with the highest fidelity possible.
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)" in 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.
Probabilistic factor analysis for spatially-aware dimension reduction across multi-section spatial transcriptomics data with millions of spatial locations.
    More details can be referred to Wei Liu, et al. (2023) <doi:10.1101/2023.07.11.548486>.
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://protobuf.dev>,
  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.
Provides tools for profiling a user-supplied log-likelihood 
    function to calculate confidence intervals for model parameters. Speed of 
    computation can be improved by adjusting the step sizes in the profiling 
    and/or starting the profiling from limits based on the approximate large 
    sample normal distribution for the maximum likelihood estimator of a 
    parameter. The accuracy of the limits can be set by the user. A plot method 
    visualises the log-likelihood and confidence interval. Cases where the 
    profile log-likelihood flattens above the value at which a confidence limit
    is defined can be handled, leading to a limit at plus or minus infinity.
    Disjoint confidence intervals will not be found.
Functional claims reserving methods based on aggregated chain-ladder data, also known as a run-off triangle, implemented in three nonparametric algorithms (PARALLAX, REACT, and MACRAME) proposed in Maciak, Mizera, and Pešta (2022) <doi:10.1017/asb.2022.4>. Additional methods including permutation bootstrap for completed run-off triangles are also provided.
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.
Help unify visual output of R analyses in the Profinit EU company. 
  So far, there are color and fill scales for 'ggplot2', plotting theme for 
  'ggplot2', color palettes and utils to make the tools default choices.
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) <doi:10.48550/arXiv.2102.00968> <doi:10.1016/j.jeconom.2021.11.008>. The package implements multiple online learning algorithms like Bernstein online aggregation; see Wintenberger (2014) <doi:10.48550/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>.
An alternative data structure and visual rendering
    for the profiling information generated by Rprof.
Calculates profile repeatability for replicate stress response 
  curves, or similar time-series data. Profile repeatability is an individual 
  repeatability metric that uses the variances at each timepoint, the maximum 
  variance, the number of crossings (lines that cross over each other), and 
  the number of replicates to compute the repeatability score. 
  For more information see Reed et al. (2019) <doi:10.1016/j.ygcen.2018.09.015>.
Tools for examining Rprof profile output.
Interactive visualizations for profiling R code.
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(), cli::cli_progress_bar(), in a graphical user interface using utils::winProgressBar(), tcltk::tkProgressBar() or shiny::withProgress(), via the speakers using beepr::beep(), 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.
A wrapper around the generic coordinate transformation software 'PROJ'
  that transforms coordinates from one coordinate reference system ('CRS')
  to another. This includes cartographic projections as well as geodetic transformations.  The intention is for this
  package to be used by user-packages such as 'reproj', and that the older 'PROJ.4' and version 5
  pathways be provided by 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 (Piironen, Paasiniemi, and Vehtari, 2020, <doi:10.1214/20-EJS1711>)
    with or without multilevel or additive terms (Catalina, Bürkner, and
    Vehtari, 2022, <https://proceedings.mlr.press/v151/catalina22a.html>), for
    some ordinal and nominal regression models (Weber, Glass, and Vehtari, 2025,
    <doi:10.1007/s00180-024-01506-0>), and for many other regression models
    (using the latent projection by Catalina, Bürkner, and Vehtari, 2021,
    <doi:10.48550/arXiv.2109.04702>, which can also be applied to most of the
    former models). The package is compatible with the 'rstanarm' and 'brms'
    packages, but other reference models can also be used. See the vignettes and
    the documentation 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>.
A user-friendly interface for creating and managing empirical crowd-sourcing studies via API access to <https://www.prolific.co>.
Procrustes matching of the posterior samples of person and item latent positions from latent space item response models. The methods implemented in this package are based on work by Borg, I., Groenen, P. (1997, ISBN:978-0-387-94845-4), Jeon, M., Jin, I. H., Schweinberger, M., Baugh, S. (2021) <doi:10.1007/s11336-021-09762-5>, and Andrew, D. M., Kevin M. Q., Jong Hee Park. (2011) <doi:10.18637/jss.v042.i09>.
Algorithms and subroutines for patient-reported outcome data analysis.
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).
PROMETHEE (Preference Ranking Organisation METHod for Enrichment of
    Evaluations) based method assesses alternatives to obtain partial and complete
    rankings. The package also provides the GLNF (Global Local Net Flow) sorting
    algorithm to classify alternatives into ordered categories, as well as an index
    function to measure the classification quality. Barrera, F., Segura, M., &
    Maroto, C. (2023) <doi:10.1111/itor.13288>. Brans, J.P.; De Smet, Y., (2016) 
    <doi:10.1007/978-1-4939-3094-4_6>.
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.
A comprehensive, user-friendly package for label-free proteomics data analysis and machine learning-based modeling. Data generated from 'MaxQuant' can be easily used to conduct differential expression analysis, build predictive models with top protein candidates, and assess model performance. promor includes a suite of tools for quality control, visualization, missing data imputation (Lazar et. al. (2016) <doi:10.1021/acs.jproteome.5b00981>), differential expression analysis (Ritchie et. al. (2015) <doi:10.1093/nar/gkv007>), and machine learning-based modeling (Kuhn (2008) <doi:10.18637/jss.v028.i05>).
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'.
Format and submit few-shot prompts to OpenAI's Large Language Models (LLMs). Designed to be particularly useful for text classification problems in the social sciences. Methods are described in Ornstein, Blasingame, and Truscott (2024) <https://joeornstein.github.io/publications/ornstein-blasingame-truscott.pdf>.
A native 'R' client library for querying the 'Prometheus' 
    time-series database, using the 'PromQL' query language.
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. Calculations of propagated uncertainties are based on matrix calculus including covariance structure according to Arras 1998 <doi:10.3929/ethz-a-010113668> (first order), Wang & Iyer 2005 <doi:10.1088/0026-1394/42/5/011> (second order) and BIPM Supplement 1 (Monte Carlo) <doi:10.59161/JCGM101-2008>.
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.
The Prognostic Regression Offsets with Propagation of
    ERrors (for Treatment Effect Estimation) package facilitates
    direct adjustment for experiments and observational studies that
    is compatible with a range of study designs and covariance
    adjustment strategies. It uses explicit specification of clusters,
    blocks and treatment allocations to furnish probability of
    assignment-based weights targeting any of several average
    treatment effect parameters, and for standard error calculations
    reflecting these design parameters. For covariance adjustment of
    its Hajek and (one-way) fixed effects estimates, it enables
    offsetting the outcome against predictions from a dedicated
    covariance model, with standard error calculations propagating
    error as appropriate from the covariance model.
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.
Calculate seat apportionment for legislative bodies with 
    various methods. The algorithms include divisor or highest averages methods
    (e.g. Jefferson, Webster or Adams), largest remainder methods and 
    biproportional apportionment.
    Gaffke, N. & Pukelsheim, F. (2008) <doi:10.1016/j.mathsocsci.2008.01.004>
    Oelbermann, K. F. (2016) <doi:10.1016/j.mathsocsci.2016.02.003>.
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.
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 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>.
It offers a wide variety of techniques, such as graphics, recoding, or regression models, for a comprehensive analysis of patient-reported outcomes (PRO). Especially novel is the broad range of regression models based on the beta-binomial distribution useful for analyzing binomial data with over-dispersion in cross-sectional, longitudinal, or multidimensional response studies (see Najera-Zuloaga J., Lee D.-J. and Arostegui I. (2019) <doi:10.1002/bimj.201700251>).
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 for scoring PRO and 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.)
We provide comprehensive draft data for major professional sports leagues, including the National Football League (NFL), National Basketball Association (NBA), and National Hockey League (NHL). It offers access to both historical and current draft data, allowing for detailed analysis and research on player biases and player performance. The package is useful for sports fans and researchers interested in identifying biases and trends within scouting reports. Created by web scraping data from leading websites that cover professional sports player scouting reports, the package allows users to filter and summarize data for analytical purposes. For further details on the methods used, please refer to Wickham (2022) "rvest: Easily Harvest (Scrape) Web Pages" <https://CRAN.R-project.org/package=rvest> and Harrison (2023) "RSelenium: R Bindings for Selenium WebDriver" <https://CRAN.R-project.org/package=RSelenium>.
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/>.
Analysis of protein expression data can be done through Principal Component Analysis (PCA), and this R package is designed to streamline the analysis. This package enables users to perform PCA and it generates biplot and scree plot for advanced graphical visualization. Optionally, it supports grouping/clustering visualization with PCA loadings and confidence ellipses. With this R package, researchers can quickly explore complex protein datasets, interpret variance contributions, and visualize sample clustering through intuitive biplots. For more details, see Jolliffe (2001) <doi:10.1007/b98835>, Gabriel (1971) <doi:10.1093/biomet/58.3.453>, Zhang et al. (2024) <doi:10.1038/s41467-024-53239-9>, and Anandan et al. (2022) <doi:10.1038/s41598-022-07781-5>.
Bayesian toolbox for quantitative proteomics. In particular, this 
    package provides functions to generate synthetic datasets, execute Bayesian
    differential analysis methods, and display results as, described in the 
    associated article Marie Chion and Arthur Leroy (2023) <arXiv:2307.08975>.
Calculates the percentage coefficient of variation (CV) for mass spectrometry-based proteomic data. The CV can be calculated with the traditional formula for raw (non log transformed) intensity data, or log transformed data.
Seq2seq time-feature analysis based on variational model, with a wide range of distributions available for the latent variable.
Calculates a comprehensive list of features from profile hidden Markov models (HMMs) of proteins. 
    Adapts and ports features for use with HMMs instead of Position Specific Scoring Matrices, in order to take 
    advantage of more accurate multiple sequence alignment by programs such as 'HHBlits' Remmert et al. (2012) 
    <DOI:10.1038/nmeth.1818> and 'HMMer' Eddy (2011) <DOI:10.1371/journal.pcbi.1002195>. Features calculated by 
    this package can be used for protein fold classification, protein structural class prediction, 
    sub-cellular localization and protein-protein interaction, among other tasks. Some examples of features 
    extracted are found in Song et al. (2018) <DOI:10.3390/app8010089>, Jin & Zhu (2021) <DOI:10.1155/2021/8629776>, 
    Lyons et al. (2015) <DOI:10.1109/tnb.2015.2457906> and Saini et al. (2015) <DOI:10.1016/j.jtbi.2015.05.030>.
An object oriented system using object-based, also
	called prototype-based, rather than class-based object oriented ideas.
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.
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.
Shiny app to interactively visualize hierarchical clustering with prototypes. 
    For details on hierarchical clustering with prototypes, see 
    Bien and Tibshirani (2011) <doi:10.1198/jasa.2011.tm10183>. This package currently launches the application.
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/doc/blast-help/downloadblastdata.html>
    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.
'ProTracker' is a popular music tracker to sequence
    music on a Commodore Amiga machine. This package offers the
    opportunity to import, export, manipulate and play 'ProTracker'
    module files. Even though the file format could be considered
    archaic, it still remains popular to this date. This package
    intends to contribute to this popularity and therewith
    keeping the legacy of 'ProTracker' and the Commodore Amiga
    alive. This package is the successor of 'ProTrackR' providing
    better performance.
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, Wasserstein-2 or Sircombe-Hazelton L2 distances. Categorical provenance proxies such as chemical compositions are compared with the Aitchison and Bray-Curtis distances,and count data with the chi-square distance. Varietal data can either be converted to one or more distributional datasets, or directly compared using the multivariate Wasserstein distance. Also included are tools to plot compositional and count 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.
A simple package to grab a Bible proverb corresponding to the
    day of the month.
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 'rdtLite' or 'rdt' packages,
    or other tools providing compatible PROV JSON output, created by the
    execution of a script or a console session, and provides a human-readable
    summary identifying the input and output files, the scripts 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 PROV JSON file created by 'rdtLite' and 'rdt' 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 Lerner, Boose, and 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, Beta and Gamma 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>.
Implements optimization techniques for Lasso regression, R.Tibshirani(1996)<doi:10.1111/j.2517-6161.1996.tb02080.x> using Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) and Iterative Shrinkage-Thresholding Algorithm (ISTA) based on proximal operators, A.Beck(2009)<doi:10.1137/080716542>. The package is useful for high-dimensional regression problems and includes cross-validation procedures to select optimal penalty parameters.
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 various built-in similarity/distance measures, computation of correlation,
    cosine similarity, Dice coefficient 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. Computing dynamic swath window sizes based on MS1 or MS2 signal distributions.
Utilize the Bayesian prior and posterior predictive checking
   approach to provide a statistical assessment of replication success 
   and failure. The package is based on the methods proposed in 
   Zhao,Y., Wen X.(2021) <arXiv:2105.03993>.
Provides a function to convert 'PRQL' strings to 'SQL' strings.
    Combined with other R functions that take 'SQL' as an argument,
    'PRQL' can be used on R.
Reverse depends for a given package are queued such that multiple
 workers can run the reverse-dependency 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>.
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.
Implementation of Probabilistic Regression Trees (PRTree),
  providing functions for model fitting and prediction, with specific adaptations
  to handle missing values. The main computations are implemented in 'Fortran'
  for high efficiency. The package is based on the PRTree methodology described in
  Alkhoury et al. (2020), "Smooth and Consistent Probabilistic Regression Trees"
  <https://proceedings.neurips.cc/paper_files/paper/2020/file/8289889263db4a40463e3f358bb7c7a1-Paper.pdf>.
  Details on the treatment of missing data and implementation aspects are presented in
  Prass, T.S.; Neimaier, A.S.; Pumi, G. (2025), "Handling Missing Data in Probabilistic Regression Trees:
  Methods and Implementation in R" <doi:10.48550/arXiv.2510.03634>.
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'.
It is often advantageous to test a hypothesis more than once
    in the context of propensity score analysis (Rosenbaum, 2012)
    <doi:10.1093/biomet/ass032>. The functions in this package facilitate
    bootstrapping for propensity score analysis (PSA). By default,
    bootstrapping using two classification tree methods (using 'rpart' and
    'ctree' functions), two matching methods (using 'Matching' and
    'MatchIt' packages), and stratification with logistic regression.  A
    framework is described for users to implement additional propensity
    score methods.  Visualizations are emphasized for diagnosing balance;
    exploring the correlation relationships between bootstrap samples and
    methods; and to summarize results.
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.
Connects to the API of <https://pushshift.io/> to search for 'Reddit' comments and submissions.
Hybrid control design is a way to borrow information from external controls to augment concurrent controls in a randomized controlled trial and is expected to overcome the feasibility issue when adequate randomized controlled trials cannot be conducted. A major challenge in the hybrid control design is its inability to eliminate a prior-data conflict caused by systematic imbalances in measured or unmeasured confounding factors between patients in the concurrent treatment/control group and external controls. To prevent the prior-data conflict, a combined use of propensity score matching and Bayesian commensurate prior has been proposed in the context of hybrid control design. The propensity score matching is first performed to guarantee the balance in baseline characteristics, and then the Bayesian commensurate prior is constructed while discounting the information based on the similarity in outcomes between the concurrent and external controls. 'psBayesborrow' is a package to implement the propensity score matching and the Bayesian analysis with commensurate prior, as well as to conduct a simulation study to assess operating characteristics of the hybrid control design, where users can choose design parameters in flexible and straightforward ways depending on their own application.
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>).
Algorithms to speed up the Bayesian Lasso Cox model (Lee et al., Int J Biostat, 2011 <doi:10.2202/1557-4679.1301>) and the Bayesian Lasso Cox with mandatory variables (Zucknick et al. Biometrical J, 2015 <doi:10.1002/bimj.201400160>).
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.
Bayesian dynamic borrowing is an approach to incorporating external
   data to supplement a randomized, controlled trial analysis in which
   external data are incorporated in a dynamic way (e.g., based on similarity
   of outcomes); see Viele 2013 <doi:10.1002/pst.1589> for an overview.
   This package implements the hierarchical commensurate prior approach to dynamic borrowing
   as described in Hobbes 2011 <doi:10.1111/j.1541-0420.2011.01564.x>.
   There are three main functionalities. First, 'psborrow2' provides a user-friendly
   interface for applying dynamic borrowing on the study results handles the Markov Chain
   Monte Carlo sampling on behalf of the user. Second, 'psborrow2' provides a
   simulation framework to compare different borrowing parameters (e.g. full borrowing, no
   borrowing, dynamic borrowing) and other trial and borrowing characteristics
   (e.g. sample size, covariates) in a unified way. Third, 'psborrow2' provides
   a set of functions to generate data for simulation studies, and also allows
   the user to specify their own data generation process. This package is designed to
   use the sampling functions from 'cmdstanr' which can be installed from
   <https://stan-dev.r-universe.dev>.
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; 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 (MetSSS) and allostatic load (AL).
  Provides a wrapper to calculate the MetSSS on new data using the
  Healthy Hearts formula.
Provide estimation for particular cases of the power series cure rate model 
             <doi:10.1080/03610918.2011.639971>. For the distribution of the concurrent causes the 
             alternative models are the Poisson, logarithmic, negative binomial and Bernoulli (which 
             are includes in the original work), the polylogarithm model 
             <doi:10.1080/00949655.2018.1451850> and the Flory-Schulz <doi:10.3390/math10244643>. 
             The estimation procedure is based on the EM algorithm discussed in 
             <doi:10.1080/03610918.2016.1202276>.
             For the distribution of the time-to-event the alternative models are slash half-normal, 
             Weibull, gamma and Birnbaum-Saunders distributions.
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 (multivariate
    analyses).
Presentation of distributions such as: two-piece power normal (TPPN), plasticizing component (PC), DS normal (DSN), expnormal (EN), Sulewski plasticizing component (SPC), easily changeable kurtosis (ECK) distributions. Density, distribution function, quantile function and random generation are presented. For details on this method see: Sulewski (2019) <doi:10.1080/03610926.2019.1674871>, Sulewski (2021) <doi:10.1080/03610926.2020.1837881>, Sulewski (2021) <doi:10.1134/S1995080221120337>, Sulewski (2022) <"New members of the Johnson family of probability dis-tributions: properties and application">, Sulewski, Volodin (2022) <doi:10.1134/S1995080222110270>, Sulewski (2023) <doi:10.17713/ajs.v52i3.1434>.
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>.
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>.
Calculates the lexicogrammatical and functional features described
  by Biber (1985) <doi:10.1515/ling.1985.23.2.337> and widely used for
  text-type, register, and genre classification tasks.
A collection of easy-to-use tools for regression analysis of survival data with a cure fraction proposed in Su et al. (2022) <doi:10.1177/09622802221108579>. The modeling framework is based on the Cox proportional hazards mixture cure model and the bounded cumulative hazard (promotion time cure) model. The pseudo-observations approach is utilized to assess covariate effects and embedded in the variable selection procedure.
Given an arbitrary set of spatial regions and road networks, 
             generate a set of representative points, or pseudohouseholds, that
             can be used for travel burden analysis. Parallel processing is 
             supported.
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. See Happ et al. (2020, <doi:10.18637/jss.v095.c01>) for details.
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 (cluster) bootstrapping. The package 
	further contains functions to pool model performance measures as ROC/AUC, Reclassification, 
	R-squared, scaled Brier score, H&L test and calibration	plots for logistic regression models. 
	Internal validation can be done across multiply imputed datasets 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. For Cox models a strata variable
	can be included.
	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>.
Functions to generate ensembles of generalized linear models using
             a greedy projected subset gradient descent algorithm. The sparsity 
             and diversity tuning parameters are selected by cross-validation.
Presentation of a new goodness-of-fit normality test based on the Lilliefors method. For details on this method see: Sulewski (2019) <doi:10.1080/03610918.2019.1664580>.
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>).
Functions which facilitate harmonization of data from multiple
    different datasets. Data harmonization involves taking data sources with
    differing values, creating coding instructions to create a harmonized
    set of values, then making those data modifications. 'psHarmonize' will
    assist with data modification once the harmonization instructions are
    written. Coding instructions are written by the user to create a
    "harmonization sheet". This sheet catalogs variable names, domains
    (e.g. clinical, behavioral, outcomes), provides R code instructions for
    mapping or conversion of data, specifies the variable name in the
    harmonized data set, and tracks notes. The package will then harmonize
    the source datasets according to the harmonization sheet to create a
    harmonized dataset. Once harmonization is finished, the package also has
    functions that will create descriptive statistics using 'RMarkdown'. Data
    Harmonization guidelines have been described by Fortier I, Raina P,
    Van den Heuvel ER, et al. (2017) <doi:10.1093/ije/dyw075>. Additional
    details of our R package have been described by Stephen JJ, Carolan P,
    Krefman AE, et al. (2024) <doi:10.1016/j.patter.2024.101003>.
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). More information about 
    the PSID can be obtained at <https://simba.isr.umich.edu/data/data.aspx>.
Streamline the management, creation, and formatting of panel data from the Panel Study of Income Dynamics ('PSID') <https://psidonline.isr.umich.edu> using this user-friendly tool. Simply define variable names and input code book details directly from the 'PSID' official website, and this toolbox will efficiently facilitate the data preparation process, transforming raw 'PSID' files into a well-organized format ready for further analysis.
The Preference Selection Index Method was created in (2010) and provides an innovative approach to determining the relative importance of criteria without pairwise comparisons, unlike the Analytic Hierarchy Process. The Preference Selection Index Method uses statistical methods to calculate the criteria weights and reflects their relative importance in the final decision-making process, offering an objective and non-subjective solution. This method is beneficial in multi-criteria decision analysis. The 'PSIM' package provides a practical and accessible tool for implementing the Preference Selection Index Method in R. It calculates the weights of criteria and makes the method available to researchers, analysts, and professionals without the need to develop complex calculations manually. More details about the Preference Selection Index Method can be found in Maniya K. and Bhatt M. G.(2010) <doi:10.1016/j.matdes.2009.11.020>.
Presentation two independence tests for two-way, three-way and four-way contingency tables. These tests are: the modular test and the logarithmic minimum test. For details on this method see: Sulewski (2017) <doi:10.18778/0208-6018.330.04>, Sulewski (2018) <doi:10.1080/02664763.2018.1424122>, Sulewski (2019) <doi:10.2478/bile-2019-0003>, Sulewski (2021) <doi:10.1080/00949655.2021.1908286>.
Considering the singly imputed synthetic data generated via plug-in sampling under the multivariate normal model, draws inference procedures including the generalized variance, the sphericity test, the test for independence between two subsets of variables, and the test for the regression of one set of variables on the other. For more details see Klein et al. (2021) <doi:10.1007/s13571-019-00215-9>.
Data and statistics of Pakistan Social and Living Standards Measurement (PSLM) survey 2014-15 from Pakistan Bureau of Statistics (<http://www.pbs.gov.pk/>).
Fits and evaluates three-state partitioned survival analyses
    (PartSAs) and Markov models (clock forward or clock reset) to
    progression and overall survival data typically collected in oncology clinical trials. These model structures are typically considered in
    cost-effectiveness modeling in advanced/metastatic cancer indications.
    Muston (2024). "Informing structural assumptions for three state oncology cost-effectiveness models through model efficiency and fit". Applied Health Economics and Health Policy.
Fit penalized splines mixed-effects models (a special case of additive models) for large longitudinal datasets. The package includes a psme() function that (1) relies on package 'mgcv' for constructing population and subject smooth functions as penalized splines, (2) transforms the constructed additive model to a linear mixed-effects model, (3) exploits package 'lme4' for model estimation and (4) backtransforms the estimated linear mixed-effects model to the additive model for interpretation and visualization. See Pedersen et al. (2019) <doi:10.7717/peerj.6876> and Bates et al. (2015) <doi:10.18637/jss.v067.i01> for an introduction. Unlike the gamm() function in 'mgcv', the psme() function is fast and memory-efficient, able to handle datasets with millions of observations.
Compute detailed and aggregated performance spectrum for event data. The detailed performance spectrum describes the event data in terms of segments, where the performance of each segment is measured and plotted for any occurrences of this segment over time and can be classified, e.g., regarding the overall population. The aggregated performance spectrum visualises the amount of cases of particular performance over time. Denisov, V., Fahland, D., & van der Aalst, W. M. P. (2018) <doi:10.1007/978-3-319-98648-7_9>.
Provides an implementation of particle swarm optimisation consistent with the standard PSO 2007/2011 by Maurice Clerc. 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>.
Estimation and inference of spatial and spatio-temporal 
    semiparametric models including spatial or spatio-temporal non-parametric 
    trends, parametric and non-parametric covariates and, possibly, a spatial 
    lag for the dependent variable and temporal correlation in the noise.
    The spatio-temporal trend can be decomposed in ANOVA way including main and 
    interaction functional terms. Use of SAP algorithm to estimate the spatial 
    or spatio-temporal trend and non-parametric covariates. The methodology of 
    these models can be found in next references
    Basile, R. et al. (2014), <doi:10.1016/j.jedc.2014.06.011>;
    Rodriguez-Alvarez, M.X. et al. (2015) <doi:10.1007/s11222-014-9464-2> and,
    particularly referred to the focus of the package, Minguez, R., 
    Basile, R. and Durban, M. (2020) <doi:10.1007/s10260-019-00492-8>.
Spearman's rank correlation test with precomputed exact
        null distribution for n <= 22.
Smoothing splines with penalties on order m derivatives.
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>.
Sample size calculations in causal inference with observational data are increasingly desired. This package is a tool to calculate sample size under prespecified power with minimal summary quantities needed.
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>.
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, gamma, 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>.
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>.
Estimating causal effects in the presence of post-treatment confounding using principal stratification. 'PStrata' allows for customized monotonicity assumptions and exclusion restriction assumptions, with automatic full Bayesian inference supported by 'Stan'. The main function to use in this package is PStrata(), which provides posterior estimates of principal causal effect with uncertainty quantification. Visualization tools are also provided for diagnosis and interpretation. See Liu and Li (2023) <arXiv:2304.02740> for details.
A unified and user-friendly framework for applying the principal sufficient dimension reduction methods for both linear and nonlinear cases. The package has an extendable power by varying loss functions for the support vector machine, even for an user-defined arbitrary function, unless those are convex and differentiable everywhere over the support (Li et al. (2011) <doi:10.1214/11-AOS932>). Also, it provides a real-time sufficient dimension reduction update procedure using the principal least squares support vector machine (Artemiou et al. (2021) <doi:10.1016/j.patcog.2020.107768>).
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, reliability coefficient, parallel analysis, 
     multi-traits multi-methods, spherical representation of a correlation matrix.
Useful for preparing and cleaning data. It includes functions to center data, reverse coding, dummy code and effect code data, and more.
A general purpose toolbox developed originally 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.  Validation and cross validation of scales developed using basic machine learning algorithms are provided, as are functions for simulating and testing particular item and test structures. Several functions  serve as a useful front end for structural equation modeling.  Graphical displays of path diagrams, including mediation models, 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 Frick et al. (2012) <doi:10.18637/jss.v048.i07> and
  Frick et al. (2015) <doi:10.1177/0013164414536183> 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, trees for 1PL, 2PL, 3PL and 4PL models and generalized partial 
  credit models.
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>.
Support functions,  data sets, and vignettes for the 'psych' package. Contains several of the biggest data sets for the 'psych' package as well as four vignettes. A few helper functions for file manipulation are included as well. For more information, see the <https://personality-project.org/r/> web page.
An integrative toolbox of word embedding research that provides:
    (1) a collection of 'pre-trained' static word vectors in the '.RData'
    compressed format <https://psychbruce.github.io/WordVector_RData.pdf>;
    (2) a group of functions to process, analyze, and visualize word vectors;
    (3) a range of tests to examine conceptual associations, including
    the Word Embedding Association Test <doi:10.1126/science.aal4230>
    and the Relative Norm Distance <doi:10.1073/pnas.1720347115>,
    with permutation test of significance; and
    (4) a set of training methods to locally train (static) word vectors
    from text corpora, including 'Word2Vec' <doi:10.48550/arXiv.1301.3781>,
    'GloVe' <doi:10.3115/v1/D14-1162>, and 'FastText' <doi:10.48550/arXiv.1607.04606>.
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.
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>).
Tabular data from statistical institutes and agencies are mostly 
    confidential and must be protected prior to publications. The cell-key 
    method is a post-tabular Statistical Disclosure Control perturbation 
    technique that adds random noise to tabular data. The statistical 
    properties of the perturbations are defined by some noise probability 
    distributions - also referred to as perturbation tables. 
    This tool can be used to create the perturbation tables based on a maximum 
    entropy approach as described for example in Giessing (2016) 
    <doi:10.1007/978-3-319-45381-1_18>. The perturbation tables created can 
    finally be used to apply a cell-key method to frequency count or magnitude 
    tables.
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?
Evaluates the strength of a surrogate marker by estimating the proportion of treatment effect explained (PTE) and relative power(RP) for the optimally-transformed version of the surrogate.  Details available in Wang et al (2022) <arXiv:2209.08414>.
Generic code for estimating treatment effects with panel data.  The idea is to break into separate steps organizing the data, looping over groups and time periods, computing group-time average treatment effects, and aggregating group-time average treatment effects.  Often, one is able to implement a new identification/estimation procedure by simply replacing the step on estimating group-time average treatment effects.  See several different examples of this approach in the package documentation.
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.
Permute treatment labels for taxa and environmental gradients to
    generate an empirical distribution of change points.  This is an extension
    for the 'TITAN2' package <https://cran.r-project.org/package=TITAN2>.
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'. 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. 
    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. (2021, <doi:10.1177/1471082X20936017>).
    Likelihood approximation based on adaptive Gauss Hermite quadrature
    rule.
Functions used for analyzing count data, mostly crime counts. Includes checking difference in two Poisson counts (e-test), checking the fit for a Poisson distribution, small sample tests for counts in bins, Weighted Displacement Difference test (Wheeler and Ratcliffe, 2018) <doi:10.1186/s40163-018-0085-5>, to evaluate crime changes over time in treated/control areas. Additionally includes functions for aggregating spatial data and spatial feature engineering.
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".
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.
A collection of functions to simulate, estimate and forecast a wide range of regression based dynamic models for positive time series. This package implements the results presented in Prass, T.S.; Pumi, G.; Taufemback, C.G. and Carlos, J.H. (2025). "Positive time series regression models: theoretical and computational aspects". Computational Statistics 40, 1185–1215. <doi:10.1007/s00180-024-01531-z>.
Includes a collection of functions presented in "Measuring stability in ecological systems without static equilibria" by Clark et al. (2022) <doi:10.1002/ecs2.4328> in Ecosphere.
	These can be used to estimate the parameters of a stochastic state space model (i.e. a model where
	a time series is observed with error). The goal of this package is to estimate the variability
	around a deterministic process, both in terms of observation error - i.e. variability due to
	imperfect observations that does not influence system state - and in terms of process noise - i.e.
	stochastic variation in the actual state of the process. Unlike classical methods for estimating
	variability, this package does not necessarily assume that the deterministic state is fixed (i.e.
	a fixed-point equilibrium), meaning that variability around a dynamic trajectory can be estimated
	(e.g. stochastic fluctuations during predator-prey dynamics).
An implementation of an S3 class based on a double vector for storing and displaying precision teaching measures, representing a growing or a decaying (multiplicative) change between two frequencies. The main format method allows researchers to display measures (including data.frame) that respect the established conventions in the precision teaching community (i.e., prefixed multiplication or division symbol, displayed number <= 1). Basic multiplication and division methods are allowed and other useful functions are provided for creating, converting or inverting precision teaching measures. For more details, see Pennypacker, Gutierrez and Lindsley (2003, ISBN: 1-881317-13-7).
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.
All 'PubChem' compounds are downloaded to a local computer, but for each compound, only partial records are used.  The data are organized into small files referenced by 'PubChem' CID.  This package also contains functions to parse the biologically relevant compounds from all 'PubChem' compounds, using biological database sources, pathway presence, and taxonomic relationships. Taxonomy is used to generate a lowest common ancestor taxonomy ID (NCBI) for each biological metabolite, which then enables creation of taxonomically specific metabolome databases for any taxon.
Provides an interface to the 'PubChem' database via the PUG REST <https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest> and 
            PUG View <https://pubchem.ncbi.nlm.nih.gov/docs/pug-view> services. This package allows users to automatically 
            access chemical and biological data from 'PubChem', including compounds, substances, assays, and various other data types. 
            Functions are available to retrieve data in different formats, perform searches, and access detailed annotations.
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.
These are harmonized datasets produced as part of the Clinical 
    Trials Network (CTN) protocol number 0094. This is a US National Institute
    of Drug Abuse (NIDA) funded project; to learn more go to
    <https://ctnlibrary.org/protocol/ctn0094/>. These are datasets which have
    the data harmonized from CTN-0027 (<https://ctnlibrary.org/protocol/ctn0027/>),
    CTN-0030 (<https://ctnlibrary.org/protocol/ctn0030/>), and CTN-0051 
    (<https://ctnlibrary.org/protocol/ctn0051/>).
Engineered features and "helper" functions ancillary to the
    'public.ctn0094data' package, extending this package for ease of use (see 
    <https://CRAN.R-project.org/package=public.ctn0094data>). This
    'public.ctn0094data' package contains harmonized datasets from some of the
    National Institute of Drug Abuse's Clinical Trials Network (NIDA's CTN)
    projects. Specifically, the CTN-0094 project is to harmonize and de-identify
    clinical trials data from the CTN-0027, CTN-0030, and CTN-51 studies for
    opioid use disorder. This current version is built from 'public.ctn0094data'
    v. 1.0.6.
Performs sensitivity analysis for publication bias in meta-analyses
    (per Mathur & VanderWeele, 2020 [<doi:10.31219/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/>.
Provides various functions for retrieving and
    interpreting information from 'Pubmed' via the 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.
Provides function declarations and inline function definitions that
    facilitate cleaning strings in C++ code before passing them to R.
Simulate and run the Gaussian puff forward atmospheric
    model in sensor (specific sensor coordinates) or grid (across the
    grid of a full oil and gas operations site) modes, following Jia, M., 
    Fish, R., Daniels, W., Sprinkle, B. and Hammerling, D. (2024) 
    <doi:10.26434/chemrxiv-2023-hc95q-v3>. Numerous visualization options, 
    including static and animated, 2D and 3D, and a site map generator 
    based on sensor and source coordinates.
Parsimonious Ultrametric Gaussian Mixture Models via grouped coordinate ascent (equivalent to EM) algorithm characterized by the inspection of hierarchical relationships among variables via parsimonious extended ultrametric covariance structures. The methodologies are described in Cavicchia, Vichi, Zaccaria (2024) <doi:10.1007/s11222-024-10405-9>, (2022) <doi:10.1007/s11634-021-00488-x> and (2020) <doi:10.1007/s11634-020-00400-z>.
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/>.
Provides a comprehensive and curated collection of datasets related to the lungs, respiratory system, and associated diseases. 
    This package includes epidemiological, clinical, experimental, and simulated datasets on conditions such as lung cancer, asthma, 
    Chronic Obstructive Pulmonary Disease (COPD), tuberculosis, whooping cough, pneumonia, influenza, and other respiratory illnesses. 
    It is designed to support data exploration, statistical modeling, teaching, and research in pulmonary medicine, public health, 
    environmental epidemiology, and respiratory disease surveillance.
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>.
Bayesian estimation and analysis methods for Probit Unfolding Models (PUMs), a novel class of scaling models designed for binary preference data. These models allow for both monotonic and non-monotonic response functions. The package supports Bayesian inference for both static and dynamic PUMs using Markov chain Monte Carlo (MCMC) algorithms with minimal or no tuning. Key functionalities include posterior sampling, hyperparameter selection, data preprocessing, model fit evaluation, and visualization. The methods are particularly suited to analyzing voting data, such as from the U.S. Congress or Supreme Court, but can also be applied in other contexts where non-monotonic responses are expected. For methodological details, see Shi et al. (2025) <doi:10.48550/arXiv.2504.00423>.
R package to query and get data out of a Pumilio sound archive system (http://ljvillanueva.github.io/pumilio/).
Estimates power, minimum detectable effect size (MDES) and sample size requirements. The context is multilevel randomized experiments with multiple outcomes. The estimation takes into account the use of multiple testing procedures. Development of this package was supported by a grant from the Institute of Education Sciences (R305D170030). For a full package description, including a detailed technical appendix, see <doi:10.18637/jss.v108.i06>.
Contains model fitting functions for linear and non-linear adsorption kinetic and diffusion models. Adsorption kinetics is used for characterizing the rate of solute adsorption and the time necessary for the adsorption process. Adsorption kinetics offers vital information on adsorption rate, adsorbent performance in response time, and mass transfer processes. In addition, diffusion models are included in the package as solute diffusion affects the adsorption kinetic experiments. This package consists of 20 adsorption and diffusion models, including Pseudo First Order (PFO), Pseudo Second Order (PSO), Elovich, and Weber-Morris model (commonly called the intraparticle model) stated by Plazinski et al. (2009) <doi:10.1016/j.cis.2009.07.009>. This package also contains a summary function where the statistical errors of each model are ranked for a more straightforward determination of the best fit model.
Provides a unified pipeline to clean, prepare, plot,
    and run basic analyses on pupillometry experiments.
Image-based color matching using the "Mycological Colour Chart" by Rayner (1970, ISBN:9780851980263) and its associated fungal pigments. This package will assist mycologists in identifying color during morphological analysis.
Contains sixteen moisture sorption isotherm models, which evaluate the fitness of adsorption and desorption curves for further understanding of the relationship between moisture content and water activity. Fitness evaluation is conducted through parameter estimation and error analysis. Moreover, graphical representation, hysteresis area estimation, and isotherm classification through the equation of Blahovec & Yanniotis (2009) <doi:10.1016/j.jfoodeng.2008.08.007> which is based on the classification system introduced by Brunauer et. al. (1940) <doi:10.1021/ja01864a025> are also included for the visualization of models and hysteresis.
Access a variety of 'PubMed' data through a single, user-friendly interface, including abstracts <https://pubmed.ncbi.nlm.nih.gov/>, bibliometrics from 'iCite' <https://icite.od.nih.gov/>, pubtations from 'PubTator3' <https://www.ncbi.nlm.nih.gov/research/pubtator3/>, and full-text records from 'PMC' <https://www.ncbi.nlm.nih.gov/pmc/>.
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>.
Send requests to the 'PurpleAir' Application Programming Interface (API; <https://community.purpleair.com/c/data/api/18>). Check a 'PurpleAir' API key and get information about the related organization. Download real-time data from a single 'PurpleAir' sensor or many sensors by sensor identifier, geographical bounding box, or time since modified. Download historical data from a single sensor. Stream real time data from monitors on a local area network.
Provides tools for retrieving and analyzing air quality data
    from 'PurpleAir' sensors through their API. Functions enable downloading
    historical measurements, accessing sensor metadata, and managing API
    request limitations through chunked data retrieval. For more information 
    about the 'PurpleAir' API, see <https://api.purpleair.com/>.
A complete and consistent functional programming toolkit for
    R.
Some functions at the intersection of 'dplyr' and
    'purrr' that formerly lived in 'purrr'.
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' <https://pushover.net>. These notifications can
    display things such as results, job status, plots, or any other text
    or numeric data.
Provides tools for extracting and processing structured annotations from 'R' 
    and 'Python' source files to facilitate workflow visualization. The package scans 
    source files for special 'PUT' annotations that define nodes, connections, and 
    metadata within a data processing workflow. These annotations can then be used 
    to generate visual representations of data flows and processing steps across 
    polyglot software environments. Builds on concepts from literate programming 
    Knuth (1984) <doi:10.1093/comjnl/27.2.97> and utilizes directed acyclic graph (DAG) 
    theory for workflow representation Foraita, Spallek, and Zeeb (2014) 
    <doi:10.1007/978-0-387-09834-0_65>. Diagram generation powered by 'Mermaid' 
    Sveidqvist (2014) <https://mermaid.js.org/>.
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.
Plot malaria parasite genetic data on two or more episodes. 
    Compute per-person posterior probabilities that each
    Plasmodium vivax (Pv) recurrence is a recrudescence, relapse,
    or reinfection (3Rs) using per-person P. vivax genetic data on two or
    more episodes and a statistical model described in 
    Taylor, Foo and White (2022) <doi:10.1101/2022.11.23.22282669>. 
    Plot per-recurrence posterior probabilities.
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.
Implements (1) panel cointegration rank tests, (2) estimators for panel
    vector autoregressive (VAR) models, and (3) identification methods for panel
    structural vector autoregressive (SVAR) models as described in the accompanying vignette.
    The implemented functions allow to account for cross-sectional dependence
    and for structural breaks in the deterministic terms of the VAR processes.
    Among the large set of functions, particularly noteworthy are those that implement
    (1) the correlation-augmented inverse normal test on the cointegration rank
    by Arsova and Oersal (2021, <doi:10.1016/j.ecosta.2020.05.002>),
    (2) the two-step estimator for pooled cointegrating vectors
    by Breitung (2005, <doi:10.1081/ETC-200067895>), and
    (3) the pooled identification based on independent component analysis
    by Herwartz and Wang (2024, <doi:10.1002/jae.3044>).
Performs partial verification bias (PVB) correction for binary 
    diagnostic tests, where PVB arises from selective patient verification in 
    diagnostic accuracy studies. Supports correction of important accuracy
    measures -- sensitivity, specificity, positive predictive values and 
    negative predictive value -- under missing-at-random and 
    missing-not-at-random missing data mechanisms. Available methods and 
    references are "Begg and Greenes' methods" in Alonzo & Pepe (2005) 
    <doi:10.1111/j.1467-9876.2005.00477.x> and deGroot et al. (2011) 
    <doi:10.1016/j.annepidem.2010.10.004>; "Multiple imputation" in 
    Harel & Zhou (2006) <doi:10.1002/sim.2494>, "EM-based logistic regression" 
    in Kosinski & Barnhart (2003) <doi:10.1111/1541-0420.00019>; 
    "Inverse probability weighting" in Alonzo & Pepe (2005) 
    <doi:10.1111/j.1467-9876.2005.00477.x>; "Inverse probability bootstrap 
    sampling" in Nahorniak et al. (2015) <doi:10.1371/journal.pone.0131765> 
    and Arifin & Yusof (2022) <doi:10.3390/diagnostics12112839>; 
    "Scaled inverse probability resampling methods" in Arifin & Yusof (2025) 
    <doi:10.1371/journal.pone.0321440>.
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>.
Tools for performing disproportionality analysis using the information component, proportional reporting rate and the reporting odds ratio. The anticipated use is passing data to the da() function, which executes the disproportionality analysis. See Norén et al (2011) <doi:10.1177/0962280211403604> and Montastruc et al (2011) <doi:10.1111/j.1365-2125.2011.04037.x> for further details.
A suite of empirical Bayes methods to use in pharmacovigilance. Contains various model fitting and post-processing functions. For more details see Tan et al. (2025) <doi:10.48550/arXiv.2502.09816>, Koenker and Mizera (2014) <doi:10.1080/01621459.2013.869224> and Efron (2016) <doi:10.1093/biomet/asv068>.
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>.
A suite of likelihood ratio test based methods to use in pharmacovigilance. Contains various testing and post-processing functions.
Estimates (and controls for) phylogenetic signal through phylogenetic eigenvectors regression (PVR) and phylogenetic signal-representation (PSR) curve, along with some plot utilities.
Speeds up the process of loading raw data from MBA (Multiplex Bead Assay) examinations, performs quality control checks, and automatically normalises the data, preparing it for more advanced, downstream tasks. The main objective of the package is to create a simple environment for a user, who does not necessarily have experience with R language. The package is developed within the project of the same name - 'PvSTATEM', which is an international project aiming for malaria elimination.
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.
Efficient algorithm for estimating piecewise exponential hazard models for right-censored data, and is useful for reliable power calculation, study design, and event/timeline prediction for study monitoring.
Price volatility refers to the degree of variation in series over a certain period of time. This volatility is especially noticeable in agricultural commodities, adding uncertainty for farmers, traders, and others in the agricultural supply chain. Commonly and popularly used four volatility models viz, GARCH, Glosten Jagannatan Runkle-GARCH (GJR-GARCH) model, exponentially weighted moving average (EWMA) model and Multiplicative Error Model (MEM) are selected and implemented. PWAVE, weighted ensemble model based on particle swarm optimization (PSO) is proposed to combine the forecast obtained from all the candidate models. This package has been developed using algorithm of Paul et al. <doi:10.1007/s40009-023-01218-x> and Yeasin and Paul (2024) <doi:10.1007/s11227-023-05542-3>.
Build piecewise exponential survival model for study design (planning) and event/timeline prediction.
A function 'PWI()' that calculates prize winner indices based on bibliometric data is provided. The default is the 'Derek de Solla Price Memorial Medal'. Users can provide recipients of other prizes.
Estimates two-level multilevel linear model and two-level multivariate linear multilevel model with weights following Probability Weighted Iterative Generalised Least Squares approach. For details see Veiga et al.(2014) <doi:10.1111/rssc.12020>.
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>.
Provides tools for calculating statistical power for experiments 
    analyzed using linear mixed models. It supports standard designs, including 
    randomized block, split-plot, and Latin Square designs, while offering flexibility 
    to accommodate a variety of other complex study designs.
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.
Provides functions for conducting power analysis in ANOVA designs, including between-, within-, and mixed-factor designs, with full support for both main effects and interactions. The package allows calculation of statistical power, required total sample size, significance level, and minimal detectable effect sizes expressed as partial eta squared or Cohen's f for ANOVA terms and planned contrasts. In addition, complementary functions are included for common related tests such as t-tests and correlation tests, making the package a convenient toolkit for power analysis in experimental psychology and related fields.
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 'pwrss' R package provides flexible and comprehensive functions for
  statistical power and minimum required sample size calculations across a wide
  range of commonly used hypothesis tests in psychological, biomedical, and
  social sciences.
Conduct dsep tests (piecewise SEM) of a directed, or mixed, acyclic graph 
    without latent variables (but possibly with implicitly marginalized or
    conditioned latent variables that create dependent errors) based on
    linear, generalized linear, or additive modelswith or without a
    nesting structure for the data. Also included are functions to do desp tests
    step-by-step,exploratory path analysis, and Monte Carlo X2 probabilities.
    This package accompanies Shipley, B, (2026).Cause and Correlation in Biology:
     A User's Guide to Path Analysis, StructuralEquations and
     Causal Inference (3rd edition).  Cambridge University Press.
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 (<https://www.rug.nl/ggdc/productivity/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.
Create PX-files from scratch or read and modify existing ones. 
    Includes a function for every PX keyword, making metadata manipulation
    simple and human-readable.
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. 
 Methods are described in Tobler Waldo R. (1979) <doi:10.1080/01621459.1979.10481647>.
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.
Provides access to the latest 'Amazon Mechanical Turk' ('MTurk') <https://www.mturk.com> Requester API (version '2017–01–17'), replacing the now deprecated 'MTurkR' package.
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'.
It enables 'sparklyr' to integrate with 'Spark Connect', and
    'Databricks Connect' by providing a wrapper over the 'PySpark' 'python'
    library.
'Google Trends' provides cross-sectional and time-series data on searches, but lacks readily available 
    longitudinal data. Researchers, who want to create longitudinal 'Google Trends' on their own, face practical challenges, such as normalized counts that make it difficult to combine 
    cross-sectional and time-series data and limitations in data formats and timelines that limit data 
    granularity over extended time periods. 
    This package addresses these issues and enables researchers to generate longitudinal 'Google Trends' data.  
    This package is built on 'pytrends', a Python library that acts as the unofficial 'Google Trends API' to collect 'Google Trends' data. As long as the 'Google Trends API', 'pytrends' and all their dependencies are working, this package will work.
    During testing, we noticed that for the same input (keyword, topic, data_format, timeline), the output index can vary from time to time. Besides, if the keyword is not very popular, then the resulting dataset will contain a lot of zeros, which will greatly affect the final result. While this package has no control over the accuracy or quality of 'Google Trends' data, once the data is created, this package coverts it to longitudinal data.  
    In addition, the user may encounter a 429 Too Many Requests error when using cross_section() and time_series() to collect 'Google Trends' data. This error indicates that the user has exceeded the rate limits set by the 'Google Trends API'. For more information about the 'Google Trends API' - 'pytrends', visit <https://pypi.org/project/pytrends/>.
Read and write 'GraphPad Prism' '.pzfx' files in R.
Mortality rates are typically provided in an abridged format, i.e., by age groups 0, [1, 5], [5, 10]', '[10, 15]', and so on. Some applications necessitate a detailed (single) age description. Despite the large number of proposed approaches in the literature, only a few methods ensure great performance at both younger and higher ages. For example, the 6-term 'Lagrange' interpolation function is well suited to mortality interpolation at younger ages (with irregular intervals), but not at older ages. The 'Karup-King' method, on the other hand, performs well at older ages but is not suitable for younger ones. Interested readers can find a full discussion of the two stated methods in the book Shryock, Siegel, and Associates (1993).The Q2q package combines the two methods to allow for the interpolation of mortality rates across all age groups. It begins by implementing each method independently, and then the resulting curves are linked using a 5-age averaged error between the two partial 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.
Functions for descriptive statistics,
  data management, and data visualization.
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>).
The Ensemble Quadratic and Affine Invariant Markov chain Monte 
    Carlo algorithms provide an efficient way to perform Bayesian inference in 
    difficult parameter space geometries. The Ensemble Quadratic Monte Carlo 
    algorithm was developed by Militzer (2023) <doi:10.3847/1538-4357/ace1f1>. 
    The Ensemble Affine Invariant algorithm was developed by Goodman and Weare 
    (2010) <doi:10.2140/camcos.2010.5.65> and it was implemented in Python by 
    Foreman-Mackey et al (2013) <doi:10.48550/arXiv.1202.3665>. The Quadratic 
    Monte Carlo method was shown to perform better than the Affine Invariant 
    method in the paper by Militzer (2023) <doi:10.3847/1538-4357/ace1f1> and 
    the Quadratic Monte Carlo method is the default method used. The Chen-Shao 
    Highest Posterior Density Estimation algorithm is used for obtaining 
    credible intervals and the potential scale reduction factor diagnostic is 
    used for checking the convergence of the chains.
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). Although, the QAP was introduced as a combinatorial optimization problem for the facility location problem in operations research, it also has many applications in data analysis. The problem is NP-hard and the package implements a simulated annealing heuristic.
Estimates QAPE using bootstrap procedures. The residual, parametric and double bootstrap is used. The test of normality using Cholesky decomposition is added. Y pop is defined.
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>.
Fuel economy, size, performance, and price data for cars in Qatar in 2025. Mirrors many of the columns in mtcars, but uses (1) non-US-centric makes and models, (2) 2025 prices, and (3) metric measurements, making it more appropriate for use as an example dataset outside the United States.
Create quantile binned and conditional plots for Exploratory Data
  Analysis. The package provides several plotting functions that are all based
  on quantile binning. The plots are created with 'ggplot2' and 'patchwork' 
  and can be further adjusted.
Implements the Bayesian quantile regression model for binary longitudinal data 
             (QBLD) developed in Rahman and Vossmeyer (2019) <DOI:10.1108/S0731-90532019000040B009>.
             The model handles both fixed and random effects and implements both a blocked
             and an unblocked Gibbs sampler for posterior inference.
This R package assists breeders in linking data systems with their analytic pipelines, 
    a crucial step in digitizing breeding processes. It supports querying and retrieving 
    phenotypic and genotypic data from systems like 'EBS' <https://ebs.excellenceinbreeding.org/>, 
    'BMS' <https://bmspro.io>, 'BreedBase' <https://breedbase.org>,
    'GIGWA' <https://github.com/SouthGreenPlatform/Gigwa2> (using 'BrAPI' <https://brapi.org> calls),
    , and 'Germinate' <https://germinateplatform.github.io/get-germinate/>. 
    Extra helper functions support environmental data sources, including 
    'TerraClimate' <https://www.climatologylab.org/terraclimate.html> and 'FAO' 
    'HWSDv2' <https://gaez.fao.org/pages/hwsd> soil database.
Programmatically access the 'Quickbase' JSON API <https://developer.quickbase.com>. 
    You supply parameters for an API call, 'qbr' delivers an http request to the 
    API endpoint and returns its response. Outputs follow 'tidyverse' philosophy.
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 minimization
       algorithm, resulting in a minimal causal configuration associated
       with a given phenomenon.
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.
Quasi-Cauchy quantile regression, proposed by de Oliveira, Ospina, Leiva, Figueroa-Zuniga and Castro (2023) <doi:10.3390/fractalfract7090667>. This regression model is useful for the case where you want to model data of a nature limited to the intervals [0,1], (0,1], [0,1) or (0,1) and you want to use a quantile approach.
Implements the Quantitative Classification-based on
    Association Rules (QCBA)  algorithm (<doi:10.1007/s10489-022-04370-x>). 
    QCBA postprocesses rule classification models making them typically smaller and in some cases more accurate. 
    Supported are 'CBA' implementations from 'rCBA', 'arulesCBA' and 'arc' packages, and 'CPAR', 'CMAR', 'FOIL2' and 'PRM' implementations 
    from 'arulesCBA' package and 'SBRL' implementation from the 'sbrl' package. The result of the post-processing is an ordered CBA-like rule list.
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 Q sets of
    p-values obtained for the same list of items. This joint analysis is
    performed by querying a composite hypothesis, i.e. an arbitrary
    complex combination of simple hypotheses, as described in Mary-Huard
    et al. (2021) <doi:10.1093/bioinformatics/btab592> and De Walsche et
    al.(2023) <doi:10.1101/2024.03.17.585412>. In this approach, the
    Q-uplet of p-values associated with each item is distributed as a
    multivariate mixture, where each of the 2^Q components corresponds to
    a specific combination of simple hypotheses. The dependence between
    the p-value series is considered using a Gaussian copula function. A
    p-value for the composite hypothesis test is derived from the
    posterior probabilities.
Performs tuning of clustering models, methods and algorithms including the problem of determining an appropriate number of clusters. Validation of cluster analysis results is performed via quadratic scoring using resampling methods, as in Coraggio, L. and Coretto, P. (2023) <doi:10.1016/j.jmva.2023.105181>.
Implements the Quantile Composite-based Path Modeling approach 
	(Davino and Vinzi, 2016 <doi:10.1007/s11634-015-0231-9>; 
	 Dolce et al., 2021 <doi:10.1007/s11634-021-00469-0>). The method complements the traditional PLS Path Modeling approach, analyzing the entire distribution of outcome variables and, therefore, overcoming the classical exploration of only average effects. It exploits quantile regression to investigate changes in the relationships among constructs and between constructs and observed variables.
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. See Flores et al. (2021) <doi:10.32614/RJ-2021-034>.
An R implementation of quality control–based robust LOESS(local
    polynomial regression fitting) signal correction for metabolomics data
    analysis, described in Dunn, W., Broadhurst, D., Begley, P. et al. (2011)
    <doi:10.1038/nprot.2011.335>. The optimisation of LOESS's span parameter
    using generalized cross-validation (GCV) is provided as an option. In
    addition to signal correction, 'qcrlscR' includes some utility functions
    like batch shifting and data filtering.
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 letters 'qe' in the package title stand for "quick and
   easy," alluding to the convenience goal of the package. We bring
   together a variety of machine learning (ML) tools from standard R
   packages, providing wrappers with a simple, convenient, 
   and uniform interface.
Quantile-based estimators (Q-estimators) can be used to fit any parametric distribution, using its quantile function. Q-estimators are usually more robust than standard maximum likelihood estimators. The method is described in: Sottile G. and Frumento P. (2022). Robust estimation and regression with parametric quantile functions. <doi:10.1016/j.csda.2022.107471>.
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.
Quantile-frequency analysis (QFA) of time series based on trigonometric quantile regression. 
 Spline quantile regression (SQR) for regression coefficient estimation.
 References:
    [1] Li, T.-H. (2012) "Quantile periodograms," Journal of the American Statistical
        Association, 107, 765–776, <doi:10.1080/01621459.2012.682815>.
    [2] Li, T.-H. (2014) Time Series with Mixed Spectra, CRC Press, <doi:10.1201/b15154>
    [3] Li, T.-H. (2022) "Quantile Fourier transform, quantile series, and nonparametric
        estimation of quantile spectra," <doi:10.48550/arXiv.2211.05844>.
    [4] Li, T.-H. (2024) "Quantile crossing spectrum and spline autoregression
         estimation," <doi:10.48550/arXiv.2412.02513>.
    [5] Li, T.-H. (2024) "Spline autoregression method for estimation of quantile spectrum,"
        <doi:10.48550/arXiv.2412.17163>.
    [6] Li, T.-H., and Megiddo, N. (2025) "Spline quantile regression," 
        <doi:10.48550/arXiv.2501.03883>.
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.
Evaluates moments of ratios (and products) of quadratic forms
    in normal variables, specifically using recursive algorithms developed by
    Bao and Kan (2013) <doi:10.1016/j.jmva.2013.03.002> and Hillier et al.
    (2014) <doi:10.1017/S0266466613000364>. Also provides distribution,
    quantile, and probability density functions of simple ratios of quadratic
    forms in normal variables with several algorithms. Originally developed as
    a supplement to Watanabe (2023) <doi:10.1007/s00285-023-01930-8> for
    evaluating average evolvability measures in evolutionary quantitative
    genetics, but can be used for a broader class of statistics. Generating 
    functions for these moments are also closely related to the top-order zonal
    and invariant polynomials of matrix arguments.
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.
Function that implements the Quantum Genetic Algorithm, first proposed by Han and Kim in 2000. This is an R implementation of the 'python' application developed by Lahoz-Beltra  (<https://github.com/ResearchCodesHub/QuantumGeneticAlgorithms>). Each optimization problem is represented as a maximization one, where each solution is a sequence of (qu)bits. Following the quantum paradigm, these qubits are in a superposition state: when measuring them, they collapse in a 0 or 1 state. After measurement, the fitness of the solution is calculated as in usual genetic algorithms. The evolution at each iteration is oriented by the application of two quantum gates to the amplitudes of the qubits: (1) a rotation gate (always); (2) a Pauli-X gate (optionally). The rotation is based on the theta angle values: higher values allow a quicker evolution, and lower values avoid local maxima. The Pauli-X gate is equivalent to the classical mutation operator and determines the swap between alfa and beta amplitudes of a given qubit. The package has been developed in such a way as to permit a complete separation between the engine, and the particular problem subject to combinatorial optimization.
Smooth additive quantile regression models, fitted using
    the methods of Fasiolo et al. (2020) <doi:10.1080/01621459.2020.1725521>.
    See Fasiolo at al. (2021) <doi:10.18637/jss.v100.i09> for an introduction to the package. 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.
Provides seamless access to the 'QGIS'
    (<https://qgis.org>) processing toolbox using the standalone
    'qgis_process' command-line utility.  Both native and third-party
    (plugin) processing providers are supported.  Beside referring data
    sources from file, also common objects from 'sf', 'terra' and 'stars'
    are supported. The native processing algorithms are documented by QGIS.org 
    (2024) <https://docs.qgis.org/latest/en/docs/user_manual/processing_algs/>.
Fork of qgraph - 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).
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.
Nomograms are constructed to predict the cumulative incidence
    rate which is calculated after adjusting for competing causes to the event of interest. 
    K-fold cross-validation is implemented to validate predictive accuracy using a competing-risk version of the concordance index.
    Methods are as described in: Kattan MW, Heller G, 
    Brennan MF (2003).
The quantity-intensity (Q/I) relationships, first introduced by Beckett
  (1964), can be employed to assess the K supplying capacity of different soils 
  based on solid-solution exchange equilibria. Such relationships describe the 
  changes in K+ concentration in the soil solution (or the intensity factor) in 
  relation to the corresponding changes in K+ at exchange sites of the soil 
  (or the capacity or quantity factor). Activity ratio of K to Ca or Ca+Mg 
  is generally used as the variable denoting the intensity, whereas, change 
  in exchangeable K is used to denote the quantity factor.
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.
Select optimal functional regression or dichotomized
       quantile predictors for survival/logistic/numeric outcome
       and perform optimistic bias correction for any optimally
       dichotomized numeric predictor(s), as in Yi, et. al.
       (2023) <doi:10.1016/j.labinv.2023.100158>.
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.
'QuantLib' bindings are provided for R using 'Rcpp' via an evolved version
 of the initial header-only 'Quantuccia' project offering an subset of 'QuantLib' (now
 maintained separately just for the calendaring subset). See the included file 'AUTHORS'
 for a full list of contributors to 'QuantLib' (and hence also 'Quantuccia').
Functionality to read, recode, and transcode data as used in 
  quantitative language comparison, specifically to deal with multilingual 
  orthographic variation (Moran & Cysouw (2018) <doi:10.5281/zenodo.1296780>) 
  and with the recoding of nominal data.
Extension of the functionality of the 'Matrix' package for using sparse matrices. Some of the functions are very general, while other are highly specific for special data format as used for quantitative language comparison.
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>.
Manages, builds and computes statistics and datasets for the 
   construction of quarterly (sub-annual) life tables by exploiting micro-data from
   either a general or an insured population.
   References: 
   Pavía and Lledó (2022) <doi:10.1111/rssa.12769>.
   Pavía and Lledó (2023) <doi:10.1017/asb.2023.16>.
   Pavía and Lledó (2025) <doi:10.1371/journal.pone.0315937>.
   Acknowledgements:
   The authors wish to thank Conselleria de Educación, Universidades y Empleo, Generalitat Valenciana (grants AICO/2021/257; CIAICO/2024/031), Ministerio de Ciencia e Innovación (grant PID2021-128228NB-I00) and Fundación Mapfre (grant 'Modelización espacial e intra-anual de la mortalidad en España. Una herramienta automática para el calculo de productos de vida') for supporting this research.
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 (2022), On a multivariate copula-based dependence measure and its estimation, Electronic Journal of Statistics, 16, 2206-2251.
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 graphical user interface (GUI) for online and offline use.
Produces quality scores for each of the US companies from 
    the Russell 3000, following the approach described in 
    "Quality Minus Junk" (Asness, Frazzini, & Pedersen, 2013) 
    <http://www.aqr.com/library/working-papers/quality-minus-junk>. 
    The package includes datasets for users who wish to view the most recently 
    uploaded quality scores. It also provides tools to automatically gather 
    relevant financials and stock price information, allowing users to update 
    their data and customize their universe for further analysis.
Implementation of methods for estimation of quantitative maps
    from Multi-Parameter Mapping (MPM) acquisitions (Weiskopf et al. (2013)
    <doi:10.3389/fnins.2013.00095>) and analysis of Inversion Recovery MRI data.
    Usage of the package is described in
    Polzehl and Tabelow (2023),
    "Magnetic Resonance Brain Imaging", 2nd Edition, Chapter 6 and 7, Springer, Use R! Series.
    <doi:10.1007/978-3-031-38949-8>.
    J. Polzehl and K. Tabelow (2023), "Magnetic Resonance Brain Imaging - Modeling and Data Analysis Using R: Code and Data."
    <doi:10.20347/WIAS.DATA.6> provides extensive example code and data.
Basic functions for building parsers, with an application to PC-AXIS format files.
The queueing model of visual search models the accuracy and response time data in a visual search experiment using queueing models with finite customer population and stopping criteria of completing the service for finite number of customers. It implements the conceptualization of a hybrid model proposed by Moore and Wolfe (2001), in which visual stimuli enter the processing one after the other and then are identified in parallel. This package provides functions that simulate the specified queueing process and calculate the Wasserstein distance between the empirical response times and the model prediction.
The new QOI file format offers a very simple but efficient image compression algorithm. This package provides an easy and simple way to read, write and display bitmap images stored in the QOI (Quite Ok Image) format. It can read and write both files and in-memory raw vectors.
The main goal is to make descriptive evaluations easier to create bigger and more complex outputs in less time with less code. Introducing format containers with multilabels <https://documentation.sas.com/doc/en/pgmsascdc/v_067/proc/p06ciqes4eaqo6n0zyqtz9p21nfb.htm>, a more powerful summarise which is capable to output every possible combination of the provided grouping variables in one go <https://documentation.sas.com/doc/en/pgmsascdc/v_067/proc/p0jvbbqkt0gs2cn1lo4zndbqs1pe.htm>, tabulation functions which can create any table in different styles <https://documentation.sas.com/doc/en/pgmsascdc/v_067/proc/n1ql5xnu0k3kdtn11gwa5hc7u435.htm> and other more readable functions. The code is optimized to work fast even with datasets of over a million observations.
There are three functions: qol, miss_qol and miss_patient takes input of the data set containing the answers of QOL questionnaire. It will compute the three types of domain based scale scores: Global, Functional, and Symptoms. In case of missing data, the miss_qol and miss_patient functions will make the required changes and then calculate the domain-wise scale scores. Finally, provide an output replacing the question columns with the domain-based scale scores in the original data set.
Methods for statistical analysis of count data and quantal data.
    For the analysis of count data an implementation of the Closure Principle Computational Approach Test ("CPCAT") is provided (Lehmann, R et al. (2016) <doi:10.1007/s00477-015-1079-4>), as well as an implementation of a "Dunnett GLM" approach using a Quasi-Poisson regression (Hothorn, L, Kluxen, F (2020) <doi:10.1101/2020.01.15.907881>).
    For the analysis of quantal data an implementation of the Closure Principle Fisher–Freeman–Halton test ("CPFISH") is provided (Lehmann, R et al. (2018) <doi:10.1007/s00477-017-1392-1>). P-values and no/lowest observed (adverse) effect concentration values are calculated.
    All implemented methods include further functions to evaluate the power and the minimum detectable difference using a bootstrapping approach.
Sigmoidal model fitting, optimal model selection and calculation of various features that are essential in the analysis of quantitative real-time polymerase chain reaction (qPCR), according to Ritz & Spiess (2008) <doi:10.1093/bioinformatics/btn227> and Spiess et al. (2008) <doi:10.1186/1471-2105-9-221>.
Computes normalized cycle threshold (Ct) values (delta Ct) from raw quantitative polymerase 
    chain reaction (qPCR) Ct values and conducts test of significance using t.test(). Plots expression 
    values based from log2(2^(-1*delta delta Ct)) across groups per gene of interest. Methods for calculation
    of delta delta Ct and relative expression (2^(-1*delta delta Ct)) values are described in: 
    Livak & Schmittgen, (2001) <doi:10.1006/meth.2001.1262>.
Content-preserving transformations transformations of PDF files such 
    as split, combine, and compress. This package interfaces directly to the 'qpdf' 
    C++ library <https://qpdf.sourceforge.io/> 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.
Provides functions for estimating ploidy levels and detecting aneuploidy in individuals using 
             allele intensities or allele count data from high-throughput genotyping platforms, including 
             single nucleotide polymorphism (SNP) arrays and sequencing-based technologies. Implements an extended version of the 'PennCNV' 
             signal standardization method by Wang et al. (2007) <doi:10.1101/gr.6861907> for higher ploidy 
             levels. Computes B-allele frequencies (BAF), z-scores, and identifies copy number variation patterns.
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>.
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).
Supports risk assessors in performing the entry step of the
    quantitative Pest Risk Assessment. It allows the estimation of the
    amount of a plant pest entering a risk assessment area (in terms of
    founder populations) through the calculation of the imported
    commodities that could be potential pathways of pest entry, and the
    development of a pathway model. Two 'Shiny' apps based on the
    functionalities of the package are included, that simplify the process
    of assessing the risk of entry of plant pests. The approach is based
    on the work of the European Food Safety Authority (EFSA PLH Panel et
    al., 2018) <doi:10.2903/j.efsa.2018.5350>.
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 <doi:10.18637/jss.v106.i10>.
Create Q-Q and manhattan plots for GWAS data from PLINK results.
Extensions of 'ggplot2' Q-Q plot functionalities.
A collection of routines for finding reference limits using, where 
    appropriate, QQ methodology.  All use a data vector X of cases from the 
    reference population. The default is to get the central 95% reference range 
    of the population, namely the 2.5 and 97.5 percentile, with optional 
    adjustment of the range.  Along with the reference limits, we want 
    confidence intervals which, for historical reasons, are typically at 90% 
    confidence.  A full analysis provides six numbers:
      – the upper and the lower reference limits, and 
      - each of their confidence intervals.
    For application details, see Hawkins and Esquivel (2024) 
    <doi:10.1093/jalm/jfad109>.
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.
This function performs QR factorization without pivoting to a real or complex matrix. It is based on Anderson. E. and ten others (1999) "LAPACK Users' Guide". Third Edition. SIAM.
Methods for detecting structural breaks, determining the
    number of breaks, and estimating break locations in linear quantile
    regression, using one or multiple quantiles, based on Qu (2008) and
    Oka and Qu (2011).  Applicable to both time series and repeated
    cross-sectional data. The main function is rq.break().

        References for detailed theoretical and empirical explanations:

        (1) Qu, Z. (2008).  "Testing for Structural Change in Regression
    Quantiles."  Journal of Econometrics, 146(1), 170-184
    <doi:10.1016/j.jeconom.2008.08.006>

        (2) Oka, T., and Qu, Z. (2011).  "Estimating Structural Changes in
    Regression Quantiles."  Journal of Econometrics, 162(2), 248-267
    <doi:10.1016/j.jeconom.2011.01.005>.
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.
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>.
Create static QR codes in R. The content of the QR code is
    exactly what the user defines. We don't add a redirect URL, making it
    impossible for us to track the usage of the QR code. This allows to
    generate fast, free to use and privacy friendly QR codes.
Implements moving-blocks bootstrap and extended tapered-blocks bootstrap, as well as smooth versions of each, for quantile regression in time series. This package accompanies the paper: Gregory, K. B., Lahiri, S. N., & Nordman, D. J. (2018). A smooth block bootstrap for quantile regression with time series. The Annals of Statistics, 46(3), 1138-1166.
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.
Provides statistical components, tables, and graphs
    that are useful in 'Quarto' and 'RMarkdown' reports and that produce 'Quarto'
    elements for special formatting such as tabs and marginal notes and graphs.
    Some of the functions produce entire report sections with tabs, e.g.,
    the missing data report created by missChk().  Functions for inserting
		variables and tables inside 'graphviz' and 'mermaid' diagrams are included,
		and so are special clinical trial graphics for adverse event reporting.
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.
A collection of functions is provided by this package to fit quantiles regression models for censored residual lifetimes. It provides various options for regression parameters estimation: the induced smoothing approach (smooth), and L1-minimization (non-smooth).  It also implements the estimation methods for standard errors of the regression parameters estimates based on an efficient partial multiplier bootstrap method and robust sandwich estimator. Furthermore, a simultaneous procedure of estimating regression parameters and their standard errors via an iterative updating procedure is implemented (iterative). For more details, see Kim, K. H., Caplan, D. J., & Kang, S. (2022), "Smoothed quantile regression for censored residual life", Computational Statistics, 1-22 <doi:10.1007/s00180-022-01262-z>.
This function aims to calculate risk of developing cardiovascular disease of individual patients in next 10 years. This unofficial package was based on published open-sourced free risk prediction algorithm QRISK3-2017 <https://qrisk.org/src.php>.
Joint estimation of quantile specific intercept and slope parameters in a linear regression setting.
A no-frills open-source solution for designing plot labels affixed with QR 
    codes. It features 'EasyQrlabelr', a 'BrAPI-compliant' 'shiny' app that 
    simplifies the process of plot label design for non-R users. It builds on the 
    methods described by Wu 'et al.' (2020) <doi:10.1111/2041-210X.13405>.
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 result
             is 
             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>.
Execute multi-step 'SQL' workflows by
    leveraging specially formatted comments to define and control
    execution. This enables users to mix queries, commands, and metadata
    within a single script. Results are returned as named objects for use
    in downstream workflows.
Provides functions for quickly writing and reading any R object to and from disk.
Streamlines and accelerates the process of saving and loading R objects, improving speed and compression compared to other methods. The package provides two compression formats: the 'qs2' format, which uses R serialization via the C API while optimizing compression and disk I/O, and the 'qdata' format, featuring custom serialization for slightly faster performance and better compression. Additionally, the 'qs2' format can be directly converted to the standard 'RDS' format, ensuring long-term compatibility with future versions of R.
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' also implements noise models.
    '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/>.
Implementations of the quantile slice sampler of Heiner et al. (2024+, in preparation) as well as other popular slice samplers are provided. 
    Helper functions for specifying pseudo-target distributions are included, both for diagnostics and for tuning the quantile slice sampler. 
    Other implemented methods include the generalized elliptical slice sampler of Nishihara et al. (2014)<https://jmlr.org/papers/v15/nishihara14a.html},
    latent slice sampler of Li and Walker (2023)<doi:10.1016/j.csda.2022.107652>, and stepping-out slice sampler of Neal (2003)<doi:10.1214/aos/1056562461>,
    and independence Metropolis-Hastings sampler.
Provides routines to create some quaternions splines:
    Barry-Goldman algorithm, De Casteljau algorithm, and Kochanek-Bartels
    algorithm. The implementations are based on the Python library
    'splines'. Quaternions splines allow to construct spherical curves. 
    References: Barry and Goldman <doi:10.1145/54852.378511>, 
    Kochanek and Bartels <doi:10.1145/800031.808575>.
Symbolic calculation and evaluation of multivariate
    polynomials with rational coefficients. This package is strongly
    inspired by the 'spray' package. It provides a function to 
    compute Gröbner bases (reference <doi:10.1007/978-3-319-16721-3>). It 
    also includes some features for symmetric polynomials, such as the Hall
    inner product.
    The header file of the C++ code can be used by other packages. It 
    provides the templated class 'Qspray' that can be used to represent and 
    to deal with multivariate polynomials with another type of coefficients.
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) <doi:10.3982/QE935>, Callaway, Li, and Oka (2018) <doi:10.1016/j.jeconom.2018.06.008>).
Provides comprehensive methods for testing, estimating, and conducting uniform inference on quantile treatment effects (QTEs) in sharp regression discontinuity (RD) designs, incorporating covariates and implementing robust bias correction methods of Qu, Yoon, Perron (2024) <doi:10.1162/rest_a_01168>.
Support package for the textbook "An Introduction to
    Quantitative Text Analysis for Linguists: Reproducible Research Using
    R" (Francom, 2024) <doi:10.4324/9781003393764>. Includes functions to
    acquire, clean, and analyze text data as well as functions to document
    and share the results of text analysis. The package is designed to be
    used in conjunction with the book, but can also be used as a standalone
    package for text analysis.
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) and QTL-by-environment interaction (QEI) mapping via ordinary or compressed variance component mixed models with random- or fixed QTL/QEI effects. 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 or on each locus curve are viewed as potential main-effect QTLs and QEIs, all their effects are included in a multi-locus model, their effects are estimated by both least angle regression and empirical Bayes (or adaptive lasso) in backcross and F2 populations, and true QTLs and QEIs are identified by likelihood radio test. See Zhou et al. (2022) <doi:10.1093/bib/bbab596> and Wen et al. (2018) <doi:10.1093/bib/bby058>.
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. (2019) <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>.
Design of QTL (quantitative trait locus) experiments
  involves choosing which strains to cross, the type of cross,
  genotyping strategies, phenotyping strategies, and the number of
  progeny to raise and phenotype. This package provides tools to help
  make such choices.
  Sen and others (2007) <doi:10.1007/s00335-006-0090-y>.
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>.
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>.
Performs random-effect multiple interval mapping (REMIM) in full-sib families of autopolyploid species based on restricted maximum likelihood (REML) estimation and score statistics, as described in Pereira et al. (2020) <doi:10.1534/genetics.120.303080>.
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, directional quantile classification, and quantile
	ratio regression.
	A vignette is given in Geraci (2016, The R Journal) <doi:10.32614/RJ-2016-037> and included in the package.
Provides three 'Quarto' website templates as an R project, which are commonly used by academics.
    Templates for personal websites and course/workshop websites are included, as well as a template with minimal content for customization.
A tool that can be customized to aid in the clean up of ecological data 
    collected using quadrats and can crop quadrats to ensure comparability between
    quadrats collected under different methodologies.
A range of quadratic forms are evaluated, using efficient methods.
 Unnecessary transposes are not performed.  Complex values are handled
 consistently.
A set of functions of increasing complexity allows users to
  (1) convert QuadKey-identified datasets, 
  based on 'Microsoft's Bing Maps Tile System', into Simple Features data frames,
  (2) transform Simple Features data frames into rasters, and 
  (3) process multiple 'Meta' ('Facebook') QuadKey-identified human mobility files
  directly into raster files.
  For more details, see D’Andrea et al. (2024) <doi:10.21105/joss.06500>.
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.
Given a dataset, the user is invited to utilize the Empirical Cumulative Distribution Function (ECDF) 
              to guess interactively the mean and the mean deviation. Thereafter,
              using the quadratic curve the user can guess the Root Mean Squared Deviation (RMSD) 
              and visualize the standard deviation (SD). 
              For details, see Sarkar and Rashid (2019)<doi:10.3126/njs.v3i0.25574>, 
              Have You Seen the Standard Deviaton?, Nepalese Journal of Statistics, Vol. 3, 1-10.
It includes test for multivariate normality, test for uniformity on the d-dimensional 
    Sphere, non-parametric two- and k-sample tests, random generation of points from the Poisson 
    kernel-based density and clustering algorithm for spherical data. For more information see
    Saraceno G., Markatou M., Mukhopadhyay R. and Golzy M. (2024)
    <doi:10.48550/arXiv.2402.02290>
    Markatou, M. and Saraceno, G. (2024) <doi:10.48550/arXiv.2407.16374>, 
    Ding, Y., Markatou, M. and Saraceno, G. (2023) <doi:10.5705/ss.202022.0347>, 
    and Golzy, M. and Markatou, M. (2020) <doi:10.1080/10618600.2020.1740713>.
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) <doi:10.48550/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.
Estimate quadratic vector autoregression models with the
    strong hierarchy using the Regularization Algorithm under Marginality
    Principle (RAMP) by Hao et al. (2018)
    <doi:10.1080/01621459.2016.1264956>, compare the performance with
    linear models, and construct networks with partial derivatives.
An easy framework to set a quality control workflow on a dataset.
  Includes a various range of functions that allow to establish an adaptable 
  data quality control.
Quality of care is compared across accountable entities, including hospitals, provider groups, and insurance plans, using standardized quality measures. However, observed variations in quality measure performance might be the result of chance sampling or measurement errors. Contains functions for estimating the reliability of unadjusted and risk-standardized quality measures.
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 maximally distinct qualitative color palettes,
    optionally tailored to color deficiency. A list of colors or a subspace
    of a color space is used as input and then projected to the DIN99d color space,
    where colors that are maximally distinct are chosen algorithmically.
Provides functions to access survey results directly into R
    using the 'Qualtrics' API. 'Qualtrics'
    <https://www.qualtrics.com/about/> is an online survey and data
    collection software platform. See <https://api.qualtrics.com/> for
    more information about the 'Qualtrics' API.  This package is
    community-maintained and is not officially supported by 'Qualtrics'.
Qualitative methods for the validation of dynamic models.
    It contains (i) an orthogonal set of deviance measures for absolute,
    relative and ordinal scale and (ii) approaches accounting for time
    shifts. The first approach transforms time to take time delays and speed
    differences into account. The second divides the time series into
    interval units according to their main features and finds the longest
    common subsequence (LCS) using a dynamic programming algorithm.
Implements indices of qualitative variation proposed by Wilcox
    (1973).
These functions use data augmentation and Bayesian techniques for the assessment of single-member and incomplete ensembles of climate projections. It provides unbiased estimates of climate change responses of all simulation chains and of all uncertainty variables. It additionally propagates uncertainty due to missing information in the estimates.
  - Evin, G., B. Hingray, J. Blanchet, N. Eckert, S. Morin, and D. Verfaillie. (2019) <doi:10.1175/JCLI-D-18-0606.1>.
Functions for interacting directly with the Quandl API to offer data in a number of formats usable in R, downloading a zip with all data from a Quandl database, and the ability to search. This R package uses the Quandl API. For more information go to <https://docs.quandl.com>. For more help on the package itself go to <https://www.quandl.com/tools/r>.
Values different types of assets and calibrates discount curves 
    for quantitative financial analysis. It covers fixed coupon assets, 
    floating note assets, interest and cross currency swaps with different 
    payment frequencies. Enables the calibration of spot, instantaneous forward 
    and basis curves, making it a powerful tool for accurate and flexible bond 
    valuation and curve generation. The valuation and calibration techniques 
    presented here are consistent with industry standards and incorporates 
    author's own calculations. Tuckman, B., Serrat, A. (2022, ISBN: 978-1-119-83555-4).
Non-parametric methods as local normal regression, polynomial local regression and penalized cubic B-splines regression are used to estimate quantiles curves. See Fan and Gijbels (1996) <doi:10.1201/9780203748725> and Perperoglou et al.(2019) <doi:10.1186/s12874-019-0666-3>.
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 n-grams, 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, the Perry and 'Benoit' (2017) <doi:10.48550/arXiv.1710.08963> class affinity scaling model, 
    and the 'Slapin' and 'Proksch' (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.
A quantitative and automated tool to extract
    (palaeo)biological information (i.e., measurements, velocities,
    similarity metrics, etc.) from the analysis of tetrapod trackways.
    Methods implemented in the package draw from several sources,
    including Alexander (1976) <doi:10.1038/261129a0>,
    Batschelet (1981, ISBN:9780120810505),
    Benhamou (2004) <doi:10.1016/j.jtbi.2004.03.016>,
    Bovet and Benhamou (1988) <doi:10.1016/S0022-5193(88)80038-9>,
    Cheung et al. (2007) <doi:10.1007/s00422-007-0158-0>,
    Cheung et al. (2008) <doi:10.1007/s00422-008-0251-z>,
    Cleasby et al. (2019) <doi:10.1007/s00265-019-2761-1>,
    Farlow et al. (1981) <doi:10.1038/294747a0>,
    Ostrom (1972) <doi:10.1016/0031-0182(72)90049-1>,
    Rohlf (2008) <https://sbmorphometrics.org/>,
    Rohlf (2009) <https://sbmorphometrics.org/>,
    Ruiz and Torices (2013) <doi:10.1080/10420940.2012.759115>,
    Scrucca et al. (2016) <doi:10.32614/RJ-2016-021>,
    Thulborn and Wade (1984) <https://www.museum.qld.gov.au/collections-and-research/memoirs/nature-21/mqm-n21-2-11-thulborn-wade>.
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.
Functions for simulation, estimation, and model selection of
        finite mixtures of Tukey g-and-h distributions.
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.
Simulating and estimating peer effect models including the quantile-based specification (Houndetoungan, 2025 <doi:10.48550/arXiv.2506.12920>), and the models with Constant Elasticity of Substitution (CES)-based social norm (Boucher et al., 2024 <doi:10.3982/ECTA21048>).
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.  It can be considered an extension of the correlogram,
    which is a correlation function over multiple lag periods that 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.  For detailed theoretical and
    empirical explanations, see Linton and Whang (2007) for univariate
    time series analysis and Han, Linton, Oka and Whang (2016) for
    multivariate time series analysis.  The full references for these key
    publications are as follows: (1) Linton, O., and Whang, Y. J. (2007).
    The quantilogram: with an application to evaluating directional
    predictability.  Journal of Econometrics, 141(1), 250-282
    <doi:10.1016/j.jeconom.2007.01.004>; (2) Han, H., Linton, O., Oka, T.,
    and Whang, Y. J. (2016).  The cross-quantilogram: measuring quantile
    dependence and testing directional predictability between time series.
    Journal of Econometrics, 193(1), 251-270
    <doi:10.1016/j.jeconom.2016.03.001>.
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 tools useful for data screening, testing
        moderation (Cohen et. al. 2003)<doi:10.4324/9780203774441>, 
        mediation (MacKinnon et. al. 2002)<doi:10.1037/1082-989x.7.1.83> 
        and estimating power (Murphy & Myors 2014)<ISBN:9781315773155>.
Estimation and inference methods for models for conditional quantile functions: 
  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, R. (2005) Quantile Regression, Cambridge U. Press,
  <doi:10.1017/CBO9780511754098> and Koenker, R. et al. (2017) Handbook of Quantile Regression, 
  CRC Press, <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.
Implements an adaptively weighted group Lasso procedure for simultaneous variable selection and structure identification in varying
  coefficient quantile regression models and additive quantile regression models with ultra-high dimensional covariates. The methodology, grounded
  in a strong sparsity condition, establishes selection consistency under certain weight conditions. To address the challenge of tuning parameter 
  selection in practice, a BIC-type criterion named high-dimensional information criterion (HDIC) is proposed. The Lasso procedure, guided by
  HDIC-determined tuning parameters, maintains selection consistency. Theoretical findings are strongly supported by simulation studies.
  (Toshio Honda, Ching-Kang Ing, Wei-Ying Wu, 2019, <DOI:10.3150/18-BEJ1091>).
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.  
    Random intercepts and variable selection are allowed via the lasso 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> or <doi:10.13140/RG.2.2.29306.21445> some code examples.
This is the implementation of quantile regression forests for the fast random forest package 'ranger'.
A sigmoidal quantile function estimator based on a newly defined 
    generalized expectile function. The generalized sigmoidal quantile function 
    can estimate quantiles beyond the range of the data, which is important for 
    certain applications given smaller sample sizes. The package is based on the 
    method introduced in Hutson (2024) <doi:10.1080/03610918.2022.2032161>.
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.
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 performance can be assessed via
    Traffic Light Test, Coverage Tests and Loss Functions. The methods of the
    package are described in Gurrola-Perez, P. and Murphy, D. (2015)
    <https://EconPapers.repec.org/RePEc:boe:boeewp:0525> as well as McNeil, J.,
    Frey, R., and Embrechts, P. (2015) <https://ideas.repec.org/b/pup/pbooks/10496.html>.
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.
Dynamically generate tabset panels
    <https://quarto.org/docs/output-formats/html-basics.html#tabsets> in
    'Quarto' HTML documents using a data frame as input.
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.
In the spirit of Anscombe's quartet, this package includes datasets
   that demonstrate the importance of visualizing your data, the importance of 
   not relying on statistical summary measures alone, and why additional 
   assumptions about the data generating mechanism are needed when estimating 
   causal effects. The package includes "Anscombe's Quartet" (Anscombe 1973) <doi:10.1080/00031305.1973.10478966>,
   D'Agostino McGowan & Barrett (2023) "Causal Quartet" <doi:10.48550/arXiv.2304.02683>, 
   "Datasaurus Dozen" (Matejka & Fitzmaurice 2017), "Interaction Triptych" (Rohrer & Arslan 2021) <doi:10.1177/25152459211007368>, "Rashomon Quartet" (Biecek et al. 2023) <doi:10.48550/arXiv.2302.13356>, and
   Gelman "Variation and Heterogeneity Causal Quartets" (Gelman et al. 2023) <doi:10.48550/arXiv.2302.12878>.
Convert R Markdown documents and 'Jupyter' notebooks to a
    variety of output formats using 'Quarto'.
Provides helper functions to work programmatically within a quarto document. It allows 
    the user to create section headers, tabsets, divs, and spans, and formats these objects
    into quarto syntax when printed into a document.
Functions for estimating the potential dispersal of tree species 
  using regeneration densities and dispersal distances to nearest seed trees. 
  A quantile regression is implemented to determine the dispersal potential.
  Spatial prediction can be used to identify natural regeneration potential for forest restoration as described in Axer et al (2021) <doi:10.1016/j.foreco.2020.118802>.
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, C., Viroli, C., Anderlucci, L. (2019) "Quantile-based clustering." Electronic Journal of Statistics. 13 (2) 4849 - 4883  <doi:10.1214/19-EJS1640>.
Syntax for defining complex filtering expressions in a programmatic way.
  A filtering query, built as a nested list configuration, can be easily stored in other formats like 'YAML' or 'JSON'.
  What's more, it's possible to convert such configuration to a valid expression that can be applied to 
  popular 'dplyr' package operations.
Translate 'SQL' 'SELECT' statements into lists of 'R' expressions.
Retrieve protein information from
    the 'UniProtKB' REST API (see <https://www.uniprot.org/help/api_queries>).
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.
A method for prediction of environmental conditions based on 
    transcriptome data linked with the environmental gradients. This package 
    provides functions to overview gene-environment relationships, to construct 
    the prediction model, and to predict environmental conditions where the 
    transcriptomes were generated. This package can quest for candidate genes 
    for the model construction even in non-model organisms' transcriptomes 
    without any genetic information.
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.
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.
Property based testing, inspired by 
    the original 'QuickCheck'. This package builds on 
    the property based testing framework provided by 
    'hedgehog' and is designed to seamlessly integrate with 
    'testthat'.
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to improve your scripts. Improve the quality and reproducibility of 'R' scripts.
An 'R' interface to the 'QuickJS' portable 'JavaScript'
    engine.  The engine and all 'R' to 'JavaScript' interoperability is bundled
    within the package, requiring no dependencies beyond a 'C' compiler.
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 N-mixture models using either FFT or asymptotic approaches. FFT N-mixture models extend the work of Cowen et al. (2017) <doi:10.1111/biom.12701>. 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 FFT models are derived and described in "Parker, M.R.P., Elliott, L., Cowen, L.L.E. (2022). Computational efficiency and precision for replicated-count and batch-marked hidden population models [Manuscript in preparation]. Department of Statistics and Actuarial Sciences, Simon Fraser University.". The asymptotic models are derived and described in: "Parker, M.R.P., Elliott, L., Cowen, L.L.E., Cao, J. (2022). Fast asymptotic solutions for N-mixtures on large populations [Manuscript in preparation]. Department of Statistics and Actuarial Sciences, Simon Fraser University.".
A high-level plotting system, compatible with `ggplot2` objects, 
    maps from `sf`, `terra`, `raster`, `sp`. It is built primarily on the 
    'grid' package. The objective of the package is to provide a plotting system 
    that is built for speed and modularity. This is useful for quick visualizations 
    when testing code and for plotting multiple figures to the same device from 
    independent sources that may be independent of one another (i.e., different 
    function or modules the create the visualizations).
Quickly fits and plots psychometric functions (normal, logistic,
    Weibull or any or any function defined by the user) for multiple groups.
A collection of functions for constructing large pairwised comparisons 
             and rating them using Elo rating system with supporting parallel processing.
             The method of random sample pairs is based on Reservoir Sampling proposed by 
             JVitter (1985) <doi:10.1145/3147.3165>.
Compile R functions annotated with type and shape
    declarations for extremely fast performance and robust runtime
    type checking. Supports both just-in-time (JIT) and ahead-of-time
    (AOT) compilation. Compilation is performed by lowering R code to
    Fortran.
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.
A toolkit for analysis and visualization of data from fluorophore-assisted seed amplification assays, such as Real-Time Quaking-Induced Conversion (RT-QuIC) and Fluorophore-Assisted Protein Misfolding Cyclic Amplification (PMCA). 'QuICSeedR' addresses limitations in existing software by automating data processing, supporting large-scale analysis, and enabling comparative studies of analysis methods. It incorporates methods described in Henderson et al. (2015) <doi:10.1099/vir.0.069906-0>, Li et al. (2020) <doi:10.1038/s41598-021-96127-8>, Rowden et al. (2023) <doi:10.3390/pathogens12020309>, Haley et al. (2013) <doi:10.1371/journal.pone.0081488>, and Mair and Wilcox (2020) <doi:10.3758/s13428-019-01246-w>. Please refer to the original publications for details.
Test whether equality and order constraints hold for all 
    individuals simultaneously by comparing Bayesian mixed models through Bayes 
    factors. A tutorial style vignette and a quickstart guide are available, via
    vignette("manual", "quid"), and vignette("quickstart", "quid") respectively.
    See Haaf and Rouder (2017) <doi:10.1037/met0000156>; Haaf, Klaassen and Rouder
    (2019) <doi:10.31234/osf.io/a4xu9>; and Rouder & Haaf (2021) <doi:10.5334/joc.131>.
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.
Functions to convert text data for labelling into format appropriate for importing into Qualtrics. Supports multiple language, including right-to-left scripts as well as different response types. Outputs an Advance Format .txt file that read into Qualtrics.
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. More information in Dusseldorp, E., Doove, L., & Van Mechelen, I. (2016) <doi:10.3758/s13428-015-0594-z>.
Functions to compute Euclidean minimum spanning trees using single-,
    sesqui-, and dual-tree Boruvka algorithms.  Thanks to K-d trees, they are
    fast in spaces of low intrinsic dimensionality.  Mutual reachability
    distances (used in the definition of the 'HDBSCAN*' algorithm)
    are also supported.  The package also features relatively fast fallback
    minimum spanning tree and nearest-neighbours algorithms for spaces of
    higher dimensionality.  The 'Python' version of 'quitefastmst' is available
    via 'PyPI'.
To construct a model in 2-D space from 2-D nonlinear
    dimension reduction data and then lift it to the high-dimensional
    space. Additionally, provides tools to visualise the model overlay the
    data in 2-D and high-dimensional space. Furthermore, provides
    summaries and diagnostics to evaluate the nonlinear dimension
    reduction layout.
Collect  your data on digital marketing campaigns from 'Quora' Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
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.
High-throughput analysis of growth curves and fluorescence
    data using three methods: linear regression, growth model fitting, and
    smooth spline fit. Analysis of dose-response relationships via
    smoothing splines or dose-response models. Complete data analysis
    workflows can be executed in a single step via user-friendly wrapper
    functions. The results of these workflows are summarized in detailed
    reports as well as intuitively navigable 'R' data containers. A 'shiny'
    application provides access to all features without
    requiring any programming knowledge. The package is described in further
    detail in Wirth et al. (2023) <doi:10.1038/s41596-023-00850-7>.
Provide a variety of Q-matrix validation methods for the generalized
    cognitive diagnosis models, including the method based on the generalized
    deterministic input, noisy, and gate model (G-DINA) by de la Torre (2011)
    <DOI:10.1007/s11336-011-9207-7> discrimination index (the GDI method) by
    de la Torre and Chiu (2016) <DOI:10.1007/s11336-015-9467-8>, the Hull method
    by Najera et al. (2021) <DOI:10.1111/bmsp.12228>, the stepwise Wald test method
    (the Wald method) by Ma and de la Torre (2020) <DOI:10.1111/bmsp.12156>, the
    multiple logistic regression‑based Q‑matrix validation method (the MLR-B method)
    by Tu et al. (2022) <DOI:10.3758/s13428-022-01880-x>, the beta method based on
    signal detection theory by Li and Chen (2024) <DOI:10.1111/bmsp.12371> and
    Q-matrix validation based on relative fit index by Chen et al. (2013)
    <DOI:10.1111/j.1745-3984.2012.00185.x>. Different research methods and iterative
    procedures during Q-matrix validating are available
    <DOI:10.3758/s13428-024-02547-5>.
Finding hidden clusters in structured data can be hindered
  by the presence of masking variables. If not detected,
  masking variables are used to calculate the overall similarities between units, 
  and therefore the cluster attribution is more imprecise.
  The algorithm q-vars implements an optimization method to find the variables
  that most separate units between clusters. In this way, masking variables can be 
  discarded from the data frame and the clustering is more accurate.
  Tests can be found in Benati et al.(2017) <doi:10.1080/01605682.2017.1398206>.
Functions to compute quasi variances and associated measures of approximation error.
Resources, tutorials, and code snippets dedicated to exploring the intersection of quantum computing and artificial intelligence (AI) in the context of analyzing Cluster of Differentiation 4 (CD4) lymphocytes and optimizing antiretroviral therapy (ART) for human immunodeficiency virus (HIV). With the emergence of quantum artificial intelligence and the development of small-scale quantum computers, there's an unprecedented opportunity to revolutionize the understanding of HIV dynamics and treatment strategies. This project leverages the R package 'qsimulatR' (Ostmeyer and Urbach, 2023, <https://CRAN.R-project.org/package=qsimulatR>), a quantum computer simulator, to explore these applications in quantum computing techniques, addressing the challenges in studying CD4 lymphocytes and enhancing ART efficacy.
Functions and tools for creating, visualizing, and investigating properties of continuous-time quantum walks, including efficient calculation of matrices such as the mixing matrix, average mixing matrix, and spectral decomposition of the Hamiltonian. E. Farhi (1997): <arXiv:quant-ph/9706062v2>; C. Godsil (2011) <arXiv:1103.2578v3>.
The modeling and prediction of graph-associated time series(GATS) based on continuous time quantum walk. This software is mainly used for feature extraction, modeling, prediction and result evaluation of GATS, including continuous time quantum walk simulation, feature selection, regression analysis, time series prediction, and series fit calculation. A paper is attached to the package for reference.
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 3.10.0 or later).
A collection of personal functions designed to simplify and streamline 
    common R programming tasks. This package provides reusable tools and shortcuts 
    for frequently used calculations and workflows.
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 use of proxies is required in certain network
    environments.  Despite the power of system level software, it is still
    inconvenient to switch proxy networks at random in R's console. This
    package is designed to provide one-click switching between proxy and
    non-proxy states.
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.
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.
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>.
Obtain information about countries around the globe. Information for names, states, languages, time, capitals, currency and many more. Data source are 'Wikipedia' <https://www.wikipedia.org>, 'TimeAndDate' <https://www.timeanddate.com> and 'CountryCode' <https://countrycode.org>.
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.
Implements the pseudo-R2D2 prior for ordinal regression from the paper "Psuedo-R2D2 prior for high-dimensional ordinal regression" by Yanchenko (2025) <doi:10.48550/arXiv.2502.17491>. In particular, it provides code to evaluate the probability distribution function for the cut-points, compute the log-likelihood, calculate the hyper-parameters for the global variance parameter, find the distribution of McFadden's coefficient-of-determination, and fit the model in 'rstan'. Please cite the paper if you use these codes.
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.
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. In doing this seemlessly, further tools are needed 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 within the R environment.
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://www.transitionmonitor.com/>). 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://www.transitionmonitor.com/>). 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://www.transitionmonitor.com/>). 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://www.transitionmonitor.com/>).
    Financial institutions use 'PACTA' to study how their capital
    allocation decisions align with climate change mitigation goals.
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.
Implementation of 'JQuery' <https://jquery.com> and 'CSS' styles to allow the display of fireworks on a document. Toolkit to easily incorporate celebratory splashes in 'Rmarkdown' and 'shiny' apps.
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>).
    Bootstrapped confidence intervals can also be calculated. To use the 
    confidence interval functionality, download bootmlm from <https://github.com/marklhc/bootmlm>.
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).
Provides a simple set of wrappers to easily use 
         RDCOMClient for generating Microsoft PowerPoint presentations. Warning:this package is soon to be archived from CRAN.
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.
R2 statistic for significance test. Variance and covariance of R2 values used to assess the 95% CI and p-value of the R2 difference.
Automatic resizing toolbar for containers, images and tables. Various resizer or expandable container functionalities are also included. Most suitable to include in 'shiny', 'markdown' and 'quarto' documents.
Area under the receiver operating characteristic curves (AUC) statistic for significance test. Variance and covariance of AUC values used to assess the 95% Confidence interval (CI) and p-value of the AUC difference for both nested and non-nested model.
Create production-ready Rich Text Format (RTF) tables and figures
    with flexible format.
The routine twosample_test() in this package runs the
    two sample test using various test statistic. The p values are
    found via permutation or large sample theory. The routine twosample_power() allows the
    calculation of the power in various cases, and plot_power() draws the corresponding power graphs.
    The routine run.studies allows a user to quickly study the power of a new method and 
    how it compares to some of the standard ones.
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.
Implementation of 'JQuery' <https://jquery.com> and 'CSS' styles to allow easy incorporation of various social media elements on a page. The elements include addition of share buttons or connect with us buttons or hyperlink buttons to 'Shiny' applications or dashboards and 'Rmarkdown' documents.Sharing capability on social media platforms including 'Facebook' <https://www.facebook.com>, 'Linkedin' <https://www.linkedin.com>, 'X/Twitter' <https://x.com>, 'Tumblr' <https://www.tumblr.com>, 'Pinterest' <https://www.pinterest.com>, 'Whatsapp' <https://www.whatsapp.com>, 'Reddit' <https://www.reddit.com>, 'Baidu' <https://www.baidu.com>, 'Blogger' <https://www.blogger.com>, 'Weibo' <https://www.weibo.com>, 'Instagram' <https://www.instagram.com>, 'Telegram' <https://www.telegram.me>, 'Youtube' <https://www.youtube.com>.
Create plots and LaTeX tables that look like SPSS output for use in teaching materials.  Rather than copying-and-pasting SPSS output into documents, R code that mocks up SPSS output can be integrated directly into dynamic LaTeX documents with tools such as knitr.  Functionality includes statistical techniques that are typically covered in introductory statistics classes: descriptive statistics, common hypothesis tests, ANOVA, and linear regression, as well as box plots, histograms, scatter plots, and line plots (including profile plots).
Converts data to STL (stereolithography) files
    that can be used to feed a 3-dimensional printer.  The
    3-dimensional output from a function can be materialized
    into a solid surface in a plastic material, therefore allowing
    more detailed examination. There are many possible uses for
    this new tool, such as to examine mathematical expressions
    with very irregular shapes, to aid teaching people with
    impaired vision, to create raised relief maps from digital
    elevation maps (DEMs), to bridge the gap between mathematical
    tools and rapid prototyping, and many more.  Ian Walker created
    the function r2stl() and Jose' Gama assembled the package.
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>.
Direct insertion of over 1000 symbols (e.g. currencies, letters, emojis, arrows, mathematical symbols and so on) into 'Rmarkdown' documents and 'Shiny' applications by incorporating 'HTML' hex codes.
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'.
  The suggested package 'BRugs' (only needed for function openbugs()) is only available from the CRAN archives, 
  see <https://cran.r-project.org/package=BRugs>.
Access and handle APIs that use the international 'open311'
    'GeoReport v2' standard for civic issue tracking
    <https://wiki.open311.org/GeoReport_v2/>. Retrieve civic service types
    and request data. Select and add available 'open311' endpoints and
    jurisdictions. Implicitly supports custom queries and 'open311'
    extensions.  Requires a minimal number of hard dependencies while
    still allowing the integration in common R formats ('xml2', 'tibble',
    'sf').
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 R and 'JavaScript' functions to allow 'WebGL'-based 3D plotting
    using the 'three.js' 'JavaScript' library. Interactivity through roll-over 
    highlighting and toggle buttons is also supported.
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().
When assigned "R for Data Science" (Wickham, Çetinkaya-Rundel, 
  and Grolemund (2023, ISBN: 1492097402)), students should read the book and
  type in all the associated R commands themselves. Sadly, that never happens.
  These tutorials allow students to demonstrate (and their instructors
  to be sure) that all work has been completed. See Kane (2023)
  <https://ppbds.github.io/tutorial.helpers/articles/instructions.html> from
  the 'tutorial.helpers' package for a background discussion.
Make optimal decisions for your personal or
    household finances. Use tools and methods that are selected carefully
    to align with academic consensus, bridging the gap between theoretical
    knowledge and practical application. They help you find
    your own personalized optimal discretionary spending or
    optimal asset allocation, and prepare you for retirement 
    or financial independence.
    The optimal solution to this problems is extremely complex,
    and we only have a single lifetime to get it right.
    Fortunately, we now have the user-friendly tools implemented,
    that integrate life-cycle models 
    with single-period net-worth mean-variance optimization models. 
    Those tools can be used by anyone who wants to see 
    what highly-personalized optimal decisions can look like.
    For more details see: 
    Idzorek T., Kaplan P. (2024, ISBN:9781952927379),
    Haghani V., White J. (2023, ISBN:9781119747918).
Interface for the 'Google Ads API'. 'Google Ads' is an online
    advertising service that enables advertisers to display advertising to web
    users (see <https://developers.google.com/google-ads/> for more information).
A collection of datasets that accompany the forthcoming book "R for Health Care Research".
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.
Datasets and utility functions to support the book
    "R for Plant Disease Epidemiology" (R4PDE). It includes functions for quantifying disease,
    assessing spatial patterns, and modeling plant disease epidemics based on weather predictors.
    These tools are intended for teaching and research in plant disease epidemiology. Several functions
    are based on classical and contemporary methods, including those discussed in Laurence V. Madden,
    Gareth Hughes, and Frank van den Bosch (2007) <doi:10.1094/9780890545058>.
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.
    This version of '{r4ss}' is compatible with Stock Synthesis versions
    3.24 through 3.30 (specifically version 3.30.19.01, from April
    2022).
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 and monetary cost 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. This version of r5r depends on R5 v7.1.
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.
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.
A comprehensive suite of statistical tools for Quality Management, designed around the Define, Measure, Analyze, Improve, and Control (DMAIC) cycle used in Six Sigma methodology. Based on the discontinued CRAN package 'qualitytools', this package refactors its original design by incorporating 'R6' object-oriented programming for increased flexibility and performance. It replaces traditional graphics with modern, interactive visualizations using 'ggplot2' and 'plotly'. Built on 'tidyverse' principles, it simplifies data manipulation and visualization, offering an intuitive approach to quality science.
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) <doi:10.1002/sim.9076>. 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. (2021) <doi:10.1002/sim.9104>.
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.
Provide function for work with 'AcademyOcean API'
    <https://academyocean.com/api>.
Application of 'RaceID' allows inference of cell types and prediction of lineage trees by the 'StemID2' algorithm (Herman, J.S., Sagar, Grun D. (2018) <DOI:10.1038/nmeth.4662>). 'VarID2' is part of this package and allows quantification of biological gene expression noise at single-cell resolution (Rosales-Alvarez, R.E., Rettkowski, J., Herman, J.S., Dumbovic, G., Cabezas-Wallscheid, N., Grun, D. (2023) <DOI:10.1186/s13059-023-02974-1>).
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.
A toolkit for making antigenic maps from immunological assay data,
    in order to quantify and visualize antigenic differences between different
    pathogen strains as described in
    Smith et al. (2004) <doi:10.1126/science.1097211> and used in the World
    Health Organization influenza vaccine strain selection process. Additional
    functions allow for the diagnostic evaluation of antigenic maps and an
    interactive viewer is provided to explore antigenic relationships amongst
    several strains and incorporate the visualization of associated genetic
    information.
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.
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.
Implementation of the MaxRank normalization method, which enables 
     standardization of Rank Abundance Distributions (RADs) to a specified 
     number of ranks. Rank abundance distributions are widely used in biology 
     and ecology to describe species abundances, and are mathematically 
     equivalent to complementary cumulative distribution functions (CCDFs) used 
     in physics, linguistics, sociology, and other fields. The method is 
     described in Saeedghalati et al. (2017) <doi:10.1371/journal.pcbi.1005362>.
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.
A straightforward model to estimate soil migration rates across various soil contexts. 
	Based on the compartmental, vertically-resolved, physically-based mass balance model of Soto and Navas (2004) 
	<doi:10.1016/j.jaridenv.2004.02.003> and Soto and Navas (2008) <doi:10.1016/j.radmeas.2008.02.024>.
	'RadEro' provides a user-friendly interface in R, utilizing input data such as 137Cs inventories and parameters directly derived 
	from soil samples (e.g., fine fraction density, effective volume) to accurately capture the 137Cs distribution within the soil profile. 
	The model simulates annual 137Cs fallout, radioactive decay, and vertical diffusion, with the diffusion coefficient calculated 
	from 137Cs reference inventory profiles. Additionally, it allows users to input custom parameters as calibration coefficients. 
	The RadEro user manual and protocol, including detailed instructions on how to format input data and configuration files, can be found at the following link: <https://github.com/eead-csic-eesa/RadEro>.
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'.
A
        collection of programs for 
        plotting SKEW-T,log p diagrams and wind profiles for data
        collected by radiosondes (the typical weather balloon-borne
        instrument). The format of this plot with companion lines to
	assess atmospheric stability are both standard  in meteorology
	and difficult to create from basic graphics functions. Hence
	this package. One novel feature is being able add several
	profiles to the same plot for comparison. 
	Use "help(ExampleSonde)" for an explanation of the variables
	needed and how they should be named in a data frame. See
	<https://github.com/dnychka/Radiosonde> for the package home page.
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>.
Make your phrase or sentence into something funny! Pass a string with the keywords in, 
    and get out a bit of humor.
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.
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 Hoffman *et al.* (1999) 
    (<doi:10.1145/331770.331775>) for original implementation, 
    see Di Caro *et al* (2012) (<doi: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.
Creating 3D radial visualizations of multivariate data.
    The package extends traditional radial coordinate visualization (RadViz) techniques
    to three-dimensional space, enabling enhanced exploration and analysis of
    high-dimensional datasets through interactive 3D plots. Zhu, Dai & Maitra (2022)
    <doi:10.1080/10618600.2021.2020129>.
Aims at loading Google Adwords data into R. Adwords is an online
    advertising service that enables advertisers to display advertising copy to web
    users (see <https://developers.google.com/adwords/> for more information). 
    Therefore the package implements three main features. First, the package
    provides an authentication process for R with the Google Adwords API (see 
    <https://developers.google.com/adwords/api/> for more information) via OAUTH2.
    Second, the package offers an interface to apply the Adwords query language in
    R and query the Adwords API with ad-hoc reports. Third, the received data are
    transformed into suitable data formats for further data processing and data
    analysis.
A model of single-layer groundwater flow in steady-state
    under the Dupuit-Forchheimer assumption can be created by placing
    elements such as wells, area-sinks and line-sinks at arbitrary
    locations in the flow field. Output variables include hydraulic head
    and the discharge vector. Particle traces can be computed numerically
    in three dimensions. The underlying theory is described in Haitjema
    (1995) <doi:10.1016/B978-0-12-316550-3.X5000-4> and references
    therein.
A series of shortcuts for routine tasks originally developed by
    Rafael A. Irizarry to facilitate data exploration.
A cross-validated minimal-optimal feature selection algorithm.
 It utilises popularity counting, hierarchical clustering with feature dissimilarity measures,
 and prefiltering with all-relevant feature selection method to obtain the minimal-optimal set of features.
Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval Method is designed to perform multi-criteria decision-making (MCDM), developed by Mališa Žižovic in 2020 (<doi:10.3390/math8061015>). 
              It calculates the final sorted rankings based on a decision matrix where rows represent alternatives 
              and columns represent criteria. The method uses:
              - A numeric vector of weights for each criterion (the sum of weights must be 1).
              - A numeric vector of ideal values for each criterion.
              - A numeric vector of anti-ideal values for each criterion.
              - Numeric values representing the extent to which the ideal value is preferred over the anti-ideal value, 
                and the extent to which the anti-ideal value is considered worse.
              The function standardizes the decision matrix, normalizes the data, applies weights, and returns the 
              final sorted rankings.
Functions for calculating life history metrics using matrix
    population models ('MPMs'). Described in Jones et al. (2021)
    <doi:10.1101/2021.04.26.441330>.
Enables Retrieval-Augmented Generation (RAG) workflows in R by combining
    local vector search using 'DuckDB' with optional web search via the 'Tavily' API.
    Supports 'OpenAI'- and 'Ollama'-compatible embedding models, full-text and 'HNSW'
    (Hierarchical Navigable Small World) indexing, and modular large language model
    (LLM) invocation. Designed for advanced question-answering, chat-based
    applications, and production-ready AI pipelines. This package is the R
    equivalent of the 'python' package 'RAGFlowChain' available at
    <https://pypi.org/project/RAGFlowChain/>.
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'.
Provides tools for implementing Retrieval-Augmented
    Generation (RAG) workflows with Large Language Models (LLM). Includes
    functions for document processing, text chunking, embedding
    generation, storage management, and content retrieval. Supports
    various document types and embedding providers ('Ollama', 'OpenAI'),
    with 'DuckDB' as the default storage backend. Integrates with the
    'ellmer' package to equip chat objects with retrieval capabilities.
    Designed to offer both sensible defaults and customization options
    with transparent access to intermediate outputs.  For a review of
    retrieval-augmented generation methods, see Gao et al. (2023)
    "Retrieval-Augmented Generation for Large Language Models: A Survey"
    <doi:10.48550/arXiv.2312.10997>.
Proper L2-penalized maximum likelihood estimators for precision 
  matrices and supporting functions to employ these estimators in a graphical 
  modeling setting. For details, see Peeters, Bilgrau, & van Wieringen (2022) 
  <doi:10.18637/jss.v102.i04> and associated publications.
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>.
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.
Efficient CRUD interface for the 'Airtable' API <https://airtable.com/developers/web/api>, supporting batch requests and parallel encoding of large data sets.
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.
Download, 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>).
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.
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 
    <https://datastorm-open.github.io/introduction_ramcharts/> 
    and <https://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 <https://www.amcharts.com/online-store/> to purchase a licence,
    and thus support its creators and get a period of Priority Support. See also
    <https://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 (2022) <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.
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'.
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.
Value-calibrated color ramps can be useful to emphasize patterns in data from complex distributions. Colors can be tied to specific values, and the association can be expanded into full color ramps that also include the relationship between colors and values. Such ramps can be used in a variety of cases when heatmap-type plots are necessary, including the visualization of vector and raster spatial data, such as topographies.
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>.
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.
Model fitting, model selection and parameter tuning procedures for a class of random network models. Many useful network modeling, estimation, and processing methods are included. 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.
A set of functions to create random Analysis Data Model
    (ADaM) datasets and cached dataset.  ADaM dataset specifications are
    described by the Clinical Data Interchange Standards Consortium
    (CDISC) Analysis Data Model Team.
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.
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>).
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 (2021) <doi:10.1080/01621459.2021.1950003>.
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.
Functions for assessing variable relations and associations 
    prior to modeling with a Random Forest algorithm (although these are 
    relevant for any predictive model).
    Metrics such as partial correlations and variance inflation factors
    are tabulated as well as plotted for the user. A function is available
    for tuning the main Random Forest hyper-parameter based on model performance 
    and variable importance metrics. This grid-search technique provides
    tables and plots showing the effect of the main hyper-parameter on each 
    of the assessment metrics. It also returns each of the evaluated models 
    to the user. The package also provides superior variable importance plots 
    for individual models. All of the plots are developed so that the 
    user has the ability to edit and improve further upon the 
    plots. Derivations and methodology are described in Bladen (2022) 
    <https://digitalcommons.usu.edu/etd/8587/>.
A bagging predictor based on generalized linear models (GLMs) is implemented. The method is published in 
   Song, Langfelder and Horvath (2013) <doi:10.1186/1471-2105-14-5>.
The Gene Ontology (GO) Consortium <https://geneontology.org/> organizes genes
  into hierarchical categories based on biological process (BP), molecular function (MF) and
  cellular component (CC, i.e., subcellular localization). Tools such as 'GoMiner' (see Zeeberg, B.R.,
  Feng, W., Wang, G. et al. (2003) <doi:10.1186/gb-2003-4-4-r28>) can leverage GO to perform
  ontological analysis of microarray and proteomics studies, typically generating a list of
  significant functional categories. The significance is traditionally determined by randomizing
  the input gene list to computing the false discovery rate (FDR) of the enrichment p-value for
  each category. We explore here the novel alternative of randomizing the GO database rather than
  the gene list.
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.
A novel ensemble method employing Support Vector Machines (SVMs) as base learners. This powerful ensemble model is designed for both classification (Ara A., et. al, 2021) <doi:10.6339/21-JDS1014>, and regression (Ara A., et. al, 2021) <doi:10.1016/j.eswa.2022.117107> problems, offering versatility and robust performance across different datasets and compared with other consolidated methods as Random Forests (Maia M, et. al, 2021) <doi:10.6339/21-JDS1025>.
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.
Performs the random projection test (Lopes et al., (2011) <doi:10.48550/arXiv.1108.2401>) for the one-sample and two-sample hypothesis testing problem for equality of means in the high dimensional setting. We are interested in detecting the mean vector in the one-sample problem or the difference between mean vectors in the two-sample problem.
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.
Generates random walks of various types by providing a set of functions
    that are compatible with the 'tidyverse'. The functions provided in the package
    make it simple to create random walks with a variety of properties, such as
    how many simulations to run, how many steps to take, and the distribution of
    random walk itself.
Carry out principal component analysis (PCA) of very large pedigrees such as found
    in breeding populations! This package exploits sparse matrices and randomised
    linear algebra to deliver a gazillion-times speed-up compared to naive singular value decoposition (SVD)
    (and eigen decomposition).
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.
Provides 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 (<doi:10.1007/978-3-540-74496-2_36>) and WELL (<doi:10.1145/1132973.1132974>) 
    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>. 
    Take a look at the Distribution task view of types and tests of random number generators.
    The package can be provided without the 'rngWELL' dependency on demand.
    Package in Memoriam of Diethelm and Barbara Wuertz.
Implements random variables by means of S4 classes and methods.
Resolve the dependency graph of R packages at a specific time point based on the information from various 'R-hub' web services <https://blog.r-hub.io/>. The dependency graph can then be used to reconstruct the R computational environment with 'Rocker' <https://rocker-project.org>.
Provides tools for filtering occurrence records, generating alpha-hull-derived range polygons and mapping species distributions.
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>.
Used for generating randomized community matrices under
             strict range cohesion. The package can handle data where species occurrence
             are recorded across sites ordered along gradients such as elevation and latitude, as well as species occurrences recorded on spatial grids with known geographic coordinates.
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.
Integrates population dynamics and dispersal into a mechanistic 
  virtual species simulator. The package can be used to study the effects of 
  environmental change on population growth and range shifts. It allows for 
  simple and straightforward definition of population dynamics (including 
  positive density dependence), extensive possibilities for defining dispersal 
  kernels, and the ability to generate virtual ecologist data. Learn more about 
  the 'rangr' at <https://docs.ropensci.org/rangr/>.
Provides a flexible alternative to the built-in rank() function called smartrank(). 
    Optionally rank categorical variables by frequency (instead of in alphabetical order), and control whether ranking is based on descending/ascending order. 
    smartrank() is suitable for both numerical and categorical data.
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>.
Polynomially bounded algorithms to aggregate complete rankings under Kemeny's axiomatic framework. 'RankAggSIgFUR' (pronounced as rank-agg-cipher) contains two heuristics algorithms: FUR and SIgFUR. For details, please see Badal and Das (2018) <doi:10.1016/j.cor.2018.06.007>.
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.
Estimates the total, between-, and within-cluster Spearman rank correlations for continuous and ordinal clustered data. See Tu et al. (2024) <DOI:10.1002/sim.10326> for details.
Implements Kornbrot's rank difference test as described in 
    <doi:10.1111/j.2044-8317.1990.tb00939.x>. This method is a modified 
    Wilcoxon signed-rank test which produces consistent and meaningful 
    results for ordinal or monotonically-transformed data.
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.
Estimates the rank intraclass correlation coefficient (ICC) for clustered continuous and ordinal data. See Tu et al. (2023) <DOI:10.1002/sim.9864> for details.
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>.
A supportive collection of functions for gathering and plotting treatment ranking metrics after network meta-analysis.
Provides convenient tools for visualising ordinal outcome data following the "Grotta Bar" approach pioneered by The National Institute of Neurological Disorders and Stroke rt-PA Stroke Study Group (1995) <doi:10.1056/NEJM199512143332401>.
Principal Component Analysis (PCA) is a statistical technique used to reduce the dimensionality of a dataset while preserving as much variability as possible. By transforming the original variables into a new set of uncorrelated variables called principal components, PCA helps in identifying patterns and simplifying the complexity of high-dimensional data. The 'RankPCA' package provides a streamlined workflow for performing PCA on datasets containing both categorical and continuous variables. It facilitates data preprocessing, encoding of categorical variables, and computes PCA to determine the optimal number of principal components based on a specified variance threshold. The package also computes composite indices for ranking observations, which can be useful for various analytical purposes. Garai, S., & Paul, R. K. (2023) <doi:10.1016/j.iswa.2023.200202>.
Statistical tools for the Mallows-Binomial model, the first joint statistical model for preference learning for rankings and ratings. This project was supported by the National Science Foundation under Grant No. 2019901.
Methods for ranking responses of a single response question or a 
  multiple response question are described in the two papers: 
  1. Wang, H. (2008). Ranking Responses in Multiple-Choice Questions. Journal of 
  Applied Statistics, 35, 465-474. <DOI:10.1080/02664760801924533> 
  2. Wang, H. and Huang, W. H. (2014). Bayesian Ranking Responses in Multiple 
  Response Questions. Journal of the Royal Statistical Society: Series A 
  (Statistics in Society), 177, 191-208. <DOI:10.1111/rssa.12009>.
Implementation of Kernelized score functions and other semi-supervised learning algorithms for node label ranking to analyze  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.
Fast computing an ensemble of rank-based trees via boosting or random forest on binary and multi-class problems. It converts continuous gene expression profiles into ranked gene pairs, for which the variable importance indices are computed and adopted for dimension reduction. Decision rules can be extracted from trees.
Finds the k nearest neighbours for every point in a
    given dataset in O(N log N) time using Arya and Mount's ANN library
    (v1.1.3). There is support for approximate as well as exact searches,
    fixed radius searches and 'bd' as well as 'kd' trees. The distance is
    computed using the L2 (Euclidean) metric. Please see package 'RANN.L1'
    for the same functionality using the L1 (Manhattan, taxicab) metric.
Provides tools for robust regression model fitting using the RANSAC (Random Sample Consensus) algorithm. RANSAC is an iterative method to estimate parameters of a model from a dataset that contains outliers. This package allows fitting both linear lm and nonlinear nls models using RANSAC, helping users obtain more reliable models in the presence of noisy or corrupted data. The methods are particularly useful in contexts where traditional least squares regression fails due to the influence of outliers. Implementations include support for performance metrics such as RMSE, MAE, and R² based on the inlier subset. For further details, see Fischler and Bolles (1981) <doi:10.1145/358669.358692>.
Implements reversal association pattern analysis for categorical data. Detects sub-tables exhibiting reversal associations in contingency tables, provides visualization tools, and supports simulation-based validation for complex I × J tables.
Interface for multiple data sources, such as the 
    `EDDS` API <https://evds2.tcmb.gov.tr/index.php?/evds/userDocs> of the 
    Central Bank of the Republic of Türkiye and the 
    `FRED` API <https://fred.stlouisfed.org/docs/api/fred/> of the Federal Reserve Bank. 
    Both data providers require API keys for access, which users can easily obtain 
    by creating accounts on their respective websites. 
    The package provides caching ability with the selection of periods to increase the 
    speed and efficiency of requests. 
    It combines datasets requested from different sources, 
    helping users when the data has common frequencies. 
    While combining data frames whenever possible, it also keeps all requested data
    available as separate data frames to increase efficiency.
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 a high-performance interface for calculating string similarities and distances, leveraging the efficient library 'RapidFuzz' <https://github.com/rapidfuzz/rapidfuzz-cpp>. This package integrates the 'C++' implementation, allowing 'R' users to access cutting-edge algorithms for fuzzy matching and text analysis.
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 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".
Rapidly estimates tree-topology from large allele frequency
    data using Root Distances Method, under a Brownian Motion Model. See
    Peng et al. (2021) <doi:10.1016/j.ympev.2021.107142>.
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.
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.
Accurately estimates the reliability of cognitive tasks using a fast and flexible permutation-based split-half reliability algorithm that supports stratified splitting while maintaining equal split sizes. See Kahveci, Bathke, and Blechert (2022) <doi:10.31234/osf.io/ta59r> for details.
Provides XML parsing capability through the 'Rapidxml' 'C++' header-only library.
Access to the internal R serialization code is provided for
 use by other packages 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 Ei-ji Nakama and Junji Nakano 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.
Run simple 'R' scripts as command line applications, with
  automatic robust and convenient support for command line arguments.
  This package provides 'Rapp', an alternative 'R' front-end similar to 
  'Rscript', that enables this.
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.
Provides functions to load and manage data from Apple Ads 
    accounts using the 'Apple Ads Campaign Management API' 
    <https://developer.apple.com/documentation/apple_ads>.
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>.
Provides access to 'Rangeland Analysis Platform' (RAP) products <https://rangelands.app/products> for arbitrary extents via 'GDAL' virtual file system.
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 (obtained from
    <https://www.gurobi.com/>). For more information on using
    this package, see Hanson et al. (2018) <doi:10.1111/2041-210X.12862>.
Offers functions for fetching JSON data from the US EPA Air Quality
             System (AQS) API with options to comply with the API rate limits.
             See <https://aqs.epa.gov/aqsweb/documents/data_api.html> for
             details of the AQS API.
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>.
Perform risk-adjusted regression and sensitivity analysis as
    developed in "Mitigating Omitted- and Included-Variable Bias in Estimates of
    Disparate Impact" Jung et al. (2024) <arXiv:1809.05651>.
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 (2020) <doi:10.1080/01621459.2020.1796677>. 
    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). Version 1.1 includes a minor adjustment to the number of combinations to be considered for multiple testing correction. This updated version is more conservative in its approach and hence more selective. <doi:10.1101/2021.10.01.462832>.
Randomization tests for the statistical comparison of i = two or more individual-based, sample-based or coverage-based rarefaction curves. The ecological null hypothesis is that the 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 i samples were all drawn from different assemblages that, nonetheless, share similar species richness and species abundance distributions. Functions are described in L. Cayuela, N.J. Gotelli & R.K. Colwell (2015) <doi:10.1890/14-1261.1>.
Calculate rarefaction-based alpha- and beta-diversity. Offer parametric extrapolation to estimate the total expected species in a single community and the total expected shared species between two communities. Visualize the curve-fitting for these estimators.
Provides functions and command-line user interface to generate allocation sequence 
             by response-adaptive randomization for clinical trials. The package currently supports 
             two families of frequentist response-adaptive randomization procedures, Doubly Adaptive 
             Biased Coin Design ('DBCD') and Sequential Estimation-adjusted Urn Model ('SEU'), for 
             binary and normal endpoints. One-sided proportion (or mean) difference and Chi-square 
             (or 'ANOVA') hypothesis testing methods are also available in the package to  facilitate 
             the inference for treatment effect. Additionally, the package provides comprehensive and 
             efficient tools to allow one to evaluate and  compare the performance of randomization 
             procedures and tests based on various criteria. For example, plots for relationship among 
             assumed treatment effects,  sample size, and power are provided. Five allocation functions 
             for 'DBCD' and six addition rule functions for 'SEU' are implemented to target allocations 
             such as 'Neyman', 'Rosenberger' Rosenberger et al. (2001) 
             <doi:10.1111/j.0006-341X.2001.00909.x> and 'Urn' allocations.
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.
Some response-adaptive randomization methods commonly found in literature are included in this package. These methods include the randomized play-the-winner rule for binary endpoint (Wei and Durham (1978) <doi:10.2307/2286290>), the doubly adaptive biased coin design with minimal variance strategy for binary endpoint (Atkinson and Biswas (2013) <doi:10.1201/b16101>, Rosenberger and Lachin (2015) <doi:10.1002/9781118742112>) and maximal power strategy targeting Neyman allocation for binary endpoint (Tymofyeyev, Rosenberger, and Hu (2007) <doi:10.1198/016214506000000906>) and RSIHR allocation with each letter representing the first character of the names of the individuals who first proposed this rule (Youngsook and Hu (2010) <doi:10.1198/sbr.2009.0056>, Bello and Sabo (2016) <doi:10.1080/00949655.2015.1114116>), A-optimal Allocation for continuous endpoint (Sverdlov and Rosenberger (2013) <doi:10.1080/15598608.2013.783726>), Aa-optimal Allocation for continuous endpoint (Sverdlov and Rosenberger (2013) <doi:10.1080/15598608.2013.783726>), generalized RSIHR allocation for continuous endpoint (Atkinson and Biswas (2013) <doi:10.1201/b16101>), Bayesian response-adaptive randomization with a control group using the Thall \& Wathen method for binary and continuous endpoints (Thall and Wathen (2007) <doi:10.1016/j.ejca.2007.01.006>) and the forward-looking Gittins index rule for binary and continuous endpoints (Villar, Wason, and Bowden (2015) <doi:10.1111/biom.12337>, Williamson and Villar (2019) <doi:10.1111/biom.13119>).
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'.
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.
Implements sample size and power calculation methods with a focus on balance and fairness in study design, inspired by the Zoroastrian deity Rashnu, the judge who weighs truth. Supports survival analysis and various hypothesis testing frameworks.
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. The 
    theoretical background of the algorithms and code examples are presented in Fuentes et al. (2022). 
    <doi:10.32614/RJ-2022-036>.
Reading, writing, manipulating, analyzing and modeling of spatial data. This package has been superseded by the "terra" package <https://CRAN.R-project.org/package=terra>.
R-based access to a large set of data variables relevant to forest ecology in British Columbia (BC), Canada. Layers
    are in raster format at 100m resolution in the BC Albers projection, hosted at the Federated Research Data Repository (FRDR)
    with <doi:10.20383/101.0283>. The collection includes: elevation; biogeoclimatic zone; wildfire; cutblocks; forest attributes from
    Hansen et al. (2013) <doi:10.1139/cjfr-2013-0401> and Beaudoin et al. (2017) <doi:10.1139/cjfr-2017-0184>; and rasterized
    Forest Insect and Disease Survey (FIDS) maps for a number of insect pest species, all covering the period 2001-2018.
    Users supply a polygon or point location in the province of BC, and 'rasterbc' will download the overlapping raster tiles
    hosted at FRDR, merging them as needed and returning the result in R as a 'SpatRaster' object. Metadata associated with these
    layers, and code for downloading them from their original sources can be found in the 'github' repository
    <https://github.com/deankoch/rasterbc_src>.
Provides methods for calculating diversity indices on numerical
    matrices, based on information theory, following Rocchini, Marcantonio
    and Ricotta (2017) <doi:10.1016/j.ecolind.2016.07.039> and Rocchini et al.
    (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 cell of a raster map. The raster of objects contains a 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.
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.
Generate 'SpatRaster' objects, as defined by the 'terra'
    package, from digital images, using a specified spatial object as a
    geographical reference.
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.
Includes algorithms to assess research productivity and patterns, such as the h-index and i-index. Cardoso et al. (2022) Cardoso, P., Fukushima, C.S. & Mammola, S. (2022) Quantifying the internationalization and representativeness in research. Trends in Ecology and Evolution, 37: 725-728.
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>.
A simplified version of the Portal Project Database designed for
    teaching. It provides a real world example of life-history, population, and
    ecological data, with sufficient complexity to teach many aspects of data
    analysis and management, but with many complexities removed to allow
    students to focus on the core ideas and skills being taught. The full
    database (which should be used for research) is available at
    <https://github.com/weecology/PortalData>.
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.
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 binomial or Poisson rates and
    their differences or ratios. Including the rate (or risk)
    difference ('RD') or rate ratio (or relative risk, 'RR') for binomial
    proportions or Poisson rates, and odds ratio ('OR', binomial only).
    Also confidence intervals for RD, RR or OR for paired binomial data, 
    and estimation of a proportion from clustered binomial data.  
    Includes skewness-corrected asymptotic score ('SCAS') methods, 
    which have been developed in Laud (2017) <doi:10.1002/pst.1813> 
    from Miettinen and Nurminen (1985) <doi:10.1002/sim.4780040211> 
    and Gart and Nam (1988) <doi:10.2307/2531848>, and in 
    Laud (2025, under review) for paired proportions. The same score produces 
    hypothesis tests that are improved versions of the non-inferiority test 
    for binomial RD and RR by Farrington and Manning (1990) 
    <doi:10.1002/sim.4780091208>, or a generalisation of the McNemar test for 
    paired data. The package also includes MOVER methods (Method Of Variance
    Estimates Recovery) for all contrasts, derived from the Newcombe
    method but with options to use equal-tailed intervals in place of the
    Wilson score method, and generalised for Bayesian applications
    incorporating prior information. So-called 'exact' methods for
    strictly conservative coverage are approximated using continuity
    adjustments, and the amount of adjustment can be selected to avoid
    over-conservative coverage.  Also includes methods for stratified
    calculations (e.g. meta-analysis), either with fixed effect assumption
    (matching the CMH test) 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.
Functions to manipulate rational functions, including basic
    arithmetic operators, derivatives, and integrals with EXPLICIT forms.
Provides functions to deal with matrix algebra for matrices
    with rational entries: determinant, rank, image and kernel, inverse,
    Cholesky decomposition. All computations are exact.
Based on the 'qspray' package, this package introduces the new 
    type 'ratioOfQsprays'. An object of type 'qspray' represents a multivariate 
    polynomial with rational coefficients while an object of type 
    'ratioOfQsprays', defined by two 'qspray' objects, represents a fraction of 
    two multivariate polynomials with rational coefficients. Arithmetic 
    operations for these objects are available, and they always return 
    irreducible fractions. Other features include: differentiation, 
    evaluation, conversion to a function, and fine control of the way to print 
    a 'ratioOfQsprays' object. The 'C++' library 'CGAL' is used to make the 
    fractions irreducible.
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).
Collection of tools for the analysis of the resilience of dynamic networks. Created as a classroom project.
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.
  Note that RGtk2 and cairoDevice have been archived on CRAN.
  See <https://rattle.togaware.com> for installation instructions.
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.
Defines storage standard for Read, process, and analyze 
    intracranial electroencephalography and deep-brain stimulation in 'RAVE', 
    a reproducible framework for analysis and visualization of iEEG
    by Magnotti, Wang, and Beauchamp, (2020, 
    <doi:10.1016/j.neuroimage.2020.117341>). 
    Supports brain imaging data structure (BIDS) 
    <https://bids.neuroimaging.io> and native file structure to
    ingest signals from 'Matlab' data files, hierarchical data format 5 (HDF5), 
    European data format (EDF), BrainVision core data format (BVCDF), or 
    BlackRock Microsystem (NEV/NSx); process images in 
    Neuroimaging informatics technology initiative (NIfTI) and 
    'FreeSurfer' formats, providing brain imaging normalization to template 
    brain, facilitating 'threeBrain' package for comprehensive electrode 
    localization via 'YAEL' (your advanced electrode localizer) by Wang, 
    Magnotti, Zhang, and Beauchamp 
    (2023, <doi:10.1523/ENEURO.0328-23.2023>).
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>.
Defines the underlying pipeline structure for reproducible 
    neuroscience, adopted by 'RAVE' (reproducible analysis and visualization 
    of intracranial electroencephalography); provides high-level class 
    definition to build, compile, set, execute, and share analysis pipelines. 
    Both R and 'Python' are supported, with 'Markdown' and 'shiny' dashboard 
    templates for extending and building customized pipelines. See the full 
    documentations at <https://rave.wiki>; to cite us, 
    check out our paper by Magnotti, Wang, and Beauchamp (2020, 
    <doi:10.1016/j.neuroimage.2020.117341>), or run 
    citation("ravepipeline") for details.
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>.
Implemented fast and memory-efficient Notch-filter, 
    Welch-periodogram, discrete wavelet spectrogram for minutes of 
    high-resolution signals, fast 3D convolution, image registration,
    3D mesh manipulation; providing fundamental toolbox for intracranial 
    Electroencephalography (iEEG) pipelines. 
    Documentation and examples about 'RAVE' project are provided at 
    <https://rave.wiki>, and the paper by John F. Magnotti, 
    Zhengjia Wang, Michael S. Beauchamp (2020) 
    <doi:10.1016/j.neuroimage.2020.117341>; see 'citation("ravetools")' for 
    details.
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.
Generates polygon straight skeletons and 3D models.  
    Provides functions to create and visualize interior polygon offsets, 
    3D beveled polygons, and 3D roof models.
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 and text overlays, generate camera vignette effects, and add titles to images.
Downloads and parses 'SDF' (Structural Description Format) and 'PDB' (Protein Database) files for 3D rendering.
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.
Download and parse public files released by B3 and convert them
    into useful formats and data structures common to data analysis
    practitioners.
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 on accumulation rates and their variability. See Blaauw & Christen (2011).
Simulation of phenotype / genotype data under assortative mating. 
    Includes functions for generating Bahadur order-2 multivariate Bernoulli 
    variables with general and diagonal-plus-low-rank correlation structures. 
    Further details are provided in: Border and Malik (2022) 
    <doi:10.1101/2022.10.13.512132>.
An interface to the 'BaM' (Bayesian Modeling) engine, 
    a 'Fortran'-based executable aimed at estimating a model 
    with a Bayesian approach and using it for prediction,
    with a particular focus on uncertainty quantification.
    Classes are defined for the various building blocks of 
    'BaM' inference (model, data, error models, Markov Chain Monte Carlo (MCMC) samplers, predictions).
    The typical usage is as follows:
    (1) specify the model to be estimated; 
    (2) specify the inference setting (dataset, parameters, error models...);
    (3) perform Bayesian-MCMC inference;
    (4) read, analyse and use MCMC samples;
    (5) perform prediction experiments.
    Technical details are available (in French) in 
    Renard (2017) <https://hal.science/hal-02606929v1>.
    Examples of applications include 
    Mansanarez et al. (2019) <doi:10.1029/2018WR023389>,
    Le Coz et al. (2021) <doi:10.1002/hyp.14169>,
    Perret et al. (2021) <doi:10.1029/2020WR027745>,
    Darienzo et al. (2021) <doi:10.1029/2020WR028607> and
    Perret et al. (2023) <doi:10.1061/JHEND8.HYENG-13101>.
'BaseX' <https://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.
A novel bias-bound approach for non-parametric inference is introduced, 
    focusing on both density and conditional expectation estimation. 
    It constructs valid confidence intervals that account for the presence of 
    a non-negligible bias and thus make it possible to perform inference with 
    optimal mean squared error minimizing bandwidths. 
    This package is based on Schennach (2020) <doi:10.1093/restud/rdz065>.
Flexible statistical modelling using a modular framework for
    regression, in which groups of transformations are composed together
    and act on probability distributions.
The Brazilian Central Bank API delivers many datasets which regard economic
  activity, regional economy, international economy, public finances, credit
  indicators and many more. For more information please see <http://dadosabertos.bcb.gov.br/>.
  These datasets can be accessed through 'rbcb' functions and can be obtained in
  different data structures common to R ('tibble', 'data.frame', 'xts', ...).
Univariate and multivariate versions of risk-based control charts. Univariate versions of control charts, such as the risk-based version of X-bar, Moving Average (MA), Exponentially Weighted Moving Average Control Charts (EWMA), and Cumulative Sum Control Charts (CUSUM) charts. The risk-based version of the multivariate T2 control chart. Plot and summary functions. Kosztyan et. al. (2016) <doi:10.1016/j.eswa.2016.06.019>.
Issues RPC-JSON calls to 'bitcoind', the daemon of Bitcoin Cash (BCH), to
    extract transaction data from the blockchain. BCH is a fork of Bitcoin that permits
    a greater number of transactions per second. A BCH daemon is available under an MIT
    license from the Bitcoin Unlimited website <https://www.bitcoinunlimited.info>.
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.
Provide estimation and data generation tools for the quantile generalized beta 
        regression model. For details, see Bourguignon, Gallardo and Saulo <arXiv:2110.04428>
        The package also provides tools to perform covariates selection.
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, breakpoints, structural breaks, or join-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.
rbenchmark is inspired by the Perl module Benchmark, and
        is intended to facilitate benchmarking of arbitrary R code. The
        library consists of just one function, benchmark, which is a
        simple wrapper around system.time.  Given a specification of
        the benchmarking process (counts of replications, evaluation
        environment) and an arbitrary number of expressions, benchmark
        evaluates each of the expressions in the specified environment,
        replicating the evaluation as many times as specified, and
        returning the results conveniently wrapped into a data frame.
An implementation of robust bent line regression. It can fit the bent line regression and test the existence of change point,
    for the paper, "Feipeng Zhang and Qunhua Li (2016). Robust bent line regression, submitted."
Tool-set to support Bayesian evidence synthesis.  This
    includes meta-analysis, (robust) prior derivation from historical
    data, operating characteristics and analysis (1 and 2 sample
    cases). Please refer to Weber et al. (2021) <doi:10.18637/jss.v100.i19>
    for details on applying this package while Neuenschwander et al. (2010)
    <doi:10.1177/1740774509356002> and Schmidli et al. (2014)
    <doi:10.1111/biom.12242> explain details on the methodology.
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.
Provides a complete interface to 'LibBi', a library for Bayesian
    inference (see <https://libbi.org> and Murray, 2015
    <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>.
Provides bioaccumulation factors from a toxicokinetic model 
    fitted to accumulation-depuration data. It is designed to fulfil the requirements
    of regulators when examining applications for market authorization of active
    substances.
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 the technicalities of
    using web services API and creates a unified and easy-to-use interface
    to biological and medical web services. This is 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.
    Read/write all 'BIOM' formats. Compute rarefaction, alpha diversity, and 
    beta diversity (including 'UniFrac'). Summarize counts by taxonomic level. 
    Subset based on metadata. Generate visualizations and statistical analyses. 
    CPU intensive operations are coded in C for speed.
Functions for connecting to BioUML server, querying BioUML repository and launching BioUML analyses.
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.
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 visualize bio-loggers time series at a microsecond precision as Acceleration, Temperature, Pressure, Light intensity. It is possible to link behavioral labels extracted
  from 'BORIS' software <http://www.boris.unito.it> or manually written in a csv file.
BM25 is a ranking function used by search engines to rank matching documents according to their relevance to a user's search query.
    This package provides a light wrapper around the 'BM25' 'rust' crate for Okapi BM25 text search.
    For more information, see Robertson et al. (1994) <https://trec.nist.gov/pubs/trec3/t3_proceedings.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>.
Implements standard and reference based multiple imputation methods for continuous
    longitudinal endpoints (Gower-Page et al. (2022) <doi:10.21105/joss.04251>). In particular,
    this package supports deterministic conditional mean imputation and jackknifing as described
    in Wolbers et al.  (2022) <doi:10.1002/pst.2234>, Bayesian multiple imputation as described
    in Carpenter et al. (2013) <doi:10.1080/10543406.2013.834911>, and bootstrapped maximum
    likelihood imputation as described in von Hippel and Bartlett (2021) <doi: 10.1214/20-STS793>.
Provides utility functions that extend the capabilities of the 
    reference-based multiple imputation package 'rbmi'. It supports clinical 
    trial analysis workflows with functions for managing imputed datasets, 
    applying analysis methods across imputations, and tidying results for 
    reporting.
Creation, manipulation, simulation of linear Gaussian Bayesian
             networks from text files and more...
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.
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. The sensitivity analysis methods in this package are documented in Rosenbaum (2002) Observational Studies, <doi:10.1007/978-1-4757-3692-2>, Springer-Verlag.
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.
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, rbwPoint(), rbwMed(), and rbwPanel(),
  that produce residual balancing weights for estimating (a), (b), (c), respectively.
Data for the examples and exercises in the book "R by Example".  
    Jim Albert and Maria Rizzo (2012, ISBN 978-1-4614-1365-3).
Relational Class Analysis (RCA) is a method for detecting
        heterogeneity in attitudinal data (as described in Goldberg
        A., 2011, Am. J. Soc, 116(5)).
Tools for preprocessing and processing canopy photographs with 
      support for raw data reading. Provides methods to address variability in 
      sky brightness and to mitigate errors from image acquisition in 
      non-diffuse light. Works with all types of fish-eye lenses, and some 
      methods also apply to conventional lenses.
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>.
Download and handle spatial and temporal data from the CAMELS-CL dataset (Catchment Attributes and Meteorology for Large Sample Studies, Chile) <https://camels.cr2.cl/>, developed by Alvarez-Garreton et al. (2018) <doi:10.5194/hess-22-5817-2018>. The package does not generate new data, it only facilitates direct access to the original dataset for hydrological analyses.
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.
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.
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 <doi:10.1007/s10618-019-00647-x>.
Generates tile maps for the East Caucasian language family, inspired by the Typological Atlas of the Languages of Daghestan (TALD, <https://lingconlab.ru/tald/>). It leverages 'ggplot2' to create visually informative maps, displaying rectangles for each language and allowing for color-coding based on linguistic features. The package includes a built-in dataset of 56 languages and the template for their distribution and provides flexibility to customize the tile map's appearance. The default template can be modified via the ability to hide or rename languages.  It's designed to be used with external data tables containing language information and features, offering a tool for visualizing the geographic distribution and linguistic characteristics of East Caucasian languages.
CausalEGM is a general causal inference framework for estimating causal effects by encoding generative modeling, which can be applied in both discrete and continuous treatment settings. A description of the methods is given in Liu (2022) <arXiv:2212.05925>.
Generate causally-simulated data to serve as ground truth for evaluating methods in causal discovery and effect estimation. The package provides tools to assist in defining functions based on specified edges, and conversely, defining edges based on functions. It enables the generation of data according to these predefined functions and causal structures. This is particularly useful for researchers in fields such as artificial intelligence, statistics, biology, medicine, epidemiology, economics, and social sciences, who are developing a general or a domain-specific methods to discover causal structures and estimate causal effects. Data simulation adheres to principles of structural causal modeling. Detailed methodologies and examples are documented in our vignette, available at <https://htmlpreview.github.io/?https://github.com/herdiantrisufriyana/rcausim/blob/master/doc/causal_simulation_exemplar.html>.
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.
	See Pimentel, et al.(2015) <doi:10.1080/01621459.2014.997879> 
	and Pimentel (2016), Obs. Studies 2(1):4-23. The rrelaxiv 
	package, which provides an alternative solver for
	the underlying network flow problems, carries an
	academic license and is not available on CRAN, but
	may be downloaded from Github at 
	<https://github.com/josherrickson/rrelaxiv/>.
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.  See Pimentel, 
	et al.(2015) <doi:10.1080/01621459.2014.997879>
	and Pimentel and Kelz (2020) 
	<doi:10.1080/01621459.2020.1720693>. The rrelaxiv 
	package, which provides an alternative solver for
	the underlying network flow problems, carries an
	academic license and is not available on CRAN, but
	may be downloaded from Github at 
	<https://github.com/josherrickson/rrelaxiv/>.
Functions to implement the parametric and non-parametric bootstrap 
    confidence interval methods described in Morrison and Simon (2017) <arXiv:1702.06986>.
Fit Class Cover Catch Digraph Classification models that can be 
    used in machine learning. Pure and proper and random walk approaches are 
    available. Methods are explained in Priebe et al. (2001) 
    <doi:10.1016/S0167-7152(01)00129-8>, Priebe et al. (2003) 
    <doi:10.1007/s00357-003-0003-7>, and Manukyan and Ceyhan (2016) 
    <doi:10.48550/arXiv.1904.04564>.
The handling of an API key (misnomer for password) for protected
    data can be difficult. This package provides secure convenience functions for 
    entering / handling API keys and pulling data directly into memory. By default
    it will load from REDCap instances, but other sources are injectable via
    inversion of control.
The RCC_PCA criterion is a tool to determine the optimal number of components to retain in PCA;See Alshammri (2021).
Provides functions to construct efficient row-column designs for 3-level factorial experiments in 3 rows. The designs ensure the estimation of all main effects (full efficiency) and two factor interactions in minimum replications. For more details, see Dey, A. and Mukerjee, R. (2012) <doi:10.1016/j.spl.2012.06.014> and Dash, S., Parsad, R., and Gupta, V. K. (2013) <doi:10.1007/s40003-013-0059-5>.
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.
With this package we provide an easy method to compute robust and conditional Data Envelopment Analysis (DEA), 
             Free Disposal Hull (FDH) and Benefit of the Doubt (BOD) scores. 
             The robust approach is based on the work of Cazals, Florens and Simar (2002) <doi:10.1016/S0304-4076(01)00080-X>. 
             The conditional approach is based on Daraio and Simar (2007) <doi:10.1007/s11123-007-0049-3>.
             Besides we provide graphs to help with the choice of m. 
             We relay on the 'Benchmarking' package to compute the efficiency scores and on the 'np' package to compute non parametric estimation of similarity among units.
Utilities for reading, writing, and managing RCDF files, including encryption and decryption support. It offers a flexible interface for handling data stored in encrypted Parquet format, along with metadata extraction, key management, and secure operations using Advanced Encryption Standard (AES) and Rivest-Shamir-Adleman (RSA) encryption.
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>.
Provides a translation layer between 'R' and 'CDO' operators. Each 
  operator is it's own function with documentation. Nested or piped functions
  will be translated into 'CDO' chains.
Performs 2D Delaunay triangulation, constrained or
    unconstrained, with the help of the C++ library 'CDT'. A function to
    plot the triangulation is provided. The constrained Delaunay
    triangulation has applications in geographic information systems.
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.
Provides functions to generate censored samples of type I, II and III, from any random sample generator. It also supplies the option to create left and right censorship. Along with this, the generation of samples with interval censoring is in the  testing phase, with two options of fixed length intervals and random lengths.
The key function 'get_vintage_data()' returns a dataframe and is 
    the window into the Census Bureau API requiring just a dataset name, 
    vintage(year), and vector of variable names for survey estimates/percentages. 
    Other functions assist in searching for available datasets, geographies, 
    group/variable concepts of interest.  Also provided are functions to access 
    and layer (via standard piping) displayable geometries for the US, states, 
    counties, blocks/tracts, roads, landmarks, places, and bodies of water. 
    Joining survey data with many of the geometry functions is built-in to 
    produce choropleth maps.
To facilitate using 'cereal' with R via 'cpp11' or '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
    <https://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.
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.
Interface to the 'ChEA3' transcription factor enrichment API.
    'ChEA3' integrates evidence from ChIP-seq, co-expression, and literature
    resources to prioritize transcription factors regulating a given set
    of genes. This package provides convenient R functions to query the API,
    retrieve ranked results across collections (including integrated scores),
    and standardize output for downstream analysis in R/Bioconductor workflows.
    See <https://maayanlab.cloud/chea3/> or Keenan (2019) <doi:10.1093/nar/gkz446>
    for further details.
Data exploration and prediction with focus on high dimensional data and chemometrics.
    The package was initially designed about partial least squares regression and discrimination models and variants, in particular locally weighted PLS models (LWPLS). Then, it has been expanded to many other methods for analyzing high dimensional data.
    The name 'rchemo' comes from the fact that the package is orientated to chemometrics, but most of the provided methods are fully generic to other domains.
    Functions such as transform(), predict(), coef() and summary() are available. Tuning the predictive models is facilitated by generic functions gridscore() (validation dataset) and gridcv() (cross-validation). Faster versions are also available for models based on latent variables (LVs) (gridscorelv() and gridcvlv()) and ridge regularization (gridscorelb() and gridcvlb()).
Provides a dataset of functions in all base and recommended packages of R versions 0.50 onwards.
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.
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.
Client for 'ChromaDB', a vector database for storing and querying embeddings. This package provides a convenient interface to interact with the REST API of 'ChromaDB' <https://docs.trychroma.com>.
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.
The concept of reliable and clinically significant change
    (Jacobson & Truax, 1991) helps you answer the following questions for a
    sample with two measurements at different points in time (pre & post):
    Which proportion of my sample has a (considering the reliability of the
    instrument) probably not-just-by-chance difference in pre- vs. post-scores?
    Which proportion of my sample does not only change in a statistically
    significant way (see question one), but also in a clinically significant way
    (e.g. change from a test score regarded "dysfunctional" to a score regarded
    "functional")?
    This package allows you to very easily create a scatterplot of your sample
    in which the x-axis maps to the pre-scores, the y-axis maps to the
    post-scores and several graphical elements (lines, colors) allow you to gain
    a quick overview about reliable changes in these scores.
    An example of this kind of plot is Figure 2 of Jacobson & Truax (1991).
    Referenced article:
    Jacobson, N. S., & Truax, P. (1991) <doi:10.1037/0022-006X.59.1.12>.
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/>.
Functions to assist manipulation of matrix 
             row and column labels for all types of matrix mathematics
             where row and column labels are to be respected.
Cross-Linguistic Data Format (CLDF) is a framework for storing 
    cross-linguistic data, ensuring compatibility and ease of data exchange between
    different linguistic datasets see Forkel et al. (2018) <doi:10.1038/sdata.2018.205>. 
    The 'rcldf' package is designed to facilitate the manipulation and analysis of 
    these datasets by simplifying the loading, querying, and visualisation of CLDF
    datasets making it easier to conduct comparative linguistic analyses, manage 
    language data, and apply statistical methods directly within R.
'Yandex Clickhouse' (<https://clickhouse.com/>) 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.
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', and a a reactive copy-to-clipboard UI link 
    component, called 'rclipLink', for 'shiny' R applications.
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().
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, refer to Aizaki and Fogarty (2023) <doi:10.1016/j.jocm.2022.100394>.
Adds menu items for case 2 (profile case) best-worst scaling (BWS2) to the R Commander. BWS2 is a question-based survey method that constructs profiles (combinations of attribute levels) using an orthogonal array, asks respondents to select the best and worst levels in each profile, and measures preferences for attribute levels by analyzing the responses. For details, see Aizaki and Fogarty (2019) <doi:10.1016/j.jocm.2019.100171>.
Adds menu items for case 3 (multi-profile) best-worst scaling (BWS3) to the R Commander. BWS3 is a question-based survey method that designs various combinations of attribute levels (profiles), asks respondents to select the best and worst profiles in each choice set, and then measures preferences for the attribute levels by analyzing the responses. For details on BWS3, refer to Louviere et al. (2015) <doi:10.1017/CBO9781107337855>.
Provides an 'Rcmdr' plug-in based on the 'cpd' package.
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.
Provides a platform-independent GUI for design of experiments. The package 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, ...).
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 10,000 scientific articles.
Rcmdr Plugin for the 'FactoMineR' 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.
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.
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.
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 package. Also it ads a Rcmdr GUI extension for Hosmer and Lemeshow GOF test from the package ResourceSelection.
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.
An R Commander plug-in for the survival
  package, with dialogs for Cox models, parametric survival regression models,
  estimation of survival curves, and testing for differences in survival
  curves, along with data-management facilities and a variety of tests, 
  diagnostics and graphs.
Provides an Rcmdr "plug-in" based on the TeachingDemos package, and is primarily for illustrative purposes.
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,
    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 work flows and scripts for first-time users.
Data sets, and functions for simulating and fitting nonlinear time series with minification and nonparametric models.
Sample size and confidence interval calculations in reversible catalytic models, with applications in malaria research. Further details can be found in the paper by Sepúlveda and Drakeley (2015, <doi:10.1186/s12936-015-0661-z>).
Detects copy number alteration events in targeted exon sequencing 
    data for tumor samples without matched normal controls. The advantage of 
    this method is that it can be applied to smaller sequencing panels including 
    evaluations of exon, transcript, gene, or even user specified genetic
    regions of interest. Functions in the package include steps for GC-content
    correction, calculation of quantile based normal karyotype ranges,
    and calculation of feature score.  Cutoffs for "normal" quantile and score
    are user-adjustable.
Functions in this package will import filtered variant call format (VCF) files of SNPs data and generate data sets to detect copy number variants, visualize them and do downstream analyses with copy number variants(e.g. Environmental association analyses).
Automatically apply different strategies to optimize R code. 
    'rco' functions take R code as input, and returns R code as output.
A data structure and toolkit for documenting and recoding
    categorical data that can be shared in other statistical software.
Simplifies integration with 'Amazon Cognito' (<https://aws.amazon.com/cognito/>) for R developers, enabling easy management of user authentication, registration, and password flows.
The 'Coinbase Advanced Trade API' <https://docs.cdp.coinbase.com/api-reference/advanced-trade-api/rest-api/introduction> lets you manage orders, portfolios, products, and fees with the new v3 endpoints.
Provides functionality to group lines that form naturally
    continuous lines in a spatial network. The algorithm implemented is based
    on the Continuity in Street Networks (COINS) method from Tripathy et al.
    (2021) <doi:10.1177/2399808320967680>, which identifies continuous
    "strokes" in the network as the line strings that maximize the angles
    between consecutive segments.
Get your data (forms, structures, answers) from Coletum 
    <https://coletum.com> to handle and analyse.
A collection of data sets relating to ADHD (Attention Deficit 
    Hyperactivity Disorder) which have been sourced from other packages on CRAN
    or from publications on other websites such as Kaggle
    <http://www.kaggle.com/>.The package also includes some simple functions for
    analysing data sets. The data sets and descriptions of the data sets may
    differ from what is on CRAN or other source websites. The aim of this
    package is to bring together data sets from a variety of ADHD research
    publications. This package would be useful for those interested in finding
    out what research has been done on the topic of ADHD, or those interested in
    comparing the results from different existing works. I started this project
    because I wanted to put together a collection of the data sets relevant to
    ADHD research, which I have a personal interest in. This work was conducted
    with the support of my mentor within the Global Talent Mentoring platform.
    <https://globaltalentmentoring.org/>.
Provides color schemes for maps (and other graphics)
        designed by Cynthia Brewer as described at http://colorbrewer2.org.
Provides a collection of palettes designed to integrate with 'ggplot', reflecting the color schemes associated with 'ConesaLab'.
'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 <https://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>.
Minimal and lightweight configuration tool that provides basic support for 'YAML' configuration files without requiring additional package dependencies. It offers a simple method for loading and parsing configuration settings, making it ideal for quick prototypes and lightweight projects.
Configuration management using files (YAML, JSON, INI, TXT),
  JSON strings, and command line arguments. Command line arguments
  can be used to override configuration. Period-separated command line
  flags are parsed as hierarchical lists. Environment variables, R global variables,
  and configuration values can be substituted.
Solve some conic related problems (intersection of conics with lines and conics, arc length of an ellipse, polar lines, etc.).
'TROLL' is coded in C++ and it typically simulates hundreds of
    thousands of individuals over hundreds of years. The 'rcontroll' R package 
    is a wrapper of 'TROLL'. 'rcontroll' includes functions that generate inputs 
    for simulations and run simulations. Finally, it is possible to analyse 
    the 'TROLL' outputs through tables, figures, and maps taking advantage of 
    other R visualisation packages. 'rcontroll' also offers the possibility to 
    generate a virtual LiDAR point cloud that corresponds to a snapshot of 
    the simulated forest.
Convert text into target classifications (e.g., ISO 3166-1) using a
    JSON mapping with regular expressions. Provides helpers to return the full
    mapping and associated metadata.
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.
Provides a set of functions to perform pathway analysis and meta-analysis from multiple gene expression datasets, as well as visualization of the results. This package wraps functionality from the following packages: Ritchie et al. (2015) <doi:10.1093/nar/gkv007>, Love et al. (2014) <doi:10.1186/s13059-014-0550-8>, Robinson et al. (2010) <doi:10.1093/bioinformatics/btp616>, Korotkevich et al. (2016) <arxiv:10.1101/060012>, Efron et al. (2015) <https://CRAN.R-project.org/package=GSA>, and Gu et al. (2012) <https://CRAN.R-project.org/package=CePa>.
Bundles the datasets and functions featured in Philip H. 
    Pollock and Barry C. Edwards<https://edge.sagepub.com/pollock>, "An R Companion to Political Analysis, 3rd Edition," Thousand Oaks, CA: Sage Publications.
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.
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 iterators implemented in
    C++ for solving problems in combinatorics and computational mathematics.
    Handles various combinatorial objects including combinations, permutations,
    integer partitions and compositions, Cartesian products, unordered
    Cartesian products, and partition of groups. Utilizes the RMatrix class
    from 'RcppParallel' for thread safety. The combination and permutation
    functions contain constraint parameters that allow for generation of all
    results of a vector meeting specific criteria (e.g. finding all
    combinations such that the sum is between two bounds). Capable of
    ranking/unranking combinatorial objects efficiently (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 aiming towards
 a good balance between speed and ease of use. It provides high-level syntax and
 functionality deliberately similar to Matlab. It is useful for algorithm development
 directly in C++, or quick conversion of research code into production environments.
 It provides efficient classes for vectors, matrices and cubes where dense and sparse
 matrices are supported. Integer, floating point and complex numbers are supported.
 A sophisticated expression evaluator (based on template meta-programming) automatically
 combines several operations to increase speed and efficiency. Dynamic evaluation
 automatically chooses optimal code paths based on detected matrix structures.
 Matrix decompositions are provided through integration with LAPACK, or one of its
 high performance drop-in replacements (such as 'MKL' or 'OpenBLAS'). It can
 automatically use 'OpenMP' multi-threading (parallelisation) to speed up
 computationally expensive operations.

   The 'RcppArmadillo' package includes the header files from the 'Armadillo' library;
 users do not need to install 'Armadillo' itself in order to use 'RcppArmadillo'.
 Starting from release 15.0.0, the minimum compilation standard is C++14 so 'Armadillo'
 version 14.6.3 is included as a fallback when an R package forces the C++11 standard.
 Package authors should set a '#define' to select the 'current' version, or select the
 'legacy' version (also chosen as default) if they must. See 'GitHub issue #475' for
 details.

   Since release 7.800.0, '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'.
Interoperability between 'Rcpp' and the 'C++11' array and tuple
    types. Linking to this package allows fixed-length 'std::array' objects to
    be converted to and from equivalent R vectors, and 'std::tuple' objects
    converted to lists, via the as() and wrap() functions. There is also
    experimental support for 'std::span' from 'C++20'.
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 64 with R)
 to present a 'ptime' object (but this needs recompilation with
 a #define set).
Exports an 'Rcpp' interface for the Bessel functions in the 'Bessel' package, which can then be called from the 'C++' code of other packages. For the original 'Fortran' implementation of these functions see Amos (1995) <doi:10.1145/212066.212078>.
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). 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.
Blaze is an open-source, high-performance 'C++' math library for dense and sparse arithmetic. 
    With its state-of-the-art Smart Expression Template implementation Blaze combines the elegance and 
    ease of use of a domain-specific language with HPC-grade performance, making it one of the most 
    intuitive and fastest 'C++' math libraries available. The 'RcppBlaze' package includes the header files 
    from the 'Blaze' library with disabling some functionalities related to link to the thread and system
    libraries which make 'RcppBlaze' be a header-only library. Therefore, users do not need to install 
    'Blaze'.
'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 parameters in linear spatial models with 
  censored/missing responses via the Expectation-Maximization (EM), the Stochastic 
  Approximation EM (SAEM), or the Monte Carlo EM (MCEM) algorithm. These algorithms 
  are widely used to compute the maximum likelihood (ML) estimates in problems with 
  incomplete data. 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. 
  In contrast, the SAEM algorithm splits the E-step into simulation and integration 
  steps. This package also approximates the standard error of the estimates using the 
  Louis method. Moreover, it has a function that performs spatial prediction in new 
  locations.
Creates a header only package to link to the 'CGAL' 
  (Computational Geometry Algorithms Library)
  header files in 'Rcpp'. There are a variety of potential uses for 
  the software such as Hilbert sorting, K-D Tree nearest neighbors, 
  and convex hull algorithms. For more information about how to use the header files, 
  see the 'CGAL' documentation at <https://www.cgal.org>. Currently
  downloads version 6.1 of the 'CGAL' header files.
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 as C++11 which should be the default on
 all platforms since the release of R 3.3.0.
In data science, it is a common practice to compute a series of columns (e.g. features) against a common response vector. Various metrics are provided with efficient computation implemented with 'Rcpp'.
Provides 'C++' header files to deal with color conversion
    from some color spaces to hexadecimal with 'Rcpp', and exports some
    color mapping functions for usage in R. Also exports functions to
    convert colors from the 'HSLuv' color space for usage in R. 'HSLuv' is
    a human-friendly alternative to HSL.
'Rcpp' Bindings for the C code of the 'Corpus Workbench' ('CWB'), an indexing and query 
  engine to efficiently analyze large corpora (<https://cwb.sourceforge.io>). '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 'pcre2' (BSD license, see <https://github.com/PCRE2Project/pcre2/blob/master/LICENCE.md>)
  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'.
'Rcpp' reimplementation of the the Bayesian non-parametric Dirichlet Process Regression model for penalized regression first published in Zeng and Zhou (2017) <doi:10.1038/s41467-017-00470-2>. A full Bayesian version is implemented with Gibbs sampling, as well as a faster but less accurate variational Bayes approximation.
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. 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' <doi:10.18637/jss.v040.i08>, 'Eigen' <doi:10.18637/jss.v052.i05> 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.
'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++14' and 'Armadillo' 10.8.2 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 <https://gallery.rcpp.org> regroups a large
 number of examples for 'Rcpp'.
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.
The header-only 'C++' template library 'FastAD' for automatic
 differentiation <https://github.com/JamesYang007/FastAD> is provided by
 this package, along with a few illustrative examples that can all be called
 from R.
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 <doi:10.1002/spe.2984>. '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>.
'Int64' values can be created and accessed via the 'bit64' package and its 'integer64'
 class which package the 'int64' representation cleverly into a 'double'. The 'nanotime' packages
 builds on this to support nanosecond-resolution timestamps. This packages helps conversions between
 'R' and 'C++' via several helper functions provided via a single header file.  A complete example
 client package is included as an illustration.
A wrapper for Jagger, a morphological analyzer proposed in Yoshinaga (2023) <arXiv:2305.19045>. Jagger uses patterns derived from morphological dictionaries and training data sets and applies them from the beginning of the input. This simultaneous and deterministic process enables it to effectively perform tokenization, POS tagging, and lemmatization.
The 'L-BFGS' algorithm is a popular optimization algorithm for unconstrained optimization problems. 
  'Blaze' is a high-performance 'C++' math library for dense and sparse arithmetic. 
  This package provides a simple interface to the 'L-BFGS' algorithm and allows users to optimize 
  their objective functions with 'Blaze' vectors and matrices in 'R' and 'Rcpp'.
The header-only modern 'C++' template library 'Magic Enum' for static reflection
 of 'enums' (to string, from string, iteration) is provided by this package. More
 information about the underlying library can be found at its repository at
 <https://github.com/Neargye/magic_enum>.
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.
A Minimal Example Package which demonstrates 'mlpack' use via C++ Code from R.
'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.
'Rcpp' bindings for 'PLANC', a highly parallel
             and extensible NMF/NTF  (Non-negative Matrix/Tensor Factorization) library.
             Wraps algorithms described in
             Kannan et. al (2018) <doi:10.1109/TKDE.2017.2767592> and
             Eswar et. al (2021) <doi:10.1145/3432185>.
             Implements algorithms described in
             Welch et al. (2019) <doi:10.1016/j.cell.2019.05.006>,
             Gao et al. (2021) <doi:10.1038/s41587-021-00867-x>, and
             Kriebel & Welch (2022) <doi:10.1038/s41467-022-28431-4>.
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' (or 'Valkey') key/value store
 using the C-language client library 'hiredis' (included as a fallback)
 with 'MsgPack' encoding provided via 'RcppMsgPack' headers. It now
 also includes the pub/sub functions from the 'rredis' package.
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
 <doi:10.48550/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
 from both their R and C or C++ code. Explicit use via 'LinkingTo:' is also supported. Also
 see the 'spdl' package which enhanced this package with a consistent R and C++ interface.
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>.
Provides 'Rcpp' bindings for 'cpptimer', a simple tic-toc timer class for benchmarking 'C++' code <https://github.com/BerriJ/cpptimer>. It's not just simple, it's blazing fast! This sleek tic-toc timer class supports overlapping timers as well as 'OpenMP' parallelism <https://www.openmp.org/>. It boasts a nanosecond-level time resolution. We did not find any overhead of the timer itself at this resolution. Results (with summary statistics) are automatically passed back to 'R' as a data frame.
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 to the 'toml++' parser written by Mark Gillard
 to R.
Using the efficient implementation in the Boost C++ library, functions are
 provided to generate vectors of 'Universally Unique Identifiers (UUID)' from R supporting
 random (version 4), name (version 5) and time (version 7) 'UUIDs'. The initial repository
 was at <https://gitlab.com/artemklevtsov/rcppuuid>.
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.
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 a 'faster but good enough'
 alternative for use with R and C++ code. See the 'zigg' package
 for a lighter implementation for much easier use in other packages.
Simplify the process of extracting and processing Clinical Practice
  Research Datalink (CPRD) data in order to build datasets ready for statistical
  analysis. This process is difficult in 'R', as the raw data is very large and 
  cannot be read into the R workspace. 'rcprd' utilises 'RSQLite' to create 
  'SQLite' databases which are stored on the hard disk. These are then  queried 
  to extract the required information for a cohort of interest, and create 
  datasets ready for statistical analysis. The  processes follow closely that 
  from the 'rEHR' package, see Springate et al., (2017) 
  <doi:10.1371/journal.pone.0171784>.
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.
Provides tools to automate the morphological delineation of
    riverside urban areas based on a method introduced in Forgaci (2018)
    <doi:10.7480/abe.2018.31>. Delineation entails the identification of
    corridor boundaries, segmentation of the corridor, and delineation of
    the river space using two-dimensional spatial information from street
    network data and digital elevation data in a projected CRS. The
    resulting delineation can be used to characterise spatial phenomena
    that can be related to the river as a central element.
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.
'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.
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>.
Restricted Cubic Splines were performed to explore the shape of association form of "U, inverted U,
    L" shape and test linearity or non-linearity base on "Cox,Logistic,linear,quasipoisson" regression, and auto output Restricted Cubic Splines figures.
    rcssci package could automatically draw RCS graphics with Y-axis "OR,HR,RR,beta".
    The Restricted Cubic Splines method were based on 
    Suli Huang (2022) <doi:10.1016/j.ecoenv.2022.113183>,Amit Kaura (2019) <doi:10.1136/bmj.l6055>,
    and Harrell Jr (2015, ISBN:978-3-319-19424-0 (Print) 978-3-319-19425-7 (Online)).
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>.
Provides various statistical methods for designing and analyzing two-stage randomized controlled 
	trials using the methods developed by Imai, Jiang, and Malani (2021) <doi:10.1080/01621459.2020.1775612> and 
	(2022+) <doi:10.48550/arXiv.2011.07677>.  The package enables the estimation of direct and spillover effects, 
	conduct hypotheses tests, and conduct sample size calculation for two-stage randomized controlled trials.
Predict fish year-class strength by calibration
  regression analysis of multiple recruitment index series.
Accurate prediction of subject recruitment for Randomized Clinical
    Trials (RCT) remains an ongoing challenge. Many previous prediction models rely
    on parametric assumptions. We present functions for non-parametric RCT
    recruitment prediction under several scenarios.
Validates estimates of (conditional) average treatment effects obtained using observational data by a) making it easy to obtain and visualize estimates derived using a large variety of methods (G-computation, inverse propensity score weighting, etc.), and b) ensuring that estimates are easily compared to a gold standard (i.e., estimates derived from randomized controlled trials). 'RCTrep' offers a generic protocol for treatment effect validation based on four simple steps, namely, set-selection, estimation, diagnosis, and validation. 'RCTrep' provides a simple dashboard to review the obtained results. The validation approach is introduced by Shen, L., Geleijnse, G. and Kaptein, M. (2023) <doi:10.21203/rs.3.rs-2559287/v2>.
Robust Clustering of Time Series (RCTS) has the functionality to cluster time series using both the classical and the robust interactive fixed effects framework. 
  The classical framework is developed in Ando & Bai (2017) <doi:10.1080/01621459.2016.1195743>. The implementation within this package excludes the SCAD-penalty on the estimations of beta. 
  This robust framework is developed in Boudt & Heyndels (2022) <doi:10.1016/j.ecosta.2022.01.002> and is made robust against different kinds of outliers.
  The algorithm iteratively updates beta (the coefficients of the observable variables), group membership, and the latent factors (which can be common and/or group-specific) along
  with their loadings. The number of groups and factors can be estimated if they are unknown.
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.
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. 3-parameter Hill equation from 'tcpl' package (Filer D et al.,  <doi:10.1093/bioinformatics/btw680>) and 4-parameter Hill equation from Curve Class2 approach (Wang Y et al.,  <doi:10.2174/1875397301004010057>) are available. 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.
Provides estimation and inference procedures for boundary regression discontinuity (RD) designs
    using local polynomial methods, based on either bivariate coordinates or distance-based approaches.
    Methods are developed in Cattaneo, Titiunik, and Yu (2025) 
    <https://mdcattaneo.github.io/papers/Cattaneo-Titiunik-Yu_2025_BoundaryRD.pdf>.
Native R only allows PDF exports of reference manuals.
    The 'Rd2md' package converts the package documentation files into
    markdown files and combines 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.
Provides functions implementing the shrunken centroids regularized discriminant analysis for
        classification purpose in high dimensional data. The method is described in 
        Guo at al. (2013) <doi:10.1093/biostatistics/kxj035>.
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 Lai J.,Zou Y., Zhang J.,Peres-Neto P.(2022) Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca.hp R package.Methods in Ecology and Evolution,13: 782-788 <DOI:10.1111/2041-210X.13800>.
Connect, execute, and parse results from the Daisi Microservice Platform <https://www.daisi.io/>. The rdaisi client
  includes a set of functionality that allows remote execution of microservices directly from R.
  Daisis allow R users to access a wide variety of Python functionality and interact with them natively.
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.
R implementation of the 'FAIR Data Pipeline API'. The 'FAIR Data 
    Pipeline' is intended to enable tracking of provenance of FAIR (findable, 
    accessible and interoperable) data used in epidemiological modelling.
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.
Download and access datasets from the Rdatasets archive
    (<https://vincentarelbundock.github.io/Rdatasets/>). The package provides
    functions to search, download, and view documentation for thousands of
    datasets from various R packages, available in both CSV and Parquet
    formats for efficient access.
A simple 'WebDAV' client that provides functions to fetch and send
  files or folders to servers using the 'WebDAV' protocol
  (see 'RFC' 4918 <https://www.rfc-editor.org/rfc/rfc4918>).
  Only a subset of the protocol is implemented (e.g. file locks are not yet
  supported).
R access to hundreds of millions data series from DBnomics API
    (<https://db.nomics.world/>).
Many packages in the 'r-dcm' family take similar arguments,
    which are checked for expected structures and values. Rather than
    duplicating code across several packages, commonly used check
    functions are included here.  This package can then be imported to
    access the check functions in other packages.
Estimation of both single- and multiple-assignment Regression Discontinuity Designs 
    (RDDs). Provides both parametric (global) and non-parametric (local) estimation choices for
    both sharp and fuzzy designs, along with power analysis and assumption checks. 
    Introductions to the underlying logic and analysis of RDDs are in 
    Thistlethwaite, D. L., Campbell, D. T. (1960) <doi:10.1037/h0044319> and
    Lee, D. S., Lemieux, T. (2010) <doi:10.1257/jel.48.2.281>.
Density discontinuity 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.
A direct interface to the underlying XML representation of DDI Codebook 2.5 with flexible API creation.
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>).
Screens all '.R', '.Rmd', and '.qmd' files to extract the name of
    packages used in a project. This package detects packages called with 
    'library(foo)', 'require(foo)', 'foo::bar()' and 'use("foo", "bar")' and 
    adds these dependencies in the DESCRIPTION file in the sections Depends, 
    Imports, and Suggests.
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.
Honest and nearly-optimal confidence intervals in fuzzy and sharp
    regression discontinuity designs and for inference at a point based on local
    linear regression. The implementation is based on Armstrong and Kolesár (2018)
    <doi:10.3982/ECTA14434>, and Kolesár and Rothe (2018)
    <doi:10.1257/aer.20160945>. Supports covariates, clustering, and weighting.
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.
Understanding heterogeneous causal effects based on pretreatment covariates is a crucial step in modern empirical work in data science. Building on the recent developments in Calonico et al (2025) <https://rdpackages.github.io/references/Calonico-Cattaneo-Farrell-Palomba-Titiunik_2025_HTERD.pdf>, this package provides tools for estimation and inference of heterogeneous treatment effects in Regression Discontinuity (RD) Designs. The package includes two main commands: 'rdhte' to conduct estimation and robust bias-corrected inference for conditional RD treatment effects (given choice of bandwidth parameter); 'rdbwhte', which implements automatic bandwidth selection methods; and 'rdhte_lincom' to test linear combinations of parameters.
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. The package can be used to construct sets of
    'SNOMED CT' concepts for research (<doi:10.1093/jamia/ocac158>).
    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 '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 and Shung (2022) <doi:10.1016/j.simpa.2022.100414>.
A common framework for calculating distance matrices.
Distance-sampling (<doi:10.1007/978-3-319-19219-2>) 
  estimates density and abundance of survey targets (e.g., animals) when 
  detection probability declines with distance. 
  Distance-sampling is popular in ecology, 
  especially when survey targets are observed from aerial platforms (e.g., 
  airplane or drone), surface vessels (e.g., boat or truck), or along 
  walking transects. 
  Distance-sampling includes line-transect studies that measure observation 
  distances as the closest approach of the sample route (transect) to the target 
  (i.e., perpendicular off-transect distance), and point-transect studies that 
  measure observation distances from stationary observers to 
  the target (i.e., radial distance). 
  The routines included here fit smooth (parametric) curves to 
  histograms of observation distances 
  and use those functions to compute effective sampling distances, density of 
  targets in the surveyed area, and abundance 
  of targets in a surrounding study area. Curve shapes include the 
  half-normal, hazard rate, and negative exponential functions.
  Physical measurement units are required and used throughout to 
  ensure density is reported correctly. The help files 
  are extensive and have been vetted by multiple authors.
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>.
Implements safe policy learning under regression discontinuity designs
    with multiple cutoffs, based on Zhang et al. (2022) <doi:10.48550/arXiv.2208.13323>.
    The learned cutoffs are guaranteed  to perform no worse than the existing
    cutoffs in terms of overall outcomes. The 'rdlearn' package also includes
    features for visualizing the learned cutoffs relative to the baseline and
    conducting sensitivity analyses.
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.
Estimates the rearranged dependence measure ('RDM') of two continuous random variables for different underlying measures. 
  Furthermore, it provides a method to estimate the (SI)-rearrangement copula using empirical checkerboard copulas.
  It is based on the theoretical results presented in Strothmann et al. (2022) <arXiv:2201.03329> and Strothmann (2021) <doi:10.17877/DE290R-22733>.
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, as described in Rodiger et al. (2017) <doi:10.1093/bioinformatics/btx528>.
    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.
A wrapper for the 'Deutsche Nationalbibliothek (German National
    Library) API', available at <https://www.dnb.de/EN/Home/home_node.html>. 
    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.
Dump source code, documentation and vignettes of an 'R'
    package into a single file. Supports installed packages, tar.gz
    archives, and package source directories. If the package is not
    installed, only its source is automatically downloaded from CRAN for
    processing. The output is a single plain text file or a character
    vector, which is useful to ingest complete package documentation and
    source into a large language model (LLM) or pass it further to other
    tools, such as 'ragnar' <https://github.com/tidyverse/ragnar> to
    create a Retrieval-Augmented Generation (RAG) workflow.
Get the category of content hosted by a domain. Use Shallalist <http://shalla.de/>,
    Virustotal (which provides access to lots of services) <https://www.virustotal.com/>,
    Alexa <https://aws.amazon.com/awis/>, DMOZ <https://curlie.org/>, University Domain list
    <https://github.com/Hipo/university-domains-list> or validated machine learning
    classifiers based on Shallalist data to learn about the kind of content hosted by a domain.
An R API Client for Valve's Dota2. RDota2 can be easily used 
    to connect to the Steam API and retrieve data for Valve's popular video 
    game Dota2. You can find out more about Dota2 at 
    <http://store.steampowered.com/app/570/>.
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.
Provide an interface for 'Drama Corpora Project' ('DraCor') API: <https://dracor.org/documentation/api>.
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).
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>, 
    Gile and Handcock (2015) <doi:10.1111/rssa.12091> and
    Gile, Beaudry, Handcock and Ott (2018) <doi:10.1146/annurev-statistics-031017-100704>.
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.
Helper functions to accompany the Blair, Coppock, and Humphreys (2022) "Research Design in the Social Sciences: Declaration, Diagnosis, and Redesign" <https://book.declaredesign.org>. 'rdss' includes datasets, helper functions, and plotting components to enable use and replication of the book.
Provides functionality for carrying out sample size estimation and power calculation in Respondent-Driven Sampling.
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 MCMC. Also see Tak et al. (2019) <doi:10.1080/10618600.2019.1704295>.
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.
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/>.
Easily interact with the 'Arduino Iot Cloud API' <https://www.arduino.cc/reference/en/iot/api/>, managing devices, things, properties and data.
Enables the use of color palettes inspired by the 'Dune' movies. These palettes are compatible with 'ggplot2'. See Wickham (2016) <doi:10.1007/978-3-319-24277-4> for more details on 'ggplot2'.
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.
A comprehensive set of regular expression functions based on those found in 'Python'
    without relying on 'reticulate'. It provides functions that intend to (1) make it easier for
    users familiar with 'Python' to work with regular expressions, (2) reduce the complexity often
    associated with regular expressions code, (3) and enable users to write more readable and
    maintainable code that relies on regular expression-based pattern matching.
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.
Partitions the phenotypic variance of a plastic trait, studied through its reaction norm. The variance partition distinguishes between the variance arising from the average shape of the reaction norms (V_Plas) and the (additive) genetic variance . The latter is itself separated into an environment-blind component (V_G/V_A) and the component arising from plasticity (V_GxE/V_AxE). The package also provides a way to further partition V_Plas into aspects (slope/curvature) of the shape of the average reaction norm (pi-decomposition) and partition V_Add (gamma-decomposition) and V_AxE (iota-decomposition) into the impact of genetic variation in the reaction norm parameters. Reference: de Villemereuil & Chevin (2025) <doi:10.32942/X2NC8B>.
Tools to help with 'shiny' reactivity. The 'react' object offers
    an alternative way to call reactive expressions to better identify them
    in the server code.
Interactive data tables for R, based on the 'React Table'
    JavaScript library. Provides an HTML widget that can be used in 'R Markdown'
    or 'Quarto' documents, 'Shiny' applications, or viewed from an R console.
Enhanced functionality for 'reactable' in 'shiny' applications, offering interactive and dynamic data table capabilities with ease.
    With 'reactable.extras', easily integrate a range of functions and components to enrich your 'shiny' apps and facilitate user-friendly data exploration.
Provides various features to streamline and enhance the styling of interactive 
    reactable tables with easy-to-use and highly-customizable functions and themes. 
    Apply conditional formatting to cells with data bars, color scales, color tiles,
    and icon sets. Utilize custom table themes inspired by popular websites such 
    and bootstrap themes. Apply sparkline line & bar charts 
    (note this feature requires the 'dataui' package which can be downloaded from
    <https://github.com/timelyportfolio/dataui>).
    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.
Provides a checkbox group input for usage in a 'Shiny'
    application. The checkbox group has a head checkbox allowing to check
    or uncheck all the checkboxes in the group. The checkboxes are
    customizable.
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.
You can easily share url pages using 'React Router' in 'shiny' applications
  and 'Quarto' documents. The package wraps the 'react-router-dom' 'React' library and
  provides access to hash routing to navigate on multiple url pages.
A robust and powerful approach is developed for replicability analysis of two Genome-wide association studies (GWASs) accounting for the linkage disequilibrium (LD) among genetic variants. The LD structure in two GWASs is captured by a four-state hidden Markov model (HMM). The unknowns involved in the HMM are estimated by an efficient expectation-maximization (EM) algorithm in combination with a non-parametric estimation of functions. By incorporating information from adjacent locations via the HMM, this approach identifies the entire clusters of genotype-phenotype associated signals, improving the power of replicability analysis while effectively controlling the false discovery rate.
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/>.
Download the latest data from the Australian Prudential Regulation
    Authority <https://www.apra.gov.au/> and import it into R as a tidy data 
    frame.
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.
Import Data from Relational Database Management Systems
    (RDBMS) and Health Information Systems ('HIS'). The current version of the
    package supports importing data from RDBMS including 'MS SQL', 'MySQL',
    'PostGRESQL', and 'SQLite', as well as from two HIS platforms: 'DHIS2' and
    'SORMAS'.
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/>.
Efficient reading of raw markdown tables into tibbles. Designed to
    accept content from strings, files, and URLs with the ability to extract
    and read multiple tables from markdown for analysis.
Convert README.md to vignettes when installing packages without vignettes.
Contains tools for reading and writing data from or to files in the formats: akterm, dmna, Scintec Format-1, and Campbell Scientific TOA5.
Clinical care data from 130 U.S. hospitals in the years 1999-2008
    adapted from the study Strack et al. (2014) <doi:10.1155/2014/781670>. 
    Each row describes an "encounter" with a patient with diabetes, including 
    variables on demographics, medications, patient history, diagnostics, 
    payment, and readmission.
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.
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.
Loads 'Blackrock' <https://blackrockneurotech.com> neural signal 
    data files into the memory, provides utility tools to extract the data into 
    common formats such as plain-text 'tsv' and 'HDF5'.
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.
Read ODS (OpenDocument Spreadsheet) into R as 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 <https://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' <https://www.fon.hum.uva.nl/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.
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 <https://rapidxml.sourceforge.net/>.
    Works on Windows, Mac and Linux without external dependencies.
A template model module, tools to help find model modules
    derived from this template and a programming syntax to use these
    modules in health economic analyses.  These elements are the
    foundation for a prototype software framework for developing living
    and transferable models and using those models in reproducible health
    economic analyses. The software framework is extended by other R
    libraries.  For detailed documentation about the framework and how to
    use it visit <https://www.ready4-dev.com/>. For a background to the
    methodological issues that the framework is attempting to help solve,
    see Hamilton et al. (2024) <doi:10.1007/s40273-024-01378-8>.
Provides a flexible and streamlined pipeline for formatting, analyzing, and 
    visualizing omics data, regardless of omics type (e.g. transcriptomics, proteomics, 
    metabolomics). The package includes tools for shaping input data into analysis-ready 
    structures, fitting linear or mixed-effect models, extracting key contrasts, and 
    generating a rich variety of ready-to-use publication-quality plots. 
    Designed for transparency and reproducibility across a wide range of study designs, 
    with customizable components for statistical modeling.
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.
Flux (mass per unit time) and Load (mass) are computed from timeseries estimates of analyte concentration and discharge. Concentration timeseries are computed from regression between surrogate and user-provided analyte. Uncertainty in calculations is estimated using bootstrap resampling. Code for the processing of acoustic backscatter from horizontally profiling acoustic Doppler current profilers is provided. All methods detailed in Livsey et al (2020) <doi:10.1007/s12237-020-00734-z>, Livsey et al (2023) <doi:10.1029/2022WR033982>, and references therein.
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.
Calculate the probability density functions (PDFs) for two threshold evidence 
    accumulation models (EAMs). These are defined using the following Stochastic 
    Differential Equation (SDE), dx(t) = v(x(t),t)*dt+D(x(t),t)*dW, where x(t) is
    the accumulated evidence at time t, v(x(t),t) is the drift rate, D(x(t),t) is 
    the noise scale, and W is the standard Wiener process. The boundary conditions 
    of this process are the upper and lower decision thresholds, represented by b_u(t) 
    and b_l(t), respectively. Upper threshold b_u(t) > 0, while lower threshold b_l(t) < 0. 
    The initial condition of this process x(0) = z where b_l(t) < z < b_u(t). We 
    represent this as the relative start point w = z/(b_u(0)-b_l(0)), defined as 
    a ratio of the initial threshold location. This package generates the PDF using
    the same approach as the 'python' package it is based upon, 'PyBEAM' by Murrow and Holmes
    (2023) <doi:10.3758/s13428-023-02162-w>. First, it converts the SDE model into the 
    forwards Fokker-Planck equation dp(x,t)/dt = d(v(x,t)*p(x,t))/dt-0.5*d^2(D(x,t)^2*p(x,t))/dx^2,
    then solves this equation using the Crank-Nicolson method to determine p(x,t). Finally, 
    it calculates the flux at the decision thresholds, f_i(t) = 0.5*d(D(x,t)^2*p(x,t))/dx 
    evaluated at x = b_i(t), where i is the relevant decision threshold, either upper (i = u) 
    or lower (i = l). The flux at each thresholds f_i(t) is the PDF for each threshold, 
    specifically its PDF. We discuss further details of this approach in this package 
    and 'PyBEAM' publications. Additionally, one can calculate the cumulative distribution 
    functions of and sampling from the EAMs.
Takes user-provided baseline data from groups of randomised controlled data and  
    assesses whether the observed distribution of baseline p-values, numbers of participants 
    in each group, or categorical variables are consistent with the expected distribution, as 
    an aid to the assessment of integrity concerns in published randomised controlled trials. 
    References (citations in PubMed format in details of each function):
    Bolland MJ, Avenell A, Gamble GD, Grey A. (2016) <doi:10.1212/WNL.0000000000003387>.
    Bolland MJ, Gamble GD, Avenell A, Grey A, Lumley T. (2019) 
    <doi:10.1016/j.jclinepi.2019.05.006>.
    Bolland MJ, Gamble GD, Avenell A, Grey A. (2019) <doi:10.1016/j.jclinepi.2019.03.001>.
    Bolland MJ, Gamble GD, Grey A, Avenell A. (2020) <doi:10.1111/anae.15165>.
    Bolland MJ, Gamble GD, Avenell A, Cooper DJ, Grey A. (2021) 
    <doi:10.1016/j.jclinepi.2020.11.012>.
    Bolland MJ, Gamble GD, Avenell A, Grey A. (2021) <doi:10.1016/j.jclinepi.2021.05.002>.
    Bolland MJ, Gamble GD, Avenell A, Cooper DJ, Grey A. (2023) 
    <doi:10.1016/j.jclinepi.2022.12.018>.
    Carlisle JB, Loadsman JA. (2017) <doi:10.1111/anae.13650>.
    Carlisle JB. (2017) <doi:10.1111/anae.13938>.
The rearrangement operator (Hardy,
        Littlewood, and Polya 1952) for univariate, bivariate, and
        trivariate point estimates of monotonic functions. The package
        additionally provides a function that creates simultaneous
        confidence intervals for univariate functions and applies the
        rearrangement operator to these confidence intervals.
Arrange data by a set of methods. Use rearrangers to reorder
    data points and mutators to change their values. From basic utilities,
    to centering the greatest value, to swirling in 3-dimensional space,
    'rearrr' enables creativity when plotting and experimenting with data.
Functions for cleaning and summarising water quality data for use in National Pollutant Discharge Elimination Service (NPDES) permit reasonable potential analyses and water quality-based effluent limitation calculations. Procedures are based on those contained in the "Technical Support Document for Water Quality-based Toxics Control", United States Environmental Protection Agency (1991).
Collection of models and analysis methods used in regional and urban economics and (quantitative) economic geography, e.g. measures of inequality, regional disparities and convergence, regional specialization as well as accessibility and spatial interaction models.
Kiefer-Wolfowitz maximum likelihood estimation for mixture models
    and some other density estimation and regression methods based on convex
    optimization.  See Koenker and Gu (2017) REBayes: An R Package for Empirical
    Bayes Mixture Methods, Journal of Statistical Software, 82, 1--26, 
    <DOI:10.18637/jss.v082.i08>.
Authors working with 'LaTeX' articles use the built-in bibliography
    options and 'BibTeX' files. While this might work with 'LaTeX', it does not
    function well with Web articles. As a way out, 'rebib' offers tools to
    convert and combine bibliographies from both sources.
A programmatic client for the eBird database 
    (<https://ebird.org/home>), including functions for searching for bird 
    observations by geographic location (latitude, longitude), eBird 
    hotspots, location identifiers, by notable sightings, by region, and by 
    taxonomic name.
Random univariate and multivariate finite mixture model generation, estimation, clustering, latent class analysis and classification. Variables can be continuous, discrete, independent or dependent and may follow normal, lognormal, Weibull, gamma, Gumbel, binomial, Poisson, Dirac, uniform or circular von Mises parametric families.
Build regular expressions piece by piece using human readable code.
    This package is designed for interactive use.  For package development, use
    the rebus.* dependencies.
Build regular expressions piece by piece using human readable code.
    This package contains core functionality, and is primarily intended to be
    used by package developers.
Build regular expressions piece by piece using human readable code.
    This package contains date and time functionality, and is primarily intended
    to be used by package developers.
Build regular expressions piece by piece using human readable code.
    This package contains number-related functionality, and is primarily intended
    to be used by package developers.
Build regular expressions piece by piece using human readable code.
    This package contains Unicode functionality, and is primarily intended to be
    used by package developers.
Relevant Component Analysis (RCA) tries to find a linear
    transformation of the feature space such that the effect of irrelevant
    variability is reduced in the transformed space.
Enables the diagnostics and enhancement of regression model calibration.It offers both global and local visualization tools for calibration diagnostics and provides one recalibration method: Torres R, Nott DJ, Sisson SA, Rodrigues T, Reis JG, Rodrigues GS (2024) <doi:10.48550/arXiv.2403.05756>. The method leverages on Probabilistic Integral Transform (PIT) values to both evaluate and perform the calibration of statistical models. For a more detailed description of the package, please refer to the bachelor's thesis available bellow.
Tools are provided for estimating, testing, and simulating
    abundance in a two-event (Petersen) mark-recapture experiment. Functions are
    given to calculate the Petersen, Chapman, and Bailey estimators and associated
    variances. However, the principal utility is a set of functions to simulate
    random draws from these estimators, and use these to conduct hypothesis
    tests and power calculations. Additionally, a set of functions are provided
    for generating confidence intervals via bootstrapping. Functions are also
    provided to test abundance estimator consistency under complete or partial
    stratification, and to calculate stratified or partially stratified estimators.
    Functions are also provided to calculate recommended sample sizes.
    Referenced methods can be found in Arnason et al. (1996) <ISSN:0706-6457>, 
    Bailey (1951) <DOI:10.2307/2332575>, 
    Bailey (1952) <DOI:10.2307/1913>, 
    Chapman (1951) NAID:20001644490, 
    Cohen (1988) ISBN:0-12-179060-6, 
    Darroch (1961) <DOI:10.2307/2332748>, and
    Robson and Regier (1964) <ISSN:1548-8659>.
Provides functions allowing the user to recursively extract frequent patterns and confident rules according to indicators of minimal support and minimal confidence. These functions are described in "Recursive Association Rule Mining" Abdelkader Mokkadem, Mariane Pelletier, Louis Raimbault (2020) <arXiv:2011.14195>.
A set of functions to see and interactively adjust a distribution
    of lessons by day, aiming at homogenizing individual distributions (for
    each class and teacher).
Sends texts to the <https://www.receptiviti.com> API to be scored,
    and facilitates the creation of custom norms and local results databases.
A toolset for 3D reconstruction and analysis of excavations. It provides methods to reconstruct natural and artificial surfaces based on field measurements. This allows to spatially contextualize documented subunits and features. Intended to be part of a 3D visualization workflow.
Analyze recurrent events with right-censored data and the potential presence of a terminal event (that prevents further occurrences, like death). 'recofest' extends the random survival forest algorithm, adapting splitting rules and node estimators to handle complexities of recurrent events. The methodology is fully described in Murris, J., Bouaziz, O., Jakubczak, M., Katsahian, S., & Lavenu, A. (2024) (<https://hal.science/hal-04612431v1/document>).
HydroBudget is a spatially distributed
  groundwater recharge model that computes a superficial water
  budget on grid cells with outputs aggregated into monthly time steps.
  It was developed as an accessible and computationally affordable model
  to simulate groundwater recharge over large areas (thousands of km2,
  regional-scale watersheds) and for long time periods (decades), in cold
  and humid climates. Model algorithms are based on the research of
  Dubois, E. et al. (2021a) <doi:10.5683/SP3/EUDV3H> and 
  Dubois, E. et al. (2021b) <doi:10.5194/hess-25-6567-2021>.
The 'Echo nest' <http://the.echonest.com> is the industry's leading
    music intelligence company, providing developer with deepest understanding of
    music content and music fans. This package can be used to access artist's data
    including songs, blogs, news, reviews etc. Song's data including audio summary,
    style, danceability, tempo etc can also be accessed.
A recipe prepares your data for modeling. We provide an
    extensible framework for pipeable sequences of feature engineering
    steps provides preprocessing tools to be applied to data. Statistical
    parameters for the steps can be estimated from an initial data set and
    then applied to other data sets. The resulting processed output can
    then be used as inputs for statistical or machine learning models.
Functions to assist in performing probabilistic record linkage and
    deduplication: generating pairs, comparing records, em-algorithm for
    estimating m- and u-probabilities
    (I. Fellegi & A. Sunter (1969) <doi:10.1080/01621459.1969.10501049>, 
    T.N. Herzog, F.J. Scheuren, & W.E. Winkler (2007), 
    "Data Quality and Record Linkage Techniques", ISBN:978-0-387-69502-0),
    forcing one-to-one matching. Can also be
    used for pre- and post-processing for machine learning methods for record
    linkage. Focus is on memory, CPU performance and flexibility.
The analysis of different aspects of biodiversity requires specific algorithms. 
	For example, in regionalisation analyses, the high frequency of ties and zero values in 
	dissimilarity matrices produced by Beta-diversity turnover produces hierarchical 
	cluster dendrograms whose topology and bootstrap supports are affected by the order of 
	rows in the original matrix. Moreover, visualisation of biogeographical regionalisation 
	can be facilitated by a combination of hierarchical clustering and multi-dimensional 
	scaling. The recluster package provides robust techniques to visualise and analyse 
	pattern of biodiversity and to improve occurrence data for cryptic taxa. 
	Other functions 	related to recluster (e.g. the biodecrypt family) are currently 
	available in GitHub at <https://github.com/leondap/recluster>.
Implements the RecMap MP2 construction heuristic
  <doi:10.1109/INFVIS.2004.57>.
  This algorithm draws maps according to a given statistical
  value, e.g., election results, population, or epidemiological data.
  The basic idea of the RecMap algorithm is that each map region,
  e.g., different countries, is represented by a rectangle.
  The area of each rectangle represents the statistical value provided
  as input to maintain zero cartographic error.
  Computationally intensive tasks are implemented in C++.
  The included vignette documents recmap algorithm usage.
Modern results of psychometric theory are implemented to provide users with a way of evaluating the internal structure of a set of items guided by theory. These methods are discussed in detail in VanderWeele and Padgett (2024) <doi:10.31234/osf.io/rnbk5>. The relative excess correlation matrices will, generally, have numerous negative entries even if all of the raw correlations between each pair of indicators are positive. The positive deviations of the relative excess correlation matrix entries help identify clusters of indicators that are more strongly related to one another, providing insights somewhat analogous to factor analysis, but without the need for rotations or decisions concerning the number of factors. A goal similar to exploratory/confirmatory factor analysis, but 'recmetrics' uses novel methods that do not rely on assumptions of latent variables or latent variable structures.
The ecocrop model estimates environmental suitability for plants using a limiting factor approach for plant growth following Hackett (1991) <doi:10.1007/BF00045728>. The implementation in this package is fast and flexible: it allows for the use of any (environmental) predictor variable. Predictors can be either static (for example, soil pH) or dynamic (for example, monthly precipitation).
Contains functions to interface with variables and variable details sheets, including recoding variables and converting them to PMML.
Simple, easy to use, and flexible functionality for recoding variables. It allows for simple piecewise definition of transformations.
Annotate text with entities and the relations between them. Annotate areas of interest in images with your labels. 
    Providing 'htmlwidgets' bindings to the 'recogito' <https://github.com/recogito/recogito-js> and 'annotorious' <https://github.com/recogito/annotorious> libraries.
Automatic, semi-automatic, and manual functions for
    generating color maps from images. The idea is to simplify
    the colors of an image according to a metric that is useful for
    the user, using deterministic methods whenever possible.
    Many images will be clustered well using the out-of-the-box
    functions, but the package also includes a toolbox of functions
    for making manual adjustments (layer merging/isolation, blurring,
    fitting to provided color clusters or those from another image, etc).
    Also includes export methods for other color/pattern analysis packages
    (pavo, patternize, colordistance).
Turns nested lists into data.frames in an orderly manner.
Calculates evaluation metrics for implicit-feedback recommender systems
  that are based on low-rank matrix factorization models, given the fitted model
  matrices and data, thus allowing to compare models from a variety of libraries.
  Metrics include P@K (precision-at-k, for top-K recommendations), R@K (recall at k),
  AP@K (average precision at k), NDCG@K (normalized discounted cumulative gain at k),
  Hit@K (from which the 'Hit Rate' is calculated), RR@K (reciprocal rank at k, from
  which the 'MRR' or 'mean reciprocal rank' is calculated), ROC-AUC (area under the
  receiver-operating characteristic curve), and PR-AUC (area under the
  precision-recall curve).
  These are calculated on a per-user basis according to the ranking of items induced
  by the model, using efficient multi-threaded routines. Also provides functions
  for creating train-test splits for model fitting and evaluation.
Provides a research infrastructure to develop and evaluate
    collaborative filtering recommender algorithms. This includes a sparse 
    representation for user-item matrices, many popular algorithms, top-N recommendations,
    and cross-validation. Hahsler (2022) <doi:10.48550/arXiv.2205.12371>.
Provides the Book-Crossing Dataset for the package recommenderlab.
Provides the Jester Dataset for package recommenderlab.
Implements solutions to canonical models of Economics such as Monopoly Profit Maximization, Cournot's Duopoly, Solow (1956, <doi:10.2307/1884513>) growth model and Mankiw, Romer and Weil (1992, <doi:10.2307/2118477>) growth model.
Functions for reconstructing individual-level data (time, status, arm) from Kaplan-MEIER curves published in academic journals (e.g. NEJM, JCO, JAMA). The individual-level data can be used for re-analysis, meta-analysis, methodology development, etc. This package was used to generate the data for commentary such as Sun, Rich, & Wei (2018) <doi:10.1056/NEJMc1808567>. Please see the vignette for a quickstart guide.
Functions to reconstruct sessions from web log or other user trace data
             and calculate various metrics around them, producing tabular,
             output that is compatible with 'dplyr' or 'data.table' centered processes.
A lightweight toolkit to validate new observations when computing
    their predictions with a predictive model. The validation process 
    consists of two steps: (1) record relevant statistics and meta data of the
    variables in the original training data for the predictive model and
    (2) use these data to run a set of basic validation tests on the new set of 
    observations.
Provides functions for linking and deduplicating data sets.
  Methods based on a stochastic approach are implemented as well as 
  classification algorithms from the machine learning domain. For details, 
  see our paper "The RecordLinkage Package: Detecting Errors in Data" 
  Sariyar M / Borg A (2010) <doi:10.32614/RJ-2010-017>.
Functions for generating k-record values and k-record
        times
Statistical tools based on the probabilistic properties of the 
  record occurrence in a sequence of independent and identically distributed 
  continuous random variables. In particular, tools to prepare a time series 
  as well as distribution-free trend and change-point tests and graphical 
  tools to study the record occurrence. Details about the implemented tools 
  can be found in Castillo-Mateo et al. (2023a) <doi:10.18637/jss.v106.i05> 
  and Castillo-Mateo et al. (2023b) <doi:10.1016/j.atmosres.2023.106934>.
R wrapper of the 'libmf' library
    <https://www.csie.ntu.edu.tw/~cjlin/libmf/> for recommender
    system using matrix factorization. It is typically used to
    approximate an incomplete matrix using the product of two
    matrices in a latent space. Other common names for this task
    include "collaborative filtering", "matrix completion",
    "matrix recovery", etc. High performance multi-core parallel
    computing is supported in this package.
Rectangle packing is a packing problem where rectangles are placed
    into a larger rectangular region (without overlapping) in order to maximise
    the use space.  Rectangles are packed using the skyline heuristic as discussed 
    in Lijun et al (2011) 'A Skyline-Based Heuristic for the 2D Rectangular Strip
    Packing Problem' <doi:10.1007/978-3-642-21827-9_29>.  A function is also 
    included for determining a good small-sized box for containing a given
    set of rectangles.
Computation of one-, two- and three-dimensional 
  pseudo-observations based on recurrent events and terminal events. 
  Generalised linear models are fitted using generalised estimating 
  equations. Technical details on the bivariate procedure can be found 
  in "Bivariate pseudo-observations for recurrent event analysis with 
  terminal events" (Furberg et al., 2021) <doi:10.1007/s10985-021-09533-5>.
Computes revisitation metrics for trajectory data, such as the number of revisitations for each location as well as the time spent for that visit and the time since the previous visit. Also includes functions to plot data.
Includes algorithms to facilitate the assessment of extinction risk of species according to the IUCN (International Union for Conservation of Nature, see <https://iucn.org/> for more information) red list criteria.
Contains implementations of recurrent event data analysis routines
    including (1) survival and recurrent event data simulation from
    stochastic process point of view by the thinning method
    proposed by Lewis and Shedler (1979) <doi:10.1002/nav.3800260304>
    and the inversion method introduced in Cinlar (1975, ISBN:978-0486497976),
    (2) the mean cumulative function (MCF) estimation by the
    Nelson-Aalen estimator of the cumulative hazard rate function,
    (3) two-sample recurrent event responses comparison with the pseudo-score
    tests proposed by Lawless and Nadeau (1995) <doi:10.2307/1269617>,
    (4) gamma frailty model with spline rate function following
    Fu, et al. (2016) <doi:10.1080/10543406.2014.992524>.
The aim of this package is to manipulate relational
   data models in R.
   It provides functions to create, modify and export data models
   in json format.
   It also allows importing models created
   with 'MySQL Workbench' (<https://www.mysql.com/products/workbench/>).
   These functions are accessible through a graphical user
   interface made with 'shiny'.
   Constraints such as types, keys, uniqueness and mandatory fields are
   automatically checked and corrected when editing a model.
   Finally, real data can be confronted to a model to check their compatibility.
Provides functions used in the 'R: Einführung durch angewandte Statistik' (second edition).
Import 'REDATAM' formats into R via the 'Open REDATAM' C++ library.
    The full context of this project and details about the implementation are
    available in <doi:10.1017/dap.2025.4> (Open Access).
Provides an API to work with 'Redatam' (see <https://redatam.org>) 
  databases in both formats: 'RXDB' (new format) and 'DICX' (old format) and running 
  'Redatam' programs written in 'SPC' language. It's a wrapper around 'Redatam' 
  core and provides functions to open/close a database (redatam_open()/redatam_close()), 
  list entities and variables from the database (redatam_entities(), redatam_variables()) 
  and execute a 'SPC' program and gets the results as data frames 
  (redatam_query(), redatam_run()).
Provides access to and analysis of data from "The Red Book of Endemic Plants of Peru" (León, B., Roque, J., Ulloa, C., Jorgensen, P.M., Pitman, N., Cano, A. 2006) <doi:10.15381/rpb.v13i2.1782>. This package offers comprehensive taxonomic, geographic, and conservation information about Peru's endemic plant species. It includes functions to verify species inclusion, obtain updated taxonomic details, and explore the dataset.
Access data stored in 'REDCap' databases using the Application
    Programming Interface (API).  'REDCap' (Research Electronic Data CAPture;
    <https://projectredcap.org>, Harris, et al. (2009) <doi:10.1016/j.jbi.2008.08.010>, 
    Harris, et al. (2019) <doi:10.1016/j.jbi.2019.103208>) is
    a web application for building and managing online surveys and databases
    developed at Vanderbilt University.  The API allows users to access data
    and project meta data (such as the data dictionary) from the web
    programmatically. The 'redcapAPI' package facilitates the process of
    accessing data with options to prepare an analysis-ready data set
    consistent with the definitions in a database's data dictionary.
Casting metadata for REDCap database creation and handling of 
    castellated data using repeated instruments and longitudinal projects in 
    'REDCap'. Keeps a focused data export approach, by allowing to only export 
    required data from the database. Also for casting new REDCap databases based 
    on datasets from other sources.
    Originally forked from the R part of 'REDCapRITS' by Paul Egeler. 
    See <https://github.com/pegeler/REDCapRITS>.
    'REDCap' (Research Electronic Data Capture) is a secure, web-based software
    platform designed to support data capture for research studies, providing
    1) an intuitive interface for validated data capture; 2) audit trails for
    tracking data manipulation and export procedures; 3) automated export
    procedures for seamless data downloads to common statistical packages; and
    4) procedures for data integration and interoperability with external 
    sources (Harris et al (2009) <doi:10.1016/j.jbi.2008.08.010>; 
    Harris et al (2019) <doi:10.1016/j.jbi.2019.103208>).
REDCap Data Management - REDCapDM is an R package that allows users to manage data exported directly from REDCap or using an API connection. This package includes several functions designed for pre-processing data, generating reports of queries such as outliers or missing values, and following up on the identified queries. 'REDCap' (Research Electronic Data CAPture; <https://projectredcap.org>) is a web application developed at Vanderbilt University, designed for creating and managing online surveys and databases and the REDCap API is an interface that allows external applications to connect to REDCap remotely, and is used to programmatically retrieve or modify project data or settings within REDCap, such as importing or exporting data.
Export all data, including metadata, from a REDCap (Research
    Electronic Data Capture) Project via the REDCap API
    <https://projectredcap.org/wp-content/resources/REDCapTechnicalOverview.pdf>.
    The exported (meta)data will be processed and formatted into a stand alone R
    data package which can be installed and shared between researchers.  Several
    default reports are generated as vignettes in the resulting package.
Encapsulates functions to streamline calls from R to the REDCap
    API.  REDCap (Research Electronic Data CAPture) is a web application for
    building and managing online surveys and databases developed at Vanderbilt
    University.  The Application Programming Interface (API) offers an avenue
    to access and modify data programmatically, improving the capacity for
    literate and reproducible programming.
Convert 'REDCap' exports into tidy tables for easy handling of 'REDCap' repeat instruments and event arms.
'REDUCE' is a portable general-purpose computer algebra system supporting scalar, vector, matrix and tensor algebra, symbolic differential and integral calculus, arbitrary precision numerical calculations and output in 'LaTeX' format. 'REDUCE' is based on 'Lisp' and is available on the two dialects 'Portable Standard Lisp' ('PSL') and 'Codemist Standard Lisp' ('CSL'). The 'redcas' package provides an interface for executing arbitrary 'REDUCE' code interactively from 'R', returning output as character vectors. 'R' code and 'REDUCE' code can be interspersed. It also provides a specialized function for calling the 'REDUCE' feature for solving systems of equations, returning the output as an 'R' object designed for the purpose. A further specialized function uses 'REDUCE' features to generate 'LaTeX' output and post-processes this for direct use in 'LaTeX' documents, e.g. using 'Sweave'.
Collect  your data on digital marketing campaigns from reddit Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
A collection of tools for extracting structured data from <https://www.reddit.com/>.
Applies quality control to daily precipitation observations; 
    reconstructs the original series by estimating precipitation in missing values; and 
    creates gridded datasets of daily precipitation.
Standard and extensible Eddy-Covariance data post-processing 
  (Wutzler et al. (2018) <doi:10.5194/bg-15-5015-2018>)
  includes  
  uStar-filtering, gap-filling, and flux-partitioning.
  The Eddy-Covariance (EC)  micrometeorological technique quantifies continuous 
  exchange fluxes of gases, energy, and momentum between an ecosystem and the atmosphere.
  It is important for understanding ecosystem dynamics and upscaling exchange fluxes.
  (Aubinet et al. (2012) <doi:10.1007/978-94-007-2351-1>).
  This package inputs pre-processed (half-)hourly data and supports further processing. 
  First, a quality-check and filtering is performed based on the relationship between 
  measured flux and friction
  velocity (uStar) to discard biased data 
  (Papale et al. (2006) <doi:10.5194/bg-3-571-2006>).
  Second, gaps in the data are filled based on information from environmental conditions
  (Reichstein et al. (2005) <doi:10.1111/j.1365-2486.2005.001002.x>).
  Third, the net flux of carbon dioxide is partitioned
  into its gross fluxes in and out of the ecosystem by night-time 
  based and day-time based approaches
  (Lasslop et al. (2010) <doi:10.1111/j.1365-2486.2009.02041.x>).
Extension to 'REddyProc' that allows reading data from netCDF files.
Implementation of the Robust Exponential Decreasing Index (REDI),
    proposed in the article by Issa Moussa, Arthur Leroy et al. (2019)
    <https://bmjopensem.bmj.com/content/bmjosem/5/1/e000573.full.pdf>.
    The REDI represents a measure of cumulated workload, robust to missing data,
    providing control of the decreasing influence of workload over time. 
    Various functions are provided to format data, compute REDI, and 
    visualise results in a simple and convenient way.
Biologically relevant, yet mathematically sound constraints are used
    to compute the propensity and thence infer the dominant direction of reactions
    of a generic biochemical network. The reactions must be unique and their 
    number must exceed that of the reactants,i.e., reactions >= reactants + 2.  
            'ReDirection', computes the null space of a user-defined stoichiometry
    matrix. The spanning non-zero and unique reaction vectors (RVs) are 
    combinatorially summed to generate one or more subspaces recursively.  
            Every reaction is represented as a sequence of identical components 
    across all RVs of a particular subspace. The terms are evaluated with 
    (biologically relevant bounds, linear maps, tests of convergence, descriptive
    statistics, vector norms) and the terms are classified into forward-, reverse- 
    and equivalent-subsets. Since, these are mutually exclusive the probability 
    of occurrence is binary (all, 1; none, 0).
            The combined propensity of a reaction is the p1-norm of the 
    sub-propensities, i.e., sum of the products of the probability and maximum
    numeric value of a subset (least upper bound, greatest lower bound). This, 
    if strictly positive is the probable rate constant, is used to infer dominant
    direction and annotate a reaction as "Forward (f)", "Reverse (b)" or 
    "Equivalent (e)".
            The inherent computational complexity (NP-hard) per iteration suggests
    that a suitable value for the number of reactions is around 20.
            Three functions comprise ReDirection. These are check_matrix() and 
    reaction_vector() which are internal, and calculate_reaction_vector()
            which is external.
Providing the container for the DockerParallel package.
An optimized method for identifying mutually 
    exclusive genomic events.  Its main contribution is a
    statistical analysis based on the Poisson-Binomial 
    distribution that takes into account that some samples
    are more mutated than others. See [Canisius, Sander, John WM Martens,
    and  Lodewyk FA Wessels. (2016)  "A novel independence test for
    somatic alterations in cancer shows that 
    biology drives mutual exclusivity but chance explains
    most co-occurrence." Genome biology 17.1 : 1-17. <doi:10.1186/s13059-016-1114-x>].
    The mutations matrices are sparse matrices. The method developed takes 
    advantage of the advantages of this type of matrix to save 
    time and computing resources.
Enables researchers to sample redistricting plans from a pre-specified
    target distribution using Sequential Monte Carlo and Markov Chain Monte Carlo
    algorithms. The package allows for the implementation of various constraints in
    the redistricting process such as geographic compactness and population parity
    requirements. Tools for analysis such as computation of various summary statistics
    and plotting functionality are also included. The package implements the SMC 
    algorithm of McCartan and Imai (2023) <doi:10.1214/23-AOAS1763>, the enumeration 
    algorithm of Fifield, Imai, Kawahara, and Kenny (2020) <doi:10.1080/2330443X.2020.1791773>,
    the Flip MCMC algorithm of Fifield, Higgins, Imai and Tarr (2020) <doi:10.1080/10618600.2020.1739532>,
    the Merge-split/Recombination algorithms of Carter et al. (2019) <doi:10.48550/arXiv.1911.01503> 
    and DeFord et al. (2021) <doi:10.1162/99608f92.eb30390f>, and the Short-burst 
    optimization algorithm of Cannon et al. (2020) <doi:10.48550/arXiv.2011.02288>.
Reliable and flexible tools for scoring redistricting plans using 
  common measures and metrics. These functions provide key direct access to 
  tools useful for non-simulation analyses of redistricting plans, such as for 
  measuring compactness or partisan fairness. Tools are designed to work with 
  the 'redist' package seamlessly.
Easy installation, loading, and control of packages for redistricting
    data downloading, spatial data processing, simulation, analysis, and 
    visualization. This package makes it easy to install and load multiple 
    'redistverse' packages at once. The 'redistverse' is developed and maintained 
    by the Algorithm-Assisted Redistricting Methodology (ALARM) Project.
    For more details see <https://alarm-redist.org>.
Provides methods to parse, query and serialize information
    stored in the Resource Description Framework (RDF). RDF is described at <https://www.w3.org/TR/rdf-primer/>.
    This package supports RDF by implementing an R interface to the Redland RDF C library,
    described at <https://librdf.org/docs/api/index.html>. In brief, RDF provides a structured graph
    consisting of Statements composed of Subject, Predicate, and Object Nodes.
Provides an interface to access data from the International Union 
    for Conservation of Nature (IUCN) Red List <https://api.iucnredlist.org/api-docs/index.html>. It allows users to retrieve up-to-date 
    information on species' conservation status, supporting biodiversity research 
    and conservation efforts.
A toolbox created by members of the International Union for
    Conservation of Nature (IUCN) Red List of Ecosystems Committee for
    Scientific Standards. Primarily, it is a set of tools suitable for
    calculating the metrics required for making assessments of species and
    ecosystems against the IUCN Red List of Threatened Species and the
    IUCN Red List of Ecosystems categories and criteria. See the IUCN
    website for detailed guidelines, the criteria, publications and other
    information.
An implementation of 'EDM' algorithms based on research software developed for internal use at the Sugihara Lab ('UCSD/SIO').  The package is implemented with 'Rcpp' wrappers around the 'cppEDM' library.  It implements the 'simplex' projection method from Sugihara & May (1990) <doi:10.1038/344734a0>, the 'S-map' algorithm from Sugihara (1994) <doi:10.1098/rsta.1994.0106>, convergent cross mapping described in Sugihara et al. (2012) <doi:10.1126/science.1227079>, and, 'multiview embedding' described in Ye & Sugihara (2016) <doi:10.1126/science.aag0863>.
Provide color schemes for maps (and other graphics) based on the
    color palettes of several Microsoft(r) products. Forked from 'RColorBrewer' v1.1-2.
A collection of 'HTML', 'JavaScript', 'CSS' and fonts
  assets that generate 'Redoc' documentation from an 'OpenAPI' Specification:
   <https://redocly.com/redoc/>.
Transfer 'REDCap' (Research Electronic Data Capture) data to a database,
    specifically optimized for 'DuckDB'. 
    Processes data in chunks to handle large datasets without exceeding available memory. 
    Features include data labeling, coded value conversion, and hearing a "quack" sound on success.
A 'hiredis' wrapper that includes support for
    transactions, pipelining, blocking subscription, serialisation of
    all keys and values, 'Redis' error handling with R errors.
    Includes an automatically generated 'R6' interface to the full
    'hiredis' API.  Generated functions are faithful to the
    'hiredis' documentation while attempting to match R's argument
    semantics.  Serialisation must be explicitly done by the user, but
    both binary and text-mode serialisation is supported.
A data mining approach for longitudinal and clustered data, 
        which combines the structure of mixed effects model with tree-based 
        estimation methods. See Sela, R.J. and Simonoff, J.S. (2012) RE-EM 
        trees: a data mining approach for longitudinal and clustered data 
        <doi:10.1007/s10994-011-5258-3>.
Implements an objective Bayes intrinsic conditional autoregressive 
    prior. This model provides an objective Bayesian approach for modeling spatially 
    correlated areal data using an intrinsic conditional autoregressive prior on a vector of 
    spatial random effects.
A robust alternative to the traditional principal component estimator is proposed within the framework of factor models, known as Robust Exponential Factor Analysis, specifically designed for the modeling of high-dimensional datasets with heavy-tailed distributions. The algorithm estimates the latent factors and the loading by minimizing the exponential squared loss function. To determine the appropriate number of factors, we propose a modified rank minimization technique, which has been shown to significantly enhance finite-sample performance.
Imputation of missing numerical outcomes for a longitudinal trial 
    with protocol deviations.
    The package uses distinct treatment arm-based assumptions for the unobserved data, following 
    the general algorithm of Carpenter, Roger, and Kenward (2013) <doi:10.1080/10543406.2013.834911>, and the causal model
    of White, Royes and Best (2020) <doi:10.1080/10543406.2019.1684308>.
    Sensitivity analyses to departures from these assumptions can be done by the 
    Delta method of Roger.
    The program uses the same algorithm as the 'mimix' 'Stata' package written by Suzie Cro, 
    with additional coding for the causal model and delta method.
    The reference-based methods are jump to reference (J2R), 
    copy increments in reference (CIR), copy reference (CR),
    and the causal model, all of which must specify the reference treatment arm.
    Other methods are
    missing at random (MAR) and the last mean carried forward (LMCF). 
    Individual-specific imputation methods
    (and their reference groups) can be specified.
Allows users to easily create references to R objects then
    'dereference' when needed or modify in place without using reference 
    classes, environments, or active bindings as workarounds. Users can 
    also create expression references that allow subsets of any object to 
    be referenced or expressions containing references to multiple 
    objects.
This is a collection of tools to allow the medical professional to
 calculate appropriate reference ranges (intervals) with confidence intervals around
 the limits for diagnostic purposes.
A suite of methods to fit and predict case count data using 
    a compartmental SIRS (Susceptible – Infectious – Recovered – Susceptible) 
    model, based on an assumed specification of the effective reproduction 
    number. The significance of this approach is that it relates epidemic 
    progression to the average number of contacts of infected individuals, 
    which decays as a function of the total susceptible fraction remaining 
    in the population. The main functions are pred.curve(), which computes 
    the epidemic curve for a set of parameters, and estimate.mle(), which 
    finds the best fitting curve to observed data. The easiest way to pass 
    arguments to the functions is via a config file, which contains input 
    settings required for prediction, and the package offers two methods, 
    navigate_to_config()  which points the user to the configuration file, 
    and re_predict() for starting the fit-predict process. The main model was published in
    Razvan G. Romanescu et al. <doi:10.1016/j.epidem.2023.100708>.
Indirect method for the estimation of reference intervals using 
	Real-World Data ('RWD'). It takes routine measurements of diagnostic 
	tests, containing pathological and non-pathological samples as input 
	and uses sophisticated statistical methods to derive a model describing 
	the distribution of the non-pathological samples. This distribution can 
	then be used to derive reference intervals. Furthermore, the package 
	offers functions for printing and plotting the results of the algorithm. 
	See ?refineR for a more comprehensive description of the features. 
	Version 1.0 of the algorithm is described in detail in 'Ammer et al. (2021)' 
	<doi:10.1038/s41598-021-95301-2>. Additional guidance on the usage of 
	the algorithm is given in 'Ammer et al. (2023)' <doi:10.1093/jalm/jfac101>.
These functions take a character vector as input, identify and 
  cluster similar values, and then merge clusters together so their values 
  become identical. The functions are an implementation of the key collision 
  and ngram fingerprint algorithms from the open source tool Open Refine 
  <https://openrefine.org/>. More info on key collision and ngram fingerprint 
  can be found here <https://openrefine.org/docs/technical-reference/clustering-in-depth>.
Reallocating the respective lessons by hours (respecting the 
    constraints induced by the existence of coupled lessons) so that 
    the total number of gaps is as small as possible.
Fits measurement error models using Monte Carlo Expectation Maximization (MCEM). For specific details on the methodology, see: Greg C. G. Wei & Martin A. Tanner (1990) A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms, Journal of the American Statistical Association, 85:411, 699-704 <doi:10.1080/01621459.1990.10474930> For more examples on measurement error modelling using MCEM, see the 'RMarkdown' vignette: "'refitME' R-package tutorial".
Automatic coding of open-ended responses to the Cognitive Reflection Test (CRT), a widely used class of tests in cognitive science and psychology that assess the tendency to override an initial intuitive (but incorrect) answer and engage in reflection to reach a correct solution. The package standardizes CRT response coding across datasets in cognitive psychology, decision-making, and related fields. Automated coding reduces manual effort and improves reproducibility by limiting variability from subjective interpretation of open-ended responses. The package supports automatic coding and machine scoring for the original English-language CRT (Frederick, 2005) <doi:10.1257/089533005775196732>, CRT4 and CRT7 (Toplak et al., 2014) <doi:10.1080/13546783.2013.844729>, CRT-long (Primi et al., 2016) <doi:10.1002/bdm.1883>, and CRT-2 (Thomson & Oppenheimer, 2016) <doi:10.1017/s1930297500007622>.
Uses an indirect method based on truncated quantile-quantile plots to estimate reference limits from routine laboratory data: Georg Hoffmann and colleagues (2024) <doi: 10.3390/jcm13154397>. The principle of the method was developed by Robert G Hoffmann (1963) <doi:10.1001/jama.1963.03060110068020> and modified by Georg Hoffmann and colleagues (2015) <doi:10.1515/labmed-2015-0104>, and Frank Klawonn and colleagues (2020) <doi:10.1515/labmed-2020-0005>, (2022) <doi:10.1007/978-3-031-15509-3_31>.
Provides tools for importing and working with bibliographic
    references. It greatly enhances the 'bibentry' class by providing a class
    'BibEntry' which stores 'BibTeX' and 'BibLaTeX' references, supports 'UTF-8'
    encoding, and can be easily searched by any field, by date ranges, and by
    various formats for name lists (author by last names, translator by full names,
    etc.). Entries can be updated, combined, sorted, printed in a number of styles,
    and exported. 'BibTeX' and 'BibLaTeX' '.bib' files can be read into 'R' and
    converted to 'BibEntry' objects. Interfaces to 'NCBI Entrez', 'CrossRef', and
    'Zotero' are provided for importing references and references can be created
    from locally stored 'PDF' files using 'Poppler'. Includes functions for citing
    and generating a bibliography with hyperlinks for documents prepared with
    'RMarkdown' or 'RHTML'.
Takes formulas including random-effects components (formatted as in 'lme4', 'glmmTMB', etc.) and processes them. Includes various helper functions.
An R package for estimating conditional multivariate reference regions. 
    The reference region is non parametrically estimated using a kernel density estimator.
    Covariates effects on the multivariate response means vector and variance-covariance
    matrix, thus on the region shape, are estimated by flexible additive predictors. 
    Continuous covariates non linear effects might be estimated using penalized splines smoothers.
    Confidence intervals for the covariates estimated effects might be derived from
    bootstrap resampling. Kernel density bandwidth can be estimated with different methods, including
    a method that optimize the region coverage. Numerical, and graphical, summaries
    can be obtained by the user in order to evaluate reference region performance with real data.
    Full mathematical details can be found in <doi:10.1002/sim.9163> and <doi:10.1007/s00477-020-01901-1>.
Connects dataframes/tables with a remote data source. Raw data downloaded
  from the data source can be further processed and transformed using data preparation 
  code that is also baked into the dataframe/table. Refreshable dataframes can 
  be shared easily (e.g. as R data files). Their users do not need to care about
  the inner workings of the data update mechanisms.
Fetches NCBI data (RefSeq <https://www.ncbi.nlm.nih.gov/refseq/> database) and provides an environment to  
    extract information at the level of gene, mRNA or protein accessions.
Provides subsets with reference semantics, i.e. subsets
    which automatically reflect changes in the original object, and which
    optionally update the original object when they are changed.
Access the 'Refuge' API, a web-application for locating trans and 
    intersex-friendly restrooms, including unisex and accessible restrooms. 
    Includes data on the location of restrooms, along with directions, 
    comments, user ratings and amenities. Coverage is global, but data is 
    most comprehensive in the United States.
    See <https://www.refugerestrooms.org/api/docs/> for full API documentation.
The Refugee Population Statistics Database published by
    The Office of The United Nations High Commissioner for Refugees (UNHCR)
    contains information about forcibly displaced populations
    spanning more than 70 years of statistical activities.
    It covers displaced populations such as refugees, asylum-seekers and
    internally displaced people, including their demographics.
    Stateless people are also included, most of who have never been displaced.
    The database also reflects the different types of solutions
    for displaced populations such as repatriation or resettlement. 
    More information on the data and methodology can be found on
    the UNHCR Refugee Data Finder <https://www.unhcr.org/refugee-statistics/>.
Methods for regression for functional
    data, including function-on-scalar, scalar-on-function, and
    function-on-function regression. Some of the functions are applicable to
    image data.
Produces Shiny applications for different types of popular functional data analyses. The functional data analyses are implemented in the refund package, then refund.shiny reads in the refund object and implements an object-specific set of plots based on the object class using S3.
It streamlines the evaluation of regression model
    assumptions, enhancing result reliability. With integrated tools for
    assessing key aspects like linearity, homoscedasticity, and more. It's
    a valuable asset for researchers and analysts working with regression
    models.
Implements regression calibration methods for correcting 
    measurement error in regression models using external or internal 
    reliability studies. Methods are described in Carroll, Ruppert, 
    Stefanski, and Crainiceanu (2006) "Measurement Error in Nonlinear 
    Models: A Modern Perspective" <doi:10.1201/9781420010138>.
Contains basic tools for visualizing, interpreting, and building regression models.  It has been designed for use with the book Introduction to Regression and Modeling with R by Adam Petrie, Cognella Publishers, ISBN: 978-1-63189-250-9.
We implement linear regression when the outcome of interest and some of the covariates are observed in two different datasets that cannot be linked, based on D'Haultfoeuille, Gaillac, Maurel (2022) <doi:10.3386/w29953>. The package allows for common regressors observed in both datasets, and for various shape constraints on the effect of covariates on the outcome of interest. It also provides the tools to perform a test of point identification. See the associated vignette <https://github.com/cgaillac/RegCombin/blob/master/RegCombin_vignette.pdf> for theory and code examples.
Regularised discriminant analysis functions. The classical regularised discriminant analysis proposed by Friedman in 1989, including cross-validation, of which the linear and quadratic discriminant analyses are special cases. Further, the regularised maximum likelihood linear discriminant analysis, including cross-validation. References: Friedman J.H. (1989): "Regularized Discriminant Analysis". Journal of the American Statistical Association 84(405): 165--175. <doi:10.2307/2289860>. Friedman J., Hastie T. and Tibshirani R. (2009). "The elements of statistical learning", 2nd edition. Springer, Berlin. <doi:10.1007/978-0-387-84858-7>. Tsagris M., Preston S. and Wood A.T.A. (2016). "Improved classification for compositional data using the alpha-transformation". Journal of Classification, 33(2): 243--261. <doi:10.1007/s00357-016-9207-5>.
Drift-Diffusion Model (DDM) has been widely used to model binary
    decision-making tasks, and many research studies the relationship between
    DDM parameters and other characteristics of the subject. This package uses
    'RStan' to perform generalized liner regression analysis over DDM parameters
    via a single Bayesian Hierarchical model. Compared to estimating DDM
    parameters followed by a separate regression model, 'RegDDM' reduces bias
    and improves statistical power.
Performs regularization of differential item functioning (DIF) parameters 
    in item response theory (IRT) models (Belzak & Bauer, 2020)
    <https://pubmed.ncbi.nlm.nih.gov/31916799/> using a penalized expectation-maximization
    algorithm.
Produces population distribution of disease risk and statistical risk categories, and predicts risks for individuals with genotype information.
Provides wrappers around base::grep() where the first argument
    is standardized to take the data object. This makes it less of a pain to use
    regular expressions with 'magrittr' or other pipe operators.
'shiny' extension that adds regular expression filtering capabilities to 
  the choice vector of the select list.
A set of functions used to automate commonly used methods in
    regression analysis. This includes plotting interactions, and calculating
    simple slopes, standardized coefficients, regions of significance
    (Johnson & Neyman, 1936; cf. Spiller et al., 2012), etc. See the reghelper
    documentation for more information, documentation, and examples.
Calculates intra-regional and inter-regional similarities based on user-provided
    spatial vector objects (regions) and spatial raster objects (cells with values).
    Implemented metrics include inhomogeneity, isolation 
    (Haralick and Shapiro (1985) <doi:10.1016/S0734-189X(85)90153-7>, 
    Jasiewicz et al. (2018) <doi:10.1016/j.cageo.2018.06.003>), 
    and distinction (Nowosad (2021) <doi:10.1080/13658816.2021.1893324>).
Provides methods to calculate approximate regional
    consistency probabilities using Method 1 and Method 2 proposed by the Japanese
    Ministry of Health, Labor and Welfare (2007) <https://www.pmda.go.jp/files/000153265.pdf>. These methods
    are useful for assessing regional consistency in multi-regional
    clinical trials.  The package can calculate unconditional, joint, and
    conditional regional consistency probabilities.  For technical
    details, please see Homma (2024) <doi:10.1002/pst.2358>.
A tool to conquer the difficulties to convert various region names and administration division codes of Chinese regions. The current version enables seamlessly converting Chinese regions' formal names, common-used names, and codes between each other at the city level from 1986 to 2019.
Validating sub-national statistical typologies, re-coding across 
    standard typologies of sub-national statistics, and making valid aggregate
    level imputation, re-aggregation, re-weighting and projection down to 
    lower hierarchical levels to create meaningful data panels and time series.
A method for performing joint registration and functional principal
    component analysis for curves (functional data) that are generated from exponential family distributions. This 
    mainly implements the algorithms described in 'Wrobel et al. (2019)' <doi:10.1111/biom.12963> and further adapts them to potentially
    incomplete curves where (some) curves are not observed from the beginning and/or until the end of the common domain. Curve registration 
    can be used to better understand patterns in functional data by separating curves into phase and amplitude variability.
    This software handles both binary and continuous functional data, and is
    especially applicable in accelerometry and wearable technology.
Provides a generic infrastructure for creating and using registries.
An algorithm is proposed to estimate regression kink model proposed by the paper, Lixiong Yang and Jen-Je Su (2018) <doi:10.1016/j.jimonfin.2018.06.002>.
Regularized (polychotomous) logistic regression 
  by Gibbs sampling. The package implements subtly different 
  MCMC schemes with varying efficiency depending on the data type 
  (binary v. binomial, say) and the desired estimator (regularized maximum
  likelihood, or Bayesian maximum a posteriori/posterior mean, etc.) through a 
  unified interface. For details, see Gramacy & Polson (2012 <doi:10.1214/12-BA719>).
Mediation analysis for multiple mediators by penalized structural equation models with different types of penalties depending on whether there are multiple mediators and only one exposure and one outcome variable (using sparse group lasso) or multiple exposures, multiple mediators, and multiple outcome variables (using lasso, L1, penalties).
This is an extension of the regression-based causal mediation analysis first proposed by Valeri and VanderWeele (2013) <doi:10.1037/a0031034> and Valeri and VanderWeele (2015) <doi:10.1097/EDE.0000000000000253>). It supports including effect measure modification by covariates(treatment-covariate and mediator-covariate product terms in mediator and outcome regression models) as proposed by Li et al (2023) <doi:10.1097/EDE.0000000000001643>. It also accommodates the original 'SAS' macro and 'PROC CAUSALMED' procedure in 'SAS' when there is no effect measure modification. Linear and logistic models are supported for the mediator model. Linear, logistic, loglinear, Poisson, negative binomial, Cox, and accelerated failure time (exponential and Weibull) models are supported for the outcome model.
Designed for longitudinal data analysis using Hidden Markov Models (HMMs). Tailored for applications in healthcare, social sciences, and economics, the main emphasis of this package is on regularization techniques for fitting HMMs. Additionally, it provides an implementation for fitting HMMs without regularization, referencing Zucchini et al. (2017, ISBN:9781315372488).
The functions in this package compute robust estimators by minimizing a kernel-based distance known as MMD (Maximum Mean Discrepancy) between the sample and a statistical model. Recent works proved that these estimators enjoy a universal consistency property, and are extremely robust to outliers. Various optimization algorithms are implemented: stochastic gradient is available for most models, but the package also allows gradient descent in a few models for which an exact formula is available for the gradient. In terms of distribution fit, a large number of continuous and discrete distributions are available: Gaussian, exponential, uniform, gamma, Poisson, geometric, etc. In terms of regression, the models available are: linear, logistic, gamma, beta and Poisson.
	Alquier, P. and Gerber, M. (2024) <doi:10.1093/biomet/asad031>
	Cherief-Abdellatif, B.-E. and Alquier, P. (2022) <doi:10.3150/21-BEJ1338>.
Network-based regularization has achieved success in variable selection for 
    high-dimensional biological data due to its ability to incorporate correlations among 
    genomic features. This package provides procedures of network-based variable selection 
    for generalized linear models (Ren et al. (2017) <doi:10.1186/s12863-017-0495-5> and 
	Ren et al.(2019) <doi:10.1002/gepi.22194>). Continuous, binary, and survival response 
	are supported. Robust network-based methods are available for continuous and survival 
	responses.
A function to plot a regression nomogram of regression 
    objects. Covariate distributions are superimposed on nomogram scales and the plot
	can be animated to allow on-the-fly changes to distribution representation and to 
	enable outcome calculation.
Provides R6 classes, methods and utilities to construct,
    analyze, summarize, and visualize regression models.
Focused on linear, quadratic and cubic regression models, it has a function for calculating the models, obtaining a list with their parameters, and a function for making the graphs for the respective models.
The graphical approach is proposed as a general framework for clinical trial designs involving multiple hypotheses, where decisions are made only based on the observed marginal p-values. A reverse graphical approach starts from a set of singleton graphs, and gradually add vertices into graphs until rejection of a set of hypotheses is made. See Gou, J. (2020). Reverse graphical approaches for multiple test procedures. Technical Report.
The visualization tool offers a nuanced understanding of regression dynamics, going beyond traditional per-unit interpretation of continuous variables versus categorical ones. It highlights the impact of unit changes as well as larger shifts like interquartile changes, acknowledging the distribution of empirical data. Furthermore, it generates visualizations depicting alterations in Odds Ratios for predictors across minimum, first quartile, median, third quartile, and maximum values, aiding in comprehending predictor-outcome interplay within empirical data distributions, particularly in logistic regression frameworks.
An R6 class "Replacer" provided by the package simplifies working with
    regex patterns containing named groups. It allows easy retrieval of matched portions
    and targeted replacements by group name, improving both code clarity and maintainability.
Functions to fit Gaussian linear model by maximising the
        residual log likelihood where the covariance structure can be
        written as a linear combination of known matrices.  Can be used
        for multivariate models and random effects models.  Easy
        straight forward manner to specify random effects models,
        including random interactions. Code now optimised to use
        Sherman Morrison Woodbury identities for matrix inversion in
        random effects models. We've added the ability to fit models
        using any kernel as well as a function to return the mean and
        covariance of random effects conditional on the data (best
        linear unbiased predictors, BLUPs).
        Clifford and McCullagh (2006)
        <https://www.r-project.org/doc/Rnews/Rnews_2006-2.pdf>.
Plot regression surfaces and marginal effects in three dimensions. The plots are 'plotly' objects and can be customized using functions and arguments from the 'plotly' package.
Simulate samples from populations with known covariate
    distributions, generate response variables according to common linear and
    generalized linear model families, draw from sampling distributions of
    regression estimates, and perform visual inference on diagnostics from model
    fits.
The expander functions rely on the mathematics developed for the Hessian-definiteness invariance theorem for linear projection transformations of variables, described in authors' paper, to generate the full, high-dimensional gradient and Hessian from the lower-dimensional derivative objects. This greatly relieves the computational burden of generating the regression-function derivatives, which in turn can be fed into any optimization routine that utilizes such derivatives. The theorem guarantees that Hessian definiteness is preserved, meaning that reasoning about this property can be performed in the low-dimensional space of the base distribution. This is often a much easier task than its equivalent in the full, high-dimensional space. Definiteness of Hessian can be useful in selecting optimization/sampling algorithms such as Newton-Raphson optimization or its sampling equivalent, the Stochastic Newton Sampler. Finally, in addition to being a computational tool, the regression expansion framework is of conceptual value by offering new opportunities to generate novel regression problems.
Perform a supervised data analysis on a database through a 'shiny' graphical interface. It includes methods such as linear regression, penalized regression, k-nearest neighbors, decision trees, ada boosting, extreme gradient boosting, random forest, neural networks, deep learning and support vector machines.
Compiling regression results into a publishable format, conducting post-hoc hypothesis testing, and plotting moderating effects (the effect of X on Y becomes stronger/weaker as Z increases).
Performs Random Subspace Method (RSM) for high-dimensional linear regression to obtain variable importance measures. The final model is chosen based on validation set or Generalized Information Criterion.
Implementation of the methods described in the paper with the above title: Langsrud, Ø. (2019) <doi:10.1007/s11222-018-9848-9>. The package can be used to generate synthetic or hybrid continuous microdata, and the relationship to the original data can be controlled in several ways. A function for replacing suppressed tabular cell frequencies with decimal numbers is included.
Uses both ridge and lasso penalties (and extensions) to penalize
    specific parameters in structural equation models. The package offers additional
    cost functions, cross validation, and other extensions beyond traditional structural
    equation models. Also contains a function to perform exploratory mediation (XMed).
Computes linear Bayesian spectral estimates from multirate	data for second-order stationary time series. Provides credible intervals and methods for plotting various spectral estimates. Please see the paper `Should we sample a time series more frequently?' (doi below) for a full description of and motivation for the methodology.
For a sequence of event occurence times, we are interested in
  finding subsequences in it that are too "regular". We define regular as being
  significantly different from a homogeneous Poisson process. The departure from
  the Poisson process is measured using a L1 distance. See Di and Perlman 2007
  for more details.
In repeated measures studies with extreme large or small values it is common 
  that the subjects measurements on average are closer to the mean of the basic population. 
  Interpreting possible changes in the mean in such situations can lead to biased results 
  since the values were not randomly selected, they come from truncated sampling.
  This method allows to estimate the range of means where treatment effects are likely to occur 
  when regression toward the mean is present. 
  Ostermann, T., Willich, Stefan N. & Luedtke, Rainer. (2008). Regression toward the mean - a detection method for unknown population mean based on Mee and Chua's algorithm. BMC Medical Research Methodology.<doi:10.1186/1471-2288-8-52>.
  Acknowledgments: We would like to acknowledge "Lena Roth" and "Nico Steckhan" for the package's initial updates (Q3 2024) and continued supervision and guidance. Both have contributed to discussing and integrating these methods into the package, ensuring they are up-to-date and contextually relevant.
Tools for linear, nonlinear and nonparametric regression
             and classification.  Novel graphical methods for assessment 
             of parametric models using nonparametric methods. One 
             vs. All and All vs. All multiclass classification, optional
             class probabilities adjustment.  Nonparametric regression 
             (k-NN) for general dimension, local-linear option.  Nonlinear 
             regression with Eickert-White method for dealing with 
             heteroscedasticity.  Utilities for converting time series
             to rectangular form.  Utilities for conversion between
             factors and indicator variables.  Some code related to
             "Statistical Regression and Classification: from Linear
             Models to Machine Learning", N. Matloff, 2017, CRC,
             ISBN 9781498710916.
Population genetic data such as 'Single Nucleotide
        Polymorphisms' (SNPs) is often used to identify genomic regions
        that have been under recent natural or artificial selection
        and might provide clues about the molecular mechanisms of adaptation. 
        One approach, the concept of an 'Extended Haplotype Homozygosity' (EHH), 
        introduced by (Sabeti 2002) <doi:10.1038/nature01140>, has given rise to 
        several statistics designed for whole genome scans. 
        The package provides functions to compute three of these,
        namely: 'iHS' (Voight 2006) <doi:10.1371/journal.pbio.0040072> for
        detecting positive or 'Darwinian' selection within a single population as well as
        'Rsb' (Tang 2007) <doi:10.1371/journal.pbio.0050171> and 
        'XP-EHH' (Sabeti 2007) <doi:10.1038/nature06250>, targeted
        at differential selection between two populations. 
        Various plotting functions are included to facilitate
        visualization and interpretation of these statistics.
Contains example data for the 'rehh' package.
Performs model-free reinforcement learning in R. This implementation enables the learning
    of an optimal policy based on sample sequences consisting of states, actions and rewards. In 
    addition, it supplies multiple predefined reinforcement learning algorithms, such as experience 
    replay. Methodological details can be found in Sutton and Barto (1998) <ISBN:0262039249>.
Functions from the book "Reinsurance: Actuarial and Statistical Aspects" (2017) by Hansjoerg Albrecher, Jan Beirlant and Jef Teugels <https://www.wiley.com/en-us/Reinsurance%3A+Actuarial+and+Statistical+Aspects-p-9780470772683>.
Search R files for not installed packages and run install.packages.
Application of reinsurance treaties to claims portfolios. 
            The package creates a class Claims whose objective is to 
            store claims and premiums, on which different treaties can be applied.
            A statistical analysis can then be applied to measure the impact of
            reinsurance, producing a table or graphical output. This package can
            be used for estimating the impact of reinsurance on several portfolios
            or for pricing treaties through statistical analysis. Documentation
            for the implemented methods can be found in "Reinsurance: Actuarial
            and Statistical Aspects" by Hansjöerg Albrecher, Jan Beirlant,
            Jozef L. Teugels (2017, ISBN: 978-0-470-77268-3) and 
            "REINSURANCE: A Basic Guide to Facultative and Treaty Reinsurance"
            by Munich Re (2010) <https://www.munichre.com/site/mram/get/documents_E96160999/mram/assetpool.mr_america/PDFs/3_Publications/reinsurance_basic_guide.pdf>.
Provides several metrics for assessing relative importance in linear models. These can be printed, plotted and bootstrapped. The recommended metric is lmg, which provides a decomposition of the model explained variance into non-negative contributions. There is a version of this package available that additionally provides a new and also recommended metric called pmvd. If you are a non-US user, you can download this extended version from Ulrike Groempings web site.
Functions to safely map from a vector of keys to a vector of values, determine properties of a given relation, or ensure a relation conforms to a given type, such as many-to-many, one-to-many, injective, surjective, or bijective. Permits default return values for use similar to a vectorised switch statement, as well as safely handling large vectors, NAs, and duplicate mappings.
Inference of relatedness coefficients from a bi-allelic genotype matrix using a Maximum Likelihood estimation, Laporte, F., Charcosset, A. and Mary-Huard, T. (2017) <doi:10.1111/biom.12634>.
Data structures and algorithms for k-ary relations with
  arbitrary domains, featuring relational algebra, predicate functions,
  and fitters for consensus relations.
Using the novel Relative Distance to cluster datasets. Implementation of a clustering approach based on the k-means algorithm that can be used with any distance. In addition, implementation of the Hartigan and Wong method to accommodate alternative distance metrics. Both methods can operate with any distance measure, provided a suitable method is available to compute cluster centers under the chosen metric. Additionally, the k-medoids algorithm is implemented, offering a robust alternative for clustering without the need of computing cluster centers under the chosen metric. All three methods are designed to support Relative distances, Euclidean distances, and any user-defined distance functions. The Hartigan and Wong method is described in Hartigan and Wong (1979) <doi:10.2307/2346830> and an explanation of the k-medoids algorithm can be found in Reynolds et al (2006) <doi:10.1007/s10852-005-9022-1>.
According to the order of the loci on the chromosome, the loci can be
    connected according to the interrelationship between them and classified according
    to different locus types.
Tools for the comparison of distributions. This includes nonparametric estimation of the relative distribution PDF and CDF and numerical summaries as described in "Relative Distribution Methods in the Social Sciences" by Mark S. Handcock and Martina Morris, Springer-Verlag, 1999, Springer-Verlag, ISBN 0387987789.
Parameters estimation and linear regression models for Reliability 
    distributions families reviewed by Almalki & Nadarajah (2014)
    <doi:10.1016/j.ress.2013.11.010> using Generalized Additive
    Models for Location, Scale and Shape, aka GAMLSS by Rigby & Stasinopoulos
    (2005) <doi:10.1111/j.1467-9876.2005.00510.x>.
Functions to obtain an important number of electoral indicators described in the package, which can be divided into two large sections: The first would be the one containing the indicators of electoral disproportionality, such as, Rae index, Loosemore–Hanby index, etc. The second group is intended to study the dimensions of the party system vote, through the indicators of electoral fragmentation, polarization, volatility, etc. Moreover, multiple seat allocation simulations can also be performed based on different allocation systems, such as the D'Hondt method, Sainte-Laguë, etc. Finally, some of these functions have been built so that, if the user wishes, the data provided by the Spanish Ministry of Home Office for different electoral processes held in Spain can be obtained automatically. All the above will allow the users to carry out deep studies on the results obtained in any type of electoral process. The methods are described in: Oñate, Pablo and Ocaña, Francisco A. (1999, ISBN:9788474762815); Ruiz Rodríguez, Leticia M. and Otero Felipe, Patricia (2011, ISBN:9788474766226).
Calculates relevance and significance values for
  simple models and for many types of regression models.
  These are introduced in
  'Stahel, Werner A.' (2021)
  "Measuring Significance and Relevance instead of p-values."
  <https://stat.ethz.ch/~stahel/relevance/stahel-relevance2103.pdf>.
  These notions are also applied to replication studies,
  as described in the manuscript
  'Stahel, Werner A.' (2022)
  "'Replicability': Terminology, Measuring Success, and Strategy"
  available in the documentation.
Tools to fit and simulate realizations from relational event models.
A method for fitting the entire regularization path of the
    reluctant generalized additive model (RGAM) for linear regression, logistic, 
    Poisson and Cox regression models. See Tay, J. K., and Tibshirani, R., (2019)
    <arXiv:1912.01808> for details.
Checking the reliability of predictions via the CORP approach,
    which generates provably statistically 'C'onsistent, 'O'ptimally binned, and
    'R'eproducible reliability diagrams using the 'P'ool-adjacent-violators
    algorithm. See Dimitriadis, Gneiting, Jordan (2021) <doi:10.1073/pnas.2016191118>.
Perform structural reliability analysis, including computation and
    simulation with system signatures, Samaniego (2007)
    <doi:10.1007/978-0-387-71797-5>, and survival signatures, Coolen and
    Coolen-Maturi (2013) <doi:10.1007/978-3-642-30662-4_8>. Additionally
    supports parametric and topological inference given system lifetime data,
    Aslett (2012) <https://www.louisaslett.com/PhD_Thesis.pdf>.
Modeling and plotting functions for Reliability Growth Analysis (RGA). Models include the Duane (1962) <doi:10.1109/TA.1964.4319640>, Non-Homogeneous Poisson Process (NHPP) by Crow (1975) <https://apps.dtic.mil/sti/citations/ADA020296>, Piecewise Weibull NHPP by Guo et al. (2010) <doi:10.1109/RAMS.2010.5448029>, and Piecewise Weibull NHPP with Change Point Detection based on the 'segmented' package by Muggeo (2024) <https://cran.r-project.org/package=segmented>.
Build interactive Reliability Probability Plots with 'plotly' by Carson Sievert (2020) <https://plotly-r.com>, an interactive web-based graphing library.
Provides a comprehensive suite of utilities for univariate continuous probability distributions and reliability models. Includes functions to compute the probability density, cumulative distribution, quantile, reliability, and hazard functions, along with random variate generation. Also offers diagnostic and model assessment tools  such as Quantile-Quantile (Q-Q) and Probability-Probability (P-P) plots, the Kolmogorov-Smirnov goodness-of-fit test, and model selection criteria including the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Currently implements the following distributions: Burr X, Chen, Exponential Extension, Exponentiated Logistic, Exponentiated Weibull, Exponential Power, Flexible Weibull, Generalized Exponential, Gompertz, Generalized Power Weibull, Gumbel, Inverse Generalized Exponential, Linear Failure Rate, Log-Gamma, Logistic-Exponential, Logistic-Rayleigh, Log-log, Marshall-Olkin Extended Exponential, Marshall-Olkin Extended Weibull, and Weibull Extension distributions. Serves as a valuable resource for teaching and research in probability theory, reliability analysis, and applied statistical modeling.
Functions to facilitate inference on the relative importance of predictors in a linear or generalized linear model, and a couple of useful Tcl/Tk widgets.
Performs Principal Components Analysis (also known as PCA) dimensionality reduction in the context of a linear regression. In most cases, PCA dimensionality reduction is performed independent of the response variable for a regression. This captures the majority of the variance of the model's predictors, but may not actually be the optimal dimensionality reduction solution for a regression against the response variable. An alternative method, optimized for a regression against the response variable, is to use both PCA and a relative importance measure. This package applies PCA to a given data frame of predictors, and then calculates the relative importance of each PCA factor against the response variable. It outputs ordered factors that are optimized for model fit. By performing dimensionality reduction with this method, an individual can achieve a the same r-squared value as performing just PCA, but with fewer PCA factors. References: Yuri Balasanov (2017) <https://ilykei.com>.
It offers random numbers generation from members of the truncated multivariate elliptical family of distribution such as the truncated versions of the Normal, Student-t, Laplace, Pearson VII, Slash, Logistic, among others. Particular distributions can be provided by specifying the density generating function. It also computes the first two moments (covariance matrix as well) for some particular distributions.
  References used for this package: Galarza, C. E., Matos, L. A., Castro, L. M., and Lachos, V. H. (2022). Moments of the doubly truncated selection elliptical distributions with emphasis on the unified multivariate skew-t distribution. Journal of Multivariate Analysis, 189, 104944 <doi:10.1016/j.jmva.2021.104944>; Ho, H. J., Lin, T. I., Chen, H. Y., and Wang, W. L. (2012). Some results on the truncated multivariate t distribution. Journal of Statistical Planning and Inference, 142(1), 25-40 <doi:10.1016/j.jspi.2011.06.006>; Valeriano, K. A., Galarza, C. E., and Matos, L. A. (2021). Moments and random number generation for the truncated elliptical family of distributions. Statistics and Computing, 33(1), 32 <doi:10.1007/s11222-022-10200-4>.
Analysis of DNA mixtures involving relatives by computation of likelihood ratios that account for dropout and drop-in, mutations, silent alleles and population 
  substructure. This is useful in kinship cases, like non-invasive prenatal paternity testing, where deductions about individuals' relationships rely on DNA mixtures, 
  and in criminal cases where the contributors to a mixed DNA stain may be related. Relationships are represented 
  by pedigrees and can include kinship between more than two individuals. The main function is relMix() and its graphical user interface relMixGUI(). 
  The implementation and method is described in Dorum et al. (2017) <doi:10.1007/s00414-016-1526-x>, Hernandis et al. (2019)
  <doi:10.1016/j.fsigss.2019.09.085> and Kaur et al. (2016) <doi:10.1007/s00414-015-1276-1>.
A set of tools to explore the behaviour statistics used for forensic DNA interpretation when close relatives are involved. The package also offers some useful tools for exploring other forensic DNA situations.
Contains functions for analysing relative survival data, including nonparametric estimators of net (marginal relative) survival, relative survival ratio, crude mortality, methods for fitting and checking additive and multiplicative regression models, transformation approach, methods for dealing with population mortality tables. Work has been described in Pohar Perme, Pavlic (2018) <doi:10.18637/jss.v087.i08>.
Calculate endogenous network effects in event sequences and fit relational event models (REM): Using network event sequences (where each tie between a sender and a target in a network is time-stamped), REMs can measure how networks form and evolve over time. Endogenous patterns such as popularity effects, inertia, similarities, cycles or triads can be calculated and analyzed over time.
The rema package implements a permutation-based approach for binary 
	meta-analyses of 2x2 tables, founded on conditional logistic regression, 
	that provides more reliable statistical tests when heterogeneity is 
	observed in rare event data (Zabriskie et al. 2021 <doi:10.1002/sim.9142>). 
	To adjust for the effect of heterogeneity, this method conditions on the 
	sufficient statistic of a proxy for the heterogeneity effect as opposed to
	estimating the heterogeneity variance. While this results in the model not
	strictly falling under the random-effects framework, it is akin to a 
	random-effects approach in that it assumes differences in variability due 
	to treatment. Further, this method does not rely on large-sample 
	approximations or continuity corrections for rare event data. This method
	uses the permutational distribution of the test statistic instead of
	asymptotic approximations for inference. The number of observed events 
	drives the computation complexity for creating this permutational 
	distribution. Accordingly, for this method to be computationally feasible,
	it should only be applied to meta-analyses with a relatively low number of
	observed events. To create this permutational distribution, a network 
	algorithm, based on the work of Mehta et al. (1992) <doi:10.2307/1390598> 
	and Corcoran et al. (2001) <doi:10.1111/j.0006-341x.2001.00941.x>, is 
	employed using C++ and integrated into the package.
Meta-analysis is widely used to summarize estimated effects sizes across multiple statistical tests. Standard fixed and random effect meta-analysis methods assume that the estimated of the effect sizes are statistically independent.  Here we relax this assumption and enable meta-analysis when the correlation matrix between effect size estimates is known.  Fixed effect meta-analysis uses the method of Lin and Sullivan (2009) <doi:10.1016/j.ajhg.2009.11.001>, and random effects meta-analysis uses the method of Han, et al. <doi:10.1093/hmg/ddw049>.
Automatically creates separate regression models for different spatial 
    regions. The prediction surface is smoothed using a regional border smoothing 
    method. If regional models are continuous, the resulting prediction surface is 
    continuous across the spatial dimensions, even at region borders. Methodology 
    is described in Wagstaff and Bean (2023) <doi:10.32614/RJ-2023-004>.
A small wrapper on 'regexpr' to extract the matches and
    captured groups from the match of a regular expression to a character
    vector.
Wrappers on 'regexpr' and 'gregexpr' to return the match
    results in tidy data frames.
An 'RStudio' addin providing shortcuts for writing in 'Markdown'. This package provides a series of 
             functions that allow the user to be more efficient when using 'Markdown'. For example, you can select 
             a word, and put it in bold or in italics, or change the alignment of elements inside you Rmd. The idea
             is to map all the functionalities from 'remedy' on keyboard shortcuts, so that it provides an interface 
             close to what you can find in any other text editor.
Methods and tools for implementing regularized multivariate functional principal component analysis ('ReMFPCA') for multivariate functional data whose variables might be observed over different dimensional domains. 'ReMFPCA' is an object-oriented interface leveraging the extensibility and scalability of R6. It employs a parameter vector to control the smoothness of each functional variable. By incorporating smoothness constraints as penalty terms within a regularized optimization framework, 'ReMFPCA' generates smooth multivariate functional principal components, offering a concise and interpretable representation of the data. For detailed information on the methods and techniques used in 'ReMFPCA', please refer to Haghbin et al. (2023) <doi:10.48550/arXiv.2306.13980>.
Efficiently processes relational event history data and transforms them into formats suitable for other packages. The primary objective of this package is to convert event history data into a format that integrates with the packages in 'remverse' and is compatible with various analytical tools (e.g., computing network statistics, estimating tie-oriented or actor-oriented social network models). Second, it can also transform the data into formats compatible with other packages out of 'remverse'. The package processes the data for two types of temporal social network models: tie-oriented modeling framework (Butts, C., 2008, <doi:10.1111/j.1467-9531.2008.00203.x>) and actor-oriented modeling framework (Stadtfeld, C., & Block, P., 2017, <doi:10.15195/v4.a14>).
Insert/extract text "reminders" into/from function source code 
    comments or as the "comment" attribute of any object.  
    The former can be handy in development as reminders of e.g. argument
    requirements, expected objects in the calling environment, required options
    settings, etc. The latter can be used to provide information of the object and 
    as simple manual "tooltips" for users, among other things.
Reference-based multiple imputation of ordinal and binary responses under Bayesian
          framework, as described in Wang and Liu (2022) <arXiv:2203.02771>. Methods for 
          missing-not-at-random include Jump-to-Reference (J2R), Copy Reference (CR), and Delta 
          Adjustment which can generate tipping point analysis.
Implementation of an algorithm in two steps to estimate parameters of a model whose latent dynamics are inferred through latent processes, jointly regularized. This package uses 'Monolix' software (<https://monolixsuite.slp-software.com/>), which provide robust statistical method for non-linear mixed effects modeling. 'Monolix' must have been installed prior to use.
Traditional latent variable models assume that the population
    is homogeneous, meaning that all individuals in the population are
    assumed to have the same latent structure. However, this assumption is
    often violated in practice given that individuals may differ in their
    age, gender, socioeconomic status, and other factors that can affect
    their latent structure. The robust expectation maximization (REM)
    algorithm is a statistical method for estimating the parameters of a
    latent variable model in the presence of population heterogeneity as recommended by 
    Nieser & Cochran (2023) <doi:10.1037/met0000413>. The REM algorithm is based on the
    expectation-maximization (EM) algorithm, but it allows for the case
    when all the data are generated by the assumed data generating model.
Implements TRACDS (Temporal Relationships 
    between Clusters for Data Streams), a generalization of 
    Extensible Markov Model (EMM). TRACDS adds a temporal or order model
    to data stream clustering by superimposing a dynamically adapting
    Markov Chain. Also provides an implementation of EMM (TRACDS on top of tNN 
    data stream clustering). Development of this 
    package was supported in part by NSF IIS-0948893 and R21HG005912 from 
    the National Human Genome Research Institute. Hahsler and Dunham (2010) <doi:10.18637/jss.v035.i05>.
An HTTP API client for 'Lemmy'
    (<https://github.com/LemmyNet/lemmy>) in R. Code and documentation are
    generated from the official 'JavaScript' client source
    (<https://github.com/LemmyNet/lemmy-js-client>).
Empirical orthogonal teleconnections in R.
    'remote' is short for 'R(-based) EMpirical Orthogonal TEleconnections'.
    It implements a collection of functions to facilitate empirical
    orthogonal teleconnection analysis. Empirical Orthogonal Teleconnections
    (EOTs) denote a regression based approach to decompose spatio-temporal
    fields into a set of independent orthogonal patterns. They are quite
    similar to Empirical Orthogonal Functions (EOFs) with EOTs producing
    less abstract results. In contrast to EOFs, which are orthogonal in both
    space and time, EOT analysis produces patterns that are orthogonal in
    either space or time.
These tools were created to test map-scale hypotheses about trends in large
  remotely sensed data sets but any data with spatial and temporal variation
  can be analyzed. Tests are conducted using the PARTS method for analyzing spatially
  autocorrelated time series
  (Ives et al., 2021: <doi:10.1016/j.rse.2021.112678>).
  The method's unique approach can handle extremely large data sets that other
  spatiotemporal models cannot, while still appropriately accounting for
  spatial and temporal autocorrelation. This is done by partitioning the data
  into smaller chunks, analyzing chunks separately and then combining the
  separate analyses into a single, correlated test of the map-scale hypotheses.
Download and install R packages stored in 'GitHub', 'GitLab',
    'Bitbucket', 'Bioconductor', or plain 'subversion' or 'git'
    repositories.  This package provides the 'install_*' functions in
    'devtools'. Indeed most of the code was copied over from 'devtools'.
Make your workflow faster and easier. Easily customizable
    plots (via 'ggplot2'), nice APA tables (following the style of the
    *American Psychological Association*) exportable to Word (via
    'flextable'), easily run statistical tests or check assumptions, and
    automatize various other tasks.
T (extent of the primary tumor), N (absence or presence and extent of regional lymph node metastasis) and M (absence or presence of distant metastasis) are three components to describe the anatomical tumor extent. TNM stage is important in treatment decision-making and outcome predicting. The existing oropharyngeal Cancer (OPC) TNM stages have not made distinction of the two sub sites of Human papillomavirus positive (HPV+) and Human papillomavirus negative (HPV-) diseases. We developed novel criteria to assess performance of the TNM stage grouping schemes based on parametric modeling adjusting on important clinical factors. These criteria evaluate the TNM stage grouping scheme in five different measures: hazard consistency, hazard discrimination, explained variation, likelihood difference, and balance. The methods are described in Xu, W., et al. (2015) <https://www.austinpublishinggroup.com/biometrics/fulltext/biometrics-v2-id1014.php>.
Computes a variety of statistics for relational event models. Relational event models enable researchers to investigate both exogenous and endogenous factors influencing the evolution of a time-ordered sequence of events. These models are categorized into tie-oriented models (Butts, C., 2008, <doi:10.1111/j.1467-9531.2008.00203.x>), where the probability of a dyad interacting next is modeled in a single step, and actor-oriented models (Stadtfeld, C., & Block, P., 2017, <doi:10.15195/v4.a14>), which first model the probability of a sender initiating an interaction and subsequently the probability of the sender's choice of receiver. The package is designed to compute a variety of statistics that summarize exogenous and endogenous influences on the event stream for both types of models.
A comprehensive set of tools designed for optimizing likelihood within a tie-oriented (Butts, C., 2008, <doi:10.1111/j.1467-9531.2008.00203.x>) or an actor-oriented modelling framework (Stadtfeld, C., & Block, P., 2017, <doi:10.15195/v4.a14>) in relational event networks. The package accommodates both frequentist and Bayesian approaches. The frequentist approaches that the package incorporates are the Maximum Likelihood Optimization (MLE) and the Gradient-based Optimization  (GDADAMAX). The Bayesian methodologies included in the package are the Bayesian Sampling Importance Resampling (BSIR) and the Hamiltonian Monte Carlo (HMC). The flexibility of choosing between frequentist and Bayesian optimization approaches allows researchers to select the estimation approach which aligns the most with their analytical preferences.
Model based simulation of dynamic networks under tie-oriented (Butts, C., 2008, <doi:10.1111/j.1467-9531.2008.00203.x>) and actor-oriented (Stadtfeld, C., & Block, P., 2017, <doi:10.15195/v4.a14>) relational event models. Supports simulation from a variety of relational event model extensions, including temporal variability in effects, heterogeneity through dyadic latent class relational event models (DLC-REM), random effects, blockmodels, and memory decay in relational event models (Lakdawala, R., 2024 <doi:10.48550/arXiv.2403.19329>). The development of this package was supported by a Vidi Grant (452-17-006) awarded by the Netherlands Organization for Scientific Research (NWO) Grant and an ERC Starting Grant  (758791).
Portfolio optimization is achieved through a combination of regularization techniques and ensemble methods that are designed to generate stable out-of-sample return predictions, particularly in the presence of strong correlations among assets. The package includes functions for data preparation, parallel processing, and portfolio analysis using methods such as Mean-Variance, James-Stein, LASSO, Ridge Regression, and Equal Weighting. It also provides visualization tools and performance metrics, such as the Sharpe ratio, volatility, and maximum drawdown, to assess the results.
ENA (Shaffer, D. W. (2017) Quantitative Ethnography. ISBN: 0578191687) is a method used to identify meaningful and quantifiable patterns in discourse or reasoning. ENA moves beyond the traditional frequency-based assessments by examining the structure of the co-occurrence, or connections in coded data. Moreover, compared to other methodological approaches, ENA has the novelty of (1) modeling whole networks of connections and (2) affording both quantitative and qualitative comparisons between different network models.   Shaffer, D.W., Collier, W., & Ruis, A.R. (2016).
Provides the datasets in the book "Methods of Multivariate Analysis (3rd)", such as Table 6.27 Blood Pressure Data, for statistical analysis,especially MANOVA. The dataset names correspond to their numbering in the third edition of the book, such as table6.27. Based on the book by Rencher and Christensen (2012, ISBN:9780470178966).
Fits linear models with endogenous regressor using latent instrumental variable approaches. 
    The methods included in the package are Lewbel's (1997) <doi:10.2307/2171884> higher moments approach as well as 
    Lewbel's (2012) <doi:10.1080/07350015.2012.643126> heteroscedasticity approach, Park and Gupta's (2012) <doi:10.1287/mksc.1120.0718> joint estimation method 
    that uses Gaussian copula and Kim and Frees's (2007) <doi:10.1007/s11336-007-9008-1> multilevel generalized
    method of moment approach that deals with endogeneity in a multilevel setting.
    These are statistical techniques to address the endogeneity problem where no external instrumental variables are needed.
    See the publication related to this package in the Journal of Statistical Software for more details: <doi:10.18637/jss.v107.i03>.
    Note that with version 2.0.0 sweeping changes were introduced which greatly improve functionality and usability but break backwards compatibility.
Peaks Over Threshold (POT) or 'methode du renouvellement'. The
    distribution for the excesses can be chosen, and heterogeneous data
    (including historical data or block data) can be used in a
    Maximum-Likelihood framework.
Supports calculations and visualization for renewable power systems and the environment. Analysis and graphical tools for DC and AC circuits and their use in electric power systems. Analysis and graphical tools for thermodynamic cycles and heat engines, supporting efficiency calculations in coal-fired power plants, gas-fired power plants. Calculations of carbon emissions and atmospheric CO2 dynamics. Analysis of power flow and demand for the grid, as well as power models for microgrids and off-grid systems. Provides resource and power generation for hydro power, wind power, and solar power.
Provides an R interface to the NCBI's 'EUtils' API, 
    allowing users to search databases like 'GenBank' 
    <https://www.ncbi.nlm.nih.gov/genbank/> and 'PubMed' 
    <https://pubmed.ncbi.nlm.nih.gov/>, process the 
    results of those searches and pull data into their R sessions.
A dependency management toolkit for R. Using 'renv', you can create
    and manage project-local R libraries, save the state of these libraries to
    a 'lockfile', and later restore your library as required. Together, these
    tools can help make your projects more isolated, portable, and reproducible.
Provides a general routine, envMU, which allows  estimation of the M envelope of span(U) given root n consistent estimators of M and U. The routine envMU does not presume a model.  This package implements response envelopes,  partial response envelopes,  envelopes in the predictor space,  heteroscedastic envelopes,  simultaneous envelopes,  scaled response envelopes,  scaled envelopes in the predictor space,  groupwise envelopes, weighted envelopes,  envelopes in logistic regression, envelopes in Poisson regression envelopes in function-on-function linear regression, envelope-based Partial Partial Least Squares,  envelopes with non-constant error covariance, envelopes with t-distributed errors, reduced rank envelopes and reduced rank envelopes with non-constant error covariance. For each of these model-based routines the package provides inference tools including bootstrap, cross validation, estimation and prediction, hypothesis testing on coefficients are included except for weighted envelopes. Tools for selection of dimension include AIC, BIC and likelihood ratio testing.   Background is available at Cook, R. D., Forzani, L. and Su, Z. (2016) <doi:10.1016/j.jmva.2016.05.006>. Optimization is based on a clockwise coordinate descent algorithm.
Provides environment modules functionality, which enables use of the Environment Modules system (<http://modules.sourceforge.net/>) from within the R environment. By default the user's login shell environment (ie. "bash -l") will be used to initialize the current session. The module function can also; load or unload specific software, list all the loaded software within the current session, and list all the applications available for loading from the module system. Lastly, the module function can remove all loaded software from the current session.
Contains utilities for the analysis of Michaelian kinetic data. Beside the classical linearization 
	methods (Lineweaver-Burk, Eadie-Hofstee, Hanes-Woolf and Eisenthal-Cornish-Bowden), features include the 
	ability to carry out weighted regression analysis that, in most cases, substantially improves the estimation 
	of kinetic parameters (Aledo (2021) <doi:10.1002/bmb.21522>). To avoid data transformation and the potential
	biases introduced by them, the package also offers functions to directly fitting data to the Michaelis-Menten 
	equation, either using ([S], v) or (time, [S]) data. Utilities to simulate substrate progress-curves (making 
	use of the Lambert W function) are also provided. The package is accompanied of vignettes that aim to orientate
	the user in the choice of the most suitable method to estimate the kinetic parameter of an Michaelian enzyme.
The complete data set of open repair data, full compliant with the Open Repair Data Standards (ORDS).
      It combines the datasets contributed by partner organizations of the Open Repair Alliance (ORA). Last updated: 2021-02-22. 
      The package also contains via quests enriched datasets on batteries,
      printer, mobiles, and tablets.
Set of utilities to facilitate the reproduction of analysis in R.
 It allow to make_structure(), clean_structure(), and run and log programs in a
 predefined order to allow secondary files, analysis and reports be constructed in
 an ordered and reproducible form.
The function RepaymentPlan() calculates repayment schedule for repayment/mortgage plans.
Various functions to fit models for non-normal repeated
    measurements, such as Binary Random Effects Models with Two Levels of Nesting,
    Bivariate Beta-binomial Regression Models, Marginal Bivariate Binomial Regression Models,
    Cormack capture-recapture models, Continuous-time Hidden Markov Chain Models, 
    Discrete-time Hidden Markov Chain Models,
    Changepoint Location Models using a Continuous-time Two-state Hidden Markov Chain,
    generalized nonlinear autoregression models, multivariate Gaussian copula models,
    generalized non-linear mixed models with one random effect,  
    generalized non-linear mixed models using h-likelihood for one random effect, 
    Repeated Measurements Models for Counts with Frailty or Serial Dependence,
    Repeated Measurements Models for Continuous Variables with Frailty or Serial Dependence,
    Ordinal Random Effects Models with Dropouts, marginal homogeneity models for square
    contingency tables, correlated negative binomial models with Kalman update.
    References include Lindsey's text books, 
    JK Lindsey (2001) <isbn:10-0198508123> and JK Lindsey (1999) <isbn:10-0198505590>.
A toolkit for the analysis of high-dimensional repeated measurements, providing functions 
    for outlier detection, differential expression analysis, gene-set tests, and binary random data generation.
Utilities for accessing RePEc (Research Papers in Economics)
    through a RESTful API. You can request a code and get detailed
    information at the following page: <https://ideas.repec.org/api.html>.
Creates reports from Trello, a collaborative, project organization 
             and list-making application. <https://trello.com/>
             Reports are created by comparing individual Trello board
             cards from two different points in time and documenting any changes made
             to the cards.
Visualization platform for T cell receptor repertoire
    analysis output results. It includes comparison of sequence frequency
    among samples, network of similar sequences and convergent
    recombination source between species. Currently repertoire analysis is
    in early stage of development and requires new approaches for
    repertoire data examination and assessment as we intend to develop.
    No publication is available yet (will be available in the near
    future), Efroni (2021) <https:>.
Estimation of Bayes and local Bayes false discovery rates for
      replicability analysis (Heller & Yekutieli, 2014 <doi:10.1214/13-AOAS697> ;
      Heller at al., 2015 <doi: 10.1093/bioinformatics/btu434>).
Updates values within csv format data files using a custom, User-built
    csv format lookup file. Based on 'data.table' package.
Create presentations and display them inside the R 'REPL'
    (Read-Eval-Print loop), aka the R console. Presentations can be written in
    'RMarkdown' or any other text format. A set of convenient navigation options
    as well as code evaluation during a presentation is provided. It is great
    for tech talks with live coding examples and tutorials. While this is not a
    replacement for standard presentation formats, it's old-school looks might
    just be what sets it apart. This project has been inspired by the
    'REPLesent' project for presentations in the 'Scala' 'REPL'.
For a multisite replication project, computes the consistency metric P_orig, which is
    the probability that the original study would observe an estimated effect size as extreme or more extreme
    than it actually did, if in fact the original study were statistically consistent with the replications. Other
    recommended metrics are: (1) the probability of a true effect of scientifically meaningful size in the same direction as the estimate
    the original study; and (2) the probability of a true effect of meaningful size in the direction opposite
    the original study's estimate. These two can be computed using the package \code{MetaUtility::prop_stronger}.
    Additionally computes older metrics used in replication projects (namely expected agreement in "statistical significance" between an original study and replication studies as well
    as prediction intervals for the replication estimates). See Mathur and VanderWeele (under review; <https://osf.io/apnjk/>)
    for details.
Performs comparative bioavailability calculations for Average
    Bioequivalence with Expanding Limits (ABEL). Implemented are 'Method A' /
    'Method B' and the detection of outliers. If the design allows, assessment
    of the empiric Type I Error and iteratively adjusting alpha to control the
    consumer risk. Average Bioequivalence - optionally with a tighter (narrow
    therapeutic index drugs) or wider acceptance range (South Africa: Cmax) -
    is implemented as well.
Test for effects of  both individual factors and their interaction on  replicated spatial patterns in a two factorial design, as explained in Ramon et al. (2016) <doi:10.1111/ecog.01848>.
Provides utilities for the design and analysis of replication
    studies.  Features both traditional methods based on statistical
    significance and more recent methods such as the sceptical p-value;
    Held L. (2020) <doi:10.1111/rssa.12493>, Held et al. (2022)
    <doi:10.1214/21-AOAS1502>, Micheloud et al. (2023) <doi:10.1111/stan.12312>.
    Also provides related methods including the harmonic mean chi-squared
    test; Held, L. (2020) <doi:10.1111/rssc.12410>, and intrinsic
    credibility; Held, L. (2019) <doi:10.1098/rsos.181534>. Contains
    datasets from five large-scale replication projects.
Create, Plot and Compare Replication Timing Profiles. The method is described in Muller et al., (2014) <doi: 10.1093/nar/gkt878>.
Tools to load 'R' packages
    and automatically generate BibTeX files citing them as well as load and
    cache plain-text and 'Excel' formatted data stored on 'GitHub', and
    from other sources.
Tools for generating descriptives and report tables for different models,
    data.frames and tables and exporting them to different formats.
A data manager meant to avoid manual storage/retrieval of
    data to/from the file system. It builds one (or more) centralized
    repository where R objects are stored with rich annotations,
    including corresponding code chunks, and easily searched and
    retrieved. See Napolitano (2017) <doi:10.1037/a0028240> for further
    information.
Generates a project and repo for easy initialization of a GitHub repo for R workshops. The repo includes a README with instructions to ensure that all users have the needed packages, an 'RStudio' project with the right directories and the proper data. The repo can then be used for hosting code taught during the workshop.
Fits linear models to repeated ordinal scores using GEE methodology.
The aim of the 'report' package is to bridge the gap between
    R’s output and the formatted results contained in your manuscript.
    This package converts statistical models and data frames into textual
    reports suited for publication, ensuring standardization and quality
    in results reporting.
Contains functions to create regulatory-style statistical reports.
    Originally designed to create tables, listings, and figures for the 
    pharmaceutical, biotechnology, and medical device industries, these
    reports are generalized enough that they could be used in any industry.
    Generates text, rich-text, PDF, HTML, and Microsoft Word file formats.  
    The package specializes 
    in printing wide and long tables with automatic page wrapping and splitting.  
    Reports can be produced with a minimum of function calls, and without 
    relying on other table packages.  The package supports titles, footnotes, 
    page header, page footers, spanning headers, page by variables, 
    and automatic page numbering.
Methods to easily build requests in the non-standard JSON
    schema required by the National Institute of Health (NIH)'s 'RePORTER
    Project API' <https://api.reporter.nih.gov/#/Search/post_v2_projects_search>.
    Also retrieve and process result sets as either a ragged or flattened 'tibble'.
Inspired by the classic 'RSA', we developed the improved 'Generalized Reporter 
    Score-based Analysis (GRSA)' method, implemented in the R package 'ReporterScore', along 
    with comprehensive visualization methods and pathway databases. 'GRSA' is a threshold-free 
    method that works well with all types of biomedical features, such as genes, chemical compounds, 
    and microbial species. Importantly, the 'GRSA' supports multi-group and longitudinal experimental 
    designs, because of the included multi-group-compatible statistical methods.
Provides an infrastructure for handling multiple R Markdown
  reports, including automated curation and time-stamping of outputs,
  parameterisation and provision of helper functions to manage dependencies.
Provides a system for reporting messages, which provides certain useful features over the standard R system, such as the incorporation of output consolidation, message filtering, assertions, expression substitution, automatic generation of stack traces for debugging, and conditional reporting based on the current "output level".
Provides an easy way to report the results of regression analysis, including:
    1. Proportional hazards regression from function 'coxph' of package 'survival';
    2. Conditional logistic regression from function 'clogit' of package 'survival';
    3. Ordered logistic regression from function 'polr' of package 'MASS';
    4. Binary logistic regression from function 'glm' of package 'stats';
    5. Linear regression from function 'lm' of package 'stats';
    6. Risk regression model for survival analysis with competing risks from function 'FGR' of package 'riskRegression';
    7. Multilevel model from function 'lme' of package 'nlme'.
Streamlined statistical reporting in 'Rmarkdown' environments. 
    Facilitates the automated reporting of descriptive statistics, multiple 
    univariate models, multivariable models and tables combining these outputs. 
    Plotting functions include customisable survival curves, forest plots from 
    logistic and ordinal regression and bivariate comparison plots.
Provides an easy way to report the results of ROC analysis, including:
    1. an ROC curve. 
    2. the value of Cutoff, AUC (Area Under Curve), ACC (accuracy),
    SEN (sensitivity), SPE (specificity),
    PLR (positive likelihood ratio), NLR (negative likelihood ratio),
    PPV (positive predictive value), NPV (negative predictive value),
    PPA (percentage of positive accordance), NPA (percentage of negative accordance), TPA (percentage of total accordance),
    KAPPA (kappa value).
Adds subtotal rows / sections (a la the 'SAS' 'Proc Tabulate' All option) to a Group By output by running a series of Group By functions with partial sets of the same variables and combining the results with the original. Can be used to add comprehensive information to a data report or to quickly aggregate Group By outputs used to gain a greater understanding of data.
These functions are especially helpful when writing reports of data analysis using "Sweave".
An R Interface to 'EPP-lab' v1.0. 'EPP-lab' is a Java program for
    projection pursuit using genetic algorithms written by Alain Berro and S. Larabi
    Marie-Sainte and is included in the package.
Performs exploratory projection pursuit  via 'REPPlab' (Daniel Fischer, Alain Berro, Klaus Nordhausen & Anne Ruiz-Gazen (2019) <doi:10.1080/03610918.2019.1626880>) using a Shiny app.
String and binary representations of objects for several formats /
    mime types.
Compute the values of various parameters evaluating how similar two multidimensional datasets' structures are in multidimensional space, as described in: Jouan-Rimbaud, D., Massart, D. L., Saby, C. A., Puel, C. (1998), <doi:10.1016/S0169-7439(98)00005-7>. The computed parameters evaluate three properties, namely, the direction of the data sets, the variance-covariance of the data points, and the location of the data sets' centroids. The package contains workhorse function jrparams(), as well as two helper functions Mboxtest() and JRsMahaldist(), and four example data sets.
An implementation of Kaplan, Betancourt, Steorts (2022) <doi:10.1080/00031305.2022.2041482> that creates representative records for use in downstream tasks after entity resolution is performed. Multiple methods for creating the representative records (data sets) are provided.
Reproducible research tools automates the creation of an analysis directory structure and work flow. There are R markdown
  skeletons which encapsulate typical analytic work flow steps. Functions will create appropriate modules which may
  pass data from one step to another.
Convenience wrapper that uses the 'rmarkdown' package to
    render small snippets of code to target formats that include both code
    and output.  The goal is to encourage the sharing of small,
    reproducible, and runnable examples on code-oriented websites, such as
    <https://stackoverflow.com> and <https://github.com>, or in email. The
    user's clipboard is the default source of input code and the default
    target for rendered output. 'reprex' also extracts clean, runnable R
    code from various common formats, such as copy/paste from an R
    session.
Includes data analysis and meta-analysis functions (e.g., to 
    calculate effect sizes and 95% Confidence Intervals (CI) on Standardised 
    Effect Sizes (d) for AB/BA cross-over repeated-measures experimental 
    designs), data presentation functions (e.g., density curve overlaid on 
    histogram),and the data sets analyzed in different research papers in 
    software engineering (e.g., related to software defect prediction or multi-
    site experiment concerning the extent to which structured abstracts were
    clearer and more complete than conventional abstracts) to streamline
    reproducible research in software engineering.
A collection of high-level, machine- and OS-independent tools
    for making reproducible and reusable content in R.
    The two workhorse functions are Cache() and prepInputs(). Cache()
    allows for nested caching, is robust to environments and objects with
    environments (like functions), and deals with some classes of 
    file-backed R objects e.g., from terra and raster packages. 
    Both functions have been developed to 
    be foundational components of data retrieval
    and processing in continuous workflow situations. In both functions,
    efforts are made to make the first and subsequent calls of functions have 
    the same result, but faster at subsequent times by way of checksums
    and digesting. Several features are still under development, including
    cloud storage of cached objects allowing for sharing between users. Several
    advanced options are available, see ?reproducibleOptions().
Provide reproducible R chunks in R Markdown document that automatically check computational results for reproducibility. This is achieved by creating json files storing metadata about computational results. A comprehensive tutorial to the package is available as preprint by Brandmaier & Peikert (2024, <doi:10.31234/osf.io/3zjvf>).
Transform coordinates from a specified source to a specified 
 target map projection. This uses the 'PROJ' library directly, by wrapping the 
 'PROJ' package which leverages 'libproj', otherwise the 'proj4' package. The 'reproj()' 
 function is generic, methods may be added to remove the need for an explicit 
 source definition. If 'proj4' is in use 'reproj()' handles the requirement for 
 conversion of angular units where necessary. This is for use primarily to 
 transform generic data formats and direct leverage of the underlying
 'PROJ' library. (There are transformations that aren't possible with 'PROJ' and
 that are provided by the 'GDAL' library, a limitation which users of this 
 package should be aware of.) The 'PROJ' library is available at 
 <https://proj.org/>.
Compute price indices using various Hedonic and
    multilateral methods, including Laspeyres, Paasche, Fisher, and HMTS (Hedonic
    Multilateral Time series re-estimation with splicing). The central function
    calculate_price_index() offers a unified interface for running these methods
    on structured datasets. This package is designed to support index construction workflows across a wide range of domains 
    — including but not limited to real estate — where quality-adjusted price comparisons over time are essential. 
    The development of this package was funded by Eurostat and Statistics Netherlands (CBS), and carried out by Statistics Netherlands.
    The HMTS method implemented here is described in Ishaak, Ouwehand and Remøy (2024)
    <doi:10.1177/0282423X241246617>. For broader methodological context, see Eurostat
    (2013, ISBN:978-92-79-25984-5, <doi:10.2785/34007>).
Root Expected Proportion Squared Difference (REPSD) is a nonparametric
  differential item functioning (DIF) method that (a) allows practitioners 
  to explore for DIF related to small, fine-grained focal groups of examinees, 
  and (b) compares the focal group directly to the composite group that will be 
  used to develop the reported test score scale. Using your provided response 
  matrix with a column that identifies focal group membership, this package
  provides the REPSD values, a simulated null distribution of possible REPSD
  values, and the simulated p-values identifying items possibly displaying DIF 
  without requiring enormous sample sizes.
Predicting regulatory DNA elements based on epigenomic signatures. This package is more of a set of building blocks than a direct solution. REPTILE regulatory prediction pipeline is built on this R package. See <https://github.com/yupenghe/REPTILE> for more information.
Provides easy access to 'The Reptile Database', a comprehensive catalogue of all living reptile species and their classification. This package includes taxonomic data for over 10,000 reptile species, approximately 2,800 of which are subspecies, covering all extant reptiles. The dataset features taxonomic names, synonyms, distribution data, type specimens, and literature references, making it ready for research and analysis. Data is sourced from 'The Reptile Database' <http://www.reptile-database.org/>.
Provides tools to search, access, and format taxonomic information 
    from the Reptile Database (<http://reptile-database.org>) directly within R. 
    Users can retrieve species-level data, distribution, etymology, synonyms, 
    common names, and other relevant information for reptiles. Designed for 
    taxonomists, ecologists, and biodiversity researchers.
Recursive lists in the form of R objects, 'JSON', and 'XML',
    for use in teaching and examples. Examples include color palettes,
    Game of Thrones characters, 'GitHub' users and repositories, music
    collections, and entities from the Star Wars universe. Data from the
    'gapminder' package is also included, as a simple data frame and in
    nested and split forms.
In order to facilitate parsing of http requests and creating
    appropriate responses this package provides two classes to handle a lot of
    the housekeeping involved in working with http exchanges. The infrastructure
    builds upon the 'rook' specification and is thus well suited to be combined
    with 'httpuv' based web servers.
Contains the function run.eqs() which calls an EQS script file, executes the EQS estimation, and, finally, imports the results as R objects. These two steps can be performed separately: call.eqs() calls and executes EQS, whereas read.eqs() imports existing EQS outputs as objects into R. It requires EQS 6.2 (build 98 or higher).
High level and easy 'HTTP' client for 'R'. Provides functions for
    building 'HTTP' queries, including query parameters, body requests, headers,
    authentication, and more.
A single key function, 'Require' that makes rerun-tolerant
    versions of 'install.packages' and `require` for CRAN packages, packages
    no longer on CRAN (i.e., archived), specific versions of packages, 
    and GitHub packages. This approach is developed to create reproducible 
    workflows that are flexible and fast enough to use while in development stages,
    while able to build snapshots once a stable package collection is found. 
    As with other functions in a reproducible workflow, this package 
    emphasizes functions that return the same result whether it is 
    the first or subsequent times running the function, with subsequent times being
    sufficiently fast that they can be run every time without undue waiting burden on 
    the user or developer.
Helper function to install packages for R using an external
    'requirements.txt' or a string containing diverse packages from
    several resources like Github or CRAN.
Modularizes source code. Keeps the global environment clean,
    explicifies interdependencies. Inspired by 'RequireJS'<http://requirejs.org/>.
Computes the power resulting from completely randomized and rerandomized experiments with two groups. Furthermore, computes the sample size necessary to obtain a desired level of power for completely randomized and rerandomized experiments.
General purpose R client for 'ERDDAP™' servers. Includes
    functions to search for 'datasets', get summary information on
    'datasets', and fetch 'datasets', in either 'csv' or 'netCDF' format.
    'ERDDAP™' information: 
    <https://upwell.pfeg.noaa.gov/erddap/information.html>.
Contains three functions that access
    environmental data from any 'ERDDAP™' data web service. The rxtracto() function extracts
    data along a trajectory for a given "radius" around the point. The
    rxtracto_3D() function extracts data in a box. The rxtractogon() function
    extracts data in a polygon. All of those three function use the 'rerddap' package
    to extract the data, and should work with any 'ERDDAP™' server.
    There are also two functions, plotBBox() and plotTrack() that use the 'plotdap'
    package to simplify the creation of maps of the data.
A comprehensive collection of practical and easy-to-use tools for regression analysis of recurrent events, with or without the presence of a (possibly) informative terminal event described in Chiou et al. (2023) <doi:10.18637/jss.v105.i05>. The modeling framework is based on a joint frailty scale-change model, that includes models described in Wang et al. (2001) <doi:10.1198/016214501753209031>, Huang and Wang (2004) <doi:10.1198/016214504000001033>, Xu et al. (2017) <doi:10.1080/01621459.2016.1173557>, and Xu et al. (2019) <doi:10.5705/SS.202018.0224> as special cases. The implemented estimating procedure does not require any parametric assumption on the frailty distribution. The package also allows the users to specify different model forms for both the recurrent event process and the terminal event.
Bootstrap, permutation tests, and jackknife,
	featuring easy-to-use syntax.
Package of data sets from "Mathematical Statistics
    with Resampling in R" (1st Ed. 2011, 2nd Ed. 2018) by Laura Chihara and Tim Hesterberg.
Data sets for Chihara and Hesterberg (2022, ISBN: 978-1-119-87404-1)
    "Mathematical Statistics with Resampling in R" (3rd Ed).
Maximum likelihood estimation for univariate reducible
 stochastic differential equation models. Discrete, possibly noisy
 observations, not necessarily evenly spaced in time. Can fit
 multiple individuals/units with global and local parameters, by
 fixed-effects or mixed-effects methods. Ref.: Garcia, O. (2019)
 "Estimating reducible stochastic differential equations by
 conversion to a least-squares problem", Computational Statistics
 34(1): 23-46, <doi:10.1007/s00180-018-0837-4>.
Functions for dissimilarity analysis and memory-based learning 
    (MBL, a.k.a local modeling) in complex spectral data sets. 
    Most of these functions are based on the methods presented in 
    Ramirez-Lopez et al. (2013) <doi:10.1016/j.geoderma.2012.12.014>.
Measure single-storage water supply system performance using resilience,
    reliability, and vulnerability metrics; assess storage-yield-reliability
    relationships; determine no-fail storage with sequent peak analysis; optimize
    release decisions for water supply, hydropower, and multi-objective reservoirs
    using deterministic and stochastic dynamic programming; generate inflow
    replicates using parametric and non-parametric models; evaluate inflow
    persistence using the Hurst coefficient.
A simple user-friendly library based on the 'python' module 'reservoirpy'.
             It provides a flexible interface to implement efficient Reservoir
             Computing (RC) architectures with a particular focus on Echo State Networks
             (ESN). Some of its features are: offline and online training, parallel implementation, 
             sparse matrix computation, fast spectral initialization, advanced learning 
             rules (e.g. Intrinsic Plasticity) etc. It also makes possible to easily create 
             complex architectures with multiple reservoirs (e.g. deep reservoirs), readouts, 
             and complex feedback loops. Moreover, graphical tools are included to easily 
             explore hyperparameters. Finally, it includes several tutorials exploring
             time series forecasting, classification and hyperparameter tuning. For more information
             about 'reservoirpy', please see Trouvain et al. (2020) <doi:10.1007/978-3-030-61616-8_40>.
             This package was developed in the framework of the University of Bordeaux’s IdEx
             "Investments for the Future" program / RRI PHDS.
Define distribution families and fit them to
    interval-censored and interval-truncated data, where the truncation
    bounds may depend on the individual observation. The defined
    distributions feature density, probability, sampling and fitting
    methods as well as efficient implementations of the log-density log
    f(x) and log-probability log P(x0 <= X <= x1) for use in 'TensorFlow'
    neural networks via the 'tensorflow' package. Allows training
    parametric neural networks on interval-censored and interval-truncated
    data with flexible parameterization. Applications include Claims
    Development in Non-Life Insurance, e.g. modelling reporting delay
    distributions from incomplete data, see Bücher, Rosenstock (2022)
    <doi:10.1007/s13385-022-00314-4>.
Contains logic for sample-level variable set scoring using randomized reduced rank reconstruction error. 
  Frost, H. Robert (2023) "Reconstruction Set Test (RESET): a computationally efficient method for 
  single sample gene set testing based on randomized reduced rank reconstruction error" <doi:10.1101/2023.04.03.535366>.
Simulates individual-based models of agricultural pest management
    and the evolution of pesticide resistance. Management occurs on a spatially
    explicit landscape that is divided into an arbitrary number of farms that
    can grow one of up to 10 crops and apply one of up to 10 pesticides. Pest
    genomes are modelled in a way that allows for any number of pest traits with
    an arbitrary covariance structure that is constructed using an evolutionary
    algorithm in the mine_gmatrix() function. Simulations are then run using the
    run_farm_sim() function. This package thereby allows for highly mechanistic
    social-ecological models of the evolution of pesticide resistance under
    different types of crop rotation and pesticide application regimes.
Flexibly restructure and aggregate data using 
  just two functions: melt and cast.
Flexibly restructure and aggregate data using just two
    functions: melt and 'dcast' (or 'acast').
Summarize model output using a robust effect size index. The index is introduced in Vandekar, Tao, & Blume (2020, <doi:10.1007/s11336-020-09698-2>). Software paper available at <doi:10.18637/jss.v112.i03>.
Developed to assist researchers with planning analysis,
    prior to obtaining data from Trusted Research Environments (TREs) also known as safe havens.
    With functionality to export and import marginal distributions as well as synthesise data, both with
    and without correlations from these marginal distributions. Using a multivariate cumulative distribution (COPULA).
    Additionally the International Stroke Trial (IST) is included as an example dataset under ODC-By licence
    Sandercock et al. (2011) <doi:10.7488/ds/104>,
    Sandercock et al. (2011) <doi:10.1186/1745-6215-12-101>.
Datasets with energy consumption data of different data measurement frequencies. 
    The data stems from several publicly funded research projects of the Chair of Information  
    Systems and Energy Efficient Systems at the University of Bamberg.
Studies of resilience in older adults employ a single-arm design where everyone experiences the stressor. The simplistic approach of regressing change versus baseline yields biased estimates due to regression-to-the-mean. This package provides a method to correct the bias. It also allows covariates to be included. The method implemented in the package is described in Varadhan, R., Zhu, J., and Bandeen-Roche, K (2023), Biostatistics (To appear).
Contains various tools to perform and visualize Response Item Networks ('ResIN's'). 'ResIN' binarizes ordered-categorical and qualitative response choices from (survey) data, calculates pairwise associations and maps the location of each item response as a node in a force-directed network. Please refer to <https://www.resinmethod.net/> for more details.
An intuitive and explainable metric of Feature Importance for Classification Problems.
    Resolution Index measures the extent to which a Feature clusters different classes when data is 
    sorted on it. User provides a DataFrame, column name of the Class, sample size and number of 
    iterations used for calculation. Resolution Index for each Feature is returned, which can be 
    effectively used to rank Features and reduce Dimensionality of Training data. For more details
    on Feature Selection see Theng and Bhoyar (2023) <doi:10.1007/s10115-023-02010-5>.
Electrical properties of resistor networks using matrix methods.
A pair of functions for calculating mean residual life (MRL)
    , median residual life, and percentile residual life using the outputs of 
    either the 'flexsurv' package or parameters
    provided by the user. Input information about the distribution, the given
    'life' value, the percentile, and the type of residual life, 
    and the function will return your desired values. For the 'flexsurv' option,
    the function allows the user to input their own data for making predictions. 
    This function is based on Jackson (2016) <doi:10.18637/jss.v070.i08>.
The Bayesian modelling of relative sea-level data
    using a comprehensive approach that incorporates
    various statistical models within a unifying framework. 
    Details regarding each statistical models; 
    linear regression (Ashe et al 2019) <doi:10.1016/j.quascirev.2018.10.032>, 
    change point models (Cahill et al 2015) <doi:10.1088/1748-9326/10/8/084002>, 
    integrated Gaussian process models (Cahill et al 2015) <doi:10.1214/15-AOAS824>, 
    temporal splines (Upton et al 2023) <arXiv:2301.09556>, 
    spatio-temporal splines (Upton et al 2023) <arXiv:2301.09556> and
    generalised additive models (Upton et al 2023) <arXiv:2301.09556>.
    This package facilitates data loading, 
    model fitting and result summarisation.
    Notably, it accommodates the inherent measurement errors
    found in relative sea-level data across multiple dimensions,
    allowing for their inclusion in the statistical models.
Compress local and online images using the 'reSmush.it' API
    service <https://resmush.it/>.
Utility functions to download data from the 'RESOURCECODE'
    hindcast database of sea-states, time series of sea-state parameters
    and time series of 1D and 2D wave spectra.  See
    <https://resourcecode.ifremer.fr> for more details about the available
    data.  Also provides facilities to plot and analyse downloaded data,
    such as computing the sea-state parameters from both the 1D and 2D
    surface elevation variance spectral density.
Includes Resourcecode hindcast database (see
    <https://resourcecode.ifremer.fr>) configuration data: nodes locations
    for both the sea-state parameters and the spectra data; examples of
    time series of 1D and 2D surface elevation variance spectral density.
A resource represents some data or a computation unit. It is 
    described by a URL and credentials. This package proposes a Resource model
    with "resolver" and "client" classes to facilitate the access and the usage of the 
    resources.
Resource Selection (Probability) Functions
  for use-availability wildlife data
  based on weighted distributions as described in
  Lele and Keim (2006) <doi:10.1890/0012-9658(2006)87%5B3021:WDAEOR%5D2.0.CO;2>,
  Lele (2009) <doi:10.2193/2007-535>,
  and Solymos & Lele (2016) <doi:10.1111/2041-210X.12432>.
A collection of several utility functions related to resolvable and affine resolvable Partially Balanced Incomplete Block Designs (PBIBDs), have been developed. In the class of resolvable designs, affine resolvable designs are said to be optimal, Bailey (1995) <doi:10.2307/2337638>. Here, the package contains three functions to generate and study the characterization properties of these designs. Developed functions are named as PBIBD1(), PBIBD2() and PBIBD3(), in which first two functions are used to generate two new series of affine resolvable PBIBDs and last one is used to generate a new series of resolvable PBIBDs, respectively.  In addition, these functions can also be used to generate design parameters (v, b, r and k), canonical efficiency factors, variance factor between associates and average variance factors of the generated designs. Here v is the number of treatments, b (= b1 + b2, in case of non-proper design) is the number of blocks, r is the number of replications and k (= k1 + k2; k1 is the size of b1 and k2 is the size of b2) is the block size.
Provides functions for the complete analysis of respiratory data. Consists of a set of functions that allow to preprocessing respiratory data,  calculate both regular statistics and nonlinear statistics, conduct group comparison and visualize the results. Especially, Power Spectral Density ('PSD') (A. Eke (2000) <doi:10.1007/s004249900135>), 'MultiScale Entropy(MSE)' ('Madalena Costa(2002)' <doi:10.1103/PhysRevLett.89.068102>) and 'MultiFractal Detrended Fluctuation Analysis(MFDFA)' ('Jan W.Kantelhardt' (2002) <doi:10.1016/S0378-4371(02)01383-3>) were applied for the analysis of respiratory data.
Provides tools to enable the researcher to more precisely conduct
    respirometry experiments. Strong emphasis is on aquatic respirometry. Tools
    focus on helping the researcher setup and conduct experiments. Functions for
    analysis of resulting respirometry data are also provided. This package
    provides tools for intermittent, flow-through, and closed respirometry
    techniques.
Some survey participants tend to respond carelessly which complicates data analysis. This package provides functions that make it easier to explore responses and identify those that may be problematic. See Gottfried et al. (2022) <doi:10.7275/vyxb-gt24> for more information.
Provides a structural, reproducible workflow for the
    processing and analysis of respirometry data. It contains analytical
    functions and utilities for working with oxygen time-series to determine
    respiration or oxygen production rates, and to make it easier to report and
    share analyses. See Harianto et al. 2019 <doi:10.1111/2041-210X.13162>.
Calculate common survey data quality indicators for multi-item scales 
             and matrix questions. Currently supports the calculation of
             response style indicators and response distribution indicators.
             For an overview on response quality indicators see Bhaktha N,
             Henning S, Clemens L (2024). 'Characterizing response quality
             in surveys with multi-item scales: A unified framework'
             <https://osf.io/9gs67/>.
Contains three functions that query AuriQ Systems' Essentia Database and return the results in R. 'essQuery' takes a single Essentia command and captures the output in R, where you can save the output to a dataframe or stream it directly into additional analysis. 'read.essentia' takes an Essentia script and captures the output csv data into R, where you can save the output to a dataframe or stream it directly into additional analysis. 'capture.essentia' takes a file containing any number of Essentia commands and captures the output of the specified statements into R dataframes. Essentia can be downloaded for free at http://www.auriq.com/documentation/source/install/index.html.
Eurostat is the statistical office of the European Union and provides high quality statistics for Europe.
             Large set of the data is disseminated through the Eurostat database (<https://ec.europa.eu/eurostat/web/main/data/database>). 
             The tools are using the REST API with the Statistical Data and Metadata eXchange (SDMX) Web Services 
             (<https://wikis.ec.europa.eu/pages/viewpage.action?pageId=44165555>) to search and download data from 
             the Eurostat database using the SDMX standard.
A RESTful API wrapper for accessing the GENESIS database of
    the German Federal Statistical Office (Destatis) as well as its Census 
    Database and the database of Germany's regional statistics. Supports data 
    search functions, credential management, result caching, and handling 
    remote background jobs for large datasets.
Duplicated restaurant data (pre-processed and formatted) for entity resolution. This package contains formatted data from a data set that contains information about different restaurants, with the Zagats portion containing 331 records and the Fodors portion containing 533 records. The following variables are included in the data set: id, name, address, city, phone, type. The data set has a respective gold data set that provides information on which records match based on id.
Download large sections of
    'GenBank' <https://www.ncbi.nlm.nih.gov/genbank/> and generate a local
    SQL-based database. A user can then query this database using 'restez'
    functions or through 'rentrez' <https://CRAN.R-project.org/package=rentrez>
    wrappers.
Models a RESTful service as if it were a nested R list.
Provides the user with functions to develop their trading strategy,
    uncover actionable trading ideas, and monitor consensus shifts with
    crowdsourced earnings and economic estimate data directly from
    <www.estimize.com>. Further information regarding the web services this
    package invokes can be found at <www.estimize.com/api>.
Implementation of the RESTK algorithm based on Markov's Inequality from  Vilardell, Sergi, Serra, Isabel, Mezzetti, Enrico, Abella, Jaume, Cazorla, Francisco  J. and Del Castillo, J. (2022). "Using Markov's Inequality with Power-Of-k Function for Probabilistic WCET Estimation". In 34th Euromicro Conference on Real-Time Systems (ECRTS 2022). Leibniz International Proceedings in Informatics (LIPIcs) 231 20:1-20:24. <doi:10.4230/LIPIcs.ECRTS.2022.20>. This work has been supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 772773).
Flexible framework for ecological restoration planning. It aims to identify priority areas for restoration efforts using optimization algorithms (based on Justeau-Allaire et al. 2021 <doi:10.1111/1365-2664.13803>). Priority areas can be identified by maximizing landscape indices, such as the effective mesh size (Jaeger 2000 <doi:10.1023/A:1008129329289>), or the integral index of connectivity (Pascual-Hortal & Saura 2006 <doi:10.1007/s10980-006-0013-z>). Additionally, constraints can be used to ensure that priority areas exhibit particular characteristics (e.g., ensure that particular places are not selected for restoration, ensure that priority areas form a single contiguous network). Furthermore, multiple near-optimal solutions can be generated to explore multiple options in restoration planning. The package leverages the 'Choco-solver' software to perform optimization using constraint programming (CP) techniques (<https://choco-solver.org/>).
A random-effects stochastic model that allows quick detection of 
  clonal dominance events from clonal tracking data collected in gene therapy studies. Starting from the Ito-type equation describing the dynamics of cells duplication, death and differentiation at clonal level, we first considered its local linear approximation as the base model. 
  The parameters of the base model, which are inferred using a maximum likelihood approach, 
  are assumed to be shared across the clones. Although this assumption makes inference easier, 
  in some cases it can be too restrictive and does not take into account possible scenarios of clonal dominance. 
  Therefore we extended the base model by introducing random effects for the clones. 
  In this extended formulation the dynamic parameters are estimated using a tailor-made 
  expectation maximization algorithm. Further details on the methods can be found in L. Del Core et al., (2022) <doi:10.1101/2022.05.31.494100>.
Allow for easy-to-use testing or evaluating of linear equality and inequality restrictions about parameters and effects in (generalized) linear statistical models.
Allows to easily create high-performance full featured HTTP APIs from R
  functions. Provides high-level classes such as 'Request', 'Response',
  'Application', 'Middleware' in order to streamline server side
  application development. Out of the box allows to serve requests using
  'Rserve' package, but flexible enough to integrate with other HTTP servers
  such as 'httpuv'.
Allows wrapping values in success() and failure() types to capture 
    the result of operations, along with any status codes. Risky expressions can be 
    wrapped in as_result() and functions wrapped in result() to catch errors
    and assign the relevant result types. Monadic functions can be bound together 
    as pipelines or transaction scripts using then_try(), to gracefully handle 
    errors at any step.
Database data model management utilities for R packages in the Observational Health Data Sciences and
    Informatics programme. 'ResultModelManager' provides utility functions to allow package
    maintainers to migrate existing SQL database models, export and import results in consistent patterns.
Using a CSV, LaTeX and R to easily build attractive resumes.
Permite obtener rápidamente una serie de medidas de resumen y gráficos para datos numéricos discretos o continuos en series simples. También permite obtener tablas de frecuencia clásicas y gráficos cuando se desea realizar un análisis de series agrupadas. Su objetivo es de aplicación didáctica para un curso introductorio de Bioestadística  utilizando el software R, para las carreras de grado las carreras de grado y otras ofertas educativas de la Facultad de Ciencias Agrarias de la UNJu / It generates summary measures and graphs for discrete or continuous numerical data in simple series. It also enables the creation of classic frequency tables and graphs when analyzing grouped series. Its purpose is for educational application in an introductory Biostatistics course using the R software, aimed at undergraduate programs and other educational offerings of the Faculty of Agricultural Sciences at the National University of Jujuy (UNJu).
Prediction of claim counts using the feature based development factors introduced in the manuscript Hiabu M., Hofman E. and Pittarello G. (2023) <doi:10.48550/arXiv.2312.14549>. 
             Implementation of Neural Networks, Extreme Gradient Boosting, 
             and Cox model with splines to optimise the partial log-likelihood of proportional hazard models.
Implements the regularized exponentially tilted empirical 
    likelihood method. Details of the method are given in Kim, MacEachern, and 
    Peruggia (2023) <doi:10.48550/arXiv.2312.17015>. This work was supported by 
    the U.S. National Science Foundation under Grants No. SES-1921523 and 
    DMS-2015552.
Implementation of the race/ethnicity prediction method, described 
    in "rethnicity: An R package for predicting ethnicity from names" 
    by Fangzhou Xie (2022) <doi:10.1016/j.softx.2021.100965> and 
    "Rethnicity: Predicting Ethnicity from Names" 
    by Fangzhou Xie (2021) <doi:10.48550/arXiv.2109.09228>.
Interface to 'Python' modules, classes, and functions. When calling
    into 'Python', R data types are automatically converted to their equivalent 'Python'
    types. When values are returned from 'Python' to R they are converted back to R
    types. Compatible with all versions of 'Python' >= 2.7.
Retime speech signals with a native Waveform Similarity Overlap-Add (WSOLA) implementation translated from the 'TSM toolbox' by Driedger & Müller (2014) <https://www.audiolabs-erlangen.de/content/resources/MIR/TSMtoolbox/2014_DriedgerMueller_TSM-Toolbox_DAFX.pdf>. Design retimings and pitch (f0) transformations with tidy data and apply them via 'Praat' interface. Produce spectrograms, spectra, and amplitude envelopes. Includes implementation of vocalic speech envelope analysis (fft_spectrum) technique and example data (mm1) from Tilsen, S., & Johnson, K. (2008) <doi:10.1121/1.2947626>.
Reconstructs retinae by morphing a flat surface with cuts
    (a dissected flat-mount retina) onto a curvilinear surface (the
    standard retinal shape). It can estimate the position of a point
    on the intact adult retina to within 8 degrees of arc (3.6% of
    nasotemporal axis). The coordinates in reconstructed retinae can
    be transformed to visuotopic coordinates. For more details see
    Sterratt, D. C., Lyngholm, D., Willshaw, D. J. and Thompson, I. D.
    (2013) <doi:10.1371/journal.pcbi.1002921>.
We provide several avenues to predict and account for user-based mortality and tag loss during mark-recapture studies. When planning a study on a target species, the retentionmort_generation() function can be used to produce multiple synthetic mark-recapture datasets to anticipate the error associated with a planned field study to guide method development to reduce error. Similarly, if field data was already collected, the retentionmort() function can be used to predict the error from already generated data to adjust for user-based mortality and tag loss. The test_dataset_retentionmort() function will provide an example dataset of how data should be inputted into the function to run properly. Lastly, the retentionmort_figure() function can be used on any dataset generated from either model function to produce an 'rmarkdown' printout of preliminary analysis associated with the model, including summary statistics and figures. Methods and results pertaining to the formation of this package can be found in McCutcheon et al. (in review, "Predicting tagging-related mortality and tag loss during mark-recapture studies").
Generates disease-specific drug-response profiles that are independent of time, concentration, and cell-line. Based on the cell lines used as surrogates, the returned profiles represent the unique transcriptional changes induced by a compound in a given disease.
Provides tools for working with Type S (Sign) and
    Type M (Magnitude) errors, as proposed in Gelman and Tuerlinckx (2000)
    <doi:10.1007/s001800000040> and
    Gelman & Carlin (2014) <doi:10.1177/1745691614551642>.
    In addition to simply calculating the probability of
    Type S/M error, the package includes functions for calculating these errors
    across a variety of effect sizes for comparison, and recommended sample size
    given "tolerances" for Type S/M errors. To improve the speed of these
    calculations, closed forms solutions for the probability of a Type S/M error
    from Lu, Qiu, and Deng (2018) <doi:10.1111/bmsp.12132>
    are implemented. As of 1.0.0, this includes
    support only for simple research designs. See the
    package vignette for a fuller exposition on how Type S/M errors arise in
    research, and how to analyze them using the type of design analysis proposed
    in the above papers.
Assist in reproducible retrospective (ex-post) harmonization
    of data, particularly individual level survey data, by providing tools
    for organizing metadata, standardizing the coding of variables, and
    variable names and value labels, including missing values, and
    documenting the data transformations, with the help of comprehensive
    s3 classes.
A collection of tools to import and structure the (currently) single-season
    event, game-log, roster, and schedule data available from <https://www.retrosheet.org>.
    In particular, the event (a.k.a. play-by-play) files can be especially difficult to parse.
    This package does the parsing on those files, returning the requested data in the most
    practical R structure to use for sabermetric or other analyses.
Provide simple mechanism to repeatedly evaluate an expression until either it succeeds or timeout exceeded. It is useful in situations that random failures could happen.
Estimates the p-probability return curve proposed by Murphy-Barltrop et al. (2023) <doi:10.1002/env.2797>. Implements pointwise and smooth estimation of the angular dependence function introduced by Wadsworth and Tawn (2013) <doi:10.3150/12-BEJ471>.
Simplified scenario testing and sensitivity analysis,
    redesigned to use packages 'future' and 'furrr'. Provides
    functions for generating function argument sets using
    one-factor-at-a-time (OFAT) and (sampled) permutations.
Provides functions for the Bayesian analysis of extreme value
    models.  The 'rust' package <https://cran.r-project.org/package=rust> is
    used to simulate a random sample from the required posterior distribution.
    The functionality of 'revdbayes' is similar to the 'evdbayes' package
    <https://cran.r-project.org/package=evdbayes>, which uses Markov Chain
    Monte Carlo ('MCMC') methods for posterior simulation.  In addition, there
    are functions for making inferences about the extremal index, using 
    the models for threshold inter-exceedance times of Suveges and Davison 
    (2010) <doi:10.1214/09-AOAS292> and Holesovsky and Fusek (2020) 
    <doi:10.1007/s10687-020-00374-3>. Also provided are d,p,q,r functions for 
    the Generalised Extreme Value ('GEV') and Generalised Pareto ('GP') 
    distributions that deal appropriately with cases where the shape parameter 
    is very close to zero.
Computation of (direct and indirect) revealed preferences, fast non-parametric tests of rationality axioms (WARP, SARP, GARP), simulation of axiom-consistent data, and detection of axiom-consistent subpopulations. Rationality tests follow Varian (1982) <doi:10.2307/1912771>, axiom-consistent subpopulations follow Crawford and Pendakur (2012) <doi:10.1111/j.1468-0297.2012.02545.x>.
R Markdown format for 'reveal.js' presentations, a framework
    for easily creating beautiful presentations using HTML.
Facilitates making a connection to the 'Revenera' 
  API and executing various queries. You can use it to get 
  event data and metadata. The 'Revenera' documentation 
  is available at <https://rui-api.redoc.ly/>. This package is 
  not supported by 'Flexera' (owner of the software).
Decoupled (e.g. separate averages) and censored (e.g. > 100 species) variables are continually reported by many well-established organizations (e.g. World Health Organization (WHO), Centers for Disease Control and Prevention (CDC), World Bank, and various national censuses).  The challenge therefore is to infer what the original data could have been given summarized information.  We present an R package that reverse engineers decoupled and/or censored count data with two main functions.  The cnbinom.pars function estimates the average and dispersion parameter of a censored univariate frequency table.  The rec function reverse engineers summarized data into an uncensored bivariate table of probabilities.
Tests linear regressions for significance reversal through leave-one(multiple)-out.
A tool for detecting reversions for a given pathogenic mutation from 
	next-generation DNA sequencing data. It analyses reads aligned to the locus of the 
	pathogenic mutation and reports reversion events where secondary mutations have 
	restored or undone the deleterious effect of the original pathogenic mutation, e.g., 
	secondary indels complement to a frameshift pathogenic mutation converting the 
	orignal frameshift mutation into inframe mutaions, deletions or SNVs that replaced 
	the original pathogenic mutation restoring the open reading frame, SNVs changing the 
	stop codon caused by the original nonsense SNV into an amino acid, etc.
Processes and visualizes the output of complex phylogenetic analyses from the 'RevBayes' phylogenetic graphical modeling software.
A portable Shiny tool to explore patient-level electronic health record data 
    and perform chart review in a single integrated framework. This tool supports 
    browsing clinical data in many different formats including multiple versions 
    of the 'OMOP' common data model as well as the 'MIMIC-III' data model. In 
    addition, chart review information is captured and stored securely via the 
    Shiny interface in a 'REDCap' (Research Electronic Data Capture) project 
    using the 'REDCap' API. See the 'ReviewR' website for additional information, 
    documentation, and examples.
Extracts tagged text from markdown manuscripts for inclusion in dynamically generated revision letters. Provides an R markdown template based on papaja::revision_letter_pdf() with comment cross-referencing, a system for managing multiple sections of extracted text, and a way to automatically determine the page number of quoted sections from PDF manuscripts.
Tools to (i) check consistency of a finite set of consumer demand observations with a number of revealed preference axioms at a given efficiency level, (ii) compute goodness-of-fit indices when the data do not obey the axioms, and (iii) compute power against uniformly random behavior.
We visualize the standard deviation of a data set as the radius of a cylinder whose volume equals the total volume of several cylinders made by revolving the empirical cumulative distribution function about the vertical line through the mean. For more details see Sarkar and Rashid (2016) <doi:10.1080/00031305.2016.1165734>.
Implements the estimation techniques described in Rousseeuw &
    Verboven (2002) <doi:10.1016/S0167-9473(02)00078-6> for the location and
    scale of very small samples.
Interaction with "RevBayes" via R. Objects created in "RevBayes" can be passed into the R environment, and many types can be converted into similar R objects. To download "RevBayes", go to <https://revbayes.github.io/download>.
Researchers commonly need to summarize scientific information, a process known as 'evidence synthesis'. The first stage of a synthesis process (such as a systematic review or meta-analysis) is to download a list of references from academic search engines such as 'Web of Knowledge' or 'Scopus'. The traditional approach to systematic review is then to sort these data manually, first by locating and removing duplicated entries, and then screening to remove irrelevant content by viewing titles and abstracts (in that order). 'revtools' provides interfaces for each of these tasks. An alternative approach, however, is to draw on tools from machine learning to visualise patterns in the corpus. In this case, you can use 'revtools' to render ordinations of text drawn from article titles, keywords and abstracts, and interactively select or exclude individual references, words or topics.
Facilitates making a connection to the 
  'Revulytics' API and executing various queries. You can use it to
  get event data and metadata. The Revulytics documentation 
  is available at <https://docs.revenera.com/ui560/report/>. This
  package is not supported by 'Flexera' (owner of the software).
Diagnostics and data preparation for random effects within estimator, random effects 
    within-idiosyncratic estimator, between-within-idiosyncratic model, and cross-classified between model.
    Mundlak, Yair (1978) <doi:10.2307/1913646>.
    Hausman, Jeffrey (1978) <doi:10.2307/1913827>.
    Allison, Paul (2009) <doi:10.4135/9781412993869>.
    Neuhaus, J.M., and J. D. Kalbfleisch (1998) <doi:10.2307/3109770>.
A friendly interface for the construction of regular
    expressions.
The main purpose of this package is to streamline the
    generation of exams that include random elements in exercises.
    Exercises can be defined in a table, based on text and figures, and
    may contain gaps to be filled with provided options. Exam documents
    can be generated in various formats. It allows us to generate a
    version for conducting the assessment and another version that
    facilitates correction, linked through a code.
Provides a user-friendly interface to NASA 'Exoplanets Archive API'
    <https://exoplanetarchive.ipac.caltech.edu/>, enabling retrieval and analysis of exoplanetary and stellar data. 
    Includes functions for querying, filtering, summarizing, and computing derived parameters from the 'Exoplanets' catalog.
Provides convenience functions to communicate with
    an Experigen server: Experigen (<http://github.com/aquincum/experigen>)
    is an online framework for creating  linguistic experiments,
    and it stores the results on a dedicated server. This package can be
    used to retrieve the results from the server, and it is especially
    helpful with registered experiments, as authentication with the server
    has to happen.
Wraps some of the matrix exponentiation 
        utilities from EXPOKIT (<http://www.maths.uq.edu.au/expokit/>), 
        a FORTRAN library that is widely recommended for matrix 
        exponentiation (Sidje RB, 1998. "Expokit: A Software Package
        for Computing Matrix Exponentials." ACM Trans. Math. Softw.
        24(1): 130-156).  EXPOKIT includes functions for 
        exponentiating both small, dense matrices, and large, sparse
        matrices (in sparse matrices, most of the cells have value 0).
        Rapid matrix exponentiation is useful in phylogenetics when we 
        have a large number of states (as we do when we are inferring 
        the history of transitions between the possible geographic 
        ranges of a species), but is probably useful in other ways as 
        well. NOTE: In case FORTRAN checks temporarily get rexpokit 
        archived on CRAN, see archived binaries at GitHub in: 
        nmatzke/Matzke_R_binaries (binaries install without compilation 
        of source code).
Provides functions to compile and load Rust code from R, similar
    to how 'Rcpp' or 'cpp11' allow easy interfacing with C++ code. Also provides
    helper functions to create R packages that use Rust code. Under the hood,
    the Rust crate 'extendr' is used to do all the heavy lifting.
Provides an interface to the Facebook API.
Load data by campaigns, ads, ad sets and insights, ad account and business manager 
    from Facebook Marketing API into R. For more details see official documents by Facebook 
    Marketing API <https://developers.facebook.com/docs/marketing-api>.
The 'rfacts' package is an R interface to the
  Fixed and Adaptive Clinical Trial Simulator ('FACTS')
  on Unix-like systems. It programmatically invokes 'FACTS' to run clinical
  trial simulations, and it aggregates simulation output data
  into tidy data frames. These capabilities provide end-to-end
  automation for large-scale simulation pipelines, and
  they enhance computational reproducibility.
  For more information on 'FACTS' itself,
  please visit <https://www.berryconsultants.com/software/>.
Easily Download Analysis-Ready Crash Data from the U.S. National Highway Traffic Safety Administration.
A collection of fast (utility) functions for data analysis. Column and row wise means, medians, variances, minimums, maximums, many t, F and G-square tests, many regressions (normal, logistic, Poisson), are some of the many fast functions. References: a) Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <doi:10.7287/peerj.preprints.26605v1>. b) Tsagris M. and Papadakis M. (2018). Forward regression in R: from the extreme slow to the extreme fast. Journal of Data Science, 16(4): 771--780. <doi:10.6339/JDS.201810_16(4).00006>. c) Chatzipantsiou C., Dimitriadis M., Papadakis M. and Tsagris M. (2020). Extremely Efficient Permutation and Bootstrap Hypothesis Tests Using Hypothesis Tests Using R. Journal of Modern Applied Statistical Methods, 18(2), eP2898. <doi:10.48550/arXiv.1806.10947>. d) Tsagris M., Papadakis M., Alenazi A. and Alzeley O. (2024). Computationally Efficient Outlier Detection for High-Dimensional Data Using the MDP Algorithm. Computation, 12(9): 185. <doi:10.3390/computation12090185>. e) Tsagris M. and Papadakis M. (2025). Fast and light-weight energy statistics using the R package Rfast. <doi:10.48550/arXiv.2501.02849>.
A collection of fast statistical and utility functions for data analysis. Functions for regression, maximum likelihood, column-wise statistics and many more have been included. C++ has been utilized to speed up the functions. References: Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <doi:10.7287/peerj.preprints.26605v1>.
Tools to perform random forest consensus clustering of different data types. The package is designed to accept a list of matrices from different assays, typically from high-throughput molecular profiling so that class discovery may be jointly performed. For references, please see Tao Shi & Steve Horvath (2006) <doi:10.1198/106186006X94072> & Monti et al (2003) <doi:10.1023/A:1023949509487> .
Methods for Resampling-based False Discovery Proportion control.
        A function is provided that provides simultaneous, multi-resolution
        False Discovery Exceedance (FDX) control as described in 
	Hemerik (2025) <doi:10.48550/arXiv.2509.02376>.
An R package for multiple imputation using chained random forests.
    Implemented methods can handle missing data in mixed types of variables by
    using prediction-based or node-based conditional distributions constructed
    using random forests. For prediction-based imputation, the method based on
    the empirical distribution of out-of-bag prediction errors of random forests
    and the method based on normality assumption for prediction errors of random
    forests are provided for imputing continuous variables. And the method based
    on predicted probabilities is provided for imputing categorical variables.
    For node-based imputation, the method based on the conditional distribution
    formed by the predicting nodes of random forests, and the method based on
    proximity measures of random forests are provided. More details of the
    statistical methods can be found in Hong et al. (2020) <arXiv:2004.14823>.
Provides the random ferns classifier by Ozuysal, Calonder, Lepetit and Fua (2009) <doi:10.1109/TPAMI.2009.23>, modified for generic and multi-label classification and featuring OOB error approximation and importance measure as introduced in Kursa (2014) <doi:10.18637/jss.v061.i10>.
The goal of 'rFIA' is to increase the accessibility and use of the United States Forest Services (USFS) Forest Inventory and Analysis (FIA) Database by providing a user-friendly, open source toolkit to easily query and analyze FIA Data. Designed to accommodate a wide range of potential user objectives, 'rFIA' simplifies the estimation of forest variables from the FIA Database and allows all R users (experts and newcomers alike) to unlock the flexibility inherent to the Enhanced FIA design. Specifically, 'rFIA' improves accessibility to the spatial-temporal estimation capacity of the FIA Database by producing space-time indexed summaries of forest variables within user-defined population boundaries. Direct integration with other popular R packages (e.g., 'dplyr', 'tidyr', and 'sf') facilitates efficient space-time query and data summary, and supports common data representations and API design. The package implements design-based estimation procedures outlined by Bechtold & Patterson (2005) <doi:10.2737/SRS-GTR-80>, and has been validated against estimates and sampling errors produced by FIA 'EVALIDator'. Current development is focused on the implementation of spatially-enabled model-assisted and model-based estimators to improve population, change, and ratio estimates.
Provides functionality to interact with the 
  'FieldClimate' API <https://api.fieldclimate.com/v2/docs/>.
An integrated package for constructing random forest prediction intervals using a fast implementation package 'ranger'. This package can apply the following three methods described in Haozhe Zhang, Joshua Zimmerman, Dan Nettleton, and Daniel J. Nordman (2019) <doi:10.1080/00031305.2019.1585288>: the out-of-bag prediction interval, the split conformal method, and the quantile regression forest.
A programmatic interface to 'FishBase', re-written
    based on an accompanying 'RESTful' API. Access tables describing over 30,000
    species of fish, their biology, ecology, morphology, and more. This package also
    supports experimental access to 'SeaLifeBase' data, which contains
    nearly 200,000 species records for all types of aquatic life not covered by
    'FishBase.'
Helps fisheries scientists collect measurements from calcified
    structures and back-calculate estimated lengths at previous ages using
    standard procedures and models. This is intended to replace much of the
    functionality provided by the now out-dated 'fishBC' software
    (<https://fisheries.org/bookstore/all-titles/software/70317/>).
A programmatic interface to 'openfisheries.org'. This package is
    part of the 'rOpenSci' suite (http://ropensci.org).
Provides data processing and summarization of data from FishNet2.net
  in text and graphical outputs. Allows efficient filtering of information and
  data cleaning.
Rank-based (R) estimation and inference for linear models.  Estimation
        is for general scores and a library of commonly used score
        functions is included.
Sets a significance level for  Random Forest MDI (Mean Decrease in Impurity, Gini or
             sum of squares) variable importance scores, using an empirical Bayes approach.
             See Dunne et al. (2022)  <doi:10.1101/2022.04.06.487300>.
Data for the vignette and examples in 'RFlocalfdr'. Contains a dataset of 1103547 importance values,
       and the table of variables used in the random forest splits. The data is Chromosome 22 taken from Auton et al.
       (2015) <doi:10.1038/nature15393>. It also contains a 51 samples by 22283 genes data set taken from
       Spira et al. (2004) <doi:10.1165/rcmb.2004-0273OC>.
Provides functions to analyse DNA fragment samples (i.e. derived from RFLP-analysis) and standalone BLAST report files (i.e. DNA sequence analysis).
Interface to the 'flsgen' neutral landscape generator <https://github.com/dimitri-justeau/flsgen>. It allows to
  - Generate fractal terrain;
  - Generate landscape structures satisfying user targets over landscape indices;
  - Generate landscape raster from landscape structures.
Tools for RFM (recency, frequency and monetary value) analysis. 
    Generate RFM score from both transaction and customer level data. Visualize the
    relationship between recency, frequency and monetary value using heatmap, 
    histograms, bar charts and scatter plots. Includes a 'shiny' app for 
    interactive segmentation. References:
    i. Blattberg R.C., Kim BD., Neslin S.A (2008) <doi:10.1007/978-0-387-72579-6_12>.
Various tools for handling fuzzy measures, calculating Shapley value and interaction index,  Choquet and Sugeno integrals, as well as fitting fuzzy measures to empirical data are provided. Construction of fuzzy measures from empirical data is done by solving a linear programming problem by using 'lpsolve' package, whose source in C adapted to the R environment 
  is included. The description of the basic theory of fuzzy measures is in the manual in the Doc folder in this package. Please refer to the following:
	[1] <https://personal-sites.deakin.edu.au/~gleb/fmtools.html>
	[2] G. Beliakov, H. Bustince, T. Calvo, 'A Practical Guide to Averaging', Springer, (2016, ISBN: 978-3-319-24753-3).
	[3] G. Beliakov, S. James, J-Z. Wu, 'Discrete Fuzzy Measures', Springer, (2020, ISBN: 978-3-030-15305-2).
R access to the 'FOAAS' (F... Off As A Service) web service is provided.
Graphics for statistics on a sphere, as applied to geological fault data, crystallography, earthquake focal mechanisms, radiation patterns, ternary plots and geographical/geological maps.  Non-double couple plotting of focal spheres and source type maps are included for statistical analysis of moment tensors.
Execute  'FOCAL' (<https://en.wikipedia.org/wiki/FOCAL_(programming_language)>) source code directly in 'R'. This is achieved by translating 'FOCAL' code into equivalent 'R' commands and controlling the sequence of execution.
Allows developers to work with many R folders inside a package. It offers functionalities to transfer R scripts (saved outside the R folder) into 
    the R folder while making additional checks.
The basic algorithm to perform the folding test of unimodality. 
  Given a dataset X (d dimensional, n samples), the test checks whether the 
  distribution of the data are rather unimodal or rather multimodal. This 
  package stems from the following research publication: 
  Siffer Alban, Pierre-Alain Fouque, Alexandre Termier, and Christine Largouët. 
  "Are your data gathered?" In Proceedings of the 24th ACM SIGKDD International 
  Conference on Knowledge Discovery Data Mining, pp. 2210-2218. ACM, 2018.
  <doi:10.1145/3219819.3219994>.
Contains all the code examples in the book "R for Dummies" (2nd
    edition) by Andrie de Vries and Joris Meys. You can view the table of 
    contents as well as the sample code for each chapter.
Provides fast implementations of Random Forests, 
    Gradient Boosting, and Linear Random Forests, with an emphasis on inference 
    and interpretability. Additionally contains methods for variable 
    importance, out-of-bag prediction, regression monotonicity, and
    several methods for missing data imputation.
The R Formatter formats R source code. It is very much based on
    formatR, but tries to improve it by heuristics. For example, spaces can be
    forced around the division operator "/".
Estimate significance of importance metrics
    for a Random Forest model by permuting the response
    variable. Produces null distribution of importance
    metrics for each predictor variable and p-value of
    observed. Provides summary and visualization functions for 'randomForest' 
    results.
A machine learning algorithm that merges satellite and ground precipitation data using Random Forest for spatial prediction, residual modeling for bias correction, and quantile mapping for adjustment, ensuring accurate estimates across temporal scales and regions.
Floating Percentile Model with additional functions for optimizing inputs and evaluating outputs and assumptions.
'FRACTRAN' is an obscure yet tantalizing programming language invented by John Conway of 'Game of Life' fame. The code consists of a sequence of fractions. The rules are simple. First, select an integer to initialize the process. Second, multiply the integer by the first fraction. If an integer results, start again with the new integer. If not, try the next fraction. Finally, if no such multiplication yields an integer, terminate the program. For more information, see <https://en.wikipedia.org/wiki/FRACTRAN> .
Assists in statistical model building to find optimal and semi-optimal higher order interactions
    and best subsets. Uses the lm(), glm(), and other R functions to fit models generated from a feasible 
    solution algorithm. Discussed in Subset Selection in Regression, A Miller (2002). Applied and explained
    for least median of squares in Hawkins (1993) <doi:10.1016/0167-9473(93)90246-P>. The feasible solution 
    algorithm comes up with model forms of a specific type that can have fixed variables, higher order 
    interactions and their lower order terms.
Methods and tools for implementing functional singular spectrum analysis and related techniques.
Set of functions that enable you to use the FUSION commands (Program available in: <http://forsys.sefs.uw.edu/fusion/fusionlatest.html>).
Provides and extends the 'Fuzzy Coco' algorithm by wrapping the
    'FuzzyCoCo' 'C++' Library, cf
    <https://github.com/Lonza-RND-Data-Science/fuzzycoco>. 'Fuzzy Coco'
    constructs systems that predict the outcome of a human decision-making
    process while providing an understandable explanation of a possible
    reasoning leading to it.  The constructed fuzzy systems are composed
    of rules and linguistic variables.  This package provides a 'S3' classic
    interface (fit_xy()/fit()/predict()/evaluate()) and a 'tidymodels'/'parsnip'
    interface, a custom engine with custom iteration stop criterion and
    progress bar support as well as a systematic implementation that do
    not rely on genetic programming but rather explore all possible
    combinations.
Computes a novel variable importance for random forests: Impurity reduction importance scores for out-of-bag (OOB) data complementing the existing inbag Gini importance, see also <doi: 10.1080/03610926.2020.1764042>. 
    The Gini impurities for inbag and OOB data are combined in three different ways, after which the information gain is computed at each split.
    This gain is aggregated for each split variable in a tree and averaged across trees.
Sequential permutation testing for statistical
  significance of predictors in random forests and other prediction methods. 
  The main function of the package is rfvimptest(), which allows to test for 
  the   statistical significance of predictors in random forests using 
  different (sequential) permutation test strategies [1]. The advantage 
  of sequential over conventional permutation tests is that they
  are computationally considerably less intensive, as the sequential
  procedure is stopped as soon as there is sufficient evidence
  for either the null or the alternative hypothesis.
  Reference:
  [1] Hapfelmeier, A., Hornung, R. & Haller, B. (2023) Efficient permutation
	  testing of variable importance measures by the example of random forests.
	  Computational Statistics & Data Analysis 181:107689, <doi:10.1016/j.csda.2022.107689>.
An interactive data visualization and exploration toolkit
    that implements Breiman and Cutler's original random forest Java based        
    visualization tools in R, for supervised and unsupervised classification and 
    regression within the algorithm random forest.
Analyze multi-level one-way
        experimental designs where there are unequal sample
        sizes and population variance homogeneity can not be assumed.
        To conduct the Gabriel test <doi:10.2307/2286265>, create two vectors: one for your 
        observations and one for the factor level of each observation. 
        The function, rgabriel, conduct the test and save the output as
        a vector to input into the gabriel.plot function, which produces 
        a confidence interval plot for Multiple Comparison.
An easy way to get started with Generative Adversarial Nets (GAN) in R. The GAN algorithm was initially 
    described by Goodfellow et al. 2014 <https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf>. A GAN can be used to learn the joint distribution of complex data by 
    comparison. A GAN consists of two neural networks a Generator and a Discriminator, where the two
    neural networks play an adversarial minimax game.
    Built-in GAN models make the training of GANs in R possible in one line and make it easy to 
    experiment with different design choices (e.g. different network architectures, value functions, optimizers).
    The built-in GAN models work with tabular data (e.g. to produce synthetic data) and image data. 
    Methods to post-process the output of GAN models to enhance the quality of samples are available.
The output gap indicates the percentage difference between the actual output of an economy and its potential. Since potential output is a latent process, the estimation of the output gap poses a challenge and numerous filtering techniques have been proposed. 'RGAP' facilitates the estimation of a Cobb-Douglas production function type output gap, as suggested by the European Commission (Havik et al. 2014) <https://ideas.repec.org/p/euf/ecopap/0535.html>. To that end, the non-accelerating wage rate of unemployment (NAWRU) and the trend of total factor productivity (TFP) can be estimated in two bivariate unobserved component models by means of Kalman filtering and smoothing. 'RGAP' features a flexible modeling framework for the appropriate state-space models and offers frequentist as well as Bayesian estimation techniques. Additional functionalities include direct access to the 'AMECO' <https://economy-finance.ec.europa.eu/economic-research-and-databases/economic-databases/ameco-database_en> database and automated model selection procedures. See the paper by Streicher (2022) <http://hdl.handle.net/20.500.11850/552089> for details.
A programmatic interface to the Web Service methods
    provided by the Global Biodiversity Information Facility (GBIF;
    <https://www.gbif.org/developer/summary>). GBIF is a database
    of species occurrence records from sources all over the globe.
    rgbif includes functions for searching for taxonomic names,
    retrieving information on data providers, getting species occurrence
    records, getting counts of occurrence records, and using the GBIF
    tile map service to make rasters summarizing huge amounts of data.
Provides an implementation of Regularized LS-TreeBoost & LAD-TreeBoost algorithm for Regulatory Network inference from any type of expression data (Microarray/RNA-seq etc).
We utilize  approximate Bayesian machinery to fit two-level conjugate hierarchical models on overdispersed Gaussian, Poisson, and Binomial data and evaluates whether the resulting approximate Bayesian interval estimates for random effects meet the nominal confidence levels via frequency coverage evaluation. The data that Rgbp assumes comprise observed sufficient statistic for each random effect, such as an average or a proportion of each group, without population-level data.  The approximate Bayesian tool equipped with the adjustment for density maximization produces approximate point and interval estimates for model parameters including second-level variance component, regression coefficients, and random effect. For the Binomial data, the package provides an option to produce  posterior samples of all the model parameters via the acceptance-rejection method. The package provides a quick way to evaluate coverage rates of the resultant Bayesian interval estimates for random effects via a parametric bootstrapping, which we call frequency method checking.
Multi-block data analysis concerns the analysis of several
    sets of variables (blocks) observed on the same group of individuals.
    The main aims of the RGCCA package are: to study the relationships
    between blocks and to identify subsets of variables of each block
    which are active in their relationships with the other blocks. This
    package allows to (i) run R/SGCCA and related methods,
    (ii) help the user to find out the optimal parameters for R/SGCCA such
    as regularization parameters (tau or sparsity), (iii) evaluate the
    stability of the RGCCA results and their significance, (iv) build predictive
    models from the R/SGCCA. (v) Generic print()
    and plot() functions apply to all these functionalities.
Toolbox for chemometrics analysis of bidimensional gas 
    chromatography data. This package import data for common scientific data
    format (NetCDF) and fold it to 2D chromatogram. Then, it can perform
    preprocessing and multivariate analysis. In the preprocessing algorithms,
    baseline correction, smoothing, and peak alignment are available.
    While in multivariate analysis, multiway principal component analysis is
    incorporated.
Allow access to both public and private end points to Coinbase Pro (erstwhile GDAX) 
    cryptocurrency exchange. For authenticated flow, users must have valid api, secret and passphrase to be able to connect.
Compute yield-stability index based on Bayesian methodology, which is useful for analyze multi-environment trials in plant breeding programs. References: Cotes Torres JM, Gonzalez Jaimes EP, and Cotes Torres A (2016) <https://revistas.unimilitar.edu.co/index.php/rfcb/article/view/2037> Seleccion de Genotipos con Alta Respuesta y Estabilidad Fenotipica en Pruebas Regionales: Recuperando el Concepto Biologico.
Earth Engine <https://earthengine.google.com/> client library for R. All
  of the 'Earth Engine' API classes, modules, and functions are made available. Additional
  functions implemented include importing (exporting) of Earth Engine spatial objects, 
  extraction of time series, interactive map display, assets management interface, 
  and metadata display. See <https://r-spatial.github.io/rgee/> for further details.
Search, composite, and download 'Google Earth Engine' imagery with 'reticulate' bindings for the 'Python' module 'geedim' by Dugal Harris. Read the 'geedim' documentation here: <https://geedim.readthedocs.io/>.
    Wrapper functions are provided to make it more convenient to use 'geedim' to download images larger than the 'Google Earth Engine' size limit <https://developers.google.com/earth-engine/apidocs/ee-image-getdownloadurl>.
    By default the "High Volume" API endpoint <https://developers.google.com/earth-engine/cloud/highvolume> is used to download data and this URL can be customized during initialization of the package.
Provides popular sampling distributions C++ routines based in 
    armadillo through a header file approach.
The GenDataSample() and GenDataPopulation() functions create, 
    respectively, a sample or population of multivariate nonnormal data 
    using methods described in Ruscio and Kaczetow (2008). Both of these 
    functions call a FactorAnalysis() function to reproduce a correlation 
    matrix. The EFACompData() function allows users to determine how many 
    factors to retain in an exploratory factor analysis of an empirical data
    set using a method described in Ruscio and Roche (2012). The latter 
    function uses populations of comparison data created by calling the 
    GenDataPopulation() function.
    <DOI: 10.1080/00273170802285693>.
    <DOI: 10.1037/a0025697>.
A method 'generate()' is implemented in this package for the random
    generation of vector time series according to models obtained by 'RMAWGEN',
    'vars' or other packages.  This package was created to generalize the
    algorithms of the 'RMAWGEN' package for the analysis and generation of any
    environmental vector time series.
The method 'generate()' is extended for spatial multi-site
    stochastic generation of daily precipitation. It generates precipitation
    occurrence in several sites using logit regression (Generalized Linear
    Models) and the approach by D.S. Wilks (1998) <doi:10.1016/S0022-1694(98)00186-3> .
Download the lyrics of your favorite songs in text and table formats. 
    Also search for related songs or song information.
    More information: <https://docs.genius.com/> .
A genetic algorithm plus derivative optimizer.
Provides spatial data analysis functionalities including Exploratory Spatial Data Analysis, 
    Spatial Cluster Detection and Clustering Analysis, Regionalization, etc. based on the C++ source code 
    of 'GeoDa', which is an open-source software tool that serves as an introduction to spatial data analysis.
    The 'GeoDa' software and its documentation are available at <https://geodacenter.github.io>.
Provides the hybrid Bayesian method Geometric Density Estimation. On the one hand, it scales the dimension of our data, on the other it performs inference. The method is fully described in the paper "Scalable Geometric Density Estimation" by Y. Wang, A. Canale, D. Dunson (2016) <http://proceedings.mlr.press/v51/wang16e.pdf>.
A lightweight implementation of the geomorphon terrain
  form classification algorithm of Jasiewicz and Stepinski (2013)
  <doi:10.1016/j.geomorph.2012.11.005> based largely on the 
  'GRASS GIS' 'r.geomorphon' module. This implementation employs 
  a novel algorithm written in C++ and 'RcppParallel'.
Provides an interface to the Python package 'Geomstats' authored by Miolane et 
    al. (2020) <arXiv:2004.04667>.
Supports analysis of spatial data processed with the 'GeoPAT' 2
    software <https://github.com/Nowosad/geopat2>. 
    Available features include creation of a grid based on the 'GeoPAT' 2
    grid header file and reading a 'GeoPAT' 2 text outputs.
Create, read, and write 'GEXF' (Graph Exchange 'XML' Format) graph files (used in 'Gephi' and others). Using the 'XML' package, rgexf allows reading and writing GEXF files, including attributes, 'GEXF' visual attributes (such as color, size, and position), network dynamics (for both edges and nodes), and edges' weights. Users can build/handle graphs element-by-element or massively through data frames, visualize the graph on a web browser through 'gexf-js' (a 'javascript' library), and interact with the 'igraph' package.
Regularized Greedy Forest wrapper of the 'Regularized Greedy Forest' <https://github.com/RGF-team/rgf/tree/master/python-package> 'python' package, which also includes a Multi-core implementation (FastRGF) <https://github.com/RGF-team/rgf/tree/master/FastRGF>.
R utilities for gff files, either general feature format (GFF3) or gene transfer format (GTF) formatted files. This package includes functions for producing summary stats, check for consistency and sorting errors, conversion from GTF to GFF3 format, file sorting, visualization and plotting of feature hierarchy, and exporting user defined feature subsets to SAF format. This tool was developed by the BioinfoGP core facility at CNB-CSIC.
Datasets from the 2021 Ghana Population and Housing Census Results. Users can access results as 'tidyverse' and 'sf'-Ready Data Frames.    The data in this package is scraped from pdf reports released by the Ghana Statistical Service website <https://census2021.statsghana.gov.gh/> . The package currently only contains datasets from the literacy and education reports. Namely, school attendance data for respondents aged 3 years and above.
Implementation of some functions to create quizzes
  in the GIFT format. This format is used by several Virtual Learning
  Environments such as Moodle.
Provides medium to high level functions for 3D interactive graphics, including
    functions modelled on base graphics (plot3d(), etc.) as well as functions for
    constructing representations of geometric objects (cube3d(), etc.).  Output
    may be on screen using OpenGL, or to various standard 3D file formats including
    WebGL, PLY, OBJ, STL as well as 2D image formats, including PNG, Postscript, SVG, PGF.
Visualizing crystal structures and selected area electron diffraction (SAED) patterns.  It provides functions cry_demo() and dp_demo() to load a file in 'CIF' (Crystallographic Information Framework) formats and display crystal structures and electron diffraction patterns.  The function dp_demo() also performs simple simulation of powder X-ray diffraction (PXRD) patterns, and the results can be saved to a file in the working directory.  The package has been tested on several platforms, including Linux on 'Crostini' with a Core™ m3-8100Y Chromebook, I found that even on this low-powered platform, the performance was acceptable.
    T. Hanashima (2001) <https://www2.kek.jp/imss/pf/tools/sasaki/sinram/sinram.html>
    Todd Helmenstine (2019) <https://sciencenotes.org/molecule-atom-colors-cpk-colors/>
    Wikipedia contributors (2023) <https://en.wikipedia.org/w/index.php?title=Atomic_radius&oldid=1179864711>.
The 'glTF' file format is used to describe 3D models.  This package 
 provides read and write functions to work with it.
A programmatic interface to the web service methods
    provided by Global Biotic Interactions (GloBI)
    (<https://www.globalbioticinteractions.org/>). GloBI provides 
    access to spatial-temporal species interaction records from 
    sources all over the world. rglobi provides methods to search 
    species interactions by location, interaction type, and 
    taxonomic name.
R interface to the GNU Linear Programming Kit.
  'GLPK' is open source software for solving large-scale linear programming (LP),
  mixed integer linear programming ('MILP') and other related problems.
Provides 3D plotting routines that facilitate the use of the 'rgl' package and extend its functionality. For example, the routines allow the user to directly control the camera position & orientation, as well as to generate 3D movies with a moving observer.
Implements state-of-the-art Random Graphical Models (RGMs) for multivariate data analysis across multiple environments, offering tools for exploring network interactions and structural relationships. Capabilities include joint inference across environments, integration of external covariates, and a Bayesian framework for uncertainty quantification. Applicable in various fields, including microbiome analysis. Methods based on Vinciotti, V., Wit, E., & Richter, F. (2023). "Random Graphical Model of Microbiome Interactions in Related Environments." <arXiv:2304.01956>.
Algorithms for estimating robustly the parameters of a Gaussian, Student, or Laplace Mixture Model.
Implementation of the Robust Gauss-Newton (RGN) algorithm,
    designed for solving optimization problems with a sum of least squares
    objective function. For algorithm details please refer to Qin et. al. (2018) 
    <doi:10.1029/2017WR022488>.
Traditional noise filtering methods aim at removing noisy samples from a classification dataset. This package adapts classic and recent filtering techniques for use in regression problems, and it also incorporates methods specifically designed for regression data. In order to do this, it uses approaches proposed in the specialized literature, such as Martin et al. (2021) [<doi:10.1109/ACCESS.2021.3123151>] and Arnaiz-Gonzalez et al. (2016) [<doi:10.1016/j.eswa.2015.12.046>]. Thus, the goal of the implemented noise filters is to eliminate samples with noise in regression datasets.
Parse scientific names using 'gnparser'
    (<https://github.com/gnames/gnparser>), written in Go. 'gnparser'
    parses scientific names into their component parts; it utilizes
    a Parsing Expression Grammar specifically for scientific names.
Routines that allow the user to run a large number of goodness-of-fit tests.  
     It allows for data to be continuous or discrete. It includes routines to estimate
     the power of the tests and display them as a power graph.
     The routine run.studies allows a user to quickly study the power of a new method and 
     how it compares to some of the standard ones.
Interface for loading data from 'Google Ads API', 
    see <https://developers.google.com/google-ads/api/docs/start>. 
    Package provide function for authorization and loading reports.
It fires a query to the API to get the unsampled data in R for Google Analytics Premium Accounts. It retrieves data from the Google drive document and stores it into the local drive. The path to the excel file is returned by this package. The user can read data from the excel file into R using read.csv() function.
This is a Google Forms and Google Classroom API Wrapper for R for managing Google Classrooms from R. The documentation for these APIs is here <https://developers.google.com/forms/api/guides> .
Provides interface to Google Fit REST API v1 (see <https://developers.google.com/fit/rest/v1/reference/>).
Serves two purposes: (i) Provide a
        comfortable R interface to query the Google server for static
        maps, and (ii) Use the map as a background image to overlay
        plots within R. This requires proper coordinate scaling.
Query functions to the GPlates <https://www.gplates.org/> Desktop Application and the GPlates Web Service <https://gws.gplates.org/> allow users to reconstruct past positions of geographic entities based on user-selected rotation models without leaving the R running environment. The online method (GPlates Web Service) makes the rotation of static plates, coastlines, and a low number of geographic coordinates available using nothing but an internet connection. The offline method requires an external installation of the GPlates Desktop Application, but allows the efficient batch rotation of thousands of coordinates, Simple Features (sf) and Spatial (sp) objects with custom reconstruction trees and partitioning polygons. Examples of such plate tectonic models are accessible via the chronosphere <https://cran.r-project.org/package=chronosphere>. This R extension is developed under the umbrella of the DFG (Deutsche Forschungsgemeinschaft) Research Unit TERSANE2 (For 2332, TEmperature Related Stressors in ANcient Extinctions).
Encode network data as strings of printable ASCII characters. Implemented 
    functions include encoding and decoding adjacency matrices, edgelists, igraph, and
    network objects to/from formats 'graph6', 'sparse6', and 'digraph6'. The formats and
    methods are described in McKay, B.D. and Piperno, A (2014)
    <doi:10.1016/j.jsc.2013.09.003>.
Data and Functions from the book R Graphics, Third Edition. There is a function to produce each figure in the book, plus several functions, classes, and methods defined in Chapter 8.
Interface to integrate 'igraph' and 'ggplot2' graphics within spatial maps. 'RGraphSpace' implements new geometric objects using 'ggplot2' prototypes, customized for representing large 'igraph' objects in a normalized coordinate system. By scaling shapes and graph elements, 'RGraphSpace' can provide a framework for layered visualizations.
An interface between the 'GRASS' geographical information system ('GIS') and 'R', based on starting 'R' from within the 'GRASS' 'GIS' environment, or running a free-standing 'R' session in a temporary 'GRASS' location; the package provides facilities for using all 'GRASS' commands from the 'R' command line. The original interface package for 'GRASS 5' (2000-2010) is described in Bivand (2000) <doi:10.1016/S0098-3004(00)00057-1> and Bivand (2001) <https://www.r-project.org/conferences/DSC-2001/Proceedings/Bivand.pdf>. This was succeeded by 'spgrass6' for 'GRASS 6' (2006-2016) and 'rgrass7' for 'GRASS 7' (2015-present). The 'rgrass' package modernizes the interface for 'GRASS 8' while still permitting the use of 'GRASS 7'.
Fully 'DBI'-compliant interface to 'Greenplum' <https://greenplum.org/>,
    an open-source parallel database. This is an extension of the 'RPostgres'
    package <https://github.com/r-dbi/RPostgres>.
The tools and utilities to estimate the model described in "Gremlin's in the
    Data: Identifying the Information Content of Research Subjects" (Howell et al.
    (2021) <doi:10.1177/0022243720965930>)  using conjoint analysis data such as
    that collected in Sawtooth Software's 'Lighthouse' or 'Discover' products.
    Additional utilities are included for formatting the input data.
Integrates the Groovy scripting language with the R Project for Statistical Computing.
Functions to calculate Sample Number and Average Sample Number for Repetitive Group Sampling Plan Based on Cpk as given in Aslam et al. (2013) (<DOI:10.1080/00949655.2012.663374>).
Useful tools for determining whether two samples are from the same distribution. Utilizes a robust method to address the problematic structure of the similarity graph constructed from high-dimensional data. The method is provided in Yichuan Bai and Lynna Chu (2023) <arXiv:2307.12325>.
This is a library to access the current API of the web speed test service 'GTmetrix'.
    It provides a convenient wrapper to start tests, get reports, and access all kinds of meta data. 
    For more information about using the API please visit <https://gtmetrix.com/api/docs/2.0/>.
Automatic open data acquisition from resources of Polish Head Office
    of Geodesy and Cartography ('Główny Urząd Geodezji i Kartografii')
    (<https://www.gov.pl/web/gugik>).
    Available datasets include various types of numeric, raster and vector data,
    such as orthophotomaps, digital elevation models (digital terrain models,
    digital surface model, point clouds), state register of borders, spatial
    databases, geometries of cadastral parcels, 3D models of buildings, and more.
    It is also possible to geocode addresses or objects using the geocodePL_get()
    function.
Reads in continuous glucose monitor data of many different formats, calculates a host of glycemic variability metrics, and plots glucose over time.
Implementation of the affine-invariant method of Goodman & Weare (2010) <DOI:10.2140/camcos.2010.5.65>, a method of producing Monte-Carlo samples from a target distribution.
DBI/RJDBC interface to h2 database. h2 version 2.3.232 is included.
An R interface to the 'Handsontable' JavaScript library, which is a
    minimalist Excel-like data grid editor.  See <https://handsontable.com/> for details.
The renewal Hawkes (RHawkes) process (Wheatley,
    Filimonov, and Sornette, 2016 <doi:10.1016/j.csda.2015.08.007>) is
    an extension to the classical Hawkes self-exciting point process
    widely used in the modelling of clustered event sequence data.
    This package provides functions to simulate the RHawkes process
    with a given immigrant hazard rate function and offspring birth
    time density function, to compute the exact likelihood of a
    RHawkes process using the recursive algorithm proposed by Chen and
    Stindl (2018) <doi:10.1080/10618600.2017.1341324>, to compute the
    Rosenblatt residuals for goodness-of-fit assessment, and to
    predict future event times based on observed event times up to a
    given time. A function implementing the linear time RHawkes
    process likelihood approximation algorithm proposed in Stindl and
    Chen (2021) <doi:10.1007/s11222-021-10002-0> is also included.
The evaluation criteria of rangeland health, condition and landscape function analysis
             based on species diversity and functional diversity of rangeland plant communities.
Non-parametric clustering of joint pattern multi-genetic/epigenetic factors. This package contains functions designed to cluster subjects based on gene features including single nucleotide polymorphisms (SNPs), DNA methylation (CPG), gene expression (GE), and covariate data. The novel concept follows the general K-means (Hartigan and Wong (1979) <doi:10.2307/2346830> framework but uses weighted Euclidean distances across the gene features to cluster subjects. This approach is unique in that it attempts to capture all pairwise interactions in an effort to cluster based on their complex biological interactions.
Here we performs robust hierarchical co-clustering between row and column entities of a data matrix in 
             absence and presence of outlying observations. It can be used to explore important co-clusters consisting of  
             important samples and their regulatory significant features. Please see Hasan, Badsha and Mollah (2020) 
             <doi:10.1101/2020.05.13.094946>.
An implementation of the 'Heroicons' icon library for 'shiny'
    applications and other 'R' web-based projects. You can search, render,
    and customize icons without 'CSS' or 'JavaScript' dependencies.
Implements the hierarchical Bayesian analysis of populations structure (hierBAPS) 
  algorithm of Cheng et al. (2013) <doi:10.1093/molbev/mst028> for clustering DNA sequences 
  from multiple sequence alignments in FASTA format. 
  The implementation includes improved defaults and plotting capabilities 
  and unlike the original 'MATLAB' version removes singleton SNPs by default.
A framework that supports creating and extending enterprise Shiny applications using best practices.
Implements simple Hamiltonian Monte Carlo routines in R for
             sampling from any desired target distribution which is continuous
             and smooth. See Neal (2017) <arXiv:1701.02434> for further details
             on Hamiltonian Monte Carlo. Automatic parameter selection is not
             supported.
Hydrologic modelling system is an object oriented tool for simulation and analysis of hydrologic events. The package proposes functions and methods for construction, simulation, visualization, and calibration of a hydrologic model.
Performs the random heteroscedastic nested error regression model described in Kubokawa, Sugasawa, Ghosh and Chaudhuri (2016) <doi:10.5705/ss.202014.0070>.
A useful statistical tool for the construction and analysis of Honeycomb Selection Designs. More information about this type of designs: Fasoula V. (2013) <doi:10.1002/9781118497869.ch6> Fasoula V.A., and Tokatlidis I.S. (2012) <doi:10.1007/s13593-011-0034-0> Fasoulas A.C., and Fasoula V.A. (1995) <doi:10.1002/9780470650059.ch3> Tokatlidis I. (2016) <doi:10.1017/S0014479715000150> Tokatlidis I., and Vlachostergios D. (2016)  <doi:10.3390/d8040029>.
Rho is used to test the generalization of inter rater reliability
    (IRR) statistics. Calculating rho starts by generating a large number of
    simulated, fully-coded data sets: a sizable collection of hypothetical
    populations, all of which have a kappa value below a given threshold -- which
    indicates unacceptable agreement. Then kappa is calculated on a sample from
    each of those sets in the collection to see if it is equal to or higher than
    the kappa in then real sample. If less than five percent of the distribution
    of samples from the simulated data sets is greater than actual observed kappa,
    the null hypothesis is rejected and one can conclude that if the two raters had
    coded the rest of the data, we would have acceptable agreement (kappa above the
    threshold).
Higher-order spectra or polyspectra of time series, such as bispectrum and bicoherence, have been investigated in abundant literature and applied to problems of signal detection in a wide range of fields. This package aims to provide a simple API to estimate and analyze them. The current implementation is based on Brillinger and Irizarry (1998) <doi:10.1016/S0165-1684(97)00217-X> for estimating bispectrum or bicoherence, Lii and Helland (1981) <doi:10.1145/355958.355961> for cross-bispectrum, and Kim and Powers (1979) <doi:10.1109/TPS.1979.4317207> for cross-bicoherence.
Evaluating risk (that a patient arises a side effect) during hospitalization is the main purpose of this package. Several methods (Parametric, non parametric and De Vielder estimation) to estimate the risk constant (R) are implemented in this package. There are also functions to simulate the different models of this issue in order to quantify the previous estimators. It is necessary to read at least the first six pages of the report to understand the topic.
Microbenchmarks for determining the run time
  performance of aspects of the R programming environment and packages
  relevant to high-performance computation.  The benchmarks are divided into
  three categories: dense matrix linear algebra kernels, sparse matrix linear
  algebra kernels, and machine learning functionality.
Control the number of threads on 'BLAS' (Aka 'GotoBLAS', 'OpenBLAS', 'ACML', 'BLIS' and 'MKL').
  And possible to control the number of threads in 'OpenMP'.
  Get a number of logical cores and physical cores if feasible.
Methods to scan RR interval data for Premature Ventricular Complexes (PVCs) and parameterise and plot the resulting Heart Rate Turbulence (HRT). The methodology of HRT analysis is based on the original publication by Schmidt et al. <doi:10.1016/S0140-6736(98)08428-1> and extended with suggestions from <doi:10.1088/1361-6579/ab98b3>.
Allows users to import data files containing heartbeat	positions in the most broadly used formats, to remove outliers or points with unacceptable physiological values present in the time series, to plot HRV data, and to perform time domain, frequency domain and nonlinear HRV analysis. See Garcia et al. (2017) <DOI:10.1007/978-3-319-65355-6>.
The Ryan-Holm step-down Bonferroni or Sidak procedure is to control the family-wise (experiment-wise) type I error rate in the multiple comparisons.
    This procedure provides the adjusting p-values and adjusting CIs. The methods used in this package are referenced from John Ludbrook (2000) <doi:10.1046/j.1440-1681.2000.03223.x>.
R-hub v2 uses GitHub Actions to run 'R CMD check' and
    similar package checks. The 'rhub' package helps you set up
    R-hub v2 for your R package, and start running checks.
Tool for the analysis Mass Spectrometry (MS) data in the context of 
             immunopeptidomic analysis for the identification of hybrid 
             peptides and the predictions of binding affinity of all peptides 
             using 'netMHCpan' <doi:10.1093/nar/gkaa379> while providing a summary 
             of the netMHCpan output. 'RHybridFinder' (RHF) is destined for 
             researchers who are looking to analyze their MS data for the purpose 
             of identification of potential spliced peptides. This package, 
             developed mainly in base R, is based on the workflow published by 
             Faridi et al. in 2018 <doi:10.1126/sciimmunol.aar3947>.
Create and manipulate hypergraph objects. This early version of
    rhype allows for the output of matrices associated with the hypergraphs 
    themselves. It also uses these matrices to calculate hypergraph spectra and
    perform spectral comparison. Functionality coming soon includes calculation
    of hyperpaths and hypergraph centrality measures.
Randomization inference procedures for simple and complex randomized designs, including multi-armed trials, as described in Gerber and Green (2012, ISBN: 978-0393979954). Users formally describe their randomization procedure and test statistic. The randomization distribution of the test statistic under some null hypothesis is efficiently simulated.
Computes attributable effects based confidence interval, permutation test confidence interval, or asymptotic confidence interval for the average treatment effect on a binary outcome. Methods outlined in further detail in Rigdon and Hudgens (2015) <doi:10.1002/sim.6384>.
Radiomics image analysis toolbox for 2D and 3D radiological images. RIA supports DICOM, NIfTI,
             nrrd and npy (numpy array) file formats.
             RIA calculates first-order, gray level co-occurrence matrix, gray level run length matrix and
             geometry-based statistics. Almost all calculations are done using vectorized formulas to
             optimize run speeds. Calculation of several thousands of parameters only takes minutes
             on a single core of a conventional PC. Detailed methodology has been published: Kolossvary
             et al. Circ: Cardiovascular Imaging. 2017;10(12):e006843 <doi: 10.1161/CIRCIMAGING.117.006843>.
Calculates the Iberian Actuarial Climate Index and its components—including temperature, precipitation, wind power, and sea level data—to support climate change analysis and risk assessment. See "Zhou et al." (2023) <doi:10.26360/2023_3> for further details.
Random-intercept accelerated failure time (AFT) model utilizing Bayesian additive regression trees (BART) for drawing causal inferences about multiple treatments while accounting for the multilevel survival data structure. It also includes an interpretable sensitivity analysis approach to evaluate how the drawn causal conclusions might be altered in response to the potential magnitude of departure from the no unmeasured confounding assumption.This package implements the methods described by Hu et al. (2022) <doi:10.1002/sim.9548>.
Allows interaction with 'Interactive Brokers' 'Trader Workstation'
    <https://interactivebrokers.github.io/tws-api/>.
    Handles the connection over the network and the exchange of messages.
    Data is encoded and decoded between user and wire formats.
    Data structures and functionality closely mirror the official implementations.
Recursive algorithms for computing various relatedness
    coefficients, including pairwise kinship, kappa and identity
    coefficients. Both autosomal and X-linked coefficients are computed.
    Founders are allowed to be inbred, which enables construction of any
    given kappa coefficients, as described in Vigeland (2020)
    <doi:10.1007/s00285-020-01505-x>. In addition to the standard
    coefficients, 'ribd' also computes a range of lesser-known
    coefficients, including generalised kinship coefficients, multi-person
    coefficients and two-locus coefficients (Vigeland, 2023,
    <doi:10.1093/g3journal/jkac326>). Many features of 'ribd' are
    available through the online app 'QuickPed' at
    <https://magnusdv.shinyapps.io/quickped>; see Vigeland (2022)
    <doi:10.1186/s12859-022-04759-y>.
The provided benchmark suite enables the automated evaluation and comparison of any existing and novel indirect method for reference interval ('RI') estimation in a systematic way.
	Indirect methods take routine measurements of diagnostic tests, containing pathological and non-pathological samples as input and use sophisticated
	statistical methods to derive a model describing the distribution of the non-pathological samples, which can then be 
	used to derive reference intervals. The benchmark suite contains 5,760 simulated test sets with varying difficulty.
	To include any indirect method, a custom wrapper function needs to be provided. 
	The package offers functions for generating the test sets, executing the indirect method and evaluating the results.
	See ?RIbench or vignette("RIbench_package") for a more comprehensive description of the features. 
	A detailed description and application is described in Ammer T., Schuetzenmeister A., Prokosch H.-U., Zierk J., Rank C.M., Rauh M. "RIbench: A Proposed Benchmark for the Standardized Evaluation of Indirect Methods for Reference Interval Estimation". Clinical Chemistry (2022) <doi:10.1093/clinchem/hvac142>.
Provides a compact 'R' interface for performing tensor calculations.
    This is achieved by allowing (upper and lower) index labeling of arrays
    and making use of Ricci calculus conventions to implicitly trigger
    contractions and diagonal subsetting.
    Explicit tensor operations, such as addition, subtraction and
    multiplication of tensors via the standard operators, raising and lowering
    indices, taking symmetric or antisymmetric tensor parts, as well as the
    Kronecker product are available. Common tensors like the Kronecker delta,
    Levi Civita epsilon, certain metric tensors, the Christoffel symbols,
    the Riemann as well as Ricci tensors are provided.
    The covariant derivative of tensor fields with respect to any metric
    tensor can be evaluated. An effort was made to provide the user with
    useful error messages.
Provides functions for the calibration of radiocarbon dates, as well as options to calculate different radiocarbon timescales (C14 age, F14C, pMC, D14C) and estimating the effects of contamination or local reservoir offsets (Reimer and Reimer 2001 <doi:10.1017/S0033822200038339>). The methods follow long-established recommendations such as Stuiver and Polach (1977) <doi:10.1017/S0033822200003672> and Reimer et al. (2004) <doi:10.1017/S0033822200033154>. This package uses the calibration curves from the data package 'rintcal'.
Gene annotation of rice (Oryza Sativa L.spp.japonica).
    The package is based on the annotation file from the website <http://plants.ensembl.org/Oryza_sativa/Info/Index>. 
    Input gene's name then return some information, including the from position, the end position, 
    the position type and the chromosome number.
Convert one biological ID to another of rice (Oryza sativa).
    Rice(Oryza sativa) has more than one form gene ID for the genome. 
    The two main gene ID for rice genome are  the RAP (The Rice Annotation Project, <https://rapdb.dna.affrc.go.jp/>, 
    and the MSU(The Rice Genome Annotation Project, <http://rice.plantbiology.msu.edu/>.
    All RAP rice gene IDs are of the form Os##g####### as explained on the website <https://rapdb.dna.affrc.go.jp/>.
    All MSU rice gene IDs are of the form LOC_Os##g##### as explained on the website <http://rice.plantbiology.msu.edu/analyses_nomenclature.shtml>.
    All SYMBOL rice gene IDs are the unique name on the NCBI(National Center for Biotechnology Information, <https://www.ncbi.nlm.nih.gov/>.
    The TRANSCRIPTID, is the transcript id of rice, are of the form Os##t#######.
    The researchers usually need to converter between various IDs. Such as converter RAP to SYMBOLS for function searching on NCBI.
    There are a lot of websites with the function for converting RAP to MSU or MSU to RA, such as 'ID Converter' <https://rapdb.dna.affrc.go.jp/tools/converter>.
    But it is difficult to convert super multiple IDs on these websites.
    The package can convert all IDs between the three IDs (RAP, MSU and SYMBOL) regardless of the number.
The Diceware method can be used to generate strong passphrases.
  In short, you roll a 6-faced dice 5 times in a row, the number obtained is
  matched against a dictionary of easily remembered words. By combining together 7
  words thus generated, you obtain a password that is relatively easy to remember,
  but would take several millions years (on average) for a powerful computer to guess.
Methods for randomization inference in group-randomized trials. Specifically, it can be used to analyze the treatment effect of stratified data with multiple clusters in each stratum with treatment given on cluster level. 
    User may also input as many covariates as they want to fit the data. Methods are described by Dylan S Small et al., (2012) <doi:10.1198/016214507000000897>.
Focused on (but not exclusive to) data sets hosted on PhysioNet
    (<https://physionet.org>), 'ricu' provides utilities for download, setup
    and access of intensive care unit (ICU) data sets. In addition to
    functions for running arbitrary queries against available data sets, a
    system for defining clinical concepts and encoding their representations
    in tabular ICU data is presented.
Provides efficient functions for detecting multiple change points in multidimensional time series. The models can be piecewise constant or polynomial. Adaptive threshold selection methods are available, see Fan and Wu (2024)	<arXiv:2403.00600>.
For whole-genome analysis, idiograms are virtually the most intuitive and effective way to map and visualize the genome-wide information. RIdeogram was developed to visualize and map whole-genome data on idiograms with no restriction of species.
Linear and logistic ridge regression functions. Additionally includes special functions for 
            genome-wide single-nucleotide polymorphism (SNP) data. More details can be found in
            <doi: 10.1002/gepi.21750> and <doi: 10.1186/1471-2105-12-372>.
Implementation of a Principal Component Analysis (PCA) in the
    torus via density ridge estimation. The main function, ridge_pca(), obtains
    the relevant density ridge for bivariate sine von Mises and bivariate
    wrapped Cauchy distribution models and provides the associated scores and
    variance decomposition. Auxiliary functions for evaluating, fitting, and
    sampling these models are also provided. The package provides replicability
    to García-Portugués and Prieto-Tirado (2023) 
    <doi:10.1007/s11222-023-10273-9>.
It is a package that provides alternative approach for finding optimum parameters of ridge regression. This package focuses on finding the ridge parameter value k which makes the variance inflation factors closest to 1, while keeping them above 1 as addressed by Michael Kutner, Christopher Nachtsheim, John Neter, William Li (2004, ISBN:978-0073108742). Moreover, the package offers end-to-end functionality to find optimum k value and presents the detailed ridge regression results. Finally it shows three sets of graphs consisting k versus variance inflation factors, regression coefficients and standard errors of them.
An interface to iDigBio's search API that allows downloading
  specimen records. Searches are returned as a data.frame. Other functions
  such as the metadata end points return lists of information. iDigBio is a US
  project focused on digitizing and serving museum specimen collections on the
  web. See <https://www.idigbio.org> for information on iDigBio.
Allows to get weather data from Automated Surface Observing
    System (ASOS) stations (airports) in the whole world thanks to the
    Iowa Environment Mesonet website.
We provide a variety of algorithms for manifold-valued data, including Fréchet summaries, hypothesis testing, clustering, visualization, and other learning tasks. See Bhattacharya and Bhattacharya (2012) <doi:10.1017/CBO9781139094764> for general exposition to statistics on manifolds.
We provide a number of algorithms to estimate fundamental statistics including Fréchet mean and geometric median for manifold-valued data. Also, C++ header files are contained that implement elementary operations on manifolds such as Sphere, Grassmann, and others. See Bhattacharya and Bhattacharya (2012) <doi:10.1017/CBO9781139094764> if you are interested in statistics on manifolds, and Absil et al (2007, ISBN:9780691132983) on computational aspects of optimization on matrix manifolds.
Implements various Riemannian metrics for symmetric positive definite matrices, including AIRM (Affine Invariant Riemannian Metric, see Pennec, Fillard, and Ayache (2006) <doi:10.1007/s11263-005-3222-z>), Log-Euclidean (see Arsigny, Fillard, Pennec, and Ayache (2006) <doi:10.1002/mrm.20965>), Euclidean, Log-Cholesky (see Lin (2019) <doi:10.1137/18M1221084>), and Bures-Wasserstein metrics (see Bhatia, Jain, and Lim (2019) <doi:10.1016/j.exmath.2018.01.002>). Provides functions for computing logarithmic and exponential maps, vectorization, and statistical operations on the manifold of positive definite matrices.
Retrieves efficiently and reliably Investors Exchange ('IEX') stock and market data using 'IEX Cloud API'. The platform is offered by Investors Exchange Group (IEX Group).
    Main goal is to leverage 'R' capabilities including existing packages to effectively provide financial and statistical analysis as well as visualization in support of fact-based decisions.
    In addition, continuously improve and enhance 'Riex' by applying best practices and being in tune with users' feedback and requirements.
    Please, make sure to review and acknowledge Investors Exchange Group (IEX Group) terms and conditions before using 'Riex' (<https://iexcloud.io/terms/>).
Implements the algorithms for solving sparse generalized eigenvalue problem by Tan, et. al. (2018). Sparse Generalized Eigenvalue Problem: Optimal Statistical Rates via Truncated Rayleigh Flow.  To appear in Journal of the Royal Statistical Society: Series B. <arXiv:1604.08697>.
Provides functions to compute recentered influence functions
    (RIF) of a distributional variable at the mean, quantiles, variance,
    gini or any custom functional of interest. The package allows to
    regress the RIF on any number of covariates. Generic print, plot and
    summary functions are also provided. Reference: Firpo, Sergio, Nicole M. Fortin, and Thomas Lemieux. (2009) <doi:10.3982/ECTA6822>. "Unconditional Quantile Regressions.".
Pointwise generation and display of attractors (prefractals) of the random iterated function system (RIFS) for various combinations of probabilistic and geometric parameters of some fixed point sets (protofractals), described by Bukhovets A.G. (2012) <doi:10.1134/S0005117912020154>.
Presentation-ready results tables for epidemiologists in an 
    automated, reproducible fashion. The user provides the final analytical 
    dataset and specifies the design of the table, with rows and/or columns 
    defined by exposure(s), effect modifier(s), and estimands as desired, 
    allowing to show descriptors and inferential estimates in one table -- 
    bridging the rift between epidemiologists and their data, one table at a 
    time. See Rothman (2017) <doi:10.1007/s10654-017-0314-3>.
The goal of Rigma is to provide a user friendly client to the
    'Figma' API <https://www.figma.com/developers/api>. It uses the latest
    `httr2` for a stable interface with the REST API. More than 20 methods
    are provided to interact with 'Figma' files, and teams. Get design
    data into R by reading published components and styles, converting and
    downloading images, getting access to the full 'Figma' file as a
    hierarchical data structure, and much more. Enhance your creativity
    and streamline the application development by automating the
    extraction, transformation, and loading of design data to your
    applications and 'HTML' documents.
A set of tools to streamline data analysis. Learning both R and introductory statistics at the same time can be challenging, and so we created 'rigr' to facilitate common data analysis tasks and enable learners to focus on statistical concepts. We provide easy-to-use interfaces for descriptive statistics, one- and two-sample inference, and regression analyses. 'rigr' output includes key information while omitting unnecessary details that can be confusing to beginners. Heteroscedasticity-robust ("sandwich") standard errors are returned by default, and multiple partial F-tests and tests for contrasts are easy to specify. A single regression function can fit both linear and generalized linear models, allowing students to more easily make connections between different classes of models.
Randomization-based inference for average treatment effects in potentially inexactly matched observational studies. It implements the inverse post-matching probability weighting framework proposed by the authors. The post-matching probability calculation follows the approach of Pimentel and Huang (2024) <doi:10.1093/jrsssb/qkae033>. The optimal full matching method is based on Hansen (2004) <doi:10.1198/106186006X137047>. The variance estimator extends the method proposed in Fogarty (2018) <doi:10.1111/rssb.12290> from the perfect randomization settings to the potentially inexact matching case. Comparisons are made with conventional methods, as described in Rosenbaum (2002) <doi:10.1007/978-1-4757-3692-2>, Fogarty (2018) <doi:10.1111/rssb.12290>, and Kang et al. (2016) <doi:10.1214/15-aoas894>.
Functionality to download stock prices,
    cryptocurrency data, and more from the 'Tiingo' API
    <https://api.tiingo.com/>.
Provides tools to access, search, and manipulate ILO's ilostat database,
  including bulk download of statistical data, dictionary lookups, and table of contents.
An interface to the powerful and fairly complete computer algebra system 'Maxima'.
    It can be used to start and control 'Maxima' from within R by entering 'Maxima' commands. 
    Results from 'Maxima' can be parsed and evaluated in R. 
    It facilitates outputting results from 'Maxima' in 'LaTeX' and 'MathML'. 
    2D and 3D plots can be displayed directly. 
    This package also registers a 'knitr'-engine enabling 'Maxima' code chunks 
    to be written in 'RMarkdown' documents.
Provides functions to read and write 'ImageJ' (<https://imagej.net>)
    Region of Interest (ROI) files, to plot the ROIs and to convert them to
    'spatstat' (<https://spatstat.org/>) spatial patterns.
A pure R implementation of the median cut algorithm.
    Extracts the dominant colors from an image, and turns them into
    a scale for use in plots or for fun!
The metrics() function calculates measures of scholarly impact. 
    These include conventional measures, such as the number of publications
    and the total citations to all publications, as well as modern and 
    robust metrics based on the vector of citations associated with each 
    publication, such as the h index and many of its variants or rivals.
    These methods are described in Ruscio et al. (2012) 
    <DOI: 10.1080/15366367.2012.711147>.
Tools for manipulating, exploring, and visualising  multiple-response data, including scored or ranked responses. Conversions to and from factors, lists, strings, matrices; reordering, lumping, flattening; set operations; tables; frequency and co-occurrence plots.
A programmatic interface to the API provided by the 'iNaturalist' website <https://www.inaturalist.org/> to download species occurrence data submitted by citizen scientists.
Relative, generalized, and Erreygers corrected concentration index; plot Lorenz curves; and decompose health
    inequalities into contributing factors. The package currently works with (generalized) linear models, survival models, complex survey models, and marginal effects probit models.
    originally forked by Brecht Devleesschauwer from the 'decomp' package  (no longer on CRAN), 'rineq' is now maintained by Kaspar Walter Meili. Compared to the earlier 'rineq' version on 'github' by Brecht Devleesschauwer (<https://github.com/brechtdv/rineq>), the regression tree functionality has been removed.
    Improvements compared to earlier versions include improved plotting of decomposition and concentration, added functionality to calculate the concentration index with different methods, calculation of robust standard errors, and support for the decomposition analysis using marginal effects probit regression models. The development version is available at <https://github.com/kdevkdev/rineq>.
Circular / ring buffers in R and C.  There are a couple
    of different buffers here with different implementations that
    represent different trade-offs.
Loading calls data from 'Ringostat API'. 
    See <https://help.ringostat.com/knowledge-base/article/integration-with-ringostat-via-api>.
C++ classes to embed R in C++ (and C) applications
 A C++ class providing the R interpreter is offered by this package
 making it easier to have "R inside" your C++ application. As R itself
 is embedded into your application, a shared library build of R is
 required. This works on Linux, OS X and even on Windows provided you
 use the same tools used to build R itself. Numerous examples are
 provided in the nine subdirectories of the examples/ directory of
 the installed package: standard, 'mpi' (for parallel computing), 'qt'
 (showing how to embed 'RInside' inside a Qt GUI application), 'wt'
 (showing how to build a "web-application" using the Wt toolkit),
 'armadillo' (for 'RInside' use with 'RcppArmadillo'), 'eigen' (for
 'RInside' use with 'RcppEigen'), and 'c_interface' for a basic C
 interface and 'Ruby' illustration.  The examples use 'GNUmakefile(s)'
 with GNU extensions, so a GNU make is required (and will use the
 'GNUmakefile' automatically). 'Doxygen'-generated documentation of
 the C++ classes is available at the 'RInside' website as well.
Functions to calculate several ecological indices of individual 
    and population niche width (Araujo's E, clustering and pairwise similarity 
    among individuals, IS, Petraitis' W, and Roughgarden's WIC/TNW) to assess 
    individual specialization based on data of resource use. Resource use can 
    be quantified by counts of categories, measures of mass or length, or 
    proportions. Monte Carlo resampling procedures are available for hypothesis 
    testing against multinomial null models.
    Details are provided in Zaccarelli et al. (2013) <doi:10.1111/2041-210X.12079>
    and associated references.
The IntCal20 radiocarbon calibration curves (Reimer et al. 2020 <doi:10.1017/RDC.2020.68>) are provided as a data package, together with previous IntCal curves (IntCal13, IntCal09, IntCal04, IntCal98), other curves (e.g., NOTCal04 [van der Plicht et al. 2004], Arnold & Libby 1951) and postbomb curves. Also provided are functions to copy the curves into memory, and to read, query and plot the data underlying the IntCal20 curves.
This tool can be used to build binary interval trees using real number inputs. 
    The tree supports queries of intervals overlapping a single number or an interval (start, end). 
    Intervals with same bounds but different names are treated as distinct intervals. 
    Insertion of intervals is also allowed. Deletion of intervals is not implemented at this point.
    See  Mark de Berg, Otfried Cheong, Marc van Kreveld, Mark Overmars (2008). Computational Geometry: Algorithms and Applications, for a reference.
Allows the user to view an image in full screen when clicking on it in 'RMarkdown' documents and 'shiny' applications. 
  The package relies on the 'JavaScript' library 'intense-images'. See <https://tholman.com/intense-images/> for more information.
A wrapper for the 'Intro.js' library (For more info: <https://introjs.com/>). 
  This package makes it easy to include step-by-step introductions, and clickable hints in a 'Shiny' 
  application. It supports both static introductions in the UI, and programmatic introductions from 
  the server-side.
Streamlined data import and export by making assumptions that
    the user is probably willing to make: 'import()' and 'export()' determine
    the data format from the file extension, reasonable defaults are used for
    data import and export, web-based import is natively supported (including
    from SSL/HTTPS), compressed files can be read directly, and fast import
    packages are used where appropriate. An additional convenience function,
    'convert()', provides a simple method for converting between file types.
Constrained clustering, transfer functions, and other methods for analysing Quaternary science data.
Turns regression models inside out. Functions decompose variances and coefficients for various regression model types. Functions also visualize regression model objects using techniques developed in Schoon, Melamed, and Breiger (2024) <doi:10.1017/9781108887205>.
Utilities to access Integrated Food Security Phase Classification
    (IPC) and Cadre Harmonisé (CH) food security data. Wrapper functions are
    available for all of the 'IPC-CH' Public API (<https://docs.api.ipcinfo.org>)
    simplified and advanced endpoints to easily download the data in a clean and
    tidy format.
Ports the Ripser <doi:10.48550/arXiv.1908.02518>
    and Cubical Ripser <doi:10.48550/arXiv.2005.12692>
    persistent homology calculation engines from C++.
    Can be used as a rapid calculation tool in topological data analysis
    pipelines.
The open sourced data management software 'Integrated
    Rule-Oriented Data System' ('iRODS') offers solutions for the whole
    data life cycle (<https://irods.org/>). The loosely constructed and
    highly configurable architecture of 'iRODS' frees the user from strict
    formatting constraints and single-vendor solutions. This package
    provides an interface to the 'iRODS' HTTP API, allowing you to manage
    your data and metadata in 'iRODS' with R. Storage of annotated files
    and R objects in 'iRODS' ensures findability, accessibility,
    interoperability, and reusability of data.
Parameter estimation, computation of probability, information, and 
    (log-)likelihood, and visualization of item/test characteristic curves and
    item/test information functions for three uni-dimensional item response theory
    models: the 3-parameter-logistic model, generalized partial credit model, 
    and graded response model. The full documentation and tutorials are at 
    <https://github.com/xluo11/Rirt>.
Numerous functions for cohort-based analyses, either for prediction or causal inference. For causal inference, it includes Inverse Probability Weighting and G-computation for marginal estimation of an exposure effect when confounders are expected. We deal with binary outcomes, times-to-events, competing events, and multi-state data. For multistate data, semi-Markov model with interval censoring may be considered, and we propose the possibility to consider the excess of mortality related to the disease compared to reference lifetime tables. For predictive studies, we propose a set of functions to estimate time-dependent receiver operating characteristic (ROC) curves with the possible consideration of right-censoring times-to-events or the presence of confounders. Finally, several functions are available to assess time-dependent ROC curves or survival curves from aggregated data.
Implements techniques for educational resource inspection, selection, and evaluation (RISE) described in Bodily, Nyland, and Wiley (2017) 
    <doi:10.19173/irrodl.v18i2.2952>.	Automates the process of identifying learning materials that are not effectively supporting student learning in 
    technology-mediated courses by synthesizing information about access to course content and performance on assessments.
Fits an Ising model to a binary dataset using L1 regularized
    logistic regression and extended BIC. Also includes a fast lasso logistic
    regression function for high-dimensional problems. Uses the 'libLBFGS'
    optimization library by Naoaki Okazaki.
Computes 26 financial risk measures for any continuous distribution.  The 26 financial risk measures  include value at risk, expected shortfall due to Artzner et al. (1999) <DOI:10.1007/s10957-011-9968-2>, tail conditional median due to Kou et al. (2013) <DOI:10.1287/moor.1120.0577>, expectiles due to Newey and Powell (1987) <DOI:10.2307/1911031>, beyond value at risk due to Longin (2001) <DOI:10.3905/jod.2001.319161>, expected proportional shortfall due to Belzunce et al. (2012) <DOI:10.1016/j.insmatheco.2012.05.003>, elementary risk measure due to Ahmadi-Javid (2012) <DOI:10.1007/s10957-011-9968-2>, omega due to Shadwick and Keating (2002), sortino ratio due to Rollinger and Hoffman (2013), kappa  due to Kaplan and Knowles  (2004), Wang (1998)'s <DOI:10.1080/10920277.1998.10595708> risk measures, Stone (1973)'s <DOI:10.2307/2978638> risk measures, Luce (1980)'s <DOI:10.1007/BF00135033> risk measures, Sarin (1987)'s <DOI:10.1007/BF00126387> risk measures, Bronshtein and Kurelenkova (2009)'s risk measures.
Provides a structured approach to assess the quality and trustworthiness of R packages 
    (documentation, testing, popularity, dependencies), supporting informed decisions in production 
    or research by highlighting strengths and potential risks in adoption or development.
A collection of functions related to the study of etiologic heterogeneity both across disease subtypes and across individual disease markers. The included functions allow one to quantify the extent of etiologic heterogeneity in the context of a case-control study, and provide p-values to test for etiologic heterogeneity across individual risk factors. Begg CB, Zabor EC, Bernstein JL, Bernstein L, Press MF, Seshan VE (2013) <doi:10.1002/sim.5902>.
Estimates flexible epidemiological effect measures including both differences and ratios using the parametric G-formula developed as an alternative to inverse probability weighting.  It is useful for estimating the impact of interventions in the presence of treatment-confounder-feedback. G-computation was originally described by Robbins (1986) <doi:10.1016/0270-0255(86)90088-6> and has been described in detail by Ahern, Hubbard, and Galea (2009) <doi:10.1093/aje/kwp015>; Snowden, Rose, and Mortimer (2011) <doi:10.1093/aje/kwq472>; and Westreich et al. (2012) <doi:10.1002/sim.5316>.
Calculates risk differences (or prevalence differences for 
    cross-sectional data) using generalized linear models with automatic 
    link function selection. Provides robust model fitting with fallback 
    methods, support for stratification and adjustment variables, inverse
    probability of treatment weighting (IPTW) for causal inference, and 
    publication-ready output formatting. Handles model convergence issues
    gracefully and provides confidence intervals using multiple approaches.
    Methods are based on approaches described in Mark W. Donoghoe and 
    Ian C. Marschner (2018) "logbin: An R Package for Relative Risk 
    Regression Using the Log-Binomial Model" <doi:10.18637/jss.v086.i09> 
    for robust GLM fitting, Peter C. Austin (2011) "An Introduction to 
    Propensity Score Methods for Reducing the Effects of Confounding in 
    Observational Studies" <doi:10.1080/00273171.2011.568786> for IPTW methods,
    and standard epidemiological methods for risk difference estimation as 
    described in Kenneth J. Rothman, Sander Greenland and Timothy L. Lash 
    (2008, ISBN:9780781755641) "Modern Epidemiology".
Geostatistical analysis of continuous and count data.
  Implements stationary Gaussian processes with Matérn correlation for spatial prediction,
  as described in Diggle and Giorgi (2019, ISBN: 978-1-138-06102-7).
Facilities for assessing R packages against a number of metrics to 
    help quantify their robustness.
Fast design of risk parity portfolios for financial investment.
    The goal of the risk parity portfolio formulation is to equalize or distribute
    the risk contributions of the different assets, which is missing if we simply
    consider the overall volatility of the portfolio as in the mean-variance
    Markowitz portfolio. In addition to the vanilla formulation, where the risk
    contributions are perfectly equalized subject to no shortselling and budget
    constraints, many other formulations are considered that allow for box
    constraints and shortselling, as well as the inclusion of additional
    objectives like the expected return and overall variance. See vignette for
    a detailed documentation and comparison, with several illustrative examples.
    The package is based on the papers:
    Y. Feng, and D. P. Palomar (2015). SCRIP: Successive Convex Optimization Methods
    for Risk Parity Portfolio Design. IEEE Trans. on Signal Processing, vol. 63,
    no. 19, pp. 5285-5300. <doi:10.1109/TSP.2015.2452219>.
    F. Spinu (2013), An Algorithm for Computing Risk Parity Weights.
    <doi:10.2139/ssrn.2297383>.
    T. Griveau-Billion, J. Richard, and T. Roncalli (2013). A fast algorithm for computing
    High-dimensional risk parity portfolios. <arXiv:1311.4057>.
Collection of functions designed to compute risk-based portfolios as described 
    in Ardia et al. (2017) <doi:10.1007/s10479-017-2474-7> and Ardia et al. (2017) <doi:10.21105/joss.00171>.
Assessing and comparing risk prediction rules for clustered data. The method is based on the paper: Rosner B, Qiu W, and Lee MLT.(2013) <doi: 10.1007/s10985-012-9240-6>.
Implementation of the following methods for event history analysis.
    Risk regression models for survival endpoints also in the presence of competing
    risks are fitted using binomial regression based on a time sequence of binary
    event status variables. A formula interface for the Fine-Gray regression model
    and an interface for the combination of cause-specific Cox regression models.
    A toolbox for assessing and comparing performance of risk predictions (risk
    markers and risk prediction models). Prediction performance is measured by the
    Brier score and the area under the ROC curve for binary possibly time-dependent
    outcome. Inverse probability of censoring weighting and pseudo values are used
    to deal with right censored data. Lists of risk markers and lists of risk models
    are assessed simultaneously. Cross-validation repeatedly splits the data, trains
    the risk prediction models on one part of each split and then summarizes and
    compares the performance across splits.
Risk ratios and risk differences are estimated using regression 
  models that allow for binary, categorical, and continuous exposures and 
  confounders. Implemented are marginal standardization after fitting logistic 
  models (g-computation) with delta-method and bootstrap standard errors, 
  Miettinen's case-duplication approach (Schouten et al. 1993, 
  <doi:10.1002/sim.4780121808>), log-binomial (Poisson) models with empirical 
  variance (Zou 2004, <doi:10.1093/aje/kwh090>), binomial models with starting 
  values from Poisson models (Spiegelman and Hertzmark 2005, 
  <doi:10.1093/aje/kwi188>), and others.
Implements an optimized approach to learning risk score models, where sparsity and integer constraints are integrated into the model-fitting process.
A tool to calculate Cardiovascular Risk Scores in large data frames as published in Perez-Vicencio, et al (2024) <doi:10.1136/openhrt-2024-002755>. Cardiovascular risk scores are statistical tools used to assess an individual's likelihood of developing a cardiovascular disease based on various risk factors, such as age, gender, blood pressure, cholesterol levels, and smoking. Here we bring together the six most commonly used in the emergency department. Using 'RiskScorescvd', you can calculate all the risk scores in an extended dataset in seconds. PCE (ASCVD) described in Goff, et al (2013) <doi:10.1161/01.cir.0000437741.48606.98>. EDACS described in Mark DG, et al (2016) <doi:10.1016/j.jacc.2017.11.064>. GRACE described in Fox KA, et al (2006) <doi:10.1136/bmj.38985.646481.55>. HEART is described in Mahler SA, et al (2017) <doi:10.1016/j.clinbiochem.2017.01.003>. SCORE2/OP described in SCORE2 working group and ESC Cardiovascular risk collaboration (2021) <doi:10.1093/eurheartj/ehab309>. TIMI described in Antman EM, et al (2000) <doi:10.1001/jama.284.7.835>. SCORE2-Diabetes described in SCORE2-Diabetes working group and ESC Cardiovascular risk collaboration (2023) <doi:10.1093/eurheartj/ehab260>. SCORE2/OP with CKD add-on described in Kunihiro M et al (2022) <doi:10.1093/eurjpc/zwac176>.
Compute time-dependent Incident/dynamic accuracy measures
        (ROC curve, AUC, integrated AUC )from censored survival data
        under proportional or non-proportional hazard assumption of
        Heagerty & Zheng (Biometrics, Vol 61 No 1, 2005, PP 92-105).
Implements efficient simulation procedures to estimate tail loss probabilities and conditional excess for a stock portfolio. The log-returns are assumed to follow a t-copula model with generalized hyperbolic or t marginals.
Risk-related information (like the prevalence of conditions, the sensitivity and specificity of diagnostic tests, or the effectiveness of interventions or treatments) can be expressed in terms of frequencies or probabilities. By providing a toolbox of corresponding metrics and representations, 'riskyr' computes, translates, and visualizes risk-related information in a variety of ways. Adopting multiple complementary perspectives provides insights into the interplay between key parameters and renders teaching and training programs on risk literacy more transparent (see <doi:10.3389/fpsyg.2020.567817>, for details).
A set of tools to extract bibliographic content from the National
    Center for Biotechnology Information (NCBI) databases, including PubMed. The
    name RISmed is a portmanteau of RIS (for Research Information Systems, a common
    tag format for bibliographic data) and PubMed.
Automated performance of common transformations used to fulfill parametric
    assumptions of normality and identification of the best performing method 
    for the user. Output for various normality tests (Thode, 2002) corresponding 
    to the best performing method and a descriptive statistical report of the 
    input data in its original units (5-number summary and mathematical moments) 
    are also presented. Lastly, the Rankit, an empirical normal quantile transformation 
    (ENQT) (Soloman & Sawilowsky, 2009), is provided to accommodate non-standard 
    use cases and facilitate adoption.  
    <DOI: 10.1201/9780203910894>.
    <DOI: 10.22237/jmasm/1257034080>.
A programmatic interface to the Web Service methods provided by ITALIC (<https://italic.units.it>).
    ITALIC is a database of lichen data in Italy and bordering European countries. 'ritalic' includes functions for retrieving information
    about lichen scientific names, geographic distribution, ecological data, morpho-functional traits and identification keys.
    More information about the data is available at <https://italic.units.it/?procedure=base&t=59&c=60>.
    The API documentation is available at <https://italic.units.it/?procedure=api>.
Selected functions for simulation and regression of integrated Isothermal Titration Calorimetry (ITC) data with the most commonly used one-to-one binding model.
An interface to the Integrated Taxonomic Information System ('ITIS')
    (<https://www.itis.gov>). Includes functions to work with the 'ITIS' REST
    API methods (<https://www.itis.gov/ws_description.html>), as well as the
    'Solr' web service (<https://www.itis.gov/solr_documentation.html>).
Tools for randomization-based inference. Current focus is on the d^2 omnibus test of differences of means following Hansen and Bowers (2008)  <doi:10.1214/08-STS254> . This test is useful for assessing balance in matched observational studies or for analysis of outcomes in block-randomized experiments.
Finds a robust instrumental variables estimator using a
             high breakdown point S-estimator of multivariate location
             and scatter matrix.
Generates graphs, CSV files, and coordinates related to river valleys when calling the riverbuilder() function.
Indices for assessing riverscape fragmentation, including the Dendritic Connectivity Index, the Population Connectivity Index, the River Fragmentation Index, the Probability of Connectivity, and the Integral Index of connectivity. For a review, see Jumani et al. (2020) <doi:10.1088/1748-9326/abcb37> and Baldan et al. (2022) <doi:10.1016/j.envsoft.2022.105470> Functions to calculate temporal indices improvement when fragmentation due to barriers is reduced are also included.
Reads river network shape files and computes network distances.
    Also included are a variety of computation and graphical tools designed
    for fisheries telemetry research, such as minimum home range, kernel density
    estimation, and clustering analysis using empirical k-functions with
    a bootstrap envelope.  Tools are also provided for editing the river
    networks, meaning there is no reliance on external software.
Functions for reading, analysing and plotting river networks.
  For this package, river networks consist of sections and nodes with associated attributes, 
  e.g. to characterise their morphological, chemical and biological state.
  The package provides functions to read this data from text files, to analyse the network
  structure and network paths and regions consisting of sections and nodes that fulfill
  prescribed criteria, and to plot the river network and associated properties.
It is devoted to the IVIVC linear level A with numerical deconvolution method. The latter is working for inequal and incompatible timepoints between impulse and response curves. A numerical convolution method is also available. Application domains include pharamaceutical industry QA/QC and R&D together with academic research.
Seamless extraction of river networks from digital 
    elevation models data. The package allows analysis of digital 
	elevation models that can be either externally provided or
	downloaded from open source repositories (thus interfacing
	with the 'elevatr' package). Extraction is performed via the 
	'D8' flow direction algorithm of TauDEM (Terrain Analysis Using 
	Digital Elevation Models), thus interfacing with the 'traudem' 
	package. Resulting river networks are compatible with functions 
	from the 'OCNet' package. See Carraro (2023) 
	<doi:10.5194/hess-27-3733-2023> for a presentation of the package.
A tool for undergraduate and graduate courses in open-channel
    hydraulics. Provides functions for computing normal and critical depths,
    steady-state water surface profiles (e.g. backwater curves) and unsteady flow
    computations (e.g. flood wave routing) as described in
    Koohafkan MC, Younis BA (2015). "Open-channel computation with R."
    The R Journal, 7(2), 249–262. <doi: 10.32614/RJ-2015-034>.
Provides access to global river gauge data from a variety of national-level river agencies. The package interfaces with the national-level agency websites to provide access to river gauge locations, river discharge, and river stage. Currently, the package is available for the following countries: Australia, Brazil, Canada, Chile, France, Japan, South Africa, the United Kingdom, and the United States.
Simplifies the creation of reproducible data science environments
    using the 'Nix' package manager, as described in
    Dolstra (2006) <ISBN 90-393-4130-3>. The included `rix()`
    function generates a complete description of the environment as a
    `default.nix` file, which can then be built using 'Nix'. This results in
    project specific software environments with pinned versions of R, packages,
    linked system dependencies, and other tools or programming languages such
    as Python or Julia. Additional helpers make it easy to run R code in
    'Nix' software environments for testing and production.
Streamlines the creation of reproducible analytical pipelines using
  'default.nix' expressions generated via the 'rix' package for reproducibility. Define
  derivations in 'R', 'Python' or 'Julia', chain them into a composition of pure
  functions and build the resulting pipeline using 'Nix' as the underlying
  end-to-end build tool. Functions to plot the pipeline as a directed acyclic
  graph are included, as well as functions to load and inspect intermediary results
  for interactive analysis. User experience heavily inspired by the 'targets' package.
Jade is a high performance template engine heavily influenced by
    Haml and implemented with JavaScript for node and browsers.
Personalized assignment to one of many treatment arms via regularized and clustered joint assignment forests as described in Ladhania, Spiess, Ungar, and Wu (2023) <doi:10.48550/arXiv.2311.00577>. The algorithm pools information across treatment arms: it considers a regularized forest-based assignment algorithm based on greedy recursive partitioning that shrinks effect estimates across arms; and it incorporates a clustering scheme that combines treatment arms with consistently similar outcomes.
Jalali calendar, or solar Hijri, is calendar of Iran and
    Afghanistan (<https://en.wikipedia.org/wiki/Solar_Hijri_calendar>).
    This package is designed to working with Jalali date. For this
    purpose, It defines JalaliDate class that is similar to Date class.
Low-level interface to Java VM very much like .C/.Call and friends. Allows creation of objects, calling methods and accessing fields.
Quickly install 'Java Development Kit (JDK)' without
    administrative privileges and set environment variables in current R
    session or project to solve common issues with 'Java' environment
    management in 'R'. Recommended to users of 'Java'/'rJava'-dependent
    'R' packages such as 'r5r', 'opentripplanner', 'xlsx', 'openNLP',
    'rWeka', 'RJDBC', 'tabulapdf', and many more. 'rJavaEnv' prevents
    common problems like 'Java' not found, 'Java' version conflicts,
    missing 'Java' installations, and the inability to install 'Java' due
    to lack of administrative privileges.  'rJavaEnv' automates the
    download, installation, and setup of the 'Java' on a per-project basis
    by setting the relevant 'JAVA_HOME' in the current 'R' session or the
    current working directory (via '.Rprofile', with the user's consent).
    Similar to what 'renv' does for 'R' packages, 'rJavaEnv' allows
    different 'Java' versions to be used across different projects, but
    can also be configured to allow multiple versions within the same
    project (e.g.  with the help of 'targets' package). Note: there are a
    few extra steps for 'Linux' users, who don't have any 'Java'
    previously installed in their system, and who prefer package
    installation from source, rather then installing binaries from 'Posit
    Package Manager'. See documentation for details.
The RJDBC package is an implementation of R's DBI interface using JDBC as a back-end. This allows R to connect to any DBMS that has a JDBC driver.
Interface around 'JDemetra+' (<https://github.com/jdemetra/jdemetra-app>), the seasonal adjustment software officially
    recommended to the members of the European Statistical System (ESS) and the European System of Central Banks.
    It offers full access to all options and outputs of 'JDemetra+', including the two leading seasonal adjustment methods
    TRAMO/SEATS+ and X-12ARIMA/X-13ARIMA-SEATS.
Functions to have nice 'rmarkdown' outputs of the 
  seasonal and trading day adjustment models made with 'RJDemetra'.
Add-in to the 'RJDemetra' package on seasonal adjustments.
    It allows to produce dashboards to summarise models and quickly check the quality of the seasonal adjustment.
Set of tools to manipulate the 'JDemetra+' workspaces.  Based
    on the 'RJDemetra' package (which interfaces with version 2 of the
    'JDemetra+' (<https://github.com/jdemetra/jdemetra-app>), the seasonal
    adjustment software officially recommended to the members of the
    European Statistical System (ESS) and the European System of Central
    Banks).  This package provides access to additional workspace
    manipulation functions such as metadata manipulation, raw paths and
    wrangling of several workspaces simultaneously.  These additional
    functionalities are useful as part of a CVS data production chain.
A series of functions in some way considered useful to the author.  These 
    include methods for subsetting tables and generating indices for arrays, 
    conditioning and intervening in probability distributions, generating 
    combinations, fast transformations, and more...
Implementation of the Johnson Quantile-Parameterised Distribution in R. 
  The Johnson Quantile-Parameterised Distribution (J-QPD) is a flexible distribution
  system that is parameterised by a symmetric percentile triplet of quantile values
  (typically the 10th-50th-90th) along with known support bounds for the distribution.
  The J-QPD system was developed by Hadlock and Bickel (2017) <doi:10.1287/deca.2016.0343>.
  This package implements the density, quantile, CDF and random number generator functions.
Provides functions to retrieve data and metadata from providers 
			 that disseminate data by means of SDMX web services. 
			 SDMX (Statistical Data and Metadata eXchange) is a standard that 
			 has been developed with the aim of simplifying the exchange of 
			 statistical information. 
			 More about the SDMX standard and the SDMX Web Services 
			 can be found at: <https://sdmx.org>.
Converts R object into JSON objects and vice-versa.
Functions to query (filter or transform), pivot (convert
    from array-of-objects to object-of-arrays, for easy import as 'R'
    data frame), search, patch (edit), and validate (against 'JSON Schema')
    'JSON' and 'NDJSON' strings, files, or URLs. Query and
    pivot support 'JSONpointer', 'JSONpath' or 'JMESpath'
    expressions. The implementation uses the 'jsoncons'
    <https://danielaparker.github.io/jsoncons/> header-only library;
    the library is easily linked to other packages for direct access
    to 'C++' functionality not implemented here.
This is a package that allows conversion to and from 
  data in Javascript object notation (JSON) format.
  This allows R objects to be inserted into Javascript/ECMAScript/ActionScript code
  and allows R programmers to read and convert JSON content to R objects.
  This is an alternative to rjson package. Originally, that was too slow for converting large R objects to JSON
  and was not extensible.  rjson's performance is now similar to this package, and perhaps slightly faster in some cases.
  This package uses methods and is readily extensible by defining methods for different classes, 
  vectorized operations, and C code and callbacks to R functions for deserializing JSON objects to R. 
  The two packages intentionally share the same basic interface. This package (RJSONIO) has many additional
  options to allow customizing the generation and processing of JSON content.
  This package uses libjson rather than implementing yet another JSON parser. The aim is to support
  other general projects by building on their work, providing feedback and benefit from their ongoing development.
Creates interactive graphs with 'R'. It joins the data analysis power of R and the visualization libraries of JavaScript in one package.
Handle 'JSON-stat' format (<https://json-stat.org>) in R.
    Not all features are supported, especially the extensive metadata
    features of 'JSON-stat'.
Create an 'R Journal' 'Rmarkdown' template article, that will
  generate html and pdf versions of your paper. Check that the paper folder 
  has all the required components needed for submission. 
  Examples of 'R Journal' publications can be found at 
  <https://journal.r-project.org>.
Provides an R interface to the 'JuliaBUGS.jl' package (<https://github.com/TuringLang/JuliaBUGS.jl>) for Bayesian inference using the BUGS modeling language. Allows R users to run models in Julia and return results as familiar R objects. Visualization and posterior analysis are supported via the 'bayesplot' and 'posterior' packages.
'JDemetra+' (<https://github.com/jdemetra/jdemetra-app>) is the seasonal adjustment software officially recommended
  to the members of the European Statistical System and the European System of Central Banks. Seasonal adjustment models performed
  with 'JDemetra+' can be stored into workspaces. 'JWSACruncher' (<https://github.com/jdemetra/jwsacruncher/releases> for v2 and <https://github.com/jdemetra/jdplus-main/releases> for v3) is a console tool that 
  re-estimates all the multi-processing defined in a workspace and to export the result. 'rjwsacruncher' allows to launch easily the 'JWSACruncher'.
The 'rkafkajars' package collects all the external jars required for the 'rkafka' package.
Easily download datasets from Kaggle <https://www.kaggle.com/> directly into your R environment using 'RKaggle'. Streamline your data analysis workflows by importing datasets effortlessly and focusing on insights rather than manual data handling. Perfect for data enthusiasts and professionals looking to integrate Kaggle datasets into their R projects with minimal hassle.
An R interface to KEA (Version 5.0).
  KEA (for Keyphrase Extraction Algorithm) allows for extracting
  keyphrases from text documents. It can be either used for free
  indexing or for indexing with a controlled vocabulary. For more
  information see <http://www.nzdl.org/Kea/>.
External jars required for package RKEA.
'KEEL' is a popular 'Java' software for a large number of different knowledge data discovery tasks.
    This package takes the advantages of 'KEEL' and R, allowing to use 'KEEL' algorithms in simple R code.
    The implemented R code layer between R and 'KEEL' makes easy both using 'KEEL' algorithms in R as implementing new algorithms for 'RKEEL' in a very simple way.
    It includes more than 100 algorithms for classification, regression, preprocess, association rules and imbalance learning, which allows a more complete experimentation process.
    For more information about 'KEEL', see <http://www.keel.es/>.
'KEEL' is a popular Java software for a large number of different knowledge data discovery tasks. Furthermore, 'RKEEL' is a package with a R code layer between R and 'KEEL', for using 'KEEL' in R code. This package includes the datasets from 'KEEL' in .dat format for its use in 'RKEEL' package. For more information about 'KEEL', see <http://www.keel.es/>.
Calculates the Kelly criterion (Kelly, J.L. (1956) <doi:10.1002/j.1538-7305.1956.tb03809.x>) for bets given quoted prices, model predictions and commissions.
    Additionally it contains helper functions to calculate the probabilities for wins and draws in multi-leg games.
Exchange rate for Kenya Shilling against other currencies, US DOLLAR, EURO, STERLING POUND, Tanzania Shilling, Uganda Shilling.
Estimates the Hoeffding decomposition of an unknown function by solving ridge group sparse optimization problem based on reproducing kernel Hilbert spaces, and approximates its sensitivity indices (see Kamari, H., Huet, S. and Taupin, M.-L. (2019) <arXiv:1905.13695>).
Applies methods used to estimate animal homerange, but
    instead of geospatial coordinates, we use isotopic coordinates. The estimation
    methods include: 1) 2-dimensional bivariate normal kernel utilization density
    estimator, 2) bivariate normal ellipse estimator, and 3) minimum convex polygon
    estimator, all applied to stable isotope data. Additionally, functions to
    determine niche area, polygon overlap between groups and levels (confidence
    contours) and plotting capabilities.
Hybrid Mortality Modelling (HMM) provides a framework in which mortality around "the accident hump" and at very old ages can be modelled under a single model. The graphics' codes necessary for visualization of the models' output are included here. Specifically, the graphics are based on the assumption that, the mortality rates can be expressed as a function of the area under the curve between the crude mortality rates plots and the tangential transform of the force of mortality.
Methods for downloading and processing data and metadata from 'Kolada', the official Swedish regions and municipalities database <https://www.kolada.se/>.
This is a analysis toolkit to streamline the analyses of minicircle sequence diversity in population-scale genome projects. rKOMICS is a user-friendly R package that has simple installation requirements and that is applicable to all 27 trypanosomatid genera. Once minicircle sequence alignments are generated, rKOMICS allows to examine, summarize and visualize minicircle sequence diversity within and between samples through the analyses of minicircle sequence clusters. We showcase the functionalities of the (r)KOMICS tool suite using a whole-genome sequencing dataset from a recently published study on the history of diversification of the Leishmania braziliensis species complex in Peru. Analyses of population diversity and structure highlighted differences in minicircle sequence richness and composition between Leishmania subspecies, and between subpopulations within subspecies. The rKOMICS package establishes a critical framework to manipulate, explore and extract biologically relevant information from mitochondrial minicircle assemblies in tens to hundreds of samples simultaneously and efficiently. This should facilitate research that aims to develop new molecular markers for identifying species-specific minicircles, or to study the ancestry of parasites for complementary insights into their evolutionary history. ***** !! WARNING: this package relies on dependencies from Bioconductor. For Mac users, this can generate errors when installing rKOMICS. Install Bioconductor and ComplexHeatmap at advance: install.packages("BiocManager"); BiocManager::install("ComplexHeatmap") *****.
A client package that makes the 'KorAP' web service API accessible from R. The corpus analysis platform 'KorAP' has been developed as a scientific tool to make potentially large, stratified and multiply annotated corpora, such as the 'German Reference Corpus DeReKo' or the 'Corpus of the Contemporary Romanian Language CoRoLa', accessible for linguists to let them verify hypotheses and to find interesting patterns in real language use. The 'RKorAPClient' package provides access to 'KorAP' and the corpora behind it for user-created R code, as a programmatic alternative to the 'KorAP' web user-interface. You can learn more about 'KorAP' and use it directly on 'DeReKo' at <https://korap.ids-mannheim.de/>.
An 'Eigen'-based computationally efficient 'C++' implementation for fitting various kriging models to data. This research is supported by U.S. National Science Foundation grant DMS-2310637.
Contains function rkt which computes the Mann-Kendall test (MK) and the Seasonal and the Regional Kendall Tests for trend (SKT and RKT) and  Theil-Sen's slope estimator.
Robust  kernel center matrix, robust  kernel cross-covariance operator for kernel unsupervised methods, kernel canonical correlation analysis, 
 influence function of identifying significant outliers or atypical objects from multimodal datasets. Alam, M. A,  Fukumizu, K., Wang  Y.-P. (2018) <doi:10.1016/j.neucom.2018.04.008>.
   Alam, M. A,  Calhoun, C. D.,  Wang  Y.-P. (2018) <doi:10.1016/j.csda.2018.03.013>.
This package provides functionality to read files
    containing observations which consist of arbitrary key/value
    pairs.
Provides functions and datasets required for the ST 370 course at North Carolina State University.
The 'LabKey' client library for R makes it easy for R users to
    load live data from a 'LabKey' Server, <https://www.labkey.com/>,
    into the R environment for analysis, provided users have permissions
    to read the data. It also enables R users to insert, update, and
    delete records stored on a 'LabKey' Server, provided they have appropriate
    permissions to do so.
Standardized methods for calculating common important derived
    physical features of lakes including water density based based on
    temperature, thermal layers, thermocline depth, lake number, Wedderburn
    number, Schmidt stability and others.
Provides access to a suite of geospatial data layers for wildfire management, fuel modeling, ecology, natural resource management, climate, conservation, etc., via the 'LANDFIRE' (<https://www.landfire.gov/>) Product Service ('LFPS') API.
A toolbox for working with base types, core R features
  like the condition system, and core 'Tidyverse' features like tidy
  evaluation.
Read and write 'las' and 'laz' binary file formats. The LAS file format is a public file format for the interchange of 3-dimensional point cloud data between data users. The LAS specifications are approved by the American Society for Photogrammetry and Remote Sensing <https://www.asprs.org/divisions-committees/lidar-division/laser-las-file-format-exchange-activities>. The LAZ file format is an open and lossless compression scheme for binary LAS format versions 1.0 to 1.4 <https://laszip.org/>.
An easy-to-use tool to employ interactivity in every-day exploratory analysis. It contains 
	a collection of most commonly used types of charts (such as scatter plots, line plots, heatmaps, bar charts),
	which can be linked to each other or to other interactive elements with just few lines of code.
Robust likelihood cross validation bandwidth for uni- and multi-variate kernel densities. It is robust against fat-tailed distributions and/or outliers. Based on "Robust Likelihood Cross-Validation for Kernel Density Estimation," Wu (2019) <doi:10.1080/07350015.2018.1424633>.
Linguistic Descriptions of Complex Phenomena (LDCP) is an architecture and methodology that allows us to model complex phenomena, interpreting input data, and generating automatic text reports customized to the user needs (see <doi:10.1016/j.ins.2016.11.002> and <doi:10.1007/s00500-016-2430-5>). The proposed package contains a set of methods that facilitates the development of LDCP systems. It main goal is increasing the visibility and practical use of this research line.
Common 'base' and 'stats' methods for 'rle' objects, aiming to make it possible to treat them transparently as vectors.
Leaf angle distribution is described by a number of functions
    (e.g. ellipsoidal, Beta and rotated ellipsoidal). The parameters of leaf angle
    distributions functions are estimated through different empirical relationship.
    This package includes estimations of parameters of different leaf angle
    distribution function, plots and evaluates leaf angle distribution functions,
    calculates extinction coefficients given leaf angle distribution.
    Reference: Wang(2007)<doi:10.1016/j.agrformet.2006.12.003>.
Display spatial data with interactive maps powered by the open-
    source JavaScript library 'Leaflet' (see <https://leafletjs.com/>). Maps can be
    rendered in a web browser or displayed in the HTML viewer pane of 'RStudio'.
    This package is designed to be easy to use and can create complex maps with
    vector and raster data, web served map tiles and interface elements.
Provides an interface to the C implementation of the
        random number generator with multiple independent streams
        developed by L'Ecuyer et al (2002). The main purpose of this
        package is to enable the use of this random number generator in
        parallel R applications.
Allows easy access to the LEMON Graph Library set of algorithms, written in C++.
             See the LEMON project page at <https://lemon.cs.elte.hu/trac/lemon>.
             Current LEMON version is 1.3.1.
An implementation to compute an optimal dose escalation rule
    using deep reinforcement learning in phase I oncology trials
    (Matsuura et al. (2023) <doi:10.1080/10543406.2023.2170402>).
    The dose escalation rule can directly optimize the percentages of correct
    selection (PCS) of the maximum tolerated dose (MTD).
Converts LESS to CSS. 
    It uses V8 engine, where LESS parser is run. Functions for
    LESS text, file or folder conversion are provided.
    This work was supported by a junior grant research project by 
    Czech Science Foundation 'GACR' no. 'GJ18-04150Y'.
Contains functions for simulating the linear fractional stable motion according to the algorithm developed by Mazur and Otryakhin <doi:10.32614/RJ-2020-008> based on the method from Stoev and Taqqu (2004) <doi:10.1142/S0218348X04002379>, as well as functions for estimation of parameters of these processes introduced by Mazur, Otryakhin and Podolskij (2018) <arXiv:1802.06373>, and also different related quantities.
Assists in the manipulation and processing of linear features with the help of the 'sf' package.
  Makes use of linear referencing to extract data from most shape files.
  Reference for this packages methods: Albeke, S.E. et al. (2010) <doi:10.1007/s10980-010-9528-4>.
An implementation of a number of Global Trend models for time series forecasting 
    that are Bayesian generalizations and extensions of some Exponential Smoothing models. 
    The main differences/additions include 1) nonlinear global trend, 2) Student-t error 
    distribution, and 3) a function for the error size, so heteroscedasticity. The methods 
    are particularly useful for short time series. When tested on the well-known M3 dataset,
    they are able to outperform all classical time series algorithms. The models are fitted 
    with MCMC using the 'rstan' package.
An R interface for libeemd (Luukko, Helske, Räsänen, 2016) <doi:10.1007/s00180-015-0603-9>, 
    a C library of highly efficient parallelizable functions  for performing the ensemble empirical mode decomposition (EEMD), 
    its complete variant (CEEMDAN), the regular empirical mode decomposition (EMD), and bivariate EMD (BEMD). 
    Due to the possible portability issues CRAN version no longer supports OpenMP, but you can install OpenMP-supported version 
    from GitHub: <https://github.com/helske/Rlibeemd/>.
Unlock the power of large-scale geospatial analysis, 
    quickly generate high-resolution kernel density visualizations, 
    supporting advanced analysis tasks such as bandwidth-tuning and spatiotemporal analysis. 
    Regardless of the size of your dataset, our library delivers efficient and accurate results.
    Tsz Nam Chan, Leong Hou U, Byron Choi, Jianliang Xu, Reynold Cheng (2023) <doi:10.1145/3555041.3589401>.
    Tsz Nam Chan, Rui Zang, Pak Lon Ip, Leong Hou U, Jianliang Xu (2023) <doi:10.1145/3555041.3589711>.
    Tsz Nam Chan, Leong Hou U, Byron Choi, Jianliang Xu (2022) <doi:10.1145/3514221.3517823>.
    Tsz Nam Chan, Pak Lon Ip, Kaiyan Zhao, Leong Hou U, Byron Choi, Jianliang Xu (2022) <doi:10.14778/3554821.3554855>.
    Tsz Nam Chan, Pak Lon Ip, Leong Hou U, Byron Choi, Jianliang Xu (2022) <doi:10.14778/3503585.3503591>.
    Tsz Nam Chan, Pak Lon Ip, Leong Hou U, Byron Choi, Jianliang Xu (2022) <doi:10.14778/3494124.3494135>.
    Tsz Nam Chan, Pak Lon Ip, Leong Hou U, Weng Hou Tong, Shivansh Mittal, Ye Li, Reynold Cheng (2021) <doi:10.14778/3476311.3476312>.
    Tsz Nam Chan, Zhe Li, Leong Hou U, Jianliang Xu, Reynold Cheng (2021) <doi:10.14778/3461535.3461540>.
    Tsz Nam Chan, Reynold Cheng, Man Lung Yiu (2020) <doi:10.1145/3318464.3380561>.
    Tsz Nam Chan, Leong Hou U, Reynold Cheng, Man Lung Yiu, Shivansh Mittal (2020) <doi:10.1109/TKDE.2020.3018376>.
    Tsz Nam Chan, Man Lung Yiu, Leong Hou U (2019) <doi:10.1109/ICDE.2019.00055>.
Interface to 'libKriging' 'C++' library <https://github.com/libKriging> that should
    provide most standard Kriging / Gaussian process regression features
    (like in 'DiceKriging', 'kergp' or 'RobustGaSP' packages).
    'libKriging' relies on Armadillo linear algebra library (Apache 2 license) by Conrad Sanderson, 
    'lbfgsb_cpp' is a 'C++' port around by Pascal Have of 'lbfgsb' library (BSD-3 license) by 
    Ciyou Zhu, Richard Byrd, Jorge Nocedal and Jose Luis Morales used for hyperparameters optimization.
Uses an extension of nonnegative matrix factorization to identify shared and dataset-specific factors. See Welch J, Kozareva V, et al (2019) <doi:10.1016/j.cell.2019.05.006>, and Liu J, Gao C, Sodicoff J, et al (2020) <doi:10.1038/s41596-020-0391-8> for more details.
A series of functions that allow users
    to access the 'LinkedIn' API to get information about connections,
    search for people and jobs, share updates with their network,
    and create group discussions.  For more information about using
    the API please visit <https://developer.linkedin.com/>.
Get data from 'Linkedin Advertising API' <https://learn.microsoft.com/en-us/linkedin/marketing/overview?view=li-lms-2023-10>.
    You can load ad account hierarchy (accounts, users, campaign groups, campaigns and creatives) and
    also you can load ad analytics data from your 'Linkedin' Ad account.
Solving a system of linear equations is one of the most fundamental
    computational problems for many fields of mathematical studies, such as
    regression problems from statistics or numerical partial differential equations.
    We provide basic stationary iterative solvers such as Jacobi, Gauss-Seidel,
    Successive Over-Relaxation and SSOR methods. Nonstationary, also known as
	Krylov subspace methods are also provided. Sparse matrix computation is also supported
    in that solving large and sparse linear systems can be manageable using 'Matrix' package
    along with 'RcppArmadillo'. For a more detailed description, see a book by Saad (2003)
    <doi:10.1137/1.9780898718003>.
Provides a set of functions for data manipulation with
    list objects, including mapping, filtering, grouping, sorting,
    updating, searching, and other useful functions. Most functions
    are designed to be pipeline friendly so that data processing with
    lists can be chained.
Listings are often part of the submission of clinical trial
    data in regulatory settings.  We provide a framework for the specific
    formatting features often used when displaying large datasets in that
    context.
Data driven approach for robust regression estimation in homoscedastic and heteroscedastic context. See Wang et al. (2007), <doi:10.1198/106186007X180156> regarding homoscedastic framework.
Estimates robust rank-based fixed effects and predicts robust
    random effects in two- and three- level random effects nested models.
    The methodology is described in Bilgic & Susmann (2013) <https://journal.r-project.org/archive/2013/RJ-2013-027/>.
R parallel implementation of Local Outlier Factor(LOF) which uses multiple CPUs to significantly speed up the LOF computation for large datasets. (Note: The overall performance depends on the computers especially the number of the cores).It also supports multiple k values to be calculated in parallel, as well as various distance measures in addition to the default Euclidean distance.
A very lightweight package that writes out log messages in an opinionated way.
  Simpler and lighter than other logging packages, 'rlog' provides a compact feature set that
  focuses on getting the job done in a Unix-like way.
Processing logical operations such as AND/OR/NOT operations
    dynamically. It also handles nesting in the operations.
An implementation to compute an optimal adaptive allocation rule
    using deep reinforcement learning in a dose-response study
    (Matsuura et al. (2022) <doi:10.1002/sim.9247>).
    The adaptive allocation rule can directly optimize a performance metric,
    such as power, accuracy of the estimated target dose, or mean absolute error
    over the estimated dose-response curve.
The goal of 'rlowdb' is to provide a lightweight, file-based JSON database. 
    Inspired by 'LowDB' in 'JavaScript', it generates an intuitive interface for
    storing, retrieving, updating, and querying structured data without requiring a full-fledged database system. 
    Ideal for prototyping, small-scale applications, and lightweight data management needs.
Rapid, simulation-based exact (restricted) likelihood ratio tests
    for testing the presence of variance components/nonparametric terms for
    models fit with nlme::lme(),lme4::lmer(), lmeTest::lmer(), gamm4::gamm4(),
    mgcv::gamm() and SemiPar::spm().
Random forest with a variety of additional features for regression, classification and survival analysis. 
             The features include: parallel computing with OpenMP, embedded model for selecting the splitting variable,
             based on Zhu, Zeng & Kosorok (2015) <doi:10.1080/01621459.2015.1036994>, subject weight, variable weight, 
             tracking subjects used in each tree, etc.
R interface to the 'LTP'-Cloud service for Natural Language Processing
    in Chinese (http://www.ltp-cloud.com/).
A collection of functions to simulate luminescence production in 
    dosimetric materials using Monte Carlo methods. Implemented are models for 
    delocalised transitions (e.g., Chen and McKeever (1997) <doi:10.1142/2781>), 
    localised transitions (e.g., Pagonis et al. (2019) <doi:10.1016/j.jlumin.2018.11.024>) 
    and tunnelling transitions (Jain et al. (2012) <doi:10.1088/0953-8984/24/38/385402> 
    and Pagonis et al. (2019) <doi:10.1016/j.jlumin.2018.11.024>). 
    Supported stimulation methods are thermal luminescence (TL), 
    continuous-wave optically stimulated luminescence (CW-OSL), 
    linearly-modulated optically stimulated luminescence (LM-OSL), 
    linearly-modulated infrared stimulated luminescence (LM-IRSL),
    and isothermal luminescence (ITL or ISO-TL).
A collection of functions to simulate luminescence signals in quartz and Al2O3 based on published models.
A collection of 'shiny' applications for the R package
 'Luminescence'. These mainly, but not exclusively, include applications for
 plotting chronometric data from e.g. luminescence or radiocarbon dating. It
 further provides access to bootstraps tooltip and popover functionality and
 contains the 'jscolor.js' library with a custom 'shiny' output binding.
R implementation of the common parsing tools 'lex' and 'yacc'.
Rasch model and extensions for survey data, using Conditional Maximum likelihood (CML). Carlo Cafiero, Sara Viviani, Mark Nord (2018) <doi:10.1016/j.measurement.2017.10.065>.
Estimation of the conditional covariance matrix using the RiskMetrics 2006 methodology of Zumbach (2007) <doi:10.2139/ssrn.1420185>.
Compute an exact CI for the population mean under a random effects model. The routines implement the algorithm described in Michael, Thronton, Xie, and Tian (2017) <https://haben-michael.github.io/research/Exact_Inference_Meta.pdf>.
Work with the 'Macrostrat' (<https://macrostrat.org/>) Web Service 
    (v.2, <https://macrostrat.org/api/v2>) to fetch geological data relevant to
    the spatial and temporal distribution of sedimentary, igneous, and
    metamorphic rocks as well as data extracted from them.
Allows for production of Czekanowski's Diagrams with clusters. See K. Bartoszek, A. Vasterlund (2020) <doi:10.2478/bile-2020-0008> and K. Bartoszek, Y. Luo (2023) <doi:10.14708/ma.v51i2.7259>.
Creates and maintains a build process for complex analytic tasks in R.
  Package allows to easily generate Makefile for the (GNU) 'make' tool, which drives the build process
  by (in parallel) executing build commands in order to update results accordingly to given dependencies
  on changed data or updated source files.
An EM algorithm to fit Mallows' Models to full or partial rankings, with or without ties.
        Based on Adkins and Flinger (1998) <doi:10.1080/03610929808832223>.
An implementation of an algorithm family for continuous
    optimization called memetic algorithms with local search chains
    (MA-LS-Chains), as proposed in Molina et al. (2010) <doi:10.1162/evco.2010.18.1.18102> and Molina et al. (2011) <doi:10.1007/s00500-010-0647-2>. Rmalschains is further discussed in Bergmeir et al. (2016) <doi:10.18637/jss.v075.i04>. Memetic algorithms are hybridizations of genetic
    algorithms with local search methods. They are especially suited
    for continuous optimization.
Edit and simplify 'geojson', 'Spatial', and 'sf'
    objects.  This is wrapper around the 'mapshaper' 'JavaScript' library
    by Matthew Bloch <https://github.com/mbloch/mapshaper/> to perform
    topologically-aware polygon simplification, as well as other
    operations such as clipping, erasing, dissolving, and converting
    'multi-part' to 'single-part' geometries.
Provides an interface to 'Mapzen'-based APIs (including 
    geocode.earth, Nextzen, and NYC GeoSearch) for geographic search 
    and geocoding, isochrone calculation, and vector data to draw map tiles. 
    See <https://www.mapzen.com/documentation/> for more information. The original 
    Mapzen has gone out of business, but 'rmapzen' can be set up to work with 
    any provider who implements the Mapzen API.
A port of the C++ routine for applying the marching cubes algorithm written by 
    Thomas Lewiner et al. (2012) <doi:10.1080/10867651.2003.10487582> into an R package. 
    The package supplies the contour3d() function, which takes a 3-dimensional array of voxel 
    data and calculates the vertices, vertex normals, and faces for a 3d mesh representing 
    the contour(s) at a given level.
Three robust marginal integration procedures for additive models based on local 
             polynomial kernel smoothers. As a preliminary estimator of the multivariate 
             function for the marginal integration procedure, a first approach uses local 
             constant M-estimators, a second one uses local polynomials of order 1 over all the
             components of covariates, and the third one uses M-estimators based on local 
             polynomials but only in the direction of interest. For this last approach, 
             estimators of the derivatives of the additive functions can be obtained. All three
             procedures can compute predictions for points outside the training set if desired. 
             See Boente and Martinez (2017) <doi:10.1007/s11749-016-0508-0> for details.
Implements a DBI-compliant interface to MariaDB
    (<https://mariadb.org/>) and MySQL (<https://www.mysql.com/>)
    databases.
An interface to the software package MARK that constructs input
    files for MARK and extracts the output. MARK was developed by Gary White
    and is freely available at <http://www.phidot.org/software/mark/downloads/>
    but is not open source.
Convert R Markdown documents into a variety of formats.
For the calculation of sample size or power in a two-group repeated measures design, accounting for attrition and accommodating a variety of correlation structures for the repeated measures; details of the method can be found in the scientific paper: Donald Hedeker, Robert D. Gibbons, Christine Waternaux (1999) <doi:10.3102/10769986024001070>.
Simulate random matrices and ensembles and compute their eigenvalue spectra and dispersions.
Read and write 'Matlab' MAT files from R. The 'rmatio'
    package supports reading MAT version 4, MAT version 5 and MAT
    compressed version 5. The 'rmatio' package can write version 5 MAT
    files and version 5 files with variable compression.
S3 and S4 functions are implemented for spatial multi-site
    stochastic generation of daily time series of temperature and
    precipitation. These tools make use of Vector AutoRegressive models (VARs).
    The weather generator model is then saved as an object and is calibrated by
    daily instrumental "Gaussianized" time series through the 'vars' package
    tools. Once obtained this model, it can it can be used for weather
    generations and be adapted to work with several climatic monthly time
    series.
A Bayesian credible interval is interpreted with respect to posterior probability, 
    and this interpretation is far more intuitive than that of a frequentist confidence interval. 
    However, standard highest-density intervals can be wide due to between-subjects variability and tends 
    to hide within-subject effects, rendering its relationship with the Bayes factor less clear 
    in within-subject (repeated-measures) designs. 
    This urgent issue can be addressed by using within-subject intervals in within-subject designs, 
    which integrate four methods including the Wei-Nathoo-Masson (2023) <doi:10.3758/s13423-023-02295-1>, 
    the Loftus-Masson (1994) <doi:10.3758/BF03210951>, 
    the Nathoo-Kilshaw-Masson (2018) <doi:10.1016/j.jmp.2018.07.005>, 
    and the Heck (2019) <doi:10.31234/osf.io/whp8t> interval estimates.
Multiscale Curvature Classification of ground returns in 3-D LiDAR 
    point clouds, designed for forested environments. 'RMCC' is a porting to R of the 
    'MCC-lidar' method by Evans and Hudak (2007) <doi:10.1109/TGRS.2006.890412>.
Supporting decision making involving multiple criteria. Annice Najafi, Shokoufeh Mirzaei (2025) RMCDA: The Comprehensive R Library for applying multi-criteria decision analysis methods, Volume 24, e100762 <doi:10.1016/j.simpa.2025.100762>.
MCFS-ID (Monte Carlo Feature Selection and Interdependency Discovery) is a Monte Carlo method-based tool for feature selection. It also allows for the discovery of interdependencies between the relevant features. MCFS-ID is particularly suitable for the analysis of high-dimensional, 'small n large p' transactional and biological data. M. Draminski, J. Koronacki (2018) <doi:10.18637/jss.v085.i12>.
Collection of methods for rating matrix completion, which is a statistical framework for recommender systems. Another relevant application is the imputation of rating-scale survey data in the social and behavioral sciences. Note that matrix completion and imputation are synonymous terms used in different streams of the literature. The main functionality implements robust matrix completion for discrete rating-scale data with a low-rank constraint on a latent continuous matrix (Archimbaud, Alfons, and Wilms (2025) <doi:10.48550/arXiv.2412.20802>). In addition, the package provides wrapper functions for 'softImpute' (Mazumder, Hastie, and Tibshirani, 2010, <https://www.jmlr.org/papers/v11/mazumder10a.html>; Hastie, Mazumder, Lee, Zadeh, 2015, <https://www.jmlr.org/papers/v16/hastie15a.html>) for easy tuning of the regularization parameter, as well as benchmark methods such as median imputation and mode imputation.
Functions for simulating Markov chains using the Barker proposal
    to compute Markov chain Monte Carlo (MCMC) estimates of expectations with
    respect to a target distribution on a real-valued vector space. The Barker
    proposal, described in Livingstone and Zanella (2022)
    <doi:10.1111/rssb.12482>, is a gradient-based MCMC algorithm inspired by the
    Barker accept-reject rule. It combines the robustness of simpler MCMC
    schemes, such as random-walk Metropolis, with the efficiency of
    gradient-based methods, such as the Metropolis adjusted Langevin algorithm. 
    The key function provided by the package is sample_chain(), which allows
    sampling a Markov chain with a specified target distribution as its
    stationary distribution. The chain is sampled by generating proposals and
    accepting or rejecting them using a Metropolis-Hasting acceptance rule.
    During an initial warm-up stage, the parameters of the proposal distribution
    can be adapted, with adapters available to both: tune the scale of the
    proposals by coercing the average acceptance rate to a target value; tune
    the shape of the proposals to match covariance estimates under the target 
    distribution. As well as the default Barker proposal, the package also
    provides implementations of alternative proposal distributions, such as
    (Gaussian) random walk and Langevin proposals. Optionally, if 'BridgeStan's
    R interface <https://roualdes.us/bridgestan/latest/languages/r.html>,
    available on GitHub <https://github.com/roualdes/bridgestan>, is installed,
    then 'BridgeStan' can be used to specify the target distribution to sample
    from.
Compute the repeated measures correlation, a statistical technique
    for determining the overall within-individual relationship among paired measures
    assessed on two or more occasions, first introduced by Bland and Altman (1995).
    Includes functions for diagnostics, p-value, effect size with confidence
    interval including optional bootstrapping, as well as graphing. Also includes
    several example datasets. For more details, see the web documentation 
    <https://lmarusich.github.io/rmcorr/index.html> and the 
    original paper: Bakdash and Marusich (2017) <doi:10.3389/fpsyg.2017.00456>.
Provides tools to evaluate the value of using a risk prediction instrument to decide treatment or intervention (versus no treatment or intervention).  Given one or more risk prediction instruments (risk models) that estimate the probability of a binary outcome, rmda provides functions to estimate and display decision curves and other figures that help assess the population impact of using a risk model for clinical decision making.   Here, "population" refers to the relevant patient population. Decision curves display estimates of the (standardized) net benefit over a range of probability thresholds used to categorize observations as 'high risk'. The curves help evaluate a treatment policy that recommends treatment for patients who are estimated to be 'high risk' by comparing the population impact of a risk-based policy to "treat all" and "treat none" intervention policies.  Curves can be estimated using data from a prospective cohort.  In addition, rmda can estimate decision curves using data from a case-control study if an estimate of the population outcome prevalence is available.  Version 1.4 of the package provides an alternative framing of the decision problem for situations where treatment is the standard-of-care and a risk model might be used to recommend that low-risk patients (i.e., patients below some risk threshold) opt out of treatment. Confidence intervals calculated using the bootstrap can be computed and displayed. A wrapper function to calculate cross-validated curves using k-fold cross-validation is also provided.
Supports concordances in 'R Markdown' 
  documents.  This currently allows the original source
  location in the '.Rmd' file of errors detected by 'HTML tidy'
  to be found more easily, and potentially allows forward
  and reverse search in 'HTML' and 'LaTeX' documents
  produced from 'R Markdown'.  The 'LaTeX' support
  has been included in the most recent development 
  version of the 'patchDVI' package.
A collection of 'Lua' filters that extend the functionality
  of R Markdown templates (e.g., count words or post-process citations).
HTML formats and templates for 'rmarkdown' documents, with some extra
    features such as automatic table of contents, lightboxed figures, dynamic
    crosstab helper.
A series of functions to aid in repeated tasks for Rmd documents. All details are to my personal preference, though I am happy to add flexibility if there are use cases I am missing. I will continue updating with new functions as I add utility functions for myself.
A system for describing and manipulating the many models that are
    generated in causal inference and data analysis projects, as based on the
		causal theory and criteria of Austin Bradford Hill (1965) 
		<doi:10.1177/003591576505800503>. This system includes the addition of 
		formal attributes that modify base `R` objects, including terms and 
		formulas, with a focus on variable roles in the "do-calculus" of modeling,
		as described in Pearl (2010) <doi:10.2202/1557-4679.1203>. For example, the 
		definition of exposure, outcome, and interaction are  implicit in the roles 
		variables take in a formula. These premises allow for a more fluent modeling
		approach focusing on variable relationships, and assessing effect 
		modification, as described by VanderWeele and Robins (2007) 
		<doi:10.1097/EDE.0b013e318127181b>. The essential goal is to help 
		contextualize formulas and models in causality-oriented workflows.
Use 'rmarkdown' partials, also know as child documents in
		'knitr', so you can make components for HTML, PDF, and Word documents. 
		The package provides various helper functions to make certain functions easier. 
		You may want to use this package, if you want to flexibly summarise objects 
		using a combination of figures, tables, text, and HTML widgets. 
		Unlike HTML widgets, the output is Markdown and can hence be turn into other
		output formats than HTML.
Formats for R Markdown that undo modifications by
    'pandoc' and 'rmarkdown' to original 'latex' templates, such as
    smaller margins, paragraph spacing, and compact titles. In addition, 
    enhancements such as author blocks with affiliations and
    headers and footers are introduced. All of this functionality is built 
    around plugins that modify the default 'pandoc' template without relying on 
    custom templates.
Computes word, character, and non-whitespace character counts in R Markdown documents and Jupyter notebooks, with or without code chunks. Returns results as a data frame.
A suite of tools useful to read, visualize and export bivariate motion energy time-series. Lagged synchrony between subjects can be analyzed through windowed cross-correlation. Surrogate data generation allows an estimation of pseudosynchrony that helps to estimate the effect size of the observed synchronization. Kleinbub, J. R., & Ramseyer, F. T. (2020). rMEA: An R package to assess nonverbal synchronization in motion energy analysis time-series. Psychotherapy research, 1-14. <doi:10.1080/10503307.2020.1844334>.
An 'Rcpp' interface for Eunjeon project <http://eunjeon.blogspot.com/>.
    The 'mecab-ko' and 'mecab-ko-dic' is based on a C++ library,
    and part-of-speech tagging with them is useful when the spacing of source Korean text is not correct.
    This package provides part-of-speech tagging and tokenization function for Korean text.
We provide functions to compute confidence intervals for a well-defined nonlinear function of the model parameters (e.g., product of k coefficients) in single--level and
    multilevel structural equation models. It also computes a chi-square test statistic for a function of indirect effects. 
    'Tofighi', D. and 'MacKinnon', D. P. (2011). 'RMediation' An R package for mediation analysis confidence intervals. Behavior Research Methods, 43, 692--700. <doi:10.3758/s13428-011-0076-x>.
    'Tofighi', D. (2020). Bootstrap Model-Based Constrained Optimization Tests of Indirect Effects. Frontiers in Psychology, 10, 2989. <doi:10.3389/fpsyg.2019.02989>.
Implementation of the MEthod based on the Removal Effects of Criteria - MEREC- a new objective weighting method for determining criteria weights for Multiple Criteria Decision Making problems, created by Mehdi Keshavarz-Ghorabaee (2021) <doi:10.3390/sym13040525>. Given a decision matrix, the function return the Merec´s weight vector and all intermediate matrix/vectors used to calculate it.
Functions for simple fixed and random effects
        meta-analysis for two-sample comparisons and cumulative
        meta-analyses. Draws standard summary plots, funnel plots, and
        computes summaries and tests for association and heterogeneity.
Implementation of the metalog distribution in R.
    The metalog distribution is a modern, highly flexible, data-driven distribution. 
    Metalogs are developed by Keelin (2016) <doi:10.1287/deca.2016.0338>.
    This package provides functions to build these distributions from raw data. 
    Resulting metalog objects are then useful for exploratory and probabilistic analysis.
The provided package implements the statistical tests for the functional repeated measures analysis problem (Kurylo and Smaga, 2023, <arXiv:2306.03883>). These procedures enable us to verify the overall hypothesis regarding equality, as well as hypotheses for pairwise comparisons (i.e., post hoc analysis) of mean functions corresponding to repeated experiments.
We introduce a robust matrix factor model that explicitly incorporates tail behavior and employs a mean-shift term to avoid efficiency losses through pre-centering of observed matrices. More details on the methods related to our paper are currently under submission. A full reference to the paper will be provided in future versions once the paper is published.
Simulation of several fractional and multifractional processes. Includes Brownian and fractional Brownian motions, bridges and Gaussian Haar-based multifractional processes (GHBMP). Implements the methods from Ayache, Olenko and Samarakoon (2025) <doi:10.48550/arXiv.2503.07286> for simulation of GHBMP. Estimation of Hurst functions and local fractal dimension. Clustering realisations based on the Hurst functions. Several functions to estimate and plot geometric statistics of the processes and time series. Provides a 'shiny' application for interactive use of the functions from the package.
Feasible multivariate GARCH models including DCC, GO-GARCH and Copula-GARCH.
A tool for multiply imputing missing data using 'MIDAS', a deep learning method based on denoising autoencoder neural networks (see Lall and Robinson, 2022; <doi:10.1017/pan.2020.49>). This algorithm offers significant accuracy and efficiency advantages over other multiple imputation strategies, particularly when applied to large datasets with complex features. Alongside interfacing with 'Python' to run the core algorithm, this package contains functions for processing data before and after model training, running imputation model diagnostics, generating multiple completed datasets, and estimating regression models on these datasets. For more information see Lall and Robinson (2023) <doi:10.18637/jss.v107.i09>.
Facilitates the use of machine learning algorithms in classification and regression (including time series forecasting) tasks by presenting a short and coherent set of functions. Versions: 1.5.0 improved mparheuristic function (new hyperparameter heuristics); 1.4.9 / 1.4.8 improved help, several warning and error code fixes (more stable version, all examples run correctly); 1.4.7 - improved Importance function and examples, minor error fixes; 1.4.6 / 1.4.5 / 1.4.4 new automated machine learning (AutoML) and ensembles, via improved fit(), mining() and mparheuristic() functions, and new categorical preprocessing, via improved delevels() function; 1.4.3 new metrics (e.g., macro precision, explained variance), new "lssvm" model and improved mparheuristic() function; 1.4.2 new "NMAE" metric, "xgboost" and "cv.glmnet" models (16 classification and 18 regression models); 1.4.1 new tutorial and more robust version; 1.4 - new classification and regression models, with a total of 14 classification and 15 regression methods, including: Decision Trees, Neural Networks, Support Vector Machines, Random Forests, Bagging and Boosting; 1.3 and 1.3.1 - new classification and regression metrics; 1.2 - new input importance methods via improved Importance() function; 1.0 - first version.